diff --git a/CMakeLists.txt b/CMakeLists.txt index 4921226ec1c90a969fa1cfc383823820500c7757..4783095194dc9c6409dc31c95588f46c9bee7c61 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -86,6 +86,14 @@ if(ANDROID OR IOS) "Disable MKLDNN when cross-compiling for Android and iOS" FORCE) set(WITH_MKLML OFF CACHE STRING "Disable MKLML package when cross-compiling for Android and iOS" FORCE) + + # Compile PaddlePaddle mobile inference library + if (NOT WITH_C_API) + set(WITH_C_API ON CACHE STRING + "Always compile the C_API when cross-compiling for Android and iOS" FORCE) + endif() + set(MOBILE_INFERENCE ON) + add_definitions(-DPADDLE_MOBILE_INFERENCE) endif() set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING @@ -160,9 +168,11 @@ endif(USE_NNPACK) add_subdirectory(proto) -# "add_subdirectory(go)" should be placed after the following loine, -# because it depends on paddle/optimizer. -add_subdirectory(paddle/optimizer) +if(NOT MOBILE_INFERENCE) + # "add_subdirectory(go)" should be placed after the following loine, + # because it depends on paddle/optimizer. + add_subdirectory(paddle/optimizer) +endif() # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be # placed after this block, because they depends on it. diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 51c3b918cc4ef4cf6c8052ccc14028a872309fcf..db8f5ab0456792f903093b9cf20e2541f00add5c 100644 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -24,6 +24,10 @@ if(WITH_DOUBLE) add_definitions(-DPADDLE_TYPE_DOUBLE) endif(WITH_DOUBLE) +if(WITH_TESTING) + add_definitions(-DPADDLE_WITH_TESTING) +endif(WITH_TESTING) + if(NOT WITH_TIMER) add_definitions(-DPADDLE_DISABLE_TIMER) endif(NOT WITH_TIMER) @@ -49,11 +53,12 @@ if(NOT WITH_GOLANG) endif(NOT WITH_GOLANG) if(NOT WITH_GPU) - add_definitions(-DPADDLE_ONLY_CPU) add_definitions(-DHPPL_STUB_FUNC) list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu) else() + add_definitions(-DPADDLE_WITH_CUDA) + FIND_PACKAGE(CUDA REQUIRED) if(${CUDA_VERSION_MAJOR} VERSION_LESS 7) diff --git a/cmake/util.cmake b/cmake/util.cmake index d1aee3e170a2d143ac06b438725e907e96f041c8..117ab7f49cdf4a568cd203b2b17767643d0b2d50 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -73,25 +73,43 @@ function(link_paddle_exe TARGET_NAME) generate_rdma_links() endif() - target_circle_link_libraries(${TARGET_NAME} - ARCHIVE_START - paddle_gserver - paddle_function - ARCHIVE_END - paddle_pserver - paddle_trainer_lib - paddle_network - paddle_math - paddle_utils - paddle_parameter - paddle_proto - paddle_cuda - paddle_optimizer - ${EXTERNAL_LIBS} - ${CMAKE_THREAD_LIBS_INIT} - ${CMAKE_DL_LIBS} - ${RDMA_LD_FLAGS} - ${RDMA_LIBS}) + if(MOBILE_INFERENCE) + target_circle_link_libraries(${TARGET_NAME} + ARCHIVE_START + paddle_gserver + paddle_function + ARCHIVE_END + paddle_math + paddle_utils + paddle_parameter + paddle_proto + paddle_cuda + ${EXTERNAL_LIBS} + ${CMAKE_THREAD_LIBS_INIT} + ${CMAKE_DL_LIBS} + ${RDMA_LD_FLAGS} + ${RDMA_LIBS}) + else() + target_circle_link_libraries(${TARGET_NAME} + ARCHIVE_START + paddle_gserver + paddle_function + ARCHIVE_END + paddle_pserver + paddle_trainer_lib + paddle_network + paddle_math + paddle_utils + paddle_parameter + paddle_proto + paddle_cuda + paddle_optimizer + ${EXTERNAL_LIBS} + ${CMAKE_THREAD_LIBS_INIT} + ${CMAKE_DL_LIBS} + ${RDMA_LD_FLAGS} + ${RDMA_LIBS}) + endif() if(ANDROID) target_link_libraries(${TARGET_NAME} log) diff --git a/doc/api/v1/index_cn.rst b/doc/api/v1/index_cn.rst index 3718cd73a2003b8ef6c406a9bd51dc68e76402dc..cf146dc088e3905a751ff55c26fd82ef0ba02c89 100644 --- a/doc/api/v1/index_cn.rst +++ b/doc/api/v1/index_cn.rst @@ -21,7 +21,7 @@ Model Config API trainer_config_helpers/optimizers.rst trainer_config_helpers/data_sources.rst trainer_config_helpers/layers.rst - trainer_config_helpers/activations.rst + trainer_config_helpers/activations.rst trainer_config_helpers/poolings.rst trainer_config_helpers/networks.rst trainer_config_helpers/evaluators.rst diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index c94627a72806fa2eca77c79da24f7f3ca18f0259..d4e9d53e5c0955912a594fe8cd9cd41a4080a2d2 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -345,6 +345,11 @@ clip .. autoclass:: paddle.v2.layer.clip :noindex: +resize +------ +.. autoclass:: paddle.v2.layer.resize + :noindex: + slope_intercept --------------- .. autoclass:: paddle.v2.layer.slope_intercept diff --git a/doc/design/block.md b/doc/design/block.md index be8800122035984df281692fc40009c397565046..9c812732d6ead76eb3aa2d1b617449c96807f21a 100644 --- a/doc/design/block.md +++ b/doc/design/block.md @@ -5,12 +5,12 @@ Both deep learning systems and programming languages help users describe computation procedures. These systems use various representations of computation: - Caffe, Torch, and Paddle: sequences of layers. -- TensorFlow, Caffe2, Mxnet: graphs of operators. +- TensorFlow, Caffe2, Mxnet: graph of operators. - PaddlePaddle: nested blocks, like C++ and Java programs. ## Block in Programming Languages and Deep Learning -In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions, or operators. +In programming languages, a block is a pair of curly braces that includes local variables definitions and a sequence of instructions or operators. Blocks work with control flow structures like `if`, `else`, and `for`, which have equivalents in deep learning: @@ -24,14 +24,14 @@ A key difference is that a C++ program describes a one pass computation, whereas ## Stack Frames and the Scope Hierarchy -The existence of the backward makes the execution of a block of traditional programs and PaddlePaddle different to each other: +The existence of the backward pass makes the execution of a block of PaddlePaddle different from traditional programs: -| programming languages | PaddlePaddle | -|-----------------------|-------------------------------| -| stack | scope hierarchy | -| stack frame | scope | -| push at entering block| push at entering block | -| pop at leaving block | destroy at minibatch completes| +| programming languages | PaddlePaddle | +|-----------------------|---------------------------------| +| stack | scope hierarchy | +| stack frame | scope | +| push at entering block| push at entering block | +| pop at leaving block | destroy when minibatch completes| 1. In traditional programs: @@ -42,9 +42,9 @@ The existence of the backward makes the execution of a block of traditional prog 1. In PaddlePaddle - When the execution enters a block, PaddlePaddle adds a new scope, where it realizes variables. - - PaddlePaddle doesn't pop a scope after the execution of the block because variables therein are to be used by the backward pass. So it has a stack forest known as a *scope hierarchy*. + - PaddlePaddle doesn't pop a scope after the execution of the block because variables therein are used by the backward pass. So it has a stack forest known as a *scope hierarchy*. - The height of the highest tree is the maximum depth of nested blocks. - - After the process of a minibatch, PaddlePaddle destroys the scope hierarchy. + - After the processing of a minibatch, PaddlePaddle destroys the scope hierarchy. ## Use Blocks in C++ and PaddlePaddle Programs @@ -55,17 +55,23 @@ Let us consolidate the discussion by presenting some examples. The following C++ programs shows how blocks are used with the `if-else` structure: ```c++ +namespace pd = paddle; + int x = 10; -int y = 20; -int out; +int y = 1; +int z = 10; bool cond = false; +int o1, o2; if (cond) { int z = x + y; - out = softmax(z); + o1 = z; + o2 = pd::layer::softmax(z); } else { - int z = fc(x); - out = z; + int d = pd::layer::fc(z); + o1 = d; + o2 = d+1; } + ``` An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator](./if_else_op.md) is as follows: @@ -73,57 +79,55 @@ An equivalent PaddlePaddle program from the design doc of the [IfElseOp operator ```python import paddle as pd -x = var(10) -y = var(20) -cond = var(false) -ie = pd.create_ifelseop(inputs=[x], output_num=1) +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] + +ie = pd.ifelse() with ie.true_block(): - x = ie.inputs(true, 0) - z = operator.add(x, y) - ie.set_output(true, 0, operator.softmax(z)) + d = pd.layer.add_scalar(x, y) + ie.output(d, pd.layer.softmax(d)) with ie.false_block(): - x = ie.inputs(false, 0) - z = layer.fc(x) - ie.set_output(true, 0, operator.softmax(z)) -out = b(cond) + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) ``` -In both examples, the left branch computes `softmax(x+y)` and the right branch computes `fc(x)`. +In both examples, the left branch computes `x+y` and `softmax(x+y)`, the right branch computes `fc(x)` and `x+1` . + +The difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. -A difference is that variables in the C++ program contain scalar values, whereas those in the PaddlePaddle programs are mini-batches of instances. The `ie.input(true, 0)` invocation returns instances in the 0-th input, `x`, that corresponds to true values in `cond` as the local variable `x`, where `ie.input(false, 0)` returns instances corresponding to false values. ### Blocks with `for` and `RNNOp` -The following RNN model from the [RNN design doc](./rnn.md) +The following RNN model in PaddlePaddle from the [RNN design doc](./rnn.md) : ```python -x = sequence([10, 20, 30]) -m = var(0) -W = tensor() -U = tensor() - -rnn = create_rnn(inputs=[input]) -with rnn.stepnet() as net: - x = net.set_inputs(0) - h = net.add_memory(init=m) - fc_out = pd.matmul(W, x) - hidden_out = pd.matmul(U, h.pre(n=1)) - sum = pd.add_two(fc_out, hidden_out) - act = pd.sigmoid(sum) - h.update(act) # update memory with act - net.set_outputs(0, act, hidden_out) # two outputs - +x = sequence([10, 20, 30]) # shape=[None, 1] +m = var(0) # shape=[1] +W = var(0.314, param=true) # shape=[1] +U = var(0.375, param=true) # shape=[1] + +rnn = pd.rnn() +with rnn.step(): + h = rnn.memory(init = m) + h_prev = rnn.previous_memory(h) + a = layer.fc(W, x) + b = layer.fc(U, h_prev) + s = pd.add(a, b) + act = pd.sigmoid(s) + rnn.update_memory(h, act) + rnn.output(a, b) o1, o2 = rnn() -print o1, o2 ``` - has its equivalent C++ program as follows ```c++ int* x = {10, 20, 30}; -int m = 0; -int W = some_value(); -int U = some_other_value(); +int* m = {0}; +int* W = {0.314}; +int* U = {0.375}; int mem[sizeof(x) / sizeof(x[0]) + 1]; int o1[sizeof(x) / sizeof(x[0]) + 1]; @@ -131,25 +135,21 @@ int o2[sizeof(x) / sizeof(x[0]) + 1]; for (int i = 1; i <= sizeof(x)/sizeof(x[0]); ++i) { int x = x[i-1]; if (i == 1) mem[0] = m; - int fc_out = W * x; - int hidden_out = Y * mem[i-1]; - int sum = fc_out + hidden_out; + int a = W * x; + int b = Y * mem[i-1]; + int s = fc_out + hidden_out; int act = sigmoid(sum); mem[i] = act; o1[i] = act; o2[i] = hidden_out; } - -print_array(o1); -print_array(o2); ``` - ## Compilation and Execution -Like TensorFlow programs, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest part executes the message for training or inference. +Like TensorFlow, a PaddlePaddle program is written in Python. The first part describes a neural network as a protobuf message, and the rest executes the message for training or inference. -The generation of this protobuf message is like what a compiler generates a binary executable file. The execution of the message that the OS executes the binary file. +The generation of this protobuf message is similar to how a compiler generates a binary executable file. The execution of the message is similar to how the OS executes the binary file. ## The "Binary Executable File Format" @@ -186,8 +186,8 @@ Also, the RNN operator in above example is serialized into a protobuf message of ``` OpDesc { - inputs = {0} // the index of x - outputs = {5, 3} // indices of act and hidden_out + inputs = {0} // the index of x in vars of BlockDesc above + outputs = {5, 3} // indices of act and hidden_out in vars of BlockDesc above attrs { "memories" : {1} // the index of h "step_net" : @@ -203,32 +203,32 @@ This `OpDesc` value is in the `ops` field of the `BlockDesc` value representing During the generation of the Protobuf message, the Block should store VarDesc (the Protobuf message which describes Variable) and OpDesc (the Protobuf message which describes Operator). VarDesc in a block should have its name scope to avoid local variables affect parent block's name scope. -Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example +Child block's name scopes should inherit the parent's so that OpDesc in child block can reference a VarDesc that stored in parent block. For example: ```python -a = pd.Varaible(shape=[20, 20]) +a = pd.Variable(shape=[20, 20]) b = pd.fc(a, params=["fc.w", "fc.b"]) rnn = pd.create_rnn() -with rnn.stepnet() as net: - x = net.set_inputs(a) +with rnn.stepnet(): + x = a.as_step_input() # reuse fc's parameter fc_without_b = pd.get_variable("fc.w") - net.set_outputs(fc_without_b) + rnn.output(fc_without_b) out = rnn() ``` -the method `pd.get_variable` can help retrieve a Variable by a name, a Variable may store in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance. +The method `pd.get_variable` can help retrieve a Variable by the name. The Variable may be stored in a parent block, but might be retrieved in a child block, so block should have a variable scope that supports inheritance. In compiler design, the symbol table is a data structure created and maintained by compilers to store information about the occurrence of various entities such as variable names, function names, classes, etc. To store the definition of variables and operators, we define a C++ class `SymbolTable`, like the one used in compilers. -`SymbolTable` can do the following stuff: +`SymbolTable` can do the following: - store the definitions (some names and attributes) of variables and operators, -- to verify if a variable was declared, -- to make it possible to implement type checking (offer Protobuf message pointers to `InferShape` handlers). +- verify if a variable was declared, +- make it possible to implement type checking (offer Protobuf message pointers to `InferShape` handlers). ```c++ @@ -240,19 +240,18 @@ class SymbolTable { OpDesc* NewOp(const string& name=""); - // TODO determine whether name is generated by python or C++ - // currently assume that a unique name will be generated by C++ if the - // argument name left default. + // TODO determine whether name is generated by python or C++. + // Currently assume that a unique name will be generated by C++ if the + // argument name is left default. VarDesc* NewVar(const string& name=""); - // find a VarDesc by name, if recursive true, find parent's SymbolTable + // find a VarDesc by name, if recursive is true, find parent's SymbolTable // recursively. // this interface is introduced to support InferShape, find protobuf messages // of variables and operators, pass pointers into InferShape. - // operator // // NOTE maybe some C++ classes such as VarDescBuilder and OpDescBuilder should - // be proposed and embedded into pybind to enable python operate on C++ pointers. + // be proposed and embedded into pybind to enable python operation on C++ pointers. VarDesc* FindVar(const string& name, bool recursive=true); OpDesc* FindOp(const string& name); @@ -270,7 +269,7 @@ class SymbolTable { After all the description of variables and operators is added into SymbolTable, the block has enough information to run. -The `Block` class takes a `BlockDesc` as input, and provide `Run` and `InferShape` functions. +The `Block` class takes a `BlockDesc` as input, and provides `Run` and `InferShape` functions. ```c++ @@ -302,7 +301,7 @@ public: void CreateVariables(const framework::Scope& scope); void CreateOperators(); - // some other necessary interfaces of NetOp are list below + // some other necessary interfaces of NetOp are listed below // ... private: @@ -316,15 +315,14 @@ private: Block inherits from OperatorBase, which has a Run method. Block's Run method will run its operators sequentially. -There is another important interface called `Eval`, which take some arguments called targets, and generate a minimal graph which takes targets as the end points and creates a new Block, -after `Run`, `Eval` will get the latest value and return the targets. +There is another important interface called `Eval`, which takes some arguments called targets and generates a minimal graph which treats targets as the end points and creates a new Block. After `Run`, `Eval` will get the latest value and return the targets. The definition of Eval is as follows: ```c++ // clean a block description by targets using the corresponding dependency graph. // return a new BlockDesc with minimal number of operators. -// NOTE not return a Block but the block's description so that this can be distributed +// NOTE: The return type is not a Block but the block's description so that this can be distributed // to a cluster. BlockDesc Prune(const BlockDesc& desc, vector targets); diff --git a/doc/design/dcgan.png b/doc/design/dcgan.png new file mode 100644 index 0000000000000000000000000000000000000000..15e8e290a111ff43900934341365cb4360d87d28 Binary files /dev/null and b/doc/design/dcgan.png differ diff --git a/doc/design/gan_api.md b/doc/design/gan_api.md new file mode 100644 index 0000000000000000000000000000000000000000..fb41df8615f73d9fd4c32995eab265833eac1a55 --- /dev/null +++ b/doc/design/gan_api.md @@ -0,0 +1,253 @@ +# Design for GAN + +GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas. + +It applies several important concepts in machine learning system design, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth. + +In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation. + +

+
+Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run. +

+ +The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563. + +

+
+Figure 2. Photo borrowed from the original DC-GAN paper. +

+ +## The Conditional-GAN might be a class. +This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure: + +- DCGAN(object): which contains everything required to build a GAN model. It provides following member functions methods as API: + +- __init__(...): Initialize hyper-parameters (like conv dimension and so forth), and declare model parameters of discriminator and generator as well. + +- generator(z, y=None): Generate a fake image from input noise z. If the label y is provided, the conditional GAN model will be chosen. +Returns a generated image. + +- discriminator(image): +Given an image, decide if it is from a real source or a fake one. +Returns a 0/1 binary label. + +- build_model(self): +build the whole GAN model, define training loss for both generator and discrimator. + +## Discussion on Engine Functions required to build GAN +- Trace the tensor and variable dependency in the engine executor. (Very critical, otherwise GAN can'be be trained correctly) +- Different optimizers responsible for optimizing different loss. + +To be more detailed, we introduce our design of DCGAN as following: + +### Class member Function: Initializer +- Set up hyper-parameters, including condtional dimension, noise dimension, batch size and so forth. +- Declare and define all the model variables. All the discriminator parameters are included in the list self.theta_D and all the generator parameters are included in the list self.theta_G. +```python +class DCGAN(object): + def __init__(self, y_dim=None): + + # hyper parameters + self.y_dim = y_dim # conditional gan or not + self.batch_size = 100 + self.z_dim = z_dim # input noise dimension + + # define parameters of discriminators + self.D_W0 = pd.Variable(shape=[3,3, 1, 128], data=pd.gaussian_normal_randomizer()) + self.D_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data + self.D_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) + self.D_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data + self.D_W2 = pd.Varialble(np.random.rand(128, 1)) + self.D_b2 = pd.Variable(np.zeros(128)) + self.theta_D = [self.D_W0, self.D_b0, self.D_W1, self.D_b1, self.D_W2, self.D_b2] + + # define parameters of generators + self.G_W0 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) + self.G_b0 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data + self.G_W1 = pd.Variable(shape=[784, 128], data=pd.gaussian_normal_randomizer()) + self.G_b1 = pd.Variable(np.zeros(128)) # variable also support initialization using a numpy data + self.G_W2 = pd.Varialble(np.random.rand(128, 1)) + self.G_b2 = pd.Variable(np.zeros(128)) + self.theta_G = [self.G_W0, self.G_b0, self.G_W1, self.G_b1, self.G_W2, self.G_b2] +``` + +### Class member Function: Generator +- Given a noisy input z, returns a fake image. +- Concatenation, batch-norm, FC operations required; +- Deconv layer required, which is missing now... +```python +class DCGAN(object): + def generator(self, z, y = None): + # input z: the random noise + # input y: input data label (optional) + # output G_im: generated fake images + + if not self.y_dim: + z = pd.layer.concat(1, [z, y]) + + G_h0 = pd.layer.fc(z, self.G_w0, self.G_b0) + G_h0_bn = pd.layer.batch_norm(G_h0) + G_h0_relu = pd.layer.relu(G_h0_bn) + + G_h1 = pd.layer.deconv(G_h0_relu, self.G_w1, self.G_b1) + G_h1_bn = pd.layer.batch_norm(G_h1) + G_h1_relu = pd.layer.relu(G_h1_bn) + + G_h2 = pd.layer.deconv(G_h1_relu, self.G_W2, self.G_b2)) + G_im = pd.layer.tanh(G_im) + return G_im +``` + +### Class member function: Discriminator +- Given a noisy input z, returns a fake image. +- Concatenation, Convolution, batch-norm, FC, Leaky-ReLU operations required; +```python +class DCGAN(object): + def discriminator(self, image): + # input image: either generated images or real ones + # output D_h2: binary logit of the label + + D_h0 = pd.layer.conv2d(image, w=self.D_w0, b=self.D_b0) + D_h0_bn = pd.layer.batchnorm(h0) + D_h0_relu = pd.layer.lrelu(h0_bn) + + D_h1 = pd.layer.conv2d(D_h0_relu, w=self.D_w1, b=self.D_b1) + D_h1_bn = pd.layer.batchnorm(D_h1) + D_h1_relu = pd.layer.lrelu(D_h1_bn) + + D_h2 = pd.layer.fc(D_h1_relu, w=self.D_w2, b=self.D_b2) + return D_h2 +``` + +### Class member function: Build the model +- Define data readers as placeholders to hold the data; +- Build generator and discriminators; +- Define two training losses for discriminator and generator, respectively. +If we have execution dependency engine to back-trace all tensors, the module building our GAN model will be like this: +```python +class DCGAN(object): + def build_model(self): + if self.y_dim: + self.y = pd.data(pd.float32, [self.batch_size, self.y_dim]) + self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) + self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) + self.z = pd.data(tf.float32, [None, self.z_size]) + + # step 1: generate images by generator, classify real/fake images with discriminator + if self.y_dim: # if conditional GAN, includes label + self.G = self.generator(self.z, self.y) + self.D_t = self.discriminator(self.images) + # generated fake images + self.sampled = self.sampler(self.z, self.y) + self.D_f = self.discriminator(self.G) + else: # original version of GAN + self.G = self.generator(self.z) + self.D_t = self.discriminator(self.images) + # generate fake images + self.sampled = self.sampler(self.z) + self.D_f = self.discriminator(self.images) + + # step 2: define the two losses + self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size)) + self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size)) + self.d_loss = self.d_loss_real + self.d_loss_fake + + self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_f, np.ones(self.batch_szie)) +``` + +If we do not have dependency engine but blocks, the module building our GAN model will be like this: +```python +class DCGAN(object): + def build_model(self, default_block): + # input data in the default block + if self.y_dim: + self.y = pd.data(pd.float32, [self.batch_size, self.y_dim]) + self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) + # self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) + self.z = pd.data(tf.float32, [None, self.z_size]) + + # step 1: generate images by generator, classify real/fake images with discriminator + with pd.default_block().g_block(): + if self.y_dim: # if conditional GAN, includes label + self.G = self.generator(self.z, self.y) + self.D_g = self.discriminator(self.G, self.y) + else: # original version of GAN + self.G = self.generator(self.z) + self.D_g = self.discriminator(self.G, self.y) + self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_g, np.ones(self.batch_szie)) + + with pd.default_block().d_block(): + if self.y_dim: # if conditional GAN, includes label + self.D_t = self.discriminator(self.images, self.y) + self.D_f = self.discriminator(self.G, self.y) + else: # original version of GAN + self.D_t = self.discriminator(self.images) + self.D_f = self.discriminator(self.G) + + # step 2: define the two losses + self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size)) + self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size)) + self.d_loss = self.d_loss_real + self.d_loss_fake +``` +Some small confusion and problems with this design: +- D\_g and D\_f are actually the same thing, but has to be written twice; i.e., if we want to run two sub-graphs conceptually, the same codes have to be written twice if they are shared by the graph. +- Requires ability to create a block anytime, rather than in if-else or rnn only; + +## Main function for the demo: +Generally, the user of GAN just need to the following things: +- Define an object as DCGAN class; +- Build the DCGAN model; +- Specify two optimizers for two different losses with respect to different parameters. +```python +# pd for short, should be more concise. +from paddle.v2 as pd +import numpy as np +import logging + +if __name__ == "__main__": + # dcgan class in the default graph/block + # if we use dependency engine as tensorflow + # the codes, will be slightly different like: + # dcgan = DCGAN() + # dcgan.build_model() + with pd.block() as def_block: + dcgan = DCGAN() + dcgan.build_model(def_block) + + # load mnist data + data_X, data_y = self.load_mnist() + + # Two subgraphs required!!! + with pd.block().d_block(): + d_optim = pd.train.Adam(lr = .001, beta= .1) + d_step = d_optim.minimize(dcgan.d_loss, dcgan.theta_D) + with pd.block.g_block(): + g_optim = pd.train.Adam(lr = .001, beta= .1) + g_step = pd.minimize(dcgan.g_loss, dcgan.theta_G) + + # executor + sess = pd.executor() + + # training + for epoch in xrange(10000): + for batch_id in range(N / batch_size): + idx = ... + # sample a batch + batch_im, batch_label = data_X[idx:idx+batch_size], data_y[idx:idx+batch_size] + # sample z + batch_z = np.random.uniform(-1., 1., [batch_size, z_dim]) + + if batch_id % 2 == 0: + sess.run(d_step, + feed_dict = {dcgan.images: batch_im, + dcgan.y: batch_label, + dcgan.z: batch_z}) + else: + sess.run(g_step, + feed_dict = {dcgan.z: batch_z}) +``` + +# More thinking about dependency engine v.s. block design: +- What if we just want to run an intermediate result? Do we need to run the whole block/graph? +- Should we call eval() to get the fake images in the first stage? And then train the discriminator in the second stage? diff --git a/doc/design/if_else_op.md b/doc/design/if_else_op.md index 954a19c0733358c235eae3cffe134c23dac94c95..26d140f06db4ecefa86be015eaa731ffddc6910c 100644 --- a/doc/design/if_else_op.md +++ b/doc/design/if_else_op.md @@ -1,41 +1,51 @@ -IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has N instances. If cond[i] == True, input instance input[i] will go through true_block() and generate output[i]; otherwise it will produce output from false_bloack(). +# The `IfElse` Operator -```python -import paddle as pd +PaddlePaddle's `IfElse` operator differs from TensorFlow's: -x = var() -y = var() -cond = var() -default_value = var() -b = pd.create_ifelseop(inputs=[x], output_num=1) -with b.true_block(): - x = b.inputs(0) - z = operator.add(x, y) - b.set_output(0, operator.softmax(z)) - -with b.false_block(): - x = b.inputs(0) - z = layer.fc(x) - b.set_output(0, operator.softmax(z)) - -out = b(cond) -``` +- the TensorFlow version takes a scalar boolean value as the condition so that the whole mini-batch goes to either the true or the false branch, whereas +- the PaddlePaddle version takes a vector of boolean value as the condition, and instances corresponding to true values go to the true branch, those corresponding to false values go to the false branch. + +## Example + +The following PaddlePaddle program shows the usage of the IfElse operator: -If only true_block is set in an IfElseOp, a special case is that we can have a default value for false as: ```python import paddle as pd -x = var() -y = var() -cond = var() -default_value = var() -b = pd.create_ifelseop(inputs=[x], output_num=1, default_value) - -with b.true_block(): - x = b.inputs(0) - z = operator.add(x, y) - b.set_output(0, operator.softmax(z)) +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] + +ie = pd.ifelse() +with ie.true_block(): + d = pd.layer.add(x, y) + ie.output(d, pd.layer.softmax(d)) +with ie.false_block(): + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) +``` -out = b(cond) +A challenge to implement the `IfElse` operator is to infer those variables to be split, or, say, to identify the variable of the mini-batch or those derived from the mini-batch. + +An equivalent C++ program is as follows: + +```c++ +namespace pd = paddle; + +int x = 10; +int y = 1; +int z = 10; +bool cond = false; +int o1, o2; +if (cond) { + int d = x + y; + o1 = z; + o2 = pd::layer::softmax(z); +} else { + int d = pd::layer::fc(z); + o1 = d; + o2 = d+1; +} ``` -where default_value is a list of vars for `cond` == False. diff --git a/doc/design/optimizer.md b/doc/design/optimizer.md new file mode 100644 index 0000000000000000000000000000000000000000..17440fae5028cfac5d58fc079ca2096d0be3a0f9 --- /dev/null +++ b/doc/design/optimizer.md @@ -0,0 +1,105 @@ +## Optimizer Design + +### The Problem + +A PaddlePaddle program, or a block, is a sequence of operators operating variables. A training program needs to do three kinds of works: + +1. the forward pass, which computes intermediate results and the cost(s), +1. the backward pass, which derives gradients from intermediate results and costs, and +1. the optimization pass, which update model parameters to optimize the cost(s). + +These works rely on three kinds of operators: + +1. forward operators, +1. gradient operators, and +1. optimization operators. + +It's true that users should be able to create all these operators manually by calling some low-level API, but it would be much more convenient if they could only describe the forward pass and let PaddlePaddle create the backward and optimization operators automatically. + +In this design, we propose a high-level API that automatically derives the optimisation pass and operators from the forward pass. + + +### High-level Python API to describe the training process + +1. User write code to describe the network: + + ```python + images = layer.data("images") + labels = layer.data("labels") + w1 = pd.var("w1") + b1 = pd.var("b1") + hidden = layer.fc(images, w=w1, b=b1) + cost = layer.mse(hidden, labels) + ``` + + The above code snippet will create forward operators in [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md). + + +2. Users create a certain kind of Optimizer with some argument. + + ```python + optimizer = AdagradOptimizer(learing_rate=0.001) + ``` + +3. Users use the optimizer to `minimize` a certain `cost` through updating parameters in parameter_list. + + ```python + opt_op_list = optimizer.minimize(cost, parameter_list=[w1, b1]) + ``` + The above code snippet will create gradient and optimization operators in Block. The return value of `minimize()` is list of optimization operators that will be run by session. + +4. Users use Session/Executor to run this opt_op_list as target to do training. + + ```python + sess.run(target= opt_op_list, ...) + ``` + +#### Optimizer Python interface: + +```python +class Optimizer(object): + """Optimizer Base class. + + """ + + def __init__(self): + pass + + def create_backward_pass(self, loss, parameter_list=None): + """ + create and add gradient Operators in BlockDesc to Compute gradients of `loss` + for parameters in parameter_list + + Args: + loss: an variable generated by cost function. + parameter_list: parameters that need to compute gradient and update to optimize the lost. + + Returns: + list of (parameters, gradients) pair. + """ + return None + + def create_optimization_pass(self, parameters_and_grads): + """Add optimization operators to update gradients to variables. + + Args: + parameters_and_grads: a list of (variable, gradient) pair to update. + + Returns: + optmization_op_list: a list of optimization operator that will update parameter using gradient. + """ + return None + + def minimize(self, loss, parameter_list): + """Add operations to minimize `loss` by updating `parameter_list`. + + This method combines interface `create_backward_pass()` and + `create_optimization_pass()` into one. + """ + params_grads = self.create_backward_pass(loss, parameter_list) + update_ops = self.create_optimization_pass(params_grads) + return update_ops + +``` + +Users can inherit the Optimizer above to create their own Optimizer with some special logic, such as AdagradOptimizer. diff --git a/doc/design/program.md b/doc/design/program.md index fb8f86ac07af403c9fee015f2a3adbfaa3c6d631..bd2456787c4e336d357a65255a8274a7c9e465cc 100644 --- a/doc/design/program.md +++ b/doc/design/program.md @@ -1,8 +1,10 @@ -# Design Doc: ProgramDesc +# Design Doc: PaddlePaddle Programs -The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program. +## Compile and Execution + +A PaddlePaddle program consists of two parts -- the first generates a `ProgramDesc` protobuf message that describes the program, and the second runs this message using a C++ class `Executor`. -As described in [graph.md](./graph.md), the first five lines of the following PaddlePaddle program +A simple example PaddlePaddle program can be found in [graph.md](./graph.md): ```python x = layer.data("images") @@ -13,36 +15,112 @@ optimize(cost) train(cost, reader=mnist.train()) ``` -generates, or compiles, a PaddelPaddle program, which is represented by the following protobuf message: +The first five lines of the following PaddlePaddle program generates, or, compiles, the `ProgramDesc` message. The last line runs it. -```protobuf -message ProgramDesc { - repeated BlockDesc blocks = 1; +## Programs and Blocks + +The basic structure of a PaddlePaddle program is some nested blocks, as a C++ or Java program. + +- program: some nested blocks +- [block](./block.md): + - some local variable definitions, and + - a sequence of operators + +The concept of block comes from usual programs. For example, the following C++ program has three blocks: + +```c++ +int main() { // block 0 + int i = 0; + if (i < 10) { // block 1 + for (int j = 0; j < 10; j++) { // block 2 + } + } + return 0; } +``` + +The following PaddlePaddle program has three blocks: + +```python +import paddle as pd // block 0 + +x = minibatch([10, 20, 30]) # shape=[None, 1] +y = var(1) # shape=[1], value=1 +z = minibatch([10, 20, 30]) # shape=[None, 1] +cond = larger_than(x, 15) # [false, true, true] +ie = pd.ifelse() +with ie.true_block(): // block 1 + d = pd.layer.add_scalar(x, y) + ie.output(d, pd.layer.softmax(d)) +with ie.false_block(): // block 2 + d = pd.layer.fc(z) + ie.output(d, d+1) +o1, o2 = ie(cond) +``` + +## `BlockDesc` and `ProgramDesc` + +All protobuf messages are defined in `framework.proto`. + +`BlockDesc` is straight-forward -- it includes local variable definitions, `vars`, and a sequence of operators, `ops`. + +```protobuf message BlockDesc { required int32 parent = 1; repeated VarDesc vars = 2; repeated OpDesc ops = 3; } +``` + +The parent ID indicates the parent block so that operators in a block can refer to variables defined locally and also those defined in their ancestor blocks. + +All hierarchical blocks in a program are flattened and stored in an array. The block ID is the index of the block in this array. + +```protobuf +message ProgramDesc { + repeated BlockDesc blocks = 1; +} +``` + + +### Global Block +The global block is the first one in the above array. + +## Operators that Use Blocks + +In the above example, the operator `IfElseOp` has two blocks -- the true branch and the false branch. + +The definition of `OpDesc` shows that an operator could have some attributes: + +```protobuf message OpDesc { AttrDesc attrs = 1; ... } +``` + +and an attribute could be of type block, which is, in fact, a block ID as described above: +``` message AttrDesc { - required AttrType type = 1; + required string name = 1; - // index into ProgramDesc::blocks when type==BLOCK - optional int32 block = 2; + enum AttrType { + INT = 1, + STRING = 2, + ... + BLOCK = ... + } + required AttrType type = 2; + + optional int32 block = 10; // when type == BLOCK ... } ``` -When each of the first five lines runs, related Python function, e.g., `layer.fc`, calls C++ InferShape functions. This InferShape function needs to access the properties of VarDesc's accessed by the current OpDesc. These VarDesc's might not be defined in the current block, but in some ancestor blocks. This requires that we can trace the parent of a block. - -A nested block is often an attribute of an operator, most likely, an IfElseOp or a WhileOp. In above solution, all blocks are in `ProgramDesc::blocks`, this implicitly assigns a zero-based ID to each block -- the index of the block in `ProgramDesc::blocks`. So that `AttrDesc::block` could be an integer block ID. +## InferShape With this design, the InferShape function should take the following parameters: diff --git a/doc/design/python_api.md b/doc/design/python_api.md new file mode 100644 index 0000000000000000000000000000000000000000..56ae1d925a96622b5576013f38e33e5f92cbbb90 --- /dev/null +++ b/doc/design/python_api.md @@ -0,0 +1,220 @@ +# Design Doc: Python API + +Due to the refactorization of the PaddlePaddle core, we need Python classes to construct corresponding protobuf messages that describe a DL program. + +| Python classes | Protobuf messages | +| --- | --- | +| Program | ProgramDesc | +| Block | BlockDesc | +| Operator | OpDesc | +| Variable | VarDesc | + +Please be aware that these Python classes need to maintain some construction-time information, which are not part of the protobuf messages. + +## Core Concepts + +### Program + +A `ProgramDesc` describes a [DL program](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md), which is composed of an array of `BlockDesc`s. The `BlockDesc`s in a `ProgramDesc` can have a tree-like hierarchical structure. However, the `ProgramDesc` onlys stores a flattened array of `BlockDesc`s. A `BlockDesc` refers to its parent block by its index in the array. For example, operators in the step block of an RNN operator need to be able to access variables in its ancestor blocks. + +Whenever we create a block, we need to set its parent block to the current block, hence the Python class `Program` needs to maintain a data member `current_block`. + +```python +class Program(objects): + def __init__(self): + self.desc = core.NewProgram() # a C++ ProgramDesc pointer. + self.blocks = vector() + self.blocks.append(Block(self, -1)) # the global block + self.current_block = 0 # initialized to the global block + + def global_block(): + return self.blocks[0] + + def current_block(): + return self.get_block(self.current_block) + + def rollback(): + self.current_block = self.current_block().parent_idx + + def create_block(): + new_block_idx = len(self.block) + self.blocks.append(Block(self, self.current_block)) + self.current_block = new_block_idx + return current_block() +``` + +`Program` is an accessor to the protobuf message `ProgramDesc`, which is created in C++ space, because the InferShape function is in C++, which manipulates `VarDesc` messages, which are in turn members of `BlockDesc`, which is a member of `ProgramDesc`. + +`Program` creates the first block as the global block in its constructor. All parameters and their initializer operators are in the global block. + +### Block + +A [Block](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md) includes + +1. a map from variable names to an instance of the Python `Variable` class, and +1. a list of `Operator` instances. + +```python +class Block(objects): + def __init__(self, program, parent_idx): + self.desc = core.NewBlock(program.desc) + self.program = program + self.vars = map() + self.ops = vector() + self.parent_idx = parent_idx + + def create_var(self, ...): + return Variable(self, ...) + + def _create_global_var(self, ...): + program.global_block().create_var(...) + + def create_parameter(self, name, ...): + # Parameter is a subclass of variable. See Parameter section for details. + self.vars[name] = Parameter(self._create_global_var(...), ...) + return self.vars[name] + + def append_operator(self, ...): + self.ops.append(Operator(self, ...)) + + def prepend_operator(self, ...): # Parameter's ctor prepands initialize operators. + self.ops.prepend(Operator(self, ...)) +``` + +`create_parameter` is necessary because parameters are global variables, defined in the global block, but can be created in some sub-blocks. For example, an FC layer in the step block of an RNN operator. + +`prepend_operator` is necessary because the constructor of `Parameter` needs to create the initialize (or load) operator of the parameter, and would like to put it in the *preamble* of the global block. + +### Operator + +The `Operator` class fills in the `OpDesc` message and calls the C++ function `InferShape` to infer the output shapes from the input shapes. + +```python +class Operator(object): + def __init__(self, + block, # Block + type, # string + inputs, # dict + outputs,# dict + attrs # dict + ): + self.desc = core.NewOpDesc(block.desc, type, inputs, outputs, attrs) + core.infer_shape(self.desc, inputs, outputs) + + def type(self): + return self.desc.type() +``` + +`Operator` creates the `OpDesc` message in C++ space, so that it can call the `InferShape` function, which is in C++. + +### Variable + +Operators take Variables as its inputs and outputs. + +```python +class Variable(object): + def __init__(self, + block=None, # Block + name=None, # string + shape, # tuple + dtype="float32", # string + lod_level=None # int + ): + if name is None: + name = unique_name_generator() + self.name = name + self.block = block + self.desc = core.NewVarDesc(block.desc, name, shape, lod_level) + self.writer = None +``` + +Please be aware of `self.writer`, that tracks operator who creates the variable. It possible that there are more than one operators who write a variable, but in Python space, each write to a variable is represented by a Variable class. This is guaranteed by the fact that **`core.NewVarDesc` must NOT create a new `VarDesc` message if its name already exists in the specified block**. + +### Parameter + +A parameter is a global variable with an initializer (or load) operator. + +```python +class Parameter(Variable): + def __init__(self, + block=None, # Block + name=None, # string + shape, # tuple + dtype="float32", # string + lod_level=None # int + trainable, # bool + initialize_op_attrs, + optimize_op_attrs): + super(Parameter, self).__init__(block, name, shape, dtype, lod_level) + self.trainable = trainable + self.optimize_op_attrs = optimize_op_attrs + block.prepend(Operator(block, # Block + initialize_op_attrs['type'], # string + None, # no inputs + self, # output is the parameter + initialize_op_attrs) +``` + +When users create a parameter, they can call + +```python +program.create_parameter( + ..., + init_attr={ + type: "uniform_random", + min: -1.0, + max: 1.0, + }) +) +``` + +In above example, `init_attr.type` names an initialize operator. It can also name the load operator + +```python +init_attr={ + type: "load", + filename: "something.numpy", +} +``` + +`optimize_op_attrs` is not in the `VarDesc` message, but kept in the Python instance, as it will be used in the Python space when creating the optimize operator's `OpDesc`, and will be in the `OpDesc` message. + +## Layer Functions + +A layer is a Python function that creates some operators and variables. Layers simplify the work of application programmers. + +### Data Layer + +```python +def data_layer(name, type, column_name): + block = the_current_program.glolal_block() + var = block.create_global_var( + name=name, + shape=[None] + type.dims(), + dtype=type.dtype) + block.prepend_operator(block, + type="Feed", + inputs = None, + outputs = [var], + {column_name: column_name}) + return var +``` + +The input to the feed operator is a special variable in the global scope, which is the output of [Python readers](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/reader/README.md). + +### FC Layer + +```python +def fc_layer(input, size, ...): + block = program.current_block() + w = block.create_parameter(...) + b = block.create_parameter(...) + out = block.create_var() + op = block.append_operator("FC", X=input, W=w, b=b, out=out) + out.writer = op + return out +``` + +## Optimizer + +[Optimizer Design Doc](./optimizer.md) diff --git a/doc/design/refactor/session.md b/doc/design/refactor/session.md new file mode 100644 index 0000000000000000000000000000000000000000..1d9a26683c14f54e3b5fe41675cd03b5620646b8 --- /dev/null +++ b/doc/design/refactor/session.md @@ -0,0 +1,180 @@ +# Design Doc: Session + +## Abstract + +The *session* object encapsulates the environment in which the +computation graph is executed. + +We will have the *local* session and *remote* session, they offer the +same [interface](#interface). The local session encapsulates the local +runtime environment and the remote session encapsulates the cluster +runtime environment. + +The local runtime environment contains: + +1. computation devices (i.e., CPU, GPU) handles, and +1. the [scope](../scope.md) which holds all variables. + +The remote runtime environment contains: + +1. computation devices (i.e., CPU and GPU on node 0, 1) in a cluster, + and +1. the distributed [scope](../scope.md) in a cluster which holds all + variables. + +The user can create a remote session on Paddle Cloud and evaluate the +computation graph with it. In this way, the user can control the +remote computation resource in a cluster from his local computer. + + +## Background + +The current design has an implicit global session in which +`paddle.eval()` is executed. The pain point is: + +Since the user is not able to explicitly switch between runtime +environments, the user cannot run a topology in two independent +environments. + +For example, in reinforcement learning, the user may want to have a +stale model for inference and a fresh model for training, and only +replace the stale model with the fresh model periodically. + +Furthermore, we have no concept that encapsulates a remote environment +that executes a computation graph. + +We need the session object to address above issues. + + +## Session + +A session is an object that owns the runtime environment. All +computations are executed through `session.eval()`. + + +### Interface + +```python +eval( + targets, + feed_dict=None, +) +``` + +Evaluates the target Operations or Variables in `targets`. + +- *targets*: the evaluation targets. Can be a single Operation or + Variable, or a list with the Operations or Variables as + elements. The value returned by `eval()` has the same shape as the + `target` argument. + + The PaddlePaddle program is represented by + the [ProgramDesc](../design/program.md), `eval()` will infer the + ProgramDesc from the given targets and run the PaddlePaddle + program. Please + see + [this graph](./distributed_architecture.md#local-training-architecture) for + the detailed illustration for the local session + and + [this graph](./distributed_architecture.md#distributed-training-architecture) for + the detailed illustration for the remote session. + +- *feed_dict*: a dictionary that contains the tensors which override + the edges of the computation graph. + + feed_dict not only can provide the input data, it can override any + OP's input as well: + + ```python + a = pd.constant(2.0, name="a") + b = pd.variable(name="b") + c = pd.mul(a,b) + sess.eval(targets=c, feed_dict={"b":3.0}) # returns 6.0 + ``` + +```python +close() +``` + +Closes the session and releases the scope that the session owns. + + +### Create a Local Session + +```python +session( + devices=None +) +``` + +Creates a new session. One session owns one global scope, so creating +multiple sessions will create different scopes. + +- *devices*: a single `string` or a list of `string` of device names, + the corresponding devices will be the computation devices for + `eval()`. If not specified, all available devices (e.g., all GPUs) + will be used. The user doesn't need to specify the CPU device since + it will be always used. Multiple sessions can use the same device. + + +#### Example + +```Python +a = paddle.constant(1.0) +b = paddle.constant(2.0) +c = a + b +sess = paddle.session(devices=["gpu:0", "gpu:1", "fpga:0"]) +sess.eval(c) +sess.close() +``` + +### Create a Remote Session + +```python +create_cloud_job( + name, + num_trainer, + mem_per_trainer, + gpu_per_trainer, + cpu_per_trainer, + num_ps, + mem_per_ps, + cpu_per_ps, +) +``` + +Creates a Paddle Cloud job. Fails if the job name exists. + +```python +get_cloud_job( + name +) +``` + +Gets a Paddle Cloud job. + +```python +remote_session( + job +) +``` + +- *job*: the Paddle Cloud job. + +#### Example + +```Python +reader = paddle.reader.recordio("/pfs/home/peter/mnist-train-*") # data stored on Paddle Cloud +image = reader.column(0) +label = reader.column(1) +fc1 = paddle.op.fc(image, size=256, act="sigmoid") +fc2 = paddle.op.fc(fc1, size=10, act="softmax") +cost = paddle.op.cross_entropy(fc2, label) +opt = paddle.optimizer.sgd(cost) + +job = paddle.create_cloud_job("test", 3, "1G", 1, 1, 2, "1G", 1) +sess = paddle.remote_ession(job) +for i in range(1000): + sess.eval(opt) +sess.close() +``` diff --git a/doc/design/refactorization.md b/doc/design/refactorization.md index ad801ca421ca31c84b0a6b0a18d1d625c87e0de5..ec51aa1a0ec667175ff7215dcd359023e296769f 100644 --- a/doc/design/refactorization.md +++ b/doc/design/refactorization.md @@ -1,40 +1,40 @@ # Design Doc: Refactorization Overview -The goal of refactorizaiton include: +The goals of refactoring include: -1. Make it easy for external contributors to write new elementory computaiton operations. -1. Make the codebase clean and readable. -1. Introduce a new design of computation representation -- a computation graph of operators and variables. -1. The graph representation helps implementing auto-scalable and auto fault recoverable distributed computing. +1. Making it easy for external contributors to write new elementary computation operations. +1. Making the codebase clean and readable. +1. Designing a new computation representation -- a computation graph of operators and variables. +1. Implementing auto-scalability and auto fault recoverable distributed computing with the help of computation graphs. ## Computation Graphs -1. PaddlePaddle represent the computation, training and inference of DL models, by computation graphs. +1. PaddlePaddle represents the computation, training and inference of Deep Learning models, by computation graphs. - 1. Please dig into [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a solid example. + 1. Please refer to [computation graphs](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/graph.md) for a concrete example. -1. Users write Python programs to describe the graphs and run it (locally or remotely). +1. Users write Python programs to describe the graphs and run them (locally or remotely). 1. A graph is composed of *variables* and *operators*. -1. The description of graphs must be able to be serialized/deserialized, so it +1. The description of graphs must be serializable/deserializable, so that: - 1. could to be sent to the cloud for distributed execution, and - 1. be sent to clients for mobile or enterprise deployment. + 1. It can be sent to the cloud for distributed execution, and + 1. It can be sent to clients for mobile or enterprise deployment. -1. The Python program do +1. The Python program does two things - 1. *compilation*: runs a Python program to generate a protobuf message representation of the graph and send it to + 1. *Compilation* runs a Python program to generate a protobuf message representation of the graph and send it to 1. the C++ library `libpaddle.so` for local execution, 1. the master process of a distributed training job for training, or 1. the server process of a Kubernetes serving job for distributed serving. - 1. *execution*: according to the protobuf message, constructs instances of class `Variable` and `OperatorBase`, and run them. + 1. *Execution* executes the graph by constructing instances of class [`Variable`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24) and [`OperatorBase`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L70), according to the protobuf message. -## Description and Realization +## Description and Realization of Computation Graph -At compile time, the Python program generates protobuf message representation of the graph, or the description of the graph. +At compile time, the Python program generates a protobuf message representation of the graph, or a description of the graph. -At runtime, the C++ program realizes the graph and run it. +At runtime, the C++ program realizes the graph and runs it. | | Representation (protobuf messages) | Realization (C++ class objects) | |---|---|---| @@ -42,30 +42,31 @@ At runtime, the C++ program realizes the graph and run it. |Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)| |Block|BlockDesc|Block| -The word *graph* is exchangable with *block* in this document. A graph represent computation steps and local variables as a C++/Java program block, or a pair of { and }. +The word *graph* is interchangeable with *block* in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`). ## Compilation and Execution -1. Run an applicaton Python program to describe the graph. In particular, +1. Run a Python program to describe the graph. In particular, the Python application program does the following: - 1. create VarDesc to represent local/intermediate variables, - 1. create operators and set attributes, - 1. validate attribute values, - 1. inference the type and the shape of variables, - 1. plan for memory-reuse for variables, - 1. generate backward and optimization part of the Graph. - 1. possiblly split the graph for distributed training. + 1. Create `VarDesc` to represent local/intermediate variables, + 1. Create operators and set attributes, + 1. Validate attribute values, + 1. Infer the type and the shape of variables, + 1. Plan memory-reuse for variables, + 1. Generate the backward graph + 1. Add optimization operators to the computation graph. + 1. Optionally, split the graph for distributed training. -1. The invocation of `train` or `infer` in the application Python program: +1. The invocation of `train` or [`infer`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/inference.py#L108) methods in the Python program does the following: - 1. create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block, + 1. Create a new Scope instance in the [scope hierarchy](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/scope.md) for each run of a block, 1. realize local variables defined in the BlockDesc message in the new scope, 1. a scope is similar to the stack frame in programming languages, - 1. create an instance of class `Block`, in which, + 1. Create an instance of class `Block`, in which, 1. realize operators in the BlockDesc message, - 1. run the Block by calling + 1. Run the Block by calling 1. `Block::Eval(vector* targets)` for forward and backward computations, or 1. `Block::Eval(vector* targets)` for optimization. @@ -76,14 +77,14 @@ The word *graph* is exchangable with *block* in this document. A graph represen Compile Time -> IR -> Runtime ``` -### Benefit +### Benefits of IR - Optimization ```text Compile Time -> IR -> Optimized IR -> Runtime ``` -- Send automatically partitioned IR to different nodes. - - Automatic data parallel +- Automatically send partitioned IR to different nodes. + - Automatic Data Parallelism ```text Compile Time |-> Single GPU IR @@ -92,7 +93,7 @@ Compile Time -> IR -> Runtime |-> Node-1 (runs trainer-IR-1) |-> Node-2 (runs pserver-IR) ``` - - Automatic model parallel (planned for future) + - Automatic Model Parallelism (planned for future) --- @@ -105,10 +106,10 @@ Compile Time -> IR -> Runtime # Operator ![class_diagram](http://api.paddlepaddle.org/graphviz?dot=https://gist.githubusercontent.com/reyoung/53df507f6749762675dff3e7ce53372f/raw/dd598e8f1976f5759f58af5e5ef94738a6b2e661/op.dot) -* `Operator` is the fundamental building block as the user interface. - * Operator stores input/output variable name, and attributes. - * The `InferShape` interface is used to infer output variable shapes by its input shapes. - * Use `Run` to compute `input variables` to `output variables`. +* `Operator` is the fundamental building block of the user interface. + * Operator stores input/output variable names and attributes. + * The `InferShape` interface is used to infer the shape of the output variables based on the shapes of the input variables. + * Use `Run` to compute the `output` variables from the `input` variables. --- @@ -126,30 +127,29 @@ Compile Time -> IR -> Runtime # Why separate Kernel and Operator * Separate GPU and CPU code. - * Make Paddle can run without GPU. -* Make one operator (which is user interface) can contain many implementations. - * Same mul op, different FP16, FP32 Kernel. different MKL, eigen kernel. + * Make Paddle capable of running without GPU. +* Make one operator (which is a user interface) and create many implementations. + * For example, same multiplication op can have different implementations kernels such as FP16 kernel, FP32 kernel, MKL, eigen kernel. --- # Libraries for Kernel development * `Eigen::Tensor` contains basic math and element-wise functions. * Note that `Eigen::Tensor` has broadcast implementation. - * Limit number of `tensor.device(dev) = ` in your code. -* `thrust::tranform` and `std::transform`. - * `thrust` has the same API as C++ standard library. Using `transform` can quickly implement a customized elementwise kernel. - * `thrust` has more complex API, like `scan`, `reduce`, `reduce_by_key`. + * Limit the number of `tensor.device(dev) = ` in your code. +* `thrust::transform` and `std::transform`. + * `thrust` has the same API as C++ standard library. Using `transform`, one can quickly implement customized element-wise kernels. + * `thrust`, in addition, supports more complex APIs, like `scan`, `reduce`, `reduce_by_key`. * Hand-writing `GPUKernel` and `CPU` code - * Do not write `.h`. CPU Kernel should be in `.cc`. GPU kernel should be in `.cu`. (`GCC` cannot compile GPU code.) + * Do not write in header (`.h`) files. CPU Kernel should be in cpp source (`.cc`) and GPU kernels should be in cuda (`.cu`) files. (GCC cannot compile GPU code.) --- -# Operator Register +# Operator Registration -## Why register is necessary? +## Why is registration necessary? We need a method to build mappings between Op type names and Op classes. -## How to do the register? - -Maintain a map, whose key is the type name and value is corresponding Op constructor. +## How is registration implemented? +Maintaining a map, whose key is the type name and the value is the corresponding Op constructor. --- # The Registry Map @@ -169,7 +169,7 @@ Maintain a map, whose key is the type name and value is corresponding Op constru # Related Concepts ### Op_Maker -It's constructor takes `proto` and `checker`. They are compeleted during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)) +It's constructor takes `proto` and `checker`. They are completed during Op_Maker's construction. ([ScaleOpMaker](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)) ### Register Macros ```cpp @@ -177,34 +177,35 @@ REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, grad_op_class) REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) ``` -### `USE` Macros -make sure the registration process is executed and linked. +### USE Macros +Make sure the registration process is executed and linked. --- -# Register Process -1. Write Op class, as well as its gradient Op class if there is. -2. Write Op maker class. In the constructor, describe its inputs, outputs, and attributes. -3. Invoke macro `REGISTER_OP`. The macro will - 1. call maker class to complete `proto` and `checker` - 2. with the completed `proto` and `checker`, build a new key-value pair in the `OpInfoMap` +# Registration Process +1. Write an Op class and its gradient Op class, if required. +2. Write an Op maker class. In the constructor of this class, describe the inputs, outputs and attributes of the operator. +3. Invoke the macro `REGISTER_OP`. This macro will + 1. Call maker class to complete `proto` and `checker` + 2. Using the completed `proto` and `checker`, it will add a new key-value pair to the `OpInfoMap` -4. Invoke `USE` macro in where the Op is used to make sure it is linked. +4. Invoke the `USE` macro in which the Op is used to make sure that it is linked. --- # Backward Module (1/2) ### Create Backward Operator -- Mapping from forwarding Op to backward Op +- Mapping from forward Op to backward Op ![backward](https://gist.githubusercontent.com/dzhwinter/a6fbd4623ee76c459f7f94591fd1abf0/raw/61026ab6e518e66bde66a889bc42557a1fccff33/backward.png) --- # Backward Module (2/2) ### Build Backward Network -- **Input** graph of forwarding operators -- **Output** graph of backward operators -- **corner case in construction** - - shared variable => insert `Add` operator - - no gradient => insert `fill_zero_grad` operator - - recursive netOp => call `Backward` recursively +- **Input**: a graph of forward operators +- **Output**: a graph of backward operators +- **Corner cases in construction** + - Shared Variables => insert an `Add` operator to combine gradients + - No Gradient => insert a `fill_zero_grad` operator + - Recursive NetOp => call `Backward` recursively + - RNN Op => recursively call `Backward` on stepnet - RNN Op => recursively call `Backward` on stepnet @@ -213,41 +214,41 @@ make sure the registration process is executed and linked. * `Tensor` is an n-dimension array with type. * Only dims and data pointers are stored in `Tensor`. - * All operators on `Tensor` is written in `Operator` or global functions. - * variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) -* `Variable` is the inputs and outputs of an operator. Not just `Tensor`. - * step_scopes in RNN is a variable and not a tensor. -* `Scope` is where variables store at. - * map - * `Scope` has a hierarchical structure. The local scope can get variable from its parent scope. + * All operations on `Tensor` are written in `Operator` or global functions. + * Variable length Tensor design [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md) +* `Variable` instances are the inputs and the outputs of an operator, not just `Tensor`. + * `step_scopes` in RNN is a variable and not a tensor. +* `Scope` is where variables are stored. + * map + * `Scope` has a hierarchical structure. The local scope can get variables from its parent scope. --- # Block (in design) -## the difference with original RNNOp -- as an operator is more intuitive than `RNNOp`, -- offers new interface `Eval(targets)` to deduce the minimal block to `Run`, -- fits the compile-time/ runtime separation design. - - during the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc` - - when graph executes, a Block with `BlockDesc` passed in creates `Op` and `Var` then `Run` +## the difference between original RNNOp and Block +- As an operator is more intuitive than `RNNOp`, +- Offers a new interface `Eval(targets)` to deduce the minimal block to `Run`, +- Fits the compile-time/ runtime separation design paradigm. + - During the compilation, `SymbolTable` stores `VarDesc`s and `OpDesc`s and serialize to a `BlockDesc` + - When graph executes, a Block with `BlockDesc` is passed. It then creates `Op` and `Var` instances and then invokes `Run`. --- # Milestone -- take Paddle/books as the main line, the requirement of the models motivates framework refactoring, -- model migration - - framework development gives **priority support** to model migration, for example, +- Take Paddle/books as the main line, the requirement of the models motivates framework refactoring, +- Model migration + - Framework development gives **priority support** to model migration, for example, - the MNIST demo needs a Python interface, - the RNN models require the framework to support `LoDTensor`. - - determine some timelines, - - heavily-relied Ops need to be migrated first, - - different models can be migrated parallelly. -- improve the framework at the same time -- accept imperfection, concentrated on solving the specific problem at the right price. + - Determine some timelines, + - Frequently used Ops need to be migrated first, + - Different models can be migrated in parallel. +- Improve the framework at the same time +- Accept imperfection, concentrate on solving the specific problem at the right price. --- # Control the migration quality -- compare the performance of migrated models with old ones. -- follow google C style -- build the automatic workflow of generating Python/C++ documentations - - the documentation of layers and ops should be written inside the code - - take the documentation quality into account when doing PR - - preview the documentations, read and improve them from users' perspective +- Compare the performance of migrated models with old ones. +- Follow the google C++ style guide. +- Build the automatic workflow of generating Python/C++ documentations. + - The documentation of layers and ops should be written inside the code. + - Take the documentation quality into account when submitting pull requests. + - Preview the documentations, read and improve them from a user's perspective. diff --git a/doc/design/register_grad_op.md b/doc/design/register_grad_op.md new file mode 100644 index 0000000000000000000000000000000000000000..3cf8a59446d244bb3a388b87b14273d9096c839a --- /dev/null +++ b/doc/design/register_grad_op.md @@ -0,0 +1,90 @@ +# Design Doc: Gradient Operators Registration + + +## The Problem Posed + +In our current operator registration mechanism, for each operator, the programmer should register a *gradient operator creator* function, which takes a C++ operator instance, and returns the corresponding gradient instance. + +However, as we decided to separate the *compilation* and *execution* of DL models, we need to reshape the creator to take a protobuf `OpDesc` message, and returns a corresponding message. + +More than that, the new registration mechanism need to support the fact that an operators' gradient computation might be a composition of operators. + +## Current Implementation + +OpInfos store in a association map which key is the operator type. The `grad_op_type` indicate associated gradient operator type. Operator can create gradient operator by `OpInfo::creator_` of gradient. The pseudo code is + +```cpp +struct OpInfo { + std::function creator_; + std::string grad_op_type_; + ... +}; + +map OpInfoMap; + +OperatorBase* CreateGradientOperator(const OperatorBase& op) { + return OpInfoMap.at(op.Type()).creator_(...); +} +``` + +## Proposed Solution + +The mapping relationship between an operator and its gradient operators is a function. The interface of that function is: + +```cpp +// (OpDesc) --> vector +std::function(const OpDescBind&)>; +``` + +The function takes an `OpDescBind` of the forward operator and returns one or many gradient operator descriptions. `OpDescBind` is a C++ wrapper for protobuf message `OpDesc` to manipulate `OpDesc` fast. + +The `GradOpDescMaker` will be registered in `OpInfo`, to replace `grad_op_type_` field. The `OpInfo` should be + +```cpp +struct OpInfo { + std::function>(const OpDescBind&)> grad_op_maker_; + ... +}; +``` + +The `grad_op_maker_ ` is `nullptr` if the operator does not have associated gradient operators. + +We propose a base class called `GradOpDescMakerBase` to let operator developers generate `Gradient Operators` easily. The public interface of that class is + +```cpp +class GradOpDescMakerBase { +public: + GradOpDescMakerBase(const OpDescBind& ); + virtual std::vector> operator()()const = 0; +}; +``` + +We can convert `GradOpDescMakerBase` to `std::function>(const OpDescBind&)>` by + +```cpp +using GradOpMaker = ...; +std::function(const OpDescBind&)> func; +func = [] (const OpDescBind& fwd_op) { + GradOpMaker maker(fwd_op); + return maker(); +}; +``` + +We can write many helper functions since the `GradOpDescMakerBase` is a class now. The basic helper functions get the variables of `Input`, `Output`, `InputGradient` and `OutputGradient` in the forwarding operator. + +We should chagne register macros at the same time. In the current solution, there is no difference between forwarding operators and backward operators. So `REGISTER_OP` just register one operator. If the `REGISTER_OPERATOR ` contains `OpProtoAndCheckerMaker` and `GradOpDescMaker`, we just list them in the same macro. It can be done by a macro contains `__VA_ARGS__`. + +The user interface should be + +```cpp +vector MinusOpGradMaker(OpDesc) {...} +REGISTER_OPERATOR(minus, MinusOp, MinusOpProtoAndCheckerMaker, SumOpGradMaker); +// Developers can still manually implement gradient operator. +REGISTER_OPERATOR(minus_grad, MinusGradOp); +``` + +The interface of current `REGISTER_OP` macro could not be changed. In `REGISTER_OP`, it will invoke `REGISTER_OPERATOR` two times and generate GradOpDescMaker inside. + +```cpp +REGISTER_OP(minus, MinusOp, MinusOpProtoAndCheckerMaker, minus_grad, MinusGradOp); +``` diff --git a/doc/design/selected_rows.md b/doc/design/selected_rows.md new file mode 100644 index 0000000000000000000000000000000000000000..9e6f3b20cbcdc55e481fbe7bf5fa555d8b3c3d45 --- /dev/null +++ b/doc/design/selected_rows.md @@ -0,0 +1,74 @@ +# Design Doc: Selected Rows + +`SelectedRows` is a kind of sparse tensor data type, which is designed to support `embedding` operators. The gradient of embedding table is a sparse tensor. Only a few rows are non-zero values in that tensor. It is straightforward to represent the sparse tensor by the following sparse tensor data structure: + +```cpp +class SelectedRows { + private: + vector rows_; + Tensor value_; + int height_; +}; +``` + +The field `height_` shows the first dimension of `SelectedRows`. The `rows` are the indices of which rows of `SelectedRows` are non-zeros. The `value_` field is an N-dim tensor and shape is `[rows.size() /* NUM_ROWS */, ...]`, which supplies values for each row. The dimension of `SelectedRows` satisfies `[height_] + value_.shape[1:]`. + +Suppose that a SelectedRows-typed variable `x` has many rows, but only two of them have values -- row 73 is `[1, 2]` and row 84 is `[3, 4]`, the `SelectedRows` representation would be: + +``` +x = SelectedRow { + rows = [73, 84], + value = [[1, 2], [3,4]] +} +``` + + +## SelectedRows in Protobuf + +`SelectedRows` is a kind of `Variable`. `VarDesc` in protobuf should describe the `SelectedRows` information. Only the tensor dimension of a `SelectedRows` will be described in compile-time since the `rows_` and `value_` are related to training data. +So we use `TensorDesc` to unify `data_type` and `dims`. A LodTensorDesc contains a `TensorDesc` and `lod_level`. The description of `SelectedRows` is a Tensor description. + +```proto +message TensorDesc { + required DataType data_type = 1; + repeated int64 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480] +} + +message LodTensorDesc { + required TensorDesc tensor = 1; + optional int lod_level = 2; +} + +message VarDesc { + required string name = 1; + enum VarType { + LOD_TENSOR = 0; + SELECTED_ROWS = 1; + } + required VarType type = 2; + optional LodTensorDesc lod_desc = 3; + optional TensorDesc selected_rows_desc = 4; + optional bool persistable = 5 [ default = false ]; +} +``` + +## InferShape for Selected Rows + +Just like `LoD` information, `InferShape` method will inference output tensor type as well. The operator should decide whether its output is a `SelectedRows` or `Dense` tensor. + +For example, the gradient operator of `TableLookup` will always generate `SelectedRows`. Its `InferShape` method should be like following + +```cpp +void TableLookupGrad::InferShape(context) { + ... + context.SetDataType("Embedding.Grad", kSelectedRows); +} +``` + + +## Sparse Operators + +There are several operators should be written to support `SelectedRows`. They are: + +1. Operators which generates `SelectedRows` gradient. e.g. Gradient of `TableLookupOp`. +2. Optimize operators which support `SelectedRows` gradient. e.g. `SGD` or `AdaGrad` for `SelectedRows`. However, there should be only one `SGD` operator. `OpWithKernel::Run` should select a suitable kernel for both `dense` tensor or `SelectedRows`. diff --git a/doc/design/tensor_array.md b/doc/design/tensor_array.md index a0419ec002159893b035fae1300fce489e68936a..8378e97bf7cfaae54c36b1b92e202b16e4fe1e28 100644 --- a/doc/design/tensor_array.md +++ b/doc/design/tensor_array.md @@ -1,39 +1,250 @@ # Design for TensorArray +This design doc presents the necessity of a new C++ class `TensorArray`. +In addition to the very simple C++ implementation + +```c++ +class TensorArray { + public: + explicit TensorArray(const LoDTensor&); + explicit TensorArray(size_t size); + + private: + vector values_; +}; +``` + +We also need to expose it to PaddlePaddle's Python API, +because users would want to use it with our very flexible operators `WhileLoop`. +An example for a RNN based on dynamic operators is + +```python +input = pd.data(...) +num_steps = Var(12) + +TensorArray states(size=num_steps) +TensorArray step_inputs(unstack_from=input) +TensorArray step_outputs(size=num_steps) + +W = Tensor(...) +U = Tensor(...) +default_state = some_op() + +step = Var(1) + +wloop = paddle.create_whileloop(loop_vars=[step]) +with wloop.frame(): + wloop.break_if(pd.equal(step, num_steps) + pre_state = states.read(step-1, default_state) + step_input = step_inputs.read(step) + state = pd.sigmoid(pd.matmul(U, pre_state) + pd.matmul(W, step_input)) + states.write(step, state) + step_outputs.write(step, state) # output state + step.update(state+1) + +output = step_outputs.stack() +``` + +## Background +Steps are one of the core concepts of RNN. In each time step of RNN, there should be several input segments, states, and output segments; all these components act like arrays, for example, call `states[step_id]` will get the state in `step_id`th time step. + +An RNN can be implemented with the following pseudocode + +```c++ +Array states; +Array input_segments; +Array output_segments; +Parameter W, U; + +step = 1 +seq_len = 12 +while_loop { + if (step == seq_len) break; + states[step] = sigmoid(W * states[step-1] + U * input_segments[step]); + output_segments[step] = states[step] // take state as output + step++; +} +``` +According to the [RNN roadmap](https://github.com/PaddlePaddle/Paddle/issues/4561), there are several different RNNs that PaddlePaddle will eventually support. + +Currently, the basic RNN implementation supported by PaddlePaddle is the `recurrent_op` which takes tensors as input and splits them into `input_segments`. + + +Since a tensor cannot store variable-length sequences directly, PaddlePaddle implements the tensor with level of details (`LoDTensor` for short). +Segmenting the `LoDTensor` is much more complicated than splitting a tensor, that makes it necessary to refactor the `recurrent_op` with `LoDTensor` segmenting support. + +As the next step in RNN support, `dynamic_recurrent_op` should be introduced to handle inputs with variable-length sequences. + +The implementation is similar to `recurrent_op`. +The key difference is the way **the original input `LoDTensors` and outupts are split to get the `input_segments` and the `output_segments`.** + + +Though it can't be built over `recurrent_op` or `dynamic_recurrent_op` directly, +the logic behind splitting a tensor or a LoD tensor into `input_segments` remains the same. + +## Why `TensorArray` +The logic behind splitting the inputs to segments, states and outputs is similar and can be shared in a seperate module. + +The array of `states`, `input_segments` and `output_segments` would be exposed to users when writing a dynamic RNN model similar to the above pseudo codes. + +So there should be an array-like container, which can store the segments of a tensor or LoD tensor. + +**This container can store an array of tensors and provides several methods to split a tensor or a LoD tensor** . +This is where the notion of `TensorArray` comes from. + +## Introduce TensorArray to uniform all the three RNNs TensorArray as a new concept is borrowed from TensorFlow, it is meant to be used with dynamic iteration primitives such as `while_loop` and `map_fn`. This concept can be used to support our new design of dynamic operations, and help to refactor some existing variant-sentence-related layers, -such as `RecurrentGradientMachine`. +such as `recurrent_op`, `RecurrentGradientMachine`. In [our design for dynamic RNN](https://github.com/PaddlePaddle/Paddle/pull/4401), `TensorArray` is used to segment inputs and store states in all time steps. By providing some methods similar to a C++ array, -the definition of some state-based dynamic models such as RNN could be more natural and highly flexible. - -## Dynamic-Related Methods -Some basic methods should be proposed as follows: - -### stack() -Pack the values in a `TensorArray` into a tensor with rank one higher than each tensor in `values`. -### unstack(axis=0) -Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. -### concat() -Return the values in the `TensorArray` as a concatenated Tensor. -### write(index, value, data_shared=true) -Write value into index of the TensorArray. -### read(index) -Read the value at location `index` in the `TensorArray`. -### size() -Return the number of values. +the definition of some state-based dynamic models such as RNN can be more natural and highly flexible. + +## Dynamic-operations on TensorArray + +`TensorArray` will be used directly when defining dynamic models, so some operators listed below should be implemented + +```python +# several helper operators for TensorArray +def tensor_array_stack(ta, tensor): + ''' + get a tensor array `ta`, return a packed `tensor`. + ''' + pass + +def tensor_array_unstack(tensor, ta): + ''' + get a `tensor`, unstack it and get a tensor array `ta`. + ''' + pass + +def tensor_array_write(ta, index, tensor, data_shared): + ''' + get a `tensor` and a scalar tensor `index`, write `tensor` into index-th + value of the tensor array `ta`. + `data_shared` is an attribute that specifies whether to copy or reference the tensors. + ''' + pass + +def tensor_array_read(ta, index, tensor): + ''' + get a tensor array `ta`, a scalar tensor `index`, read the index-th value of + `ta` and return as the `tensor`. + ''' + pass + +def tensor_array_size(ta, tensor): + ''' + get a tensor array `ta`, return the size of `ta` and return as the scalar `tensor`. + ''' + pass +``` + +It is trivial for users to use so many low-level operators, so some helper methods should be proposed in python wrapper to make `TensorArray` easier to use, +for example + +```python +class TensorArray: + def __init__(self, name): + self.name = name + self.desc = TensorArrayDesc() + + def stack(self, name=None): + ''' + Pack the values in a `TensorArray` into a tensor with rank one higher + than each tensor in `values`. + `stack` can be used to split tensor into time steps for RNN or whileloop. + + @name: str + the name of the variable to output. + ''' + tensor = NewVar(name) + tensor_array_stack(self.name, tensor) + return tensor + + def unstack(self, input): + ''' + Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors. + `unstack` can be used to concatenate all the time steps for RNN or whileloop. + + @input: str + the name of input tensor + ''' + tensor_array_unstack(tensor, self.name) + + def write(self, index, value, data_shared=True): + ''' + Write value into index of the TensorArray. + If `data_shared` is set to True, than the index-th value in TensorArray will + be shared with the tensor passed in. + + @index: str + name of a scalar tensor + @value: str + name of a tensor + @data_shared: bool + ''' + tensor_array_write(self.name, index, value, data_shared) + + def read(self, index, output): + ''' + Read the value at location `index` in the `TensorArray`. + + @index: str + name of a scalar tensor + @output: + name of a output variable + ''' + tensor_array_read(self.name, index, output) + + + def size(self, output): + ''' + Return the number of values. + + @output: str + name of a scalar tensor + ''' + tensor_array_size(self.name, output) +``` ## LoDTensor-related Supports -The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes variant length sequences as input, -because each step of RNN could only take a tensor-represented batch of data as input, +The `RecurrentGradientMachine` in Paddle serves as a flexible RNN layer; it takes varience-length sequences as input, and output sequences too. + +Since each step of RNN can only take a tensor-represented batch of data as input, some preprocess should be taken on the inputs such as sorting the sentences by their length in descending order and cut each word and pack to new batches. -Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`. +Such cut-like operations can be embedded into `TensorArray` as general methods called `unpack` and `pack`, +these two operations are similar to `stack` and `unstack` except that they operate on variable-length sequences formated as a LoD tensor rather than a tensor. + +Some definitions are like + +```python +def unpack(level): + ''' + Split LodTensor in some `level` and generate batches, if set `sort_by_length`, + will sort by length. -With these two methods, a variant-sentence-RNN can be implemented like + Returns: + - a new `TensorArray`, whose values are LodTensors and represents batches + of data. + - an int32 Tensor, which stores the map from the new batch's indices to + original LoDTensor + ''' + pass + +def pack(level, indices_map): + ''' + Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` + and `level` and `indices_map`. + ''' + pass +``` + +With these two methods, a varience-length sentence supported RNN can be implemented like ```c++ // input is the varient-length data @@ -58,16 +269,3 @@ LoDTensor rnn_output = ta.pack(ta, indice_map); ``` the code above shows that by embedding the LoDTensor-related preprocess operations into `TensorArray`, the implementation of a RNN that supports varient-length sentences is far more concise than `RecurrentGradientMachine` because the latter mixes all the codes together, hard to read and extend. - - -some details are as follows. - -### unpack(level, sort_by_length) -Split LodTensor in some `level` and generate batches, if set `sort_by_length`, will sort by length. - -Returns: - -- a new `TensorArray`, whose values are LodTensors and represents batches of data. -- an int32 Tensor, which stores the map from the new batch's indices to original LoDTensor -### pack(level, indices_map) -Recover the original LoD-arranged LoDTensor with the values in a `TensorArray` and `level` and `indices_map`. diff --git a/doc/design/test.dot b/doc/design/test.dot new file mode 100644 index 0000000000000000000000000000000000000000..62c69b8fc8010a26a54a6ee8ef1488aad94d747a --- /dev/null +++ b/doc/design/test.dot @@ -0,0 +1,35 @@ + +digraph Test { + z -> generator -> G_img; + G_img -> discriminator -> D_f -> d_loss_f; + label0 -> d_loss_f -> d_loss; + + img -> discriminator -> D_t -> d_loss_t; + label1 -> d_loss_t -> d_loss; + + d_loss -> d_loss_t[color=red, style=dashed]; + d_loss -> d_loss_f[color=red, style=dashed]; + d_loss_t -> D_t[color=red, style=dashed]; + d_loss_f -> D_f[color=red, style=dashed]; + D_t -> discriminator[color=red, style=dashed]; + D_f -> discriminator[color=red, style=dashed]; + + D_f -> g_loss; + label2 -> g_loss; + + g_loss -> D_f[color=green, style=dashed]; + D_f -> discriminator[color=green, style=dashed]; + discriminator -> G_img[color=green, style=dashed]; + G_img -> generator[color=green, style=dashed]; + + discriminator [color=red, shape=box]; + generator [color=green, shape=box]; + z [shape=diamond]; + img [shape=diamond]; + label0 [shape=diamond]; + label1 [shape=diamond]; + label2 [shape=diamond]; + + d_loss [color=red]; + g_loss [color=green]; +} diff --git a/doc/design/test.dot.png b/doc/design/test.dot.png new file mode 100644 index 0000000000000000000000000000000000000000..4e121a40b9f7b2232d7cdda315bad15926446f55 Binary files /dev/null and b/doc/design/test.dot.png differ diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index 264b998f50df016da0741d97d4b26f759ee90900..c823d7e9fcd63dd7719ac1403952b03c2d2f03c0 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -206,7 +206,7 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`。 - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。 - - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。 + - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulGradKernel`类。 - 在 `.cu`文件中注册GPU Kernel。 @@ -285,41 +285,27 @@ class TestMulGradOp(GradientChecker): 'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) ``` 下面解释代码中一些关键的地方: - 调用`create_op("mul")`创建反向Op对应的前向Op。 -- 调用`compare_grad`函数对比CPU、GPU计算结果。 -- `test_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。 - - 第一个参数`self.op` : 前向Op。 - - 第二个参数`self.inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。 - - 第三个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。 - - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out` -- `test_ignore_x`和`test_ignore_y`分支用来测试只需要计算一个输入梯度的情况。 +- `test_check_grad_normal`中调用`check_grad`使用数值法检测梯度正确性和稳定性。 + - 第一个参数`["X", "Y"]` : 指定对输入变量`X`、`Y`做梯度检测。 + - 第二个参数`"Out"` : 指定前向网络最终的输出目标变量`Out`。 + - 第三个参数`max_relative_error`：指定检测梯度时能容忍的最大错误值。 +- `test_check_grad_ingore_x`和`test_check_grad_ingore_y`分支用来测试只需要计算一个输入梯度的情况。 ### 编译和执行单元测试 diff --git a/doc/howto/dev/new_op_en.md b/doc/howto/dev/new_op_en.md index bad1dbc1de9cc5bd11914fddf397857f0bda7976..1e88e1f5b4df710f1b69f0305d8d8a2921c4249a 100644 --- a/doc/howto/dev/new_op_en.md +++ b/doc/howto/dev/new_op_en.md @@ -205,7 +205,7 @@ The definition of its corresponding backward operator, if applicable, is similar - `REGISTER_OP` registers the `ops::MulOp` class, type named `mul`, its type `ProtoMaker` is `ops::MulOpMaker`, registering `ops::MulOpGrad` as `mul_grad`. - `REGISTER_OP_WITHOUT_GRADIENT` registers an operator without gradient. - - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulKernel`. + - `REGISTER_OP_CPU_KERNEL` registers `ops::MulKernel` class and specialized template types `paddle::platform::CPUPlace` and `float`, which also registers `ops::MulGradKernel`. - Registering GPU Kernel in `.cu` files @@ -293,41 +293,27 @@ class TestMulGradOp(GradientChecker): 'Y': np.random.random((84, 100)).astype("float32") } - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) - - def test_normal(self): + def test_check_grad_normal(self): # mul op will enlarge the relative error - self.check_grad( - self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) - def test_ignore_x(self): + def test_check_grad_ingore_x(self): self.check_grad( - self.op, - self.inputs, ["Y"], - "Out", - max_relative_error=0.5, - no_grad_set={"X"}) + ['Y'], 'Out', max_relative_error=0.5, no_grad_set=set("X")) - def test_ignore_y(self): + def test_check_grad_ingore_y(self): self.check_grad( - self.op, - self.inputs, ["X"], - "Out", - max_relative_error=0.5, - no_grad_set={"Y"}) + ['X'], 'Out', max_relative_error=0.5, no_grad_set=set('Y')) ``` Some key points in the code above include: - `create_op("mul")` creates the backward operator's corresponding forward operator. -- `compare_grad` compares results between utilizing the CPU and the GPU. - `test_normal` calls `check_grad` to validate scaling tests' correctness and stability through numeric methods. - - The first variable `self.op` denotes the forward operator. - - The second variable `self.inputs` denotes the input dictionary, which has its key value identical to its `ProtoMaker` definitions. - - The third variable `["X", "Y"]` appoints `X` and `Y` to be scale tested. - - The fourth variable `"Out"` points to the network's final output target `Out`. -- `test_ignore_x` and `test_ignore_y`branches test the cases where there is only one scaling input. + - The first variable `["X", "Y"]` appoints `X` and `Y` to be scale tested. + - The second variable `"Out"` points to the network's final output target `Out`. + - The third variable `max_relative_error` points to the maximum relative tolerance error during scaling tests. +- `test_check_grad_ingore_x` and `test_check_grad_ingore_y`branches test the cases where there is only one scaling input. ### Compiling and Running diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index b435de80a224571d16efdee168541aa301c3f73a..7d2becbdd772747d77890321fce6721d8d17fb30 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -1,27 +1,32 @@ add_subdirectory(cuda) add_subdirectory(function) add_subdirectory(utils) -add_subdirectory(testing) add_subdirectory(math) -add_subdirectory(parameter) add_subdirectory(gserver) -add_subdirectory(pserver) -add_subdirectory(trainer) -add_subdirectory(scripts) -add_subdirectory(string) - -if(Boost_FOUND) - add_subdirectory(memory) - add_subdirectory(platform) - add_subdirectory(framework) - add_subdirectory(operators) - add_subdirectory(pybind) -endif() +add_subdirectory(parameter) +add_subdirectory(testing) -if(WITH_C_API) +if(MOBILE_INFERENCE) add_subdirectory(capi) -endif() +else() + add_subdirectory(pserver) + add_subdirectory(trainer) + add_subdirectory(string) + add_subdirectory(scripts) + + if(WITH_C_API) + add_subdirectory(capi) + endif() + + if(Boost_FOUND) + add_subdirectory(memory) + add_subdirectory(platform) + add_subdirectory(framework) + add_subdirectory(operators) + add_subdirectory(pybind) + endif() -if(WITH_SWIG_PY) - add_subdirectory(api) + if(WITH_SWIG_PY) + add_subdirectory(api) + endif() endif() diff --git a/paddle/api/Util.cpp b/paddle/api/Util.cpp index d369df5d4e04b4a8d822db0e72a8051150868ce6..11bd05c09d1ecbbcec6b6596c16416c26635a072 100644 --- a/paddle/api/Util.cpp +++ b/paddle/api/Util.cpp @@ -47,7 +47,7 @@ bool isUsingGpu() { return FLAGS_use_gpu; } void setUseGpu(bool useGpu) { FLAGS_use_gpu = useGpu; } bool isGpuVersion() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index b9bbe58951c643f1b1649858880fbd2ba3a2a7b7..2c458a78c598bf206b30c0c07599ce605af77701 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -37,9 +37,7 @@ set(PADDLE_CAPI_INFER_LIBS paddle_cuda paddle_function paddle_gserver - paddle_proto - paddle_pserver - paddle_network) + paddle_proto) cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS}) diff --git a/paddle/capi/Matrix.cpp b/paddle/capi/Matrix.cpp index d898ebe2612d749ca261d35139d1cd45bd355eef..4547afaf1dc9af8bc7909a684db766fdd7b159c0 100644 --- a/paddle/capi/Matrix.cpp +++ b/paddle/capi/Matrix.cpp @@ -46,7 +46,7 @@ paddle_error paddle_matrix_set_row(paddle_matrix mat, if (rowID >= ptr->mat->getHeight()) return kPD_OUT_OF_RANGE; paddle::real* buf = ptr->mat->getRowBuf(rowID); size_t width = ptr->mat->getWidth(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA hl_memcpy(buf, rowArray, sizeof(paddle::real) * width); #else std::copy(rowArray, rowArray + width, buf); diff --git a/paddle/capi/tests/CMakeLists.txt b/paddle/capi/tests/CMakeLists.txt index 8208808b94f54f2ddaf4d426a65b8db562b36aca..bb38ace62808db5ce95a1a57ff465e8edc059213 100644 --- a/paddle/capi/tests/CMakeLists.txt +++ b/paddle/capi/tests/CMakeLists.txt @@ -4,11 +4,12 @@ add_unittest(capi_test_mats test_Vector.cpp target_include_directories(capi_test_mats PUBLIC ${PADDLE_CAPI_INC_PATH}) target_link_libraries(capi_test_mats paddle_capi) - -add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp) -target_include_directories(capi_test_gradientMachine PUBLIC - ${PADDLE_CAPI_INC_PATH}) -target_link_libraries(capi_test_gradientMachine paddle_capi) -add_test(NAME capi_test_gradientMachine - COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests) +if(NOT MOBILE_INFERENCE) + add_unittest_without_exec(capi_test_gradientMachine test_GradientMachine.cpp) + target_include_directories(capi_test_gradientMachine PUBLIC + ${PADDLE_CAPI_INC_PATH}) + target_link_libraries(capi_test_gradientMachine paddle_capi) + add_test(NAME capi_test_gradientMachine + COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/capi_test_gradientMachine + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/capi/tests) +endif() diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 4aaa43d79612111856dd4dfc954ca2bfd8f4fa63..148610aa2c7821542f9aa19690c3dc857ec9ab2e 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -19,17 +19,15 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope) proto_library(framework_proto SRCS framework.proto) cc_library(attribute SRCS attribute.cc DEPS framework_proto) -cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute) +cc_library(proto_desc SRCS var_desc.cc op_desc.cc block_desc.cc program_desc.cc DEPS attribute ddim) cc_library(op_proto_maker SRCS op_proto_maker.cc DEPS framework_proto attribute) cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker) -cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto) -cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope) +cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto proto_desc) +cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope proto_desc) cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry) -cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator) -cc_library(op_registry SRCS op_registry.cc DEPS grad_op_builder op_proto_maker op_info) +cc_library(op_registry SRCS op_registry.cc DEPS op_proto_maker op_info operator) cc_test(op_registry_test SRCS op_registry_test.cc DEPS op_registry) -cc_test(grad_op_builder_test SRCS grad_op_builder_test.cc DEPS grad_op_builder op_registry add_op) py_proto_compile(framework_py_proto SRCS framework.proto) # Generate an empty __init__.py to make framework_py_proto as a valid python module. @@ -43,3 +41,13 @@ add_custom_command(TARGET framework_py_proto POST_BUILD cc_library(backward SRCS backward.cc DEPS net_op) cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context) + +cc_library(executor SRCS executor.cc DEPS op_registry device_context scope framework_proto backward ${GLOB_OP_LIB}) +if(WITH_GPU) + nv_test(executor_test SRCS executor_test.cc DEPS executor) +else() + cc_test(executor_test SRCS executor_test.cc DEPS executor) +endif() + +cc_library(tensor_array SRCS tensor_array.cc DEPS lod_tensor) +cc_test(tensor_array_test SRCS tensor_array_test.cc DEPS tensor_array place) diff --git a/paddle/framework/attribute.h b/paddle/framework/attribute.h index c7559cefb6415ee141f32e4357459653564cd2ac..d13530e3408a54c7ecab87c3bd9e6288e342f9af 100644 --- a/paddle/framework/attribute.h +++ b/paddle/framework/attribute.h @@ -21,20 +21,12 @@ limitations under the License. */ #include #include "paddle/framework/framework.pb.h" +#include "paddle/framework/type_defs.h" #include "paddle/platform/enforce.h" -#include "paddle/platform/variant.h" namespace paddle { namespace framework { -// The order should be as same as framework.proto -typedef boost::variant, - std::vector, std::vector, bool, - std::vector, BlockDesc*> - Attribute; - -typedef std::unordered_map AttributeMap; - ProgramDesc& GetProgramDesc(); template diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index 0ec18de5b8a0e7cebdb91c30d2b45596b02dfa51..063b108500d95c94d5859cf6e1a5a88dcdb2ed31 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -13,10 +13,13 @@ limitations under the License. */ #include "paddle/framework/backward.h" +#include "paddle/operators/net_op.h" +#include #include #include +#include "paddle/framework/block_desc.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -24,6 +27,35 @@ namespace paddle { namespace framework { +static inline std::unique_ptr CreateGradOp( + const OperatorBase& op) { + OpDescBind op_desc; + op_desc.SetInputMap(op.Inputs()); + op_desc.SetOutputMap(op.Outputs()); + op_desc.SetType(op.Type()); + op_desc.SetAttrMap(op.Attrs()); + auto& info = OpInfoMap::Instance().Get(op.Type()); + auto grad_descs = info.GradOpMaker()(op_desc); + std::vector> grad_ops; + grad_ops.reserve(grad_descs.size()); + std::transform(grad_descs.begin(), grad_descs.end(), + std::back_inserter(grad_ops), + [](const std::unique_ptr& grad_desc) { + return OpRegistry::CreateOp(*grad_desc); + }); + PADDLE_ENFORCE(!grad_ops.empty()); + if (grad_ops.size() == 1) { + return std::move(grad_ops[0]); + } else { + auto net_op = new operators::NetOp(); + for (auto& grad_op : grad_ops) { + net_op->AppendOp(std::move(grad_op)); + } + net_op->CompleteAddOp(); + return std::unique_ptr(net_op); + } +} + template static void ForEachVarName(const Map& names, T callback) { for (auto& name : names) { @@ -140,13 +172,14 @@ static std::unique_ptr BackwardRecursive( std::to_string(i)); net->ops_[op_offset]->Rename(name, dup_outputs.back()); } - // collect all the offset to append `add` op for each alias + // collect all the offset for each alias, + // insert a sum operator to add all aliases to output insert_position.push_back( - {dup_op.back(), OpRegistry::CreateOp("add", {{"X", {dup_outputs}}}, + {dup_op.back(), OpRegistry::CreateOp("sum", {{"X", dup_outputs}}, {{"Out", {name}}}, {})}); } - // make sure the inserted `add` ops follow the BFS order. + // make sure the inserted `sum` ops follow the BFS order. insert_position.sort( [](const Pos& l, const Pos& r) { return l.first > r.first; }); @@ -154,7 +187,7 @@ static std::unique_ptr BackwardRecursive( net->InsertOp(pos.first + 1, std::move(pos.second)); } } else { - std::unique_ptr grad_op(OpRegistry::CreateGradOp(forwardOp)); + std::unique_ptr grad_op(CreateGradOp(forwardOp)); ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op]( const std::string& grad_input) { @@ -182,7 +215,8 @@ static std::unique_ptr BackwardRecursive( // process recurrent gradient op as a special operator. if (forwardOp.Type() == "recurrent") { - // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or + // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), + // or // this will result in infinite loop. const auto& rnnop = *static_cast(&forwardOp); @@ -222,5 +256,145 @@ std::unique_ptr Backward( return BackwardRecursive(forwardOp, no_grad_names, uid); } +// ==================================== // + +static bool AllGradInSet(const std::vector& names, + const std::unordered_set& set) { + for (const std::string& name : names) { + if (!set.count(GradVarName(name))) { + return false; + } + } + return true; +} + +std::vector> MakeOpGrad( + const std::unique_ptr& op_desc, + std::unordered_set& no_grad_vars) { + std::vector> grad_op_descs; + // All input gradients of forwarding operator do not need to calculat. + const std::vector& inputs = op_desc->InputArgumentNames(); + if (AllGradInSet(inputs, no_grad_vars)) { + return grad_op_descs; // empty vector + } + // All output gradients of forwarding operator do not need to calculate. + const std::vector& outputs = op_desc->OutputArgumentNames(); + if (AllGradInSet(outputs, no_grad_vars)) { + for (const std::string& name : inputs) { + no_grad_vars.insert(GradVarName(name)); + } + return grad_op_descs; // empty vector + } + + grad_op_descs = OpRegistry::CreateGradOpDescs(op_desc.get()); + + std::list> pending_fill_zeros_ops; + for (auto& desc : grad_op_descs) { + for (const std::string& in_name : desc->InputArgumentNames()) { + if (no_grad_vars.count(in_name)) { + std::string prefix = in_name.substr( + 0, in_name.size() - sizeof(kGradVarSuffix) / sizeof(char) + 1); + std::string new_name = prefix + kZeroVarSuffix; + desc->Rename(in_name, new_name); + std::unique_ptr fill_zeros_op(new OpDescBind( + "fill_zeros_like", {{"X", {prefix}}}, {{"Y", {new_name}}}, {})); + pending_fill_zeros_ops.push_back(std::move(fill_zeros_op)); + } + } + for (const std::string& out_name : desc->OutputArgumentNames()) { + if (no_grad_vars.count(out_name)) { + desc->Rename(out_name, kEmptyVarName); + } + } + } + + for (auto& p : pending_fill_zeros_ops) { + grad_op_descs.insert(grad_op_descs.begin(), std::move(p)); + } + return grad_op_descs; +} + +std::vector> MakeBlockBackward( + ProgramDescBind& program_desc, int block_idx, + std::unordered_set& no_grad_vars) { + BlockDescBind* cur_block = program_desc.Block(block_idx); + std::deque>& op_descs = cur_block->ops_; + std::unordered_map> dup_out_ops; + size_t grad_desc_idx = 0; + std::vector> backward_descs; + for (auto it = op_descs.rbegin(); it != op_descs.rend(); ++it) { + std::vector> op_grads = + MakeOpGrad(*it, no_grad_vars); + + if ((*it)->Type() == "recurrent") { + PADDLE_ENFORCE_EQ( + op_grads.size(), size_t(1), + "rnn_op's gradient process should contain only one op."); + int step_block_idx = (*it)->GetBlockAttr("stop_block"); + auto backward_block_op_descs = + MakeBlockBackward(program_desc, step_block_idx, no_grad_vars); + BlockDescBind* backward_block = program_desc.AppendBlock(*cur_block); + for (auto& ptr : backward_block_op_descs) { + backward_block->ops_.push_back(std::move(ptr)); + } + op_grads[0]->SetBlockAttr("step_block", *backward_block); + } + + for (const auto& desc : op_grads) { + for (const std::string& out_name : desc->OutputArgumentNames()) { + dup_out_ops[out_name].emplace_back(grad_desc_idx); + } + ++grad_desc_idx; + } + std::transform( + op_grads.begin(), op_grads.end(), std::back_inserter(backward_descs), + [](std::unique_ptr& ptr) { return std::move(ptr); }); + } + // Check whether some variables are written more than once + std::list>> pending_sum_ops; + for (const auto& dup : dup_out_ops) { + const std::string& out_name = dup.first; + const std::vector dup_op = dup.second; + if (out_name != kEmptyVarName && dup_op.size() > 1) { + std::vector sum_op_inputs; + for (size_t i = 0; i < dup_op.size(); ++i) { + std::string new_name = out_name + "@RENAME@" + std::to_string(i); + backward_descs[dup_op[i]]->Rename(out_name, new_name); + sum_op_inputs.emplace_back(new_name); + } + std::unique_ptr sum_op(new OpDescBind( + "sum", {{"X", sum_op_inputs}}, {{"Out", {out_name}}}, {})); + pending_sum_ops.push_back({dup_op.back(), std::move(sum_op)}); + } + } + pending_sum_ops.sort( + [](const std::pair>& a, + const std::pair>& b) { + return a.first > b.first; + }); + for (auto& p : pending_sum_ops) { + backward_descs.insert(backward_descs.begin() + p.first + 1, + std::move(p.second)); + } + return backward_descs; +} + +void AppendBackward(ProgramDescBind& program_desc, + const std::unordered_set& no_grad_vars) { + std::unordered_set no_grad_var_names; + no_grad_var_names.reserve(no_grad_vars.size() + 1); + no_grad_var_names.insert(std::string(kEmptyVarName) + kGradVarSuffix); + for (auto& name : no_grad_vars) { + no_grad_var_names.insert(GradVarName(name)); + } + const int root_block_idx = 0; + auto backward_op_descs = + MakeBlockBackward(program_desc, root_block_idx, no_grad_var_names); + auto& forw_op_descs = program_desc.Block(root_block_idx)->ops_; + for (auto& ptr : backward_op_descs) { + forw_op_descs.push_back(std::move(ptr)); + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/backward.h b/paddle/framework/backward.h index 1ecf69881b3126c2904920b9f4b77bfcccc9cf86..7ffe4c28103f9d6a9f179422d1beb86106ef786e 100644 --- a/paddle/framework/backward.h +++ b/paddle/framework/backward.h @@ -13,8 +13,11 @@ limitations under the License. */ #pragma once + #include -#include "operator.h" +#include "paddle/framework/operator.h" +#include "paddle/framework/program_desc.h" + namespace paddle { namespace framework { @@ -23,5 +26,9 @@ namespace framework { extern std::unique_ptr Backward( const OperatorBase& forwardOp, const std::unordered_set& no_grad_vars); + +void AppendBackward(ProgramDescBind& program_desc, + const std::unordered_set& no_grad_vars); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index 6932f5b989a3e21ebc44ec4fec9f5223f2547d7a..3b7cbcd98927be829d185590147adf74cd3d10d1 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -15,30 +15,42 @@ #include "paddle/framework/backward.h" #include +#include "paddle/framework/block_desc.h" +#include "paddle/framework/op_desc.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" namespace paddle { namespace framework { -using OperatorBase = framework::OperatorBase; -using OpProtoAndCheckerMaker = framework::OpProtoAndCheckerMaker; -using OpProto = framework::OpProto; -using OpAttrChecker = framework::OpAttrChecker; -using Scope = framework::Scope; using DeviceContext = platform::DeviceContext; class RowWiseAddOpMaker : public OpProtoAndCheckerMaker { public: RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "Input X of Add").NotInGradient(); - AddInput("b", "Bias of Add").NotInGradient(); - AddOutput("Out", "Out of Add").NotInGradient(); + AddInput("X", "Input X of Add"); + AddInput("b", "Bias of Add"); + AddOutput("Out", "Out of Add"); AddComment("Add Op"); } }; +class RowWiseAddGradMaker : public SingleGradOpDescMaker { + public: + using SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto grad_op = new OpDescBind(); + grad_op->SetInput(GradVarName("Out"), OutputGrad("Out")); + grad_op->SetOutput(GradVarName("X"), InputGrad("X")); + grad_op->SetOutput(GradVarName("b"), InputGrad("b")); + grad_op->SetType("rowwise_add_grad"); + return std::unique_ptr(grad_op); + } +}; + class MulOpMaker : public OpProtoAndCheckerMaker { public: MulOpMaker(OpProto *proto, OpAttrChecker *op_checker) @@ -46,6 +58,8 @@ class MulOpMaker : public OpProtoAndCheckerMaker { AddInput("X", "A"); AddInput("Y", "B"); AddOutput("Out", "Out"); + AddAttr("x_num_col_dims", "").SetDefault(1).EqualGreaterThan(1); + AddAttr("y_num_col_dims", "").SetDefault(1).EqualGreaterThan(1); AddComment("Mul"); } }; @@ -133,42 +147,46 @@ class FillZeroOpMaker : public OpProtoAndCheckerMaker { } }; -class AddOpMaker : public OpProtoAndCheckerMaker { +class SumOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "the input tensors of sum operator.").AsDuplicable(); + AddOutput("Out", "the output tensor of sum operator."); + AddComment(""); + } +}; + +class MultInOutOpMaker : public OpProtoAndCheckerMaker { public: - AddOpMaker(OpProto *proto, OpAttrChecker *op_checker) + MultInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "x").AsDuplicable(); - AddOutput("Out", "out"); + AddInput("X", "x"); + AddInput("H", "h"); + AddOutput("Y", "y"); + AddOutput("Z", "z"); AddComment(""); } }; + } // namespace framework } // namespace paddle namespace f = paddle::framework; namespace ops = paddle::operators; using EnforceNotMet = paddle::platform::EnforceNotMet; -REGISTER_OP(rowwise_add, f::NOP, f::RowWiseAddOpMaker, rowwise_add_grad, - f::NOP); +REGISTER_OPERATOR(rowwise_add, f::NOP, f::RowWiseAddOpMaker, + f::RowWiseAddGradMaker); +REGISTER_OPERATOR(rowwise_add_grad, f::NOP); REGISTER_OP(mul, f::NOP, f::MulOpMaker, mul_grad, f::NOP); REGISTER_OP(sigmoid, f::NOP, f::SigmoidOpMaker, sigmoid_grad, f::NOP); REGISTER_OP_WITHOUT_GRADIENT(nograd, f::NOP, f::NoGradOpMaker); REGISTER_OP_WITHOUT_GRADIENT(fill_zeros_like, f::NOP, f::FillZeroOpMaker); -REGISTER_OP(add, f::NOP, f::AddOpMaker, add_grad, f::NOP); +REGISTER_OP(sum, f::NOP, f::SumOpMaker, sum_grad, f::NOP); REGISTER_OP_WITHOUT_GRADIENT(fc, f::FcOp, f::FcOpMaker); REGISTER_OP(many_output_op, f::NOP, f::ManyOutputOpMaker, many_output_op_grad, f::NOP); - -TEST(Backward, simple_op_grad) { - auto fwd = f::OpRegistry::CreateOp( - "rowwise_add", {{"X", {"x"}}, {"b", {"b"}}}, {{"Out", {"out"}}}, {}); - ASSERT_NE(fwd, nullptr); - auto gop = f::OpRegistry::CreateGradOp(*fwd); - ASSERT_EQ(1UL, gop->Inputs().size()); - ASSERT_EQ("rowwise_add_grad", gop->Type()); - ASSERT_EQ(f::GradVarName("x"), gop->Output(f::GradVarName("X"))); - ASSERT_EQ(f::GradVarName("b"), gop->Output(f::GradVarName("b"))); -} +REGISTER_OP(mult_in_out, f::NOP, f::MultInOutOpMaker, mult_in_out_grad, f::NOP); TEST(Backward, simple_op_not_need_grad) { auto fwd = f::OpRegistry::CreateOp( @@ -283,18 +301,7 @@ TEST(Backward, net_shared_weight) { ASSERT_TRUE(bwd->IsNetOp()); auto bwd_net = static_cast(bwd.get()); ASSERT_EQ(3UL, bwd_net->ops_.size()); - ASSERT_EQ("add", bwd_net->ops_[2]->Type()); -} - -TEST(Backward, op_register_grad_not_for_network) { - auto fwd = - f::OpRegistry::CreateOp("fc", {{"X", {"x"}}, {"W", {"w"}}, {"b", {"b"}}}, - {{"mul_result", {"mul_out"}}, - {"add_result", {"add_out"}}, - {"Out", {"out1"}}}, - {{"temporary_index", std::vector{0, 1}}}); - - ASSERT_THROW(f::OpRegistry::CreateGradOp(*fwd), EnforceNotMet); + ASSERT_EQ("sum", bwd_net->ops_[2]->Type()); } TEST(Backward, op_all_input_are_not_need) { @@ -399,3 +406,315 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) { EXPECT_EQ(bwd_net->ops_[2]->Inputs(all).size(), 0UL); EXPECT_EQ(bwd_net->ops_[2]->Outputs(all).size(), 0UL); } + +// =================================== // + +f::ProgramDesc *GetNewProgramDesc() { + auto *program_desc = new f::ProgramDesc(); + auto *root_block = program_desc->add_blocks(); + root_block->set_idx(0); + root_block->set_parent_idx(-1); + return program_desc; +} + +TEST(Backward, simple_single_op) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op = block->AppendOp(); + op->SetType("rowwise_add"); + op->SetInput("X", {"x"}); + op->SetInput("b", {"b"}); + op->SetOutput("Out", {"out"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 2UL); + f::OpDescBind *grad_op = block->AllOps()[1]; + EXPECT_EQ(grad_op->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op->InputNames().size(), 1UL); + ASSERT_EQ(grad_op->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out")})); + EXPECT_EQ(grad_op->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x")})); + EXPECT_EQ(grad_op->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b")})); +} + +TEST(Backward, default_attribute) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op = block->AppendOp(); + op->SetType("mul"); + op->SetInput("X", {"x"}); + op->SetInput("Y", {"y"}); + op->SetOutput("Out", {"out"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 2UL); + EXPECT_EQ(boost::get(op->GetAttr("x_num_col_dims")), 1); + EXPECT_EQ(boost::get(op->GetAttr("y_num_col_dims")), 1); + + f::OpDescBind *grad_op = block->AllOps()[1]; + ASSERT_EQ(grad_op->Type(), "mul_grad"); + EXPECT_EQ(boost::get(grad_op->GetAttr("x_num_col_dims")), 1); + EXPECT_EQ(boost::get(grad_op->GetAttr("y_num_col_dims")), 1); +} + +TEST(Backward, simple_mult_op) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"out1"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out2"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 6UL); + f::OpDescBind *grad_op1 = block->AllOps()[5]; + EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); + + f::OpDescBind *grad_op2 = block->AllOps()[4]; + EXPECT_EQ(grad_op2->Type(), "mul_grad"); + ASSERT_EQ(grad_op2->InputNames().size(), 4UL); + ASSERT_EQ(grad_op2->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op2->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op2->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op2->Input("Out"), std::vector({"out2"})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + + f::OpDescBind *grad_op3 = block->AllOps()[3]; + EXPECT_EQ(grad_op3->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op3->InputNames().size(), 1UL); + ASSERT_EQ(grad_op3->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out3")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b3")})); +} + +TEST(Backward, intermedia_var_no_grad) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"x2"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out2"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + f::OpDescBind *op4 = block->AppendOp(); + op4->SetType("mul"); + op4->SetInput("X", {"out1"}); + op4->SetInput("Y", {"out3"}); + op4->SetOutput("Out", {"out4"}); + + AppendBackward(program, {"out3"}); + + ASSERT_EQ(block->AllOps().size(), 6UL); + f::OpDescBind *grad_op1 = block->AllOps()[5]; + EXPECT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); + + f::OpDescBind *grad_op4 = block->AllOps()[4]; + EXPECT_EQ(grad_op4->Type(), "mul_grad"); + ASSERT_EQ(grad_op4->InputNames().size(), 4UL); + ASSERT_EQ(grad_op4->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op4->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op4->Input("Y"), std::vector({"out3"})); + EXPECT_EQ(grad_op4->Input("Out"), std::vector({"out4"})); + EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out4")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), + std::vector({f::kEmptyVarName})); +} + +TEST(Backward, var_no_grad) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("mult_in_out"); + op1->SetInput("X", {"x1"}); + op1->SetInput("H", {"h1"}); + op1->SetOutput("Y", {"y1"}); + op1->SetOutput("Z", {"z1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mult_in_out"); + op2->SetInput("X", {"y1"}); + op2->SetInput("H", {"z1"}); + op2->SetOutput("Y", {"y2"}); + op2->SetOutput("Z", {"z2"}); + + AppendBackward(program, {"z1"}); + + ASSERT_EQ(block->AllOps().size(), 5UL); + f::OpDescBind *grad_op2 = block->AllOps()[2]; + ASSERT_EQ(grad_op2->Type(), "mult_in_out_grad"); + ASSERT_EQ(grad_op2->InputNames().size(), 6UL); + ASSERT_EQ(grad_op2->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op2->Input("X"), std::vector({"y1"})); + EXPECT_EQ(grad_op2->Input("H"), std::vector({"z1"})); + EXPECT_EQ(grad_op2->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op2->Input("Z"), std::vector({"z2"})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + EXPECT_EQ(grad_op2->Input(f::GradVarName("Z")), + std::vector({f::GradVarName("z2")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("X")), + std::vector({f::GradVarName("y1")})); + EXPECT_EQ(grad_op2->Output(f::GradVarName("H")), + std::vector({f::kEmptyVarName})); + + f::OpDescBind *fill_zero_op = block->AllOps()[3]; + ASSERT_EQ(fill_zero_op->Type(), "fill_zeros_like"); + ASSERT_EQ(fill_zero_op->InputNames().size(), 1UL); + ASSERT_EQ(fill_zero_op->OutputNames().size(), 1UL); + EXPECT_EQ(fill_zero_op->Input("X"), std::vector({"z1"})); + EXPECT_EQ(fill_zero_op->Output("Y"), + std::vector({std::string("z1") + f::kZeroVarSuffix})); + + f::OpDescBind *grad_op1 = block->AllOps()[4]; + ASSERT_EQ(grad_op1->Type(), "mult_in_out_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 6UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input("X"), std::vector({"x1"})); + EXPECT_EQ(grad_op1->Input("H"), std::vector({"h1"})); + EXPECT_EQ(grad_op1->Input("Y"), std::vector({"y1"})); + EXPECT_EQ(grad_op1->Input("Z"), std::vector({"z1"})); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Y")), + std::vector({f::GradVarName("y1")})); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Z")), + std::vector({std::string("z1") + f::kZeroVarSuffix})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("H")), + std::vector({f::GradVarName("h1")})); +} + +TEST(Backward, shared_var) { + f::ProgramDesc *program_desc = GetNewProgramDesc(); + f::ProgramDescBind &program = f::ProgramDescBind::Instance(program_desc); + f::BlockDescBind *block = program.Block(0); + f::OpDescBind *op1 = block->AppendOp(); + op1->SetType("rowwise_add"); + op1->SetInput("X", {"x1"}); + op1->SetInput("b", {"b1"}); + op1->SetOutput("Out", {"out1"}); + + f::OpDescBind *op2 = block->AppendOp(); + op2->SetType("mul"); + op2->SetInput("X", {"out1"}); + op2->SetInput("Y", {"y2"}); + op2->SetOutput("Out", {"out2"}); + + f::OpDescBind *op3 = block->AppendOp(); + op3->SetType("rowwise_add"); + op3->SetInput("X", {"out1"}); + op3->SetInput("b", {"b3"}); + op3->SetOutput("Out", {"out3"}); + + AppendBackward(program, {}); + + ASSERT_EQ(block->AllOps().size(), 7UL); + f::OpDescBind *grad_op3 = block->AllOps()[3]; + ASSERT_EQ(grad_op3->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op3->InputNames().size(), 1UL); + ASSERT_EQ(grad_op3->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op3->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out3")})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1") + "@RENAME@0"})); + EXPECT_EQ(grad_op3->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b3")})); + + f::OpDescBind *grad_op4 = block->AllOps()[4]; + ASSERT_EQ(grad_op4->Type(), "mul_grad"); + ASSERT_EQ(grad_op4->InputNames().size(), 4UL); + ASSERT_EQ(grad_op4->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op4->Input("X"), std::vector({"out1"})); + EXPECT_EQ(grad_op4->Input("Y"), std::vector({"y2"})); + EXPECT_EQ(grad_op4->Input("Out"), std::vector({"out2"})); + EXPECT_EQ(grad_op4->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out2")})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("X")), + std::vector({f::GradVarName("out1") + "@RENAME@1"})); + EXPECT_EQ(grad_op4->Output(f::GradVarName("Y")), + std::vector({f::GradVarName("y2")})); + + f::OpDescBind *sum_op = block->AllOps()[5]; + ASSERT_EQ(sum_op->Type(), "sum"); + ASSERT_EQ(sum_op->InputNames().size(), 1UL); + ASSERT_EQ(sum_op->OutputNames().size(), 1UL); + EXPECT_EQ(sum_op->Input("X"), + std::vector({f::GradVarName("out1") + "@RENAME@0", + f::GradVarName("out1") + "@RENAME@1"})); + EXPECT_EQ(sum_op->Output("Out"), + std::vector({f::GradVarName("out1")})); + + f::OpDescBind *grad_op1 = block->AllOps()[6]; + ASSERT_EQ(grad_op1->Type(), "rowwise_add_grad"); + ASSERT_EQ(grad_op1->InputNames().size(), 1UL); + ASSERT_EQ(grad_op1->OutputNames().size(), 2UL); + EXPECT_EQ(grad_op1->Input(f::GradVarName("Out")), + std::vector({f::GradVarName("out1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("X")), + std::vector({f::GradVarName("x1")})); + EXPECT_EQ(grad_op1->Output(f::GradVarName("b")), + std::vector({f::GradVarName("b1")})); +} \ No newline at end of file diff --git a/paddle/framework/block_desc.cc b/paddle/framework/block_desc.cc index 9570aedfdda332b797a8f348e0f6cf81bb2aee2f..509aa235d3ee226adef15f08f5785866700499f1 100644 --- a/paddle/framework/block_desc.cc +++ b/paddle/framework/block_desc.cc @@ -34,6 +34,10 @@ VarDescBind *BlockDescBind::Var(const std::string &name) const { return it->second.get(); } +bool BlockDescBind::HasVar(const std::string &name) const { + return vars_.find(name) != vars_.end(); +} + std::vector BlockDescBind::AllVars() const { std::vector res; for (const auto &p : vars_) { @@ -70,6 +74,12 @@ void BlockDescBind::Sync() { for (auto &op_desc : ops_) { op_field.AddAllocated(op_desc->Proto()); } + auto &var_field = *this->desc_->mutable_vars(); + var_field.Clear(); + var_field.Reserve(static_cast(vars_.size())); + for (auto &var_desc : vars_) { + var_field.AddAllocated(var_desc.second->Proto()); + } need_update_ = false; } } diff --git a/paddle/framework/block_desc.h b/paddle/framework/block_desc.h index 1a1135bab44cd27bb7d784c3b486188aa40635e4..3437e89923da8de79eeaa88d0466cf7eb0b5926d 100644 --- a/paddle/framework/block_desc.h +++ b/paddle/framework/block_desc.h @@ -15,10 +15,12 @@ limitations under the License. */ #pragma once #include +#include #include #include #include "paddle/framework/op_desc.h" #include "paddle/framework/var_desc.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -31,12 +33,17 @@ class ProgramDescBind; class BlockDescBind { public: + friend std::vector> MakeBlockBackward( + ProgramDescBind &program_desc, int block_idx, + std::unordered_set &no_grad_vars); + + friend void AppendBackward( + ProgramDescBind &program_desc, + const std::unordered_set &no_grad_vars); + BlockDescBind(ProgramDescBind *prog, BlockDesc *desc) : prog_(prog), desc_(desc), need_update_(false) {} - BlockDescBind(const BlockDescBind &o) = delete; - BlockDescBind &operator=(const BlockDescBind &o) = delete; - int32_t ID() const { return desc_->idx(); } int32_t Parent() const { return desc_->parent_idx(); } @@ -45,6 +52,8 @@ class BlockDescBind { VarDescBind *Var(const std::string &name_bytes) const; + bool HasVar(const std::string &var_name) const; + std::vector AllVars() const; BlockDescBind *ParentBlock() const; @@ -66,6 +75,8 @@ class BlockDescBind { std::deque> ops_; std::unordered_map> vars_; + + DISABLE_COPY_AND_ASSIGN(BlockDescBind); }; } // namespace framework } // namespace paddle diff --git a/paddle/framework/data_type.h b/paddle/framework/data_type.h new file mode 100644 index 0000000000000000000000000000000000000000..649899d42572c9a22adca5337dcd56b0bcf42e7c --- /dev/null +++ b/paddle/framework/data_type.h @@ -0,0 +1,35 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include +#include "paddle/framework/framework.pb.h" + +namespace paddle { +namespace framework { + +inline DataType ToDataType(std::type_index type) { + if (typeid(float).hash_code() == type.hash_code()) { + return DataType::FP32; + } else if (typeid(double).hash_code() == type.hash_code()) { + return DataType::FP64; + } else if (typeid(int).hash_code() == type.hash_code()) { + return DataType::INT32; + } else { + PADDLE_THROW("Not supported"); + } +} + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/details/op_registry.h b/paddle/framework/details/op_registry.h new file mode 100644 index 0000000000000000000000000000000000000000..daa474e8c5a223589018720da29a5c3363b5934d --- /dev/null +++ b/paddle/framework/details/op_registry.h @@ -0,0 +1,109 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once + +#include "paddle/framework/grad_op_desc_maker.h" +#include "paddle/framework/op_info.h" +#include "paddle/framework/op_proto_maker.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace framework { +namespace details { + +enum OpInfoFillType { + kOperator = 0, + kOpProtoAndCheckerMaker = 1, + kGradOpDescMaker = 2 +}; + +template +struct OpInfoFillTypeID { + static constexpr OpInfoFillType ID() { + return std::is_base_of::value + ? kOperator + : (std::is_base_of::value + ? kOpProtoAndCheckerMaker + : (std::is_base_of::value + ? kGradOpDescMaker + : static_cast(-1))); + } +}; + +template ::ID()> +struct OpInfoFiller; + +template +class OperatorRegistrarRecursive; + +template +class OperatorRegistrarRecursive { + public: + using T = typename std::tuple_element>::type; + OperatorRegistrarRecursive(const char* op_type, OpInfo* info) { + OpInfoFiller fill; + fill(op_type, info); + constexpr auto size = sizeof...(ARGS); + OperatorRegistrarRecursive reg(op_type, + info); + (void)(reg); + } +}; + +template +class OperatorRegistrarRecursive { + public: + OperatorRegistrarRecursive(const char* op_type, OpInfo* info) {} +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->creator_ = [](const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, + const AttributeMap& attrs) { + return new T(type, inputs, outputs, attrs); + }; + } +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->proto_ = new OpProto; + info->checker_ = new OpAttrChecker(); + auto maker = T(info->proto_, info->checker_); + maker.Validate(); + info->proto_->set_type(op_type); + PADDLE_ENFORCE( + info->proto_->IsInitialized(), + "Fail to initialize %s's OpProto, because %s is not initialized", + op_type, info->proto_->InitializationErrorString()); + } +}; + +template +struct OpInfoFiller { + void operator()(const char* op_type, OpInfo* info) const { + info->grad_op_maker_ = [](const OpDescBind& fwd_op) { + T maker(fwd_op); + return maker(); + }; + } +}; +} // namespace details + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/executor.cc b/paddle/framework/executor.cc new file mode 100644 index 0000000000000000000000000000000000000000..c388b2198e4fbf75d6584d710e00d3deca93eb51 --- /dev/null +++ b/paddle/framework/executor.cc @@ -0,0 +1,163 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/framework/executor.h" + +#include +#include +#include +#include +#include + +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/op_registry.h" +#include "paddle/framework/scope.h" + +namespace paddle { +namespace framework { + +const std::string kFeedOpType = "feed"; +const std::string kFetchOpType = "fetch"; + +Executor::Executor(const std::vector& places) { + PADDLE_ENFORCE_GT(places.size(), 0); + device_contexts_.resize(places.size()); + for (size_t i = 0; i < places.size(); i++) { + if (platform::is_cpu_place(places[i])) { + device_contexts_[i] = new platform::CPUDeviceContext( + boost::get(places[i])); + } else if (platform::is_gpu_place(places[i])) { +#ifdef PADDLE_WITH_CUDA + device_contexts_[i] = new platform::CUDADeviceContext( + boost::get(places[i])); +#else + PADDLE_THROW( + "'GPUPlace' is not supported, Please re-compile with WITH_GPU " + "option"); +#endif + } + } +} + +Executor::~Executor() { + for (auto& device_context : device_contexts_) { + delete device_context; + } +} + +void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) { + // TODO(tonyyang-svail): + // - only runs on the first device (i.e. no interdevice communication) + // - will change to use multiple blocks for RNN op and Cond Op + PADDLE_ENFORCE_GT(pdesc.blocks_size(), block_id); + auto& block = pdesc.blocks(block_id); + auto& device = device_contexts_[0]; + + // Instantiate all the vars in the global scope + for (auto& var : block.vars()) { + scope->NewVar(var.name()); + } + + Scope& local_scope = scope->NewScope(); + + std::vector should_run = Prune(pdesc, block_id); + PADDLE_ENFORCE_EQ(should_run.size(), static_cast(block.ops_size())); + for (size_t i = 0; i < should_run.size(); ++i) { + if (should_run[i]) { + for (auto& var : block.ops(i).outputs()) { + for (auto& argu : var.arguments()) { + if (local_scope.FindVar(argu) == nullptr) { + local_scope.NewVar(argu); + } + } + } + auto op = paddle::framework::OpRegistry::CreateOp(block.ops(i)); + op->Run(local_scope, *device); + } + } + + // TODO(tonyyang-svail): + // - Destroy local_scope +} + +std::vector Prune(const ProgramDesc& pdesc, int block_id) { + // TODO(tonyyang-svail): + // - will change to use multiple blocks for RNN op and Cond Op + + auto& block = pdesc.blocks(block_id); + auto& ops = block.ops(); + + bool expect_feed = true; + for (auto& op_desc : ops) { + PADDLE_ENFORCE(op_desc.type() != kFeedOpType || expect_feed, + "All FeedOps are at the beginning of the ProgramDesc"); + expect_feed = (op_desc.type() == kFeedOpType); + } + + bool expect_fetch = true; + for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) { + auto& op_desc = *op_iter; + PADDLE_ENFORCE(op_desc.type() != kFetchOpType || expect_fetch, + "All FetchOps must at the end of the ProgramDesc"); + expect_fetch = (op_desc.type() == kFetchOpType); + } + + std::set dependent_vars; + std::vector should_run; + for (auto op_iter = ops.rbegin(); op_iter != ops.rend(); ++op_iter) { + auto& op_desc = *op_iter; + + bool found_dependent_vars = false; + for (auto& var : op_desc.outputs()) { + for (auto& argu : var.arguments()) { + if (dependent_vars.count(argu) != 0) { + found_dependent_vars = true; + } + } + } + + if (op_desc.type() == kFetchOpType || found_dependent_vars) { + // erase its output to the dependency graph + for (auto& var : op_desc.outputs()) { + for (auto& argu : var.arguments()) { + dependent_vars.erase(argu); + } + } + + // insert its input to the dependency graph + for (auto& var : op_desc.inputs()) { + for (auto& argu : var.arguments()) { + dependent_vars.insert(argu); + } + } + + should_run.push_back(true); + } else { + should_run.push_back(false); + } + } + + // TODO(tonyyang-svail): + // - check this after integration of Init + // PADDLE_ENFORCE(dependent_vars.empty()); + + // since we are traversing the ProgramDesc in reverse order + // we reverse the should_run vector + std::reverse(should_run.begin(), should_run.end()); + + return should_run; +} + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/executor.h b/paddle/framework/executor.h new file mode 100644 index 0000000000000000000000000000000000000000..4e3bc2c0a59dfee5b9993037671f14a109dc8a74 --- /dev/null +++ b/paddle/framework/executor.h @@ -0,0 +1,55 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/framework/framework.pb.h" +#include "paddle/framework/op_info.h" +#include "paddle/framework/scope.h" +#include "paddle/framework/tensor.h" + +namespace paddle { +namespace framework { + +class Executor { + public: + explicit Executor(const std::vector& places); + ~Executor(); + + /* @Brief + * Runtime evaluation of the given ProgramDesc under certain Scope + * + * @param + * ProgramDesc + * Scope + */ + void Run(const ProgramDesc&, Scope*, int); + + private: + std::vector device_contexts_; +}; + +/* @Brief + * Pruning the graph + * + * @param + * ProgramDesc + * + * @return + * vector Same size as ops. Indicates whether an op should be run. + */ +std::vector Prune(const ProgramDesc& pdesc, int block_id); + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/executor_test.cc b/paddle/framework/executor_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..137e53d849542e48080228e0002931867c4d7fb2 --- /dev/null +++ b/paddle/framework/executor_test.cc @@ -0,0 +1,318 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/framework/executor.h" + +#include +#include + +#include "gtest/gtest.h" +#include "paddle/framework/attribute.h" +#include "paddle/framework/backward.h" +#include "paddle/framework/block_desc.h" +#include "paddle/framework/op_desc.h" +#include "paddle/framework/op_registry.h" +#include "paddle/framework/operator.h" + +USE_OP(elementwise_add); +USE_OP(gaussian_random); +USE_OP(feed); +USE_OP(fetch); +USE_OP(mul); +USE_OP(sum); +USE_OP(squared_l2_distance); +USE_OP(fill_constant); +USE_OP(sgd); + +using namespace paddle::platform; +using namespace paddle::framework; + +void AddOp(const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, AttributeMap attrs, + paddle::framework::BlockDescBind* block) { + // insert output + for (auto kv : outputs) { + for (auto v : kv.second) { + auto var = block->NewVar(v); + var->SetDataType(paddle::framework::DataType::FP32); + } + } + + // insert op + auto op = block->AppendOp(); + op->SetType(type); + for (auto& kv : inputs) { + op->SetInput(kv.first, kv.second); + } + for (auto& kv : outputs) { + op->SetOutput(kv.first, kv.second); + } + op->SetAttrMap(attrs); +} + +// Tensors in feed value variable will only be in CPUPlace +// So we can memcpy the data from vector to feed_value +template +void SetFeedVariable(const std::vector>& inputs, + const std::vector>& dims) { + Variable* g_feed_value = GetGlobalScope().FindVar("feed_value"); + auto& feed_inputs = + *(g_feed_value->GetMutable>()); + size_t size = inputs.size(); + feed_inputs.resize(size); + for (size_t i = 0; i < size; i++) { + T* dst = feed_inputs[i].mutable_data(make_ddim(dims[i]), CPUPlace()); + memcpy(dst, inputs[i].data(), inputs[i].size() * sizeof(T)); + } +} + +// Tensors in fetch value variable will only be in CPUPlace +// So we can memcpy the data from fetch_value to vector +template +std::vector> GetFetchVariable() { + Variable* g_fetch_value = GetGlobalScope().FindVar("fetch_value"); + auto& fetch_outputs = + *(g_fetch_value->GetMutable>()); + + size_t size = fetch_outputs.size(); + std::vector> result; + result.reserve(size); + for (size_t i = 0; i < size; i++) { + std::vector tmp; + tmp.resize(fetch_outputs[i].numel()); + memcpy(tmp.data(), fetch_outputs[i].data(), + fetch_outputs[i].numel() * sizeof(T)); + result.push_back(tmp); + } + + return result; +} + +class ExecutorTesterRandom : public ::testing::Test { + public: + virtual void SetUp() override { + int input_dim = 3, batch_size = 2, embed_dim = 5; + + auto temp_init_root_block = init_pdesc_.add_blocks(); + temp_init_root_block->set_idx(0); + temp_init_root_block->set_parent_idx(-1); + paddle::framework::ProgramDescBind& init_program = + paddle::framework::ProgramDescBind::Instance(&init_pdesc_); + paddle::framework::BlockDescBind* init_root_block = init_program.Block(0); + + AddOp("gaussian_random", {}, {{"Out", {"w1"}}}, + {{"dims", std::vector{input_dim, embed_dim}}}, init_root_block); + AddOp("gaussian_random", {}, {{"Out", {"w2"}}}, + {{"dims", std::vector{embed_dim, input_dim}}}, init_root_block); + AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, init_root_block); + AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, init_root_block); + + // flush + init_program.Proto(); + + // run block + auto temp_root_block = pdesc_.add_blocks(); + temp_root_block->set_idx(0); + temp_root_block->set_parent_idx(-1); + paddle::framework::ProgramDescBind& program = + paddle::framework::ProgramDescBind::Instance(&pdesc_); + paddle::framework::BlockDescBind* root_block = program.Block(0); + + // feed data + inputs_.push_back({1.0, 1.0, 1.0, 1.0, 1.0, 1.0}); + dims_.push_back({batch_size, input_dim}); + AddOp("feed", {}, {{"Out", {"a"}}}, + {{"dims", std::vector{batch_size, input_dim}}, {"col", 0}}, + root_block); + + // forward + AddOp("mul", {{"X", {"a"}}, {"Y", {"w1"}}}, {{"Out", {"b"}}}, {}, + root_block); + AddOp("mul", {{"X", {"b"}}, {"Y", {"w2"}}}, {{"Out", {"a_out"}}}, {}, + root_block); + AddOp("squared_l2_distance", {{"X", {"a"}}, {"Y", {"a_out"}}}, + {{"Out", {"l2_distance"}}, {"sub_result", {"l2_distance_sub"}}}, {}, + root_block); + + // backward + AddOp("fill_constant", {}, {{"Out", {"l2_distance@GRAD"}}}, + {{"shape", std::vector{batch_size, 1}}, {"value", float(1.0)}}, + root_block); + AppendBackward(program, {}); + + // update + AddOp("fill_constant", {}, {{"Out", {"learning_rate"}}}, + {{"shape", std::vector{1}}, {"value", float(0.001)}}, + root_block); + AddOp("sgd", {{"Param", {"w1"}}, + {"LearningRate", {"learning_rate"}}, + {"Grad", {"w1@GRAD"}}}, + {{"ParamOut", {"w1"}}}, {}, root_block); + AddOp("sgd", {{"Param", {"w2"}}, + {"LearningRate", {"learning_rate"}}, + {"Grad", {"w2@GRAD"}}}, + {{"ParamOut", {"w2"}}}, {}, root_block); + + AddOp("fetch", {{"Input", {"w1"}}}, {}, {{"col", 0}}, root_block); + AddOp("fetch", {{"Input", {"w2"}}}, {}, {{"col", 1}}, root_block); + AddOp("fetch", {{"Input", {"l2_distance"}}}, {}, {{"col", 0}}, root_block); + + // flush + program.Proto(); + } + + protected: + ProgramDesc init_pdesc_; + ProgramDesc pdesc_; + std::vector> inputs_; + std::vector> dims_; +}; + +class ExecutorTesterFeedAndFetch : public ::testing::Test { + public: + virtual void SetUp() override { + auto temp_root_block = pdesc_.add_blocks(); + temp_root_block->set_idx(0); + temp_root_block->set_parent_idx(-1); + + // wrap to BlockDescBind + paddle::framework::ProgramDescBind& program = + paddle::framework::ProgramDescBind::Instance(&pdesc_); + paddle::framework::BlockDescBind* root_block = program.Block(0); + + std::vector dim{6}; + + AddOp("feed", {}, {{"Out", {"a"}}}, {{"dims", dim}, {"col", 0}}, + root_block); + AddOp("feed", {}, {{"Out", {"b"}}}, {{"dims", dim}, {"col", 1}}, + root_block); + AddOp("fetch", {{"Input", {"a"}}}, {}, {{"col", 0}}, root_block); + AddOp("fetch", {{"Input", {"b"}}}, {}, {{"col", 1}}, root_block); + + // flush + program.Proto(); + + std::vector vec1 = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0}; + std::vector vec2 = {4.0, 5.0, 6.0, 7.0, 8.0, 9.0}; + inputs_.push_back(vec1); + inputs_.push_back(vec2); + dims_.push_back({static_cast(vec1.size())}); + dims_.push_back({static_cast(vec2.size())}); + } + + protected: + ProgramDesc pdesc_; + std::vector> inputs_; + std::vector> dims_; +}; + +#ifndef PADDLE_WITH_CUDA +TEST_F(ExecutorTesterRandom, CPU) { + std::vector places; + CPUPlace cpu_place; + places.push_back(cpu_place); + + // We have a global Scope and BuddyAllocator, and we must ensure + // global BuddyAllocator is initialized before global Scope. Thus, + // global Scope will deconstruct before BuddyAllocator. Otherwise, + // "pointer being freed was not allocated" error will appear. + paddle::memory::Used(cpu_place); + + std::unique_ptr executor(new Executor(places)); + + executor->Run(init_pdesc_, &GetGlobalScope(), 0); + SetFeedVariable(inputs_, dims_); + executor->Run(pdesc_, &GetGlobalScope(), 0); + std::vector> result = GetFetchVariable(); +} + +TEST_F(ExecutorTesterFeedAndFetch, CPU) { + std::vector places; + CPUPlace cpu_place; + places.push_back(cpu_place); + + // We have a global Scope and BuddyAllocator, and we must ensure + // global BuddyAllocator is initialized before global Scope. Thus, + // global Scope will deconstruct before BuddyAllocator. Otherwise, + // "pointer being freed was not allocated" error will appear. + paddle::memory::Used(cpu_place); + + std::unique_ptr executor(new Executor(places)); + + for (int batch_id = 0; batch_id < 3; batch_id++) { + SetFeedVariable(inputs_, dims_); + executor->Run(pdesc_, &GetGlobalScope(), 0); + std::vector> result = GetFetchVariable(); + PADDLE_ENFORCE_EQ(result.size(), inputs_.size()); + for (size_t i = 0; i < result.size(); ++i) { + PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size()); + for (size_t j = 0; j < result[i].size(); ++j) { + PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]); + } + } + } +} +#else +TEST_F(ExecutorTesterRandom, GPU) { + std::vector places; + GPUPlace gpu_place(0); + places.push_back(gpu_place); + + // We have a global Scope and BuddyAllocator, and we must ensure + // global BuddyAllocator is initialized before global Scope. Thus, + // global Scope will deconstruct before BuddyAllocator. Otherwise, + // "pointer being freed was not allocated" error will appear. + // If paddle is compiled with GPU, both CPU and GPU BuddyAllocator + // need to be used at first. + paddle::memory::Used(CPUPlace()); + paddle::memory::Used(gpu_place); + + std::unique_ptr executor(new Executor(places)); + + executor->Run(init_pdesc_, &GetGlobalScope(), 0); + for (int batch_id = 0; batch_id < 3; batch_id++) { + SetFeedVariable(inputs_, dims_); + executor->Run(pdesc_, &GetGlobalScope(), 0); + } +} + +TEST_F(ExecutorTesterFeedAndFetch, GPU) { + std::vector places; + GPUPlace gpu_place(0); + places.push_back(gpu_place); + // We have a global Scope and BuddyAllocator, and we must ensure + // global BuddyAllocator is initialized before global Scope. Thus, + // global Scope will deconstruct before BuddyAllocator. Otherwise, + // "pointer being freed was not allocated" error will appear. + // If paddle is compiled with GPU, both CPU and GPU BuddyAllocator + // need to be used at first. + paddle::memory::Used(CPUPlace()); + paddle::memory::Used(gpu_place); + + std::unique_ptr executor(new Executor(places)); + + for (int batch_id = 0; batch_id < 3; batch_id++) { + SetFeedVariable(inputs_, dims_); + executor->Run(pdesc_, &GetGlobalScope(), 0); + std::vector> result = GetFetchVariable(); + PADDLE_ENFORCE_EQ(result.size(), inputs_.size()); + for (size_t i = 0; i < result.size(); ++i) { + PADDLE_ENFORCE_EQ(result[i].size(), inputs_[i].size()); + for (size_t j = 0; j < result[i].size(); ++j) { + PADDLE_ENFORCE_EQ(result[i][j], inputs_[i][j]); + } + } + } +} +#endif diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto index 951c7afbc14e2d9119169c1351d38ff0b67bdc5b..b7a63f9ba10b77acff516d75cf1be0d4eeda40d4 100644 --- a/paddle/framework/framework.proto +++ b/paddle/framework/framework.proto @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ syntax = "proto2"; +option optimize_for = LITE_RUNTIME; package paddle.framework; enum AttrType { @@ -66,7 +67,6 @@ message OpProto { optional bool duplicable = 3 [ default = false ]; optional bool intermediate = 4 [ default = false ]; - optional bool not_in_gradient = 5 [ default = false ]; } // AttrProto describes the C++ type Attribute. @@ -106,6 +106,7 @@ message LoDTensorDesc { message VarDesc { required string name = 1; optional LoDTensorDesc lod_tensor = 2; + optional bool persistable = 3 [ default = false ]; } message BlockDesc { @@ -115,4 +116,7 @@ message BlockDesc { repeated OpDesc ops = 4; } +// Please refer to +// https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/program.md +// for more details. message ProgramDesc { repeated BlockDesc blocks = 1; } diff --git a/paddle/framework/grad_op_builder.cc b/paddle/framework/grad_op_builder.cc deleted file mode 100644 index b02a599a800668b22e7fe39a10fa6dc132e305bd..0000000000000000000000000000000000000000 --- a/paddle/framework/grad_op_builder.cc +++ /dev/null @@ -1,58 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOpArgType::OUT WARRANTIES OR CONDITIONS OF ANY KOpArgType::IND, either -express or implied. See the License for the specific language governing -permissions and limitations under the License. */ - -#include "paddle/framework/grad_op_builder.h" -#include "paddle/framework/op_registry.h" - -namespace paddle { -namespace framework { -enum class OpArgType { IN, OUT }; - -static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, - bool is_grad, VariableNameMap* vars) { - const auto& src_inout = - src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs(); - auto& dst_inout = *vars; - auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto(); - const auto& src_arg_list = - src_type == OpArgType::IN ? proto.inputs() : proto.outputs(); - for (const auto& arg : src_arg_list) { - if (arg.not_in_gradient() && !is_grad) continue; - const std::string src_name = arg.name(); - std::string dst_name = is_grad ? GradVarName(src_name) : src_name; - dst_inout[dst_name].reserve(src_inout.at(src_name).size()); - for (auto& var_name : src_inout.at(src_name)) { - std::string s = is_grad ? GradVarName(var_name) : var_name; - dst_inout[dst_name].emplace_back(s); - } - } -} - -OperatorBase* BuildGradOp(const OperatorBase* op) { - auto& info = OpInfoMap::Instance().Get(op->Type()); - PADDLE_ENFORCE(info.HasGradientOp()); - - VariableNameMap inputs; - VariableNameMap outputs; - TransOpArg(op, OpArgType::IN, false, &inputs); // I - TransOpArg(op, OpArgType::OUT, false, &inputs); // O - TransOpArg(op, OpArgType::OUT, true, &inputs); // OG - TransOpArg(op, OpArgType::IN, true, &outputs); // IG - - auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_); - return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs()); -} - -} // namespace framework -} // namespace paddle diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc deleted file mode 100644 index 9e3ca563c6765637f8471d142d32cec447f0b977..0000000000000000000000000000000000000000 --- a/paddle/framework/grad_op_builder_test.cc +++ /dev/null @@ -1,122 +0,0 @@ -#include "paddle/framework/grad_op_builder.h" -#include -#include "paddle/framework/op_registry.h" -#include "paddle/framework/operator.h" - -USE_OP(add); - -namespace paddle { -namespace framework { - -class MutiInOutOpMaker : public OpProtoAndCheckerMaker { - public: - MutiInOutOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("In1", "a single input"); - AddInput("In2_mult", "a multiple input").AsDuplicable(); - AddInput("In3", "another single input"); - AddOutput("Out1", "a single output"); - AddOutput("Out2_mult", "a multiple output").AsDuplicable(); - AddComment("test op with multiple inputs and outputs"); - } -}; - -class IOIgnoredOpMaker : public OpProtoAndCheckerMaker { - public: - IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("In1", "a single input"); - AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient(); - AddInput("In3_mult", "another multiple input").AsDuplicable(); - AddOutput("Out1_mult", "a multiple output").AsDuplicable(); - AddOutput("Out2", "a single output").NotInGradient(); - AddComment("op with inputs and outputs ignored in gradient calculating"); - } -}; - -} // namespace framework -} // namespace paddle - -namespace f = paddle::framework; - -TEST(GradOpBuilder, AddTwo) { - std::shared_ptr add_op(f::OpRegistry::CreateOp( - "add", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {})); - std::shared_ptr grad_add_op = - f::OpRegistry::CreateGradOp(*add_op); - EXPECT_EQ(grad_add_op->Inputs().size(), 4UL); - EXPECT_EQ(grad_add_op->Outputs().size(), 2UL); - EXPECT_EQ(grad_add_op->Input("X"), "x"); - EXPECT_EQ(grad_add_op->Input("Y"), "y"); - EXPECT_EQ(grad_add_op->Input("Out"), "out"); - EXPECT_EQ(grad_add_op->Input(f::GradVarName("Out")), f::GradVarName("out")); - EXPECT_EQ(grad_add_op->Output(f::GradVarName("X")), f::GradVarName("x")); - EXPECT_EQ(grad_add_op->Output(f::GradVarName("Y")), f::GradVarName("y")); -} - -REGISTER_OP(mult_io, f::NOP, f::MutiInOutOpMaker, mult_io_grad, f::NOP); -REGISTER_OP(io_ignored, f::NOP, f::IOIgnoredOpMaker, io_ignored_grad, f::NOP); - -TEST(GradOpBuilder, MutiInOut) { - std::shared_ptr test_op(f::OpRegistry::CreateOp( - "mult_io", {{"In1", {"in1"}}, - {"In2_mult", {"in2_1", "in2_2", "in2_3"}}, - {"In3", {"in3"}}}, - {{"Out1", {"out1"}}, {"Out2_mult", {"out2_1", "out2_2"}}}, {})); - std::shared_ptr grad_test_op = - f::OpRegistry::CreateGradOp(*test_op); - - ASSERT_EQ(grad_test_op->Inputs().size(), 3UL + 2UL + 2UL); - EXPECT_EQ(grad_test_op->Input("In1"), "in1"); - EXPECT_EQ(grad_test_op->Inputs("In2_mult"), - std::vector({"in2_1", "in2_2", "in2_3"})); - EXPECT_EQ(grad_test_op->Input("In3"), "in3"); - EXPECT_EQ(grad_test_op->Input("Out1"), "out1"); - EXPECT_EQ(grad_test_op->Inputs("Out2_mult"), - std::vector({"out2_1", "out2_2"})); - EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out1")), - f::GradVarName("out1")); - EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out2_mult")), - std::vector( - {f::GradVarName("out2_1"), f::GradVarName("out2_2")})); - - ASSERT_EQ(grad_test_op->Outputs().size(), 3UL); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1")); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")), - std::vector({f::GradVarName("in2_1"), - f::GradVarName("in2_2"), - f::GradVarName("in2_3")})); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In3")), f::GradVarName("in3")); -} - -TEST(GradOpBuilder, IOIgnoredInGradient) { - std::shared_ptr test_op(f::OpRegistry::CreateOp( - "io_ignored", {{"In1", {"in1"}}, - {"In2_mult", {"in2_1", "in2_2"}}, - {"In3_mult", {"in3_1", "in3_2"}}}, - {{"Out1_mult", {"out1_1", "out1_2"}}, {"Out2", {"out2"}}}, {})); - std::shared_ptr grad_test_op = - f::OpRegistry::CreateGradOp(*test_op); - - // 'In2' and 'Out2' are ignored in gradient calculating - ASSERT_EQ(grad_test_op->Inputs().size(), 2UL + 1UL + 2UL); - EXPECT_EQ(grad_test_op->Input("In1"), "in1"); - EXPECT_EQ(grad_test_op->Inputs("In3_mult"), - std::vector({"in3_1", "in3_2"})); - EXPECT_EQ(grad_test_op->Inputs("Out1_mult"), - std::vector({"out1_1", "out1_2"})); - EXPECT_EQ(grad_test_op->Inputs(f::GradVarName("Out1_mult")), - std::vector( - {f::GradVarName("out1_1"), f::GradVarName("out1_2")})); - EXPECT_EQ(grad_test_op->Input(f::GradVarName("Out2")), - f::GradVarName("out2")); - - ASSERT_EQ(grad_test_op->Outputs().size(), 3UL); - EXPECT_EQ(grad_test_op->Output(f::GradVarName("In1")), f::GradVarName("in1")); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In2_mult")), - std::vector( - {f::GradVarName("in2_1"), f::GradVarName("in2_2")})); - EXPECT_EQ(grad_test_op->Outputs(f::GradVarName("In3_mult")), - std::vector( - {f::GradVarName("in3_1"), f::GradVarName("in3_2")})); -} diff --git a/paddle/framework/grad_op_desc_maker.h b/paddle/framework/grad_op_desc_maker.h new file mode 100644 index 0000000000000000000000000000000000000000..e9ae6e22060850fe229998d3b651d08a5ca2033a --- /dev/null +++ b/paddle/framework/grad_op_desc_maker.h @@ -0,0 +1,124 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/op_desc.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace framework { + +class GradOpDescMakerBase { + public: + explicit GradOpDescMakerBase(const OpDescBind& fwd_op) : fwd_op_(fwd_op) {} + + virtual ~GradOpDescMakerBase() = default; + virtual std::vector> operator()() const = 0; + + protected: + static std::vector ToGradNames( + const std::vector& var_names) { + std::vector ret_val; + ret_val.reserve(var_names.size()); + std::transform(var_names.begin(), var_names.end(), + std::back_inserter(ret_val), GradVarName); + return ret_val; + } + + std::vector InputGrad(const std::string& name) const { + return ToGradNames(fwd_op_.Input(name)); + } + + std::vector OutputGrad(const std::string& name) const { + return ToGradNames(fwd_op_.Output(name)); + } + + std::vector InputNames() const { + return this->fwd_op_.InputNames(); + } + + std::vector OutputNames() const { + return this->fwd_op_.OutputNames(); + } + + std::vector Input(const std::string& name) const { + return fwd_op_.Input(name); + } + + std::vector Output(const std::string& name) const { + return fwd_op_.Output(name); + } + + const std::unordered_map& Attrs() const { + return fwd_op_.GetAttrMap(); + } + + const Attribute& GetAttr(const std::string& name) const { + auto& map = fwd_op_.GetAttrMap(); + auto it = map.find(name); + PADDLE_ENFORCE(it != map.end(), "Cannot find attribute %s", name); + return it->second; + } + + std::string ForwardOpType() const { return this->fwd_op_.Type(); } + + private: + const OpDescBind& fwd_op_; +}; + +class SingleGradOpDescMaker : public GradOpDescMakerBase { + public: + using GradOpDescMakerBase::GradOpDescMakerBase; + + std::vector> operator()() const { + std::vector> retv; + retv.emplace_back(this->Apply()); + return retv; + } + + protected: + virtual std::unique_ptr Apply() const = 0; +}; + +class DefaultGradOpDescMaker : public SingleGradOpDescMaker { + public: + using SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + virtual std::unique_ptr Apply() const { + auto* grad = new OpDescBind(); + grad->SetType(this->GradOpType()); + + for (auto& input_param : this->InputNames()) { + grad->SetInput(input_param, this->Input(input_param)); + grad->SetOutput(GradVarName(input_param), this->InputGrad(input_param)); + } + + for (auto& output_param : this->OutputNames()) { + grad->SetInput(output_param, this->Output(output_param)); + grad->SetInput(GradVarName(output_param), this->OutputGrad(output_param)); + } + + grad->SetAttrMap(this->Attrs()); + + return std::unique_ptr(grad); + } + + virtual std::string GradOpType() const { + return this->ForwardOpType() + "_grad"; + } +}; + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 49786a4a6635f1b39356dbf9633c4e7da443f04e..4db36ee76609ac6360fe2fc7b4a366e0284d1016 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -15,7 +15,7 @@ #pragma once #include -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include #include @@ -29,7 +29,7 @@ namespace paddle { namespace framework { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA template using Vector = std::vector; #else diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md index 07bbdf9416c432052b3222757a61ac4bfd70fe14..d147f1c4257eec14664301edab8d1fe2f128d2b0 100644 --- a/paddle/framework/lod_tensor.md +++ b/paddle/framework/lod_tensor.md @@ -1,147 +1,175 @@ # Design Doc: LoD (Level-of-Detail) Tensor -PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor. +Like other deep learning systems, PaddlePaddle supports training models from sequence data. Also, like other systems, PaddlePaddle represent a mini-batch of sequences as a Tensor. What is different is that PaddlePaddle doesn't require all sequences in a mini-batch to be of the same length. Thus no need for padding zeros. -## Challenge of Variable-length Inputs +| | TensorFlow | PaddlePaddle | +|-----------------------|------------|--------------| +| RNN | Support | Support | +| recursive RNN | Support | Support | +| padding zeros | Must | No need | +| blob data type | Tensor | LoDTensor | -People usually represent a mini-batch by a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. So a transformation, T, of all images can be a matrix multiplication of the 10xOx32-dimensional tensor T and the 10x32x32 Tensor. +PaddlePaddle achieves this flexibility by passing through a new data type, *LoD Tensor*, which is a Tensor attached with segmentation index known as *LoD*, between operators. The LoD index doesn't only segment a tensor, but also recursively segments sub-sequences. This document presents the design of LoD and LoDTensor. -Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector. If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor. However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths. -## LoD as a Solution +## The Challenge: Variable-length Sequences -### Mini-Batch of variable-length sentences +Most deep learning systems represent a mini-batch as a Tensor. For example, a mini-batch of 10 images, each of size 32x32, is a 10x32x32 Tensor. Another example is that each mini-batch contains N sentences, where each word is a D-dimensional one-hot vector. Suppose that all sentences have the same length L, we can represent this mini-batch by a NxLxD tensor. -Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively. We can represent it by a (3+1+2)xD tensor plus some index information: +Both examples show that the elements of sequences are usually of the same size. In the first example, all images are 32x32, and in the second one, all words are D-dimensional vectors. It doesn't make sense to allow variable-sized images, as that would require transformations like convolution to handle variable-sized Tensors. + +The real challenge is that in most cases, sentences have variable lengths, and we will need an index data structure to segment the tensor into sequences. Also, sequences might consist of sub-sequences. + + +## A Solution: The LoD Index + +To understand our solution, it is best to look at some examples. + +### A Mini-Batch of Sentences + +Let's imagine a mini-batch of 3 variable lengths sentences composed of 3, 1, and 2 words, respectively. We can represent the mini-batch by a (3+1+2)xD tensor plus some index information: ``` - 3 3 1 2 ||| | || ``` -Each `|` represents a D-dimensional word vectors. The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence. +where each `|` represents a D-dimensional word vector. The numbers, 3, 1, and 2, form a 1-level LoD. + +### Recursive Sequences + +Let check another example of a 2-level LoD Tensor. Consider a mini-batch of three articles with 3, 1, and 2 sentences, and each sentence consists of a variable number of words: + +``` +3 1 2 +3 2 4 1 2 3 +||| || |||| | || ||| +``` -### Mini-Batch of variable-length videos +### A Mini-Batch of Videos -This approach generalizes to the case where elements are not words, but higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively. The underlying tensor is of size (3+1+2)x640x480. The index information illustrates as: +LoD tensors generalize to the case where elements are higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. Here is a mini-batch of 3 videos with 3, 1, and 2 frames, respectively. ``` - 3 3 1 2 口口口 口 口口 ``` -where each `口` represents an image. +The underlying tensor is of size (3+1+2)x640x480, and each `口` represents a 640x480 image. -### Mini-Batch of fixed-size images +### A Mini-Batch of Images -Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images. The LoD Tensor representation is +In traditional cases like a mini-batch with N fixed-sized images, the LoD Tensor representation is as ``` - M 1 1 1 1 1 口口口口 ... 口 ``` -The many 1's on the second level seem duplicated. For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index. - -### Design and summarization +In this case, we don't lose any information by ignoring the many 1's in the index and simply considering this LoD Tensor as a usual Tensor: -In summary, as long as that the essential elements (words or images) have the same size, we can represent mini-batches by a LoD Tensor: +``` +口口口口 ... 口 +``` -- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and -- The first dimension size L has an additonal property -- a LoD index as a nested vector: +### Model Parameters - ```c++ - typedef std::vector> LoD; - ``` +A model parameter is just a usual Tensor, which, just like the above example, is a **0-level LoD Tensor**. -- The LoD index is not necessary when there are only two levels and all elements of the second level have length 1. -## Slicing of LoD Tensor +## The LoD Tensor -Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words. This network requires that mini-batches represented by 3 level LoD Tensor, for example, +Let us revisit above example of the 2-level LoD Tensor ``` - 3 3 1 2 3 2 4 1 2 3 ||| || |||| | || ||| ``` -To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the -slice** +It is indeed a tree, where leaves are elementary sequences identified by **branches**. + +For example, the third sentence in above example is identified by branch <0,2>, where 0 indicates the first article with length 3, and 2 indicates the third sentence in this article with length 4. + +### The LoD Index -For example, the <2,1>-slice of above slice is +We can save the LoD index in the above example ``` -2 -|| +3 1 2 +3 2 4 1 2 3 ``` -and the <1,2>-slice of above example is +in a not-full 2D matrix: +```c++ +typedef std::vector > LoD; ``` -2 -2 3 -|| ||| -``` -Let's go on slicing this slice. Its <1,1>-slice is +where + +- `LoD.size()` is the number of levels, or the maximum length of branches, +- `LoD[i][j]` is the length of the j-th segment at the i-th level. + +## The Offset Representation + +To quickly access elementary sequences, we adopt an offset representation -- instead of saving the lengths, we save the beginning and ending elements of sequences. + +In the above example, we accumulate the length of elementary sequences: ``` -1 -1 -| +3 2 4 1 2 3 ``` -### The Slicing Algorithm +into offsets -The algorithm, with over-simplified data structure, is defined as +``` +0 3 5 9 10 12 15 + = = = = = = + 3 2+3 4+5 1+9 2+10 3+12 +``` -```c++ -typedef std::vector> LoD; +so we know that the first sentence is from word 0 to word 3, and the second sentence from work 3 to word 5. -struct LoDTensor { - LoD lod_; - float* tensor_; -}; +Similarly, the lengths in the top level LoD -LoDTensor Slice(const LoDTensor& lodt, int level, int sequence); +``` +3 1 2 ``` -Let us revisit the example above +are transformed into offsets of elements/words as follows: ``` - 3 -3 1 2 -3 2 4 1 2 3 -||| || |||| | || ||| +0 9 10 15 + = = = + 3+2+4 1+9 2+3+10 ``` -Suppose that we want to retrieve the <1,2>-slice +so we can tell that the first article is from word 0 to word 9, and the second article is from word 9 to word 10. + +The complete offset representation is as follows: ``` -2 -2 3 -|| ||| +0 9 10 15 +0 3 5 9 10 12 15 + ||| || |||| | || ||| ``` -we will need to find out the starting position of this slice by summing over all leaf nodes in `LoD` to the left of the slice, i.e., 3 + 2 + 4 + 1 = 10. +## Slicing of LoD Tensors + +When we use the above 2-level LoD Tensor as the input to a nested-RNN, we need to retrieve certain sequences. Here we define the sequence identified by branch as the **-slice**. -To avoid the traversal of the LoD tree at slicing time, we can do it at the construction time -- instead of saving the lengths of the next level in the LoD tree, we can save the starting offset of the next level. For example, above LoD Tensor can be transformed into +For example, the <2>-slice of above example is ``` - 0 -0 9 10 -0 3 5 9 10 12 -||| || |||| | || ||| +10 15 +10 12 15 + || ||| ``` -We don't really need the 0 on top, so the LoD Tensor could be +and the <2,0>-slice of above slice is ``` -0 9 10 -0 3 5 9 10 12 -||| || |||| | || ||| +10 12 + || ``` diff --git a/paddle/framework/op_desc.cc b/paddle/framework/op_desc.cc index 99b5a9c37700adce56f9a83af3792ef113a873ff..e7538b4af3429e566a439d5a0db8496efcd94969 100644 --- a/paddle/framework/op_desc.cc +++ b/paddle/framework/op_desc.cc @@ -13,11 +13,24 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/framework/op_desc.h" +#include +#include #include "paddle/framework/block_desc.h" +#include "paddle/framework/operator.h" namespace paddle { namespace framework { +OpDescBind::OpDescBind(const std::string &type, const VariableNameMap &inputs, + const VariableNameMap &outputs, + const AttributeMap &attrs) { + op_desc_.set_type(type); + inputs_ = inputs; + outputs_ = outputs; + attrs_ = attrs; + need_update_ = true; +} + OpDesc *OpDescBind::Proto() { Sync(); return &op_desc_; @@ -31,11 +44,10 @@ const std::vector &OpDescBind::Input( return it->second; } -std::vector OpDescBind::InputNames() const { +std::vector OpDescBind::InputArgumentNames() const { std::vector retv; - retv.reserve(this->inputs_.size()); for (auto &ipt : this->inputs_) { - retv.push_back(ipt.first); + retv.insert(retv.end(), ipt.second.begin(), ipt.second.end()); } return retv; } @@ -54,11 +66,10 @@ const std::vector &OpDescBind::Output( return it->second; } -std::vector OpDescBind::OutputNames() const { +std::vector OpDescBind::OutputArgumentNames() const { std::vector retv; - retv.reserve(this->outputs_.size()); for (auto &ipt : this->outputs_) { - retv.push_back(ipt.first); + retv.insert(retv.end(), ipt.second.begin(), ipt.second.end()); } return retv; } @@ -89,6 +100,12 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) { need_update_ = true; } +void OpDescBind::SetAttrMap( + const std::unordered_map &attr_map) { + attrs_ = attr_map; + need_update_ = true; +} + Attribute OpDescBind::GetAttr(const std::string &name) const { auto it = attrs_.find(name); PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name); @@ -101,6 +118,47 @@ int OpDescBind::GetBlockAttr(const std::string &name) const { return boost::get(it->second)->idx(); } +const std::unordered_map &OpDescBind::GetAttrMap() + const { + return attrs_; +} + +void OpDescBind::Rename(const std::string &old_name, + const std::string &new_name) { + for (auto &input : inputs_) { + std::replace(input.second.begin(), input.second.end(), old_name, new_name); + } + for (auto &output : outputs_) { + std::replace(output.second.begin(), output.second.end(), old_name, + new_name); + } + need_update_ = true; +} + +struct SetAttrDescVisitor : public boost::static_visitor { + explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {} + mutable OpDesc::Attr *attr_; + void operator()(int v) const { attr_->set_i(v); } + void operator()(float v) const { attr_->set_f(v); } + void operator()(const std::string &v) const { attr_->set_s(v); } + void operator()(bool b) const { attr_->set_b(b); } + + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_ints()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_floats()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_strings()); + } + void operator()(const std::vector &v) const { + VectorToRepeated(v, attr_->mutable_bools()); + } + void operator()(BlockDesc *desc) const { attr_->set_block_idx(desc->idx()); } + void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); } +}; + void OpDescBind::Sync() { if (need_update_) { this->op_desc_.mutable_inputs()->Clear(); @@ -123,11 +181,45 @@ void OpDescBind::Sync() { attr_desc->set_name(attr.first); attr_desc->set_type( static_cast(attr.second.which() - 1)); - boost::apply_visitor(SetAttrDescVisitor(attr_desc), attr.second); + SetAttrDescVisitor visitor(attr_desc); + boost::apply_visitor(visitor, attr.second); } need_update_ = false; } } + +using InferShapeFuncMap = + std::unordered_map>; + +static InferShapeFuncMap &InferShapeFuncs() { + static InferShapeFuncMap *g_map = nullptr; + if (g_map == nullptr) { + g_map = new InferShapeFuncMap(); + auto &info_map = OpInfoMap::Instance(); + // all registered kernels + for (auto &pair : OperatorWithKernel::AllOpKernels()) { + auto &info = info_map.Get(pair.first); + // use empty type here to avoid runtime checks. + auto op = + static_cast(info.Creator()("", {}, {}, {})); + g_map->insert( + {pair.first, [op](InferShapeContext *ctx) { op->InferShape(ctx); }}); + } + } + return *g_map; +} + +void OpDescBind::InferShape(const BlockDescBind &block) const { + auto &funcs = InferShapeFuncs(); + auto it = funcs.find(this->Type()); + if (it == funcs.end()) { + PADDLE_THROW("Operator %s has not been registered", this->Type()); + } + CompileTimeInferShapeContext ctx(*this, block); + it->second(&ctx); +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/op_desc.h b/paddle/framework/op_desc.h index ffc8ac61abfb74e4716f10c457d0fbc18b2e2ab8..81c4225041157ac600d1db73ef2363ebcd4abfc0 100644 --- a/paddle/framework/op_desc.h +++ b/paddle/framework/op_desc.h @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include "paddle/framework/attribute.h" +#include "paddle/framework/type_defs.h" #include "paddle/framework/var_desc.h" namespace paddle { @@ -26,6 +27,11 @@ class BlockDescBind; class OpDescBind { public: + OpDescBind() {} + + OpDescBind(const std::string &type, const VariableNameMap &inputs, + const VariableNameMap &outputs, const AttributeMap &attrs); + OpDesc *Proto(); std::string Type() const { return op_desc_.type(); } @@ -34,20 +40,18 @@ class OpDescBind { const std::vector &Input(const std::string &name) const; - std::vector InputNames() const; + std::vector InputArgumentNames() const; void SetInput(const std::string ¶m_name, const std::vector &args); const std::vector &Output(const std::string &name) const; - std::vector OutputNames() const; + std::vector OutputArgumentNames() const; void SetOutput(const std::string ¶m_name, const std::vector &args); - std::string DebugString() { return this->Proto()->DebugString(); } - bool HasAttr(const std::string &name) const { return attrs_.find(name) != attrs_.end(); } @@ -64,39 +68,55 @@ class OpDescBind { int GetBlockAttr(const std::string &name) const; - private: - struct SetAttrDescVisitor : public boost::static_visitor { - explicit SetAttrDescVisitor(OpDesc::Attr *attr) : attr_(attr) {} - mutable OpDesc::Attr *attr_; - void operator()(int v) const { attr_->set_i(v); } - void operator()(float v) const { attr_->set_f(v); } - void operator()(const std::string &v) const { attr_->set_s(v); } - void operator()(bool b) const { attr_->set_b(b); } - - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_ints()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_floats()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_strings()); - } - void operator()(const std::vector &v) const { - VectorToRepeated(v, attr_->mutable_bools()); - } - void operator()(BlockDesc *desc) const { - attr_->set_block_idx(desc->idx()); - } - void operator()(boost::blank) const { PADDLE_THROW("Unexpected branch"); } - }; + void Rename(const std::string &old_name, const std::string &new_name); + + // Only be used in C++ + const AttributeMap &GetAttrMap() const; + + // Only be used in C++ + void SetAttrMap(const AttributeMap &attr_map); + + std::vector InputNames() const { return MapKeys(inputs_); } + std::vector OutputNames() const { return MapKeys(outputs_); } + + void SetInputMap(const VariableNameMap &input) { + this->inputs_ = input; + this->need_update_ = true; + } + + void SetOutputMap(const VariableNameMap &output) { + this->outputs_ = output; + this->need_update_ = true; + } void Sync(); + const VariableNameMap &Inputs() const { return inputs_; } + + const VariableNameMap &Outputs() const { return outputs_; } + + AttributeMap *MutableAttrMap() { + this->need_update_ = true; + return &this->attrs_; + } + + void InferShape(const BlockDescBind &block) const; + + private: + template + static std::vector MapKeys(const MapType &map) { + std::vector ret_val; + ret_val.reserve(map.size()); + std::transform( + map.begin(), map.end(), std::back_inserter(ret_val), + [](const typename MapType::value_type &pair) { return pair.first; }); + return ret_val; + } + OpDesc op_desc_; - std::unordered_map> inputs_; - std::unordered_map> outputs_; - std::unordered_map attrs_; + VariableNameMap inputs_; + VariableNameMap outputs_; + AttributeMap attrs_; // need_update_ indicate there some local changes not be synchronized. If // local changes should be synchronized, need_update_ should be set to true. diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h index b98d8f23a14cf6fbe787953ad16b5c9ab99222ad..c504f69e30bb899c183bd4281d2eadb50fd3b376 100644 --- a/paddle/framework/op_info.h +++ b/paddle/framework/op_info.h @@ -19,21 +19,18 @@ #include #include "paddle/framework/attribute.h" +#include "paddle/framework/op_desc.h" +#include "paddle/framework/type_defs.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { -class OperatorBase; -using VariableNameMap = std::map>; - -using OpCreator = std::function; struct OpInfo { OpCreator creator_; - std::string grad_op_type_; - OpProto* proto_; - OpAttrChecker* checker_; + GradOpMakerFN grad_op_maker_; + OpProto* proto_{nullptr}; + OpAttrChecker* checker_{nullptr}; bool HasOpProtoAndChecker() const { return proto_ != nullptr && checker_ != nullptr; @@ -46,30 +43,25 @@ struct OpInfo { return *proto_; } - const OpAttrChecker& Checker() const { - PADDLE_ENFORCE_NOT_NULL(checker_, - "Operator Checker has not been registered"); - return *checker_; - } - const OpCreator& Creator() const { PADDLE_ENFORCE_NOT_NULL(creator_, "Operator Creator has not been registered"); return creator_; } - bool HasGradientOp() const { return !grad_op_type_.empty(); } + const GradOpMakerFN& GradOpMaker() const { + PADDLE_ENFORCE_NOT_NULL(grad_op_maker_, + "Operator GradOpMaker has not been registered."); + return grad_op_maker_; + } + + const OpAttrChecker* Checker() const { return checker_; } }; class OpInfoMap { public: static OpInfoMap& Instance(); - OpInfoMap(const OpInfoMap& o) = delete; - OpInfoMap(OpInfoMap&& o) = delete; - OpInfoMap& operator=(const OpInfoMap& o) = delete; - OpInfoMap& operator=(OpInfoMap&& o) = delete; - bool Has(const std::string& op_type) const { return map_.find(op_type) != map_.end(); } @@ -105,6 +97,8 @@ class OpInfoMap { private: OpInfoMap() = default; std::unordered_map map_; + + DISABLE_COPY_AND_ASSIGN(OpInfoMap); }; } // namespace framework diff --git a/paddle/framework/op_proto_maker.h b/paddle/framework/op_proto_maker.h index 4d55a37db9f0a3deac7b3489c8bc288ea41f4799..a134befd90a1eaeff6f6ea62f11412df63cdc394 100644 --- a/paddle/framework/op_proto_maker.h +++ b/paddle/framework/op_proto_maker.h @@ -44,11 +44,6 @@ class OpProtoAndCheckerMaker { var_->set_intermediate(true); return *this; } - - VariableBuilder& NotInGradient() { - var_->set_not_in_gradient(true); - return *this; - } }; VariableBuilder AddInput(const std::string& name, const std::string& comment); diff --git a/paddle/framework/op_proto_maker_test.cc b/paddle/framework/op_proto_maker_test.cc index b01e30f75371ca4aa63dae86ddfb966b1d4c7830..988a14cf4de8fdf052ca7e8c41bff0c05ba2daaa 100644 --- a/paddle/framework/op_proto_maker_test.cc +++ b/paddle/framework/op_proto_maker_test.cc @@ -48,4 +48,4 @@ TEST(ProtoMaker, DuplicatedInOut) { paddle::framework::OpAttrChecker op_checker; auto proto_maker = TestInOutProtoMaker(&op_proto, &op_checker); ASSERT_THROW(proto_maker.Validate(), paddle::platform::EnforceNotMet); -} \ No newline at end of file +} diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index b0e85dd49f97da4a7f889fde0b5f060954947be8..b118edae17430c8a4dd5c96a2a0c675766e08166 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -23,7 +23,9 @@ std::unique_ptr OpRegistry::CreateOp( const std::string& type, const VariableNameMap& inputs, const VariableNameMap& outputs, AttributeMap attrs) { auto& info = OpInfoMap::Instance().Get(type); - info.Checker().Check(attrs); + if (info.Checker() != nullptr) { + info.Checker()->Check(attrs); + } auto op = info.Creator()(type, inputs, outputs, attrs); return std::unique_ptr(op); } @@ -52,9 +54,20 @@ std::unique_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { return CreateOp(op_desc.type(), inputs, outputs, attrs); } -std::unique_ptr OpRegistry::CreateGradOp(const OperatorBase& op) { - PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); - return std::unique_ptr(BuildGradOp(&op)); +std::unique_ptr OpRegistry::CreateOp(const OpDescBind& op_desc) { + return CreateOp(op_desc.Type(), op_desc.Inputs(), op_desc.Outputs(), + op_desc.GetAttrMap()); +} + +std::vector> OpRegistry::CreateGradOpDescs( + OpDescBind* op_desc) { + auto& info = OpInfoMap::Instance().Get(op_desc->Type()); + + if (info.Checker() != nullptr) { + info.Checker()->Check(*op_desc->MutableAttrMap()); + } + + return info.grad_op_maker_(*op_desc); } } // namespace framework diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 90077d0192421f3678a049a723972fcb1e8d67af..5ca3af52a6909eeee21f647d0e60c7a690f90190 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -21,49 +21,54 @@ limitations under the License. */ #include #include #include "paddle/framework/attribute.h" +#include "paddle/framework/details/op_registry.h" #include "paddle/framework/framework.pb.h" -#include "paddle/framework/grad_op_builder.h" -#include "paddle/framework/op_info.h" -#include "paddle/framework/op_proto_maker.h" +#include "paddle/framework/grad_op_desc_maker.h" +#include "paddle/framework/op_desc.h" #include "paddle/framework/operator.h" #include "paddle/framework/scope.h" namespace paddle { namespace framework { +class Registrar { + public: + // In our design, various kinds of classes, e.g., operators and kernels, + // have their corresponding registry and registrar. The action of + // registration is in the constructor of a global registrar variable, which, + // however, are not used in the code that calls package framework, and would + // be removed from the generated binary file by the linker. To avoid such + // removal, we add Touch to all registrar classes and make USE_OP macros to + // call this method. So, as long as the callee code calls USE_OP, the global + // registrar variable won't be removed by the linker. + void Touch() {} +}; + +template +struct OperatorRegistrar : public Registrar { + explicit OperatorRegistrar(const char* op_type) : op_type(op_type) { + PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type), + "'%s' is registered more than once.", op_type); + static_assert(sizeof...(ARGS) != 0, + "OperatorRegistrar should be invoked at least by OpClass"); + details::OperatorRegistrarRecursive<0, false, ARGS...>(op_type, &info); + OpInfoMap::Instance().Insert(op_type, info); + } + + const char* op_type; + + OpInfo info; +}; class OpRegistry { public: template static void RegisterOp(const std::string& op_type, const std::string& grad_op_type) { - PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type), - "'%s' is registered more than once.", op_type); - OpInfo op_info; - op_info.creator_ = []( - const std::string& type, const VariableNameMap& inputs, - const VariableNameMap& outputs, const AttributeMap& attrs) { - return new OpType(type, inputs, outputs, attrs); - }; - op_info.grad_op_type_ = grad_op_type; - if (std::type_index(typeid(ProtoMakerType)) != - std::type_index(typeid(NOPMaker))) { - op_info.proto_ = new OpProto; - op_info.checker_ = new OpAttrChecker; - auto maker = ProtoMakerType(op_info.proto_, op_info.checker_); - maker.Validate(); - op_info.proto_->set_type(op_type); - PADDLE_ENFORCE( - op_info.proto_->IsInitialized(), - "Fail to initialize %s's OpProto, because %s is not initialized", - op_type, op_info.proto_->InitializationErrorString()); - } else { - op_info.proto_ = nullptr; - op_info.checker_ = nullptr; - } - OpInfoMap::Instance().Insert(op_type, op_info); + OperatorRegistrar reg(op_type.c_str()); + reg.info.grad_op_type_ = grad_op_type; // register gradient op if (!grad_op_type.empty()) { - RegisterOp(grad_op_type, ""); + OperatorRegistrar grad_reg(grad_op_type.c_str()); } } @@ -74,20 +79,10 @@ class OpRegistry { static std::unique_ptr CreateOp(const OpDesc& op_desc); - static std::unique_ptr CreateGradOp(const OperatorBase& op); -}; + static std::vector> CreateGradOpDescs( + OpDescBind* op_desc); -class Registrar { - public: - // In our design, various kinds of classes, e.g., operators and kernels, - // have their corresponding registry and registrar. The action of - // registration is in the constructor of a global registrar variable, which, - // however, are not used in the code that calls package framework, and would - // be removed from the generated binary file by the linker. To avoid such - // removal, we add Touch to all registrar classes and make USE_OP macros to - // call this method. So, as long as the callee code calls USE_OP, the global - // registrar variable won't be removed by the linker. - void Touch() {} + static std::unique_ptr CreateOp(const OpDescBind& op_desc); }; template @@ -100,13 +95,39 @@ class OpRegistrar : public Registrar { } }; -template +template +struct OpKernelRegistrarFunctor; + +template +struct OpKernelRegistrarFunctor { + using KERNEL_TYPE = + typename std::tuple_element>::type; + + void operator()(const char* op_type) const { + using T = typename KERNEL_TYPE::ELEMENT_TYPE; + OperatorWithKernel::OpKernelKey key(ToDataType(std::type_index(typeid(T))), + PlaceType()); + OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KERNEL_TYPE); + + constexpr auto size = std::tuple_size>::value; + OpKernelRegistrarFunctor + func; + func(op_type); + } +}; + +template +struct OpKernelRegistrarFunctor { + void operator()(const char* op_type) const {} +}; + +// User can register many kernel in one place. The data type could be different. +template class OpKernelRegistrar : public Registrar { public: explicit OpKernelRegistrar(const char* op_type) { - OperatorWithKernel::OpKernelKey key; - key.place_ = PlaceType(); - OperatorWithKernel::AllOpKernels()[op_type][key].reset(new KernelType); + OpKernelRegistrarFunctor func; + func(op_type); } }; @@ -119,33 +140,41 @@ class OpKernelRegistrar : public Registrar { __test_global_namespace_##uniq_name##__>::value, \ msg) +#define REGISTER_OPERATOR(op_type, op_class, ...) \ + STATIC_ASSERT_GLOBAL_NAMESPACE( \ + __reg_op__##op_type, \ + "REGISTER_OPERATOR must be called in global namespace"); \ + class _OpClass_##op_type##_ : public op_class { \ + public: \ + DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ + DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \ + }; \ + static ::paddle::framework::OperatorRegistrar<_OpClass_##op_type##_, \ + ##__VA_ARGS__> \ + __op_registrar_##op_type##__(#op_type); \ + int TouchOpRegistrar_##op_type() { \ + __op_registrar_##op_type##__.Touch(); \ + return 0; \ + } + /** * Macro to register Operator. */ -#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \ - grad_op_class) \ - STATIC_ASSERT_GLOBAL_NAMESPACE( \ - __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \ - class _OpClass_##op_type##_ : public op_class { \ - public: \ - DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ - DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \ - }; \ - class _OpGradClass_##op_type##_ : public grad_op_class { \ - public: \ - DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_); \ - DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class); \ - }; \ - static ::paddle::framework::OpRegistrar< \ - _OpClass_##op_type##_, op_maker_class, _OpGradClass_##op_type##_> \ - __op_registrar_##op_type##__(#op_type, #grad_op_type); \ - int TouchOpRegistrar_##op_type() { \ - __op_registrar_##op_type##__.Touch(); \ - return 0; \ - } +#define REGISTER_OP(op_type, op_class, op_maker_class, grad_op_type, \ + grad_op_class) \ + REGISTER_OPERATOR(grad_op_type, grad_op_class); \ + class _GradOpDescMaker_##grad_op_type##_ \ + : public ::paddle::framework::DefaultGradOpDescMaker { \ + using ::paddle::framework::DefaultGradOpDescMaker::DefaultGradOpDescMaker; \ + \ + protected: \ + virtual std::string GradOpType() const { return #grad_op_type; } \ + }; \ + REGISTER_OPERATOR(op_type, op_class, _GradOpDescMaker_##grad_op_type##_, \ + op_maker_class); #define REGISTER_OP_WITHOUT_GRADIENT(op_type, op_class, op_maker_class) \ - REGISTER_OP(op_type, op_class, op_maker_class, , ::paddle::framework::NOP) + REGISTER_OPERATOR(op_type, op_class, op_maker_class) /** * Macro to register OperatorKernel. @@ -192,7 +221,7 @@ class OpKernelRegistrar : public Registrar { // TODO(fengjiayi): The following macros // seems ugly, do we have better method? -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA #define USE_OP_KERNEL(op_type) USE_OP_DEVICE_KERNEL(op_type, CPU) #else #define USE_OP_KERNEL(op_type) \ diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc index b6fc0409d5cb22b13352df41b8e911c79bc4825a..b860fe6cac773d1e85adecc43f5dfec42b6c7661 100644 --- a/paddle/framework/op_registry_test.cc +++ b/paddle/framework/op_registry_test.cc @@ -173,3 +173,14 @@ TEST(OpRegistry, CustomChecker) { int test_attr = op->Attr("test_attr"); ASSERT_EQ(test_attr, 4); } + +class CosineOpComplete : public paddle::framework::CosineOp { + public: + DEFINE_OP_CONSTRUCTOR(CosineOpComplete, paddle::framework::CosineOp); + DEFINE_OP_CLONE_METHOD(CosineOpComplete); +}; + +TEST(OperatorRegistrar, Test) { + using namespace paddle::framework; + OperatorRegistrar reg("cos"); +} diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index d7beff5bc1df1def6bf35381e103cf87eeb68fd0..2fca816f353635d3bff184323755961ee82fbb68 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -22,14 +22,14 @@ namespace framework { template <> Eigen::DefaultDevice& ExecutionContext::GetEigenDevice< platform::CPUPlace, Eigen::DefaultDevice>() const { - return *device_context_.get_eigen_device(); + return *device_context_.GetEigenDevice(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> Eigen::GpuDevice& ExecutionContext::GetEigenDevice() const { - return *device_context_.get_eigen_device(); + return *device_context_.GetEigenDevice(); } #endif @@ -205,13 +205,13 @@ void OperatorBase::GenerateTemporaryNames() { } template <> -const Tensor* InferShapeContext::Input(const std::string& name) const { +const Tensor* ExecutionContext::Input(const std::string& name) const { auto* var = InputVar(name); return var == nullptr ? nullptr : GetTensorFromVar(var); } template <> -const std::vector InferShapeContext::MultiInput( +const std::vector ExecutionContext::MultiInput( const std::string& name) const { auto names = op().Inputs(name); std::vector res; @@ -225,13 +225,13 @@ const std::vector InferShapeContext::MultiInput( } template <> -Tensor* InferShapeContext::Output(const std::string& name) const { +Tensor* ExecutionContext::Output(const std::string& name) const { auto var = OutputVar(name); return var == nullptr ? nullptr : var->GetMutable(); } template <> -std::vector InferShapeContext::MultiOutput( +std::vector ExecutionContext::MultiOutput( const std::string& name) const { auto names = op().Outputs(name); std::vector res; @@ -245,5 +245,12 @@ std::vector InferShapeContext::MultiOutput( return res; } +std::ostream& operator<<(std::ostream& os, + const OperatorWithKernel::OpKernelKey& kernel_key) { + os << "place[" << kernel_key.place_ << "]:data_type[" << kernel_key.data_type_ + << "]"; + return os; +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 79bda2e2f9173ab632307bc52167d7d8c17d4418..15f80b57206c90f689acfdcac60a0d9011025fc0 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -22,6 +22,8 @@ limitations under the License. */ #include "op_info.h" #include "paddle/framework/attribute.h" +#include "paddle/framework/block_desc.h" +#include "paddle/framework/data_type.h" #include "paddle/framework/framework.pb.h" #include "paddle/framework/lod_tensor.h" #include "paddle/framework/scope.h" @@ -55,7 +57,6 @@ inline std::string GradVarName(const std::string& var_name) { } class OperatorBase; -class InferShapeContext; class ExecutionContext; extern const Tensor* GetTensorFromVar(const Variable* var); @@ -141,9 +142,9 @@ class OperatorBase { // Macro for define a clone method. // If you are writing an kernel operator, `Clone` will be defined when you // register it. i.e. `Clone` method is not needed to define by yourself. -#define DEFINE_OP_CLONE_METHOD(cls) \ - std::unique_ptr Clone() const final { \ - return std::unique_ptr(new cls(*this)); \ +#define DEFINE_OP_CLONE_METHOD(cls) \ + std::unique_ptr<::paddle::framework::OperatorBase> Clone() const final { \ + return std::unique_ptr<::paddle::framework::OperatorBase>(new cls(*this)); \ } // Macro for define a default constructor for Operator. @@ -167,10 +168,11 @@ class NOP : public OperatorBase { } }; -class InferShapeContext { +class ExecutionContext { public: - InferShapeContext(const OperatorBase& op, const Scope& scope) - : op_(op), scope_(scope) {} + ExecutionContext(const OperatorBase& op, const Scope& scope, + const platform::DeviceContext& device_context) + : op_(op), scope_(scope), device_context_(device_context) {} const OperatorBase& op() const { return op_; } @@ -276,101 +278,201 @@ class InferShapeContext { out_tensor->set_lod(in_tensor.lod()); } + template ::EigenDeviceType> + DeviceType& GetEigenDevice() const; + + platform::Place GetPlace() const { return device_context_.GetPlace(); } + + const platform::DeviceContext& device_context() const { + return device_context_; + } + private: const OperatorBase& op_; const Scope& scope_; + const platform::DeviceContext& device_context_; }; template <> -const Tensor* InferShapeContext::Input(const std::string& name) const; +const Tensor* ExecutionContext::Input(const std::string& name) const; template <> -const std::vector InferShapeContext::MultiInput( +const std::vector ExecutionContext::MultiInput( const std::string& name) const; template <> -Tensor* InferShapeContext::Output(const std::string& name) const; +Tensor* ExecutionContext::Output(const std::string& name) const; template <> -std::vector InferShapeContext::MultiOutput( +std::vector ExecutionContext::MultiOutput( const std::string& name) const; -template -struct EigenDeviceConverter; +class CompileTimeInferShapeContext : public InferShapeContext { + public: + CompileTimeInferShapeContext(const OpDescBind& op, const BlockDescBind& block) + : op_(op), block_(block) {} + + bool HasInput(const std::string& name) const override { + const std::vector& input_names = op_.Input(name); + auto length = input_names.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Input(%s) should have only one value, " + "but it have %d now", + name, length); + return block_.HasVar(input_names[0]); + } -template <> -struct EigenDeviceConverter { - using EigenDeviceType = Eigen::DefaultDevice; -}; + bool HasOutput(const std::string& name) const override { + const std::vector& output_names = op_.Output(name); + auto length = output_names.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Output(%s) should have only one value, " + "but it have %d now", + name, length); + return block_.HasVar(output_names[0]); + } -#ifndef PADDLE_ONLY_CPU -template <> -struct EigenDeviceConverter { - using EigenDeviceType = Eigen::GpuDevice; -}; -#endif + bool HasInputs(const std::string& name) const override { + const std::vector& input_names = op_.Input(name); + PADDLE_ENFORCE(!input_names.empty(), "Inputs(%s) length is 0", name); + for (auto& input : input_names) { + if (!block_.HasVar(input)) return false; + } + return true; + } -class ExecutionContext : public InferShapeContext { - public: - ExecutionContext(const OperatorBase& op, const Scope& scope, - const platform::DeviceContext& device_context) - : InferShapeContext(op, scope), device_context_(device_context) {} + bool HasOutputs(const std::string& name) const override { + const std::vector& output_names = op_.Output(name); + PADDLE_ENFORCE(!output_names.empty(), "Inputs(%s) length is 0", name); + for (auto& output : output_names) { + if (!block_.HasVar(output)) return false; + } + return true; + } - template ::EigenDeviceType> - DeviceType& GetEigenDevice() const; + DDim GetInputDim(const std::string& name) const override { + std::vector ddims = GetInputsDim(name); + auto length = ddims.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Input(%s) should have 1 value, " + "but it has %d now", + name, length); + return ddims[0]; + } - platform::Place GetPlace() const { return device_context_.GetPlace(); } + void SetInputDim(const std::string& name, const DDim& dim) override { + SetInputsDim(name, {dim}); + } - const platform::DeviceContext& device_context() const { - return device_context_; + DDim GetOutputDim(const std::string& name) const override { + std::vector ddims = GetOutputsDim(name); + auto length = ddims.size(); + PADDLE_ENFORCE_EQ(length, 1UL, + "Output(%s) should have 1 value, " + "but it has %d now", + name, length); + return ddims[0]; + } + + void SetOutputDim(const std::string& name, const DDim& dim) override { + SetOutputsDim(name, {dim}); + } + + AttrReader Attrs() const override { return AttrReader(op_.GetAttrMap()); } + + const std::vector& Inputs( + const std::string& name) const override { + return op_.Input(name); + } + + const std::vector& Outputs( + const std::string& name) const override { + return op_.Output(name); } private: - const platform::DeviceContext& device_context_; + DDim GetDim(const std::string& name) const override { + return framework::make_ddim(block_.Var(name)->Shape()); + } + + void SetDim(const std::string& name, const DDim& dim) override { + block_.Var(name)->SetShape(framework::vectorize(dim)); + } + + const OpDescBind& op_; + const BlockDescBind& block_; }; -class RuntimeInferShapeContext : public InferShapeContextBase { +class RuntimeInferShapeContext : public InferShapeContext { public: RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope) : op_(op), scope_(scope) {} - bool HasInput(const std::string& name) const { + bool HasInput(const std::string& name) const override { auto ipt = op_.Input(name); auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt); return var != nullptr; } - bool HasOutput(const std::string& name) const { + bool HasOutput(const std::string& name) const override { auto ipt = op_.Output(name); auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt); return var != nullptr; } - DDim GetInputDim(const std::string& name) const { + bool HasInputs(const std::string& name) const override { + auto inputs = op_.Inputs(name); + if (inputs.empty()) { + return false; + } + for (auto& input : inputs) { + if (scope_.FindVar(input) == nullptr) { + return false; + } + } + return true; + } + + bool HasOutputs(const std::string& name) const override { + auto outputs = op_.Outputs(name); + if (outputs.empty()) { + return false; + } + for (auto& output : outputs) { + if (scope_.FindVar(output) == nullptr) { + return false; + } + } + return true; + } + + DDim GetInputDim(const std::string& name) const override { return GetDim(op_.Input(name)); } - void SetInputDim(const std::string& name, const DDim& dim) { + void SetInputDim(const std::string& name, const DDim& dim) override { SetDim(op_.Input(name), dim); } - DDim GetOutputDim(const std::string& name) const { + DDim GetOutputDim(const std::string& name) const override { return GetDim(op_.Output(name)); } - void SetOutputDim(const std::string& name, const DDim& dim) { + void SetOutputDim(const std::string& name, const DDim& dim) override { SetDim(op_.Output(name), dim); } - AttrReader Attrs() const { return AttrReader(op_.Attrs()); } + AttrReader Attrs() const override { return AttrReader(op_.Attrs()); } - const std::vector& Inputs(const std::string& name) const { + const std::vector& Inputs( + const std::string& name) const override { return op_.Inputs(name); } - const std::vector& Outputs(const std::string& name) const { + const std::vector& Outputs( + const std::string& name) const override { return op_.Outputs(name); } @@ -391,11 +493,11 @@ class RuntimeInferShapeContext : public InferShapeContextBase { return t; } - DDim GetDim(const std::string& name) const { + DDim GetDim(const std::string& name) const override { return GetTensor(name)->dims(); } - void SetDim(const std::string& name, const DDim& dim) { + void SetDim(const std::string& name, const DDim& dim) override { GetTensor(name)->Resize(dim); } @@ -403,7 +505,7 @@ class RuntimeInferShapeContext : public InferShapeContextBase { const Scope& scope_; }; -class OpKernel { +class OpKernelBase { public: /** * ExecutionContext is the only parameter of Kernel Run function. @@ -414,33 +516,47 @@ class OpKernel { virtual void Compute(const ExecutionContext& context) const = 0; - virtual ~OpKernel() {} + virtual ~OpKernelBase() = default; +}; + +template +class OpKernel : public OpKernelBase { + public: + using ELEMENT_TYPE = T; }; class OperatorWithKernel : public OperatorBase { public: struct OpKernelKey { platform::Place place_; + DataType data_type_; - OpKernelKey() = default; - explicit OpKernelKey(const platform::DeviceContext& dev_ctx) { - place_ = dev_ctx.GetPlace(); - } + OpKernelKey(DataType data_type, platform::Place place) + : place_(place), data_type_(data_type) {} + + OpKernelKey(DataType data_type, const platform::DeviceContext& dev_ctx) + : place_(dev_ctx.GetPlace()), data_type_(data_type) {} bool operator==(const OpKernelKey& o) const { - return platform::places_are_same_class(place_, o.place_); + return platform::places_are_same_class(place_, o.place_) && + data_type_ == o.data_type_; } }; struct OpKernelHash { - std::hash hash_; + std::hash hash_; size_t operator()(const OpKernelKey& key) const { - return hash_(platform::is_gpu_place(key.place_)); + int place = key.place_.which(); + int data_type = static_cast(key.data_type_); + int pre_hash = data_type << NUM_PLACE_TYPE_LIMIT_IN_BIT | + (place & ((1 << NUM_PLACE_TYPE_LIMIT_IN_BIT) - 1)); + return hash_(pre_hash); } }; using OpKernelMap = - std::unordered_map, OpKernelHash>; + std::unordered_map, + OpKernelHash>; OperatorWithKernel(const std::string& type, const VariableNameMap& inputs, const VariableNameMap& outputs, const AttributeMap& attrs) @@ -451,8 +567,26 @@ class OperatorWithKernel : public OperatorBase { RuntimeInferShapeContext infer_shape_ctx(*this, scope); this->InferShape(&infer_shape_ctx); - auto& opKernel = AllOpKernels().at(type_).at(OpKernelKey(dev_ctx)); - opKernel->Compute(ExecutionContext(*this, scope, dev_ctx)); + ExecutionContext ctx(*this, scope, dev_ctx); + + // check if op[type] has kernel registered. + auto& all_op_kernels = AllOpKernels(); + auto kernels_iter = all_op_kernels.find(type_); + if (kernels_iter == all_op_kernels.end()) { + PADDLE_THROW("op[%s] has no kernel", type_); + } + + // check if op[type] have kernel for kernel_key + OpKernelMap& kernels = kernels_iter->second; + auto kernel_key = OpKernelKey(IndicateDataType(ctx), dev_ctx); + auto kernel_iter = kernels.find(kernel_key); + + if (kernel_iter == kernels.end()) { + PADDLE_THROW("op[%s] has no kernel with kernel_key[%s]", type_, + kernel_key); + } + + kernel_iter->second->Compute(ctx); } static std::unordered_map& @@ -462,14 +596,47 @@ class OperatorWithKernel : public OperatorBase { } bool SupportGPU() const override { - OperatorWithKernel::OpKernelKey key; - key.place_ = platform::GPUPlace(); - return OperatorWithKernel::AllOpKernels().at(type_).count(key) != 0; + auto& op_kernels = OperatorWithKernel::AllOpKernels().at(type_); + return std::any_of(op_kernels.begin(), op_kernels.end(), + [](OpKernelMap::const_reference kern_pair) { + return platform::is_gpu_place(kern_pair.first.place_); + }); } + virtual void InferShape(InferShapeContext* ctx) const = 0; + protected: - virtual void InferShape(InferShapeContextBase* ctx) const = 0; + // indicate kernel DataType by input data. Defaultly all input data must be + // same. + virtual DataType IndicateDataType(const ExecutionContext& ctx) const { + auto& scope = ctx.scope(); + int data_type = -1; + for (auto& input : this->inputs_) { + for (auto& ipt_name : input.second) { + auto* var = scope.FindVar(ipt_name); + if (var != nullptr) { + const Tensor* t = nullptr; + if (var->IsType()) { + t = &var->Get(); + } else if (var->IsType()) { + t = &var->Get(); + } + if (t != nullptr) { + int tmp = static_cast(ToDataType(t->type())); + PADDLE_ENFORCE(tmp == data_type || data_type == -1, + "DataType of Paddle Op must be same."); + data_type = tmp; + } + } + } + } + PADDLE_ENFORCE(data_type != -1, "DataType should be indicated by input"); + return static_cast(data_type); + } }; +std::ostream& operator<<(std::ostream& os, + const OperatorWithKernel::OpKernelKey& kernel_key); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index e1d8f040b837a6ad598351dae0427cc7c231e79f..a02f4668bca2360995cc05206f7f97e027db0907 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -113,11 +113,14 @@ class OpWithKernelTest : public OperatorWithKernel { using OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override {} + void InferShape(framework::InferShapeContext* ctx) const override {} + DataType IndicateDataType(const ExecutionContext& ctx) const override { + return DataType::FP32; + } }; template -class CPUKernelTest : public OpKernel { +class CPUKernelTest : public OpKernel { public: void Compute(const ExecutionContext& ctx) const { std::cout << "this is cpu kernel" << std::endl; @@ -144,7 +147,7 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker } }; -class CPUKernalMultiInputsTest : public OpKernel { +class CPUKernalMultiInputsTest : public OpKernel { public: void Compute(const ExecutionContext& ctx) const { auto xs = ctx.op().Inputs("xs"); diff --git a/paddle/framework/program_desc.h b/paddle/framework/program_desc.h index 06ffcd4b15078f62ea8b7a3714e73de799530785..f29b1c54e7160ac477229f64e5471939131a2d8f 100644 --- a/paddle/framework/program_desc.h +++ b/paddle/framework/program_desc.h @@ -14,8 +14,10 @@ limitations under the License. */ #pragma once +#include #include #include "paddle/framework/framework.pb.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -26,15 +28,10 @@ class ProgramDescBind { public: static ProgramDescBind &Instance(ProgramDesc *prog); - ProgramDescBind(const ProgramDescBind &o) = delete; - ProgramDescBind &operator=(const ProgramDescBind &o) = delete; - BlockDescBind *AppendBlock(const BlockDescBind &parent); BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); } - std::string DebugString() { return Proto()->DebugString(); } - size_t Size() const { return blocks_.size(); } ProgramDesc *Proto(); @@ -46,6 +43,8 @@ class ProgramDescBind { ProgramDesc *prog_; std::vector> blocks_; + + DISABLE_COPY_AND_ASSIGN(ProgramDescBind); }; } // namespace framework } // namespace paddle diff --git a/paddle/framework/scope.cc b/paddle/framework/scope.cc index 080b4ac621c1b8c0d4b4e7b26f394cf2be263894..5821bac928ed898971d61a3e2a86f59155d76991 100644 --- a/paddle/framework/scope.cc +++ b/paddle/framework/scope.cc @@ -13,6 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/framework/scope.h" + +#include // for unique_ptr +#include // for call_once #include "paddle/string/printf.h" namespace paddle { @@ -62,5 +65,17 @@ void Scope::DropKids() { kids_.clear(); } +std::once_flag feed_variable_flag; + +framework::Scope& GetGlobalScope() { + static std::unique_ptr g_scope{nullptr}; + std::call_once(feed_variable_flag, [&]() { + g_scope.reset(new framework::Scope()); + g_scope->NewVar("feed_value"); + g_scope->NewVar("fetch_value"); + }); + return *(g_scope.get()); +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/scope.h b/paddle/framework/scope.h index c93b03e48130afe9568089b6a7586c4185d1d5b4..a8cfb107c25ccd62039db7349cc1c1dbff772f39 100644 --- a/paddle/framework/scope.h +++ b/paddle/framework/scope.h @@ -19,6 +19,7 @@ limitations under the License. */ #include #include "paddle/framework/variable.h" +#include "paddle/platform/macros.h" namespace paddle { namespace framework { @@ -38,11 +39,6 @@ class Scope { Scope() {} ~Scope(); - // Disable Copy, Assign, Move. - Scope(const Scope& other) = delete; - Scope& operator=(const Scope& other) = delete; - Scope(Scope&& other) = delete; - /// Create a sub-scope. Returns a reference other than a pointer so /// to prevent from manual deletion. /// Mark it to const because that new kid scope cannot change parent scope. @@ -73,7 +69,11 @@ class Scope { std::unordered_map vars_; mutable std::list kids_; Scope const* parent_{nullptr}; + + DISABLE_COPY_AND_ASSIGN(Scope); }; +framework::Scope& GetGlobalScope(); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/shape_inference.h b/paddle/framework/shape_inference.h index b07fc788124413f728c713027609d9d2d1c39538..64aab16ae54d34fd614add348c7c420b4a8f771d 100644 --- a/paddle/framework/shape_inference.h +++ b/paddle/framework/shape_inference.h @@ -19,11 +19,18 @@ limitations under the License. */ namespace paddle { namespace framework { -class InferShapeContextBase { +// TODO(longfei): Once after both CompileTimeInferShapeContext and +// RuntimeInferShapeContext get merged, we can rename InferShapeContext into +// InferShapeContext so to replace the current InferShapeContext. +class InferShapeContext { public: - virtual ~InferShapeContextBase() {} + virtual ~InferShapeContext() {} virtual bool HasInput(const std::string &name) const = 0; virtual bool HasOutput(const std::string &name) const = 0; + + virtual bool HasInputs(const std::string &name) const = 0; + virtual bool HasOutputs(const std::string &name) const = 0; + virtual framework::DDim GetInputDim(const std::string &name) const = 0; std::vector GetInputsDim(const std::string &name) const { const std::vector &names = Inputs(name); diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h index f040c09c089ec75c9773d752685be5e232e8f4b7..ba82127d9c028eb39b9dc1a7f34fcf546524142b 100644 --- a/paddle/framework/tensor.h +++ b/paddle/framework/tensor.h @@ -29,20 +29,10 @@ limitations under the License. */ namespace paddle { -namespace pybind { -namespace details { -template -struct CastToPyBufferImpl; -} -} // namespace pybind - namespace framework { class Tensor { public: - template - friend struct pybind::details::CastToPyBufferImpl; - template friend struct EigenTensor; @@ -105,6 +95,19 @@ class Tensor { template inline void CopyFrom(const Tensor& src, const platform::Place& dst_place); + /** + * @brief Copy the content of an external vector to a tensor. + * + * @param[in] src The external vector. + * @param[in] ctx The device context contains place where to store. + * + * * @note CopyFromVector assumes that the tensor has been resized + * before invoking. + */ + template + inline void CopyFromVector(const std::vector& src, + const platform::Place& dst_place); + /** * @brief Return the slice of the tensor. * @@ -119,6 +122,8 @@ class Tensor { return holder_->place(); } + std::type_index type() const { return holder_->type(); } + private: template inline void check_memory_size() const; diff --git a/paddle/framework/tensor_array.cc b/paddle/framework/tensor_array.cc new file mode 100644 index 0000000000000000000000000000000000000000..2728bce1c1af848285e80d8ee8b3b61ec046342e --- /dev/null +++ b/paddle/framework/tensor_array.cc @@ -0,0 +1,283 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + + + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/framework/tensor_array.h" + +#include +#include +#include + +namespace paddle { +namespace framework { + +namespace detail { + +/* + * Offer an iterator over the length-sorted lod-tensor's top level. The top + * level of a lod-tensor stores batch-size of sequences, each top-level sequence + * may contains several lower-level sequences, sort top-level lod by the numbers + * of lower-level sequences in descending order, so that during RNN's running, + * the batch-size will keep decreasing, the short sentences will end at the tail + * of each batch. + * + * Let's take a simple lod-tensor for example + * + * |(0) |(1) top-level has two instances + * ||| ||||| lower-level + * + * sort by lower-level's length + * + * |(1) |(0) + * ||||| ||| + * + * when RNN runs, it get 5 batches (equals the number of elements the longest + * sequence has) + * + * ||||| + * ||| + * + * the first three batches has two elements, the last two elements just has 1 + * element each. + */ +struct DynamicBatchUnpacker { + using value_type = float; + + DynamicBatchUnpacker(const LoDTensor& source, size_t level, + bool descend = true) + : source(&source), level(level) { + BuildLengthSortedMeta(descend); + } + + LoDTensor GetBatch(size_t index); + + std::vector meta; + + LoDTensor const* source; + size_t level; + + protected: + void BuildLengthSortedMeta(bool descend); +}; + +LoDTensor PackDynamicBatch(const std::vector& source, + const std::vector& meta, const LoD& lod, + size_t level); + +} // namespace detail + +const LoDTensor& TensorArray::Read(size_t index) const { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + if (index >= size()) { + values_.resize(index + 1); + } + return values_[index]; +} + +void TensorArray::Write(size_t index, const LoDTensor& value) { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + + if (index >= size()) { + values_.resize(index + 1); + } + + values_[index].Resize(value.dims()); + values_[index].mutable_data(platform::CPUPlace()); + values_[index].CopyFrom(value, platform::CPUPlace()); +} + +void TensorArray::WriteShared(size_t index, const LoDTensor& value) { + PADDLE_ENFORCE_LE(index, MAX_SIZE, "index[%d] too large", index); + if (index >= size()) { + values_.resize(index + 1); + } + + values_[index].ShareDataWith(value); +} + +LoDTensor TensorArray::Pack(size_t level, const std::vector& meta, + const LoD& lod) const { + return detail::PackDynamicBatch(values_, meta, lod, level); +} + +std::vector TensorArray::Unpack(const LoDTensor& source, int level, + bool length_desend) { + detail::DynamicBatchUnpacker unpacker(source, level, + length_desend /*descend*/); + + // find max length of all the sequences + size_t max_length = 0; + for (const auto& seq : unpacker.meta) { + max_length = std::max(max_length, seq.end - seq.begin); + } + + // write batches to values + for (size_t batch_id = 0; batch_id < max_length; batch_id++) { + Write(batch_id, unpacker.GetBatch(batch_id)); + } + + return unpacker.meta; +} + +LoDTensor TensorArray::Stack() const { + LoDTensor result; + if (size() == 0) return result; + + const auto& first_dims = values_.front().dims(); + // check all the values have the same shape + // TODO(superjom) check the same dtypes + for (size_t idx = 1; idx < size(); idx++) { + const auto& value_dims = values_[idx].dims(); + PADDLE_ENFORCE_EQ(first_dims, value_dims); + } + + // copy + auto result_dims = vectorize(first_dims); + result_dims.insert(result_dims.begin(), size()); + result.Resize(make_ddim(result_dims)); + result.mutable_data(platform::CPUPlace()); + + for (size_t idx = 0; idx < size(); idx++) { + result.Slice(idx, idx + 1) + .CopyFrom(Read(idx), platform::CPUPlace()); + } + return result; +} + +void TensorArray::Unstack(const LoDTensor& source) const { + Unstack(source, false /*data_shared*/); +} + +void TensorArray::UnstackShared(const LoDTensor& source) const { + Unstack(source, true /*data_shared*/); +} + +void TensorArray::Unstack(const LoDTensor& source, bool data_shared) const { + size_t first_dim = source.dims()[0]; + DDim value_dims = slice_ddim(source.dims(), 1, source.dims().size()); + PADDLE_ENFORCE_GT(first_dim, 0, + "source should have some data to be unstacked"); + + values_.resize(first_dim); + + for (size_t elem = 0; elem < first_dim; elem++) { + // create a new value + auto& value = values_[elem]; + if (data_shared) { + // share memory + value.ShareDataWith(source.Slice(elem, elem + 1)); + } else { + // copy + value.Resize(value_dims); + value.CopyFrom(source.Slice(elem, elem + 1), + platform::CPUPlace()); + } + } +} + +size_t TensorArray::size() const { return values_.size(); } + +namespace detail { + +void DynamicBatchUnpacker::BuildLengthSortedMeta(bool descend) { + PADDLE_ENFORCE(meta.empty(), "duplicate build meta"); + // collect meta for each sequence in some level + auto lod = SliceLevels(source->lod(), level, level + 1)[0]; + + for (size_t seq_id = 0; seq_id < lod.size() - 1; seq_id++) { + DySeqMeta seq_meta({lod[seq_id], lod[seq_id + 1], seq_id}); + meta.push_back(seq_meta); + } + + PADDLE_ENFORCE_GT(meta.size(), 0, "meta is empty"); + + // sort by length + sort(meta.begin(), meta.end(), + [descend](const DySeqMeta& a, const DySeqMeta& b) { + bool a_ge_b = (a.end - a.begin) > (b.end - b.begin); + return descend ? a_ge_b : !a_ge_b; + }); +} + +LoDTensor DynamicBatchUnpacker::GetBatch(size_t index) { + PADDLE_ENFORCE(!meta.empty(), "should build meta first"); + LoDTensor result; + + // collect indice need to copy to the batch + std::vector indice; + for (const auto& seq : meta) { + size_t id = seq.begin + index; + if (id >= seq.end) break; + indice.push_back(id); + } + PADDLE_ENFORCE(!indice.empty(), "invalid batch at %d", index); + + // copy the indice of records in LoDTensor + auto record_dims = slice_ddim(source->dims(), 1, source->dims().size()); + auto record_dims_vec = vectorize(record_dims); + record_dims_vec.insert(record_dims_vec.begin(), indice.size()); + result.Resize(make_ddim(record_dims_vec)); + result.mutable_data(platform::CPUPlace()); + + for (size_t i = 0; i < indice.size(); i++) { + auto index = indice[i]; + auto target = result.Slice(i, i + 1); + auto source_ = source->Slice(index, index + 1); + + target.CopyFrom(source_, platform::CPUPlace()); + } + + return result; +} + +// TODO(supejom) to cache lod if reasonable +LoDTensor PackDynamicBatch(const std::vector& source, + const std::vector& meta, const LoD& lod, + size_t level) { + PADDLE_ENFORCE(!source.empty()); + PADDLE_ENFORCE(!meta.empty()); + PADDLE_ENFORCE(!lod.empty()); + + LoDTensor result; + + // init result space + auto record_dims = slice_ddim(source[0].dims(), 1, source[0].dims().size()); + auto record_dims_vec = vectorize(record_dims); + auto height = lod[level].back(); + record_dims_vec.insert(record_dims_vec.begin(), height); + result.Resize(make_ddim(record_dims_vec)); + result.mutable_data(platform::CPUPlace()); + + for (size_t batch_id = 0; batch_id < source.size(); batch_id++) { + for (size_t seq_id = 0; seq_id < meta.size(); seq_id++) { + const auto& seq_meta = meta[seq_id]; + // source is source[batch_id][seq_id] + // target is result[index] + auto index = seq_meta.begin + batch_id; + if (index >= seq_meta.end) break; + auto source_ = source[batch_id].Slice(seq_id, seq_id + 1); + auto target = result.Slice(index, index + 1); + target.CopyFrom(source_, platform::CPUPlace()); + } + } + + result.set_lod(lod); + return result; +} + +} // namespace detail + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_array.h b/paddle/framework/tensor_array.h new file mode 100644 index 0000000000000000000000000000000000000000..293da04997304be41810446cb3e866d545805f83 --- /dev/null +++ b/paddle/framework/tensor_array.h @@ -0,0 +1,113 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include + +#include "paddle/framework/lod_tensor.h" + +namespace paddle { +namespace framework { + +/* + * DyBatchSeqPosition stores indices of the basic element in tensor. It is used + * after lod-tensor's re-assembling, its info can be used to recover the order + * in original lod-tensor. + */ +struct DySeqMeta { + DySeqMeta(size_t begin, size_t end, size_t ori_idx) + : begin(begin), end(end), ori_idx(ori_idx) {} + + size_t begin; + size_t end; // not included + size_t ori_idx; +}; + +/* + * TensorArray is a C-array-like array of tensors, it is meant to be used with + * dynamic iteration primitives such as while_loop. It is used to segment inputs + * and store states in all time steps. + * + * By providing some methods similar to a C++ array, the difinition of some + * state-based dynamic models such as RNN cound be more natural and highly + * flexible. + */ +class TensorArray { + public: + using value_type = float; + + // max number of values allowed to store. + const size_t MAX_SIZE{100000}; + + /* + * Read the value at location `index` in the `TensorArray`. + */ + const LoDTensor &Read(size_t index) const; + + /* + * Write value into the index of the TensorArray. + */ + void Write(size_t index, const LoDTensor &value); + + /* + * Write value into the index of the TensorArray, with memory shared. + */ + void WriteShared(size_t index, const LoDTensor &value); + + /* + * Recover the original LoD-arranged LoDTensor with the `values`, `level` and + * `indice_map`. + */ + LoDTensor Pack(size_t level, const std::vector &meta, + const LoD &lod) const; + + /* + * Split LoDTensor in some `level` and write the generated batches to + * `values`, if set `desend`, will sort by length in descending order else in + * ascending order. + */ + std::vector Unpack(const LoDTensor &source, int level, + bool length_desend); + + /* + * Pack the values into a tensor with rank one higher than each tensor in + * values. + */ + LoDTensor Stack() const; + + /* + * Unstacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors. + */ + void Unstack(const LoDTensor &source) const; + + /* + * Unstacks the given division of a rank-`R` tensor into rank-`(R-1)` tensors, + * with memory of tensors shared. + */ + void UnstackShared(const LoDTensor &source) const; + + /* + * Return the number of values. + */ + size_t size() const; + + protected: + void Unstack(const LoDTensor &source, bool data_shared) const; + + private: + mutable std::vector values_; +}; // class TensorArray + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_array_test.cc b/paddle/framework/tensor_array_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..d9f52509cdd1b79f6d53b5d4922f9e44279de08b --- /dev/null +++ b/paddle/framework/tensor_array_test.cc @@ -0,0 +1,130 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/framework/tensor_array.h" + +#include + +namespace paddle { +namespace framework { + +class TensorArrayTester : public ::testing::Test { + protected: + void SetUp() override { + LoDTensor source; + source.Resize(make_ddim({batch_size, dim})); + int* data = source.mutable_data(platform::CPUPlace()); + for (int i = 0; i < 16 * 32; i++) { + data[i] = i; + } + ta.Unstack(source); + } + + TensorArray ta; + const int batch_size = 16; + const int dim = 32; +}; + +TEST_F(TensorArrayTester, Read) { + for (int i = 0; i < batch_size; i++) { + const auto& tensor = ta.Read(i); + ASSERT_EQ(tensor.dims()[0], 1); + ASSERT_EQ(tensor.dims()[1], dim); + } +} + +TEST_F(TensorArrayTester, Write) { + LoDTensor source; + source.Resize(make_ddim({1, dim})); + for (int i = 0; i < dim; i++) { + *(source.mutable_data(platform::CPUPlace()) + i) = i; + } + + ta.Write(2, source); + + const auto& tensor = ta.Read(2); + for (int i = 0; i < dim; i++) { + EXPECT_EQ(*(tensor.data() + i), *(source.data() + i)); + } +} + +TEST_F(TensorArrayTester, WriteShared) { + LoDTensor source; + source.Resize(make_ddim({1, dim})); + for (int i = 0; i < dim; i++) { + *(source.mutable_data(platform::CPUPlace()) + i) = i; + } + + ta.WriteShared(2, source); + + const auto& tensor = ta.Read(2); + for (int i = 0; i < dim; i++) { + EXPECT_EQ(*(tensor.data() + i), *(source.data() + i)); + } + + EXPECT_EQ(source.data(), tensor.data()); +} + +class TensorArrayPackTester : public ::testing::Test { + protected: + virtual void SetUp() override { + lod.push_back(std::vector{0, 2, 9, 13}); + + source.set_lod(lod); + source.Resize(make_ddim({13, 128})); + source.mutable_data(platform::CPUPlace()); + + // content of each setence: 0 1 2 3 4 + const auto& level = lod.front(); + for (size_t i = 0; i < level.size() - 1; i++) { + size_t begin = level[i]; + size_t end = level[i + 1]; + for (size_t j = begin; j < end; j++) { + auto record = source.Slice(j, j + 1); + for (int dim = 0; dim < 128; dim++) { + record.mutable_data(platform::CPUPlace())[dim] = j - begin; + } + } + } + + // unpack + meta = ta.Unpack(source, 0, true); + } + + LoD lod; + TensorArray ta; + LoDTensor source; + std::vector meta; +}; + +TEST_F(TensorArrayPackTester, Unpack) { + ASSERT_EQ(ta.size(), 7UL); + + const auto& t0 = ta.Read(0); + const auto& t1 = ta.Read(1); + + ASSERT_EQ(t0.data()[0], int(0)); + ASSERT_EQ(t1.data()[0], int(1)); +} + +TEST_F(TensorArrayPackTester, Pack) { + LoDTensor packed = ta.Pack(0, meta, lod); +} + +TEST_F(TensorArrayTester, size) { + ASSERT_EQ(ta.size(), static_cast(batch_size)); +} + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h index a5405f9c31543b5733f9db923c2a6f8b968cfc2d..8ee9941982cdd8f78fdbace9dca085097b08eeb8 100644 --- a/paddle/framework/tensor_impl.h +++ b/paddle/framework/tensor_impl.h @@ -65,7 +65,7 @@ inline T* Tensor::mutable_data(platform::Place place) { holder_.reset(new PlaceholderImpl( boost::get(place), size)); } else if (platform::is_gpu_place(place)) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA PADDLE_THROW("'GPUPlace' is not supported in CPU only device."); } #else @@ -103,7 +103,7 @@ inline void Tensor::CopyFrom(const Tensor& src, memory::Copy(boost::get(dst_place), dst_ptr, boost::get(src_place), src_ptr, size); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA else if (platform::is_gpu_place(src_place) && platform::is_cpu_place(dst_place)) { memory::Copy(boost::get(dst_place), dst_ptr, @@ -123,6 +123,29 @@ inline void Tensor::CopyFrom(const Tensor& src, #endif } +template +inline void Tensor::CopyFromVector(const std::vector& src, + const platform::Place& dst_place) { + auto src_ptr = static_cast(src.data()); + platform::CPUPlace src_place; + auto dst_ptr = static_cast(mutable_data(dst_place)); + auto size = src.size() * sizeof(T); + + if (platform::is_cpu_place(dst_place)) { + memory::Copy(boost::get(dst_place), dst_ptr, src_place, + src_ptr, size); + } +#ifdef PADDLE_WITH_CUDA + else if (platform::is_gpu_place(dst_place)) { + memory::Copy(boost::get(dst_place), dst_ptr, src_place, + src_ptr, size, 0); + } + PADDLE_ENFORCE(cudaStreamSynchronize(0), + "cudaStreamSynchronize failed in Tensor CopyFromVector"); + +#endif +} + template inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const { check_memory_size(); diff --git a/paddle/framework/tensor_test.cc b/paddle/framework/tensor_test.cc index e2ec738de35c90c6a06c9a46b062d4cce55f5eda..492eba69e1ea483eca1da782004231af61fc60be 100644 --- a/paddle/framework/tensor_test.cc +++ b/paddle/framework/tensor_test.cc @@ -74,7 +74,7 @@ TEST(Tensor, MutableData) { EXPECT_EQ(p1, p2); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; float* p1 = nullptr; @@ -126,7 +126,7 @@ TEST(Tensor, ShareDataWith) { ASSERT_EQ(src_tensor.data(), dst_tensor.data()); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; Tensor dst_tensor; @@ -163,7 +163,7 @@ TEST(Tensor, Slice) { EXPECT_EQ(src_data_address + 3 * 4 * 1 * sizeof(int), slice_data_address); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; src_tensor.mutable_data(make_ddim({6, 9}), GPUPlace()); @@ -218,7 +218,7 @@ TEST(Tensor, CopyFrom) { EXPECT_EQ(dst_ptr[i], slice_ptr[i]); } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA { Tensor src_tensor; Tensor gpu_tensor; @@ -263,6 +263,93 @@ TEST(Tensor, CopyFrom) { #endif } +TEST(Tensor, CopyFromVector) { + using namespace paddle::framework; + using namespace paddle::platform; + { + std::vector src_vec = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + Tensor cpu_tensor; + + // Copy to CPU Tensor + cpu_tensor.Resize(make_ddim({3, 3})); + auto cpu_place = new paddle::platform::CPUPlace(); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + + // Compare Tensors + const int* cpu_ptr = cpu_tensor.data(); + const int* src_ptr = src_vec.data(); + ASSERT_NE(src_ptr, cpu_ptr); + for (size_t i = 0; i < 9; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + } + + src_vec.erase(src_vec.begin(), src_vec.begin() + 5); + cpu_tensor.Resize(make_ddim({2, 2})); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + cpu_ptr = cpu_tensor.data(); + src_ptr = src_vec.data(); + ASSERT_NE(src_ptr, cpu_ptr); + for (size_t i = 0; i < 5; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + } + + delete cpu_place; + } + +#ifdef PADDLE_WITH_CUDA + { + std::vector src_vec = {1, 2, 3, 4, 5, 6, 7, 8, 9}; + Tensor cpu_tensor; + Tensor gpu_tensor; + Tensor dst_tensor; + + // Copy to CPU Tensor + cpu_tensor.Resize(make_ddim({3, 3})); + auto cpu_place = new paddle::platform::CPUPlace(); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + + // Copy to GPUTensor + gpu_tensor.Resize(make_ddim({3, 3})); + auto gpu_place = new paddle::platform::GPUPlace(); + gpu_tensor.CopyFromVector(src_vec, *gpu_place); + // Copy from GPU to CPU tensor for comparison + dst_tensor.CopyFrom(gpu_tensor, *cpu_place); + + // Compare Tensors + const int* src_ptr = src_vec.data(); + const int* cpu_ptr = cpu_tensor.data(); + const int* dst_ptr = dst_tensor.data(); + ASSERT_NE(src_ptr, cpu_ptr); + ASSERT_NE(src_ptr, dst_ptr); + for (size_t i = 0; i < 9; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + EXPECT_EQ(src_ptr[i], dst_ptr[i]); + } + + src_vec.erase(src_vec.begin(), src_vec.begin() + 5); + + cpu_tensor.Resize(make_ddim({2, 2})); + cpu_tensor.CopyFromVector(src_vec, *cpu_place); + gpu_tensor.Resize(make_ddim({2, 2})); + gpu_tensor.CopyFromVector(src_vec, *gpu_place); + dst_tensor.CopyFrom(gpu_tensor, *cpu_place); + + src_ptr = src_vec.data(); + cpu_ptr = cpu_tensor.data(); + dst_ptr = dst_tensor.data(); + ASSERT_NE(src_ptr, cpu_ptr); + ASSERT_NE(src_ptr, dst_ptr); + for (size_t i = 0; i < 5; ++i) { + EXPECT_EQ(src_ptr[i], cpu_ptr[i]); + EXPECT_EQ(src_ptr[i], dst_ptr[i]); + } + + delete cpu_place; + delete gpu_place; + } +#endif +} + TEST(Tensor, ReshapeToMatrix) { using namespace paddle::framework; using namespace paddle::platform; diff --git a/paddle/framework/type_defs.h b/paddle/framework/type_defs.h new file mode 100644 index 0000000000000000000000000000000000000000..6f65a942ba2a4073e6aa1047875ec5c3283c23a6 --- /dev/null +++ b/paddle/framework/type_defs.h @@ -0,0 +1,43 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include +#include +#include +#include "paddle/platform/variant.h" + +namespace paddle { +namespace framework { +class OperatorBase; +class OpDescBind; +using VariableNameMap = std::map>; + +// The order should be as same as framework.proto +using Attribute = + boost::variant, + std::vector, std::vector, bool, + std::vector, BlockDesc*>; + +using AttributeMap = std::unordered_map; + +using OpCreator = std::function; + +using GradOpMakerFN = + std::function>(const OpDescBind&)>; + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/var_desc.cc b/paddle/framework/var_desc.cc index 13b9c5f3cdf98e6d22f4217fa1cf9a48910a78d8..a88e813b5e7c7e6420cb0ba8a25bba4f4d658e80 100644 --- a/paddle/framework/var_desc.cc +++ b/paddle/framework/var_desc.cc @@ -32,5 +32,13 @@ std::vector VarDescBind::Shape() const { DataType VarDescBind::GetDataType() const { return desc_.lod_tensor().data_type(); } + +void VarDescBind::SetLoDLevel(int32_t lod_level) { + desc_.mutable_lod_tensor()->set_lod_level(lod_level); +} + +int32_t VarDescBind::GetLodLevel() const { + return desc_.lod_tensor().lod_level(); +} } // namespace framework } // namespace paddle diff --git a/paddle/framework/var_desc.h b/paddle/framework/var_desc.h index 4763bf09d004539ab24e4aad3bf429667f1fcc73..464fece85fe5c674690c2034054e551f14db2138 100644 --- a/paddle/framework/var_desc.h +++ b/paddle/framework/var_desc.h @@ -66,6 +66,10 @@ class VarDescBind { DataType GetDataType() const; + void SetLoDLevel(int32_t lod_level); + + int32_t GetLodLevel() const; + private: VarDesc desc_; }; diff --git a/paddle/function/BlockExpandOp.cpp b/paddle/function/BlockExpandOp.cpp index a89b6bba45843d81264819cad6ba053f28314f6b..bd0fe119ce46df9c333258c9c1ad7b5b2bdc544f 100644 --- a/paddle/function/BlockExpandOp.cpp +++ b/paddle/function/BlockExpandOp.cpp @@ -194,7 +194,7 @@ public: REGISTER_TYPED_FUNC(BlockExpand, CPU, BlockExpandForward); REGISTER_TYPED_FUNC(BlockExpandGrad, CPU, BlockExpandBackward); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(BlockExpand, GPU, BlockExpandForward); REGISTER_TYPED_FUNC(BlockExpandGrad, GPU, BlockExpandBackward); #endif diff --git a/paddle/function/ContextProjectionOp.cpp b/paddle/function/ContextProjectionOp.cpp index b87750b74247bd0eb822340bc5a85d41b86ecec2..23916c0f4b6319004ca0793bc9305b8a1dd0ae89 100644 --- a/paddle/function/ContextProjectionOp.cpp +++ b/paddle/function/ContextProjectionOp.cpp @@ -395,7 +395,7 @@ REGISTER_TYPED_FUNC(ContextProjectionForward, REGISTER_TYPED_FUNC(ContextProjectionBackward, CPU, ContextProjectionBackwardFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(ContextProjectionForward, GPU, ContextProjectionForwardFunc); diff --git a/paddle/function/CosSimOp.cpp b/paddle/function/CosSimOp.cpp index 7ece7b2dfedaf460741c97b5a700eb632d85cabc..2e5c281f37d8ffb1062121b5dc5b4f790ab52089 100644 --- a/paddle/function/CosSimOp.cpp +++ b/paddle/function/CosSimOp.cpp @@ -233,7 +233,7 @@ private: REGISTER_TYPED_FUNC(CosSimForward, CPU, CosSimForwardFunc); REGISTER_TYPED_FUNC(CosSimBackward, CPU, CosSimBackwardFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(CosSimForward, GPU, CosSimForwardFunc); REGISTER_TYPED_FUNC(CosSimBackward, GPU, CosSimBackwardFunc); #endif diff --git a/paddle/function/CropOp.cpp b/paddle/function/CropOp.cpp index f12ee43e3d72f9ac776eaff93914228850694dd2..46f98f12c1f150fdf3ed53a7a96e5cf0020e14a4 100644 --- a/paddle/function/CropOp.cpp +++ b/paddle/function/CropOp.cpp @@ -169,7 +169,7 @@ private: REGISTER_TYPED_FUNC(Crop, CPU, CropFunc); REGISTER_TYPED_FUNC(CropGrad, CPU, CropGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(Crop, GPU, CropFunc); REGISTER_TYPED_FUNC(CropGrad, GPU, CropGradFunc); #endif diff --git a/paddle/function/CrossMapNormalOp.cpp b/paddle/function/CrossMapNormalOp.cpp index ef878bfbba961bdd3d5212e19fb83bb1e285e47f..9e88669d37bd50179dcc0464e8c1cd6e2fed74db 100644 --- a/paddle/function/CrossMapNormalOp.cpp +++ b/paddle/function/CrossMapNormalOp.cpp @@ -336,7 +336,7 @@ private: REGISTER_TYPED_FUNC(CrossMapNormal, CPU, CrossMapNormalFunc); REGISTER_TYPED_FUNC(CrossMapNormalGrad, CPU, CrossMapNormalGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(CrossMapNormal, GPU, CrossMapNormalFunc); REGISTER_TYPED_FUNC(CrossMapNormalGrad, GPU, CrossMapNormalGradFunc); #endif diff --git a/paddle/function/DepthwiseConvOp.cpp b/paddle/function/DepthwiseConvOp.cpp index 2f3112fe657cd381891dc53c7179e7520911e8c9..9863e3ae1d5fcb1eece5267fd4f2a6b593b799df 100644 --- a/paddle/function/DepthwiseConvOp.cpp +++ b/paddle/function/DepthwiseConvOp.cpp @@ -292,7 +292,7 @@ REGISTER_TYPED_FUNC(DepthwiseConvGradInput, REGISTER_TYPED_FUNC(DepthwiseConvGradFilter, CPU, DepthwiseConvGradFilterFunction); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(DepthwiseConv, GPU, DepthwiseConvFunction); REGISTER_TYPED_FUNC(DepthwiseConvGradInput, GPU, diff --git a/paddle/function/DepthwiseConvOpTest.cpp b/paddle/function/DepthwiseConvOpTest.cpp index d8e8c889d5c23bf9b2b5fd0b0393395883188fd8..b1a90da7db2b647dd384e3772820294140e5ec9d 100644 --- a/paddle/function/DepthwiseConvOpTest.cpp +++ b/paddle/function/DepthwiseConvOpTest.cpp @@ -17,7 +17,7 @@ limitations under the License. */ namespace paddle { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(DepthwiseConv, Forward) { DepthwiseConvolution( "GemmConv-CPU", "DepthwiseConv-GPU", forward); diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index f8cf4ebea8d724f0291b981647622b63e3d84495..bdb56ddac38b91d756fc6f31282f29c0489fd660 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -340,7 +340,7 @@ public: REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvFunction); REGISTER_TYPED_FUNC(GemmConvGradInput, CPU, GemmConvGradInputFunction); REGISTER_TYPED_FUNC(GemmConvGradFilter, CPU, GemmConvGradFilterFunction); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(GemmConv, GPU, GemmConvFunction); REGISTER_TYPED_FUNC(GemmConvGradInput, GPU, GemmConvGradInputFunction); REGISTER_TYPED_FUNC(GemmConvGradFilter, GPU, GemmConvGradFilterFunction); diff --git a/paddle/function/GemmConvOpTest.cpp b/paddle/function/GemmConvOpTest.cpp index 5283d79a5a53d979ae4e134f7e46b7ee106e9c44..b5b5e1f35b79e422b14f7495bc321533cc1d618a 100644 --- a/paddle/function/GemmConvOpTest.cpp +++ b/paddle/function/GemmConvOpTest.cpp @@ -24,7 +24,7 @@ TEST(GemmConv, NaiveConv) { "NaiveConv-CPU", "GemmConv-CPU", forward); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(GemmConv, Forward) { Convolution( "GemmConv-CPU", "GemmConv-GPU", forward); diff --git a/paddle/function/Im2ColTest.cpp b/paddle/function/Im2ColTest.cpp index acc88a553abe7ac58b629aba9b850df58cee7f81..a0a01a5fc7fc055dce6ddb3ee51c7ab18f8a4ca7 100644 --- a/paddle/function/Im2ColTest.cpp +++ b/paddle/function/Im2ColTest.cpp @@ -116,7 +116,7 @@ void TestIm2ColFunctor() { TEST(Im2ColFunctor, CPU) { TestIm2ColFunctor(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Im2ColFunctor, GPU) { TestIm2ColFunctor(); } diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 25e41edad54bec0f76a3de4799fab14241407272..704a8c41325ef86067a3bd8ed6d772b77df147c5 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -341,7 +341,7 @@ private: }; REGISTER_TYPED_FUNC(MulOp, CPU, MulFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(MulOp, GPU, MulFunc); #endif } // namespace paddle diff --git a/paddle/function/PadOp.cpp b/paddle/function/PadOp.cpp index adba7c92ece505eecc74edce6b393cf27fa10ccc..eed2f2e3089b6b6167ef7c5a7acb7ecaa08945e1 100644 --- a/paddle/function/PadOp.cpp +++ b/paddle/function/PadOp.cpp @@ -207,7 +207,7 @@ private: REGISTER_TYPED_FUNC(Pad, CPU, PadFunc); REGISTER_TYPED_FUNC(PadGrad, CPU, PadGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(Pad, GPU, PadFunc); REGISTER_TYPED_FUNC(PadGrad, GPU, PadGradFunc); #endif diff --git a/paddle/function/RowConvOp.cpp b/paddle/function/RowConvOp.cpp index b6501e8f4db7fd33891cd80e07a6f36dd0b34532..7c802d66273c6f7aa56b2f460e3dff4401967517 100644 --- a/paddle/function/RowConvOp.cpp +++ b/paddle/function/RowConvOp.cpp @@ -217,7 +217,7 @@ public: REGISTER_TYPED_FUNC(RowConv, CPU, RowConvFunc); REGISTER_TYPED_FUNC(RowConvGrad, CPU, RowConvGradFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(RowConv, GPU, RowConvFunc); REGISTER_TYPED_FUNC(RowConvGrad, GPU, RowConvGradFunc); #endif diff --git a/paddle/function/SwitchOp.cpp b/paddle/function/SwitchOp.cpp index 01e252a8dc0cd5fa1e964efa01d04cf282b3dfe7..597723a2dded6a6a116e05b7d4c942cd633e2c99 100644 --- a/paddle/function/SwitchOp.cpp +++ b/paddle/function/SwitchOp.cpp @@ -132,7 +132,7 @@ public: REGISTER_TYPED_FUNC(NCHW2NHWC, CPU, NCHW2NHWCFunc); REGISTER_TYPED_FUNC(NHWC2NCHW, CPU, NHWC2NCHWFunc); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA REGISTER_TYPED_FUNC(NCHW2NHWC, GPU, NCHW2NHWCFunc); REGISTER_TYPED_FUNC(NHWC2NCHW, GPU, NHWC2NCHWFunc); #endif diff --git a/paddle/gserver/CMakeLists.txt b/paddle/gserver/CMakeLists.txt index 62cff9361ccba3ae3b9359ddb932f5b26146eb97..5f39167afc34affbea7858fa0794ef52b786a383 100644 --- a/paddle/gserver/CMakeLists.txt +++ b/paddle/gserver/CMakeLists.txt @@ -60,6 +60,36 @@ if(NOT WITH_PYTHON) dataproviders/PyDataProvider.h) endif() +if(MOBILE_INFERENCE) + # Remove evaluators + list(REMOVE_ITEM GSERVER_SOURCES + layers/ValidationLayer.cpp + evaluators/Evaluator.cpp + evaluators/DetectionMAPEvaluator.cpp + evaluators/CTCErrorEvaluator.cpp + evaluators/ChunkEvaluator.cpp) + + # Remove dataproviders + list(REMOVE_ITEM GSERVER_SOURCES + dataproviders/DataProvider.cpp + dataproviders/MultiDataProvider.cpp + dataproviders/ProtoDataProvider.cpp + dataproviders/PyDataProvider2.cpp + dataproviders/PyDataProvider.cpp) + + # Remove useless gradientmachines + list(REMOVE_ITEM GSERVER_SOURCES + gradientmachines/MultiNetwork.cpp + gradientmachines/RecurrentGradientMachine.cpp + gradientmachines/ParallelNeuralNetwork.cpp + gradientmachines/GradientMachineMode.cpp + gradientmachines/MultiGradientMachine.cpp) + + # Remove useless layers + list(REMOVE_ITEM GSERVER_SOURCES + layers/RecurrentLayerGroup.cpp) +endif() + if(WITH_GPU) cuda_add_library(paddle_gserver ${GSERVER_SOURCES}) else() diff --git a/paddle/gserver/gradientmachines/GradientMachine.cpp b/paddle/gserver/gradientmachines/GradientMachine.cpp index b44e4dc202f01956ed21c175aa897ced8e92546b..de5faf5e1e2b3e73bc07fe7f1635110f4efd7eec 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.cpp +++ b/paddle/gserver/gradientmachines/GradientMachine.cpp @@ -17,12 +17,15 @@ limitations under the License. */ #include #include "paddle/utils/Logging.h" +#include "NeuralNetwork.h" +#include "hl_gpu.h" + +#ifndef PADDLE_MOBILE_INFERENCE #include "GradientMachineMode.h" #include "MultiGradientMachine.h" #include "MultiNetwork.h" -#include "NeuralNetwork.h" #include "ParallelNeuralNetwork.h" -#include "hl_gpu.h" +#endif namespace paddle { @@ -30,13 +33,16 @@ GradientMachine* GradientMachine::create( const ModelConfig& config, int mode, const std::vector& parameterTypes) { +#ifndef PADDLE_MOBILE_INFERENCE if (auto gm = IGradientMachineMode::tryCreateGradientMachine(mode, config)) { return gm; } if (FLAGS_trainer_count > 1) { return new MultiGradientMachine(config, FLAGS_use_gpu); } +#endif if (FLAGS_trainer_count == 1) { // single +#ifndef PADDLE_MOBILE_INFERENCE NeuralNetwork* nn; if (config.type() == "multi_nn") { /* multi submodel calculate, thread(s) will be initialized inside */ @@ -48,6 +54,9 @@ GradientMachine* GradientMachine::create( /* single thread calculate */ nn = NeuralNetwork::create(config); } +#else + NeuralNetwork* nn = NeuralNetwork::create(config); +#endif ParamInitCallback testParamInitCb = [](int paramId, Parameter* para) { para->enableType(PARAMETER_VALUE); }; diff --git a/paddle/gserver/gradientmachines/GradientMachine.h b/paddle/gserver/gradientmachines/GradientMachine.h index f9c82a2bef82b4e6bcbf0c73583505d2692f3926..ebfe0573cfdbfb2ef54a29b038e8b85356cc6c27 100644 --- a/paddle/gserver/gradientmachines/GradientMachine.h +++ b/paddle/gserver/gradientmachines/GradientMachine.h @@ -20,13 +20,16 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "TrainerConfig.pb.h" #include "paddle/gserver/dataproviders/DataProvider.h" -#include "paddle/gserver/evaluators/Evaluator.h" #include "paddle/gserver/layers/Layer.h" #include "paddle/math/Matrix.h" #include "paddle/parameter/Parameter.h" #include "paddle/parameter/ParameterUpdaterBase.h" #include "paddle/utils/Thread.h" +#ifndef PADDLE_MOBILE_INFERENCE +#include "paddle/gserver/evaluators/Evaluator.h" +#endif + namespace paddle { /** * @brief A gradient machine is capable of calculating some outputs given @@ -147,6 +150,7 @@ public: virtual void onPassEnd() = 0; +#ifndef PADDLE_MOBILE_INFERENCE /** * Create an evaluator which can be used for eval() */ @@ -156,6 +160,7 @@ public: * evaluate using the given evaluator */ virtual void eval(Evaluator* evaluator) const = 0; +#endif std::vector& getParameters() { return parameters_; } diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index 26cff3e67710b2f38d93572c5d58849aa94a5135..dcf0acb5a2cc7698625a2e254d4c12a32bc9631d 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -14,15 +14,17 @@ limitations under the License. */ #include "paddle/utils/Util.h" +#include "NeuralNetwork.h" +#include "hl_gpu.h" +#include "paddle/gserver/layers/AgentLayer.h" #include "paddle/utils/CustomStackTrace.h" #include "paddle/utils/Logging.h" +#include "paddle/utils/Stat.h" +#ifndef PADDLE_MOBILE_INFERENCE #include "MultiNetwork.h" -#include "NeuralNetwork.h" #include "RecurrentGradientMachine.h" -#include "hl_gpu.h" -#include "paddle/gserver/layers/AgentLayer.h" -#include "paddle/utils/Stat.h" +#endif namespace paddle { void parameterInitNN(int paramId, @@ -54,6 +56,7 @@ void parameterInitNN(int paramId, } NeuralNetwork* NeuralNetwork::create(const ModelConfig& config) { +#ifndef PADDLE_MOBILE_INFERENCE if (config.type() == "recurrent_nn") { return newNeuralNetwork("root"); } else if (config.type() == "multi_nn") { @@ -61,6 +64,9 @@ NeuralNetwork* NeuralNetwork::create(const ModelConfig& config) { } else { return newNeuralNetwork(); } +#else + return new NeuralNetwork(); +#endif } std::map NeuralNetwork::dllInitMap; @@ -304,6 +310,8 @@ void NeuralNetwork::onPassEnd() { } } +#ifndef PADDLE_MOBILE_INFERENCE + class CombinedEvaluator : public Evaluator { public: void addEvaluator(std::unique_ptr&& evaluator) { @@ -466,6 +474,8 @@ Evaluator* NeuralNetwork::makeEvaluator() const { void NeuralNetwork::eval(Evaluator* evaluator) const { evaluator->eval(*this); } +#endif + void NeuralNetwork::setOutputGrad(const std::vector& args) { CHECK_GE(outputLayers_.size(), args.size()); for (size_t i = 0; i < args.size(); ++i) { diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.h b/paddle/gserver/gradientmachines/NeuralNetwork.h index 12810f642519b7965fc1b7d751290445e3350dd5..56a1ec78460731554c9b47cf3f517f7654dc314f 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.h +++ b/paddle/gserver/gradientmachines/NeuralNetwork.h @@ -97,9 +97,12 @@ public: virtual void onPassEnd(); +#ifndef PADDLE_MOBILE_INFERENCE virtual Evaluator* makeEvaluator() const; virtual void eval(Evaluator* evaluator) const; +#endif + virtual void resetState(); virtual void setOutputGrad(const std::vector& args); diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp index f7a80e23e1bd49549bec57b360587adc6b423794..bc7d1c83a48aefeb4bc6d3baa32b78aba712e58d 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.cpp +++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp @@ -16,7 +16,7 @@ limitations under the License. */ #include "BatchNormalizationLayer.h" #include "Layer.h" #include "paddle/utils/Stat.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "CudnnBatchNormLayer.h" #endif diff --git a/paddle/gserver/layers/BatchNormalizationLayer.cpp b/paddle/gserver/layers/BatchNormalizationLayer.cpp index 412762d38475422be98ffeb87ffcfb028c3e035f..dacff25e5927daf9c991577a71be86b160228317 100644 --- a/paddle/gserver/layers/BatchNormalizationLayer.cpp +++ b/paddle/gserver/layers/BatchNormalizationLayer.cpp @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/utils/Stat.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "hl_batch_transpose.h" #endif #include "BatchNormalizationLayer.h" @@ -90,7 +90,7 @@ void BatchNormalizationLayer::expandMat(const MatrixPtr& in, MatrixPtr& out) { size_t batchSize = in->getHeight(); CHECK_EQ(out->getHeight(), batchSize * imgPixels_); if (useGpu_) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA LOG(FATAL) << "paddle is compiled only for cpu"; #else batchTranspose( @@ -127,7 +127,7 @@ void BatchNormalizationLayer::shrinkMat(const MatrixPtr& in, MatrixPtr& out) { } CHECK_EQ(in->getHeight(), static_cast(batchSize * imgPixels_)); if (useGpu_) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA LOG(FATAL) << "paddle is compiled only for cpu"; #else batchTranspose( diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index e95f42c863b3733ca66055e1b3939e734cae8ad1..01f2aae6cf88d47296da804061b9b039cca593db 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -15,11 +15,14 @@ limitations under the License. */ #include "paddle/utils/Util.h" #include "CostLayer.h" -#include "ValidationLayer.h" #include "paddle/math/SparseMatrix.h" #include "paddle/utils/Error.h" #include "paddle/utils/Logging.h" +#ifndef PADDLE_MOBILE_INFERENCE +#include "ValidationLayer.h" +#endif + DEFINE_bool(log_error_clipping, false, "enable log error clipping or not"); namespace paddle { @@ -103,10 +106,12 @@ LayerPtr Layer::create(const LayerConfig& config) { return LayerPtr(new MultiClassCrossEntropy(config)); else if (type == "rank-cost") return LayerPtr(new RankingCost(config)); +#ifndef PADDLE_MOBILE_INFERENCE else if (type == "auc-validation") return LayerPtr(new AucValidation(config)); else if (type == "pnpair-validation") return LayerPtr(new PnpairValidation(config)); +#endif return LayerPtr(registrar_.createByType(config.type(), config)); } diff --git a/paddle/gserver/layers/PoolLayer.cpp b/paddle/gserver/layers/PoolLayer.cpp index 96d5c54accc047b685502a178de2d290f3158731..7b932d5a76e9c4fe7cbe5882bbc19eb3de4b503a 100644 --- a/paddle/gserver/layers/PoolLayer.cpp +++ b/paddle/gserver/layers/PoolLayer.cpp @@ -15,7 +15,7 @@ limitations under the License. */ #include "PoolLayer.h" #include "PoolProjectionLayer.h" #include "paddle/utils/Logging.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "CudnnPoolLayer.h" #endif namespace paddle { @@ -53,7 +53,7 @@ Layer* PoolLayer::create(const LayerConfig& config) { const std::string& pool = config.inputs(0).pool_conf().pool_type(); if (pool == "max-projection" || pool == "avg-projection") { return new PoolProjectionLayer(config); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA } else if (CudnnPoolLayer::typeCheck(pool)) { return new CudnnPoolLayer(config); #endif diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index de9b8e63dfc4291f8f42ca8c57cb5eb6baed8d8e..fcee19415c13e9731bd47eb53bbff9b52cf6450b 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -1,15 +1,17 @@ # gserver pacakge unittests +if(NOT MOBILE_INFERENCE) ################### test_ProtoDataProvider ############ -add_unittest_without_exec(test_ProtoDataProvider - test_ProtoDataProvider.cpp) - -# test_ProtoDataProvider will mkdir as same name, -# so if WORKING_DIRECTORY is default directory, then -# mkdir will get error. -add_test(NAME test_ProtoDataProvider - COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + add_unittest_without_exec(test_ProtoDataProvider + test_ProtoDataProvider.cpp) + + # test_ProtoDataProvider will mkdir as same name, + # so if WORKING_DIRECTORY is default directory, then + # mkdir will get error. + add_test(NAME test_ProtoDataProvider + COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_ProtoDataProvider + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) +endif() ################# test_LayerGrad ####################### add_unittest_without_exec(test_LayerGrad @@ -98,9 +100,11 @@ add_unittest_without_exec(test_KmaxSeqScore add_test(NAME test_KmaxSeqScore COMMAND test_KmaxSeqScore) +if(NOT MOBILE_INFERENCE) ################## test_Evaluator ####################### -add_unittest(test_Evaluator - test_Evaluator.cpp) + add_unittest(test_Evaluator + test_Evaluator.cpp) +endif() ################ test_LinearChainCRF #################### add_simple_unittest(test_LinearChainCRF) @@ -131,27 +135,31 @@ if(NOT WITH_DOUBLE) WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) endif() +if(NOT MOBILE_INFERENCE) ############### test_RecurrentGradientMachine ############### -# TODO(yuyang18): There is some bug in test_RecurrentGradientMachine -# I will fix it. -add_unittest_without_exec(test_RecurrentGradientMachine - test_RecurrentGradientMachine.cpp) -add_test(NAME test_RecurrentGradientMachine - COMMAND .set_python_path.sh -d - ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests - ${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) - -add_unittest_without_exec(test_NetworkCompare - test_NetworkCompare.cpp) -if(WITH_GPU) - add_test(NAME test_NetworkCompare - COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=true - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) -else() - add_test(NAME test_NetworkCompare - COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=false - WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + # TODO(yuyang18): There is some bug in test_RecurrentGradientMachine + # I will fix it. + add_unittest_without_exec(test_RecurrentGradientMachine + test_RecurrentGradientMachine.cpp) + add_test(NAME test_RecurrentGradientMachine + COMMAND .set_python_path.sh -d + ${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests + ${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) +endif() + +if(NOT MOBILE_INFERENCE) + add_unittest_without_exec(test_NetworkCompare + test_NetworkCompare.cpp) + if(WITH_GPU) + add_test(NAME test_NetworkCompare + COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=true + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + else() + add_test(NAME test_NetworkCompare + COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=false + WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle) + endif() endif() diff --git a/paddle/gserver/tests/LayerGradUtil.cpp b/paddle/gserver/tests/LayerGradUtil.cpp index a38880e14cdfcef05461dae567d198e5400c6bb1..cd957c7c0bca4c6089cc07e8f4226b8260190f07 100644 --- a/paddle/gserver/tests/LayerGradUtil.cpp +++ b/paddle/gserver/tests/LayerGradUtil.cpp @@ -674,7 +674,7 @@ void testLayerGradKernel(TestConfig testConf, bool useGpu, bool useWeight, float epsilon) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) return; #endif FLAGS_use_gpu = useGpu; diff --git a/paddle/gserver/tests/LayerGradUtil.h b/paddle/gserver/tests/LayerGradUtil.h index 88e831f78bd165f63806df6c081d84411be51502..e10a27eedfa3d207d77a9bf1c5bfb23480dcca69 100644 --- a/paddle/gserver/tests/LayerGradUtil.h +++ b/paddle/gserver/tests/LayerGradUtil.h @@ -15,7 +15,6 @@ limitations under the License. */ #pragma once #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/testing/TestUtil.h" using namespace std; // NOLINT diff --git a/paddle/gserver/tests/test_ActivationGrad.cpp b/paddle/gserver/tests/test_ActivationGrad.cpp index de93972a5880518dfbfb9f8582e17c594e54b9b8..f4c2a07c4426da36ff0b0570339a3a972dadec1f 100644 --- a/paddle/gserver/tests/test_ActivationGrad.cpp +++ b/paddle/gserver/tests/test_ActivationGrad.cpp @@ -17,7 +17,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_BatchNorm.cpp b/paddle/gserver/tests/test_BatchNorm.cpp index 659eefa31bdb1f2433d03a59d5bf4782c71bdecf..41116f480957153eca33d211d09095903d6a00d9 100644 --- a/paddle/gserver/tests/test_BatchNorm.cpp +++ b/paddle/gserver/tests/test_BatchNorm.cpp @@ -17,7 +17,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -119,7 +118,7 @@ TEST(Layer, batchNorm) { CHECK_EQ(static_cast(convLayer->getOutputValue()->getWidth()), 576); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void batchNormInference(int n, int c, int h, int w) { MatrixPtr input = std::make_shared(n, c * h * w); MatrixPtr cudnnOut = std::make_shared(n, c * h * w); diff --git a/paddle/gserver/tests/test_CRFLayerGrad.cpp b/paddle/gserver/tests/test_CRFLayerGrad.cpp index df14449291e9ec08f45718de07bbb101f6dbea58..f010066ebc6c33eff17715ba20b4e238583f1966 100644 --- a/paddle/gserver/tests/test_CRFLayerGrad.cpp +++ b/paddle/gserver/tests/test_CRFLayerGrad.cpp @@ -16,7 +16,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/gserver/layers/LinearChainCRF.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_ConvTrans.cpp b/paddle/gserver/tests/test_ConvTrans.cpp index 6035a866b4eee4c6a61fa93f3adbf5e1d2d549f7..5f2f9665478ad4bdfb00421ec57b3ecc1b41b417 100644 --- a/paddle/gserver/tests/test_ConvTrans.cpp +++ b/paddle/gserver/tests/test_ConvTrans.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" diff --git a/paddle/gserver/tests/test_ConvUnify.cpp b/paddle/gserver/tests/test_ConvUnify.cpp index e7325e0cc3b7195b5fec77c878e3e087cfc643e0..8634355b5206f5cde0aa0717df50ade39e173ae7 100644 --- a/paddle/gserver/tests/test_ConvUnify.cpp +++ b/paddle/gserver/tests/test_ConvUnify.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -117,7 +116,7 @@ MatrixPtr doOneConvTest(size_t imgSize, } TEST(Layer, convParaUnified) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA MatrixPtr input, resultCpu, resultGpu; /// TEST1 for conv /// diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp index c922237d33da5de0ece61df732334bee5592249d..477638426fe91f2c5b1f4d5011496385f07c2e90 100644 --- a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" diff --git a/paddle/gserver/tests/test_DetectionOutput.cpp b/paddle/gserver/tests/test_DetectionOutput.cpp index af43dc51fad35c834635b543b1a016f6d717de1e..dc39c97a87f8b346dc9cc09d6158b1b4069bcf2d 100644 --- a/paddle/gserver/tests/test_DetectionOutput.cpp +++ b/paddle/gserver/tests/test_DetectionOutput.cpp @@ -150,7 +150,7 @@ TEST(Layer, detectionOutputLayerFwd) { useGpu, result2); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // GPU case 1. useGpu = true; inputLoc = Matrix::create(1, 16, false, useGpu); diff --git a/paddle/gserver/tests/test_Evaluator.cpp b/paddle/gserver/tests/test_Evaluator.cpp index 93996392d221d531f65caf465decbffdbc2d0384..62a131171fa5ae973cb3069151a582aaeac9ee0e 100644 --- a/paddle/gserver/tests/test_Evaluator.cpp +++ b/paddle/gserver/tests/test_Evaluator.cpp @@ -51,7 +51,7 @@ void testEvaluator(TestConfig testConf, string testEvaluatorName, size_t batchSize, bool useGpu) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) return; #endif FLAGS_use_gpu = useGpu; diff --git a/paddle/gserver/tests/test_KmaxSeqScore.cpp b/paddle/gserver/tests/test_KmaxSeqScore.cpp index 308abe6816428bc0f98ec32e892622fa4a23b1ae..ffe5cfb8dbb55d0b70a5699969abaa101f05f9ce 100644 --- a/paddle/gserver/tests/test_KmaxSeqScore.cpp +++ b/paddle/gserver/tests/test_KmaxSeqScore.cpp @@ -18,7 +18,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "paddle/utils/GlobalConstants.h" #include "LayerGradUtil.h" @@ -97,7 +96,7 @@ TEST(Layer, kmaxSeqScoreLayer) { Matrix::create(subSeqStartPosition.back(), 1, false, false); std::vector mode = {false}; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA mode.push_back(true); #endif diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 090bde7b203652e3ffb1662b8f5b8937885d2608..1a46fb49153a0aa4228f58db481b950bc2d6de83 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #endif #include @@ -21,7 +21,6 @@ limitations under the License. */ #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" #include "paddle/math/MathUtils.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" @@ -258,7 +257,7 @@ void testProjectionConv(size_t groups, bool isDeconv) { true); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Projection, conv) { /// test ConvProjection testProjectionConv(1, false); @@ -422,7 +421,7 @@ TEST(Layer, depthwiseConvLayer) { // 'depthwise_conv' is a sepecial case of 'exconv' whose // groups size equals to the input channels size. testDepthwiseConvLayer("exconv", /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testDepthwiseConvLayer("exconv", /* useGpu= */ true); #endif } @@ -480,7 +479,7 @@ void testConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, convLayer) { testConvLayer("exconv", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testConvLayer("exconv", /* trans= */ false, /* useGpu= */ true); testConvLayer("cudnn_conv", /* trans= */ false, /* useGpu= */ true); #endif @@ -525,7 +524,7 @@ TEST(Layer, convTransLayer) { for (auto useGpu : {false, true}) { testConvTransLayer("exconvt", /* trans= */ false, /* useGpu= */ useGpu); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testConvTransLayer("cudnn_convt", /* trans= */ false, /* useGpu= */ true); #endif } @@ -638,7 +637,7 @@ TEST(Layer, SelectiveFullyConnectedLayer) { /* trans= */ false, /* useGup= */ false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testLayerGrad(config, "selective_fc", 100, @@ -1210,7 +1209,7 @@ void testPoolLayer(const string& poolType, bool trans, bool useGpu) { testLayerGrad(config, "pool", 100, trans, useGpu); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void testPoolLayer2(const string& poolType, bool trans, bool useGpu) { TestConfig config; config.inputDefs.push_back({INPUT_DATA, "layer_0", 3200, 0}); @@ -1236,7 +1235,7 @@ TEST(Layer, PoolLayer) { testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ false); testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPoolLayer("avg-projection", /* trans= */ false, /* useGpu= */ true); testPoolLayer("max-projection", /* trans= */ false, /* useGpu= */ true); testPoolLayer("cudnn-max-pool", /* trans= */ false, /* useGpu= */ true); @@ -1309,7 +1308,7 @@ void testPool3DLayer(const string& poolType, bool trans, bool useGpu) { TEST(Layer, Pool3DLayer) { testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ false); testPool3DLayer("max", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ true); testPool3DLayer("max", /* trans= */ false, /* useGpu= */ true); #endif @@ -1695,7 +1694,7 @@ void testBatchNormLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, BatchNormalizationLayer) { testBatchNormLayer("batch_norm", false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testBatchNormLayer("batch_norm", false, true); if (hl_get_cudnn_lib_version() >= int(4000)) { testBatchNormLayer("cudnn_batch_norm", false, true); @@ -1744,7 +1743,7 @@ void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, testBatchNorm3DLayer) { testBatchNorm3DLayer("batch_norm", false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testBatchNorm3DLayer("batch_norm", false, true); if (hl_get_cudnn_lib_version() >= int(4000)) { testBatchNorm3DLayer("cudnn_batch_norm", false, true); @@ -2262,7 +2261,7 @@ void test3DConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, test3DConvLayer) { test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ true); #endif } @@ -2339,7 +2338,7 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { TEST(Layer, test3DDeConvLayer) { test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ true); #endif } diff --git a/paddle/gserver/tests/test_MKLDNN.cpp b/paddle/gserver/tests/test_MKLDNN.cpp index 857d07df3e3088be28943d9e2fe58017e9e57f4a..a70b2f17f4f1130322f3c50d244f70fdcf34468b 100644 --- a/paddle/gserver/tests/test_MKLDNN.cpp +++ b/paddle/gserver/tests/test_MKLDNN.cpp @@ -215,13 +215,13 @@ struct testActDesc { static void getAddtoConfig(TestConfig& cfg, const testActDesc& pm) { cfg.biasSize = 0; cfg.layerConfig.set_type("addto"); - size_t layerSize = pm.ih * pm.ih * pm.iw; + size_t layerSize = pm.ic * pm.ih * pm.iw; cfg.layerConfig.set_size(layerSize); cfg.inputDefs.push_back({INPUT_DATA, "layer_0", layerSize, 0}); cfg.layerConfig.add_inputs(); } -void testActivation(std::string& actType, const testActDesc& pm) { +void testActivation(std::string actType, const testActDesc& pm) { // TODO(TJ): remove me when paddle support elu activation if (actType == "mkldnn_elu") { return; @@ -240,6 +240,7 @@ TEST(MKLDNNActivation, Activations) { for (auto type : types) { /* bs, c, h, w*/ testActivation(type, {16, 64, 32, 32}); + testActivation(type, {2, 8, 1, 1}); } } diff --git a/paddle/gserver/tests/test_NetworkCompare.cpp b/paddle/gserver/tests/test_NetworkCompare.cpp index d36f72360f8ebd2033fb3e8c0e1b30911abba362..2b92211936aad1a034369bda0830bed3438cf401 100644 --- a/paddle/gserver/tests/test_NetworkCompare.cpp +++ b/paddle/gserver/tests/test_NetworkCompare.cpp @@ -243,7 +243,7 @@ TEST(Compare, concat_slice) { compareNetwork(config_file_a, config_file_b); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(Compare, img_pool) { std::string config_file_a = "./gserver/tests/img_pool_a.conf"; std::string config_file_b = "./gserver/tests/img_pool_b.conf"; diff --git a/paddle/gserver/tests/test_PriorBox.cpp b/paddle/gserver/tests/test_PriorBox.cpp index ae0e3bc3d24c54eb84c7b5f5053e629607ef4310..8dc5568784295b5a2e7d4decd178d612432a1a18 100644 --- a/paddle/gserver/tests/test_PriorBox.cpp +++ b/paddle/gserver/tests/test_PriorBox.cpp @@ -151,7 +151,7 @@ TEST(Layer, priorBoxLayerFwd) { useGpu, result); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // reset the input parameters variance[1] = 0.1; variance[3] = 0.2; diff --git a/paddle/gserver/tests/test_ProtoDataProvider.cpp b/paddle/gserver/tests/test_ProtoDataProvider.cpp index e11bf402c27898b8fdbd3fceeb8aeff8906352db..af6472619d1840e82787974d265d601b4a406c09 100644 --- a/paddle/gserver/tests/test_ProtoDataProvider.cpp +++ b/paddle/gserver/tests/test_ProtoDataProvider.cpp @@ -485,7 +485,7 @@ TEST(ProtoDataProvider, test) { // Currently in async mode, useGpu is not supported continue; } -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } @@ -525,7 +525,7 @@ TEST(ProtoDataProvider, constant_slots) { for (int numConstantSlots : {1, 2}) { for (int useGpu : numTwoArray) { for (int dataCompression : numTwoArray) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } @@ -708,7 +708,7 @@ TEST(ProtoSequenceDataProvider, test) { // Currently in async mode, useGpu is not supported continue; } -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { continue; } diff --git a/paddle/gserver/tests/test_PyDataProvider.cpp b/paddle/gserver/tests/test_PyDataProvider.cpp index db883543c306c1938eb9da188ce20ed768018efb..fe54799259d86064c4fcaec0e53707247981a1b4 100644 --- a/paddle/gserver/tests/test_PyDataProvider.cpp +++ b/paddle/gserver/tests/test_PyDataProvider.cpp @@ -37,7 +37,7 @@ TEST(PyDataProvider, py_fill_slots) { config.clear_files(); std::string dataFile = "gserver/tests/pyDataProvider/pyDataProviderList"; config.set_files(dataFile); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA bool useGpu = false; #else bool useGpu = true; @@ -71,7 +71,7 @@ TEST(PyDataProvider, py_fill_nest_slots) { std::string dataFile = "gserver/tests/pyDataProvider/pyDataProviderList"; config.set_files(dataFile); EXPECT_EQ(config.IsInitialized(), true); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA bool useGpu = false; #else bool useGpu = true; diff --git a/paddle/gserver/tests/test_SelectiveFCLayer.cpp b/paddle/gserver/tests/test_SelectiveFCLayer.cpp index ab23d00a2cb6077147f5b89664a8e2437b4cd63b..d164e382c4a804aef2417135b64cf709474d12f1 100644 --- a/paddle/gserver/tests/test_SelectiveFCLayer.cpp +++ b/paddle/gserver/tests/test_SelectiveFCLayer.cpp @@ -24,7 +24,6 @@ limitations under the License. */ #include "paddle/gserver/layers/Layer.h" #include "paddle/gserver/layers/SelectiveFullyConnectedLayer.h" #include "paddle/math/CpuSparseMatrix.h" -#include "paddle/trainer/Trainer.h" using namespace paddle; // NOLINT using namespace std; // NOLINT @@ -321,7 +320,7 @@ TEST(Layer, SelectiveFcLayer_train_dense_mul) { "filelist=gserver/tests/SelectiveFcTest/dense_mul_list"; for (auto useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) { break; } @@ -388,7 +387,7 @@ void testSelectiveFcLayerTrainSparseMul(const LayerConfig& config, outMatSelfc->getWidth(), outMatSelfc->getElementCnt())); cpuOutMatSelfc->copyFrom(*outMatSelfc, HPPL_STREAM_DEFAULT); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (useGpu) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } @@ -418,7 +417,7 @@ void testSelectiveFcLayerTrainSparseMul(const LayerConfig& config, MatrixPtr cpuOutMatFc( new CpuMatrix(outMatFc->getHeight(), outMatFc->getWidth())); cpuOutMatFc->copyFrom(*outMatFc, HPPL_STREAM_DEFAULT); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (useGpu) { hl_stream_synchronize(HPPL_STREAM_DEFAULT); } @@ -443,7 +442,7 @@ TEST(Layer, SelectiveFcLayer_train_sparse_mul) { selLayerConfig.set_size(fcLayerWidth); testSelectiveFcLayerTrainSparseMul(selLayerConfig, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testSelectiveFcLayerTrainSparseMul(selLayerConfig, true); #endif } diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp index e1d4ae16176433b898ba88dd60550e44b4fe37be..3dbffc563462973bdc1da529d486b2a2d5a677d3 100644 --- a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp +++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp @@ -15,7 +15,6 @@ limitations under the License. */ #include #include "ModelConfig.pb.h" #include "paddle/gserver/layers/DataLayer.h" -#include "paddle/trainer/Trainer.h" #include "LayerGradUtil.h" #include "paddle/testing/TestUtil.h" @@ -195,7 +194,7 @@ TEST(Layer, SeqSliceLayer) { vector> ends; std::vector mode = {false}; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA mode.push_back(true); #endif genSeqInfo(seqStartPos, subSeqStartPos); diff --git a/paddle/gserver/tests/test_WarpCTCLayer.cpp b/paddle/gserver/tests/test_WarpCTCLayer.cpp index 55427e2f12fd7b77c6eea1f65b3229e6fd29d71d..da829460061d38f363317e33daeb65cfa705bb8e 100644 --- a/paddle/gserver/tests/test_WarpCTCLayer.cpp +++ b/paddle/gserver/tests/test_WarpCTCLayer.cpp @@ -199,7 +199,7 @@ TEST(Layer, WarpCTCLayer) { for (auto batchSize : {1, 10, 32}) { for (auto normByTimes : {false, true}) { for (auto useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif LOG(INFO) << "layerSize=" << layerSize << " batchSize=" << batchSize diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 0023b4d0f5da500f380ecb836b7c54e050b13d67..c3e34d5309d9ca8a32d7b0a8043e668cdb5be54b 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -670,7 +670,7 @@ void GpuMatrix::leftMul(Matrix& a, real scaleAB, real scaleT) { } void GpuMatrix::selectRows(Matrix& table, IVector& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(dynamic_cast(&table)); CHECK(table.useGpu()); CHECK(ids.useGpu()); @@ -694,7 +694,7 @@ void GpuMatrix::selectRows(Matrix& table, IVector& ids) { } void GpuMatrix::addToRows(Matrix& table, IVector& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(dynamic_cast(&table)); CHECK(table.useGpu()); CHECK(ids.useGpu()); @@ -741,7 +741,7 @@ void GpuMatrix::rowMax(Matrix& max) { } void GpuMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(maxIds.useGpu() && maxVal.useGpu()) << "Matrix type are not equal"; size_t numSamples = getHeight(); size_t beam = maxVal.getWidth(); diff --git a/paddle/math/RowBuffer.h b/paddle/math/RowBuffer.h index dbb829c4e24a659e4a97c0a3ba4c5c78b68815d3..9ef5b89680b00981188d78cb312dc75e2c0a79ee 100644 --- a/paddle/math/RowBuffer.h +++ b/paddle/math/RowBuffer.h @@ -99,7 +99,11 @@ public: /** * @brief clear local buffer. It only affect auto-growth buffer. */ - inline void clear() { rowStore_.clear(); } + inline void clear() { + // swap an empty vector to it to free the memory. + std::vector> empty; + rowStore_.swap(empty); + } /** * @brief get current number of rows. diff --git a/paddle/math/SparseMatrix.cpp b/paddle/math/SparseMatrix.cpp index 6370c77386688a334fa0de8b4e2b272882e9e2b0..284b68d590ba655395c0186d8ea86d6855c6fc50 100644 --- a/paddle/math/SparseMatrix.cpp +++ b/paddle/math/SparseMatrix.cpp @@ -836,7 +836,7 @@ void GpuSparseMatrix::zeroMem() { } void GpuSparseMatrix::rowMax(IVector& maxIds, Matrix& maxVal) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA CHECK(maxIds.useGpu() && maxVal.useGpu()) << "Matrix type are not equal"; size_t numSamples = getHeight(); size_t beam = maxVal.getWidth(); diff --git a/paddle/math/Vector.cpp b/paddle/math/Vector.cpp index eb87ee9bb7936d27c0c32a1a4b35ff49871c0a10..ff72672e3ab77212b309fcfea835839a916fa632 100644 --- a/paddle/math/Vector.cpp +++ b/paddle/math/Vector.cpp @@ -172,7 +172,7 @@ void GpuVectorT::isEqualTo(const VectorT& b, const T& value) { template void GpuVectorT::selectFrom(const VectorT& src, const VectorT& ids) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA hl_vector_select_from(this->getData(), this->getSize(), src.getData(), @@ -850,7 +850,7 @@ CpuGpuVectorT::CpuGpuVectorT(CpuGpuVectorT& src, size_t size) : sync_(nullptr) { CHECK_LE(offset + size, static_cast(src.getSize())); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA SyncedFlag* flag = src.getSync(); if (*flag == DATA_AT_CPU) { src.copyToGpu(); // will set synchronous data between CPU and GPU @@ -861,7 +861,7 @@ CpuGpuVectorT::CpuGpuVectorT(CpuGpuVectorT& src, auto cMemHandle = (src.getVector(false))->getMemoryHandle(); cpuVectorT_ = std::make_shared>( size, std::dynamic_pointer_cast(cMemHandle), offset); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA auto gMemHandle = (src.getVector(true))->getMemoryHandle(); gpuVectorT_ = std::make_shared>( size, std::dynamic_pointer_cast(gMemHandle), offset); diff --git a/paddle/math/tests/test_Allocator.cpp b/paddle/math/tests/test_Allocator.cpp index 1ca70ea84c867b83013625eaee141f5b75fad4ae..1fecf659e5080c7d25f5f76b92b15f75eaab6ce3 100644 --- a/paddle/math/tests/test_Allocator.cpp +++ b/paddle/math/tests/test_Allocator.cpp @@ -68,7 +68,7 @@ void testPoolAllocator() { TEST(Allocator, Pool) { testPoolAllocator(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testPoolAllocator(); #endif } @@ -92,7 +92,7 @@ TEST(MemoryHandle, Cpu) { EXPECT_EQ(ptr1, ptr2); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(MemoryHandle, Gpu) { int numGpu = hl_get_device_count(); diff --git a/paddle/math/tests/test_BaseMatrix.cpp b/paddle/math/tests/test_BaseMatrix.cpp index 22ce39701fca7b650fc03794cb0701e0987d2dae..1766257860b0b13e9f0ce898438e7c2d644f545e 100644 --- a/paddle/math/tests/test_BaseMatrix.cpp +++ b/paddle/math/tests/test_BaseMatrix.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * This test file use autotest::AutoCompare and cmpWithoutArg to compares the * implementation of CPU and GPU member function in diff --git a/paddle/math/tests/test_CpuGpuVector.cpp b/paddle/math/tests/test_CpuGpuVector.cpp index 58bc43a38ba9465a832fcd0652e6309c403577e3..c72f89c8244b1209e490b09387c2ee6352426ce1 100644 --- a/paddle/math/tests/test_CpuGpuVector.cpp +++ b/paddle/math/tests/test_CpuGpuVector.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include "paddle/math/Vector.h" diff --git a/paddle/math/tests/test_ExecViaCpu.cpp b/paddle/math/tests/test_ExecViaCpu.cpp index 04c856453d2ec4ad764e37ae430e3e30ac0dea0b..25e0ba11ded96dd78aedc3c297507d0555d80d74 100644 --- a/paddle/math/tests/test_ExecViaCpu.cpp +++ b/paddle/math/tests/test_ExecViaCpu.cpp @@ -94,7 +94,7 @@ void testWrapper(F&& f) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(ExecViaCpu, test1) { testWrapper(f); testWrapper(&f); diff --git a/paddle/math/tests/test_GpuProfiler.cpp b/paddle/math/tests/test_GpuProfiler.cpp index e6b5dba446b5a0022ade76b188895c4e0e2a22b4..d9f146f0d1f63480ddee784071b43ff85da0b15c 100644 --- a/paddle/math/tests/test_GpuProfiler.cpp +++ b/paddle/math/tests/test_GpuProfiler.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include "paddle/math/Matrix.h" @@ -162,4 +162,4 @@ int main(int argc, char** argv) { return RUN_ALL_TESTS(); } -#endif /* PADDLE_ONLY_CPU */ +#endif diff --git a/paddle/math/tests/test_Matrix.cpp b/paddle/math/tests/test_Matrix.cpp index 1c21da5b76e95603258a5006d0c57b00126e65b9..2f99fa3581e14b91acc0b294856619f4ae2b3483 100644 --- a/paddle/math/tests/test_Matrix.cpp +++ b/paddle/math/tests/test_Matrix.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * This test file use autotest::AutoCompare and cmpWithArg to compares the * implementation of CPU and GPU member function in Matrix.cpp. diff --git a/paddle/math/tests/test_SparseMatrix.cpp b/paddle/math/tests/test_SparseMatrix.cpp index c0572dfdbf738a4dfad04811b3a3e1b65487ff6d..8abbe8d82e02b7d1738fe7e6d0c8d494166e7892 100644 --- a/paddle/math/tests/test_SparseMatrix.cpp +++ b/paddle/math/tests/test_SparseMatrix.cpp @@ -47,7 +47,7 @@ struct MatrixPara { SparseFormat format; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void test_sparse_matrix_mul(MatrixPara paraA, MatrixPara paraB, MatrixPara paraC) { @@ -452,7 +452,7 @@ TEST(Matrix, SparseMatrixCSRFormatTrimFrom) { matB->trimFrom(*mat); checkSMatrixEqual2(matA, matB); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuSparseMatrixPtr matC = std::make_shared( height, trimedWidth, height, FLOAT_VALUE, SPARSE_CSR, true); matC->trimFrom(*mat); @@ -546,7 +546,7 @@ TEST(Matrix, SparseMatrixCSCFormatTrimFrom) { matB->trimFrom(*mat); checkSMatrixEqual2(matA, matB); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuSparseMatrixPtr matC = std::make_shared( height, trimedWidth, height, FLOAT_VALUE, SPARSE_CSC, true); matC->trimFrom(*mat); diff --git a/paddle/math/tests/test_Tensor.cu b/paddle/math/tests/test_Tensor.cu index 31b693afa8bd50f77a8efb67769e6215dd755bd3..d03698dee25fdd6dd49f2a3fdb5c605333440f49 100644 --- a/paddle/math/tests/test_Tensor.cu +++ b/paddle/math/tests/test_Tensor.cu @@ -270,7 +270,7 @@ TEST(Unary, BaseOp) { TestUnaryVectorT testCpuIVector( testUnaryBaseOpInt); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpuMatrix(testUnaryBaseOp); TestUnaryVectorT testGpuVector(testUnaryBaseOp); TestUnaryVectorT testGpuIVector( @@ -317,7 +317,7 @@ void testUnayrMathOp(Tensor& A1, Tensor& A2) { TEST(Unary, MathOp) { TestUnaryMatrix testCpu(testUnayrMathOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpu(testUnayrMathOp); #endif } @@ -374,7 +374,7 @@ void testUnayrCompareOp(Tensor& A1, Tensor& A2) { TEST(Unary, CompareOp) { TestUnaryMatrix testCpu(testUnayrCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestUnaryMatrix testGpu(testUnayrCompareOp); #endif } @@ -536,7 +536,7 @@ void testBinaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, BaseOp) { TestBinaryMatrix testCpu(testBinaryBaseOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryBaseOp); #endif } @@ -710,7 +710,7 @@ void testBinaryMathOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, MathOp) { TestBinaryMatrix testCpu(testBinaryMathOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryMathOp); #endif } @@ -810,7 +810,7 @@ void testBinaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B) { TEST(Binary, CompareOp) { TestBinaryMatrix testCpu(testBinaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestBinaryMatrix testGpu(testBinaryCompareOp); #endif } @@ -955,7 +955,7 @@ void testTernaryBaseOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) { TEST(Ternary, BaseOp) { TestTernaryMatrix testCpu(testTernaryBaseOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestTernaryMatrix testGpu(testTernaryBaseOp); #endif } @@ -1058,7 +1058,7 @@ void testTernaryCompareOp(Tensor& A1, Tensor& A2, Tensor& B, Tensor& C) { TEST(Ternary, CompareOp) { TestTernaryMatrix testCpu(testTernaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestTernaryMatrix testGpu(testTernaryCompareOp); #endif } @@ -1086,7 +1086,7 @@ void testQuaternaryAdd( TEST(Quaternary, BaseOp) { TestQuaternaryMatrix testCpu(testQuaternaryAdd); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestQuaternaryMatrix testGpu(testQuaternaryAdd); #endif } @@ -1156,7 +1156,7 @@ void testQuaternaryCompareOp( TEST(Quaternary, CompareOp) { TestQuaternaryMatrix testCpu(testQuaternaryCompareOp); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TestQuaternaryMatrix testGpu(testQuaternaryCompareOp); #endif } diff --git a/paddle/math/tests/test_TrainingAlgorithm.cpp b/paddle/math/tests/test_TrainingAlgorithm.cpp index 4a88844b43ef40af988d2b391d2bef4568dea9b7..5ae0aa036f6bfc1e5bd4e955277c4efff8c739ce 100644 --- a/paddle/math/tests/test_TrainingAlgorithm.cpp +++ b/paddle/math/tests/test_TrainingAlgorithm.cpp @@ -91,7 +91,7 @@ int VectorCheckErr(const VectorPtr& vector1, const VectorPtr& vector2) { typedef std::function testMatrixFunc; void testCase(testMatrixFunc matrixFunc) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA for (auto useGpu : {false, true}) { #else for (auto useGpu : {false}) { diff --git a/paddle/math/tests/test_batchTranspose.cpp b/paddle/math/tests/test_batchTranspose.cpp index 4eb9837909ffaaf0f483ab65ece7a0b29fd49319..b70a61976402fd0a7cfee8382fd926fcf28486d5 100644 --- a/paddle/math/tests/test_batchTranspose.cpp +++ b/paddle/math/tests/test_batchTranspose.cpp @@ -17,7 +17,7 @@ limitations under the License. */ using namespace paddle; // NOLINT -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(MatrixBatchTransTest, test_batch_matrix_transpose) { const int nx = 100; const int ny = 50; diff --git a/paddle/math/tests/test_lazyAssign.cu b/paddle/math/tests/test_lazyAssign.cu index 92afab4ff7f5ff4acc219c5ac783733340c5726a..04f23cff55db45c39049538545430bc5996cce5d 100644 --- a/paddle/math/tests/test_lazyAssign.cu +++ b/paddle/math/tests/test_lazyAssign.cu @@ -72,7 +72,7 @@ void testLazyAssign(int height, int width) { TEST(lazyAssign, CPU) { testMatrixCase(testLazyAssign); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TEST(lazyAssign, GPU) { testMatrixCase(testLazyAssign); } #endif @@ -142,6 +142,6 @@ void testSgdUpdate(int height, int width) { TEST(sgdUpdate, CPU) { testMatrixCase(testSgdUpdate); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_GPU TEST(sgdUpdate, GPU) { testMatrixCase(testSgdUpdate); } #endif diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 061fb22e3fd744d9d9895fd1008089e4a6ce6a0f..7e5a1db44a5302e3b4e5d2768755824666e880ba 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /// This unittest checks GpuMatrix/CpuMatrix get same result, so disable when /// only cpu version. diff --git a/paddle/math/tests/test_perturbation.cpp b/paddle/math/tests/test_perturbation.cpp index 60ebae015381a3901c14d0cd4c1225e54ac5726f..c7c07c817a08d78ddcbf8218e8c4a9d22f4990bc 100644 --- a/paddle/math/tests/test_perturbation.cpp +++ b/paddle/math/tests/test_perturbation.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include diff --git a/paddle/math/tests/test_sparseMatrixCompare.cpp b/paddle/math/tests/test_sparseMatrixCompare.cpp index a9185a4b24b13ca0287b0f67375c4599e8b9ac78..2b2a391b9d04a9f7fa4986a6b6dd5cd8e5385f1f 100644 --- a/paddle/math/tests/test_sparseMatrixCompare.cpp +++ b/paddle/math/tests/test_sparseMatrixCompare.cpp @@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /// This unittest checks GpuSparseMatrix/CpuSparseMatrix get same result, // so disable when /// only cpu version. diff --git a/paddle/memory/detail/buddy_allocator.cc b/paddle/memory/detail/buddy_allocator.cc index bb44970109c05d239e6b92d90b2079b752fa0104..e212f7737a4093125857126cabb5b1a7b3e055b1 100644 --- a/paddle/memory/detail/buddy_allocator.cc +++ b/paddle/memory/detail/buddy_allocator.cc @@ -175,14 +175,14 @@ void* BuddyAllocator::SystemAlloc(size_t size) { } BuddyAllocator::PoolSet::iterator BuddyAllocator::RefillPool() { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (system_allocator_->UseGpu()) { if ((total_used_ + total_free_) == 0) { // Compute the maximum allocation size for the first allocation. max_chunk_size_ = platform::GpuMaxChunkSize(); } } -#endif // PADDLE_ONLY_CPU +#endif // Allocate a new maximum sized block size_t index = 0; diff --git a/paddle/memory/detail/system_allocator.cc b/paddle/memory/detail/system_allocator.cc index a270bd59581520859d43cddd2fc0cfa72080f46d..33166d9ce23a4a345fc00a65adf63281b13643c3 100644 --- a/paddle/memory/detail/system_allocator.cc +++ b/paddle/memory/detail/system_allocator.cc @@ -62,7 +62,7 @@ void CPUAllocator::Free(void* p, size_t size, size_t index) { bool CPUAllocator::UseGpu() const { return false; } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA void* GPUAllocator::Alloc(size_t& index, size_t size) { // CUDA documentation doesn't explain if cudaMalloc returns nullptr @@ -134,7 +134,7 @@ void GPUAllocator::Free(void* p, size_t size, size_t index) { bool GPUAllocator::UseGpu() const { return true; } -#endif // PADDLE_ONLY_CPU +#endif } // namespace detail } // namespace memory diff --git a/paddle/memory/detail/system_allocator.h b/paddle/memory/detail/system_allocator.h index 82ba322e057575c460b1d51d719c9b0fa459273e..552cab4f96ff21a6f3c66209eb62150e92996826 100644 --- a/paddle/memory/detail/system_allocator.h +++ b/paddle/memory/detail/system_allocator.h @@ -40,7 +40,7 @@ class CPUAllocator : public SystemAllocator { virtual bool UseGpu() const; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA class GPUAllocator : public SystemAllocator { public: virtual void* Alloc(size_t& index, size_t size); @@ -51,7 +51,7 @@ class GPUAllocator : public SystemAllocator { size_t gpu_alloc_size_ = 0; size_t fallback_alloc_size_ = 0; }; -#endif // PADDLE_ONLY_CPU +#endif } // namespace detail } // namespace memory diff --git a/paddle/memory/detail/system_allocator_test.cc b/paddle/memory/detail/system_allocator_test.cc index ba44e06ddb68e92e4086a8006b868557b0c89b50..6a8558937bf0c924e5f48605ff066e2789fd59b6 100644 --- a/paddle/memory/detail/system_allocator_test.cc +++ b/paddle/memory/detail/system_allocator_test.cc @@ -56,10 +56,10 @@ TEST(CPUAllocator, LockMem) { TestAllocator(a, 0); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(GPUAllocator, Alloc) { paddle::memory::detail::GPUAllocator a; TestAllocator(a, 2048); TestAllocator(a, 0); } -#endif // PADDLE_ONLY_CPU +#endif diff --git a/paddle/memory/memcpy.cc b/paddle/memory/memcpy.cc index c96a697a7e022684688b31c05da43e52812100d8..1df88a6da9fb0c50d0d7ecd083c0533d8a886a67 100644 --- a/paddle/memory/memcpy.cc +++ b/paddle/memory/memcpy.cc @@ -26,7 +26,7 @@ void Copy(platform::CPUPlace, void* dst, std::memcpy(dst, src, num); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template <> void Copy(platform::CPUPlace dst_place, void* dst, @@ -89,7 +89,7 @@ void Copy(platform::GPUPlace dst_place, platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToDevice); } -#endif // PADDLE_ONLY_CPU +#endif } // namespace memory } // namespace paddle diff --git a/paddle/memory/memcpy.h b/paddle/memory/memcpy.h index 2b9c0eada6e8406fc81baec7f331a8dd5b8b0ec1..9b36182c2b619317da31310141823442d8fd3f94 100644 --- a/paddle/memory/memcpy.h +++ b/paddle/memory/memcpy.h @@ -33,7 +33,7 @@ namespace memory { template void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA /** * \brief Copy memory from one place to another place. @@ -53,7 +53,7 @@ template void Copy(DstPlace, void* dst, SrcPlace, const void* src, size_t num, cudaStream_t stream); -#endif // PADDLE_ONLY_CPU +#endif } // namespace memory } // namespace paddle diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 29bc26f9d3bca0e30896657431f9a9bb1dac0d1d..5087c02385f7f37d78d134b739f3f22522977fb8 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -62,7 +62,7 @@ size_t Used(platform::CPUPlace place) { return GetCPUBuddyAllocator()->Used(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { using BuddyAllocVec = std::vector; @@ -77,7 +77,7 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { // GPU buddy allocator initialization std::call_once(gpu_allocator_flag, [&]() { - int gpu_num = platform::GetDeviceCount(); + int gpu_num = platform::GetCUDADeviceCount(); allocators.reserve(gpu_num); for (int gpu = 0; gpu < gpu_num; gpu++) { platform::SetDeviceId(gpu); @@ -111,7 +111,7 @@ size_t Used(platform::GPUPlace place) { return GetGPUBuddyAllocator(place.device)->Used(); } -#endif // PADDLE_ONLY_CPU +#endif } // namespace memory } // namespace paddle diff --git a/paddle/memory/memory_test.cc b/paddle/memory/memory_test.cc index 53cc63a098d0802479e3a371717adb7596c249ed..2444931e26774ae80b916fbb7bd46ff93025d9ed 100644 --- a/paddle/memory/memory_test.cc +++ b/paddle/memory/memory_test.cc @@ -80,7 +80,7 @@ TEST(BuddyAllocator, CPUMultAlloc) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA size_t align(size_t size, paddle::platform::GPUPlace place) { size += sizeof(paddle::memory::detail::Metadata); @@ -135,4 +135,4 @@ TEST(BuddyAllocator, GPUMultAlloc) { } } -#endif // PADDLE_ONLY_CPU +#endif diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index e56895c63a426b782f7b46091bc86c367d49899d..7dae8fe2f99f9ec1233d0a0f6180cc9f287fc150 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -55,12 +55,33 @@ function(op_library TARGET) set(pybind_flag 1) endif() + # pool_op contains several operators + if ("${TARGET}" STREQUAL "pool_op") + set(pybind_flag 1) + # It's enough to just adding one operator to pybind + file(APPEND ${pybind_file} "USE_OP(pool2d);\n") + endif() + + # pool_with_index_op contains several operators + if ("${TARGET}" STREQUAL "pool_with_index_op") + set(pybind_flag 1) + # It's enough to just adding one operator to pybind + file(APPEND ${pybind_file} "USE_OP(max_pool2d_with_index);\n") + endif() + # activation_op contains several operators if ("${TARGET}" STREQUAL "activation_op") set(pybind_flag 1) # It's enough to just adding one operator to pybind file(APPEND ${pybind_file} "USE_OP(sigmoid);\n") endif() + + # reduce_op contains several operators + if ("${TARGET}" STREQUAL "reduce_op") + set(pybind_flag 1) + # It's enough to just adding one operator to pybind + file(APPEND ${pybind_file} "USE_OP(reduce_sum);\n") + endif() # pybind USE_NO_KERNEL_OP file(READ ${TARGET}.cc TARGET_CONTENT) @@ -90,12 +111,16 @@ set(DEPS_OPS recurrent_op cond_op cross_entropy_op - softmax_with_cross_entropy_op) + softmax_with_cross_entropy_op + sum_op) + + op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor net_op) op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op) -op_library(cross_entropy_op DEPS cross_entropy_function) -op_library(softmax_with_cross_entropy_op DEPS cross_entropy_function softmax_function) +op_library(cross_entropy_op DEPS cross_entropy) +op_library(softmax_with_cross_entropy_op DEPS cross_entropy softmax) +op_library(sum_op DEPS net_op) list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS}) foreach(src ${GENERAL_OPS}) @@ -108,3 +133,4 @@ cc_test(gather_test SRCS gather_test.cc DEPS tensor) cc_test(net_op_test SRCS net_op_test.cc DEPS net_op) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory) +cc_test(dynamic_recurrent_op_test SRCS dynamic_recurrent_op_test.cc DEPS dynamic_recurrent_op recurrent_op tensor_array) diff --git a/paddle/operators/accuracy_op.cc b/paddle/operators/accuracy_op.cc index 82010bfb53e58a0836c99c353590f4e32e25ac4a..c5fb113e0f4455ec852fd48542ea2917398df5f3 100644 --- a/paddle/operators/accuracy_op.cc +++ b/paddle/operators/accuracy_op.cc @@ -22,7 +22,7 @@ class AccuracyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Inference"), "Input(Inference) of AccuracyOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Label"), diff --git a/paddle/operators/accuracy_op.cu b/paddle/operators/accuracy_op.cu index 75e8a989036f0b818687e1fec3e600bb90e86b22..0ca9ef941d4cb15619caea2b6baed197e4b15e5a 100644 --- a/paddle/operators/accuracy_op.cu +++ b/paddle/operators/accuracy_op.cu @@ -47,7 +47,7 @@ __global__ void AccuracyCudaKernel(const int N, const int D, const int* Xdata, } template -class AccuracyOpCUDAKernel : public framework::OpKernel { +class AccuracyOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), diff --git a/paddle/operators/accuracy_op.h b/paddle/operators/accuracy_op.h index fe704efe1c979f4fc6a5a37184e51b416f5e517f..12c6b9aac8819caedbc02017cee81b37322bb72a 100644 --- a/paddle/operators/accuracy_op.h +++ b/paddle/operators/accuracy_op.h @@ -35,7 +35,7 @@ template ; template -class AccuracyKernel : public framework::OpKernel { +class AccuracyKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* inference = ctx.Input("Inference"); diff --git a/paddle/operators/activation_op.cc b/paddle/operators/activation_op.cc index f77e1c572e33533ac672e3d476a7e6dad122031f..ced14a8923140ec6b08e3e6725a5780b61033daf 100644 --- a/paddle/operators/activation_op.cc +++ b/paddle/operators/activation_op.cc @@ -22,7 +22,7 @@ class ActivationOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputDim("Y", ctx->GetInputDim("X")); ctx->ShareLoD("X", /*->*/ "Y"); } @@ -33,7 +33,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y")); } }; @@ -49,6 +49,18 @@ class SigmoidOpMaker : public framework::OpProtoAndCheckerMaker { } }; +class LogSigmoidOpMaker : public framework::OpProtoAndCheckerMaker { + public: + LogSigmoidOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of LogSigmoid operator"); + AddOutput("Y", "Output of LogSigmoid operator"); + AddComment( + "Logsigmoid activation operator, logsigmoid = log (1 / (1 + exp(-x)))"); + } +}; + class ExpOpMaker : public framework::OpProtoAndCheckerMaker { public: ExpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -69,6 +81,39 @@ class ReluOpMaker : public framework::OpProtoAndCheckerMaker { } }; +template +class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker { + public: + LeakyReluOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of LeakyRelu operator"); + AddOutput("Y", "Output of LeakyRelu operator"); + AddComment( + "LeakyRelu activation operator, " + "leaky_relu = max(x, alpha * x)"); + AddAttr("alpha", "The small negative slope") + .SetDefault(static_cast(0.02f)); + } +}; + +template +class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SoftShrinkOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Softshrink operator"); + AddOutput("Y", "Output of Softshrink operator"); + AddComment( + "Softshrink activation operator, " + "softshrink = x - lambda, if x > lambda;" + " x + lambda, if x < lambda; 0 otherwise"); + AddAttr("lambda", "non-negative offset") + .SetDefault(static_cast(0.5f)); + } +}; + class TanhOpMaker : public framework::OpProtoAndCheckerMaker { public: TanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -81,6 +126,35 @@ class TanhOpMaker : public framework::OpProtoAndCheckerMaker { } }; +class TanhShrinkOpMaker : public framework::OpProtoAndCheckerMaker { + public: + TanhShrinkOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of TanhShrink operator"); + AddOutput("Y", "Output of TanhShrink operator"); + AddComment("TanhShrink activation operator, tanhshrink(x) = x - tanh(x)"); + } +}; + +template +class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker { + public: + HardShrinkOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of HardShrink operator"); + AddOutput("Y", "Output of HardShrink operator"); + AddComment( + "HardShrink activation operator, " + "hard_shrink(x) = x if x > lambda" + "hard_shrink(x) = x if x < -lambda" + "hard_shrink(x) = 0 otherwise"); + AddAttr("threshold", "The value of threshold for HardShrink") + .SetDefault(static_cast(0.5)); + } +}; + class SqrtOpMaker : public framework::OpProtoAndCheckerMaker { public: SqrtOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -132,6 +206,28 @@ class SquareOpMaker : public framework::OpProtoAndCheckerMaker { } }; +class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SoftplusOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Softplus operator"); + AddOutput("Y", "Output of Softplus operator"); + AddComment("Softplus activation operator, softplus(x) = log(1 + exp(x))"); + } +}; + +class SoftsignOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SoftsignOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Softsign operator"); + AddOutput("Y", "Output of Softsign operator"); + AddComment("Softsign activation operator, softsign(x) = x / (1 + |x|)"); + } +}; + template class BReluOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -163,6 +259,40 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker { } }; +template +class ELUOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(Tensor) The input of ELU operator, it shouldn't be empty. Input " + "is flattened and treated as a 1D array."); + AddOutput("Y", + "(Tensor) The output of ELU operator. It has the same shape as " + "the input."); + AddAttr( + "alpha", "(float, default 1.0) Alpha value in the elu formulation.") + .SetDefault(static_cast(1.)); + AddComment(R"DOC( + ELU activation operator. It applies this element-wise computation on + the input: f(x) = max(0, x) + min(0, alpha * (exp(x) - 1)). + Check .. _Link: https://arxiv.org/abs/1511.07289 for more details.)DOC"); + } +}; + +template +class Relu6OpMaker : public framework::OpProtoAndCheckerMaker { + public: + Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of Relu6 operator"); + AddOutput("Y", "Output of Relu6 operator"); + AddComment("Relu6 activation operator, relu6 = min(max(0, x), 6)"); + AddAttr("threshold", "The threshold value of Relu6") + .SetDefault(static_cast(6)); + } +}; + template class PowOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -195,111 +325,79 @@ class STanhOpMaker : public framework::OpProtoAndCheckerMaker { } // namespace paddle namespace ops = paddle::operators; + REGISTER_OP(sigmoid, ops::ActivationOp, ops::SigmoidOpMaker, sigmoid_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(sigmoid, - ops::ActivationKernel>); -REGISTER_OP_CPU_KERNEL( - sigmoid_grad, ops::ActivationGradKernel>); + +REGISTER_OP(logsigmoid, ops::ActivationOp, ops::LogSigmoidOpMaker, + logsigmoid_grad, ops::ActivationOpGrad); REGISTER_OP(exp, ops::ActivationOp, ops::ExpOpMaker, exp_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL( - exp, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL(exp_grad, - ops::ActivationGradKernel); REGISTER_OP(relu, ops::ActivationOp, ops::ReluOpMaker, relu_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(relu, - ops::ActivationKernel>); -REGISTER_OP_CPU_KERNEL( - relu_grad, ops::ActivationGradKernel>); REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL( - tanh, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL( - tanh_grad, ops::ActivationGradKernel>); + +REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker, + tanh_shrink_grad, ops::ActivationOpGrad); + +REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker, + softshrink_grad, ops::ActivationOpGrad); REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL( - sqrt, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL( - sqrt_grad, ops::ActivationGradKernel>); REGISTER_OP(abs, ops::ActivationOp, ops::AbsOpMaker, abs_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL( - abs, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL(abs_grad, - ops::ActivationGradKernel); REGISTER_OP(reciprocal, ops::ActivationOp, ops::ReciprocalOpMaker, reciprocal_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(reciprocal, - ops::ActivationKernel>); -REGISTER_OP_CPU_KERNEL( - reciprocal_grad, - ops::ActivationGradKernel>); REGISTER_OP(log, ops::ActivationOp, ops::LogOpMaker, log_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL( - log, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL( - log_grad, ops::ActivationGradKernel>); REGISTER_OP(square, ops::ActivationOp, ops::SquareOpMaker, square_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(square, - ops::ActivationKernel); -REGISTER_OP_CPU_KERNEL( - square_grad, ops::ActivationGradKernel>); + +REGISTER_OP(softplus, ops::ActivationOp, ops::SoftplusOpMaker, softplus_grad, + ops::ActivationOpGrad); + +REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad, + ops::ActivationOpGrad); REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker, brelu_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(brelu, - ops::BReluKernel); -REGISTER_OP_CPU_KERNEL(brelu_grad, - ops::BReluGradKernel); + +REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker, + leaky_relu_grad, ops::ActivationOpGrad); REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker, soft_relu_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(soft_relu, - ops::SoftReluKernel); -REGISTER_OP_CPU_KERNEL( - soft_relu_grad, ops::SoftReluGradKernel); + +REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker, elu_grad, + ops::ActivationOpGrad); + +REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker, relu6_grad, + ops::ActivationOpGrad); REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker, pow_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(pow, ops::PowKernel); -REGISTER_OP_CPU_KERNEL(pow_grad, - ops::PowGradKernel); REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker, stanh_grad, ops::ActivationOpGrad); -REGISTER_OP_CPU_KERNEL(stanh, - ops::STanhKernel); -REGISTER_OP_CPU_KERNEL(stanh_grad, - ops::STanhGradKernel); + +REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker, + hard_shrink_grad, ops::ActivationOpGrad); + +#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \ + REGISTER_OP_CPU_KERNEL( \ + act_type, \ + ops::ActivationKernel>); \ + REGISTER_OP_CPU_KERNEL(act_type##_grad, \ + ops::ActivationGradKernel>); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_CPU_KERNEL); diff --git a/paddle/operators/activation_op.cu b/paddle/operators/activation_op.cu index feed1302b292a546f88fa35457c86aa2cfdaa307..7b7644519d4e9cadcc4ca62ccb599262feffa660 100644 --- a/paddle/operators/activation_op.cu +++ b/paddle/operators/activation_op.cu @@ -17,84 +17,12 @@ namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(sigmoid, - ops::ActivationKernel>); -REGISTER_OP_GPU_KERNEL( - sigmoid_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL( - exp, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL(exp_grad, - ops::ActivationGradKernel); -REGISTER_OP_GPU_KERNEL(relu, - ops::ActivationKernel>); -REGISTER_OP_GPU_KERNEL( - relu_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL( - tanh, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL( - tanh_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL( - sqrt, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL( - sqrt_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL( - abs, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL(abs_grad, - ops::ActivationGradKernel); - -REGISTER_OP_GPU_KERNEL(reciprocal, - ops::ActivationKernel>); -REGISTER_OP_GPU_KERNEL( - reciprocal_grad, - ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL( - log, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL( - log_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL(square, - ops::ActivationKernel); -REGISTER_OP_GPU_KERNEL( - square_grad, ops::ActivationGradKernel>); - -REGISTER_OP_GPU_KERNEL(brelu, - ops::BReluKernel); -REGISTER_OP_GPU_KERNEL(brelu_grad, - ops::BReluGradKernel); - -REGISTER_OP_GPU_KERNEL(soft_relu, - ops::SoftReluKernel); -REGISTER_OP_GPU_KERNEL( - soft_relu_grad, ops::SoftReluGradKernel); - -REGISTER_OP_GPU_KERNEL(pow, ops::PowKernel); -REGISTER_OP_GPU_KERNEL(pow_grad, - ops::PowGradKernel); - -REGISTER_OP_GPU_KERNEL(stanh, - ops::STanhKernel); -REGISTER_OP_GPU_KERNEL(stanh_grad, - ops::STanhGradKernel); +#define REGISTER_ACTIVATION_GPU_KERNEL(act_type, functor, grad_functor) \ + REGISTER_OP_GPU_KERNEL( \ + act_type, \ + ops::ActivationKernel>); \ + REGISTER_OP_GPU_KERNEL(act_type##_grad, \ + ops::ActivationGradKernel>); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_ACTIVATION_GPU_KERNEL); diff --git a/paddle/operators/activation_op.h b/paddle/operators/activation_op.h index 15f8afb4ba45cc989fe7576b82b8bf853b1df7de..f88c9c48eb9fcb779de5a99a45a832e582d76ab0 100644 --- a/paddle/operators/activation_op.h +++ b/paddle/operators/activation_op.h @@ -19,9 +19,12 @@ namespace paddle { namespace operators { -template -class ActivationKernel : public framework::OpKernel { +template +class ActivationKernel + : public framework::OpKernel { public: + using T = typename Functor::ELEMENT_TYPE; + void Compute(const framework::ExecutionContext& context) const override { auto* X = context.Input("X"); auto* Y = context.Output("Y"); @@ -31,13 +34,20 @@ class ActivationKernel : public framework::OpKernel { auto y = framework::EigenVector::Flatten(*Y); auto place = context.GetEigenDevice(); Functor functor; + + auto attrs = functor.GetAttrs(); + for (auto& attr : attrs) { + *attr.second = context.Attr(attr.first); + } functor(place, x, y); } }; -template -class ActivationGradKernel : public framework::OpKernel { +template +class ActivationGradKernel + : public framework::OpKernel { public: + using T = typename Functor::ELEMENT_TYPE; void Compute(const framework::ExecutionContext& context) const override { auto* X = context.Input("X"); auto* Y = context.Input("Y"); @@ -51,303 +61,558 @@ class ActivationGradKernel : public framework::OpKernel { auto dx = framework::EigenVector::Flatten(*dX); auto place = context.GetEigenDevice(); Functor functor; + auto attrs = functor.GetAttrs(); + for (auto& attr : attrs) { + *attr.second = context.Attr(attr.first); + } functor(place, x, y, dy, dx); } }; +template +struct BaseActivationFunctor { + using ELEMENT_TYPE = T; + + using AttrPair = std::vector>; + + AttrPair GetAttrs() { return AttrPair(); } +}; + // sigmoid(x) = 1 / (1 + exp(-x)) template -struct SigmoidFunctor { +struct SigmoidFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = static_cast(1) / (static_cast(1) + (-x).exp()); } }; template -struct SigmoidGradFunctor { +struct SigmoidGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * y * (static_cast(1) - y); } }; +// Originally: logsigmoid(x) = -log (1 + exp(-x)) +// For numerical stability, we can use the log-sum-exp trick: +// https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/ +// We can rewrite the above equation as: +// y = -log( exp(0) + exp(-x)) [since exp(0) = 1] +// = -log( exp(max(-x, 0) - max(-x, 0)) + exp(-x + max(-x, 0) - max(-x, 0))) +// = -log( exp(max(-x, 0)) * exp(-max(-x, 0)) - exp(max(-x, 0)) * exp(-x - +// max(-x, 0))) +// = -log( exp(max(-x, 0)) * (exp(-max(-x, 0)) + exp(-x - max(-x, 0)))) +// = -log( exp(max(-x, 0)) - log(exp(-max(-x, 0)) + exp(-x - max(-x, 0))) +// +// Hence, logsigmoid(x) = - (max(-x, 0) + log(exp(-max(-x, 0)) +// + exp(-x - max(-x, 0)))) +template +struct LogSigmoidFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y) const { + auto temp = (-x).cwiseMax(static_cast(0)); // temp = max(-x, 0) + y.device(d) = -temp - (((-temp).exp() + (-x - temp).exp()).log()); + } +}; + +// Originally: f' = exp(-x) / (1 + exp(-x)) +// For numerical stability: f' = exp(-x - max(-x, 0)) / (exp(-max(-x, 0)) + +// exp(-x - max(-x, 0))) +template +struct LogSigmoidGradFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + auto temp = (-x).cwiseMax(static_cast(0)); // temp = max(-x, 0) + dx.device(d) = + dy * ((-x - temp).exp() / ((-temp).exp() + (-x - temp).exp())); + } +}; + // exp(x) = e^x -struct ExpFunctor { +template +struct ExpFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.exp(); } }; -struct ExpGradFunctor { +template +struct ExpGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * y; } }; // relu(x) = max(x, 0) template -struct ReluFunctor { +struct ReluFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.cwiseMax(static_cast(0)); } }; template -struct ReluGradFunctor { +struct ReluGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * (x > static_cast(0)).template cast(); } }; // tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x)) -struct TanhFunctor { +template +struct TanhFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.tanh(); } }; template -struct TanhGradFunctor { +struct TanhGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * (static_cast(1) - y * y); } }; +// tanhshrink(x) = x - tanh(x) +// where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x)) +template +struct TanhShrinkFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x - x.tanh(); + } +}; + +template +struct TanhShrinkGradFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = dy * (x.tanh() * x.tanh()); + } +}; + +// tanhshrink(x) = x - tanh(x) +// where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x)) +template +struct HardShrinkFunctor : public BaseActivationFunctor { + float threshold; + + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + template + void operator()(Device d, X x, Y y) const { + auto temp1 = (x < (threshold * -1)).template cast().eval(); + auto temp2 = (x > threshold).template cast().eval(); + y.device(d) = x * (temp1 + temp2); + } +}; + +template +struct HardShrinkGradFunctor : public BaseActivationFunctor { + float threshold; + + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + auto temp1 = (x < (threshold * -1)).template cast().eval(); + auto temp2 = (x > threshold).template cast().eval(); + dx.device(d) = dy * (temp1 + temp2).template cast(); + } +}; + +// softshrink(x) = x - lambda, if x > lambda; x + lambda, if x < lambda; 0 +// otherwise +template +struct SoftShrinkFunctor : public BaseActivationFunctor { + float lambda; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"lambda", &lambda}}; + } + + template + void operator()(Device d, X x, Y y) const { + auto temp1 = (x > lambda).template cast().eval(); + auto temp2 = (x < -lambda).template cast().eval(); + y.device(d) = temp1 * (x - lambda) + temp2 * (x + lambda); + } +}; + +template +struct SoftShrinkGradFunctor : public BaseActivationFunctor { + float lambda; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"lambda", &lambda}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + auto temp1 = (x > lambda).template cast().eval(); + auto temp2 = (x < -lambda).template cast().eval(); + dx.device(d) = dy * (temp1 + temp2).template cast(); + } +}; + // sqrt(x) = x^(1/2) -struct SqrtFunctor { +template +struct SqrtFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.sqrt(); } }; template -struct SqrtGradFunctor { +struct SqrtGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { const Y y_conj = Eigen::numext::conj(y); dx.device(d) = static_cast(0.5) * dy / y_conj; } }; // abs(x) = |x| -struct AbsFunctor { +template +struct AbsFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.abs(); } }; -struct AbsGradFunctor { +template +struct AbsGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * x.sign(); } }; // reciprocal(x) = 1 / x template -struct ReciprocalFunctor { +struct ReciprocalFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = static_cast(1) / x; } }; template -struct ReciprocalGradFunctor { +struct ReciprocalGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * static_cast(-1) * y * y; } }; // log(x) = natural logarithm of x -struct LogFunctor { +template +struct LogFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.log(); } }; template -struct LogGradFunctor { +struct LogGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * (static_cast(1) / x); } }; // square(x) = x^2 -struct SquareFunctor { +template +struct SquareFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y) { + void operator()(Device d, X x, Y y) const { y.device(d) = x.square(); } }; template -struct SquareGradFunctor { +struct SquareGradFunctor : public BaseActivationFunctor { template - void operator()(Device d, X x, Y y, dY dy, dX dx) { + void operator()(Device d, X x, Y y, dY dy, dX dx) const { dx.device(d) = dy * static_cast(2) * x; } }; -template -class BReluKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Output("Y"); - auto t_min = static_cast(context.Attr("t_min")); - auto t_max = static_cast(context.Attr("t_max")); - Y->mutable_data(context.GetPlace()); +template +struct BReluFunctor : public BaseActivationFunctor { + float t_min; + float t_max; + + // NOTE: Explicit hides the `BaseActivationFunctor::GetAttrs` + // not polymorphism for speed. + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"t_min", &t_min}, {"t_max", &t_max}}; + } - auto x = framework::EigenVector::Flatten(*X); - auto y = framework::EigenVector::Flatten(*Y); - auto place = context.GetEigenDevice(); - y.device(place) = x.cwiseMax(t_min).cwiseMin(t_max); + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.cwiseMax(t_min).cwiseMin(t_max); } }; -template -class BReluGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* dY = context.Input(framework::GradVarName("Y")); - auto* dX = context.Output(framework::GradVarName("X")); - auto t_min = static_cast(context.Attr("t_min")); - auto t_max = static_cast(context.Attr("t_max")); - dX->mutable_data(context.GetPlace()); +template +struct BReluGradFunctor : public BaseActivationFunctor { + float t_min; + float t_max; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"t_min", &t_min}, {"t_max", &t_max}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = dy * ((x > t_min) * (x < t_max)).template cast(); + } +}; - auto dy = framework::EigenVector::Flatten(*dY); - auto x = framework::EigenVector::Flatten(*X); - auto dx = framework::EigenVector::Flatten(*dX); - auto place = context.GetEigenDevice(); +// relu6(x) = min(max(0, x), 6) +template +struct Relu6Functor : public BaseActivationFunctor { + float threshold; - dx.device(place) = dy * ((x > t_min) * (x < t_max)).template cast(); + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.cwiseMax(static_cast(0)).cwiseMin(threshold); } }; -template -class SoftReluKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Output("Y"); - auto threshold = static_cast(context.Attr("threshold")); - Y->mutable_data(context.GetPlace()); +template +struct Relu6GradFunctor : public BaseActivationFunctor { + float threshold; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = + dy * ((x > static_cast(0)) * (x < threshold)).template cast(); + } +}; - auto x = framework::EigenVector::Flatten(*X); - auto y = framework::EigenVector::Flatten(*Y); - auto place = context.GetEigenDevice(); - auto temp = x.cwiseMax(-threshold).cwiseMin(threshold).eval(); - y.device(place) = (static_cast(1) + temp.exp()).log(); +// softplus(x) = log(1 + exp(x)) +// When x is a very large positive number, exp(x) may explode to inf, +// Using trick below for numerical stability +// https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/ +// Then: softplus(x) = max(x, 0) + log(exp(-max(x, 0)) + exp(x - max(x, 0))) +template +struct SoftplusFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y) { + auto temp = x.cwiseMax(static_cast(0)); // temp = max(x, 0) + y.device(d) = temp + (((-temp).exp() + (x - temp).exp()).log()); } }; -template -class SoftReluGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Input("Y"); - auto* dY = context.Input(framework::GradVarName("Y")); - auto* dX = context.Output(framework::GradVarName("X")); - auto threshold = static_cast(context.Attr("threshold")); - dX->mutable_data(context.GetPlace()); +// d(softplus(x))/dx = exp(x) / (1 + exp(x)) +// For numerical stability: +// d(softplus(x))/dx = exp(x - max(x, 0)) / (exp(-max(x, 0)) + +// exp(x - max(x, 0))) +template +struct SoftplusGradFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y, dY dy, dX dx) { + auto temp = x.cwiseMax(static_cast(0)); // temp = max(x, 0) + dx.device(d) = dy * ((x - temp).exp() / ((-temp).exp() + (x - temp).exp())); + } +}; - auto x = framework::EigenVector::Flatten(*X); - auto y = framework::EigenVector::Flatten(*Y); - auto dy = framework::EigenVector::Flatten(*dY); - auto dx = framework::EigenVector::Flatten(*dX); - auto place = context.GetEigenDevice(); +// softsign(x) = x / (1 + |x|) +template +struct SoftsignFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y) { + y.device(d) = x / (static_cast(1) + x.abs()); + } +}; + +// d(softsign(x))/dx = 1 / (1 + |x|)^2 +// Taken from https://en.wikipedia.org/wiki/Activation_function +template +struct SoftsignGradFunctor : public BaseActivationFunctor { + template + void operator()(Device d, X x, Y y, dY dy, dX dx) { + dx.device(d) = + dy * (static_cast(1) / (static_cast(1) + x.abs()).square()); + } +}; + +template +struct SoftReluFunctor : public BaseActivationFunctor { + float threshold; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + + template + void operator()(Device d, X x, Y y) const { + auto temp = x.cwiseMax(-threshold).cwiseMin(threshold); + y.device(d) = (static_cast(1) + temp.exp()).log(); + } +}; + +template +struct SoftReluGradFunctor : public BaseActivationFunctor { + float threshold; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"threshold", &threshold}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { auto temp = ((x > -threshold) * (x < threshold)).template cast().eval(); - dx.device(place) = dy * (static_cast(1) - (-y).exp()) * temp; + dx.device(d) = dy * (static_cast(1) - (-y).exp()) * temp; } }; -template -class PowKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Output("Y"); - auto factor = static_cast(context.Attr("factor")); - Y->mutable_data(context.GetPlace()); +template +struct LeakyReluFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } - auto x = framework::EigenVector::Flatten(*X); - auto y = framework::EigenVector::Flatten(*Y); - auto place = context.GetEigenDevice(); - y.device(place) = x.pow(factor); + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.cwiseMax(alpha * x); } }; -template -class PowGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* dY = context.Input(framework::GradVarName("Y")); - auto* dX = context.Output(framework::GradVarName("X")); - auto factor = static_cast(context.Attr("factor")); - dX->mutable_data(context.GetPlace()); +template +struct LeakyReluGradFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + auto temp1 = alpha * (x < static_cast(0)).template cast().eval(); + auto temp2 = (x >= static_cast(0)).template cast().eval(); + dx.device(d) = dy * (temp1 + temp2).template cast(); + } +}; - auto dy = framework::EigenVector::Flatten(*dY); - auto x = framework::EigenVector::Flatten(*X); - auto dx = framework::EigenVector::Flatten(*dX); - auto place = context.GetEigenDevice(); +template +struct ELUFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } - dx.device(place) = dy * factor * x.pow(factor - static_cast(1)); + template + void operator()(Device d, X x, Y y) const { + y.device(d) = + x.cwiseMax(static_cast(0)) + + (alpha * (x.exp() - static_cast(1))).cwiseMin(static_cast(0)); } }; -template -class STanhKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* Y = context.Output("Y"); - auto scale_a = static_cast(context.Attr("scale_a")); - auto scale_b = static_cast(context.Attr("scale_b")); - Y->mutable_data(context.GetPlace()); +template +struct ELUGradFunctor : public BaseActivationFunctor { + float alpha; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"alpha", &alpha}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = + dy * (x > static_cast(0)).template cast() + + dy * (y + alpha) * (x < static_cast(0)).template cast(); + } +}; - auto x = framework::EigenVector::Flatten(*X); - auto y = framework::EigenVector::Flatten(*Y); - auto place = context.GetEigenDevice(); - y.device(place) = scale_b * (scale_a * x).tanh(); +template +struct PowFunctor : public BaseActivationFunctor { + float factor; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"factor", &factor}}; + } + template + void operator()(Device d, X x, Y y) const { + y.device(d) = x.pow(factor); } }; -template -class STanhGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* X = context.Input("X"); - auto* dY = context.Input(framework::GradVarName("Y")); - auto* dX = context.Output(framework::GradVarName("X")); - auto scale_a = static_cast(context.Attr("scale_a")); - auto scale_b = static_cast(context.Attr("scale_b")); - dX->mutable_data(context.GetPlace()); +template +struct PowGradFunctor : public BaseActivationFunctor { + float factor; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"factor", &factor}}; + } + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { + dx.device(d) = dy * factor * x.pow(factor - static_cast(1)); + } +}; - auto dy = framework::EigenVector::Flatten(*dY); - auto x = framework::EigenVector::Flatten(*X); - auto dx = framework::EigenVector::Flatten(*dX); - auto place = context.GetEigenDevice(); +template +struct STanhFunctor : public BaseActivationFunctor { + float scale_a; + float scale_b; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"scale_a", &scale_a}, {"scale_b", &scale_b}}; + } + + template + void operator()(Device d, X x, Y y) const { + y.device(d) = scale_b * (scale_a * x).tanh(); + } +}; +template +struct STanhGradFunctor : public BaseActivationFunctor { + float scale_a; + float scale_b; + typename BaseActivationFunctor::AttrPair GetAttrs() { + return {{"scale_a", &scale_a}, {"scale_b", &scale_b}}; + } + + template + void operator()(Device d, X x, Y y, dY dy, dX dx) const { auto temp = (scale_a * x).tanh() * (scale_a * x).tanh(); - dx.device(place) = dy * scale_a * scale_b * (static_cast(1) - temp); + dx.device(d) = dy * scale_a * scale_b * (static_cast(1) - temp); } }; } // namespace operators } // namespace paddle + +#define FOR_EACH_KERNEL_FUNCTOR(__macro) \ + __macro(sigmoid, SigmoidFunctor, SigmoidGradFunctor); \ + __macro(logsigmoid, LogSigmoidFunctor, LogSigmoidGradFunctor); \ + __macro(exp, ExpFunctor, ExpGradFunctor); \ + __macro(relu, ReluFunctor, ReluGradFunctor); \ + __macro(tanh, TanhFunctor, TanhGradFunctor); \ + __macro(softshrink, SoftShrinkFunctor, SoftShrinkGradFunctor); \ + __macro(sqrt, SqrtFunctor, SqrtGradFunctor); \ + __macro(abs, AbsFunctor, AbsGradFunctor); \ + __macro(reciprocal, ReciprocalFunctor, ReciprocalGradFunctor); \ + __macro(log, LogFunctor, LogGradFunctor); \ + __macro(square, SquareFunctor, SquareGradFunctor); \ + __macro(brelu, BReluFunctor, BReluGradFunctor); \ + __macro(soft_relu, SoftReluFunctor, SoftReluGradFunctor); \ + __macro(pow, PowFunctor, PowGradFunctor); \ + __macro(stanh, STanhFunctor, STanhGradFunctor); \ + __macro(softplus, SoftplusFunctor, SoftplusGradFunctor); \ + __macro(softsign, SoftsignFunctor, SoftsignGradFunctor); \ + __macro(relu6, Relu6Functor, Relu6GradFunctor); \ + __macro(leaky_relu, LeakyReluFunctor, LeakyReluGradFunctor); \ + __macro(tanh_shrink, TanhShrinkFunctor, TanhShrinkGradFunctor); \ + __macro(elu, ELUFunctor, ELUGradFunctor); \ + __macro(hard_shrink, HardShrinkFunctor, HardShrinkGradFunctor) diff --git a/paddle/operators/adadelta_op.cc b/paddle/operators/adadelta_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..cf1bca1658f3fb4cd14465791a0f94865871a120 --- /dev/null +++ b/paddle/operators/adadelta_op.cc @@ -0,0 +1,115 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adadelta_op.h" + +namespace paddle { +namespace operators { + +class AdadeltaOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("AvgSquaredGrad"), + "Input(AvgSquaredGrad) of AdadeltaOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"), + "Input(AvgSquaredUpdate) of AdadeltaOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdadeltaOp should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("AvgSquaredGradOut"), + "Output(AvgSquaredGradOut) of AdadeltaOp should not be null."); + PADDLE_ENFORCE( + ctx->HasOutput("AvgSquaredUpdateOut"), + "Output(AvgSquaredUpdateOut) of AdadeltaOp should not be null."); + + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dim, ctx->GetInputDim("Grad"), + "param and grad input of AdadeltaOp should have same dimension"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredGrad"), + "Param and AvgSquaredGrad input of AdadeltaOp " + "should have same dimension"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("AvgSquaredUpdate"), + "Param and AvgSquaredUpdate input of AdadeltaOp " + "should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dim); + ctx->SetOutputDim("AvgSquaredGradOut", param_dim); + ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim); + } +}; + +class AdadeltaOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdadeltaOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("AvgSquaredGrad", + "(Tensor) Input expectation of squared gradient"); + AddInput("AvgSquaredUpdate", + "(Tensor) Input expectation of squared parameter updates"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("AvgSquaredGradOut", + "(Tensor) Output expectation of squared gradient"); + AddOutput("AvgSquaredUpdateOut", + "(Tensor) Output expectation of squared parameter updates"); + + AddAttr("rho", + "(float, default 0.95) Exponential decay rate " + "for squared gradients.") + .SetDefault(0.95f); + AddAttr("epsilon", + "(float, default 1.0e-6) Constant for " + "numerical stability") + .SetDefault(1.0e-6f); + AddComment(R"DOC( +Adadelta Updates Operator. + +This implements the Adadelta optimizer[1]. Adadelta is a per-dimension +adaptive learning rate method for gradient descent. + +Adadelta updates: + +avg_squared_grad_out = rho * avg_squared_grad + (1 - rho) * grad * grad +param_update = - sqrt((avg_squared_update + epsilon) / + (avg_squared_grad_out + epsilon)) * grad +avg_squared_update_out = rho * avg_squared_update + (1 - rho) * param_update**2 +param_out = param + param_update + +References: + [1] ADADELTA: An Adaptive Learning Rate Method + https://arxiv.org/abs/1212.5701 + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker); +REGISTER_OP_CPU_KERNEL( + adadelta, ops::AdadeltaOpKernel); diff --git a/paddle/operators/adadelta_op.cu b/paddle/operators/adadelta_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..3af1c8c8e9861138a33b3156818f704c3b20363f --- /dev/null +++ b/paddle/operators/adadelta_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/adadelta_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL( + adadelta, ops::AdadeltaOpKernel); diff --git a/paddle/operators/adadelta_op.h b/paddle/operators/adadelta_op.h new file mode 100644 index 0000000000000000000000000000000000000000..d29e15c43583bd447fbacb548a326f303f7d1463 --- /dev/null +++ b/paddle/operators/adadelta_op.h @@ -0,0 +1,69 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdadeltaOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto avg_squared_grad_out_tensor = + ctx.Output("AvgSquaredGradOut"); + auto avg_squared_update_out_tensor = + ctx.Output("AvgSquaredUpdateOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + avg_squared_grad_out_tensor->mutable_data(ctx.GetPlace()); + avg_squared_update_out_tensor->mutable_data(ctx.GetPlace()); + + float rho = ctx.Attr("rho"); + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + // Squared gradient accumulator + auto avg_squared_grad = framework::EigenVector::Flatten( + *ctx.Input("AvgSquaredGrad")); + // Squared updates accumulator + auto avg_squared_update = framework::EigenVector::Flatten( + *ctx.Input("AvgSquaredUpdate")); + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto avg_squared_grad_out = + framework::EigenVector::Flatten(*avg_squared_grad_out_tensor); + auto avg_squared_update_out = + framework::EigenVector::Flatten(*avg_squared_update_out_tensor); + auto place = ctx.GetEigenDevice(); + + avg_squared_grad_out.device(place) = + rho * avg_squared_grad + (1 - rho) * grad.square(); + auto update = + -((avg_squared_update + epsilon) / (avg_squared_grad_out + epsilon)) + .sqrt() * + grad; + avg_squared_update_out.device(place) = + rho * avg_squared_update + (1 - rho) * update.square(); + param_out.device(place) = param + update; + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/adagrad_op.cc b/paddle/operators/adagrad_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..a17747efb79d76a6beffb27bfc03ee1b62c5618d --- /dev/null +++ b/paddle/operators/adagrad_op.cc @@ -0,0 +1,93 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adagrad_op.h" + +namespace paddle { +namespace operators { + +class AdagradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdagradOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdagradOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(MomentOut) of AdagradOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "LearningRate should have one element"); + auto param_dims = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdagradOp should have the same dimension."); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment"), + "Param and Moment input of AdagradOp should have the same dimension."); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("MomentOut", param_dims); + } +}; + +class AdagradOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdagradOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("Moment", "(Tensor) Second moment"); + AddInput("LearningRate", "(Tensor) Learning rate"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("MomentOut", "(Tensor) Output second moment"); + + AddAttr("epsilon", + "(float, default 1.0e-6) " + "Constant for numerical stability") + .SetDefault(1.0e-6f); + AddComment(R"DOC( + +Adaptive Gradient Algorithm (Adagrad). + +moment_out = moment + grad * grad +param_out = param - learning_rate * grad / (sqrt(moment_out) + epsilon) + +The original paper(http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) +does not have the epsilon attribute. It is added here for numerical stability +by avoiding division by zero. + +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adagrad, ops::AdagradOp, ops::AdagradOpMaker); +REGISTER_OP_CPU_KERNEL(adagrad, + ops::AdagradOpKernel); diff --git a/paddle/operators/adagrad_op.cu b/paddle/operators/adagrad_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..a5b7951121360f78612f9008a522235104708112 --- /dev/null +++ b/paddle/operators/adagrad_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/adagrad_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(adagrad, + ops::AdagradOpKernel); diff --git a/paddle/operators/adagrad_op.h b/paddle/operators/adagrad_op.h new file mode 100644 index 0000000000000000000000000000000000000000..c5d8f751d3527f89b96d4274328ba0bb5f6efa44 --- /dev/null +++ b/paddle/operators/adagrad_op.h @@ -0,0 +1,55 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdagradOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto moment_out_tensor = ctx.Output("MomentOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + moment_out_tensor->mutable_data(ctx.GetPlace()); + + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + auto moment = framework::EigenVector::Flatten( + *ctx.Input("Moment")); + auto lr = framework::EigenVector::Flatten( + *ctx.Input("LearningRate")); + + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto moment_out = framework::EigenVector::Flatten(*moment_out_tensor); + auto place = ctx.GetEigenDevice(); + + moment_out.device(place) = moment + grad * grad; + Eigen::DSizes m_dsize(moment_out_tensor->numel()); + param_out.device(place) = + param - lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/adamax_op.cc b/paddle/operators/adamax_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..5cf727742c770f8f0d59cd49345c5c055722a56c --- /dev/null +++ b/paddle/operators/adamax_op.cc @@ -0,0 +1,139 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/adamax_op.h" + +namespace paddle { +namespace operators { + +class AdamaxOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("InfNorm"), + "Input(InfNorm) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"), + "Input(Beta1Pow) of AdamaxOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(MomentOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("InfNormOut"), + "Output(InfNormOut) of AdamaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Beta1PowOut"), + "Output(Beta1PowOut) of AdamaxOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 dimension"); + auto beta1_pow_dims = ctx->GetInputDim("Beta1Pow"); + PADDLE_ENFORCE_EQ(framework::product(beta1_pow_dims), 1, + "Beta1 power accumulator should have 1 dimension"); + auto param_dims = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Grad"), + "Param and Grad input of AdamaxOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("Moment"), + "Param and Moment input of AdamaxOp should have same dimension"); + PADDLE_ENFORCE_EQ( + param_dims, ctx->GetInputDim("InfNorm"), + "Param and InfNorm input of AdamaxOp should have same dimension"); + + ctx->SetOutputDim("ParamOut", param_dims); + ctx->SetOutputDim("MomentOut", param_dims); + ctx->SetOutputDim("InfNormOut", param_dims); + ctx->SetOutputDim("Beta1PowOut", beta1_pow_dims); + } +}; + +class AdamaxOpMaker : public framework::OpProtoAndCheckerMaker { + public: + AdamaxOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", "(Tensor) Input parameter"); + AddInput("Grad", "(Tensor) Input gradient"); + AddInput("LearningRate", "(Tensor) Learning rate"); + AddInput("Moment", "(Tensor) First moment"); + AddInput("InfNorm", + "(Tensor) " + "Input exponentially weighted infinity norm"); + AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator"); + + AddOutput("ParamOut", "(Tensor) Output parameter"); + AddOutput("MomentOut", "(Tensor) Output first moment"); + AddOutput("InfNormOut", + "(Tensor) " + "Output exponentially weighted infinity norm"); + AddOutput("Beta1PowOut", "(Tensor) Output beta1 power accumulator"); + + AddAttr("beta1", + "(float, default 0.9) " + "Exponential decay rate for the " + "1st moment estimates.") + .SetDefault(0.9f); + AddAttr("beta2", + "(float, default 0.999) " + "exponential decay rate for the weighted " + "infinity norm estimates.") + .SetDefault(0.999f); + AddAttr("epsilon", + "(float, default 1.0e-8) " + "Constant for numerical stability") + .SetDefault(1.0e-8f); + AddComment(R"DOC( +Adamax Updates Operator. + +This implements the Adamax optimizer from Section 7 of the Adam +paper[1]. Adamax is a variant of the +Adam algorithm based on the infinity norm. + +Adamax updates: + +moment_out = beta1 * moment + (1 - beta1) * grad +inf_norm_out = max(beta2 * inf_norm + epsilon, abs(grad)) +beta1_pow_out = beta1_pow * beta1 +learning_rate_t = learning_rate/(1 - beta1_pow_out) +param_out = param - learning_rate_t * moment_out/inf_norm_out + +The original paper does not have an epsilon attribute. +However, it is added here for numerical stability +by preventing divide by 0. + +References: + [1] Adam: A Method for Stochastic Optimization + (https://arxiv.org/abs/1412.6980) + +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker); +REGISTER_OP_CPU_KERNEL(adamax, + ops::AdamaxOpKernel); diff --git a/paddle/operators/add_op.cu b/paddle/operators/adamax_op.cu similarity index 80% rename from paddle/operators/add_op.cu rename to paddle/operators/adamax_op.cu index d9c6d20a6c320b59e57ed25da3dd8b093833f8c7..fee3b6fc6b656917d79b84f48da8e63be7683890 100644 --- a/paddle/operators/add_op.cu +++ b/paddle/operators/adamax_op.cu @@ -12,7 +12,9 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include "paddle/operators/add_op.h" +#define EIGEN_USE_GPU +#include "paddle/operators/adamax_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(add, ops::AddKernel); +REGISTER_OP_GPU_KERNEL(adamax, + ops::AdamaxOpKernel); diff --git a/paddle/operators/adamax_op.h b/paddle/operators/adamax_op.h new file mode 100644 index 0000000000000000000000000000000000000000..9677b1bb786002aadfaeb571b2ba2e6aa2481ca5 --- /dev/null +++ b/paddle/operators/adamax_op.h @@ -0,0 +1,72 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class AdamaxOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto param_out_tensor = ctx.Output("ParamOut"); + auto moment_out_tensor = ctx.Output("MomentOut"); + auto inf_norm_out_tensor = ctx.Output("InfNormOut"); + auto beta1_pow_out_tensor = ctx.Output("Beta1PowOut"); + + param_out_tensor->mutable_data(ctx.GetPlace()); + moment_out_tensor->mutable_data(ctx.GetPlace()); + inf_norm_out_tensor->mutable_data(ctx.GetPlace()); + beta1_pow_out_tensor->mutable_data(ctx.GetPlace()); + + float beta1 = ctx.Attr("beta1"); + float beta2 = ctx.Attr("beta2"); + float epsilon = ctx.Attr("epsilon"); + + auto param = framework::EigenVector::Flatten( + *ctx.Input("Param")); + auto grad = framework::EigenVector::Flatten( + *ctx.Input("Grad")); + auto moment = framework::EigenVector::Flatten( + *ctx.Input("Moment")); + auto inf_norm = framework::EigenVector::Flatten( + *ctx.Input("InfNorm")); + auto lr = framework::EigenVector::Flatten( + *ctx.Input("LearningRate")); + auto beta1_pow = framework::EigenVector::Flatten( + *ctx.Input("Beta1Pow")); + auto param_out = framework::EigenVector::Flatten(*param_out_tensor); + auto moment_out = framework::EigenVector::Flatten(*moment_out_tensor); + auto inf_norm_out = + framework::EigenVector::Flatten(*inf_norm_out_tensor); + auto beta1_pow_out = + framework::EigenVector::Flatten(*beta1_pow_out_tensor); + auto place = ctx.GetEigenDevice(); + + moment_out.device(place) = beta1 * moment + (1 - beta1) * grad; + inf_norm_out.device(place) = + grad.abs().cwiseMax((beta2 * inf_norm) + epsilon); + beta1_pow_out.device(place) = beta1_pow * beta1; + auto lr_t = lr / (1 - beta1_pow_out); + Eigen::DSizes m_dsize(moment_out_tensor->numel()); + param_out.device(place) = + param - lr_t.broadcast(m_dsize) * (moment_out / inf_norm_out); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc deleted file mode 100644 index 3914d1323083ede6a7ea07e7b4ef76b9e4afd26d..0000000000000000000000000000000000000000 --- a/paddle/operators/add_op.cc +++ /dev/null @@ -1,68 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#include "paddle/operators/add_op.h" - -namespace paddle { -namespace operators { - -class AddOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of AddOp should not be null."); - - auto x_dims = ctx->GetInputDim("X"); - auto y_dims = ctx->GetInputDim("Y"); - PADDLE_ENFORCE_EQ(x_dims, y_dims, - "Two input of Add Op's dimension must be same."); - ctx->SetOutputDim("Out", x_dims); - } -}; - -class AddOpMaker : public framework::OpProtoAndCheckerMaker { - public: - AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The first input of add op"); - AddInput("Y", "The second input of add op"); - AddOutput("Out", "The output of add op"); - AddComment(R"DOC( -Two Element Add Operator. - -The equation is: Out = X + Y -)DOC"); - } -}; - -class AddOpGrad : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override {} -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad); - -REGISTER_OP_CPU_KERNEL(add, ops::AddKernel); diff --git a/paddle/operators/clip_op.cc b/paddle/operators/clip_op.cc index b3dd060fd725fc9056b25e4affd82fdb345e77f7..3e9b0d82ba06a918d52124e791715277640fd4ff 100644 --- a/paddle/operators/clip_op.cc +++ b/paddle/operators/clip_op.cc @@ -22,7 +22,7 @@ class ClipOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ClipOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -61,7 +61,7 @@ class ClipOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/clip_op.h b/paddle/operators/clip_op.h index ce1d4e1f460414e6e4acee4fa3207f309c55d86b..ac702e9935201ba5263a80ebeb1ab22fa0bd1340 100644 --- a/paddle/operators/clip_op.h +++ b/paddle/operators/clip_op.h @@ -56,7 +56,7 @@ class ClipGradFunctor { }; template -class ClipKernel : public framework::OpKernel { +class ClipKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto max = context.Attr("max"); @@ -73,7 +73,7 @@ class ClipKernel : public framework::OpKernel { }; template -class ClipGradKernel : public framework::OpKernel { +class ClipGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto max = context.Attr("max"); diff --git a/paddle/operators/concat_op.cc b/paddle/operators/concat_op.cc index 1ffa02c8f94c01a385d3ba376c1fd0dc3c1bd372..235c4449ace79e87d209615199d6628f269961a9 100644 --- a/paddle/operators/concat_op.cc +++ b/paddle/operators/concat_op.cc @@ -24,7 +24,7 @@ class ConcatOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE_GE(ctx->Inputs("X").size(), 1UL, "Inputs(X) of ConcatOp should be empty.") PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -83,7 +83,7 @@ class ConcatOpGrad : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X")); } }; diff --git a/paddle/operators/concat_op.h b/paddle/operators/concat_op.h index b37063261123bce1f22c39ab021e88f2faf58e9f..c113f19fb5cf806709bff845ee0f1078b34014bb 100644 --- a/paddle/operators/concat_op.h +++ b/paddle/operators/concat_op.h @@ -22,7 +22,7 @@ namespace paddle { namespace operators { template -class ConcatKernel : public framework::OpKernel { +class ConcatKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto ins = ctx.MultiInput("X"); @@ -44,7 +44,7 @@ class ConcatKernel : public framework::OpKernel { }; template -class ConcatGradKernel : public framework::OpKernel { +class ConcatGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* in = ctx.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/cond_op.cc b/paddle/operators/cond_op.cc index aaffa6661fe4686d09f20f0f0682219772638202..2737104a205cbc1e18ce4a3a45592a416d38a874 100644 --- a/paddle/operators/cond_op.cc +++ b/paddle/operators/cond_op.cc @@ -14,12 +14,7 @@ limitations under the License. */ #include "paddle/operators/cond_op.h" -#include -#include - -#include "paddle/framework/op_registry.h" #include "paddle/operators/gather.h" -#include "paddle/operators/net_op.h" #include "paddle/operators/scatter.h" namespace paddle { @@ -31,175 +26,183 @@ using Tensor = framework::Tensor; using LoDTensor = framework::LoDTensor; using DDim = framework::DDim; -void CondOp::CreateScope(const Scope& scope) const { +framework::Scope& CondOp::AddSubScope(const Scope& scope) const { auto sub_scopes_var = scope.FindVar("SubScopes"); PADDLE_ENFORCE_NOT_NULL(sub_scopes_var, "Output(SubScopes) of CondOp should not be null."); auto sub_scopes = sub_scopes_var->GetMutable>(); auto& sub_scope = scope.NewScope(); sub_scopes->push_back(&sub_scope); + return sub_scope; } -void CondOp::CreateIndexTensor(const Scope& scope) const { +std::vector& CondOp::GetSubScopes( + const framework::Scope& scope) const { + auto sub_scopes_var = scope.FindVar("SubScopes"); + PADDLE_ENFORCE_NOT_NULL(sub_scopes_var, + "Output(SubScopes) of CondOp should not be null."); + return *sub_scopes_var->GetMutable>(); +} + +LoDTensor& CondOp::AddIndexTensor(const Scope& scope) const { auto index_tensors_var = scope.FindVar("IndexTensors"); PADDLE_ENFORCE_NOT_NULL(index_tensors_var, "Output(IndexTensors) of CondOp should not be null."); auto& index_tensors = *index_tensors_var->GetMutable>(); index_tensors.push_back(LoDTensor()); + return index_tensors.back(); } -void CondOp::InferShape(const Scope& scope) const { - auto sub_scopes_var = scope.FindVar("SubScopes"); - PADDLE_ENFORCE_NOT_NULL(sub_scopes_var, - "Output(SubScopes) of CondOp should not be null."); - auto& sub_scopes = *sub_scopes_var->GetMutable>(); - - for (int i = 0; i < 2; ++i) { - // Create two sub scopes for true and false branches - // sub_scopes[0] for the true branch and sub_scopes[1] for the false - // branch - CreateScope(scope); - - // Create two tensors for true and false indices - // index_tensors[0] for the true branch and index_tensors[1] for the false - // branch - CreateIndexTensor(scope); - - PADDLE_ENFORCE(!Inputs("Xs").empty(), - "Inputs(Xs) of CondOp can't be empty."); - for (auto& input : Inputs("Xs")) { - // Create a new tensor in sub-scope for input-type tensor - Variable* v = sub_scopes[i]->NewVar(input); - LoDTensor* sub_input = v->GetMutable(); - sub_input->Resize(scope.FindVar(input)->GetMutable()->dims()); - } - - for (auto& output : (*sub_net_op_[i]).Outputs()) { - for (auto& var_name : output.second) { - sub_scopes[i]->NewVar(var_name); - } - } - - // each net calls InferShape - // sub_net_op_[i]->InferShape(*sub_scopes[i]); - } - - for (auto& output : Outputs("Outs")) { - LoDTensor* tensor_t_out = - sub_scopes[0]->FindVar(output)->GetMutable(); - PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL"); - LoDTensor* tensor_f_out = - sub_scopes[1]->FindVar(output)->GetMutable(); - PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL"); - - auto* tensor_out_var = scope.FindVar(output); - PADDLE_ENFORCE_NOT_NULL(tensor_out_var, "Output not found"); - LoDTensor* tensor_out = tensor_out_var->GetMutable(); - PADDLE_ENFORCE_NOT_NULL(tensor_t_out, - "True output tensor should not be NULL"); - - // check output size should be same - PADDLE_ENFORCE_EQ(tensor_t_out->dims(), tensor_f_out->dims(), - "Outputs not of the same shape"); - tensor_out->Resize(tensor_t_out->dims()); - // tensor_out->mutable_data(tensor_out->dims(), - // platform::CPUPlace()); - tensor_out->mutable_data(platform::CPUPlace()); - } -} - -void CondOp::Run(const Scope& scope, - const platform::DeviceContext& dev_ctx) const { - auto* sub_scopes_var = scope.FindVar("SubScopes"); - PADDLE_ENFORCE_NOT_NULL(sub_scopes_var, - "Output(SubScopes) of CondOp should not be null."); - auto sub_scopes = sub_scopes_var->Get>(); +std::vector& CondOp::GetIndexTensors( + const framework::Scope& scope) const { auto* index_tensors_var = scope.FindVar("IndexTensors"); PADDLE_ENFORCE_NOT_NULL(index_tensors_var, "Output(IndexTensors) of CondOp should not be null."); - auto index_tensors = index_tensors_var->Get>(); + return *index_tensors_var->GetMutable>(); +} - std::string cond_name = Input("Cond"); - Variable* cond_var = scope.FindVar(cond_name); +void CondOp::PrepareDataForSubnet( + const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const { + PADDLE_ENFORCE(!Inputs("Xs").empty(), "Inputs(Xs) of CondOp can't be empty."); + + for (int i = 0; i < BRANCH_NUM; ++i) { + // Create two sub scopes for true and false branches + // sub_scopes[0] for the true branch + // sub_scopes[1] for the false branch + AddSubScope(scope); + // Create two tensors for true and false indices: + // index_tensors[0] for the true branch + // index_tensors[1] for the false branch + AddIndexTensor(scope); + } + + Variable* cond_var = scope.FindVar(Input("Cond")); PADDLE_ENFORCE_NOT_NULL(cond_var, "Input(Cond) of CondOp should not be null."); const LoDTensor* cond = cond_var->GetMutable(); - // Step 1: get the true/false index at runtime - // index_[0]: vector, contains all index for cond[i] == true - // index_[1]: vector, contains all index for cond[i] == false - for (int i = 0; i < 2; ++i) index_[i].clear(); + // get the true/false index at runtime according to cond tensor + // index_vectors[0]: vector, contains all index for cond[i] == true + // index_vectors[1]: vector, contains all index for cond[i] == false + std::vector> index_vectors; + index_vectors.resize(BRANCH_NUM); const int* cond_data = cond->data(); for (int i = 0; i < cond->dims()[0]; ++i) { if (cond_data[i]) - index_[0].push_back(i); + index_vectors[TRUE_BRANCH].push_back(i); else - index_[1].push_back(i); + index_vectors[FALSE_BRANCH].push_back(i); } - // put index_[0] and index_[1] into two tensors: - // index_tensor_[0] and index_tensor_[1] - DDim dim = paddle::framework::make_ddim({0}); - for (int i = 0; i < 2; ++i) { - dim[0] = index_[i].size(); - int* tmp_ptr = + // put index_vectors[0] and index_vectors[1] into two tensors: + // index_tensors[0] and index_tensors[1] + std::vector& index_tensors = GetIndexTensors(scope); + std::vector& sub_scopes = GetSubScopes(scope); + + for (int i = 0; i < BRANCH_NUM; ++i) { + DDim dim = {static_cast(index_vectors[i].size())}; + int* index_tensor_data_ptr = index_tensors[i].mutable_data(dim, platform::CPUPlace()); - index_tensors[i].Resize(dim); - memcpy(tmp_ptr, index_[i].data(), dim[0] * sizeof(int)); + memcpy(index_tensor_data_ptr, index_vectors[i].data(), + dim[0] * sizeof(int)); } - // Step 2: collect data by calling gather - for (int i = 0; i < 2; ++i) { - // i= 0/i for True and False branches respectively - for (auto& input : Inputs("Xs")) { - // find Tensor - Variable* v = scope.FindVar(input); - PADDLE_ENFORCE_NOT_NULL(v); - LoDTensor* tensor_parent = v->GetMutable(); + // create input in subscopes according to index_vectors + for (auto& input : Inputs("Xs")) { + Variable* var_parent = scope.FindVar(input); + PADDLE_ENFORCE_NOT_NULL(var_parent); + const auto* tensor_parent = &var_parent->Get(); - v = sub_scopes[i]->FindVar(input); - PADDLE_ENFORCE_NOT_NULL(v); - LoDTensor* tensor_child = v->GetMutable(); + for (int i = 0; i < BRANCH_NUM; ++i) { + Variable* var_child = sub_scopes[i]->FindVar(input); + PADDLE_ENFORCE_NOT_NULL(var_child); + auto* tensor_child = var_child->GetMutable(); // Resize child - DDim dim = tensor_child->dims(); - dim[0] = index_[i].size(); - tensor_child->Resize(dim); + DDim dim = tensor_parent->dims(); + dim[0] = index_tensors[i].dims()[0]; tensor_child->mutable_data(dim, platform::CPUPlace()); - Gather(dev_ctx.GetPlace(), tensor_parent, &index_tensors[i], - tensor_child); + CPUGather(dev_ctx, *tensor_parent, index_tensors[i], tensor_child); } } - // Step 3: run - for (int i = 0; i < 2; ++i) { - sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx); + // create output_tensors in subscope for sub_net + for (int i = 0; i < BRANCH_NUM; ++i) { + for (auto& output : (*sub_net_op_[i]).Outputs()) { + for (auto& var_name : output.second) { + sub_scopes[i]->NewVar(var_name); + } + } } +} - // Step 4: merge output results +void CondOp::MergeDataFromSubnet(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const { + std::vector& sub_scopes = GetSubScopes(scope); + const std::vector& index_tensors = + GetIndexTensors(scope); + + // Infer the output dim, out_dim[0] = true_dim[0] + false_dim[0] PADDLE_ENFORCE(!Outputs("Outs").empty(), "Outputs(Outs) of CondOp can't be empty."); - for (int i = 0; i < 2; ++i) { - // i= 0/i for True and False branches respectively - for (auto& output : Outputs("Outs")) { - // find Tensor - Variable* v = scope.FindVar(output); - PADDLE_ENFORCE_NOT_NULL(v); - LoDTensor* tensor_parent = v->GetMutable(); - - v = sub_scopes[i]->FindVar(output); - PADDLE_ENFORCE_NOT_NULL(v); - LoDTensor* tensor_child = v->GetMutable(); - - ScatterUpdate(dev_ctx.GetPlace(), tensor_child, &index_tensors[i], + for (auto& output : Outputs("Outs")) { + const LoDTensor* tensor_t_out = + &sub_scopes[TRUE_BRANCH]->FindVar(output)->Get(); + PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL"); + const LoDTensor* tensor_f_out = + &sub_scopes[FALSE_BRANCH]->FindVar(output)->Get(); + PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL"); + + auto* var_out = scope.FindVar(output); + PADDLE_ENFORCE_NOT_NULL(var_out, "Output not found"); + LoDTensor* tensor_out = var_out->GetMutable(); + PADDLE_ENFORCE_NOT_NULL(tensor_t_out, + "True output tensor should not be NULL"); + + DDim true_dim = tensor_t_out->dims(); + DDim false_dim = tensor_f_out->dims(); + true_dim[0] = 0; + false_dim[0] = 0; + PADDLE_ENFORCE_EQ(true_dim, false_dim, + "Outputs not of the same shape except the first dim"); + + DDim out_dim = tensor_t_out->dims(); + out_dim[0] = tensor_t_out->dims()[0] + tensor_f_out->dims()[0]; + tensor_out->Resize(out_dim); + tensor_out->mutable_data(platform::CPUPlace()); + } + + // merge output results: + // output_tensor = true_output_tensor + false_output_tensor + for (auto& output : Outputs("Outs")) { + Variable* var_parent = scope.FindVar(output); + PADDLE_ENFORCE_NOT_NULL(var_parent); + auto* tensor_parent = var_parent->GetMutable(); + + for (int i = 0; i < BRANCH_NUM; ++i) { + Variable* var_child = sub_scopes[i]->FindVar(output); + PADDLE_ENFORCE_NOT_NULL(var_child); + auto* tensor_child = &var_child->Get(); + ScatterAssign(dev_ctx, *tensor_child, index_tensors[i], tensor_parent); } } } +void CondOp::Run(const Scope& scope, + const platform::DeviceContext& dev_ctx) const { + PrepareDataForSubnet(scope, dev_ctx); + std::vector& sub_scopes = GetSubScopes(scope); + for (int i = 0; i < BRANCH_NUM; ++i) { + sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx); + } + MergeDataFromSubnet(scope, dev_ctx); +} + class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker { public: CondOpProtoAndCheckerMaker(framework::OpProto* proto, diff --git a/paddle/operators/cond_op.h b/paddle/operators/cond_op.h index 9a88ee35f108204348baddc57e0c0d8e63c3fb6d..93121fb31be287794249b5a62386d5a8dd268a0c 100644 --- a/paddle/operators/cond_op.h +++ b/paddle/operators/cond_op.h @@ -40,8 +40,7 @@ class CondOp : public framework::OperatorBase { const framework::VariableNameMap& outputs, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { - index_.resize(2); - sub_net_op_.resize(2); + sub_net_op_.resize(BRANCH_NUM); } CondOp(const CondOp& o) @@ -51,42 +50,44 @@ class CondOp : public framework::OperatorBase { PADDLE_THROW("Not implemented"); } - void CreateScope(const framework::Scope& scope) const; + framework::Scope& AddSubScope(const framework::Scope& scope) const; + std::vector& GetSubScopes( + const framework::Scope& scope) const; - void CreateIndexTensor(const framework::Scope& scope) const; + framework::LoDTensor& AddIndexTensor(const framework::Scope& scope) const; + std::vector& GetIndexTensors( + const framework::Scope& scope) const; - /* - * InferShape must be called before Run. - * FIXME(yuyang18): Since InferShape has been removed, this implementation - * could be wrong. - */ - void InferShape(const framework::Scope& scope) const; + void PrepareDataForSubnet(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const; + void MergeDataFromSubnet(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const; /* * Set True Block */ void set_truenet(std::unique_ptr&& net) { - sub_net_op_[0] = std::move(net); + sub_net_op_[TRUE_BRANCH] = std::move(net); } /* * Set False Block */ void set_falsenet(std::unique_ptr&& net) { - sub_net_op_[1] = std::move(net); + sub_net_op_[FALSE_BRANCH] = std::move(net); } void Run(const framework::Scope& scope, const platform::DeviceContext& dev_ctx) const override; private: + const int TRUE_BRANCH = 0; + const int FALSE_BRANCH = 1; + const int BRANCH_NUM = 2; + // sub_net_op_[0]: subnet_t // sub_net_op_[1]: subnet_f std::vector> sub_net_op_; - - // index_[0]: True_index; - // index_[1]: False_index; - mutable std::vector> index_; }; } // namespace operators diff --git a/paddle/operators/conv2d_op.cc b/paddle/operators/conv2d_op.cc index 5cc82944bb6b9a4fc5cd94cf2233ab84fc105fe7..6325d4248f10ea8a12ae5398d9fe0e579db3f7ae 100644 --- a/paddle/operators/conv2d_op.cc +++ b/paddle/operators/conv2d_op.cc @@ -27,7 +27,7 @@ class Conv2DOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Input"), "Input(Input) of Conv2DOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Filter"), @@ -106,7 +106,7 @@ class Conv2DOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto in_dims = ctx->GetInputDim("Input"); auto filter_dims = ctx->GetInputDim("Filter"); if (ctx->HasOutput(framework::GradVarName("Input"))) { diff --git a/paddle/operators/conv_shift_op.cc b/paddle/operators/conv_shift_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..e1e321ed5fce6ce4e4089cc5c5e488a2cbad6c82 --- /dev/null +++ b/paddle/operators/conv_shift_op.cc @@ -0,0 +1,206 @@ +/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/operators/conv_shift_op.h" +#include "paddle/framework/eigen.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; +template +using EigenMatrix = framework::EigenMatrix; + +class ConvShiftOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); + PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null."); + + auto x_dims = ctx->GetInputDim("X"); + auto y_dims = ctx->GetInputDim("Y"); + PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2."); + PADDLE_ENFORCE_EQ(y_dims.size(), 2, "Input(Y)'s rank should be 2."); + PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0], + "The 1st dimension of Input(X) and Input(Y) should " + "be equal."); + PADDLE_ENFORCE_EQ(y_dims[1] % 2, 1, + "The 2nd dimension of Input(Y) should be odd."); + PADDLE_ENFORCE_LE(y_dims[1], x_dims[1], + "The 2nd dimension of Input(Y) should be less than or " + "equal to the 2nd dimension of Input(X)."); + ctx->SetOutputDim("Out", x_dims); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; + +class ConvShiftGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); + PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null."); + PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), + "Input(Out@GRAD) should be not null."); + + auto x_grad_name = framework::GradVarName("X"); + if (ctx->HasOutput(x_grad_name)) { + auto x_dims = ctx->GetInputDim("X"); + ctx->SetOutputDim(x_grad_name, x_dims); + } + + auto y_grad_name = framework::GradVarName("Y"); + if (ctx->HasOutput(y_grad_name)) { + auto y_dims = ctx->GetInputDim("Y"); + ctx->SetOutputDim(y_grad_name, y_dims); + } + } +}; + +class ConvShiftOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ConvShiftOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : framework::OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(Tensor, default Tensor), a 2-D tensor with shape B x M, " + "where B is the batch size and M is the data dimension."); + AddInput("Y", + "(Tensor, default Tensor), a 2-D tensor with shape B x N, " + "where B is the batch size and N is the data dimension. N must " + "be odd."); + AddOutput("Out", + "(Tensor, default Tensor), a 2-D tensor with shape B x M, " + "i.e., the same shape as X."); + AddComment(R"DOC( +ConvShift Operator. + +A layer for circular convolution of two vectors, +as used in the Neural Turing Machine: https://arxiv.org/abs/1410.5401 + +The equation is: + + \f[ + Out[i] = \sum_{j=-(N-1)/2}^{(N-1)/2} X_{i+j} * Y_{j} + \f] + +where X's index is computed modulo M, and b's index is computed modulo N. + +Both of the input `X` and `Y` can carry LoD (Level of Details) information. +However, the output only shares the LoD information with input `X`. +)DOC"); + } +}; + +template +class ConvShiftKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + auto *X = context.Input("X"); + auto *Y = context.Input("Y"); + auto *Out = context.Output("Out"); + Out->mutable_data(context.GetPlace()); + + auto x = EigenMatrix::From(*X); + auto y = EigenMatrix::From(*Y); + auto out = EigenMatrix::From(*Out); + out.setZero(); + + size_t batch_size = X->dims()[0]; + size_t x_width = X->dims()[1]; + size_t y_width = Y->dims()[1]; + size_t y_half_width = (y_width - 1) / 2; + + for (size_t k = 0; k < batch_size; ++k) { + for (size_t i = 0; i < x_width; ++i) { + for (size_t j = 0; j < y_width; ++j) { + int index = (i + j - y_half_width + x_width) % x_width; + out(k, i) += x(k, index) * y(k, j); + } + } + } + } +}; + +template +class ConvShiftGradKernel + : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + auto *X = context.Input("X"); + auto *Y = context.Input("Y"); + auto *dOut = context.Input(framework::GradVarName("Out")); + auto *dX = context.Output(framework::GradVarName("X")); + auto *dY = context.Output(framework::GradVarName("Y")); + + auto x = EigenMatrix::From(*X); + auto y = EigenMatrix::From(*Y); + auto dout = EigenMatrix::From(*dOut); + + auto x_dims = X->dims(); + auto y_dims = Y->dims(); + size_t batch_size = x_dims[0]; + size_t x_width = x_dims[1]; + size_t y_width = y_dims[1]; + size_t y_half_width = (y_width - 1) / 2; + + // The below trades code duplication for efficiency (keeping the if + // statement outside of the loop). + if (dX) { + dX->mutable_data(context.GetPlace()); + auto dx = EigenMatrix::From(*dX); + dx.setZero(); + for (size_t k = 0; k < batch_size; ++k) { + for (size_t i = 0; i < x_width; ++i) { + for (size_t j = 0; j < y_width; ++j) { + int index = (i + j - y_half_width + x_width) % x_width; + dx(k, index) += dout(k, i) * y(k, j); + } + } + } + } + + if (dY) { + dY->mutable_data(context.GetPlace()); + auto dy = EigenMatrix::From(*dY); + dy.setZero(); + for (size_t k = 0; k < batch_size; ++k) { + for (size_t i = 0; i < x_width; ++i) { + for (size_t j = 0; j < y_width; ++j) { + int index = (i + j - y_half_width + x_width) % x_width; + dy(k, j) += x(k, index) * dout(k, i); + } + } + } + } + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(conv_shift, ops::ConvShiftOp, ops::ConvShiftOpMaker, + conv_shift_grad, ops::ConvShiftGradOp); +REGISTER_OP_CPU_KERNEL(conv_shift, + ops::ConvShiftKernel); +REGISTER_OP_CPU_KERNEL( + conv_shift_grad, + ops::ConvShiftGradKernel); diff --git a/paddle/operators/conv_shift_op.cu b/paddle/operators/conv_shift_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..145e966fe9caa68f7485bb258fa78fd34bfd4c04 --- /dev/null +++ b/paddle/operators/conv_shift_op.cu @@ -0,0 +1,194 @@ +/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/operators/conv_shift_op.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; + +namespace { + +inline int div_up(int x, int y) { return (x + y - 1) / y; } + +// Some notes on the design: +// +// Each thread is responsible for computing a single output out[k, i]. +// Thread blocks are based on tiles of x with height 1 in the batch dimension. +// +// This design is based on the typical use case where the filter +// y is fairly small. For large y, it would probably be more efficient +// to also tile across y. +template +__global__ void conv_shift_forward(const T *x, const T *y, T *out, int x_width, + int y_width, int y_half_width, + int batch_size) { + extern __shared__ T mem[]; + + int tx = threadIdx.x; + int i = blockIdx.x * blockDim.x + tx; // global x index + int k = blockIdx.y; // batch index + + // Check if we are in a boundary block with fewer x's to process than + // blockDim.x. + int num_x = + (blockIdx.x == gridDim.x - 1) ? (x_width % blockDim.x) : blockDim.x; + + T *sx = mem; + T *sx_pad = &mem[num_x]; + T *sy = &mem[blockDim.x + y_width]; + + // Collaboratively load y[k, :] and length-y padding of x into shared memory. + int pad_start = blockIdx.x * blockDim.x + num_x + x_width - y_half_width; + for (int j = tx; j < y_width; j += blockDim.x) { + sy[j] = y[k * y_width + j]; + sx_pad[j] = x[k * x_width + (pad_start + j) % x_width]; + } + + // Load a cyclically shifted slice of x into shared memory. + if (tx < num_x) { + int load_i = (i - y_half_width + x_width) % x_width; + sx[tx] = x[k * x_width + load_i]; + } else { + return; + } + __syncthreads(); + + // Compute dot product of sx[tx:tx + y_width] and sy. + T sum = 0; + for (int j = 0; j < y_width; ++j) { + sum += sx[tx + j] * sy[j]; + } + + // Save to out[k, i]. + out[k * x_width + i] = sum; +} + +// Compute x gradient - initial naive implementation with atomic add. +template +__global__ void conv_shift_dx(const T *dout, const T *y, T *dx, int x_width, + int y_width, int y_half_width, int batch_size) { + int i = blockIdx.x * blockDim.x + threadIdx.x; // x index + int j = blockIdx.y; // y index + int k = blockIdx.z; // batch index + + if (i < x_width) { + int index = (i + j - y_half_width + x_width) % x_width; + atomicAdd(&dx[k * x_width + index], + dout[k * x_width + i] * y[k * y_width + j]); + } +} + +// Compute y gradient - initial naive implementation with atomic add. +template +__global__ void conv_shift_dy(const T *x, const T *dout, T *dy, int x_width, + int y_width, int y_half_width, int batch_size) { + int i = blockIdx.x * blockDim.x + threadIdx.x; // x index + int j = blockIdx.y; // y index + int k = blockIdx.z; // batch index + + if (i < x_width) { + int index = (i + j - y_half_width + x_width) % x_width; + atomicAdd(&dy[k * y_width + j], + x[k * x_width + index] * dout[k * x_width + i]); + } +} +} // namespace + +template +class ConvShiftKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + const Tensor *X = context.Input("X"); + const Tensor *Y = context.Input("Y"); + Tensor *Out = context.Output("Out"); + const T *x_data = X->data(); + const T *y_data = Y->data(); + T *out_data = Out->mutable_data(context.GetPlace()); + + int batch_size = X->dims()[0]; + int x_width = X->dims()[1]; + int y_width = Y->dims()[1]; + int y_half_width = (y_width - 1) / 2; + + const int x_per_block = 256; + int num_x_blocks = div_up(x_width, x_per_block); + int mem_per_block = (x_per_block + 2 * y_width) * sizeof(T); + + dim3 grid_dim(num_x_blocks, batch_size); + + auto stream = reinterpret_cast( + context.device_context()) + .stream(); + + conv_shift_forward<<>>( + x_data, y_data, out_data, x_width, y_width, y_half_width, batch_size); + } +}; + +template +class ConvShiftGradKernel + : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + const Tensor *X = context.Input("X"); + const Tensor *Y = context.Input("Y"); + const Tensor *dOut = context.Input(framework::GradVarName("Out")); + const T *x_data = X->data(); + const T *y_data = Y->data(); + const T *dout_data = dOut->data(); + + Tensor *dX = context.Output(framework::GradVarName("X")); + Tensor *dY = context.Output(framework::GradVarName("Y")); + + int batch_size = X->dims()[0]; + int x_width = X->dims()[1]; + int y_width = Y->dims()[1]; + int y_half_width = (y_width - 1) / 2; + + auto stream = reinterpret_cast( + context.device_context()) + .stream(); + + const int x_per_block = 256; + int num_x_blocks = div_up(x_width, x_per_block); + dim3 grid_dim(num_x_blocks, y_width, batch_size); + + if (dX) { + T *dx_data = dX->mutable_data(context.GetPlace()); + cudaMemsetAsync(dx_data, 0, dX->numel() * sizeof(T), stream); + conv_shift_dx<<>>( + dout_data, y_data, dx_data, x_width, y_width, y_half_width, + batch_size); + } + if (dY) { + T *dy_data = dY->mutable_data(context.GetPlace()); + cudaMemsetAsync(dy_data, 0, dY->numel() * sizeof(T), stream); + conv_shift_dy<<>>( + x_data, dout_data, dy_data, x_width, y_width, y_half_width, + batch_size); + } + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(conv_shift, + ops::ConvShiftKernel); +REGISTER_OP_GPU_KERNEL( + conv_shift_grad, + ops::ConvShiftGradKernel); diff --git a/paddle/operators/conv_shift_op.h b/paddle/operators/conv_shift_op.h new file mode 100644 index 0000000000000000000000000000000000000000..5a160b0f1696c70868fc48d219b38cde2018e8a3 --- /dev/null +++ b/paddle/operators/conv_shift_op.h @@ -0,0 +1,33 @@ +/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class ConvShiftKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override; +}; + +template +class ConvShiftGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override; +}; +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/cos_sim_op.cc b/paddle/operators/cos_sim_op.cc index 040546f1a6fe1af6d17a5e363a11d27de88d03c2..2b4c4b9c45d22e9d0b124145b55cfaeeaf1583d5 100644 --- a/paddle/operators/cos_sim_op.cc +++ b/paddle/operators/cos_sim_op.cc @@ -24,7 +24,7 @@ class CosSimOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { // notnull check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of CosSimOp should not be null."); @@ -98,7 +98,7 @@ class CosSimOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { // notnull check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) must not be null."); diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h index bcf6f758cae561a2e22f5be6c7a242647ef1c144..68c56f531f941e1b8f66ac7ba6bf318881642c4f 100644 --- a/paddle/operators/cos_sim_op.h +++ b/paddle/operators/cos_sim_op.h @@ -28,7 +28,7 @@ template ; template -class CosSimKernel : public framework::OpKernel { +class CosSimKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { // get Tensor @@ -67,7 +67,7 @@ class CosSimKernel : public framework::OpKernel { }; template -class CosSimGradKernel : public framework::OpKernel { +class CosSimGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { // get Tensor diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc index 9b2305e90e85a6f39d4c584a3251b25f67e81aca..a1424993cc3076f2755164393c5429d79fc1ee78 100644 --- a/paddle/operators/crop_op.cc +++ b/paddle/operators/crop_op.cc @@ -25,7 +25,7 @@ class CropOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of CropOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -115,7 +115,7 @@ class CropOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/crop_op.h b/paddle/operators/crop_op.h index ac3aeaf41e206c1deb74c7022c36f02c4777a84b..2e72583d68d0acf0e2f5044637dba55de3b57209 100644 --- a/paddle/operators/crop_op.h +++ b/paddle/operators/crop_op.h @@ -27,7 +27,7 @@ using EigenTensor = framework::EigenTensor; using framework::Tensor; template -class CropKernel : public framework::OpKernel { +class CropKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* x = context.Input("X"); @@ -69,7 +69,7 @@ void CropGradFunction(const framework::ExecutionContext& context) { } template -class CropGradKernel : public framework::OpKernel { +class CropGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { size_t rank = diff --git a/paddle/operators/cross_entropy_op.cc b/paddle/operators/cross_entropy_op.cc index 26fc9b51c44d21d92851030449e116538f937846..708e80e96a0f007be1594d8b4781d1296d6b398d 100644 --- a/paddle/operators/cross_entropy_op.cc +++ b/paddle/operators/cross_entropy_op.cc @@ -22,7 +22,7 @@ class CrossEntropyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null."); @@ -47,6 +47,12 @@ class CrossEntropyOp : public framework::OperatorWithKernel { ctx->SetOutputDim("Y", {x_dims[0], 1}); ctx->ShareLoD("X", /*->*/ "Y"); } + + // CrossEntropy's data type just determined by "X" + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("X")->type()); + } }; class CrossEntropyGradientOp : public framework::OperatorWithKernel { @@ -54,7 +60,7 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), @@ -87,6 +93,12 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel { } ctx->SetOutputDim(framework::GradVarName("X"), x_dims); } + + // CrossEntropy's data type just determined by "X" + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("X")->type()); + } }; class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker { diff --git a/paddle/operators/cross_entropy_op.cu b/paddle/operators/cross_entropy_op.cu index 1cfeb7a53b047541322ac53c5b7249e660039d5c..5e2024e0ea9040b758e1cec4dbaa4b329bbb727e 100644 --- a/paddle/operators/cross_entropy_op.cu +++ b/paddle/operators/cross_entropy_op.cu @@ -18,14 +18,6 @@ namespace paddle { namespace operators { namespace { -// TODO(qingqing): make zero setting a common function. -template -__global__ void Zero(T* X, const int N) { - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; - i += blockDim.x * gridDim.x) { - X[i] = 0.0; - } -} template __global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X, @@ -53,7 +45,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* dX, const T* dY, const T* X, } // namespace template -class CrossEntropyOpCUDAKernel : public framework::OpKernel { +class CrossEntropyOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), @@ -64,12 +56,12 @@ class CrossEntropyOpCUDAKernel : public framework::OpKernel { y->mutable_data(ctx.GetPlace()); math::CrossEntropyFunctor()( - ctx, y, x, label, ctx.Attr("softLabel")); + ctx.device_context(), y, x, label, ctx.Attr("softLabel")); } }; template -class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel { +class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), @@ -99,11 +91,7 @@ class CrossEntropyGradientOpCUDAKernel : public framework::OpKernel { .stream()>>>(dx_data, dy_data, x_data, label_data, batch_size, class_num); } else { - Zero<<( - ctx.device_context()) - .stream()>>>(dx_data, batch_size * class_num); - + math::SetConstant(ctx.device_context(), dx, 0); auto* label_data = label->data(); grid = (batch_size + block - 1) / block; CrossEntropyGradientKernel<<< diff --git a/paddle/operators/cross_entropy_op.h b/paddle/operators/cross_entropy_op.h index 1f67461d3fadb1a979832ad049d4e0098256b834..d2d321aa7ed8e32cc19d5a171beea34d36195b10 100644 --- a/paddle/operators/cross_entropy_op.h +++ b/paddle/operators/cross_entropy_op.h @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/framework/eigen.h" #include "paddle/framework/op_registry.h" #include "paddle/operators/math/cross_entropy.h" +#include "paddle/operators/math/math_function.h" namespace paddle { namespace operators { @@ -26,7 +27,7 @@ template ; template -class CrossEntropyOpKernel : public framework::OpKernel { +class CrossEntropyOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), @@ -37,12 +38,12 @@ class CrossEntropyOpKernel : public framework::OpKernel { y->mutable_data(ctx.GetPlace()); math::CrossEntropyFunctor()( - ctx, y, x, labels, ctx.Attr("softLabel")); + ctx.device_context(), y, x, labels, ctx.Attr("softLabel")); } }; template -class CrossEntropyGradientOpKernel : public framework::OpKernel { +class CrossEntropyGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), @@ -69,8 +70,7 @@ class CrossEntropyGradientOpKernel : public framework::OpKernel { const T* x_data = x->data(); const int* label_data = label->data(); - // TODO(qingqing): make zero setting a common function. - memset(dx_data, 0, sizeof(T) * batch_size * class_num); + math::SetConstant(ctx.device_context(), dx, 0); for (int i = 0; i < batch_size; ++i) { PADDLE_ASSERT(label_data[i] >= 0 || label_data[i] < class_num); diff --git a/paddle/operators/detail/strided_memcpy.h b/paddle/operators/detail/strided_memcpy.h index b165224b37fb091c094a823179256c3dd40a37c9..068c82f399316a1587d7322d8dab75823656800e 100644 --- a/paddle/operators/detail/strided_memcpy.h +++ b/paddle/operators/detail/strided_memcpy.h @@ -34,7 +34,7 @@ struct StridedMemcpyFunctor { auto& cpu_place = boost::get(place); memory::Copy(cpu_place, dst, cpu_place, src, sizeof(T) * dst_dim.head); } else { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA auto& gpu_place = boost::get(place); auto& cuda_ctx = reinterpret_cast(dev_ctx); diff --git a/paddle/operators/dropout_op.cc b/paddle/operators/dropout_op.cc index a669b5cf00f1f4ad351486e2977bf8a76aa5bf62..708ccfa0bfc163ea75fcd9dbcb5ef6140b4b5f36 100644 --- a/paddle/operators/dropout_op.cc +++ b/paddle/operators/dropout_op.cc @@ -24,7 +24,7 @@ class DropoutOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE_GE(ctx->Attrs().Get("dropout_prob"), 0); PADDLE_ENFORCE_LE(ctx->Attrs().Get("dropout_prob"), 1); @@ -70,7 +70,7 @@ class DropoutOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE_EQ(ctx->Attrs().Get("is_training"), 1, "GradOp is only callable when is_training is true"); diff --git a/paddle/operators/dropout_op.cu b/paddle/operators/dropout_op.cu index a04e4a22cc09d4e8106a528e490ccf8e90681c08..30c769000f2b98c69eaa78a4c139630dd0956386 100644 --- a/paddle/operators/dropout_op.cu +++ b/paddle/operators/dropout_op.cu @@ -47,7 +47,7 @@ struct MaskGenerator { // Use std::random and thrust::random(thrust is a std library in CUDA) to // implement uniform random. template -class GPUDropoutKernel : public framework::OpKernel { +class GPUDropoutKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* x = context.Input("X"); diff --git a/paddle/operators/dropout_op.h b/paddle/operators/dropout_op.h index d57f64afcb3558aeea6aed23fae06866e9af874a..745525fe81dadb22cbb64d66203f5a75608d3718 100644 --- a/paddle/operators/dropout_op.h +++ b/paddle/operators/dropout_op.h @@ -26,7 +26,7 @@ template ; template -class CPUDropoutKernel : public framework::OpKernel { +class CPUDropoutKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* x = context.Input("X"); @@ -62,7 +62,7 @@ class CPUDropoutKernel : public framework::OpKernel { }; template -class DropoutGradKernel : public framework::OpKernel { +class DropoutGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { PADDLE_ENFORCE(context.Attr("is_training"), diff --git a/paddle/operators/dynamic_recurrent_op.cc b/paddle/operators/dynamic_recurrent_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..b919aef8fb62e5b2331c2d842556e0642ea6b095 --- /dev/null +++ b/paddle/operators/dynamic_recurrent_op.cc @@ -0,0 +1,276 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve . + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/operators/dynamic_recurrent_op.h" + +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using framework::Scope; +using framework::TensorArray; +using framework::LoDTensor; +using framework::Variable; + +namespace detail { + +inline void CreateVariables(Scope& scope, + const std::vector& var_names) { + for (const auto& name : var_names) { + scope.NewVar(name); + } +} + +} // namespace detail + +class DynamicRecurrentOpProtoAndCheckerMaker + : public framework::OpProtoAndCheckerMaker { + public: + DynamicRecurrentOpProtoAndCheckerMaker(framework::OpProto* proto, + framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + const auto& name = DynamicRecurrentOp::kArgName; + // inputs and outputs stored in proto + AddInput(name.inlinks, + "the inputs that need to be segmented for each step.") + .AsDuplicable(); + AddInput(name.boot_memories, "variables to initialize memories.") + .AsDuplicable(); + + AddOutput(name.outlinks, "the outputs that need to concated for all steps.") + .AsDuplicable(); + AddOutput(name.step_scopes, "step scopes"); + + // Attributes stored in AttributeMap + AddAttr>(name.pre_memories, + "names of pre-memories"); + AddAttr>(name.memories, "names of memories"); + + AddComment("This is a RNN operator for varience-length sequences."); + } +}; + +void DynamicRecurrentOp::Run(const Scope& scope, + const platform::DeviceContext& dev_ctx) const { + cache_.Init(kArgName, *this, scope, &arg_); + SplitInputs(); + CreateScopes(); + WriteStepInputs(); + InitStates(); + + // call stepnet in all the time steps + for (size_t step = 0; step < cache_.num_steps; step++) { + auto& step_scope = cache_.GetScope(step); + stepnet_->Run(step_scope, dev_ctx); + } + + WriteStepOutputs(); + ConcatOutputs(); +} + +void DynamicRecurrentOp::SplitInputs() const { + // TODO(superjom) make level a config + // TODO(superjom) check all the inputs has the same LoD + int level = 0; + const auto& inlinks = cache_.inlinks; + for (const auto& item : inlinks) { + const auto& var = item.second; + const auto& tensor = var->Get(); + TensorArray& ta = step_inputs_[item.first]; + dy_seq_metas_[item.first] = + ta.Unpack(tensor, level, true /*length_descend*/); + + if (cache_.num_steps) { + PADDLE_ENFORCE_EQ(ta.size(), cache_.num_steps, + "inputs should have the same steps"); + } else { + cache_.num_steps = ta.size(); + } + } +} + +void DynamicRecurrentOp::WriteStepInputs() const { + for (const auto& item : cache_.inlinks) { + auto ta_it = step_inputs_.find(item.first); + PADDLE_ENFORCE(ta_it != step_inputs_.end(), + "step_inputs_ not compatible with memory set"); + TensorArray& ta = ta_it->second; + for (size_t step = 0; step < ta.size(); step++) { + auto tensor = ta.Read(step); + auto& step_scope = cache_.GetScope(step); + Variable* var = step_scope.FindVar(item.first); + if (var == nullptr) { + var = step_scope.NewVar(item.first); + } + var->GetMutable()->ShareDataWith(tensor); + } + } +} + +void DynamicRecurrentOp::WriteStepOutputs() const { + for (size_t step = 0; step < cache_.scopes->size(); step++) { + auto& scope = cache_.GetScope(step); + for (auto& item : step_outputs_) { + auto* var = scope.FindVar(item.first); + if (var == nullptr) { + var = scope.NewVar(item.first); + } + auto* tensor = var->GetMutable(); + item.second.WriteShared(step, *tensor); + } + } +} + +void DynamicRecurrentOp::CreateScopes() const { + PADDLE_ENFORCE_GT(cache_.num_steps, 0); + // resize scopes + size_t num_scopes_need_create = cache_.num_steps - cache_.scopes->size(); + for (size_t i = 0; i < num_scopes_need_create; i++) { + cache_.scopes->emplace_back(&cache_.scope->NewScope()); + } + + // init temporary inputs + PADDLE_ENFORCE_NOT_NULL(stepnet_, "stepnet should be set first"); + std::vector memories; + std::vector pre_memories; + std::transform(arg_.memories.begin(), arg_.memories.end(), + std::back_inserter(memories), + [](const rnn::MemoryAttr& m) { return m.var; }); + std::transform(arg_.memories.begin(), arg_.memories.end(), + std::back_inserter(pre_memories), + [](const rnn::MemoryAttr& m) { return m.pre_var; }); + + for (size_t step = 0; step < cache_.num_steps; step++) { + auto& scope = cache_.GetScope(step); + detail::CreateVariables(scope, arg_.inlinks); + detail::CreateVariables(scope, arg_.outlinks); + detail::CreateVariables(scope, memories); + detail::CreateVariables(scope, pre_memories); + } +} + +void DynamicRecurrentOp::ConcatOutputs() const { + // TODO(superjom) transform this to a config + int level = 0; + // TODO(superjom) pass in some lod + // just a placeholder + framework::LoD lod; + for (auto& item : step_outputs_) { + auto tensor = item.second.Pack(level, dy_seq_metas_[item.first], lod); + auto& output = cache_.outlinks[item.first]->Get(); + const_cast(&output)->ShareDataWith(tensor); + } +} + +void DynamicRecurrentOp::InitStates() const { + // init the first state + // TODO(superjom) parepare the scenerio that boot state not exists + for (auto memory : arg_.memories) { + auto* boot_state_var = cache_.scope->FindVar(memory.boot_var); + PADDLE_ENFORCE_NOT_NULL(boot_state_var); + auto& boot_state = boot_state_var->Get(); + const auto& dims = boot_state.dims(); + + for (size_t step = 0; step < cache_.num_steps; step++) { + auto& cur_scope = cache_.GetScope(step); + // link pre-state to boot_state + // init state and pre-state + auto* pre_state = cur_scope.FindVar(memory.pre_var); + PADDLE_ENFORCE_NOT_NULL(pre_state); + pre_state->GetMutable(); + + auto* state = cur_scope.FindVar(memory.var); + PADDLE_ENFORCE_NOT_NULL(state); + state->GetMutable()->Resize(dims); + state->GetMutable()->mutable_data( + platform::CPUPlace()); + + if (step == 0) { + auto* pre_state_tensor = pre_state->GetMutable(); + pre_state_tensor->Resize(boot_state.dims()); + pre_state_tensor->ShareDataWith(boot_state); + } else { + auto& pre_scope = cache_.GetScope(step - 1); + auto* state_pre = pre_scope.FindVar(memory.var); + PADDLE_ENFORCE_NOT_NULL(state_pre); + pre_state->GetMutable()->ShareDataWith( + *state_pre->GetMutable()); + } + } + } +} + +void DynamicRecurrentOp::ArgCache::Init( + const rnn::ArgumentName& name, const paddle::framework::OperatorBase& op, + const paddle::framework::Scope& scope, rnn::Argument* arg) { + this->scope = &scope; + InitArgument(name, op, arg); + CacheScopes(scope, *arg); + CacheInlinks(scope, arg->inlinks); + CacheOutlinks(scope, arg->outlinks); +} + +void DynamicRecurrentOp::ArgCache::InitArgument(const rnn::ArgumentName& name, + const OperatorBase& op, + rnn::Argument* arg) { + rnn::InitArgument(name, arg, op, false /*is_grad*/); +} + +void DynamicRecurrentOp::ArgCache::CacheScopes(const Scope& scope, + const rnn::Argument& arg) { + auto scopes_var = scope.FindVar(arg.step_scopes); + PADDLE_ENFORCE(scopes_var != nullptr, + "the step_scopes output argument [%s] should be created first " + "by framework.", + arg.step_scopes); + this->scopes = scopes_var->GetMutable>(); +} + +void DynamicRecurrentOp::ArgCache::CacheInlinks( + const Scope& scope, const std::vector& names) { + for (auto name : names) { + auto* var = GetVariable(scope, name); + inlinks[name] = var; + } +} + +void DynamicRecurrentOp::ArgCache::CacheOutlinks( + const Scope& scope, const std::vector& names) { + for (auto name : names) { + auto* var = GetVariable(scope, name); + outlinks[name] = var; + } +} + +Variable* DynamicRecurrentOp::ArgCache::GetVariable(const Scope& scope, + const std::string& name) { + auto* var = scope.FindVar(name); + PADDLE_ENFORCE_NOT_NULL(var, "variable [%s] not exist in scope", name); + return var; +} + +const rnn::ArgumentName DynamicRecurrentOp::kArgName{ + "step_net", "step_scopes", "inlinks", "outlinks", + "memories", "pre_memories", "boot_memories"}; + +void DynamicRecurrentGradientOp::Run( + const Scope& scope, const platform::DeviceContext& dev_ctx) const {} + +} // namespace operators +} // namespace paddle + +REGISTER_OP_WITHOUT_GRADIENT( + dynamic_recurrent, paddle::operators::DynamicRecurrentOp, + paddle::operators::DynamicRecurrentOpProtoAndCheckerMaker); diff --git a/paddle/operators/dynamic_recurrent_op.h b/paddle/operators/dynamic_recurrent_op.h new file mode 100644 index 0000000000000000000000000000000000000000..6a2970f27fd5bcb25e924dbc567e254159b55a3e --- /dev/null +++ b/paddle/operators/dynamic_recurrent_op.h @@ -0,0 +1,158 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once + +#ifdef PADDLE_WITH_TESTING +#include "gtest/gtest.h" +#endif + +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/operator.h" +#include "paddle/framework/tensor_array.h" +#include "paddle/framework/variable.h" +#include "paddle/operators/rnn/recurrent_op_utils.h" + +namespace paddle { +namespace operators { + +class DynamicRecurrentOp : public framework::OperatorBase { + public: + static const rnn::ArgumentName kArgName; + using value_type = float; + + DynamicRecurrentOp(const std::string& type, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + + DynamicRecurrentOp(const DynamicRecurrentOp& o) + : framework::OperatorBase( + static_cast(o)) { + // TODO(yuyang18): Implement copy ctor well. + PADDLE_THROW("Not implemented"); + } + + void Run(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const override; + + /* + * Split the inputs(LoDTensors) to segments for each time step. + */ + void SplitInputs() const; + + /* + * Create step-scopes to store temporary outputs in each time steps. + */ + void CreateScopes() const; + + /* + * Link TensorArray steps to the corresponding variables located in + * step-scopes. + */ + void WriteStepInputs() const; + + /* + * Write output of each step to the corresponding TensorArray. + */ + void WriteStepOutputs() const; + + /* + * Initialize the states, each state will have a corresponding pre-state, + * which share the memory with the state in the previous time state. The + * pre-state in the first time step will be initialized with an zero tensor or + * a tensor in parent scope if is provided. + */ + void InitStates() const; + + /* + * Concatenate outputs in each time step and generate a LoDTensor. + */ + void ConcatOutputs() const; + + /* + * set a stepnet that is created according to a RecurrentOp's stepnet. + */ + void SetStepNet(std::unique_ptr net) { + PADDLE_ENFORCE_NOT_NULL(net); + stepnet_ = std::move(net); + } + const OperatorBase& GetStepNet() const { return *stepnet_; } + + protected: + struct ArgCache { + framework::Scope const* scope; + std::vector* scopes; + std::map inlinks; + std::map outlinks; + + size_t num_steps{0}; + + void Init(const rnn::ArgumentName& name, const OperatorBase& op, + const framework::Scope& scope, rnn::Argument* arg); + + framework::Scope& GetScope(size_t index) { + PADDLE_ENFORCE_LT(index, num_steps); + return *scopes->at(index); + } + + private: + void InitArgument(const rnn::ArgumentName& name, const OperatorBase& op, + rnn::Argument* arg); + void CacheScopes(const framework::Scope& scope, const rnn::Argument& arg); + void CacheInlinks(const framework::Scope& scope, + const std::vector& names); + void CacheOutlinks(const framework::Scope& scope, + const std::vector& names); + framework::Variable* GetVariable(const framework::Scope& scope, + const std::string& name); + }; + + private: + std::unique_ptr stepnet_; + mutable framework::TensorArray states_; + mutable std::map step_inputs_; + mutable std::map step_outputs_; + mutable std::map> + dy_seq_metas_; + mutable rnn::Argument arg_; + mutable ArgCache cache_; + +#ifdef PADDLE_WITH_TESTING + friend class DynamicRecurrentOpTestHelper; + FRIEND_TEST(DynamicRecurrentOpTestHelper, SplitInputs); + FRIEND_TEST(DynamicRecurrentOpTestHelper, CreateCache); + FRIEND_TEST(DynamicRecurrentOpTestHelper, CreateScopes); + FRIEND_TEST(DynamicRecurrentOpTestHelper, WriteStepInputs); + FRIEND_TEST(DynamicRecurrentOpTestHelper, WriteStepOutputs); + FRIEND_TEST(DynamicRecurrentOpTestHelper, InitStates); + FRIEND_TEST(DynamicRecurrentOpTestHelper, ConcatOutputs); +#endif +}; + +class DynamicRecurrentGradientOp : public framework::OperatorBase { + public: + DynamicRecurrentGradientOp(const std::string& type, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + + void Run(const framework::Scope& scope, + const platform::DeviceContext& dev_ctx) const override; +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/dynamic_recurrent_op_test.cc b/paddle/operators/dynamic_recurrent_op_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..675a7890f3fa6bb7ab9dbbdb04894b2557214a8a --- /dev/null +++ b/paddle/operators/dynamic_recurrent_op_test.cc @@ -0,0 +1,222 @@ +#include "paddle/operators/dynamic_recurrent_op.h" + +#include + +#include "paddle/framework/ddim.h" +#include "paddle/framework/lod_tensor.h" +#include "paddle/framework/op_desc.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +using framework::Scope; +using framework::TensorArray; +using framework::LoDTensor; +using framework::Variable; + +class TestOp : public framework::OperatorBase { + public: + using framework::OperatorBase::OperatorBase; + DEFINE_OP_CLONE_METHOD(TestOp); + void Run(const Scope& scope, + const platform::DeviceContext& dev_ctx) const override {} +}; + +void OpDescNewVar(const std::string& param_name, + std::initializer_list arguments, + paddle::framework::OpDesc::Var* var) { + var->set_parameter(param_name); + for (auto& arg_name : arguments) { + var->add_arguments(arg_name); + } +} + +// create a LoD tensor in scope with specific dims +LoDTensor* CreateVar(Scope& scope, std::string name, framework::DDim dims, + const platform::Place& place) { + auto* var = scope.NewVar(name); + auto* tensor = var->GetMutable(); + tensor->Resize(dims); + tensor->mutable_data(place); + return tensor; +} + +class DynamicRecurrentOpTestHelper : public ::testing::Test { + protected: + const rnn::ArgumentName argname = DynamicRecurrentOp::kArgName; + + virtual void SetUp() override { + CreateGlobalVariables(); + + auto op_desc = CreateOpDesc(); + op = paddle::framework::OpRegistry::CreateOp(op_desc); + dop = dynamic_cast(op.get()); + InitCacheManually(); + InitStepNet(); + } + + framework::OpDesc CreateOpDesc() { + // create op + paddle::framework::OpDesc op_desc; + op_desc.set_type("dynamic_recurrent"); + + OpDescNewVar(argname.inlinks, {"in0"}, op_desc.add_inputs()); + OpDescNewVar(argname.boot_memories, {"boot_mem"}, op_desc.add_inputs()); + OpDescNewVar(argname.step_scopes, {"step_scopes"}, op_desc.add_outputs()); + OpDescNewVar(argname.outlinks, {"out0"}, op_desc.add_outputs()); + + // set pre-memories + auto pre_memories = op_desc.mutable_attrs()->Add(); + pre_memories->set_name(argname.pre_memories); + pre_memories->set_type(paddle::framework::AttrType::STRINGS); + auto pre_memories_item = pre_memories->add_strings(); + *pre_memories_item = "mem@pre"; + + // set memories + auto memories = op_desc.mutable_attrs()->Add(); + memories->set_name(argname.memories); + memories->set_type(paddle::framework::AttrType::STRINGS); + auto memories_item = memories->add_strings(); + *memories_item = "mem"; + return op_desc; + } + + void CreateGlobalVariables() { + platform::CPUPlace place; + scope.NewVar("step_scopes"); + CreateVar(scope, "boot_mem", framework::make_ddim({10, 20}), place); + // auto* out0 = + CreateVar(scope, "out0", framework::make_ddim({10, 20}), place); + auto* in0 = CreateVar(scope, "in0", framework::make_ddim({10, 8}), place); + // 10 instanes with 4 sentences, length is 4, 3, 2, 1 respectively. + framework::LoD in0_lod(1); + for (int x : std::vector{0, 4, 7, 9, 10}) { + in0_lod[0].push_back(x); + } + in0->set_lod(in0_lod); + in0->Resize(framework::make_ddim({10, 8})); + // set the content, each sentence content is seqid.batchid + // the seqid starts from 0 + int start = 0; + for (size_t seqid = 0; seqid < in0_lod.size() - 1; seqid++) { + for (size_t batchid = 0; + batchid < in0_lod[0][seqid + 1] - in0_lod[0][seqid]; batchid++) { + float v = seqid + batchid * 0.1; + + for (size_t dim = 0; dim < 8; dim++) { + in0->data()[start * 8 + dim] = v; + } + start++; + } + } + } + + void InitCacheManually() { + dop->cache_.Init(DynamicRecurrentOp::kArgName, *dop, scope, &dop->arg_); + } + + void InitStepNet() { + std::unique_ptr stepnet{new NetOp}; + dynamic_cast(stepnet.get()) + ->AppendOp(std::unique_ptr(new TestOp( + "test", {{"inlinks", {"in0"}}, {"boot_memories", {"boot_mem"}}}, + {{"outlinks", {"out0"}}, {"step_scopes", {"step_scopes"}}}, {}))); + dop->SetStepNet(std::move(stepnet)); + } + + protected: + DynamicRecurrentOp* dop; + std::unique_ptr op; + paddle::platform::CPUDeviceContext device_context; + paddle::framework::Scope scope; +}; + +TEST_F(DynamicRecurrentOpTestHelper, CreateCache) { + const rnn::Argument& arg = dop->arg_; + ASSERT_EQ(arg.inlinks.size(), 1UL); + ASSERT_EQ(arg.outlinks.size(), 1UL); +} + +TEST_F(DynamicRecurrentOpTestHelper, SplitInputs) { + dop->SplitInputs(); + auto& in0_ta = dop->step_inputs_["in0"]; + ASSERT_EQ(in0_ta.size(), 4UL); + + const auto& batch0 = in0_ta.Read(0); + const auto& batch1 = in0_ta.Read(1); + const auto& batch2 = in0_ta.Read(2); + const auto& batch3 = in0_ta.Read(3); + EXPECT_EQ(batch0.dims()[0], 4); + EXPECT_EQ(batch1.dims()[0], 3); + EXPECT_EQ(batch2.dims()[0], 2); + EXPECT_EQ(batch3.dims()[0], 1); +} + +TEST_F(DynamicRecurrentOpTestHelper, CreateScopes) { + dop->SplitInputs(); + dop->CreateScopes(); + ASSERT_EQ(dop->cache_.num_steps, 4UL); + ASSERT_EQ(dop->cache_.scopes->size(), 4UL); +} + +TEST_F(DynamicRecurrentOpTestHelper, WriteStepInputs) { + dop->SplitInputs(); + dop->CreateScopes(); + dop->WriteStepInputs(); + + for (size_t step = 0; step < dop->cache_.num_steps; step++) { + auto& scope = dop->cache_.GetScope(step); + for (auto name : std::vector({"in0"})) { + ASSERT_TRUE(scope.FindVar(name) != nullptr); + } + } +} + +TEST_F(DynamicRecurrentOpTestHelper, WriteStepOutputs) { + dop->SplitInputs(); + dop->CreateScopes(); + dop->WriteStepInputs(); + dop->WriteStepOutputs(); + + for (size_t step = 0; step < dop->cache_.num_steps; step++) { + auto& scope = dop->cache_.GetScope(step); + for (auto name : std::vector({"out0"})) { + ASSERT_TRUE(scope.FindVar(name)); + } + } +} + +TEST_F(DynamicRecurrentOpTestHelper, ConcatOutputs) { + // Let's leave this test to python unittest. +} + +TEST_F(DynamicRecurrentOpTestHelper, InitStates) { + dop->SplitInputs(); + dop->CreateScopes(); + dop->WriteStepInputs(); + dop->WriteStepOutputs(); + dop->InitStates(); + + for (size_t step = 0; step < dop->cache_.num_steps; step++) { + auto& scope = dop->cache_.GetScope(step); + auto state = scope.FindVar("mem"); + ASSERT_TRUE(state != nullptr); + + auto* pre_state = scope.FindVar("mem@pre"); + ASSERT_TRUE(pre_state != nullptr); + + auto* boot_state = scope.FindVar("boot_mem"); + ASSERT_TRUE(boot_state != nullptr); + + if (step == 0) { + // check pre_state is a reference of boot_state + ASSERT_EQ(boot_state->Get().data(), + pre_state->Get().data()); + } + } +} + +} // operators +} // namespace paddle diff --git a/paddle/operators/elementwise_add_op.h b/paddle/operators/elementwise_add_op.h index e9f78ef26e05878053d968c35f17b456c128827a..f04fe3ec6069ab1bf227be6a3a5c10ee908e4824 100644 --- a/paddle/operators/elementwise_add_op.h +++ b/paddle/operators/elementwise_add_op.h @@ -20,7 +20,7 @@ namespace paddle { namespace operators { template -class ElementwiseAddKernel : public framework::OpKernel { +class ElementwiseAddKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseCompute(ctx); @@ -101,7 +101,7 @@ struct ElementwiseAddBroadCast2GradFunctor { }; template -class ElementwiseAddGradKernel : public framework::OpKernel { +class ElementwiseAddGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseGradCompute, diff --git a/paddle/operators/elementwise_div_op.h b/paddle/operators/elementwise_div_op.h index 99b6d9c1991edfb0018f8a459dfa373948cec434..8946ff3d25c2aff3dc3aa69368f0083371cd2fef 100644 --- a/paddle/operators/elementwise_div_op.h +++ b/paddle/operators/elementwise_div_op.h @@ -20,7 +20,7 @@ namespace paddle { namespace operators { template -class ElementwiseDivKernel : public framework::OpKernel { +class ElementwiseDivKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseCompute(ctx); @@ -103,7 +103,7 @@ struct ElementwiseDivBroadCast2GradFunctor { }; template -class ElementwiseDivGradKernel : public framework::OpKernel { +class ElementwiseDivGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseGradCompute, diff --git a/paddle/operators/elementwise_mul_op.cc b/paddle/operators/elementwise_mul_op.cc index bda5dfe03e974740fe4a07191ae6b68ebfcd5d3a..da7765aa6a7a81c9e0b4f462022cad54c16aec47 100644 --- a/paddle/operators/elementwise_mul_op.cc +++ b/paddle/operators/elementwise_mul_op.cc @@ -36,7 +36,9 @@ REGISTER_OP(elementwise_mul, ops::ElementwiseOp, ops::ElementwiseMulOpMaker, elementwise_mul_grad, ops::ElementwiseOpGrad); REGISTER_OP_CPU_KERNEL( elementwise_mul, - ops::ElementwiseMulKernel); + ops::ElementwiseMulKernel, + ops::ElementwiseMulKernel); REGISTER_OP_CPU_KERNEL( elementwise_mul_grad, - ops::ElementwiseMulGradKernel); + ops::ElementwiseMulGradKernel, + ops::ElementwiseMulGradKernel); diff --git a/paddle/operators/elementwise_mul_op.cu b/paddle/operators/elementwise_mul_op.cu index da08a75596c4d3b89dc8892bd4405464fec96389..056f081d3e6ac349978ff00689700c035bed8e39 100644 --- a/paddle/operators/elementwise_mul_op.cu +++ b/paddle/operators/elementwise_mul_op.cu @@ -19,7 +19,9 @@ namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL( elementwise_mul, - ops::ElementwiseMulKernel); + ops::ElementwiseMulKernel, + ops::ElementwiseMulKernel); REGISTER_OP_GPU_KERNEL( elementwise_mul_grad, - ops::ElementwiseMulGradKernel); + ops::ElementwiseMulGradKernel, + ops::ElementwiseMulGradKernel); diff --git a/paddle/operators/elementwise_mul_op.h b/paddle/operators/elementwise_mul_op.h index 6ab642378bb0af8593ca0677014aede3c03cff8e..4469b07eaa08a3b011a88e58f1d645dd30b10ced 100644 --- a/paddle/operators/elementwise_mul_op.h +++ b/paddle/operators/elementwise_mul_op.h @@ -19,7 +19,7 @@ namespace paddle { namespace operators { template -class ElementwiseMulKernel : public framework::OpKernel { +class ElementwiseMulKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseCompute(ctx); @@ -102,7 +102,7 @@ struct ElementwiseMulBroadCast2GradFunctor { }; template -class ElementwiseMulGradKernel : public framework::OpKernel { +class ElementwiseMulGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseGradCompute, diff --git a/paddle/operators/elementwise_op.h b/paddle/operators/elementwise_op.h index 3082f37422faa990bbf03c8a1a87b025d481a290..66f1910a4738f737c7581e17729c201a7e3eaeae 100644 --- a/paddle/operators/elementwise_op.h +++ b/paddle/operators/elementwise_op.h @@ -25,7 +25,7 @@ class ElementwiseOp : public framework::OperatorWithKernel { protected: using Tensor = framework::Tensor; - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of elementwise op should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -106,7 +106,7 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel { using Tensor = framework::Tensor; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), diff --git a/paddle/operators/elementwise_sub_op.h b/paddle/operators/elementwise_sub_op.h index 3ca1376c73b3332b76a5973e201f9e4fba77cd21..3f40c1c5bcea5e8473765b039de4ee2a16054f0c 100644 --- a/paddle/operators/elementwise_sub_op.h +++ b/paddle/operators/elementwise_sub_op.h @@ -19,7 +19,7 @@ namespace paddle { namespace operators { template -class ElementwiseSubKernel : public framework::OpKernel { +class ElementwiseSubKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseCompute(ctx); @@ -102,7 +102,7 @@ struct ElementwiseSubBroadCast2GradFunctor { }; template -class ElementwiseSubGradKernel : public framework::OpKernel { +class ElementwiseSubGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { ElementwiseGradCompute, diff --git a/paddle/operators/fc_op.cc b/paddle/operators/fc_op.cc index 5ac0e8cc45f007d42f1b6d7f86333f5cbedb3ea8..7c422c81fc479fa2e317bdee1b66017096381d27 100644 --- a/paddle/operators/fc_op.cc +++ b/paddle/operators/fc_op.cc @@ -100,7 +100,7 @@ class FCOp : public NetOp { add_out = Output("AddOut"); AppendOp(framework::OpRegistry::CreateOp( - "rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}}, + "elementwise_add", {{"X", {sum_out}}, {"Y", {Input("B")}}}, {{"Out", {add_out}}}, {})); } else { if (Output("AddOut") != framework::kEmptyVarName) { diff --git a/paddle/operators/feed_op.cc b/paddle/operators/feed_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..fa325bb28299afe24a67772473529fb76b9c73e1 --- /dev/null +++ b/paddle/operators/feed_op.cc @@ -0,0 +1,59 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/feed_op.h" + +namespace paddle { +namespace operators { + +class FeedOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output should be not null."); + auto& shape = ctx->Attrs().Get>("dims"); + std::vector shape_int64(shape.size(), 0); + std::transform(shape.begin(), shape.end(), shape_int64.begin(), + [](int a) { return static_cast(a); }); + ctx->SetOutputDim("Out", framework::make_ddim(shape_int64)); + // TODO(qijun): need to handle LodTensor later + } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return static_cast(Attr("dataType")); + } +}; + +class FeedOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FeedOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddAttr("dataType", "output data type") + .SetDefault(framework::DataType::FP32); + AddAttr("col", "The col in global feed variable").SetDefault(0); + AddAttr>("dims", "The dimension of feed tensor."); + AddOutput("Out", "The output of feed op."); + AddComment(R"DOC(Feed data from global feed variable)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(feed, ops::FeedOp, ops::FeedOpMaker); +REGISTER_OP_CPU_KERNEL(feed, ops::FeedKernel); diff --git a/paddle/framework/grad_op_builder.h b/paddle/operators/feed_op.cu similarity index 69% rename from paddle/framework/grad_op_builder.h rename to paddle/operators/feed_op.cu index 998f8ebbb5f2f4fb8b7e938b5916afd0f8a7930d..7b6a2ac91e7b8d306804ca3d27b1eaf8177302f9 100644 --- a/paddle/framework/grad_op_builder.h +++ b/paddle/operators/feed_op.cu @@ -4,7 +4,7 @@ Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 +http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, @@ -12,14 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. */ -#pragma once +#include "paddle/operators/feed_op.h" -#include "paddle/framework/operator.h" - -namespace paddle { -namespace framework { - -OperatorBase* BuildGradOp(const OperatorBase* op); - -} // namespace framework -} // namespace paddle +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(feed, ops::FeedKernel); diff --git a/paddle/operators/feed_op.h b/paddle/operators/feed_op.h new file mode 100644 index 0000000000000000000000000000000000000000..9d8158299fea97a464a7bb64321b1adf8b7b2fab --- /dev/null +++ b/paddle/operators/feed_op.h @@ -0,0 +1,42 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class FeedKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + framework::Tensor* out = ctx.Output("Out"); + out->mutable_data(ctx.GetPlace()); + framework::Variable* g_feed_variable = + framework::GetGlobalScope().FindVar("feed_value"); + const auto& tensors = + g_feed_variable->Get>(); + int col = ctx.template Attr("col"); + PADDLE_ENFORCE_GT(tensors.size(), static_cast(col)); + // TODO(qijun): + // check tensors[col].dims() with attribute, + // except the first dimenson. + out->CopyFrom(tensors[col], ctx.GetPlace()); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/fetch_op.cc b/paddle/operators/fetch_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..90737c8c550ca18f03c6a9ad0d9323d0b4d0b96d --- /dev/null +++ b/paddle/operators/fetch_op.cc @@ -0,0 +1,52 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/fetch_op.h" + +namespace paddle { +namespace operators { + +class FetchOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Input"), "Input should be not null."); + } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return static_cast(Attr("dataType")); + } +}; + +class FetchOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FetchOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddAttr("dataType", "output data type") + .SetDefault(framework::DataType::FP32); + AddAttr("col", "The col in global fetch variable").SetDefault(0); + AddInput("Input", "The output of fetch op."); + AddComment(R"DOC(Fetch data to global fetch variable)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(fetch, ops::FetchOp, ops::FetchOpMaker); +REGISTER_OP_CPU_KERNEL(fetch, ops::FetchKernel); diff --git a/paddle/operators/fetch_op.cu b/paddle/operators/fetch_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..ca39d24c791ded71149777acc53e3b5cc240329f --- /dev/null +++ b/paddle/operators/fetch_op.cu @@ -0,0 +1,18 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + +http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/fetch_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(fetch, ops::FetchKernel); diff --git a/paddle/operators/fetch_op.h b/paddle/operators/fetch_op.h new file mode 100644 index 0000000000000000000000000000000000000000..eb9c3a7b593b84da7c8dc12d71c4f748269c64e6 --- /dev/null +++ b/paddle/operators/fetch_op.h @@ -0,0 +1,44 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class FetchKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + const framework::Tensor* input = ctx.Input("Input"); + framework::Variable* g_fetch_variable = + framework::GetGlobalScope().FindVar("fetch_value"); + auto* tensors = + g_fetch_variable->GetMutable>(); + int col = ctx.template Attr("col"); + if (tensors->size() < static_cast(col + 1)) { + tensors->resize(col + 1); + } + PADDLE_ENFORCE_GT(tensors->size(), static_cast(col)); + (*tensors)[col].Resize(input->dims()); + (*tensors)[col].mutable_data(platform::CPUPlace()); + (*tensors)[col].CopyFrom(*input, platform::CPUPlace()); + // TODO(qijun): need to handle LodTensor later + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/fill_constant_op.cc b/paddle/operators/fill_constant_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..65d03d5fa4426ef4229c1155753c67e59ce98857 --- /dev/null +++ b/paddle/operators/fill_constant_op.cc @@ -0,0 +1,68 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/fill_constant_op.h" + +namespace paddle { +namespace operators { + +class FillConstantOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of FillConstantOp should not be null."); + auto &shape = ctx->Attrs().Get>("shape"); + std::vector shape_int64(shape.size(), 0); + std::transform(shape.begin(), shape.end(), shape_int64.begin(), + [](int a) { return static_cast(a); }); + auto dims = framework::make_ddim(shape_int64); + ctx->SetOutputDim("Out", dims); + } + + framework::DataType IndicateDataType( + const framework::ExecutionContext &ctx) const override { + return static_cast(ctx.Attr("dataType")); + } +}; + +class FillConstantOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FillConstantOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : framework::OpProtoAndCheckerMaker(proto, op_checker) { + AddAttr("dataType", + "(int, default 5 (FP32)) " + "Output data type") + .SetDefault(framework::DataType::FP32); + AddAttr>("shape", "(vector) The shape of the output"); + AddAttr("value", "(float, default 0) The value to be filled") + .SetDefault(0.0f); + AddOutput("Out", + "(Tensor) Tensor of specified shape will be filled " + "with the specified value"); + AddComment(R"DOC(Fill up a variable with specified constant value.)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(fill_constant, ops::FillConstantOp, + ops::FillConstantOpMaker); +REGISTER_OP_CPU_KERNEL( + fill_constant, + ops::FillConstantOpKernel); diff --git a/paddle/operators/rowwise_add_op.cu b/paddle/operators/fill_constant_op.cu similarity index 75% rename from paddle/operators/rowwise_add_op.cu rename to paddle/operators/fill_constant_op.cu index 4a57f64c890ce99d6060faec6a4a01b107403344..eef8fcbd7f65a9891126e039c4d46a106a6daa60 100644 --- a/paddle/operators/rowwise_add_op.cu +++ b/paddle/operators/fill_constant_op.cu @@ -13,11 +13,10 @@ limitations under the License. */ #define EIGEN_USE_GPU -#include "paddle/operators/rowwise_add_op.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/fill_constant_op.h" namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL( - rowwise_add, ops::RowwiseAddKernel); -REGISTER_OP_GPU_KERNEL( - rowwise_add_grad, - ops::RowwiseAddGradKernel); + fill_constant, + ops::FillConstantOpKernel); diff --git a/paddle/operators/add_op.h b/paddle/operators/fill_constant_op.h similarity index 52% rename from paddle/operators/add_op.h rename to paddle/operators/fill_constant_op.h index a7307b6818aa3d10ff215d06281e2b53196fd101..53b8b548eca6dfe035c326d95f91d3e279f63318 100644 --- a/paddle/operators/add_op.h +++ b/paddle/operators/fill_constant_op.h @@ -19,28 +19,17 @@ limitations under the License. */ namespace paddle { namespace operators { -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - template -class AddKernel : public framework::OpKernel { +class FillConstantOpKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& context) const override { - auto* input0 = context.Input("X"); - auto* input1 = context.Input("Y"); - auto* output = context.Output("Out"); - - output->mutable_data(context.GetPlace()); - - auto X = EigenVector::Flatten(*input0); - auto Y = EigenVector::Flatten(*input1); - auto Z = EigenVector::Flatten(*output); - - auto place = context.GetEigenDevice(); - - Z.device(place) = X + Y; + void Compute(const framework::ExecutionContext& ctx) const override { + auto* out = ctx.Output("Out"); + out->mutable_data(ctx.GetPlace()); + auto value = ctx.Attr("value"); + + auto out_eigen = framework::EigenVector::Flatten(*out); + auto place = ctx.GetEigenDevice(); + out_eigen.device(place) = out_eigen.constant(static_cast(value)); } }; diff --git a/paddle/operators/fill_zeros_like_op.cc b/paddle/operators/fill_zeros_like_op.cc index e164de6584e7350283781019cc74118c2d13646e..4c70b9a36bb8f3a2184a8f2ac738378e9d3271e2 100644 --- a/paddle/operators/fill_zeros_like_op.cc +++ b/paddle/operators/fill_zeros_like_op.cc @@ -22,7 +22,7 @@ class FillZerosLikeOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of FillZerosLikeOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), diff --git a/paddle/operators/fill_zeros_like_op.h b/paddle/operators/fill_zeros_like_op.h index 4474581784531faee1741f0b143743e31cc3788f..cdf56a723b117fe7b08ef2749aa2c2978c923d44 100644 --- a/paddle/operators/fill_zeros_like_op.h +++ b/paddle/operators/fill_zeros_like_op.h @@ -20,7 +20,7 @@ namespace paddle { namespace operators { template -class FillZerosLikeKernel : public framework::OpKernel { +class FillZerosLikeKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* output = context.Output("Y"); diff --git a/paddle/operators/gather.cu.h b/paddle/operators/gather.cu.h new file mode 100644 index 0000000000000000000000000000000000000000..8d04ecd284226c7b4c6cdd5531915fee2d94ce61 --- /dev/null +++ b/paddle/operators/gather.cu.h @@ -0,0 +1,79 @@ +/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/tensor.h" +#include "paddle/platform/place.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; +using platform::Place; + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +template +__global__ void GatherCUDAKernel(const T* params, const int* indices, T* output, + size_t index_size, size_t slice_size) { + CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + int gather_i = indices[indices_i]; + int params_i = gather_i * slice_size + slice_i; + *(output + i) = *(params + params_i); + } +} + +/** + * A thin wrapper on gpu tensor + * Return a new tensor from source tensor, gathered according to index + * input[src]: type-T source Tensor + * input[index]: type-int index Tensor (1-D) + * return: output tensor + */ +template +void GPUGather(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + // PADDLE_ENFORCE(platform::is_gpu_place(place)); + // check index of shape 1-D + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; + + auto src_dims = src.dims(); + framework::DDim output_dims(src_dims); + output_dims[0] = index_size; + + // slice size + int slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + + int block = 512; + int n = slice_size * index_size; + int grid = (n + block - 1) / block; + + GatherCUDAKernel<<< + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + p_src, p_index, p_output, index_size, slice_size); +} + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/gather.h b/paddle/operators/gather.h index 92fb51ec17709bc6f8abb2f516a9240fb5dc3a77..052db49cb3c2594eca8b9a5e3716689480089703 100644 --- a/paddle/operators/gather.h +++ b/paddle/operators/gather.h @@ -24,49 +24,40 @@ limitations under the License. */ namespace paddle { namespace operators { -// Implementation of CPU copy -template -void CPUGather(const T* src, const int* indices, const int slice_size, - const int index_size, T* output) { - const size_t slice_bytes = slice_size * sizeof(T); - - for (int i = 0; i < index_size; ++i) { - int index_ = indices[i]; - memcpy(output + i * slice_size, src + index_ * slice_size, slice_bytes); - } -} - -// Implementation of GPU copy: -template -void GPUGather(const T* src, const int* index, const int slice_size, - const int index_size, T* output); +using framework::Tensor; /** + * A thin wrapper for gathering on cpu tensor * Return a new tensor from source tensor, gathered according to index * input[src]: type-T source Tensor * input[index]: type-int index Tensor (1-D) * return: output tensor */ template -void Gather(const platform::Place& place, const paddle::framework::Tensor* src, - const paddle::framework::Tensor* index, - paddle::framework::Tensor* output) { +void CPUGather(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); // check index of shape 1-D - PADDLE_ENFORCE(index->dims().size() == 1); - int index_size = index->dims()[0]; + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; - auto src_dims = src->dims(); + auto src_dims = src.dims(); framework::DDim output_dims(src_dims); output_dims[0] = index_size; + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + // slice size int slice_size = 1; for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; - // Gathering - if (platform::is_cpu_place(place)) { - CPUGather(src->data(), index->data(), slice_size, index_size, - output->data()); + const size_t slice_bytes = slice_size * sizeof(T); + + for (int i = 0; i < index_size; ++i) { + int index_ = p_index[i]; + memcpy(p_output + i * slice_size, p_src + index_ * slice_size, slice_bytes); } } diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 0e3cd174adee1e50d0a63861286a26d325484efb..fb99c6c01670fcbacd012573b0679ac755638c57 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -23,7 +23,7 @@ class GatherOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of GatherOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Index"), @@ -31,12 +31,19 @@ class GatherOp : public framework::OperatorWithKernel { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of GatherOp should not be null."); + auto index_dims = ctx->GetInputDim("Index"); + PADDLE_ENFORCE(index_dims.size() == 1); int batch_size = ctx->GetInputDim("Index")[0]; PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0"); framework::DDim output_dims(ctx->GetInputDim("X")); output_dims[0] = batch_size; ctx->SetOutputDim("Out", output_dims); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("X")->type()); + } }; class GatherGradOp : public framework::OperatorWithKernel { @@ -44,9 +51,14 @@ class GatherGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("X")->type()); + } }; class GatherOpMaker : public framework::OpProtoAndCheckerMaker { @@ -69,8 +81,5 @@ Out = X[Index] namespace ops = paddle::operators; REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad, ops::GatherGradOp); -REGISTER_OP_CPU_KERNEL(gather, - ops::GatherOpKernel); -REGISTER_OP_CPU_KERNEL( - gather_grad, - ops::GatherGradientOpKernel); +REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel); +REGISTER_OP_CPU_KERNEL(gather_grad, ops::GatherGradientOpKernel); diff --git a/paddle/operators/gather_op.cu b/paddle/operators/gather_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..92219d6a433e6db0bb9886ed8670cbafaa843ff8 --- /dev/null +++ b/paddle/operators/gather_op.cu @@ -0,0 +1,64 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "gather.cu.h" +#include "paddle/framework/eigen.h" +#include "paddle/operators/gather_op.h" +#include "scatter.cu.h" + +namespace paddle { +namespace operators { + +template +class GatherOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *x = ctx.Input("X"); + auto *index = ctx.Input("Index"); + auto *output = ctx.Output("Out"); + + output->mutable_data(ctx.GetPlace()); + + GPUGather(ctx.device_context(), *x, *index, output); + } +}; + +template +class GatherGradOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *Index = ctx.Input("Index"); + auto *dX = ctx.Output(framework::GradVarName("X")); + auto *dO = ctx.Input(framework::GradVarName("Out")); + auto *x = ctx.Input("X"); + + dX->mutable_data(ctx.GetPlace()); + auto dxt = framework::EigenVector::Flatten(*dX); + auto place = ctx.GetEigenDevice(); + dxt.device(place) = dxt.constant(static_cast(0)); + + GPUScatterAssign(ctx.device_context(), *dO, *Index, dX); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(gather, ops::GatherOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(gather_grad, ops::GatherGradOpCUDAKernel); diff --git a/paddle/operators/gather_op.h b/paddle/operators/gather_op.h index 381854f301870beadb72d9e9b4eb17ff199960fb..8276ed0d3d8b676aafab45fae70942e78b72b8e6 100644 --- a/paddle/operators/gather_op.h +++ b/paddle/operators/gather_op.h @@ -23,29 +23,40 @@ namespace operators { using Tensor = framework::Tensor; -template -class GatherOpKernel : public framework::OpKernel { +template +class GatherOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { - auto *X = ctx.Input("X"); - auto *Index = ctx.Input("Index"); - auto *Y = ctx.Output("Out"); + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); + + auto *x = ctx.Input("X"); + auto *index = ctx.Input("Index"); + auto *output = ctx.Output("Out"); + + output->mutable_data(ctx.GetPlace()); - Y->mutable_data(ctx.GetPlace()); - Gather(ctx.GetPlace(), X, Index, Y); + CPUGather(ctx.device_context(), *x, *index, output); } }; -template -class GatherGradientOpKernel : public framework::OpKernel { +template +class GatherGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); + auto *Index = ctx.Input("Index"); auto *dX = ctx.Output(framework::GradVarName("X")); auto *dO = ctx.Input(framework::GradVarName("Out")); dX->mutable_data(ctx.GetPlace()); - ScatterUpdate(ctx.GetPlace(), dO, Index, dX); + auto dxt = framework::EigenVector::Flatten(*dX); + auto place = ctx.GetEigenDevice(); + dxt.device(place) = dxt.constant(static_cast(0)); + + ScatterAssign(ctx.device_context(), *dO, *Index, dX); } }; diff --git a/paddle/operators/gather_test.cc b/paddle/operators/gather_test.cc index 0ae1e99452973feb6d085dd6ef51e2afca988f59..cbd86b87961ee24aa889e208de5ac38e03a33135 100644 --- a/paddle/operators/gather_test.cc +++ b/paddle/operators/gather_test.cc @@ -41,7 +41,9 @@ TEST(Gather, GatherData) { int* p_output = output->mutable_data(make_ddim({2, 4}), CPUPlace()); - Gather(CPUPlace(), src, index, output); + auto* cpu_place = new paddle::platform::CPUPlace(); + paddle::platform::CPUDeviceContext ctx(*cpu_place); + CPUGather(ctx, *src, *index, output); for (int i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4); for (int i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4); diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 05120a6e7bcfdb8641c722731f462c89e4223339..ca7fb385057dda4ee6f7dea7edc21d9ebbf6b48e 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -16,7 +16,7 @@ namespace paddle { namespace operators { template -class CPUGaussianRandomKernel : public framework::OpKernel { +class CPUGaussianRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { float mean = context.Attr("mean"); @@ -43,7 +43,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of GaussianRandomOp should not be null."); auto dims = ctx->Attrs().Get>("dims"); @@ -56,6 +56,11 @@ class GaussianRandomOp : public framework::OperatorWithKernel { "dims can be one int or array. dims must be set."); ctx->SetOutputDim("Out", framework::make_ddim(temp)); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return static_cast(Attr("data_type")); + } }; class GaussianRandomOpMaker : public framework::OpProtoAndCheckerMaker { @@ -76,6 +81,8 @@ Use to initialize tensor with gaussian random generator. "Random seed of generator." "0 means use system wide seed") .SetDefault(0); + AddAttr("data_type", "output data type") + .SetDefault(framework::DataType::FP32); } }; diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 2d63b3049988cfc3135a87a57dad56b970df3eab..315560bf1ba8a66b9a3b7d79510d202885e845d6 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -37,7 +37,7 @@ struct GaussianGenerator { }; template -class GPUGaussianRandomKernel : public framework::OpKernel { +class GPUGaussianRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); diff --git a/paddle/operators/gemm_conv2d_op.h b/paddle/operators/gemm_conv2d_op.h index 5c9e81732aa72211c2021382cf9a907880c53c17..323e3f7c3bd506c6b63bf4d1152384649f5da575 100644 --- a/paddle/operators/gemm_conv2d_op.h +++ b/paddle/operators/gemm_conv2d_op.h @@ -25,7 +25,7 @@ namespace operators { using Tensor = framework::Tensor; template -class GemmConv2DKernel : public framework::OpKernel { +class GemmConv2DKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { const Tensor* input = context.Input("Input"); @@ -98,7 +98,7 @@ class GemmConv2DKernel : public framework::OpKernel { }; template -class GemmConvGrad2DKernel : public framework::OpKernel { +class GemmConvGrad2DKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { const Tensor* input = context.Input("Input"); diff --git a/paddle/operators/interp_op.cc b/paddle/operators/interp_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..d02b01c3f3a1b30ec27253140203b076a98ce0c2 --- /dev/null +++ b/paddle/operators/interp_op.cc @@ -0,0 +1,113 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/framework/op_registry.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +class InterpOp : public NetOp { + public: + InterpOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName, + "Input(X) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Input("Y"), framework::kEmptyVarName, + "Input(Y) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Input("W"), framework::kEmptyVarName, + "Input(W) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("SubOut"), framework::kEmptyVarName, + "Output(SubOut) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("MulOut"), framework::kEmptyVarName, + "Output(MulOut) of InterpOp should not be null."); + PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName, + "Output(Out) of InterpOp should not be null."); + + // SubOut = X - Y + auto x = Input("X"); + auto y = Input("Y"); + auto sub_out = Output("SubOut"); + AppendOp(framework::OpRegistry::CreateOp( + "elementwise_sub", {{"X", {x}}, {"Y", {y}}}, {{"Out", {sub_out}}}, {})); + + // MulOut = SubOut * W = (X - Y) * W + auto w = Input("W"); + auto mul_out = Output("MulOut"); + AppendOp(framework::OpRegistry::CreateOp( + "elementwise_mul", {{"X", {sub_out}}, {"Y", {w}}}, {{"Out", {mul_out}}}, + {{"axis", 0}})); + + // Out = MulOut + Y = (X - Y) * W + Y = X * W + Y * (1 - W) + AppendOp(framework::OpRegistry::CreateOp("elementwise_add", + {{"X", {mul_out}}, {"Y", {y}}}, + {{"Out", {Output("Out")}}}, {})); + + CompleteAddOp(false); + } +}; + +class InterpOpMaker : public framework::OpProtoAndCheckerMaker { + public: + InterpOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(Tensor), 2-D Matrix of shape [batch_size, data_dim]" + "containing data samples, the first input of interp_op"); + AddInput("Y", + "(Tensor), 2-D Matrix of shape `[batch_size, data_dim]`" + "containing data samples, the second input of interp_op"); + AddInput("W", + "(Tensor), 1-D Vector of shape [batch_size]," + "the interpolated values in the half-open interval [0.0, 1.0)"); + AddOutput("SubOut", + "(Tensor), the intermediate subtraction outputs, saving X - Y.") + .AsIntermediate(); + AddOutput("MulOut", + "(Tensor), the intermediate multiplication outputs," + "saving the elementwise multiplication of (X - Y) and W.") + .AsIntermediate(); + AddOutput("Out", + "(Tensor), the output of interp_op, same shape with X," + "returns the first-dimensional piecewise linear interpolant " + "between X and Y"); + AddComment(R"DOC( + Linear Interpolation with two inputs, used in NEURAL TURING MACHINE. + + Equation: + Out.row[i] = X.row[i] * W[i] + Y.row[i] * (1 - W[i]) + = (X.row[i] - Y.row[i]) * W[i] + Y.row[i] + + Example: + X = [[1,2],[3,4]], + Y = [[2,1],[4,3]], + W = [0.3, 0.4] + + Then, Out = [[1.7,1.3],[3.6,3.4]] + + where 1.7 = 1*0.3+2*(1-0.3), + 1.3 = 2*0.3+1*(1-0.3), + 3.6 = 3*0.4+4*(1-0.4), + 3.4 = 4*0.4+3*(1-0.4) +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(interp, ops::InterpOp, ops::InterpOpMaker); diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc index 9b1314bfbade8551d98b0fbabb7c2968d7600db5..3f8d4ab85756ec007004e397a55ea60e2fa704ae 100644 --- a/paddle/operators/lookup_table_op.cc +++ b/paddle/operators/lookup_table_op.cc @@ -22,7 +22,7 @@ class LookupTableOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("W"), "Input(W) of LookupTableOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Ids"), @@ -36,6 +36,11 @@ class LookupTableOp : public framework::OperatorWithKernel { ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]}); ctx->ShareLoD("Ids", /*->*/ "Out"); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("W")->type()); + } }; class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker { @@ -65,10 +70,15 @@ class LookupTableOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto table_dims = ctx->GetInputDim("W"); ctx->SetOutputDim(framework::GradVarName("W"), table_dims); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("W")->type()); + } }; } // namespace operators diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu index 62f63b4f3c876e084e2468001e8bcb9310d16a82..c3808fa9a8de031fcae3ac0417e8c4330b2f5aad 100644 --- a/paddle/operators/lookup_table_op.cu +++ b/paddle/operators/lookup_table_op.cu @@ -61,7 +61,7 @@ __global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids, } template -class LookupTableCUDAKernel : public framework::OpKernel { +class LookupTableCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto table_t = context.Input("W"); @@ -85,7 +85,7 @@ class LookupTableCUDAKernel : public framework::OpKernel { }; template -class LookupTableGradCUDAKernel : public framework::OpKernel { +class LookupTableGradCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto ids_t = context.Input("Ids"); diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h index a1298906dd4b4209644fe06584f70169519de01c..dfead2fc5b25b9be26bb19cd74a3a94daf62cca6 100644 --- a/paddle/operators/lookup_table_op.h +++ b/paddle/operators/lookup_table_op.h @@ -23,7 +23,7 @@ namespace operators { using Tensor = framework::Tensor; template -class LookupTableKernel : public framework::OpKernel { +class LookupTableKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto table_t = context.Input("W"); // float tensor @@ -44,7 +44,7 @@ class LookupTableKernel : public framework::OpKernel { }; template -class LookupTableGradKernel : public framework::OpKernel { +class LookupTableGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto ids_t = context.Input("Ids"); diff --git a/paddle/operators/lstm_unit_op.cc b/paddle/operators/lstm_unit_op.cc index bd75b001cb87d914f6c56ea35dcb5013d68145b2..13a45ec246988558b0c090ab14e627b5b0e44532 100644 --- a/paddle/operators/lstm_unit_op.cc +++ b/paddle/operators/lstm_unit_op.cc @@ -22,7 +22,7 @@ class LstmUnitOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null."); PADDLE_ENFORCE(ctx->HasInput("C_prev"), "Input(C_prev) of LSTM should not be null."); @@ -47,7 +47,6 @@ class LstmUnitOp : public framework::OperatorWithKernel { } }; -template class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker { public: LstmUnitOpMaker(framework::OpProto* proto, @@ -68,7 +67,7 @@ Equation: H = C * sigm(o) )DOC"); - AddAttr("forget_bias", "The forget bias of Lstm Unit.") + AddAttr("forget_bias", "The forget bias of Lstm Unit.") .SetDefault(0.0); } }; @@ -78,7 +77,7 @@ class LstmUnitGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("C")), "Input(C@GRAD) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("H")), @@ -93,9 +92,11 @@ class LstmUnitGradOp : public framework::OperatorWithKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker, - lstm_unit_grad, ops::LstmUnitGradOp); +REGISTER_OP(lstm_unit, ops::LstmUnitOp, ops::LstmUnitOpMaker, lstm_unit_grad, + ops::LstmUnitGradOp); REGISTER_OP_CPU_KERNEL(lstm_unit, - ops::LstmUnitKernel); + ops::LstmUnitKernel, + ops::LstmUnitKernel); REGISTER_OP_CPU_KERNEL( - lstm_unit_grad, ops::LstmUnitGradKernel); + lstm_unit_grad, ops::LstmUnitGradKernel, + ops::LstmUnitGradKernel); diff --git a/paddle/operators/lstm_unit_op.cu b/paddle/operators/lstm_unit_op.cu index 6e5e4978994c281416a65af5f8ffdec688768d63..49ea550b6f49a13bf31d14321d7a9eb13a834d4b 100644 --- a/paddle/operators/lstm_unit_op.cu +++ b/paddle/operators/lstm_unit_op.cu @@ -89,8 +89,8 @@ __global__ void LSTMUnitGradientKernel(const int nthreads, const int dim, } } -template -class LstmUnitOpCUDAKernel : public framework::OpKernel { +template +class LstmUnitOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), @@ -101,7 +101,7 @@ class LstmUnitOpCUDAKernel : public framework::OpKernel { auto* c_tensor = ctx.Output("C"); auto* h_tensor = ctx.Output("H"); - auto forget_bias = static_cast(ctx.Attr("forget_bias")); + auto forget_bias = static_cast(ctx.Attr("forget_bias")); int b_size = c_tensor->dims()[0]; int D = c_tensor->dims()[1]; @@ -120,8 +120,8 @@ class LstmUnitOpCUDAKernel : public framework::OpKernel { } }; -template -class LstmUnitGradOpCUDAKernel : public framework::OpKernel { +template +class LstmUnitGradOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), @@ -153,7 +153,7 @@ class LstmUnitGradOpCUDAKernel : public framework::OpKernel { int N = c_tensor->dims()[0]; int D = c_tensor->dims()[1]; - auto forget_bias = static_cast(ctx.Attr("forget_bias")); + auto forget_bias = static_cast(ctx.Attr("forget_bias")); int block = 512; int n = N * D; @@ -169,5 +169,7 @@ class LstmUnitGradOpCUDAKernel : public framework::OpKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(lstm_unit, ops::LstmUnitOpCUDAKernel); -REGISTER_OP_GPU_KERNEL(lstm_unit_grad, ops::LstmUnitGradOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(lstm_unit, ops::LstmUnitOpCUDAKernel, + ops::LstmUnitOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(lstm_unit_grad, ops::LstmUnitGradOpCUDAKernel, + ops::LstmUnitGradOpCUDAKernel); diff --git a/paddle/operators/lstm_unit_op.h b/paddle/operators/lstm_unit_op.h index 683034fe15df8cabfdff5e856adb5c0467055064..a0ff498c1d3ed2aaa10f5473ef91de168c250649 100644 --- a/paddle/operators/lstm_unit_op.h +++ b/paddle/operators/lstm_unit_op.h @@ -32,8 +32,8 @@ inline T tanh(T x) { return 2. * sigmoid(2. * x) - 1.; } -template -class LstmUnitKernel : public framework::OpKernel { +template +class LstmUnitKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), @@ -44,7 +44,7 @@ class LstmUnitKernel : public framework::OpKernel { auto* c_tensor = ctx.Output("C"); auto* h_tensor = ctx.Output("H"); - auto forget_bias = static_cast(ctx.Attr("forget_bias")); + auto forget_bias = static_cast(ctx.Attr("forget_bias")); int b_size = c_tensor->dims()[0]; int D = c_tensor->dims()[1]; @@ -75,8 +75,8 @@ class LstmUnitKernel : public framework::OpKernel { } }; -template -class LstmUnitGradKernel : public framework::OpKernel { +template +class LstmUnitGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), @@ -108,7 +108,7 @@ class LstmUnitGradKernel : public framework::OpKernel { int N = c_tensor->dims()[0]; int D = c_tensor->dims()[1]; - auto forget_bias = static_cast(ctx.Attr("forget_bias")); + auto forget_bias = static_cast(ctx.Attr("forget_bias")); for (int n = 0; n < N; ++n) { for (int d = 0; d < D; ++d) { diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index 91ae3d49f1df51d9524547f7765285bff9dbb5c5..2fd559e90a22d01cbaf89c0fbd0f011bfdf66596 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,16 +1,16 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc - im2col.cu DEPS cblas device_context operator) - nv_library(softmax_function SRCS softmax.cc softmax.cu - DEPS operator) - nv_library(cross_entropy_function SRCS cross_entropy.cc cross_entropy.cu - DEPS operator) + nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc im2col.cu pooling.cc pooling.cu DEPS cblas device_context operator) + nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) + nv_library(softmax SRCS softmax.cc softmax.cu DEPS operator) + nv_library(cross_entropy SRCS cross_entropy.cc cross_entropy.cu DEPS operator) + nv_library(vol2col SRCS vol2col.cc vol2col.cu DEPS device_context) else() - cc_library(math_function SRCS math_function.cc im2col.cc - DEPS cblas device_context operator) - cc_library(softmax_function SRCS softmax.cc DEPS operator) - cc_library(cross_entropy_function SRCS cross_entropy.cc DEPS operator) + cc_library(math_function SRCS math_function.cc im2col.cc pooling.cc DEPS cblas device_context operator) + cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) + cc_library(softmax SRCS softmax.cc DEPS operator) + cc_library(cross_entropy SRCS cross_entropy.cc DEPS operator) + cc_library(vol2col SRCS vol2col.cc DEPS device_context) endif() -nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) cc_test(im2col_test SRCS im2col_test.cc DEPS math_function tensor) +cc_test(vol2col_test SRCS vol2col_test.cc DEPS vol2col tensor) diff --git a/paddle/operators/math/cross_entropy.cc b/paddle/operators/math/cross_entropy.cc index a5a426bc7b16852e67afd790df7a91d89a458c8a..150a65f2751aaeac17f9403404d2efd990a0c72b 100644 --- a/paddle/operators/math/cross_entropy.cc +++ b/paddle/operators/math/cross_entropy.cc @@ -26,8 +26,8 @@ using EigenMatrix = framework::EigenMatrix; template class CrossEntropyFunctor { public: - void operator()(const framework::ExecutionContext& ctx, - framework::Tensor* out, const framework::Tensor* prob, + void operator()(const platform::DeviceContext& ctx, framework::Tensor* out, + const framework::Tensor* prob, const framework::Tensor* labels, const bool softLabel) { const int batch_size = prob->dims()[0]; if (softLabel) { @@ -35,7 +35,7 @@ class CrossEntropyFunctor { auto lbl = EigenMatrix::From(*labels); auto loss = EigenMatrix::From(*out); - loss.device(ctx.GetEigenDevice()) = + loss.device(*ctx.GetEigenDevice()) = -((lbl * in.log().unaryExpr(math::TolerableValue())) .sum(Eigen::DSizes(1)) .reshape(Eigen::DSizes(batch_size, 1))); diff --git a/paddle/operators/math/cross_entropy.cu b/paddle/operators/math/cross_entropy.cu index d14a75a30c01deb86937a3ced43005aed4066d86..367190e6b0682ec62550e869e2f04c3a2b2cbec3 100644 --- a/paddle/operators/math/cross_entropy.cu +++ b/paddle/operators/math/cross_entropy.cu @@ -74,8 +74,8 @@ using Tensor = framework::Tensor; template class CrossEntropyFunctor { public: - void operator()(const framework::ExecutionContext& ctx, - framework::Tensor* out, const framework::Tensor* prob, + void operator()(const platform::DeviceContext& ctx, framework::Tensor* out, + const framework::Tensor* prob, const framework::Tensor* labels, bool softLabel) { const T* prob_data = prob->data(); T* loss_data = out->mutable_data(ctx.GetPlace()); @@ -87,20 +87,18 @@ class CrossEntropyFunctor { const T* label_data = labels->data(); int block = class_num > 512 ? 512 : pow(2, int(std::log2(class_num))); - SoftCrossEntropyKernel< - T><<( - ctx.device_context()) - .stream()>>>(loss_data, prob_data, label_data, class_num); + SoftCrossEntropyKernel<<< + batch_size, block, block * sizeof(T), + reinterpret_cast(ctx).stream()>>>( + loss_data, prob_data, label_data, class_num); } else { const int* label_data = labels->data(); int block = 512; int grid = (batch_size + block - 1) / block; CrossEntropyKernel<<< - grid, block, 0, reinterpret_cast( - ctx.device_context()) - .stream()>>>(loss_data, prob_data, label_data, - batch_size, class_num); + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + loss_data, prob_data, label_data, batch_size, class_num); } } }; diff --git a/paddle/operators/math/cross_entropy.h b/paddle/operators/math/cross_entropy.h index 18e637cf9186b5dc21e94f1ab15b3d858ec93c67..0ab6827ffa8f8b90b432a801607a97206e010cf4 100644 --- a/paddle/operators/math/cross_entropy.h +++ b/paddle/operators/math/cross_entropy.h @@ -37,9 +37,7 @@ struct TolerableValue { template class CrossEntropyFunctor { public: - // (TODO caoying) it is much better to use DeviceContext as the first - // parameter. - void operator()(const framework::ExecutionContext& context, + void operator()(const platform::DeviceContext& context, framework::Tensor* out, const framework::Tensor* prob, const framework::Tensor* labels, const bool softLabel); }; diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc index f0b8c885918afe7f80edc465c6d9be7c11ac066f..40bdbfe73351a609a4ab9fdc27ac5ff6710df2a2 100644 --- a/paddle/operators/math/im2col_test.cc +++ b/paddle/operators/math/im2col_test.cc @@ -71,7 +71,7 @@ void testIm2col() { context = new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace()); } else { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA context = new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace()); #else @@ -116,7 +116,7 @@ void testIm2col() { TEST(math, im2col) { testIm2col(); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA testIm2col(); #endif } diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index 43306fca73387b7b212f556a2b187df113a1b327..473eff4d198ca9b17b6af8eebd6dfe39d49d138d 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -52,6 +52,7 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda, #include +#include "paddle/framework/eigen.h" #include "paddle/framework/tensor.h" #include "paddle/platform/device_context.h" #include "paddle/platform/enforce.h" @@ -84,6 +85,13 @@ void matmul(const platform::DeviceContext& context, const framework::Tensor& matrix_b, bool trans_b, T alpha, framework::Tensor* matrix_out, T beta); +template +void SetConstant(const platform::DeviceContext& context, + framework::Tensor* tensor, T num) { + auto t = framework::EigenVector::Flatten(*tensor); + t.device(*context.GetEigenDevice()) = t.constant(static_cast(num)); +} + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function_test.cc b/paddle/operators/math/math_function_test.cc index f272f7e5135e7092618b8c94ee55faf1cfd8e8a5..9945ba101d719848aa0c06fa65629d59f167c083 100644 --- a/paddle/operators/math/math_function_test.cc +++ b/paddle/operators/math/math_function_test.cc @@ -1,7 +1,7 @@ #include "paddle/operators/math/math_function.h" #include "gtest/gtest.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(math_function, notrans_mul_trans) { paddle::framework::Tensor input1; paddle::framework::Tensor input1_gpu; @@ -243,3 +243,24 @@ TEST(math_function, gemm_trans_clbas) { EXPECT_EQ(input3_ptr[6], 86); EXPECT_EQ(input3_ptr[7], 99); } + +TEST(math_function, zero) { + paddle::framework::Tensor tensor; + auto* cpu_place = new paddle::platform::CPUPlace(); + float* t = tensor.mutable_data({2, 2}, *cpu_place); + paddle::platform::CPUDeviceContext context(*cpu_place); + paddle::operators::math::SetConstant( + context, &tensor, 0); + EXPECT_EQ(t[0], 0); + EXPECT_EQ(t[1], 0); + EXPECT_EQ(t[2], 0); + EXPECT_EQ(t[3], 0); + + paddle::operators::math::SetConstant( + context, &tensor, 1); + + EXPECT_EQ(t[0], 1); + EXPECT_EQ(t[1], 1); + EXPECT_EQ(t[2], 1); + EXPECT_EQ(t[3], 1); +} diff --git a/paddle/operators/math/pooling.cc b/paddle/operators/math/pooling.cc new file mode 100644 index 0000000000000000000000000000000000000000..50cfb88bb5700dda3785e63e0ccc6457cc928da0 --- /dev/null +++ b/paddle/operators/math/pooling.cc @@ -0,0 +1,740 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/math/pooling.h" + +namespace paddle { +namespace operators { +namespace math { + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class Pool2dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const int input_stride = input_height * input_width; + const int output_stride = output_height * output_width; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + + T ele = pool_process.initial(); + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + pool_process.compute(ele, input_data[h * input_width + w]); + } + } + int pool_size = (hend - hstart) * (wend - wstart); + pool_process.finalize(ele, (static_cast(pool_size))); + output_data[ph * output_width + pw] = ele; + } + } + input_data += input_stride; + output_data += output_stride; + } + } + } +}; + +/* +* All tensors are in NCHW format. +* Ksize, strides, paddings are two elements. These two elements represent height +* and width, respectively. +*/ +template +class Pool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_grad_process) { + const int batch_size = input.dims()[0]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + const int input_stride = input_height * input_width; + const int output_stride = output_height * output_width; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + int pool_size = (hend - hstart) * (wend - wstart); + float scale = 1.0 / pool_size; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + pool_grad_process.compute( + input_data[h * input_width + w], + output_data[ph * output_width + pw], + output_grad_data[ph * output_width + pw], + input_grad_data[h * input_width + w], + static_cast(scale)); + } + } + } + } + input_data += input_stride; + output_data += output_stride; + input_grad_data += input_stride; + output_grad_data += output_stride; + } + } + } +}; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + const int input_stride = input_height * input_width; + const int output_stride = output_height * output_width; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + + bool stop = false; + for (int h = hstart; h < hend && !stop; ++h) { + for (int w = wstart; w < wend && !stop; ++w) { + int input_idx = h * input_width + w; + int output_idx = ph * output_width + pw; + if (input_data[input_idx] == output_data[output_idx]) { + input_grad_data[input_idx] += output_grad_data[output_idx]; + stop = true; + } + } + } + } + } + input_data += input_stride; + output_data += output_stride; + input_grad_data += input_stride; + output_grad_data += output_stride; + } + } + } +}; + +template class MaxPool2dGradFunctor; +template class MaxPool2dGradFunctor; + +template class Pool2dFunctor, float>; +template class Pool2dFunctor, float>; +template class Pool2dGradFunctor< + platform::CPUPlace, paddle::operators::math::MaxPoolGrad, float>; +template class Pool2dGradFunctor< + platform::CPUPlace, paddle::operators::math::AvgPoolGrad, float>; +template class Pool2dFunctor, double>; +template class Pool2dFunctor, double>; +template class Pool2dGradFunctor< + platform::CPUPlace, paddle::operators::math::MaxPoolGrad, double>; +template class Pool2dGradFunctor< + platform::CPUPlace, paddle::operators::math::AvgPoolGrad, double>; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class Pool3dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const int input_stride = input_depth * input_height * input_width; + const int output_stride = output_depth * output_height * output_width; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int pd = 0; pd < output_depth; ++pd) { + int dstart = pd * stride_depth - padding_depth; + int dend = std::min(dstart + ksize_depth, input_depth); + dstart = std::max(dstart, 0); + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + int output_idx = (pd * output_height + ph) * output_width + pw; + T ele = pool_process.initial(); + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + pool_process.compute( + ele, + input_data[(d * input_height + h) * input_width + w]); + } + } + } + int pool_size = + (dend - dstart) * (hend - hstart) * (wend - wstart); + pool_process.finalize(ele, static_cast(pool_size)); + output_data[output_idx] = ele; + } + } + } + input_data += input_stride; + output_data += output_stride; + } + } + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class Pool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_grad_process) { + const int batch_size = input.dims()[0]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + const int input_stride = input_depth * input_height * input_width; + const int output_stride = output_depth * output_height * output_width; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int pd = 0; pd < output_depth; ++pd) { + int dstart = pd * stride_depth - padding_depth; + int dend = std::min(dstart + ksize_depth, input_depth); + dstart = std::max(dstart, 0); + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + + int pool_size = + (dend - dstart) * (hend - hstart) * (wend - wstart); + float scale = 1.0 / pool_size; + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int input_idx = (d * input_height + h) * input_width + w; + int output_idx = + (pd * output_height + ph) * output_width + pw; + pool_grad_process.compute( + input_data[input_idx], output_data[output_idx], + output_grad_data[output_idx], + input_grad_data[input_idx], static_cast(scale)); + } + } + } + } + } + } + input_data += input_stride; + output_data += output_stride; + input_grad_data += input_stride; + output_grad_data += output_stride; + } + } + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + const int input_stride = input_depth * input_height * input_width; + const int output_stride = output_depth * output_height * output_width; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int pd = 0; pd < output_depth; ++pd) { + int dstart = pd * stride_depth - padding_depth; + int dend = std::min(dstart + ksize_depth, input_depth); + dstart = std::max(dstart, 0); + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + bool stop = false; + for (int d = dstart; d < dend && !stop; ++d) { + for (int h = hstart; h < hend && !stop; ++h) { + for (int w = wstart; w < wend && !stop; ++w) { + int input_idx = (d * input_height + h) * input_width + w; + int output_idx = + (pd * output_height + ph) * output_width + pw; + + if (input_data[input_idx] == output_data[output_idx]) { + input_grad_data[input_idx] += + output_grad_data[output_idx]; + stop = true; + } + } + } + } + } + } + } + input_data += input_stride; + output_data += output_stride; + input_grad_data += input_stride; + output_grad_data += output_stride; + } + } + } +}; + +template class MaxPool3dGradFunctor; +template class MaxPool3dGradFunctor; + +template class Pool3dFunctor, float>; +template class Pool3dFunctor, float>; +template class Pool3dGradFunctor< + platform::CPUPlace, paddle::operators::math::MaxPoolGrad, float>; +template class Pool3dGradFunctor< + platform::CPUPlace, paddle::operators::math::AvgPoolGrad, float>; +template class Pool3dFunctor, double>; +template class Pool3dFunctor, double>; +template class Pool3dGradFunctor< + platform::CPUPlace, paddle::operators::math::MaxPoolGrad, double>; +template class Pool3dGradFunctor< + platform::CPUPlace, paddle::operators::math::AvgPoolGrad, double>; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + const int input_stride = input_height * input_width; + const int output_stride = output_height * output_width; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + T* mask_data = mask.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + + T ele = static_cast(-FLT_MAX); + int index = -1; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + if (ele < input_data[h * input_width + w]) { + ele = input_data[h * input_width + w]; + index = h * input_width + w; + } + } + } + output_data[ph * output_width + pw] = ele; + mask_data[ph * output_width + pw] = index; + } + } + // offset + input_data += input_stride; + output_data += output_stride; + mask_data += output_stride; + } + } + } +}; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input_grad.dims()[0]; + const int input_height = input_grad.dims()[2]; + const int input_width = input_grad.dims()[3]; + const int output_channels = output_grad.dims()[1]; + const int output_height = output_grad.dims()[2]; + const int output_width = output_grad.dims()[3]; + const int input_stride = input_height * input_width; + const int output_stride = output_height * output_width; + + const T* mask_data = mask.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int n = 0; n < batch_size; ++n) { + for (int c = 0; c < output_channels; ++c) { + for (int ph = 0; ph < output_height; ++ph) { + for (int pw = 0; pw < output_width; ++pw) { + const int output_idx = ph * output_width + pw; + const int input_idx = static_cast(mask_data[output_idx]); + input_grad_data[input_idx] += output_grad_data[output_idx]; + } + } + // offset + input_grad_data += input_stride; + output_grad_data += output_stride; + mask_data += output_stride; + } + } + } +}; + +template class MaxPool2dWithIndexFunctor; +template class MaxPool2dWithIndexGradFunctor; +template class MaxPool2dWithIndexFunctor; +template class MaxPool2dWithIndexGradFunctor; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + const int input_stride = input_depth * input_height * input_width; + const int output_stride = output_depth * output_height * output_width; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + T* mask_data = mask.mutable_data(context.GetPlace()); + + for (int i = 0; i < batch_size; i++) { + for (int c = 0; c < output_channels; ++c) { + for (int pd = 0; pd < output_depth; ++pd) { + int dstart = pd * stride_depth - padding_depth; + int dend = std::min(dstart + ksize_depth, input_depth); + dstart = std::max(dstart, 0); + for (int ph = 0; ph < output_height; ++ph) { + int hstart = ph * stride_height - padding_height; + int hend = std::min(hstart + ksize_height, input_height); + hstart = std::max(hstart, 0); + for (int pw = 0; pw < output_width; ++pw) { + int wstart = pw * stride_width - padding_width; + int wend = std::min(wstart + ksize_width, input_width); + wstart = std::max(wstart, 0); + + int output_idx = (pd * output_height + ph) * output_width + pw; + T ele = static_cast(-FLT_MAX); + int index = -1; + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int input_idx = (d * input_height + h) * input_width + w; + if (ele < input_data[input_idx]) { + index = input_idx; + ele = input_data[input_idx]; + } + } + } + } + output_data[output_idx] = ele; + mask_data[output_idx] = index; + } + } + } + // offset + input_data += input_stride; + output_data += output_stride; + mask_data += output_stride; + } + } + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input_grad.dims()[0]; + const int input_depth = input_grad.dims()[2]; + const int input_height = input_grad.dims()[3]; + const int input_width = input_grad.dims()[4]; + const int output_channels = output_grad.dims()[1]; + const int output_depth = output_grad.dims()[2]; + const int output_height = output_grad.dims()[3]; + const int output_width = output_grad.dims()[4]; + const int input_stride = input_depth * input_height * input_width; + const int output_stride = output_depth * output_height * output_width; + + const T* mask_data = mask.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + for (int n = 0; n < batch_size; ++n) { + for (int c = 0; c < output_channels; ++c) { + for (int pd = 0; pd < output_depth; ++pd) { + for (int ph = 0; ph < output_height; ++ph) { + for (int pw = 0; pw < output_width; ++pw) { + const int output_idx = + (pd * output_height + ph) * output_width + pw; + const int input_idx = static_cast(mask_data[output_idx]); + input_grad_data[input_idx] += output_grad_data[output_idx]; + } + } + } + // offset + input_grad_data += input_stride; + output_grad_data += output_stride; + mask_data += output_stride; + } + } + } +}; + +template class MaxPool3dWithIndexFunctor; +template class MaxPool3dWithIndexGradFunctor; +template class MaxPool3dWithIndexFunctor; +template class MaxPool3dWithIndexGradFunctor; +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/pooling.cu b/paddle/operators/math/pooling.cu new file mode 100644 index 0000000000000000000000000000000000000000..736327f4b7b9e9df9ce8f7f60b0437fc1d2d373a --- /dev/null +++ b/paddle/operators/math/pooling.cu @@ -0,0 +1,1059 @@ +/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/math/pooling.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { +namespace math { + +template +__global__ void KernelPool2D(const int nthreads, const T* input_data, + T* output_data, const int channels, + const int input_height, const int input_width, + const int output_height, const int output_width, + const int ksize_height, const int ksize_width, + const int stride_height, const int stride_width, + const int padding_height, const int padding_width, + PoolProcess pool_process) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int c = (index / output_width / output_height) % channels; + int batch_idx = index / output_width / output_height / channels; + + int hstart = ph * stride_height - padding_height; + int hend = min(hstart + ksize_height, input_height); + hstart = max(hstart, 0); + + int wstart = pw * stride_width - padding_width; + int wend = min(wstart + ksize_width, input_width); + wstart = max(wstart, 0); + + input_data += (batch_idx * channels + c) * input_height * input_width; + T ele = pool_process.initial(); + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + pool_process.compute(ele, input_data[h * input_width + w]); + } + } + int pool_size = (hend - hstart) * (wend - wstart); + pool_process.finalize(ele, (static_cast(pool_size))); + output_data[index] = ele; + } +} + +template +__global__ void KernelPool2DGrad( + const int nthreads, const T* input_data, const T* output_data, + const T* output_grad, T* input_grad, const int channels, + const int input_height, const int input_width, const int output_height, + const int output_width, const int ksize_height, const int ksize_width, + const int stride_height, const int stride_width, const int padding_height, + const int padding_width, PoolProcess pool_process) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int offsetW = index % input_width + padding_width; + int offsetH = (index / input_width) % input_height + padding_height; + int offsetC = (index / input_width / input_height) % channels; + int batch_idx = index / input_width / input_height / channels; + + int phstart = (offsetH < ksize_height) + ? 0 + : (offsetH - ksize_height) / stride_height + 1; + int pwstart = (offsetW < ksize_width) + ? 0 + : (offsetW - ksize_width) / stride_width + 1; + int phend = min(offsetH / stride_height + 1, output_height); + int pwend = min(offsetW / stride_width + 1, output_width); + T gradient = 0; + T input = input_data[index]; + int output_idx = + (batch_idx * channels + offsetC) * output_height * output_width; + output_data += output_idx; + output_grad += output_idx; + for (int ph = phstart; ph < phend; ++ph) { + for (int pw = pwstart; pw < pwend; ++pw) { + int hstart = ph * stride_height - padding_height; + int wstart = pw * stride_width - padding_width; + int hend = min(hstart + ksize_height, input_height); + int wend = min(wstart + ksize_width, input_width); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + int pool_size = (hend - hstart) * (wend - wstart); + int output_sub_idx = ph * output_width + pw; + pool_process.compute(input, output_data[output_sub_idx], + output_grad[output_sub_idx], gradient, + static_cast(1.0 / pool_size)); + } + } + input_grad[index] = gradient; + } +} + +template +__global__ void KernelMaxPool2DGrad( + const int nthreads, const T* input_data, const T* output_data, + const T* output_grad, T* input_grad, const int channels, + const int input_height, const int input_width, const int output_height, + const int output_width, const int ksize_height, const int ksize_width, + const int stride_height, const int stride_width, const int padding_height, + const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int c = (index / output_width / output_height) % channels; + int batch_idx = index / output_width / output_height / channels; + + int hstart = ph * stride_height - padding_height; + int hend = min(hstart + ksize_height, input_height); + hstart = max(hstart, 0); + + int wstart = pw * stride_width - padding_width; + int wend = min(wstart + ksize_width, input_width); + wstart = max(wstart, 0); + + input_data += (batch_idx * channels + c) * input_height * input_width; + input_grad += (batch_idx * channels + c) * input_height * input_width; + + T ele = output_data[index]; + int maxIndex = -1; + bool stop = false; + for (int h = hstart; h < hend && !stop; ++h) { + for (int w = wstart; w < wend && !stop; ++w) { + if (ele == input_data[h * input_width + w]) { + maxIndex = h * input_width + w; + stop = true; + } + } + } + + if (maxIndex != -1) { + // atomic add + platform::CudaAtomicAdd(input_grad + maxIndex, output_grad[index]); + } + } +} + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class Pool2dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_height * output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelPool2D< + PoolProcess, + T><<(context) + .stream()>>>(nthreads, input_data, output_data, input_channels, + input_height, input_width, output_height, + output_width, ksize_height, ksize_width, + stride_height, stride_width, padding_height, + padding_width, pool_process); + } +}; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class Pool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = batch_size * input_channels * input_height * input_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelPool2DGrad< + PoolProcess, + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, output_grad_data, input_grad_data, + input_channels, input_height, input_width, output_height, output_width, + ksize_height, ksize_width, stride_height, stride_width, padding_height, + padding_width, pool_process); + } +}; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_height * output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool2DGrad< + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, output_grad_data, input_grad_data, + input_channels, input_height, input_width, output_height, output_width, + ksize_height, ksize_width, stride_height, stride_width, padding_height, + padding_width); + } +}; + +template class MaxPool2dGradFunctor; +template class MaxPool2dGradFunctor; + +template class Pool2dFunctor, float>; +template class Pool2dFunctor, float>; +template class Pool2dGradFunctor< + platform::GPUPlace, paddle::operators::math::MaxPoolGrad, float>; +template class Pool2dGradFunctor< + platform::GPUPlace, paddle::operators::math::AvgPoolGrad, float>; +template class Pool2dFunctor, double>; +template class Pool2dFunctor, double>; +template class Pool2dGradFunctor< + platform::GPUPlace, paddle::operators::math::MaxPoolGrad, double>; +template class Pool2dGradFunctor< + platform::GPUPlace, paddle::operators::math::AvgPoolGrad, double>; + +template +__global__ void KernelPool3D( + const int nthreads, const T* input_data, T* output_data, const int channels, + const int input_depth, const int input_height, const int input_width, + const int output_depth, const int output_height, const int output_width, + const int ksize_depth, const int ksize_height, const int ksize_width, + const int stride_depth, const int stride_height, const int stride_width, + const int padding_depth, const int padding_height, const int padding_width, + PoolProcess pool_process) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int pd = (index / output_width / output_height) % output_depth; + int c = (index / output_width / output_height / output_depth) % channels; + int batch_idx = + index / output_width / output_height / output_depth / channels; + int dstart = pd * stride_depth - padding_depth; + int hstart = ph * stride_height - padding_height; + int wstart = pw * stride_width - padding_width; + int dend = min(dstart + ksize_depth, input_depth); + int hend = min(hstart + ksize_height, input_height); + int wend = min(wstart + ksize_width, input_width); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + T ele = pool_process.initial(); + input_data += + (batch_idx * channels + c) * input_depth * input_height * input_width; + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + pool_process.compute( + ele, input_data[(d * input_height + h) * input_width + w]); + } + } + } + int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); + pool_process.finalize(ele, static_cast(pool_size)); + output_data[index] = ele; + } +} + +template +__global__ void KernelPool3DGrad( + const int nthreads, const T* input_data, const T* output_data, + const T* output_grad, T* input_grad, const int channels, + const int input_depth, const int input_height, const int input_width, + const int output_depth, const int output_height, const int output_width, + const int ksize_depth, const int ksize_height, const int ksize_width, + const int stride_depth, const int stride_height, const int stride_width, + const int padding_depth, const int padding_height, const int padding_width, + PoolProcess pool_process) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int offsetW = index % input_width + padding_width; + int offsetH = (index / input_width) % input_height + padding_height; + int offsetD = + (index / input_width / input_height) % input_depth + padding_depth; + int offsetC = (index / input_width / input_height / input_depth) % channels; + int batch_idx = index / input_width / input_height / input_depth / channels; + + int pdstart = (offsetD < ksize_depth) + ? 0 + : (offsetD - ksize_depth) / stride_depth + 1; + int phstart = (offsetH < ksize_height) + ? 0 + : (offsetH - ksize_height) / stride_height + 1; + int pwstart = (offsetW < ksize_width) + ? 0 + : (offsetW - ksize_width) / stride_width + 1; + int pdend = min((offsetD) / stride_depth + 1, output_depth); + int phend = min((offsetH) / stride_height + 1, output_height); + int pwend = min((offsetW) / stride_width + 1, output_width); + + T gradient = 0; + T input = input_data[index]; + int output_idx = (batch_idx * channels + offsetC) * output_depth * + output_height * output_width; + output_data += output_idx; + output_grad += output_idx; + + for (int pd = pdstart; pd < pdend; ++pd) { + for (int ph = phstart; ph < phend; ++ph) { + for (int pw = pwstart; pw < pwend; ++pw) { + // figure out the pooling size + int dstart = pd * stride_depth - padding_depth; + int hstart = ph * stride_height - padding_height; + int wstart = pw * stride_width - padding_width; + int dend = min(dstart + ksize_depth, input_depth); + int hend = min(hstart + ksize_height, input_height); + int wend = min(wstart + ksize_width, input_width); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); + int output_sub_idx = (pd * output_height + ph) * output_width + pw; + pool_process.compute(input, output_data[output_sub_idx], + output_grad[output_sub_idx], gradient, + static_cast(1.0 / pool_size)); + } + } + } + input_grad[index] = gradient; + } +} + +template +__global__ void KernelMaxPool3DGrad( + const int nthreads, const T* input_data, const T* output_data, + const T* output_grad, T* input_grad, const int channels, + const int input_depth, const int input_height, const int input_width, + const int output_depth, const int output_height, const int output_width, + const int ksize_depth, const int ksize_height, const int ksize_width, + const int stride_depth, const int stride_height, const int stride_width, + const int padding_depth, const int padding_height, + const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int pd = (index / output_width / output_height) % output_depth; + int c = (index / output_width / output_height / output_depth) % channels; + int batch_idx = + index / output_width / output_height / output_depth / channels; + int dstart = pd * stride_depth - padding_depth; + int hstart = ph * stride_height - padding_height; + int wstart = pw * stride_width - padding_width; + int dend = min(dstart + ksize_depth, input_depth); + int hend = min(hstart + ksize_height, input_height); + int wend = min(wstart + ksize_width, input_width); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + T ele = output_data[index]; + bool stop = false; + int maxIdx = -1; + input_data += + (batch_idx * channels + c) * input_depth * input_height * input_width; + input_grad += + (batch_idx * channels + c) * input_depth * input_height * input_width; + + for (int d = dstart; d < dend && !stop; ++d) { + for (int h = hstart; h < hend && !stop; ++h) { + for (int w = wstart; w < wend && !stop; ++w) { + if (ele == input_data[(d * input_height + h) * input_width + w]) { + stop = true; + maxIdx = (d * input_height + h) * input_width + w; + } + } + } + } + if (maxIdx != -1) { + // atomic add + platform::CudaAtomicAdd(input_grad + maxIdx, output_grad[index]); + } + } +} + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class Pool3dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_depth * output_height * + output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelPool3D< + PoolProcess, + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, input_channels, input_depth, + input_height, input_width, output_depth, output_height, output_width, + ksize_depth, ksize_height, ksize_width, stride_depth, stride_height, + stride_width, padding_depth, padding_height, padding_width, + pool_process); + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class Pool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_process) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = + batch_size * input_channels * input_depth * input_height * input_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelPool3DGrad< + PoolProcess, + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, output_grad_data, input_grad_data, + input_channels, input_depth, input_height, input_width, output_depth, + output_height, output_width, ksize_depth, ksize_height, ksize_width, + stride_depth, stride_height, stride_width, padding_depth, + padding_height, padding_width, pool_process); + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const T* input_data = input.data(); + const T* output_data = output.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_depth * output_height * + output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool3DGrad< + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, output_grad_data, input_grad_data, + input_channels, input_depth, input_height, input_width, output_depth, + output_height, output_width, ksize_depth, ksize_height, ksize_width, + stride_depth, stride_height, stride_width, padding_depth, + padding_height, padding_width); + } +}; + +template class MaxPool3dGradFunctor; +template class MaxPool3dGradFunctor; + +template class Pool3dFunctor, float>; +template class Pool3dFunctor, float>; +template class Pool3dGradFunctor< + platform::GPUPlace, paddle::operators::math::MaxPoolGrad, float>; +template class Pool3dGradFunctor< + platform::GPUPlace, paddle::operators::math::AvgPoolGrad, float>; +template class Pool3dFunctor, double>; +template class Pool3dFunctor, double>; +template class Pool3dGradFunctor< + platform::GPUPlace, paddle::operators::math::MaxPoolGrad, double>; +template class Pool3dGradFunctor< + platform::GPUPlace, paddle::operators::math::AvgPoolGrad, double>; + +template +__global__ void KernelMaxPool2dWithIdx( + const int nthreads, const T* input_data, T* output_data, T* mask_data, + const int channels, const int input_height, const int input_width, + const int output_height, const int output_width, const int ksize_height, + const int ksize_width, const int stride_height, const int stride_width, + const int padding_height, const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int c = (index / output_width / output_height) % channels; + int batch_idx = index / output_width / output_height / channels; + + int hstart = ph * stride_height - padding_height; + int hend = min(hstart + ksize_height, input_height); + hstart = max(hstart, 0); + + int wstart = pw * stride_width - padding_width; + int wend = min(wstart + ksize_width, input_width); + wstart = max(wstart, 0); + + input_data += (batch_idx * channels + c) * input_height * input_width; + T ele = -FLT_MAX; + int max_index = -1; + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + int input_index = h * input_width + w; + if (ele < input_data[input_index]) { + max_index = input_index; + ele = input_data[input_index]; + } + } + } + output_data[index] = ele; + mask_data[index] = max_index; + } +} + +template +__global__ void KernelMaxPool2DWithIdxGrad( + const int nthreads, T* input_grad, const T* output_grad, const T* mask_data, + const int channels, const int input_height, const int input_width, + const int output_height, const int output_width, const int ksize_height, + const int ksize_width, const int stride_height, const int stride_width, + const int padding_height, const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int w_offset = index % input_width; + int h_offset = (index / input_width) % input_height; + int c_offset = (index / input_width / input_height) % channels; + int batch_idx = index / input_width / input_height / channels; + + int ph_start = + (h_offset + padding_height < ksize_height) + ? 0 + : (h_offset + padding_height - ksize_height) / stride_height + 1; + int pw_start = + (w_offset + padding_width < ksize_width) + ? 0 + : (w_offset + padding_width - ksize_width) / stride_width + 1; + int ph_end = + min((h_offset + padding_height) / stride_height + 1, output_height); + int pw_end = + min((w_offset + padding_width) / stride_width + 1, output_width); + + T gradient = 0; + int input_current_featuremap_idx = h_offset * input_width + w_offset; + int output_idx = + (batch_idx * channels + c_offset) * output_height * output_width; + + mask_data += output_idx; + output_grad += output_idx; + for (int ph = ph_start; ph < ph_end; ++ph) { + for (int pw = pw_start; pw < pw_end; ++pw) { + if (mask_data[ph * output_width + pw] == input_current_featuremap_idx) + gradient += output_grad[ph * output_width + pw]; + } + } + input_grad[index] = gradient; + } +} + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_height = input.dims()[2]; + const int input_width = input.dims()[3]; + const int output_channels = output.dims()[1]; + const int output_height = output.dims()[2]; + const int output_width = output.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + T* mask_data = mask.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_height * output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool2dWithIdx< + T><<(context) + .stream()>>>(nthreads, input_data, output_data, mask_data, + input_channels, input_height, input_width, + output_height, output_width, ksize_height, + ksize_width, stride_height, stride_width, + padding_height, padding_width); + } +}; + +/* + * All tensors are in NCHW format. + * Ksize, strides, paddings are two elements. These two elements represent + * height and width, respectively. + */ +template +class MaxPool2dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input_grad.dims()[0]; + const int input_channels = input_grad.dims()[1]; + const int input_height = input_grad.dims()[2]; + const int input_width = input_grad.dims()[3]; + const int output_height = output_grad.dims()[2]; + const int output_width = output_grad.dims()[3]; + const int ksize_height = ksize[0]; + const int ksize_width = ksize[1]; + const int stride_height = strides[0]; + const int stride_width = strides[1]; + const int padding_height = paddings[0]; + const int padding_width = paddings[1]; + + const T* mask_data = mask.data(); + const T* output_grad_data = output_grad.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = batch_size * input_channels * input_height * input_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool2DWithIdxGrad< + T><<(context) + .stream()>>>(nthreads, input_grad_data, output_grad_data, + mask_data, input_channels, input_height, + input_width, output_height, output_width, + ksize_height, ksize_width, stride_height, + stride_width, padding_height, padding_width); + } +}; + +template class MaxPool2dWithIndexFunctor; +template class MaxPool2dWithIndexGradFunctor; +template class MaxPool2dWithIndexFunctor; +template class MaxPool2dWithIndexGradFunctor; + +template +__global__ void KernelMaxPool3DWithIdx( + const int nthreads, const T* input_data, T* output_data, T* mask_data, + const int channels, const int input_depth, const int input_height, + const int input_width, const int output_depth, const int output_height, + const int output_width, const int ksize_depth, const int ksize_height, + const int ksize_width, const int stride_depth, const int stride_height, + const int stride_width, const int padding_depth, const int padding_height, + const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int pw = index % output_width; + int ph = (index / output_width) % output_height; + int pd = (index / output_width / output_height) % output_depth; + int c = (index / output_width / output_height / output_depth) % channels; + int batch_idx = + index / output_width / output_height / output_depth / channels; + + int dstart = pd * stride_depth - padding_depth; + int hstart = ph * stride_height - padding_height; + int wstart = pw * stride_width - padding_width; + int dend = min(dstart + ksize_depth, input_depth); + int hend = min(hstart + ksize_height, input_height); + int wend = min(wstart + ksize_width, input_width); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + + T ele = -FLT_MAX; + int max_index = -1; + input_data += + (batch_idx * channels + c) * input_depth * input_height * input_width; + + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + if (ele < input_data[(d * input_height + h) * input_width + w]) { + max_index = (d * input_height + h) * input_width + w; + ele = input_data[max_index]; + } + } + } + } + output_data[index] = ele; + mask_data[index] = max_index; + } +} + +template +__global__ void KernelMaxPool3DWithIdxGrad( + const int nthreads, T* input_grad, const T* output_grad, const T* mask, + const int channels, const int input_depth, const int input_height, + const int input_width, const int output_depth, const int output_height, + const int output_width, const int ksize_depth, const int ksize_height, + const int ksize_width, const int stride_depth, const int stride_height, + const int stride_width, const int padding_depth, const int padding_height, + const int padding_width) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; + index += blockDim.x * gridDim.x) { + int w_offset = index % input_width; + int h_offset = (index / input_width) % input_height; + int d_offset = (index / input_width / input_height) % input_depth; + int c_offset = + (index / input_width / input_height / input_depth) % channels; + int batch_idx = index / input_width / input_height / input_depth / channels; + + int pd_start = + (d_offset + padding_depth < ksize_depth) + ? 0 + : (d_offset + padding_depth - ksize_depth) / stride_depth + 1; + int ph_start = + (h_offset + padding_height < ksize_height) + ? 0 + : (h_offset + padding_height - ksize_height) / stride_height + 1; + int pw_start = + (w_offset + padding_width < ksize_width) + ? 0 + : (w_offset + padding_width - ksize_width) / stride_width + 1; + int pd_end = + min((d_offset + padding_depth) / stride_depth + 1, output_depth); + int ph_end = + min((h_offset + padding_height) / stride_height + 1, output_height); + int pw_end = + min((w_offset + padding_width) / stride_width + 1, output_width); + + T gradient = 0; + int input_current_feature_map_idx = + (d_offset * input_height + h_offset) * input_width + w_offset; + int output_idx = (batch_idx * channels + c_offset) * output_depth * + output_height * output_width; + mask += output_idx; + output_grad += output_idx; + + for (int pd = pd_start; pd < pd_end; ++pd) { + for (int ph = ph_start; ph < ph_end; ++ph) { + for (int pw = pw_start; pw < pw_end; ++pw) { + if (mask[(pd * output_height + ph) * output_width + pw] == + input_current_feature_map_idx) + gradient += + output_grad[(pd * output_height + ph) * output_width + pw]; + } + } + } + input_grad[index] = gradient; + } +} + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input.dims()[0]; + const int input_channels = input.dims()[1]; + const int input_depth = input.dims()[2]; + const int input_height = input.dims()[3]; + const int input_width = input.dims()[4]; + const int output_channels = output.dims()[1]; + const int output_depth = output.dims()[2]; + const int output_height = output.dims()[3]; + const int output_width = output.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const T* input_data = input.data(); + T* output_data = output.mutable_data(context.GetPlace()); + T* mask_data = mask.mutable_data(context.GetPlace()); + + int nthreads = batch_size * output_channels * output_depth * output_height * + output_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool3DWithIdx< + T><<(context) + .stream()>>>( + nthreads, input_data, output_data, mask_data, input_channels, + input_depth, input_height, input_width, output_depth, output_height, + output_width, ksize_depth, ksize_height, ksize_width, stride_depth, + stride_height, stride_width, padding_depth, padding_height, + padding_width); + } +}; + +/* + * All tensors are in NCDHW format. + * Ksize, strides, paddings are three elements. These three elements represent + * depth, height and width, respectively. + */ +template +class MaxPool3dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings) { + const int batch_size = input_grad.dims()[0]; + const int input_channels = input_grad.dims()[1]; + const int input_depth = input_grad.dims()[2]; + const int input_height = input_grad.dims()[3]; + const int input_width = input_grad.dims()[4]; + const int output_depth = output_grad.dims()[2]; + const int output_height = output_grad.dims()[3]; + const int output_width = output_grad.dims()[4]; + const int ksize_depth = ksize[0]; + const int ksize_height = ksize[1]; + const int ksize_width = ksize[2]; + const int stride_depth = strides[0]; + const int stride_height = strides[1]; + const int stride_width = strides[2]; + const int padding_depth = paddings[0]; + const int padding_height = paddings[1]; + const int padding_width = paddings[2]; + + const T* output_grad_data = output_grad.data(); + const T* mask_data = mask.data(); + T* input_grad_data = input_grad.mutable_data(context.GetPlace()); + + int nthreads = + batch_size * input_channels * input_depth * input_height * input_width; + int blocks = (nthreads + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KernelMaxPool3DWithIdxGrad< + T><<(context) + .stream()>>>( + nthreads, input_grad_data, output_grad_data, mask_data, input_channels, + input_depth, input_height, input_width, output_depth, output_height, + output_width, ksize_depth, ksize_height, ksize_width, stride_depth, + stride_height, stride_width, padding_depth, padding_height, + padding_width); + } +}; + +template class MaxPool3dWithIndexFunctor; +template class MaxPool3dWithIndexGradFunctor; +template class MaxPool3dWithIndexFunctor; +template class MaxPool3dWithIndexGradFunctor; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/pooling.h b/paddle/operators/math/pooling.h new file mode 100644 index 0000000000000000000000000000000000000000..c50c57b5c52cdc5c12425cb119b80502aef5451e --- /dev/null +++ b/paddle/operators/math/pooling.h @@ -0,0 +1,194 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/tensor.h" +#include "paddle/platform/device_context.h" +#include "paddle/platform/hostdevice.h" + +namespace paddle { +namespace operators { +namespace math { + +#define FLT_MAX \ + __FLT_MAX__ // It might need to be placed in another file, but I'm still + // wondering where to put it. + +/* + * \brief Extracting simple operations from pooling. + * Both MaxPool and AvgPool need "initial", "compute" and "finalize" + * operation. + * MaxPool initializes temp variable to the negative maximum to find the + * maximum value in the pooling field. + * AvgPool initializes temp variable to the zero to accumulate all values + * in pool pooling, and finally takes the average. + * MaxPoolGrad and AvgPoolGrad are gradient operations respectively. + */ +template +class MaxPool { + public: + DEVICE inline T initial() { return static_cast(-FLT_MAX); } + DEVICE inline void compute(T& y, const T& x) { y = y > x ? y : x; } + DEVICE inline void finalize(T& y, const T& pool_field) {} +}; + +template +class AvgPool { + public: + DEVICE inline T initial() { return static_cast(0); } + DEVICE inline void compute(T& y, const T& x) { y += x; } + DEVICE inline void finalize(T& y, const T& pool_field) { y /= pool_field; } +}; + +template +class MaxPoolGrad { + public: + DEVICE inline void compute(const T& x, const T& y, const T& dy, T& dx, + T scale) { + dx += dy * (x == y); + } +}; + +template +class AvgPoolGrad { + public: + DEVICE inline void compute(const T& x, const T& y, const T& dy, T& dx, + T scale) { + dx += (scale * dy); + } +}; + +/* + * \brief Getting pooling results, and calculating gradient. + * + * In pool2d, all tensors are in NCHW format. Where N is batch size, C is the + * number of channels, H and W is the height and width of feature. + * In pool3d, all tensors are in NCDHW format. Where N is batch size, C is the + * number of channels, D, H and W is the depth, height and width of feature. + * + * In max pooling, it is possible that the pooling region has multiple maximum + * elements. In this case, we should compute the gradient of the first maximum + * element. + * This is different from average pooling. So we rewrite the max_pool_grad: + * MaxPool2dGradFunctor, MaxPool3dGradFunctor. + */ +template +class Pool2dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_compute); +}; + +template +class Pool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_compute); +}; + +template +class MaxPool2dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +template +class Pool3dFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + std::vector& ksize, std::vector& strides, + std::vector& paddings, PoolProcess pool_compute); +}; + +template +class Pool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings, + PoolProcess pool_compute); +}; + +template +class MaxPool3dGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& input_grad, + const framework::Tensor& output, + const framework::Tensor& output_grad, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +/* + * \brief Getting max pooling results and corresponding max index, and + * calculating gradient. + * In up-sampling-pooling, it is necessary to know max element index. + * In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in + * NCDHW format. + */ +template +class MaxPool2dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +template +class MaxPool2dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +template +class MaxPool3dWithIndexFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& input, framework::Tensor& output, + framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +template +class MaxPool3dWithIndexGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& input_grad, + const framework::Tensor& output_grad, + const framework::Tensor& mask, std::vector& ksize, + std::vector& strides, std::vector& paddings); +}; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/softmax.cc b/paddle/operators/math/softmax.cc index ac9f3c4bf61bf8e13faa17387f1112756db9a100..0ba8197ab8b64649c8adcf67771ba01eca7f1d10 100644 --- a/paddle/operators/math/softmax.cc +++ b/paddle/operators/math/softmax.cc @@ -1,16 +1,16 @@ /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 + http://www.apache.org/licenses/LICENSE-2.0 - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. */ +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ #include "paddle/operators/math/softmax.h" @@ -19,6 +19,7 @@ namespace operators { namespace math { template class SoftmaxFunctor; +template class SoftmaxGradFunctor; } // namespace math } // namespace operators diff --git a/paddle/operators/math/softmax.cu b/paddle/operators/math/softmax.cu index 4c3df0550e7ca6f4310db1d35cc34d5c73a2dd16..99f988d51e4b16c3f3bfd9c76b411bb53619603e 100644 --- a/paddle/operators/math/softmax.cu +++ b/paddle/operators/math/softmax.cu @@ -1,16 +1,16 @@ /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 + http://www.apache.org/licenses/LICENSE-2.0 - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. */ +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ #define EIGEN_USE_GPU @@ -21,6 +21,7 @@ namespace operators { namespace math { template class SoftmaxFunctor; +template class SoftmaxGradFunctor; } // namespace math } // namespace operators diff --git a/paddle/operators/math/softmax.h b/paddle/operators/math/softmax.h index 3d2f0d0aecffcd0fe51166c3d863aa8b91bba196..b7f627eee7f8fe68a83595a3390a55d438c97afb 100644 --- a/paddle/operators/math/softmax.h +++ b/paddle/operators/math/softmax.h @@ -1,16 +1,16 @@ /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 + http://www.apache.org/licenses/LICENSE-2.0 - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. */ +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ #pragma once #include "paddle/framework/eigen.h" @@ -36,7 +36,7 @@ struct ValueClip { template class SoftmaxFunctor { public: - void operator()(const framework::ExecutionContext& context, + void operator()(const platform::DeviceContext& context, const framework::Tensor* X, framework::Tensor* Y) { auto logits = EigenMatrix::From(*X); auto softmax = EigenMatrix::From(*Y); @@ -58,8 +58,8 @@ class SoftmaxFunctor { .broadcast(one_by_class)) .unaryExpr(ValueClip()); - softmax.device(context.GetEigenDevice()) = shifted_logits.exp(); - softmax.device(context.GetEigenDevice()) = + softmax.device(*context.GetEigenDevice()) = shifted_logits.exp(); + softmax.device(*context.GetEigenDevice()) = (softmax * softmax.sum(along_class) .inverse() @@ -68,6 +68,37 @@ class SoftmaxFunctor { .broadcast(one_by_class)); } }; + +template +class SoftmaxGradFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor* y, const framework::Tensor* y_grad, + framework::Tensor* x_grad) { + auto softmax = EigenMatrix::From(*y); + auto softmax_grad = EigenMatrix::From(*y_grad); + auto logits_grad = EigenMatrix::From(*x_grad); + + const int kBatchDim = 0; + const int kClassDim = 1; + + const int batch_size = softmax.dimension(kBatchDim); + const int num_classes = softmax.dimension(kClassDim); + + Eigen::DSizes along_class(kClassDim); + Eigen::DSizes batch_by_one(batch_size, 1); + Eigen::DSizes one_by_class(1, num_classes); + + auto dot = (softmax * softmax_grad) + .sum(along_class) + .eval() + .reshape(batch_by_one) + .broadcast(one_by_class); + logits_grad.device(*context.GetEigenDevice()) = + (softmax_grad - dot) * softmax; + } +}; + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/vol2col.cc b/paddle/operators/math/vol2col.cc new file mode 100644 index 0000000000000000000000000000000000000000..e9718a047381596a1570b4b00546622968b70227 --- /dev/null +++ b/paddle/operators/math/vol2col.cc @@ -0,0 +1,155 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/math/vol2col.h" + +namespace paddle { +namespace operators { +namespace math { + +/* + * vol = [input_channels, input_depth, input_height, input_width] + * col = + * [input_channels, filter_depth, filter_height, filter_width, + * output_depth, output_height, output_width] + */ +template +class Vol2ColFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& vol, framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const { + PADDLE_ENFORCE(vol.dims().size() == 4); + PADDLE_ENFORCE(col.dims().size() == 7); + + int input_channels = vol.dims()[0]; + int input_depth = vol.dims()[1]; + int input_height = vol.dims()[2]; + int input_width = vol.dims()[3]; + int filter_depth = col.dims()[1]; + int filter_height = col.dims()[2]; + int filter_width = col.dims()[3]; + int output_depth = col.dims()[4]; + int output_height = col.dims()[5]; + int output_width = col.dims()[6]; + int channels_col = + input_channels * filter_depth * filter_height * filter_width; + + const T* vol_data = vol.data(); + T* col_data = col.data(); + + for (int c = 0; c < channels_col; ++c) { + int w_offset = c % filter_width; + int h_offset = (c / filter_width) % filter_height; + int d_offset = (c / filter_width / filter_height) % filter_depth; + int c_in = c / filter_width / filter_height / filter_depth; + for (int d = 0; d < output_depth; ++d) { + int d_pad = d * stride_depth - padding_depth + d_offset; + for (int h = 0; h < output_height; ++h) { + int h_pad = h * stride_height - padding_height + h_offset; + for (int w = 0; w < output_width; ++w) { + int w_pad = w * stride_width - padding_width + w_offset; + + int col_idx = + ((c * output_depth + d) * output_height + h) * output_width + w; + if (h_pad < 0 || h_pad >= input_height || w_pad < 0 || + w_pad >= input_width || d_pad < 0 || d_pad >= input_depth) { + col_data[col_idx] = static_cast(0); + } else { + int vol_idx = + ((c_in * input_depth + d_pad) * input_height + h_pad) * + input_width + + w_pad; + col_data[col_idx] = vol_data[vol_idx]; + } + } + } + } + } + } +}; + +/* + * vol = [input_channels,input_depth, input_height, input_width] + * col = + * [input_channels, filter_depth, filter_height, filter_width, + * output_depth, output_height, output_width] + */ +template +class Col2VolFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& vol, const framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const { + PADDLE_ENFORCE(vol.dims().size() == 4); + PADDLE_ENFORCE(col.dims().size() == 7); + + int input_channels = vol.dims()[0]; + int input_depth = vol.dims()[1]; + int input_height = vol.dims()[2]; + int input_width = vol.dims()[3]; + int filter_depth = col.dims()[1]; + int filter_height = col.dims()[2]; + int filter_width = col.dims()[3]; + int output_depth = col.dims()[4]; + int output_height = col.dims()[5]; + int output_width = col.dims()[6]; + int channels_col = + input_channels * filter_depth * filter_height * filter_width; + + T* vol_data = vol.data(); + const T* col_data = col.data(); + + for (int c = 0; c < channels_col; ++c) { + int w_offset = c % filter_width; + int h_offset = (c / filter_width) % filter_height; + int d_offset = (c / filter_width / filter_height) % filter_depth; + int cIm = c / filter_width / filter_height / filter_depth; + for (int d = 0; d < output_depth; ++d) { + int d_pad = d * stride_depth - padding_depth + d_offset; + for (int h = 0; h < output_height; ++h) { + int h_pad = h * stride_height - padding_height + h_offset; + for (int w = 0; w < output_width; ++w) { + int w_pad = w * stride_width - padding_width + w_offset; + + if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 && + w_pad < input_width && d_pad >= 0 && d_pad < input_depth) { + int vol_idx = + ((cIm * input_depth + d_pad) * input_height + h_pad) * + input_width + + w_pad; + int col_idx = + ((c * output_depth + d) * output_height + h) * output_width + + w; + vol_data[vol_idx] += col_data[col_idx]; + } + } + } + } + } + } +}; + +template class Vol2ColFunctor; +template class Vol2ColFunctor; +template class Col2VolFunctor; +template class Col2VolFunctor; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/vol2col.cu b/paddle/operators/math/vol2col.cu new file mode 100644 index 0000000000000000000000000000000000000000..27b11fb237575fd25a789a5fcc24ed4e30607009 --- /dev/null +++ b/paddle/operators/math/vol2col.cu @@ -0,0 +1,204 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/math/vol2col.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { +namespace math { + +template +__global__ void vol2col(int num_kernels, const T* data_vol, int depth, + int height, int width, int filter_depth, + int filter_height, int filter_width, int stride_depth, + int stride_height, int stride_width, int padding_depth, + int padding_height, int padding_width, int output_detph, + int output_height, int output_width, T* data_col) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels; + index += blockDim.x * gridDim.x) { + int w_out = index % output_width; + int h_out = (index / output_width) % output_height; + int d_out = (index / output_width / output_height) % output_detph; + int channel_in = index / output_width / output_height / output_detph; + int channel_out = channel_in * filter_depth * filter_height * filter_width; + int w_in = w_out * stride_width - padding_width; + int h_in = h_out * stride_height - padding_height; + int d_in = d_out * stride_depth - padding_depth; + + data_col += ((channel_out * output_detph + d_out) * output_height + h_out) * + output_width + + w_out; + data_vol += ((channel_in * depth + d_in) * height + h_in) * width + w_in; + for (int k = 0; k < filter_depth; ++k) { + for (int i = 0; i < filter_height; ++i) { + for (int j = 0; j < filter_width; ++j) { + int d = d_in + k; + int h = h_in + i; + int w = w_in + j; + *data_col = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 && + w < width) + ? data_vol[(k * height + i) * width + j] + : 0; + data_col += output_detph * output_height * output_width; + } + } + } + } +} + +/* + * im = [input_channels,intpu_depth, input_height, input_width] + * col = + * [input_channels, filter_depth, filter_height, filter_width, + * output_depth, output_height, output_width] + */ +template +class Vol2ColFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& vol, framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const { + PADDLE_ENFORCE(vol.dims().size() == 4); + PADDLE_ENFORCE(col.dims().size() == 7); + + int input_channels = vol.dims()[0]; + int input_depth = vol.dims()[1]; + int input_height = vol.dims()[2]; + int input_width = vol.dims()[3]; + int filter_depth = col.dims()[1]; + int filter_height = col.dims()[2]; + int filter_width = col.dims()[3]; + int output_depth = col.dims()[4]; + int output_height = col.dims()[5]; + int output_width = col.dims()[6]; + + int num_outputs = + input_channels * output_depth * output_height * output_width; + + const int threads = 1024; + const int blocks = (num_outputs + 1024 - 1) / 1024; + vol2col<<(context) + .stream()>>>( + num_outputs, vol.data(), input_depth, input_height, input_width, + filter_depth, filter_height, filter_width, stride_depth, stride_height, + stride_width, padding_depth, padding_height, padding_width, + output_depth, output_height, output_width, col.data()); + } +}; + +template +__global__ void col2vol(int num_kernels, const T* data_col, int depth, + int height, int width, int filter_depth, + int filter_height, int filter_width, int stride_depth, + int stride_height, int stride_width, int padding_depth, + int padding_height, int padding_width, int output_detph, + int output_height, int output_width, T* data_vol) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels; + index += blockDim.x * gridDim.x) { + T src_val = 0; + int w = index % width + padding_width; + int h = (index / width) % height + padding_height; + int d = (index / width / height) % depth + padding_depth; + int c = index / width / height / depth; + // compute the start and end of the output + int w_col_start = + (w < filter_width) ? 0 : (w - filter_width) / stride_width + 1; + int w_col_end = min(w / stride_width + 1, output_width); + int h_col_start = + (h < filter_height) ? 0 : (h - filter_height) / stride_height + 1; + int h_col_end = min(h / stride_height + 1, output_height); + int d_col_start = + (d < filter_depth) ? 0 : (d - filter_depth) / stride_depth + 1; + int d_col_end = min(d / stride_depth + 1, output_detph); + + int offset = (c * filter_depth * filter_height * filter_width + + d * filter_width * filter_height + h * filter_width + w) * + output_detph * output_height * output_width; + + int coeff_d_col = + (1 - stride_depth * filter_width * filter_height * output_detph) * + output_height * output_width; + int coeff_h_col = + (1 - stride_height * filter_width * output_detph * output_height) * + output_width; + int coeff_w_col = + (1 - stride_width * output_detph * output_height * output_width); + + for (int d_col = d_col_start; d_col < d_col_end; ++d_col) { + for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { + for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { + src_val += data_col[offset + d_col * coeff_d_col + + h_col * coeff_h_col + w_col * coeff_w_col]; + } + } + } + data_vol[index] = src_val; + } +} + +/* + * im = [input_channels, input_depth, input_height, input_width] + * col = + * [input_channels, filter_depth, filter_height, filter_width, + * output_depth, output_height, output_width] + */ +template +class Col2VolFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& vol, const framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const { + PADDLE_ENFORCE(vol.dims().size() == 4); + PADDLE_ENFORCE(col.dims().size() == 7); + + int input_channels = vol.dims()[0]; + int input_depth = vol.dims()[1]; + int input_height = vol.dims()[2]; + int input_width = vol.dims()[3]; + int filter_depth = col.dims()[1]; + int filter_height = col.dims()[2]; + int filter_width = col.dims()[3]; + int output_depth = col.dims()[4]; + int output_height = col.dims()[5]; + int output_width = col.dims()[6]; + + int num_kernels = input_channels * input_depth * input_height * input_width; + + const int threads = 1024; + const int blocks = (num_kernels + 1024 - 1) / 1024; + + col2vol<<(context) + .stream()>>>( + num_kernels, col.data(), input_depth, input_height, input_width, + filter_depth, filter_height, filter_width, stride_depth, stride_height, + stride_width, padding_depth, padding_height, padding_width, + output_depth, output_height, output_width, vol.data()); + } +}; + +template class Vol2ColFunctor; +template class Vol2ColFunctor; +template class Col2VolFunctor; +template class Col2VolFunctor; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/vol2col.h b/paddle/operators/math/vol2col.h new file mode 100644 index 0000000000000000000000000000000000000000..f022365a16fbf61981e94bedbd8b21a32887b235 --- /dev/null +++ b/paddle/operators/math/vol2col.h @@ -0,0 +1,78 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/framework/tensor.h" +#include "paddle/platform/device_context.h" + +namespace paddle { +namespace operators { +namespace math { +/* + * \brief Converts the feature data of four dimensions(CDHW) into a colData of + * seven dimensions in the Vol2ColFunctor calculation, + * And in the Col2VolFunctor calculation, it is reversed. + * + * \param volData Vol data. + * \param volShape The shape of volData, + * [input_channels, input_depth, input_height, input_width]. + * \param colData Column data. + * \param colShape The shape of colData. + * + * The shape of colData is: + * [input_channels, filter_depth, filter_height, filter_width, output_depth, + * output_height, output_width] + * So, it is easy to reshape into a convolution matrix for convolution + * calculation based on matrix multiplication. + * The shape of convolution matrix is [height, width], where the height is equal + * input_channels * filter_depth * filter_height * filter_width, and the width + * is equal output_depth * output_height * output_width. + * + * Reshape: + * shape of colData shape of convolution matrix + * [input_channels, + * filter_depth, + * filter_height, + * filter_width, ======> [height, width] + * output_depth, + * output_height, + * output_width] + * + * \note The caller needs to ensure that volShape.inputChannels is equal to + * colShape.inputChannels. + */ +template +class Vol2ColFunctor { + public: + void operator()(const platform::DeviceContext& context, + const framework::Tensor& vol, framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const; +}; + +template +class Col2VolFunctor { + public: + void operator()(const platform::DeviceContext& context, + framework::Tensor& vol, const framework::Tensor& col, + int stride_depth, int stride_height, int stride_width, + int padding_depth, int padding_height, + int padding_width) const; +}; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/vol2col_test.cc b/paddle/operators/math/vol2col_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..81225e9a9803ce371d23620876ac22da63a8e2d1 --- /dev/null +++ b/paddle/operators/math/vol2col_test.cc @@ -0,0 +1,135 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/math/vol2col.h" +#include +#include + +template +void testVol2col() { + paddle::framework::Tensor input; + paddle::framework::Tensor input_tmp; + paddle::framework::Tensor output; + paddle::framework::Tensor output_tmp; + + auto* place = new Place(); + paddle::platform::DeviceContext* context; + if (paddle::platform::is_cpu_place(*place)) { + context = + new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace()); + } else { +#ifdef PADDLE_WITH_CUDA + context = + new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace()); +#else + PADDLE_THROW("no GPU support"); +#endif // PADDLE_WITH_CUDA + } + + /** + * input = [[0, 1, 2, + * 3, 4, 5] + * [6, 7, 8, + * 9, 10, 11]] + * + * output = [0, 1 + * 1, 2 + * 3, 4 + * 4, 5 + * 6, 7 + * 7, 8 + * 9, 10 + * 10, 11] + * + * col2vol = [[0, 2, 2, + * 3, 8, 5] + * [6, 14, 8, + * 9, 20, 11]] + * + */ + int input_depth = 2; + int input_height = 2; + int input_width = 3; + int filter_size = 2; + int stride = 1; + int padding = 0; + int output_depth = (input_depth - filter_size + 2 * padding) / stride + 1; + int output_height = (input_height - filter_size + 2 * padding) / stride + 1; + int output_width = (input_width - filter_size + 2 * padding) / stride + 1; + + // Vol2Col test + float* input_ptr = + input_tmp.mutable_data({1, input_depth, input_height, input_width}, + paddle::platform::CPUPlace()); + float arr[12] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}; + memcpy(input_ptr, arr, 12 * sizeof(float)); + + if (paddle::platform::is_cpu_place(*place)) { + input = input_tmp; + } else { + input.CopyFrom(input_tmp, *place); + } + output.mutable_data({1, filter_size, filter_size, filter_size, + output_depth, output_height, output_width}, + *place); + + paddle::operators::math::Vol2ColFunctor vol2col; + vol2col(*context, input, output, stride, stride, stride, padding, padding, + padding); + + float vol_2_col[] = {0, 1, 1, 2, 3, 4, 4, 5, 6, 7, 7, 8, 9, 10, 10, 11}; + float* out_cfo_ptr; + if (paddle::platform::is_cpu_place(*place)) { + out_cfo_ptr = output.data(); + } else { + output_tmp.CopyFrom(output, paddle::platform::CPUPlace()); + out_cfo_ptr = output_tmp.data(); + } + + for (int i = 0; i < 16; ++i) { + EXPECT_EQ(out_cfo_ptr[i], vol_2_col[i]); + } + + // Col2Vol test + float col_2_vol[] = {0, 2, 2, 3, 8, 5, 6, 14, 8, 9, 20, 11}; + memset(input_ptr, 0, 12 * sizeof(float)); + if (paddle::platform::is_cpu_place(*place)) { + input = input_tmp; + } else { + input.CopyFrom(input_tmp, *place); + } + + paddle::operators::math::Col2VolFunctor col2vol; + col2vol(*context, input, output, stride, stride, stride, padding, padding, + padding); + + float* in_ptr; + if (paddle::platform::is_cpu_place(*place)) { + in_ptr = input.data(); + } else { + input_tmp.CopyFrom(input, paddle::platform::CPUPlace()); + in_ptr = input_tmp.data(); + } + + for (int i = 0; i < 12; ++i) { + EXPECT_EQ(in_ptr[i], col_2_vol[i]); + } +} + +TEST(math, vol2col) { + testVol2col(); +#ifdef PADDLE_WITH_CUDA + testVol2col(); +#endif // PADDLE_WITH_CUDA +} diff --git a/paddle/operators/mean_op.cc b/paddle/operators/mean_op.cc index d799239d4ed6d230578c77921a1a454b476b63fa..441543049faf96c8fbc14e84448a348c3287c1e2 100644 --- a/paddle/operators/mean_op.cc +++ b/paddle/operators/mean_op.cc @@ -22,7 +22,7 @@ class MeanOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MeanOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -36,7 +36,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker { MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The input of mean op"); - AddOutput("Out", "The output of mean op").NotInGradient(); + AddOutput("Out", "The output of mean op"); AddComment(R"DOC( Mean Operator )DOC"); } @@ -47,16 +47,32 @@ class MeanGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } }; +class MeanGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("mean_grad"); + grad_op->SetInput("X", Input("X")); + grad_op->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); + grad_op->SetOutput(framework::GradVarName("X"), InputGrad("X")); + return std::unique_ptr(grad_op); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(mean, ops::MeanOp, ops::MeanOpMaker, mean_grad, ops::MeanGradOp); +REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanGradMaker); +REGISTER_OPERATOR(mean_grad, ops::MeanGradOp); REGISTER_OP_CPU_KERNEL(mean, ops::MeanKernel); REGISTER_OP_CPU_KERNEL(mean_grad, diff --git a/paddle/operators/mean_op.h b/paddle/operators/mean_op.h index ce31e178d8e375dc59be80a6c05133201308da70..c99286a5b928f1edcd845b01b21b95654c25db07 100644 --- a/paddle/operators/mean_op.h +++ b/paddle/operators/mean_op.h @@ -28,7 +28,7 @@ template ; template -class MeanKernel : public framework::OpKernel { +class MeanKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* input = context.Input("X"); @@ -45,7 +45,7 @@ class MeanKernel : public framework::OpKernel { }; template -class MeanGradKernel : public framework::OpKernel { +class MeanGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto OG = context.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc index ce049d4d7bd96a6758d71b381e6e6b4edbcc8b5c..d7fd2f901b766c3105a5ea8847fb8a3fe456a945 100644 --- a/paddle/operators/minus_op.cc +++ b/paddle/operators/minus_op.cc @@ -26,7 +26,7 @@ class MinusOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MinusOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -49,9 +49,9 @@ class MinusOpMaker : public framework::OpProtoAndCheckerMaker { public: MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The left tensor of minus operator.").NotInGradient(); - AddInput("Y", "The right tensor of minus operator.").NotInGradient(); - AddOutput("Out", "The output tensor of minus operator.").NotInGradient(); + AddInput("X", "The left tensor of minus operator."); + AddInput("Y", "The right tensor of minus operator."); + AddOutput("Out", "The output tensor of minus operator."); AddComment(R"DOC(Minus Operator @@ -64,26 +64,35 @@ or not. But the output only shares the LoD with input `X`. )DOC"); } }; -template -class MinusGradOp : public NetOp { + +class MinusGradMaker : public framework::GradOpDescMakerBase { public: - MinusGradOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : NetOp(type, inputs, outputs, attrs) { - auto out_grad = Input(framework::GradVarName("Out")); - auto x_grad = Output(framework::GradVarName("X")); - auto y_grad = Output(framework::GradVarName("Y")); - - // x_grad = out_grad - AppendOp(framework::OpRegistry::CreateOp("identity", {{"X", {out_grad}}}, - {{"Y", {x_grad}}}, {})); - - framework::AttributeMap scale_attr; - scale_attr["scale"] = static_cast(-1); - AppendOp(framework::OpRegistry::CreateOp("scale", {{"X", {out_grad}}}, - {{"Out", {y_grad}}}, scale_attr)); - CompleteAddOp(false); + using framework::GradOpDescMakerBase::GradOpDescMakerBase; + + std::vector> operator()() + const override { + std::vector> ops; + auto x_g = InputGrad("X"); + if (!x_g.empty()) { + auto *x_g_op = new framework::OpDescBind(); + x_g_op->SetType("scale"); + x_g_op->SetInput("X", OutputGrad("Out")); + x_g_op->SetOutput("Out", x_g); + x_g_op->SetAttr("scale", 1.0f); + ops.emplace_back(x_g_op); + } + + auto y_g = InputGrad("Y"); + if (!y_g.empty()) { + auto *y_g_op = new framework::OpDescBind(); + y_g_op->SetType("scale"); + y_g_op->SetInput("X", OutputGrad("Out")); + y_g_op->SetOutput("Out", y_g); + y_g_op->SetAttr("scale", -1.0f); + ops.emplace_back(y_g_op); + } + + return ops; } }; @@ -91,7 +100,6 @@ class MinusGradOp : public NetOp { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad, - ops::MinusGradOp); +REGISTER_OPERATOR(minus, ops::MinusOp, ops::MinusOpMaker, ops::MinusGradMaker); REGISTER_OP_CPU_KERNEL(minus, ops::MinusKernel); diff --git a/paddle/operators/minus_op.h b/paddle/operators/minus_op.h index 6310a4fd5141516cff4fc7acbe1d17913a1b5506..bd9a2790aa2b208c2d3dfc792031283eb6c42397 100644 --- a/paddle/operators/minus_op.h +++ b/paddle/operators/minus_op.h @@ -20,7 +20,7 @@ namespace paddle { namespace operators { template -class MinusKernel : public framework::OpKernel { +class MinusKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* left_tensor = context.Input("X"); diff --git a/paddle/operators/modified_huber_loss_op.cc b/paddle/operators/modified_huber_loss_op.cc index 84212a2b3be1ac3664ebd77c7a0ae4d86abad3a0..6522327fdcf646a60df76909d049341b5a9021c9 100644 --- a/paddle/operators/modified_huber_loss_op.cc +++ b/paddle/operators/modified_huber_loss_op.cc @@ -22,7 +22,7 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); @@ -74,7 +74,7 @@ class ModifiedHuberLossGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"), diff --git a/paddle/operators/modified_huber_loss_op.cu b/paddle/operators/modified_huber_loss_op.cu index bce760f95e72cfec05b07591e0fa1250168b112f..8854e166cd99ce914d7f9f9bcead3234b0649506 100644 --- a/paddle/operators/modified_huber_loss_op.cu +++ b/paddle/operators/modified_huber_loss_op.cu @@ -39,7 +39,7 @@ struct ModifiedHuberLossBackward { }; template -class ModifiedHuberLossGradGPUKernel : public framework::OpKernel { +class ModifiedHuberLossGradGPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("Y"); diff --git a/paddle/operators/modified_huber_loss_op.h b/paddle/operators/modified_huber_loss_op.h index cb51007749e3c59572d4852959f4119ac377decc..aba75efad9c19e3e113b4f09bc1fbd4732f4e187 100644 --- a/paddle/operators/modified_huber_loss_op.h +++ b/paddle/operators/modified_huber_loss_op.h @@ -47,7 +47,7 @@ struct ModifiedHuberLossForward { }; template -class ModifiedHuberLossKernel : public framework::OpKernel { +class ModifiedHuberLossKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("X"); @@ -73,7 +73,7 @@ class ModifiedHuberLossKernel : public framework::OpKernel { // CPU backward kernel template -class ModifiedHuberLossGradCPUKernel : public framework::OpKernel { +class ModifiedHuberLossGradCPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("Y"); diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 9858c4d9c2195c7bd0e767aaa86a950e0a791443..ec0683d8875a945746b676a1f859e614af557441 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -1,16 +1,16 @@ /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at - http://www.apache.org/licenses/LICENSE-2.0 + http://www.apache.org/licenses/LICENSE-2.0 - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. */ +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ #include "paddle/operators/mul_op.h" @@ -24,7 +24,7 @@ class MulOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -35,12 +35,14 @@ class MulOp : public framework::OperatorWithKernel { int x_num_col_dims = ctx->Attrs().Get("x_num_col_dims"); int y_num_col_dims = ctx->Attrs().Get("y_num_col_dims"); - PADDLE_ENFORCE(x_dims.size() > x_num_col_dims, - "The rank of input tensor X should be larger than " - "`mul_op`'s `x_num_col_dims`."); - PADDLE_ENFORCE(y_dims.size() > y_num_col_dims, - "The rank of input tensor Y should be larger than " - "`mul_op`'s `y_num_col_dims`."); + PADDLE_ENFORCE_GT( + x_dims.size(), x_num_col_dims, + "The input tensor X's rank of MulOp should be larger than " + "x_num_col_dims."); + PADDLE_ENFORCE_GT( + y_dims.size(), y_num_col_dims, + "The input tensor Y's rank of MulOp should be larger than " + "y_num_col_dims."); auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims); auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims); @@ -95,7 +97,7 @@ class MulOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index ac7136a76933d1f3ead86518c65d589747227631..684b1ea0c0c8ddabc9809cc05ed985e0cc250955 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -28,7 +28,7 @@ template ; template -class MulKernel : public framework::OpKernel { +class MulKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { const Tensor* x = context.Input("X"); @@ -52,7 +52,7 @@ class MulKernel : public framework::OpKernel { }; template -class MulGradKernel : public framework::OpKernel { +class MulGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { int x_num_col_dims = ctx.template Attr("x_num_col_dims"); diff --git a/paddle/operators/multiplex_op.cc b/paddle/operators/multiplex_op.cc index 9896d269ccc86d8fdc3bf6375e44ef5bf3e6b9c7..a86685b6dde4761cf74f9521bd9609b0864b9bdf 100644 --- a/paddle/operators/multiplex_op.cc +++ b/paddle/operators/multiplex_op.cc @@ -24,7 +24,7 @@ class MultiplexOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Ids"), "Input(Ids) shouldn't be null."); PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "MultiInput(X) shouldn't be empty."); @@ -50,6 +50,11 @@ class MultiplexOp : public framework::OperatorWithKernel { } ctx->SetOutputDim("Out", in_dim); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.MultiInput("X")[0]->type()); + } }; class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker { @@ -85,7 +90,7 @@ class MultiplexGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null."); PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(), "Output(X@Grad) should not be null."); @@ -99,6 +104,11 @@ class MultiplexGradOp : public framework::OperatorWithKernel { } ctx->SetOutputsDim(framework::GradVarName("X"), d_ins); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.MultiInput("X")[0]->type()); + } }; } // namespace operators diff --git a/paddle/operators/multiplex_op.cu b/paddle/operators/multiplex_op.cu index 505776612e7119e568493506b113661a839e5bd1..72b1f96eafde37976b4b067b534112b17e02b807 100644 --- a/paddle/operators/multiplex_op.cu +++ b/paddle/operators/multiplex_op.cu @@ -21,7 +21,7 @@ namespace operators { using Tensor = framework::Tensor; template -class MultiplexGPUKernel : public framework::OpKernel { +class MultiplexGPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto ins = ctx.MultiInput("X"); @@ -51,7 +51,7 @@ class MultiplexGPUKernel : public framework::OpKernel { }; template -class MultiplexGradGPUKernel : public framework::OpKernel { +class MultiplexGradGPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* d_out = ctx.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/multiplex_op.h b/paddle/operators/multiplex_op.h index 637c63a34af394f5f54997c46c00a9ff00577476..ab3cafaa324a29d6f249cf1f73db92e1364eebc8 100644 --- a/paddle/operators/multiplex_op.h +++ b/paddle/operators/multiplex_op.h @@ -23,7 +23,7 @@ namespace paddle { namespace operators { template -class MultiplexCPUKernel : public framework::OpKernel { +class MultiplexCPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto ins = ctx.MultiInput("X"); @@ -48,7 +48,7 @@ class MultiplexCPUKernel : public framework::OpKernel { }; template -class MultiplexGradCPUKernel : public framework::OpKernel { +class MultiplexGradCPUKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* d_out = ctx.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 2388b094d228562a4c9bfd1ad6840ef1c2068533..ebeb262d9621fa35c870b6407992f6b6d2bf7c70 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include "paddle/framework/framework.pb.h" #include "paddle/framework/op_registry.h" diff --git a/paddle/operators/pad_op.cc b/paddle/operators/pad_op.cc index 04ebb14f6ee6c73f48aa2f75811a22f9b8a25006..2f26ada85e477a6e5cd24c881f6369548331e8d0 100644 --- a/paddle/operators/pad_op.cc +++ b/paddle/operators/pad_op.cc @@ -24,7 +24,7 @@ class PadOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of PadOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of PadOp should not be null."); @@ -56,8 +56,7 @@ class PadOpMaker : public framework::OpProtoAndCheckerMaker { "The input should be a k-D tensor(k > 0 and k < 7)"); AddOutput("Out", "The output of pad op." - "A tensor with the same shape as X.") - .NotInGradient(); + "A tensor with the same shape as X."); AddComment(R"DOC( Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example: @@ -99,7 +98,7 @@ class PadOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); @@ -111,11 +110,29 @@ class PadOpGrad : public framework::OperatorWithKernel { } }; +class PadOpGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* bind = new framework::OpDescBind(); + bind->SetInput("X", Input("X")); + bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out")); + bind->SetOutput(framework::GradVarName("X"), InputGrad("X")); + bind->SetAttrMap(Attrs()); + bind->SetType("pad_grad"); + return std::unique_ptr(bind); + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad); + +REGISTER_OPERATOR(pad, ops::PadOp, ops::PadOpMaker, ops::PadOpGradMaker); +REGISTER_OPERATOR(pad_grad, ops::PadOpGrad); REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel); REGISTER_OP_CPU_KERNEL(pad_grad, ops::PadGradKernel); diff --git a/paddle/operators/pad_op.h b/paddle/operators/pad_op.h index 2cc3b945ae5b2e2e93d8531c7f99e4c215d1d806..9534dbf54529e3b9ae2b6640d51fe291e9521927 100644 --- a/paddle/operators/pad_op.h +++ b/paddle/operators/pad_op.h @@ -47,7 +47,7 @@ void PadFunction(const framework::ExecutionContext& context) { } template -class PadKernel : public framework::OpKernel { +class PadKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { int rank = context.Input("X")->dims().size(); @@ -97,7 +97,7 @@ void PadGradFunction(const framework::ExecutionContext& context) { } template -class PadGradKernel : public framework::OpKernel { +class PadGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { size_t rank = diff --git a/paddle/operators/pool_op.cc b/paddle/operators/pool_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..ba3b5ed2075ceca284b49ecddb90ba5950b820c3 --- /dev/null +++ b/paddle/operators/pool_op.cc @@ -0,0 +1,195 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/pool_op.h" + +namespace paddle { +namespace operators { + +int OutputSizePool(int input_size, int filter_size, int padding, int stride) { + int output_size = (input_size - filter_size + 2 * padding) / stride + 1; + return output_size; +} + +class PoolOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "X(Input) of Pooling should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Out(Output) of Pooling should not be null."); + + auto in_x_dims = ctx->GetInputDim("X"); + + std::string pooling_type = ctx->Attrs().Get("poolingType"); + std::vector ksize = ctx->Attrs().Get>("ksize"); + std::vector strides = ctx->Attrs().Get>("strides"); + std::vector paddings = ctx->Attrs().Get>("paddings"); + + PADDLE_ENFORCE(pooling_type == "max" || pooling_type == "avg", + "pooling_type should be 'max' or 'avg'"); + PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5, + "Pooling intput should be 4-D or 5-D"); + + if (ctx->Attrs().Get("globalPooling")) { + ksize.resize(static_cast(in_x_dims.size()) - 2); + for (size_t i = 0; i < ksize.size(); ++i) + ksize[i] = static_cast(in_x_dims[i + 2]); + } + + PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U, + "Input size and Pooling size should be consistent."); + PADDLE_ENFORCE(ksize.size() == 2 || ksize.size() == 3, + "Pooling size should be 2 elements. or 3 elements."); + PADDLE_ENFORCE_EQ(ksize.size(), strides.size(), + "strides size and pooling size should be the same."); + PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(), + "paddings size and pooling size should be the same."); + + std::vector output_shape({in_x_dims[0], in_x_dims[1]}); + for (size_t i = 0; i < ksize.size(); ++i) { + output_shape.push_back( + OutputSizePool(in_x_dims[i + 2], ksize[i], paddings[i], strides[i])); + } + ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); + } +}; + +class PoolOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "X(Input) of Pooling should not be null."); + PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), + "Input@Grad of Pooling should not be null."); + ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); + } +}; + +class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker { + public: + Pool2dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "The input tensor of pooling operator. " + "The format of input tensor is NCHW. Where N is batch size, C is the " + "number of channels, H and W is the height and width of feature."); + AddOutput("Out", + "The output tensor of pooling operator." + "The format of output tensor is also NCHW."); + + AddAttr("poolingType", + "PoolingType of pooling operator." + "Str constant equal to 'max' or 'avg'.") + .InEnum({"max", "avg"}); + AddAttr>( + "ksize", + "Pooling size(depth, height, width) of pooling operator." + "If globalPooling = true, ksize is ignored and need not be " + "specified."); // TODO(Add checker) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>("strides", + "Strides(height, width) of pooling operator." + "Default {1,1}") + .SetDefault({1, 1}); // TODO(Add checker) + AddAttr>("paddings", + "Paddings(height, width) of pooling operator." + "Default {0,0}.") + .SetDefault({0, 0}); // TODO(Add checker) + AddComment(R"DOC( +The pooling2d operation calculates the output based on +the input, poolingType and ksize, strides, paddings parameters. +)DOC"); + } +}; + +class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker { + public: + Pool3dOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "The input tensor of pooling operator. " + "The format of input tensor is NCDHW. Where N is batch size, C is " + "the " + "number of channels, D, H and W is the depth, height and width of " + "feature."); + AddOutput("Out", + "The output tensor of pooling operator." + "The format of output tensor is also NCDHW."); + + AddAttr("poolingType", + "PoolingType of pooling operator." + "str constant equal to 'max' or 'avg'.") + .InEnum({"max", "avg"}); + AddAttr>( + "ksize", + "Pooling size(depth, height, width) of pooling operator." + "If globalPooling = true, ksize is ignored and need not be " + "specified."); // TODO(Add checker) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>( + "strides", + "Strides(depth, height, width) of pooling operator." + "Default {1,1,1}.") + .SetDefault({1, 1, 1}); // TODO(Add checker) + AddAttr>( + "paddings", + "Paddings(depth, height, width) of pooling operator." + "Default {0,0,0}.") + .SetDefault({0, 0, 0}); // TODO(Add checker) + AddComment(R"DOC( +The pooling3d operation calculates the output based on +the input, poolingType and ksize, strides, paddings parameters. +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad, + ops::PoolOpGrad); + +REGISTER_OP_CPU_KERNEL(pool2d, + ops::PoolKernel); +REGISTER_OP_CPU_KERNEL(pool2d_grad, + ops::PoolGradKernel) + +REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad, + ops::PoolOpGrad); + +REGISTER_OP_CPU_KERNEL(pool3d, + ops::PoolKernel); +REGISTER_OP_CPU_KERNEL(pool3d_grad, + ops::PoolGradKernel); diff --git a/paddle/operators/pool_op.cu b/paddle/operators/pool_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..0e3b80868f7b9d1697d619889160856d65ad59a3 --- /dev/null +++ b/paddle/operators/pool_op.cu @@ -0,0 +1,27 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/pool_op.h" + +namespace ops = paddle::operators; + +REGISTER_OP_GPU_KERNEL(pool2d, + ops::PoolKernel); +REGISTER_OP_GPU_KERNEL(pool2d_grad, + ops::PoolGradKernel); + +REGISTER_OP_GPU_KERNEL(pool3d, + ops::PoolKernel); +REGISTER_OP_GPU_KERNEL(pool3d_grad, + ops::PoolGradKernel); diff --git a/paddle/operators/pool_op.h b/paddle/operators/pool_op.h new file mode 100644 index 0000000000000000000000000000000000000000..c2bc358def42959f2cc8f61cb00436fae1b7514b --- /dev/null +++ b/paddle/operators/pool_op.h @@ -0,0 +1,147 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/math_function.h" +#include "paddle/operators/math/pooling.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class PoolKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const Tensor* in_x = context.Input("X"); + Tensor* out = context.Output("Out"); + + std::string pooling_type = context.Attr("poolingType"); + std::vector ksize = context.Attr>("ksize"); + std::vector strides = context.Attr>("strides"); + std::vector paddings = context.Attr>("paddings"); + if (context.Attr("globalPooling")) { + for (size_t i = 0; i < ksize.size(); ++i) { + ksize[i] = static_cast(in_x->dims()[i + 2]); + } + } + + switch (ksize.size()) { + case 2: { + if (pooling_type == "max") { + paddle::operators::math::Pool2dFunctor< + Place, paddle::operators::math::MaxPool, T> + pool2d_forward; + paddle::operators::math::MaxPool pool_process; + pool2d_forward(context.device_context(), *in_x, *out, ksize, strides, + paddings, pool_process); + + } else if (pooling_type == "avg") { + paddle::operators::math::Pool2dFunctor< + Place, paddle::operators::math::AvgPool, T> + pool2d_forward; + paddle::operators::math::AvgPool pool_process; + pool2d_forward(context.device_context(), *in_x, *out, ksize, strides, + paddings, pool_process); + } + } break; + case 3: { + if (pooling_type == "max") { + paddle::operators::math::Pool3dFunctor< + Place, paddle::operators::math::MaxPool, T> + pool3d_forward; + paddle::operators::math::MaxPool pool_process; + pool3d_forward(context.device_context(), *in_x, *out, ksize, strides, + paddings, pool_process); + } else if (pooling_type == "avg") { + paddle::operators::math::Pool3dFunctor< + Place, paddle::operators::math::AvgPool, T> + pool3d_forward; + paddle::operators::math::AvgPool pool_process; + pool3d_forward(context.device_context(), *in_x, *out, ksize, strides, + paddings, pool_process); + } + } break; + } + } +}; + +template +class PoolGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const Tensor* in_x = context.Input("X"); + const Tensor* out = context.Input("Out"); + const Tensor* out_grad = + context.Input(framework::GradVarName("Out")); + Tensor* in_x_grad = context.Output(framework::GradVarName("X")); + + std::string pooling_type = context.Attr("poolingType"); + std::vector ksize = context.Attr>("ksize"); + std::vector strides = context.Attr>("strides"); + std::vector paddings = context.Attr>("paddings"); + + if (context.Attr("globalPooling")) { + for (size_t i = 0; i < ksize.size(); ++i) + ksize[i] = static_cast(in_x->dims()[i + 2]); + } + + if (in_x_grad) { + in_x_grad->mutable_data(context.GetPlace()); + auto temp = framework::EigenVector::Flatten(*in_x_grad); + temp.device(context.GetEigenDevice()) = + temp.constant(static_cast(0)); + + switch (ksize.size()) { + case 2: { + if (pooling_type == "max") { + paddle::operators::math::MaxPool2dGradFunctor + pool2d_backward; + pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out, + *out_grad, ksize, strides, paddings); + } else if (pooling_type == "avg") { + paddle::operators::math::Pool2dGradFunctor< + Place, paddle::operators::math::AvgPoolGrad, T> + pool2d_backward; + paddle::operators::math::AvgPoolGrad pool_process; + pool2d_backward(context.device_context(), *in_x, *in_x_grad, *out, + *out_grad, ksize, strides, paddings, pool_process); + } + } break; + case 3: { + if (pooling_type == "max") { + paddle::operators::math::MaxPool3dGradFunctor + pool3d_backward; + pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out, + *out_grad, ksize, strides, paddings); + } else if (pooling_type == "avg") { + paddle::operators::math::Pool3dGradFunctor< + Place, paddle::operators::math::AvgPoolGrad, T> + pool3d_backward; + paddle::operators::math::AvgPoolGrad pool_process; + pool3d_backward(context.device_context(), *in_x, *in_x_grad, *out, + *out_grad, ksize, strides, paddings, pool_process); + } + } break; + } + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/pool_with_index_op.cc b/paddle/operators/pool_with_index_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..7b6afcfd1f7e30624cb6859228892677cba58856 --- /dev/null +++ b/paddle/operators/pool_with_index_op.cc @@ -0,0 +1,228 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/pool_with_index_op.h" + +namespace paddle { +namespace operators { + +inline int OutputSizeMaxPool(int input_size, int filter_size, int padding, + int stride) { + int output_size = (input_size - filter_size + 2 * padding) / stride + 1; + return output_size; +} + +class MaxPoolWithIndexOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "X(Input) of Pooling should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Out(Output) of Pooling should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Mask"), + "Mask(Output) of Pooling should not be null."); + + auto in_x_dims = ctx->GetInputDim("X"); + + std::vector ksize = ctx->Attrs().Get>("ksize"); + std::vector strides = ctx->Attrs().Get>("strides"); + std::vector paddings = ctx->Attrs().Get>("paddings"); + + PADDLE_ENFORCE(in_x_dims.size() == 4 || in_x_dims.size() == 5, + "Pooling intput should be 4-D or 5-D"); + + if (ctx->Attrs().Get("globalPooling")) { + ksize.resize(static_cast(in_x_dims.size()) - 2); + for (size_t i = 0; i < ksize.size(); ++i) + ksize[i] = static_cast(in_x_dims[i + 2]); + } + + PADDLE_ENFORCE(in_x_dims.size() - ksize.size() == 2U, + "Intput size and pooling size should be consistent."); + PADDLE_ENFORCE_EQ(ksize.size(), strides.size(), + "Strides size and pooling size should be the same."); + PADDLE_ENFORCE_EQ(ksize.size(), paddings.size(), + "Paddings size and pooling size should be the same."); + + std::vector output_shape({in_x_dims[0], in_x_dims[1]}); + for (size_t i = 0; i < ksize.size(); ++i) { + output_shape.push_back(OutputSizeMaxPool(in_x_dims[i + 2], ksize[i], + paddings[i], strides[i])); + } + ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); + ctx->SetOutputDim("Mask", framework::make_ddim(output_shape)); + } +}; + +class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); + PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), + "Input(X@GRAD) should not be null."); + ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); + } +}; + +class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MaxPool2dWithIndexOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "The input tensor of pooling operator. " + "The format of input tensor is NCHW. Where N is batch size, C is the " + "number of channels, H and W is the height and width of image."); + AddOutput("Out", + "The output tensor of pooling operator." + "The format of output tensor is also NCHW." + "Where N is batch size, C is " + "the number of channels, H and W is the height and " + "width of image."); + AddOutput("Mask", + "The Mask tensor of pooling operator." + "The format of output tensor is also NCHW." + "Where N is batch size, C is the number of channels, H and W " + "is the height and width of image." + "The value in it is the index in current feature map"); + + AddAttr>( + "ksize", + "The pooling size(height, width) of pooling operator." + "If globalPooling = true, ksize is ignored and need not be " + "specified."); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>("strides", + "Strides(height, width) of pooling operator." + "Default {1,1}.") + .SetDefault({1, 1}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr>("paddings", + "Paddings(height, width) of pooling operator." + "Default {0,0}.") + .SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + + AddComment(R"DOC( +The maxPooling2d with index operation calculates the output and the mask +based on the input and ksize, strides, paddings parameters. Input(X) and +output(Out, Mask) are in NCHW format. Where N is batch size, C is the +number of channels, H and W is the height and width of feature. +Parameters(ksize, strides, paddings) are two elements. +These two elements represent height and width, respectively. +)DOC"); + } +}; + +class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MaxPool3dWithIndexOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "The input tensor of pooling operator. " + "The format of input tensor is NCDHW. Where N is batch size, C is " + "the number of channels, D, H and W is the depth, height and width of " + "image."); + AddOutput("Out", + "The output tensor of pooling operator." + "The format of output tensor is also NCDHW." + "Where N is batch size, C is " + "the number of channels, D, H and W is the depth, height and " + "width of image."); + AddOutput("Mask", + "The Mask tensor of pooling operator." + "The format of output tensor is also NCDHW." + "Where N is batch size, C is the number of channels, D, H and W " + "is the depth, height and width of image." + "The value in it is the index in current feature map"); + + AddAttr>( + "ksize", + "The pooling size(depth, height, width) of pooling operator." + "If globalPooling = true, ksize is ignored and need not be " + "specified."); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr( + "globalPooling", + "Whether to use the globalPooling." + "Bool constant equal to false or true." + "Default false." + "If globalPooling = true, ksize is ignored and need not be specified.") + .SetDefault(false); + AddAttr>( + "strides", + "Strides(depth, height, width) of pooling operator." + "Default {1,1,1}.") + .SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + AddAttr>( + "paddings", + "Paddings(depth, height, width) of pooling operator." + "Default {0,0,0}.") + .SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently, + // TypedAttrChecker don't support vector type.) + + AddComment(R"DOC( +The maxpooling3d with index operation calculates the output and the mask +based on the input and ksize, strides, paddings parameters. +Input(X) and output(Out, Mask) are in NCDHW format. Where N is batch +size, C is the number of channels, D, H and W is the depth, height and +width of feature. Parameters(ksize, strides, paddings) are three elements. +These three elements represent depth, height and width, respectively. +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP(max_pool2d_with_index, ops::MaxPoolWithIndexOp, + ops::MaxPool2dWithIndexOpMaker, max_pool2d_with_index_grad, + ops::MaxPoolWithIndexOpGrad); + +REGISTER_OP_CPU_KERNEL( + max_pool2d_with_index, + ops::MaxPoolWithIndexKernel); +REGISTER_OP_CPU_KERNEL( + max_pool2d_with_index_grad, + ops::MaxPoolWithIndexGradKernel) + +REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp, + ops::MaxPool3dWithIndexOpMaker, max_pool3d_with_index_grad, + ops::MaxPoolWithIndexOpGrad); + +REGISTER_OP_CPU_KERNEL( + max_pool3d_with_index, + ops::MaxPoolWithIndexKernel); +REGISTER_OP_CPU_KERNEL( + max_pool3d_with_index_grad, + ops::MaxPoolWithIndexGradKernel) diff --git a/paddle/operators/pool_with_index_op.cu b/paddle/operators/pool_with_index_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..287657d4b1c57f354ef050885f71261092bdc062 --- /dev/null +++ b/paddle/operators/pool_with_index_op.cu @@ -0,0 +1,31 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/pool_with_index_op.h" + +namespace ops = paddle::operators; + +REGISTER_OP_GPU_KERNEL( + max_pool2d_with_index, + ops::MaxPoolWithIndexKernel); +REGISTER_OP_GPU_KERNEL( + max_pool2d_with_index_grad, + ops::MaxPoolWithIndexGradKernel) + +REGISTER_OP_GPU_KERNEL( + max_pool3d_with_index, + ops::MaxPoolWithIndexKernel); +REGISTER_OP_GPU_KERNEL( + max_pool3d_with_index_grad, + ops::MaxPoolWithIndexGradKernel) diff --git a/paddle/operators/pool_with_index_op.h b/paddle/operators/pool_with_index_op.h new file mode 100644 index 0000000000000000000000000000000000000000..01b961ca8295f723bea7335e43ec5ab100dfc65c --- /dev/null +++ b/paddle/operators/pool_with_index_op.h @@ -0,0 +1,103 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/math_function.h" +#include "paddle/operators/math/pooling.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class MaxPoolWithIndexKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const Tensor* in_x = context.Input("X"); + Tensor* out = context.Output("Out"); + Tensor* mask = context.Output("Mask"); + + std::vector ksize = context.Attr>("ksize"); + std::vector strides = context.Attr>("strides"); + std::vector paddings = context.Attr>("paddings"); + if (context.Attr("globalPooling")) { + for (size_t i = 0; i < ksize.size(); ++i) { + ksize[i] = static_cast(in_x->dims()[i + 2]); + } + } + + switch (ksize.size()) { + case 2: { + paddle::operators::math::MaxPool2dWithIndexFunctor + pool2d_forward; + pool2d_forward(context.device_context(), *in_x, *out, *mask, ksize, + strides, paddings); + } break; + case 3: { + paddle::operators::math::MaxPool3dWithIndexFunctor + pool3d_forward; + pool3d_forward(context.device_context(), *in_x, *out, *mask, ksize, + strides, paddings); + } break; + } + } +}; + +template +class MaxPoolWithIndexGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + const Tensor* mask = context.Input("Mask"); + const Tensor* out_grad = + context.Input(framework::GradVarName("Out")); + Tensor* in_x_grad = context.Output(framework::GradVarName("X")); + + std::vector ksize = context.Attr>("ksize"); + std::vector strides = context.Attr>("strides"); + std::vector paddings = context.Attr>("paddings"); + if (context.Attr("globalPooling")) { + for (size_t i = 0; i < ksize.size(); ++i) { + ksize[i] = static_cast(in_x_grad->dims()[i + 2]); + } + } + + if (in_x_grad) { + in_x_grad->mutable_data(context.GetPlace()); + auto temp = framework::EigenVector::Flatten(*in_x_grad); + temp.device(context.GetEigenDevice()) = + temp.constant(static_cast(0)); + + switch (ksize.size()) { + case 2: { + paddle::operators::math::MaxPool2dWithIndexGradFunctor + pool2d_backward; + pool2d_backward(context.device_context(), *in_x_grad, *out_grad, + *mask, ksize, strides, paddings); + } break; + case 3: { + paddle::operators::math::MaxPool3dWithIndexGradFunctor + pool3d_backward; + pool3d_backward(context.device_context(), *in_x_grad, *out_grad, + *mask, ksize, strides, paddings); + } break; + } + } + } +}; +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/prelu_op.cc b/paddle/operators/prelu_op.cc index 1692464f2833a59243ccc1598422180262a59282..166fe2682422c61a264c88b8683752ee323e940e 100644 --- a/paddle/operators/prelu_op.cc +++ b/paddle/operators/prelu_op.cc @@ -26,7 +26,7 @@ class PReluOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null"); PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1, @@ -63,7 +63,7 @@ class PReluGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/prelu_op.h b/paddle/operators/prelu_op.h index 6b78ed295cbac060d816fb3dd27a4b80145cb1ce..5ad31c2203ae6c9bf6f48bb9ecf9a714597e7da8 100644 --- a/paddle/operators/prelu_op.h +++ b/paddle/operators/prelu_op.h @@ -40,7 +40,7 @@ class PReluFunctor { }; template -class PReluKernel : public framework::OpKernel { +class PReluKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* x = context.Input("X"); @@ -77,7 +77,7 @@ class PReluGradFunctor { }; template -class PReluGradKernel : public framework::OpKernel { +class PReluGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* dx = context.Output(framework::GradVarName("X")); diff --git a/paddle/operators/rank_loss_op.cc b/paddle/operators/rank_loss_op.cc index 1ba22006f27abc963e7f161636a964863513a40c..e0abbc4db1723e297c313d50fc1f9cbb77acb9f3 100644 --- a/paddle/operators/rank_loss_op.cc +++ b/paddle/operators/rank_loss_op.cc @@ -25,7 +25,7 @@ class RankLossOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { // input check PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null"); PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null"); @@ -90,7 +90,7 @@ class RankLossGradOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null."); diff --git a/paddle/operators/rank_loss_op.h b/paddle/operators/rank_loss_op.h index 7df195ff47ecfd79388385eed4bd37b8c9b45979..f184d6efcb496a1d7f38540712b6c431f816482e 100644 --- a/paddle/operators/rank_loss_op.h +++ b/paddle/operators/rank_loss_op.h @@ -21,7 +21,7 @@ namespace paddle { namespace operators { template -class RankLossKernel : public framework::OpKernel { +class RankLossKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* out_t = ctx.Output("Out"); @@ -42,7 +42,7 @@ class RankLossKernel : public framework::OpKernel { }; template -class RankLossGradKernel : public framework::OpKernel { +class RankLossGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* d_left_t = diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 80de229c333f645fb3098b97fa076c6b77bb7ca9..04c4c24951f5db572486ded5edfc26948a821682 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -30,36 +30,39 @@ using LoDTensor = framework::LoDTensor; void RecurrentAlgorithm::Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const { - auto step_scopes = GetStepScopes(scope); - rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, - false /*infer_shape_mode*/); - InitMemories(step_scopes[0], false /*infer_shape_mode*/); + auto* input0 = scope.FindVar(arg_->inlinks[0]); + PADDLE_ENFORCE_NOT_NULL(input0); + size_t seq_len = input0->GetMutable()->dims()[0]; + PADDLE_ENFORCE_GT(seq_len, 0); - for (size_t step_id = 0; step_id < seq_len_; step_id++) { - // create output alias variables + CreateScopes(scope, seq_len); + auto& step_scopes = GetStepScopes(scope); + rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len); + InitMemories(step_scopes[0]); + + for (size_t step_id = 0; step_id < seq_len; step_id++) { if (step_id > 0) { - rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, - false /*infer_shape_mode*/); + rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1); } (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } - rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, - false /*infer_shape_mode*/); + rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len); } -void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { +void RecurrentAlgorithm::CreateScopes(const Scope& scope, + size_t seq_len) const { // TODO(superjom) Only two scopes are needed for inference, this case will be // supported later. - auto step_scopes_var = scope.FindVar(arg_->step_scopes); + auto* step_scopes_var = scope.FindVar(arg_->step_scopes); PADDLE_ENFORCE(step_scopes_var != nullptr, ""); - auto step_scopes = step_scopes_var->GetMutable>(); + auto* step_scopes = step_scopes_var->GetMutable>(); // Now all variables in scope must be created outside of op. PADDLE_ENFORCE_NOT_NULL(stepnet_); PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs"); - if (seq_len_ > step_scopes->size()) { - for (size_t i = step_scopes->size(); i < seq_len_; ++i) { + if (seq_len > step_scopes->size()) { + for (size_t i = step_scopes->size(); i < seq_len; ++i) { auto& step_scope = scope.NewScope(); // create step net's temp inputs @@ -82,8 +85,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { } } -void RecurrentAlgorithm::InitMemories(Scope* step_scope, - bool infer_shape_mode) const { +void RecurrentAlgorithm::InitMemories(Scope* step_scope) const { for (auto& attr : arg_->memories) { auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable(); PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr, @@ -91,12 +93,9 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope, attr.boot_var); auto* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable(); - if (infer_shape_mode) { - pre_mem->Resize(boot_mem->dims()); - PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2); - } else { - pre_mem->ShareDataWith(*boot_mem); - } + pre_mem->Resize(boot_mem->dims()); + PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2); + pre_mem->ShareDataWith(*boot_mem); } } @@ -146,23 +145,23 @@ class RecurrentAlgorithmProtoAndCheckerMaker void RecurrentGradientAlgorithm::Run( const Scope& scope, const platform::DeviceContext& dev_ctx) const { - auto step_scopes = GetStepScopes(scope); - rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_, - false /*infer_shape_mode*/); - for (int step_id = seq_len_ - 1; step_id >= 0; --step_id) { - if (static_cast(step_id) != seq_len_ - 1) { - rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, - false /*infer_shape_mode*/); + auto* input0 = scope.FindVar(arg_->inlinks[0]); + PADDLE_ENFORCE_NOT_NULL(input0); + size_t seq_len = input0->GetMutable()->dims()[0]; + auto& step_scopes = GetStepScopes(scope); + rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len); + for (int step_id = seq_len - 1; step_id >= 0; --step_id) { + if (step_id != seq_len - 1) { + rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1); } (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } - LinkBootMemoryGradients(step_scopes[0], false); - rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, - false /*infer_shape_mode*/); + rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len); + LinkBootMemoryGradients(step_scopes[0]); } void RecurrentGradientAlgorithm::LinkBootMemoryGradients( - Scope* step_scope, bool infer_shape_mode) const { + Scope* step_scope) const { for (auto& attr : arg_->memories) { PADDLE_ENFORCE(step_scope->FindVar(attr.var) != nullptr, "memory variable [%s] does not exists", attr.var); @@ -171,11 +170,8 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients( auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable(); auto* boot_mem_grad = step_scope->NewVar(attr.boot_var)->GetMutable(); - if (infer_shape_mode) { - boot_mem_grad->Resize(mem_grad->dims()); - } else { - boot_mem_grad->ShareDataWith(*mem_grad); - } + boot_mem_grad->Resize(mem_grad->dims()); + boot_mem_grad->ShareDataWith(*mem_grad); } } diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index c6b9a5533eece9057449b5c875ddcb3cefe716f0..253d7e3284360ceaddce9ef5f8f9a3ea4793d740 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -48,7 +48,7 @@ class RecurrentAlgorithm { * NOTE the scopes are reused in both the forward and backward, so just * create once and expand its size if more steps need. */ - void CreateScopes(const framework::Scope& scope) const; + void CreateScopes(const framework::Scope& scope, size_t seq_len) const; const std::vector& GetStepScopes( const framework::Scope& scope) const { @@ -56,12 +56,11 @@ class RecurrentAlgorithm { ->GetMutable>(); } - void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const; + void InitMemories(framework::Scope* step_scopes) const; private: std::unique_ptr* stepnet_; rnn::Argument* arg_; - mutable size_t seq_len_; }; class RecurrentGradientAlgorithm { @@ -86,8 +85,7 @@ class RecurrentGradientAlgorithm { void Run(const framework::Scope& scope, const platform::DeviceContext& dev_ctx) const; - void LinkBootMemoryGradients(framework::Scope* step_scopes, - bool infer_shape_mode) const; + void LinkBootMemoryGradients(framework::Scope* step_scopes) const; protected: inline const std::vector& GetStepScopes( @@ -98,7 +96,6 @@ class RecurrentGradientAlgorithm { private: rnn::Argument* arg_; - mutable size_t seq_len_; std::unique_ptr* stepnet_; }; @@ -123,6 +120,7 @@ class RecurrentOp : public framework::OperatorBase { void set_stepnet(std::unique_ptr net) { stepnet_ = std::move(net); } + const OperatorBase& stepnet() const { return *stepnet_; } static const rnn::ArgumentName kArgName; diff --git a/paddle/operators/reduce_op.cc b/paddle/operators/reduce_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..005f88b57cb47b2c00f9a81da8afa49439665a22 --- /dev/null +++ b/paddle/operators/reduce_op.cc @@ -0,0 +1,189 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/operators/reduce_op.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; + +class ReduceOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of ReduceOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of ReduceOp should not be null."); + auto x_dims = ctx->GetInputDim("X"); + auto x_rank = x_dims.size(); + PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported."); + int dim = ctx->Attrs().Get("dim"); + if (dim < 0) dim = x_rank + dim; + PADDLE_ENFORCE_LT( + dim, x_rank, + "The dim should be in the range [-rank(input), rank(input))."); + bool keep_dim = ctx->Attrs().Get("keep_dim"); + auto dims_vector = vectorize(x_dims); + if (keep_dim || x_rank == 1) { + dims_vector[dim] = 1; + } else { + dims_vector.erase(dims_vector.begin() + dim); + } + auto out_dims = framework::make_ddim(dims_vector); + ctx->SetOutputDim("Out", out_dims); + if (dim != 0) { + // Only pass LoD when not reducing on the first dim. + ctx->ShareLoD("X", /*->*/ "Out"); + } + } +}; + +class ReduceGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null."); + PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), + "Input(Out@GRAD) should not be null."); + auto x_dims = ctx->GetInputDim("X"); + auto x_rank = x_dims.size(); + PADDLE_ENFORCE_LE(x_rank, 6, "Tensors with rank at most 6 are supported."); + int dim = ctx->Attrs().Get("dim"); + if (dim < 0) dim = x_rank + dim; + PADDLE_ENFORCE_LT( + dim, x_rank, + "The dim should be in the range [-rank(input), rank(input))."); + auto x_grad_name = framework::GradVarName("X"); + if (ctx->HasOutput(x_grad_name)) { + ctx->SetOutputDim(x_grad_name, x_dims); + } + } +}; + +class ReduceOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ReduceOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "X", + "(Tensor) The input tensor. Tensors with rank at most 6 are supported"); + AddOutput("Out", "(Tensor) The result tensor."); + AddAttr( + "dim", + "(int, default 1) The dimension to reduce. " + "Must be in the range [-rank(input), rank(input)). " + "If `dim < 0`, the dim to reduce is `rank + dim`. " + "Noting that reducing on the first dim will make the LoD info lost.") + .SetDefault(0); + AddAttr("keep_dim", + "(bool, default false) " + "If true, retain the reduced dimension with length 1.") + .SetDefault(false); + comment_ = R"DOC( +{ReduceOP} operator computes the {reduce} of input tensor along the given dimension. +The result tensor has 1 fewer dimension than the input unless `keep_dim` is true. +)DOC"; + AddComment(comment_); + } + + protected: + std::string comment_; + + void Replace(std::string &src, std::string from, std::string to) { + std::size_t len_from = std::strlen(from.c_str()); + std::size_t len_to = std::strlen(to.c_str()); + for (std::size_t pos = src.find(from); pos != std::string::npos; + pos = src.find(from, pos + len_to)) { + src.replace(pos, len_from, to); + } + } + + void SetComment(std::string name, std::string op) { + Replace(comment_, "{ReduceOP}", name); + Replace(comment_, "{reduce}", op); + } +}; + +class ReduceSumOpMaker : public ReduceOpMaker { + public: + ReduceSumOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : ReduceOpMaker(proto, op_checker) { + SetComment("ReduceSum", "sum"); + AddComment(comment_); + } +}; + +class ReduceMeanOpMaker : public ReduceOpMaker { + public: + ReduceMeanOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : ReduceOpMaker(proto, op_checker) { + SetComment("ReduceMean", "mean"); + AddComment(comment_); + } +}; + +class ReduceMaxOpMaker : public ReduceOpMaker { + public: + ReduceMaxOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : ReduceOpMaker(proto, op_checker) { + SetComment("ReduceMax", "max"); + AddComment(comment_); + } +}; + +class ReduceMinOpMaker : public ReduceOpMaker { + public: + ReduceMinOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : ReduceOpMaker(proto, op_checker) { + SetComment("ReduceMin", "min"); + AddComment(comment_); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP(reduce_sum, ops::ReduceOp, ops::ReduceSumOpMaker, reduce_sum_grad, + ops::ReduceGradOp); + +REGISTER_OP(reduce_mean, ops::ReduceOp, ops::ReduceMeanOpMaker, + reduce_mean_grad, ops::ReduceGradOp); + +REGISTER_OP(reduce_max, ops::ReduceOp, ops::ReduceMaxOpMaker, reduce_max_grad, + ops::ReduceGradOp); + +REGISTER_OP(reduce_min, ops::ReduceOp, ops::ReduceMinOpMaker, reduce_min_grad, + ops::ReduceGradOp); + +#define REGISTER_REDUCE_CPU_KERNEL(reduce_type, functor, grad_functor) \ + REGISTER_OP_CPU_KERNEL( \ + reduce_type, \ + ops::ReduceKernel); \ + REGISTER_OP_CPU_KERNEL(reduce_type##_grad, \ + ops::ReduceGradKernel); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_REDUCE_CPU_KERNEL); diff --git a/paddle/operators/reduce_op.cu b/paddle/operators/reduce_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..d306e1a24096d737438d71d4d4abc35328d160cb --- /dev/null +++ b/paddle/operators/reduce_op.cu @@ -0,0 +1,28 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/reduce_op.h" + +namespace ops = paddle::operators; + +#define REGISTER_REDUCE_GPU_KERNEL(reduce_type, functor, grad_functor) \ + REGISTER_OP_GPU_KERNEL( \ + reduce_type, \ + ops::ReduceKernel); \ + REGISTER_OP_GPU_KERNEL(reduce_type##_grad, \ + ops::ReduceGradKernel); + +FOR_EACH_KERNEL_FUNCTOR(REGISTER_REDUCE_GPU_KERNEL); diff --git a/paddle/operators/reduce_op.h b/paddle/operators/reduce_op.h new file mode 100644 index 0000000000000000000000000000000000000000..45043c440bc8017e97f8be00d08f1cb60d201e20 --- /dev/null +++ b/paddle/operators/reduce_op.h @@ -0,0 +1,206 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once + +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +using DDim = framework::DDim; +template +using EigenTensor = framework::EigenTensor; + +struct SumFunctor { + template + void operator()(const Place& place, X& x, Y& y, const Dim& dim) { + y.device(place) = x.sum(dim); + } +}; + +struct SumGradFunctor { + template + void operator()(const Place& place, X& x, Y& y, DX& dx, DY& dy, + const Dim& dim, int size) { + dx.device(place) = dy.broadcast(dim); + } +}; + +struct MeanFunctor { + template + void operator()(const Place& place, X& x, Y& y, const Dim& dim) { + y.device(place) = x.mean(dim); + } +}; + +struct MeanGradFunctor { + template + void operator()(const Place& place, X& x, Y& y, DX& dx, DY& dy, + const Dim& dim, int size) { + dx.device(place) = dy.broadcast(dim) / dx.constant(size); + } +}; + +struct MaxFunctor { + template + void operator()(const Place& place, X& x, Y& y, const Dim& dim) { + y.device(place) = x.maximum(dim); + } +}; + +struct MinFunctor { + template + void operator()(const Place& place, X& x, Y& y, const Dim& dim) { + y.device(place) = x.minimum(dim); + } +}; + +struct MaxOrMinGradFunctor { + template + void operator()(const Place& place, X& x, Y& y, DX& dx, DY& dy, + const Dim& dim, int size) { + auto equals = x == y.broadcast(dim); + auto ones = dx.constant(1); + auto zeros = dx.constant(0); + // If there are multiple minimum or maximum elements, the subgradient of + // each is the set [0, 1], and we pass gradient to all of them here. + dx.device(place) = dy.broadcast(dim) * equals.select(ones, zeros); + } +}; + +template +class ReduceKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + int rank = context.Input("X")->dims().size(); + switch (rank) { + case 1: + ReduceCompute<1>(context); + break; + case 2: + ReduceCompute<2>(context); + break; + case 3: + ReduceCompute<3>(context); + break; + case 4: + ReduceCompute<4>(context); + break; + case 5: + ReduceCompute<5>(context); + break; + case 6: + ReduceCompute<6>(context); + break; + } + } + + private: + template + void ReduceCompute(const framework::ExecutionContext& context) const { + auto* input = context.Input("X"); + auto* output = context.Output("Out"); + output->mutable_data(context.GetPlace()); + + auto x = EigenTensor::From(*input); + auto x_rank = static_cast(x.dimensions().size()); + int dim = static_cast(context.Attr("dim")); + if (dim < 0) dim = x_rank + dim; + auto reduce_dim = Eigen::array({{dim}}); + // construct the squeezed output tensor + bool keep_dim = context.Attr("keep_dim"); + DDim dims = output->dims(); + auto dims_vector = vectorize(dims); + if (keep_dim && x_rank > 1) { + dims_vector.erase(dims_vector.begin() + dim); + dims = framework::make_ddim(dims_vector); + } + auto out = EigenTensor < T, D == 1 ? 1 : (D - 1) > ::From(*output, dims); + auto& place = context.GetEigenDevice(); + Functor functor; + functor(place, x, out, reduce_dim); + } +}; + +template +class ReduceGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + int rank = context.Input("X")->dims().size(); + switch (rank) { + case 1: + ReduceGradCompute<1>(context); + break; + case 2: + ReduceGradCompute<2>(context); + break; + case 3: + ReduceGradCompute<3>(context); + break; + case 4: + ReduceGradCompute<4>(context); + break; + case 5: + ReduceGradCompute<5>(context); + break; + case 6: + ReduceGradCompute<6>(context); + break; + } + } + + private: + template + void ReduceGradCompute(const framework::ExecutionContext& context) const { + auto* input0 = context.Input("X"); + auto* input1 = context.Input("Out"); + auto* input2 = context.Input(framework::GradVarName("Out")); + auto* output = context.Output(framework::GradVarName("X")); + + output->mutable_data(context.GetPlace()); + auto x = EigenTensor::From(*input0); + auto x_grad = EigenTensor::From(*output); + auto x_rank = static_cast(x.dimensions().size()); + int dim = static_cast(context.Attr("dim")); + if (dim < 0) dim = x_rank + dim; + DDim dims = input0->dims(); + dims[dim] = 1; + auto x_reduce = EigenTensor::From(*input1, dims); + auto x_reduce_grad = EigenTensor::From(*input2, dims); + + Eigen::array braodcast_dim; + for (size_t i = 0; i < D; ++i) braodcast_dim[i] = 1; + braodcast_dim[dim] = input0->dims()[dim]; + auto& place = context.GetEigenDevice(); + Functor functor; + functor(place, x, x_reduce, x_grad, x_reduce_grad, braodcast_dim, + braodcast_dim[dim]); + } +}; + +} // namespace operators +} // namespace paddle + +#define FOR_EACH_KERNEL_FUNCTOR(__macro) \ + __macro(reduce_sum, SumFunctor, SumGradFunctor); \ + __macro(reduce_mean, MeanFunctor, MeanGradFunctor); \ + __macro(reduce_max, MaxFunctor, MaxOrMinGradFunctor); \ + __macro(reduce_min, MinFunctor, MaxOrMinGradFunctor); diff --git a/paddle/operators/reshape_op.cc b/paddle/operators/reshape_op.cc index a3c3fa2716ad9f6487e3eff2d98b2c76d964ddef..3cd54930a0919b7c6aad624b442eb15167b4c354 100644 --- a/paddle/operators/reshape_op.cc +++ b/paddle/operators/reshape_op.cc @@ -26,7 +26,7 @@ class ReshapeOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { // input check PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ReshapeOp should not be null."); @@ -94,7 +94,7 @@ class ReshapeGradOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) shouldn't be null."); diff --git a/paddle/operators/reshape_op.h b/paddle/operators/reshape_op.h index 873acf30782d390cdca5e7e864c76e1f743f9a7c..628dfe4c0fadcfeec188d8ae5049a994e3281bc1 100644 --- a/paddle/operators/reshape_op.h +++ b/paddle/operators/reshape_op.h @@ -21,7 +21,7 @@ namespace paddle { namespace operators { template -class ReshapeKernel : public framework::OpKernel { +class ReshapeKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* out = ctx.Output("Out"); @@ -39,7 +39,7 @@ class ReshapeKernel : public framework::OpKernel { }; template -class ReshapeGradKernel : public framework::OpKernel { +class ReshapeGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const { auto* d_out = ctx.Input(framework::GradVarName("Out")); diff --git a/paddle/operators/rmsprop_op.cc b/paddle/operators/rmsprop_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..ada6f2bc3cd5de0424139144d8c0696158c319d9 --- /dev/null +++ b/paddle/operators/rmsprop_op.cc @@ -0,0 +1,120 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/rmsprop_op.h" + +namespace paddle { +namespace operators { + +class RmspropOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("MeanSquare"), + "Input(MeanSquare) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Moment"), + "Input(Moment) of RmspropOp should not be null."); + + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(param_out) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MomentOut"), + "Output(Momentum_out) of RmspropOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("MeanSquareOut"), + "Output(MeanSquareOut) of RmspropOp should not be null."); + + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ( + param_dim, ctx->GetInputDim("Grad"), + "Param and grad input of RmspropOp should have the same dimension."); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Moment"), + "Param and Momentum input of RmspropOp " + "should have the same dimension."); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("MeanSquare"), + "Param and Momentum input of RmspropOp " + "should have the same dimension."); + + auto lr_dim = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dim), 1, + "Learning Rate should be a scalar."); + + ctx->SetOutputDim("ParamOut", param_dim); + ctx->SetOutputDim("MomentOut", param_dim); + ctx->SetOutputDim("MeanSquareOut", param_dim); + } +}; + +class RmspropOpMaker : public framework::OpProtoAndCheckerMaker { + public: + RmspropOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Param", + "(Tensor, default Tensor) " + "Input parameter value that has to be updated"); + AddInput("MeanSquare", + "(Tensor, default Tensor)" + " The mean square value that gets updated"); + AddInput("LearningRate", + "(Tensor, default Tensor) " + "The learning rate should be a tensor of size 1"); + AddInput("Grad", + "(Tensor, default Tensor) " + "Input gradient of the parameter"); + AddInput("Moment", + "(Tensor, default Tensor) The moment that gets updated"); + + AddOutput("ParamOut", "(Tensor) Output updated parameter value"); + AddOutput("MomentOut", "(Tensor) Output updated moment"); + AddOutput("MeanSquareOut", "(Tensor) Output Mean squared updated value"); + + AddAttr("epsilon", + "(float, default 1e-10) Constant " + "for numerical stability.") + .SetDefault(1.0e-10f); + AddAttr("decay", + "(float, default 0.9) " + "Discounting factor for coming gradient.") + .SetDefault(0.9f); + AddAttr("momentum", "(float, default 0.0) Constant value") + .SetDefault(0.0f); + AddComment(R"DOC( + +RMSprop + +MeanSquareOut = decay * MeanSquare + (1 - decay) * Grad * Grad +MomentOut = momentum * Moment + + LearningRate * Grad / sqrt(MeanSquareOut + epsilon) +ParamOut = Param - MomentOut + +The original slides that proposed RMSprop: Slide 29 of +http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf) + +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(rmsprop, ops::RmspropOp, ops::RmspropOpMaker); +REGISTER_OP_CPU_KERNEL(rmsprop, + ops::RmspropOpKernel); diff --git a/paddle/operators/rmsprop_op.cu b/paddle/operators/rmsprop_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..52634a54816bcd5ad0ba82a56f1df95110112265 --- /dev/null +++ b/paddle/operators/rmsprop_op.cu @@ -0,0 +1,20 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/rmsprop_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(rmsprop, + ops::RmspropOpKernel); diff --git a/paddle/operators/rmsprop_op.h b/paddle/operators/rmsprop_op.h new file mode 100644 index 0000000000000000000000000000000000000000..7bf2129010f994966d79ef11d5cec30159b47068 --- /dev/null +++ b/paddle/operators/rmsprop_op.h @@ -0,0 +1,67 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +template +using EigenVector = framework::EigenVector; + +template +class RmspropOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* param_out = ctx.Output("ParamOut"); + auto* moment_out = ctx.Output("MomentOut"); + auto* mean_square_out = ctx.Output("MeanSquareOut"); + + auto grad = ctx.Input("Grad"); + + param_out->mutable_data(ctx.GetPlace()); + moment_out->mutable_data(ctx.GetPlace()); + mean_square_out->mutable_data(ctx.GetPlace()); + + float epsilon = ctx.Attr("epsilon"); + float rho = ctx.Attr("decay"); + float momentum = ctx.Attr("momentum"); + + auto p = EigenVector::Flatten(*ctx.Input("Param")); + auto ms = EigenVector::Flatten(*ctx.Input("MeanSquare")); + auto lr = EigenVector::Flatten(*ctx.Input("LearningRate")); + auto g = EigenVector::Flatten(*grad); + auto mom = EigenVector::Flatten(*ctx.Input("Moment")); + + auto p_out = EigenVector::Flatten(*param_out); + auto mom_out = EigenVector::Flatten(*moment_out); + auto ms_out = EigenVector::Flatten(*mean_square_out); + auto place = ctx.GetEigenDevice(); + + Eigen::DSizes grad_dsize(grad->numel()); + + ms_out.device(place) = rho * ms + (1 - rho) * g * g; + mom_out.device(place) = + momentum * mom + + lr.broadcast(grad_dsize) * g / (ms_out + epsilon).sqrt(); + p_out.device(place) = p - mom_out; + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/rnn/recurrent_op_utils.cc b/paddle/operators/rnn/recurrent_op_utils.cc index a767009d2366e20d2ebd35f562b8df7d408f2d4e..ef317a71f12c6de974bd8715bb08122b761fae37 100644 --- a/paddle/operators/rnn/recurrent_op_utils.cc +++ b/paddle/operators/rnn/recurrent_op_utils.cc @@ -25,7 +25,7 @@ using LoDTensor = framework::LoDTensor; void SegmentInputs(const std::vector& step_scopes, const std::vector& inlinks, - const size_t seq_len, bool infer_shape_mode) { + const size_t seq_len) { PADDLE_ENFORCE(!inlinks.empty(), "no in links are provided."); for (size_t i = 0; i < inlinks.size(); ++i) { // global inputs @@ -41,11 +41,9 @@ void SegmentInputs(const std::vector& step_scopes, for (size_t j = 0; j < seq_len; j++) { Tensor* step_input = step_scopes[j]->NewVar(inlinks[i])->GetMutable(); - if (!infer_shape_mode) { - // The input of operators of each step is Tensor here. - // Maybe need to modify Slice function. - *step_input = input->Slice(j, j + 1); - } + // The input of operators of each step is Tensor here. + // Maybe need to modify Slice function. + *step_input = input->Slice(j, j + 1); step_input->Resize(step_dims); } } @@ -53,39 +51,35 @@ void SegmentInputs(const std::vector& step_scopes, void ConcatOutputs(const std::vector& step_scopes, const std::vector& outlinks, - const size_t seq_len, bool infer_shape_mode) { + const size_t seq_len) { for (size_t i = 0; i < outlinks.size(); i++) { - auto output_var = step_scopes[0]->parent().FindVar(outlinks[i]); + auto* output_var = step_scopes[0]->parent().FindVar(outlinks[i]); PADDLE_ENFORCE_NOT_NULL(output_var, "output link [%s] is not in scope.", outlinks[i]); LoDTensor* output = output_var->GetMutable(); - if (infer_shape_mode) { - auto step_scope_var = step_scopes[0]->FindVar(outlinks[i]); - PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]); - f::DDim step_dims = - step_scope_var->template GetMutable()->dims(); - std::vector dims_vec = vectorize(step_dims); - dims_vec.insert(dims_vec.begin(), seq_len); - output->Resize(f::make_ddim(dims_vec)); - } else { - output->mutable_data(platform::CPUPlace()); - for (size_t j = 0; j < seq_len; j++) { - LoDTensor* step_output = - step_scopes[j]->FindVar(outlinks[i])->GetMutable(); - // TODO(luotao02) data type and platform::DeviceContext() should set - // correctly - (output->Slice(j, j + 1)) - .CopyFrom(*step_output, platform::CPUPlace()); - } + auto* step_scope_var = step_scopes[0]->FindVar(outlinks[i]); + PADDLE_ENFORCE_NOT_NULL(step_scope_var, "%s not in scope", outlinks[i]); + f::DDim step_dims = + step_scope_var->template GetMutable()->dims(); + std::vector dims_vec = vectorize(step_dims); + dims_vec.insert(dims_vec.begin(), seq_len); + output->Resize(f::make_ddim(dims_vec)); + output->mutable_data(platform::CPUPlace()); + for (size_t j = 0; j < seq_len; j++) { + LoDTensor* step_output = + step_scopes[j]->FindVar(outlinks[i])->GetMutable(); + // TODO(luotao02) data type and platform::DeviceContext() should set + // correctly + (output->Slice(j, j + 1)) + .CopyFrom(*step_output, platform::CPUPlace()); } } } void LinkMemories(const std::vector& scopes, const std::vector& memories, - const size_t step_id, const int offset, - bool infer_shape_mode) { + const size_t step_id, const int offset) { PADDLE_ENFORCE_LT(step_id, scopes.size(), "step [%d] is out of range of step scopes' size [%d]", step_id, scopes.size()); @@ -95,16 +89,13 @@ void LinkMemories(const std::vector& scopes, step_id + offset, scopes.size(), "offset [%d] is out of range, it must be less than (%d - %d)", offset, scopes.size(), step_id); - auto scope = scopes[step_id]; - auto linked_scope = scopes[step_id + offset]; + auto* scope = scopes[step_id]; + auto* linked_scope = scopes[step_id + offset]; for (auto& attr : memories) { - auto mem = scope->FindVar(attr.pre_var)->GetMutable(); - auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable(); - if (infer_shape_mode) { - mem->Resize(linked_mem->dims()); - } else { - mem->ShareDataWith(*linked_mem); - } + auto* mem = scope->FindVar(attr.pre_var)->GetMutable(); + auto* linked_mem = linked_scope->FindVar(attr.var)->GetMutable(); + mem->Resize(linked_mem->dims()); + mem->ShareDataWith(*linked_mem); } } @@ -115,11 +106,11 @@ void InitArgument(const ArgumentName& name, Argument* arg, arg->inlinks = op.Inputs(name.inlinks); arg->outlinks = op.Outputs(name.outlinks); - auto boot_memories = + auto& boot_memories = is_grad ? op.Outputs(name.boot_memories) : op.Inputs(name.boot_memories); // attributes - auto memories = op.Attr>(name.memories); - auto pre_memories = op.Attr>(name.pre_memories); + auto& memories = op.Attr>(name.memories); + auto& pre_memories = op.Attr>(name.pre_memories); PADDLE_ENFORCE(memories.size() == boot_memories.size(), "the size of memories, boot_memories don't match:%d,%d", diff --git a/paddle/operators/rnn/recurrent_op_utils.h b/paddle/operators/rnn/recurrent_op_utils.h index 9c777f1e9067a3e2ceb9d23f7bf7d3c73343c91f..fd17b9b88915cf458ff2836b5c5d8f84cd9b65b5 100644 --- a/paddle/operators/rnn/recurrent_op_utils.h +++ b/paddle/operators/rnn/recurrent_op_utils.h @@ -64,18 +64,18 @@ struct ArgumentName { */ void SegmentInputs(const std::vector& step_scopes, const std::vector& inlinks, - const size_t seq_len, bool infer_shape_mode); + const size_t seq_len); /** * Process outputs of step nets and merge to variables. */ void ConcatOutputs(const std::vector& step_scopes, const std::vector& outlinks, - const size_t seq_len, bool infer_shape_mode); + const size_t seq_len); void LinkMemories(const std::vector& step_scopes, const std::vector& memories, const size_t step_id, - const int offset, bool infer_shape_mode); + const int offset); void InitArgument(const ArgumentName& name, Argument* arg, const framework::OperatorBase& op, bool is_grad = false); diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc deleted file mode 100644 index 1fcf0959dffd6a68d97dec4e2b5b509d06c0d09c..0000000000000000000000000000000000000000 --- a/paddle/operators/rowwise_add_op.cc +++ /dev/null @@ -1,109 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - - Licensed under the Apache License, Version 2.0 (the "License"); - you may not use this file except in compliance with the License. - You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - - Unless required by applicable law or agreed to in writing, software - distributed under the License is distributed on an "AS IS" BASIS, - WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - See the License for the specific language governing permissions and - limitations under the License. */ - -#include "paddle/operators/rowwise_add_op.h" - -namespace paddle { -namespace operators { - -using framework::Tensor; - -class RowwiseAddOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of RowwiseAddOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("b"), - "Input(b) of RowwiseAddOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of RowwiseAddOp should not be null."); - - auto x_dims = ctx->GetInputDim("X"); - auto b_dims = ctx->GetInputDim("b"); - PADDLE_ENFORCE_GT( - x_dims.size(), b_dims.size(), - "The rank of input `X` must be larger than the one of input `b`."); - - int num_col_dims = x_dims.size() - b_dims.size(); - - PADDLE_ENFORCE_EQ( - framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims, - "The width of two operands must be same"); - PADDLE_ENFORCE_EQ(ctx->Outputs("Out").size(), 1, - "The output size must be 1"); - ctx->SetOutputDim("Out", x_dims); - ctx->ShareLoD("X", /*->*/ "Out"); - } -}; - -class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker { - public: - RowwiseAddOpMaker(framework::OpProto* proto, - framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The left input of row-wise add op, must be matrix"); - AddInput("b", "The right input of row-wise add op, must be vector"); - AddOutput("Out", "The output of row-wise add op"); - AddComment(R"DOC(Row-wise Add operator - -for i in xrange(X.shape[0]): - Out = X[i] + b -)DOC"); - } -}; -class RowwiseAddGradOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "X should not be null"); - PADDLE_ENFORCE(ctx->HasInput("b"), "b should not be null"); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) should not be null"); - auto x_dims = ctx->GetInputDim("X"); - auto b_dims = ctx->GetInputDim("b"); - PADDLE_ENFORCE_GT( - x_dims.size(), b_dims.size(), - "The rank of input `X` must be larger than the one of input `b`."); - - int64_t num_col_dims = x_dims.size() - b_dims.size(); - PADDLE_ENFORCE_EQ( - framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims, - "The width of two operands must be same"); - auto x_grad_name = framework::GradVarName("X"); - auto b_grad_name = framework::GradVarName("b"); - if (ctx->HasOutput(x_grad_name)) { - ctx->SetOutputDim(x_grad_name, x_dims); - } - if (ctx->HasOutput(b_grad_name)) { - ctx->SetOutputDim(b_grad_name, b_dims); - } - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; -REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker, - rowwise_add_grad, ops::RowwiseAddGradOp); -REGISTER_OP_CPU_KERNEL( - rowwise_add, ops::RowwiseAddKernel); -REGISTER_OP_CPU_KERNEL( - rowwise_add_grad, - ops::RowwiseAddGradKernel); diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h deleted file mode 100644 index 35774b940926f77167b8f19597027e74d3477e5b..0000000000000000000000000000000000000000 --- a/paddle/operators/rowwise_add_op.h +++ /dev/null @@ -1,80 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#pragma once -#include "paddle/framework/eigen.h" -#include "paddle/framework/op_registry.h" - -namespace paddle { -namespace operators { - -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; -template -using EigenMatrix = framework::EigenMatrix; - -template -class RowwiseAddKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto out = context.Output("Out"); - out->mutable_data(context.GetPlace()); - int num_col_dims = context.Input("X")->dims().size() - - context.Input("b")->dims().size(); - auto input = - EigenMatrix::Reshape(*context.Input("X"), num_col_dims); - auto bias = EigenVector::Flatten(*context.Input("b")); - auto output = EigenMatrix::Reshape(*out, num_col_dims); - - const int bias_size = bias.dimension(0); - const int rest_size = input.size() / bias_size; - Eigen::DSizes one_d(input.size()); - Eigen::DSizes bcast(rest_size); - output.reshape(one_d).device(context.GetEigenDevice()) = - input.reshape(one_d) + bias.broadcast(bcast).reshape(one_d); - } -}; - -template -class RowwiseAddGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* dout = context.Input(framework::GradVarName("Out")); - auto* dx = context.Output(framework::GradVarName("X")); - auto* db = context.Output(framework::GradVarName("b")); - int num_col_dims = context.Input("X")->dims().size() - - context.Input("b")->dims().size(); - - auto out_grad = EigenMatrix::Reshape(*dout, num_col_dims); - auto place = context.GetEigenDevice(); - - if (dx) { - dx->mutable_data(context.GetPlace()); - EigenMatrix::Reshape(*dx, num_col_dims).device(place) = out_grad; - } - - if (db) { - db->mutable_data(context.GetPlace()); - // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html - // colwise add - Eigen::array dims{{0}}; /* dimension to reduce */ - EigenVector::Flatten(*db).device(place) = out_grad.sum(dims); - } - } -}; -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc index e92501e12834b92875f494de401672344f50e3b5..ac297da6b72250eaa5eb6ea2177fbcc7a6c1ec56 100644 --- a/paddle/operators/scale_op.cc +++ b/paddle/operators/scale_op.cc @@ -26,7 +26,7 @@ class ScaleOp : public framework::OperatorWithKernel { : OperatorWithKernel(type, inputs, outputs, attrs) {} protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of ScaleOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), @@ -41,8 +41,8 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { public: ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The input tensor of scale operator.").NotInGradient(); - AddOutput("Out", "The output tensor of scale operator.").NotInGradient(); + AddInput("X", "The input tensor of scale operator."); + AddOutput("Out", "The output tensor of scale operator."); AddComment(R"DOC(Scale operator The equation is: Out = scale*X @@ -52,21 +52,18 @@ The equation is: Out = scale*X } }; -// The operator to calculate gradients of a scale operator is just the scale -// operator itself. -// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out)) -template -class ScaleGradOp : public NetOp { +class ScaleGradMaker : public framework::SingleGradOpDescMaker { public: - ScaleGradOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : NetOp(type, inputs, outputs, attrs) { - AppendOp(framework::OpRegistry::CreateOp( - "scale", {{"X", {Input(framework::GradVarName("Out"))}}}, - {{"Out", {Output(framework::GradVarName("X"))}}}, - {{"scale", Attr("scale")}})); - CompleteAddOp(false); + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto *grad_op = new framework::OpDescBind(); + grad_op->SetType("scale"); + grad_op->SetInput("X", OutputGrad("Out")); + grad_op->SetOutput("Out", InputGrad("X")); + grad_op->SetAttr("scale", GetAttr("scale")); + return std::unique_ptr(grad_op); } }; @@ -75,7 +72,7 @@ class ScaleGradOp : public NetOp { namespace ops = paddle::operators; -REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker, scale_grad, - ops::ScaleGradOp); +REGISTER_OPERATOR(scale, ops::ScaleOp, ops::ScaleOpMaker, + ops::ScaleGradMaker); REGISTER_OP_CPU_KERNEL(scale, ops::ScaleKernel); diff --git a/paddle/operators/scale_op.h b/paddle/operators/scale_op.h index 02fbdc52bbf89c9f2acc5eeaa1197e4ccbca9d31..dc6bc768997f4fdd049bb63bdc11252ab52fcda9 100644 --- a/paddle/operators/scale_op.h +++ b/paddle/operators/scale_op.h @@ -20,7 +20,7 @@ namespace paddle { namespace operators { template -class ScaleKernel : public framework::OpKernel { +class ScaleKernel : public framework::OpKernel { public: virtual void Compute(const framework::ExecutionContext& context) const { auto* tensor = context.Output("Out"); diff --git a/paddle/operators/scatter.cu.h b/paddle/operators/scatter.cu.h new file mode 100644 index 0000000000000000000000000000000000000000..d95436be4f25b9df4aaef57ddb249ecf944f0666 --- /dev/null +++ b/paddle/operators/scatter.cu.h @@ -0,0 +1,80 @@ +/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/tensor.h" +#include "paddle/platform/place.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ + i += blockDim.x * gridDim.x) + +template +__global__ void ScatterCUDAKernel(const T* params, const int* indices, + T* output, size_t index_size, + size_t slice_size) { + CUDA_1D_KERNEL_LOOP(i, index_size * slice_size) { + int indices_i = i / slice_size; + int slice_i = i - indices_i * slice_size; // offset inside the slice + int scatter_i = indices[indices_i]; + int out_i = scatter_i * slice_size + slice_i; + *(output + out_i) = *(params + i); + } +} + +/** + * A thin wrapper on gpu tensor + * Return a new updated tensor from source tensor, scatter-assigned according to + * index + * input[src]: type-T source Tensor + * input[index]: type-int index Tensor (1-D) + * return: output tensor + */ +template +void GPUScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + // PADDLE_ENFORCE(platform::is_gpu_place(place)); + // check index of shape 1-D + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; + + auto src_dims = src.dims(); + framework::DDim output_dims(src_dims); + output_dims[0] = index_size; + + // slice size + int slice_size = 1; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + + int block = 512; + int n = slice_size * index_size; + int grid = (n + block - 1) / block; + + ScatterCUDAKernel<<< + grid, block, 0, + reinterpret_cast(ctx).stream()>>>( + p_src, p_index, p_output, index_size, slice_size); +} + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/scatter.h b/paddle/operators/scatter.h index 6b542675c291607b35f180123cf42fee6a783a85..c1fb844ebd2ff7ca7dbdb8e8ac3c1fff4c0c6607 100644 --- a/paddle/operators/scatter.h +++ b/paddle/operators/scatter.h @@ -24,67 +24,42 @@ namespace paddle { namespace operators { using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - -// Implementation of CPU copy -template -void CPUScatterUpdate(const paddle::framework::Tensor* src, const int* index, - const size_t index_size, - paddle::framework::Tensor* output) { - paddle::framework::DDim output_dims = output->dims(); - - for (size_t i = 0; i < index_size; ++i) { - int index_ = index[i]; - - paddle::framework::Tensor src_ = *src; - paddle::framework::Tensor output_ = *output; - if (index_size > 1) src_ = src->Slice(i, i + 1); - if (output_dims[0] > 1) output_ = output->Slice(index_, index_ + 1); - - auto X = EigenVector::Flatten(src_); - auto Y = EigenVector::Flatten(output_); - - Y = X + Y; - } -} - -// Implementation of GPU scatter: -template -void GPUScatterUpdate(const T* src, const int* index, const int slice_size, - const int index_size, T* output); /** * Return a updated tensor from source tensor, scattered according to index: - * dst[i] += src[index[i]] + * dst[i] = src[index[i]] * input[src]: type-T source Tensor * input[index]: type-int index Tensor (1-D) * return: output tensor */ template -void ScatterUpdate(const platform::Place& place, - const paddle::framework::Tensor* src, - const paddle::framework::Tensor* index, - paddle::framework::Tensor* output) { +void ScatterAssign(const platform::DeviceContext& ctx, const Tensor& src, + const Tensor& index, Tensor* output) { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace())); // check index of shape 1-D - PADDLE_ENFORCE(index->dims().size() == 1); - int index_size = index->dims()[0]; + PADDLE_ENFORCE(index.dims().size() == 1); + int index_size = index.dims()[0]; - auto src_dims = src->dims(); + auto src_dims = src.dims(); auto dst_dims = output->dims(); + const T* p_src = src.data(); + const int* p_index = index.data(); + T* p_output = output->data(); + // check src shape and dst shape should match for (int i = 1; i < src_dims.size(); i++) PADDLE_ENFORCE(src_dims[i] == dst_dims[i]); // slice size size_t slice_size = 1; - for (int i = 0; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + for (int i = 1; i < src_dims.size(); ++i) slice_size *= src_dims[i]; + + const size_t slice_bytes = slice_size * sizeof(T); - if (platform::is_cpu_place(place)) { - CPUScatterUpdate(src, index->data(), index_size, output); - } else { + for (int i = 0; i < index_size; ++i) { + int index_ = p_index[i]; + memcpy(p_output + index_ * slice_size, p_src + i * slice_size, slice_bytes); } } diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc index 3fc4a39ebc5526bfed61ba667c3cdc214cdd056c..fbea01a8db007a5b8cfe17f545f1cce6ecff3afb 100644 --- a/paddle/operators/scatter_op.cc +++ b/paddle/operators/scatter_op.cc @@ -23,7 +23,7 @@ class ScatterOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Ref"), "Input(Ref) of ScatterOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Index"), @@ -48,6 +48,11 @@ class ScatterOp : public framework::OperatorWithKernel { } ctx->SetOutputDim("Out", ref_dims); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("Ref")->type()); + } }; class ScatterGradOp : public framework::OperatorWithKernel { @@ -55,11 +60,16 @@ class ScatterGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { ctx->SetOutputDim(framework::GradVarName("Updates"), ctx->GetInputDim("Updates")); ctx->SetOutputDim(framework::GradVarName("Ref"), ctx->GetInputDim("Ref")); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("Ref")->type()); + } }; class ScatterOpMaker : public framework::OpProtoAndCheckerMaker { @@ -87,8 +97,5 @@ Out[Index] = Ref[Index] + Updates namespace ops = paddle::operators; REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad, ops::ScatterGradOp); -REGISTER_OP_CPU_KERNEL(scatter, - ops::ScatterOpKernel); -REGISTER_OP_CPU_KERNEL( - scatter_grad, - ops::ScatterGradientOpKernel); +REGISTER_OP_CPU_KERNEL(scatter, ops::ScatterOpKernel); +REGISTER_OP_CPU_KERNEL(scatter_grad, ops::ScatterGradientOpKernel); diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/scatter_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..06f4d759447b6dcd28b50576dfc246fc466d9336 --- /dev/null +++ b/paddle/operators/scatter_op.cu @@ -0,0 +1,63 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "gather.cu.h" +#include "paddle/operators/gather_op.h" +#include "scatter.cu.h" + +namespace paddle { +namespace operators { + +template +class ScatterOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *Ref = ctx.Input("Ref"); + auto *Index = ctx.Input("Index"); + auto *Updates = ctx.Input("Updates"); + auto *Out = ctx.Output("Out"); + + Out->ShareDataWith(*Ref); + + GPUScatterAssign(ctx.device_context(), *Updates, *Index, Out); + } +}; + +template +class ScatterGradOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "This kernel only runs on GPU device."); + auto *dRef = ctx.Output(framework::GradVarName("Ref")); + auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); + auto *Index = ctx.Input("Index"); + auto *dOut = ctx.Input(framework::GradVarName("Out")); + + // In place gradient: dRef = dO + dRef->ShareDataWith(*dOut); + dUpdates->mutable_data(ctx.GetPlace()); + // Gradient by Gather: dUpdates = dO[Index] + GPUGather(ctx.device_context(), *dOut, *Index, dUpdates); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(scatter, ops::ScatterOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(scatter_grad, ops::ScatterGradOpCUDAKernel); diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h index e9595638a86a4a4536ddad4e6f20fd80a54b1608..6101219006414e4865f676e3ca5d2a88949ad17a 100644 --- a/paddle/operators/scatter_op.h +++ b/paddle/operators/scatter_op.h @@ -23,10 +23,12 @@ namespace operators { using Tensor = framework::Tensor; -template -class ScatterOpKernel : public framework::OpKernel { +template +class ScatterOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); auto *Ref = ctx.Input("Ref"); auto *Index = ctx.Input("Index"); auto *Updates = ctx.Input("Updates"); @@ -35,14 +37,16 @@ class ScatterOpKernel : public framework::OpKernel { // In place output: Out = Ref, Out[Index] += Updates Out->ShareDataWith(*Ref); // Apply ScatterUpdate: Out[index] += Updates[:] - ScatterUpdate(ctx.GetPlace(), Updates, Index, Out); + ScatterAssign(ctx.device_context(), *Updates, *Index, Out); } }; -template -class ScatterGradientOpKernel : public framework::OpKernel { +template +class ScatterGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext &ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "This kernel only runs on CPU."); auto *dRef = ctx.Output(framework::GradVarName("Ref")); auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); auto *Index = ctx.Input("Index"); @@ -52,7 +56,7 @@ class ScatterGradientOpKernel : public framework::OpKernel { dRef->ShareDataWith(*dOut); dUpdates->mutable_data(ctx.GetPlace()); // Gradient by Gather: dUpdates += dO[Index] - Gather(ctx.GetPlace(), dOut, Index, dUpdates); + CPUGather(ctx.device_context(), *dOut, *Index, dUpdates); } }; diff --git a/paddle/operators/scatter_test.cc b/paddle/operators/scatter_test.cc index 26fdaff1460a297fa638181641991f732533fe52..00dbdacbfef7af826790472acc6caa285c259e0e 100644 --- a/paddle/operators/scatter_test.cc +++ b/paddle/operators/scatter_test.cc @@ -40,7 +40,9 @@ TEST(scatter, ScatterUpdate) { float* p_output = output->mutable_data(make_ddim({4, 4}), CPUPlace()); - ScatterUpdate(CPUPlace(), src, index, output); + auto* cpu_place = new paddle::platform::CPUPlace(); + paddle::platform::CPUDeviceContext ctx(*cpu_place); + ScatterAssign(ctx, *src, *index, output); for (size_t i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], float(0)); for (size_t i = 0; i < 4; ++i) EXPECT_EQ(output->data()[i], float(0)); diff --git a/paddle/operators/sequence_pool_op.cc b/paddle/operators/sequence_pool_op.cc index 17685ea654715f6996e17f6228f266c3aa1ee424..06c00d31eac9ff09084db951ce2929850f9d14c2 100644 --- a/paddle/operators/sequence_pool_op.cc +++ b/paddle/operators/sequence_pool_op.cc @@ -22,11 +22,11 @@ class SequencePoolOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of SequenceAvgPoolOp should not be null."); + "Input(X) of SequencePoolOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of SequenceAvgPoolOp should not be null."); + "Output(Out) of SequencePoolOp should not be null."); ctx->SetOutputDim("Out", ctx->GetInputDim("X")); } }; @@ -74,7 +74,7 @@ class SequencePoolGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Gradient of Out should not be null."); PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null."); diff --git a/paddle/operators/sequence_pool_op.h b/paddle/operators/sequence_pool_op.h index cb80586e88f8d9e31b7b91a54f5e05ac6fa73f0f..752d714125578b2d1f926765b183495ec5cc203e 100644 --- a/paddle/operators/sequence_pool_op.h +++ b/paddle/operators/sequence_pool_op.h @@ -38,7 +38,7 @@ enum SeqPoolType { }; template -class SequencePoolKernel : public framework::OpKernel { +class SequencePoolKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in = context.Input("X"); @@ -85,7 +85,7 @@ class SequencePoolKernel : public framework::OpKernel { }; template -class SequencePoolGradKernel : public framework::OpKernel { +class SequencePoolGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in = context.Input("X"); diff --git a/paddle/operators/sequence_softmax_op.cc b/paddle/operators/sequence_softmax_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..ea217ba4597c24ec323c6509f097874d977fcf81 --- /dev/null +++ b/paddle/operators/sequence_softmax_op.cc @@ -0,0 +1,103 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/operators/sequence_softmax_op.h" + +namespace paddle { +namespace operators { + +class SequenceSoftmaxOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of SequenceSoftmaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of SequenceSoftmaxOp should not be null."); + ctx->SetOutputDim("Out", ctx->GetInputDim("X")); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; + +class SequenceSoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SequenceSoftmaxOpMaker(framework::OpProto* proto, + framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(LoDTensor) 1-D or 2-D input LoDTensor with the 2-nd dimension " + "of length 1."); + AddOutput("Out", + "(LoDTensor) 1-D or 2-D output LoDTensor with the 2-nd dimension " + "of length 1."); + AddComment(R"DOC( +SequenceSoftmaxOp computes softmax activation among all time-steps for each +sequence. The dimension of each time-step should be 1. Thus, the shape of +input Tensor can be either [N, 1] or [N], where N is the sum of all sequences' +lengths. + +Equation: + for i-th sequence in a mini-batch: + Out(X[lod[i]:lod[i+1]], :) = + exp(X[lod[i]:lod[i+1], :]) / sum(exp(X[lod[i]:lod[i+1], :])) + +For example, for a mini-batch of 3 sequences with variable-length, +each containing 2, 3, 2 time-steps, the lod of which is [0, 2, 5, 7], +then softmax will be computed among X[0:2, :], X[2:5, :], X[5:7, :] +and N turns out to be 7. +)DOC"); + } +}; + +class SequenceSoftmaxGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Out"), + "Input(Out) of SequenceSoftmaxGradOp should not be null."); + PADDLE_ENFORCE( + ctx->HasInput(framework::GradVarName("Out")), + "Input(Out@GRAD) of SequenceSoftmaxGradOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of SequenceSoftmaxOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), + "Output(X@GRAD) of SequenceSoftmaxOp should not be null."); + + PADDLE_ENFORCE_EQ( + ctx->GetInputDim("Out"), + ctx->GetInputDim(framework::GradVarName("Out")), + "Input(Out) and Input(Out@GRAD) of SequenceSoftmaxGradOp should be of " + "the same shape."); + + ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(sequence_softmax, ops::SequenceSoftmaxOp, + ops::SequenceSoftmaxOpMaker, sequence_softmax_grad, + ops::SequenceSoftmaxGradOp); +REGISTER_OP_CPU_KERNEL( + sequence_softmax, + ops::SequenceSoftmaxKernel); +REGISTER_OP_CPU_KERNEL( + sequence_softmax_grad, + ops::SequenceSoftmaxGradKernel); diff --git a/paddle/operators/sequence_softmax_op.cu b/paddle/operators/sequence_softmax_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..f2a1e3d5e31ef21b95a51b287bdd1d4aa9221e89 --- /dev/null +++ b/paddle/operators/sequence_softmax_op.cu @@ -0,0 +1,25 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#define EIGEN_USE_GPU + +#include "paddle/operators/sequence_softmax_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL( + sequence_softmax, + ops::SequenceSoftmaxKernel) +REGISTER_OP_GPU_KERNEL( + sequence_softmax_grad, + ops::SequenceSoftmaxGradKernel); diff --git a/paddle/operators/sequence_softmax_op.h b/paddle/operators/sequence_softmax_op.h new file mode 100644 index 0000000000000000000000000000000000000000..96d87c404d217280d74bd088e7a23f539ef6e7ce --- /dev/null +++ b/paddle/operators/sequence_softmax_op.h @@ -0,0 +1,94 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "paddle/operators/math/softmax.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +using LoDTensor = framework::LoDTensor; + +template +class SequenceSoftmaxKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* x = ctx.Input("X"); + auto* out = ctx.Output("Out"); + + auto lod = x->lod(); + auto dims = x->dims(); + + const size_t level = lod.size() - 1; + PADDLE_ENFORCE_EQ(dims[0], static_cast(lod[level].back()), + "The first dimension of Input(X) should be equal to the " + "sum of all sequences' lengths."); + PADDLE_ENFORCE_EQ(dims[0], x->numel(), + "The width of each timestep in Input(X) of " + "SequenceSoftmaxOp should be 1."); + + out->mutable_data(ctx.GetPlace()); + for (int i = 0; i < static_cast(lod[level].size()) - 1; ++i) { + int start_pos = static_cast(lod[level][i]); + int end_pos = static_cast(lod[level][i + 1]); + Tensor x_i = x->Slice(start_pos, end_pos); + Tensor out_i = out->Slice(start_pos, end_pos); + + // Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos) + framework::DDim dims_i = framework::make_ddim({1UL, end_pos - start_pos}); + x_i.Resize(dims_i); + out_i.Resize(dims_i); + math::SoftmaxFunctor()(ctx.device_context(), &x_i, &out_i); + } + } +}; + +template +class SequenceSoftmaxGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* out = ctx.Input("Out"); + auto* out_grad = ctx.Input(framework::GradVarName("Out")); + auto* x = ctx.Input("X"); + auto* x_grad = ctx.Output(framework::GradVarName("X")); + + auto lod = x->lod(); + const size_t level = lod.size() - 1; + + x_grad->mutable_data(ctx.GetPlace()); + for (int i = 0; i < static_cast(lod[level].size()) - 1; ++i) { + int start_pos = static_cast(lod[level][i]); + int end_pos = static_cast(lod[level][i + 1]); + + Tensor out_i = out->Slice(start_pos, end_pos); + Tensor out_grad_i = out_grad->Slice(start_pos, end_pos); + Tensor x_grad_i = x_grad->Slice(start_pos, end_pos); + + // Reshape from (end_pos - start_pos) x 1UL to 1UL x (end_pos - start_pos) + framework::DDim dims_i = framework::make_ddim({1UL, end_pos - start_pos}); + out_i.Resize(dims_i); + out_grad_i.Resize(dims_i); + x_grad_i.Resize(dims_i); + math::SoftmaxGradFunctor()(ctx.device_context(), &out_i, + &out_grad_i, &x_grad_i); + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/sgd_op.cc b/paddle/operators/sgd_op.cc index 3bce95535cf10c0df95b503c6e362b3f0ba2e723..2a6a162a02507fa42c5ecbba59384f9c32aba7a9 100644 --- a/paddle/operators/sgd_op.cc +++ b/paddle/operators/sgd_op.cc @@ -22,18 +22,23 @@ class SGDOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("param"), - "Input(param) of SGDOp should not be null."); - PADDLE_ENFORCE(ctx->HasInput("grad"), - "Input(grad) of SGDOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("param_out"), - "Output(param_out) of SGDOp should not be null."); - - auto param_dim = ctx->GetInputDim("param"); - PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("grad"), + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("Param"), + "Input(Param) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("Grad"), + "Input(Grad) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasInput("LearningRate"), + "Input(LearningRate) of SGDOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("ParamOut"), + "Output(ParamOut) of SGDOp should not be null."); + + auto lr_dims = ctx->GetInputDim("LearningRate"); + PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1, + "Learning rate should have 1 element"); + auto param_dim = ctx->GetInputDim("Param"); + PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("Grad"), "Two input of SGD Op's dimension must be same."); - ctx->SetOutputDim("param_out", param_dim); + ctx->SetOutputDim("ParamOut", param_dim); } }; @@ -41,10 +46,10 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker { public: SGDOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("param", "input parameter"); - AddInput("grad", "input gradient"); - AddOutput("param_out", "output parameter"); - AddAttr("learning_rate", "learning rate of sgd"); + AddInput("Param", "Input parameter"); + AddInput("LearningRate", "Learning rate of SGD"); + AddInput("Grad", "Input gradient"); + AddOutput("ParamOut", "output parameter"); AddComment(R"DOC( Simplest sgd algorithm. diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h index f8888f9c362e1c39af42236bb3a23be37aa3ae15..26f4012f258771794c736dbfad4af174b017f410 100644 --- a/paddle/operators/sgd_op.h +++ b/paddle/operators/sgd_op.h @@ -19,28 +19,25 @@ limitations under the License. */ namespace paddle { namespace operators { -using Tensor = framework::Tensor; -template -using EigenVector = framework::EigenVector; - template -class SGDOpKernel : public framework::OpKernel { +class SGDOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - auto param = ctx.Input("param"); - auto grad = ctx.Input("grad"); - auto param_out = ctx.Output("param_out"); - float lr = ctx.Attr("learning_rate"); + auto param = ctx.Input("Param"); + auto grad = ctx.Input("Grad"); + auto param_out = ctx.Output("ParamOut"); + auto learning_rate = ctx.Input("LearningRate"); param_out->mutable_data(ctx.GetPlace()); - auto p = EigenVector::Flatten(*param); - auto g = EigenVector::Flatten(*grad); - auto o = EigenVector::Flatten(*param_out); + auto p = framework::EigenVector::Flatten(*param); + auto g = framework::EigenVector::Flatten(*grad); + auto o = framework::EigenVector::Flatten(*param_out); + auto lr = framework::EigenVector::Flatten(*learning_rate); auto place = ctx.GetEigenDevice(); - o.device(place) = p - lr * g; + Eigen::DSizes grad_dsize(grad->numel()); + o.device(place) = p - lr.broadcast(grad_dsize) * g; } }; diff --git a/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..b6653e1cc754bd626f04c28c42ce59f83c2fc87f --- /dev/null +++ b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cc @@ -0,0 +1,150 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#include "paddle/operators/sigmoid_cross_entropy_with_logits_op.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; + +class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); + PADDLE_ENFORCE(ctx->HasInput("Labels"), + "Input(Labels) should be not null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null."); + + auto x_dims = ctx->GetInputDim("X"); + auto labels_dims = ctx->GetInputDim("Labels"); + PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2."); + PADDLE_ENFORCE_EQ(labels_dims.size(), 2, + "Input(Labels)'s rank should be 2."); + PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0], + "The 1st dimension of Input(X) and Input(Labels) should " + "be equal."); + PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1], + "The 2nd dimension of Input(X) and Input(Labels) should " + "be equal."); + + ctx->SetOutputDim("Out", x_dims); + ctx->ShareLoD("X", /*->*/ "Out"); + } +}; + +class SigmoidCrossEntropyWithLogitsGradOp + : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); + PADDLE_ENFORCE(ctx->HasInput("Labels"), + "Input(Labels) should be not null."); + PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), + "Input(Out@GRAD) shoudl be not null."); + PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), + "Output(X@GRAD) should be not null."); + + auto x_dims = ctx->GetInputDim("X"); + auto labels_dims = ctx->GetInputDim("Labels"); + auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out")); + PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2."); + PADDLE_ENFORCE_EQ(labels_dims.size(), 2, + "Input(Labels)'s rank should be 2."); + PADDLE_ENFORCE_EQ(dout_dims.size(), 2, + "Input(Out@Grad)'s rank should be 2."); + PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0], + "The 1st dimension of Input(X) and Input(Labels) should " + "be equal."); + PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1], + "The 2nd dimension of Input(X) and Input(Labels) should " + "be equal."); + PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0], + "The 1st dimension of Input(X) and Input(Out@Grad) " + "should be equal."); + PADDLE_ENFORCE_EQ(x_dims[1], dout_dims[1], + "The 2nd dimension of Input(X) and Input(Out@Grad) " + "should be equal."); + + ctx->SetOutputDim(framework::GradVarName("X"), x_dims); + } +}; + +class SigmoidCrossEntropyWithLogitsOpMaker + : public framework::OpProtoAndCheckerMaker { + public: + SigmoidCrossEntropyWithLogitsOpMaker(framework::OpProto* proto, + framework::OpAttrChecker* op_checker) + : framework::OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", + "(Tensor, default Tensor), a 2-D tensor with shape N x D, " + "where N is the batch size and D is the number of classes. " + "This input is a tensor of logits computed by the previous " + " operator. Logits are unscaled log probabilities given as " + "log(p/(1-p))."); + AddInput("Labels", + "(Tensor, default Tensor), a 2-D tensor of the same type " + "and shape as X. This input is a tensor of probabalistic labels " + "for each logit"); + AddOutput("Out", + "(Tensor, default Tensor), a 2-D tensor with shape N x D " + " of elementwise logistic losses."); + AddComment(R"DOC( +SigmoidCrossEntropyWithLogits Operator. + +This measures the elementwise probability error in discrete classification tasks +in which each class is independent. This can be thought of as predicting labels +for a data-point that are not mutually exclusive. For example, a news article +can be about politics, technology or sports at the same time or none of these. + +The logistic loss is given as follows: + + loss = -Labels * log(sigmoid(X)) - (1 - Labels) * log(1 - sigmoid(X)) + +We know that sigmoid(X) = (1 / (1 + exp(-X))). By substituting this we get + + loss = X - X * Labels + log(1 + exp(-X)) + +For stability and to prevent overflow of exp(-X) when X < 0, +we can reformulate the loss as follows: + + loss = max(X, 0) - X * Labels + log(1 + exp(-abs(X))) + +Both the input `X` and `Labels` can carry the LoD (Level of Details) information. +However the output only shares the LoD with input `X`. +)DOC"); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(sigmoid_cross_entropy_with_logits, + ops::SigmoidCrossEntropyWithLogitsOp, + ops::SigmoidCrossEntropyWithLogitsOpMaker, + sigmoid_cross_entropy_with_logits_grad, + ops::SigmoidCrossEntropyWithLogitsGradOp); +REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits, + ops::SigmoidCrossEntropyWithLogitsKernel< + paddle::platform::CPUPlace, float>); +REGISTER_OP_CPU_KERNEL(sigmoid_cross_entropy_with_logits_grad, + ops::SigmoidCrossEntropyWithLogitsGradKernel< + paddle::platform::CPUPlace, float>); diff --git a/paddle/operators/sigmoid_cross_entropy_with_logits_op.cu b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..32a39956a14a206373b7b4c141dad19577d171f0 --- /dev/null +++ b/paddle/operators/sigmoid_cross_entropy_with_logits_op.cu @@ -0,0 +1,24 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/sigmoid_cross_entropy_with_logits_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(sigmoid_cross_entropy_with_logits, + ops::SigmoidCrossEntropyWithLogitsKernel< + paddle::platform::GPUPlace, float>); +REGISTER_OP_GPU_KERNEL(sigmoid_cross_entropy_with_logits_grad, + ops::SigmoidCrossEntropyWithLogitsGradKernel< + paddle::platform::GPUPlace, float>); diff --git a/paddle/operators/sigmoid_cross_entropy_with_logits_op.h b/paddle/operators/sigmoid_cross_entropy_with_logits_op.h new file mode 100644 index 0000000000000000000000000000000000000000..41c619f181c878f08959a8ca461c60af5ffdff2a --- /dev/null +++ b/paddle/operators/sigmoid_cross_entropy_with_logits_op.h @@ -0,0 +1,75 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. */ + +#pragma once +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X))) +template +class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + const framework::Tensor *X = context.Input("X"); + const framework::Tensor *Labels = + context.Input("Labels"); + framework::Tensor *Out = context.Output("Out"); + Out->mutable_data(context.GetPlace()); + + auto x = framework::EigenVector::Flatten(*X); + auto labels = framework::EigenVector::Flatten(*Labels); + auto out = framework::EigenVector::Flatten(*Out); + auto place = context.GetEigenDevice(); + + // term1 = max(x, 0) + auto term1 = x.cwiseMax(static_cast(0)); + // term2 = x * labels + auto term2 = x * labels; + // term3 = log(1 + exp(-abs(x))) + auto term3 = (static_cast(1) + (-(x.abs())).exp()).log(); + + out.device(place) = term1 - term2 + term3; + } +}; + +// dX = sigmoid(X) - labels +template +class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + const framework::Tensor *X = context.Input("X"); + const framework::Tensor *Labels = + context.Input("Labels"); + const framework::Tensor *dOut = + context.Input(framework::GradVarName("Out")); + framework::Tensor *dX = + context.Output(framework::GradVarName("X")); + dX->mutable_data(context.GetPlace()); + + auto x = framework::EigenVector::Flatten(*X); + auto labels = framework::EigenVector::Flatten(*Labels); + auto dout = framework::EigenVector::Flatten(*dOut); + auto dx = framework::EigenVector::Flatten(*dX); + auto place = context.GetEigenDevice(); + + auto sigmoid_x = static_cast(1) / (static_cast(1) + (-x).exp()); + dx.device(place) = dout * (sigmoid_x - labels); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/smooth_l1_loss_op.cc b/paddle/operators/smooth_l1_loss_op.cc index 2d197e3b1b763fa87939623d47728aab3bff7cd1..91391dc945be3d3d94966339bf9232eb21dcc38f 100644 --- a/paddle/operators/smooth_l1_loss_op.cc +++ b/paddle/operators/smooth_l1_loss_op.cc @@ -22,7 +22,7 @@ class SmoothL1LossOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized."); PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized."); @@ -94,7 +94,7 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { auto in_dims = ctx->GetInputDim("X"); auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); diff --git a/paddle/operators/smooth_l1_loss_op.h b/paddle/operators/smooth_l1_loss_op.h index 0604fb5e1c2f17c702208520a1d23bd5c3c65b5d..39d0070b6c8909b8f433de48038240e851d9d6cf 100644 --- a/paddle/operators/smooth_l1_loss_op.h +++ b/paddle/operators/smooth_l1_loss_op.h @@ -45,7 +45,7 @@ struct SmoothL1LossForward { }; template -class SmoothL1LossKernel : public framework::OpKernel { +class SmoothL1LossKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("X"); @@ -115,7 +115,7 @@ struct SmoothL1LossBackward { }; template -class SmoothL1LossGradKernel : public framework::OpKernel { +class SmoothL1LossGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("InsideWeight"); diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc index e353afee3e10247fbd5c7f4282c366cd1bc39552..4c131ed44de1c1567193c7cabff6ddf2dc63d9be 100644 --- a/paddle/operators/softmax_op.cc +++ b/paddle/operators/softmax_op.cc @@ -22,7 +22,7 @@ class SoftmaxOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SoftmaxOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Y"), @@ -69,7 +69,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null."); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")), "Input(Y@GRAD) should be not null."); diff --git a/paddle/operators/softmax_op.h b/paddle/operators/softmax_op.h index 7220f486be055e1b841a06b15f519717c54f575c..2c08853f4f615bfe95f51aa20776ddddcdaa8f61 100644 --- a/paddle/operators/softmax_op.h +++ b/paddle/operators/softmax_op.h @@ -26,46 +26,31 @@ template ; template -class SoftmaxKernel : public framework::OpKernel { +class SoftmaxKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto X = context.Input("X"); - auto Y = context.Output("Y"); + auto* X = context.Input("X"); + auto* Y = context.Output("Y"); // allocate memory on device. Y->mutable_data(context.GetPlace()); - math::SoftmaxFunctor()(context, X, Y); + math::SoftmaxFunctor()(context.device_context(), X, Y); } }; template -class SoftmaxGradKernel : public framework::OpKernel { +class SoftmaxGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto Y = context.Input("Y"); - auto dY = context.Input(framework::GradVarName("Y")); - auto dX = context.Output(framework::GradVarName("X")); - dX->mutable_data(context.GetPlace()); - - const int batch_size = Y->dims()[0]; - const int class_num = Y->dims()[1]; - - Eigen::DSizes along_class(1); - Eigen::DSizes batch_by_one(batch_size, 1); - Eigen::DSizes one_by_class(1, class_num); + auto* Y = context.Input("Y"); + auto* dY = context.Input(framework::GradVarName("Y")); + auto* dX = context.Output(framework::GradVarName("X")); - auto Y_eigen = EigenMatrix::From(*Y); - auto dY_eigen = EigenMatrix::From(*dY); - auto dX_eigen = EigenMatrix::From(*dX); - auto place = context.GetEigenDevice(); + // allocate memory on device. + dX->mutable_data(context.GetPlace()); - auto dot = (Y_eigen * dY_eigen) - .sum(along_class) - .eval() - .reshape(batch_by_one) - .broadcast(one_by_class); - dX_eigen.device(place) = (dY_eigen - dot) * Y_eigen; + math::SoftmaxGradFunctor()(context.device_context(), Y, dY, dX); } }; diff --git a/paddle/operators/softmax_with_cross_entropy_op.cc b/paddle/operators/softmax_with_cross_entropy_op.cc index e2299b254458cdd42dee4683561d4d5c81653fb1..5431a1657c300d9c60100616ca7ea2e1196824ec 100644 --- a/paddle/operators/softmax_with_cross_entropy_op.cc +++ b/paddle/operators/softmax_with_cross_entropy_op.cc @@ -13,6 +13,8 @@ limitations under the License. */ #include "paddle/operators/softmax_with_cross_entropy_op.h" +#include +#include namespace paddle { namespace operators { @@ -26,15 +28,14 @@ class SoftmaxWithCrossEntropyOpMaker AddInput("Logits", "(Tensor, default: Tensor), The unscaled log probabilities " "which is a 2-D tensor with shape [N x K]. N is the batch_size, " - "and K is the class number.") - .NotInGradient(); - AddInput( - "Label", - "(Tensor, default: Tensor), The ground truth which is a 2-D " - "tensor. " - "If softLable is set to 0, Label is a Tensor with shape [N x 1]. " - "If softLable is set to 1, Label is a Tensor " - "with shape [N x K]."); + "and K is the class number."); + AddInput("Label", + "(Tensor, default: Tensor), The ground truth which is a 2-D " + "tensor. " + "If softLable is set to 0, Label is a Tensor with shape [N x " + "1]. " + "If softLable is set to 1, Label is a Tensor " + "with shape [N x K]."); AddOutput( "Softmax", "(Tensor, default: Tensor), A 2-D tensor with shape [N x K]. " @@ -82,7 +83,7 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("Logits"), "Input(Logits) should be not null."); PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); @@ -115,6 +116,11 @@ class SoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel { ctx->ShareLoD("Logits", /*->*/ "Softmax"); ctx->ShareLoD("Logits", /*->*/ "Loss"); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType(ctx.Input("Logits")->type()); + } }; class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { @@ -122,7 +128,7 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")), "Input(Loss@Grad) should not be null."); PADDLE_ENFORCE(ctx->HasInput("Softmax"), @@ -149,6 +155,31 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { ctx->SetOutputDim(framework::GradVarName("Logits"), ctx->GetInputDim("Softmax")); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return framework::ToDataType( + ctx.Input(framework::GradVarName("Loss"))->type()); + } +}; + +class SoftmaxGradMaker : public framework::SingleGradOpDescMaker { + public: + using framework::SingleGradOpDescMaker::SingleGradOpDescMaker; + + protected: + std::unique_ptr Apply() const override { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("softmax_with_cross_entropy_grad"); + grad_op->SetInput("Label", Input("Label")); + grad_op->SetInput("Softmax", Output("Softmax")); + grad_op->SetInput("Loss", Output("Loss")); + grad_op->SetInput(framework::GradVarName("Softmax"), OutputGrad("Softmax")); + grad_op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss")); + grad_op->SetOutput(framework::GradVarName("Logits"), InputGrad("Logits")); + grad_op->SetAttrMap(Attrs()); + return std::unique_ptr(grad_op); + } }; } // namespace operators @@ -156,10 +187,10 @@ class SoftmaxWithCrossEntropyOpGrad : public framework::OperatorWithKernel { namespace ops = paddle::operators; -REGISTER_OP(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp, - ops::SoftmaxWithCrossEntropyOpMaker, - softmax_with_cross_entropy_grad, - ops::SoftmaxWithCrossEntropyOpGrad); +REGISTER_OPERATOR(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyOp, + ops::SoftmaxWithCrossEntropyOpMaker, ops::SoftmaxGradMaker); +REGISTER_OPERATOR(softmax_with_cross_entropy_grad, + ops::SoftmaxWithCrossEntropyOpGrad); REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy, ops::SoftmaxWithCrossEntropyKernel); REGISTER_OP_CPU_KERNEL(softmax_with_cross_entropy_grad, diff --git a/paddle/operators/softmax_with_cross_entropy_op.cu b/paddle/operators/softmax_with_cross_entropy_op.cu index 1cf4296dccf68aece6fdfb7910a9c68449633b76..2bc53ecf871eb1800a920ba85e8eac31d7037efe 100644 --- a/paddle/operators/softmax_with_cross_entropy_op.cu +++ b/paddle/operators/softmax_with_cross_entropy_op.cu @@ -53,7 +53,7 @@ __global__ void SoftCrossEntropyGradientKernel(T* logit_grad, } // namespace template -class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel { +class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()), @@ -66,14 +66,16 @@ class SoftmaxWithCrossEntropyCUDAKernel : public framework::OpKernel { softmax->mutable_data(context.GetPlace()); loss->mutable_data(context.GetPlace()); - math::SoftmaxFunctor()(context, logits, softmax); + math::SoftmaxFunctor()(context.device_context(), + logits, softmax); math::CrossEntropyFunctor()( - context, loss, softmax, labels, context.Attr("softLabel")); + context.device_context(), loss, softmax, labels, + context.Attr("softLabel")); } }; template -class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel { +class SoftmaxWithCrossEntropyGradCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()), diff --git a/paddle/operators/softmax_with_cross_entropy_op.h b/paddle/operators/softmax_with_cross_entropy_op.h index bf792c1f59e2e43a98c93bddbc2aa63d646dee6f..cffd422f1827b646a8abcd881fdcb5455e6a663a 100644 --- a/paddle/operators/softmax_with_cross_entropy_op.h +++ b/paddle/operators/softmax_with_cross_entropy_op.h @@ -27,7 +27,7 @@ template ; template -class SoftmaxWithCrossEntropyKernel : public framework::OpKernel { +class SoftmaxWithCrossEntropyKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { PADDLE_ENFORCE(platform::is_cpu_place(context.GetPlace()), @@ -40,14 +40,16 @@ class SoftmaxWithCrossEntropyKernel : public framework::OpKernel { softmax->mutable_data(context.GetPlace()); loss->mutable_data(context.GetPlace()); - math::SoftmaxFunctor()(context, logits, softmax); + math::SoftmaxFunctor()(context.device_context(), + logits, softmax); math::CrossEntropyFunctor()( - context, loss, softmax, labels, context.Attr("softLabel")); + context.device_context(), loss, softmax, labels, + context.Attr("softLabel")); } }; template -class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel { +class SoftmaxWithCrossEntropyGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { const Tensor* out_grad = diff --git a/paddle/operators/split_op.cc b/paddle/operators/split_op.cc index 5f4b5539affef6fe1d3c4d15fff77d983b5e107f..d5dd4df2a2a6f424421ae7f117c26acff08d37d0 100644 --- a/paddle/operators/split_op.cc +++ b/paddle/operators/split_op.cc @@ -24,7 +24,7 @@ class SplitOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SplitOp should not be null."); PADDLE_ENFORCE_GE(ctx->Outputs("Out").size(), 1UL, diff --git a/paddle/operators/split_op.h b/paddle/operators/split_op.h index 8ab8e0ee4fea621b34da73507c53846100d61a17..fa26e5f677b18c84b45dd583004d02cab4c1d375 100644 --- a/paddle/operators/split_op.h +++ b/paddle/operators/split_op.h @@ -22,7 +22,7 @@ namespace paddle { namespace operators { template -class SplitOpKernel : public framework::OpKernel { +class SplitOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* in = ctx.Input("X"); diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc index 5a0cb596008a98aacf5e7b5ff70307ea1b8508e6..cce4e527c36bc0f4735165ff88ebba85b00eb898 100644 --- a/paddle/operators/squared_l2_distance_op.cc +++ b/paddle/operators/squared_l2_distance_op.cc @@ -22,7 +22,7 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of SquaredL2DistanceOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("Y"), @@ -86,7 +86,7 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Gradient of Out should not be null"); auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h index 097ac04fc09a10b3b624f491a847e281e41a802c..259ef4029646914f83a112b9c6d7fdf8401483f6 100644 --- a/paddle/operators/squared_l2_distance_op.h +++ b/paddle/operators/squared_l2_distance_op.h @@ -28,7 +28,7 @@ template ; template -class SquaredL2DistanceKernel : public framework::OpKernel { +class SquaredL2DistanceKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("X"); @@ -68,7 +68,7 @@ class SquaredL2DistanceKernel : public framework::OpKernel { }; template -class SquaredL2DistanceGradKernel : public framework::OpKernel { +class SquaredL2DistanceGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* in0 = context.Input("sub_result"); diff --git a/paddle/operators/strided_memcpy_test.cc b/paddle/operators/strided_memcpy_test.cc index 05882a88738cfc9cc23480efe0afe504008377ca..68f064eaee5851333ddf9767b7138da83a28503d 100644 --- a/paddle/operators/strided_memcpy_test.cc +++ b/paddle/operators/strided_memcpy_test.cc @@ -72,7 +72,7 @@ TEST(StridedMemcpy, CPUConcat) { } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(StridedMemcpy, GPUCrop) { // clang-format off int src[] = { @@ -157,4 +157,4 @@ TEST(StridedMemcpy, GPUConcat) { #endif } // namespace operators -} // namespace paddle \ No newline at end of file +} // namespace paddle diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc index 8f62a9f4db8d39edc11949df513aebf4fa257d45..ffb0cb92111bfb8490d35e4f5cfc9e405b0e3250 100644 --- a/paddle/operators/sum_op.cc +++ b/paddle/operators/sum_op.cc @@ -11,6 +11,7 @@ limitations under the License. */ #include "paddle/operators/sum_op.h" #include +#include "paddle/operators/net_op.h" namespace paddle { namespace operators { @@ -21,15 +22,16 @@ class SumOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { + PADDLE_ENFORCE(ctx->HasInputs("X"), "Inputs(X) should not be null"); auto x_dims = ctx->GetInputsDim("X"); - PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of SumOp should not be null."); - auto in_dim = x_dims[0]; size_t N = x_dims.size(); PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1."); + + auto in_dim = x_dims[0]; for (size_t i = 1; i < N; i++) { auto dim = x_dims[i]; PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape"); @@ -54,21 +56,26 @@ or not. But the output only shares the LoD with the first input. } }; -class SumGradOp : public framework::OperatorWithKernel { +class SumGradMaker : public framework::GradOpDescMakerBase { public: - using framework::OperatorWithKernel::OperatorWithKernel; + using framework::GradOpDescMakerBase::GradOpDescMakerBase; - protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { - auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out")); - auto x_grad_names = ctx->Outputs(framework::GradVarName("X")); - size_t x_length = x_grad_names.size(); - std::vector x_grad_dims; - x_grad_dims.reserve(x_length); - for (size_t i = 0; i < x_length; ++i) { - x_grad_dims.push_back(out_grad_dims); - } - ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims); + std::vector> operator()() + const override { + auto x_grads = InputGrad("X"); + std::vector> grad_ops; + grad_ops.reserve(x_grads.size()); + auto og = OutputGrad("Out"); + std::transform(x_grads.begin(), x_grads.end(), std::back_inserter(grad_ops), + [&og](const std::string& x_grad) { + auto* grad_op = new framework::OpDescBind(); + grad_op->SetType("scale"); + grad_op->SetInput("X", og); + grad_op->SetOutput("Out", {x_grad}); + grad_op->SetAttr("scale", 1.0f); + return std::unique_ptr(grad_op); + }); + return grad_ops; } }; @@ -76,7 +83,6 @@ class SumGradOp : public framework::OperatorWithKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp); + +REGISTER_OPERATOR(sum, ops::SumOp, ops::SumOpMaker, ops::SumGradMaker); REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel); -REGISTER_OP_CPU_KERNEL(sum_grad, - ops::SumGradKernel); diff --git a/paddle/operators/sum_op.cu b/paddle/operators/sum_op.cu index a465cf3659ba7c51338abadfc62962fb6755a39d..b1896d3cd87f47bd2573287ee37b1b72ae9ec6e8 100644 --- a/paddle/operators/sum_op.cu +++ b/paddle/operators/sum_op.cu @@ -14,5 +14,3 @@ limitations under the License. */ namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL(sum, ops::SumKernel); -REGISTER_OP_GPU_KERNEL(sum_grad, - ops::SumGradKernel); diff --git a/paddle/operators/sum_op.h b/paddle/operators/sum_op.h index 0b1e9ebaa38d455fb5e3ce8c1a39cbbcdad9a940..91e5da8b40d452db8715990cdbe2731b3aea44b9 100644 --- a/paddle/operators/sum_op.h +++ b/paddle/operators/sum_op.h @@ -22,7 +22,7 @@ template ; template -class SumKernel : public framework::OpKernel { +class SumKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto ins = context.MultiInput("X"); @@ -42,24 +42,5 @@ class SumKernel : public framework::OpKernel { } }; -template -class SumGradKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* input = context.Input(framework::GradVarName("Out")); - auto outs = context.MultiOutput(framework::GradVarName("X")); - for (auto out : outs) { - out->mutable_data(context.GetPlace()); - } - - auto place = context.GetEigenDevice(); - auto in = EigenVector::Flatten(*input); - for (auto out : outs) { - auto result = EigenVector::Flatten(*out); - result.device(place) = in; - } - } -}; - } // namespace operators } // namespace paddle diff --git a/paddle/operators/top_k_op.cc b/paddle/operators/top_k_op.cc index 5f22bf1df8720b60aba7cd75896d88cd1ad77635..c95481991229dd40e162094ccbf15de8ab627c8b 100644 --- a/paddle/operators/top_k_op.cc +++ b/paddle/operators/top_k_op.cc @@ -22,7 +22,7 @@ class TopkOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase *ctx) const override { + void InferShape(framework::InferShapeContext *ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of TopkOp should not be null."); PADDLE_ENFORCE(ctx->HasOutput("Out"), diff --git a/paddle/operators/top_k_op.cu b/paddle/operators/top_k_op.cu index 53fe505b77bfac8a33803f082f8e935d3ed403b6..7be6932f1e301d06e0e232367a38bfa673ff45be 100644 --- a/paddle/operators/top_k_op.cu +++ b/paddle/operators/top_k_op.cu @@ -279,7 +279,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int* indices, } template -class TopkOpCUDAKernel : public framework::OpKernel { +class TopkOpCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), diff --git a/paddle/operators/top_k_op.h b/paddle/operators/top_k_op.h index ef66acc1d569282a42be64b7a5e90f3fbdb20690..4b248faa120bcfd20e70d288cce2d485d3e6371e 100644 --- a/paddle/operators/top_k_op.h +++ b/paddle/operators/top_k_op.h @@ -28,7 +28,7 @@ template ; template -class TopkKernel : public framework::OpKernel { +class TopkKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { // Get the top k elements of each row of input tensor diff --git a/paddle/operators/transpose_op.cc b/paddle/operators/transpose_op.cc index 0672f9342dac00ecc3f358450a9a203327cbb51f..1101bbe3efe3d313400663ade561063da4e34dfd 100644 --- a/paddle/operators/transpose_op.cc +++ b/paddle/operators/transpose_op.cc @@ -24,7 +24,7 @@ class TransposeOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null"); auto x_dims = ctx->GetInputDim("X"); @@ -93,7 +93,7 @@ class TransposeOpGrad : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null"); PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); diff --git a/paddle/operators/transpose_op.h b/paddle/operators/transpose_op.h index ea299dce72ad340b0a65ee50582dc156b5ad7abb..aaa3f47ab5545accd4d1108e0ad6f5a3062186d0 100644 --- a/paddle/operators/transpose_op.h +++ b/paddle/operators/transpose_op.h @@ -38,7 +38,7 @@ void EigenTranspose(const framework::ExecutionContext& context, } template -class TransposeKernel : public framework::OpKernel { +class TransposeKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* x = context.Input("X"); @@ -73,7 +73,7 @@ class TransposeKernel : public framework::OpKernel { }; template -class TransposeGradKernel : public framework::OpKernel { +class TransposeGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* out_grad = diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index 2771df56086ff261728af84edcdf01cda3e45e9f..e330877fc4283b796dcb5c5d745881884ae491ae 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -21,7 +21,7 @@ namespace operators { // Use std::random and thrust::random(thrust is a std library in CUDA) to // implement uniform random. template -class CPUUniformRandomKernel : public framework::OpKernel { +class CPUUniformRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* tensor = ctx.Output("Out"); @@ -47,7 +47,7 @@ class UniformRandomOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(framework::InferShapeContextBase* ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) of UniformRandomOp should not be null."); @@ -62,6 +62,11 @@ class UniformRandomOp : public framework::OperatorWithKernel { } ctx->SetOutputDim("Out", framework::make_ddim(temp)); } + + framework::DataType IndicateDataType( + const framework::ExecutionContext& ctx) const override { + return static_cast(Attr("data_type")); + } }; class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker { @@ -80,6 +85,8 @@ Used to initialize tensor with uniform random generator. "Random seed of uniform random. " "0 means generate a seed by system") .SetDefault(0); + AddAttr("data_type", "output tensor data type") + .SetDefault(framework::DataType::FP32); } }; } // namespace operators diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index 6614b53b3f990d10c82633f3c1f079acea0cd827..5612ce9eb1c644d6271b4a9bb949f685848e05c0 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -40,7 +40,7 @@ struct UniformGenerator { // Use std::random and thrust::random(thrust is a std library in CUDA) to // implement uniform random. template -class GPUUniformRandomKernel : public framework::OpKernel { +class GPUUniformRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index 93b472b41c8a4c3a2bfada9d4fbf0e9e1b0cc736..36450e926891342f37424447703781a33c1190ae 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -16,8 +16,8 @@ namespace paddle { namespace platform { template <> -Eigen::DefaultDevice* DeviceContext::get_eigen_device() - const { +Eigen::DefaultDevice* DeviceContext::GetEigenDevice< + platform::CPUPlace, Eigen::DefaultDevice>() const { return reinterpret_cast(this)->eigen_device(); } @@ -35,7 +35,13 @@ Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const { Place CPUDeviceContext::GetPlace() const { return CPUPlace(); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA + +template <> +Eigen::GpuDevice* +DeviceContext::GetEigenDevice() const { + return reinterpret_cast(this)->eigen_device(); +} class EigenCudaStreamDevice : public Eigen::StreamInterface { public: @@ -90,11 +96,6 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface { mutable unsigned int* semaphore_; }; -template <> -Eigen::GpuDevice* DeviceContext::get_eigen_device() const { - return reinterpret_cast(this)->eigen_device(); -} - CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) { SetDeviceId(place_.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); @@ -135,7 +136,7 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; } cudaStream_t CUDADeviceContext::stream() const { return stream_; } -#endif // PADDLE_ONLY_CPU +#endif } // namespace platform } // namespace paddle diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index f6a39a8e26c301296aac0af7f4e8b2c6c97ece24..ef5f19214d9ccb23b9c946bee28cb764122bd7cd 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -14,7 +14,7 @@ limitations under the License. */ #include "paddle/platform/enforce.h" #include "paddle/platform/place.h" -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" #include "paddle/platform/gpu_info.h" @@ -27,13 +27,23 @@ limitations under the License. */ namespace paddle { namespace platform { +template +struct EigenDeviceConverter; + +template <> +struct EigenDeviceConverter { + using EigenDeviceType = Eigen::DefaultDevice; +}; + class DeviceContext { public: virtual ~DeviceContext() {} virtual Place GetPlace() const = 0; - template - DeviceType* get_eigen_device() const; + template ::EigenDeviceType> + DeviceType* GetEigenDevice() const; virtual void Wait() const {} }; @@ -51,7 +61,12 @@ class CPUDeviceContext : public DeviceContext { std::unique_ptr eigen_device_; }; -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA +template <> +struct EigenDeviceConverter { + using EigenDeviceType = Eigen::GpuDevice; +}; + class EigenCudaStreamDevice; class CUDADeviceContext : public DeviceContext { diff --git a/paddle/platform/device_context_test.cc b/paddle/platform/device_context_test.cc index 5883a55272f0f24c94d48bc43c62ddb7bef15465..8bf5174c4a5579f6f5602dd38e5a87ed3ef444a7 100644 --- a/paddle/platform/device_context_test.cc +++ b/paddle/platform/device_context_test.cc @@ -20,11 +20,11 @@ TEST(Device, Init) { using paddle::platform::CUDADeviceContext; using paddle::platform::GPUPlace; - int count = paddle::platform::GetDeviceCount(); + int count = paddle::platform::GetCUDADeviceCount(); for (int i = 0; i < count; i++) { DeviceContext* device_context = new CUDADeviceContext(GPUPlace(i)); Eigen::GpuDevice* gpu_device = - device_context->template get_eigen_device(); + device_context->template GetEigenDevice(); ASSERT_NE(nullptr, gpu_device); delete device_context; } @@ -34,7 +34,7 @@ TEST(Device, CUDADeviceContext) { using paddle::platform::CUDADeviceContext; using paddle::platform::GPUPlace; - int count = paddle::platform::GetDeviceCount(); + int count = paddle::platform::GetCUDADeviceCount(); for (int i = 0; i < count; i++) { CUDADeviceContext* device_context = new CUDADeviceContext(GPUPlace(i)); Eigen::GpuDevice* gpu_device = device_context->eigen_device(); diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h index b523ef03c0053622bfda5b4bf07515c1b480b4af..cd906c3fa9375cd6edaed0377a596771e25043d4 100644 --- a/paddle/platform/enforce.h +++ b/paddle/platform/enforce.h @@ -29,7 +29,7 @@ limitations under the License. */ #include // for __cxa_demangle #endif -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" @@ -41,7 +41,7 @@ limitations under the License. */ #include #include -#endif // PADDLE_ONLY_CPU +#endif namespace paddle { namespace platform { @@ -113,7 +113,7 @@ inline typename std::enable_if::type throw_on_error( } } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template inline typename std::enable_if::type throw_on_error( @@ -185,7 +185,7 @@ inline void throw_on_error(T e) { std::make_exception_ptr( \ std::runtime_error(paddle::string::Sprintf(__VA_ARGS__))), \ __FILE__, __LINE__); \ - } while (0) + } while (false) #define PADDLE_ENFORCE(...) \ do { \ @@ -195,7 +195,7 @@ inline void throw_on_error(T e) { throw ::paddle::platform::EnforceNotMet(std::current_exception(), \ __FILE__, __LINE__); \ } \ - } while (0) + } while (false) /* * Some enforce helpers here, usage: diff --git a/paddle/platform/enforce_test.cc b/paddle/platform/enforce_test.cc index 80bdee3d9dfbe38ef707a6ba60cdb7f7b99714de..8206a055eabf4abf584962e921610d5029e2f571 100644 --- a/paddle/platform/enforce_test.cc +++ b/paddle/platform/enforce_test.cc @@ -213,4 +213,4 @@ TEST(ENFORCE_USER_DEFINED_CLASS, EQ) { TEST(ENFORCE_USER_DEFINED_CLASS, NE) { Dims a{{1, 2, 3, 4}}, b{{5, 6, 7, 8}}; ASSERT_THROW(PADDLE_ENFORCE_EQ(a, b), paddle::platform::EnforceNotMet); -} \ No newline at end of file +} diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc index be381a4e26cf0eb41f5b3de88bd03ad8901683cc..0cab5ffc5609bbd6fd08c74329d8370fb95f8102 100644 --- a/paddle/platform/gpu_info.cc +++ b/paddle/platform/gpu_info.cc @@ -26,11 +26,11 @@ DEFINE_double(fraction_of_gpu_memory_to_use, 0.95, namespace paddle { namespace platform { -int GetDeviceCount() { +int GetCUDADeviceCount() { int count; PADDLE_ENFORCE( cudaGetDeviceCount(&count), - "cudaGetDeviceCount failed in paddle::platform::GetDeviceCount"); + "cudaGetDeviceCount failed in paddle::platform::GetCUDADeviceCount"); return count; } @@ -43,6 +43,8 @@ int GetCurrentDeviceId() { } void SetDeviceId(int id) { + // TODO(qijun): find a better way to cache the cuda device count + PADDLE_ENFORCE_LT(id, GetCUDADeviceCount(), "id must less than GPU count"); PADDLE_ENFORCE(cudaSetDevice(id), "cudaSetDevice failed in paddle::platform::SetDeviceId"); } diff --git a/paddle/platform/gpu_info.h b/paddle/platform/gpu_info.h index f0c825bd9b0bc41396b8fdb95f0b4337cbe3db02..37665b97d764fbcfe0964127d230b1d28d90b687 100644 --- a/paddle/platform/gpu_info.h +++ b/paddle/platform/gpu_info.h @@ -14,7 +14,7 @@ limitations under the License. */ #pragma once -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA #include #include @@ -28,7 +28,7 @@ const std::string kEnvFractionGpuMemoryToUse = "PADDLE_FRACTION_GPU_MEMORY_TO_USE"; //! Get the total number of GPU devices in system. -int GetDeviceCount(); +int GetCUDADeviceCount(); //! Get the current GPU device id in system. int GetCurrentDeviceId(); @@ -63,4 +63,4 @@ void GpuMemcpyPeer(void *dst, int dst_device, const void *src, int src_device, } // namespace platform } // namespace paddle -#endif // PADDLE_ONLY_CPU +#endif diff --git a/paddle/platform/hostdevice.h b/paddle/platform/hostdevice.h index e7de86b7b2f75d206e730ec409bbee5d0a08942e..eb2df291cceef553d6422e6166e1fef2c63e2a47 100644 --- a/paddle/platform/hostdevice.h +++ b/paddle/platform/hostdevice.h @@ -2,8 +2,10 @@ #ifdef __CUDACC__ #define HOSTDEVICE __host__ __device__ +#define DEVICE __device__ #define HOST __host__ #else #define HOSTDEVICE +#define DEVICE #define HOST #endif diff --git a/paddle/platform/macros.h b/paddle/platform/macros.h index 4a04a38c0c6f905639004dea2f4416ecc57c8620..feae7bdd77e3a0d02f33fb33991648408f542d0e 100644 --- a/paddle/platform/macros.h +++ b/paddle/platform/macros.h @@ -16,8 +16,10 @@ limitations under the License. */ // Disable the copy and assignment operator for a class. #ifndef DISABLE_COPY_AND_ASSIGN -#define DISABLE_COPY_AND_ASSIGN(classname) \ - private: \ - classname(const classname&) = delete; \ - classname& operator=(const classname&) = delete +#define DISABLE_COPY_AND_ASSIGN(classname) \ + private: \ + classname(const classname&) = delete; \ + classname(const classname&&) = delete; \ + classname& operator=(const classname&) = delete; \ + classname& operator=(const classname&&) = delete #endif diff --git a/paddle/platform/place.cc b/paddle/platform/place.cc index b31515e1f028acac885a506ff1c20479407a05e3..856e54df89c1c18ade040957188a2fbda0901473 100644 --- a/paddle/platform/place.cc +++ b/paddle/platform/place.cc @@ -47,7 +47,7 @@ bool is_cpu_place(const Place &p) { } bool places_are_same_class(const Place &p1, const Place &p2) { - return is_gpu_place(p1) == is_gpu_place(p2); + return p1.which() == p2.which(); } std::ostream &operator<<(std::ostream &os, const Place &p) { diff --git a/paddle/platform/place.h b/paddle/platform/place.h index 1117476bb37f1b0f3876c55e610803d5ee2558ce..0efc6932349a5b3ad295d195a16737a642e18943 100644 --- a/paddle/platform/place.h +++ b/paddle/platform/place.h @@ -15,6 +15,7 @@ limitations under the License. */ #pragma once #include + #include "paddle/platform/variant.h" namespace paddle { @@ -46,8 +47,18 @@ struct IsGPUPlace : public boost::static_visitor { bool operator()(const GPUPlace &gpu) const { return true; } }; +// Define the max number of Place in bit length. i.e., the max number of places +// should be less equal than 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) +#define NUM_PLACE_TYPE_LIMIT_IN_BIT 4 + typedef boost::variant Place; +// static check number of place types is less equal than +// 2^(NUM_PLACE_TYPE_LIMIT_IN_BIT) +BOOST_MPL_ASSERT((boost::mpl::less_equal< + Place::types::size, + boost::mpl::long_<1 << NUM_PLACE_TYPE_LIMIT_IN_BIT>>)); + void set_place(const Place &); const Place &get_place(); diff --git a/paddle/platform/variant.h b/paddle/platform/variant.h index c2257af1b5dd1a1e284979bf17e1a947072baa85..619897ca19eb2e6f4dbfd9160edf8c4bc58c89a9 100644 --- a/paddle/platform/variant.h +++ b/paddle/platform/variant.h @@ -16,7 +16,7 @@ #include -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA // Because boost's variadic templates has bug on nvcc, boost will disable // variadic template support when GPU enabled on nvcc. @@ -29,4 +29,6 @@ #endif #endif +#include +#include #include diff --git a/paddle/pserver/test/SocketTest.cpp b/paddle/pserver/test/SocketTest.cpp index 6f6c9e596cfb7a2547d5b6c5de69381eb9c29132..b43461d61bab21747e85090bbf7af21a87a670c6 100644 --- a/paddle/pserver/test/SocketTest.cpp +++ b/paddle/pserver/test/SocketTest.cpp @@ -215,7 +215,7 @@ int main(int argc, char** argv) { uint64_t dataSize = FLAGS_dim * sizeof(real); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA GpuVector gpuParam(FLAGS_dim); GpuVector gpuGrad(FLAGS_dim); #else diff --git a/paddle/pserver/test/test_ProtoServer.cpp b/paddle/pserver/test/test_ProtoServer.cpp index 04236fda2fb62b928b5c06ff38acfd3eb7217b08..ad8ffed9c1c8e4bdef27689ab21950db6b5cf0a2 100644 --- a/paddle/pserver/test/test_ProtoServer.cpp +++ b/paddle/pserver/test/test_ProtoServer.cpp @@ -99,7 +99,7 @@ TEST(ProtoServer, regular) { } TEST(ProtoServer, extended) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA ProtoClient* client; if (FLAGS_rdma_tcp == "rdma") client = new ProtoClient(FLAGS_server_addr, FLAGS_port, F_RDMA); diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index 18ecbd1aa34c82d63ae7f8ec1bd8f81b35eee30b..97364f2db9523c0629616692631d8372657a2128 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -1,6 +1,6 @@ if(WITH_PYTHON) cc_library(paddle_pybind SHARED SRCS pybind.cc exception.cc protobuf.cc - DEPS pybind python backward proto_desc + DEPS pybind python backward proto_desc tensor_array ${GLOB_OP_LIB}) endif(WITH_PYTHON) diff --git a/paddle/pybind/protobuf.cc b/paddle/pybind/protobuf.cc index 218821b35bb6947181fedc56e002ad0285f6307d..116c99bd2c1ca59b093392f9e6cc481c089309bc 100644 --- a/paddle/pybind/protobuf.cc +++ b/paddle/pybind/protobuf.cc @@ -117,7 +117,6 @@ void BindProgramDesc(py::module &m) { .def("append_block", &ProgramDescBind::AppendBlock, py::return_value_policy::reference) .def("block", &ProgramDescBind::Block, py::return_value_policy::reference) - .def("__str__", &ProgramDescBind::DebugString) .def("num_blocks", &ProgramDescBind::Size); } @@ -167,7 +166,9 @@ void BindVarDsec(py::module &m) { .def("set_shape", &VarDescBind::SetShape) .def("set_data_type", &VarDescBind::SetDataType) .def("shape", &VarDescBind::Shape, py::return_value_policy::reference) - .def("data_type", &VarDescBind::GetDataType); + .def("data_type", &VarDescBind::GetDataType) + .def("lod_level", &VarDescBind::GetLodLevel) + .def("set_lod_level", &VarDescBind::SetLoDLevel); } void BindOpDesc(py::module &m) { @@ -191,15 +192,14 @@ void BindOpDesc(py::module &m) { .def("output", &OpDescBind::Output) .def("output_names", &OpDescBind::OutputNames) .def("set_output", &OpDescBind::SetOutput) - .def("__str__", &OpDescBind::DebugString) - .def("__repr__", &OpDescBind::DebugString) .def("has_attr", &OpDescBind::HasAttr) .def("attr_type", &OpDescBind::GetAttrType) .def("attr_names", &OpDescBind::AttrNames) .def("set_attr", &OpDescBind::SetAttr) .def("attr", &OpDescBind::GetAttr) .def("set_block_attr", &OpDescBind::SetBlockAttr) - .def("get_block_attr", &OpDescBind::GetBlockAttr); + .def("get_block_attr", &OpDescBind::GetBlockAttr) + .def("infer_shape", &OpDescBind::InferShape); } } // namespace pybind diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index d85bf6c7faa5f65c7b39682f7639fe269bdfa345..0f6e3101e26c5ac249664ce8badc10adc939305f 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include "paddle/framework/backward.h" #include "paddle/framework/lod_tensor.h" +#include "paddle/framework/tensor_array.h" #include "paddle/operators/cond_op.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -34,7 +35,7 @@ static size_t UniqueIntegerGenerator() { } bool IsCompileGPU() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; @@ -77,20 +78,18 @@ PYBIND11_PLUGIN(core) { }) .def("set", PyCPUTensorSetFromArray) .def("set", PyCPUTensorSetFromArray) -#ifndef PADDLE_ONLY_CPU + .def("set", PyCPUTensorSetFromArray) +#ifdef PADDLE_WITH_CUDA .def("set", PyCUDATensorSetFromArray) .def("set", PyCUDATensorSetFromArray) + .def("set", PyCUDATensorSetFromArray) #endif .def("shape", [](Tensor &self) { return vectorize(self.dims()); }) - .def("set_float_element", - [](Tensor &self, size_t offset, float f) { - // TODO(yuyang18): Only support GPU now. - self.data()[offset] = f; - }) - .def("get_float_element", [](Tensor &self, size_t offset) -> float { - // TODO(yuyang18): Only support GPU now. - return self.data()[offset]; - }); + .def("set_float_element", TensorSetElement) + .def("get_float_element", TensorGetElement) + .def("set_double_element", TensorSetElement) + .def("get_double_element", TensorGetElement) + .def("dtype", [](Tensor &self) { return ToDataType(self.type()); }); py::class_(m, "LoDTensor") .def_buffer( @@ -98,7 +97,7 @@ PYBIND11_PLUGIN(core) { .def( "__init__", [](LoDTensor &instance, const std::vector> &lod) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA new (&instance) LoDTensor(lod); #else LoD new_lod; @@ -109,7 +108,7 @@ PYBIND11_PLUGIN(core) { }) .def("set_lod", [](LoDTensor &self, const std::vector> &lod) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA self.set_lod(lod); #else LoD new_lod; @@ -119,7 +118,7 @@ PYBIND11_PLUGIN(core) { #endif }) .def("lod", [](LoDTensor &self) -> std::vector> { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return self.lod(); #else auto lod = self.lod(); @@ -145,6 +144,13 @@ All parameter, weight, gradient are variables in Paddle. .def("set_int", [](Variable &var, int val) -> void { *var.GetMutable() = val; }) .def("get_int", [](const Variable &var) -> int { return var.Get(); }) + .def("is_float", [](const Variable &var) { return var.IsType(); }) + .def("set_float", + [](Variable &var, float val) -> void { + *var.GetMutable() = val; + }) + .def("get_float", + [](const Variable &var) -> float { return var.Get(); }) .def("get_tensor", [](Variable &self) -> LoDTensor * { return self.GetMutable(); @@ -198,7 +204,7 @@ All parameter, weight, gradient are variables in Paddle. .def_static("create", [](paddle::platform::GPUPlace& place) -> paddle::platform::DeviceContext* { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA PADDLE_THROW("GPUPlace is not supported in CPU device."); #else return new paddle::platform::CUDADeviceContext(place); @@ -266,6 +272,56 @@ All parameter, weight, gradient are variables in Paddle. self->CompleteAddOp(); }); + py::class_(m, "TensorArray") + .def("__init__", + [](TensorArray &instance) { new (&instance) TensorArray(); }) + .def("read", + [](TensorArray &self, size_t index) { return self.Read(index); }) + .def("write", [](TensorArray &self, size_t index, + LoDTensor &value) { self.Write(index, value); }) + .def("write_shared", + [](TensorArray &self, size_t index, const LoDTensor &value) { + self.WriteShared(index, value); + }) + .def("size", [](TensorArray &self) { return self.size(); }) + .def("pack", + [](TensorArray &self, size_t level, + const std::vector> &meta_info, + const std::vector> &lod) { + std::vector meta; + for (auto &info : meta_info) { + PADDLE_ENFORCE_EQ(info.size(), 3UL); + meta.emplace_back(info[0], info[1], info[2]); + } +#ifndef PADDLE_WITH_CUDA + return self.Pack(level, meta, lod); +#else + LoD new_lod; + new_lod.reserve(lod.size()); + std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); + return self.Pack(level, meta, new_lod); +#endif + }) + .def("unpack", + [](TensorArray &self, const LoDTensor &source, int level, + bool length_descend) { + auto metas = self.Unpack(source, level, length_descend); + std::vector> meta_info; + for (auto meta : metas) { + meta_info.emplace_back( + std::vector({meta.begin, meta.end, meta.ori_idx})); + } + return meta_info; + }) + .def("stack", [](TensorArray &self) { return self.Stack(); }) + .def("unstack", + [](TensorArray &self, const LoDTensor &source) { + return self.Unstack(source); + }) + .def("unstack_shared", [](TensorArray &self, const LoDTensor &source) { + return self.UnstackShared(source); + }); + // recurrent_op py::class_(m, "RecurrentOp") .def_static( diff --git a/paddle/pybind/tensor_py.h b/paddle/pybind/tensor_py.h index f0d5a6f9ff963ecd80d0c261daff56bff50663d4..9e73f79cbdd545db558bd8641bc52e4bf3b0664f 100644 --- a/paddle/pybind/tensor_py.h +++ b/paddle/pybind/tensor_py.h @@ -42,7 +42,7 @@ template struct CastToPyBufferImpl { using CUR_TYPE = typename std::tuple_element>::type; py::buffer_info operator()(framework::Tensor &tensor) { - if (std::type_index(typeid(CUR_TYPE)) == tensor.holder_->type()) { + if (std::type_index(typeid(CUR_TYPE)) == tensor.type()) { auto dim_vec = framework::vectorize(tensor.dims()); std::vector dims_outside; std::vector strides; @@ -56,13 +56,13 @@ struct CastToPyBufferImpl { prod *= dims_outside[i - 1]; } framework::Tensor dst_tensor; - if (paddle::platform::is_gpu_place(tensor.holder_->place())) { + if (paddle::platform::is_gpu_place(tensor.place())) { dst_tensor.CopyFrom(tensor, platform::CPUPlace()); - } else if (paddle::platform::is_cpu_place(tensor.holder_->place())) { + } else if (paddle::platform::is_cpu_place(tensor.place())) { dst_tensor = tensor; } return py::buffer_info( - dst_tensor.mutable_data(dst_tensor.holder_->place()), + dst_tensor.mutable_data(dst_tensor.place()), sizeof(CUR_TYPE), py::format_descriptor::format(), (size_t)framework::arity(dst_tensor.dims()), dims_outside, strides); } else { @@ -73,10 +73,23 @@ struct CastToPyBufferImpl { }; } // namespace details inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) { - auto buffer_info = details::CastToPyBufferImpl()(tensor); + auto buffer_info = + details::CastToPyBufferImpl()(tensor); return buffer_info; } +template +T TensorGetElement(framework::Tensor &self, size_t offset) { + PADDLE_ENFORCE(platform::is_cpu_place(self.place())); + return self.data()[offset]; +} + +template +void TensorSetElement(framework::Tensor &self, size_t offset, T elem) { + PADDLE_ENFORCE(platform::is_cpu_place(self.place())); + self.data()[offset] = elem; +} + template void PyCPUTensorSetFromArray( framework::Tensor &self, @@ -93,7 +106,7 @@ void PyCPUTensorSetFromArray( std::memcpy(dst, array.data(), sizeof(T) * array.size()); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA template void PyCUDATensorSetFromArray( framework::Tensor &self, diff --git a/paddle/scripts/submit_local.sh.in b/paddle/scripts/submit_local.sh.in index 26f9c0fcd4e045f5d603fc4e4b16691a418823ca..5c4b5a2495182ea5d2b3341cff650dfb4d8b0c0f 100755 --- a/paddle/scripts/submit_local.sh.in +++ b/paddle/scripts/submit_local.sh.in @@ -18,7 +18,7 @@ function version(){ echo "PaddlePaddle @PADDLE_VERSION@, compiled with" echo " with_avx: @WITH_AVX@" echo " with_gpu: @WITH_GPU@" - echo " with_mkldnn: @WITH_MKLDNN" + echo " with_mkldnn: @WITH_MKLDNN@" echo " with_mklml: @WITH_MKLML@" echo " with_double: @WITH_DOUBLE@" echo " with_python: @WITH_PYTHON@" diff --git a/paddle/string/to_string_test.cc b/paddle/string/to_string_test.cc index 542c771a98ec8ae187cd4f821ed1ee4373427041..971484dd0c073762e99f3926576eb21b96197769 100644 --- a/paddle/string/to_string_test.cc +++ b/paddle/string/to_string_test.cc @@ -36,4 +36,4 @@ TEST(to_string, user_defined) { using namespace paddle::string; UserDefinedClass instance; ASSERT_EQ(kOutputString, to_string(instance)); -} \ No newline at end of file +} diff --git a/paddle/trainer/MergeModel.cpp b/paddle/trainer/MergeModel.cpp index 91d89b61a32259b8bbe70fda2579f87ec6b9af00..6c52eaf4494bb247324b29981d94d7e97e0f212a 100644 --- a/paddle/trainer/MergeModel.cpp +++ b/paddle/trainer/MergeModel.cpp @@ -29,7 +29,7 @@ int main(int argc, char** argv) { initMain(argc, argv); initPython(argc, argv); string confFile = TrainerConfigHelper::getConfigNameFromPath(FLAGS_model_dir); -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA FLAGS_use_gpu = false; #endif auto config = std::make_shared(confFile); diff --git a/paddle/trainer/tests/test_Compare.cpp b/paddle/trainer/tests/test_Compare.cpp index e855a8fe2e09aa0f16a73f3e7bcc2f32921092f8..f3a964acb69be059a43470f7b68910a3b6cecaab 100644 --- a/paddle/trainer/tests/test_Compare.cpp +++ b/paddle/trainer/tests/test_Compare.cpp @@ -146,7 +146,7 @@ void compareGradient(comData& comDataCpu, comData& comDataGpu) { } int main(int argc, char** argv) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA exit(0); #endif paddle::initMain(argc, argv); diff --git a/paddle/trainer/tests/test_CompareSparse.cpp b/paddle/trainer/tests/test_CompareSparse.cpp index 813275518e411d6e963e23df634541f771096e0f..5f1834bd730375fc10762fc19788d0c693f8e752 100644 --- a/paddle/trainer/tests/test_CompareSparse.cpp +++ b/paddle/trainer/tests/test_CompareSparse.cpp @@ -174,7 +174,7 @@ TEST(compareSparse, multiGradientMachine) { FLAGS_local = local; FLAGS_ports_num_for_sparse = 5; for (bool useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif FLAGS_parallel_nn = useGpu; @@ -198,7 +198,7 @@ TEST(compareSparse, NeuralNetwork) { FLAGS_local = local; FLAGS_ports_num_for_sparse = 5; for (bool useGpu : {false, true}) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA if (useGpu) continue; #endif FLAGS_parallel_nn = useGpu; diff --git a/paddle/trainer/tests/test_Trainer.cpp b/paddle/trainer/tests/test_Trainer.cpp index 264bc46ebcd0aa17fd605e537fcb2c316ef31162..425b3d10a38086463784ba2a18db1293efe96e92 100644 --- a/paddle/trainer/tests/test_Trainer.cpp +++ b/paddle/trainer/tests/test_Trainer.cpp @@ -51,7 +51,7 @@ void checkGradientTest(const string& configFile, TEST(checkGradient, cpu) { checkGradientTest(configFile1, false, false); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(checkGradient, gpu) { checkGradientTest(configFile1, true, false); } TEST(checkGradient, multiGpu) { @@ -97,7 +97,7 @@ TEST(checkGradient, hsigmoid) { checkGradientTest(configFile2, false, false); } TEST(checkGradient, chunk) { checkGradientTest(configFile3, false, false); -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA checkGradientTest(configFile3, true, true); #endif } diff --git a/paddle/trainer/tests/test_TrainerOnePass.cpp b/paddle/trainer/tests/test_TrainerOnePass.cpp index 00ba61377aeff17d82e03f7560c0d71b3570d14f..b2a93d4d5eea37ad716b59427f2aa4409d2f537d 100644 --- a/paddle/trainer/tests/test_TrainerOnePass.cpp +++ b/paddle/trainer/tests/test_TrainerOnePass.cpp @@ -79,7 +79,7 @@ void trainerOnePassTest(const string& configFile, // 1. test trainer (cpu, gpu). TEST(trainerOnePass, cpu) { trainerOnePassTest(configFile1, false, false); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(trainerOnePass, gpu) { trainerOnePassTest(configFile1, true, false); } TEST(trainerOnePass, gpu2) { trainerOnePassTest(configFile1, true, false, 2); } @@ -94,7 +94,7 @@ TEST(trainerOnePass, parallel) { #endif // 2. test average_window. -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(average_window, gpu) { trainerOnePassTest(configFile1, true, false, 4, 0.01); } @@ -266,7 +266,7 @@ TEST(checkRemoteUpdater, cpuTrainerOldUpdater) { checkRemoteParameterUpdaterTest(configFile1, false, false, 1, true); } -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA TEST(checkRemoteUpdater, gpuTrainer) { checkRemoteParameterUpdaterTest(configFile1, true, false); } diff --git a/paddle/trainer/tests/test_recurrent_machine_generation.cpp b/paddle/trainer/tests/test_recurrent_machine_generation.cpp index 1322e77178a4f5674f41943f886a17be8337bd75..a8fbe31c2b1e228107dfc19483444409bfcbf788 100644 --- a/paddle/trainer/tests/test_recurrent_machine_generation.cpp +++ b/paddle/trainer/tests/test_recurrent_machine_generation.cpp @@ -113,7 +113,7 @@ void testGeneration(const string& configFile, #ifndef PADDLE_TYPE_DOUBLE TEST(RecurrentGradientMachine, test_generation) { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA const auto useGpuConfs = {false}; #else const auto useGpuConfs = {true, false}; diff --git a/paddle/utils/Flags.cpp b/paddle/utils/Flags.cpp index ab1c181c62cdbee8cc5f804ec9aaf63ac5464ad6..8f100f02e90bcbc7fdcf6f053aec6f95cfb09c1a 100644 --- a/paddle/utils/Flags.cpp +++ b/paddle/utils/Flags.cpp @@ -14,7 +14,7 @@ limitations under the License. */ #include "Flags.h" -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA DEFINE_bool(use_gpu, false, "Only support CPU training"); #else DEFINE_bool(use_gpu, true, "Whether to use GPU for training"); diff --git a/paddle/utils/Util.h b/paddle/utils/Util.h index 22ce2534d3468ded36221810aa61c15b37f13f3d..9579881ea3b92abab0189631184bab515afb67a3 100644 --- a/paddle/utils/Util.h +++ b/paddle/utils/Util.h @@ -218,7 +218,7 @@ protected: * *d2* is peer device to enable direct access to by the d1 device. */ inline void enablePeerAccess(int d1, int d2) { -#ifndef PADDLE_ONLY_CPU +#ifdef PADDLE_WITH_CUDA if (hl_device_can_access_peer(d1, d2)) { SetDevice dev(d1); hl_device_enable_peer_access(d2); diff --git a/paddle/utils/Version.h b/paddle/utils/Version.h index f53d6420bbbdf66f8f355af95c6b11c30a3bfab9..004d62451cddfee8fbd687938086e04ecb2332a9 100644 --- a/paddle/utils/Version.h +++ b/paddle/utils/Version.h @@ -48,7 +48,7 @@ void printVersion(std::ostream& os); * @return return true if paddle compiled with GPU */ constexpr bool isWithGpu() { -#ifdef PADDLE_ONLY_CPU +#ifndef PADDLE_WITH_CUDA return false; #else return true; diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 74025d2a7bb68f87afd24bb4b70ec425ba0dcb64..d37f29d2c4bf9177398ea82c99bc40affdd952c2 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -142,6 +142,7 @@ __all__ = [ 'img_pool3d_layer', 'scale_shift_layer', 'img_conv3d_layer', + 'resize_layer', ] @@ -250,6 +251,8 @@ class LayerType(object): KMAX_SEQ_SCORE = 'kmax_seq_score' SCALE_SHIFT_LAYER = 'scale_shift' + RESIZE = 'resize' + @staticmethod def is_layer_type(type_name): """ @@ -6473,7 +6476,7 @@ def switch_order_layer(input, act=None, layer_attr=None): """ - This layer switch dimension order of image input. + This layer switch dimension order of image input. From order "batchSize, channels, height, width" to order "batchSize, height, width, channels". @@ -6932,3 +6935,23 @@ def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): bias=ParamAttr.to_bias(bias_attr)) return LayerOutput( name, LayerType.SCALE_SHIFT_LAYER, parents=[input], size=input.size) + + +@wrap_name_default("resize") +def resize_layer(input, size, name=None): + """ + The resize layer resizes the input matrix with a shape of [Height, Width] + into the output matrix with a shape of [Height x Width / size, size], + where size is the parameter of this layer indicating the output dimension. + + :param input: The input to this layer. + :type input: LayerOutput. + :param name: The name of this layer. It is optional. + :type name: basestring + :param size: The resized output dimesion of this layer. + :type size: int + :return: A LayerOutput object. + :rtype: LayerOutput + """ + Layer(name=name, type=LayerType.RESIZE, inputs=Input(input.name), size=size) + return LayerOutput(name, LayerType.RESIZE, parents=[input], size=input.size) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 8a204a96f3ef57673cef65306d0bf8e8c3409751..6a4550c209762362d40f8a2afaf526a1fe53ca6b 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -10,6 +10,6 @@ test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_la test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer -test_conv3d_layer test_deconv3d_layer test_BatchNorm3D) +test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr new file mode 100644 index 0000000000000000000000000000000000000000..9399252b23d0ec0cce918196bf4077a51e757eaf --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_resize_layer.protostr @@ -0,0 +1,27 @@ +type: "nn" +layers { + name: "input" + type: "data" + size: 300 + active_type: "" +} +layers { + name: "__resize_0__" + type: "resize" + size: 150 + active_type: "" + inputs { + input_layer_name: "input" + } +} +input_layer_names: "input" +output_layer_names: "__resize_0__" +sub_models { + name: "root" + layer_names: "input" + layer_names: "__resize_0__" + input_layer_names: "input" + output_layer_names: "__resize_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py new file mode 100644 index 0000000000000000000000000000000000000000..09a6f507338c1da8e9ce60555f8ca2576704170c --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_resize_layer.py @@ -0,0 +1,6 @@ +from paddle.trainer_config_helpers import * + +data = data_layer(name='input', size=300) +resized = resize_layer(input=data, size=150) + +outputs(resized) diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index e66bf67d7949057486eb54c46f39128fad5dae55..a0ffd31c545eb10dd8c2f14746ee90df58700e61 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -10,7 +10,8 @@ There are: * EndPass """ __all__ = [ - 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult' + 'EndIteration', 'BeginIteration', 'BeginPass', 'EndPass', 'TestResult', + 'EndForwardBackward' ] @@ -73,6 +74,17 @@ class BeginIteration(object): self.batch_id = batch_id +class EndForwardBackward(object): + """ + Event On One Batch ForwardBackward Complete. + """ + + def __init__(self, pass_id, batch_id, gm): + self.pass_id = pass_id + self.batch_id = batch_id + self.gm = gm + + class EndIteration(WithMetric): """ Event On One Batch Training Complete. diff --git a/python/paddle/v2/framework/graph.py b/python/paddle/v2/framework/graph.py new file mode 100644 index 0000000000000000000000000000000000000000..0f0a2847e58a1ca172bf1ba382abb2ebc1ecb8ed --- /dev/null +++ b/python/paddle/v2/framework/graph.py @@ -0,0 +1,240 @@ +import paddle.v2.framework.core as core +import collections +import numpy as np +import copy + +__all__ = ['Block', 'Variable', 'Program', 'Operator'] + + +class Variable(object): + def __init__(self, + block, + name=None, + shape=None, + dtype=None, + lod_level=None, + **kwargs): + self.block = block + + if name is None: + name = Variable._unique_var_name_() + try: + self.desc = self.block.desc.var(name) + is_new_var = False + except core.EnforceNotMet: + self.desc = self.block.desc.new_var(name) + is_new_var = True + + if shape is not None: + if is_new_var: + self.desc.set_shape(shape) + else: + old_shape = self.shape + shape = tuple(shape) + if shape != old_shape: + raise ValueError( + "Variable {0} has been created before. the previous " + "shape is {1}; the new shape is {2}. They are not " + "matched.".format(self.name, old_shape, shape)) + if dtype is not None: + if not isinstance(dtype, core.DataType): + dtype = Variable._convert_np_dtype_to_dtype_(dtype) + if is_new_var: + self.desc.set_data_type(dtype) + else: + old_dtype = self.data_type() + if dtype != old_shape: + raise ValueError("Variable {0} has been created before. " + "The previous data type is {1}; the new " + "data type is {2}. They are not " + "matched.".format(self.name, old_dtype, + dtype)) + + if lod_level is not None: + if is_new_var: + self.desc.set_lod_level(lod_level) + else: + if lod_level != self.lod_level: + raise ValueError("Variable {0} has been created before. " + "The previous lod_level is {1}; the new " + "lod_level is {2}. They are not " + "matched".format(self.name, self.lod_level, + lod_level)) + self.block.vars[name] = self + self.op = None + + @property + def name(self): + return self.desc.name() + + @property + def shape(self): + # convert to tuple, make it as same as numpy API. + return tuple(self.desc.shape()) + + @property + def data_type(self): + return self.desc.data_type() + + @property + def lod_level(self): + return self.desc.lod_level() + + @staticmethod + def _unique_var_name_(): + uid = core.unique_integer() # unique during whole process. + return "_generated_var_%d" % uid + + @staticmethod + def _convert_np_dtype_to_dtype_(np_dtype): + dtype = np.dtype(np_dtype) + if dtype == np.float32: + return core.DataType.FP32 + elif dtype == np.float64: + return core.DataType.FP64 + elif dtype == np.float16: + return core.DataType.FP16 + elif dtype == np.int32: + return core.DataType.INT32 + elif dtype == np.int16: + return core.DataType.INT16 + elif dtype == np.int64: + return core.DataType.INT64 + elif dtype == np.bool: + return core.DataType.BOOL + else: + raise ValueError("Not supported numpy dtype " + str(dtype)) + + +class Operator(object): + def __init__(self, + block, + desc, + type=None, + inputs=None, + outputs=None, + attrs=None): + self.block = block + self.desc = desc + if type is not None: + # TODO. + pass + if inputs is not None: + # TODO + pass + if outputs is not None: + # TODO + pass + if attrs is not None: + # TODO + pass + + # TODO: Getters + + +class Block(object): + def __init__(self, program, idx): + self.desc = program.desc.block(idx) + self.vars = dict() # var_name --> var + self.ops = collections.deque() # operator list + self.program = program + + @property + def parent_idx(self): + return self.desc.parent + + @property + def idx(self): + return self.desc.id + + def create_var(self, *args, **kwargs): + return Variable(self, *args, **kwargs) + + def create_parameter(self, *args, **kwargs): + global_block = self.program.global_block() + return Parameter(global_block, *args, **kwargs) + + def append_op(self, *args, **kwargs): + op_desc = self.desc.append_op() + op = Operator(self, op_desc, *args, **kwargs) + self.ops.append(op) + return op + + def prepend_op(self, *args, **kwargs): + op_desc = self.desc.prepend_op() + op = Operator(self, op_desc, *args, **kwargs) + self.ops.appendleft(op) + return op + + +class Program(object): + @classmethod + def instance(cls): + # From https://stackoverflow.com/questions/8212053 + # Making Program as a Singleton class. + if not hasattr(cls, '_instance'): + cls._instance = cls() + return cls._instance + + def __init__(self): + assert not hasattr(self.__class__, + '_instance'), 'Do not call constructor directly!' + self.desc = core.ProgramDesc.instance() + self.blocks = [Block(self, 0)] + self.current_block_idx = 0 + + def global_block(self): + return self.blocks[0] + + def current_block(self): + return self.blocks[self.current_block_idx] + + def create_block(self): + new_block_idx = len(self.blocks) + self.desc.append_block(self.current_block().desc) + self.current_block_idx = new_block_idx + self.blocks.append(Block(self, self.current_block_idx)) + return self.current_block() + + def rollback(self): + self.current_block_idx = self.current_block().parent_idx + + +class Parameter(Variable): + def __init__(self, block, shape, dtype, **kwargs): + if shape is None or dtype is None: + raise ValueError("Parameter must set shape and dtype") + if len(shape) == 0: + raise ValueError("Parameter shape cannot be empty") + + for each in shape: + if each < 0: + raise ValueError("Parameter shape should not be related with " + "batch-size") + + Variable.__init__(self, block, shape=shape, dtype=dtype, **kwargs) + self.trainable = kwargs.get('trainable', True) + self.init_attr = kwargs.get('initialize_attr', { + 'type': 'uniform_random', + 'min': -1.0, + 'max': 1.0 + }) + + self.optimize_attr = kwargs.get('optimize_attr', {'learning_rate': 1.0}) + self._append_initialize_ops_() + + def _append_initialize_ops_(self): + attr = copy.deepcopy(self.init_attr) + op_type = attr.pop('type', None) + block = self.block + assert isinstance(block, Block) + shape = self.shape + attr['dims'] = shape + attr['data_type'] = int(self.data_type) + op = block.prepend_op( + type=op_type, inputs=None, outputs={'Out': [self]}, attrs=attr) + self.op = op + + +# program is a global instance. +g_program = Program.instance() diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py index 89979044f29a301daa7435ff903ae902c981ea1b..81067f38bbf64ac1ab4ccf02aa43b0a38b7d48ad 100644 --- a/python/paddle/v2/framework/tests/op_test.py +++ b/python/paddle/v2/framework/tests/op_test.py @@ -1,5 +1,6 @@ import unittest import numpy as np +import random import itertools import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator @@ -12,17 +13,19 @@ def grad_var_name(var_name): def create_op(scope, op_type, inputs, outputs, attrs): kwargs = dict() + def __create_var__(name, var_name): + scope.new_var(var_name) + kwargs[name].append(var_name) + for in_name, in_dup in Operator.get_op_inputs(op_type): if in_name in inputs: kwargs[in_name] = [] if in_dup: sub_in = inputs[in_name] for sub_in_name, _ in sub_in: - var = scope.new_var(sub_in_name) - kwargs[in_name].append(sub_in_name) + __create_var__(in_name, sub_in_name) else: - var = scope.new_var(in_name) - kwargs[in_name].append(in_name) + __create_var__(in_name, in_name) for out_name, out_dup in Operator.get_op_outputs(op_type): if out_name in outputs: @@ -30,11 +33,9 @@ def create_op(scope, op_type, inputs, outputs, attrs): if out_dup: sub_out = outputs[out_name] for sub_out_name, _ in sub_out: - var = scope.new_var(sub_out_name) - kwargs[out_name].append(sub_out_name) + __create_var__(out_name, sub_out_name) else: - var = scope.new_var(out_name) - kwargs[out_name].append(out_name) + __create_var__(out_name, out_name) for attr_name in Operator.get_op_attr_names(op_type): if attr_name in attrs: @@ -44,49 +45,51 @@ def create_op(scope, op_type, inputs, outputs, attrs): def set_input(scope, op, inputs, place): + def __set_input__(var_name, var): + if isinstance(var, tuple) or isinstance(var, np.ndarray): + tensor = scope.find_var(var_name).get_tensor() + if isinstance(var, tuple): + tensor.set_lod(var[1]) + var = var[0] + tensor.set_dims(var.shape) + tensor.set(var, place) + elif isinstance(var, float): + scope.find_var(var_name).set_float(var) + elif isinstance(var, int): + scope.find_var(var_name).set_int(var) + for in_name, in_dup in Operator.get_op_inputs(op.type()): if in_name in inputs: if in_dup: sub_in = inputs[in_name] for sub_in_name, sub_in_val in sub_in: - var = scope.find_var(sub_in_name) - tensor = var.get_tensor() - sub_in_array = sub_in_val[0] \ - if isinstance(sub_in_val, tuple) else sub_in_val - tensor.set_dims(sub_in_array.shape) - tensor.set(sub_in_array, place) - if isinstance(sub_in_val, tuple): - tensor.set_lod(sub_in_val[1]) + __set_input__(sub_in_name, sub_in_val) else: - var = scope.find_var(in_name) - tensor = var.get_tensor() - in_val = inputs[in_name] - in_array = in_val[0] if isinstance(in_val, tuple) else in_val - tensor.set_dims(in_array.shape) - tensor.set(in_array, place) - if isinstance(in_val, tuple): - tensor.set_lod(in_val[1]) + __set_input__(in_name, inputs[in_name]) def set_output_grad(scope, op, outputs, place): + def __set_tensor__(name): + out_tensor = scope.find_var(name).get_tensor() + grad_tensor = scope.new_var(grad_var_name(name)).get_tensor() + out_dtype = out_tensor.dtype() + if out_dtype == core.DataType.FP64: + data = np.ones(out_tensor.shape(), dtype=np.float64) + elif out_dtype == core.DataType.FP32: + data = np.ones(out_tensor.shape(), dtype=np.float32) + else: + raise ValueError("Not supported data type " + str(out_dtype)) + + grad_tensor.set(data, place) + for out_name, out_dup in Operator.get_op_outputs(op.type()): if out_name in outputs: if out_dup: sub_out = outputs[out_name] for sub_out_name, _ in sub_out: - out_tensor = scope.find_var(sub_out_name).get_tensor() - grad_tensor = scope.new_var(grad_var_name( - sub_out_name)).get_tensor() - grad_tensor.set_dims(out_tensor.shape()) - data = np.ones(out_tensor.shape(), dtype=np.float32) - grad_tensor.set(data, place) + __set_tensor__(sub_out_name) else: - out_tensor = scope.find_var(out_name).get_tensor() - grad_tensor = scope.new_var(grad_var_name(out_name)).get_tensor( - ) - grad_tensor.set_dims(out_tensor.shape()) - data = np.ones(out_tensor.shape(), dtype=np.float32) - grad_tensor.set(data, place) + __set_tensor__(out_name) def get_numeric_gradient(scope, @@ -96,7 +99,6 @@ def get_numeric_gradient(scope, output_names, delta=0.005, in_place=False): - set_input(scope, op, inputs, core.CPUPlace()) tensor_to_check = scope.find_var(input_to_check).get_tensor() @@ -115,7 +117,29 @@ def get_numeric_gradient(scope, tensor_to_check = scope.find_var(input_to_check).get_tensor() tensor_size = product(tensor_to_check.get_dims()) - gradient_flat = np.zeros(shape=(tensor_size, ), dtype='float32') + tensor_to_check_dtype = tensor_to_check.dtype() + if tensor_to_check_dtype == core.DataType.FP32: + tensor_to_check_dtype = np.float32 + elif tensor_to_check_dtype == core.DataType.FP64: + tensor_to_check_dtype = np.float64 + else: + raise ValueError("Not supported data type " + str( + tensor_to_check_dtype)) + + gradient_flat = np.zeros(shape=(tensor_size, ), dtype=tensor_to_check_dtype) + + def __get_elem__(tensor, i): + if tensor_to_check_dtype == np.float32: + return tensor.get_float_element(i) + else: + return tensor.get_double_element(i) + + def __set_elem__(tensor, i, e): + if tensor_to_check_dtype == np.float32: + tensor.set_float_element(i, e) + else: + tensor.set_double_element(i, e) + # we only compute gradient of one element each time. # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): @@ -123,20 +147,20 @@ def get_numeric_gradient(scope, set_input(scope, op, inputs, core.CPUPlace()) # get one input element throw it's index i. - origin = tensor_to_check.get_float_element(i) + origin = __get_elem__(tensor_to_check, i) # add delta to it, run op and then get the sum of the result tensor. x_pos = origin + delta - tensor_to_check.set_float_element(i, x_pos) + __set_elem__(tensor_to_check, i, x_pos) y_pos = get_output() if in_place: set_input(scope, op, inputs, core.CPUPlace()) x_neg = origin - delta - tensor_to_check.set_float_element(i, x_neg) + __set_elem__(tensor_to_check, i, x_neg) y_neg = get_output() - tensor_to_check.set_float_element(i, origin) + __set_elem__(tensor_to_check, i, origin) gradient_flat[i] = (y_pos - y_neg) / delta / 2 return gradient_flat.reshape(tensor_to_check.get_dims()) @@ -174,6 +198,21 @@ def get_gradient(scope, op, inputs, outputs, grad_name, place, class OpTest(unittest.TestCase): + @classmethod + def setUpClass(cls): + '''Fix random seeds to remove randomness from tests''' + cls._np_rand_state = np.random.get_state() + cls._py_rand_state = random.getstate() + + np.random.seed(123) + random.seed(124) + + @classmethod + def tearDownClass(cls): + '''Restore random seeds''' + np.random.set_state(cls._np_rand_state) + random.setstate(cls._py_rand_state) + def check_output_with_place(self, place, atol): self.scope = core.Scope() op_inputs = self.inputs if hasattr(self, "inputs") else dict() diff --git a/python/paddle/v2/framework/tests/test_activation_op.py b/python/paddle/v2/framework/tests/test_activation_op.py index 8f6d2be17758b7f6604d2db74fe466fb30695bd5..a28c4431e1ae9230750247c0ed16c9aff37364fa 100644 --- a/python/paddle/v2/framework/tests/test_activation_op.py +++ b/python/paddle/v2/framework/tests/test_activation_op.py @@ -33,6 +33,21 @@ class TestSigmoid(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.008) +class TestLogSigmoid(OpTest): + def setUp(self): + self.op_type = "logsigmoid" + self.inputs = { + 'X': np.random.uniform(-1, 1, [11, 17]).astype("float32") + } + self.outputs = {'Y': np.log(1 / (1 + np.exp(-self.inputs['X'])))} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.008) + + class TestTanh(OpTest): def setUp(self): self.op_type = "tanh" @@ -48,6 +63,61 @@ class TestTanh(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.007) +class TestTanhShrink(OpTest): + def setUp(self): + self.op_type = "tanh_shrink" + self.inputs = { + 'X': np.random.uniform(0.1, 1, [10, 17]).astype("float32") + } + self.outputs = {'Y': self.inputs['X'] - np.tanh(self.inputs['X'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.008) + + +class TestHardShrink(OpTest): + def setUp(self): + self.op_type = "hard_shrink" + x = np.random.uniform(-1, 1, [4, 4]).astype("float32") + threshold = 0.5 + + self.inputs = {'X': x} + self.attrs = {'lambda': threshold} + + t = np.copy(x) + t[(t >= -threshold) & (t <= threshold)] = 0 + self.outputs = {'Y': t} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.005) + + +class TestSoftShrink(OpTest): + def setUp(self): + self.op_type = "softshrink" + lambda_val = 0.1 + self.attrs = {'lambda': lambda_val} + self.inputs = { + 'X': np.random.uniform(0.25, 10, [4, 4]).astype("float32") + } + y = np.copy(self.inputs['X']) + y = (y < -lambda_val) * (y + lambda_val) + (y > lambda_val) * ( + y - lambda_val) + self.outputs = {'Y': y} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.007) + + class TestSqrt(OpTest): def setUp(self): self.op_type = "sqrt" @@ -122,6 +192,28 @@ class TestBRelu(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.02) +class TestRelu6(OpTest): + def setUp(self): + self.op_type = "relu6" + x = np.random.uniform(-1, 1, [4, 10]).astype("float32") + threshold = 6.0 + # The same with TestAbs + x[np.abs(x) < 0.005] = 0.02 + x[np.abs(x - threshold) < 0.005] = threshold + 0.02 + + self.inputs = {'X': x} + self.attrs = {'threshold': threshold} + self.outputs = { + 'Y': np.minimum(np.maximum(self.inputs['X'], 0), threshold) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.02) + + class TestSoftRelu(OpTest): def setUp(self): self.op_type = "soft_relu" @@ -144,6 +236,26 @@ class TestSoftRelu(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.02) +class TestELU(OpTest): + def setUp(self): + self.op_type = "elu" + x = np.random.uniform(-3, 3, [4, 4]).astype("float32") + alpha = 1. + # Note: unlike other Relu extensions, point 0 on standard ELU function (i.e. alpha = 1) + # is differentiable, so we can skip modifications like x[np.abs(x) < 0.005] = 0.02 here + self.inputs = {'X': x} + self.attrs = {'alpha': alpha} + self.outputs = { + 'Y': np.maximum(0, x) + np.minimum(0, alpha * (np.exp(x) - 1)) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.02) + + class TestReciprocal(OpTest): def setUp(self): self.op_type = "reciprocal" @@ -219,5 +331,37 @@ class TestSTanh(OpTest): self.check_grad(['X'], 'Y', max_relative_error=0.007) +class TestSoftplus(OpTest): + def setUp(self): + self.op_type = "softplus" + self.inputs = { + 'X': np.random.uniform(-1, 1, [11, 17]).astype("float32") + } + self.outputs = {'Y': np.log(1 + np.exp(self.inputs['X']))} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.007) + + +class TestSoftsign(OpTest): + def setUp(self): + self.op_type = "softsign" + self.inputs = { + 'X': np.random.uniform(-1, 1, [11, 17]).astype("float32") + } + self.outputs = { + 'Y': np.divide(self.inputs['X'], 1 + np.abs(self.inputs['X'])) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Y', max_relative_error=0.007) + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_adadelta_op.py b/python/paddle/v2/framework/tests/test_adadelta_op.py new file mode 100644 index 0000000000000000000000000000000000000000..7105593a98aee9885ba16e3ee0649a6024033ee7 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adadelta_op.py @@ -0,0 +1,96 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdadeltaOp1(OpTest): + def setUp(self): + self.op_type = "adadelta" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The squared gradient is positive + avg_squared_grad = np.random.random((102, 105)).astype("float32") + # The squared update is positive + avg_squared_update = np.random.random((102, 105)).astype("float32") + + rho = 0.95 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'AvgSquaredGrad': avg_squared_grad, + 'AvgSquaredUpdate': avg_squared_update + } + + self.attrs = {'rho': rho, 'epsilon': epsilon} + + avg_squared_grad_out = rho * avg_squared_grad + \ + (1 - rho) * np.square(grad) + update = -np.multiply( + np.sqrt( + np.divide(avg_squared_update + epsilon, avg_squared_grad_out + + epsilon)), grad) + + avg_squared_update_out = rho * avg_squared_update + \ + (1 - rho) * np.square(update) + + param_out = param + update + + self.outputs = { + 'ParamOut': param_out, + 'AvgSquaredGradOut': avg_squared_grad_out, + 'AvgSquaredUpdateOut': avg_squared_update_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdadeltaOp2(OpTest): + '''Test Adadelta op with default attribute values + ''' + + def setUp(self): + self.op_type = "adadelta" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The squared gradient is positive + avg_squared_grad = np.random.random((102, 105)).astype("float32") + # The squared update is positive + avg_squared_update = np.random.random((102, 105)).astype("float32") + + rho = 0.95 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'AvgSquaredGrad': avg_squared_grad, + 'AvgSquaredUpdate': avg_squared_update + } + + avg_squared_grad_out = rho * avg_squared_grad + \ + (1 - rho) * np.square(grad) + update = -np.multiply( + np.sqrt( + np.divide(avg_squared_update + epsilon, avg_squared_grad_out + + epsilon)), grad) + + avg_squared_update_out = rho * avg_squared_update + \ + (1 - rho) * np.square(update) + + param_out = param + update + + self.outputs = { + 'ParamOut': param_out, + 'AvgSquaredGradOut': avg_squared_grad_out, + 'AvgSquaredUpdateOut': avg_squared_update_out + } + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_adagrad_op.py b/python/paddle/v2/framework/tests/test_adagrad_op.py new file mode 100644 index 0000000000000000000000000000000000000000..66bad349e59b608cb3cc965401c81ef4c716b318 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adagrad_op.py @@ -0,0 +1,69 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdagradOp1(OpTest): + ''' Test Adagrad operator with explicit attributes + ''' + + def setUp(self): + self.op_type = "adagrad" + + param = np.random.random((123, 321)).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + lr = 0.01 + epsilon = 1e-8 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'LearningRate': np.array([lr]).astype("float32") + } + + self.attrs = {'epsilon': epsilon} + + moment_out = moment + grad * grad + param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon) + + self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out} + + def test_check_output(self): + self.check_output() + + +class TestAdagradOp2(OpTest): + ''' Test Adagrad operator with default attributes + ''' + + def setUp(self): + self.op_type = "adagrad" + + param = np.random.random((123, 321)).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + lr = 0.01 + epsilon = 1e-6 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'LearningRate': np.array([lr]).astype("float32") + } + + self.attrs = {'epsilon': epsilon} + + moment_out = moment + grad * grad + param_out = param - lr * grad / (np.sqrt(moment_out) + epsilon) + + self.outputs = {'ParamOut': param_out, 'MomentOut': moment_out} + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_adamax_op.py b/python/paddle/v2/framework/tests/test_adamax_op.py new file mode 100644 index 0000000000000000000000000000000000000000..af81075d6ad508dcd473ed596b00b036d87d894f --- /dev/null +++ b/python/paddle/v2/framework/tests/test_adamax_op.py @@ -0,0 +1,178 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestAdamaxOp1(OpTest): + def setUp(self): + '''Test Adamax Operator with supplied attributes + ''' + self.op_type = "adamax" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.78 + beta2 = 0.899 + epsilon = 1e-5 + beta1_pow = beta1**10 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamaxOp2(OpTest): + '''Test Adamax Operator with default attributes + ''' + + def setUp(self): + self.op_type = "adamax" + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.9 + beta2 = 0.999 + epsilon = 1e-8 + beta1_pow = beta1**8 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + def test_check_output(self): + self.check_output() + + +class TestAdamaxOpMultipleSteps(OpTest): + def setUp(self): + '''Test Adamax Operator with supplied attributes + ''' + self.op_type = "adamax" + self.num_steps = 10 + + param = np.random.uniform(-1, 1, (102, 105)).astype("float32") + grad = np.random.uniform(-1, 1, (102, 105)).astype("float32") + moment = np.random.uniform(-1, 1, (102, 105)).astype("float32") + # The infinity norm is positive + inf_norm = np.random.random((102, 105)).astype("float32") + + learning_rate = 0.002 + beta1 = 0.8 + beta2 = 0.99 + epsilon = 1e-5 + beta1_pow = 1 + + self.inputs = { + 'Param': param, + 'Grad': grad, + 'Moment': moment, + 'InfNorm': inf_norm, + 'LearningRate': np.array([learning_rate]).astype("float32"), + 'Beta1Pow': np.array([beta1_pow]).astype("float32") + } + + self.attrs = {'beta1': beta1, 'beta2': beta2, 'epsilon': epsilon} + + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + def test_check_output(self): + for _ in range(self.num_steps): + param_out, moment_out, inf_norm_out, beta1_pow_out = adamax_step( + self.inputs, self.attrs) + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'InfNormOut': inf_norm_out, + 'Beta1PowOut': beta1_pow_out + } + + # Verify output for this step + self.check_output() + + # Output of this step becomes input for next step + self.inputs['Param'] = param_out + self.inputs['Moment'] = moment_out + self.inputs['InfNorm'] = inf_norm_out + self.inputs['Beta1Pow'] = beta1_pow_out + + # Randomize gradient for next step + self.inputs['Grad'] = np.random.uniform( + -1, 1, (102, 105)).astype("float32") + + +def adamax_step(inputs, attributes): + ''' + Simulate one step of the adamax optimizer + :param inputs: dict of inputs + :param attributes: dict of attributes + :return tuple: tuple of output param, moment, inf_norm and + beta1 power accumulator + ''' + param = inputs['Param'] + grad = inputs['Grad'] + moment = inputs['Moment'] + inf_norm = inputs['InfNorm'] + lr = inputs['LearningRate'] + beta1_pow = inputs['Beta1Pow'] + + beta1 = attributes['beta1'] + beta2 = attributes['beta2'] + epsilon = attributes['epsilon'] + + moment_out = beta1 * moment + (1 - beta1) * grad + inf_norm_out = np.maximum(beta2 * inf_norm + epsilon, np.abs(grad)) + beta1_pow_out = beta1_pow * beta1 + lr_t = (lr / (1 - beta1_pow_out)) + param_out = param - lr_t * np.divide(moment_out, inf_norm_out) + + return param_out, moment_out, inf_norm_out, beta1_pow_out + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_add_op.py b/python/paddle/v2/framework/tests/test_add_op.py deleted file mode 100644 index 3ca34d9b9fc2b7b54cc25ca0e0d1a08a71e37c52..0000000000000000000000000000000000000000 --- a/python/paddle/v2/framework/tests/test_add_op.py +++ /dev/null @@ -1,20 +0,0 @@ -import unittest -import numpy as np -from op_test import OpTest - - -class TestAddOp(OpTest): - def setUp(self): - self.op_type = "add" - self.inputs = { - 'X': np.random.random((102, 105)).astype("float32"), - 'Y': np.random.random((102, 105)).astype("float32") - } - self.outputs = {'Out': self.inputs['X'] + self.inputs['Y']} - - def test_check_output(self): - self.check_output() - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_cond_op.py b/python/paddle/v2/framework/tests/test_cond_op.py index e7a506f2775a3f1edbacceb91e84ad49a9db67c0..76323b5e10c59822b4de82a70ebd57b3e57c8392 100644 --- a/python/paddle/v2/framework/tests/test_cond_op.py +++ b/python/paddle/v2/framework/tests/test_cond_op.py @@ -15,7 +15,7 @@ class PySimpleCond(object): for i in range(1, 10, 2): array[i] = 0 self.cond = np.array(array) - self.x = np.ones(shape=(10, 1)) + self.x = np.ones(shape=(10, 1)).astype("float32") def forward(self): self.index_t = np.where(self.cond == 1) @@ -112,7 +112,4 @@ class TestCondOp(unittest.TestCase): if __name__ == "__main__": - exit( - 0 - ) # FIXME(yuyang18): Since infer_shape has been removed, cond op may error unittest.main() diff --git a/python/paddle/v2/framework/tests/test_conv_shift_op.py b/python/paddle/v2/framework/tests/test_conv_shift_op.py new file mode 100644 index 0000000000000000000000000000000000000000..b9ab21a06a1c6e8e2d1e936a0b4b8a07a59f57b9 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_conv_shift_op.py @@ -0,0 +1,47 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def conv_shift_forward(x, y): + out = np.zeros_like(x) + M = x.shape[1] + N = y.shape[1] + y_half_width = (N - 1) / 2 + for i in xrange(M): + for j in xrange(N): + out[:, i] += x[:, (i + j + M - y_half_width) % M] * y[:, j] + return out + + +class TestConvShiftOp(OpTest): + def setUp(self): + self.op_type = "conv_shift" + + batch_size = 4 + x_dim = 17 + y_dim = 3 # must be odd and <= x_dim + x = np.random.random((batch_size, x_dim)).astype("float32") + y = np.random.random((batch_size, y_dim)).astype("float32") + self.inputs = {'X': x, 'Y': y} + + out = conv_shift_forward(x, y) + self.outputs = {'Out': out} + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05) + + def test_check_grad_ignore_x(self): + self.check_grad( + ['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X")) + + def test_check_grad_ignore_y(self): + self.check_grad( + ['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y')) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py index 1de514dff487158e0823fd628d9b3b50f36fdd9b..4ea14da7fd3d84870965d62514d6a79b4926a6ec 100644 --- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py @@ -80,7 +80,7 @@ class TestCrossEntropyOp3(OpTest): cross_entropy2 = (-label * np.log(X)).sum( axis=1, keepdims=True).astype("float32") - self.inputs = {"X": X, "Label": label} + self.inputs = {"X": X, "Label": label.astype(np.float32)} self.outputs = {"Y": cross_entropy} self.attrs = {"softLabel": True} diff --git a/python/paddle/v2/framework/tests/test_elementwise_mul_op.py b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py index cee4385a8176f7a441a280e3cd40c39ca51493c5..261ca9cb3da90dee91b016fee98f67b4c19356a1 100644 --- a/python/paddle/v2/framework/tests/test_elementwise_mul_op.py +++ b/python/paddle/v2/framework/tests/test_elementwise_mul_op.py @@ -7,8 +7,8 @@ class ElementwiseMulOp(OpTest): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"), - 'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32") + 'X': np.random.uniform(0.1, 1, [13, 17]).astype("float64"), + 'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float64") } self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])} @@ -16,23 +16,21 @@ class ElementwiseMulOp(OpTest): self.check_output() def test_check_grad_normal(self): - self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.1) + self.check_grad(['X', 'Y'], 'Out') def test_check_grad_ingore_x(self): - self.check_grad( - ['Y'], 'Out', max_relative_error=0.1, no_grad_set=set("X")) + self.check_grad(['Y'], 'Out', no_grad_set=set("X")) def test_check_grad_ingore_y(self): - self.check_grad( - ['X'], 'Out', max_relative_error=0.1, no_grad_set=set('Y')) + self.check_grad(['X'], 'Out', no_grad_set=set('Y')) class TestElementwiseMulOp_Vector(ElementwiseMulOp): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.random((32, )).astype("float32"), - 'Y': np.random.random((32, )).astype("float32") + 'X': np.random.random((32, )).astype("float64"), + 'Y': np.random.random((32, )).astype("float64") } self.outputs = {'Out': np.multiply(self.inputs['X'], self.inputs['Y'])} @@ -41,8 +39,8 @@ class TestElementwiseMulOp_broadcast_0(ElementwiseMulOp): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.rand(2, 3, 4).astype(np.float32), - 'Y': np.random.rand(2).astype(np.float32) + 'X': np.random.rand(2, 3, 4).astype(np.float64), + 'Y': np.random.rand(2).astype(np.float64) } self.attrs = {'axis': 0} @@ -55,8 +53,8 @@ class TestElementwiseMulOp_broadcast_1(ElementwiseMulOp): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.rand(2, 3, 4).astype(np.float32), - 'Y': np.random.rand(3).astype(np.float32) + 'X': np.random.rand(2, 3, 4).astype(np.float64), + 'Y': np.random.rand(3).astype(np.float64) } self.attrs = {'axis': 1} @@ -69,8 +67,8 @@ class TestElementwiseMulOp_broadcast_2(ElementwiseMulOp): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.rand(2, 3, 4).astype(np.float32), - 'Y': np.random.rand(4).astype(np.float32) + 'X': np.random.rand(2, 3, 4).astype(np.float64), + 'Y': np.random.rand(4).astype(np.float64) } self.outputs = { @@ -82,8 +80,8 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp): def setUp(self): self.op_type = "elementwise_mul" self.inputs = { - 'X': np.random.rand(2, 3, 4, 5).astype(np.float32), - 'Y': np.random.rand(3, 4).astype(np.float32) + 'X': np.random.rand(2, 3, 4, 5).astype(np.float64), + 'Y': np.random.rand(3, 4).astype(np.float64) } self.attrs = {'axis': 1} diff --git a/python/paddle/v2/framework/tests/test_fill_constant_op.py b/python/paddle/v2/framework/tests/test_fill_constant_op.py new file mode 100644 index 0000000000000000000000000000000000000000..dff7b615aa378b0ef932df47241db07eace61a86 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_fill_constant_op.py @@ -0,0 +1,35 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestFillConstantOp1(OpTest): + def setUp(self): + '''Test fill_constant op with specified value + ''' + self.op_type = "fill_constant" + + self.inputs = {} + self.attrs = {'shape': [123, 92], 'value': 3.8} + self.outputs = {'Out': np.full((123, 92), 3.8)} + + def test_check_output(self): + self.check_output() + + +class TestFillConstantOp2(OpTest): + def setUp(self): + '''Test fill_constant op with default value + ''' + self.op_type = "fill_constant" + + self.inputs = {} + self.attrs = {'shape': [123, 92]} + self.outputs = {'Out': np.full((123, 92), 0.0)} + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py deleted file mode 100644 index 85117bf9600975ea5d61dfb5b34335792bf6d8b2..0000000000000000000000000000000000000000 --- a/python/paddle/v2/framework/tests/test_gradient_checker.py +++ /dev/null @@ -1,46 +0,0 @@ -import unittest -import numpy as np -import paddle.v2.framework.core as core -from op_test import get_numeric_gradient -from op_test import create_op - - -class GetNumericGradientTest(unittest.TestCase): - def test_add_op(self): - x = np.random.random((10, 1)).astype("float32") - y = np.random.random((10, 1)).astype("float32") - z = x + y - scope = core.Scope() - add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict()) - arr = get_numeric_gradient(scope, add_op, {'X': x, - 'Y': y}, 'X', ['Out']) - self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4) - - def test_softmax_op(self): - def stable_softmax(x): - """Compute the softmax of vector x in a numerically stable way.""" - shiftx = x - np.max(x) - exps = np.exp(shiftx) - return exps / np.sum(exps) - - def label_softmax_grad(Y, dY): - dX = Y * 0.0 - for i in range(Y.shape[0]): - d = np.dot(Y[i, :], dY[i, :]) - dX[i, :] = Y[i, :] * (dY[i, :] - d) - return dX - - X = np.random.random((2, 2)).astype("float32") - Y = np.apply_along_axis(stable_softmax, 1, X) - dY = np.ones(Y.shape) - dX = label_softmax_grad(Y, dY) - - scope = core.Scope() - softmax_op = create_op(scope, "softmax", {"X": X}, {"Y": Y}, dict()) - - arr = get_numeric_gradient(scope, softmax_op, {"X": X}, "X", "Y") - np.testing.assert_almost_equal(arr, dX, decimal=1e-2) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_infer_shape.py b/python/paddle/v2/framework/tests/test_infer_shape.py new file mode 100644 index 0000000000000000000000000000000000000000..99562890fdd4d8b10f420869f1ba9f694db5969a --- /dev/null +++ b/python/paddle/v2/framework/tests/test_infer_shape.py @@ -0,0 +1,63 @@ +import unittest + +import paddle.v2.framework.core as core + + +class TestInferShape(unittest.TestCase): + def test_sum_op(self): + prog = core.ProgramDesc.__create_program_desc__() + self.assertIsNotNone(prog) + block = prog.block(0) + self.assertIsNotNone(block) + + shape = [10, 20] + + # prepare input/output + x1 = block.new_var("x1") + x1.set_shape(shape) + x2 = block.new_var("x2") + x2.set_shape(shape) + + out = block.new_var("out") + + # prepare the operator + sum_op_desc = block.append_op() + sum_op_desc.set_type("sum") + sum_op_desc.set_input("X", ["x1", "x2"]) + sum_op_desc.set_output("Out", ["out"]) + + sum_op_desc.infer_shape(block) + self.assertEqual(out.shape(), shape) + + def test_mul_op(self): + prog = core.ProgramDesc.__create_program_desc__() + self.assertIsNotNone(prog) + block = prog.block(0) + self.assertIsNotNone(block) + + x_shape = [10, 20] + y_shape = [20, 30] + + # prepare input/output + x1 = block.new_var("x") + x1.set_shape(x_shape) + x2 = block.new_var("y") + x2.set_shape(y_shape) + + out = block.new_var("out") + + # prepare the operator + mul_op_desc = block.append_op() + mul_op_desc.set_type("mul") + mul_op_desc.set_input("X", ["x"]) + mul_op_desc.set_input("Y", ["y"]) + mul_op_desc.set_output("Out", ["out"]) + mul_op_desc.set_attr("x_num_col_dims", 1) + mul_op_desc.set_attr("y_num_col_dims", 1) + + mul_op_desc.infer_shape(block) + self.assertEqual(out.shape(), [x_shape[0], y_shape[1]]) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_interp_op.py b/python/paddle/v2/framework/tests/test_interp_op.py new file mode 100644 index 0000000000000000000000000000000000000000..066569b96c9611bd20e7192f8bd6caa6e467202f --- /dev/null +++ b/python/paddle/v2/framework/tests/test_interp_op.py @@ -0,0 +1,28 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestInterpOp(OpTest): + def setUp(self): + self.op_type = "interp" + x = np.random.random((2, 3)).astype("float32") + y = np.random.random((2, 3)).astype("float32") + w = np.random.random(2).astype("float32") + + sub_out = x - y + mul_out = sub_out * w.reshape(2, 1) + out = mul_out + y + + self.inputs = {'X': x, 'Y': y, 'W': w} + self.outputs = {'Out': out, 'SubOut': sub_out, 'MulOut': mul_out} + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(['X', 'Y'], 'Out') + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_lstm_unit_op.py b/python/paddle/v2/framework/tests/test_lstm_unit_op.py index 8ce65bfc31d9fa2d3988759a197e2f497b8161b1..365ee560e14e322cd8cfcdc068a8b004f6e365ad 100644 --- a/python/paddle/v2/framework/tests/test_lstm_unit_op.py +++ b/python/paddle/v2/framework/tests/test_lstm_unit_op.py @@ -14,8 +14,8 @@ def tanh_np(x): class LstmUnitTest(OpTest): def setUp(self): self.op_type = "lstm_unit" - x_np = np.random.normal(size=(5, 16)).astype("float32") - c_np = np.random.normal(size=(5, 4)).astype("float32") + x_np = np.random.normal(size=(5, 16)).astype("float64") + c_np = np.random.normal(size=(5, 4)).astype("float64") i_np, f_np, o_np, j_np = np.split(x_np, 4, axis=1) forget_bias_np = 0. self.attrs = {'forget_bias': 0.} @@ -31,7 +31,7 @@ class LstmUnitTest(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['X', 'C_prev'], ['C', 'H'], max_relative_error=0.01) + self.check_grad(['X', 'C_prev'], ['C', 'H']) if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py index 50cfb855f2b01d8fd32342855d46716da7e07856..8503257feb8e1a5802f3f889f72c559a2aaa583a 100644 --- a/python/paddle/v2/framework/tests/test_net.py +++ b/python/paddle/v2/framework/tests/test_net.py @@ -15,7 +15,7 @@ def fc(X, W, Y): class TestNet(unittest.TestCase): def test_net_all(self): net = core.Net.create() - op1 = Operator("add", X="X", Y="Y", Out="Out") + op1 = Operator("sum", X=["X", "Y"], Out="Out") net.append_op(op1) net2 = core.Net.create() @@ -26,7 +26,7 @@ class TestNet(unittest.TestCase): expected = ''' Op(plain_net), inputs:{all[W, X, Y]}, outputs:{all[Out, fc.out, pre_activation]}. - Op(add), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}. + Op(sum), inputs:{X[X, Y]}, outputs:{Out[Out]}. Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}. Op(plain_net), inputs:{all[W, X]}, outputs:{all[fc.out, pre_activation]}. Op(mul), inputs:{X[X], Y[W]}, outputs:{Out[pre_activation]}. diff --git a/python/paddle/v2/framework/tests/test_operator.py b/python/paddle/v2/framework/tests/test_operator.py index 040556322d79cbb594eb9af585a5b9920d7ab625..98f6b2f5ee639120557cb85b3ada6d2931f7d0d2 100644 --- a/python/paddle/v2/framework/tests/test_operator.py +++ b/python/paddle/v2/framework/tests/test_operator.py @@ -193,10 +193,10 @@ class TestOpDescCreationMethod(unittest.TestCase): class TestOpCreations(unittest.TestCase): def test_all(self): - add_op = op.Operator("add", X="a", Y="b", Out="z") + add_op = op.Operator("sum", X=["a", "b"], Out="z") self.assertIsNotNone(add_op) # Invoke C++ DebugString() - self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.', + self.assertEqual('Op(sum), inputs:{X[a, b]}, outputs:{Out[z]}.', str(add_op)) diff --git a/python/paddle/v2/framework/tests/test_parameter.py b/python/paddle/v2/framework/tests/test_parameter.py new file mode 100644 index 0000000000000000000000000000000000000000..3b5d38f257e6f51be30d9f1fa42285461b2a0eb7 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_parameter.py @@ -0,0 +1,27 @@ +import unittest +from paddle.v2.framework.graph import g_program +import paddle.v2.framework.core as core + + +class TestParameter(unittest.TestCase): + def test_param(self): + b = g_program.create_block() + param = b.create_parameter( + name='fc.w', + shape=[784, 100], + dtype='float32', + initialize_attr={ + 'type': 'uniform_random', + 'seed': 13, + 'min': -5.0, + 'max': 5.0 + }) + self.assertIsNotNone(param) + self.assertEqual('fc.w', param.name) + self.assertEqual((784, 100), param.shape) + self.assertEqual(core.DataType.FP32, param.data_type) + self.assertEqual(0, param.block.idx) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_pool2d_op.py b/python/paddle/v2/framework/tests/test_pool2d_op.py new file mode 100644 index 0000000000000000000000000000000000000000..2941fda81b23998072810d8c6f6597a6f3db7e30 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pool2d_op.py @@ -0,0 +1,144 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0): + + N, C, H, W = x.shape + if global_pool == 1: + ksize = [H, W] + H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + out = np.zeros((N, C, H_out, W_out)) + for i in xrange(H_out): + for j in xrange(W_out): + r_start = np.max((i * strides[0] - paddings[0], 0)) + r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + c_start = np.max((j * strides[1] - paddings[1], 0)) + c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, r_start:r_end, c_start:c_end] + + out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) + return out + + +def avg_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0): + + N, C, H, W = x.shape + if global_pool == 1: + ksize = [H, W] + H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + out = np.zeros((N, C, H_out, W_out)) + for i in xrange(H_out): + for j in xrange(W_out): + r_start = np.max((i * strides[0] - paddings[0], 0)) + r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + c_start = np.max((j * strides[1] - paddings[1], 0)) + c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, r_start:r_end, c_start:c_end] + + out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / ( + (r_end - r_start) * (c_end - c_start)) + return out + + +class TestPool2d_Op(OpTest): + def setUp(self): + self.initTestCase() + input = np.random.random(self.shape).astype("float32") + output = self.pool2D_forward_naive(input, self.ksize, self.strides, + self.paddings, self.global_pool) + self.inputs = {'X': input} + + self.attrs = { + 'strides': self.strides, + 'paddings': self.paddings, + 'ksize': self.ksize, + 'poolingType': self.pool_type, + 'globalPooling': self.global_pool, + } + + self.outputs = {'Out': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + if self.pool_type != "max": + self.check_grad(set(['X']), 'Out', max_relative_error=0.07) + + def initTestCase(self): + self.global_pool = True + self.op_type = "pool2d" + self.pool_type = "avg" + self.pool2D_forward_naive = avg_pool2D_forward_naive + self.shape = [2, 3, 5, 5] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [0, 0] + + +class TestCase1(TestPool2d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool2d" + self.pool_type = "avg" + self.pool2D_forward_naive = avg_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [0, 0] + + +class TestCase2(TestPool2d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool2d" + self.pool_type = "avg" + self.pool2D_forward_naive = avg_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [1, 1] + + +class TestCase3(TestPool2d_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "pool2d" + self.pool_type = "max" + self.pool2D_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 5, 5] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [0, 0] + + +class TestCase4(TestPool2d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool2d" + self.pool_type = "max" + self.pool2D_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [0, 0] + + +class TestCase5(TestPool2d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool2d" + self.pool_type = "max" + self.pool2D_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [1, 1] + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_pool3d_op.py b/python/paddle/v2/framework/tests/test_pool3d_op.py new file mode 100644 index 0000000000000000000000000000000000000000..8792b492e3da6541f71185be82b8bfc4f52d821d --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pool3d_op.py @@ -0,0 +1,152 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0): + + N, C, D, H, W = x.shape + if global_pool == 1: + ksize = [D, H, W] + D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1 + out = np.zeros((N, C, D_out, H_out, W_out)) + for k in xrange(D_out): + d_start = np.max((k * strides[0] - paddings[0], 0)) + d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) + for i in xrange(H_out): + h_start = np.max((i * strides[0] - paddings[0], 0)) + h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + for j in xrange(W_out): + w_start = np.max((j * strides[1] - paddings[1], 0)) + w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] + + out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4)) + return out + + +def avg_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0): + + N, C, D, H, W = x.shape + if global_pool == 1: + ksize = [D, H, W] + D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1 + out = np.zeros((N, C, D_out, H_out, W_out)) + for k in xrange(D_out): + d_start = np.max((k * strides[0] - paddings[0], 0)) + d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) + for i in xrange(H_out): + h_start = np.max((i * strides[0] - paddings[0], 0)) + h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + for j in xrange(W_out): + w_start = np.max((j * strides[1] - paddings[1], 0)) + w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] + + out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / ( + (d_end - d_start) * (h_end - h_start) * (w_end - w_start)) + return out + + +class TestPool3d_Op(OpTest): + def setUp(self): + self.initTestCase() + input = np.random.random(self.shape).astype("float32") + output = self.pool3D_forward_naive(input, self.ksize, self.strides, + self.paddings, self.global_pool) + self.inputs = {'X': input} + + self.attrs = { + 'strides': self.strides, + 'paddings': self.paddings, + 'ksize': self.ksize, + 'poolingType': self.pool_type, + 'globalPooling': self.global_pool, + } + + self.outputs = {'Out': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + if self.pool_type != "max": + self.check_grad(set(['X']), 'Out', max_relative_error=0.07) + + def initTestCase(self): + self.global_pool = True + self.op_type = "pool3d" + self.pool_type = "avg" + self.pool3D_forward_naive = avg_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [0, 0, 0] + + +class TestCase1(TestPool3d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool3d" + self.pool_type = "avg" + self.pool3D_forward_naive = avg_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [0, 0, 0] + + +class TestCase2(TestPool3d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool3d" + self.pool_type = "avg" + self.pool3D_forward_naive = avg_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +class TestCase3(TestPool3d_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "pool3d" + self.pool_type = "max" + self.pool3D_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [0, 0, 0] + + +class TestCase4(TestPool3d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool3d" + self.pool_type = "max" + self.pool3D_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [0, 0, 0] + + +class TestCase5(TestPool3d_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "pool3d" + self.pool_type = "max" + self.pool3D_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_pool_max_op.py b/python/paddle/v2/framework/tests/test_pool_max_op.py new file mode 100644 index 0000000000000000000000000000000000000000..f0f8aa6089c74d31702a6a5d37362099205d96b2 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pool_max_op.py @@ -0,0 +1,212 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def max_pool3D_forward_naive(x, + ksize, + strides, + paddings=[0, 0, 0], + global_pool=0): + + N, C, D, H, W = x.shape + if global_pool == 1: + ksize = [D, H, W] + D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1 + out = np.zeros((N, C, D_out, H_out, W_out)) + mask = np.zeros((N, C, D_out, H_out, W_out)) + for k in xrange(D_out): + d_start = np.max((k * strides[0] - paddings[0], 0)) + d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) + for i in xrange(H_out): + h_start = np.max((i * strides[0] - paddings[0], 0)) + h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + for j in xrange(W_out): + w_start = np.max((j * strides[1] - paddings[1], 0)) + w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] + + out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4)) + + for n in xrange(N): + for c in xrange(C): + arr = x_masked[n, c, :, :, :] + index = np.where(arr == np.max(arr)) + sub_deep = index[0][0] + sub_row = index[1][0] + sub_col = index[2][0] + index = ((d_start + sub_deep) * H + + (h_start + sub_row)) * W + w_start + sub_col + mask[n, c, k, i, j] = index + + return out, mask + + +def max_pool2D_forward_naive(x, ksize, strides, paddings=[0, 0], global_pool=0): + + N, C, H, W = x.shape + if global_pool == 1: + ksize = [H, W] + H_out = (H - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + W_out = (W - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + out = np.zeros((N, C, H_out, W_out)) + mask = np.zeros((N, C, H_out, W_out)) + for i in xrange(H_out): + for j in xrange(W_out): + r_start = np.max((i * strides[0] - paddings[0], 0)) + r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + c_start = np.max((j * strides[1] - paddings[1], 0)) + c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + x_masked = x[:, :, r_start:r_end, c_start:c_end] + + out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) + + for n in xrange(N): + for c in xrange(C): + arr = x_masked[n, c, :, :] + index = np.where(arr == np.max(arr)) + sub_row = index[0][0] + sub_col = index[1][0] + index = (r_start + sub_row) * W + c_start + sub_col + mask[n, c, i, j] = index + + return out, mask + + +class TestMaxPoolWithIndex_Op(OpTest): + def setUp(self): + self.initTestCase() + input = np.random.random(self.shape).astype("float32") + output, mask = self.pool_forward_naive(input, self.ksize, self.strides, + self.paddings, self.global_pool) + + self.attrs = { + 'strides': self.strides, + 'paddings': self.paddings, + 'ksize': self.ksize, + 'globalPooling': self.global_pool, + } + + self.inputs = {'X': input} + self.outputs = {'Out': output, "Mask": mask} + + def test_check_output(self): + self.check_output() + + # def test_check_grad(self): + # self.check_grad(set(['X']), ['Out'], max_relative_error=0.07) + + def initTestCase(self): + self.global_pool = True + self.index = "max_pool3d_with_index" + self.op_type = "%s" % self.index + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +class TestCase1(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "max_pool3d_with_index" + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +class TestCase2(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "max_pool3d_with_index" + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +class TestCase3(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "max_pool3d_with_index" + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [2, 2, 2] + self.paddings = [0, 0, 0] + + +class TestCase4(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "max_pool3d_with_index" + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +class TestCase5(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "max_pool3d_with_index" + self.pool_forward_naive = max_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [2, 2, 2] + self.paddings = [0, 0, 0] + + +class TestCase6(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "max_pool2d_with_index" + self.pool_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [1, 1] + + +class TestCase7(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = False + self.op_type = "max_pool2d_with_index" + self.pool_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 7, 7] + self.ksize = [3, 3] + self.strides = [2, 2] + self.paddings = [0, 0] + + +class TestCase8(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "max_pool2d_with_index" + self.pool_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 5, 5] + self.ksize = [3, 3] + self.strides = [1, 1] + self.paddings = [1, 1] + + +class TestCase9(TestMaxPoolWithIndex_Op): + def initTestCase(self): + self.global_pool = True + self.op_type = "max_pool2d_with_index" + self.pool_forward_naive = max_pool2D_forward_naive + self.shape = [2, 3, 5, 5] + self.ksize = [3, 3] + self.strides = [2, 2] + self.paddings = [0, 0] + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_prelu_op.py b/python/paddle/v2/framework/tests/test_prelu_op.py index 676fd9f7c555fd5c8544e760345ab954cd137dc5..7be932ac8f6b82283fecd32ac4b3b7bb9aff0338 100644 --- a/python/paddle/v2/framework/tests/test_prelu_op.py +++ b/python/paddle/v2/framework/tests/test_prelu_op.py @@ -17,7 +17,7 @@ class PReluTest(OpTest): x_np_sign = np.sign(x_np) x_np = x_np_sign * np.maximum(x_np, .005) - alpha_np = np.array([.1]) + alpha_np = np.array([.1], dtype="float32") self.inputs = {'X': x_np, 'Alpha': alpha_np} out_np = np.maximum(self.inputs['X'], 0.) out_np = out_np + np.minimum(self.inputs['X'], diff --git a/python/paddle/v2/framework/tests/test_program.py b/python/paddle/v2/framework/tests/test_program.py new file mode 100644 index 0000000000000000000000000000000000000000..b82d1760d65a24401aaa336bc41f75ed60af8ae9 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_program.py @@ -0,0 +1,36 @@ +import unittest +from paddle.v2.framework.graph import g_program + + +class TestProgram(unittest.TestCase): + def test_program(self): + b = g_program.current_block() + self.assertEqual(-1, b.parent_idx) + self.assertEqual(0, b.idx) + + b = g_program.create_block() + self.assertEqual(1, b.idx) + self.assertEqual(0, b.parent_idx) + + b = g_program.create_block() + self.assertEqual(2, b.idx) + self.assertEqual(1, b.parent_idx) + + g_program.rollback() + + b = g_program.current_block() + self.assertEqual(1, b.idx) + self.assertEqual(0, b.parent_idx) + + b = g_program.create_block() + self.assertEqual(3, b.idx) + self.assertEqual(1, b.parent_idx) + + g_program.rollback() + b = g_program.current_block() + self.assertEqual(1, b.idx) + self.assertEqual(0, b.parent_idx) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 92161ae5dd93d34d898a2027435cc5e55611bcd0..1f114432c09f29fab6cd56de00dff341785ae0e4 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -16,14 +16,17 @@ class PySimpleRNN(object): ''' def __init__(self, input_dim=30, batch_size=50, weight_dim=15, sent_len=11): - self.x = np.random.normal(size=(sent_len, batch_size, input_dim)) - self.W = np.random.normal(size=(input_dim, input_dim)) - self.U = np.random.normal(size=(input_dim, input_dim)) - self.h_boot = np.random.normal(size=(batch_size, input_dim)) + self.x = np.random.normal(size=(sent_len, batch_size, + input_dim)).astype("float32") + self.W = np.random.normal(size=(input_dim, input_dim)).astype("float32") + self.U = np.random.normal(size=(input_dim, input_dim)).astype("float32") + self.h_boot = np.random.normal(size=(batch_size, + input_dim)).astype("float32") # memories self.mems = [ - np.zeros(shape=(batch_size, input_dim)) for i in range(sent_len) + np.zeros(shape=(batch_size, input_dim)).astype("float32") + for i in range(sent_len) ] def forward(self): @@ -36,7 +39,7 @@ class PySimpleRNN(object): return [self.x[i] for i in range(self.x.shape[0])] def concat_outputs(self): - return np.array(self.mems) + return np.array(self.mems).astype("float32") def step(self, step_id, x): ''' @@ -47,8 +50,8 @@ class PySimpleRNN(object): pre_mem = self.mems[step_id - 1] else: pre_mem = self.h_boot - xW = np.matmul(x, self.W) - hU = np.matmul(pre_mem, self.U) + xW = np.matmul(x, self.W).astype("float32") + hU = np.matmul(pre_mem, self.U).astype("float32") sum = xW + hU self.mems[step_id] = py_sigmoid(sum) @@ -102,7 +105,8 @@ class RecurrentOpTest(unittest.TestCase): self.create_step_net() ctx = core.DeviceContext.create(core.CPUPlace()) self.rnnop.run(self.scope, ctx) - return np.array(self.scope.find_var("h@mem").get_tensor()) + return np.array(self.scope.find_var("h@mem").get_tensor()).astype( + "float32") def create_global_variables(self): # create inlink @@ -142,7 +146,7 @@ class RecurrentOpTest(unittest.TestCase): stepnet = core.Net.create() x_fc_op = Operator("mul", X="x", Y="W", Out="Wx") h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") - sum_op = Operator("add", X="Wx", Y="Uh", Out="sum") + sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum") sig_op = Operator("sigmoid", X="sum", Y="h@mem") for op in [x_fc_op, h_fc_op, sum_op, sig_op]: @@ -179,7 +183,7 @@ class RecurrentGradientOpTest(unittest.TestCase): stepnet = core.Net.create() x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx") h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") - sum_op = Operator("add", X="Wx", Y="Uh", Out="sum") + sum_op = Operator("sum", X=["Wx", "Uh"], Out="sum") sig_op = Operator("sigmoid", X="sum", Y="h@alias") for op in [x_fc_op, h_fc_op, sum_op, sig_op]: @@ -197,7 +201,4 @@ class RecurrentGradientOpTest(unittest.TestCase): if __name__ == '__main__': - exit( - 0 - ) # FIXME(yuyang18): InferShape has been removed, this unittest may error unittest.main() diff --git a/python/paddle/v2/framework/tests/test_reduce_op.py b/python/paddle/v2/framework/tests/test_reduce_op.py new file mode 100644 index 0000000000000000000000000000000000000000..70359d60cbe656150877673c63e81eae92d8ab9a --- /dev/null +++ b/python/paddle/v2/framework/tests/test_reduce_op.py @@ -0,0 +1,89 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestSumOp(OpTest): + def setUp(self): + self.op_type = "reduce_sum" + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")} + self.outputs = {'Out': self.inputs['X'].sum(axis=0)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestMeanOp(OpTest): + def setUp(self): + self.op_type = "reduce_mean" + self.inputs = {'X': np.random.random((5, 6, 2, 10)).astype("float32")} + self.attrs = {'dim': 1} + self.outputs = {'Out': self.inputs['X'].mean(axis=self.attrs['dim'])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestMaxOp(OpTest): + """Remove Max with subgradient from gradient check to confirm the success of CI.""" + + def setUp(self): + self.op_type = "reduce_max" + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")} + self.attrs = {'dim': -1} + self.outputs = {'Out': self.inputs['X'].max(axis=self.attrs['dim'])} + + def test_check_output(self): + self.check_output() + + +class TestMinOp(OpTest): + """Remove Min with subgradient from gradient check to confirm the success of CI.""" + + def setUp(self): + self.op_type = "reduce_min" + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")} + self.attrs = {'dim': 2} + self.outputs = {'Out': self.inputs['X'].min(axis=self.attrs['dim'])} + + def test_check_output(self): + self.check_output() + + +class TestKeepDimReduce(OpTest): + def setUp(self): + self.op_type = "reduce_sum" + self.inputs = {'X': np.random.random((5, 6, 10)).astype("float32")} + self.attrs = {'dim': -2, 'keep_dim': True} + self.outputs = { + 'Out': self.inputs['X'].sum(axis=self.attrs['dim'], keepdims=True) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class Test1DReduce(OpTest): + def setUp(self): + self.op_type = "reduce_sum" + self.inputs = {'X': np.random.random(20).astype("float32")} + self.outputs = {'Out': self.inputs['X'].sum(axis=0)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_rmsprop_op.py b/python/paddle/v2/framework/tests/test_rmsprop_op.py new file mode 100644 index 0000000000000000000000000000000000000000..3e5ff733e9b55fe8c9727e9721e25083a494be15 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_rmsprop_op.py @@ -0,0 +1,89 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestRmspropOp1(OpTest): + ''' Test RMSProp with explicit inputs + ''' + + def setUp(self): + self.op_type = "rmsprop" + + param = np.random.random((123, 321)).astype("float32") + mean_square = np.random.random((123, 321)).astype("float32") + learning_rate = np.array([0.01]).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + + epsilon = 1e-6 + decay = 0.9 + momentum = 0.0 + + self.inputs = { + 'Param': param, + 'MeanSquare': mean_square, + 'LearningRate': learning_rate, + 'Grad': grad, + 'Moment': moment, + } + + self.attrs = {'epsilon': epsilon, 'decay': decay, 'momentum': momentum} + + ms_out = decay * mean_square + (1 - decay) * grad * grad + moment_out = momentum * moment + \ + learning_rate * grad / np.sqrt(ms_out + epsilon) + param_out = param - moment_out + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'MeanSquareOut': ms_out + } + + def test_check_output(self): + self.check_output() + + +class TestRmspropOp2(OpTest): + '''Test RMSProp with defaukt values for attributes + ''' + + def setUp(self): + self.op_type = "rmsprop" + + param = np.random.random((123, 321)).astype("float32") + mean_square = np.random.random((123, 321)).astype("float32") + learning_rate = np.array([0.01]).astype("float32") + grad = np.random.random((123, 321)).astype("float32") + moment = np.zeros((123, 321)).astype("float32") + + epsilon = 1.0e-10 + decay = 0.9 + momentum = 0.0 + + self.inputs = { + 'Param': param, + 'MeanSquare': mean_square, + 'LearningRate': learning_rate, + 'Grad': grad, + 'Moment': moment, + } + + ms_out = decay * mean_square + (1 - decay) * grad * grad + moment_out = momentum * moment + \ + learning_rate * grad / np.sqrt(ms_out + epsilon) + param_out = param - moment_out + + self.outputs = { + 'ParamOut': param_out, + 'MomentOut': moment_out, + 'MeanSquareOut': ms_out + } + + def test_check_output(self): + self.check_output() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py deleted file mode 100644 index 336645bd993ff743cbe20bb5cae5cd278db57ce7..0000000000000000000000000000000000000000 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ /dev/null @@ -1,51 +0,0 @@ -import unittest -import numpy as np -from op_test import OpTest - - -class TestRowwiseAddOp(OpTest): - def setUp(self): - self.op_type = "rowwise_add" - self.inputs = { - 'X': np.random.uniform(0.1, 1, [5, 10]).astype("float32"), - 'b': np.random.uniform(0.1, 1, [10]).astype("float32") - } - self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'b'], 'Out') - - def test_check_grad_ingore_b(self): - self.check_grad(['X'], 'Out', no_grad_set=set('b')) - - def test_check_grad_ingore_x(self): - self.check_grad(['b'], 'Out', no_grad_set=set('X')) - - -class TestRowwiseAddOp2(OpTest): - def setUp(self): - self.op_type = "rowwise_add" - self.inputs = { - 'X': np.random.uniform(0.1, 1, [2, 3, 2, 5]).astype("float32"), - 'b': np.random.uniform(0.1, 1, [2, 5]).astype("float32") - } - self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} - - def test_check_output(self): - self.check_output() - - def test_check_grad_normal(self): - self.check_grad(['X', 'b'], 'Out') - - def test_check_grad_ignore_b(self): - self.check_grad(['X'], 'Out', no_grad_set=set('b')) - - def test_check_grad_ignore_x(self): - self.check_grad(['b'], 'Out', no_grad_set=set('X')) - - -if __name__ == "__main__": - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py index 33c73c52631a09ea0fefdeb9467991ae9c04321c..1032269d5dfb02e3518b9ef2820d5d0dcc8a51a0 100644 --- a/python/paddle/v2/framework/tests/test_scatter_op.py +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -10,7 +10,7 @@ class TestScatterOp(OpTest): index_np = np.array([1, 2]).astype("int32") updates_np = np.random.random((2, 3)).astype("float32") output_np = np.copy(ref_np) - output_np[index_np] += updates_np + output_np[index_np] = updates_np self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} self.outputs = {'Out': output_np} @@ -18,7 +18,7 @@ class TestScatterOp(OpTest): self.check_output() def test_check_grad(self): - self.check_grad(['Updates', 'Ref'], 'Out', in_place=True) + self.check_grad(['Updates'], 'Out', in_place=True) if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_sequence_softmax_op.py b/python/paddle/v2/framework/tests/test_sequence_softmax_op.py new file mode 100644 index 0000000000000000000000000000000000000000..b54a56aa6d3f76baa4d1fc6ba8f963332deba002 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_sequence_softmax_op.py @@ -0,0 +1,38 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def stable_softmax(x): + """Compute the softmax of vector x in a numerically stable way.""" + shiftx = x - np.max(x).clip(-64.) + exps = np.exp(shiftx) + return exps / np.sum(exps) + + +class TestSequenceSoftmaxOp(OpTest): + def setUp(self): + self.op_type = "sequence_softmax" + x = np.random.uniform(0.1, 1, (11, 1)).astype("float32") + lod = [[0, 4, 5, 8, 11]] + + out = np.zeros((11, 1)).astype("float32") + for i in range(4): + sub_x = x[lod[0][i]:lod[0][i + 1], :] + sub_x = sub_x.reshape(1, lod[0][i + 1] - lod[0][i]) + sub_out = stable_softmax(sub_x) + out[lod[0][i]:lod[0][i + 1], :] = sub_out.reshape( + lod[0][i + 1] - lod[0][i], 1) + + self.inputs = {"X": (x, lod)} + self.outputs = {"Out": out} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out", max_relative_error=0.01) + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_sgd_op.py b/python/paddle/v2/framework/tests/test_sgd_op.py index 64e54d1500c1bc134cc1efe33d41a16dbc08f2d4..2dd881e5e107249277a91bd8e3a72567269e1cd4 100644 --- a/python/paddle/v2/framework/tests/test_sgd_op.py +++ b/python/paddle/v2/framework/tests/test_sgd_op.py @@ -8,11 +8,10 @@ class TestSGDOp(OpTest): self.op_type = "sgd" w = np.random.random((102, 105)).astype("float32") g = np.random.random((102, 105)).astype("float32") - lr = 0.1 + lr = np.array([0.1]).astype("float32") - self.inputs = {'param': w, 'grad': g} - self.attrs = {'learning_rate': lr} - self.outputs = {'param_out': w - lr * g} + self.inputs = {'Param': w, 'Grad': g, 'LearningRate': lr} + self.outputs = {'ParamOut': w - lr * g} def test_check_output(self): self.check_output() diff --git a/python/paddle/v2/framework/tests/test_sigmoid_cross_entropy_with_logits_op.py b/python/paddle/v2/framework/tests/test_sigmoid_cross_entropy_with_logits_op.py new file mode 100644 index 0000000000000000000000000000000000000000..e53856b38aa5ddd6061b350a66e9fe86bc23923c --- /dev/null +++ b/python/paddle/v2/framework/tests/test_sigmoid_cross_entropy_with_logits_op.py @@ -0,0 +1,66 @@ +import numpy as np +from op_test import OpTest +from scipy.special import logit +from scipy.special import expit + + +class TestSigmoidCrossEntropyWithLogitsOp1(OpTest): + '''Test sigmoid_cross_entropy_with_logit_op with binary labels + ''' + + def setUp(self): + self.op_type = "sigmoid_cross_entropy_with_logits" + batch_size = 64 + num_classes = 20 + self.inputs = { + 'X': logit( + np.random.uniform(0, 1, (batch_size, num_classes)) + .astype("float32")), + 'Labels': np.random.randint(0, 2, (batch_size, num_classes)) + .astype("float32") + } + + # Fw Pass is implemented as elementwise sigmoid followed by + # elementwise logistic loss + # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X)) + sigmoid_X = expit(self.inputs['X']) + term1 = self.inputs['Labels'] * np.log(sigmoid_X) + term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X) + self.outputs = {'Out': -term1 - term2} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') + + +class TestSigmoidCrossEntropyWithLogitsOp2(OpTest): + '''Test sigmoid_cross_entropy_with_logit_op with probabalistic labels + ''' + + def setUp(self): + self.op_type = "sigmoid_cross_entropy_with_logits" + batch_size = 64 + num_classes = 20 + self.inputs = { + 'X': logit( + np.random.uniform(0, 1, (batch_size, num_classes)) + .astype("float32")), + 'Labels': np.random.uniform(0, 1, (batch_size, num_classes)) + .astype("float32") + } + + # Fw Pass is implemented as elementwise sigmoid followed by + # elementwise logistic loss + # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X)) + sigmoid_X = expit(self.inputs['X']) + term1 = self.inputs['Labels'] * np.log(sigmoid_X) + term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X) + self.outputs = {'Out': -term1 - term2} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(['X'], 'Out') diff --git a/python/paddle/v2/framework/tests/test_softmax_with_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_softmax_with_cross_entropy_op.py index 428395b76c8fbcbc07b19ee1979419f0e64aca85..377d07fb5927a108e9bd39ab227da4f40a9cd447 100644 --- a/python/paddle/v2/framework/tests/test_softmax_with_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_softmax_with_cross_entropy_op.py @@ -43,7 +43,7 @@ class TestSoftmaxWithCrossEntropyOp2(OpTest): def setUp(self): self.op_type = "softmax_with_cross_entropy" batch_size = 2 - class_num = 17 + class_num = 37 logits = np.random.uniform(0.1, 1.0, [batch_size, class_num]).astype("float32") diff --git a/python/paddle/v2/framework/tests/test_tensor_array.py b/python/paddle/v2/framework/tests/test_tensor_array.py new file mode 100644 index 0000000000000000000000000000000000000000..11f8a01f9224fcbd6dd6cbc8c37cc81036ad3e07 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_tensor_array.py @@ -0,0 +1,106 @@ +import logging +import paddle.v2.framework.core as core +import unittest +import numpy as np + + +class TestTensorArray(unittest.TestCase): + def setUp(self): + self.ta = core.TensorArray() + + self.batch_size = 10 + self.dim = 2 + + # create a LoDTensor + self.scope = core.Scope() + var = self.scope.new_var("test_tensor") + self.place = core.CPUPlace() + tensor = var.get_tensor() + tensor.set_dims([self.batch_size, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + tensor_array[0, 0] = 0 + tensor_array[1, 0] = 1 + tensor_array[2, 0] = 2 + tensor_array[3, 0] = 3 + tensor_array[4, 0] = 4 + tensor_array[5, 0] = 5 + tensor_array[6, 0] = 6 + tensor_array[7, 0] = 7 + tensor_array[8, 0] = 8 + tensor_array[9, 0] = 9 + + lod_py = [[0, 2, 5, 10]] + lod_tensor = core.LoDTensor(lod_py) + lod_tensor.set(tensor_array, self.place) + + self.py_seq_meta = [[5, 10, 2], [2, 5, 1], [0, 2, 0]] + + self.tensor = lod_tensor + + def test_unstack(self): + self.ta.unstack(self.tensor) + self.assertEqual(self.tensor.get_dims()[0], self.ta.size()) + + def test_read(self): + self.ta.unstack(self.tensor) + for i in range(self.batch_size): + tensor = self.ta.read(i) + + def test_write(self): + self.ta.unstack(self.tensor) + + # create a tensor with shape of [1, self.dim] + var = self.scope.new_var("hell") + tensor = var.get_tensor() + tensor.set_dims([1, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + for i in range(self.dim): + tensor_array[0, i] = i + tensor.set(tensor_array, self.place) + + self.ta.write(2, tensor) + + ta_tensor = self.ta.read(2) + ta_tensor_array = np.array(ta_tensor) + self.assertEqual(ta_tensor.get_dims(), [1, self.dim]) + self.assertTrue((tensor_array == ta_tensor_array).all()) + + def test_write_shared(self): + self.ta.unstack(self.tensor) + + # create a tensor with shape of [1, self.dim] + var = self.scope.new_var("hell") + tensor = var.get_tensor() + tensor.set_dims([1, self.dim]) + tensor.alloc_float(self.place) + tensor_array = np.array(tensor) + for i in range(self.dim): + tensor_array[0, i] = i + tensor.set(tensor_array, self.place) + + self.ta.write_shared(2, tensor) + + ta_tensor = self.ta.read(2) + ta_tensor_array = np.array(ta_tensor) + self.assertEqual(ta_tensor.get_dims(), [1, self.dim]) + self.assertTrue((tensor_array == ta_tensor_array).all()) + + def test_unpack(self): + meta = self.ta.unpack(self.tensor, 0, True) + self.assertEqual(self.ta.size(), 5) + self.assertEqual(meta, self.py_seq_meta) + + def test_pack(self): + meta = self.ta.unpack(self.tensor, 0, True) + print "meta", meta + tensor = self.ta.pack(0, meta, self.tensor.lod()) + print np.array(self.tensor) + print np.array(tensor) + self.assertTrue((np.array(self.tensor) == np.array(tensor)).all()) + self.assertTrue(tensor.lod(), self.tensor.lod()) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_variable.py b/python/paddle/v2/framework/tests/test_variable.py new file mode 100644 index 0000000000000000000000000000000000000000..8ea1083ff6535d2d517f2ac587a956bfed906f03 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_variable.py @@ -0,0 +1,40 @@ +import unittest +from paddle.v2.framework.graph import Variable, g_program +import paddle.v2.framework.core as core +import numpy as np + + +class TestVariable(unittest.TestCase): + def test_np_dtype_convert(self): + DT = core.DataType + convert = Variable._convert_np_dtype_to_dtype_ + self.assertEqual(DT.FP32, convert(np.float32)) + self.assertEqual(DT.FP16, convert("float16")) + self.assertEqual(DT.FP64, convert("float64")) + self.assertEqual(DT.INT32, convert("int32")) + self.assertEqual(DT.INT16, convert("int16")) + self.assertEqual(DT.INT64, convert("int64")) + self.assertEqual(DT.BOOL, convert("bool")) + self.assertRaises(ValueError, lambda: convert("int8")) + + def test_var(self): + b = g_program.current_block() + w = b.create_var( + dtype="float64", shape=[784, 100], lod_level=0, name="fc.w") + self.assertEqual(core.DataType.FP64, w.data_type) + self.assertEqual((784, 100), w.shape) + self.assertEqual("fc.w", w.name) + self.assertEqual(0, w.lod_level) + + w = b.create_var(name='fc.w') + self.assertEqual(core.DataType.FP64, w.data_type) + self.assertEqual((784, 100), w.shape) + self.assertEqual("fc.w", w.name) + self.assertEqual(0, w.lod_level) + + self.assertRaises(ValueError, + lambda: b.create_var(name="fc.w", shape=(24, 100))) + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py index e80456d9bbeb3c34ac9eab873a84dbf8f06e34df..9148cb56cf78e1ebb994f4a4a34d4a1b6e2e6ef4 100644 --- a/python/paddle/v2/inference.py +++ b/python/paddle/v2/inference.py @@ -96,6 +96,9 @@ class Inference(object): for i, item in enumerate(result): retv[i].append(item) + if retv == None: + return [] + if flatten_result: retv = [numpy.concatenate(out) for out in retv] diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index ca95ef13bd440ac0ba3d46f6e4680d4d7aa94c42..076e75593991415bc3fbcbd36a108c8c7de66932 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -164,11 +164,18 @@ class SGD(object): pass_type) self.__gradient_machine__.eval(pass_evaluator) self.__gradient_machine__.eval(batch_evaluator) + event_handler( + v2_event.EndForwardBackward( + pass_id=pass_id, + batch_id=batch_id, + gm=self.__gradient_machine__)) for each_param in self.__gradient_machine__.getNonStaticParameters( ): self.__parameter_updater__.update(each_param) cost_sum = out_args.sum() cost = cost_sum / len(data_batch) + self.__parameter_updater__.finishBatch(cost) + batch_evaluator.finish() event_handler( v2_event.EndIteration( pass_id=pass_id, @@ -176,8 +183,6 @@ class SGD(object): cost=cost, evaluator=batch_evaluator, gm=self.__gradient_machine__)) - self.__parameter_updater__.finishBatch(cost) - batch_evaluator.finish() self.__parameter_updater__.finishPass() pass_evaluator.finish()