diff --git a/.travis.yml b/.travis.yml index b4b83fcdbc84ce0fb0c91c816ebc3c964acfa590..e217c8f5a740ef5ab7315656ed7839ffa219c805 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,7 +4,6 @@ cache: - $HOME/.ccache - $HOME/.cache/pip - $TRAVIS_BUILD_DIR/build/third_party - - $TRAVIS_BUILD_DIR/build_android/third_party sudo: required dist: trusty os: @@ -12,7 +11,6 @@ os: env: - JOB=build_doc - JOB=check_style - - JOB=build_android addons: apt: packages: @@ -23,7 +21,6 @@ addons: - python - python-pip - python2.7-dev - - python-numpy - python-wheel - libboost-dev - curl @@ -37,8 +34,8 @@ before_install: - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python # protobuf version. - - pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt - - pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker + - sudo pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt + - sudo pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker - curl https://glide.sh/get | bash - eval "$(GIMME_GO_VERSION=1.8.3 gimme)" - go get -u github.com/alecthomas/gometalinter diff --git a/CMakeLists.txt b/CMakeLists.txt index ad559672ad2f83a3d62cdf332b47c6cf1e730f70..08237cd850ae20c515a39c8783a18deaac431626 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,8 +65,8 @@ if(NOT CMAKE_BUILD_TYPE) endif() if(ANDROID) - if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21") - message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 21") + if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16") + message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16") endif() set(WITH_GPU OFF CACHE STRING diff --git a/Dockerfile.android b/Dockerfile.android index c0fa58c384f9ebcae60477ffce49ea4ffa929db9..452aa1574550627c2cce6375e12e154a9763254d 100644 --- a/Dockerfile.android +++ b/Dockerfile.android @@ -4,9 +4,15 @@ MAINTAINER PaddlePaddle Authors ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' +# ENV variables +ARG ANDROID_ABI + +ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"} + ENV HOME=/root \ ANDROID_NDK_HOME=/opt/android-ndk-linux \ - ANDROID_STANDALONE_TOOLCHAIN=/opt/android-toolchain-gcc + ANDROID_ARM_STANDALONE_TOOLCHAIN=/opt/arm-toolchain \ + ANDROID_ARM64_STANDALONE_TOOLCHAIN=/opt/arm64-toolchain RUN apt-get update && \ apt-get install -y \ @@ -15,12 +21,11 @@ RUN apt-get update && \ apt-get clean -y # Install Go and glide -RUN wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \ - tar -C /usr/local -xzf go.tgz && \ +RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \ + tar -xz -C /usr/local && \ mkdir /root/gopath && \ mkdir /root/gopath/bin && \ - mkdir /root/gopath/src && \ - rm go.tgz + mkdir /root/gopath/src ENV GOROOT=/usr/local/go GOPATH=/root/gopath # should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT. ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin @@ -42,7 +47,8 @@ RUN mkdir /opt/android-ndk-tmp && \ wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \ unzip -q android-ndk-r14b-linux-x86_64.zip && \ mv android-ndk-r14b ${ANDROID_NDK_HOME} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-21 --install-dir=${ANDROID_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-23 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-23 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \ rm -rf /opt/android-ndk-tmp && \ rm -rf ${ANDROID_NDK_HOME} diff --git a/cmake/cross_compiling/android.cmake b/cmake/cross_compiling/android.cmake index 5e3e437a8da9624df35a5c754fe77be73f20361d..84219cfa5587f5b765b2e8f35180797d7053169f 100644 --- a/cmake/cross_compiling/android.cmake +++ b/cmake/cross_compiling/android.cmake @@ -20,6 +20,7 @@ # The supported variables are listed belows: # # ANDROID_STANDALONE_TOOLCHAIN +# ANDROID_TOOLCHAIN # ANDROID_ABI # ANDROID_NATIVE_API_LEVEL # ANDROID_ARM_MODE @@ -57,6 +58,10 @@ IF(NOT DEFINED CMAKE_SYSTEM_VERSION AND ANDROID_NATIVE_API_LEVEL) ENDIF() ENDIF() +IF(NOT DEFINED ANDROID_TOOLCHAIN) + SET(ANDROID_TOOLCHAIN clang) +ENDIF() + IF(NOT DEFINED ANDROID_ABI) SET(ANDROID_ABI "armeabi-v7a") ENDIF() @@ -82,6 +87,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") "${CMAKE_VERSION}), when cross-compiling for Android.") IF(ANDROID_STANDALONE_TOOLCHAIN) + # Use standalone toolchain SET(CMAKE_SYSROOT "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot") IF(NOT CMAKE_SYSTEM_VERSION) @@ -96,26 +102,44 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") ENDIF() # Toolchain - SET(ANDROID_TOOLCHAIN "gcc") SET(ANDROID_TOOLCHAIN_ROOT ${ANDROID_STANDALONE_TOOLCHAIN}) - IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$") - SET(ANDROID_TOOLCHAIN_NAME arm-linux-androideabi) - IF(ANDROID_ABI STREQUAL "armeabi") - SET(CMAKE_SYSTEM_PROCESSOR armv5te) - ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a") - SET(CMAKE_SYSTEM_PROCESSOR armv7-a) - ENDIF() - ENDIF() - IF(ANDROID_ABI STREQUAL "arm64-v8a") - SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android) - SET(CMAKE_SYSTEM_PROCESSOR aarch64) + ELSE(ANDROID_NDK) + # TODO: use android ndk + ENDIF() + + IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$") + SET(ANDROID_TOOLCHAIN_NAME arm-linux-androideabi) + IF(ANDROID_ABI STREQUAL "armeabi") + SET(CMAKE_SYSTEM_PROCESSOR armv5te) + SET(ANDROID_CLANG_TRIPLE armv5te-none-linux-androideabi) + ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a") + SET(CMAKE_SYSTEM_PROCESSOR armv7-a) + SET(ANDROID_CLANG_TRIPLE armv7-none-linux-androideabi) ENDIF() - SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-") + ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a") + SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android) + SET(CMAKE_SYSTEM_PROCESSOR aarch64) + SET(ANDROID_CLANG_TRIPLE aarch64-none-linux-android) + ELSE() + MESSAGE(FATAL_ERROR "Invalid Android ABI: ${ANDROID_ABI}.") + ENDIF() + SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-") + + IF(ANDROID_TOOLCHAIN STREQUAL clang) + SET(ANDROID_C_COMPILER_NAME clang) + SET(ANDROID_CXX_COMPILER_NAME clang++) + SET(CMAKE_C_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE}) + SET(CMAKE_CXX_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE}) + ELSEIF(ANDROID_TOOLCHAIN STREQUAL gcc) + SET(ANDROID_C_COMPILER_NAME gcc) + SET(ANDROID_CXX_COMPILER_NAME g++) + ELSE() + MESSAGE(FATAL_ERROR "Invalid Android toolchain: ${ANDROID_TOOLCHAIN}") ENDIF() # C compiler IF(NOT CMAKE_C_COMPILER) - SET(ANDROID_C_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}gcc") + SET(ANDROID_C_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_C_COMPILER_NAME}") ELSE() GET_FILENAME_COMPONENT(ANDROID_C_COMPILER ${CMAKE_C_COMPILER} PROGRAM) ENDIF() @@ -125,7 +149,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") # CXX compiler IF(NOT CMAKE_CXX_COMPILER) - SET(ANDROID_CXX_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}g++") + SET(ANDROID_CXX_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_CXX_COMPILER_NAME}") ELSE() GET_FILENAME_COMPONENT(ANDROID_CXX_COMPILER ${CMAKE_CXX_COMPILER} PROGRAM) ENDIF() @@ -137,7 +161,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") SET(CMAKE_CXX_COMPILER ${ANDROID_CXX_COMPILER} CACHE PATH "CXX compiler" FORCE) # Toolchain and ABI specific flags. - SET(ANDROID_COMPILER_FLAGS "-ffunction-sections -fdata-sections -finline-limit=64") + SET(ANDROID_COMPILER_FLAGS "-ffunction-sections -fdata-sections") SET(ANDROID_LINKER_FLAGS "-Wl,--gc-sections") IF(ANDROID_ABI STREQUAL "armeabi") @@ -145,8 +169,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") -march=armv5te -mtune=xscale -msoft-float) - ENDIF() - IF(ANDROID_ABI STREQUAL "armeabi-v7a") + ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a") LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv7-a -mfloat-abi=softfp) @@ -156,6 +179,8 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") LIST(APPEND ANDROID_COMPILER_FLAGS -mfpu=vfpv3-d16) ENDIF() LIST(APPEND ANDROID_LINKER_FLAGS -Wl,--fix-cortex-a8) + ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a") + LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a) ENDIF() IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$") @@ -164,10 +189,18 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0") ELSE() LIST(APPEND ANDROID_COMPILER_FLAGS -mthumb) ENDIF() + IF(ANDROID_TOOLCHAIN STREQUAL clang) + # Disable integrated-as for better compatibility. + LIST(APPEND ANDROID_COMPILER_FLAGS -fno-integrated-as) + ENDIF() ENDIF() - IF(ANDROID_ABI STREQUAL "arm64-v8a") - LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a) + IF(ANDROID_TOOLCHAIN STREQUAL clang) + # CMake automatically forwards all compiler flags to the linker, + # and clang doesn't like having -Wa flags being used for linking. + # To prevent CMake from doing this would require meddling with + # the CMAKE__COMPILE_OBJECT rules, which would get quite messy. + LIST(APPEND ANDROID_LINKER_FLAGS -Qunused-arguments) ENDIF() STRING(REPLACE ";" " " ANDROID_COMPILER_FLAGS "${ANDROID_COMPILER_FLAGS}") diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 0002a470d90f722e3f9106ca56d70e6bf2cea339..f9e05af59fed7a8ad049390eda2c94d8577db1e7 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -12,6 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +IF(USE_EIGEN_FOR_BLAS) + return() +ENDIF(USE_EIGEN_FOR_BLAS) + INCLUDE(cblas) IF(NOT ${CBLAS_FOUND}) diff --git a/doc/design/functions_operators_layers.md b/doc/design/functions_operators_layers.md index 7a2e8fd0ace2e3f4462b15215de22c31e944b7cb..d23ba56b5773a36d448a99e4abdebc1475ed789c 100644 --- a/doc/design/functions_operators_layers.md +++ b/doc/design/functions_operators_layers.md @@ -86,12 +86,13 @@ def layer.fc(X): We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example: -``` + | C++ functions/functors | mul | add | | | +|------------------------|--------------|--------------|-------------|----------| | C++ operator class | mulOp | addOp | FCOp | | | Python binding | operator.mul | operator.add | operator.fc | | | Python function | | | | layer.fc | -``` + This is how we differentiate layer and operators in PaddlePaddle: diff --git a/doc/design/graph.md b/doc/design/graph.md index 87f696f90f164a639ad5182823ddfb14aab7e065..51b7f87638f8ddff752328a562fe0dd0fe56cfd1 100644 --- a/doc/design/graph.md +++ b/doc/design/graph.md @@ -1,4 +1,4 @@ -# Design Doc: Computations as Graphs +# Design Doc: Computations as a Graph A primary goal of the refactorization of PaddlePaddle is a more flexible representation of deep learning computation, in particular, a graph of operators and variables, instead of sequences of layers as before. @@ -8,6 +8,8 @@ This document explains that the construction of a graph as three steps: - construct the backward part - construct the optimization part +## The Construction of a Graph + Let us take the problem of image classification as a simple example. The application program that trains the model looks like: ```python @@ -25,7 +27,9 @@ The first four lines of above program build the forward part of the graph. ![](images/graph_construction_example_forward_only.png) -In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b. +In particular, the first line `x = layer.data("images")` creates variable x and a Feed operator that copies a column from the minibatch to x. `y = layer.fc(x)` creates not only the FC operator and output variable y, but also two parameters, W and b, and the initialization operators. + +Initialization operators are kind of "run-once" operators -- the `Run` method increments a class data member counter so to run at most once. By doing so, a parameter wouldn't be initialized repeatedly, say, in every minibatch. In this example, all operators are created as `OpDesc` protobuf messages, and all variables are `VarDesc`. These protobuf messages are saved in a `BlockDesc` protobuf message. @@ -49,3 +53,18 @@ According to the chain rule of gradient computation, `ConstructBackwardGraph` wo For each parameter, like W and b created by `layer.fc`, marked as double circles in above graphs, `ConstructOptimizationGraph` creates an optimization operator to apply its gradient. Here results in the complete graph: ![](images/graph_construction_example_all.png) + +## Block and Graph + +The word block and graph are interchangable in the desgin of PaddlePaddle. A [Block[(https://github.com/PaddlePaddle/Paddle/pull/3708) is a metaphore of the code and local variables in a pair of curly braces in programming languages, where operators are like statements or instructions. A graph of operators and variables is a representation of the block. + +A Block keeps operators in an array `BlockDesc::ops` + +```protobuf +message BlockDesc { + repeated OpDesc ops = 1; + repeated VarDesc vars = 2; +} +``` + +in the order that there appear in user programs, like the Python program at the beginning of this article. We can imagine that in `ops`, we have some forward operators, followed by some gradient operators, and then some optimization operators. diff --git a/doc/design/images/graph_construction_example.dot b/doc/design/images/graph_construction_example.dot index bedb6de0111a8ccab4030d034d65cf72705fc25a..8d1b673abf6b78c851676fa379dc850c4818f0e5 100644 --- a/doc/design/images/graph_construction_example.dot +++ b/doc/design/images/graph_construction_example.dot @@ -2,6 +2,8 @@ digraph ImageClassificationGraph { ///////// The forward part ///////// FeedX [label="Feed", color=blue, shape=box]; FeedY [label="Feed", color=blue, shape=box]; + InitW [label="Init", color=blue, shape=diamond]; + Initb [label="Init", color=blue, shape=diamond]; FC [label="FC", color=blue, shape=box]; MSE [label="MSE", color=blue, shape=box]; @@ -14,6 +16,8 @@ digraph ImageClassificationGraph { FeedX -> x -> FC -> y -> MSE -> cost [color=blue]; FeedY -> l [color=blue]; + InitW -> W [color=blue]; + Initb -> b [color=blue]; W -> FC [color=blue]; b -> FC [color=blue]; l -> MSE [color=blue]; diff --git a/doc/design/images/graph_construction_example_all.png b/doc/design/images/graph_construction_example_all.png index 18d8330b60e12720bb993c8cf588d64ff8db1ea9..181187503472d15779b87284105841168b3945c4 100644 Binary files a/doc/design/images/graph_construction_example_all.png and b/doc/design/images/graph_construction_example_all.png differ diff --git a/doc/design/images/graph_construction_example_forward_backward.png b/doc/design/images/graph_construction_example_forward_backward.png index 61c3a02a04bc8891ab5b921a889829bcce386df8..3049a9315fd616464dec54e33064cb75598ca536 100644 Binary files a/doc/design/images/graph_construction_example_forward_backward.png and b/doc/design/images/graph_construction_example_forward_backward.png differ diff --git a/doc/design/images/graph_construction_example_forward_only.png b/doc/design/images/graph_construction_example_forward_only.png index 14805df11fc09f64d6bc17f5e969f1400d615148..25d19088cbf0b5f68cf734f2ff21eba8af4a2860 100644 Binary files a/doc/design/images/graph_construction_example_forward_only.png and b/doc/design/images/graph_construction_example_forward_only.png differ diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md new file mode 100644 index 0000000000000000000000000000000000000000..fa3c5d7990213cf2b0d236e66e592dd2699da876 --- /dev/null +++ b/doc/design/ops/dist_train.md @@ -0,0 +1,106 @@ +# Design Doc: Operation Graph Based Parameter Server + +## Abstract + +We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and +the parameter server: they both run subgraphs, but subgraphs of +different purposes. + +## Background + +The previous implementations of the parameter server does not run a +subgraph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server. + +It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given that after the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension. + +## Design + +### Graph Converter + +The *graph converter* converts the user-defined operation (OP) graph +into subgraphs to be scheduled on different nodes with the following +steps: + +1. OP placement: the OPs will be placed on different nodes according + to heuristic that minimizes estimated total computation + time. Currently we will use a simple heuristic that puts parameter + varable on parameter server workers and everything else on trainer + workers. + +1. Add communication OPs to enable the communication between nodes. + +We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. + +Below is an example of converting the user defined graph to the +subgraphs for the trainer and the parameter server: + + + +After converting: + + + +1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. +1. Operators are added to the subgraphs. + - *Send* sends data to the connected *Recv* operator. The + scheduler on the receive node will only schedule *Recv* operator + to run when the *Send* operator has ran (the *Send* OP will mark + the *Recv* OP runnable automatically). + - *Enueue* enqueues the input variable, it can block until space + become available in the queue. + - *Dequeue* outputs configurable numbers of tensors from the + queue. It will block until the queue have the required number of + tensors. + + +### Benefits + +- Model parallelism become easier to implement: it's an extension to + the trainer - parameter server approach. we already have the + communication OPs, but need to extend the graph converter's + placement functionality. + +- User-defined optimizer is easier to add - user can now express it as + a subgraph. + +- No more duplication logic inside the trainer and the parameter + server mentioned in the background section. + +### Challenges + +- It might be hard for the graph converter to cut a general graph + (without any hint for which subgraph is the optimizer). We may need + to label which subgraph inside the OP graph is the optimizer. + +- It's important to balance the parameter shards of on multiple + parameter server. If a single parameter is very big (some + word-embedding, fully connected, softmax layer), we need to + automatically partition the single parameter onto different + parameter servers when possible (only element-wise optimizer depends + on the parameter variable). + +### Discussion + +- In the "Aync SGD" figure, the "W" variable on the parameter server + could be read and wrote concurrently, what is our locking strategy? + E.g., each variable have a lock cpp method to be invoked by every + OP, or, have a lock OP. + +- Can the Enqueue OP be implemented under our current tensor design + (puts the input tensor into the queue tensor)? + +- *Dequeue* OP will have variable numbers of output (depends on the + `min_count` attribute), does our current design support it? (similar + question for the *Add* OP) + + +### References: +[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf) diff --git a/doc/design/ops/src/dist-graph.graffle b/doc/design/ops/src/dist-graph.graffle new file mode 100644 index 0000000000000000000000000000000000000000..941399c6ced8d5f65b6c595522b770c88259df4b Binary files /dev/null and b/doc/design/ops/src/dist-graph.graffle differ diff --git a/doc/design/ops/src/dist-graph.png b/doc/design/ops/src/dist-graph.png new file mode 100644 index 0000000000000000000000000000000000000000..3546b09f1c2ee3e4f60f519d5e47f823f08051a7 Binary files /dev/null and b/doc/design/ops/src/dist-graph.png differ diff --git a/doc/design/ops/src/local-graph.graffle b/doc/design/ops/src/local-graph.graffle new file mode 100644 index 0000000000000000000000000000000000000000..19e509bd9af3c1e9a3f5e0f16ddd281457a339c5 Binary files /dev/null and b/doc/design/ops/src/local-graph.graffle differ diff --git a/doc/design/ops/src/local-graph.png b/doc/design/ops/src/local-graph.png new file mode 100644 index 0000000000000000000000000000000000000000..ada51200f793a9bb18911e7d63cfdb3244b967d7 Binary files /dev/null and b/doc/design/ops/src/local-graph.png differ diff --git a/doc/design/simple_op_design.md b/doc/design/simple_op_design.md index 5e07c29c56d21728599195d420d3222213d77e7c..fded4a68612396a262121a5a886a8ae573dfa662 100644 --- a/doc/design/simple_op_design.md +++ b/doc/design/simple_op_design.md @@ -147,7 +147,7 @@ class CosineOp { struct CosineOpProtoMaker : public OpProtoMaker { CosineOpProtoMaker(OpProto* proto) : OpProtoMaker(proto) { AddInput("input", "input of cosine op"); - AddAttr("scale", "scale of cosine op", float).Default(1.0).LargerThan(0.0); + AddAttr("scale", "scale of cosine op", float).Default(1.0).GreaterThan(0.0); AddType("cos"); AddComment("This is cos op"); } diff --git a/doc/design/var_desc.md b/doc/design/var_desc.md new file mode 100644 index 0000000000000000000000000000000000000000..86a95c10d5729704f86c285c9fe92db0cf2158be --- /dev/null +++ b/doc/design/var_desc.md @@ -0,0 +1,124 @@ +## Background +PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime. + +PaddlePaddle use proto message to describe compile time graph for + +1. Computation graph should be able to be saved to a file. +1. In distributed training, the graph will be serialized and send to multiple workers. + +The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below. + +| |compile time|runtime| +|---|---|---| +|Data|VarDesc(proto)|Variable(cpp)| +|Operation|OpDesc(proto)|Operator(cpp)| + + +## Definition of VarDesc + +A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it. + +```proto +message VarDesc { + required string name = 1; + optional LoDTensorDesc lod_tensor = 2; +} +``` + +## Definition of LodTensorDesc + +```proto +enum DataType { + BOOL = 0; + INT16 = 1; + INT32 = 2; + INT64 = 3; + FP16 = 4; + FP32 = 5; + FP64 = 6; +} + +message LoDTensorDesc { + required DataType data_type = 1; + repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480] + optional int32 lod_level = 3 [default=0]; +} +``` + +## Definition of Variable in Python + +In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable. + +```python +image = Variable(dims=[-1, 640, 480]) +# fc1 and fc2 are both Variable +fc1 = layer.fc(input=image, output_size=10) +fc2 = layer.fc(input=fc1, output_size=20) +``` +### what should class `Variable` Have +1. `name`.a name of string type is used to mark the value of the Variable. +1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator. +1. `operator`. Variable should record which operator produce itself. The reaon is: + - we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable. + +In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph. + +```python +import VarDesc +import LoDTensorDesc +import framework + +def AddInitialOperator(variable, initializer): + # add an initialize Operator to block to init this Variable + +class Variable(object): + def __init__(self, name, dims, type, initializer): + self._block = get_default_block() + self._name = name + self.op = None + + tensor_desc = LoDTensorDesc(data_type=type, dims=dims) + _var_desc = VarDesc(name=name, lod_tensor=tensor_desc) + self._var = framework.CreateVar(_var_desc) + self._block.add_var(self) + + # add initial op according to initializer + if initializer is not None: + AddInitialOperator(self, initializer) + + def dims(self): + return self._var.dims() + + def data_type(self): + return self._var.data_type() + + def to_proto(self): + pass +``` + +Then we can use this Variable to create a fc layer in Python. + +```python +import paddle as pd + +def flatten_size(X, num_flatten_dims): + prod = 1 # of last num_flatten_dims + for i in xrange(num_flatten_dims): + prod = prod * X.dims[-i-1] + return prod + +def layer.fc(X, output_size, num_flatten_dims): + W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size]) + b = Variable(pd.random_uniform(), type=FP32, dims=[output_size]) + out = Variable(type=FP32) + y = operator.fc(X, W, b, output=out) # fc will put fc op input into out + pd.InferShape(y) + return out + +x = Variable(dims=[-1, 640, 480]) +y = layer.fc(x, output_size=100) +z = layer.fc(y, output_size=200) + +paddle.eval(targets=[z], ...) +print(z) +``` diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index e3bee32f8eeac0b2db9e15430fd7c950c6fc777a..58665e9f2b6299ec3959ed6858ab01d459f64dd8 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -23,17 +23,20 @@ - `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。 - `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成 -依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下： +依据是否包含kernel，可以将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下： - - 内容 | 定义位置 --------------- | :---------------------- + + 内容 | 定义位置 +-------------- | :---------------------- OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake -Op定义 | `.cc`文件 -Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。 -注册Op | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件 - - +Op定义 | `.cc`文件 +Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，GPU 实现在`.cu`文件中。 +注册Op | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，GPU实现在`.cu`文件中 + + +实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。 + + 下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。 @@ -42,9 +45,11 @@ Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在` ### 1. 定义ProtoMaker类 -矩阵乘的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释： - -``` +矩阵乘法的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。 + +首先定义`ProtoMaker`来描述该Op的输入、输出，并添加注释： + +```cpp class MulOpMaker : public framework::OpProtoAndCheckerMaker { public: MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -59,20 +64,20 @@ The equation is: Out = X * Y } }; ``` - -[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个： + +[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数含有2个参数： - `framework::OpProto` ： 前者存储Op的输入输出和参数属性，将用于Python API接口的生成。 - `framework::OpAttrChecker` ：后者用于检查参数属性的合法性。 - -构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加该Op的注释，这些函数会将对应内容添加到`OpProto`中。 -在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，该命名尽可能的规范。 +构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加Op的注释。这些函数会将对应内容添加到`OpProto`中。 - -再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子： - -``` +上面的代码在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，命名请遵守命名规范。 + + +再以[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)为例： + +```cpp template class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -87,17 +92,19 @@ The equation is: Out = scale*X } }; ``` - - 在这个例子里，两处不同： - - - `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。 - - `AddAttr("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。 - + +这个例子有两处不同： + +- `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中，如果Op的某个输入不参与反向梯度的计算，请显示地调用`.NotInGradient()`进行设置。 + +- `AddAttr("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。 + ### 2. 定义Operator类 +下面的点实现了MulOp的定义： -```c++ +```cpp class MulOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -121,33 +128,46 @@ class MulOp : public framework::OperatorWithKernel { ``` [`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel`。`public`成员： - -```c++ + +```cpp using framework::OperatorWithKernel::OperatorWithKernel; ``` 这句表示使用基类`OperatorWithKernel`的构造函数，也可写成： - -```c++ + +```cpp MulOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, const framework::AttributeMap &attrs) : OperatorWithKernel(type, inputs, outputs, attrs) {} -``` - +``` + 还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是： - 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。 - 2). 设置输出Tensor的形状。 -通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中 +通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和下面将要介绍的注册函数一起放在`.cc`中 ### 3. 定义OpKernel类 -```C++ -template -class MulKernel : public framework::OpKernel { - public: +`MulKernel`继承自`framework::OpKernel`，带有下面两个模板参数: + +- `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 + +- `typename T` : 表示数据类型，如`float`, `double`等。 + +需要为`MulKernel`类重写`Compute`接口。 +- `Compute`接受一个输入参数：`const framework::ExecutionContext& context`。 +- 与`InferShapeContext`相比，`ExecutionContext`增加了设备类型，同样可获取到输入输出和属性参数。 +- `Compute`函数里实现`OpKernel`的具体计算逻辑。 + +下面是 `MulKernel` `Compute`的实现： + + ```cpp + template + class MulKernel : public framework::OpKernel { + public: void Compute(const framework::ExecutionContext& context) const override { auto* X = context.Input("X"); auto* Y = context.Input("Y"); @@ -157,141 +177,136 @@ class MulKernel : public framework::OpKernel { const_cast(context.device_context_); math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); } -}; -``` + }; + ``` -`MulKernel`继承自`framework::OpKernel`，带有模板参数: +需要注意：**不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。** - - `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 - - - `typename T` : 表示数据类型，如`float`, `double`等。 - -`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。 - -注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 +`MulOp`的CPU、GPU实现共享同一个`Kernel`。`OpKernel`不共享的例子可以参考：[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 + +为了使`OpKernel`的计算过程书写更加简单，并且CPU、GPU的代码可以复用，我们通常借助 Eigen unsupported Tensor模块来实现`Compute`接口。关于在PaddlePaddle中如何使用Eigen库，请参考[使用文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md)。 + + +到此，前向Op实现完成。接下来，需要在`.cc`文件中注册该op和kernel。 +反向Op类的定义，反向OpKernel的定义与前向Op类似，这里不再赘述。**但需注意反向Op没有`ProtoMaker`**。 -为了使得`OpKernel`的计算过程书写较为简单，CPU、GPU的代码可以复用，我们通常借助Eigen unsupported Tensor模块来实现。关于在paddle中如何使用Eigen库，请参考对应的使用[文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md) - -到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。 - ### 4. 注册Operator -在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 +- 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 + + ```cpp + namespace ops = paddle::operators; + REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); + REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); + REGISTER_OP_CPU_KERNEL(mul_grad, + ops::MulGradKernel); + ``` + + 在上面的代码中： + + - `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。 -```c++ -namespace ops = paddle::operators; -REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); -REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); -REGISTER_OP_CPU_KERNEL(mul_grad, - ops::MulGradKernel); -``` - - - `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。 - -在 `.cu`文件中注册GPU Kernel。请注意，如果GPU Kernel的实现是基于Eigen unsupported模块，那么在 `.cu`的最前面请加上宏定义 `#define EIGEN_USE_GPU` - -```c++ -// if use Eigen unsupported module before include head files -#define EIGEN_USE_GPU - -namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); -REGISTER_OP_GPU_KERNEL(mul_grad, - ops::MulGradKernel); -``` + +- 在 `.cu`文件中注册GPU Kernel。 + - 请注意，如果GPU Kernel的实现基于Eigen unsupported模块，那么在 `.cu`的开始请加上宏定义 `#define EIGEN_USE_GPU`，代码示例如下： + + ```cpp + // if use Eigen unsupported module before include head files + #define EIGEN_USE_GPU + + namespace ops = paddle::operators; + REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); + REGISTER_OP_GPU_KERNEL(mul_grad, + ops::MulGradKernel); + ``` ### 5. 编译 -在[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件中添加编译。 - -``` -op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) -``` - -下面命令可以编译： - -``` -make mul_op -``` +- 简单**无特殊依赖**的OP无需修改CMakeList.txt文件。[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt) 会自动将 `paddle/operators` 目录下新增的 `*_op.cc` 文件加入编译。 +- 较为复杂、**有额外依赖** 的operator仍需要修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)。如，`mul_op` 依赖 `math_function`，需要在`CMakeLists.txt`中添加如下内容： + + ``` + op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) + + ``` + +- 运行下面命令可以进行编译： + + ``` + make mul_op + ``` ## 绑定Python -- 绑定Python - - 在 [`paddle/pybind/pybind.cc -`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类： +- 绑定Python + + 在 [`paddle/pybind/pybind.cc +`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) 使用`USE_OP`告知编译器需要链接的Op，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。 ``` USE_OP(mul); ``` 如果只实现了CPU版本，则使用`USE_CPU_ONLY_OP`: - + ``` USE_CPU_ONLY_OP(gather); ``` - + 如果OP不带Kernel，则使用`USE_NO_KENREL_OP`: - + ``` USE_NO_KENREL_OP(recurrent); ``` - - 使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。 - - + + - 生成库 - 在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中，使得该Op可以链接到生成的lib库中。 - - ``` - if(WITH_PYTHON) - cc_library(paddle_pybind SHARED - SRCS pybind.cc - DEPS pybind python backward - mul_op - minus_op) - endif(WITH_PYTHON) - ``` + 无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。 ## 实现单元测试 -单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。 +单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。 -### 前向Operator单测 +### 前向Operator单元测试 -前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。 +前向Op单元测试继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`。各项更加具体的单元测试在`OpTestMeta`里完成。测试前向Operator，需要： -``` -import unittest -import numpy as np -from gradient_checker import GradientChecker, create_op -from op_test_util import OpTestMeta +1. 在`setUp`函数定义输入、输出，以及相关的属性参数。 +2. 生成随机的输入数据。 +3. 在Python脚本中实现与前向operator相同的计算逻辑，得到输出值，与operator前向计算的输出进行对比。 -class TestMulOp(unittest.TestCase): - __metaclass__ = OpTestMeta - def setUp(self): - self.type = "mul" - self.inputs = { - 'X': np.random.random((32, 84)).astype("float32"), - 'Y': np.random.random((84, 100)).astype("float32") - } - self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} -``` - 首先需要`import`必要的包,下面详细解释其他值： - - - `self.type = "mul" ` : 定义类型，和注册的类型一致。 - - `self.inputs` : 定义输入，类型为Numpy.array，并初始化。 - - `self.outputs` : 定义输出，并得到Python结算结果。 + ```python + import unittest + import numpy as np + from gradient_checker import GradientChecker, create_op + from op_test_util import OpTestMeta - -### 反向Operator单测 + class TestMulOp(unittest.TestCase): + __metaclass__ = OpTestMeta -反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。 + def setUp(self): + self.type = "mul" + self.inputs = { + 'X': np.random.random((32, 84)).astype("float32"), + 'Y': np.random.random((84, 100)).astype("float32") + } + self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} + ``` -``` +上面的代码首先导入依赖的包，下面是对`setUp`函数中操作的重要变量的详细解释： + +- `self.type = "mul" ` : 定义类型，与operator注册时注册的类型一致。 +- `self.inputs` : 定义输入，类型为`numpy.array`，并初始化。 +- `self.outputs` : 定义输出，并在Python脚本中完成与operator同样的计算逻辑，返回Python端的计算结果。 + + +### 反向Operator单元测试 + +反向Op单元测试继承自`GradientChecker`，而`GradientChecker`继承自`unittest.TestCase`，因此，**反向单元测试函数需要以`test_`开头**。 + +```python class TestMulGradOp(GradientChecker): def setUp(self): self.op = create_op("mul") @@ -325,33 +340,34 @@ class TestMulGradOp(GradientChecker): no_grad_set={"Y"}) ``` -下面解释一些关键的地方: +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`检查梯度稳定性，这里采用数值法检测梯度正确性。 - - 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`分支测试只需要计算一个输入梯度的情况。 +- 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。 -### 编译和执行 +### 编译和执行单元测试 -单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译： +单元测试编写完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容，将单元测试加入工程： ``` py_test(test_mul_op SRCS test_mul_op.py) ``` -编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`，编译成功之后执行单测命令为： +请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单元测试： -``` +```bash make test ARGS="-R test_mul_op -V" ``` + 或者: -``` +```bash ctest -R test_mul_op ``` diff --git a/paddle/cuda/include/hl_cpu_gru.cuh b/paddle/cuda/include/hl_cpu_gru.cuh index c0a37ced2a72a1ab410025e2aa45313c23f1349a..e4f6bf42c61694e9826a127c9628730cfd43ada7 100644 --- a/paddle/cuda/include/hl_cpu_gru.cuh +++ b/paddle/cuda/include/hl_cpu_gru.cuh @@ -18,14 +18,6 @@ limitations under the License. */ #ifndef __NVCC__ -#include "paddle/math/MathFunctions.h" - -#ifndef PADDLE_TYPE_DOUBLE -#define CBLAS_GEMM paddle::gemm -#else -#define CBLAS_GEMM paddle::gemm -#endif - template void hl_naive_gru_forward_reset_output(OpResetOutput opResetOutput, real *gateValue, @@ -210,51 +202,6 @@ inline void forward_final_output(OpFinalOutput opFinalOutput, } } -template -void hl_cpu_gru_forward(OpResetOutput opResetOutput, - OpFinalOutput opFinalOutput, - hl_gru_value value, - int frameSize, - int batchSize, - hl_activation_mode_t active_node, - hl_activation_mode_t active_gate) { - if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - 2 * frameSize, - frameSize, - 1, - value.prevOutValue, - frameSize, - value.gateWeight, - frameSize * 2, - 1, - value.gateValue, - frameSize * 3); - } - - forward_reset_output(opResetOutput, value, frameSize, batchSize, active_gate); - - if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - frameSize, - frameSize, - 1, - value.resetOutputValue, - frameSize, - value.stateWeight, - frameSize, - 1, - value.gateValue + frameSize * 2, - frameSize * 3); - } - - forward_final_output(opFinalOutput, value, frameSize, batchSize, active_node); -} - template void hl_naive_gru_backward_state_grad(OpStateGrad opStateGrad, real *gateValue, @@ -525,86 +472,6 @@ inline void backward_reset_grad(OpResetGrad opResetGrad, } } -template -void hl_cpu_gru_backward(OpStateGrad opStateGrad, - OpResetGrad opResetGrad, - hl_gru_value value, - hl_gru_grad grad, - int frameSize, - int batchSize, - hl_activation_mode_t active_node, - hl_activation_mode_t active_gate) { - backward_state_grad(opStateGrad, value, grad, - frameSize, batchSize, active_node); - - if (value.prevOutValue && grad.prevOutGrad) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize, - 1, - grad.gateGrad + frameSize * 2, - frameSize * 3, - value.stateWeight, - frameSize, - 0, - grad.resetOutputGrad, - frameSize); - - if (grad.stateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize, - batchSize, - 1, - value.resetOutputValue, - frameSize, - grad.gateGrad + frameSize * 2, - frameSize * 3, - 1, - grad.stateWeightGrad, - frameSize); - } - } - - backward_reset_grad(opResetGrad, value, grad, - frameSize, batchSize, active_gate); - - if (grad.prevOutGrad && value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize * 2, - 1, - grad.gateGrad, - frameSize * 3, - value.gateWeight, - frameSize * 2, - 1, - grad.prevOutGrad, - frameSize); - - if (grad.gateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize * 2, - batchSize, - 1, - value.prevOutValue, - frameSize, - grad.gateGrad, - frameSize * 3, - 1, - grad.gateWeightGrad, - frameSize * 2); - } - } -} - #endif #endif // HL_CPU_GRU_CUH_ diff --git a/paddle/framework/attribute.h b/paddle/framework/attribute.h index 071879a9d453377ccc2e9e71b62e8568a7ef1c9b..2b788a76cafe198abb9aed8ba842e37cc6ff73a6 100644 --- a/paddle/framework/attribute.h +++ b/paddle/framework/attribute.h @@ -41,11 +41,23 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc); // check whether a value(attribute) fit a certain limit template -class LargerThanChecker { +class GreaterThanChecker { public: - explicit LargerThanChecker(T lower_bound) : lower_bound_(lower_bound) {} + explicit GreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {} void operator()(T& value) const { - PADDLE_ENFORCE(value > lower_bound_, "larger_than check fail"); + PADDLE_ENFORCE(value > lower_bound_, "larger_than check fails."); + } + + private: + T lower_bound_; +}; + +template +class EqualGreaterThanChecker { + public: + explicit EqualGreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {} + void operator()(T& value) const { + PADDLE_ENFORCE_GE(value, lower_bound_, "equal_larger_than check fails."); } private: @@ -110,8 +122,13 @@ class TypedAttrChecker { return *this; } - TypedAttrChecker& LargerThan(const T& lower_bound) { - value_checkers_.push_back(LargerThanChecker(lower_bound)); + TypedAttrChecker& GreaterThan(const T& lower_bound) { + value_checkers_.push_back(GreaterThanChecker(lower_bound)); + return *this; + } + + TypedAttrChecker& EqualGreaterThan(const T& lower_bound) { + value_checkers_.push_back(EqualGreaterThanChecker(lower_bound)); return *this; } diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index 8aa6728a95bc464ab8884986f0cec6c817d3303b..c762811dfc190b255e0a3389885a081ce8315caf 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -2,20 +2,20 @@ ## Motivation -In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass. - +In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the gradient operators/expressions together with the chain rule. Every forward network needs a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass. + ## Backward Operator Registry -A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. +A backward network is built up with several backward operators. Backward operators take forward operators' inputs outputs, and output gradients and then calculate its input gradients. | | forward operator | backward operator | ---------------------- | ---------------- |------------------------- | | **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients | | **Operator::outputs_** | Outputs | InputGradients | - In most cases, there is a one-to-one correspondence between forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced. + In most cases, there is a one-to-one correspondence between the forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced. -For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro: +For example, we have got a `mul_op`, and we can register its information and corresponding backward operator by the following macro: ```cpp REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad); @@ -27,17 +27,17 @@ REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad); ## Backward Opeartor Creating -Given a certain forward operator, we can get its corresponding backward opeartor by calling: +Given a certain forward operator, we can get its corresponding backward operator by calling: ```cpp OperatorBase* bwd_op = BuildGradOp(const OperatorBase* fwd_op); -``` +``` The function `BuildGradOp` will sequentially execute following processes: 1. Get the `type_` of given forward operator, and then get the corresponding backward operator's type by looking up the `OpInfoMap`. -2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these are not necessary for gradient computing. +2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these, are not necessary for gradient computing. 3. Add forward inputs' gradient variables into map `output`, adding forward outputs' gradient variables into map `input`. @@ -49,31 +49,31 @@ A backward network is a series of backward operators. The main idea of building In our design, the network itself is also a kind of operator. So the operators contained by a big network may be some small network. -given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`,`InputGradients`. +given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`, `InputGradients`. 1. Op - when the input forward network is a Op, return its gradient Operator Immediately. + when the input forward network is an Op, return its gradient Operator Immediately. 2. NetOp - when the input forward network is a NetOp, it need to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to forward NetOp. + when the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp. - **shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwirte their shared input variable. + **shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwrite their shared input variable.

-
+
- 1. shared variable in two operators. + 1. Shared variable in operators.

- Share variable between operators or same input variable used in multiple operators lead to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively, and add a generic add operator replace the overwirte links. + Share variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator replace the overwrite links.

-
+
- 2. replace shared variable gradient with `Add` Operator + 2. Replace shared variable's gradient with `Add` operator.

diff --git a/paddle/framework/ddim.cc b/paddle/framework/ddim.cc index 85b7de79743bb0390d66b8999f2e8342a51d14a9..fc3d508553c0e966978b28d58127bdbff10d45f1 100644 --- a/paddle/framework/ddim.cc +++ b/paddle/framework/ddim.cc @@ -283,5 +283,14 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) { DDim::DDim(std::initializer_list init_list) { *this = make_ddim(init_list); } + +DDim flatten_to_2d(const DDim& src, int num_col_dims) { + int rank = src.size(); + return make_ddim({product(slice_ddim(src, 0, num_col_dims)), + product(slice_ddim(src, num_col_dims, rank))}); +} + +DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); } + } // namespace framework } // namespace paddle diff --git a/paddle/framework/ddim.h b/paddle/framework/ddim.h index db30c523948b1d437615aa0e9bfecb5e25569296..ca29e7e8c7776de6adf3e3b0e8f11f0d4d8487c3 100644 --- a/paddle/framework/ddim.h +++ b/paddle/framework/ddim.h @@ -115,6 +115,12 @@ int arity(const DDim& ddim); std::ostream& operator<<(std::ostream&, const DDim&); +// Reshape a tensor to a matrix. The matrix's first dimension(column length) +// will be the product of tensor's first `num_col_dims` dimensions. +DDim flatten_to_2d(const DDim& src, int num_col_dims); + +DDim flatten_to_1d(const DDim& src); + } // namespace framework } // namespace paddle diff --git a/paddle/framework/eigen.h b/paddle/framework/eigen.h index 2d8d9ae10c56e0632414a5bbc754d35bfa9ce6a5..54bbeafcabdeeb1e2c1017c156b3512c83dada3a 100644 --- a/paddle/framework/eigen.h +++ b/paddle/framework/eigen.h @@ -63,20 +63,35 @@ struct EigenTensor { template -struct EigenMatrix : public EigenTensor {}; +struct EigenMatrix : public EigenTensor { + static typename EigenMatrix::Type Reshape(Tensor& tensor, int num_col_dims) { + int rank = tensor.dims_.size(); + PADDLE_ENFORCE(num_col_dims > 0 && num_col_dims < rank, + "`num_col_dims` must be between (0, rank_of_tensor)."); + return EigenMatrix::From(tensor, + flatten_to_2d(tensor.dims(), num_col_dims)); + } + + static typename EigenMatrix::ConstType Reshape(const Tensor& tensor, + int num_col_dims) { + int rank = tensor.dims_.size(); + PADDLE_ENFORCE(num_col_dims > 0 && num_col_dims < rank, + "`num_col_dims` must be between (0, rank_of_tensor)."); + return EigenMatrix::From(tensor, + flatten_to_2d(tensor.dims(), num_col_dims)); + } +}; template struct EigenVector : public EigenTensor { // Flatten reshapes a Tensor into an EigenVector. static typename EigenVector::Type Flatten(Tensor& tensor) { - return EigenVector::From( - tensor, make_ddim({static_cast(product(tensor.dims_))})); + return EigenVector::From(tensor, {product(tensor.dims_)}); } static typename EigenVector::ConstType Flatten(const Tensor& tensor) { - return EigenVector::From( - tensor, make_ddim({static_cast(product(tensor.dims_))})); + return EigenVector::From(tensor, {product(tensor.dims_)}); } }; diff --git a/paddle/framework/eigen_test.cc b/paddle/framework/eigen_test.cc index dc1957691b1a202826e10e84c21ac8874df9e378..bc4a2db32cfba66bef2c444e1f822e0d2a57b91e 100644 --- a/paddle/framework/eigen_test.cc +++ b/paddle/framework/eigen_test.cc @@ -108,5 +108,24 @@ TEST(Eigen, Matrix) { } } +TEST(Eigen, MatrixReshape) { + Tensor t; + float* p = t.mutable_data({2, 3, 6, 4}, platform::CPUPlace()); + for (int i = 0; i < 2 * 3 * 6 * 4; ++i) { + p[i] = static_cast(i); + } + + EigenMatrix::Type em = EigenMatrix::Reshape(t, 2); + + ASSERT_EQ(2 * 3, em.dimension(0)); + ASSERT_EQ(6 * 4, em.dimension(1)); + + for (int i = 0; i < 2 * 3; i++) { + for (int j = 0; j < 6 * 4; j++) { + ASSERT_NEAR(i * 6 * 4 + j, em(i, j), 1e-6f); + } + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto index 368136a9729dd2c745cc71bc391031e0a390fc87..dfcb5fb6210a08f35193b83e3b5f7cee92f618d7 100644 --- a/paddle/framework/framework.proto +++ b/paddle/framework/framework.proto @@ -87,3 +87,24 @@ message OpProto { repeated Attr attrs = 4; required string comment = 5; } + +enum DataType { + BOOL = 0; + INT16 = 1; + INT32 = 2; + INT64 = 3; + FP16 = 4; + FP32 = 5; + FP64 = 6; +} + +message LoDTensorDesc { + required DataType data_type = 1; + repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480] + optional int32 lod_level = 3 [ default = 0 ]; +} + +message VarDesc { + required string name = 1; + optional LoDTensorDesc lod_tensor = 2; +} diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc index 902c2655e9182d74a48ad13e17a39a3304d5fa57..9e3ca563c6765637f8471d142d32cec447f0b977 100644 --- a/paddle/framework/grad_op_builder_test.cc +++ b/paddle/framework/grad_op_builder_test.cc @@ -3,7 +3,7 @@ #include "paddle/framework/op_registry.h" #include "paddle/framework/operator.h" -USE_OP(add_two); +USE_OP(add); namespace paddle { namespace framework { @@ -41,7 +41,7 @@ namespace f = paddle::framework; TEST(GradOpBuilder, AddTwo) { std::shared_ptr add_op(f::OpRegistry::CreateOp( - "add_two", {{"X", {"x"}}, {"Y", {"y"}}}, {{"Out", {"out"}}}, {})); + "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); diff --git a/paddle/framework/images/duplicate_op2.graffle b/paddle/framework/images/duplicate_op2.graffle index 2b658085d6a55d368c320051ba7f94ec2900f13c..ede3bca30ae17d5af52505fd94dc2f79b23b57e0 100644 Binary files a/paddle/framework/images/duplicate_op2.graffle and b/paddle/framework/images/duplicate_op2.graffle differ diff --git a/paddle/framework/images/duplicate_op2.png b/paddle/framework/images/duplicate_op2.png index c5588015d1450fd8c1bda3580680d884494868bb..4e872dc2caf3b0cbd0d5176f11a14801b538dc86 100644 Binary files a/paddle/framework/images/duplicate_op2.png and b/paddle/framework/images/duplicate_op2.png differ diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc index 71eac4a10b34c3010a2758120c25754af58f669d..908a1f2fd0abe0aa4016c72dbcbc18dcc144232c 100644 --- a/paddle/framework/lod_tensor.cc +++ b/paddle/framework/lod_tensor.cc @@ -19,8 +19,8 @@ namespace paddle { namespace framework { -LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end) { - LOD new_lod; +LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end) { + LoD new_lod; new_lod.reserve(level_end - level_begin); for (size_t i = level_begin; i < level_end; i++) { new_lod.emplace_back(in.at(i)); @@ -28,10 +28,10 @@ LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end) { return new_lod; } -LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin, +LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin, size_t elem_end) { // slice the lod. - LOD new_lod; + LoD new_lod; new_lod.reserve(in.size() - level); auto start = in.at(level)[elem_begin]; auto end = in.at(level)[elem_end]; @@ -46,13 +46,13 @@ LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin, std::transform(new_lod.back().begin(), new_lod.back().end(), new_lod.back().begin(), [start](int v) { return v - start; }); - PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD"); + PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LoD"); } PADDLE_ENFORCE_LE(new_lod.size(), in.size()); return new_lod; } -bool operator==(const LOD& a, const LOD& b) { +bool operator==(const LoD& a, const LoD& b) { if (a.size() != b.size()) { return false; } @@ -72,12 +72,12 @@ bool operator==(const LOD& a, const LOD& b) { return true; } -void LODTensor::SliceLevels(size_t level_begin, size_t level_end) { +void LoDTensor::SliceLevels(size_t level_begin, size_t level_end) { auto new_lod = framework::SliceLevels(lod_, level_begin, level_end); lod_ = new_lod; } -void LODTensor::SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) { +void LoDTensor::SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) { PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level, NumLevels()); PADDLE_ENFORCE(elem_begin < NumElements(level), diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 9e6b6b4aca41ed464292b56bf6f2d27514f874f7..154068fef69bc96edbd85b731fe8091b3b1ff823 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -35,34 +35,34 @@ template using Vector = thrust::host_vector; #endif -using LOD = std::vector>; +using LoD = std::vector>; -LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end); +LoD SliceLevels(const LoD& in, size_t level_begin, size_t level_end); -LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin, +LoD SliceInLevel(const LoD& in, size_t level, size_t elem_begin, size_t elem_end); -bool operator==(const LOD& a, const LOD& b); +bool operator==(const LoD& a, const LoD& b); /* - * LODTensor (Level of details Tensor) + * LoDTensor (Level of details Tensor) * see https://en.wikipedia.org/wiki/Level_of_details for reference. */ -class LODTensor { +class LoDTensor { public: - LODTensor() {} - LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {} + LoDTensor() {} + LoDTensor(const LoD& lod, Tensor* t) : lod_(lod), tensor_(t) {} - void set_lod(const LOD& lod) { lod_ = lod; } + void set_lod(const LoD& lod) { lod_ = lod; } void set_tensor(Tensor* tensor) { tensor_ = tensor; } Tensor& tensor() { return *tensor_; } - LOD lod() { return lod_; } + LoD lod() { return lod_; } /* - * Get a element from LOD. + * Get a element from LoD. */ size_t lod_element(size_t level, size_t elem) const { PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level, @@ -74,7 +74,7 @@ class LODTensor { } /* - * Number of LODTensor's levels, each level has units of data, for example, + * Number of LoDTensor's levels, each level has units of data, for example, * in the sentence's view, article, paragraph, sentence are 3 levels. */ size_t NumLevels() const { return lod_.size(); } @@ -100,7 +100,7 @@ class LODTensor { void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end); private: - LOD lod_; + LoD lod_; Tensor* tensor_; // not owned }; } // namespace framework diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md index 8dfe3ee823084cb8c38550a82e761a741eabe135..769b61f175a2f462258c1242d027c04c0abd12a9 100644 --- a/paddle/framework/lod_tensor.md +++ b/paddle/framework/lod_tensor.md @@ -94,7 +94,7 @@ Let's go on slicing this slice. Its <1,1>-slice is ||| ``` -### The General Slicing Algorithm +### The Slicing Algorithm The algorithm, with over-simplified data structure, is defined as @@ -106,17 +106,41 @@ struct LoDTensor { float* tensor_; }; -LoDTensor Slice(const LoDTensor& lodt, int level, int sequence) { +LoDTensor Slice(const LoDTensor& lodt, int level, int sequence); +``` + +Let us revisit the example above -} +``` + 3 +3 1 2 +3 2 4 1 2 3 +||| || |||| | || ||| ``` -### Slicing the Top Level +Suppose that we want to retrieve the <1,2>-slice -Please be aware that an RNN operator only slices the top level of a LoD Tensor to get the step inputs. +``` +2 +2 3 +|| ||| +``` -```c++ -LoDTensor Slice(const LoDTensor& lodt, int sequence) { +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. + +To avoid the traversal of the LoD tree at slcing 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 + +``` + 0 +0 9 10 +0 3 5 9 10 12 +||| || |||| | || ||| +``` + +We don't really need the 0 on top, so the LoD Tensor could be -} +``` +0 9 10 +0 3 5 9 10 12 +||| || |||| | || ||| ``` diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc index 9a351605edb5013bdab2c6193bdd9ce401acc937..1da8553134f377f7a4fbe8008d12fe8d4a0e47f4 100644 --- a/paddle/framework/lod_tensor_test.cc +++ b/paddle/framework/lod_tensor_test.cc @@ -21,7 +21,7 @@ namespace paddle { namespace framework { -class LODTensorTester : public ::testing::Test { +class LoDTensorTester : public ::testing::Test { public: virtual void SetUp() override { // tensor's batch_size: 30 @@ -29,7 +29,7 @@ class LODTensorTester : public ::testing::Test { // 0 10 20 // 0 5 10 15 20 // 0 2 5 7 10 12 15 20 - LOD lod; + LoD lod; lod.push_back(std::vector{0, 10, 20}); lod.push_back(std::vector{0, 5, 10, 15, 20}); lod.push_back(std::vector{0, 2, 5, 7, 10, 12, 15, 17, 20}); @@ -47,21 +47,21 @@ class LODTensorTester : public ::testing::Test { protected: platform::CPUPlace place; Tensor tensor; - LODTensor lod_tensor; + LoDTensor lod_tensor; }; -TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); } +TEST_F(LoDTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); } -TEST_F(LODTensorTester, NumElements) { +TEST_F(LoDTensorTester, NumElements) { ASSERT_EQ(lod_tensor.NumElements(0), 2UL); ASSERT_EQ(lod_tensor.NumElements(1), 4UL); ASSERT_EQ(lod_tensor.NumElements(2), 8UL); } -TEST_F(LODTensorTester, SliceLevels) { +TEST_F(LoDTensorTester, SliceLevels) { // slice 1 level for (size_t level = 0; level < 3UL; ++level) { - LODTensor new_lod_tensor = lod_tensor; + LoDTensor new_lod_tensor = lod_tensor; new_lod_tensor.SliceLevels(level, level + 1); ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL); ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); @@ -70,7 +70,7 @@ TEST_F(LODTensorTester, SliceLevels) { } // slice 2 level for (size_t level = 0; level < 2UL; ++level) { - LODTensor new_lod_tensor = lod_tensor; + LoDTensor new_lod_tensor = lod_tensor; new_lod_tensor.SliceLevels(level, level + 2); ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); @@ -80,9 +80,9 @@ TEST_F(LODTensorTester, SliceLevels) { } } -TEST_F(LODTensorTester, SliceInLevel) { +TEST_F(LoDTensorTester, SliceInLevel) { size_t level = 0; - LODTensor new_lod_tensor = lod_tensor; + LoDTensor new_lod_tensor = lod_tensor; new_lod_tensor.SliceInLevel(level, 0, 2); EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL); EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL); diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc index b43f6a8cc56fdd2dc483bef303cf1213b171a5e4..e00c6e8d904508ec9985537fc703c7c61a14e0de 100644 --- a/paddle/framework/op_registry_test.cc +++ b/paddle/framework/op_registry_test.cc @@ -21,7 +21,7 @@ class CosineOpProtoAndCheckerMaker : public OpProtoAndCheckerMaker { AddOutput("output", "output of cosine op"); AddAttr("scale", "scale of cosine op") .SetDefault(1.0) - .LargerThan(0.0); + .GreaterThan(0.0); AddComment("This is cos op"); } }; @@ -80,7 +80,7 @@ TEST(OpRegistry, CreateOp) { paddle::framework::Scope scope; paddle::platform::CPUDeviceContext dev_ctx; op->Run(scope, dev_ctx); - float scale_get = op->GetAttr("scale"); + float scale_get = op->Attr("scale"); ASSERT_EQ(scale_get, scale); } @@ -121,7 +121,7 @@ TEST(OpRegistry, DefaultValue) { paddle::framework::Scope scope; paddle::platform::CPUDeviceContext dev_ctx; op->Run(scope, dev_ctx); - ASSERT_EQ(op->GetAttr("scale"), 1.0); + ASSERT_EQ(op->Attr("scale"), 1.0); } TEST(OpRegistry, CustomChecker) { @@ -172,6 +172,6 @@ TEST(OpRegistry, CustomChecker) { paddle::platform::CPUDeviceContext dev_ctx; paddle::framework::Scope scope; op->Run(scope, dev_ctx); - int test_attr = op->GetAttr("test_attr"); + int test_attr = op->Attr("test_attr"); ASSERT_EQ(test_attr, 4); } \ No newline at end of file diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index 790cfc4746b1d34da413fa3c29a266f962c6dde6..e1e122091f7759b1a68f1f982bc2a35e8241f9f0 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -123,6 +123,15 @@ OperatorBase::OperatorBase(const std::string& type, CheckAllInputOutputSet(); } +std::vector OperatorBase::InputVars() const { + std::vector ret_val; + for (auto& o : outputs_) { + ret_val.reserve(ret_val.size() + o.second.size()); + ret_val.insert(ret_val.end(), o.second.begin(), o.second.end()); + } + return ret_val; +} + std::vector OperatorBase::OutputVars(bool has_intermediate) const { std::vector ret_val; if (has_intermediate) { diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index da92220b04e313e4743cc77241755b685d0791ad..4600b06009bcef7d0774d25b816aac4733f30795 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -69,7 +69,7 @@ class OperatorBase { virtual ~OperatorBase() {} template - inline const T& GetAttr(const std::string& name) const { + inline const T& Attr(const std::string& name) const { PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap", name); return boost::get(attrs_.at(name)); @@ -94,11 +94,14 @@ class OperatorBase { const VariableNameMap& Inputs() const { return inputs_; } const VariableNameMap& Outputs() const { return outputs_; } + //! Get a input with argument's name described in `op_proto` std::string Input(const std::string& name) const; //! Get a input which has multiple variables. const std::vector& Inputs(const std::string& name) const; + std::vector InputVars() const; + //! Get a output with argument's name described in `op_proto` std::string Output(const std::string& name) const; //! Get an output which has multiple variables. @@ -238,8 +241,8 @@ class InferShapeContext { const Scope& scope() const { return scope_; } template - inline const T& GetAttr(const std::string& name) const { - return op_.GetAttr(name); + inline const T& Attr(const std::string& name) const { + return op_.Attr(name); } size_t InputSize(const std::string& name) const { @@ -311,9 +314,9 @@ class InferShapeContext { } template - std::vector MultiOutput(const std::string& name) const { + std::vector MultiOutput(const std::string& name) const { auto names = op_.Outputs(name); - std::vector res; + std::vector res; res.reserve(names.size()); std::transform(names.begin(), names.end(), std::back_inserter(res), [&](const std::string& sub_name) { diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index 8a1970c7a8aa5f76abed49bfde445fc743544e66..20bbb11896a4c6f11079669f0b25773f6460594d 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -102,7 +102,7 @@ class OpKernelTestProtoAndCheckerMaker : public OpProtoAndCheckerMaker { AddOutput("y", "output of test op"); AddAttr("scale", "scale of cosine op") .SetDefault(1.0) - .LargerThan(0.0); + .GreaterThan(0.0); AddComment("This is test op"); } }; @@ -140,7 +140,7 @@ class OpKernelTestMultiInputsProtoAndCheckerMaker AddOutput("ys", "outputs of test op").AsDuplicable(); AddAttr("scale", "scale of cosine op") .SetDefault(1.0) - .LargerThan(0.0); + .GreaterThan(0.0); AddComment("This is test op"); } }; diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h index 643f875491724bf443bd7727391734377ee6180c..ce938b21437195fed8c1adad4329fd139f3f96ab 100644 --- a/paddle/framework/tensor.h +++ b/paddle/framework/tensor.h @@ -43,6 +43,9 @@ class Tensor { template friend struct EigenTensor; + template + friend struct EigenMatrix; + template friend struct EigenVector; diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h index 94f436294f350e2a39785a09959efb3b17bd00a5..637f04ae0037bd402d855b8bcde8087bfe8328d1 100644 --- a/paddle/framework/tensor_impl.h +++ b/paddle/framework/tensor_impl.h @@ -148,5 +148,13 @@ inline Tensor& Tensor::Resize(const DDim& dims) { inline const DDim& Tensor::dims() const { return dims_; } +template +inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) { + Tensor res; + res.ShareDataWith(src); + res.Resize(flatten_to_2d(src.dims(), num_col_dims)); + return res; +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/tensor_test.cc b/paddle/framework/tensor_test.cc index 7db38d5caeebccf710334e854faf785ef0f64063..55302ea47120f420e952b26830c8ea4cbcce6435 100644 --- a/paddle/framework/tensor_test.cc +++ b/paddle/framework/tensor_test.cc @@ -262,3 +262,16 @@ TEST(Tensor, CopyFrom) { } #endif } + +TEST(Tensor, ReshapeToMatrix) { + using namespace paddle::framework; + using namespace paddle::platform; + Tensor src; + int* src_ptr = src.mutable_data({2, 3, 4, 9}, CPUPlace()); + for (int i = 0; i < 2 * 3 * 4 * 9; ++i) { + src_ptr[i] = i; + } + Tensor res = ReshapeToMatrix(src, 2); + ASSERT_EQ(res.dims()[0], 2 * 3); + ASSERT_EQ(res.dims()[1], 4 * 9); +} \ No newline at end of file diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index f43f15e5cacb70b625d7791e1e02ce7780286200..4fd72d64a90ae6f16dd1499ceb7fba6e40fe4cea 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -44,6 +44,7 @@ if(WITH_GPU) add_simple_unittest(RowConvOpTest) add_simple_unittest(BlockExpandOpTest) add_simple_unittest(CropOpTest) + add_simple_unittest(SwitchOpTest) endif() add_simple_unittest(Im2ColTest) diff --git a/paddle/function/EigenGemm.cpp b/paddle/function/EigenGemm.cpp index 674141ed39b7f5573948348e3ba3bb526ae43c66..b3e666e860d29d89650d48a23cf44917035a02d7 100644 --- a/paddle/function/EigenGemm.cpp +++ b/paddle/function/EigenGemm.cpp @@ -83,9 +83,9 @@ struct EigenBlasGemm { }; #ifdef PADDLE_TYPE_DOUBLE -template class EigenBlasGemm; +template struct EigenBlasGemm; #else -template class EigenBlasGemm; +template struct EigenBlasGemm; #endif } // namespace paddle diff --git a/paddle/function/GruFunctor.h b/paddle/function/GruFunctor.h new file mode 100644 index 0000000000000000000000000000000000000000..9f6392198ea360502f313cbe15dfae46ece69758 --- /dev/null +++ b/paddle/function/GruFunctor.h @@ -0,0 +1,159 @@ +/* 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 "GemmFunctor.h" +#include "hl_cpu_gru.cuh" + +namespace paddle { + +template +struct GruFunctor { + template + static void compute(OpResetOutput opResetOutput, + OpFinalOutput opFinalOutput, + hl_gru_value value, + int frameSize, + int batchSize, + hl_activation_mode_t active_node, + hl_activation_mode_t active_gate) { +#ifndef __NVCC__ + if (value.prevOutValue) { + BlasGemm::compute(false, + false, + batchSize, + 2 * frameSize, + frameSize, + 1, + value.prevOutValue, + frameSize, + value.gateWeight, + frameSize * 2, + 1, + value.gateValue, + frameSize * 3); + } + + forward_reset_output( + opResetOutput, value, frameSize, batchSize, active_gate); + + if (value.prevOutValue) { + BlasGemm::compute(false, + false, + batchSize, + frameSize, + frameSize, + 1, + value.resetOutputValue, + frameSize, + value.stateWeight, + frameSize, + 1, + value.gateValue + frameSize * 2, + frameSize * 3); + } + + forward_final_output( + opFinalOutput, value, frameSize, batchSize, active_node); +#endif + } +}; + +template +struct GruGradFunctor { + template + static void compute(OpStateGrad opStateGrad, + OpResetGrad opResetGrad, + hl_gru_value value, + hl_gru_grad grad, + int frameSize, + int batchSize, + hl_activation_mode_t active_node, + hl_activation_mode_t active_gate) { +#ifndef __NVCC__ + backward_state_grad( + opStateGrad, value, grad, frameSize, batchSize, active_node); + + if (value.prevOutValue && grad.prevOutGrad) { + BlasGemm::compute(false, + true, + batchSize, + frameSize, + frameSize, + 1, + grad.gateGrad + frameSize * 2, + frameSize * 3, + value.stateWeight, + frameSize, + 0, + grad.resetOutputGrad, + frameSize); + + if (grad.stateWeightGrad) { + BlasGemm::compute(true, + false, + frameSize, + frameSize, + batchSize, + 1, + value.resetOutputValue, + frameSize, + grad.gateGrad + frameSize * 2, + frameSize * 3, + 1, + grad.stateWeightGrad, + frameSize); + } + } + + backward_reset_grad( + opResetGrad, value, grad, frameSize, batchSize, active_gate); + + if (grad.prevOutGrad && value.prevOutValue) { + BlasGemm::compute(false, + true, + batchSize, + frameSize, + frameSize * 2, + 1, + grad.gateGrad, + frameSize * 3, + value.gateWeight, + frameSize * 2, + 1, + grad.prevOutGrad, + frameSize); + + if (grad.gateWeightGrad) { + BlasGemm::compute(true, + false, + frameSize, + frameSize * 2, + batchSize, + 1, + value.prevOutValue, + frameSize, + grad.gateGrad, + frameSize * 3, + 1, + grad.gateWeightGrad, + frameSize * 2); + } + } +#endif + } +}; + +} // namespace paddle diff --git a/paddle/function/Im2Col.h b/paddle/function/Im2Col.h index 9b91e223a6a28586b11fe7ed4a44421e029a67bb..1e0cff436ff60d5a029e89657d00af2b0bf8b454 100644 --- a/paddle/function/Im2Col.h +++ b/paddle/function/Im2Col.h @@ -94,95 +94,4 @@ public: int paddingWidth); }; -template -struct Padding { - static void run(const T* src, - T* dest, - int channels, - int inputHeight, - int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; - for (int c = 0; c < channels; c++) { - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; - } - - for (int i = 0; i < inputHeight; i++) { - // padding head - for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); - } - - memcpy(dest, src, inputWidth * sizeof(T)); - dest += inputWidth; - src += inputWidth; - - // padding tail - for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); - } - } - - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; - } - } - } -}; - -#if defined(__ARM_NEON__) || defined(__ARM_NEON) -template <> -struct Padding { - static void run(const float* src, - float* dest, - int channels, - int inputHeight, - int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; - for (int c = 0; c < channels; c++) { - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; - } - - for (int i = 0; i < inputHeight; i++) { - // padding head - for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); - } - - int step = inputWidth >> 2; - int remain = inputWidth & 3; - for (int s = 0; s < step; s++) { - float32x4_t s0 = vld1q_f32(src); - vst1q_f32(dest, s0); - src += 4; - dest += 4; - } - for (int r = 0; r < remain; r++) { - *dest++ = *src++; - } - - // padding tail - for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); - } - } - - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; - } - } - } -}; - -#endif - } // namespace paddle diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 91b4b8ed91b6055babcfbab8f7adb2c55e2747d0..25e41edad54bec0f76a3de4799fab14241407272 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -13,18 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "MulOp.h" -/// todo(tianbing), delete it -#include -#include "paddle/math/MathFunctions.h" +#include "GemmFunctor.h" #include "paddle/math/SIMDFunctions.h" #include "paddle/utils/ThreadLocal.h" -#ifndef PADDLE_TYPE_DOUBLE -#define GEMM paddle::gemm -#else -#define GEMM paddle::gemm -#endif - namespace { inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { for (unsigned int i = 0; i < len; ++i) { @@ -114,19 +106,20 @@ void MulOp(CpuMatrix& out, real scaleT, bool aTrans, bool bTrans) { - GEMM(aTrans ? CblasTrans : CblasNoTrans, - bTrans ? CblasTrans : CblasNoTrans, - out.getHeight(), - out.getWidth(), - !aTrans ? a.getWidth() : a.getHeight(), - scaleAB, - a.getData(), - a.getStride(), - b.getData(), - b.getStride(), - scaleT, - out.getData(), - out.getStride()); + BlasGemm::compute( + aTrans, + bTrans, + out.getHeight(), + out.getWidth(), + !aTrans ? a.getWidth() : a.getHeight(), + scaleAB, + a.getData(), + a.getStride(), + b.getData(), + b.getStride(), + scaleT, + out.getData(), + out.getStride()); } /// dense matrix (+)= sparse matrix * dense matrix diff --git a/paddle/function/SwitchOp.cpp b/paddle/function/SwitchOp.cpp new file mode 100644 index 0000000000000000000000000000000000000000..01e252a8dc0cd5fa1e964efa01d04cf282b3dfe7 --- /dev/null +++ b/paddle/function/SwitchOp.cpp @@ -0,0 +1,140 @@ +/* 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 "SwitchOp.h" +#include "paddle/math/Vector.h" + +namespace paddle { + +template <> +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int argType) { + for (int n = 0; n < num; ++n) { + for (int c = 0; c < inC; ++c) { + for (int h = 0; h < inH; ++h) { + for (int w = 0; w < inW; ++w) { + if (argType == ADD_TO) { + outputs[((n * inH + h) * inW + w) * inC + c] += *(inputs++); + } else { + outputs[((n * inH + h) * inW + w) * inC + c] = *(inputs++); + } + } + } + } + } +} + +template <> +void NHWC2NCHW(real* outputs, + const real* inputs, + const int num, + const int inH, + const int inW, + const int inC, + const int argType) { + for (int n = 0; n < num; ++n) { + for (int h = 0; h < inH; ++h) { + for (int w = 0; w < inW; ++w) { + for (int c = 0; c < inC; ++c) { + if (argType == ADD_TO) { + outputs[((n * inC + c) * inH + h) * inW + w] += *(inputs++); + } else { + outputs[((n * inC + c) * inH + h) * inW + w] = *(inputs++); + } + } + } + } + } +} + +/** + * \brief Switch dimension order of image input. + * The input and output is a 4D tensor. Switch order + * 'batch_size,channels, height, width' to + * order 'batch_size, height, width, channels'. + * + * Argument in this Function: + * \param inputs input data with order 'batch_size,channels, height, width'. + * \param outputs output data with order 'batch_size, height, width, channels'. + */ +template +class NCHW2NHWCFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override {} + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); + + size_t num = inputs[0].shape()[0]; + size_t inC = inputs[0].shape()[1]; + size_t inH = inputs[0].shape()[2]; + size_t inW = inputs[0].shape()[3]; + NCHW2NHWC(outputs[0].data(), + inputs[0].data(), + num, + inC, + inH, + inW, + outputs[0].getArgType()); + } +}; + +/** + * \brief Switch dimension order of image input. + * The input and output is a 4D tensor. Switch order + * 'batch_size, height, width, channels' to + * order 'batch_size, channels, height, width'. + * + * Argument in this Function: + * \param inputs input data with order 'batch_size, height, width, channels'. + * \param outputs output data with order 'batch_size, channels, height, width'. + */ +template +class NHWC2NCHWFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override {} + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); + + size_t num = inputs[0].shape()[0]; + size_t inH = inputs[0].shape()[1]; + size_t inW = inputs[0].shape()[2]; + size_t inC = inputs[0].shape()[3]; + + NHWC2NCHW(outputs[0].data(), + inputs[0].data(), + num, + inH, + inW, + inC, + outputs[0].getArgType()); + } +}; + +REGISTER_TYPED_FUNC(NCHW2NHWC, CPU, NCHW2NHWCFunc); +REGISTER_TYPED_FUNC(NHWC2NCHW, CPU, NHWC2NCHWFunc); +#ifndef PADDLE_ONLY_CPU +REGISTER_TYPED_FUNC(NCHW2NHWC, GPU, NCHW2NHWCFunc); +REGISTER_TYPED_FUNC(NHWC2NCHW, GPU, NHWC2NCHWFunc); +#endif + +} // namespace paddle diff --git a/paddle/function/SwitchOp.h b/paddle/function/SwitchOp.h new file mode 100644 index 0000000000000000000000000000000000000000..e4c1c3ac922f88c3e5424b5943082810aabfacdb --- /dev/null +++ b/paddle/function/SwitchOp.h @@ -0,0 +1,66 @@ +/* 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 "Function.h" + +namespace paddle { + +/** + * \brief This funtion switch dimension order of image input. + * The input and output is a 4D tensor. Switch order 'batch_size, + *channels, height, width' to + * order 'batch_size, height, width, channels'. + * + * \param[out] outputs save results. + * \param[in] inputs input data. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + * \param[in] argType type of output argument. + */ +template +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int argtype); + +/** + * \brief This funtion switch dimension order of image input. + * The input and output is a 4D tensor. Switch order 'batch_size, + *height, width, channels' to + * order 'batch_size, channels, height, width'. + * + * \param[out] inGrad gradients of previous layer. + * \param[in] outGrad output gradients. + * \param[in] num batch size of input data. + * \param[in] inH height of input data. + * \param[in] inW with of input data. + * \param[in] inC channel number of input data. + * \param[in] argType type of output argument. + */ +template +void NHWC2NCHW(real* inGrad, + const real* outGrad, + const int num, + const int inH, + const int inW, + const int inC, + const int argType); +} // namespace paddle diff --git a/paddle/function/SwitchOpGpu.cu b/paddle/function/SwitchOpGpu.cu new file mode 100644 index 0000000000000000000000000000000000000000..45390a56c3f776ec18a65a6ba2f7149a7a6ef6c3 --- /dev/null +++ b/paddle/function/SwitchOpGpu.cu @@ -0,0 +1,98 @@ +/* Copyright (c) 2016 Paddle + +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 "SwitchOp.h" +#include "hl_base.h" + +namespace paddle { + +__global__ void KeNCHW2NHWC(real* outputs, + const real* inputs, + int inC, + int inH, + int inW, + int nthreads, + int argType) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int w = idx % inW; + const int h = (idx / inW) % inH; + const int c = (idx / inW / inH) % inC; + const int n = idx / inW / inH / inC; + + const int off = ((n * inH + h) * inW + w) * inC + c; + if (argType == ADD_TO) { + outputs[off] += inputs[idx]; + } else { + outputs[off] = inputs[idx]; + } + } +} + +template <> +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW, + const int argType) { + size_t nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + KeNCHW2NHWC<<>>( + outputs, inputs, inC, inH, inW, nth, argType); + CHECK_SYNC("NCHW2NHWC"); +} + +__global__ void KeNHWC2NCHW(real* outputs, + const real* inputs, + int inH, + int inW, + int inC, + int nthreads, + int argType) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int c = idx % inC; + const int w = (idx / inC) % inW; + const int h = (idx / inC / inW) % inH; + const int n = idx / inW / inH / inC; + + const int off = ((n * inC + c) * inH + h) * inW + w; + if (argType == ADD_TO) { + outputs[off] += inputs[idx]; + } else { + outputs[off] = inputs[idx]; + } + } +} + +template <> +void NHWC2NCHW(real* outputs, + const real* inputs, + const int num, + const int inH, + const int inW, + const int inC, + const int argType) { + int nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + KeNHWC2NCHW<<>>( + outputs, inputs, inH, inW, inC, nth, argType); + CHECK_SYNC("NHWC2NCHW"); +} + +} // namespace paddle diff --git a/paddle/function/SwitchOpTest.cpp b/paddle/function/SwitchOpTest.cpp new file mode 100644 index 0000000000000000000000000000000000000000..03b0dd66ddcbab713969ed747601ecb1b2eb7955 --- /dev/null +++ b/paddle/function/SwitchOpTest.cpp @@ -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. */ + +#include +#include "FunctionTest.h" + +namespace paddle { + +TEST(Pad, real) { + for (size_t numSamples : {1, 4, 8, 16}) { + for (size_t channels : {1, 4, 8, 16}) { + for (size_t imgSizeH : {1, 4, 8, 16}) { + for (size_t imgSizeW : {1, 4, 8, 16}) { + VLOG(3) << " numSamples=" << numSamples << " channels=" << channels + << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; + for (bool test_grad : {true, false}) { + CpuGpuFuncCompare compare(test_grad ? "NHWC2NCHW" : "NCHW2NHWC", + FuncConfig()); + TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW}; + TensorShape outDims{numSamples, imgSizeH, imgSizeW, channels}; + compare.addInputs( + BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims)); + compare.addOutputs(BufferArg( + VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO)); + compare.run(); + } + } + } + } + } +} + +} // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index f09e98587d1681d29a79a9cb0303c2d4356c6935..18126152ea0b4ebfe4ec5c8084479787814ed173 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -12,468 +12,13 @@ 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 "neon_util.h" +#include "NeonDepthwiseConv.h" #include "paddle/function/ConvOp.h" -#include "paddle/function/Im2Col.h" namespace paddle { -namespace neon { - #if defined(__ARM_NEON__) || defined(__ARM_NEON) -template -struct DepthwiseConvKernel {}; - -inline float32_t conv3x3(float32x4_t r0, - float32x4_t r1, - float32x4_t r2, - float32x4_t k0, - float32x4_t k1, - float32x4_t k2) { - float32x4_t tmp; - tmp = vmulq_f32(r0, k0); - tmp = vmlaq_f32(tmp, r1, k1); - tmp = vmlaq_f32(tmp, r2, k2); - return vaddvq_f32(tmp); -} - -inline float32_t conv4x4(float32x4_t r0, - float32x4_t r1, - float32x4_t r2, - float32x4_t r3, - float32x4_t k0, - float32x4_t k1, - float32x4_t k2, - float32x4_t k3) { - float32x4_t tmp; - tmp = vmulq_f32(r0, k0); - tmp = vmlaq_f32(tmp, r1, k1); - tmp = vmlaq_f32(tmp, r2, k2); - tmp = vmlaq_f32(tmp, r3, k3); - return vaddvq_f32(tmp); -} - -/** - * Each step calculates four elements of the output. - * First step: - * R0[0, 1, 2, 3...] * K[0][0] - * R0[1, 2, 3, 4...] * K[0][1] - * R0[2, 3, 4, 5...] * K[0][2] - * R1[0, 1, 2, 3...] * K[1][0] - * R1[1, 2, 3, 4...] * K[1][1] - * R1[2, 3, 4, 5...] * K[1][2] - * R2[0, 1, 2, 3...] * K[2][0] - * R2[1, 2, 3, 4...] * K[2][1] - * + R2[2, 3, 4, 5...] * K[2][2] - * ------------------------------ - * Output[0, 1, 2, 3] - */ -template <> -struct DepthwiseConvKernel<3, 1> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 9) { - // Load the filters - float32x4_t k[3]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 3); - k[2] = vld1q_f32(filterData + 6); - k[0] = vsetq_lane_f32(0.f, k[0], 3); - k[1] = vsetq_lane_f32(0.f, k[1], 3); - k[2] = vsetq_lane_f32(0.f, k[2], 3); - - const float* r0 = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - const float* r1 = r0 + inputWidth; - const float* r2 = r0 + inputWidth * 2; - float32x4_t input[3][3]; - for (int h = 0; h < outputHeight; h++) { - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t tmp; - input[0][0] = vld1q_f32(r0); - tmp = vld1q_f32(r0 + 4); - input[0][1] = vextq_f32(input[0][0], tmp, 1); - input[0][2] = vextq_f32(input[0][0], tmp, 2); - input[1][0] = vld1q_f32(r1); - tmp = vld1q_f32(r1 + 4); - input[1][1] = vextq_f32(input[1][0], tmp, 1); - input[1][2] = vextq_f32(input[1][0], tmp, 2); - input[2][0] = vld1q_f32(r2); - tmp = vld1q_f32(r2 + 4); - input[2][1] = vextq_f32(input[2][0], tmp, 1); - input[2][2] = vextq_f32(input[2][0], tmp, 2); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 4; - r1 += 4; - r2 += 4; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); - r0++; - r1++; - r2++; - outputData++; - } - - r0 += 2; - r1 += 2; - r2 += 2; - } - } - } -}; - -/** - * Each step calculates four elements of the output. - * First step: - * R0[0, 2, 4, 6...] * K[0][0] - * R0[1, 3, 5, 7...] * K[0][1] - * R0[2, 4, 6, 8...] * K[0][2] - * R1[0, 2, 4, 6...] * K[1][0] - * R1[1, 3, 5, 7...] * K[1][1] - * R1[2, 4, 6, 8...] * K[1][2] - * R2[0, 2, 4, 6...] * K[2][0] - * R2[1, 3, 5, 7...] * K[2][1] - * R2[2, 4, 6, 8...] * K[2][2] - * ------------------------------ - * Output[0, 1, 2, 3] - */ -template <> -struct DepthwiseConvKernel<3, 2> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 9) { - // Load the filters - float32x4_t k[3]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 3); - k[2] = vld1q_f32(filterData + 6); - k[0] = vsetq_lane_f32(0.f, k[0], 3); - k[1] = vsetq_lane_f32(0.f, k[1], 3); - k[2] = vsetq_lane_f32(0.f, k[2], 3); - - const float* start = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - float32x4_t input[3][3]; - for (int h = 0; h < outputHeight; h++) { - const float* r0 = start + 2 * h * inputWidth; - const float* r1 = start + (2 * h + 1) * inputWidth; - const float* r2 = start + (2 * h + 2) * inputWidth; - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t data1; - float32x4x2_t data2; - - data2 = vld2q_f32(r0); - input[0][0] = data2.val[0]; - input[0][1] = data2.val[1]; - data1 = vld1q_f32(r0 + 8); - input[0][2] = vextq_f32(data2.val[0], data1, 1); - - data2 = vld2q_f32(r1); - input[1][0] = data2.val[0]; - input[1][1] = data2.val[1]; - data1 = vld1q_f32(r1 + 8); - input[1][2] = vextq_f32(data2.val[0], data1, 1); - - data2 = vld2q_f32(r2); - input[2][0] = data2.val[0]; - input[2][1] = data2.val[1]; - data1 = vld1q_f32(r2 + 8); - input[2][2] = vextq_f32(data2.val[0], data1, 1); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 8; - r1 += 8; - r2 += 8; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); - r0 += 2; - r1 += 2; - r2 += 2; - outputData++; - } - } - } - } -}; - -/** - * Each step calculates four elements of the output. - */ -template <> -struct DepthwiseConvKernel<4, 1> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 16) { - // Load the filters - float32x4_t k[4]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 4); - k[2] = vld1q_f32(filterData + 8); - k[3] = vld1q_f32(filterData + 12); - - const float* r0 = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - const float* r1 = r0 + inputWidth; - const float* r2 = r0 + inputWidth * 2; - const float* r3 = r0 + inputWidth * 3; - float32x4_t input[4][4]; - for (int h = 0; h < outputHeight; h++) { - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t tmp; - input[0][0] = vld1q_f32(r0); - tmp = vld1q_f32(r0 + 4); - input[0][1] = vextq_f32(input[0][0], tmp, 1); - input[0][2] = vextq_f32(input[0][0], tmp, 2); - input[0][3] = vextq_f32(input[0][0], tmp, 3); - - input[1][0] = vld1q_f32(r1); - tmp = vld1q_f32(r1 + 4); - input[1][1] = vextq_f32(input[1][0], tmp, 1); - input[1][2] = vextq_f32(input[1][0], tmp, 2); - input[1][3] = vextq_f32(input[1][0], tmp, 3); - - input[2][0] = vld1q_f32(r2); - tmp = vld1q_f32(r2 + 4); - input[2][1] = vextq_f32(input[2][0], tmp, 1); - input[2][2] = vextq_f32(input[2][0], tmp, 2); - input[2][3] = vextq_f32(input[2][0], tmp, 3); - - input[3][0] = vld1q_f32(r3); - tmp = vld1q_f32(r3 + 4); - input[3][1] = vextq_f32(input[3][0], tmp, 1); - input[3][2] = vextq_f32(input[3][0], tmp, 2); - input[3][3] = vextq_f32(input[3][0], tmp, 3); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 4; - r1 += 4; - r2 += 4; - r3 += 4; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - float32x4_t i3 = vld1q_f32(r3); - *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); - r0++; - r1++; - r2++; - r3++; - outputData++; - } - - r0 += 3; - r1 += 3; - r2 += 3; - r3 += 3; - } - } - } -}; - -/** - * Each step calculates four elements of the output. - */ -template <> -struct DepthwiseConvKernel<4, 2> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 16) { - // Load the filters - float32x4_t k[4]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 4); - k[2] = vld1q_f32(filterData + 8); - k[3] = vld1q_f32(filterData + 12); - - const float* start = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - float32x4_t input[4][4]; - for (int h = 0; h < outputHeight; h++) { - const float* r0 = start + 2 * h * inputWidth; - const float* r1 = start + (2 * h + 1) * inputWidth; - const float* r2 = start + (2 * h + 2) * inputWidth; - const float* r3 = start + (2 * h + 3) * inputWidth; - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4x2_t data1; - float32x4x2_t data2; - - data1 = vld2q_f32(r0); - data2 = vld2q_f32(r0 + 8); - input[0][0] = data1.val[0]; - input[0][1] = data1.val[1]; - input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r1); - data2 = vld2q_f32(r1 + 8); - input[1][0] = data1.val[0]; - input[1][1] = data1.val[1]; - input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r2); - data2 = vld2q_f32(r2 + 8); - input[2][0] = data1.val[0]; - input[2][1] = data1.val[1]; - input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r3); - data2 = vld2q_f32(r3 + 8); - input[3][0] = data1.val[0]; - input[3][1] = data1.val[1]; - input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 8; - r1 += 8; - r2 += 8; - r3 += 8; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - float32x4_t i3 = vld1q_f32(r3); - *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); - r0 += 2; - r1 += 2; - r2 += 2; - r3 += 2; - outputData++; - } - } - } - } -}; - template class NeonDepthwiseConvFunction : public ConvFunctionBase { public: @@ -497,16 +42,16 @@ public: const TensorShape& filter = inputs[1].shape(); const TensorShape& output = outputs[0].shape(); - size_t batchSize = input[0]; - size_t inputChannels = input[1]; - size_t inputHeight = input[2]; - size_t inputWidth = input[3]; - size_t filterHeight = getFilterHeight(filter); - size_t filterWidth = getFilterWidth(filter); - size_t outputChannels = output[1]; - size_t outputHeight = output[2]; - size_t outputWidth = output[3]; - size_t filterMultiplier = outputChannels / groups_; + int batchSize = input[0]; + int inputChannels = input[1]; + int inputHeight = input[2]; + int inputWidth = input[3]; + int filterHeight = getFilterHeight(filter); + int filterWidth = getFilterWidth(filter); + int outputChannels = output[1]; + int outputHeight = output[2]; + int outputWidth = output[3]; + int filterMultiplier = outputChannels / groups_; CHECK_EQ(inputChannels, groups_); // only support strideH() == strideW() and filterHeight == filterWidth. @@ -519,22 +64,19 @@ public: // padding the input float* inputPadding = inputData; + int padInputHeight = inputHeight + 2 * paddingH(); + int padInputWidth = inputWidth + 2 * paddingW(); if (paddingH() > 0 || paddingW() > 0) { - int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) * - (inputWidth + 2 * paddingW()); + int newSize = batchSize * inputChannels * padInputHeight * padInputWidth; resizeBuffer(newSize); inputPadding = reinterpret_cast(memory_->getBuf()); - Padding::run(inputData, - inputPadding, - batchSize * inputChannels, - inputHeight, - inputWidth, - paddingH(), - paddingW()); - - // height and width of padding data - inputHeight += 2 * paddingH(); - inputWidth += 2 * paddingW(); + neon::Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); } std::function::run; + DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run; } else if (filterWidth == 3 && strideW() == 2) { - DepthWiseConv = DepthwiseConvKernel<3, 2>::run; + DepthWiseConv = neon::DepthwiseConvKernel<3, 2>::run; } else if (filterWidth == 4 && strideW() == 1) { - DepthWiseConv = DepthwiseConvKernel<4, 1>::run; + DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run; } else if (filterWidth == 4 && strideW() == 2) { - DepthWiseConv = DepthwiseConvKernel<4, 2>::run; + DepthWiseConv = neon::DepthwiseConvKernel<4, 2>::run; } else { LOG(FATAL) << "Not supported"; } - for (size_t i = 0; i < batchSize; i++) { + for (int i = 0; i < batchSize; i++) { DepthWiseConv(inputPadding, filterData, - inputHeight, - inputWidth, + padInputHeight, + padInputWidth, outputChannels, outputHeight, outputWidth, filterMultiplier, outputData); - inputPadding += inputChannels * inputHeight * inputWidth; + inputPadding += inputChannels * padInputHeight * padInputWidth; outputData += outputChannels * outputHeight * outputWidth; } } }; +#ifndef PADDLE_TYPE_DOUBLE REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction); +#endif #endif -} // namespace neon } // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h new file mode 100644 index 0000000000000000000000000000000000000000..aefeea78badbca3d0d09e292e4e1e148618f8ac6 --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -0,0 +1,631 @@ +/* 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 "neon_util.h" + +namespace paddle { + +namespace neon { + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +template +struct DepthwiseConvKernel {}; + +inline float32_t conv3x3(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + return vaddvq_f32(tmp); +} + +inline float32_t conv4x4(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t r3, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2, + float32x4_t k3) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + tmp = vmlaq_f32(tmp, r3, k3); + return vaddvq_f32(tmp); +} + +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 1, 2, 3...] * K[0][0] + * R0[1, 2, 3, 4...] * K[0][1] + * R0[2, 3, 4, 5...] * K[0][2] + * R1[0, 1, 2, 3...] * K[1][0] + * R1[1, 2, 3, 4...] * K[1][1] + * R1[2, 3, 4, 5...] * K[1][2] + * R2[0, 1, 2, 3...] * K[2][0] + * R2[1, 2, 3, 4...] * K[2][1] + * + R2[2, 3, 4, 5...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0++; + r1++; + r2++; + outputData++; + } + + r0 += 2; + r1 += 2; + r2 += 2; + } + } + } +}; + +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 2, 4, 6...] * K[0][0] + * R0[1, 3, 5, 7...] * K[0][1] + * R0[2, 4, 6, 8...] * K[0][2] + * R1[0, 2, 4, 6...] * K[1][0] + * R1[1, 3, 5, 7...] * K[1][1] + * R1[2, 4, 6, 8...] * K[1][2] + * R2[0, 2, 4, 6...] * K[2][0] + * R2[1, 3, 5, 7...] * K[2][1] + * R2[2, 4, 6, 8...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t data1; + float32x4x2_t data2; + + data2 = vld2q_f32(r0); + input[0][0] = data2.val[0]; + input[0][1] = data2.val[1]; + data1 = vld1q_f32(r0 + 8); + input[0][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r1); + input[1][0] = data2.val[0]; + input[1][1] = data2.val[1]; + data1 = vld1q_f32(r1 + 8); + input[1][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r2); + input[2][0] = data2.val[0]; + input[2][1] = data2.val[1]; + data1 = vld1q_f32(r2 + 8); + input[2][2] = vextq_f32(data2.val[0], data1, 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0 += 2; + r1 += 2; + r2 += 2; + outputData++; + } + } + } + } +}; + +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + const float* r3 = r0 + inputWidth * 3; + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[0][3] = vextq_f32(input[0][0], tmp, 3); + + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[1][3] = vextq_f32(input[1][0], tmp, 3); + + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + input[2][3] = vextq_f32(input[2][0], tmp, 3); + + input[3][0] = vld1q_f32(r3); + tmp = vld1q_f32(r3 + 4); + input[3][1] = vextq_f32(input[3][0], tmp, 1); + input[3][2] = vextq_f32(input[3][0], tmp, 2); + input[3][3] = vextq_f32(input[3][0], tmp, 3); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + r3 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0++; + r1++; + r2++; + r3++; + outputData++; + } + + r0 += 3; + r1 += 3; + r2 += 3; + r3 += 3; + } + } + } +}; + +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + const float* r3 = start + (2 * h + 3) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4x2_t data1; + float32x4x2_t data2; + + data1 = vld2q_f32(r0); + data2 = vld2q_f32(r0 + 8); + input[0][0] = data1.val[0]; + input[0][1] = data1.val[1]; + input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r1); + data2 = vld2q_f32(r1 + 8); + input[1][0] = data1.val[0]; + input[1][1] = data1.val[1]; + input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r2); + data2 = vld2q_f32(r2 + 8); + input[2][0] = data1.val[0]; + input[2][1] = data1.val[1]; + input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r3); + data2 = vld2q_f32(r3 + 8); + input[3][0] = data1.val[0]; + input[3][1] = data1.val[1]; + input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + r3 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0 += 2; + r1 += 2; + r2 += 2; + r3 += 2; + outputData++; + } + } + } + } +}; + +template +struct Padding { + static void run(const T* input, + T* inputPadding, + int channels, + int inputHeight, + int inputWidth, + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - inputHeight) / 2; + const int paddingWidth = (padInputWidth - inputWidth) / 2; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T)); + inputPadding += padInputWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = T(0); + } + + memcpy(inputPadding, input, inputWidth * sizeof(T)); + inputPadding += inputWidth; + input += inputWidth; + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = T(0); + } + } + + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T)); + inputPadding += padInputWidth * paddingHeight; + } + } + } +}; + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) +template <> +struct Padding { + static void run(const float* input, + float* inputPadding, + int channels, + int inputHeight, + int inputWidth, + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - inputHeight) / 2; + const int paddingWidth = (padInputWidth - inputWidth) / 2; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + + int step = inputWidth >> 2; + int remain = inputWidth & 3; + for (int s = 0; s < step; s++) { + float32x4_t s0 = vld1q_f32(input); + vst1q_f32(inputPadding, s0); + input += 4; + inputPadding += 4; + } + for (int r = 0; r < remain; r++) { + *inputPadding++ = *input++; + } + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + } + + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + } + } +}; + +// for stride is 2 +struct StridePadding { + static void run(const float* input, + float* inputPadding, + int channels, + int inputHeight, + int inputWidth, + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - (inputHeight * 2 - 1)) / 2; + const int paddingWidth = (padInputWidth - (inputWidth * 2 - 1)) / 2; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + + int step = inputWidth >> 2; + int remain = inputWidth & 3; + float32x4_t s1 = vdupq_n_f32(0.f); + for (int s = 0; s < step; s++) { + float32x4_t s0 = vld1q_f32(input); + float32x4x2_t v = {s0, s1}; + vst2q_f32(inputPadding, v); + input += 4; + inputPadding += 8; + } + for (int r = 0; r < remain; r++) { + *inputPadding++ = *input++; + *inputPadding++ = float(0); + } + inputPadding--; + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + if (i != inputHeight - 1) { + memset(inputPadding, 0, padInputWidth * sizeof(float)); + inputPadding += padInputWidth; + } + } + + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + } + } +}; + +#endif + +#endif + +} // namespace neon +} // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConvTranspose.cpp b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp new file mode 100644 index 0000000000000000000000000000000000000000..49ca4bc8a0947ba329bd991e9f7d001623901a67 --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp @@ -0,0 +1,136 @@ +/* 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 "NeonDepthwiseConv.h" +#include "paddle/function/ConvOp.h" + +namespace paddle { + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +template +class NeonDepthwiseConvTransposeFunction : public ConvFunctionBase { +public: + void init(const FuncConfig& config) override { + ConvFunctionBase::init(config); + } + + void check(const BufferArgs& inputs, const BufferArgs& outputs) override { + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + checkShape(input, filter, output); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); + check(inputs, outputs); + + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + + int batchSize = input[0]; + int inputChannels = input[1]; + int inputHeight = input[2]; + int inputWidth = input[3]; + int filterHeight = getFilterHeight(filter); + int filterWidth = getFilterWidth(filter); + int outputChannels = output[1]; + int outputHeight = output[2]; + int outputWidth = output[3]; + int filterMultiplier = outputChannels / groups_; + CHECK_EQ(inputChannels, groups_); + + // only support strideH() == strideW() and filterHeight == filterWidth. + CHECK_EQ(strideH(), strideW()); + CHECK_EQ(paddingH(), paddingW()); + CHECK_EQ(filterHeight, filterWidth); + + float* inputData = inputs[0].data(); + float* filterData = inputs[1].data(); + float* outputData = outputs[0].data(); + + // padding the input, input -> inputPadding + float* inputPadding = inputData; + int padInputHeight = + (inputHeight - 1) * strideH() + 2 * filterHeight - 1 - 2 * paddingH(); + int padInputWidth = + (inputWidth - 1) * strideW() + 2 * filterWidth - 1 - 2 * paddingW(); + + if (padInputHeight > inputHeight || padInputWidth > inputWidth) { + int newSize = batchSize * inputChannels * padInputHeight * padInputWidth; + resizeBuffer(newSize); + inputPadding = reinterpret_cast(memory_->getBuf()); + if (strideH() == 1) { + neon::Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); + } else if (strideH() == 2) { + neon::StridePadding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); + } else { + LOG(FATAL) << "Not supported"; + } + } + + std::function + DepthWiseConv; + + if (filterWidth == 3) { + DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run; + } else if (filterWidth == 4) { + DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run; + } else { + LOG(FATAL) << "Not supported"; + } + + for (int i = 0; i < batchSize; i++) { + DepthWiseConv(inputPadding, + filterData, + padInputHeight, + padInputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); + inputPadding += inputChannels * padInputHeight * padInputWidth; + outputData += outputChannels * outputHeight * outputWidth; + } + } +}; + +#ifndef PADDLE_TYPE_DOUBLE + +REGISTER_TYPED_FUNC(NeonDepthwiseConvTranspose, + CPU, + NeonDepthwiseConvTransposeFunction); + +#endif + +#endif + +} // namespace paddle diff --git a/paddle/function/neon/neon_util.h b/paddle/function/neon/neon_util.h index 56b3febe2d27bb4fbf57e49079b3ad071d556914..e2db0450675084345ad55559d8988c5375801cc9 100644 --- a/paddle/function/neon/neon_util.h +++ b/paddle/function/neon/neon_util.h @@ -33,12 +33,8 @@ inline float32_t vaddvq_f32(float32x4_t a) { return vget_lane_f32(vpadd_f32(v, v), 0); } -inline float32x4_t vmlaq_laneq_f32(float32x4_t a, - float32x4_t b, - float32x4_t v, - const int lane) { - return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane)); -} +#define vmlaq_laneq_f32(a, b, v, lane) \ + vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane)) #endif } // namespace neon diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp index 1ceaaaa206ee3cbc5421238574c7f310011ccaa5..f7a80e23e1bd49549bec57b360587adc6b423794 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.cpp +++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp @@ -62,14 +62,18 @@ void BatchNormBaseLayer::calFeatureMapSize() { const ImageConfig& conf = config_.inputs(0).image_conf(); imageH_ = inputLayers_[0]->getOutput().getFrameHeight(); imageW_ = inputLayers_[0]->getOutput().getFrameWidth(); + imageD_ = inputLayers_[0]->getOutput().getFrameDepth(); + + if (0 == imageD_) imageD_ = conf.img_size_z(); if (imageH_ == 0 && imageW_ == 0) { imageH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); imageW_ = conf.img_size(); } else { getOutput().setFrameHeight(imageH_); getOutput().setFrameWidth(imageW_); + getOutput().setFrameDepth(imageD_); } - imgPixels_ = imageH_ * imageW_; + imgPixels_ = imageH_ * imageW_ * imageD_; } } // namespace paddle diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h index 230bafc31d96bbd49481a7ed135be6888688627e..e721d2d267a31cae46407673b8b1281e87055608 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.h +++ b/paddle/gserver/layers/BatchNormBaseLayer.h @@ -80,6 +80,7 @@ protected: /// Height or width of input image feature. /// Both of them are 1 if the input is fully-connected layer. + int imageD_; int imageH_; int imageW_; /// Height * Width. diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp index 3887aa58b283d319c5b9afec3a38ad676669a8d1..9deda2de989a55d34510560c49b213ea1a52fd07 100644 --- a/paddle/gserver/layers/Conv3DLayer.cpp +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -83,8 +83,8 @@ void Conv3DLayer::forward(PassType passType) { int outWidth = getSize(); resetOutput(batchSize, outWidth); + REGISTER_TIMER_INFO("FwdConv3D", getName().c_str()); for (size_t i = 0; i != inputLayers_.size(); ++i) { - REGISTER_TIMER_INFO("FwdConv3D", getName().c_str()); const MatrixPtr &inMat = getInputValue(i); const MatrixPtr &outMat = getOutputValue(); int M = M_[i]; @@ -120,7 +120,6 @@ void Conv3DLayer::forward(PassType passType) { } } if (nullptr != this->biasParameter_) { - REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); this->addBias(); } forwardActivation(); @@ -134,15 +133,14 @@ void Conv3DLayer::backward(const UpdateCallback &callback) { biases_->getParameterPtr()->incUpdate(callback); } + REGISTER_TIMER_INFO("BwdConv3D", getName().c_str()); for (size_t i = 0; i != inputLayers_.size(); ++i) { - REGISTER_TIMER_INFO("BwdConv3D", getName().c_str()); if (weights_[i]->getWGrad()) { bpropWeights(i); } if (getInputGrad(i)) { bpropData(i); } - REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); weights_[i]->getParameterPtr()->incUpdate(callback); } } diff --git a/paddle/gserver/layers/CudnnBatchNormLayer.cpp b/paddle/gserver/layers/CudnnBatchNormLayer.cpp index 44ba2c4b7d1562d2ce839b5f4b4de1af35e6925f..49a9540c0b6e36b59ed786287ff5c4569b69a6a5 100644 --- a/paddle/gserver/layers/CudnnBatchNormLayer.cpp +++ b/paddle/gserver/layers/CudnnBatchNormLayer.cpp @@ -37,7 +37,7 @@ bool CudnnBatchNormLayer::init(const LayerMap& layerMap, } void CudnnBatchNormLayer::reshape(int batchSize) { - hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_, imageW_); + hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_ * imageD_, imageW_); } void CudnnBatchNormLayer::forward(PassType passType) { @@ -104,7 +104,7 @@ void CudnnBatchNormLayer::forward(PassType passType) { EPS, batchSize, channels_, - imageH_, + imageH_ * imageD_, imageW_); } } diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index 2838980a973d3dbcce9716f21f2ea07e3a2fa660..1b59ed60c57fe3bbfa814befa8a63408a2621715 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -84,8 +84,8 @@ void DeConv3DLayer::forward(PassType passType) { resetOutput(batchSize, outWidth); const MatrixPtr outMat = getOutputValue(); + REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str()); for (size_t i = 0; i != inputLayers_.size(); ++i) { - REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str()); const MatrixPtr &inMat = getInputValue(i); int M = M_[i]; int N = N_[i]; @@ -120,7 +120,6 @@ void DeConv3DLayer::forward(PassType passType) { } } if (nullptr != this->biasParameter_) { - REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); this->addBias(); } forwardActivation(); @@ -133,12 +132,12 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) { bpropBiases(); biases_->getParameterPtr()->incUpdate(callback); } + REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str()); for (size_t i = 0; i < inputLayers_.size(); ++i) { if (weights_[i]->getWGrad() || this->needGradient_) { int M = M_[i]; int N = N_[i]; int K = K_[i]; - REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str()); Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); const MatrixPtr &inMat = getInputValue(i); for (int n = 0; n < batchSize; ++n) { @@ -182,7 +181,6 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) { } } } - REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); weights_[i]->getParameterPtr()->incUpdate(callback); } } diff --git a/paddle/gserver/layers/DetectionOutputLayer.cpp b/paddle/gserver/layers/DetectionOutputLayer.cpp index 8ab838e191314ab25469631626c0b0564d7fffda..0cf0a92bf4bd8f9b8eba2016b2377d9dfb18c70a 100644 --- a/paddle/gserver/layers/DetectionOutputLayer.cpp +++ b/paddle/gserver/layers/DetectionOutputLayer.cpp @@ -139,7 +139,13 @@ void DetectionOutputLayer::forward(PassType passType) { allDecodedBBoxes, &allIndices); - resetOutput(numKept, 7); + if (numKept > 0) { + resetOutput(numKept, 7); + } else { + MatrixPtr outV = getOutputValue(); + outV = NULL; + return; + } MatrixPtr outV = getOutputValue(); getDetectionOutput(confBuffer_->getData(), numKept, diff --git a/paddle/gserver/layers/DetectionUtil.cpp b/paddle/gserver/layers/DetectionUtil.cpp index 3e61adc66e60c54250e4f323452aa13045310879..d83674f45a70212a8adc94a31ff58eb0e01baa00 100644 --- a/paddle/gserver/layers/DetectionUtil.cpp +++ b/paddle/gserver/layers/DetectionUtil.cpp @@ -469,7 +469,7 @@ size_t getDetectionIndices( const size_t numClasses, const size_t backgroundId, const size_t batchSize, - const size_t confThreshold, + const real confThreshold, const size_t nmsTopK, const real nmsThreshold, const size_t keepTopK, diff --git a/paddle/gserver/layers/DetectionUtil.h b/paddle/gserver/layers/DetectionUtil.h index fe4f9f075e4cf011c97f68f49598a828d62327b3..641ed873b4c8645b6455e5ef5e63593e3005b770 100644 --- a/paddle/gserver/layers/DetectionUtil.h +++ b/paddle/gserver/layers/DetectionUtil.h @@ -275,7 +275,7 @@ size_t getDetectionIndices( const size_t numClasses, const size_t backgroundId, const size_t batchSize, - const size_t confThreshold, + const real confThreshold, const size_t nmsTopK, const real nmsThreshold, const size_t keepTopK, diff --git a/paddle/gserver/layers/GruCompute.cpp b/paddle/gserver/layers/GruCompute.cpp index 06907768e98f4bad952706cffbbd65d1f86cc6df..148516391c6cad8feff34b9bd1c10c27d1a8a0e6 100644 --- a/paddle/gserver/layers/GruCompute.cpp +++ b/paddle/gserver/layers/GruCompute.cpp @@ -14,6 +14,7 @@ limitations under the License. */ #include "GruCompute.h" #include "hl_recurrent_apply.cuh" +#include "paddle/function/GruFunctor.h" #include "paddle/utils/Util.h" namespace paddle { @@ -25,13 +26,13 @@ void GruCompute::init(LayerConfig &config) { template <> void GruCompute::forward<0>(hl_gru_value value, int frameSize, int batchSize) { - hl_cpu_gru_forward(hppl::forward::gru_resetOutput(), - hppl::forward::gru_finalOutput(), - value, - frameSize, - batchSize, - activeNode_, - activeGate_); + GruFunctor::compute(hppl::forward::gru_resetOutput(), + hppl::forward::gru_finalOutput(), + value, + frameSize, + batchSize, + activeNode_, + activeGate_); } template <> @@ -39,14 +40,15 @@ void GruCompute::backward<0>(hl_gru_value value, hl_gru_grad grad, int frameSize, int batchSize) { - hl_cpu_gru_backward(hppl::backward::gru_stateGrad(), - hppl::backward::gru_resetGrad(), - value, - grad, - frameSize, - batchSize, - activeNode_, - activeGate_); + GruGradFunctor::compute( + hppl::backward::gru_stateGrad(), + hppl::backward::gru_resetGrad(), + value, + grad, + frameSize, + batchSize, + activeNode_, + activeGate_); } } // namespace paddle diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..d7eee6eaf078dab8d48adc4c7ee758a433672ac6 --- /dev/null +++ b/paddle/gserver/layers/SwitchOrderLayer.cpp @@ -0,0 +1,110 @@ +/* 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 "SwitchOrderLayer.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +REGISTER_LAYER(switch_order, SwitchOrderLayer); + +bool SwitchOrderLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + auto& img_conf = config_.inputs(0).image_conf(); + size_t inD = img_conf.img_size_z(); + size_t inH = + img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(); + size_t inW = img_conf.img_size(); + size_t inC = img_conf.channels(); + inH = inH * inD; + inDims_ = TensorShape({0, inC, inH, inW}); + outDims_ = TensorShape(4); + + auto& reshape_conf = config_.reshape_conf(); + for (int i = 0; i < reshape_conf.height_axis_size(); i++) { + heightAxis_.push_back(reshape_conf.height_axis(i)); + } + for (int i = 0; i < reshape_conf.width_axis_size(); i++) { + widthAxis_.push_back(reshape_conf.width_axis(i)); + } + createFunction(nchw2nhwc_, "NCHW2NHWC", FuncConfig()); + createFunction(nhwc2nchw_, "NHWC2NCHW", FuncConfig()); + return true; +} + +void SwitchOrderLayer::setOutDims() { + outDims_.setDim(0, inDims_[0]); + outDims_.setDim(1, inDims_[2]); + outDims_.setDim(2, inDims_[3]); + outDims_.setDim(3, inDims_[1]); + reshapeHeight_ = 1; + for (size_t i = 0; i < heightAxis_.size(); i++) { + reshapeHeight_ *= outDims_[heightAxis_[i]]; + } + output_.setFrameHeight(reshapeHeight_); + reshapeWidth_ = 1; + for (size_t i = 0; i < widthAxis_.size(); i++) { + reshapeWidth_ *= outDims_[widthAxis_[i]]; + } + output_.setFrameWidth(reshapeWidth_); +} + +void SwitchOrderLayer::setInDims() { + MatrixPtr input = inputLayers_[0]->getOutputValue(); + size_t batchSize = input->getHeight(); + inDims_.setDim(0, batchSize); + int d = inputLayers_[0]->getOutput().getFrameDepth(); + d = (d == 0 ? 1 : d); + int h = inputLayers_[0]->getOutput().getFrameHeight(); + if (h != 0) inDims_.setDim(2, h * d); + int w = inputLayers_[0]->getOutput().getFrameWidth(); + if (w != 0) inDims_.setDim(3, w); + int totalCount = input->getElementCnt(); + int channels = totalCount / (inDims_[0] * inDims_[2] * inDims_[3]); + if (channels != 0) inDims_.setDim(1, channels); +} + +void SwitchOrderLayer::forward(PassType passType) { + Layer::forward(passType); + setInDims(); + setOutDims(); + resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]); + if (heightAxis_.size() > 0) { + getOutputValue()->reshape(reshapeHeight_, reshapeWidth_); + getOutputGrad()->reshape(reshapeHeight_, reshapeWidth_); + } + + // switch NCHW to NHWC + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getInputValue(0), inDims_); + outputs.addArg(*getOutputValue(), outDims_); + nchw2nhwc_[0]->calc(inputs, outputs); + forwardActivation(); +} + +void SwitchOrderLayer::backward(const UpdateCallback& callback) { + (void)callback; + backwardActivation(); + + // switch NHWC to NCHW + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getOutputGrad(), outDims_); + outputs.addArg(*getInputGrad(0), inDims_, ADD_TO); + nhwc2nchw_[0]->calc(inputs, outputs); +} +} // namespace paddle diff --git a/paddle/gserver/layers/SwitchOrderLayer.h b/paddle/gserver/layers/SwitchOrderLayer.h new file mode 100644 index 0000000000000000000000000000000000000000..47b1f7f73ee783b3eae3c9cfe08b1459cef16a71 --- /dev/null +++ b/paddle/gserver/layers/SwitchOrderLayer.h @@ -0,0 +1,47 @@ +/* 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 "Layer.h" + +namespace paddle { + +/** + * \brief This layer calculate softmax in image channel dimension. + */ +class SwitchOrderLayer : public Layer { +public: + explicit SwitchOrderLayer(const LayerConfig& config) : Layer(config) {} + + ~SwitchOrderLayer() {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; + void setInDims(); + void setOutDims(); + +protected: + std::vector> nchw2nhwc_; + std::vector> nhwc2nchw_; + TensorShape inDims_; + TensorShape outDims_; + std::vector heightAxis_; + std::vector widthAxis_; + size_t reshapeHeight_; + size_t reshapeWidth_; +}; +} // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index a831ffbc73fbd6ad42fa31b2d6d583718474e59b..0e6be2df9ef5f0fae8ed2b0c65ac6c032fe45ab1 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1703,6 +1703,55 @@ TEST(Layer, BatchNormalizationLayer) { #endif } +void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) { + TestConfig config; + const int CHANNELS = 10; + const int IMG_SIZE = 16; + const int IMG_SIZE_Y = 8; + const int IMG_SIZE_Z = 8; + size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y * IMG_SIZE_Z; + config.layerConfig.set_type(type); + config.layerConfig.set_size(size); + config.layerConfig.set_active_type("sigmoid"); + config.biasSize = CHANNELS; + config.inputDefs.push_back({INPUT_DATA, + "layer_0", + /* dim= */ size, + /* paraSize= */ CHANNELS}); + + config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + + LayerInputConfig* input = config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + ImageConfig* img_conf = input->mutable_image_conf(); + img_conf->set_channels(CHANNELS); + img_conf->set_img_size(IMG_SIZE); + img_conf->set_img_size_y(IMG_SIZE_Y); + img_conf->set_img_size_z(IMG_SIZE_Z); + + testLayerGrad(config, + "batch_norm", + 64, + /* trans= */ trans, + useGpu, + /* useWeight */ true); +} + +TEST(Layer, testBatchNorm3DLayer) { + testBatchNorm3DLayer("batch_norm", false, false); +#ifndef PADDLE_ONLY_CPU + testBatchNorm3DLayer("batch_norm", false, true); + if (hl_get_cudnn_lib_version() >= int(4000)) { + testBatchNorm3DLayer("cudnn_batch_norm", false, true); + } +#endif +} + void testConvOperator(bool isDeconv) { TestConfig config; const int NUM_FILTERS = 16; @@ -2008,6 +2057,31 @@ TEST(Layer, CropLayer) { } } +TEST(Layer, SwitchOrderLayer) { + TestConfig config; + // config input_0 + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + ImageConfig* img = input->mutable_image_conf(); + img->set_channels(4); + img->set_img_size(16); + img->set_img_size_y(16); + + ReshapeConfig* reshape = config.layerConfig.mutable_reshape_conf(); + reshape->add_height_axis(0); + reshape->add_height_axis(1); + reshape->add_height_axis(2); + reshape->add_width_axis(3); + + // config softmax layer + config.layerConfig.set_type("switch_order"); + config.layerConfig.set_name("switchOrderLayer"); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "switch_order", 100, false, useGpu, true); + } +} + vector randSampling(real range, int n) { CHECK_GE(range, n); vector num(range); diff --git a/paddle/math/MathFunctions.cpp b/paddle/math/MathFunctions.cpp index c8ba1074a1555bbddde7e5f0fb2a046138b27c09..c2f17beeb87942ea681f5d388659c0d280157b26 100644 --- a/paddle/math/MathFunctions.cpp +++ b/paddle/math/MathFunctions.cpp @@ -84,6 +84,7 @@ LAPACK_ROUTINE_EACH(DYNAMIC_LOAD_LAPACK_WRAP) namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -143,6 +144,7 @@ void gemm(const CBLAS_TRANSPOSE transA, C, ldc); } +#endif template <> int getrf(const CBLAS_ORDER order, @@ -182,6 +184,7 @@ int getri(const CBLAS_ORDER order, return dynload::PADDLE_DGETRI(order, N, A, lda, ipiv); } +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void axpy(const int n, const float alpha, const float* x, float* y) { cblas_saxpy(n, alpha, x, 1, y, 1); @@ -201,6 +204,7 @@ template <> double dotProduct(const int n, const double* x, const double* y) { return cblas_ddot(n, x, 1, y, 1); } +#endif #if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML) diff --git a/paddle/math/MathFunctions.h b/paddle/math/MathFunctions.h index 637643838ff433753e0cbb9154ee069c2f7c6d15..e8ea6e37ac527a19c529d1731b94bed970211755 100644 --- a/paddle/math/MathFunctions.h +++ b/paddle/math/MathFunctions.h @@ -40,7 +40,14 @@ extern "C" { #ifndef LAPACK_FOUND extern "C" { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS #include +#else +typedef enum CBLAS_ORDER { + CblasRowMajor = 101, + CblasColMajor = 102 +} CBLAS_ORDER; +#endif int LAPACKE_sgetrf( int matrix_layout, int m, int n, float* a, int lda, int* ipiv); int LAPACKE_dgetrf( @@ -56,6 +63,7 @@ int LAPACKE_dgetri( namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -70,6 +78,7 @@ void gemm(const CBLAS_TRANSPOSE transA, const T beta, T* C, const int ldc); +#endif template int getrf(const CBLAS_ORDER Order, @@ -84,10 +93,21 @@ int getri( const CBLAS_ORDER Order, const int N, T* A, const int lda, const int* ipiv); template -void axpy(const int n, const T alpha, const T* x, T* y); +void axpy(const int n, const T alpha, const T* x, T* y) { + /// y = y + alpha * x + for (int i = 0; i < n; i++) { + y[i] = y[i] + alpha * x[i]; + } +} template -T dotProduct(const int n, const T* x, const T* y); +T dotProduct(const int n, const T* x, const T* y) { + T result = static_cast(0); + for (int i = 0; i < n; i++) { + result += x[i] * y[i]; + } + return result; +} template void vExp(const int n, const T* a, T* r); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 8bc42571f7c141aa31e18d0504b95b2ed4f0da77..4a2132c8d1bfa329ced575f9b78052bdbfe3e4d5 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -28,6 +28,7 @@ limitations under the License. */ #include "hl_top_k.h" #include "paddle/utils/Logging.h" +#include "paddle/function/GemmFunctor.h" #include "paddle/utils/ThreadLocal.h" #include "SIMDFunctions.h" @@ -2773,24 +2774,24 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { CHECK(!isTransposed()) << "Not supported"; size_t a_col, b_col, a_row, b_row; - CBLAS_TRANSPOSE a_trans, b_trans; + bool a_trans, b_trans; if (!a->isTransposed()) { a_col = a->getWidth(); a_row = a->getHeight(); - a_trans = CblasNoTrans; + a_trans = false; } else { a_col = a->getHeight(); a_row = a->getWidth(); - a_trans = CblasTrans; + a_trans = true; } if (!b->isTransposed()) { b_col = b->getWidth(); b_row = b->getHeight(); - b_trans = CblasNoTrans; + b_trans = false; } else { b_col = b->getHeight(); b_row = b->getWidth(); - b_trans = CblasTrans; + b_trans = true; } CHECK_EQ(a_col, b_row); @@ -2807,7 +2808,7 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { int lda = a->getStride(); int ldb = b->getStride(); int ldc = getStride(); - gemm( + BlasGemm::compute( a_trans, b_trans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc); } diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index 431d4e071072317c8fdfdc4f0d13e7cd4e3d062b..44180bca8bca53e74d71ce7bed3516399c01c81d 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -1616,6 +1616,10 @@ public: }; class CpuMatrix : public Matrix { +private: + MatrixPtr sftmaxSum_; + MatrixPtr sftmaxDot_; + public: CpuMatrix(size_t height, size_t width, bool trans = false); CpuMatrix(real* data, size_t height, size_t width, bool trans = false) diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 25dbd236e6432d99b27f1a6ffc4e07bf0f994155..f9ea25ab045a02be5ab9ed81ef9c679126d3a188 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -14,27 +14,31 @@ function(op_library TARGET) cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) - foreach(src ${op_library_SRCS}) - if (${src} MATCHES ".*\\.cu$") - list(APPEND cu_srcs ${src}) - elseif(${src} MATCHES ".*\\.cc$") - list(APPEND cc_srcs ${src}) - else() - message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu") + list(LENGTH op_library_SRCS op_library_SRCS_len) + if (${op_library_SRCS_len} EQUAL 0) + if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc) + list(APPEND cc_srcs ${TARGET}.cc) endif() - endforeach() + if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cu) + list(APPEND cu_srcs ${TARGET}.cu) + endif() + else() + foreach(src ${op_library_SRCS}) + if (${src} MATCHES ".*\\.cu$") + list(APPEND cu_srcs ${src}) + elseif(${src} MATCHES ".*\\.cc$") + list(APPEND cc_srcs ${src}) + else() + message(FATAL_ERROR "${TARGET} Source file ${src} should only be .cc or .cu") + endif() + endforeach() + endif() list(LENGTH cc_srcs cc_srcs_len) if (${cc_srcs_len} EQUAL 0) message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file") endif() - list(LENGTH cu_srcs cu_srcs_len) - list(LENGTH op_library_DEPS dep_len) - if (${cu_srcs_len} EQUAL 0 AND ${dep_len} EQUAL 0) - message(WARNING "The op library ${TARGET} not support GPU!") - endif() - if (WITH_GPU) nv_library(${TARGET} SRCS ${cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS} ${op_common_deps}) @@ -46,22 +50,22 @@ endfunction() add_subdirectory(math) -list(REMOVE_ITEM GENERAL_OPS - net_op - minus_op - mul_op - recurrent_op - scale_op) - -op_library(net_op SRCS net_op.cc) -op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op) -op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) +set(DEPS_OPS + identity_op + minus_op + mul_op + recurrent_op + scale_op) +op_library(identity_op DEPS scale_op) +op_library(minus_op DEPS scale_op) +op_library(mul_op DEPS math_function) op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor operator net_op) -op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op) +op_library(scale_op DEPS net_op) +list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS}) foreach(src ${GENERAL_OPS}) - op_library(${src} SRCS ${src}.cc ${src}.cu) + op_library(${src}) endforeach() set(GLOB_OP_LIB ${OP_LIBRARY} CACHE INTERNAL "Global OP library") diff --git a/paddle/operators/add_op.cc b/paddle/operators/add_op.cc index 8ab748ed71e9a5dc0ee0259a78a2b886870bec5b..8dbd47cf0dfbc265032a9966343eed5c7bd8692e 100644 --- a/paddle/operators/add_op.cc +++ b/paddle/operators/add_op.cc @@ -57,7 +57,6 @@ class AddOpGrad : public framework::OperatorWithKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(add_two, ops::AddOp, ops::AddOpMaker, add_two_grad, ops::AddOpGrad); +REGISTER_OP(add, ops::AddOp, ops::AddOpMaker, add_grad, ops::AddOpGrad); -REGISTER_OP_CPU_KERNEL(add_two, - ops::AddKernel); +REGISTER_OP_CPU_KERNEL(add, ops::AddKernel); diff --git a/paddle/operators/add_op.cu b/paddle/operators/add_op.cu index cec5f558cbc161124620ad4241d6bd8a5324277c..d9c6d20a6c320b59e57ed25da3dd8b093833f8c7 100644 --- a/paddle/operators/add_op.cu +++ b/paddle/operators/add_op.cu @@ -12,10 +12,7 @@ See the License for the specific language governing permissions and limitations under the License. */ -#define EIGEN_USE_GPU -#include "paddle/framework/op_registry.h" #include "paddle/operators/add_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(add_two, - ops::AddKernel); +REGISTER_OP_GPU_KERNEL(add, ops::AddKernel); diff --git a/paddle/operators/cos_sim_op.h b/paddle/operators/cos_sim_op.h index 9e3ff26815644e11d8f9c9a3c3a3840159401c17..9e2bcebe3b5432c157fac895a9bbab5164193dbb 100644 --- a/paddle/operators/cos_sim_op.h +++ b/paddle/operators/cos_sim_op.h @@ -23,6 +23,9 @@ using Tensor = framework::Tensor; template using EigenMatrix = framework::EigenMatrix; +template +using EigenVector = framework::EigenVector; template class CosSimKernel : public framework::OpKernel { @@ -43,14 +46,14 @@ class CosSimKernel : public framework::OpKernel { auto new_dims = framework::make_ddim({dims[0], size / dims[0]}); auto x = EigenMatrix::From(*input_x, new_dims); auto y = EigenMatrix::From(*input_y, new_dims); - auto z = EigenMatrix::From(*output_z); - auto x_norm = EigenMatrix::From(*output_x_norm); - auto y_norm = EigenMatrix::From(*output_y_norm); + auto z = EigenVector::Flatten(*output_z); + auto x_norm = EigenVector::Flatten(*output_x_norm); + auto y_norm = EigenVector::Flatten(*output_y_norm); auto place = context.GetEigenDevice(); - auto xy = (x * y).sum(Eigen::array({1})); - x_norm.device(place) = x.square().sum(Eigen::array({1})).sqrt(); - y_norm.device(place) = y.square().sum(Eigen::array({1})).sqrt(); + auto xy = (x * y).sum(Eigen::array({{1}})); + x_norm.device(place) = x.square().sum(Eigen::array({{1}})).sqrt(); + y_norm.device(place) = y.square().sum(Eigen::array({{1}})).sqrt(); z.device(place) = xy / x_norm / y_norm; } }; diff --git a/paddle/operators/crop_op.cc b/paddle/operators/crop_op.cc index 77ea51ea79cc611177c1affe71bd7a45159cd0f9..6033a45dc5be9f1ee8849ae191490291de563240 100644 --- a/paddle/operators/crop_op.cc +++ b/paddle/operators/crop_op.cc @@ -26,19 +26,18 @@ class CropOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - auto dim0 = ctx.Input("X")->dims(); + auto x_dim = ctx.Input("X")->dims(); auto Y = ctx.Input("Y"); if (Y == nullptr) { - auto shape = GetAttr>("shape"); + auto shape = Attr>("shape"); PADDLE_ENFORCE_EQ( - shape.size(), dim0.size(), + int64_t(shape.size()), x_dim.size(), "Shape size should be equal to dimention size of input tensor."); std::vector tensor_shape(shape.size()); - for (int i = 0; i < shape.size(); ++i) { + for (size_t i = 0; i < shape.size(); ++i) { tensor_shape[i] = (int64_t)shape[i]; } - ctx.Output("Out")->Resize( - paddle::framework::make_ddim(tensor_shape)); + ctx.Output("Out")->Resize(framework::make_ddim(tensor_shape)); } else { ctx.Output("Out")->Resize(Y->dims()); } @@ -49,14 +48,57 @@ class CropOpMaker : public framework::OpProtoAndCheckerMaker { public: CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The input of crop op"); - AddInput("Y", "The input used as reference for cropping. "); - AddOutput("Out", "The output of crop op."); + AddInput("X", + "The input of pad op. " + "The input should be a k-D tensor(k > 0 and k < 7)"); + AddInput("Y", + "The input used as reference for cropping" + " with the same dimension as X. "); + AddOutput("Out", + "The output of crop op " + "with the same dimension as X."); AddComment(R"DOC( Crop Operator. +Crop input into output, as specified by offsets and shape. + +There are two ways to set shape: +1. referenc input: crop input X as shape as reference input. + The dimension of reference input should + be as same as input X. +2. shape list: crop input X by shape described by a list. + The size of shape list should be as same as + dimension size of input X. + +The input should be a k-D tensor(k > 0 and k < 7). As an example: + +Given: + +X = [[0, 1, 2, 0, 0] + [0, 3, 4, 0, 0] + [0, 0, 0, 0, 0]] + +and + +offsets = [0, 1] + +and + +shape = [2, 2] + +then we get + +Out = [[1, 2], + [3, 4]] + )DOC"); - AddAttr>("offsets", "The offsets for cropping."); - AddAttr>("shape", "The shape for cropping."); + AddAttr>("offsets", + "A list describing offsets to be cropped." + "The size of offsets list should be as same as " + "dimension size of input X."); + AddAttr>("shape", + "A list describing the shape of output." + "The size of shape list should be as same as " + "dimension size of input X."); } }; @@ -76,12 +118,42 @@ class CropOpGrad : public framework::OperatorWithKernel { } }; +template +class CropCPUKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &context) const override { + auto *x = context.Input("X"); + auto *out = context.Output("Out"); + auto x_data = x->data(); + T *out_data = out->mutable_data(paddle::platform::CPUPlace()); + auto x_dims = x->dims(); + auto out_dims = out->dims(); + int64_t out_count = framework::product(out_dims); + std::vector x_shape = framework::vectorize(x_dims); + std::vector out_shape = framework::vectorize(out_dims); + + auto offsets = context.op().Attr>("offsets"); + PADDLE_ENFORCE_EQ( + x_dims.size(), offsets.size(), + "Offsets size should be equal to dimension size of input tensor."); + + std::vector> crop_rules(x_dims.size()); + for (size_t i = 0; i < crop_rules.size(); ++i) { + crop_rules[i].first = offsets[i]; + crop_rules[i].second = x_dims[i] - out_dims[i] - offsets[i]; + } + + for (int64_t i = 0; i < out_count; ++i) { + out_data[i] = x_data[transIndex(out_shape, x_shape, crop_rules, i)]; + } + } +}; + } // namespace operators } // namespace paddle namespace ops = paddle::operators; REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad); -REGISTER_OP_CPU_KERNEL(crop, - ops::CropKernel); +REGISTER_OP_CPU_KERNEL(crop, ops::CropCPUKernel); REGISTER_OP_CPU_KERNEL(crop_grad, ops::CropGradKernel); diff --git a/paddle/operators/crop_op.cu b/paddle/operators/crop_op.cu index 5afed4946598b89c6a72fb5e9a921bc2b1b631da..7977a3fe60162aed58cb2796485b554196aa39b6 100644 --- a/paddle/operators/crop_op.cu +++ b/paddle/operators/crop_op.cu @@ -15,8 +15,104 @@ #define EIGEN_USE_GPU #include "paddle/operators/crop_op.h" +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +__global__ void CropKernel(const int N, const int64_t* out_shape, + const int64_t* x_shape, const int* crop_rules, + const T* x_data, T* out_data) { + CUDA_1D_KERNEL_LOOP(index, N) { + // int64_t dim_size = out_shape.size(); + int64_t pos[D]; + + for (int64_t i = D - 1; i >= 0; --i) { + pos[i] = (index % out_shape[i]) + crop_rules[i * 2]; + index = index / out_shape[i]; + } + + int64_t result = pos[0]; + for (size_t i = 1; i < D; ++i) { + result = result * x_shape[i] + pos[i]; + } + + out_data[index] = x_data[result]; + } +} + +template +void CropCUDAFunctoin(const framework::ExecutionContext& context) { + auto* x = context.Input("X"); + auto* out = context.Output("Out"); + auto x_data = x->data(); + T* out_data = out->mutable_data(paddle::platform::CPUPlace()); + auto x_dims = x->dims(); + auto out_dims = out->dims(); + int64_t out_count = framework::product(out_dims); + int64_t* x_shape = &(framework::vectorize(x_dims))[0]; + int64_t* out_shape = &(framework::vectorize(out_dims))[0]; + + auto offsets = context.op().Attr>("offsets"); + PADDLE_ENFORCE_EQ( + x_dims.size(), offsets.size(), + "Offsets size should be equal to dimension size of input tensor."); + + int crop_rules[D * 2]; + for (size_t i = 0; i < x_dims.size(); ++i) { + crop_rules[i * 2] = offsets[i]; + crop_rules[i * 2 + 1] = x_dims[i] - out_dims[i] - offsets[i]; + } + + int n = out_dims[0]; + int d = out_dims[1]; + int block = 512; + int grid = (n * d + block - 1) / block; + + CropKernel<<>>(out_count, out_shape, x_shape, crop_rules, + x_data, out_data); +} + +template +class CropOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + size_t rank = context.Input("X")->dims().size(); + switch (rank) { + case 1: + CropCUDAFunctoin(context); + break; + case 2: + CropCUDAFunctoin(context); + break; + case 3: + CropCUDAFunctoin(context); + break; + case 4: + CropCUDAFunctoin(context); + break; + case 5: + CropCUDAFunctoin(context); + break; + case 6: + CropCUDAFunctoin(context); + break; + default: + PADDLE_THROW( + "CropOp only support tensors with no more than 6 dimensions."); + } + } +}; + +} // namespace operators +} // namespace paddle + namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(crop, - ops::CropKernel); +REGISTER_OP_GPU_KERNEL(crop, ops::CropOpCUDAKernel); REGISTER_OP_GPU_KERNEL(crop_grad, ops::CropGradKernel); diff --git a/paddle/operators/crop_op.h b/paddle/operators/crop_op.h index 40e05869ddde1652b590455f15b41656e8604999..54e7b6abd12e27fe57224fbc2a316eb14c74e7df 100644 --- a/paddle/operators/crop_op.h +++ b/paddle/operators/crop_op.h @@ -18,7 +18,7 @@ #include "paddle/framework/op_registry.h" namespace paddle { -namespace operators { +namespace operators { // Internal template @@ -26,60 +26,22 @@ using EigenTensor = framework::EigenTensor; using Tensor = framework::Tensor; -template -void CropFunction(const framework::ExecutionContext& context) { - auto* x = context.Input("X"); - auto* out = context.Output("Out"); - out->mutable_data(context.GetPlace()); - auto x_dims = x->dims(); - auto out_dims = out->dims(); - - auto offsets = context.op().GetAttr>("offsets"); - PADDLE_ENFORCE_EQ( - x_dims.size(), offsets.size(), - "Offsets size should be equal to dimension size of input tensor."); +int64_t transIndex(std::vector out_shape, std::vector x_shape, + std::vector> crop_rules, size_t index) { + int64_t dim_size = out_shape.size(); + int64_t pos[dim_size]; - Eigen::array, D> paddings; - for (size_t i = 0; i < D; ++i) { - paddings[i].first = -(offsets[i]); - paddings[i].second = -(x_dims[i] - out_dims[i] - offsets[i]); + for (int64_t i = out_shape.size() - 1; i >= 0; --i) { + pos[i] = (index % out_shape[i]) + crop_rules[i].first; + index = index / out_shape[i]; } - auto x_tensor = EigenTensor::From(*x); - auto out_tensor = EigenTensor::From(*out); - auto place = context.GetEigenDevice(); - out_tensor.device(place) = x_tensor.pad(paddings, 0); -} - -template -class CropKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - int dim = context.Input("X")->dims().size(); - switch (dim) { - case 1: - CropFunction(context); - break; - case 2: - CropFunction(context); - break; - case 3: - CropFunction(context); - break; - case 4: - CropFunction(context); - break; - case 5: - CropFunction(context); - break; - case 6: - CropFunction(context); - break; - default: - LOG(ERROR) << "Only ranks up to 6 supported."; - } + size_t result = pos[0]; + for (size_t i = 1; i < x_shape.size(); ++i) { + result = result * x_shape[i] + pos[i]; } -}; + return result; +} template void CropGradFunction(const framework::ExecutionContext& context) { @@ -89,7 +51,7 @@ void CropGradFunction(const framework::ExecutionContext& context) { auto d_x_dims = d_x->dims(); auto d_out_dims = d_out->dims(); - auto offsets = context.op().GetAttr>("offsets"); + auto offsets = context.op().Attr>("offsets"); Eigen::array, D> paddings; for (int i = 0; i < d_out_dims.size(); ++i) { @@ -107,9 +69,9 @@ template class CropGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - size_t dim = + size_t rank = context.Input(framework::GradVarName("Out"))->dims().size(); - switch (dim) { + switch (rank) { case 1: CropGradFunction(context); break; @@ -129,7 +91,8 @@ class CropGradKernel : public framework::OpKernel { CropGradFunction(context); break; default: - LOG(ERROR) << "Only ranks up to 6 supported."; + PADDLE_THROW( + "CropOp only support tensors with no more than 6 dimensions."); } } }; diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 8bb61275badfccec49953015a47b87b0879153bf..6574880c0eb6324b2dd175e39a364d2ef46e735e 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -19,12 +19,12 @@ template class CPUGaussianRandomKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - float mean = context.GetAttr("mean"); - float std = context.GetAttr("std"); + float mean = context.Attr("mean"); + float std = context.Attr("std"); auto* tensor = context.Output("Out"); T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = static_cast(context.GetAttr("seed")); + unsigned int seed = static_cast(context.Attr("seed")); std::minstd_rand engine; if (seed == 0) { seed = std::random_device()(); @@ -45,7 +45,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext& context) const override { auto* tensor = context.Output("Out"); - auto dims = GetAttr>("dims"); + auto dims = Attr>("dims"); std::vector temp; temp.reserve(dims.size()); for (auto dim : dims) { diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 833a82bbf293a0892531283dc681ca2edd72f6a1..d9dbc1dcfe6a6676938d64be93c879ea69148018 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -42,13 +42,13 @@ class GPUGaussianRandomKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = static_cast(context.GetAttr("seed")); + unsigned int seed = static_cast(context.Attr("seed")); if (seed == 0) { std::random_device rd; seed = rd(); } - T mean = static_cast(context.GetAttr("mean")); - T std = static_cast(context.GetAttr("std")); + T mean = static_cast(context.Attr("mean")); + T std = static_cast(context.Attr("std")); thrust::counting_iterator index_sequence_begin(0); ssize_t N = framework::product(tensor->dims()); thrust::transform(index_sequence_begin, index_sequence_begin + N, diff --git a/paddle/operators/identity_op.cc b/paddle/operators/identity_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..7d9d4fa519d1c690feacbadc5175aeab49082282 --- /dev/null +++ b/paddle/operators/identity_op.cc @@ -0,0 +1,57 @@ +/* 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/net_op.h" +#include "paddle/operators/scale_op.h" + +namespace paddle { +namespace operators { + +// The identity operator is an alias of the scale operator. This is also an +// example for creating an alias for an existing operator. +template +class IdentityOpMaker : public framework::OpProtoAndCheckerMaker { + public: + IdentityOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input tensor of identity operator."); + AddOutput("Out", "The output tensor of identity operator."); + AddComment(R"DOC( +The identity operator is an alias of the scale operator +with the attribute scale fixed to 1.0. +)DOC"); + } +}; + +template +class IdentityOp : public NetOp { + public: + IdentityOp(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("X")}}}, {{"Out", {Output("Out")}}}, + {{"scale", static_cast(1)}})); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp, + ops::IdentityOpMaker); diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index ed51d416ed9497eee45ba826ad672b8fb1ad3678..f8333f34f7b4c7b0f9a0f14a7a33f9d98e1d331c 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,8 +1,10 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context) + nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc + im2col.cu DEPS cblas device_context) else() - cc_library(math_function SRCS math_function.cc DEPS cblas device_context) + cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas 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) diff --git a/paddle/operators/math/im2col.cc b/paddle/operators/math/im2col.cc new file mode 100644 index 0000000000000000000000000000000000000000..5727c1cab16c1379ffe77f5594c057e93a042785 --- /dev/null +++ b/paddle/operators/math/im2col.cc @@ -0,0 +1,260 @@ +/* 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/im2col.h" + +namespace paddle { +namespace operators { +namespace math { + +/* + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] + */ +template +class Im2ColFunctor { + public: + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + int channels_col = input_channels * filter_height * filter_width; + + const T* im_data = im.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 c_im = c / filter_width / filter_height; + for (int h = 0; h < output_height; ++h) { + for (int w = 0; w < output_width; ++w) { + int im_row_idx = h * stride_height + h_offset; + int im_col_idx = w * stride_width + w_offset; + if ((im_row_idx - padding_height) < 0 || + (im_row_idx - padding_height) >= input_height || + (im_col_idx - padding_width) < 0 || + (im_col_idx - padding_width) >= input_width) { + col_data[(c * output_height + h) * output_width + w] = T(0); + } else { + im_row_idx += c_im * input_height - padding_height; + im_col_idx -= padding_width; + col_data[(c * output_height + h) * output_width + w] = + im_data[im_row_idx * input_width + im_col_idx]; + } + } + } + } + } +}; + +/* + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] + */ +template +class Col2ImFunctor { + public: + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + int channels_col = input_channels * filter_height * filter_width; + + T* im_data = im.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 c_im = c / filter_width / filter_height; + for (int h = 0; h < output_height; ++h) { + for (int w = 0; w < output_width; ++w) { + int im_row_idx = h * stride_height + h_offset; + int im_col_idx = w * stride_width + w_offset; + if ((im_row_idx - padding_height) >= 0 && + (im_row_idx - padding_height) < input_height && + (im_col_idx - padding_width) >= 0 && + (im_col_idx - padding_width) < input_width) { + im_row_idx += c_im * input_height - padding_height; + im_col_idx -= padding_width; + im_data[im_row_idx * input_width + im_col_idx] += + col_data[(c * output_height + h) * output_width + w]; + } + } + } + } + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +/* + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] + */ +template +class Im2ColFunctor { + public: + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + const T* im_data = im.data(); + T* col_data = col.data(); + + for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) { + for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) { + for (int channel = 0; channel < input_channels; ++channel) { + for (int filter_row_idx = 0; filter_row_idx < filter_height; + ++filter_row_idx) { + for (int filter_col_idx = 0; filter_col_idx < filter_width; + ++filter_col_idx) { + int im_row_offset = + col_row_idx * stride_height + filter_row_idx - padding_height; + int im_col_offset = + col_col_idx * stride_width + filter_col_idx - padding_width; + int col_offset = (((col_row_idx * output_width + col_col_idx) * + input_channels + + channel) * + filter_height + + filter_row_idx) * + filter_width + + filter_col_idx; + if (im_row_offset < 0 || im_row_offset >= input_height || + im_col_offset < 0 || im_col_offset >= input_width) { + col_data[col_offset] = T(0); + } else { + int im_offset = + (channel * input_height + im_row_offset) * input_width + + im_col_offset; + col_data[col_offset] = im_data[im_offset]; + } + } + } + } + } + } + } +}; + +/* + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] + */ +template +class Col2ImFunctor { + public: + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + T* im_data = im.data(); + const T* col_data = col.data(); + + for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) { + for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) { + for (int channel = 0; channel < input_channels; ++channel) { + for (int filter_row_idx = 0; filter_row_idx < filter_height; + ++filter_row_idx) { + for (int filter_col_idx = 0; filter_col_idx < filter_width; + ++filter_col_idx) { + int im_row_offset = + col_row_idx * stride_height + filter_row_idx - padding_height; + int im_col_offset = + col_col_idx * stride_width + filter_col_idx - padding_width; + int col_offset = (((col_row_idx * output_width + col_col_idx) * + input_channels + + channel) * + filter_height + + filter_row_idx) * + filter_width + + filter_col_idx; + if (im_row_offset >= 0 && im_row_offset < input_height && + im_col_offset >= 0 && im_col_offset < input_width) { + int im_offset = + (channel * input_height + im_row_offset) * input_width + + im_col_offset; + im_data[im_offset] += col_data[col_offset]; + } + } + } + } + } + } + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/im2col.cu b/paddle/operators/math/im2col.cu new file mode 100644 index 0000000000000000000000000000000000000000..9bff7bee3c95093852305d392af0949b831e5665 --- /dev/null +++ b/paddle/operators/math/im2col.cu @@ -0,0 +1,374 @@ +/* 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/im2col.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { +namespace math { + +template +__global__ void im2col(const T* data_im, int num_outs, int height, int width, + int filter_height, int filter_width, int stride_height, + int stride_width, int padding_height, int padding_width, + int output_height, int output_width, T* data_col) { + int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; + if (index < num_outs) { + int w_out = index % output_width; + index /= output_width; + int h_out = index % output_height; + int channel_in = index / output_height; + int channel_out = channel_in * filter_height * filter_width; + int h_in = h_out * stride_height; + int w_in = w_out * stride_width; + + data_col += (channel_out * output_height + h_out) * output_width + w_out; + for (int i = 0; i < filter_height; ++i) { + for (int j = 0; j < filter_width; ++j) { + int rIdx = int(h_in + i); + int cIdx = int(w_in + j); + if ((rIdx - (int)padding_height) >= (int)height || + (rIdx - (int)padding_height) < 0 || + (cIdx - (int)padding_width) >= (int)width || + (cIdx - (int)padding_width) < 0) { + *data_col = 0; + } else { + rIdx = rIdx + channel_in * height - padding_height; + cIdx = cIdx - padding_width; + *data_col = data_im[rIdx * width + cIdx]; + } + data_col += output_height * output_width; + } + } + } +} + +/* + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] + */ +template +class Im2ColFunctor { + public: + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + + int num_outputs = input_channels * output_height * output_width; + int blocks = (num_outputs + 1024 - 1) / 1024; + int block_x = 512; + int block_y = (blocks + 512 - 1) / 512; + dim3 threads(1024, 1); + dim3 grid(block_x, block_y); + im2col<<< + grid, threads, 0, + reinterpret_cast(context)->stream()>>>( + im.data(), num_outputs, input_height, input_width, filter_height, + filter_width, stride_height, stride_width, padding_height, + padding_width, output_height, output_width, col.data()); + } +}; + +template +__global__ void col2im(size_t n, const T* data_col, size_t height, size_t width, + size_t channels, size_t filter_height, + size_t filter_width, size_t stride_height, + size_t stride_width, size_t padding_height, + size_t padding_width, size_t output_height, + size_t output_width, T* data_im) { + size_t index = + (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; + if (index < n) { + T val = 0; + int w = int(index % width); + int h = int((index / width) % height); + int c = int(index / (width * height)); + if ((w - (int)padding_width) >= 0 && + (w - (int)padding_width) < (width - 2 * padding_width) && + (h - (int)padding_height) >= 0 && + (h - padding_height) < (height - 2 * padding_height)) { + // compute the start and end of the output + int w_col_start = (w < (int)filter_width) + ? 0 + : (w - int(filter_width)) / (int)stride_width + 1; + int w_col_end = + min((int)(w / (int)stride_width + 1), (int)(output_width)); + int h_col_start = (h < (int)filter_height) + ? 0 + : (h - (int)filter_height) / (int)stride_height + 1; + int h_col_end = min(int(h / stride_height + 1), int(output_height)); + 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) { + // the col location: [c * width * height + h_out, w_out] + int c_col = int(c * filter_height * filter_width) + + (h - h_col * (int)stride_height) * (int)filter_width + + (w - w_col * (int)stride_width); + val += + data_col[(c_col * output_height + h_col) * output_width + w_col]; + } + } + h -= padding_height; + w -= padding_width; + data_im[c * ((width - 2 * padding_width) * + (height - 2 * padding_height)) + + h * (width - 2 * padding_width) + w] += val; + } + } +} + +/* + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] + */ +template +class Col2ImFunctor { + public: + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + + size_t num_kernels = input_channels * (input_height + 2 * padding_height) * + (input_width + 2 * padding_width); + + size_t blocks = (num_kernels + 1024 - 1) / 1024; + size_t block_x = 512; + size_t block_y = (blocks + 512 - 1) / 512; + dim3 threads(1024, 1); + dim3 grid(block_x, block_y); + + // To avoid involving atomic operations, we will launch one kernel per + // bottom dimension, and then in the kernel add up the top dimensions. + col2im<<< + grid, threads, 0, + reinterpret_cast(context)->stream()>>>( + num_kernels, col.data(), input_height + 2 * padding_height, + input_width + 2 * padding_width, input_channels, filter_height, + filter_width, stride_height, stride_width, padding_height, + padding_width, output_height, output_width, im.data()); + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +template +__global__ void im2colOCF(const T* im_data, T* col_data, int input_channels, + int input_height, int input_width, int filter_height, + int filter_width, int stride_height, int stride_width, + int padding_height, int padding_width, + int output_height, int output_width) { + int swid = blockIdx.x; + int shid = blockIdx.y; + for (int channelid = threadIdx.z; channelid < input_channels; + channelid += blockDim.z) { + for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) { + int width_offset = idx + swid * stride_width - padding_width; + int height_offset = idy + shid * stride_height - padding_height; + int im_offset = width_offset + height_offset * input_width + + channelid * input_height * input_width; + + int col_offset = idx + idy * filter_width + + channelid * filter_height * filter_width + + (shid * output_width + swid) * + (input_channels * filter_height * filter_width); + + if (height_offset >= input_height || height_offset < 0 || + width_offset >= input_width || width_offset < 0) { + col_data[col_offset] = T(0); + } else { + col_data[col_offset] = im_data[im_offset]; + } + } + } + } +} + +/* + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] + */ +template +class Im2ColFunctor { + public: + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + int block_dim_x = 0; + int block_dim_y = 0; + if (filter_height <= 4 && filter_width <= 4) { + block_dim_x = 4; + block_dim_y = 4; + } else if (filter_height <= 8 && filter_width <= 8) { + block_dim_x = 8; + block_dim_y = 8; + } else if (filter_height <= 16 && filter_width <= 16) { + block_dim_x = 16; + block_dim_y = 16; + } else { + block_dim_x = 32; + block_dim_y = 32; + } + + int block_dim_z = 1024 / block_dim_x / block_dim_y; + dim3 threads(block_dim_x, block_dim_y, + std::min(block_dim_z, input_channels)); + dim3 grid(output_width, output_height); + im2colOCF<<< + grid, threads, 0, + reinterpret_cast(context)->stream()>>>( + im.data(), col.data(), input_channels, input_height, input_width, + filter_height, filter_width, stride_height, stride_width, + padding_height, padding_width, output_height, output_width); + } +}; + +template +__global__ void col2imOCF(T* im_data, const T* col_data, int input_channels, + int input_height, int input_width, int filter_height, + int filter_width, int stride_height, int stride_width, + int padding_height, int padding_width, + int output_height, int output_width) { + int swid = blockIdx.x; + int shid = blockIdx.y; + for (int channelid = threadIdx.z; channelid < input_channels; + channelid += blockDim.z) { + for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) { + int width_offset = idx + swid * stride_width - padding_width; + int height_offset = idy + shid * stride_height - padding_height; + int im_offset = width_offset + height_offset * input_width + + channelid * input_height * input_width; + + int col_offset = idx + idy * filter_width + + channelid * filter_height * filter_width + + (shid * output_width + swid) * + (input_channels * filter_height * filter_width); + + if (height_offset >= 0 && height_offset < input_height && + width_offset >= 0 && width_offset < input_width) { + paddle::platform::CudaAtomicAdd(im_data + im_offset, + col_data[col_offset]); + } + } + } + } +} + +/* + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] + */ +template +class Col2ImFunctor { + public: + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + int block_dim_x = 0; + int block_dim_y = 0; + if (filter_height <= 4 && filter_width <= 4) { + block_dim_x = 4; + block_dim_y = 4; + } else if (filter_height <= 8 && filter_width <= 8) { + block_dim_x = 8; + block_dim_y = 8; + } else if (filter_height <= 16 && filter_width <= 16) { + block_dim_x = 16; + block_dim_y = 16; + } else { + block_dim_x = 32; + block_dim_y = 32; + } + + int block_dim_z = 1024 / block_dim_x / block_dim_y; + dim3 threads(block_dim_x, block_dim_y, + std::min(block_dim_z, input_channels)); + dim3 grid(output_width, output_height); + col2imOCF<<< + grid, threads, 0, + reinterpret_cast(context)->stream()>>>( + im.data(), col.data(), input_channels, input_height, input_width, + filter_height, filter_width, stride_height, stride_width, + padding_height, padding_width, output_height, output_width); + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h new file mode 100644 index 0000000000000000000000000000000000000000..8958c5457cc2c3034c34ca82fb2e98cc06be63c5 --- /dev/null +++ b/paddle/operators/math/im2col.h @@ -0,0 +1,90 @@ +/* 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 { + +/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */ +enum class ColFormat { kCFO = 0, kOCF = 1 }; + +/* + * \brief Converts the image data of three dimensions(CHW) into a colData of + * five dimensions in the Im2ColFunctor calculation, + * And in the Col2ImFunctor calculation, it is reversed. + * + * \param imData Image data. + * \param imShape The shape of imData, + * [input_channels, input_height, input_width]. + * \param colData Column data. + * \param colShape The shape of colData. + * + * If the template argument Format is kCFO, the shape of colData is: + * [input_channels, filter_height, filter_width, 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_height * filter_width, and the width is equal + * output_height * output_width. + * + * Reshape: + * shape of colData shape of convolution matrix + * [input_channels, + * filter_height, + * filter_width, ======> [height, width] + * output_height, + * output_width] + * + * If the template argument Format is kOCF, the shape of colData is: + * [output_height, output_width, input_channels, filter_height, filter_width] + * So, it is easy to reshape into a sequence matrix for rnn calculation. + * The shape of sequence matrix is [seq_length, step_size], where the seq_length + * is equal output_height * output_width, and the step_size is equal + * input_channels * filter_height * filter_width. + * + * Reshape: + * shape of colData shape of sequence matrix + * [output_height, + * output_width, + * input_channels, ======> [seqLength, stepSize] + * filter_height, + * filter_width] + * + * \note The caller needs to ensure that imShape.inputChannels is equal to + * colShape.inputChannels. + */ +template +class Im2ColFunctor { + public: + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context); +}; + +template +class Col2ImFunctor { + public: + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width, platform::DeviceContext* context); +}; + +} // namespace math +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/im2col_test.cc b/paddle/operators/math/im2col_test.cc new file mode 100644 index 0000000000000000000000000000000000000000..186a33edcec88bd5e51091a524a778eeb27ad526 --- /dev/null +++ b/paddle/operators/math/im2col_test.cc @@ -0,0 +1,122 @@ +/* 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/im2col.h" +#include +#include + +template +void testIm2col() { + paddle::framework::Tensor input_tmp; + paddle::framework::Tensor input; + paddle::framework::Tensor output_cfo; + paddle::framework::Tensor output_ocf; + paddle::framework::Tensor output_tmp; + + /** + * input = [0, 1, 2, + * 3, 4, 5] + * + * output_cfo = [0, 1 + * 1, 2 + * 3, 4 + * 4, 5] + * + * output_ocf = [0, 1, 3, 4 + * 1, 2, 4, 5] + */ + int input_height = 2; + int input_width = 3; + int filter_size = 2; + int stride = 1; + int padding = 0; + int output_height = (input_height - filter_size + 2 * padding) / stride + 1; + int output_width = (input_width - filter_size + 2 * padding) / stride + 1; + float* input_ptr = input_tmp.mutable_data( + {1, input_height, input_width}, paddle::platform::CPUPlace()); + float arr[6] = {0, 1, 2, 3, 4, 5}; + memcpy(input_ptr, arr, 6 * sizeof(float)); + + auto* place = new Place(); + if (paddle::platform::is_cpu_place(*place)) { + input = input_tmp; + } else { + input.CopyFrom(input_tmp, *place); + } + output_cfo.mutable_data( + {1, filter_size, filter_size, output_height, output_width}, *place); + output_ocf.mutable_data( + {output_height, output_width, 1, filter_size, filter_size}, *place); + + paddle::operators::math::Im2ColFunctor< + paddle::operators::math::ColFormat::kCFO, Place, float> + im2col; + paddle::operators::math::Im2ColFunctor< + paddle::operators::math::ColFormat::kOCF, Place, float> + im2col_ocf; + + paddle::platform::DeviceContext* context; + if (paddle::platform::is_cpu_place(*place)) { + context = + new paddle::platform::CPUDeviceContext(paddle::platform::CPUPlace()); + } else { +#ifndef PADDLE_ONLY_CPU + context = + new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace()); +#else + PADDLE_THROW("no GPU support"); +#endif // PADDLE_ONLY_CPU + } + im2col(input, output_cfo, stride, stride, padding, padding, context); + im2col_ocf(input, output_ocf, stride, stride, padding, padding, context); + + float* out_cfo_ptr; + if (paddle::platform::is_cpu_place(*place)) { + out_cfo_ptr = output_cfo.data(); + } else { + output_tmp.CopyFrom(output_cfo, paddle::platform::CPUPlace()); + out_cfo_ptr = output_tmp.data(); + } + EXPECT_EQ(out_cfo_ptr[0], 0); + EXPECT_EQ(out_cfo_ptr[1], 1); + EXPECT_EQ(out_cfo_ptr[2], 1); + EXPECT_EQ(out_cfo_ptr[3], 2); + EXPECT_EQ(out_cfo_ptr[4], 3); + EXPECT_EQ(out_cfo_ptr[5], 4); + EXPECT_EQ(out_cfo_ptr[6], 4); + EXPECT_EQ(out_cfo_ptr[7], 5); + + float* out_ocf_ptr; + if (paddle::platform::is_cpu_place(*place)) { + out_ocf_ptr = output_ocf.data(); + } else { + output_tmp.CopyFrom(output_ocf, paddle::platform::CPUPlace()); + out_ocf_ptr = output_tmp.data(); + } + EXPECT_EQ(out_ocf_ptr[0], 0); + EXPECT_EQ(out_ocf_ptr[1], 1); + EXPECT_EQ(out_ocf_ptr[2], 3); + EXPECT_EQ(out_ocf_ptr[3], 4); + EXPECT_EQ(out_ocf_ptr[4], 1); + EXPECT_EQ(out_ocf_ptr[5], 2); + EXPECT_EQ(out_ocf_ptr[6], 4); + EXPECT_EQ(out_ocf_ptr[7], 5); +} + +TEST(math, im2col) { + testIm2col(); +#ifndef PADDLE_ONLY_CPU + testIm2col(); +#endif +} \ No newline at end of file diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 28a47cdff2e9b7a965ff9f99e787bb8315010823..710a56a0e8e2d17162d7d000df226f1537104eb9 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -25,18 +25,27 @@ class MulOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - auto dim0 = ctx.Input("X")->dims(); - auto dim1 = ctx.Input("Y")->dims(); - PADDLE_ENFORCE_EQ(dim0.size(), 2, - "input X(%s) should be a tensor with 2 dims, a matrix", - ctx.op().Input("X")); - PADDLE_ENFORCE_EQ(dim1.size(), 2, - "input Y(%s) should be a tensor with 2 dims, a matrix", - ctx.op().Input("Y")); + auto x_dims = ctx.Input("X")->dims(); + auto y_dims = ctx.Input("Y")->dims(); + int x_num_col_dims = Attr("x_num_col_dims"); + int y_num_col_dims = Attr("y_num_col_dims"); + + PADDLE_ENFORCE(x_dims.size() > x_num_col_dims, + "The rank of input tensor X(%s) should be larger than " + "`mul_op`'s `x_num_col_dims`.", + ctx.op().Input("X")); + PADDLE_ENFORCE(y_dims.size() > y_num_col_dims, + "The rank of input tensor Y(%s) should be larger than " + "`mul_op`'s `y_num_col_dims`.", + ctx.op().Input("Y")); + + 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); + PADDLE_ENFORCE_EQ( - dim0[1], dim1[0], + x_mat_dims[1], y_mat_dims[0], "First matrix's width must be equal with second matrix's height."); - ctx.Output("Out")->Resize({dim0[0], dim1[1]}); + ctx.Output("Out")->Resize({x_mat_dims[0], y_mat_dims[1]}); } }; @@ -47,6 +56,23 @@ class MulOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("X", "The first input of mul op"); AddInput("Y", "The second input of mul op"); AddOutput("Out", "The output of mul op"); + AddAttr( + "x_num_col_dims", + R"DOC(mul_op can take tensors with more than two dimensions as input `X`, + in that case, tensors will be reshaped to a matrix. The matrix's first + dimension(column length) will be the product of tensor's last + `num_col_dims` dimensions, and the matrix's second dimension(row length) + will be the product of tensor's first `rank - num_col_dims` dimensions. + )DOC") + .SetDefault(1) + .EqualGreaterThan(1); + AddAttr( + "y_num_col_dims", + R"DOC(mul_op can take tensors with more than two dimensions as input `Y`, + in that case, tensors will be reshaped to a matrix. Just like input `X`. + )DOC") + .SetDefault(1) + .EqualGreaterThan(1); AddComment(R"DOC( Two Element Mul Operator. @@ -70,10 +96,20 @@ class MulOpGrad : public framework::OperatorWithKernel { auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); auto *x_grad = ctx.Output(framework::GradVarName("X")); auto *y_grad = ctx.Output(framework::GradVarName("Y")); - PADDLE_ENFORCE(x_dims[0] == out_dims[0], - "Out@GRAD M X N must equal to X dims 0, M "); - PADDLE_ENFORCE(y_dims[1] == out_dims[1], - "Out@GRAD M X N must equal to Y dims 1, N "); + + auto x_mat_dims = + framework::flatten_to_2d(x_dims, Attr("x_num_col_dims")); + auto y_mat_dims = + framework::flatten_to_2d(y_dims, Attr("y_num_col_dims")); + + PADDLE_ENFORCE_EQ( + x_mat_dims[0], out_dims[0], + "The first dimension of Out@GRAD must equal to the first dimension of " + "the first operand."); + PADDLE_ENFORCE_EQ( + y_mat_dims[1], out_dims[1], + "The second dimension of Out@GRAD must equal to the second " + "dimension of the second operand."); if (x_grad) x_grad->Resize(x_dims); if (y_grad) y_grad->Resize(y_dims); diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index 05a79e13b3470e39a5ebd0394ba05629553a5075..3c01f868bda8cba488b3403df456d63d6b082fa6 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -1,7 +1,7 @@ /* 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 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 @@ -31,13 +31,25 @@ template class MulKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto* x = context.Input("X"); - auto* y = context.Input("Y"); - auto* z = context.Output("Out"); + const Tensor* x = context.Input("X"); + const Tensor* y = context.Input("Y"); + Tensor* z = context.Output("Out"); + const Tensor x_matrix = + x->dims().size() > 2 + ? framework::ReshapeToMatrix( + *x, context.template Attr("x_num_col_dims")) + : *x; + const Tensor y_matrix = + y->dims().size() > 2 + ? framework::ReshapeToMatrix( + *y, context.template Attr("y_num_col_dims")) + : *y; + z->mutable_data(context.GetPlace()); auto* device_context = const_cast(context.device_context_); - math::matmul(*x, false, *y, false, 1, z, 0, device_context); + math::matmul(x_matrix, false, y_matrix, false, 1, z, 0, + device_context); } }; @@ -45,23 +57,39 @@ template class MulGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - auto* x = ctx.Input("X"); - auto* y = ctx.Input("Y"); - auto* dout = ctx.Input(framework::GradVarName("Out")); + int x_num_col_dims = ctx.template Attr("x_num_col_dims"); + int y_num_col_dims = ctx.template Attr("y_num_col_dims"); + const Tensor* x = ctx.Input("X"); + const Tensor* y = ctx.Input("Y"); + const Tensor x_matrix = + x->dims().size() > 2 ? framework::ReshapeToMatrix(*x, x_num_col_dims) + : *x; + const Tensor y_matrix = + y->dims().size() > 2 ? framework::ReshapeToMatrix(*y, y_num_col_dims) + : *y; + const Tensor* dout = ctx.Input(framework::GradVarName("Out")); - auto* dx = ctx.Output(framework::GradVarName("X")); - auto* dy = ctx.Output(framework::GradVarName("Y")); + Tensor* dx = ctx.Output(framework::GradVarName("X")); + Tensor* dy = ctx.Output(framework::GradVarName("Y")); auto* device_context = const_cast(ctx.device_context_); if (dx) { dx->mutable_data(ctx.GetPlace()); + Tensor dx_matrix = dx->dims().size() > 2 ? framework::ReshapeToMatrix( + *dx, x_num_col_dims) + : *dx; // dx = dout * y'. dx: M x K, dout : M x N, y : K x N - math::matmul(*dout, false, *y, true, 1, dx, 0, device_context); + math::matmul(*dout, false, y_matrix, true, 1, &dx_matrix, 0, + device_context); } if (dy) { dy->mutable_data(ctx.GetPlace()); + Tensor dy_matrix = dy->dims().size() > 2 ? framework::ReshapeToMatrix( + *dy, y_num_col_dims) + : *dy; // dy = x' * dout. dy K x N, dout : M x N, x : M x K - math::matmul(*x, true, *dout, false, 1, dy, 0, device_context); + math::matmul(x_matrix, true, *dout, false, 1, &dy_matrix, 0, + device_context); } } }; diff --git a/paddle/operators/rnn/recurrent_op_utils.cc b/paddle/operators/rnn/recurrent_op_utils.cc index 69e723b4019fe553426bafbf02b3334ea4acfcf1..97872c67ac99fbf6c9c177d52f1d4069163e8548 100644 --- a/paddle/operators/rnn/recurrent_op_utils.cc +++ b/paddle/operators/rnn/recurrent_op_utils.cc @@ -109,7 +109,7 @@ void InitArgument(const ArgumentName& name, Argument* arg, arg->step_scopes = op.Output(name.step_scopes); auto inlinks = op.Inputs(name.inlinks); - auto inlink_alias = op.GetAttr>(name.inlink_alias); + auto inlink_alias = op.Attr>(name.inlink_alias); PADDLE_ENFORCE(inlinks.size() == inlink_alias.size(), "the size of inlinks and inlink_alias don't match:%d,%d", inlinks.size(), inlink_alias.size()); @@ -121,7 +121,7 @@ void InitArgument(const ArgumentName& name, Argument* arg, } auto outlinks = op.Outputs(name.outlinks); - auto outlink_alias = op.GetAttr>(name.outlink_alias); + auto outlink_alias = op.Attr>(name.outlink_alias); PADDLE_ENFORCE(outlinks.size() == outlink_alias.size(), "the size of outlinks and outlink_alias don't match:%d,%d", outlinks.size(), outlink_alias.size()); @@ -135,8 +135,8 @@ void InitArgument(const ArgumentName& name, Argument* arg, auto boot_memories = op.Inputs(name.boot_memories); // attributes - auto memories = op.GetAttr>(name.memories); - auto pre_memories = op.GetAttr>(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/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index 30b4b404315a9f041e21d79b75fd06307e33f7f9..fa8f0ff1a858143af427b51025279c726f1628e0 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -25,14 +25,19 @@ class RowwiseAddOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - auto dim0 = ctx.Input("X")->dims(); - auto dim1 = ctx.Input("b")->dims(); - - PADDLE_ENFORCE(dim0.size() == 2, "Input 0 must be matrix"); - PADDLE_ENFORCE(dim1.size() == 1, "The second input must be vector"); - PADDLE_ENFORCE(dim0[1] == dim1[0], "The width of two input must be same"); - PADDLE_ENFORCE(ctx.OutputSize("Out") == 1, "The output size must be 1"); - ctx.Output("Out")->Resize(ctx.Input("X")->dims()); + auto x_dims = ctx.Input("X")->dims(); + auto b_dims = ctx.Input("b")->dims(); + 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.OutputSize("Out"), 1, "The output size must be 1"); + ctx.Output("Out")->Resize(x_dims); } }; @@ -61,13 +66,20 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); - auto dims0 = ctx.Input("X")->dims(); - auto dims1 = ctx.Input("b")->dims(); - PADDLE_ENFORCE_EQ(1, dims1.size(), "b dims should be 1") + auto x_dims = ctx.Input("X")->dims(); + auto b_dims = ctx.Input("b")->dims(); + 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"); auto *dx = ctx.Output(framework::GradVarName("X")); auto *db = ctx.Output(framework::GradVarName("b")); - if (dx) dx->Resize(dims0); - if (db) db->Resize(dims1); + if (dx) dx->Resize(x_dims); + if (db) db->Resize(b_dims); } }; diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 4e926d9f2947f37b71e81c0fa592b0c66b19c640..35774b940926f77167b8f19597027e74d3477e5b 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -33,10 +33,12 @@ class RowwiseAddKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { auto out = context.Output("Out"); out->mutable_data(context.GetPlace()); - - auto input = EigenMatrix::From(*context.Input("X")); - auto bias = EigenVector::From(*context.Input("b")); - auto output = EigenMatrix::From(*out); + 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; @@ -54,12 +56,15 @@ class RowwiseAddGradKernel : public framework::OpKernel { 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::From(*dout); + auto out_grad = EigenMatrix::Reshape(*dout, num_col_dims); auto place = context.GetEigenDevice(); + if (dx) { dx->mutable_data(context.GetPlace()); - EigenMatrix::From(*dx).device(place) = out_grad; + EigenMatrix::Reshape(*dx, num_col_dims).device(place) = out_grad; } if (db) { diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc index 8e96a74c94ab7ff4d8c3266695e5157aff67905b..ea991f683d841b3dc4624a0d8aa3c88367fd3c6d 100644 --- a/paddle/operators/scale_op.cc +++ b/paddle/operators/scale_op.cc @@ -44,11 +44,13 @@ class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { The equation is: Out = scale*X )DOC"); - AddAttr("scale", "scale of scale operator.").SetDefault(1.0); + AddAttr("scale", "The scaling factor of the scale operator.") + .SetDefault(1.0); } }; -// Identity Op's gradient is identity op, too. +// 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 { @@ -60,38 +62,11 @@ class ScaleGradOp : public NetOp { AppendOp(framework::OpRegistry::CreateOp( "scale", {{"X", {Input(framework::GradVarName("Out"))}}}, {{"Out", {Output(framework::GradVarName("X"))}}}, - {{"scale", GetAttr("scale")}})); + {{"scale", Attr("scale")}})); CompleteAddOp(false); } }; -// identity is a alias of scale op. This is also a example for creating a alias -// operator. -template -class IdentityOpMaker : public framework::OpProtoAndCheckerMaker { - public: - IdentityOpMaker(framework::OpProto *proto, - framework::OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "input tensor of identity op"); - AddOutput("Out", "output tensor of identity op"); - AddComment("identity operator. Just a alias of scale op which scale = 1.0"); - } -}; - -template -class IdentityOp : public NetOp { - public: - IdentityOp(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("X")}}}, {{"Out", {Output("Out")}}}, - {{"scale", static_cast(1)}})); - } -}; - } // namespace operators } // namespace paddle @@ -101,5 +76,3 @@ REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker, scale_grad, ops::ScaleGradOp); REGISTER_OP_CPU_KERNEL(scale, ops::ScaleKernel); -REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp, - ops::IdentityOpMaker); diff --git a/paddle/operators/scale_op.h b/paddle/operators/scale_op.h index 65fb77eefad812fa52ac053b791ba1b8f480375f..02fbdc52bbf89c9f2acc5eeaa1197e4ccbca9d31 100644 --- a/paddle/operators/scale_op.h +++ b/paddle/operators/scale_op.h @@ -27,7 +27,7 @@ class ScaleKernel : public framework::OpKernel { auto* in = context.Input("X"); tensor->mutable_data(in->place()); - auto scale = static_cast(context.GetAttr("scale")); + auto scale = static_cast(context.Attr("scale")); auto eigen_out = framework::EigenVector::Flatten(*tensor); auto eigen_in = framework::EigenVector::Flatten(*in); diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h index 8422b622ee54ba76fb98b7dacfa9618031c1c88c..f8888f9c362e1c39af42236bb3a23be37aa3ae15 100644 --- a/paddle/operators/sgd_op.h +++ b/paddle/operators/sgd_op.h @@ -31,7 +31,7 @@ class SGDOpKernel : public framework::OpKernel { auto param = ctx.Input("param"); auto grad = ctx.Input("grad"); auto param_out = ctx.Output("param_out"); - float lr = ctx.GetAttr("learning_rate"); + float lr = ctx.Attr("learning_rate"); param_out->mutable_data(ctx.GetPlace()); diff --git a/paddle/operators/softmax_op.cc b/paddle/operators/softmax_op.cc index 7d062ad67c048bc6bef68121f86334eb3f1efe92..7166b2f60be8a6088ab3a81686f7bed1b7181d97 100644 --- a/paddle/operators/softmax_op.cc +++ b/paddle/operators/softmax_op.cc @@ -51,7 +51,7 @@ the other dimensions in the K-dimensional vector input. Then the ratio of the exponential of the given dimension and the sum of exponential values of all the other dimensions is the output of the softmax operator. -For each row `i` and each column `j` in X, we have: +For each row `i` and each column `j` in input X, we have: Y[i, j] = exp(X[i, j]) / sum_j(exp(X[i, j])) )DOC"); @@ -64,14 +64,15 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - PADDLE_ENFORCE(ctx.InputVar("Y") != nullptr, "Input(Y) should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should be not null."); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")), - "Input(Y@GRAD) should not be null"); - PADDLE_ENFORCE(ctx.Input("Y")->dims() == - ctx.Input(framework::GradVarName("Y"))->dims(), - "the shape of Input(0) and Input(1) should be the same"); + "Input(Y@GRAD) should be not null."); + PADDLE_ENFORCE_EQ(ctx.Input("Y")->dims(), + ctx.Input(framework::GradVarName("Y"))->dims(), + "Input(Y) and its gradients should have a same shape."); + ctx.Output(framework::GradVarName("X")) - ->Resize(ctx.Input("Y")->dims()); + ->Resize(ctx.Input("X")->dims()); } }; diff --git a/paddle/operators/softmax_op.h b/paddle/operators/softmax_op.h index 4fa6b59540498638c3b7df639ae10a66c0fa1c16..8a3a5ab927c0e2937936fcc973f000d4d95c3dbc 100644 --- a/paddle/operators/softmax_op.h +++ b/paddle/operators/softmax_op.h @@ -28,12 +28,12 @@ template class SoftmaxKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto input = context.Input("X"); - auto output = context.Output("Y"); - output->mutable_data(context.GetPlace()); + auto X = context.Input("X"); + auto Y = context.Output("Y"); + Y->mutable_data(context.GetPlace()); - auto logits = EigenMatrix::From(*input); - auto softmax = EigenMatrix::From(*output); + auto logits = EigenMatrix::From(*X); + auto softmax = EigenMatrix::From(*Y); const int kBatchDim = 0; const int kClassDim = 1; diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..dc30644a5e7e33d4289e48cac093aa5fde7e75e7 --- /dev/null +++ b/paddle/operators/squared_l2_distance_op.cc @@ -0,0 +1,118 @@ +/* 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/squared_l2_distance_op.h" + +namespace paddle { +namespace operators { + +class SquaredL2DistanceOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext& ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), + "Input of SquaredL2DistanceOp " + "must be initialized."); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), + "Target of SquaredL2DistanceOp " + "must be initialized."); + + auto* x = ctx.Input("X"); + auto x_dims = x->dims(); + auto* y = ctx.Input("Y"); + auto y_dims = y->dims(); + + PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims), + "Tensor rank of both SquaredL2DistanceOp's " + "inputs must be same."); + + int rank = framework::arity(x_dims); + PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2."); + PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0], + framework::product(y_dims) / y_dims[0], + "Product of dimensions expcet the first dimension of " + "input and target must be equal."); + PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0], + "First dimension of target must be equal to input " + "or to 1."); + + ctx.Output("sub_result") + ->Resize({static_cast(x_dims[0]), + static_cast(framework::product(x_dims) / x_dims[0])}); + ctx.Output("Out")->Resize({x_dims[0], 1}); + } +}; + +class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SquaredL2DistanceOpMaker(framework::OpProto* proto, + framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input of SquaredL2DistanceOp."); + AddInput("Y", "Target of SquaredL2DistanceOp."); + AddOutput("sub_result", + "Buffering substraction result which " + "will be reused in backward.") + .AsIntermediate(); + AddOutput("Out", "Squared l2 distance between input and target."); + AddComment(R"DOC( + SquaredL2DistanceOp will cacluate the squared L2 distance for + input and target. Number of distance value equals to the + first dimension of input. First dimension of target could be equal to + input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp + will broadcast target's first dimension to input's first dimension. + You can decide whether calculate the gradient of input and target. + )DOC"); + } +}; + +class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext& ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Gradient of Out should not be null"); + auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); + auto x_dims = ctx.Input("X")->dims(); + auto y_dims = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0], + "First dimension of output gradient and " + "input value must be equal."); + PADDLE_ENFORCE_EQ(out_dims[1], 1, + "Second dimension of output gradient " + "must be 1."); + auto* x_grad = ctx.Output(framework::GradVarName("X")); + auto* y_grad = ctx.Output(framework::GradVarName("Y")); + if (x_grad) x_grad->Resize(x_dims); + if (y_grad) y_grad->Resize(y_dims); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(squared_l2_distance, ops::SquaredL2DistanceOp, + ops::SquaredL2DistanceOpMaker, squared_l2_distance_grad, + ops::SquaredL2DistanceGradOp); +REGISTER_OP_CPU_KERNEL( + squared_l2_distance, + ops::SquaredL2DistanceKernel); +REGISTER_OP_CPU_KERNEL( + squared_l2_distance_grad, + ops::SquaredL2DistanceGradKernel); diff --git a/paddle/operators/gather_op.cu b/paddle/operators/squared_l2_distance_op.cu similarity index 69% rename from paddle/operators/gather_op.cu rename to paddle/operators/squared_l2_distance_op.cu index 3f04a7b3f8142106917975cd1e0413fa1633a298..3fe62f1a9cb56722ea544b0fed052ac384e799aa 100644 --- a/paddle/operators/gather_op.cu +++ b/paddle/operators/squared_l2_distance_op.cu @@ -13,8 +13,13 @@ limitations under the License. */ #define EIGEN_USE_GPU -#include "paddle/operators/gather_op.h" + +#include "paddle/operators/squared_l2_distance_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(gather, - ops::GatherOpKernel); +REGISTER_OP_GPU_KERNEL( + squared_l2_distance, + ops::SquaredL2DistanceKernel); +REGISTER_OP_GPU_KERNEL( + squared_l2_distance_grad, + ops::SquaredL2DistanceGradKernel); diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h new file mode 100644 index 0000000000000000000000000000000000000000..ad3347a0b35f3385c5adbcd7ceaa94fe134105e3 --- /dev/null +++ b/paddle/operators/squared_l2_distance_op.h @@ -0,0 +1,123 @@ +/* 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 SquaredL2DistanceKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* in0 = context.Input("X"); + auto* in1 = context.Input("Y"); + auto* out0 = context.Output("sub_result"); + auto* out1 = context.Output("Out"); + + auto in0_dims = in0->dims(); + auto in1_dims = in1->dims(); + + int cols = framework::product(in0_dims) / in0_dims[0]; + // reduce dimensions except the first + auto x = + EigenMatrix::From(*in0, framework::make_ddim({in0_dims[0], cols})); + auto y = + EigenMatrix::From(*in1, framework::make_ddim({in1_dims[0], cols})); + + out0->mutable_data(context.GetPlace()); + out1->mutable_data(context.GetPlace()); + auto sub_result = EigenMatrix::From(*out0); + auto z = EigenVector::Flatten(*out1); + + auto place = context.GetEigenDevice(); + auto x_dims = x.dimensions(); + auto y_dims = y.dimensions(); + // buffer the substraction result + if (y_dims[0] == 1 && x_dims[0] > y_dims[0]) { + sub_result.device(place) = + x - + y.broadcast(Eigen::array({{static_cast(x_dims[0]), 1}})); + } else { + sub_result.device(place) = x - y; + } + auto sub_res_pow2 = sub_result * sub_result; + z.device(place) = sub_res_pow2.sum(Eigen::array({{1}})); + } +}; + +template +class SquaredL2DistanceGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* in0 = context.Input("sub_result"); + auto* in1 = context.Input(framework::GradVarName("Out")); + auto* x_g = context.Output(framework::GradVarName("X")); + auto* y_g = context.Output(framework::GradVarName("Y")); + + auto sub_result = EigenMatrix::From(*in0); + auto out_grad = EigenMatrix::From(*in1); + + auto x_dims = x_g->dims(); + auto y_dims = y_g->dims(); + + int cols = framework::product(x_dims) / x_dims[0]; + // calculate gradient + auto grad_mat = 2 * + (out_grad.broadcast(Eigen::array({{1, cols}}))) * + sub_result; + + // propagate back to input + auto eigen_place = context.GetEigenDevice(); + if (x_g) { + x_g->mutable_data(context.GetPlace()); + // eigen matrix + auto x_grad = + EigenMatrix::From(*x_g, framework::make_ddim({x_dims[0], cols})); + // dimensions are same with subResult + x_grad.device(eigen_place) = grad_mat; + } + + if (y_g) { + y_g->mutable_data(context.GetPlace()); + + PADDLE_ENFORCE_GE(sub_result.dimensions()[0], y_dims[0], + "First dimension of gradient must be greater or " + "equal than first dimension of target."); + + if (sub_result.dimensions()[0] == y_dims[0]) { + auto y_grad = + EigenMatrix::From(*y_g, framework::make_ddim({y_dims[0], cols})); + y_grad.device(eigen_place) = -1 * grad_mat; + } else { + auto col_sum_res = -1 * (grad_mat.sum(Eigen::array({{0}}))); + auto y_grad = EigenVector::Flatten(*y_g); + y_grad.device(eigen_place) = col_sum_res; + } + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/sum_op.cc b/paddle/operators/sum_op.cc new file mode 100644 index 0000000000000000000000000000000000000000..5805826ee8a555ca6dfc1ca81feaadffea9e1012 --- /dev/null +++ b/paddle/operators/sum_op.cc @@ -0,0 +1,73 @@ +/* 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/sum_op.h" +#include + +namespace paddle { +namespace operators { +using framework::Tensor; + +class SumOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto ins = ctx.MultiInput("X"); + auto *out = ctx.Output("Out"); + int N = ins.size(); + + auto in_dim = ins[0]->dims(); + + PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1."); + for (int i = 1; i < N; i++) { + auto dim = ins[i]->dims(); + PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape"); + } + out->Resize(in_dim); + } +}; + +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(R"DOC( + Sum the input tensors. + )DOC"); + } +}; + +class SumGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto outputs = ctx.MultiOutput(framework::GradVarName("X")); + auto dims = ctx.Input(framework::GradVarName("Out"))->dims(); + for (auto output : outputs) { + output->Resize(dims); + } + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(sum, ops::SumOp, ops::SumOpMaker, sum_grad, ops::SumGradOp); +REGISTER_OP_CPU_KERNEL(sum, ops::SumKernel); +REGISTER_OP_CPU_KERNEL(sum_grad, + ops::SumGradKernel); diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/sum_op.cu similarity index 68% rename from paddle/operators/scatter_op.cu rename to paddle/operators/sum_op.cu index 6716b478833ff3adb6112cdb1ee25b7f1744ea1f..a465cf3659ba7c51338abadfc62962fb6755a39d 100644 --- a/paddle/operators/scatter_op.cu +++ b/paddle/operators/sum_op.cu @@ -1,11 +1,8 @@ /* 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 - +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. @@ -13,8 +10,9 @@ See the License for the specific language governing permissions and limitations under the License. */ #define EIGEN_USE_GPU -#include "paddle/operators/scatter_op.h" +#include "paddle/operators/sum_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(scatter, - ops::ScatterOpKernel); +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 new file mode 100644 index 0000000000000000000000000000000000000000..0b1e9ebaa38d455fb5e3ce8c1a39cbbcdad9a940 --- /dev/null +++ b/paddle/operators/sum_op.h @@ -0,0 +1,65 @@ +/* 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 SumKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto ins = context.MultiInput("X"); + auto* out = context.Output("Out"); + out->mutable_data(context.GetPlace()); + + auto place = context.GetEigenDevice(); + auto result = EigenVector::Flatten(*out); + + int N = ins.size(); + auto in = EigenVector::Flatten(*(ins[0])); + result.device(place) = in; + for (int i = 1; i < N; i++) { + auto in = EigenVector::Flatten(*(ins[i])); + result.device(place) = result + in; + } + } +}; + +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 new file mode 100644 index 0000000000000000000000000000000000000000..38d2f0a09aec751734864947a2f3cfa20107e22f --- /dev/null +++ b/paddle/operators/top_k_op.cc @@ -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. */ + +#include "paddle/operators/top_k_op.h" + +namespace paddle { +namespace operators { + +class TopkOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), + "Input of TopkOP must be initialized."); + auto *input = ctx.Input("X"); + const int k = static_cast(ctx.Attr("k")); + + PADDLE_ENFORCE_GE(k, 1, "k must >= 1"); + PADDLE_ENFORCE_GE(input->dims().size(), 1, "input must have >= 1d shape"); + PADDLE_ENFORCE_GE(input->dims()[input->dims().size() - 1], k, + "input must have >= k columns"); + + framework::DDim dims = input->dims(); + dims[dims.size() - 1] = k; + ctx.Output("Out")->Resize(dims); + ctx.Output("Indices")->Resize(dims); + } +}; + +class TopkOpMaker : public framework::OpProtoAndCheckerMaker { + public: + TopkOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input of Topk op"); + AddOutput("Out", "The output tensor of Topk op"); + AddOutput("Indices", "The indices of Topk elements of input"); + AddComment( + R"DOC(If the input is a vector (1d tensor), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus values[j] is the j-th largest entry in input, and its index is indices[j]. + + For matrices, computes the top k entries in each row. )DOC"); + AddAttr("k", + "Number of top elements to look for along the last " + "dimension (along each row for matrices).") + .SetDefault(1); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_WITHOUT_GRADIENT(top_k, ops::TopkOp, ops::TopkOpMaker); +REGISTER_OP_CPU_KERNEL(top_k, + ops::TopkKernel); diff --git a/paddle/operators/top_k_op.cu b/paddle/operators/top_k_op.cu new file mode 100644 index 0000000000000000000000000000000000000000..afe4d149c53819c45e20353bc9d16393f3f61e0f --- /dev/null +++ b/paddle/operators/top_k_op.cu @@ -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/op_registry.h" +#include "paddle/platform/assert.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +struct Pair { + __device__ __forceinline__ Pair() {} + __device__ __forceinline__ Pair(T value, int id) : v(value), id(id) {} + + __device__ __forceinline__ void set(T value, int id) { + v = value; + id = id; + } + + __device__ __forceinline__ void operator=(const Pair& in) { + v = in.v; + id = in.id; + } + + __device__ __forceinline__ bool operator<(const T value) const { + return (v < value); + } + + __device__ __forceinline__ bool operator<(const Pair& in) const { + return (v < in.v) || ((v == in.v) && (id > in.id)); + } + + __device__ __forceinline__ bool operator>(const Pair& in) const { + return (v > in.v) || ((v == in.v) && (id < in.id)); + } + + T v; + int id; +}; + +template +__device__ __forceinline__ void AddTo(Pair topk[], const Pair& p, + int beam_size) { + for (int k = beam_size - 2; k >= 0; k--) { + if (topk[k] < p) { + topk[k + 1] = topk[k]; + } else { + topk[k + 1] = p; + return; + } + } + topk[0] = p; +} + +template +__device__ __forceinline__ void AddTo(Pair topk[], const Pair& p) { + for (int k = beam_size - 2; k >= 0; k--) { + if (topk[k] < p) { + topk[k + 1] = topk[k]; + } else { + topk[k + 1] = p; + return; + } + } + topk[0] = p; +} + +template +__device__ __forceinline__ void GetTopK(Pair topk[], const T* src, int idx, + int dim, int beam_size) { + while (idx < dim) { + if (topk[beam_size - 1] < src[idx]) { + Pair tmp(src[idx], idx); + AddTo(topk, tmp, beam_size); + } + idx += BlockSize; + } +} + +template +__device__ __forceinline__ void GetTopK(Pair topk[], const T* src, int idx, + int dim, const Pair& max, + int beam_size) { + while (idx < dim) { + if (topk[beam_size - 1] < src[idx]) { + Pair tmp(src[idx], idx); + if (tmp < max) { + AddTo(topk, tmp, beam_size); + } + } + idx += BlockSize; + } +} + +template +__device__ __forceinline__ void GetTopK(Pair topk[], const T* val, int* col, + int idx, int dim, int beam_size) { + while (idx < dim) { + if (topk[beam_size - 1] < val[idx]) { + Pair tmp(val[idx], col[idx]); + AddTo(topk, tmp, beam_size); + } + idx += BlockSize; + } +} + +template +__device__ __forceinline__ void GetTopK(Pair topk[], const T* val, int* col, + int idx, int dim, const Pair& max, + int beam_size) { + while (idx < dim) { + if (topk[beam_size - 1] < val[idx]) { + Pair tmp(val[idx], col[idx]); + if (tmp < max) { + AddTo(topk, tmp, beam_size); + } + } + idx += BlockSize; + } +} + +template +__device__ __forceinline__ void ThreadGetTopK(Pair topk[], int& beam, + int beam_size, const T* src, + bool& firstStep, bool& is_empty, + Pair& max, int dim, + const int tid) { + if (beam > 0) { + int length = beam < beam_size ? beam : beam_size; + if (firstStep) { + firstStep = false; + GetTopK(topk, src, tid, dim, length); + } else { + for (int k = 0; k < MaxLength; k++) { + if (k < MaxLength - beam) { + topk[k] = topk[k + beam]; + } else { + topk[k].set(-INFINITY, -1); + } + } + if (!is_empty) { + GetTopK(topk + MaxLength - beam, src, tid, dim, max, + length); + } + } + + max = topk[MaxLength - 1]; + if (max.v == -1) is_empty = true; + beam = 0; + } +} + +template +__device__ __forceinline__ void ThreadGetTopK(Pair topk[], int& beam, + int beam_size, const T* val, + int* col, bool& firstStep, + bool& is_empty, Pair& max, + int dim, const int tid) { + if (beam > 0) { + int length = beam < beam_size ? beam : beam_size; + if (firstStep) { + firstStep = false; + GetTopK(topk, val, col, tid, dim, length); + } else { + for (int k = 0; k < MaxLength; k++) { + if (k < MaxLength - beam) { + topk[k] = topk[k + beam]; + } else { + topk[k].set(-INFINITY, -1); + } + } + if (!is_empty) { + GetTopK(topk + MaxLength - beam, val, col, tid, dim, max, + length); + } + } + + max = topk[MaxLength - 1]; + if (max.v == -1) is_empty = true; + beam = 0; + } +} + +template +__device__ __forceinline__ void BlockReduce(Pair* sh_topk, int* maxid, + Pair topk[], T** topVal, + int** topIds, int& beam, int& k, + const int tid, const int warp) { + while (true) { + __syncthreads(); + if (tid < BlockSize / 2) { + if (sh_topk[tid] < sh_topk[tid + BlockSize / 2]) { + maxid[tid] = tid + BlockSize / 2; + } else { + maxid[tid] = tid; + } + } + __syncthreads(); + for (int stride = BlockSize / 4; stride > 0; stride = stride / 2) { + if (tid < stride) { + if (sh_topk[maxid[tid]] < sh_topk[maxid[tid + stride]]) { + maxid[tid] = maxid[tid + stride]; + } + } + __syncthreads(); + } + __syncthreads(); + + if (tid == 0) { + **topVal = sh_topk[maxid[0]].v; + **topIds = sh_topk[maxid[0]].id; + (*topVal)++; + (*topIds)++; + } + if (tid == maxid[0]) beam++; + if (--k == 0) break; + __syncthreads(); + + if (tid == maxid[0]) { + if (beam < MaxLength) { + sh_topk[tid] = topk[beam]; + } + } + if (maxid[0] / 32 == warp) { + if (__shfl(beam, (maxid[0]) % 32, 32) == MaxLength) break; + } + } +} + +/** + * Each block compute one sample. + * In a block: + * 1. every thread get top MaxLength value; + * 2. merge to sh_topk, block reduce and get max value; + * 3. go to the second setp, until one thread's topk value is null; + * 4. go to the first setp, until get the topk value. + */ +template +__global__ void KeMatrixTopK(T* output, int output_stride, int* indices, + const T* src, int lds, int dim, int k) { + __shared__ Pair sh_topk[BlockSize]; + __shared__ int maxid[BlockSize / 2]; + const int tid = threadIdx.x; + const int warp = threadIdx.x / 32; + output += blockIdx.x * output_stride; + indices += blockIdx.x * k; + + Pair topk[MaxLength]; + int beam = MaxLength; + Pair max; + bool is_empty = false; + bool firststep = true; + + for (int k = 0; k < MaxLength; k++) { + topk[k].set(-INFINITY, -1); + } + while (k) { + ThreadGetTopK(topk, beam, k, + src + blockIdx.x * lds, firststep, + is_empty, max, dim, tid); + + sh_topk[tid] = topk[0]; + BlockReduce(sh_topk, maxid, topk, &output, + &indices, beam, k, tid, warp); + } +} + +template +class TopkOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "It must use GPUPlace."); + auto* input = ctx.Input("X"); + auto* output = ctx.Output("Out"); + auto* indices = ctx.Output("Indices"); + size_t k = static_cast(ctx.Attr("k")); + + const T* input_data = input->data(); + + T* output_data = output->mutable_data(ctx.GetPlace()); + // FIXME(typhoonzero): data is always converted to type T? + int* indices_data = indices->mutable_data(ctx.GetPlace()); + + size_t input_height = input->dims()[0]; + size_t input_width = input->dims()[1]; + if (k > input_width) k = input_width; + + // NOTE: pass lds and dim same to input width. + // NOTE: old matrix implementation of stride is different to eigen. + // TODO(typhoonzero): launch kernel on specified stream. + // TODO(typhoonzero): refine this kernel. + dim3 threads(256, 1); + dim3 grid(input_height, 1); + + KeMatrixTopK<<>>( + output_data, output->dims()[1], indices_data, input_data, input_width, + input_width, int(k)); + } +}; + +} // namespace operators +} // namespace paddle + +REGISTER_OP_GPU_KERNEL(top_k, paddle::operators::TopkOpCUDAKernel); diff --git a/paddle/operators/top_k_op.h b/paddle/operators/top_k_op.h new file mode 100644 index 0000000000000000000000000000000000000000..ef66acc1d569282a42be64b7a5e90f3fbdb20690 --- /dev/null +++ b/paddle/operators/top_k_op.h @@ -0,0 +1,76 @@ +/* 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 "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +using EigenMatrix = framework::EigenMatrix; + +template +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 + // FIXME: only deal with matrix(2d tensor). + auto* input = ctx.Input("X"); + auto* output = ctx.Output("Out"); + auto* indices = ctx.Output("Indices"); + // k is determined by Attr + const size_t k = static_cast(ctx.Attr("k")); + + T* output_data = output->mutable_data(ctx.GetPlace()); + T* indices_data = indices->mutable_data(ctx.GetPlace()); + + auto eg_input = EigenMatrix::From(*input); + + // reshape input to a flattern matrix(like flat_inner_dims) + framework::DDim inputdims = input->dims(); + const size_t row = framework::product( + framework::slice_ddim(inputdims, 0, inputdims.size() - 1)); + const size_t col = inputdims[inputdims.size() - 1]; + Eigen::DSizes flat2dims(row, col); + // NOTE: eigen shape doesn't affect paddle tensor. + eg_input.reshape(flat2dims); + + for (size_t i = 0; i < row; i++) { + std::vector> vec; + for (size_t j = 0; j < col; j++) { + vec.push_back(std::pair(eg_input(i, j), j)); + } + + std::partial_sort( + vec.begin(), vec.begin() + k, vec.end(), + [](const std::pair& l, const std::pair& r) { + return l.first > r.first; + }); + for (size_t j = 0; j < k; j++) { + output_data[i * k + j] = vec[j].first; + indices_data[i * k + j] = vec[j].second; + } + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index 40cef8942a3648af5629e5a5db0f021ae3d6f1c1..f2aeef6c310df8535e67fa3906301a87f8ec4694 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -26,15 +26,15 @@ class CPUUniformRandomKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = static_cast(context.GetAttr("seed")); + unsigned int seed = static_cast(context.Attr("seed")); std::minstd_rand engine; if (seed == 0) { seed = std::random_device()(); } engine.seed(seed); std::uniform_real_distribution dist( - static_cast(context.GetAttr("min")), - static_cast(context.GetAttr("max"))); + static_cast(context.Attr("min")), + static_cast(context.Attr("max"))); int64_t size = framework::product(tensor->dims()); for (int64_t i = 0; i < size; ++i) { data[i] = dist(engine); @@ -48,10 +48,10 @@ class UniformRandomOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext& ctx) const override { - PADDLE_ENFORCE(GetAttr("min") < GetAttr("max"), + PADDLE_ENFORCE(Attr("min") < Attr("max"), "uniform_random's min must less then max"); auto* tensor = ctx.Output("Out"); - auto dims = GetAttr>("dims"); + auto dims = Attr>("dims"); std::vector temp; temp.reserve(dims.size()); for (auto dim : dims) { diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index df993c07794b0b2408e4edc8a45fae9a17aef01c..c2c041b144b6ca1f019f972e1301b756ec1c9301 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -45,13 +45,13 @@ class GPUUniformRandomKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& context) const override { auto* tensor = context.Output("Out"); T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = static_cast(context.GetAttr("seed")); + unsigned int seed = static_cast(context.Attr("seed")); if (seed == 0) { std::random_device rd; seed = rd(); } - T min = static_cast(context.GetAttr("min")); - T max = static_cast(context.GetAttr("max")); + T min = static_cast(context.Attr("min")); + T max = static_cast(context.Attr("max")); thrust::counting_iterator index_sequence_begin(0); ssize_t N = framework::product(tensor->dims()); thrust::transform(index_sequence_begin, index_sequence_begin + N, diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h index 24ddf3441caa6e5f45a7b96af26a23ed324dc1b6..2841d2a2dbec5c17ef098a06c976ca01247820f5 100644 --- a/paddle/platform/cudnn_helper.h +++ b/paddle/platform/cudnn_helper.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include "paddle/platform/dynload/cudnn.h" #include "paddle/platform/enforce.h" #include "paddle/platform/macros.h" diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h index 81448897e95eb05f4ce7de8683d98e05bade77cb..64fcbd93b6c4d5d9b36f2636c3ef4f7327f08d25 100644 --- a/paddle/platform/enforce.h +++ b/paddle/platform/enforce.h @@ -25,10 +25,6 @@ limitations under the License. */ #include "paddle/string/printf.h" #include "paddle/string/to_string.h" -#ifdef __GNUC__ -#include // for __cxa_demangle -#endif - #ifndef PADDLE_ONLY_CPU #include "paddle/platform/dynload/cublas.h" @@ -46,19 +42,6 @@ limitations under the License. */ namespace paddle { namespace platform { -namespace { -#ifdef __GNUC__ -inline std::string demangle(std::string name) { - int status = -4; // some arbitrary value to eliminate the compiler warning - std::unique_ptr res{ - abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free}; - return (status == 0) ? res.get() : name; -} -#else -inline std::string demangle(std::string name) { return name; } -#endif -} - struct EnforceNotMet : public std::exception { std::exception_ptr exp_; std::string err_str_; @@ -79,7 +62,7 @@ struct EnforceNotMet : public std::exception { Dl_info info; for (int i = 0; i < size; ++i) { if (dladdr(call_stack[i], &info)) { - auto demangled = demangle(info.dli_sname); + auto demangled = info.dli_sname; auto addr_offset = static_cast(call_stack[i]) - static_cast(info.dli_saddr); sout << string::Sprintf("%-3d %*0p %s + %zd\n", i, diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 1b76dc0c17b199ad7e66897efeb53b8ac201eea5..5aeae4dff3db96a30a956712eee52eebdc1fd397 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -30,7 +30,7 @@ limitations under the License. */ namespace py = pybind11; -USE_OP(add_two); +USE_OP(add); USE_OP(onehot_cross_entropy); USE_OP(sgd); USE_OP(mul); @@ -50,6 +50,9 @@ USE_OP(cos_sim); USE_CPU_ONLY_OP(gather); USE_CPU_ONLY_OP(scatter); USE_OP(crop); +USE_OP(top_k); +USE_OP(squared_l2_distance); +USE_OP(sum); namespace paddle { namespace framework { @@ -215,7 +218,10 @@ All parameter, weight, gradient are variables in Paddle. -> std::map> { return op.Outputs(); }) + .def("output_vars", + [](const OperatorBase &op) { return op.OutputVars(true); }) .def("inputs", [](const OperatorBase &op) { return op.Inputs(); }) + .def("input_vars", [](const OperatorBase &op) { return op.InputVars(); }) .def("__str__", &OperatorBase::DebugString) .def("no_intermediate_outputs", [](const OperatorBase &op) { return op.OutputVars(false); }) diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 17986420220fec173bbf3ecff240d4c504f8adbd..2ac455d771bf78377ce4ee7d921393d3b3958e3c 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -30,6 +30,8 @@ Configuring cmake in /paddle/build ... -DCMAKE_BUILD_TYPE=Release -DWITH_DOC=OFF -DWITH_GPU=${WITH_GPU:-OFF} + -DWITH_MKLDNN=${WITH_MKLDNN:-ON} + -DWITH_MKLML=${WITH_MKLML:-ON} -DWITH_AVX=${WITH_AVX:-OFF} -DWITH_GOLANG=${WITH_GOLANG:-ON} -DWITH_SWIG_PY=ON @@ -37,7 +39,7 @@ Configuring cmake in /paddle/build ... -DWITH_PYTHON=${WITH_PYTHON:-ON} -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} -DCUDNN_ROOT=/usr/ - -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} + -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} -DWITH_TESTING=${WITH_TESTING:-ON} -DCMAKE_EXPORT_COMPILE_COMMANDS=ON ======================================== @@ -50,6 +52,8 @@ cmake .. \ -DCMAKE_BUILD_TYPE=Release \ -DWITH_DOC=OFF \ -DWITH_GPU=${WITH_GPU:-OFF} \ + -DWITH_MKLDNN=${WITH_MKLDNN:-ON} \ + -DWITH_MKLML=${WITH_MKLML:-ON} \ -DWITH_AVX=${WITH_AVX:-OFF} \ -DWITH_GOLANG=${WITH_GOLANG:-ON} \ -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} \ diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index 5584e29e2a155a8062f7d4f2016bd389bd9803f3..aabd2da5e499c8e648f2967e56c661ec37f025a1 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -2,22 +2,58 @@ set -xe -mkdir -p /paddle/build_android -cd /paddle/build_android -rm -rf /paddle/install 2>/dev/null || true -cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \ - -DANDROID_ABI=armeabi-v7a \ - -DANDROID_ARM_NEON=ON \ - -DANDROID_ARM_MODE=ON \ - -DHOST_C_COMPILER=/usr/bin/gcc \ - -DHOST_CXX_COMPILER=/usr/bin/g++ \ - -DCMAKE_INSTALL_PREFIX=/paddle/install \ - -DCMAKE_BUILD_TYPE=RelWithDebInfo \ - -DCMAKE_C_FLAGS_RELWITHDEBINFO="-O3" \ - -DCMAKE_CXX_FLAGS_RELWITHDEBINFO="-O3" \ - -DWITH_C_API=ON \ - -DWITH_SWIG_PY=OFF \ - .. +BUILD_ROOT=/paddle/build_android +DEST_ROOT=/paddle/install + +rm -rf $BUILD_ROOT 2>/dev/null || true +mkdir -p $BUILD_ROOT +cd $BUILD_ROOT + +if [ $ANDROID_ABI == "armeabi-v7a" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_NEON=ON \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=$DEST_ROOT \ + -DCMAKE_BUILD_TYPE=Release \ + -DUSE_EIGEN_FOR_BLAS=ON \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + -DWITH_STYLE_CHECK=OFF \ + .. +elif [ $ANDROID_ABI == "arm64-v8a" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=$DEST_ROOT \ + -DCMAKE_BUILD_TYPE=Release \ + -DUSE_EIGEN_FOR_BLAS=OFF \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + -DWITH_STYLE_CHECK=OFF \ + .. +elif [ $ANDROID_ABI == "armeabi" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DCMAKE_BUILD_TYPE=Release \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + -DWITH_STYLE_CHECK=OFF \ + .. +else + echo "Invalid ANDROID_ABI: $ANDROID_ABI" +fi + make -j `nproc` make install -j `nproc` diff --git a/paddle/scripts/travis/build_android.sh b/paddle/scripts/travis/build_android.sh index 004067a8f55351509caaf2bbf6d5c349a4698a79..9da71d1e8cdec4047167fe354973e6bac85fb9f0 100755 --- a/paddle/scripts/travis/build_android.sh +++ b/paddle/scripts/travis/build_android.sh @@ -22,6 +22,7 @@ cmake -DCMAKE_SYSTEM_NAME=Android \ -DANDROID_ABI=armeabi-v7a \ -DANDROID_ARM_NEON=ON \ -DANDROID_ARM_MODE=ON \ + -DUSE_EIGEN_FOR_BLAS=ON \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ -DWITH_STYLE_CHECK=OFF \ diff --git a/paddle/utils/Util.cpp b/paddle/utils/Util.cpp index b18b73e06a6c39c3bf9717280bc6323917c80efb..2755fdd9cd1c2509cad996557c6fb24363d42d8a 100644 --- a/paddle/utils/Util.cpp +++ b/paddle/utils/Util.cpp @@ -320,6 +320,9 @@ void loadFileList(const std::string& fileListFileName, } double getMemoryUsage() { +#if defined(__ANDROID__) + return 0.0; +#else FILE* fp = fopen("/proc/meminfo", "r"); CHECK(fp) << "failed to fopen /proc/meminfo"; size_t bufsize = 256 * sizeof(char); @@ -357,6 +360,7 @@ double getMemoryUsage() { delete[] buf; double usedMem = 1.0 - 1.0 * (freeMem + bufMem + cacheMem) / totalMem; return usedMem; +#endif } SyncThreadPool* getGlobalSyncThreadPool() { diff --git a/paddle/utils/Util.h b/paddle/utils/Util.h index 613844669d2495ada7b8f7a841f47b821b7fdeba..22ce2534d3468ded36221810aa61c15b37f13f3d 100644 --- a/paddle/utils/Util.h +++ b/paddle/utils/Util.h @@ -33,6 +33,13 @@ limitations under the License. */ #include "Flags.h" #include "hl_gpu.h" +#if defined(__ANDROID__) && (__ANDROID_API__ < 21) +inline int rand_r(unsigned int* seedp) { + (void)seedp; + return rand(); +} +#endif + /** * Loop over the elements in a container * TODO(yuyang18): It's this foreach useful? Why not use C++ 11 foreach, diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4ddf023780c704cb10c51ee9e5d7cb63420f9d73..ebf0911d6ea0b39d51447859ae2aef485b50b0e6 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -271,6 +271,7 @@ message ImageConfig { // The size of input feature map. required uint32 img_size = 8; optional uint32 img_size_y = 9; + optional uint32 img_size_z = 10 [ default = 1 ]; } message PriorBoxConfig { @@ -287,6 +288,11 @@ message PadConfig { repeated uint32 pad_w = 4; } +message ReshapeConfig { + repeated uint32 height_axis = 1; + repeated uint32 width_axis = 2; +} + message MultiBoxLossConfig { required uint32 num_classes = 1; required float overlap_threshold = 2; @@ -339,7 +345,6 @@ message LayerInputConfig { } message LayerConfig { - required string name = 1; required string type = 2; optional uint64 size = 3; @@ -515,7 +520,11 @@ message LayerConfig { // for HuberRegressionLoss optional double delta = 57 [ default = 1.0 ]; + // for 3D data optional uint64 depth = 58 [ default = 1 ]; + + // for switch order layer + optional ReshapeConfig reshape_conf = 59; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 152a56190c1ffddbf9590ed8f71308ceb88403f4..356e1d8b6fa9173db33a340744afd8d513a83a96 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1332,6 +1332,12 @@ def parse_image(image, input_layer_name, image_conf): get_img_size(input_layer_name, image_conf.channels) +def parse_image3d(image, input_layer_name, image_conf): + image_conf.channels = image.channels + image_conf.img_size, image_conf.img_size_y, image_conf.img_size_z = \ + get_img3d_size(input_layer_name, image_conf.channels) + + def parse_norm(norm, input_layer_name, norm_conf): norm_conf.norm_type = norm.norm_type config_assert( @@ -2365,9 +2371,11 @@ class BatchNormLayer(LayerBase): name, inputs, bias=True, + img3D=False, use_global_stats=True, moving_average_fraction=0.9, batch_norm_type=None, + mean_var_names=None, **xargs): if inputs is None: inputs = [] @@ -2409,24 +2417,69 @@ class BatchNormLayer(LayerBase): input_layer = self.get_input_layer(0) image_conf = self.config.inputs[0].image_conf - parse_image(self.inputs[0].image, input_layer.name, image_conf) - - # Only pass the width and height of input to batch_norm layer - # when either of it is non-zero. - if input_layer.width != 0 or input_layer.height != 0: - self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, - image_conf.channels, False) + if img3D: + parse_image3d(self.inputs[0].image, input_layer.name, image_conf) + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. + if input_layer.width != 0 or input_layer.height != 0: + self.set_cnn_layer( + input_layer_name=name, + depth=image_conf.img_size_z, + height=image_conf.img_size_y, + width=image_conf.img_size, + channels=image_conf.channels, + is_print=True) + else: + self.set_layer_size(input_layer.size) else: - self.set_layer_size(input_layer.size) + parse_image(self.inputs[0].image, input_layer.name, image_conf) + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. + if input_layer.width != 0 or input_layer.height != 0: + self.set_cnn_layer( + input_layer_name=name, + height=image_conf.img_size_y, + width=image_conf.img_size, + channels=image_conf.channels, + is_print=True) + else: + self.set_layer_size(input_layer.size) psize = self.calc_parameter_size(image_conf) dims = [1, psize] + if mean_var_names is not None: + assert len(mean_var_names) == 2 + self.inputs[1].parameter_name = mean_var_names[0] + self.inputs[2].parameter_name = mean_var_names[1] + self.create_input_parameter(0, psize) self.create_input_parameter(1, psize, dims) self.create_input_parameter(2, psize, dims) self.create_bias_parameter(bias, psize) + def set_cnn_layer(self, + input_layer_name, + depth=None, + height=None, + width=None, + channels=None, + is_print=True): + depthIsNone = False + if depth is None: + depth = 1 + depthIsNone = True + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(height, width) + self.set_layer_depth(depth) + if is_print and depthIsNone: + print("output for %s: c = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, height, width, size)) + elif is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + def calc_parameter_size(self, image_conf): return image_conf.channels @@ -2688,9 +2741,20 @@ class AddToLayer(LayerBase): super(AddToLayer, self).__init__( name, 'addto', 0, inputs=inputs, **xargs) config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer') - for input_index in xrange(len(self.inputs)): - input_layer = self.get_input_layer(input_index) - self.set_layer_size(input_layer.size) + + if len(self.inputs) > 1: + for input_index in xrange(len(self.inputs)): + assert self.get_input_layer(0).height == self.get_input_layer( + input_index).height + assert self.get_input_layer(0).width == self.get_input_layer( + input_index).width + assert self.get_input_layer(0).depth == self.get_input_layer( + input_index).depth + + self.set_layer_size(self.get_input_layer(0).size) + self.set_layer_height_width(self.get_input_layer(0).height, \ + self.get_input_layer(0).width) + self.set_layer_depth(self.get_input_layer(0).depth) self.create_bias_parameter(bias, self.config.size) @@ -3370,11 +3434,20 @@ class ConcatenateLayer(LayerBase): name, 'concat', 0, inputs=inputs, **xargs) size = 0 for input_index in xrange(len(self.inputs)): + assert self.get_input_layer(0).height == self.get_input_layer( + input_index).height + assert self.get_input_layer(0).width == self.get_input_layer( + input_index).width + assert self.get_input_layer(0).depth == self.get_input_layer( + input_index).depth input_layer = self.get_input_layer(input_index) input = self.inputs[input_index] if self.config.size == 0: size += input_layer.size + self.set_layer_height_width(self.get_input_layer(0).height, \ + self.get_input_layer(0).width) + self.set_layer_depth(self.get_input_layer(0).depth) self.set_layer_size(size) @@ -3670,6 +3743,15 @@ class RecurrentLayerGroup(LayerBase): name, 'recurrent_layer_group', 0, inputs=[], device=device) +@config_layer('switch_order') +class SwitchOrderLayer(LayerBase): + def __init__(self, name, inputs, reshape, **xargs): + super(SwitchOrderLayer, self).__init__( + name, 'switch_order', 0, inputs=inputs, **xargs) + self.config.reshape_conf.height_axis.extend(reshape['height']) + self.config.reshape_conf.width_axis.extend(reshape['width']) + + # Deprecated, use a new layer specific class instead @config_func def Layer(name, type, **xargs): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 47ac601e678013aceb62005d6f25595f49673d2c..4b1d80d3db924bfa2ad0e081f785d8f5dd719fce 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -131,6 +131,7 @@ __all__ = [ 'row_conv_layer', 'dropout_layer', 'prelu_layer', + 'switch_order_layer', 'gated_unit_layer', 'crop_layer', 'sub_nested_seq_layer', @@ -239,6 +240,7 @@ class LayerType(object): SMOOTH_L1 = 'smooth_l1' PRELU = 'prelu' + SWITCH_ORDER_LAYER = 'switch_order' CROP_LAYER = 'crop' SUB_NESTED_SEQ = 'sub_nested_seq' CLIP_LAYER = 'clip' @@ -352,6 +354,10 @@ class LayerOutput(object): def height(self): return cp.g_layer_map[self.full_name].height + @property + def depth(self): + return cp.g_layer_map[self.full_name].depth + def set_input(self, input): """ Set the input for a memory layer. Can only be used for memory layer @@ -941,7 +947,7 @@ def data_layer(name, size, depth=None, height=None, width=None, if height is not None and width is not None: num_filters = size / (width * height * depth) assert num_filters * width * height * depth == size, \ - "size=%s width=%s height=%s depth=%s" % (size, width, height, depth) + "size=%s width=%s height=%s depth=%s" % (size, width, height, depth) return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters) @@ -1217,7 +1223,8 @@ def detection_output_layer(input_loc, name=None): """ Apply the NMS to the output of network and compute the predict bounding - box location. + box location. The output of this layer could be None if there is no valid + bounding box. :param name: The Layer Name. :type name: basestring @@ -2951,13 +2958,15 @@ def img_cmrnorm_layer(input, def batch_norm_layer(input, act=None, name=None, + img3D=False, num_channels=None, bias_attr=None, param_attr=None, layer_attr=None, batch_norm_type=None, moving_average_fraction=0.9, - use_global_stats=None): + use_global_stats=None, + mean_var_names=None): """ Batch Normalization Layer. The notation of this layer as follow. @@ -3024,6 +3033,8 @@ def batch_norm_layer(input, :math:`runningMean = newMean*(1-factor) + runningMean*factor` :type moving_average_fraction: float. + :param mean_var_names: [mean name, variance name] + :type mean_var_names: string list :return: LayerOutput object. :rtype: LayerOutput """ @@ -3037,6 +3048,7 @@ def batch_norm_layer(input, (batch_norm_type == "cudnn_batch_norm") l = Layer( name=name, + img3D=img3D, inputs=Input( input.name, image=Image(channels=num_channels), **param_attr.attr), active_type=act.name, @@ -3045,6 +3057,7 @@ def batch_norm_layer(input, bias=ParamAttr.to_bias(bias_attr), moving_average_fraction=moving_average_fraction, use_global_stats=use_global_stats, + mean_var_names=mean_var_names, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( @@ -6404,6 +6417,55 @@ def gated_unit_layer(input, layer_attr=layer_attr) +@layer_support() +@wrap_name_default('switch_order') +def switch_order_layer(input, + name=None, + reshape_axis=None, + act=None, + layer_attr=None): + """ + This layer switch dimension order of image input. + From order "batchSize, channels, height, width" + to order "batchSize, height, width, channels". + + The example usage is: + + .. code-block:: python + reshape_axis = 3 + switch = switch_order(input=layer, name='switch', reshape_axis=reshape_axis) + reshape = {'height':[ 0, 1, 2], 'width':[3]} + + :param input: The input layer. + :type input: LayerOutput + :param name: Name of this layer. + :type name: basestring + :param reshape: reshape matrix by axises. + :type reshape: Dict + :return: LayerOutput object. + :rtype: LayerOutput + """ + assert isinstance(input, LayerOutput) + assert reshape_axis != None and (reshape_axis > 0 and reshape_axis < 4) + height = [ele for ele in xrange(reshape_axis)] + width = [ele for ele in range(reshape_axis, 4)] + reshape = {'height': height, 'width': width} + + l = Layer( + name=name, + inputs=input.name, + reshape=reshape, + type=LayerType.SWITCH_ORDER_LAYER, + active_type=act.name, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name=name, + layer_type=LayerType.SWITCH_ORDER_LAYER, + activation=act, + parents=input, + size=l.config.size) + + @wrap_name_default() @layer_support() def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None): 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 df872a90ff388f0d96cef44763dbd076bc768ab9..8a204a96f3ef57673cef65306d0bf8e8c3409751 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_conv3d_layer test_deconv3d_layer test_BatchNorm3D) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr index 1a577b8d9b1e1915236ba6afcfa97040d70c707a..5ddf6052df021b055390a42c25ce6c0d650e4aee 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr @@ -62,6 +62,7 @@ layers { moving_average_fraction: 0.9 height: 227 width: 227 + depth: 1 } layers { name: "__crmnorm_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr index 2818389b16cca75f5030b75fc4de8c89c06c5e02..c0252b945b4c7fd6b4dad8770e3e1dccb88df28a 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr @@ -62,6 +62,7 @@ layers { moving_average_fraction: 0.9 height: 256 width: 256 + depth: 1 } layers { name: "__crmnorm_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr new file mode 100644 index 0000000000000000000000000000000000000000..832ed24a31dd2bedba9a4fce77d7a088d1796fdb --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr @@ -0,0 +1,92 @@ +type: "nn" +layers { + name: "data3D" + type: "data" + size: 360 + active_type: "" + height: 6 + width: 20 + depth: 3 +} +layers { + name: "__batch_norm_0__" + type: "batch_norm" + size: 360 + active_type: "relu" + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w0" + image_conf { + channels: 1 + img_size: 20 + img_size_y: 6 + img_size_z: 3 + } + } + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w1" + } + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w2" + } + bias_parameter_name: "___batch_norm_0__.wbias" + moving_average_fraction: 0.9 + height: 6 + width: 20 + depth: 3 +} +parameters { + name: "___batch_norm_0__.w0" + size: 1 + initial_mean: 1.0 + initial_std: 0.0 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "___batch_norm_0__.w1" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false + is_static: true + is_shared: true +} +parameters { + name: "___batch_norm_0__.w2" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false + is_static: true + is_shared: true +} +parameters { + name: "___batch_norm_0__.wbias" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +input_layer_names: "data3D" +output_layer_names: "__batch_norm_0__" +sub_models { + name: "root" + layer_names: "data3D" + layer_names: "__batch_norm_0__" + input_layer_names: "data3D" + output_layer_names: "__batch_norm_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr index b110e91498ce7d112987714bd769868179141c54..8a1399efad0ff339e35f69400ac654a4787a6018 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_bi_grumemory.protostr @@ -74,6 +74,9 @@ layers { inputs { input_layer_name: "__bidirectional_gru_0___bw" } + height: 0 + width: 0 + depth: 1 } parameters { name: "___bidirectional_gru_0___fw_transform.w0" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr index 8133aa9c8d3e7c6843d1b27b70e87d394a1e0e47..046037936a6d85f54095c65f206e468aa69065d7 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_recursive_topology.protostr @@ -16,6 +16,9 @@ layers { inputs { input_layer_name: "data" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_1__" @@ -28,6 +31,9 @@ layers { inputs { input_layer_name: "__addto_0__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_2__" @@ -40,6 +46,9 @@ layers { inputs { input_layer_name: "__addto_1__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_3__" @@ -52,6 +61,9 @@ layers { inputs { input_layer_name: "__addto_2__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_4__" @@ -64,6 +76,9 @@ layers { inputs { input_layer_name: "__addto_3__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_5__" @@ -76,6 +91,9 @@ layers { inputs { input_layer_name: "__addto_4__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_6__" @@ -88,6 +106,9 @@ layers { inputs { input_layer_name: "__addto_5__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_7__" @@ -100,6 +121,9 @@ layers { inputs { input_layer_name: "__addto_6__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_8__" @@ -112,6 +136,9 @@ layers { inputs { input_layer_name: "__addto_7__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_9__" @@ -124,6 +151,9 @@ layers { inputs { input_layer_name: "__addto_8__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_10__" @@ -136,6 +166,9 @@ layers { inputs { input_layer_name: "__addto_9__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_11__" @@ -148,6 +181,9 @@ layers { inputs { input_layer_name: "__addto_10__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_12__" @@ -160,6 +196,9 @@ layers { inputs { input_layer_name: "__addto_11__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_13__" @@ -172,6 +211,9 @@ layers { inputs { input_layer_name: "__addto_12__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_14__" @@ -184,6 +226,9 @@ layers { inputs { input_layer_name: "__addto_13__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_15__" @@ -196,6 +241,9 @@ layers { inputs { input_layer_name: "__addto_14__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_16__" @@ -208,6 +256,9 @@ layers { inputs { input_layer_name: "__addto_15__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_17__" @@ -220,6 +271,9 @@ layers { inputs { input_layer_name: "__addto_16__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_18__" @@ -232,6 +286,9 @@ layers { inputs { input_layer_name: "__addto_17__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_19__" @@ -244,6 +301,9 @@ layers { inputs { input_layer_name: "__addto_18__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_20__" @@ -256,6 +316,9 @@ layers { inputs { input_layer_name: "__addto_19__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_21__" @@ -268,6 +331,9 @@ layers { inputs { input_layer_name: "__addto_20__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_22__" @@ -280,6 +346,9 @@ layers { inputs { input_layer_name: "__addto_21__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_23__" @@ -292,6 +361,9 @@ layers { inputs { input_layer_name: "__addto_22__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_24__" @@ -304,6 +376,9 @@ layers { inputs { input_layer_name: "__addto_23__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_25__" @@ -316,6 +391,9 @@ layers { inputs { input_layer_name: "__addto_24__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_26__" @@ -328,6 +406,9 @@ layers { inputs { input_layer_name: "__addto_25__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_27__" @@ -340,6 +421,9 @@ layers { inputs { input_layer_name: "__addto_26__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_28__" @@ -352,6 +436,9 @@ layers { inputs { input_layer_name: "__addto_27__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_29__" @@ -364,6 +451,9 @@ layers { inputs { input_layer_name: "__addto_28__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_30__" @@ -376,6 +466,9 @@ layers { inputs { input_layer_name: "__addto_29__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__addto_31__" @@ -388,6 +481,9 @@ layers { inputs { input_layer_name: "__addto_30__" } + height: 0 + width: 0 + depth: 1 } layers { name: "__fc_layer_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr index d0ad388165007b8f96f059e5b003c52f756383e5..7a2f3eab38808a031c27cf7ab9d6273952e389eb 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/util_layers.protostr @@ -22,6 +22,9 @@ layers { inputs { input_layer_name: "b" } + height: 0 + width: 0 + depth: 1 } layers { name: "__concat_0__" @@ -34,6 +37,9 @@ layers { inputs { input_layer_name: "b" } + height: 0 + width: 0 + depth: 1 } layers { name: "__concat_1__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py new file mode 100644 index 0000000000000000000000000000000000000000..a991b22252ba10eed895efd931108c2d8b0e52f1 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py @@ -0,0 +1,11 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-4) + +#data = data_layer(name='data', size=180, width=30, height=6) +#batchNorm = batch_norm_layer(data, num_channels=1) +#outputs(batchNorm) + +data3D = data_layer(name='data3D', size=120 * 3, width=20, height=6, depth=3) +batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True) +outputs(batchNorm3D) diff --git a/python/paddle/v2/event.py b/python/paddle/v2/event.py index 7589cc9917f26375d595e200245d5ba099bc38d7..e66bf67d7949057486eb54c46f39128fad5dae55 100644 --- a/python/paddle/v2/event.py +++ b/python/paddle/v2/event.py @@ -53,10 +53,13 @@ class BeginPass(object): class EndPass(WithMetric): """ Event On One Pass Training Complete. + To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')" + in your event_handler call back """ - def __init__(self, pass_id, evaluator): + def __init__(self, pass_id, evaluator, gm): self.pass_id = pass_id + self.gm = gm WithMetric.__init__(self, evaluator) @@ -73,10 +76,13 @@ class BeginIteration(object): class EndIteration(WithMetric): """ Event On One Batch Training Complete. + To get the output of a specific layer, add "event.gm.getLayerOutputs('predict_layer')" + in your event_handler call back """ - def __init__(self, pass_id, batch_id, cost, evaluator): + def __init__(self, pass_id, batch_id, cost, evaluator, gm): self.pass_id = pass_id self.batch_id = batch_id self.cost = cost + self.gm = gm WithMetric.__init__(self, evaluator) diff --git a/python/paddle/v2/framework/op.py b/python/paddle/v2/framework/op.py index 0349407a851ebb48f69d7daef7a318cf348aad5d..4e91924a50cf6401d4002510e940ddc84edbe61a 100644 --- a/python/paddle/v2/framework/op.py +++ b/python/paddle/v2/framework/op.py @@ -4,8 +4,8 @@ import paddle.v2.framework.proto.framework_pb2 as framework_pb2 def get_all_op_protos(): """ - Get all registered op proto from Paddle C++ - :return: list of OpProto + Get all registered op proto from PaddlePaddle C++ end. + :return: A list of registered OpProto. """ protostrs = core.get_all_op_protos() ret_values = [] @@ -21,8 +21,8 @@ def is_str(s): class OpDescCreationMethod(object): """ - A Functor object to convert user input(use key word args) to OpDesc based on - OpProto. + Convert the user's input(only keyword arguments are supported) to OpDesc + based on the OpProto. :param op_proto: The OpProto object. :type op_proto: op_proto_pb2.OpProto @@ -30,17 +30,18 @@ class OpDescCreationMethod(object): def __init__(self, op_proto): if not isinstance(op_proto, framework_pb2.OpProto): - raise TypeError("Argument should be OpProto") + raise TypeError( + "Type of op_proto should be OpProto in PaddlePaddle.") self.__op_proto__ = op_proto def __call__(self, *args, **kwargs): """ - Convert user input to OpDesc. Only key-word args are supported. - :return: OpDesc based on user input + Convert user's input to OpDesc. Only keyword arguments are supported. + :return: The OpDesc based on user input. :rtype: op_desc_pb2.OpDesc """ if len(args) != 0: - raise ValueError("Only keyword arguments is supported by Paddle") + raise ValueError("Only keyword arguments are supported.") op_desc = framework_pb2.OpDesc() for input_parameter in self.__op_proto__.inputs: @@ -49,8 +50,9 @@ class OpDescCreationMethod(object): input_arguments = [input_arguments] if not input_parameter.duplicable and len(input_arguments) > 1: - raise ValueError("Input %s only accepts one input, but give %d" - % (input_parameter.name, len(input_arguments))) + raise ValueError( + "Input %s expects only one input, but %d are given." % + (input_parameter.name, len(input_arguments))) ipt = op_desc.inputs.add() ipt.parameter = input_parameter.name @@ -63,7 +65,7 @@ class OpDescCreationMethod(object): if not output_parameter.duplicable and len(output_arguments) > 1: raise ValueError( - "Output %s only accepts one output, but give %d" % + "Output %s expects only one output, but %d are given." % (output_parameter.name, len(output_arguments))) out = op_desc.outputs.add() @@ -100,15 +102,17 @@ class OpDescCreationMethod(object): pair.first = p[0] pair.second = p[1] else: - raise NotImplementedError("Not support attribute type " + - str(attr.type)) + raise NotImplementedError( + "A not supported attribute type: %s." % ( + str(attr.type))) return op_desc @staticmethod def any_is_true(generator): """ - Reduce a bool array to one. If any of them is True, then return True. + Reduce a boolean array to a single boolean parameter. If any element in + the array is True, this function will return True, otherwise False. """ for flag in generator: if flag: @@ -127,7 +131,7 @@ class OpInfo(object): def create_op_creation_method(op_proto): """ - Generate op creation method for an OpProto + Generate op creation method for an OpProto. """ method = OpDescCreationMethod(op_proto) @@ -138,28 +142,31 @@ def create_op_creation_method(op_proto): return OpInfo( method=__impl__, name=op_proto.type, - inputs=[var.name for var in op_proto.inputs], - outputs=[var.name for var in op_proto.outputs], + inputs=[(var.name, var.duplicable) for var in op_proto.inputs], + outputs=[(var.name, var.duplicable) for var in op_proto.outputs], attrs=[attr.name for attr in op_proto.attrs]) class OperatorFactory(object): def __init__(self): self.op_methods = dict() + for op_proto in get_all_op_protos(): method = create_op_creation_method(op_proto) self.op_methods[method.name] = method def __call__(self, *args, **kwargs): - if 'type' in kwargs: + if "type" in kwargs: if len(args) != 0: - raise ValueError("All Paddle argument should be key-word " - "argument except type") - t = kwargs.pop('type') + raise ValueError( + "Except the argument \"type\"," + "all of the other arguments should be keyword arguments.") + t = kwargs.pop("type") else: if len(args) != 1: - raise ValueError("All Paddle argument should be key-word " - "argument except type") + raise ValueError( + "Except the argument \"type\"," + "all of the other arguments should be keyword arguments.") t = args[0] return self.get_op_info(t).method(**kwargs) @@ -169,13 +176,19 @@ class OperatorFactory(object): def get_op_info(self, t): if t not in self.op_methods: - raise ValueError("operator %s is not registered", t) + raise ValueError("The operator: %s is not registered." % t) return self.op_methods.get(t) def get_op_input_names(self, type): + return map(lambda x: x[0], self.get_op_info(type).inputs) + + def get_op_inputs(self, type): return self.get_op_info(type).inputs def get_op_output_names(self, type): + return map(lambda x: x[0], self.get_op_info(type).outputs) + + def get_op_outputs(self, type): return self.get_op_info(type).outputs def get_op_attr_names(self, type): @@ -184,7 +197,7 @@ class OperatorFactory(object): class __RecurrentOp__(object): __proto__ = None - type = 'recurrent' + type = "recurrent" def __init__(self): # cache recurrent_op's proto @@ -194,8 +207,8 @@ class __RecurrentOp__(object): self.__proto__ = op_proto def __call__(self, *args, **kwargs): - if self.type not in args and 'type' not in kwargs: - kwargs['type'] = self.type + if self.type not in args and "type" not in kwargs: + kwargs["type"] = self.type # create proto create_method = OpDescCreationMethod(self.__proto__) proto = create_method(*args, **kwargs) @@ -203,5 +216,5 @@ class __RecurrentOp__(object): return core.RecurrentOp.create(proto.SerializeToString()) -Operator = OperatorFactory() # Default global factory +Operator = OperatorFactory() # The default global factory RecurrentOp = __RecurrentOp__() diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index e0f77d797390be0461f466726f63a70dd485a290..2117fdf0d58520a008d2bd01d56d96dd248be025 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -17,6 +17,7 @@ py_test(test_cross_entropy_op SRCS test_cross_entropy_op.py) py_test(test_gather_op SRCS test_gather_op.py) py_test(test_scatter_op SRCS test_scatter_op.py) py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py) +py_test(test_top_k_op SRCS test_top_k_op.py) py_test(gradient_checker SRCS gradient_checker.py) @@ -32,4 +33,6 @@ py_test(test_sgd_op SRCS test_sgd_op.py) py_test(test_gradient_checker SRCS test_gradient_checker.py) py_test(test_lookup_table SRCS test_lookup_table.py) py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py) +py_test(test_sum_op SRCS test_sum_op.py) py_test(mnist SRCS mnist.py) +py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index a68f302f9c344bf6d63e8d9b48836d69338c3d0b..f6f8f49b797fb6e5016a5e309f12f192d5096431 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -38,9 +38,9 @@ def feed_data(name, data): assert isinstance(data, numpy.ndarray) tensor = scope.find_var(name).get_tensor() tensor.set_dims(data.shape) - if data.dtype == numpy.dtype('int32'): + if data.dtype == numpy.dtype("int32"): tensor.alloc_int(place) - elif data.dtype == numpy.dtype('float32'): + elif data.dtype == numpy.dtype("float32"): tensor.alloc_float(place) else: raise ValueError("data type not supported") @@ -74,22 +74,25 @@ def init_param(net, param_name, dims): # fc_layer def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): """ - Add a fc layer to net + The fully connected layer. - :param input: input variable name. + :param input: The name of input variable. :type input: str - :param size: fully connected layer size. - :param act: activation name - :param param: parameter attribute, used for initialize parameters. - :param bias: bias attribute. False will not have a bias. - :param name: the name of fc layer. If not set, model will generate a - readable name - :return: output variable name. + :param size: The size of fully connected layer. + :param act: The name of activation. + :param param: The attribute of learnable parameter which can be used to + modify initialization mean and std of the parameter. + :param bias: The attribute of bias. If set False, this layer does not have + a bias. + :param name: The name of this layer. If it is not set explictly, a name + will be generated automatically. + :return: The name of the output variable. """ + if name is None: - name = 'fc_%d' % uniq_id() + name = "fc_%d" % uniq_id() if not isinstance(name, str): - raise ValueError("name should be string") + raise ValueError("The name of a layer should be a string.") input_dims = scope.find_var(input).get_tensor().get_dims() @@ -123,7 +126,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): def cross_entropy_layer(net, input, label): - cost_name = 'cross_entropy_%d' % uniq_id() + cost_name = "cross_entropy_%d" % uniq_id() cross_entropy_op = Operator( "onehot_cross_entropy", X=input, label=label, Y=cost_name) net.append_op(cross_entropy_op) @@ -177,8 +180,8 @@ def error_rate(predict, label): return error_num / float(len(label)) -images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) -labels = data_layer(name='label', dims=[BATCH_SIZE]) +images = data_layer(name="pixel", dims=[BATCH_SIZE, 784]) +labels = data_layer(name="label", dims=[BATCH_SIZE]) fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid") fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid") predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax") diff --git a/python/paddle/v2/framework/tests/op_test.py b/python/paddle/v2/framework/tests/op_test.py new file mode 100644 index 0000000000000000000000000000000000000000..3a6a5dca4c4ddc1399d80e491e4072f24707c01e --- /dev/null +++ b/python/paddle/v2/framework/tests/op_test.py @@ -0,0 +1,275 @@ +import unittest +import numpy as np +import itertools +import paddle.v2.framework.core as core +from paddle.v2.framework.op import Operator + + +def grad_var_name(var_name): + return var_name + "@GRAD" + + +def create_op(scope, op_type, inputs, outputs, attrs=None): + kwargs = dict() + + 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) + else: + var = scope.new_var(in_name) + kwargs[in_name].append(in_name) + + for out_name, out_dup in Operator.get_op_outputs(op_type): + if out_name in outputs: + kwargs[out_name] = [] + if out_dup: + sub_in = outputs[out_name] + for sun_in_name in sub_in: + var = scope.new_var(sun_in_name) + kwargs[out_name].append(sun_in_name) + else: + var = scope.new_var(out_name) + kwargs[out_name].append(out_name) + + for attr_name in Operator.get_op_attr_names(op_type): + kwargs[attr_name] = attrs[attr_name] + return Operator(op_type, **kwargs) + + +def set_input(scope, op, inputs, place): + 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 in sub_in: + var = scope.find_var(sub_in_name) + tensor = var.get_tensor() + arr = sub_in[sub_in_name] + tensor.set_dims(arr.shape) + tensor.set(arr, place) + else: + var = scope.find_var(in_name) + tensor = var.get_tensor() + arr = inputs[in_name] + tensor.set_dims(arr.shape) + tensor.set(arr, place) + + +def set_output_grad(scope, op, outputs, 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) + 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) + + +def get_numeric_gradient(scope, + op, + inputs, + input_to_check, + output_name, + delta=0.005, + in_place=False): + + set_input(scope, op, inputs, core.CPUPlace()) + op.infer_shape(scope) + + tensor_to_check = scope.find_var(input_to_check).get_tensor() + + def product(dim): + return reduce(lambda a, b: a * b, dim, 1) + + ctx = core.DeviceContext.create(core.CPUPlace()) + + def get_output(): + op.run(scope, ctx) + return np.array(scope.find_var(output_name).get_tensor()).sum() + + 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') + # 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): + if in_place: + set_input(op, inputs, core.CPUPlace()) + + # get one input element throw it's index i. + origin = tensor_to_check.get_float_element(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) + y_pos = get_output() + + if in_place: + set_input(op, inputs, core.CPUPlace()) + + x_neg = origin - delta + tensor_to_check.set_float_element(i, x_neg) + y_neg = get_output() + + tensor_to_check.set_float_element(i, origin) + gradient_flat[i] = (y_pos - y_neg) / delta / 2 + + return gradient_flat.reshape(tensor_to_check.get_dims()) + + +def get_backward_op(scope, op, no_grad_set): + backward_op = core.Operator.backward(op, no_grad_set) + for input in backward_op.input_vars(): + var = scope.new_var(input) + var.get_tensor() + for output in backward_op.output_vars(): + var = scope.new_var(output) + var.get_tensor() + return backward_op + + +def get_gradient(scope, op, inputs, outputs, grad_name, place, + no_grad_set=None): + ctx = core.DeviceContext.create(place) + + set_input(scope, op, inputs, place) + + op.infer_shape(scope) + op.run(scope, ctx) + + if no_grad_set is None: + no_grad_set = set() + + backward_op = get_backward_op(scope, op, no_grad_set) + set_output_grad(scope, op, outputs, place) + + backward_op.infer_shape(scope) + backward_op.run(scope, ctx) + + out = np.array(scope.find_var(grad_name).get_tensor()) + return out + + +class OpTest(unittest.TestCase): + def check_output_with_place(self, place): + self.scope = core.Scope() + self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs) + if isinstance(place, core.GPUPlace) and not self.op.support_gpu(): + return + set_input(self.scope, self.op, self.inputs, place) + self.op.infer_shape(self.scope) + ctx = core.DeviceContext.create(place) + self.op.run(self.scope, ctx) + + for out_name, out_dup in Operator.get_op_outputs(self.op.type()): + if out_dup: + sub_out = self.outputs[out_name] + for sub_out_name in sub_out: + actual = np.array( + self.scope.find_var(sub_out_name).get_tensor()) + expect = sub_out[sub_out_name] + self.assertTrue( + np.allclose( + actual, expect, atol=1e-05), + "output name: " + out_name + "has diff") + else: + actual = np.array(self.scope.find_var(out_name).get_tensor()) + expect = self.outputs[out_name] + self.assertTrue( + np.allclose( + actual, expect, atol=1e-05), + "output name: " + out_name + "has diff") + + def check_output(self): + places = [core.CPUPlace()] + if core.is_compile_gpu(): + places.append(core.GPUPlace(0)) + for place in places: + self.check_output_with_place(place) + + def __assert_is_close(self, numeric_grads, analytic_grads, names, + max_relative_error, msg_prefix): + + for a, b, name in itertools.izip(numeric_grads, analytic_grads, names): + abs_a = np.abs(a) + abs_a[abs_a < 1e-3] = 1 + + diff_mat = np.abs(a - b) / abs_a + max_diff = np.max(diff_mat) + + def err_msg(): + offset = np.argmax(diff_mat > max_relative_error) + return "%s Variable %s max gradient diff %f over limit %f, the first " \ + "error element is %d" % ( + msg_prefix, name, max_diff, max_relative_error, offset) + + self.assertLessEqual(max_diff, max_relative_error, err_msg()) + + def check_grad(self, + inputs_to_check, + output_name, + no_grad_set=None, + in_place=False, + max_relative_error=0.005): + self.scope = core.Scope() + self.op = create_op(self.scope, self.op_type, self.inputs, self.outputs) + if no_grad_set is None: + no_grad_set = set() + + numeric_grads = [ + get_numeric_gradient( + self.scope, + self.op, + self.inputs, + input_to_check, + output_name, + in_place=in_place) for input_to_check in inputs_to_check + ] + grad_names = [ + grad_var_name(input_to_check) for input_to_check in inputs_to_check + ] + + cpu_place = core.CPUPlace() + cpu_analytic_grads = [ + get_gradient(self.scope, self.op, self.inputs, self.outputs, + grad_name, cpu_place, no_grad_set) + for grad_name in grad_names + ] + + self.__assert_is_close(numeric_grads, cpu_analytic_grads, grad_names, + max_relative_error, + "Gradient Check On %s" % str(cpu_place)) + + if core.is_compile_gpu() and self.op.support_gpu(): + gpu_place = core.GPUPlace(0) + gpu_analytic_grads = [ + get_gradient(self.scope, self.op, self.inputs, self.outputs, + grad_name, gpu_place, no_grad_set) + for grad_name in grad_names + ] + + self.__assert_is_close(numeric_grads, gpu_analytic_grads, + grad_names, max_relative_error, + "Gradient Check On %s" % str(gpu_place)) + + for c_grad, g_grad, name in itertools.izip( + cpu_analytic_grads, gpu_analytic_grads, grad_names): + self.assertTrue( + np.allclose( + c_grad, g_grad, atol=1e-4), + "output name: " + name + " has diff") diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py index a4899355b53d62903b97999ebf9c2c7ecfc6c4cd..88adede7c74b72ac1fcf6491d2e5c5a303157e04 100644 --- a/python/paddle/v2/framework/tests/op_test_util.py +++ b/python/paddle/v2/framework/tests/op_test_util.py @@ -63,10 +63,12 @@ class OpTestMeta(type): for out_name in Operator.get_op_output_names(self.type): actual = numpy.array(scope.find_var(out_name).get_tensor()) expect = self.outputs[out_name] + print "actual: %s" % actual + print "expect: %s" % expect self.assertTrue( numpy.allclose( actual, expect, atol=1e-05), - "output name: " + out_name + "has diff") + "output name: " + out_name + " has diff") obj.test_all = test_all return obj diff --git a/python/paddle/v2/framework/tests/test_add_two_op.py b/python/paddle/v2/framework/tests/test_add_two_op.py index 0def484eddb88604398ee10390d3f28058714a57..a578e74eca9a3c4327a4881f853028e2347c98ad 100644 --- a/python/paddle/v2/framework/tests/test_add_two_op.py +++ b/python/paddle/v2/framework/tests/test_add_two_op.py @@ -11,7 +11,7 @@ class TestAddOp(unittest.TestCase): __metaclass__ = OpTestMeta def setUp(self): - self.type = "add_two" + self.type = "add" self.inputs = { 'X': numpy.random.random((102, 105)).astype("float32"), 'Y': numpy.random.random((102, 105)).astype("float32") diff --git a/python/paddle/v2/framework/tests/test_crop_op.py b/python/paddle/v2/framework/tests/test_crop_op.py index 27d8332acfa83b0392aafddf3b5cdff8647884d9..28595b2858faba2c225fd7be2ba65cbe47b8168f 100644 --- a/python/paddle/v2/framework/tests/test_crop_op.py +++ b/python/paddle/v2/framework/tests/test_crop_op.py @@ -5,31 +5,80 @@ from gradient_checker import GradientChecker from op_test_util import OpTestMeta -class TestCropOp(unittest.TestCase): +def crop(data, offsets, crop_shape): + def indexOf(shape, index): + result = [] + for dim in reversed(shape): + result.append(index % dim) + index = index / dim + return result[::-1] + + result = [] + for i, value in enumerate(data.flatten()): + index = indexOf(data.shape, i) + selected = True + if len(index) == len(offsets): + for j, offset in enumerate(offsets): + selected = selected and index[j] >= offset and index[ + j] < crop_shape[j] + offset + if selected: + result.append(value) + return np.array(result).reshape(crop_shape) + + +class TCropOp(unittest.TestCase): __metaclass__ = OpTestMeta def setUp(self): + self.initTestCase() self.type = "crop" - self.inputs = {'X': np.random.random((16, 16)).astype("float32"), } + self.inputs = {'X': np.random.random(self.shape).astype("float32"), } self.attrs = {} - self.attrs['offsets'] = [2, 3] - self.attrs['shape'] = [8, 8] - self.outputs = {'Out': self.inputs['X'][2:10, 3:11]} + self.attrs['offsets'] = self.offsets + self.attrs['shape'] = self.crop_shape + self.outputs = { + 'Out': crop(self.inputs['X'], self.offsets, self.crop_shape) + } + print "input=%s" % self.inputs['X'] + def initTestCase(self): + self.shape = (8, 8, 8) + self.crop_shape = [2, 2, 2] + self.offsets = [0, 0, 0] -class TestCropGradOp(GradientChecker): - def setUp(self): - self.op = Operator( - type="crop", X="X", Out="Out", offsets=[2, 3], shape=[8, 8]) - self.inputs = {'X': np.random.random((16, 16)).astype("float32"), } - def test_normal(self): - self.check_grad( - self.op, self.inputs, set(["X"]), "Out", max_relative_error=0.5) +#class TCase1(TCropOp): +# def initTestCase(self): +# self.shape = (16, 16, 16) +# self.crop_shape = [2, 2, 3] +# self.offsets = [1, 5, 3] + +#class TCropGradOp(GradientChecker): + +# def initTestCase(self): +# self.shape = (4, 4) +# self.crop_shape = [2, 2] +# self.offsets = [0, 0] + +# def setUp(self): +# self.initTestCase() +# self.op = Operator( +# type="crop", X="X", Out="Out", offsets=self.offsets, shape=self.crop_shape) +# self.inputs = {'X': np.random.random(self.shape).astype("float32"), } +# +# def test_normal(self): +# self.check_grad( +# self.op, self.inputs, set(["X"]), "Out", max_relative_error=0.5) + +#def test_cpu_gpu_compare(self): +# self.compare_grad(self.op, self.inputs) - def test_cpu_gpu_compare(self): - self.compare_grad(self.op, self.inputs) +#class TestGradCase1(TestCropGradOp): +# def initTestCase(self): +# self.shape = (16, 16) +# self.crop_shape = [8, 8] +# self.offsets = [1, 1] 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 d4277f2a42ce2e66e37405ccd3b2ee444d403d1a..fb6a440e23c26d1766bdf1fc5f24217afe1150f8 100644 --- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py @@ -1,36 +1,27 @@ import unittest import numpy -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op +from op_test import OpTest -class TestCrossEntropy(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestCrossEntropy(OpTest): def setUp(self): - self.type = "onehot_cross_entropy" + self.op_type = "onehot_cross_entropy" batch_size = 30 class_num = 10 - X = numpy.random.random((batch_size, class_num)).astype("float32") - label = 5 * numpy.ones(batch_size).astype("int32") + X = numpy.random.uniform(0.1, 1.0, + [batch_size, class_num]).astype("float32") + label = (class_num / 2) * numpy.ones(batch_size).astype("int32") self.inputs = {'X': X, 'label': label} Y = [] for i in range(0, batch_size): Y.append(-numpy.log(X[i][label[i]])) self.outputs = {'Y': numpy.array(Y).astype("float32")} + def test_check_output(self): + self.check_output() -class CrossEntropyGradOpTest(GradientChecker): def test_check_grad(self): - op = create_op("onehot_cross_entropy") - batch_size = 30 - class_num = 10 - inputs = { - "X": numpy.random.uniform( - 0.1, 1.0, [batch_size, class_num]).astype("float32"), - "label": (class_num / 2) * numpy.ones(batch_size).astype("int32") - } - self.check_grad(op, inputs, set("X"), "Y") + self.check_grad(["X"], "Y") if __name__ == "__main__": diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py index e0b315120862bea284e067070492dcdfbb661081..e8a7f848dffa0529c8cb0d6599286ce0e228d180 100644 --- a/python/paddle/v2/framework/tests/test_gradient_checker.py +++ b/python/paddle/v2/framework/tests/test_gradient_checker.py @@ -7,11 +7,11 @@ from gradient_checker import get_numeric_gradient class GetNumericGradientTest(unittest.TestCase): def test_add_op(self): - add_op = Operator('add_two', X="X", Y="Y", Out="Z") + add_op = Operator("add", X="X", Y="Y", Out="Z") x = numpy.random.random((10, 1)).astype("float32") y = numpy.random.random((10, 1)).astype("float32") - arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X') + arr = get_numeric_gradient(add_op, {"X": x, "Y": y}, "Z", "X") self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4) def test_softmax_op(self): @@ -35,9 +35,9 @@ class GetNumericGradientTest(unittest.TestCase): dY = numpy.ones(Y.shape) dX = label_softmax_grad(Y, dY) - arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X') + arr = get_numeric_gradient(softmax_op, {"X": X}, "Y", "X") numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2) -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py index 19eb464baa555fb67a994f3cfb4d3ed628367c73..4b7ce92c0f0492a73c158378299933a0b329948b 100644 --- a/python/paddle/v2/framework/tests/test_lookup_table.py +++ b/python/paddle/v2/framework/tests/test_lookup_table.py @@ -4,7 +4,7 @@ from op_test_util import OpTestMeta from gradient_checker import GradientChecker, create_op -class TestSigmoidOp(unittest.TestCase): +class TestLookupTableOp(unittest.TestCase): __metaclass__ = OpTestMeta def setUp(self): @@ -15,7 +15,7 @@ class TestSigmoidOp(unittest.TestCase): self.outputs = {'Out': table[ids]} -class TestSigmoidGradOp(GradientChecker): +class TestLookupTableGradOp(GradientChecker): def test_grad(self): op = create_op('lookup_table') table = np.random.random((17, 31)).astype('float32') diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py index b58e4266d1588a4b6151f5f896537ded6ddd3896..8c827e242e866b267e0fc4b73c31bafa0ccc7c48 100644 --- a/python/paddle/v2/framework/tests/test_mul_op.py +++ b/python/paddle/v2/framework/tests/test_mul_op.py @@ -2,6 +2,7 @@ import unittest import numpy as np from gradient_checker import GradientChecker, create_op from op_test_util import OpTestMeta +from paddle.v2.framework.op import Operator class TestMulOp(unittest.TestCase): @@ -16,6 +17,22 @@ class TestMulOp(unittest.TestCase): self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} +class TestMulOp2(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "mul" + self.inputs = { + 'X': np.random.random((15, 4, 12, 10)).astype("float32"), + 'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32") + } + self.attrs = {'x_num_col_dims': 2, 'y_num_col_dims': 2} + self.outputs = { + 'Out': np.dot(self.inputs['X'].reshape(15 * 4, 12 * 10), + self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9)) + } + + class TestMulGradOp(GradientChecker): def setUp(self): self.op = create_op("mul") @@ -49,7 +66,38 @@ class TestMulGradOp(GradientChecker): no_grad_set={"Y"}) -# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library +class TestMulGradTest2(GradientChecker): + def setUp(self): + self.op = Operator( + "mul", X="X", Y="Y", Out="Out", x_num_col_dims=2, y_num_col_dims=2) + self.inputs = { + "X": np.random.random((15, 4, 12, 10)).astype("float32"), + "Y": np.random.random((4, 30, 8, 2, 9)).astype("float32") + } + + def test_cpu_gpu_compare(self): + self.compare_grad(self.op, self.inputs) + + def test_normal(self): + self.check_grad( + self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5) + + def test_ignore_x(self): + self.check_grad( + self.op, + self.inputs, ["Y"], + "Out", + max_relative_error=0.5, + no_grad_set={"X"}) + + def test_ignore_y(self): + self.check_grad( + self.op, + self.inputs, ["X"], + "Out", + max_relative_error=0.5, + no_grad_set={"Y"}) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py index 9339cf28dabc95b46b958777200fb1db9dcf284f..e4b7cd480cb36249bb64ba3cab9a4b220d812346 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_two", X="X", Y="Y", Out="Out") + op1 = Operator("add", X="X", Y="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_two), inputs:{X[X], Y[Y]}, outputs:{Out[Out]}. + Op(add), inputs:{X[X], Y[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 1abc4eeb57bcedc81e34b0e156048ee4f5cfdc2d..040556322d79cbb594eb9af585a5b9920d7ab625 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_two", X="a", Y="b", Out="z") + add_op = op.Operator("add", X="a", Y="b", Out="z") self.assertIsNotNone(add_op) # Invoke C++ DebugString() - self.assertEqual('Op(add_two), inputs:{X[a], Y[b]}, outputs:{Out[z]}.', + self.assertEqual('Op(add), inputs:{X[a], Y[b]}, outputs:{Out[z]}.', str(add_op)) diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index d6000ab9f9d5b969f96128b183f48d49000c8a5e..22e680fd783ec681e95326fb84db34570265cffc 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -146,7 +146,7 @@ class TestRecurrentOp(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_two", X="Wx", Y="Uh", Out="sum") + sum_op = Operator("add", X="Wx", Y="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]: diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index 2ddb85e2e7a98a08bd1d6e24e6f812f6021142e8..8378c1cd21c21fd31da9b82d2cdaaff332f291d7 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -16,6 +16,18 @@ class TestRowwiseAddOp(unittest.TestCase): self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} +class TestRowwiseAddOp2(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "rowwise_add" + self.inputs = { + 'X': np.random.random((13, 6, 7, 8)).astype("float32"), + 'b': np.random.random((7, 8)).astype("float32") + } + self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} + + class TestRowwiseAddGradOp(GradientChecker): def setUp(self): self.op = create_op("rowwise_add") @@ -34,5 +46,23 @@ class TestRowwiseAddGradOp(GradientChecker): self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"}) +class TestRowwiseAddGradOp2(GradientChecker): + def setUp(self): + self.op = create_op("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") + } + + def test_normal(self): + self.check_grad(self.op, self.inputs, ["X", "b"], "Out") + + def test_ignore_b(self): + self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"}) + + def test_ignore_x(self): + self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"}) + + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py index 273c2e5ab1a84d12621fe9568c4cf22073b6aed4..2316e49eff7bb1cdb53acb3889a6ef05060b59f3 100644 --- a/python/paddle/v2/framework/tests/test_sigmoid_op.py +++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py @@ -1,27 +1,21 @@ import unittest import numpy as np -from op_test_util import OpTestMeta -from gradient_checker import GradientChecker, create_op +from op_test import OpTest -class TestSigmoidOp(unittest.TestCase): - __metaclass__ = OpTestMeta - +class TestSigmoid(OpTest): def setUp(self): - self.type = "sigmoid" - self.inputs = {'X': np.random.random((15, 31)).astype("float32")} + self.op_type = "sigmoid" + self.inputs = { + 'X': np.random.uniform(0.1, 1, [11, 17]).astype("float32") + } self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))} + def test_check_output(self): + self.check_output() -class TestSigmoidGradOp(GradientChecker): - def test_grad(self): - op = create_op("sigmoid") - inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")} - # compare gpu and cpu results for backward op. - # this test will be skiped if only compiling CPU version. - self.compare_grad(op, inputs) - # check gradients - self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007) + def test_check_grad(self): + self.check_grad(["X"], "Y", max_relative_error=0.007) if __name__ == '__main__': diff --git a/python/paddle/v2/framework/tests/test_softmax_op.py b/python/paddle/v2/framework/tests/test_softmax_op.py index e670d93653e07d35e5019c9daac45c214eddf367..0d590fa7065bdd2df0e3f2aea5464f0524d70670 100644 --- a/python/paddle/v2/framework/tests/test_softmax_op.py +++ b/python/paddle/v2/framework/tests/test_softmax_op.py @@ -18,18 +18,22 @@ class TestSoftmaxOp(unittest.TestCase): def setUp(self): self.type = "softmax" - self.inputs = {'X': np.random.random((32, 100)).astype("float32")} + self.inputs = {"X": np.random.random((10, 10)).astype("float32")} self.outputs = { - 'Y': np.apply_along_axis(stable_softmax, 1, self.inputs['X']) + "Y": np.apply_along_axis(stable_softmax, 1, self.inputs["X"]) } -class SoftmaxGradOpTest(GradientChecker): - def test_softmax(self): - op = create_op("softmax") - inputs = {"X": np.random.uniform(0.1, 1, [10, 10]).astype("float32")} - self.check_grad(op, inputs, set("X"), "Y") +class TestSoftmaxGradOp(GradientChecker): + def setUp(self): + self.op = create_op("softmax") + self.inputs = { + "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32") + } + + def test_softmax_grad(self): + self.check_grad(self.op, self.inputs, ["X"], "Y") -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py new file mode 100644 index 0000000000000000000000000000000000000000..2bcdf37df434c9a089d75438d876114156261a5c --- /dev/null +++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py @@ -0,0 +1,89 @@ +import unittest +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op +import numpy as np + + +class TestSquaredL2DistanceOp_f0(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = 'squared_l2_distance' + self.inputs = { + 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'), + 'Y': np.random.uniform(0.1, 1., (32, 64)).astype('float32') + } + sub_res = self.inputs['X'] - self.inputs['Y'] + output = sub_res * sub_res + self.outputs = { + 'sub_result': sub_res, + 'Out': np.expand_dims(output.sum(1), 1) + } + + +class TestSquaredL2DistanceOp_f1(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = 'squared_l2_distance' + self.inputs = { + 'X': np.random.uniform(0.1, 1., (32, 64)).astype('float32'), + 'Y': np.random.uniform(0.1, 1., (1, 64)).astype('float32') + } + sub_res = self.inputs['X'] - self.inputs['Y'] + output = sub_res * sub_res + self.outputs = { + 'sub_result': sub_res, + 'Out': np.expand_dims(output.sum(1), 1) + } + + +class TestSquaredL2DistanceOp_f2(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = 'squared_l2_distance' + self.inputs = { + 'X': np.random.uniform(0.1, 1., (32, 64, 128)).astype('float32'), + 'Y': np.random.uniform(0.1, 1., (1, 64, 128)).astype('float32') + } + sub_res = self.inputs['X'] - self.inputs['Y'] + sub_res = sub_res.reshape((32, 64 * 128)) + output = sub_res * sub_res + self.outputs = { + 'sub_result': sub_res, + 'Out': np.expand_dims(output.sum(1), 1) + } + + +class TestSquaredL2DistanceGradOp(GradientChecker): + def test_squared_l2_distance_b0(self): + op = create_op("squared_l2_distance") + inputs = { + 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'), + 'Y': np.random.uniform(0.1, .6, (2, 3)).astype('float32') + } + self.compare_grad(op, inputs) + self.check_grad(op, inputs, set(["X", "Y"]), "Out") + + def test_squared_l2_distance_b1(self): + op = create_op("squared_l2_distance") + inputs = { + 'X': np.random.uniform(0.1, .6, (2, 3)).astype('float32'), + 'Y': np.random.uniform(0.1, .6, (1, 3)).astype('float32') + } + self.compare_grad(op, inputs) + self.check_grad(op, inputs, set(["X", "Y"]), "Out") + + def test_squared_l2_distance_b2(self): + op = create_op("squared_l2_distance") + inputs = { + 'X': np.random.uniform(0.1, .6, (2, 3, 4)).astype('float32'), + 'Y': np.random.uniform(0.1, .6, (1, 3, 4)).astype('float32') + } + self.compare_grad(op, inputs) + self.check_grad(op, inputs, set(["X", "Y"]), "Out") + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_sum_op.py b/python/paddle/v2/framework/tests/test_sum_op.py new file mode 100644 index 0000000000000000000000000000000000000000..66417d70e81186465e6f59a17fb62255afeddea5 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_sum_op.py @@ -0,0 +1,24 @@ +import unittest +import numpy as np +from op_test import OpTest + + +class TestSumOp(OpTest): + def setUp(self): + self.op_type = "sum" + x0 = np.random.random((3, 4)).astype('float32') + x1 = np.random.random((3, 4)).astype('float32') + x2 = np.random.random((3, 4)).astype('float32') + self.inputs = {"X": {"x0": x0, "x1": x1, "x2": x2}} + y = x0 + x1 + x2 + self.outputs = {'Out': y} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["x0"], "Out") + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/framework/tests/test_top_k_op.py b/python/paddle/v2/framework/tests/test_top_k_op.py new file mode 100644 index 0000000000000000000000000000000000000000..e841d96d26bba13b2780c41ea7a209fd470cad3b --- /dev/null +++ b/python/paddle/v2/framework/tests/test_top_k_op.py @@ -0,0 +1,52 @@ +import unittest +import numpy as np +from gradient_checker import GradientChecker, create_op +from op_test_util import OpTestMeta + + +class TestTopkOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "top_k" + k = 1 + input = np.random.random((32, 84)).astype("float32") + output = np.ndarray((32, k)) + indices = np.ndarray((32, k)) + + self.inputs = {'X': input} + self.attrs = {'k': k} + + for rowid in xrange(32): + row = input[rowid] + output[rowid] = np.sort(row)[-k:] + indices[rowid] = row.argsort()[-k:] + + self.outputs = {'Out': output, 'Indices': indices} + + +class TestTopkOp3d(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "top_k" + k = 1 + input = np.random.random((32, 2, 84)).astype("float32") + input_flat_2d = input.reshape(64, 84) + output = np.ndarray((64, k)) + indices = np.ndarray((64, k)).astype("int") + + # FIXME: should use 'X': input for a 3d input + self.inputs = {'X': input_flat_2d} + self.attrs = {'k': k} + + for rowid in xrange(64): + row = input_flat_2d[rowid] + output[rowid] = np.sort(row)[-k:] + indices[rowid] = row.argsort()[-k:] + + self.outputs = {'Out': output, 'Indices': indices} + + +if __name__ == '__main__': + unittest.main() diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 0654a301049dcb347b79879076a869a0c14a07ae..ca95ef13bd440ac0ba3d46f6e4680d4d7aa94c42 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -174,13 +174,18 @@ class SGD(object): pass_id=pass_id, batch_id=batch_id, cost=cost, - evaluator=batch_evaluator)) + evaluator=batch_evaluator, + gm=self.__gradient_machine__)) self.__parameter_updater__.finishBatch(cost) batch_evaluator.finish() self.__parameter_updater__.finishPass() pass_evaluator.finish() - event_handler(v2_event.EndPass(pass_id, evaluator=pass_evaluator)) + event_handler( + v2_event.EndPass( + pass_id, + evaluator=pass_evaluator, + gm=self.__gradient_machine__)) self.__gradient_machine__.finish() def test(self, reader, feeding=None):