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提交 6c1cf609 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'ups/develop' into fix

上级 c4535732 0329ee74
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AUTHORS.md
浏览文件 @ 6c1cf609
......@@ -22,6 +22,7 @@
| jczaja | Jacek Czaja |
| JiayiFeng | Jia-Yi Feng |
| kbinias | Krzysztof Binias |
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| lipeng-unisound | Peng Li |
......
cmake/inference_lib.cmake
浏览文件 @ 6c1cf609
......@@ -39,7 +39,7 @@ function(copy TARGET)
message(FATAL_ERROR "${TARGET} source numbers are not equal to destination numbers")
endif()
math(EXPR len "${copy_lib_SRCS_len} - 1")
add_custom_target(${TARGET} DEPENDS ${copy_lib_DEPS})
foreach(index RANGE ${len})
list(GET copy_lib_SRCS ${index} src)
......@@ -155,6 +155,15 @@ copy(inference_lib DEPS paddle_fluid_shared paddle_fluid
DSTS ${dst_dir}/${module} ${dst_dir}/${module}
)
if(WITH_CONTRIB)
set(contrib_dst_dir "${FLUID_INSTALL_DIR}/contrib/inference")
copy(contrib_inference_lib DEPS paddle_inference_api
SRCS ${PADDLE_SOURCE_DIR}/paddle/contrib/inference/paddle_inference_api.h
${PADDLE_BINARY_DIR}/paddle/contrib/inference/libpaddle_inference_api.*
DSTS ${contrib_dst_dir} ${contrib_dst_dir}
)
endif()
set(module "platform")
copy(platform_lib DEPS profiler_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/dynload/*.h ${src_dir}/${module}/details/*.h
......
doc/fluid/api/layers.rst
浏览文件 @ 6c1cf609
......@@ -342,6 +342,12 @@ conv2d
.. autofunction:: paddle.fluid.layers.conv2d
:noindex:
conv3d
------
.. autofunction:: paddle.fluid.layers.conv3d
:noindex:
sequence_pool
-------------
......@@ -366,6 +372,12 @@ pool2d
.. autofunction:: paddle.fluid.layers.pool2d
:noindex:
pool3d
------
.. autofunction:: paddle.fluid.layers.pool3d
:noindex:
batch_norm
----------
......@@ -384,6 +396,13 @@ conv2d_transpose
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
conv3d_transpose
----------------
.. autofunction:: paddle.fluid.layers.conv2d_transpose
:noindex:
sequence_expand
---------------
......
doc/v2/dev/contribute_to_paddle_cn.md
浏览文件 @ 6c1cf609
......@@ -104,7 +104,7 @@ no changes added to commit (use "git add" and/or "git commit -a")
➜ docker run -it -v $(pwd):/paddle paddle:latest-dev bash -c "cd /paddle/build && ctest"
```
关于构建和测试的更多信息，请参见[这篇文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。
关于构建和测试的更多信息，请参见[使用Docker安装运行](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/v2/build_and_install/docker_install_cn.rst)。
## 提交（commit）
......
paddle/contrib/inference/CMakeLists.txt
浏览文件 @ 6c1cf609
......@@ -50,7 +50,7 @@ cc_test(test_paddle_inference_api
inference_api_test(test_paddle_inference_api_impl
ARGS test_word2vec test_image_classification)
if (WITH_ANAKIN)
if (WITH_ANAKIN AND WITH_TESTING) # only needed in CI
# Due to Anakin do not have official library releases and the versions of protobuf and cuda do not match Paddle's,
# so anakin library will not be merged to our official inference library. To use anakin prediction API, one need to
# compile the libinference_anakin_api.a and compile with anakin.so.
......
paddle/contrib/tape/CMakeLists.txt
浏览文件 @ 6c1cf609
......@@ -17,7 +17,7 @@ if(APPLE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-error=pessimizing-move")
endif(APPLE)
cc_library(tape_variable SRCS variable.cc DEPS ${FLUID_CORE_MODULES})
cc_library(tape_variable SRCS variable.cc DEPS ${FLUID_CORE_MODULES} device_context framework_proto proto_desc operator)
cc_library(tape SRCS tape.cc DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} tape_variable)
cc_test(test_tape
......
paddle/fluid/framework/init.cc
浏览文件 @ 6c1cf609
......@@ -18,6 +18,7 @@ limitations under the License. */
#include "paddle/fluid/framework/init.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/string/piece.h"
......@@ -113,6 +114,9 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
}
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
#ifndef PADDLE_WITH_MKLDNN
operators::math::SetNumThreads(1);
#endif
}
void InitGLOG(const std::string &prog_name) {
......
paddle/fluid/inference/tensorrt/convert/op_converter.h
浏览文件 @ 6c1cf609
......@@ -64,7 +64,8 @@ class OpConverter {
(*it)(op, scope, test_mode);
}
// convert fluid block to tensorrt network
// Convert a fluid block to tensorrt network, NOTE it just convert operators,
// the INetwork's inputs and outputs should specified in some other modules.
void ConvertBlock(const framework::proto::BlockDesc& block,
const std::unordered_set<std::string>& parameters,
const framework::Scope& scope, TensorRTEngine* engine) {
......
paddle/fluid/inference/tensorrt/engine.h
浏览文件 @ 6c1cf609
......@@ -51,11 +51,12 @@ class TensorRTEngine : public EngineBase {
nvinfer1::Weights w_;
};
TensorRTEngine(int max_batch, int max_workspace, cudaStream_t* stream,
TensorRTEngine(int max_batch, int max_workspace,
cudaStream_t* stream = nullptr,
nvinfer1::ILogger& logger = NaiveLogger::Global())
: max_batch_(max_batch),
max_workspace_(max_workspace),
stream_(stream),
stream_(stream ? stream : &default_stream_),
logger_(logger) {}
virtual ~TensorRTEngine();
......@@ -121,6 +122,8 @@ class TensorRTEngine : public EngineBase {
// the max memory size the engine uses
int max_workspace_;
cudaStream_t* stream_;
// If stream_ is not set from outside, hold its own stream.
cudaStream_t default_stream_;
nvinfer1::ILogger& logger_;
std::vector<Buffer> buffers_;
......@@ -165,20 +168,31 @@ class TensorRTEngine : public EngineBase {
*/
class TRT_EngineManager {
public:
TensorRTEngine* Create(int max_batch, int max_workspace,
cudaStream_t* stream) {
engines_.emplace_back(new TensorRTEngine(max_batch, max_workspace, stream));
return engines_.back().get();
bool HasEngine(const std::string& name) const {
return engines_.count(name) != 0;
}
// Get an engine called `name`.
TensorRTEngine* Get(const std::string& name) const {
return engines_.at(name).get();
}
// Create or get an engine called `name`
TensorRTEngine* Create(int max_batch, int max_workspace, cudaStream_t* stream,
const std::string& name) {
auto* p = new TensorRTEngine(max_batch, max_workspace, stream);
engines_[name].reset(p);
return p;
}
void DeleteALl() {
for (auto& ptr : engines_) {
ptr.reset(nullptr);
for (auto& item : engines_) {
item.second.reset(nullptr);
}
}
private:
std::vector<std::unique_ptr<TensorRTEngine>> engines_;
std::unordered_map<std::string, std::unique_ptr<TensorRTEngine>> engines_;
};
} // namespace tensorrt
......
paddle/fluid/operators/activation_op.cc
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......@@ -252,15 +252,14 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out", "Output of Softshrink operator");
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
AddComment(R"DOC(
Softshrink Activation Operator.
:strong:`Softshrink Activation Operator`
$$
out = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x - \lambda, \text{if } x > \lambda \\
x + \lambda, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
)DOC");
}
......@@ -271,18 +270,18 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "Input of HardShrink operator");
AddOutput("Out", "Output of HardShrink operator");
AddAttr<float>("threshold", "The value of threshold for HardShrink")
AddAttr<float>("threshold",
"The value of threshold for HardShrink. [default: 0.5]")
.SetDefault(0.5f);
AddComment(R"DOC(
HardShrink Activation Operator.
:strong:`HardShrink activation operator`
$$
out = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x, \text{if } x > \lambda \\
x, \text{if } x < -\lambda \\
0, \text{otherwise}
\end{cases}
)DOC");
}
......@@ -394,18 +393,18 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
void Make() override {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Out", "Output of ThresholdedRelu operator");
AddAttr<float>("threshold", "The threshold location of activation")
AddAttr<float>("threshold",
"The threshold location of activation. [default 1.0].")
.SetDefault(1.0f);
AddComment(R"DOC(
ThresholdedRelu Activation Operator.
:strong:`ThresholdedRelu activation operator`
$$
out = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
$$
.. math::
out = \begin{cases}
x, \text{if } x > threshold \\
0, \text{otherwise}
\end{cases}
)DOC");
}
};
......
paddle/fluid/operators/compare_op.cc
浏览文件 @ 6c1cf609
......@@ -23,30 +23,26 @@ class CompareOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
OpComment comment;
AddInput("X",
string::Sprintf("(LoDTensor) the left hand operand of %s operator",
comment.type));
AddInput("Y", string::Sprintf(
"(LoDTensor) the right hand operand of %s operator",
comment.type));
AddInput("X", string::Sprintf("the left hand operand of %s operator",
comment.type));
AddInput("Y", string::Sprintf("the right hand operand of %s operator",
comment.type));
AddAttr<bool>("force_cpu",
"(bool, default false) Force fill output variable to cpu "
"Force fill output variable to cpu "
"memory. Otherwise, fill output variable to the running "
"device")
.SetDefault(false);
AddOutput("Out", string::Sprintf(
"(LoDTensor) n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(%s Operator
"device [default true].")
.SetDefault(true);
AddOutput("Out", string::Sprintf("n-dim bool tensor. Each element is %s",
comment.equation));
AddComment(string::Sprintf(R"DOC(
It operates element-wise on X and Y, and returns the Out. Each of them is a
N-dim tensor. X and Y could be any type. The each element of the Out tensor is
calculated by %s
calculated by $%s$
)DOC",
comment.type, comment.equation));
AddAttr<int>("axis",
"(int, default -1). The start dimension index "
"for broadcasting Y onto X.")
comment.equation));
AddAttr<int>(
"axis",
"The start dimension index for broadcasting Y onto X. [default -1]")
.SetDefault(-1)
.EqualGreaterThan(-1);
}
......
paddle/fluid/operators/concat_op.cc
浏览文件 @ 6c1cf609
......@@ -107,7 +107,13 @@ REGISTER_OPERATOR(concat, ops::ConcatOp, ops::ConcatOpMaker,
false> /* set false to disable empty grad */);
REGISTER_OPERATOR(concat_grad, ops::ConcatOpGrad);
REGISTER_OP_CPU_KERNEL(
concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>);
concat, ops::ConcatKernel<paddle::platform::CPUDeviceContext, double>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, float>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::ConcatKernel<paddle::platform::CPUDeviceContext, int>);
REGISTER_OP_CPU_KERNEL(
concat_grad,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, double>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
ops::ConcatGradKernel<paddle::platform::CPUDeviceContext, int>);
paddle/fluid/operators/concat_op.cu.cc
浏览文件 @ 6c1cf609
......@@ -15,7 +15,13 @@ limitations under the License. */
#include "paddle/fluid/operators/concat_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
concat, ops::ConcatKernel<paddle::platform::CUDADeviceContext, float>);
concat, ops::ConcatKernel<paddle::platform::CUDADeviceContext, double>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, float>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::ConcatKernel<paddle::platform::CUDADeviceContext, int>);
REGISTER_OP_CUDA_KERNEL(
concat_grad,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::ConcatGradKernel<paddle::platform::CUDADeviceContext, int>);
paddle/fluid/operators/cumsum_op.cc
浏览文件 @ 6c1cf609
......@@ -30,19 +30,19 @@ class CumOp : public framework::OperatorWithKernel {
class CumsumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "Input of Cumsum operator");
AddOutput("Out", "Output of Cumsum operator");
AddInput("X", "Input of cumsum operator");
AddOutput("Out", "Output of cumsum operator");
AddAttr<int>("axis",
"(int, default -1). The dimenstion to accumulate along. "
"-1 means the last dimenstion")
"The dimenstion to accumulate along. -1 means the last "
"dimenstion [default -1].")
.SetDefault(-1)
.EqualGreaterThan(-1);
AddAttr<bool>("exclusive",
"bool, default false). Whether to perform exclusive cumsum")
"Whether to perform exclusive cumsum. [default false].")
.SetDefault(false);
AddAttr<bool>("reverse",
"bool, default false). If true, the cumsum is performed in "
"the reversed direction")
"If true, the cumsum is performed in the reversed direction. "
"[default false].")
.SetDefault(false);
AddComment(R"DOC(
The cumulative sum of the elements along a given axis.
......
paddle/fluid/operators/get_places_op.cc
浏览文件 @ 6c1cf609
......@@ -85,7 +85,7 @@ class GetPlacesOpProtoMaker : public framework::OpProtoAndCheckerMaker {
.InEnum({"CUDA", "CPU", "AUTO"})
.SetDefault("AUTO");
AddComment(R"DOC(
Returns a list of places based on flags. The list will be used for parallel
Returns a list of places based on arguments. The list will be used for parallel
execution.
)DOC");
}
......
paddle/fluid/operators/layer_norm_op.cc
浏览文件 @ 6c1cf609
......@@ -62,36 +62,33 @@ class LayerNormOp : public framework::OperatorWithKernel {
class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(LoDTensor) The input tensor.");
AddInput("X", "The input tensor.");
AddInput("Scale",
"(Tensor, optional) Scale is a 1-dimensional tensor of size "
"(optional) Scale is a 1-dimensional tensor of size "
"H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
"It is applied to the output.")
.AsDispensable();
AddInput("Bias",
"(Tensor, optional) Bias is a 1-dimensional tensor of size "
"(optional) Bias is a 1-dimensional tensor of size "
"H(`begin_norm_axis` splits the tensor(`X`) to a matrix [N,H])."
"It is applied to the output.")
.AsDispensable();
AddOutput("Y", "(LoDTensor) Result after normalization.");
AddOutput("Mean", "(Tensor) Mean of the current mini batch.")
.AsIntermediate();
AddOutput("Variance", "(Tensor) Variance of the current mini batch.")
AddOutput("Y", "Result after normalization.");
AddOutput("Mean", "Mean of the current mini batch.").AsIntermediate();
AddOutput("Variance", "Variance of the current mini batch.")
.AsIntermediate();
AddAttr<float>("epsilon",
"(float, default 1e-5) Constant for "
"numerical stability")
"Constant for numerical stability [default 1e-5].")
.SetDefault(1e-5)
.AddCustomChecker([](const float &epsilon) {
PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
"'epsilon' should be between 0.0 and 0.001.");
});
AddAttr<int>("begin_norm_axis",
"(int default:1), the "
"axis of `begin_norm_axis ... Rank(X) - 1` will be "
"the axis of `begin_norm_axis ... Rank(X) - 1` will be "
"normalized. `begin_norm_axis` splits the tensor(`X`) to a "
"matrix [N,H].")
"matrix [N,H]. [default 1].")
.SetDefault(1)
.AddCustomChecker([](const int &begin_norm_axis) {
PADDLE_ENFORCE_GT(begin_norm_axis, 0,
......@@ -99,10 +96,14 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
});
AddComment(R"DOC(
Layer Normalization.
Layer Norm has been implemented as discussed in the paper:
https://arxiv.org/abs/1607.06450
...
Assume feature vectors exist on dimensions
:attr:`begin_norm_axis ... rank(input)` and calculate the moment statistics
along these dimensions for each feature vector :math:`a` with size
:math:`H`, then normalize each feature vector using the corresponding
statistics. After that, apply learnable gain and bias on the normalized
tensor to scale and shift if :attr:`scale` and :attr:`shift` are set.
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_
)DOC");
}
};
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 6c1cf609
......@@ -33,12 +33,10 @@ class MeanOp : public framework::OperatorWithKernel {
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").Reuse("X");
AddInput("X", "(Tensor) The input of mean op");
AddOutput("Out", "(Tensor) The output of mean op").Reuse("X");
AddComment(R"DOC(
Mean Operator.
Out is a scalar which is the mean of all elements in X.
Mean Operator calculates the mean of all elements in X.
)DOC");
}
......
paddle/fluid/operators/multiplex_op.cc
浏览文件 @ 6c1cf609
......@@ -62,26 +62,46 @@ class MultiplexOp : public framework::OperatorWithKernel {
class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Ids", "The index tensor of multiplex operator.");
AddInput("X", "The candidate tensors of multiplex operator.")
AddInput("Ids",
"Tensor<int32>, index variable which is a 2-D tensor with shape "
"[M, 1] where M is the batch size.");
AddInput("X",
"A list of variables to gather from. All variables have the same "
"shape and the rank is at least 2.")
.AsDuplicable();
AddOutput("Out", "The output tensor of multiplex operator.");
AddComment(R"DOC(
Multiplex Operator.
Multiplex multiple tensors according to the index provided by the index tensor.
Ids: the index tensor.
X[0 : N - 1]: the candidate tensors for output (N >= 2).
For each index i from 0 to batchSize - 1, the output is the i-th row of the
Referring to the given index variable, this layer selects rows from the
input variables to construct a multiplex variable. Assuming that there are
:math:`m` input variables and :math:`I_i` represents the i-th input
variable and :math:`i` is in [0, :math:`m`). All input variables are
tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
Please note that rank of the input tensor should be at least 2. Each input
variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
* ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
variable. The given index variable should be a 2-D tensor with shape
[:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
Then the output variable will be a tensor with shape [:math:`d_0`,
:math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
* Ids: the index tensor.
* X[0 : N - 1]: the candidate tensors for output (N >= 2).
* For each index i from 0 to batchSize - 1, the output is the i-th row of the
the (Ids[i])-th tensor.
For i-th row of the output tensor:
$$y[i] = x_{k}[i]$$
$$
y[i] = x_{k}[i]
$$
where `y` is the output tensor, `x_{k}` is the k-th input tensor,
and `k = Ids[i]`.
where $y$ is the output tensor, $x_{k}$ is the k-th input tensor,
and $k = Ids[i]$.
)DOC");
}
......
paddle/fluid/operators/reader/create_recordio_file_reader_op.cc
浏览文件 @ 6c1cf609
......@@ -78,11 +78,15 @@ class CreateRecordIOReaderOp : public framework::OperatorBase {
class CreateRecordIOReaderOpMaker : public FileReaderMakerBase {
protected:
void Apply() override {
AddAttr<std::string>("filename", "The filename of record io reader");
AddAttr<std::string>(
"filename",
"The filename of record file. This file will given to reader.");
AddComment(R"DOC(
CreateRecordIOReader Operator
Open a recordio file and return the reader object. The returned reader object
is thread-safe.
Create a reader from a record io file
NOTE: This is a very low-level API. It is used for debugging data file or
training. Please use `open_files` instead of this API for production usage.
)DOC");
}
};
......
paddle/fluid/operators/reader/reader_op_registry.cc
浏览文件 @ 6c1cf609
......@@ -54,7 +54,7 @@ std::unique_ptr<framework::ReaderBase> CreateReaderByFileName(
}
void FileReaderMakerBase::Make() {
AddOutput("Out", "(ReaderHolder) The created random reader.").AsDuplicable();
AddOutput("Out", "(ReaderHolder): The created random reader.").AsDuplicable();
AddAttr<std::vector<int>>("shape_concat", "The concat of all data's shapes.");
AddAttr<std::vector<int>>(
"ranks",
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ 6c1cf609
......@@ -78,23 +78,23 @@ class RowConvOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(LoDTensor), the input(X) is a LodTensor, which supports "
"the input(X) is a LodTensor, which supports "
"variable time-length input sequences. The underlying tensor "
"in this LoDTensor is a matrix with shape (T x N), where T "
"is the total time steps in this mini-batch and N is the input "
"data dimension.");
AddInput("Filter",
"(Tensor), the input(Filter) is a learnable parameter. It "
"the input(Filter) is a learnable parameter. It "
"is a 2-D tensor with shape (future_context x N), where, "
"future_context is the future context length and N is the data "
"dimension.");
AddOutput("Out",
"(LoDTensor), the output(Out) is a LodTensor, which supports "
"the output(Out) is a LodTensor, which supports "
"variable time-length input sequences. The underlying tensor "
"in this LodTensor is a matrix with shape T x N, i.e., the "
"same shape as X.");
AddComment(R"DOC(
Row-convolution Operator.
:strong:`Row-convolution operator`
The row convolution is called lookahead convolution. This operator was
introduced in the following paper for DeepSpeech2:
......@@ -114,9 +114,23 @@ and a filter ($W$) of size $context \times d$,
the output sequence is convolved as:
$$
out_{i, :} = \sum_{j=i}^{i + context} in_{j,:} \dot W_{i-j, :}
out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
$$
In the above equation:
* $Out_{i}$: The i-th row of output variable with shape [1, D].
* $\\tau$: Future context size.
* $X_{j}$: The j-th row of input variable with shape [1, D].
* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
More details about row_conv please refer to
the design document
https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645 .
)DOC");
}
};
......
paddle/fluid/operators/split_op.cc
浏览文件 @ 6c1cf609
......@@ -115,4 +115,7 @@ USE_CPU_ONLY_OP(concat);
REGISTER_OPERATOR(split, ops::SplitOp, ops::SplitOpMaker, ops::SplitGradMaker);
REGISTER_OP_CPU_KERNEL(split,
ops::SplitOpKernel<paddle::platform::CPUPlace, float>);
ops::SplitOpKernel<paddle::platform::CPUPlace, double>,
ops::SplitOpKernel<paddle::platform::CPUPlace, float>,
ops::SplitOpKernel<paddle::platform::CPUPlace, int64_t>,
ops::SplitOpKernel<paddle::platform::CPUPlace, int>);
paddle/fluid/operators/split_op.cu.cc
浏览文件 @ 6c1cf609
......@@ -15,4 +15,7 @@ limitations under the License. */
#include "paddle/fluid/operators/split_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
split, ops::SplitOpKernel<paddle::platform::CUDADeviceContext, float>);
split, ops::SplitOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::SplitOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::SplitOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::SplitOpKernel<paddle::platform::CUDADeviceContext, int>);
paddle/fluid/operators/tensorrt_engine_op.cc
浏览文件 @ 6c1cf609
......@@ -66,17 +66,25 @@ nvinfer1::Dims Vec2TRT_Dims(const std::vector<int64_t> &shape) {
} // namespace
template <typename DeviceContext, typename T>
void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
void TensorRTEngineKernel<DeviceContext, T>::Prepare(
const framework::ExecutionContext &context) const {
VLOG(4) << "Prepare engine";
// Get the ProgramDesc and pass to convert.
framework::proto::BlockDesc block_desc;
block_desc.ParseFromString(context.Attr<std::string>("subgraph"));
max_batch_ = context.Attr<int>("max_batch");
int max_batch = context.Attr<int>("max_batch");
auto max_workspace = context.Attr<int>("max_workspace");
engine_ = Singleton<TRT_EngineManager>::Global().Create(
max_batch_, max_workspace, &stream_);
engine_->InitNetwork();
auto params = context.Attr<std::vector<std::string>>("parameters");
std::unordered_set<std::string> parameters;
for (const auto &param : params) {
parameters.insert(param);
}
// TODO(Superjomn) replace this with a different stream
auto *engine = Singleton<TRT_EngineManager>::Global().Create(
max_batch, max_workspace, nullptr /*engine hold its own stream*/,
context.Attr<std::string>("engine_uniq_key"));
engine->InitNetwork();
framework::BlockDesc block(nullptr /*programdesc*/, &block_desc);
// Add inputs
......@@ -87,24 +95,23 @@ void paddle::operators::TensorRTEngineKernel<DeviceContext, T>::Prepare(
PADDLE_ENFORCE_EQ(var->GetType(), FluidDT::VarType_Type_LOD_TENSOR,
"TensorRT engine only takes LoDTensor as input");
auto shape = var->GetShape();
engine_->DeclareInput(
engine->DeclareInput(
input, FluidDataType2TRT(
var->Proto()->type().lod_tensor().tensor().data_type()),
Vec2TRT_Dims(var->GetShape()));
}
// TODO(Superjomn) parameters should be passed after analysised from outside.
inference::Singleton<inference::tensorrt::OpConverter>::Global().ConvertBlock(
block_desc, {}, context.scope(), engine_);
block_desc, parameters, context.scope(), engine);
// Add outputs
VLOG(4) << "declare outputs";
for (auto &output : context.Outputs("Ys")) {
VLOG(4) << "declare output " << output;
engine_->DeclareOutput(output);
engine->DeclareOutput(output);
}
engine_->FreezeNetwork();
engine->FreezeNetwork();
}
class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -113,6 +120,7 @@ class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Xs", "A list of inputs.").AsDuplicable();
AddOutput("Ys", "A list of outputs").AsDuplicable();
AddAttr<std::string>("subgraph", "the subgraph.");
AddAttr<std::string>("engine_uniq_key", "unique key for the TRT engine.");
AddAttr<int>("max_batch", "the maximum batch size.");
AddAttr<int>("max_workspace", "the maximum batch size.");
AddComment("TensorRT engine operator.");
......
paddle/fluid/operators/tensorrt_engine_op.h
浏览文件 @ 6c1cf609
......@@ -19,10 +19,14 @@
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/inference/analysis/helper.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
#include "paddle/fluid/inference/tensorrt/engine.h"
namespace paddle {
namespace operators {
using inference::Singleton;
using inference::tensorrt::TRT_EngineManager;
class TensorRTEngineOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -47,16 +51,18 @@ template <typename DeviceContext, typename T>
class TensorRTEngineKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
if (!engine_) {
auto engine_name = context.Attr<std::string>("engine_uniq_key");
if (!Singleton<TRT_EngineManager>::Global().HasEngine(engine_name)) {
Prepare(context);
}
auto* engine = Singleton<TRT_EngineManager>::Global().Get(engine_name);
auto input_names = context.op().Inputs("Xs");
PADDLE_ENFORCE(!input_names.empty(), "should pass more than one inputs");
// Try to determine a batch_size
auto& tensor0 = inference::analysis::GetFromScope<framework::LoDTensor>(
context.scope(), input_names.front());
int batch_size = tensor0.dims()[0];
PADDLE_ENFORCE_LE(batch_size, max_batch_);
PADDLE_ENFORCE_LE(batch_size, context.Attr<int>("max_batch"));
// Convert input tensor from fluid to engine.
for (const auto& x : context.Inputs("Xs")) {
......@@ -64,20 +70,20 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
auto& t = inference::analysis::GetFromScope<framework::LoDTensor>(
context.scope(), x);
if (platform::is_cpu_place(t.place())) {
engine_->SetInputFromCPU(x, static_cast<const void*>(t.data<void>()),
t.memory_size());
engine->SetInputFromCPU(x, static_cast<const void*>(t.data<void>()),
t.memory_size());
} else {
engine_->SetInputFromGPU(x, static_cast<const void*>(t.data<void>()),
t.memory_size());
engine->SetInputFromGPU(x, static_cast<const void*>(t.data<void>()),
t.memory_size());
}
}
// Execute the engine.
PADDLE_ENFORCE_GT(batch_size, 0);
engine_->Execute(batch_size);
engine->Execute(batch_size);
// Convert output tensor from engine to fluid
for (const auto& y : context.Outputs("Ys")) {
// convert output and copy to fluid.
nvinfer1::ITensor* trt_t = engine_->GetITensor(y);
nvinfer1::ITensor* trt_t = engine->GetITensor(y);
auto dims = trt_t->getDimensions();
// Use the output ITensor's dims to reshape the Fluid Tensor.
std::vector<int> ddim(dims.d, dims.d + dims.nbDims);
......@@ -89,27 +95,22 @@ class TensorRTEngineKernel : public framework::OpKernel<T> {
auto size = inference::analysis::AccuDims(dims.d, dims.nbDims);
if (platform::is_cpu_place(fluid_t->place())) {
// TODO(Superjomn) change this float to dtype size.
engine_->GetOutputInCPU(
engine->GetOutputInCPU(
y, fluid_t->mutable_data<float>(platform::CPUPlace()),
size * sizeof(float));
} else {
engine_->GetOutputInGPU(
engine->GetOutputInGPU(
y, fluid_t->mutable_data<float>(platform::CUDAPlace()),
size * sizeof(float));
}
}
cudaStreamSynchronize(stream_);
cudaStreamSynchronize(*engine->stream());
}
protected:
// Build the engine.
void Prepare(const framework::ExecutionContext& context) const;
private:
mutable cudaStream_t stream_;
mutable inference::tensorrt::TensorRTEngine* engine_{nullptr};
mutable int max_batch_{0};
};
} // namespace operators
......
paddle/fluid/operators/tensorrt_engine_op_test.cc
浏览文件 @ 6c1cf609
......@@ -79,6 +79,17 @@ void SetAttr<int64_t>(framework::proto::OpDesc* op, const std::string& name,
attr->set_type(paddle::framework::proto::AttrType::LONG);
attr->set_l(data);
}
template <>
void SetAttr<std::vector<std::string>>(framework::proto::OpDesc* op,
const std::string& name,
const std::vector<std::string>& data) {
auto* attr = op->add_attrs();
attr->set_name(name);
attr->set_type(paddle::framework::proto::AttrType::STRINGS);
for (const auto& s : data) {
attr->add_strings(s.c_str());
}
}
} // namespace
......@@ -123,11 +134,15 @@ TEST(TensorRTEngineOp, manual) {
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z0"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch", 30);
SetAttr<int>(engine_op_desc.Proto(), "max_batch", 100);
SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 1 << 10);
SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "a_engine");
SetAttr<std::vector<std::string>>(engine_op_desc.Proto(), "parameters",
std::vector<std::string>({}));
LOG(INFO) << "create engine op";
auto engine_op = framework::OpRegistry::CreateOp(*engine_op_desc.Proto());
LOG(INFO) << "engine_op " << engine_op.get();
framework::Scope scope;
platform::CPUPlace place;
......@@ -145,6 +160,88 @@ TEST(TensorRTEngineOp, manual) {
engine_op->Run(scope, place);
}
void Execute(int batch_size, int input_dim, int output_dim, int nlayers = 1) {
framework::ProgramDesc program;
framework::Scope scope;
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
auto* block_ = program.Proto()->add_blocks();
block_->set_idx(0);
block_->set_parent_idx(-1);
using shape_t = std::vector<int64_t>;
LOG(INFO) << "create block desc";
framework::BlockDesc block_desc(&program, block_);
auto AddFCLayer = [&](const std::string& x_name, const std::string& y_name,
const std::string& z_name, bool x_created,
const shape_t& x_shape, const shape_t& y_shape,
const shape_t& z_shape) {
LOG(INFO) << "create fc op";
auto* fc = block_desc.AppendOp();
fc->SetType("mul");
fc->SetInput("X", std::vector<std::string>({x_name}));
fc->SetInput("Y", std::vector<std::string>({y_name}));
fc->SetOutput("Out", std::vector<std::string>({z_name}));
// Set inputs' variable shape in BlockDesc
if (!x_created) {
AddTensorToBlockDesc(block_, x_name,
std::vector<int64_t>({batch_size, input_dim, 1, 1}));
}
AddTensorToBlockDesc(block_, y_name,
std::vector<int64_t>({input_dim, output_dim}));
AddTensorToBlockDesc(block_, z_name,
std::vector<int64_t>({batch_size, output_dim}));
// Prepare variables.
if (!x_created) {
CreateCPUTensor(&scope, x_name, std::vector<int64_t>(x_shape));
}
CreateCPUTensor(&scope, y_name, std::vector<int64_t>(y_shape));
CreateCPUTensor(&scope, z_name, std::vector<int64_t>(z_shape));
// It is wired, need to copy manually.
*block_->add_ops() = *fc->Proto();
};
// Test with 4 layer FC
AddFCLayer("x0", "y0", "z0", false, {batch_size, input_dim},
{input_dim, output_dim}, {batch_size, output_dim});
AddFCLayer("z0", "y1", "z1", true, {}, {output_dim, output_dim},
{batch_size, output_dim});
AddFCLayer("z1", "y2", "z2", true, {}, {output_dim, output_dim},
{batch_size, output_dim});
AddFCLayer("z2", "y3", "z3", true, {}, {output_dim, output_dim},
{batch_size, output_dim});
LOG(INFO) << "create tensorrt desc";
framework::OpDesc engine_op_desc(nullptr);
engine_op_desc.SetType("tensorrt_engine");
engine_op_desc.SetInput("Xs", std::vector<std::string>({"x0"}));
engine_op_desc.SetOutput("Ys", std::vector<std::string>({"z3"}));
SetAttr<std::string>(engine_op_desc.Proto(), "subgraph",
block_->SerializeAsString());
SetAttr<int>(engine_op_desc.Proto(), "max_batch", batch_size);
SetAttr<int>(engine_op_desc.Proto(), "max_workspace", 2 << 10);
SetAttr<std::vector<std::string>>(
engine_op_desc.Proto(), "parameters",
std::vector<std::string>({"y0", "y1", "y2", "y3"}));
SetAttr<std::string>(engine_op_desc.Proto(), "engine_uniq_key", "b_engine");
auto engine_op = framework::OpRegistry::CreateOp(*engine_op_desc.Proto());
// Execute them.
engine_op->Run(scope, place);
}
// Test with a larger FC layer.
TEST(TensorRTEngineOp, fc) { Execute(40, 256, 256); }
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/uniform_random_op.cc
浏览文件 @ 6c1cf609
......@@ -86,32 +86,24 @@ class UniformRandomOp : public framework::OperatorWithKernel {
class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddOutput("Out", "(Tensor) The output tensor of uniform random op");
AddOutput("Out", "The output tensor of uniform random op");
AddComment(R"DOC(
Uniform random operator.
This operator initializes a tensor with random values sampled from a
uniform distribution.
uniform distribution. The random result is in set [min, max].
)DOC");
AddAttr<std::vector<int>>("shape",
"(vector<int>) The shape of the output tensor");
AddAttr<float>("min",
"(float, default -1.0) "
"Minimum value of uniform random")
AddAttr<std::vector<int>>("shape", "The shape of the output tensor");
AddAttr<float>("min", "Minimum value of uniform random. [default -1.0].")
.SetDefault(-1.0f);
AddAttr<float>("max",
"(float, default 1.0) "
"Maximun value of uniform random")
AddAttr<float>("max", "Maximun value of uniform random. [default 1.0].")
.SetDefault(1.0f);
AddAttr<int>("seed",
"(int, default 0) "
"Random seed used for generating samples. "
"0 means use a seed generated by the system."
"Note that if seed is not 0, this operator will always "
"generate the same random numbers every time.")
"generate the same random numbers every time. [default 0].")
.SetDefault(0);
AddAttr<int>("dtype", "(int, default 5(FP32)) Output tensor data type")
AddAttr<int>("dtype", "Output tensor data type. [default 5(FP32)].")
.SetDefault(framework::proto::VarType::FP32);
}
};
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 6c1cf609
......@@ -20,6 +20,7 @@ from ..framework import Program, Variable, Operator
from ..layer_helper import LayerHelper, unique_name
from ..initializer import force_init_on_cpu
from ops import logical_and, logical_not, logical_or
import numpy
__all__ = [
'split_lod_tensor',
......@@ -706,7 +707,7 @@ def lod_rank_table(x, level=0):
.. code-block:: python
x = fluid.layers.data(name='x', shape=[10],
dtype='float32', lod_level=1)
dtype='float32', lod_level=1)
out = layers.lod_rank_table(x=x, level=0)
"""
helper = LayerHelper("lod_rank_table", **locals())
......@@ -909,37 +910,40 @@ def create_array(dtype):
dtype=dtype)
def less_than(x, y, force_cpu=True, cond=None, **ignored):
@templatedoc()
def less_than(x, y, force_cpu=None, cond=None, **ignored):
"""
**Less than**
${comment}
This layer returns the truth value of :math:`x < y` elementwise.
>>> import paddle.fluid as fluid
>>> less = fluid.layers.less_than(x=label, y=limit)
Args:
x(Variable): First operand of *less_than*
y(Variable): Second operand of *less_than*
force_cpu(Bool|True): The output data will be on CPU if set true.
x(${x_type}): ${x_comment}.
y(${y_type}): ${y_comment}.
force_cpu(${force_cpu_type}): ${force_cpu_comment}.
cond(Variable|None): Optional output variable to store the result of *less_than*
Returns:
Variable: The tensor variable storing the output of *less_than*.
Examples:
.. code-block:: python
less = fluid.layers.less_than(x=label, y=limit)
${out_comment}.
"""
helper = LayerHelper("less_than", **locals())
if cond is None:
cond = helper.create_tmp_variable(dtype='bool')
cond.stop_gradient = True
attrs = dict()
if force_cpu is not None:
attrs['force_cpu'] = force_cpu
elif force_init_on_cpu():
attrs['force_cpu'] = force_init_on_cpu()
helper.append_op(
type='less_than',
inputs={'X': [x],
'Y': [y]},
outputs={'Out': [cond]},
attrs={'force_cpu': force_cpu or force_init_on_cpu()})
attrs=attrs)
return cond
......@@ -1004,8 +1008,28 @@ def array_read(array, i):
def shrink_memory(x, i, table):
"""
This function creates an operator to shrink_rnn_memory using the RankTable
This function creates an operator to shrink rnn memory using the RankTable
as mentioned in the input parameter.
NOTE: This API is very low-level API. It is used by DynamicRNN only.
Since the Dynamic RNN uses no-padding way to implement RNN. The sequence
will be sorted by order, and the length of valid memory will be shrink after
each time step.
Args:
x(Variable): The memory object in the previous time step.
i(Variable): The step count variable. A int scalar as LoDTensor.
table(Variable): The RNNRankTable object.
Returns:
the memory variable after shrink.
Examples:
Since this API is very low level API. The example is not provided.
Please reference the implementation of class DynamicRNN for detail
usage.
"""
helper = LayerHelper('shrink_memory', **locals())
out = helper.create_tmp_variable(dtype=x.dtype)
......@@ -1209,6 +1233,34 @@ class IfElseBlockGuard(object):
class IfElse(object):
"""
if-else control flow.
Args:
cond (Variable): condition used to compare.
name (str, default None): The name of this layer.
Examples:
.. code-block:: python
limit = fluid.layers.fill_constant_batch_size_like(
input=label, dtype='int64', shape=[1], value=5.0)
cond = fluid.layers.less_than(x=label, y=limit)
ie = fluid.layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = fluid.layers.fc(input=true_image, size=100, act='tanh')
prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = fluid.layers.fc(
input=false_image, size=200, act='tanh')
prob = fluid.layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
"""
OUT_IF_ELSE_BLOCKS = 0
IN_IF_ELSE_TRUE_BLOCKS = 1
IN_IF_ELSE_FALSE_BLOCKS = 2
......@@ -1311,6 +1363,38 @@ class IfElse(object):
class DynamicRNN(object):
"""
The dynamic RNN can process a batch of sequence data. The length of each
sample sequence can be different. This API automatically process them in
batch.
The input lod must be set. Please reference `lod_tensor`
>>> import paddle.fluid as fluid
>>> data = fluid.layers.data(name='sentence', dtype='int64', lod_level=1)
>>> embedding = fluid.layers.embedding(input=data, size=[65535, 32],
>>> is_sparse=True)
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(embedding)
>>> prev = drnn.memory(shape=[200])
>>> hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
>>> drnn.update_memory(prev, hidden) # set prev to hidden
>>> drnn.output(hidden)
>>>
>>> # last is the last time step of rnn. It is the encoding result.
>>> last = fluid.layers.sequence_last_step(drnn())
The dynamic RNN will unfold sequence into timesteps. Users need to define
how to process each time step during the :code:`with` block.
The `memory` is used staging data cross time step. The initial value of
memory can be zero or another variable.
The dynamic RNN can mark multiple variables as its output. Use `drnn()` to
get the output sequence.
"""
BEFORE_RNN = 0
IN_RNN = 1
AFTER_RNN = 2
......@@ -1333,6 +1417,15 @@ class DynamicRNN(object):
self.mem_link = []
def step_input(self, x):
"""
Mark a sequence as a dynamic RNN input.
Args:
x(Variable): The input sequence.
Returns:
The current timestep in the input sequence.
"""
self._assert_in_rnn_block_("step_input")
if not isinstance(x, Variable):
raise TypeError(
......@@ -1376,6 +1469,15 @@ class DynamicRNN(object):
return array_read(array=input_array, i=self.step_idx)
def static_input(self, x):
"""
Mark a variable as a RNN input. The input will not be scattered into
time steps.
Args:
x(Variable): The input variable.
Returns:
The input variable that can access in RNN.
"""
self._assert_in_rnn_block_("static_input")
if not isinstance(x, Variable):
raise TypeError(
......@@ -1397,6 +1499,10 @@ class DynamicRNN(object):
@contextlib.contextmanager
def block(self):
"""
The block for user to define operators in RNN. See the class docstring
for more details.
"""
if self.status != DynamicRNN.BEFORE_RNN:
raise ValueError("rnn.block() can only be invoke once")
self.step_idx = fill_constant(
......@@ -1423,6 +1529,9 @@ class DynamicRNN(object):
x=each_array, table=self.lod_rank_table))
def __call__(self, *args, **kwargs):
"""
Get the output of RNN. This API should only be invoked after RNN.block()
"""
if self.status != DynamicRNN.AFTER_RNN:
raise ValueError(("Output of the dynamic RNN can only be visited "
"outside the rnn block."))
......@@ -1437,6 +1546,70 @@ class DynamicRNN(object):
value=0.0,
need_reorder=False,
dtype='float32'):
"""
Create a memory variable for dynamic rnn.
If the :code:`init` is not None, :code:`memory` will be initialized by
this variable. The :code:`need_reorder` is used to reorder the memory as
the input variable. It should be set to true when the initialized memory
depends on the input sample.
For example,
>>> import paddle.fluid as fluid
>>> sentence = fluid.layers.data(
>>> name='sentence', dtype='float32', shape=[32])
>>> boot_memory = fluid.layers.data(
>>> name='boot', dtype='float32', shape=[10])
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(sentence)
>>> memory = drnn.memory(init=boot_memory, need_reorder=True)
>>> hidden = fluid.layers.fc(
>>> input=[word, memory], size=10, act='tanh')
>>> drnn.update_memory(ex_mem=memory, new_mem=hidden)
>>> drnn.output(hidden)
>>> rnn_output = drnn()
Otherwise, if :code:`shape`, :code:`value`, :code:`dtype` are set, the
:code:`memory` will be initialized by this :code:`value`.
For example,
>>> import paddle.fluid as fluid
>>> sentence = fluid.layers.data(
>>> name='sentence', dtype='float32', shape=[32])
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(sentence)
>>> memory = drnn.memory(shape=[10], dtype='float32', value=0)
>>> hidden = fluid.layers.fc(
>>> input=[word, memory], size=10, act='tanh')
>>> drnn.update_memory(ex_mem=memory, new_mem=hidden)
>>> drnn.output(hidden)
>>> rnn_output = drnn()
Args:
init(Variable|None): The initialized variable.
shape(list|tuple): The memory shape. NOTE the shape does not contain
batch_size.
value(float): the initalized value.
need_reorder(bool): True if the initialized memory depends on the
input sample.
dtype(str|numpy.dtype): The data type of the initialized memory.
Returns:
the memory variable.
"""
self._assert_in_rnn_block_('memory')
if init is not None:
if not isinstance(init, Variable):
......@@ -1504,6 +1677,16 @@ class DynamicRNN(object):
return self.memory(init=init)
def update_memory(self, ex_mem, new_mem):
"""
Update the memory from ex_mem to new_mem. NOTE that the shape and data
type of :code:`ex_mem` and :code:`new_mem` must be same.
Args:
ex_mem(Variable): the memory variable.
new_mem(Variable): the plain variable generated in RNN block.
Returns:
None
"""
self._assert_in_rnn_block_('update_memory')
if not isinstance(ex_mem, Variable):
raise TypeError("The input arg `ex_mem` of update_memory() must "
......@@ -1521,6 +1704,15 @@ class DynamicRNN(object):
self.mem_link.append((new_mem, mem_array))
def output(self, *outputs):
"""
mark the RNN output variables.
Args:
outputs: The output variables.
Returns:
None
"""
self._assert_in_rnn_block_('output')
parent_block = self._parent_block_()
for each in outputs:
......
python/paddle/fluid/layers/detection.py
浏览文件 @ 6c1cf609
......@@ -210,53 +210,68 @@ def bipartite_match(dist_matrix,
dist_threshold=None,
name=None):
"""
**Bipartite matchint operator**
This operator is a greedy bipartite matching algorithm, which is used to
obtain the matching with the maximum distance based on the input
This operator implements a greedy bipartite matching algorithm, which is
used to obtain the matching with the maximum distance based on the input
distance matrix. For input 2D matrix, the bipartite matching algorithm can
find the matched column for each row, also can find the matched row for
each column. And this operator only calculate matched indices from column
to row. For each instance, the number of matched indices is the number of
of columns of the input ditance matrix.
There are two outputs to save matched indices and distance.
A simple description, this algothrim matched the best (maximum distance)
find the matched column for each row (matched means the largest distance),
also can find the matched row for each column. And this operator only
calculate matched indices from column to row. For each instance,
the number of matched indices is the column number of the input distance
matrix.
There are two outputs, matched indices and distance.
A simple description, this algorithm matched the best (maximum distance)
row entity to the column entity and the matched indices are not duplicated
in each row of ColToRowMatchIndices. If the column entity is not matched
any row entity, set -1 in ColToRowMatchIndices.
Please note that the input DistMat can be LoDTensor (with LoD) or Tensor.
NOTE: the input DistMat can be LoDTensor (with LoD) or Tensor.
If LoDTensor with LoD, the height of ColToRowMatchIndices is batch size.
If Tensor, the height of ColToRowMatchIndices is 1.
NOTE: This API is a very low level API. It is used by :code:`ssd_loss`
layer. Please consider to use :code:`ssd_loss` instead.
Args:
dist_matrix(Variable): This input is a 2-D LoDTensor with shape
[K, M]. It is pair-wise distance matrix between the entities
represented by each row and each column. For example, assumed one
entity is A with shape [K], another entity is B with shape [M]. The
dist_matirx[i][j] is the distance between A[i] and B[j]. The bigger
the distance is, the better macthing the pairs are. Please note,
This tensor can contain LoD information to represent a batch of
inputs. One instance of this batch can contain different numbers of
entities.
dist_matrix[i][j] is the distance between A[i] and B[j]. The bigger
the distance is, the better matching the pairs are.
NOTE: This tensor can contain LoD information to represent a batch
of inputs. One instance of this batch can contain different numbers
of entities.
match_type(string|None): The type of matching method, should be
'bipartite' or 'per_prediction', 'bipartite' by defalut.
'bipartite' or 'per_prediction'. [default 'bipartite'].
dist_threshold(float|None): If `match_type` is 'per_prediction',
this threshold is to determine the extra matching bboxes based
on the maximum distance, 0.5 by defalut.
on the maximum distance, 0.5 by default.
Returns:
match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
N is the batch size. If match_indices[i][j] is -1, it
means B[j] does not match any entity in i-th instance.
Otherwise, it means B[j] is matched to row
match_indices[i][j] in i-th instance. The row number of
i-th instance is saved in match_indices[i][j].
match_distance(Variable): A 2-D Tensor with shape [N, M] in float type.
N is batch size. If match_indices[i][j] is -1,
match_distance[i][j] is also -1.0. Otherwise, assumed
match_distance[i][j] = d, and the row offsets of each instance
are called LoD. Then match_distance[i][j] = dist_matrix[d+LoD[i]][j].
tuple: a tuple with two elements is returned. The first is
matched_indices, the second is matched_distance.
The matched_indices is a 2-D Tensor with shape [N, M] in int type.
N is the batch size. If match_indices[i][j] is -1, it
means B[j] does not match any entity in i-th instance.
Otherwise, it means B[j] is matched to row
match_indices[i][j] in i-th instance. The row number of
i-th instance is saved in match_indices[i][j].
The matched_distance is a 2-D Tensor with shape [N, M] in float type
. N is batch size. If match_indices[i][j] is -1,
match_distance[i][j] is also -1.0. Otherwise, assumed
match_distance[i][j] = d, and the row offsets of each instance
are called LoD. Then match_distance[i][j] =
dist_matrix[d+LoD[i]][j].
Examples:
>>> x = fluid.layers.data(name='x', shape=[4], dtype='float32')
>>> y = fluid.layers.data(name='y', shape=[4], dtype='float32')
>>> iou = fluid.layers.iou_similarity(x=x, y=y)
>>> matched_indices, matched_dist = fluid.layers.bipartite_match(iou)
"""
helper = LayerHelper('bipartite_match', **locals())
match_indices = helper.create_tmp_variable(dtype='int32')
......@@ -364,7 +379,7 @@ def ssd_loss(location,
normalize=True,
sample_size=None):
"""
**Multi-box loss layer for object dection algorithm of SSD**
**Multi-box loss layer for object detection algorithm of SSD**
This layer is to compute dection loss for SSD given the location offset
predictions, confidence predictions, prior boxes and ground-truth boudding
......@@ -372,21 +387,35 @@ def ssd_loss(location,
is a weighted sum of the localization loss (or regression loss) and
confidence loss (or classification loss) by performing the following steps:
1. Find matched boundding box by bipartite matching algorithm.
1. Find matched bounding box by bipartite matching algorithm.
1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
1.2 Compute matched boundding box by bipartite matching algorithm.
2. Compute confidence for mining hard examples
2.1. Get the target label based on matched indices.
2.2. Compute confidence loss.
3. Apply hard example mining to get the negative example indices and update
the matched indices.
4. Assign classification and regression targets
4.1. Encoded bbox according to the prior boxes.
4.2. Assign regression targets.
4.3. Assign classification targets.
5. Compute the overall objective loss.
5.1 Compute confidence loss.
5.1 Compute localization loss.
5.3 Compute the overall weighted loss.
Args:
......@@ -421,39 +450,36 @@ def ssd_loss(location,
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
normalize (bool): Whether to normalize the SSD loss by the total number
of output locations, True by defalut.
of output locations, True by default.
sample_size (int): The max sample size of negative box, used only when
mining_type is 'hard_example'.
Returns:
Variable: The weighted sum of the localization loss and confidence loss,
with shape [N * Np, 1], N and Np are the same as they are
in `location`.
The weighted sum of the localization loss and confidence loss, with \
shape [N * Np, 1], N and Np are the same as they are in `location`.
Raises:
ValueError: If mining_type is 'hard_example', now only support
mining type of `max_negative`.
ValueError: If mining_type is 'hard_example', now only support mining \
type of `max_negative`.
Examples:
.. code-block:: python
pb = layers.data(
name='prior_box',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
pbv = layers.data(
name='prior_box_var',
shape=[10, 4],
append_batch_size=False,
dtype='float32')
loc = layers.data(name='target_box', shape=[10, 4], dtype='float32')
scores = layers.data(name='scores', shape=[10, 21], dtype='float32')
gt_box = layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
gt_label = layers.data(
name='gt_label', shape=[1], lod_level=1, dtype='float32')
loss = layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
>>> pb = fluid.layers.data(
>>> name='prior_box',
>>> shape=[10, 4],
>>> append_batch_size=False,
>>> dtype='float32')
>>> pbv = fluid.layers.data(
>>> name='prior_box_var',
>>> shape=[10, 4],
>>> append_batch_size=False,
>>> dtype='float32')
>>> loc = fluid.layers.data(name='target_box', shape=[10, 4], dtype='float32')
>>> scores = fluid.layers.data(name='scores', shape=[10, 21], dtype='float32')
>>> gt_box = fluid.layers.data(
>>> name='gt_box', shape=[4], lod_level=1, dtype='float32')
>>> gt_label = fluid.layers.data(
>>> name='gt_label', shape=[1], lod_level=1, dtype='float32')
>>> loss = fluid.layers.ssd_loss(loc, scores, gt_box, gt_label, pb, pbv)
"""
helper = LayerHelper('ssd_loss', **locals())
......
python/paddle/fluid/layers/io.py
浏览文件 @ 6c1cf609
......@@ -292,6 +292,7 @@ def _copy_reader_create_op_(block, op):
return new_op
@templatedoc(op_type='create_recordio_file_reader')
def open_recordio_file(filename,
shapes,
lod_levels,
......@@ -299,34 +300,30 @@ def open_recordio_file(filename,
pass_num=1,
for_parallel=True):
"""
Open a RecordIO file
This layer takes a RecordIO file to read from and returns a Reader Variable.
Via the Reader Variable, we can get data from the given RecordIO file.
${comment}
Args:
filename(str): The RecordIO file's name.
filename(${filename_type}): ${filename_comment}.
shapes(list): List of tuples which declaring data shapes.
lod_levels(list): List of ints which declaring data lod_level.
lod_levels(${lod_levels_type}): ${lod_levels_comment}.
dtypes(list): List of strs which declaring data type.
pass_num(int): Number of passes to run.
for_parallel(Bool): Set it as True if you are going to run
subsequent operators in parallel.
Returns:
Variable: A Reader Variable via which we can get RecordIO file data.
${out_comment}.
Examples:
.. code-block:: python
reader = fluid.layers.io.open_recordio_file(
filename='./data.recordio',
shapes=[(3,224,224), (1)],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
# Via the reader, we can use 'read_file' layer to get data:
image, label = fluid.layers.io.read_file(reader)
>>> import paddle.fluid as fluid
>>> reader = fluid.layers.io.open_recordio_file(
>>> filename='./data.recordio',
>>> shapes=[(3,224,224), (1)],
>>> lod_levels=[0, 0],
>>> dtypes=['float32', 'int64'])
>>> # Via the reader, we can use 'read_file' layer to get data:
>>> image, label = fluid.layers.io.read_file(reader)
"""
dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
shape_concat = []
......@@ -544,6 +541,9 @@ def __create_unshared_decorated_reader__(op_type, reader, attrs, name=None):
def shuffle(reader, buffer_size):
"""
Shuffle the reader.
"""
return __create_unshared_decorated_reader__(
'create_shuffle_reader', reader, {'buffer_size': int(buffer_size)})
......@@ -554,6 +554,29 @@ def batch(reader, batch_size):
def double_buffer(reader, place=None, name=None):
"""
Wrap a double buffer reader. The data will copy to target place with a
double buffer queue. If the target place is None, the place that executor
perform on will be used.
Args:
reader(Variable): the reader variable need to be wrapped.
place(Place): the place of target data. Default is the sample place of
executor perform.
name(str): Variable name. None if the user does not care.
Returns:
wrapped reader with double buffer.
Examples:
>>> reader = fluid.layers.open_files(filenames=['somefile'],
>>> shapes=[[-1, 784], [-1, 1]],
>>> dtypes=['float32', 'int64'])
>>> reader = fluid.layers.double_buffer(reader)
>>> img, label = fluid.layers.read_file(reader)
"""
attrs = dict()
if place is not None:
attrs['place'] = str(place).upper()
......@@ -587,6 +610,26 @@ def read_file(file_obj):
class Preprocessor(object):
"""
A block for data pre-processing in reader.
Args:
reader (Variable): A reader variable.
name (str, default None): The name of the reader.
Examples:
.. code-block:: python
preprocessor = fluid.layers.io.Preprocessor(reader=reader)
with preprocessor.block():
img, lbl = preprocessor.inputs()
img_out = img / 2
lbl_out = lbl + 1
preprocessor.outputs(img_out, lbl_out)
data_file = fluid.layers.io.double_buffer(preprocessor())
"""
BEFORE_SUB_BLOCK = 0
IN_SUB_BLOCK = 1
AFTER_SUB_BLOCK = 2
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 6c1cf609
......@@ -25,20 +25,72 @@ import utils
import random
__all__ = [
'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d',
'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'batch_norm',
'beam_search_decode', 'conv2d_transpose', 'sequence_expand', 'lstm_unit',
'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
'beam_search', 'row_conv', 'multiplex', 'layer_norm',
'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
'image_resize_short', 'resize_bilinear', 'gather', 'random_crop', 'mean_iou'
'fc',
'embedding',
'dynamic_lstm',
'dynamic_lstmp',
'dynamic_gru',
'gru_unit',
'linear_chain_crf',
'crf_decoding',
'cos_sim',
'cross_entropy',
'square_error_cost',
'chunk_eval',
'sequence_conv',
'conv2d',
'conv3d',
'sequence_pool',
'sequence_softmax',
'softmax',
'pool2d',
'pool3d',
'batch_norm',
'beam_search_decode',
'conv2d_transpose',
'conv3d_transpose',
'sequence_expand',
'lstm_unit',
'reduce_sum',
'reduce_mean',
'reduce_max',
'reduce_min',
'reduce_prod',
'sequence_first_step',
'sequence_last_step',
'dropout',
'split',
'ctc_greedy_decoder',
'edit_distance',
'l2_normalize',
'matmul',
'topk',
'warpctc',
'sequence_reshape',
'transpose',
'im2sequence',
'nce',
'beam_search',
'row_conv',
'multiplex',
'layer_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
'autoincreased_step_counter',
'reshape',
'lod_reset',
'lrn',
'pad',
'label_smooth',
'roi_pool',
'dice_loss',
'image_resize',
'image_resize_short',
'resize_bilinear',
'gather',
'random_crop',
'mean_iou',
]
......@@ -173,11 +225,11 @@ def embedding(input,
have two elements which indicate the size of the dictionary of
embeddings and the size of each embedding vector respectively.
is_sparse(bool): The flag indicating whether to use sparse update.
is_distributed (bool): Whether to run lookup table from remote parameter server.
is_distributed(bool): Whether to run lookup table from remote parameter server.
padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
Otherwise the given :attr:`padding_idx` indicates padding the output
with zeros whenever lookup encounters it in :attr:`input`. If
:math:`padding_idx < 0`, the padding_idx to use in lookup is
:math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is
:math:`size[0] + dim`.
param_attr(ParamAttr): Parameters for this layer
dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
......@@ -1183,14 +1235,17 @@ def conv2d(input,
act=None,
name=None):
"""
**Convlution2D Layer**
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCHW format. Where N is batch size, C is the number of
and strides, paddings, dilations, groups parameters. Input and
Output are in NCHW format, where N is batch size, C is the number of
channels, H is the height of the feature, and W is the width of the feature.
The details of convolution layer, please refer UFLDL's `convolution,
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
Filter is in MCHW format, where M is the number of output image channels,
C is the number of input image channels, H is the height of the filter,
and W is the width of the filter. If the groups is greater than 1,
C will equal the number of input image channels divided by the groups.
Please refer to UFLDL's `convolution
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
for more detials.
If bias attribution and activation type are provided, bias is added to the
output of the convolution, and the corresponding activation function is
applied to the final result.
......@@ -1201,15 +1256,14 @@ def conv2d(input,
Out = \sigma (W \\ast X + b)
In the above equation:
Where:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
......@@ -1220,6 +1274,7 @@ def conv2d(input,
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
......@@ -1231,7 +1286,7 @@ def conv2d(input,
Args:
input (Variable): The input image with [N, C, H, W] format.
num_filters(int): The number of filter. It is as same as the output
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
......@@ -1254,7 +1309,8 @@ def conv2d(input,
bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
use_mkldnn (bool): Use mkldnn kernels or not, it is valid only when compiled
with mkldnn library. Default: False
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -1275,8 +1331,6 @@ def conv2d(input,
conv2d = fluid.layers.conv2d(
input=data, num_filters=2, filter_size=3, act="relu")
"""
if stride is None:
stride = [1, 1]
num_channels = input.shape[1]
......@@ -1339,6 +1393,171 @@ def conv2d(input,
return helper.append_activation(pre_act)
def conv3d(input,
num_filters,
filter_size,
stride=1,
padding=0,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
use_mkldnn=False,
act=None,
name=None):
"""
**Convlution3D Layer**
The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCDHW format. Where N is batch size C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. Convlution3D is similar with Convlution2D
but adds one dimension(depth). If bias attribution and activation type are
provided, bias is added to the output of the convolution, and the
corresponding activation function is applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \sigma (W \\ast X + b)
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
Where
.. math::
D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups (int): The groups number of the Conv3d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr (ParamAttr): The parameters to the Conv3d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
use_mkldnn (bool): Use mkldnn kernels or not.
act (str): Activation type. Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d = fluid.layers.conv3d(
input=data, num_filters=2, filter_size=3, act="relu")
"""
l_type = 'conv3d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
num_channels = input.shape[1]
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
num_filter_channels = num_channels / groups
filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
stride = utils.convert_to_list(stride, 3, 'stride')
padding = utils.convert_to_list(padding, 3, 'padding')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
input_shape = input.shape
filter_shape = [num_filters, num_filter_channels] + filter_size
def _get_default_param_initializer():
std = (2.0 / (filter_size[0]**3 * num_channels))**0.5
return Normal(0.0, std, 0)
filter_param = helper.create_parameter(
attr=helper.param_attr,
shape=filter_shape,
dtype=dtype,
default_initializer=_get_default_param_initializer())
pre_bias = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
inputs={
'Input': input,
'Filter': filter_param,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': use_mkldnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
return helper.append_activation(pre_act)
def sequence_pool(input, pool_type):
"""
This function add the operator for sequence pooling.
......@@ -1526,12 +1745,84 @@ def pool2d(input,
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
helper = LayerHelper('pool2d', **locals())
l_type = 'pool2d'
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="pool2d",
type=l_type,
inputs={"X": input},
outputs={"Out": pool_out},
attrs={
"pooling_type": pool_type,
"ksize": pool_size,
"global_pooling": global_pooling,
"strides": pool_stride,
"paddings": pool_padding,
"use_cudnn": use_cudnn,
"ceil_mode": ceil_mode,
"use_mkldnn": use_mkldnn
})
return pool_out
def pool3d(input,
pool_size=-1,
pool_type="max",
pool_stride=1,
pool_padding=0,
global_pooling=False,
use_cudnn=True,
ceil_mode=False,
use_mkldnn=False,
name=None):
"""
This function adds the operator for pooling in 3-dimensions, using the
pooling configurations mentioned in input parameters.
Args:
input (Variable): ${input_comment}
pool_size (int): ${ksize_comment}
pool_type (str): ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling (bool): ${global_pooling_comment}
use_cudnn (bool): ${use_cudnn_comment}
ceil_mode (bool): ${ceil_mode_comment}
use_mkldnn (bool): ${use_mkldnn_comment}
name (str): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: output of pool3d layer.
"""
if pool_type not in ["max", "avg"]:
raise ValueError(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
str(pool_type))
if global_pooling is False and pool_size == -1:
raise ValueError(
"When the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: " + str(pool_size))
pool_size = utils.convert_to_list(pool_size, 3, 'pool_size')
pool_padding = utils.convert_to_list(pool_padding, 3, 'pool_padding')
pool_stride = utils.convert_to_list(pool_stride, 3, 'pool_stride')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
l_type = "pool3d"
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type=l_type,
inputs={"X": input},
outputs={"Out": pool_out},
attrs={
......@@ -1665,6 +1956,7 @@ def batch_norm(input,
return helper.append_activation(batch_norm_out)
@templatedoc()
def layer_norm(input,
scale=True,
shift=True,
......@@ -1675,20 +1967,11 @@ def layer_norm(input,
act=None,
name=None):
"""
**Layer Normalization**
Assume feature vectors exist on dimensions
:attr:`begin_norm_axis ... rank(input)` and calculate the moment statistics
along these dimensions for each feature vector :math:`a` with size
:math:`H`, then normalize each feature vector using the corresponding
statistics. After that, apply learnable gain and bias on the normalized
tensor to scale and shift if :attr:`scale` and :attr:`shift` are set.
Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_
${comment}
The formula is as follows:
.. math::
.. math::
\\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
......@@ -1696,6 +1979,15 @@ def layer_norm(input,
h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
* :math:`a`: the vector representation of the summed inputs to the neurons
in that layer.
* :math:`H`: the number of hidden units in a layers
* :math:`g`: the trainable scale parameter.
* :math:`b`: the trainable bias parameter.
Args:
input(Variable): The input tensor variable.
scale(bool): Whether to learn the adaptive gain :math:`g` after
......@@ -1714,14 +2006,13 @@ def layer_norm(input,
name (str): The name of this layer. It is optional.
Returns:
Variable: A tensor variable with the same shape as the input.
${y_comment}
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 32, 32], dtype='float32')
x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
>>> data = fluid.layers.data(name='data', shape=[3, 32, 32],
>>> dtype='float32')
>>> x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
"""
helper = LayerHelper('layer_norm', **locals())
dtype = helper.input_dtype()
......@@ -1952,6 +2243,173 @@ def conv2d_transpose(input,
return out
def conv3d_transpose(input,
num_filters,
output_size=None,
filter_size=None,
padding=0,
stride=1,
dilation=1,
groups=None,
param_attr=None,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
"""
**Convlution3D transpose layer**
The convolution3D transpose layer calculates the output based on the input,
filter, and dilations, strides, paddings. Input(Input) and output(Output)
are in NCDHW format. Where N is batch size, C is the number of channels,
D is the depth of the feature, H is the height of the feature, and W
is the width of the feature. Parameters(dilations, strides, paddings) are
two elements. These two elements represent height and width, respectively.
The details of convolution transpose layer, please refer to the following
explanation and references `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
For each input :math:`X`, the equation is:
.. math::
Out = W \\ast X
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast` : Convolution transpose operation.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
different.
Example:
- Input:
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
- Output:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
.. math::
D_{out} &= (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\\\
H_{out} &= (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\\\
W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
Args:
input(Variable): The input image with [N, C, D, H, W] format.
num_filters(int): The number of the filter. It is as same as the output
image channel.
output_size(int|tuple|None): The output image size. If output size is a
tuple, it must contain three integers, (image_D, image_H, image_W). This
parameter only works when filter_size is None.
filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
it must contain three integers, (filter_size_D, filter_size_H, filter_size_W).
Otherwise, the filter will be a square. None if use output size to
calculate filter_size.
padding(int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_D, padding_H, padding_W). Otherwise, the
padding_D = padding_H = padding_W = padding. Default: padding = 0.
stride(int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_D, stride_H, stride_W). Otherwise, the
stride_D = stride_H = stride_W = stride. Default: stride = 1.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_D, dilation_H, dilation_W). Otherwise, the
dilation_D = dilation_H = dilation_W = dilation. Default: dilation = 1.
groups(int): The groups number of the Conv3d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv3d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv3d layer. Default: None
use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act(str): Activation type. Default: None
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
Variable: The tensor variable storing the convolution transpose result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[3, 12, 32, 32], dtype='float32')
conv2d_transpose = fluid.layers.conv3d_transpose(
input=data, num_filters=2, filter_size=3)
"""
l_type = "conv3d_transpose"
helper = LayerHelper(l_type, **locals())
if not isinstance(input, Variable):
raise TypeError("Input of conv3d_transpose must be Variable")
input_channel = input.shape[1]
padding = utils.convert_to_list(padding, 3, 'padding')
stride = utils.convert_to_list(stride, 3, 'stride')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
if filter_size is None:
if output_size is None:
raise ValueError("output_size must be set when filter_size is None")
if isinstance(output_size, int):
output_size = [output_size, output_size]
d_in = input.shape[2]
h_in = input.shape[3]
w_in = input.shape[4]
filter_size_d = (output_size[0] - (d_in - 1) * stride[0] + 2 *
padding[0] - 1) / dilation[0] + 1
filter_size_h = (output_size[1] - (h_in - 1) * stride[1] + 2 *
padding[1] - 1) / dilation[1] + 1
filter_size_w = (output_size[2] - (w_in - 1) * stride[2] + 2 *
padding[2] - 1) / dilation[2] + 1
filter_size = [filter_size_d, filter_size_h, filter_size_w]
else:
filter_size = utils.convert_to_list(filter_size, 3,
'conv3d_transpose.filter_size')
groups = 1 if groups is None else groups
filter_shape = [input_channel, num_filters / groups] + filter_size
img_filter = helper.create_parameter(
dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
pre_bias = helper.create_tmp_variable(dtype=input.dtype)
helper.append_op(
type=l_type,
inputs={'Input': [input],
'Filter': [img_filter]},
outputs={'Output': pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
out = helper.append_activation(pre_act)
return out
def sequence_expand(x, y, ref_level=-1, name=None):
"""Sequence Expand Layer. This layer will expand the input variable **x**
according to specified level lod of **y**. Please note that lod level of
......@@ -3237,29 +3695,13 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
return out
@templatedoc()
def row_conv(input, future_context_size, param_attr=None, act=None):
"""Row Conv Operator. This layer will apply lookahead convolution to
**input**. The input variable should be a 2D LoDTensor with shape [T, D].
Parameters with shape [future_context_size + 1, D] will be created. The math
equation of row convolution is as follows:
.. math::
Out_{i} = \sum_{j = i} ^ {i + \\tau} X_{j} \odot W_{i - j}
In the above equation:
* :math:`Out_{i}`: The i-th row of output variable with shape [1, D].
* :math:`\\tau`: Future context size.
* :math:`X_{j}`: The j-th row of input variable with shape [1, D].
* :math:`W_{i-j}`: The (i-j)-th row of parameters with shape [1, D].
More details about row_conv please refer to the paper \
(http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and
the design document \
(https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).
"""
${comment}
Args:
input (Variable): Input variable, a 2D LoDTensor with shape [T, D].
input (${x_type}): ${x_comment}.
future_context_size (int): Future context size. Please note, the shape
of convolution kernel is [future_context_size + 1, D].
param_attr (ParamAttr): Attributes of parameters, including
......@@ -3267,14 +3709,13 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
act (str): Non-linear activation to be applied to output variable.
Returns:
Variable: The output tensor with same shape as input tensor.
${out_comment}.
Examples:
.. code-block:: python
x = fluid.layers.data(name='x', shape=[16],
dtype='float32', lod_level=1)
out = fluid.layers.row_conv(input=x, future_context_size=2)
>>> import paddle.fluid as fluid
>>> x = fluid.layers.data(name='x', shape=[16],
>>> dtype='float32', lod_level=1)
>>> out = fluid.layers.row_conv(input=x, future_context_size=2)
"""
helper = LayerHelper('row_conv', **locals())
dtype = helper.input_dtype()
......@@ -3290,42 +3731,23 @@ def row_conv(input, future_context_size, param_attr=None, act=None):
return helper.append_activation(out)
@templatedoc()
def multiplex(inputs, index):
"""
**Multiplex Layer**
Referring to the given index variable, this layer selects rows from the
input variables to construct a multiplex variable. Assuming that there are
:math:`m` input variables and :math:`I_i` represents the i-th input
variable and :math:`i` is in [0, :math:`m`). All input variables are
tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
Please note that rank of the input tensor should be at least 2. Each input
variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
* ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
variable. The given index variable should be a 2-D tensor with shape
[:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
Then the output variable will be a tensor with shape [:math:`d_0`,
:math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
${comment}
>>> import paddle.fluid as fluid
>>> x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
>>> x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
>>> index = fluid.layers.data(name='index', shape=[1], dtype='int32')
>>> out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
Args:
inputs (list): A list of variables to gather from. All variables have the
same shape and the rank is at least 2.
index (Variable): Tensor<int32>, index variable which is a 2-D tensor
with shape [M, 1] where M is the batch size.
inputs (list): ${x_comment}.
index (${ids_type}): ${ids_comment}.
Returns:
Variable: Multiplex variable gathered from input variables.
Examples:
.. code-block:: python
x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
index = fluid.layers.data(name='index', shape=[1], dtype='int32')
out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
${out_comment}.
"""
helper = LayerHelper('multiplex', **locals())
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 6c1cf609
......@@ -40,8 +40,6 @@ __activations__ = [
'relu6',
'pow',
'stanh',
'hard_shrink',
'thresholded_relu',
'hard_sigmoid',
'swish',
]
......@@ -64,11 +62,9 @@ __all__ = [
'logical_or',
'logical_xor',
'logical_not',
'uniform_random',
'uniform_random_batch_size_like',
'gaussian_random',
'gaussian_random_batch_size_like',
'cumsum',
'scatter',
'sum',
'slice',
......@@ -79,3 +75,88 @@ __all__ = [
for _OP in set(__all__):
globals()[_OP] = generate_layer_fn(_OP)
__all__ += ["uniform_random"]
_uniform_random_ = generate_layer_fn('uniform_random')
def uniform_random(shape, dtype=None, min=None, max=None, seed=None):
kwargs = dict()
for name in locals():
val = locals()[name]
if val is not None:
kwargs[name] = val
return _uniform_random_(**kwargs)
uniform_random.__doc__ = _uniform_random_.__doc__ + """
Examples:
>>> result = fluid.layers.uniform_random(shape=[32, 784])
"""
__all__ += ['hard_shrink']
_hard_shrink_ = generate_layer_fn('hard_shrink')
def hard_shrink(x, threshold=None):
kwargs = dict()
for name in locals():
val = locals()[name]
if val is not None:
kwargs[name] = val
return _hard_shrink_(**kwargs)
hard_shrink.__doc__ = _hard_shrink_.__doc__ + """
Examples:
>>> data = fluid.layers.data(name="input", shape=[784])
>>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""
__all__ += ['cumsum']
_cum_sum_ = generate_layer_fn('cumsum')
def cumsum(x, axis=None, exclusive=None, reverse=None):
kwargs = dict()
for name in locals():
val = locals()[name]
if val is not None:
kwargs[name] = val
return _cum_sum_(**kwargs)
cumsum.__doc__ = _cum_sum_.__doc__ + """
Examples:
>>> data = fluid.layers.data(name="input", shape=[32, 784])
>>> result = fluid.layers.cumsum(data, axis=0)
"""
__all__ += ['thresholded_relu']
_thresholded_relu_ = generate_layer_fn('thresholded_relu')
def thresholded_relu(x, threshold=None):
kwargs = dict()
for name in locals():
val = locals()[name]
if val is not None:
kwargs[name] = val
_thresholded_relu_(**kwargs)
thresholded_relu.__doc__ = _thresholded_relu_.__doc__ + """
Examples:
>>> data = fluid.layers.data(name="input", shape=[1])
>>> result = fluid.layers.thresholded_relu(data, threshold=0.4)
"""
python/paddle/fluid/layers/tensor.py
浏览文件 @ 6c1cf609
......@@ -6,7 +6,7 @@
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# Unlessf 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
......@@ -51,7 +51,12 @@ def create_parameter(shape,
is_bias=False,
default_initializer=None):
"""
Create a parameter
Create a parameter. The parameter is a learnable variable, which can have
gradient, and can be optimized.
NOTE: this is a very low-level API. This API is useful when you create
operator by your self. instead of using layers.
Args:
shape(list[int]): shape of the parameter
dtype(string): element type of the parameter
......@@ -63,7 +68,12 @@ def create_parameter(shape,
default_initializer(Initializer): initializer for the parameter
Returns:
Parameter: the created parameter
the created parameter.
Examples:
>>> W = fluid.layers.create_parameter(shape=[784, 200], dtype='float32')
>>> data = fluid.layers.data(name="img", shape=[64, 784], append_batch_size=False)
>>> hidden = fluid.layers.matmul(x=data, y=W)
"""
helper = LayerHelper("create_parameter", **locals())
if attr is None:
......@@ -191,6 +201,7 @@ def assign(input, output):
Examples:
.. code-block:: python
out = fluid.layers.create_tensor(dtype='float32')
hidden = fluid.layers.fc(input=data, size=10)
fluid.layers.assign(hidden, out)
......
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 6c1cf609
......@@ -76,8 +76,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
emb_layers.append(mark_embedding)
hidden_0_layers = [
fluid.layers.fc(input=emb, size=hidden_dim, act='tanh')
for emb in emb_layers
fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
]
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
......@@ -94,8 +93,8 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
for i in range(1, depth):
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim, act='tanh'),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim, act='tanh')
fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
])
lstm = fluid.layers.dynamic_lstm(
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 6c1cf609
......@@ -41,8 +41,8 @@ function(py_test_modules TARGET_NAME)
endfunction()
list(REMOVE_ITEM TEST_OPS test_warpctc_op)
list(REMOVE_ITEM TEST_OPS test_dist_train)
#list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
#list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_crf)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_fetch_feed)
# TODO(wuyi): this test hungs on CI, will add it back later
list(REMOVE_ITEM TEST_OPS test_listen_and_serv_op)
foreach(TEST_OP ${TEST_OPS})
......@@ -50,3 +50,5 @@ foreach(TEST_OP ${TEST_OPS})
endforeach(TEST_OP)
py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 6c1cf609
......@@ -157,9 +157,11 @@ class ControlFlowGraph(object):
if op.type() == "fill_constant" and op.attr("force_cpu") == True:
self._skip_opt.update(op.output_arg_names())
def release_memory(self):
def release_memory(self, skip_opt_set=None):
self._dataflow_analyze()
self._update_skip_opt_set()
if skip_opt_set:
self._skip_opt.update(skip_opt_set)
fwd_id = 0
bwd_id = 0
for i in range(self.op_size):
......@@ -183,7 +185,7 @@ class ControlFlowGraph(object):
else:
bwd_id += 1
def memory_optimize(self, level=0):
def memory_optimize(self, skip_opt_set=None, level=0):
def compare_shape(x_shape, cache_shape, opt_level):
if opt_level == 0:
return x_shape == cache_shape
......@@ -200,6 +202,9 @@ class ControlFlowGraph(object):
self._dataflow_analyze()
self._update_skip_opt_set()
# update skip set to meet users' demand
if skip_opt_set:
self._skip_opt.update(skip_opt_set)
self.pool = []
for i in range(self.op_size):
op = self._ops[i]
......@@ -358,7 +363,7 @@ def _get_cfgs(input_program):
return cfgs
def memory_optimize(input_program, print_log=False, level=0):
def memory_optimize(input_program, skip_opt_set=None, print_log=False, level=0):
"""Optimize memory by reusing var memory.
Note: it doesn't not support subblock nested in subblock.
......@@ -374,10 +379,10 @@ def memory_optimize(input_program, print_log=False, level=0):
PRINT_LOG = print_log
cfgs = _get_cfgs(input_program)
for cfg in cfgs:
cfg.memory_optimize(level)
cfg.memory_optimize(skip_opt_set=skip_opt_set, level=level)
def release_memory(input_program):
def release_memory(input_program, skip_opt_set=None):
cfgs = _get_cfgs(input_program)
for cfg in cfgs:
cfg.release_memory()
cfg.release_memory(skip_opt_set=skip_opt_set)
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