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提交 6253b152 编写于 作者: Q Qiao Longfei
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Merge branch 'optimize-sum-seq-pooling-op' of... 

Merge branch 'optimize-sum-seq-pooling-op' of https://github.com/jacquesqiao/Paddle into optimize-sum-seq-pooling-op
上级 14f5a408 f4e6fe07
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Showing with 11499 addition and 1701 deletion
CMakeLists.txt
浏览文件 @ 6253b152
......@@ -127,6 +127,9 @@ set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
"A path setting fluid shared and static libraries")
set(FLUID_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_inference_install_dir" CACHE STRING
"A path setting fluid inference shared and static libraries")
if (WITH_C_API AND WITH_PYTHON)
message(WARNING "It is suggest not embedded a python interpreter in Paddle "
"when using C-API. It will give an unpredictable behavior when using a "
......
README.md
浏览文件 @ 6253b152
......@@ -2,8 +2,8 @@
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -19,7 +19,7 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 0.15.0](https://github.com/PaddlePaddle/Paddle/tree/v0.15.0)
### Latest PaddlePaddle Release: [Fluid 1.0.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -27,9 +27,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==0.15.0.post87
pip install paddlepaddle-gpu==1.0.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==0.15.0.post85
pip install paddlepaddle-gpu==1.0.1.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......@@ -76,26 +76,26 @@ pip install paddlepaddle-gpu==0.15.0.post85
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/beginners_guide/install/install_doc.html) on our website.
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/0.15.0/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/beginners_guide/index.html) documentation.
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.0.0/getstarted/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/user_guides/howto/training/cluster_howto.html)
- [Distributed Training](http://paddlepaddle.org/documentation/docs/zh/1.0/user_guides/howto/training/cluster_howto.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/api/zh/0.15.0/fluid.html)
- [Python API](http://paddlepaddle.org/documentation/api/zh/1.0/fluid.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/0.15.0/new_docs/advanced_usage/development/contribute_to_paddle.html)
- [How to Contribute](http://paddlepaddle.org/documentation/docs/zh/1.0/advanced_usage/development/contribute_to_paddle.html)
We appreciate your contributions!
......
benchmark/fluid/run.sh 100644 → 100755
浏览文件 @ 6253b152
文件模式从 100644 更改为 100755
cmake/generic.cmake
浏览文件 @ 6253b152
......@@ -261,6 +261,13 @@ function(cc_library TARGET_NAME)
add_dependencies(${TARGET_NAME} mklml)
target_link_libraries(${TARGET_NAME} "-L${MKLML_LIB_DIR} -liomp5 -Wl,--as-needed")
endif()
# remove link to python, see notes at:
# https://github.com/pybind/pybind11/blob/master/docs/compiling.rst#building-manually
if("${cc_library_DEPS};" MATCHES "python;")
list(REMOVE_ITEM cc_library_DEPS python)
add_dependencies(${TARGET_NAME} python)
target_link_libraries(${TARGET_NAME} "-Wl,-undefined,dynamic_lookup")
endif()
target_link_libraries(${TARGET_NAME} ${cc_library_DEPS})
add_dependencies(${TARGET_NAME} ${cc_library_DEPS})
endif()
......@@ -311,6 +318,8 @@ function(cc_test TARGET_NAME)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
# No unit test should exceed 10 minutes.
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
endif()
endfunction(cc_test)
......@@ -629,6 +638,8 @@ function(py_test TARGET_NAME)
PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
# No unit test should exceed 10 minutes.
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
endif()
endfunction()
......
cmake/inference_lib.cmake
浏览文件 @ 6253b152
......@@ -150,16 +150,16 @@ if (WITH_ANAKIN AND WITH_MKL)
SRCS
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/libinference_anakin_api* # compiled anakin api
${ANAKIN_INSTALL_DIR} # anakin release
DSTS ${dst_dir}/inference/anakin ${FLUID_INSTALL_DIR}/third_party/install/anakin)
DSTS ${FLUID_INSTALL_DIR}/third_party/install/anakin ${FLUID_INSTALL_DIR}/third_party/install/anakin)
list(APPEND inference_deps anakin_inference_lib)
endif()
set(module "inference")
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
${src_dir}/${module}/api/paddle_inference_api.h ${src_dir}/${module}/api/demo_ci
${src_dir}/${module}/api/paddle_inference_api.h
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
)
set(module "platform")
......@@ -188,18 +188,38 @@ copy(cmake_cache
# This command generates a complete fluid library for both train and inference
add_custom_target(fluid_lib_dist DEPENDS ${fluid_lib_dist_dep})
# Following commands generate a inference-only fluid library
# third_party, version.txt and CMakeCache.txt are the same position with ${FLUID_INSTALL_DIR}
copy(third_party DEPS fluid_lib_dist
SRCS ${FLUID_INSTALL_DIR}/third_party ${FLUID_INSTALL_DIR}/CMakeCache.txt
DSTS ${FLUID_INFERENCE_INSTALL_DIR} ${FLUID_INFERENCE_INSTALL_DIR}
)
# only need libpaddle_fluid.so/a and paddle_inference_api.h for inference-only library
copy(inference_api_lib DEPS fluid_lib_dist
SRCS ${FLUID_INSTALL_DIR}/paddle/fluid/inference/libpaddle_fluid.*
${FLUID_INSTALL_DIR}/paddle/fluid/inference/paddle_inference_api.h
DSTS ${FLUID_INFERENCE_INSTALL_DIR}/paddle/lib ${FLUID_INFERENCE_INSTALL_DIR}/paddle/include
)
add_custom_target(inference_lib_dist DEPENDS third_party inference_api_lib)
# paddle fluid version
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
set(version_file ${FLUID_INSTALL_DIR}/version.txt)
file(WRITE ${version_file}
"GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
"WITH_MKL: ${WITH_MKL}\n"
"WITH_GPU: ${WITH_GPU}\n")
if(WITH_GPU)
file(APPEND ${version_file}
"CUDA version: ${CUDA_VERSION}\n"
"CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
endif()
function(version version_file)
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=format:%H -1
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}
OUTPUT_VARIABLE PADDLE_GIT_COMMIT)
file(WRITE ${version_file}
"GIT COMMIT ID: ${PADDLE_GIT_COMMIT}\n"
"WITH_MKL: ${WITH_MKL}\n"
"WITH_MKLDNN: ${WITH_MKLDNN}\n"
"WITH_GPU: ${WITH_GPU}\n")
if(WITH_GPU)
file(APPEND ${version_file}
"CUDA version: ${CUDA_VERSION}\n"
"CUDNN version: v${CUDNN_MAJOR_VERSION}\n")
endif()
endfunction()
version(${FLUID_INSTALL_DIR}/version.txt)
version(${FLUID_INFERENCE_INSTALL_DIR}/version.txt)
paddle/fluid/API.spec
浏览文件 @ 6253b152
......@@ -61,12 +61,12 @@ paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None
paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None))
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None))
paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn'], varargs=None, keywords=None, defaults=(None, None, False))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
......@@ -75,7 +75,8 @@ paddle.fluid.layers.conv2d_transpose ArgSpec(args=['input', 'num_filters', 'outp
paddle.fluid.layers.conv3d_transpose ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_expand ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.sequence_expand_as ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_pad ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.sequence_unpad ArgSpec(args=['x', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.lstm_unit ArgSpec(args=['x_t', 'hidden_t_prev', 'cell_t_prev', 'forget_bias', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(0.0, None, None, None))
paddle.fluid.layers.reduce_sum ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.reduce_mean ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
......@@ -84,6 +85,7 @@ paddle.fluid.layers.reduce_min ArgSpec(args=['input', 'dim', 'keep_dim', 'name']
paddle.fluid.layers.reduce_prod ArgSpec(args=['input', 'dim', 'keep_dim', 'name'], varargs=None, keywords=None, defaults=(None, False, None))
paddle.fluid.layers.sequence_first_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_last_step ArgSpec(args=['input'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_slice ArgSpec(args=['input', 'offset', 'length', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.dropout ArgSpec(args=['x', 'dropout_prob', 'is_test', 'seed', 'name'], varargs=None, keywords=None, defaults=(False, None, None))
paddle.fluid.layers.split ArgSpec(args=['input', 'num_or_sections', 'dim', 'name'], varargs=None, keywords=None, defaults=(-1, None))
paddle.fluid.layers.ctc_greedy_decoder ArgSpec(args=['input', 'blank', 'name'], varargs=None, keywords=None, defaults=(None,))
......@@ -95,8 +97,8 @@ paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_ti
paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
......@@ -114,6 +116,7 @@ paddle.fluid.layers.pad ArgSpec(args=['x', 'paddings', 'pad_value', 'name'], var
paddle.fluid.layers.pad_constant_like ArgSpec(args=['x', 'y', 'pad_value', 'name'], varargs=None, keywords=None, defaults=(0.0, None))
paddle.fluid.layers.label_smooth ArgSpec(args=['label', 'prior_dist', 'epsilon', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 0.1, 'float32', None))
paddle.fluid.layers.roi_pool ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1, 1, 1.0))
paddle.fluid.layers.roi_align ArgSpec(args=['input', 'rois', 'pooled_height', 'pooled_width', 'spatial_scale', 'sampling_ratio', 'name'], varargs=None, keywords=None, defaults=(1, 1, 1.0, -1, None))
paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], varargs=None, keywords=None, defaults=(1e-05,))
paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
......@@ -171,6 +174,7 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True))
paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None))
paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/details/var_handle.h
浏览文件 @ 6253b152
......@@ -49,6 +49,8 @@ struct VarHandleBase {
void AddOutput(OpHandleBase* out, ir::Node* node) {
if (pending_ops_.find(out) == pending_ops_.end()) {
PADDLE_ENFORCE(out != nullptr, "The output of %s should not be nullptr",
this->Node()->Name());
pending_ops_.insert(out);
node_->outputs.push_back(node);
}
......
paddle/fluid/framework/executor.cc
浏览文件 @ 6253b152
......@@ -101,7 +101,7 @@ void InitializeVariable(Variable* var, proto::VarType::Type var_type) {
} else if (var_type == proto::VarType::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
var->GetMutable<std::vector<framework::Scope*>>();
} else if (var_type == proto::VarType::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == proto::VarType::LOD_TENSOR_ARRAY) {
......
paddle/fluid/framework/feed_fetch_method.cc
浏览文件 @ 6253b152
......@@ -27,8 +27,7 @@ void SetFeedVariable(Scope* scope, const LoDTensor& input,
// be created.
VLOG(3) << "SetFeedVariable name=" << var_name << " index=" << index;
Variable* g_feed_value = scope->Var(var_name);
auto& feed_inputs =
*(g_feed_value->GetMutable<std::vector<paddle::framework::LoDTensor>>());
auto& feed_inputs = *(g_feed_value->GetMutable<FeedFetchList>());
if (index >= feed_inputs.size()) {
feed_inputs.resize(index + 1);
}
......
paddle/fluid/framework/framework.proto
浏览文件 @ 6253b152
......@@ -80,7 +80,6 @@ message OpProto {
optional bool duplicable = 3 [ default = false ];
optional bool intermediate = 4 [ default = false ];
optional bool dispensable = 5 [ default = false ];
optional string reuse = 6;
}
// AttrProto describes the C++ type Attribute.
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 6253b152
......@@ -10,7 +10,7 @@ function(pass_library TARGET DEST)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass ${op_library_DEPS})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass fuse_pass_base ${op_library_DEPS})
# add more DEST here, such as train, dist and collect USE_PASS into a file automatically.
if (${DEST} STREQUAL "base" OR ${DEST} STREQUAL "inference")
message(STATUS "add pass ${TARGET} ${DEST}")
......@@ -25,13 +25,11 @@ cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph graph_helper graph_traits)
cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(fc_fuse_pass inference)
if (WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif ()
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
pass_library(fc_lstm_fuse_pass inference)
......@@ -39,6 +37,13 @@ pass_library(embedding_fc_lstm_fuse_pass inference)
pass_library(fc_gru_fuse_pass inference)
pass_library(seq_concat_fc_fuse_pass inference)
pass_library(conv_bn_fuse_pass inference)
pass_library(seqconv_eltadd_relu_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(conv_bias_mkldnn_fuse_pass inference)
pass_library(conv_relu_mkldnn_fuse_pass inference)
pass_library(conv_elementwise_add_mkldnn_fuse_pass inference)
endif()
cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
......@@ -54,4 +59,5 @@ cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS g
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
if (WITH_MKLDNN)
cc_test(test_conv_relu_mkldnn_fuse_pass SRCS conv_relu_mkldnn_fuse_pass_tester.cc DEPS conv_relu_mkldnn_fuse_pass)
cc_test(test_conv_elementwise_add_mkldnn_fuse_pass SRCS conv_elementwise_add_mkldnn_fuse_pass_tester.cc DEPS conv_elementwise_add_mkldnn_fuse_pass)
endif ()
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ 6253b152
......@@ -262,7 +262,7 @@ std::unique_ptr<ir::Graph> AttentionLSTMFusePass::ApplyImpl(
std::unordered_set<std::string> specified_vars({"data_lod_attention",
"cell_init", "hidden_init",
"data", "week", "minute"});
int count = 0;
size_t count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsVar() && specified_vars.count(node->Name())) {
++count;
......
paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.cc 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.h"
#include <functional>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
template <typename BinaryOperation>
LoDTensor tensor_apply_eltwise(const LoDTensor& vec_a, const LoDTensor& vec_b,
BinaryOperation f) {
PADDLE_ENFORCE_EQ(vec_a.dims(), vec_b.dims());
LoDTensor vec_y;
vec_y.Resize(vec_a.dims());
const float* a = vec_a.data<float>();
const float* b = vec_b.data<float>();
float* y = vec_y.mutable_data<float>(platform::CPUPlace());
for (int i = 0; i < vec_a.numel(); i++) {
y[i] = f(a[i], b[i]);
}
return vec_y;
}
std::unique_ptr<ir::Graph> ConvBiasFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init(name_scope_, graph.get());
auto* scope = param_scope();
PADDLE_ENFORCE(scope);
GraphPatternDetector gpd;
auto* conv_input =
gpd.mutable_pattern()
->NewNode(patterns::PDNodeName(name_scope_, "conv_input"))
->AsInput()
->assert_is_op_input("conv2d", "Input");
patterns::ConvBias conv_bias_pattern(gpd.mutable_pattern(), name_scope_);
conv_bias_pattern(conv_input);
int found_conv_bias_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle ConvBias fuse";
GET_IR_NODE_FROM_SUBGRAPH(conv_weight, conv_weight,
conv_bias_pattern); // Filter
GET_IR_NODE_FROM_SUBGRAPH(conv_out, conv_out, conv_bias_pattern); // tmp
GET_IR_NODE_FROM_SUBGRAPH(conv, conv, conv_bias_pattern); // CONV op
// bias
GET_IR_NODE_FROM_SUBGRAPH(eltwise_bias, eltwise_bias, conv_bias_pattern);
// output
GET_IR_NODE_FROM_SUBGRAPH(eltwise_out, eltwise_out, conv_bias_pattern);
// elementwise_add op
GET_IR_NODE_FROM_SUBGRAPH(eltwise, eltwise, conv_bias_pattern);
PADDLE_ENFORCE(subgraph.count(conv_input));
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *eltwise);
if (fuse_option == DO_NOT_FUSE || fuse_option == FUSE_NATIVE) {
VLOG(3) << "do not perform conv+bias fuse";
return;
}
auto* eltwise_bias_tensor =
scope->FindVar(eltwise_bias->Name())->GetMutable<LoDTensor>();
auto input_names = conv->Op()->InputNames();
bool has_bias = std::find(input_names.begin(), input_names.end(), "Bias") !=
input_names.end();
if (has_bias && conv->Op()->Input("Bias").size() > 0) {
auto conv_bias_names = conv->Op()->Input("Bias");
// add eltwise bias to existing conv bias
PADDLE_ENFORCE_EQ(conv_bias_names.size(), 1);
auto* conv_bias_var = scope->FindVar(conv_bias_names[0]);
auto* conv_bias_tensor = conv_bias_var->GetMutable<LoDTensor>();
PADDLE_ENFORCE_EQ(conv_bias_tensor->dims(), eltwise_bias_tensor->dims());
*conv_bias_tensor = tensor_apply_eltwise(
*conv_bias_tensor, *eltwise_bias_tensor, std::plus<float>());
conv->Op()->SetOutput("Output",
std::vector<std::string>({eltwise_out->Name()}));
GraphSafeRemoveNodes(graph.get(), {eltwise, conv_out});
IR_NODE_LINK_TO(conv, eltwise_out);
} else {
// take eltwise bias as conv bias
OpDesc desc;
desc.SetInput(
"Input", std::vector<std::string>({subgraph.at(conv_input)->Name()}));
desc.SetInput("Filter", std::vector<std::string>({conv_weight->Name()}));
desc.SetInput("Bias", std::vector<std::string>({eltwise_bias->Name()}));
desc.SetOutput("Output", std::vector<std::string>({eltwise_out->Name()}));
desc.SetType("conv2d");
for (auto& attr : conv->Op()->GetAttrMap()) {
desc.SetAttr(attr.first, attr.second);
}
auto conv_bias_node = g->CreateOpNode(&desc);
IR_NODE_LINK_TO(subgraph.at(conv_input), conv_bias_node);
IR_NODE_LINK_TO(conv_weight, conv_bias_node);
IR_NODE_LINK_TO(eltwise_bias, conv_bias_node);
IR_NODE_LINK_TO(conv_bias_node, eltwise_out);
GraphSafeRemoveNodes(graph.get(), {conv, eltwise, conv_out});
}
found_conv_bias_count++;
};
gpd(graph.get(), handler);
AddStatis(found_conv_bias_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(conv_bias_mkldnn_fuse_pass,
paddle::framework::ir::ConvBiasFusePass);
paddle/fluid/framework/ir/conv_bias_mkldnn_fuse_pass.h 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
/*
* Fuse the Conv and Elementwise_add to a ConvBiasOp.
*/
class ConvBiasFusePass : public FusePassBase {
public:
virtual ~ConvBiasFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"conv_bias_mkldnn_fuse"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
浏览文件 @ 6253b152
......@@ -126,12 +126,21 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
// conv, batch_norm,
// conv_weight, conv_out,
// bn_scale, bn_bias, bn_mean, bn_variance,
// bn_out, bn_mean_out, bn_variance_out, bn_saved_mean, bn_saved_variance
// bn_out, bn_mean_out, bn_variance_out, bn_saved_mean,
// bn_saved_variance
GET_CONV_BN_NODES(conv_bn_pattern);
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *batch_norm);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+bn fuse";
return;
}
// Create eltwise_y (conv bias) variable
VarDesc eltwise_y_in_desc(
patterns::PDNodeName(name_scope_, "eltwise_y_in"));
eltwise_y_in_desc.SetPersistable(true);
auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
auto* eltwise_y_in_tensor =
scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
......@@ -151,27 +160,59 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
*bn_mean, *bn_variance, eltwise_y_in_tensor,
epsilon);
// Create an elementwise add node
OpDesc desc;
desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
desc.SetOutput("Out", std::vector<std::string>({bn_out->Name()}));
desc.SetType("elementwise_add");
desc.SetAttr("axis", 1);
bool a = boost::get<bool>(conv->Op()->GetAttr("use_mkldnn"));
desc.SetAttr("use_mkldnn", a);
auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
GraphSafeRemoveNodes(graph.get(), {bn_scale, bn_bias, bn_mean, bn_variance,
batch_norm, bn_mean_out, bn_variance_out,
bn_saved_mean, bn_saved_variance});
PADDLE_ENFORCE(subgraph.count(conv_input));
IR_NODE_LINK_TO(conv_out, eltwise_op);
IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
IR_NODE_LINK_TO(eltwise_op, bn_out);
found_conv_bn_count++;
// with MKL-DNN fuse conv+bn into conv with bias
// without MKL-DNN fuse conv+bn into conv+elementwise_add
if (fuse_option == FUSE_MKLDNN) {
auto input_names = conv->Op()->InputNames();
bool has_bias = std::find(input_names.begin(), input_names.end(),
"Bias") != input_names.end();
if (has_bias && conv->Op()->Input("Bias").size() > 0) {
// reuse existing conv bias node
auto conv_bias_names = conv->Op()->Input("Bias");
PADDLE_ENFORCE_EQ(conv_bias_names.size(), 1);
auto* conv_bias_var = scope->FindVar(conv_bias_names[0]);
auto* conv_bias_tensor = conv_bias_var->GetMutable<LoDTensor>();
PADDLE_ENFORCE_EQ(conv_bias_tensor->dims(),
eltwise_y_in_tensor->dims());
auto eigen_conv_bias = EigenVector<float>::From(*conv_bias_tensor);
eigen_conv_bias += EigenVector<float>::From(*eltwise_y_in_tensor);
} else {
// add new conv_bias node
conv->Op()->SetInput(
"Bias", std::vector<std::string>({eltwise_y_in_node->Name()}));
IR_NODE_LINK_TO(eltwise_y_in_node, conv);
}
conv->Op()->SetOutput("Output",
std::vector<std::string>({bn_out->Name()}));
GraphSafeRemoveNodes(
graph.get(),
{conv_out, bn_scale, bn_bias, bn_mean, bn_variance, batch_norm,
bn_mean_out, bn_variance_out, bn_saved_mean, bn_saved_variance});
IR_NODE_LINK_TO(conv, bn_out);
found_conv_bn_count++;
} else { // fuse_option == FUSE_NATIVE
// create an elementwise add node.
OpDesc desc;
desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
desc.SetOutput("Out", std::vector<std::string>({bn_out->Name()}));
desc.SetType("elementwise_add");
desc.SetAttr("axis", 1);
auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
GraphSafeRemoveNodes(
graph.get(),
{bn_scale, bn_bias, bn_mean, bn_variance, batch_norm, bn_mean_out,
bn_variance_out, bn_saved_mean, bn_saved_variance});
IR_NODE_LINK_TO(conv_out, eltwise_op);
IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
IR_NODE_LINK_TO(eltwise_op, bn_out);
found_conv_bn_count++;
}
};
gpd(graph.get(), handler);
......@@ -237,7 +278,6 @@ std::unique_ptr<ir::Graph> ConvEltwiseAddBNFusePass::ApplyImpl(
{bn_scale, bn_bias, bn_mean, bn_variance, batch_norm, bn_mean_out,
bn_variance_out, bn_saved_mean, bn_saved_variance, eltwise_out});
PADDLE_ENFORCE(subgraph.count(conv_input));
IR_NODE_LINK_TO(eltwise, bn_out);
found_conv_bn_count++;
......
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.cc 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h"
#include <functional>
#include <utility>
#include "paddle/fluid/framework/ir/graph_traits.h"
namespace paddle {
namespace framework {
namespace ir {
namespace {
// The function keeps the graph consistent by replacing
// a node 'from' in the set of inputs nodes
// of the visited node by a node 'to'.
void CorrectGraphEdges(Graph* graph, Node* from, Node* to) {
for (auto& node : GraphTraits::DFS(*graph)) {
auto from_in_inputs =
std::find(std::begin(node.inputs), std::end(node.inputs), from);
if (from_in_inputs != std::end(node.inputs)) {
IR_NODE_LINK_TO(to, (&node));
auto inputs = node.Op()->Inputs();
using input_type = VariableNameMap::value_type;
std::for_each(std::begin(inputs), std::end(inputs),
[from, to, &node](const input_type& i) -> void {
auto param_names = i.second;
auto pi = std::find(std::begin(param_names),
std::end(param_names), from->Name());
if (pi != std::end(param_names)) {
node.Op()->SetInput(i.first, {to->Name()});
}
});
}
}
}
} // namespace
using graph_ptr = std::unique_ptr<ir::Graph>;
graph_ptr ConvElementwiseAddMKLDNNFusePass::ApplyImpl(graph_ptr graph) const {
FusePassBase::Init(name_scope_, graph.get());
GraphPatternDetector gpd;
auto pattern = gpd.mutable_pattern();
patterns::Conv conv_pattern{pattern, name_scope_};
auto conv_output = conv_pattern();
patterns::ElementwiseAdd elementwise_add_pattern{pattern, name_scope_};
elementwise_add_pattern(conv_output);
conv_output->AsIntermediate();
auto conv_op_has_bias = [](const Node& conv_op) -> std::pair<bool, Node*> {
auto bias_input_names = conv_op.Op()->Inputs();
auto bias_it = bias_input_names.find("Bias");
if (bias_it != std::end(bias_input_names)) {
bool has_bias = !bias_it->second.empty();
if (has_bias) {
auto conv_bias_names = bias_it->second;
auto conv_bias_names_it =
std::find_if(std::begin(conv_op.inputs), std::end(conv_op.inputs),
[&conv_bias_names](Node* n) -> bool {
return n->Name() == conv_bias_names[0];
});
return std::make_pair(has_bias, *conv_bias_names_it);
}
}
return std::make_pair(false, nullptr);
};
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
GET_IR_NODE_FROM_SUBGRAPH(conv_op, conv_op, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(conv_input, conv_input, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(conv_filter, conv_filter, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(conv_output, conv_output, conv_pattern);
GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_op, elementwise_add_op,
elementwise_add_pattern);
GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_x, elementwise_add_x,
elementwise_add_pattern);
GET_IR_NODE_FROM_SUBGRAPH(elementwise_add_out, elementwise_add_out,
elementwise_add_pattern);
if (FindFuseOption(*conv_op, *elementwise_add_op) != FUSE_MKLDNN) return;
OpDesc op_desc;
op_desc.SetType("conv2d");
op_desc.SetInput("Input", {conv_input->Name()});
op_desc.SetInput("Filter", {conv_filter->Name()});
op_desc.SetInput("ResidualData", {elementwise_add_x->Name()});
op_desc.SetOutput("Output", {conv_output->Name()});
bool has_bias;
Node* conv_bias;
std::tie(has_bias, conv_bias) = conv_op_has_bias(*conv_op);
if (has_bias) {
op_desc.SetInput("Bias", {conv_bias->Name()});
}
for (const auto& attr : conv_op->Op()->GetAttrMap()) {
op_desc.SetAttr(attr.first, attr.second);
}
op_desc.SetAttr("fuse_residual_connection", true);
auto fused_conv_op = g->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(conv_input, fused_conv_op);
IR_NODE_LINK_TO(conv_filter, fused_conv_op);
IR_NODE_LINK_TO(elementwise_add_x, fused_conv_op);
IR_NODE_LINK_TO(fused_conv_op, conv_output);
if (has_bias) {
IR_NODE_LINK_TO(conv_bias, fused_conv_op);
}
CorrectGraphEdges(g, elementwise_add_out, conv_output);
GraphSafeRemoveNodes(g, {elementwise_add_out, conv_op, elementwise_add_op});
};
gpd(graph.get(), handler);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(conv_elementwise_add_mkldnn_fuse_pass,
paddle::framework::ir::ConvElementwiseAddMKLDNNFusePass);
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
class ConvElementwiseAddMKLDNNFusePass : public FusePassBase {
public:
virtual ~ConvElementwiseAddMKLDNNFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"residual_connections_fuse_pass"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass_tester.cc 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <gtest/gtest.h>
#include <string>
#include "paddle/fluid/framework/ir/conv_elementwise_add_mkldnn_fuse_pass.h"
#include "paddle/fluid/framework/ir/graph_traits.h"
namespace paddle {
namespace framework {
namespace ir {
namespace {
constexpr int nodes_removed = 3;
constexpr int nodes_added = 1;
void SetOp(ProgramDesc* prog, const std::string& type,
const std::vector<std::pair<std::string, std::string>>& inputs,
const std::pair<std::string, std::string>& output) {
auto op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
op->SetAttr("use_mkldnn", true);
for (const auto& input : inputs) {
op->SetInput(input.first, {input.second});
}
op->SetOutput(output.first, {output.second});
}
struct IsReachable {
using func = std::function<bool(const std::string&, const std::string&)>;
auto operator()(const std::unique_ptr<ir::Graph>& graph) -> func {
auto find_node = [](const std::unique_ptr<ir::Graph>& graph,
const std::string& name) -> Node* {
for (auto& node : GraphTraits::DFS(*graph)) {
if (name == node.Name()) {
return &node;
}
}
return nullptr;
};
return [&](std::string from, const std::string to) -> bool {
if (from == to) return true;
std::map<std::string, bool> visited;
for (auto& node : GraphTraits::DFS(*graph)) {
visited[node.Name()] = false;
}
visited[from] = true;
std::list<std::string> queue;
queue.push_back(from);
while (!queue.empty()) {
auto cur = find_node(graph, queue.front());
queue.pop_front();
if (cur == nullptr) return false;
for (auto n : cur->outputs) {
if (n->Name() == to) return true;
if (!visited[n->Name()]) {
visited[n->Name()] = true;
queue.push_back(n->Name());
}
}
}
return false;
};
}
};
void AssertOpsCount(const std::unique_ptr<ir::Graph>& graph) {
int conv_count = 0;
int elementwise_add_count = 0;
for (auto* node : graph->Nodes()) {
if (node->IsOp() && node->Op()->Type() == "conv2d") {
++conv_count;
}
if (node->IsOp() && node->Op()->Type() == "elementwise_add") {
++elementwise_add_count;
}
}
EXPECT_EQ(conv_count, 1);
EXPECT_EQ(elementwise_add_count, 0);
}
ProgramDesc BuildProgramDesc(const std::vector<std::string>& transient_vars,
const std::vector<std::string>& persistent_vars) {
ProgramDesc prog;
auto add_var_to_prog = [&prog](const std::string& var_name) -> VarDesc* {
auto var = prog.MutableBlock(0)->Var(var_name);
var->SetType(proto::VarType::LOD_TENSOR);
return var;
};
for (const auto& v : transient_vars) {
add_var_to_prog(v);
}
for (const auto& v : persistent_vars) {
auto var = add_var_to_prog(v);
var->SetPersistable(true);
}
return prog;
}
} // namespace
TEST(ConvElementwiseAddMKLDNNFusePass, ConvolutionWithElementwiseAddRelu) {
auto prog =
BuildProgramDesc({"a", "b", "c", "d", "e", "f"}, {"bias", "weights"});
SetOp(&prog, "conv2d",
{{"Input", "a"}, {"Bias", "bias"}, {"Filter", "weights"}},
{"Output", "b"});
SetOp(&prog, "elementwise_add", {{"X", "b"}, {"Y", "c"}}, {"Out", "d"});
SetOp(&prog, "relu", {{"X", "d"}}, {"Out", "e"});
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
IsReachable is_reachable;
EXPECT_TRUE(is_reachable(graph)("a", "relu"));
auto pass =
PassRegistry::Instance().Get("conv_elementwise_add_mkldnn_fuse_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
EXPECT_TRUE(is_reachable(graph)("a", "relu"));
EXPECT_EQ(original_nodes_num - nodes_removed + nodes_added,
current_nodes_num);
AssertOpsCount(graph);
}
TEST(ConvElementwiseAddMKLDNNFusePass,
ConvolutionWithElementwiseAddReluNoBias) {
auto prog = BuildProgramDesc({"a", "b", "c", "d", "e"}, {"weights"});
SetOp(&prog, "conv2d", {{"Input", "a"}, {"Filter", "weights"}},
{"Output", "b"});
SetOp(&prog, "elementwise_add", {{"X", "b"}, {"Y", "c"}}, {"Out", "d"});
SetOp(&prog, "relu", {{"X", "d"}}, {"Out", "e"});
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
IsReachable is_reachable;
EXPECT_TRUE(is_reachable(graph)("a", "relu"));
auto pass =
PassRegistry::Instance().Get("conv_elementwise_add_mkldnn_fuse_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
EXPECT_TRUE(is_reachable(graph)("a", "relu"));
EXPECT_EQ(original_nodes_num - nodes_removed + nodes_added,
current_nodes_num);
AssertOpsCount(graph);
}
TEST(ConvElementwiseAddMKLDNNFusePass, ConvolutionElementwiseAdd) {
auto prog = BuildProgramDesc({"a", "b", "c", "d"}, {"bias", "weights"});
SetOp(&prog, "conv2d",
{{"Input", "a"}, {"Bias", "bias"}, {"Filter", "weights"}},
{"Output", "b"});
SetOp(&prog, "elementwise_add", {{"X", "b"}, {"Y", "c"}}, {"Out", "d"});
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
IsReachable is_reachable;
EXPECT_TRUE(is_reachable(graph)("a", "d"));
auto pass =
PassRegistry::Instance().Get("conv_elementwise_add_mkldnn_fuse_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
EXPECT_FALSE(is_reachable(graph)("a", "d"));
EXPECT_EQ(original_nodes_num - nodes_removed + nodes_added,
current_nodes_num);
AssertOpsCount(graph);
}
TEST(ConvElementwiseAddMKLDNNFusePass, SigmoidConvolutionAddElementwiseRelu) {
auto prog =
BuildProgramDesc({"a", "b", "c", "d", "e", "f"}, {"bias", "weights"});
SetOp(&prog, "sigmoid", {{"X", "a"}}, {"Out", "b"});
SetOp(&prog, "conv2d",
{{"Input", "b"}, {"Bias", "bias"}, {"Filter", "weights"}},
{"Output", "c"});
SetOp(&prog, "elementwise_add", {{"X", "c"}, {"Y", "d"}}, {"Out", "e"});
SetOp(&prog, "relu", {{"X", "e"}}, {"Out", "f"});
std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
IsReachable is_reachable;
EXPECT_TRUE(is_reachable(graph)("a", "f"));
auto pass =
PassRegistry::Instance().Get("conv_elementwise_add_mkldnn_fuse_pass");
int original_nodes_num = graph->Nodes().size();
graph = pass->Apply(std::move(graph));
int current_nodes_num = graph->Nodes().size();
EXPECT_TRUE(is_reachable(graph)("a", "f"));
EXPECT_EQ(original_nodes_num - nodes_removed + nodes_added,
current_nodes_num);
AssertOpsCount(graph);
}
} // namespace ir
} // namespace framework
} // namespace paddle
USE_PASS(conv_elementwise_add_mkldnn_fuse_pass);
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.cc
浏览文件 @ 6253b152
......@@ -46,6 +46,12 @@ std::unique_ptr<ir::Graph> ConvReLUFusePass::ApplyImpl(
GET_IR_NODE_FROM_SUBGRAPH(relu_out, relu_out, conv_relu_pattern); // Out
GET_IR_NODE_FROM_SUBGRAPH(relu, relu, conv_relu_pattern); // ReLU op
FuseOptions fuse_option = FindFuseOption(*conv, *relu);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+relu fuse";
return;
}
// Transform Conv node into ConvReLU node.
OpDesc* desc = conv->Op();
desc->SetOutput("Output", std::vector<std::string>({relu_out->Name()}));
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc
浏览文件 @ 6253b152
......@@ -20,17 +20,19 @@ namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog, const std::string& type,
void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs) {
const std::vector<std::string>& outputs, bool use_mkldnn = false) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
if (type == "conv2d") {
op->SetAttr("use_mkldnn", true);
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("name", name);
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
op->SetInput("Bias", {inputs[2]});
} else if (type == "relu") {
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetInput("X", inputs);
}
op->SetOutput("Out", outputs);
......@@ -43,7 +45,8 @@ void SetOp(ProgramDesc* prog, const std::string& type,
ProgramDesc BuildProgramDesc() {
ProgramDesc prog;
for (auto& v :
std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g"})) {
std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
"h", "weights2", "bias2", "k", "l"})) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::SELECTED_ROWS);
if (v == "weights" || v == "bias") {
......@@ -51,14 +54,24 @@ ProgramDesc BuildProgramDesc() {
}
}
SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
SetOp(&prog, "OP0", "op0", std::vector<std::string>({"a"}),
std::vector<std::string>({"b"}));
SetOp(&prog, "OP1", std::vector<std::string>({"b"}),
SetOp(&prog, "OP1", "op1", std::vector<std::string>({"b"}),
std::vector<std::string>({"c"}));
SetOp(&prog, "conv2d", std::vector<std::string>({"c", "weights", "bias"}),
std::vector<std::string>({"f"}));
SetOp(&prog, "relu", std::vector<std::string>({"f"}),
std::vector<std::string>({"g"}));
// conv+relu, both with MKL-DNN
SetOp(&prog, "conv2d", "conv1",
std::vector<std::string>({"c", "weights", "bias"}),
std::vector<std::string>({"f"}), true);
SetOp(&prog, "relu", "relu1", std::vector<std::string>({"f"}),
std::vector<std::string>({"g"}), true);
SetOp(&prog, "OP3", "op3", std::vector<std::string>({"g"}),
std::vector<std::string>({"h"}));
// conv+relu, only one with MKL-DNN
SetOp(&prog, "conv2d", "conv2",
std::vector<std::string>({"h", "weights2", "bias2"}),
std::vector<std::string>({"k"}), true);
SetOp(&prog, "relu", "relu2", std::vector<std::string>({"k"}),
std::vector<std::string>({"l"}));
return prog;
}
......@@ -88,10 +101,16 @@ TEST(ConvReLUFusePass, basic) {
auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(boost::get<bool>(op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_relu"));
bool fuse_relu = boost::get<bool>(op->GetAttr("fuse_relu"));
if (fuse_relu) {
++conv_relu_count;
// check if only "conv1" convolution is fused
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "conv1") {
ASSERT_TRUE(op->HasAttr("fuse_relu"));
bool fuse_relu = boost::get<bool>(op->GetAttr("fuse_relu"));
if (fuse_relu) {
++conv_relu_count;
}
} else if (op_name == "conv2") {
ASSERT_FALSE(op->HasAttr("fuse_relu"));
}
}
}
......
paddle/fluid/framework/ir/fuse_pass_base.cc 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
void FusePassBase::Init(const std::string& repr, Graph* graph) const {
repr_ = repr;
graph_ = graph;
}
Scope* FusePassBase::param_scope() const {
PADDLE_ENFORCE(graph_->Has(kParamScopeAttr));
return graph_->Get<framework::Scope*>(kParamScopeAttr);
}
void FusePassBase::AddStatis(int count_of_fused) const {
PADDLE_ENFORCE(graph_);
PADDLE_ENFORCE(!repr_.empty());
if (!graph_->Has(kFuseStatisAttr)) {
graph_->Set(kFuseStatisAttr, new std::unordered_map<std::string, int>);
}
auto& info =
graph_->Get<std::unordered_map<std::string, int>>(kFuseStatisAttr);
info[repr_] = count_of_fused;
}
FuseOptions FusePassBase::FindFuseOption(const Node& node1,
const Node& node2) const {
#ifdef PADDLE_WITH_MKLDNN
bool node1_mkldnn = node1.Op()->HasAttr("use_mkldnn") &&
boost::get<bool>(node1.Op()->GetAttr("use_mkldnn"));
bool node2_mkldnn = node2.Op()->HasAttr("use_mkldnn") &&
boost::get<bool>(node2.Op()->GetAttr("use_mkldnn"));
if (node1_mkldnn && node2_mkldnn)
return FUSE_MKLDNN;
else if (!node1_mkldnn && !node2_mkldnn)
return FUSE_NATIVE;
else
return DO_NOT_FUSE;
#else
return FUSE_NATIVE;
#endif
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/fuse_pass_base.h
浏览文件 @ 6253b152
......@@ -25,32 +25,24 @@ namespace ir {
static const char kParamScopeAttr[] = "__param_scope__";
static const char kFuseStatisAttr[] = "__fuse_statis__";
enum FuseOptions {
DO_NOT_FUSE, // fusing will not be done
FUSE_NATIVE, // fusing will be done without MKL-DNN
FUSE_MKLDNN // fusing will be done with MKL-DNN
};
class FusePassBase : public Pass {
public:
void Init(const std::string& repr, Graph* graph) const {
repr_ = repr;
graph_ = graph;
}
Scope* param_scope() const {
PADDLE_ENFORCE(graph_->Has(kParamScopeAttr));
return graph_->Get<framework::Scope*>(kParamScopeAttr);
}
void AddStatis(int count_of_fused) const {
PADDLE_ENFORCE(graph_);
PADDLE_ENFORCE(!repr_.empty());
if (!graph_->Has(kFuseStatisAttr)) {
graph_->Set(kFuseStatisAttr, new std::unordered_map<std::string, int>);
}
auto& info =
graph_->Get<std::unordered_map<std::string, int>>(kFuseStatisAttr);
info[repr_] = count_of_fused;
}
void Init(const std::string& repr, Graph* graph) const;
Scope* param_scope() const;
void AddStatis(int count_of_fused) const;
virtual ~FusePassBase() {}
protected:
virtual FuseOptions FindFuseOption(const Node& node1,
const Node& node2) const;
mutable Graph* graph_;
mutable std::string repr_;
};
......
paddle/fluid/framework/ir/graph_helper_test.cc
浏览文件 @ 6253b152
......@@ -200,15 +200,15 @@ TEST(GraphHelperTest, GraphNum) {
Graph g(prog);
BuildZeroGraph(&g);
ASSERT_EQ(GraphNum(g), 0);
ASSERT_EQ(GraphNum(g), 0UL);
Graph g2(prog);
BuildOneGraph(&g2);
ASSERT_EQ(GraphNum(g2), 1);
ASSERT_EQ(GraphNum(g2), 1UL);
Graph g3(prog);
BuildTwoGraphs(&g3);
ASSERT_EQ(GraphNum(g3), 2);
ASSERT_EQ(GraphNum(g3), 2UL);
}
} // namespace ir
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 6253b152
......@@ -259,6 +259,8 @@ GraphPatternDetector::DetectPatterns() {
return result;
}
// TODO(Superjomn) enhance the function as it marks unique unique as duplicates
// see https://github.com/PaddlePaddle/Paddle/issues/13550
void GraphPatternDetector::UniquePatterns(
std::vector<GraphPatternDetector::subgraph_t> *subgraphs) {
if (subgraphs->empty()) return;
......@@ -759,6 +761,51 @@ PDNode *patterns::ConvReLU::operator()(
return relu_out_var;
}
PDNode *patterns::SeqConvEltAddRelu::operator()(
paddle::framework::ir::PDNode *seqconv_input) {
// Create Operators
seqconv_input->assert_is_op_input("sequence_conv", "X");
auto *seqconv_op = pattern->NewNode(seqconv_repr())
->assert_is_op("sequence_conv")
->assert_op_attr<bool>("paddingTrainable", false)
->assert_op_attr<int>("contextStride", 1);
auto *eltadd_op =
pattern->NewNode(eltadd_repr())->assert_is_op("elementwise_add");
auto *relu_op = pattern->NewNode(relu_repr())->assert_is_op("relu");
// Create variables
// Filter
auto *seqconv_weight_var =
pattern->NewNode(seqconv_weight_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("sequence_conv", "Filter");
// Bias
auto *eltadd_bias_var = pattern->NewNode(eltadd_bias_repr())
->AsInput()
->assert_is_op_input("elementwise_add");
// intermediate variable, will be removed in the IR after fuse.
auto *seqconv_out_var = pattern->NewNode(seqconv_out_repr())
->AsIntermediate()
->assert_is_only_output_of_op("sequence_conv")
->assert_is_op_input("elementwise_add");
auto *eltadd_out_var = pattern->NewNode(eltadd_out_repr())
->AsIntermediate()
->assert_is_only_output_of_op("elementwise_add")
->assert_is_only_input_of_op("relu");
// output
auto *relu_out_var = pattern->NewNode(relu_out_repr())
->AsOutput()
->assert_is_op_output("relu");
seqconv_op->LinksFrom({seqconv_input, seqconv_weight_var})
.LinksTo({seqconv_out_var});
eltadd_op->LinksFrom({seqconv_out_var, eltadd_bias_var})
.LinksTo({eltadd_out_var});
relu_op->LinksFrom({eltadd_out_var}).LinksTo({relu_out_var});
return relu_out_var;
}
PDNode *patterns::FC::operator()(paddle::framework::ir::PDNode *x,
bool with_bias) {
// Create shared nodes.
......@@ -964,6 +1011,79 @@ PDNode *patterns::ElewiseAddActInplaceGrad::operator()(
return ele_add_grad;
}
PDNode *patterns::ConvBias::operator()(
paddle::framework::ir::PDNode *conv_input) {
// Create Operators
conv_input->assert_is_op_input("conv2d", "Input");
auto *conv_op = pattern->NewNode(conv_repr())->assert_is_op("conv2d");
auto *eltiwse_op =
pattern->NewNode(eltwise_repr())->assert_is_op("elementwise_add");
// Create variables
// Filter
auto *conv_weight_var = pattern->NewNode(conv_weight_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("conv2d", "Filter");
// intermediate variable, will be removed in the IR after fuse.
auto *conv_out_var = pattern->NewNode(conv_out_repr())
->AsIntermediate()
->assert_is_only_output_of_op("conv2d")
->assert_is_op_input("elementwise_add");
// Bias stored in elementwise_add
auto *eltwise_bias_var = pattern->NewNode(eltwise_bias_repr())
->AsInput()
->assert_is_persistable_var()
->assert_is_op_input("elementwise_add", "Y");
// output
auto *eltwise_out_var = pattern->NewNode(eltwise_out_repr())
->AsOutput()
->assert_is_op_output("elementwise_add");
conv_op->LinksFrom({conv_input, conv_weight_var}).LinksTo({conv_out_var});
eltiwse_op->LinksFrom({conv_out_var, eltwise_bias_var})
.LinksTo({eltwise_out_var});
return eltwise_out_var;
}
PDNode *patterns::Conv::operator()() {
auto conv_op = pattern->NewNode(conv_op_repr())->assert_is_op("conv2d");
auto input_var = pattern->NewNode(conv_input_repr())
->AsInput()
->assert_is_op_input("conv2d", "Input");
auto filter_var = pattern->NewNode(conv_filter_repr())
->AsInput()
->assert_is_op_input("conv2d", "Filter");
auto output_var = pattern->NewNode(conv_output_repr())
->AsOutput()
->assert_is_op_output("conv2d", "Output");
conv_op->LinksFrom({input_var, filter_var});
conv_op->LinksTo({output_var});
return output_var;
}
PDNode *patterns::ElementwiseAdd::operator()(PDNode *x_var) {
auto elementwise_add_op = pattern->NewNode(elementwise_add_op_repr())
->assert_is_op("elementwise_add");
x_var->assert_is_op_input("elementwise_add", "X");
auto y_var = pattern->NewNode(elementwise_add_x_repr())
->AsInput()
->assert_is_op_input("elementwise_add", "Y");
auto out_var = pattern->NewNode(elementwise_add_out_repr())
->AsOutput()
->assert_is_op_output("elementwise_add", "Out");
elementwise_add_op->LinksFrom({x_var, y_var});
elementwise_add_op->LinksTo({out_var});
return out_var;
}
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ 6253b152
......@@ -128,6 +128,15 @@ struct PDNode {
const std::unordered_set<std::string>& op_types,
const std::string& argument, int nth);
template <typename T>
PDNode* assert_op_attr(const std::string& attr_name, const T& attr) {
asserts_.emplace_back([=](Node* x) {
return x && x->IsOp() && x->Op()->HasAttr(attr_name) &&
boost::get<T>(x->Op()->GetAttr(attr_name)) == attr;
});
return this;
}
private:
PDNode(PDPattern* pattern, const std::string& name = "",
Type type = Type::kVar)
......@@ -434,6 +443,31 @@ struct ConvReLU : public PatternBase {
PATTERN_DECL_NODE(relu_out);
};
// SEQCONV with Elementwise_Add ReLU
// op: seqconv + elementwise_add + relu
// named nodes:
// seqconv_input, seqconv_weight,
// seqconv_out, seqconv,
// elementwise_add_bias, elementwise_add_out, elementwise_add
// relu_out, relu
struct SeqConvEltAddRelu : public PatternBase {
SeqConvEltAddRelu(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "seqconv_eltadd_relu") {}
PDNode* operator()(PDNode* seqconv_input);
// declare operator node's name
PATTERN_DECL_NODE(seqconv);
PATTERN_DECL_NODE(eltadd);
PATTERN_DECL_NODE(relu);
// declare variable node's name
PATTERN_DECL_NODE(seqconv_weight);
PATTERN_DECL_NODE(seqconv_out);
PATTERN_DECL_NODE(eltadd_bias);
PATTERN_DECL_NODE(eltadd_out);
PATTERN_DECL_NODE(relu_out);
};
// FC with bias
// op: mul + elementwise_add
// named nodes:
......@@ -578,6 +612,65 @@ struct ElewiseAddActInplaceGrad : public PatternBase {
PATTERN_DECL_NODE(d_ele_y);
PATTERN_DECL_NODE(ele_y);
};
// Conv with Elementwise_add as bias
// op: conv + elementwise_add
// named nodes:
// conv_input, conv_weight,
// conv_out, conv,
// eltwise_bias, eltwise_out,
// elementwise_add
struct ConvBias : public PatternBase {
ConvBias(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "conv_bias") {}
PDNode* operator()(PDNode* conv_input);
// declare operator node's name
PATTERN_DECL_NODE(conv);
PATTERN_DECL_NODE(eltwise);
// declare variable node's name
PATTERN_DECL_NODE(conv_weight);
PATTERN_DECL_NODE(conv_out);
PATTERN_DECL_NODE(eltwise_bias);
PATTERN_DECL_NODE(eltwise_out);
};
// Convolution op
// Forward pass for convolution.
// conv_input, conv_bias and conv_filter are inputs.
// conv_output is a result of the operator.
// residual_data is data used by skip connection.
// If residual connection fusion is on, the formula is:
// conv_output = conv_op(conv_filter, conv_input, conv_bias)
// + conv_residual_data
// If the fusion is off, conv_residual_data is not added.
struct Conv : public PatternBase {
Conv(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "convolution") {}
PDNode* operator()();
PATTERN_DECL_NODE(conv_op);
PATTERN_DECL_NODE(conv_input);
PATTERN_DECL_NODE(conv_filter);
PATTERN_DECL_NODE(conv_residual_data);
PATTERN_DECL_NODE(conv_output);
};
// ElementwiseAdd used in residual connections.
// y_var is used and convolution output.
// The operator is removed, when residual
// connection fusion is on.
struct ElementwiseAdd : public PatternBase {
ElementwiseAdd(PDPattern* pattern, const std::string& name_scope)
: PatternBase(pattern, name_scope, "elementwise_add") {}
PDNode* operator()(PDNode* x_var);
PATTERN_DECL_NODE(elementwise_add_op);
PATTERN_DECL_NODE(elementwise_add_x);
PATTERN_DECL_NODE(elementwise_add_y);
PATTERN_DECL_NODE(elementwise_add_out);
};
} // namespace patterns
// Link two ir::Nodes from each other.
......
paddle/fluid/framework/ir/graph_test.cc
浏览文件 @ 6253b152
......@@ -124,7 +124,7 @@ TEST(GraphTest, Basic) {
ASSERT_EQ(n->outputs.size(), 0UL);
}
}
ASSERT_EQ(nodes.size(), 5);
ASSERT_EQ(nodes.size(), 5UL);
}
TEST(GraphTest, WriteAfterRead) {
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc 0 → 100644
浏览文件 @ 6253b152
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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/fluid/framework/ir/mkldnn_placement_pass.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Aplies MKL-DNN placement strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp() && n->Op()->HasAttr("use_mkldnn")) {
n->Op()->SetAttr("use_mkldnn", true);
}
}
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(mkldnn_placement_pass,
paddle::framework::ir::MKLDNNPlacementPass);
paddle/fluid/framework/ir/mkldnn_placement_pass.h 0 → 100644
浏览文件 @ 6253b152
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class MKLDNNPlacementPass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.cc 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.h"
#include <string>
#include "paddle/fluid/framework/lod_tensor.h"
namespace paddle {
namespace framework {
namespace ir {
int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope) {
GraphPatternDetector gpd;
auto* pattern = gpd.mutable_pattern();
PDNode* x = pattern->NewNode(patterns::PDNodeName(name_scope, "X"))
->assert_is_op_input("sequence_conv")
->assert_var_not_persistable();
patterns::SeqConvEltAddRelu fuse_pattern(pattern, name_scope);
fuse_pattern(x);
// Create New OpDesc
auto fuse_creator = [&](Node* seqconv, Node* input, Node* seqconv_weight,
Node* eltadd_bias, Node* relu_out) {
OpDesc op_desc;
op_desc.SetType("fusion_seqconv_eltadd_relu");
op_desc.SetInput("X", {input->Name()});
op_desc.SetInput("Filter", {seqconv_weight->Name()});
op_desc.SetInput("Bias", {eltadd_bias->Name()});
op_desc.SetAttr("contextLength", seqconv->Op()->GetAttr("contextLength"));
op_desc.SetAttr("contextStart", seqconv->Op()->GetAttr("contextStart"));
op_desc.SetAttr("contextStride", seqconv->Op()->GetAttr("contextStride"));
PADDLE_ENFORCE(graph->Has(kParamScopeAttr));
auto* scope = graph->Get<Scope*>(kParamScopeAttr);
const std::string ColMat = patterns::UniqueKey("SeqConvColMat");
op_desc.SetOutput("ColMat", {ColMat});
op_desc.SetOutput("Out", {relu_out->Name()});
scope->Var(ColMat)->GetMutable<LoDTensor>();
auto* op = graph->CreateOpNode(&op_desc);
IR_NODE_LINK_TO(input, op);
IR_NODE_LINK_TO(seqconv_weight, op);
IR_NODE_LINK_TO(eltadd_bias, op);
IR_NODE_LINK_TO(op, relu_out);
return op;
};
int fusion_count{0};
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
Graph* g) {
VLOG(4) << "handle SeqConv EltAdd Relu fuse";
GET_IR_NODE_FROM_SUBGRAPH(seqconv, seqconv, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(seqconv_weight, seqconv_weight, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(seqconv_out, seqconv_out, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(eltadd, eltadd, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(eltadd_bias, eltadd_bias, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(eltadd_out, eltadd_out, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(relu, relu, fuse_pattern);
GET_IR_NODE_FROM_SUBGRAPH(relu_out, relu_out, fuse_pattern);
fuse_creator(seqconv, subgraph.at(x), seqconv_weight, eltadd_bias,
relu_out);
std::unordered_set<const Node*> marked_nodes(
{seqconv, seqconv_out, eltadd, eltadd_out, relu});
GraphSafeRemoveNodes(graph, marked_nodes);
++fusion_count;
};
gpd(graph, handler);
return fusion_count;
}
std::unique_ptr<ir::Graph> SeqConvEltAddReluFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
FusePassBase::Init(name_scope_, graph.get());
int fusion_count = BuildFusion(graph.get(), name_scope_, param_scope());
AddStatis(fusion_count);
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(seqconv_eltadd_relu_fuse_pass,
paddle::framework::ir::SeqConvEltAddReluFusePass);
paddle/fluid/framework/ir/seqconv_eltadd_relu_fuse_pass.h 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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 <string>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
class SeqConvEltAddReluFusePass : public FusePassBase {
public:
virtual ~SeqConvEltAddReluFusePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
const std::string name_scope_{"seqconv_eltadd_relu_fuse"};
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/naive_executor.cc
浏览文件 @ 6253b152
......@@ -37,7 +37,7 @@ static void InitializeVariable(Variable *var, proto::VarType::Type var_type) {
} else if (var_type == proto::VarType::FETCH_LIST) {
var->GetMutable<FeedFetchList>();
} else if (var_type == proto::VarType::STEP_SCOPES) {
var->GetMutable<std::vector<framework::Scope>>();
var->GetMutable<std::vector<framework::Scope *>>();
} else if (var_type == proto::VarType::LOD_RANK_TABLE) {
var->GetMutable<LoDRankTable>();
} else if (var_type == proto::VarType::LOD_TENSOR_ARRAY) {
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 6253b152
......@@ -85,10 +85,6 @@ class CompileTimeInferShapeContext : public InferShapeContext {
VLOG(3) << "input " << in << " is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarType::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
out_var->SetLoDLevel(in_var->GetLoDLevel());
}
......@@ -519,20 +515,14 @@ void OpDesc::InferShape(const BlockDesc &block) const {
}
void OpDesc::InferVarType(BlockDesc *block) const {
// There are a few places that var type can be set.
// When VarDesc is created, default set to LOD_TENSOR.
// When output variable is created, default is defaut set to LOD_TENSOR.
// We limit here to be the only place that operator defines its customized
// var type inference. Hence, we don't do any "default" setting here.
auto &info = OpInfoMap::Instance().Get(this->Type());
if (info.infer_var_type_) {
info.infer_var_type_(*this, block);
} else {
// all output type is LoDTensor by default
VLOG(10) << this->Type()
<< " has not registered InferVarType. Set output variables to "
"LOD_TENSOR";
for (auto &out_pair : this->outputs_) {
for (auto &out_var_name : out_pair.second) {
block->FindRecursiveOrCreateVar(out_var_name)
.SetType(proto::VarType::LOD_TENSOR);
}
}
}
}
......
paddle/fluid/framework/op_desc.h
浏览文件 @ 6253b152
......@@ -100,16 +100,6 @@ class OpDesc {
std::vector<std::string> InputNames() const { return MapKeys(inputs_); }
std::vector<std::string> OutputNames() const { return MapKeys(outputs_); }
void SetInputMap(const VariableNameMap &input) {
this->inputs_ = input;
this->need_update_ = true;
}
void SetOutputMap(const VariableNameMap &output) {
this->outputs_ = output;
this->need_update_ = true;
}
const VariableNameMap &Inputs() const { return inputs_; }
const VariableNameMap &Outputs() const { return outputs_; }
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 6253b152
......@@ -21,7 +21,6 @@ namespace framework {
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
CheckReuseVars();
}
OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput(
......@@ -40,40 +39,6 @@ OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput(
return OpProtoAndCheckerMaker::VariableBuilder{output};
}
void OpProtoAndCheckerMaker::Reuse(const std::string& name,
const std::string& reused_name) {
bool found = false;
proto::OpProto::Var* var;
for (auto& var : proto_->inputs()) {
if (var.name() == reused_name) {
found = true;
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
found = false;
for (int i = 0; i < proto_->outputs().size(); ++i) {
var = proto_->mutable_outputs()->Mutable(i);
if (var->name() == name) {
PADDLE_ENFORCE(!var->has_reuse(),
"Output(%s) has been set reused var of %s", name,
var->reuse());
found = true;
var->set_reuse(reused_name);
break;
}
}
PADDLE_ENFORCE(found == true,
"Input/Output name: %s reused_name: %s, one of them is not "
"exists or not matched.",
name, reused_name);
}
void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
std::unordered_set<std::string> names;
auto checker = [&](const std::string& name) {
......@@ -91,24 +56,6 @@ void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() {
}
}
void OpProtoAndCheckerMaker::CheckReuseVars() {
std::unordered_set<std::string> names;
for (auto& input : proto_->inputs()) {
names.insert(input.name());
}
auto checker = [&](const std::string& name, const std::string& reused) {
PADDLE_ENFORCE(
names.count(reused),
"Output [%s] reuse Input [%s], but the input is not registered.", name,
reused);
};
for (auto& output : proto_->outputs()) {
if (output.has_reuse()) {
checker(output.name(), output.reuse());
}
}
}
void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
OpAttrChecker* attr_checker) {
proto_ = proto;
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 6253b152
......@@ -14,8 +14,6 @@ limitations under the License. */
#pragma once
#include <string>
#include <unordered_set>
#include "glog/logging.h"
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/framework.pb.h"
......@@ -73,11 +71,6 @@ class OpProtoAndCheckerMaker {
var_->set_dispensable(true);
return *this;
}
VariableBuilder &Reuse(const std::string &name) {
var_->set_reuse(name);
return *this;
}
};
VariableBuilder AddInput(const std::string &name, const std::string &comment);
......@@ -85,8 +78,6 @@ class OpProtoAndCheckerMaker {
VariableBuilder AddOutput(const std::string &name,
const std::string &comment);
void Reuse(const std::string &name, const std::string &reused_name);
template <typename T>
TypedAttrChecker<T> &AddAttr(const std::string &name,
const std::string &comment,
......@@ -105,8 +96,6 @@ class OpProtoAndCheckerMaker {
void CheckNoDuplicatedInOutAttrs();
void Validate();
void CheckReuseVars();
proto::OpProto *proto_;
OpAttrChecker *op_checker_;
bool validated_{false};
......
paddle/fluid/framework/op_proto_maker_test.cc
浏览文件 @ 6253b152
......@@ -47,120 +47,3 @@ TEST(ProtoMaker, DuplicatedInOut) {
ASSERT_THROW(proto_maker(&op_proto, &op_checker),
paddle::platform::EnforceNotMet);
}
class TestInplaceProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
}
};
class TestInplaceProtoMaker2
: public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddOutput("XOut", "output of test op").Reuse("X");
AddOutput("NoOut", "output of test op").Reuse("NotExists");
}
};
TEST(ProtoMaker, InplaceOutput) {
paddle::framework::proto::OpProto op_proto, op_proto2;
paddle::framework::OpAttrChecker op_checker;
TestInplaceProtoMaker proto_maker;
TestInplaceProtoMaker2 proto_maker2;
proto_maker(&op_proto, &op_checker);
ASSERT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
}
// normal reuse
class TestReuseProtoMaker : public paddle::framework::OpProtoAndCheckerMaker {
public:
void Make() {
AddInput("X", "input of test op");
AddInput("Y", "input of test op");
AddOutput("Out", "output of test op");
AddOutput("XOut", "output of test op");
// avoid destructor exception.
// Validate();
TestReuse();
}
virtual void TestReuse() {}
};
// test duplicate reuse error
class TestReuseProtoMaker2 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "X");
Reuse("Out", "Y");
}
};
// NotExists Input
class TestReuseProtoMaker3 : public TestReuseProtoMaker {
public:
void TestReuse() {
Reuse("Out", "NotExists");
Reuse("XOut", "X");
}
};
// NotExists Output
class TestReuseProtoMaker4 : public TestReuseProtoMaker {
public:
void TestReuse() { Reuse("NotExists", "X"); }
};
TEST(ProtoMaker, Reuse) {
paddle::framework::proto::OpProto op_proto;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker proto_maker;
proto_maker(&op_proto, &op_checker);
}
// NOTE(dzhwinter):
// There is a Fatal CHECK on base class destructor, which will call abort inside
// instead of
// throw an exception. If we throw an exception in Make(), we will trigger the
// CHECK and terminate the tests.
//
// I had tried to replace the default CHECK with a exception, however, it's
// still not supported by glog.
// the details:
// https://github.com/google/glog/issues/249
// https://github.com/facebookresearch/TensorComprehensions/issues/351
/*
TEST(ProtoMaker, ReuseWithException) {
paddle::framework::proto::OpProto op_proto2, op_proto3, op_proto4;
paddle::framework::OpAttrChecker op_checker;
TestReuseProtoMaker2 proto_maker2;
TestReuseProtoMaker3 proto_maker3;
TestReuseProtoMaker4 proto_maker4;
EXPECT_THROW(proto_maker2(&op_proto2, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker3(&op_proto3, &op_checker),
paddle::platform::EnforceNotMet);
EXPECT_THROW(proto_maker4(&op_proto4, &op_checker),
paddle::platform::EnforceNotMet);
}
void FailureFunction() {
throw std::runtime_error("Check failed in destructor.");
// return 0;
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
google::InstallFailureFunction(&FailureFunction);
return RUN_ALL_TESTS();
}
*/
paddle/fluid/framework/operator.cc
浏览文件 @ 6253b152
......@@ -149,9 +149,17 @@ void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
platform::SetDeviceId(dev_id);
#endif
}
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
// The profile has a process-wide mutex, results in serious performance issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
} else {
RunImpl(scope, place);
}
VLOG(3) << place << " " << DebugStringEx(&scope);
}
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 6253b152
......@@ -156,12 +156,10 @@ ParallelExecutor::ParallelExecutor(
params, member_->local_scopes_, member_->use_cuda_);
#endif
if (VLOG_IS_ON(5)) {
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
// If the loss_var_name is given, the number of graph should be only one.
if (loss_var_name.size()) {
PADDLE_ENFORCE_EQ(ir::GraphNum(*graph), 1,
"The number of graph should be only one");
}
if (exec_strategy.type_ == ExecutionStrategy::kDefault) {
......@@ -299,6 +297,12 @@ void ParallelExecutor::FeedAndSplitTensorIntoLocalScopes(
}
ParallelExecutor::~ParallelExecutor() {
const auto dev_ctxs =
platform::DeviceContextPool::Instance().GetAllDeviceContexts();
for (auto &dev_ctx : dev_ctxs) {
dev_ctx->Wait();
}
if (member_->own_local_scope_) {
for (size_t i = 1; i < member_->local_scopes_.size(); ++i) {
Scope *local_scope = member_->local_scopes_[i];
......
paddle/fluid/framework/program_desc.cc
浏览文件 @ 6253b152
......@@ -126,7 +126,7 @@ const std::vector<std::string> ProgramDesc::GetFeedTargetNames() {
std::vector<std::string> feed_target_names;
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFeedOpType) {
int col = boost::get<int>(op->GetAttr("col"));
size_t col = boost::get<int>(op->GetAttr("col"));
if (col >= feed_target_names.size()) {
feed_target_names.resize(col + 1);
}
......@@ -143,7 +143,7 @@ const std::vector<std::string> ProgramDesc::GetFetchTargetNames() {
std::vector<std::string> fetch_target_names;
for (auto *op : global_block.AllOps()) {
if (op->Type() == kFetchOpType) {
int col = boost::get<int>(op->GetAttr("col"));
size_t col = boost::get<int>(op->GetAttr("col"));
if (col >= fetch_target_names.size()) {
fetch_target_names.resize(col + 1);
}
......
paddle/fluid/framework/program_desc_test.cc
浏览文件 @ 6253b152
......@@ -103,7 +103,7 @@ TEST(ProgramDesc, copy_ctor) {
ASSERT_EQ(1, op->GetBlockAttrId("sub_block"));
found_sub_block = true;
ASSERT_EQ(2, op->GetBlocksAttrIds("sub_blocks").size());
ASSERT_EQ(2UL, op->GetBlocksAttrIds("sub_blocks").size());
found_sub_blocks = true;
}
}
......
paddle/fluid/framework/reader_test.cc
浏览文件 @ 6253b152
......@@ -39,8 +39,8 @@ TEST(READER, decorate_chain) {
{
auto endpoints = root->GetEndPoints();
ASSERT_EQ(endpoints.size(), 2U);
ASSERT_NE(endpoints.count(end_point1.get()), 0);
ASSERT_NE(endpoints.count(end_point2.get()), 0);
ASSERT_NE(endpoints.count(end_point1.get()), 0UL);
ASSERT_NE(endpoints.count(end_point2.get()), 0UL);
}
{
......
paddle/fluid/framework/selected_rows_test.cc
浏览文件 @ 6253b152
......@@ -91,7 +91,7 @@ TEST(SelectedRows, SparseTable) {
ASSERT_TRUE(table.HasKey(10));
ASSERT_TRUE(table.HasKey(8));
ASSERT_TRUE(table.HasKey(6));
ASSERT_EQ(table.rows().size(), 3);
ASSERT_EQ(table.rows().size(), 3UL);
framework::Tensor ids;
ids.Resize(framework::make_ddim({4}));
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 6253b152
......@@ -36,6 +36,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
auto size = src.numel() * SizeOfType(src.type());
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
boost::get<platform::CPUPlace>(src_place), src_ptr, size);
}
......@@ -71,6 +76,11 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
auto stream =
reinterpret_cast<const platform::CUDADeviceContext&>(ctx).stream();
if (platform::is_same_place(src_place, dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data async from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size,
stream);
} else {
......@@ -114,6 +124,11 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
auto dst_ptr = dst->mutable_data(dst_place, src.type());
auto size = src.numel() * SizeOfType(src.type());
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
if (src_ptr == dst_ptr) {
VLOG(3) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
return;
}
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
boost::get<platform::CPUPlace>(src_place), src_ptr, size);
}
......@@ -130,6 +145,11 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
memory::Copy(dst_gpu_place, dst_ptr, src_cpu_place, src_ptr, size, nullptr);
} else if (platform::is_gpu_place(src_place) &&
platform::is_gpu_place(dst_place)) {
if (src_ptr == dst_ptr && platform::is_same_place(src_place, dst_place)) {
VLOG(3) << "Skip copy the same data from " << src_place << " to "
<< dst_place;
return;
}
auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
......
paddle/fluid/framework/tensor_util_test.cc
浏览文件 @ 6253b152
......@@ -41,6 +41,11 @@ TEST(TensorCopy, Tensor) {
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
}
TensorCopy(dst_tensor, *cpu_place, &dst_tensor);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
}
EXPECT_TRUE(dst_tensor.layout() == src_tensor.layout());
Tensor slice_tensor = src_tensor.Slice(1, 2);
......@@ -82,6 +87,15 @@ TEST(TensorCopy, Tensor) {
EXPECT_EQ(src_ptr[i], dst_ptr[i]);
}
// Copy the same tensor
TensorCopy(gpu_tensor, *gpu_place, gpu_ctx, &gpu_tensor);
gpu_ctx.Wait();
const int* dst_ptr_tmp = dst_tensor.data<int>();
EXPECT_NE(src_ptr, dst_ptr_tmp);
for (size_t i = 0; i < 9; ++i) {
EXPECT_EQ(src_ptr[i], dst_ptr_tmp[i]);
}
Tensor slice_tensor = src_tensor.Slice(1, 2);
// CPU Slice Tensor to GPU Tensor
......
paddle/fluid/framework/var_desc.h
浏览文件 @ 6253b152
......@@ -59,6 +59,7 @@ class VarDesc {
public:
explicit VarDesc(const std::string &name) {
desc_.set_name(name);
// TODO(paddle-dev): Why default to lodtensor.
desc_.mutable_type()->set_type(proto::VarType::LOD_TENSOR);
}
......
paddle/fluid/framework/variable.h
浏览文件 @ 6253b152
......@@ -38,8 +38,12 @@ class Variable {
template <typename T>
T* GetMutable() {
if (!IsType<T>()) {
if (!holder_) {
holder_.reset(new PlaceholderImpl<T>(new T()));
} else {
PADDLE_ENFORCE(IsType<T>(),
"Variable must be type %s, the holding type is %s",
typeid(T).name(), holder_->Type().name());
}
return static_cast<T*>(holder_->Ptr());
}
......
paddle/fluid/framework/variable_test.cc
浏览文件 @ 6253b152
......@@ -33,9 +33,10 @@ TEST(Variable, GetMutable) {
const Tensor& tt = v->Get<Tensor>();
EXPECT_EQ(1234, tt.content_);
std::string* s = v->GetMutable<std::string>();
*s = "hello";
const std::string& ss = v->Get<std::string>();
EXPECT_EQ("hello", ss);
try {
v->GetMutable<std::string>();
} catch (std::exception& e) {
return;
}
EXPECT_TRUE(false);
}
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ 6253b152
......@@ -101,7 +101,13 @@ Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
void Analyzer::Run(Argument* argument) {
std::vector<std::string> passes;
for (auto& pass : all_ir_passes_) {
#ifdef PADDLE_WITH_MKLDNN
if (use_mkldnn_) {
VLOG(3) << "Adding MKL-DNN placement pass";
passes.push_back("mkldnn_placement_pass");
}
#endif
for (auto& pass : ir_passes_) {
if (!disabled_ir_passes_.count(pass)) {
passes.push_back(pass);
passes.push_back("graph_viz_pass"); // add graphviz for debug.
......@@ -117,11 +123,26 @@ void Analyzer::Run(Argument* argument) {
}
}
Analyzer& Analyzer::IncludeAllIrPasses() {
ir_passes_ = all_ir_passes_;
return *this;
}
Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
disabled_ir_passes_.insert(passes.begin(), passes.end());
return *this;
}
Analyzer& Analyzer::IncludeIrPasses(const std::vector<std::string>& passes) {
ir_passes_ = passes;
return *this;
}
Analyzer& Analyzer::SetUseMkldnn(bool use_mkldnn) {
use_mkldnn_ = use_mkldnn;
return *this;
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ 6253b152
......@@ -54,6 +54,9 @@ class Analyzer : public OrderedRegistry<PassManager> {
void Run(Argument* argument);
Analyzer& DisableIrPasses(const std::vector<std::string>& passes);
Analyzer& IncludeIrPasses(const std::vector<std::string>& passes);
Analyzer& IncludeAllIrPasses();
Analyzer& SetUseMkldnn(bool use_mkldnn);
DISABLE_COPY_AND_ASSIGN(Analyzer);
......@@ -64,23 +67,29 @@ class Analyzer : public OrderedRegistry<PassManager> {
// larger fusion.
const std::vector<std::string> all_ir_passes_{{
// Manual update the passes here.
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
"embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
#ifdef PADDLE_WITH_MKLDNN
"conv_relu_mkldnn_fuse_pass", //
"conv_bias_mkldnn_fuse_pass", //
"conv_relu_mkldnn_fuse_pass", //
"conv_elementwise_add_mkldnn_fuse_pass", //
#endif
}};
std::unordered_set<std::string> disabled_ir_passes_;
// Ir passes to run
std::vector<std::string> ir_passes_;
bool use_mkldnn_;
};
} // namespace analysis
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ 6253b152
......@@ -51,9 +51,7 @@ void TestWord2vecPrediction(const std::string& model_path) {
config.model_dir = model_path;
config.use_gpu = false;
config.device = 0;
auto predictor =
::paddle::CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
auto predictor = ::paddle::CreatePaddlePredictor<NativeConfig>(config);
// One single batch
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 6253b152
......@@ -25,9 +25,11 @@
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
DECLARE_bool(profile);
DECLARE_int32(paddle_num_threads);
namespace paddle {
......@@ -47,6 +49,9 @@ bool AnalysisPredictor::Init(
}
#endif
// no matter with or without MKLDNN
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
LOG(WARNING) << "ir optimize only supports CPU currently, enable_ir_optim "
......@@ -72,10 +77,6 @@ bool AnalysisPredictor::Init(
inference_program_ = program;
}
if (config_._use_mkldnn) {
executor_->EnableMKLDNN(*inference_program_);
}
executor_->Prepare(scope_.get(), *inference_program_, 0,
config_.use_feed_fetch_ops);
......@@ -220,10 +221,24 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
PADDLE_ENFORCE(
config_.ir_mode == contrib::AnalysisConfig::IrPassMode::kExclude,
"Only kExclude is supported yet.");
Analyzer().DisableIrPasses(config_.ir_passes).Run(&argument_);
switch (config_.ir_mode) {
case contrib::AnalysisConfig::IrPassMode::kExclude:
Analyzer()
.IncludeAllIrPasses()
.SetUseMkldnn(config_._use_mkldnn)
.DisableIrPasses(config_.ir_passes)
.Run(&argument_);
break;
case contrib::AnalysisConfig::IrPassMode::kInclude:
Analyzer()
.SetUseMkldnn(config_._use_mkldnn)
.IncludeIrPasses(config_.ir_passes)
.Run(&argument_);
break;
default:
LOG(ERROR) << "Only kExclude and kInclude modes are supoorted yet.";
}
CHECK(argument_.transformed_program_desc);
VLOG(5) << "to prepare executor";
......@@ -335,6 +350,19 @@ bool AnalysisPredictor::LoadProgramDesc() {
}
return true;
}
AnalysisPredictor::~AnalysisPredictor() {
#if !defined(_WIN32)
if (FLAGS_profile) {
platform::DisableProfiler(platform::EventSortingKey::kTotal,
"./profile.log");
}
#endif
if (sub_scope_) {
scope_->DeleteScope(sub_scope_);
}
}
std::unique_ptr<PaddlePredictor> AnalysisPredictor::Clone() {
auto *x = new AnalysisPredictor(config_);
x->Init(scope_, inference_program_);
......
paddle/fluid/inference/api/analysis_predictor.h
浏览文件 @ 6253b152
......@@ -72,6 +72,7 @@ class AnalysisPredictor : public PaddlePredictor {
template <typename T>
void GetFetchOne(const framework::LoDTensor &fetchs,
PaddleTensor *output_data);
~AnalysisPredictor();
private:
contrib::AnalysisConfig config_;
......
paddle/fluid/inference/api/analysis_predictor_tester.cc
浏览文件 @ 6253b152
......@@ -27,9 +27,7 @@ TEST(AnalysisPredictor, ZeroCopy) {
config.model_dir = FLAGS_dirname + "/word2vec.inference.model";
config.use_feed_fetch_ops = false;
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
auto w0 = predictor->GetInputTensor("firstw");
auto w1 = predictor->GetInputTensor("secondw");
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 6253b152
......@@ -23,9 +23,11 @@ limitations under the License. */
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/inference/api/api_impl.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/platform/cpu_helper.h"
#include "paddle/fluid/platform/profiler.h"
DEFINE_bool(profile, false, "Turn on profiler for fluid");
DECLARE_int32(paddle_num_threads);
namespace paddle {
namespace {
......@@ -72,6 +74,9 @@ bool NativePaddlePredictor::Init(
}
#endif
// no matter with or without MKLDNN
paddle::platform::SetNumThreads(FLAGS_paddle_num_threads);
if (config_.use_gpu) {
place_ = paddle::platform::CUDAPlace(config_.device);
} else {
......
paddle/fluid/inference/api/api_impl_tester.cc
浏览文件 @ 6253b152
......@@ -205,7 +205,7 @@ void MainThreadsWord2Vec(bool use_gpu) {
float* ref_data = refs[tid].data<float>();
EXPECT_EQ(refs[tid].numel(), static_cast<int64_t>(len / sizeof(float)));
for (int i = 0; i < refs[tid].numel(); ++i) {
EXPECT_NEAR(ref_data[i], data[i], ACC_DIFF);
EXPECT_NEAR(ref_data[i], data[i], 2e-3);
}
});
}
......
paddle/fluid/inference/api/api_tensorrt_subgraph_engine_tester.cc
浏览文件 @ 6253b152
......@@ -41,11 +41,8 @@ void CompareTensorRTWithFluid(bool enable_tensorrt) {
config1.device = 0;
config1.max_batch_size = 10;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
for (int batch_id = 0; batch_id < 1; batch_id++) {
//# 2. Prepare input.
......
paddle/fluid/inference/api/demo_ci/CMakeLists.txt
浏览文件 @ 6253b152
......@@ -77,7 +77,7 @@ endif(NOT WIN32)
link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
link_directories("${PADDLE_LIB}/paddle/fluid/inference")
link_directories("${PADDLE_LIB}/paddle/lib")
add_executable(${DEMO_NAME} ${DEMO_NAME}.cc)
......@@ -97,10 +97,10 @@ endif()
# Note: libpaddle_inference_api.so/a must put before libpaddle_fluid.so/a
if(WITH_STATIC_LIB)
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_STATIC_LIBRARY_SUFFIX})
else()
set(DEPS
${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
${PADDLE_LIB}/paddle/lib/libpaddle_fluid${CMAKE_SHARED_LIBRARY_SUFFIX})
endif()
if (NOT WIN32)
......
paddle/fluid/inference/api/demo_ci/run.sh
浏览文件 @ 6253b152
......@@ -5,12 +5,13 @@ TEST_GPU_CPU=$3 # test both GPU/CPU mode or only CPU mode
DATA_DIR=$4 # dataset
TENSORRT_INCLUDE_DIR=$5 # TensorRT header file dir, defalut to /usr/local/TensorRT/include
TENSORRT_LIB_DIR=$6 # TensorRT lib file dir, default to /usr/local/TensorRT/lib
inference_install_dir=${PADDLE_ROOT}/build/fluid_inference_install_dir
cd `dirname $0`
current_dir=`pwd`
if [ $2 == ON ]; then
# You can export yourself if move the install path
MKL_LIB=${PADDLE_ROOT}/build/fluid_install_dir/third_party/install/mklml/lib
MKL_LIB=${inference_install_dir}/third_party/install/mklml/lib
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${MKL_LIB}
fi
if [ $3 == ON ]; then
......@@ -20,7 +21,7 @@ else
fi
USE_TENSORRT=OFF
if [ [-d"$TENSORRT_INCLUDE_DIR"] -a [-d"$TENSORRT_LIB_DIR"] ]; then
if [ -d "$TENSORRT_INCLUDE_DIR" -a -d "$TENSORRT_LIB_DIR" ]; then
USE_TENSORRT=ON
fi
......@@ -55,7 +56,7 @@ cd build
for WITH_STATIC_LIB in ON OFF; do
# -----simple_on_word2vec-----
rm -rf *
cmake .. -DPADDLE_LIB=${PADDLE_ROOT}/build/fluid_install_dir/ \
cmake .. -DPADDLE_LIB=${inference_install_dir} \
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=simple_on_word2vec \
-DWITH_GPU=$TEST_GPU_CPU \
......@@ -75,7 +76,7 @@ for WITH_STATIC_LIB in ON OFF; do
fi
# ---------vis_demo---------
rm -rf *
cmake .. -DPADDLE_LIB=${PADDLE_ROOT}/build/fluid_install_dir/ \
cmake .. -DPADDLE_LIB=${inference_install_dir} \
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=vis_demo \
-DWITH_GPU=$TEST_GPU_CPU \
......@@ -98,7 +99,7 @@ for WITH_STATIC_LIB in ON OFF; do
# --------tensorrt mobilenet------
if [ $USE_TENSORRT == ON -a $TEST_GPU_CPU == ON ]; then
rm -rf *
cmake .. -DPADDLE_LIB=${PADDLE_ROOT}/build/fluid_install_dir/ \
cmake .. -DPADDLE_LIB=${inference_install_dir} \
-DWITH_MKL=$TURN_ON_MKL \
-DDEMO_NAME=trt_mobilenet_demo \
-DWITH_GPU=$TEST_GPU_CPU \
......
paddle/fluid/inference/api/demo_ci/simple_on_word2vec.cc
浏览文件 @ 6253b152
......@@ -23,7 +23,7 @@ limitations under the License. */
#include <memory>
#include <thread> //NOLINT
#include "paddle/fluid/inference/paddle_inference_api.h"
#include "paddle/include/paddle_inference_api.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
DEFINE_bool(use_gpu, false, "Whether use gpu.");
......@@ -42,8 +42,7 @@ void Main(bool use_gpu) {
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
auto predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
auto predictor = CreatePaddlePredictor<NativeConfig>(config);
for (int batch_id = 0; batch_id < 3; batch_id++) {
//# 2. Prepare input.
......@@ -85,8 +84,7 @@ void MainThreads(int num_threads, bool use_gpu) {
config.use_gpu = use_gpu;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
auto main_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
auto main_predictor = CreatePaddlePredictor<NativeConfig>(config);
std::vector<std::thread> threads;
for (int tid = 0; tid < num_threads; ++tid) {
......
paddle/fluid/inference/api/demo_ci/trt_mobilenet_demo.cc
浏览文件 @ 6253b152
......@@ -18,7 +18,7 @@ limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of CHECK to avoid importing other paddle header files.
#include "paddle/fluid/inference/demo_ci/utils.h"
#include "utils.h" // NOLINT
DECLARE_double(fraction_of_gpu_memory_to_use);
DEFINE_string(modeldir, "", "Directory of the inference model.");
......
paddle/fluid/inference/api/demo_ci/utils.h
浏览文件 @ 6253b152
......@@ -18,7 +18,7 @@
#include <iostream>
#include <string>
#include <vector>
#include "paddle/fluid/inference/paddle_inference_api.h"
#include "paddle/include/paddle_inference_api.h"
namespace paddle {
namespace demo {
......
paddle/fluid/inference/api/demo_ci/vis_demo.cc
浏览文件 @ 6253b152
......@@ -18,7 +18,7 @@ limitations under the License. */
#include <gflags/gflags.h>
#include <glog/logging.h> // use glog instead of CHECK to avoid importing other paddle header files.
#include "paddle/fluid/inference/demo_ci/utils.h"
#include "utils.h" // NOLINT
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
......@@ -34,12 +34,13 @@ DEFINE_bool(use_gpu, false, "Whether use gpu.");
namespace paddle {
namespace demo {
using contrib::AnalysisConfig;
/*
* Use the native fluid engine to inference the demo.
* Use the native and analysis fluid engine to inference the demo.
*/
void Main(bool use_gpu) {
std::unique_ptr<PaddlePredictor> predictor;
NativeConfig config;
std::unique_ptr<PaddlePredictor> predictor, analysis_predictor;
AnalysisConfig config;
config.param_file = FLAGS_modeldir + "/__params__";
config.prog_file = FLAGS_modeldir + "/__model__";
config.use_gpu = use_gpu;
......@@ -49,8 +50,8 @@ void Main(bool use_gpu) {
}
VLOG(3) << "init predictor";
predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
predictor = CreatePaddlePredictor<NativeConfig>(config);
analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
VLOG(3) << "begin to process data";
// Just a single batch of data.
......@@ -68,7 +69,7 @@ void Main(bool use_gpu) {
input.dtype = PaddleDType::FLOAT32;
VLOG(3) << "run executor";
std::vector<PaddleTensor> output;
std::vector<PaddleTensor> output, analysis_output;
predictor->Run({input}, &output, 1);
VLOG(3) << "output.size " << output.size();
......@@ -77,6 +78,10 @@ void Main(bool use_gpu) {
// compare with reference result
CheckOutput(FLAGS_refer, tensor);
// the analysis_output has some diff with native_output,
// TODO(luotao): add CheckOutput for analysis_output later.
analysis_predictor->Run({input}, &analysis_output, 1);
}
} // namespace demo
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 6253b152
......@@ -259,10 +259,17 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
void SetIncludeMode() {
ir_mode = IrPassMode::kInclude;
// this pass has to be run at the beginning of all fuse passes
ir_passes = {"infer_clean_graph_pass"};
}
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
IrPassMode ir_mode{IrPassMode::kExclude};
// passes to be excluded/included
std::vector<std::string> ir_passes{"embedding_fc_lstm_fuse_pass"};
// NOT stable yet.
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ 6253b152
......@@ -42,16 +42,22 @@ class Pool2dOpConverter : public OpConverter {
boost::get<std::vector<int>>(op_desc.GetAttr("strides"));
std::vector<int> paddings =
boost::get<std::vector<int>>(op_desc.GetAttr("paddings"));
bool ceil_mode = boost::get<bool>(op_desc.GetAttr("ceil_mode"));
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
if (global_pooling == true) {
nvinfer1::Dims input_shape = input1->getDimensions();
int nbDims = input_shape.nbDims;
nv_ksize.d[0] = input_shape.d[nbDims - 2];
nv_ksize.d[1] = input_shape.d[nbDims - 1];
nv_strides.h() = 1;
nv_strides.w() = 1;
nv_paddings.h() = 0;
nv_paddings.w() = 0;
}
const nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
const nvinfer1::DimsHW nv_paddings(paddings[0], paddings[1]);
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
......@@ -64,6 +70,36 @@ class Pool2dOpConverter : public OpConverter {
PADDLE_THROW("TensorRT unsupported pooling type!");
}
if (ceil_mode) {
nvinfer1::DimsHW pre_pad(0, 0);
nvinfer1::DimsHW post_pad(0, 0);
int input_height = input_shape.d[nbDims - 2];
int input_width = input_shape.d[nbDims - 1];
int floor_h_output_size =
(input_height - ksize[0] + 2 * paddings[0]) / strides[0] + 1;
int ceil_h_output_size =
(input_height - ksize[0] + 2 * paddings[0] + strides[0] - 1) /
strides[0] +
1;
int floor_w_output_size =
(input_width - ksize[1] + 2 * paddings[1]) / strides[1] + 1;
int ceil_w_output_size =
(input_width - ksize[1] + 2 * paddings[1] + strides[1] - 1) /
strides[1] +
1;
if (floor_h_output_size != ceil_h_output_size) {
post_pad.h() = strides[0] - 1;
}
if (floor_w_output_size != ceil_w_output_size) {
post_pad.w() = strides[1] - 1;
}
auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
post_pad);
input1 = layer->getOutput(0);
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
nv_pool_type, nv_ksize);
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @ 6253b152
......@@ -20,18 +20,20 @@ namespace paddle {
namespace inference {
namespace tensorrt {
void test_pool2d(bool global_pooling) {
void test_pool2d(bool global_pooling, bool ceil_mode) {
framework::Scope scope;
std::unordered_set<std::string> parameters;
TRTConvertValidation validator(5, parameters, scope, 1 << 15);
// The ITensor's Dims should not contain the batch size.
// So, the ITensor's Dims of input and output should be C * H * W.
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 4, 4));
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 13, 14));
if (global_pooling)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 1, 1));
else if (ceil_mode)
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 7));
else
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 2, 2));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
// Prepare Op description
framework::OpDesc desc;
......@@ -39,7 +41,7 @@ void test_pool2d(bool global_pooling) {
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({2, 2});
std::vector<int> ksize({3, 3});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
......@@ -49,6 +51,7 @@ void test_pool2d(bool global_pooling) {
desc.SetAttr("strides", strides);
desc.SetAttr("paddings", paddings);
desc.SetAttr("global_pooling", global_pooling);
desc.SetAttr("ceil_mode", ceil_mode);
LOG(INFO) << "set OP";
validator.SetOp(*desc.Proto());
......@@ -57,9 +60,10 @@ void test_pool2d(bool global_pooling) {
validator.Execute(3);
}
TEST(Pool2dOpConverter, normal) { test_pool2d(false); }
TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true); }
TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tests/api/analyzer_resnet50_tester.cc
浏览文件 @ 6253b152
......@@ -52,9 +52,10 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
}
// Easy for profiling independently.
TEST(Analyzer_resnet50, profile) {
void profile(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
cfg._use_mkldnn = use_mkldnn;
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
......@@ -69,6 +70,11 @@ TEST(Analyzer_resnet50, profile) {
}
}
TEST(Analyzer_resnet50, profile) { profile(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_resnet50, profile_mkldnn) { profile(true /* use_mkldnn */); }
#endif
// Check the fuse status
TEST(Analyzer_resnet50, fuse_statis) {
AnalysisConfig cfg;
......@@ -82,15 +88,21 @@ TEST(Analyzer_resnet50, fuse_statis) {
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_resnet50, compare) {
void compare(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
cfg._use_mkldnn = use_mkldnn;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(cfg, input_slots_all);
}
TEST(Analyzer_resnet50, compare) { compare(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_resnet50, compare_mkldnn) { compare(true /* use_mkldnn */); }
#endif
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/analyzer_rnn1_tester.cc
浏览文件 @ 6253b152
......@@ -308,18 +308,13 @@ TEST(Analyzer_rnn1, ZeroCopy) {
PaddlePlace place;
int output_size{0};
auto predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto predictor = CreatePaddlePredictor<AnalysisConfig>(config);
config.use_feed_fetch_ops = true;
auto native_predictor =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config);
auto native_predictor = CreatePaddlePredictor<NativeConfig>(config);
config.use_feed_fetch_ops = true; // the analysis predictor needs feed/fetch.
auto analysis_predictor =
CreatePaddlePredictor<AnalysisConfig, PaddleEngineKind::kAnalysis>(
config);
auto analysis_predictor = CreatePaddlePredictor<AnalysisConfig>(config);
#define NEW_TENSOR(name__) \
auto name__##_tensor = predictor->GetInputTensor(#name__);
......
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
浏览文件 @ 6253b152
......@@ -18,12 +18,12 @@ namespace paddle {
namespace inference {
using namespace framework; // NOLINT
static std::vector<float> result_data;
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
std::vector<size_t> lod;
std::vector<std::vector<float>> rnn_link_data;
std::vector<float> result_data;
size_t num_samples; // total number of samples
size_t batch_iter{0};
size_t batch_size{1};
......@@ -57,6 +57,7 @@ struct DataRecord {
std::ifstream file(path);
std::string line;
int num_lines = 0;
result_data.clear();
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
......@@ -135,13 +136,12 @@ TEST(Analyzer_rnn2, profile) {
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
// the first inference result
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
PADDLE_ENFORCE_GT(outputs.size(), 0);
size_t size = GetSize(outputs[0]);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(outputs[0].data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(result[i], data.result_data[i], 1e-3);
EXPECT_NEAR(result[i], result_data[i], 1e-3);
}
}
}
......
paddle/fluid/inference/tests/api/analyzer_seq_conv1_tester.cc
浏览文件 @ 6253b152
......@@ -183,7 +183,13 @@ TEST(Analyzer_seq_conv1, fuse_statis) {
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
GetFuseStatis(predictor.get(), &num_ops);
auto fuse_statis = GetFuseStatis(predictor.get(), &num_ops);
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
ASSERT_TRUE(fuse_statis.count("seqconv_eltadd_relu_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 2);
EXPECT_EQ(fuse_statis.at("seqconv_eltadd_relu_fuse"), 6);
EXPECT_EQ(num_ops, 32);
}
// Compare result of NativeConfig and AnalysisConfig
......
paddle/fluid/inference/tests/api/analyzer_vis_tester.cc
浏览文件 @ 6253b152
......@@ -59,9 +59,6 @@ void SetConfig(AnalysisConfig *cfg) {
cfg->specify_input_name = true;
// TODO(TJ): fix fusion gru
cfg->ir_passes.push_back("fc_gru_fuse_pass");
#ifdef PADDLE_WITH_MKLDNN
cfg->_use_mkldnn = true;
#endif
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
......@@ -84,9 +81,10 @@ void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
// Easy for profiling independently.
// ocr, mobilenet and se_resnext50
TEST(Analyzer_vis, profile) {
void profile(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
cfg._use_mkldnn = use_mkldnn;
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
......@@ -108,6 +106,12 @@ TEST(Analyzer_vis, profile) {
}
}
TEST(Analyzer_vis, profile) { profile(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_vis, profile_mkldnn) { profile(true /* use_mkldnn */); }
#endif
// Check the fuse status
TEST(Analyzer_vis, fuse_statis) {
AnalysisConfig cfg;
......@@ -118,15 +122,21 @@ TEST(Analyzer_vis, fuse_statis) {
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_vis, compare) {
void compare(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
cfg._use_mkldnn = use_mkldnn;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(cfg, input_slots_all);
}
TEST(Analyzer_vis, compare) { compare(); }
#ifdef PADDLE_WITH_MKLDNN
TEST(Analyzer_vis, compare_mkldnn) { compare(true /* use_mkldnn */); }
#endif
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 6253b152
......@@ -50,7 +50,7 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
auto &ref_out = ref_outputs[i];
size_t size = VecReduceToInt(out.shape);
size_t ref_size = VecReduceToInt(ref_out.shape);
EXPECT_GT(size, 0);
EXPECT_GT(size, 0UL);
EXPECT_EQ(size, ref_size);
EXPECT_EQ(out.dtype, ref_out.dtype);
switch (out.dtype) {
......@@ -77,11 +77,9 @@ void CompareResult(const std::vector<PaddleTensor> &outputs,
std::unique_ptr<PaddlePredictor> CreateTestPredictor(
const AnalysisConfig &config, bool use_analysis = true) {
if (use_analysis) {
return CreatePaddlePredictor<contrib::AnalysisConfig,
PaddleEngineKind::kAnalysis>(config);
return CreatePaddlePredictor<contrib::AnalysisConfig>(config);
} else {
return CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(
config);
return CreatePaddlePredictor<NativeConfig>(config);
}
}
......@@ -165,7 +163,8 @@ void TestPrediction(const AnalysisConfig &config,
const std::vector<std::vector<PaddleTensor>> &inputs,
std::vector<PaddleTensor> *outputs, int num_threads,
bool use_analysis = FLAGS_use_analysis) {
LOG(INFO) << "use_analysis: " << use_analysis;
LOG(INFO) << "use_analysis: " << use_analysis
<< ", use_mkldnn: " << config._use_mkldnn;
if (num_threads == 1) {
TestOneThreadPrediction(config, inputs, outputs, use_analysis);
} else {
......@@ -177,6 +176,7 @@ void TestPrediction(const AnalysisConfig &config,
void CompareNativeAndAnalysis(
const AnalysisConfig &config,
const std::vector<std::vector<PaddleTensor>> &inputs) {
LOG(INFO) << "use_mkldnn: " << config._use_mkldnn;
std::vector<PaddleTensor> native_outputs, analysis_outputs;
TestOneThreadPrediction(config, inputs, &native_outputs, false);
TestOneThreadPrediction(config, inputs, &analysis_outputs, true);
......
paddle/fluid/inference/tests/api/trt_models_tester.cc
浏览文件 @ 6253b152
......@@ -51,11 +51,8 @@ void CompareTensorRTWithFluid(int batch_size, std::string model_dirname) {
config1.model_dir = model_dirname;
config1.max_batch_size = batch_size;
auto predictor0 =
CreatePaddlePredictor<NativeConfig, PaddleEngineKind::kNative>(config0);
auto predictor1 =
CreatePaddlePredictor<MixedRTConfig,
PaddleEngineKind::kAutoMixedTensorRT>(config1);
auto predictor0 = CreatePaddlePredictor<NativeConfig>(config0);
auto predictor1 = CreatePaddlePredictor<MixedRTConfig>(config1);
// Prepare inputs
int height = 224;
int width = 224;
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 6253b152
......@@ -86,7 +86,7 @@ function(op_library TARGET)
# remove windows unsupported op, because windows has no nccl, no warpctc such ops.
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op" "hierarchical_sigmoid_op"
"crf_decoding_op" "select_op" "lstmp_op" "gru_op" "fusion_gru_op" "lstm_op" "fusion_lstm_op" "cumsum_op"
"channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
"fusion_seqconv_eltadd_relu_op" "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......@@ -284,10 +284,10 @@ op_library(max_sequence_len_op DEPS lod_rank_table)
op_library(sequence_conv_op DEPS context_project)
op_library(sequence_pool_op DEPS sequence_pooling)
if (NOT WIN32)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library(hierarchical_sigmoid_op DEPS matrix_bit_code)
op_library(lstmp_op DEPS sequence2batch lstm_compute)
op_library(gru_op DEPS sequence2batch gru_compute)
endif(NOT WIN32)
op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
......@@ -300,11 +300,12 @@ op_library(flatten_op DEPS reshape_op)
op_library(sequence_pad_op DEPS sequence_padding)
op_library(unstack_op DEPS stack_op)
op_library(fake_quantize_op DEPS memory)
op_library(fusion_lstm_op DEPS cpu_lstm_compute)
op_library(fusion_lstm_op DEPS jit_kernel)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
op_library(layer_norm_op DEPS cub)
op_library(reduce_mean_op DEPS cub)
op_library(affine_channel_op DEPS cub)
else()
op_library(conv_op DEPS vol2col im2col)
endif()
......@@ -315,7 +316,7 @@ op_library(save_op DEPS lod_tensor)
op_library(load_op DEPS lod_tensor)
op_library(save_combine_op DEPS lod_tensor)
op_library(load_combine_op DEPS lod_tensor)
op_library(concat_op DEPS concat)
op_library(concat_op DEPS concat_and_split)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
......@@ -347,6 +348,6 @@ cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
if(NOT WIN32)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
endif()
nv_test(dropout_op_test SRCS dropout_op_test.cc DEPS dropout_op tensor)
paddle/fluid/operators/activation_op.cc
浏览文件 @ 6253b152
......@@ -28,7 +28,7 @@ using paddle::framework::Tensor;
public: \
void Make() override { \
AddInput("X", "Input of " #OP_NAME " operator"); \
AddOutput("Out", "Output of " #OP_NAME " operator").Reuse("X"); \
AddOutput("Out", "Output of " #OP_NAME " operator"); \
AddAttr<bool>("use_mkldnn", \
"(bool, default false) Only used in mkldnn kernel") \
.SetDefault(false); \
......
paddle/fluid/operators/adam_op.cc
浏览文件 @ 6253b152
......@@ -92,9 +92,9 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Beta1Pow", "(Tensor) Input beta1 power accumulator");
AddInput("Beta2Pow", "(Tensor) Input beta2 power accumulator");
AddOutput("ParamOut", "(Tensor) Output parameter").Reuse("Param");
AddOutput("Moment1Out", "(Tensor) Output first moment").Reuse("Moment1");
AddOutput("Moment2Out", "(Tensor) Output second moment").Reuse("Moment2");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddOutput("Moment1Out", "(Tensor) Output first moment");
AddOutput("Moment2Out", "(Tensor) Output second moment");
AddAttr<float>("beta1",
"(float, default 0.9) "
......
paddle/fluid/operators/affine_channel_op.cc 0 → 100644
浏览文件 @ 6253b152
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class AffineChannelOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"(Tensor) Feature map input can be a 4D tensor with order NCHW "
"or NHWC. It also can be a 2D tensor and C is the second "
"dimension.");
AddInput("Scale",
"(Tensor) 1D input of shape (C), the c-th element "
"is the scale factor of the affine transformation "
"for the c-th channel of the input.");
AddInput("Bias",
"(Tensor) 1D input of shape (C), the c-th element "
"is the bias of the affine transformation for the "
"c-th channel of the input.");
AddAttr<std::string>(
"data_layout",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
AddOutput("Out", "(Tensor) A tensor of the same shape and order with X.");
AddComment(R"DOC(
Applies a separate affine transformation to each channel of the input. Useful
for replacing spatial batch norm with its equivalent fixed transformation.
The input also can be 2D tensor and applies a affine transformation in second
dimension.
$$Out = Scale*X + Bias$$
)DOC");
}
};
class AffineChannelOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of AffineChannelOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Scale"),
"Input(Scale) of AffineChannelOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Bias"),
"Input(Bias) of AffineChannelOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AffineChannelOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", "Out");
}
};
class AffineChannelOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
if (ctx->HasOutput(framework::GradVarName("X"))) {
PADDLE_ENFORCE(ctx->HasInput("Scale"),
"Input(Scale) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
}
if (ctx->HasOutput(framework::GradVarName("Scale"))) {
// Scale@GRAD and Bias@GRAD must exist at the same time.
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")),
"Output(Scale@GRAD) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
ctx->SetOutputDim(framework::GradVarName("Scale"),
ctx->GetInputDim("Scale"));
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Scale"));
}
}
};
template <typename T>
using EigenArrayMap =
Eigen::Map<Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using ConstEigenArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, Eigen::Dynamic>>;
template <typename T>
using EigenVectorArrayMap = Eigen::Map<Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename T>
using ConstEigenVectorArrayMap =
Eigen::Map<const Eigen::Array<T, Eigen::Dynamic, 1>>;
template <typename DeviceContext, typename T>
class AffineChannelKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* scale = ctx.Input<framework::Tensor>("Scale");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* y = ctx.Output<framework::Tensor>("Out");
y->mutable_data<T>(ctx.GetPlace());
const framework::DataLayout layout =
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
auto dims = x->dims();
int N = dims[0];
int C = layout == framework::DataLayout::kNCHW ? dims[1]
: dims[dims.size() - 1];
int HxW = x->numel() / N / C;
auto* scale_d = scale->data<T>();
auto* bias_d = bias->data<T>();
ConstEigenVectorArrayMap<T> a_e(scale_d, C);
ConstEigenVectorArrayMap<T> b_e(bias_d, C);
auto* x_d = x->data<T>();
auto* y_d = y->data<T>();
if (layout == framework::DataLayout::kNCHW) {
int stride = C * HxW;
for (int i = 0; i < N; i++) {
ConstEigenArrayMap<T> x_e(x_d, HxW, C);
EigenArrayMap<T> y_e(y_d, HxW, C);
y_e = (x_e.rowwise() * a_e.transpose()).rowwise() + b_e.transpose();
x_d += stride;
y_d += stride;
}
} else {
int num = N * HxW;
ConstEigenArrayMap<T> x_e(x_d, C, num);
EigenArrayMap<T> y_e(y_d, C, num);
y_e = (x_e.colwise() * a_e).colwise() + b_e;
}
}
};
template <typename DeviceContext, typename T>
class AffineChannelGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* scale = ctx.Input<framework::Tensor>("Scale");
auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* dscale =
ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
const framework::DataLayout layout =
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
auto dims = x->dims();
int N = dims[0];
int C = layout == framework::DataLayout::kNCHW ? dims[1]
: dims[dims.size() - 1];
int HxW = x->numel() / N / C;
auto* x_d = x->data<T>();
auto* dy_d = dy->data<T>();
auto* scale_d = scale->data<T>();
ConstEigenVectorArrayMap<T> scale_e(scale_d, C);
T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
T* dscale_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
T* dbias_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
EigenVectorArrayMap<T> dscale_e(dscale_d, C);
EigenVectorArrayMap<T> dbias_e(dbias_d, C);
if (layout == framework::DataLayout::kNCHW) {
// compute dx
int stride = C * HxW;
if (dx) {
for (int i = 0; i < N; i++) {
ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
EigenArrayMap<T> dx_e(dx_d, HxW, C);
dx_e = dy_e.rowwise() * scale_e.transpose();
dy_d += stride;
dx_d += stride;
}
}
// compute dscale and dbias
if (dscale && dbias) {
dy_d = dy->data<T>();
for (int i = 0; i < N; i++) {
ConstEigenArrayMap<T> x_e(x_d, HxW, C);
ConstEigenArrayMap<T> dy_e(dy_d, HxW, C);
if (i == 0) {
dscale_e = (x_e * dy_e).colwise().sum();
} else {
dscale_e += (x_e * dy_e).colwise().sum();
}
if (i == 0) {
dbias_e = dy_e.colwise().sum();
} else {
dbias_e += dy_e.colwise().sum();
}
x_d += stride;
dy_d += stride;
}
}
} else {
int num = N * HxW;
ConstEigenArrayMap<T> dy_e(dy_d, C, num);
// compute dx
if (dx) {
EigenArrayMap<T> dx_e(dx_d, C, num);
dx_e = dy_e.colwise() * scale_e;
}
// compute dscale and dbias
if (dscale && dbias) {
ConstEigenArrayMap<T> x_e(x_d, C, num);
dscale_e = (x_e * dy_e).rowwise().sum();
dbias_e = dy_e.rowwise().sum();
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CPU = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(affine_channel, ops::AffineChannelOp,
ops::AffineChannelOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(affine_channel_grad, ops::AffineChannelOpGrad);
REGISTER_OP_CPU_KERNEL(affine_channel, ops::AffineChannelKernel<CPU, float>,
ops::AffineChannelKernel<CPU, double>);
REGISTER_OP_CPU_KERNEL(affine_channel_grad,
ops::AffineChannelGradKernel<CPU, float>,
ops::AffineChannelGradKernel<CPU, double>);
paddle/fluid/operators/affine_channel_op.cu 0 → 100644
浏览文件 @ 6253b152
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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 "cub/cub.cuh"
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace paddle {
namespace operators {
template <typename T, framework::DataLayout layout, bool HasBias>
__global__ void KeAffineChannelCUDA(const T* x, const T* scale, const T* bias,
const int C, const int HxW, const int num,
T* y) {
int gid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = gid; i < num; i += stride) {
const int c = layout == framework::DataLayout::kNCHW ? i / HxW % C : i % C;
if (HasBias) {
y[i] = scale[c] * x[i] + bias[c];
} else {
y[i] = scale[c] * x[i];
}
}
}
template <typename DeviceContext, typename T>
class AffineChannelCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* scale = ctx.Input<framework::Tensor>("Scale");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* y = ctx.Output<framework::Tensor>("Out");
y->mutable_data<T>(ctx.GetPlace());
const framework::DataLayout layout =
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto dims = x->dims();
const int num = x->numel();
int N = dims[0];
int C = layout == framework::DataLayout::kNCHW ? dims[1]
: dims[dims.size() - 1];
int HxW = num / N / C;
const T* x_d = x->data<T>();
const T* scale_d = scale->data<T>();
const T* bias_d = bias->data<T>();
T* y_d = y->data<T>();
int block = 1024;
int grid = (num + block - 1) / block;
if (layout == framework::DataLayout::kNCHW) {
KeAffineChannelCUDA<T, framework::DataLayout::kNCHW,
true><<<grid, block, 0, dev_ctx.stream()>>>(
x_d, scale_d, bias_d, C, HxW, num, y_d);
} else {
KeAffineChannelCUDA<T, framework::DataLayout::kNHWC,
true><<<grid, block, 0, dev_ctx.stream()>>>(
x_d, scale_d, bias_d, C, HxW, num, y_d);
}
}
};
template <typename T, int BlockDim, framework::DataLayout layout>
__global__ void AffineChannelScaleBiasGradientCUDAKernel(
const T* dy, const T* x, const int N, const int C, const int HxW, T* dscale,
T* dbias) {
const int outer_size = C;
const int inner_size = N * HxW;
typedef cub::BlockReduce<T, BlockDim> BlockReduce;
__shared__ typename BlockReduce::TempStorage ds_storage;
__shared__ typename BlockReduce::TempStorage db_storage;
for (int i = blockIdx.x; i < outer_size; i += gridDim.x) {
T ds_sum = 0;
T db_sum = 0;
for (int j = threadIdx.x; j < inner_size; j += blockDim.x) {
const int index = layout == framework::DataLayout::kNCHW
? (j / HxW * C + i) * HxW + j % HxW
: j * outer_size + i;
ds_sum += dy[index] * x[index];
db_sum += dy[index];
}
ds_sum = BlockReduce(ds_storage).Reduce(ds_sum, cub::Sum());
db_sum = BlockReduce(db_storage).Reduce(db_sum, cub::Sum());
if (threadIdx.x == 0) {
dscale[i] = ds_sum;
dbias[i] = db_sum;
}
__syncthreads();
}
}
template <typename DeviceContext, typename T>
class AffineChannelGradCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* x = ctx.Input<framework::Tensor>("X");
auto* scale = ctx.Input<framework::Tensor>("Scale");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* dy = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* dscale =
ctx.Output<framework::Tensor>(framework::GradVarName("Scale"));
auto* dbias = ctx.Output<framework::Tensor>(framework::GradVarName("Bias"));
const framework::DataLayout layout =
framework::StringToDataLayout(ctx.Attr<std::string>("data_layout"));
auto& dev_ctx = ctx.template device_context<DeviceContext>();
auto dims = x->dims();
const int num = x->numel();
int N = dims[0];
int C = layout == framework::DataLayout::kNCHW ? dims[1]
: dims[dims.size() - 1];
int HxW = num / N / C;
const T* x_d = x->data<T>();
const T* dy_d = dy->data<T>();
const T* s_d = scale->data<T>();
T* dx_d = dx ? dx->mutable_data<T>(ctx.GetPlace()) : nullptr;
T* ds_d = dscale ? dscale->mutable_data<T>(ctx.GetPlace()) : nullptr;
T* db_d = dbias ? dbias->mutable_data<T>(ctx.GetPlace()) : nullptr;
const int block = 1024;
int max_threads = dev_ctx.GetMaxPhysicalThreadCount();
const int max_blocks = std::max(max_threads / block, 1);
int grid1 = (num + block - 1) / block;
int grid2 = std::min(C, max_blocks);
if (layout == framework::DataLayout::kNCHW) {
if (dx) {
KeAffineChannelCUDA<T, framework::DataLayout::kNCHW,
false><<<grid1, block, 0, dev_ctx.stream()>>>(
dy_d, s_d, nullptr, C, HxW, num, dx_d);
}
if (dscale && dbias) {
AffineChannelScaleBiasGradientCUDAKernel<
T, block, framework::DataLayout::kNCHW><<<grid2, block, 0,
dev_ctx.stream()>>>(
dy_d, x_d, N, C, HxW, ds_d, db_d);
}
} else {
if (dx) {
KeAffineChannelCUDA<T, framework::DataLayout::kNCHW,
false><<<grid1, block, 0, dev_ctx.stream()>>>(
dy_d, s_d, nullptr, C, HxW, num, dx_d);
}
if (dscale && dbias) {
AffineChannelScaleBiasGradientCUDAKernel<
T, block, framework::DataLayout::kNHWC><<<grid2, block, 0,
dev_ctx.stream()>>>(
dy_d, x_d, N, C, HxW, ds_d, db_d);
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
REGISTER_OP_CUDA_KERNEL(affine_channel,
ops::AffineChannelCUDAKernel<CUDA, float>,
ops::AffineChannelCUDAKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(affine_channel_grad,
ops::AffineChannelGradCUDAKernel<CUDA, float>,
ops::AffineChannelGradCUDAKernel<CUDA, double>);
paddle/fluid/operators/array_to_lod_tensor_op.cc
浏览文件 @ 6253b152
......@@ -11,7 +11,7 @@ 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/fluid/operators/math/concat.h>
#include <paddle/fluid/operators/math/concat_and_split.h>
#include <numeric>
#include "paddle/fluid/framework/lod_rank_table.h"
......
paddle/fluid/operators/batch_norm_op.cc
浏览文件 @ 6253b152
......@@ -135,15 +135,13 @@ class BatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Variance",
"The global variance (for training) "
"or estimated Variance (for testing)");
AddOutput("Y", "result after normalization").Reuse("X");
AddOutput("Y", "result after normalization");
AddOutput("MeanOut",
"Share memory with Mean. "
"Store the global mean when training")
.Reuse("Mean");
"Store the global mean when training");
AddOutput("VarianceOut",
"Share memory with Variance. "
"Store the global Variance when training")
.Reuse("Variance");
"Store the global Variance when training");
AddOutput("SavedMean",
"Mean of the current mini batch, "
"will apply to output when training")
......
paddle/fluid/operators/concat_op.h
浏览文件 @ 6253b152
......@@ -17,7 +17,7 @@ limitations under the License. */
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/strided_memcpy.h"
namespace paddle {
......@@ -89,29 +89,17 @@ class ConcatGradKernel : public framework::OpKernel<T> {
outputs.push_back(nullptr);
}
}
auto& dev_ctx = ctx.template device_context<DeviceContext>();
// Sometimes direct copies will be faster, this maybe need deeply analysis.
if (axis == 0 && outs.size() < 10) {
size_t input_offset = 0;
const auto in_stride = framework::stride_numel(out_grad->dims());
for (size_t i = 0; i < outs.size(); ++i) {
auto out_stride = framework::stride_numel(ins[i]->dims());
auto* out = outputs[i];
if (out != nullptr) {
StridedNumelCopyWithAxis<T>(
ctx.device_context(), axis, out->data<T>(), out_stride,
out_grad->data<T>() + input_offset, in_stride, out_stride[axis]);
}
input_offset += out_stride[axis];
}
std::vector<const framework::Tensor*> ref_shape;
ref_shape.insert(ref_shape.begin(), ins.begin(), ins.end());
StridedMemcpyWithAxis0<T>(dev_ctx, *out_grad, ref_shape, &outputs);
} else {
auto& dev_ctx = ctx.template device_context<DeviceContext>();
paddle::operators::math::ConcatGradFunctor<DeviceContext, T>
concat_grad_functor;
concat_grad_functor(dev_ctx, *out_grad,
ctx.MultiInput<framework::Tensor>("X"),
static_cast<int>(axis), &outputs);
math::SplitFunctor<DeviceContext, T> split_functor;
split_functor(dev_ctx, *out_grad, ctx.MultiInput<framework::Tensor>("X"),
static_cast<int>(axis), &outputs);
}
}
};
......
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @ 6253b152
......@@ -300,10 +300,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
bool fuse_relu = ctx.Attr<bool>("fuse_relu");
bool fuse_eltwise = ctx.Attr<bool>("fuse_eltwise");
bool fuse_residual_conn = ctx.Attr<bool>("fuse_residual_connection");
int groups = ctx.Attr<int>("groups");
// TODO: add support for dilation
// TODO(tpatejko): add support for dilation
PADDLE_ENFORCE(
dilations.size() == 2 && dilations[0] == 1 && dilations[1] == 1,
"dilation in convolution is not implemented yet");
......@@ -369,11 +369,11 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias_tz, platform::MKLDNNGetDataType<T>(), memory::format::x);
conv_pd = ConvFwdPrimitiveDesc(src_md, weights_md, bias_md, dst_md,
strides, paddings, mkldnn_engine,
fuse_relu, fuse_eltwise);
fuse_relu, fuse_residual_conn);
} else {
conv_pd =
ConvFwdPrimitiveDesc(src_md, weights_md, dst_md, strides, paddings,
mkldnn_engine, fuse_relu, fuse_eltwise);
mkldnn_engine, fuse_relu, fuse_residual_conn);
}
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx.SetBlob(key_conv_pd, conv_pd);
......@@ -386,8 +386,26 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto user_weights_memory_p = handler.AcquireWeightsMemory(
user_weights_md, to_void_cast<T>(filter_data));
T* output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
T* output_data = nullptr;
if (fuse_residual_conn) {
auto residual_param = ctx.Input<Tensor>("ResidualData");
auto residual_param_data = residual_param->data<T>();
PADDLE_ENFORCE(
residual_param_data != nullptr,
"Provide data if you want MKLDNN conv+elementwise_add fusion");
PADDLE_ENFORCE_EQ(output->dims(), residual_param->dims(),
"Output and elementwise parameter need to have the "
"same dimension sizes");
output->ShareDataWith(*residual_param);
output_data = output->mutable_data<T>(ctx.GetPlace());
} else {
output_data =
output->mutable_data<T>(ctx.GetPlace(), handler.GetDstMemorySize());
}
// create reorder primitive if the input format is not the preferred one
auto src_memory_p =
handler.AcquireSrcMemoryFromPrimitive(user_src_memory_p, pipeline);
......@@ -424,14 +442,15 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
private:
mkldnn::primitive_attr CreatePostOps(bool fuse_relu,
bool fuse_eltwise) const {
bool fuse_residual_conn) const {
mkldnn::primitive_attr conv_attr;
mkldnn::post_ops post_operations;
// Fusion with Elementwise layer relies on adding a sum post-operation with
// the scale parameter. It is assumed that when fuse_eltwise is true, the
// Output tensor contains the data coming from residual connection. The
// result of this post_op is: Output = scale * Output + Conv_Out.
if (fuse_eltwise) {
// the scale parameter. It is assumed that when fuse_residual_connection is
// true, the output tensor contains the data coming from residual
// connection. The result of this post_op is:
// Output = scale * Output + Conv_Out.
if (fuse_residual_conn) {
post_operations.append_sum(1.0f);
}
// Fusion with ReLU layer is executed through the PostOps feature. Create a
......@@ -452,7 +471,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const memory::desc& dst, const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_eltwise) const {
const bool fuse_residual_conn) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
......@@ -461,7 +480,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr = CreatePostOps(fuse_relu, fuse_eltwise);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, fuse_residual_conn);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
......@@ -476,7 +496,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
const std::vector<int>& strides,
const std::vector<int>& paddings,
const mkldnn::engine& engine, const bool fuse_relu,
const bool fuse_eltwise) const {
const bool fuse_residual_conn) const {
memory::dims stride_dims = {strides[0], strides[1]};
memory::dims padding_dims = {paddings[0], paddings[1]};
......@@ -485,7 +505,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
bias, dst, stride_dims, padding_dims, padding_dims,
mkldnn::padding_kind::zero);
mkldnn::primitive_attr conv_attr = CreatePostOps(fuse_relu, fuse_eltwise);
mkldnn::primitive_attr conv_attr =
CreatePostOps(fuse_relu, fuse_residual_conn);
auto p_conv_pd = new mkldnn::convolution_forward::primitive_desc(
conv_desc, conv_attr, engine);
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ 6253b152
......@@ -130,8 +130,12 @@ void Conv2DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.")
.Reuse("Input");
"The format of output tensor is also NCHW.");
AddInput("ResidualData",
"(Tensor) Tensor with residual data "
"to which convolution output will be added."
"Used with fuse_residual_connection fusion.")
.AsDispensable();
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
......@@ -164,10 +168,10 @@ void Conv2DOpMaker::Make() {
.SetDefault(false);
AddAttr<bool>("fuse_relu", "(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<bool>("fuse_eltwise",
AddAttr<bool>("fuse_residual_connection",
"(bool, default false) Only used in mkldnn kernel. Used "
"whenever convolution output is connected via skip connection "
"to a previous layer.")
"whenever convolution output is as an input to residual "
"connection.")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
......@@ -233,8 +237,7 @@ void Conv3DOpMaker::Make() {
"input image channels divided by the groups.");
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.")
.Reuse("Input");
"The format of output tensor is also NCDHW.");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 6253b152
......@@ -20,7 +20,7 @@ detection_library(box_coder_op SRCS box_coder_op.cc box_coder_op.cu)
detection_library(iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op.cu)
detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc)
detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
detection_library(anchor_generator_op SRCS anchor_generator_op.cc
anchor_generator_op.cu)
......
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ 6253b152
......@@ -16,7 +16,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
......
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ 6253b152
......@@ -12,10 +12,12 @@ 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 <cmath>
#include <cstring>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -25,21 +27,17 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
struct AppendProposalsFunctor {
LoDTensor *out_;
int64_t offset_;
Tensor *to_add_;
static const double kBBoxClipDefault = std::log(1000.0 / 16.0);
AppendProposalsFunctor(LoDTensor *out, int64_t offset, Tensor *to_add)
: out_(out), offset_(offset), to_add_(to_add) {}
template <typename T>
void apply() const {
auto *out_data = out_->data<T>();
auto *to_add_data = to_add_->data<T>();
memcpy(out_data + offset_, to_add_data, to_add_->numel() * sizeof(T));
}
};
static void AppendProposals(Tensor *dst, int64_t offset, const Tensor &src) {
auto *out_data = dst->data<void>();
auto *to_add_data = src.data<void>();
size_t size_of_t = framework::SizeOfType(src.type());
offset *= size_of_t;
std::memcpy(
reinterpret_cast<void *>(reinterpret_cast<uintptr_t>(out_data) + offset),
to_add_data, src.numel() * size_of_t);
}
class GenerateProposalsOp : public framework::OperatorWithKernel {
public:
......@@ -75,8 +73,9 @@ class GenerateProposalsOp : public framework::OperatorWithKernel {
};
template <class T>
void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
Tensor *bbox_deltas, Tensor *variances, Tensor *proposals) {
static inline void BoxCoder(const platform::DeviceContext &ctx,
Tensor *all_anchors, Tensor *bbox_deltas,
Tensor *variances, Tensor *proposals) {
T *proposals_data = proposals->mutable_data<T>(ctx.GetPlace());
int64_t row = all_anchors->dims()[0];
......@@ -108,11 +107,11 @@ void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
anchor_center_y;
bbox_width = std::exp(std::min<T>(variances_data[i * len + 2] *
bbox_deltas_data[i * len + 2],
std::log(1000.0 / 16.0))) *
kBBoxClipDefault)) *
anchor_width;
bbox_height = std::exp(std::min<T>(variances_data[i * len + 3] *
bbox_deltas_data[i * len + 3],
std::log(1000.0 / 16.0))) *
kBBoxClipDefault)) *
anchor_height;
} else {
bbox_center_x =
......@@ -120,10 +119,10 @@ void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
bbox_center_y =
bbox_deltas_data[i * len + 1] * anchor_height + anchor_center_y;
bbox_width = std::exp(std::min<T>(bbox_deltas_data[i * len + 2],
std::log(1000.0 / 16.0))) *
kBBoxClipDefault)) *
anchor_width;
bbox_height = std::exp(std::min<T>(bbox_deltas_data[i * len + 3],
std::log(1000.0 / 16.0))) *
kBBoxClipDefault)) *
anchor_height;
}
......@@ -136,30 +135,32 @@ void BoxCoder(const platform::DeviceContext &ctx, Tensor *all_anchors,
}
template <class T>
void ClipTiledBoxes(const platform::DeviceContext &ctx, const Tensor &im_info,
Tensor *boxes) {
static inline void ClipTiledBoxes(const platform::DeviceContext &ctx,
const Tensor &im_info, Tensor *boxes) {
T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
const T *im_info_data = im_info.data<T>();
T zero(0);
for (int64_t i = 0; i < boxes->numel(); ++i) {
if (i % 4 == 0) {
boxes_data[i] =
std::max(std::min(boxes_data[i], im_info_data[1] - 1), 0.0f);
std::max(std::min(boxes_data[i], im_info_data[1] - 1), zero);
} else if (i % 4 == 1) {
boxes_data[i] =
std::max(std::min(boxes_data[i], im_info_data[0] - 1), 0.0f);
std::max(std::min(boxes_data[i], im_info_data[0] - 1), zero);
} else if (i % 4 == 2) {
boxes_data[i] =
std::max(std::min(boxes_data[i], im_info_data[1] - 1), 0.0f);
std::max(std::min(boxes_data[i], im_info_data[1] - 1), zero);
} else {
boxes_data[i] =
std::max(std::min(boxes_data[i], im_info_data[0] - 1), 0.0f);
std::max(std::min(boxes_data[i], im_info_data[0] - 1), zero);
}
}
}
template <class T>
void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
float min_size, const Tensor &im_info, Tensor *keep) {
static inline void FilterBoxes(const platform::DeviceContext &ctx,
Tensor *boxes, float min_size,
const Tensor &im_info, Tensor *keep) {
const T *im_info_data = im_info.data<T>();
T *boxes_data = boxes->mutable_data<T>(ctx.GetPlace());
T im_scale = im_info_data[2];
......@@ -185,24 +186,24 @@ void FilterBoxes(const platform::DeviceContext &ctx, Tensor *boxes,
keep->Resize({keep_len});
}
bool SortScorePairDescend(const std::pair<float, int> &pair1,
const std::pair<float, int> &pair2) {
return pair1.first > pair2.first;
}
template <class T>
void GetMaxScoreIndex(const std::vector<T> &scores,
std::vector<std::pair<T, int>> *sorted_indices) {
static inline std::vector<std::pair<T, int>> GetSortedScoreIndex(
const std::vector<T> &scores) {
std::vector<std::pair<T, int>> sorted_indices;
sorted_indices.reserve(scores.size());
for (size_t i = 0; i < scores.size(); ++i) {
sorted_indices->push_back(std::make_pair(scores[i], i));
sorted_indices.emplace_back(scores[i], i);
}
// Sort the score pair according to the scores in descending order
std::stable_sort(sorted_indices->begin(), sorted_indices->end(),
SortScorePairDescend);
std::stable_sort(sorted_indices.begin(), sorted_indices.end(),
[](const std::pair<T, int> &a, const std::pair<T, int> &b) {
return a.first < b.first;
});
return sorted_indices;
}
template <class T>
T BBoxArea(const T *box, const bool normalized) {
static inline T BBoxArea(const T *box, bool normalized) {
if (box[2] < box[0] || box[3] < box[1]) {
// If coordinate values are is invalid
// (e.g. xmax < xmin or ymax < ymin), return 0.
......@@ -220,7 +221,7 @@ T BBoxArea(const T *box, const bool normalized) {
}
template <class T>
T JaccardOverlap(const T *box1, const T *box2, const bool normalized) {
static inline T JaccardOverlap(const T *box1, const T *box2, bool normalized) {
if (box2[0] > box1[2] || box2[2] < box1[0] || box2[1] > box1[3] ||
box2[3] < box1[1]) {
return static_cast<T>(0.);
......@@ -229,8 +230,8 @@ T JaccardOverlap(const T *box1, const T *box2, const bool normalized) {
const T inter_ymin = std::max(box1[1], box2[1]);
const T inter_xmax = std::min(box1[2], box2[2]);
const T inter_ymax = std::min(box1[3], box2[3]);
const T inter_w = std::max(0.0f, inter_xmax - inter_xmin + 1);
const T inter_h = std::max(0.0f, inter_ymax - inter_ymin + 1);
const T inter_w = std::max(T(0), inter_xmax - inter_xmin + 1);
const T inter_h = std::max(T(0), inter_ymax - inter_ymin + 1);
const T inter_area = inter_w * inter_h;
const T bbox1_area = BBoxArea<T>(box1, normalized);
const T bbox2_area = BBoxArea<T>(box2, normalized);
......@@ -238,9 +239,21 @@ T JaccardOverlap(const T *box1, const T *box2, const bool normalized) {
}
}
template <typename T>
static inline Tensor VectorToTensor(const std::vector<T> &selected_indices,
int selected_num) {
Tensor keep_nms;
keep_nms.Resize({selected_num});
auto *keep_data = keep_nms.mutable_data<T>(platform::CPUPlace());
for (int i = 0; i < selected_num; ++i) {
keep_data[i] = selected_indices[i];
}
return keep_nms;
}
template <class T>
Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, Tensor *scores,
const T nms_threshold, const float eta) {
static inline Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox,
Tensor *scores, T nms_threshold, float eta) {
PADDLE_ENFORCE_NOT_NULL(bbox);
int64_t num_boxes = bbox->dims()[0];
// 4: [xmin ymin xmax ymax]
......@@ -248,20 +261,18 @@ Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, Tensor *scores,
std::vector<T> scores_data(num_boxes);
std::copy_n(scores->data<T>(), num_boxes, scores_data.begin());
std::vector<std::pair<T, int>> sorted_indices;
GetMaxScoreIndex<T>(scores_data, &sorted_indices);
std::vector<std::pair<T, int>> sorted_indices =
GetSortedScoreIndex<T>(scores_data);
std::vector<int> selected_indices;
int selected_num = 0;
T adaptive_threshold = nms_threshold;
const T *bbox_data = bbox->data<T>();
bool flag;
while (sorted_indices.size() != 0) {
int idx = sorted_indices.front().second;
flag = true;
for (size_t k = 0; k < selected_indices.size(); ++k) {
int idx = sorted_indices.back().second;
bool flag = true;
for (int kept_idx : selected_indices) {
if (flag) {
const int kept_idx = selected_indices[k];
T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
bbox_data + kept_idx * box_size, false);
flag = (overlap <= adaptive_threshold);
......@@ -271,32 +282,29 @@ Tensor NMS(const platform::DeviceContext &ctx, Tensor *bbox, Tensor *scores,
}
if (flag) {
selected_indices.push_back(idx);
selected_num++;
++selected_num;
}
sorted_indices.erase(sorted_indices.begin());
sorted_indices.erase(sorted_indices.end());
if (flag && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}
}
Tensor keep_nms;
keep_nms.Resize({selected_num});
int *keep_data = keep_nms.mutable_data<int>(ctx.GetPlace());
for (int i = 0; i < selected_num; ++i) {
keep_data[i] = selected_indices[i];
}
return keep_nms;
return VectorToTensor(selected_indices, selected_num);
}
template <typename DeviceContext, typename T>
template <typename T>
class GenerateProposalsKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext &context) const override {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto *anchors = context.Input<Tensor>("Anchors");
auto *variances = context.Input<Tensor>("Variances");
auto anchors = detail::Ref(context.Input<Tensor>("Anchors"),
"Cannot find input Anchors(%s) in scope",
context.Inputs("Anchors")[0]);
auto variances = detail::Ref(context.Input<Tensor>("Variances"),
"Cannot find input Variances(%s) in scope",
context.Inputs("Variances")[0]);
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
......@@ -307,15 +315,16 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
float min_size = context.Attr<float>("min_size");
float eta = context.Attr<float>("eta");
auto &dev_ctx = context.template device_context<DeviceContext>();
auto &dev_ctx =
context.template device_context<platform::CPUDeviceContext>();
auto scores_dim = scores->dims();
auto &scores_dim = scores->dims();
int64_t num = scores_dim[0];
int64_t c_score = scores_dim[1];
int64_t h_score = scores_dim[2];
int64_t w_score = scores_dim[3];
auto bbox_dim = bbox_deltas->dims();
auto &bbox_dim = bbox_deltas->dims();
int64_t c_bbox = bbox_dim[1];
int64_t h_bbox = bbox_dim[2];
int64_t w_bbox = bbox_dim[3];
......@@ -330,17 +339,17 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
scores_swap.mutable_data<T>({num, h_score, w_score, c_score},
dev_ctx.GetPlace());
math::Transpose<DeviceContext, T, 4> trans;
math::Transpose<platform::CPUDeviceContext, T, 4> trans;
std::vector<int> axis = {0, 2, 3, 1};
trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
trans(dev_ctx, *scores, &scores_swap, axis);
framework::LoD lod;
std::vector<size_t> lod0(1, 0);
Tensor *anchor = const_cast<framework::Tensor *>(anchors);
anchor->Resize({anchors->numel() / 4, 4});
Tensor *var = const_cast<framework::Tensor *>(variances);
var->Resize({var->numel() / 4, 4});
lod.resize(1);
auto &lod0 = lod[0];
lod0.push_back(0);
anchors.Resize({anchors.numel() / 4, 4});
variances.Resize({variances.numel() / 4, 4});
int64_t num_proposals = 0;
for (int64_t i = 0; i < num; ++i) {
......@@ -352,24 +361,17 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
scores_slice.Resize({h_score * w_score * c_score, 1});
std::pair<Tensor, Tensor> tensor_pair =
ProposalForOneImage(dev_ctx, im_info_slice, *anchor, *var,
ProposalForOneImage(dev_ctx, im_info_slice, anchors, variances,
bbox_deltas_slice, scores_slice, pre_nms_top_n,
post_nms_top_n, nms_thresh, min_size, eta);
Tensor proposals = tensor_pair.first;
Tensor scores = tensor_pair.second;
framework::VisitDataType(
framework::ToDataType(rpn_rois->type()),
AppendProposalsFunctor(rpn_rois, 4 * num_proposals, &proposals));
framework::VisitDataType(
framework::ToDataType(rpn_roi_probs->type()),
AppendProposalsFunctor(rpn_roi_probs, num_proposals, &scores));
Tensor &proposals = tensor_pair.first;
Tensor &scores = tensor_pair.second;
AppendProposals(rpn_rois, 4 * num_proposals, proposals);
AppendProposals(rpn_roi_probs, num_proposals, scores);
num_proposals += proposals.dims()[0];
lod0.emplace_back(num_proposals);
lod0.push_back(num_proposals);
}
lod.emplace_back(lod0);
rpn_rois->set_lod(lod);
rpn_roi_probs->set_lod(lod);
rpn_rois->Resize({num_proposals, 4});
......@@ -377,7 +379,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
}
std::pair<Tensor, Tensor> ProposalForOneImage(
const DeviceContext &ctx, const Tensor &im_info_slice,
const platform::CPUDeviceContext &ctx, const Tensor &im_info_slice,
const Tensor &anchors, const Tensor &variances,
const Tensor &bbox_deltas_slice, // [M, 4]
const Tensor &scores_slice, // [N, 1]
......@@ -392,10 +394,9 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
for (int i = 0; i < scores_slice.numel(); ++i) {
index[i] = i;
}
std::function<bool(const int64_t &, const int64_t &)> compare =
[scores_data](const int64_t &i, const int64_t &j) {
return scores_data[i] > scores_data[j];
};
auto compare = [scores_data](const int64_t &i, const int64_t &j) {
return scores_data[i] > scores_data[j];
};
if (pre_nms_top_n <= 0 || pre_nms_top_n >= scores_slice.numel()) {
std::sort(index, index + scores_slice.numel(), compare);
......@@ -452,33 +453,45 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
class GenerateProposalsOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Scores", "The scores of anchors should be foreground.");
AddInput("BboxDeltas", "bbox_deltas.");
AddInput("ImInfo", "Information for image reshape.");
AddInput("Anchors", "All anchors.");
AddInput("Variances", " variances");
AddOutput("RpnRois", "Anchors.");
AddOutput("RpnRoiProbs", "Anchors.");
AddAttr<int>("pre_nms_topN", "pre_nms_topN");
AddAttr<int>("post_nms_topN", "post_nms_topN");
AddAttr<float>("nms_thresh", "nms_thres");
AddAttr<float>("min_size", "min size");
AddInput("Scores",
"(Tensor) The scores from conv is in shape (N, A, H, W), "
"N is batch size, A is number of anchors, "
"H and W are height and width of the feature map");
AddInput("BboxDeltas",
"(Tensor) Bounding box deltas from conv is in "
"shape (N, 4*A, H, W).");
AddInput("ImInfo",
"(Tensor) Information for image reshape is in shape (N, 3), "
"in format (height, width, scale)");
AddInput("Anchors",
"(Tensor) Bounding box anchors from anchor_generator_op "
"is in shape (A, H, W, 4).");
AddInput("Variances",
"(Tensor) Bounding box variances with same shape as `Anchors`.");
AddOutput("RpnRois",
"(LoDTensor), Output proposals with shape (rois_num, 4).");
AddOutput("RpnRoiProbs",
"(LoDTensor) Scores of proposals with shape (rois_num, 1).");
AddAttr<int>("pre_nms_topN",
"Number of top scoring RPN proposals to keep before "
"applying NMS.");
AddAttr<int>("post_nms_topN",
"Number of top scoring RPN proposals to keep after "
"applying NMS");
AddAttr<float>("nms_thresh", "NMS threshold used on RPN proposals.");
AddAttr<float>("min_size",
"Proposal height and width both need to be greater "
"than this min_size.");
AddAttr<float>("eta", "The parameter for adaptive NMS.");
AddComment(R"DOC(
Generate Proposals OP
This operator proposes rois according to each box with their probability to be a foreground object and
the box can be calculated by anchors. Bbox_deltais and scores are the output of RPN. Final proposals
could be used to train detection net.
Scores is the probability for each box to be an object. In format of (N, A, H, W) where N is batch size, A is number
of anchors, H and W are height and width of the feature map.
BboxDeltas is the differece between predicted box locatoin and anchor location. In format of (N, 4*A, H, W)
This operator Generate bounding box proposals for Faster RCNN.
The propoasls are generated for a list of images based on image
score 'Scores', bounding box regression result 'BboxDeltas' as
well as predefined bounding box shapes 'anchors'. Greedy
non-maximum suppression is applied to generate the final bounding
boxes.
For generating proposals, this operator transposes and resizes scores and bbox_deltas in size of (H*W*A, 1) and (H*W*A, 4) and
calculate box locations as proposals candidates. Then clip boxes to image and remove predicted boxes with small area.
Finally, apply nms to get final proposals as output.
)DOC");
}
};
......@@ -490,6 +503,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(generate_proposals, ops::GenerateProposalsOp,
ops::GenerateProposalsOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
generate_proposals,
ops::GenerateProposalsKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(generate_proposals, ops::GenerateProposalsKernel<float>,
ops::GenerateProposalsKernel<double>);
paddle/fluid/operators/detection/generate_proposals_op.cu
浏览文件 @ 6253b152
......@@ -16,10 +16,13 @@ limitations under the License. */
#include <string>
#include <vector>
#include "cub/cub.cuh"
#include "paddle/fluid/framework/mixed_vector.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/gather.cu.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/for_range.h"
namespace paddle {
namespace operators {
......@@ -36,36 +39,38 @@ namespace {
int const kThreadsPerBlock = sizeof(uint64_t) * 8;
template <typename T>
__global__ void RangeInitKernel(const T start, const T delta, const int size,
T *out) {
CUDA_1D_KERNEL_LOOP(i, size) { out[i] = start + i * delta; }
}
static const double kBBoxClipDefault = std::log(1000.0 / 16.0);
struct RangeInitFunctor {
int start_;
int delta_;
int *out_;
__device__ void operator()(size_t i) { out_[i] = start_ + i * delta_; }
};
template <typename T>
void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
Tensor *value_out, Tensor *index_out) {
int num = value.numel();
static void SortDescending(const platform::CUDADeviceContext &ctx,
const Tensor &value, Tensor *value_out,
Tensor *index_out) {
int num = static_cast<int>(value.numel());
Tensor index_in_t;
int *idx_in = index_in_t.mutable_data<int>({num}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
RangeInitKernel<<<DIVUP(num, block), block, 0, stream>>>(0, 1, num, idx_in);
platform::ForRange<platform::CUDADeviceContext> for_range(ctx, num);
for_range(RangeInitFunctor{0, 1, idx_in});
int *idx_out = index_out->mutable_data<int>({num}, ctx.GetPlace());
const T *keys_in = value.data<T>();
T *keys_out = value_out->mutable_data<T>({num}, ctx.GetPlace());
// Determine temporary device storage requirements
void *d_temp_storage = NULL;
size_t temp_storage_bytes = 0;
cub::DeviceRadixSort::SortPairsDescending<T, int>(
d_temp_storage, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out,
num);
nullptr, temp_storage_bytes, keys_in, keys_out, idx_in, idx_out, num);
// Allocate temporary storage
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
d_temp_storage = memory::Alloc(place, temp_storage_bytes);
void *d_temp_storage = memory::Alloc(place, temp_storage_bytes);
// Run sorting operation
cub::DeviceRadixSort::SortPairsDescending<T, int>(
......@@ -76,22 +81,27 @@ void SortDescending(const platform::CUDADeviceContext &ctx, const Tensor &value,
}
template <typename T>
__device__ __forceinline__ T Min(T x, T y) {
return x < y ? x : y;
}
template <typename T>
__device__ __forceinline__ T Max(T x, T y) {
return x > y ? x : y;
}
template <typename T>
__global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
const T *var, const int *index,
const T *im_info, const int num,
T *proposals) {
T kBBoxClipDefault = log(1000.0 / 16.0);
CUDA_1D_KERNEL_LOOP(i, num) {
struct BoxDecodeAndClipFunctor {
const T *anchor;
const T *deltas;
const T *var;
const int *index;
const T *im_info;
T *proposals;
BoxDecodeAndClipFunctor(const T *anchor, const T *deltas, const T *var,
const int *index, const T *im_info, T *proposals)
: anchor(anchor),
deltas(deltas),
var(var),
index(index),
im_info(im_info),
proposals(proposals) {}
T bbox_clip_default{static_cast<T>(kBBoxClipDefault)};
__device__ void operator()(size_t i) {
int k = index[i] * 4;
T axmin = anchor[k];
T aymin = anchor[k + 1];
......@@ -108,17 +118,17 @@ __global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
T dxmax = deltas[k + 2];
T dymax = deltas[k + 3];
T d_cx = 0., d_cy = 0., d_w = 0., d_h = 0.;
T d_cx, d_cy, d_w, d_h;
if (var) {
d_cx = cx + dxmin * w * var[k];
d_cy = cy + dymin * h * var[k + 1];
d_w = exp(Min<T>(dxmax * var[k + 2], kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax * var[k + 3], kBBoxClipDefault)) * h;
d_w = exp(Min(dxmax * var[k + 2], bbox_clip_default)) * w;
d_h = exp(Min(dymax * var[k + 3], bbox_clip_default)) * h;
} else {
d_cx = cx + dxmin * w;
d_cy = cy + dymin * h;
d_w = exp(Min<T>(dxmax, kBBoxClipDefault)) * w;
d_h = exp(Min<T>(dymax, kBBoxClipDefault)) * h;
d_w = exp(Min(dxmax, bbox_clip_default)) * w;
d_h = exp(Min(dymax, bbox_clip_default)) * h;
}
T oxmin = d_cx - d_w * 0.5;
......@@ -126,17 +136,21 @@ __global__ void BoxDecodeAndClipKernel(const T *anchor, const T *deltas,
T oxmax = d_cx + d_w * 0.5 - 1.;
T oymax = d_cy + d_h * 0.5 - 1.;
proposals[i * 4] = Max<T>(Min<T>(oxmin, im_info[1] - 1.), 0.);
proposals[i * 4 + 1] = Max<T>(Min<T>(oymin, im_info[0] - 1.), 0.);
proposals[i * 4 + 2] = Max<T>(Min<T>(oxmax, im_info[1] - 1.), 0.);
proposals[i * 4 + 3] = Max<T>(Min<T>(oymax, im_info[0] - 1.), 0.);
proposals[i * 4] = Max(Min(oxmin, im_info[1] - 1.), 0.);
proposals[i * 4 + 1] = Max(Min(oymin, im_info[0] - 1.), 0.);
proposals[i * 4 + 2] = Max(Min(oxmax, im_info[1] - 1.), 0.);
proposals[i * 4 + 3] = Max(Min(oymax, im_info[0] - 1.), 0.);
}
}
__device__ __forceinline__ T Min(T a, T b) const { return a > b ? b : a; }
__device__ __forceinline__ T Max(T a, T b) const { return a > b ? a : b; }
};
template <typename T, int BlockSize>
__global__ void FilterBBoxes(const T *bboxes, const T *im_info,
const T min_size, const int num, int *keep_num,
int *keep) {
static __global__ void FilterBBoxes(const T *bboxes, const T *im_info,
const T min_size, const int num,
int *keep_num, int *keep) {
T im_h = im_info[0];
T im_w = im_info[1];
T im_scale = im_info[2];
......@@ -181,7 +195,7 @@ __global__ void FilterBBoxes(const T *bboxes, const T *im_info,
}
}
__device__ inline float IoU(const float *a, const float *b) {
static __device__ inline float IoU(const float *a, const float *b) {
float left = max(a[0], b[0]), right = min(a[2], b[2]);
float top = max(a[1], b[1]), bottom = min(a[3], b[3]);
float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);
......@@ -191,8 +205,9 @@ __device__ inline float IoU(const float *a, const float *b) {
return inter_s / (s_a + s_b - inter_s);
}
__global__ void NMSKernel(const int n_boxes, const float nms_overlap_thresh,
const float *dev_boxes, uint64_t *dev_mask) {
static __global__ void NMSKernel(const int n_boxes,
const float nms_overlap_thresh,
const float *dev_boxes, uint64_t *dev_mask) {
const int row_start = blockIdx.y;
const int col_start = blockIdx.x;
......@@ -234,9 +249,9 @@ __global__ void NMSKernel(const int n_boxes, const float nms_overlap_thresh,
}
template <typename T>
void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const Tensor &sorted_indices, const T nms_threshold,
Tensor *keep_out) {
static void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const Tensor &sorted_indices, const T nms_threshold,
Tensor *keep_out) {
int boxes_num = proposals.dims()[0];
PADDLE_ENFORCE_EQ(boxes_num, sorted_indices.dims()[0]);
......@@ -247,13 +262,10 @@ void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
const T *boxes = proposals.data<T>();
auto place = boost::get<platform::CUDAPlace>(ctx.GetPlace());
int size_bytes = boxes_num * col_blocks * sizeof(uint64_t);
uint64_t *d_mask =
reinterpret_cast<uint64_t *>(memory::Alloc(place, size_bytes));
NMSKernel<<<blocks, threads>>>(boxes_num, nms_threshold, boxes, d_mask);
uint64_t *h_mask = reinterpret_cast<uint64_t *>(
memory::Alloc(platform::CPUPlace(), size_bytes));
memory::Copy(platform::CPUPlace(), h_mask, place, d_mask, size_bytes, 0);
framework::Vector<uint64_t> mask(boxes_num * col_blocks);
NMSKernel<<<blocks, threads>>>(
boxes_num, nms_threshold, boxes,
mask.CUDAMutableData(boost::get<platform::CUDAPlace>(ctx.GetPlace())));
std::vector<uint64_t> remv(col_blocks);
memset(&remv[0], 0, sizeof(uint64_t) * col_blocks);
......@@ -267,7 +279,7 @@ void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
if (!(remv[nblock] & (1ULL << inblock))) {
++num_to_keep;
keep_vec.push_back(i);
uint64_t *p = &h_mask[0] + i * col_blocks;
uint64_t *p = &mask[0] + i * col_blocks;
for (int j = nblock; j < col_blocks; j++) {
remv[j] |= p[j];
}
......@@ -276,12 +288,10 @@ void NMS(const platform::CUDADeviceContext &ctx, const Tensor &proposals,
int *keep = keep_out->mutable_data<int>({num_to_keep}, ctx.GetPlace());
memory::Copy(place, keep, platform::CPUPlace(), keep_vec.data(),
sizeof(int) * num_to_keep, 0);
memory::Free(place, d_mask);
memory::Free(platform::CPUPlace(), h_mask);
}
template <typename T>
std::pair<Tensor, Tensor> ProposalForOneImage(
static std::pair<Tensor, Tensor> ProposalForOneImage(
const platform::CUDADeviceContext &ctx, const Tensor &im_info,
const Tensor &anchors, const Tensor &variances,
const Tensor &bbox_deltas, // [M, 4]
......@@ -300,18 +310,20 @@ std::pair<Tensor, Tensor> ProposalForOneImage(
// 2. box decode and clipping
Tensor proposals;
proposals.mutable_data<T>({pre_nms_num, 4}, ctx.GetPlace());
int block = 512;
auto stream = ctx.stream();
BoxDecodeAndClipKernel<T><<<DIVUP(pre_nms_num, block), block, 0, stream>>>(
anchors.data<T>(), bbox_deltas.data<T>(), variances.data<T>(),
index_sort.data<int>(), im_info.data<T>(), pre_nms_num,
proposals.data<T>());
{
platform::ForRange<platform::CUDADeviceContext> for_range(ctx, pre_nms_num);
for_range(BoxDecodeAndClipFunctor<T>{
anchors.data<T>(), bbox_deltas.data<T>(), variances.data<T>(),
index_sort.data<int>(), im_info.data<T>(), proposals.data<T>()});
}
// 3. filter
Tensor keep_index, keep_num_t;
keep_index.mutable_data<int>({pre_nms_num}, ctx.GetPlace());
keep_num_t.mutable_data<int>({1}, ctx.GetPlace());
min_size = std::max(min_size, 1.0f);
auto stream = ctx.stream();
FilterBBoxes<T, 512><<<1, 512, 0, stream>>>(
proposals.data<T>(), im_info.data<T>(), min_size, pre_nms_num,
keep_num_t.data<int>(), keep_index.data<int>());
......@@ -355,8 +367,12 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
auto *scores = context.Input<Tensor>("Scores");
auto *bbox_deltas = context.Input<Tensor>("BboxDeltas");
auto *im_info = context.Input<Tensor>("ImInfo");
auto *anchors = context.Input<Tensor>("Anchors");
auto *variances = context.Input<Tensor>("Variances");
auto anchors = detail::Ref(context.Input<Tensor>("Anchors"),
"Cannot find input Anchors(%s) in scope",
context.Inputs("Anchors")[0]);
auto variances = detail::Ref(context.Input<Tensor>("Variances"),
"Cannot find input Variances(%s) in scope",
context.Inputs("Variances")[0]);
auto *rpn_rois = context.Output<LoDTensor>("RpnRois");
auto *rpn_roi_probs = context.Output<LoDTensor>("RpnRoiProbs");
......@@ -392,10 +408,8 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis);
trans(dev_ctx, *scores, &scores_swap, axis);
Tensor *anchor = const_cast<framework::Tensor *>(anchors);
anchor->Resize({anchors->numel() / 4, 4});
Tensor *var = const_cast<framework::Tensor *>(variances);
var->Resize({var->numel() / 4, 4});
anchors.Resize({anchors.numel() / 4, 4});
variances.Resize({variances.numel() / 4, 4});
rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
context.GetPlace());
......@@ -417,12 +431,12 @@ class CUDAGenerateProposalsKernel : public framework::OpKernel<T> {
scores_slice.Resize({h_score * w_score * c_score, 1});
std::pair<Tensor, Tensor> box_score_pair =
ProposalForOneImage<T>(dev_ctx, im_info_slice, *anchor, *var,
ProposalForOneImage<T>(dev_ctx, im_info_slice, anchors, variances,
bbox_deltas_slice, scores_slice, pre_nms_top_n,
post_nms_top_n, nms_thresh, min_size, eta);
Tensor proposals = box_score_pair.first;
Tensor scores = box_score_pair.second;
Tensor &proposals = box_score_pair.first;
Tensor &scores = box_score_pair.second;
memory::Copy(place, rpn_rois_data + num_proposals * 4, place,
proposals.data<T>(), sizeof(T) * proposals.numel(), 0);
......
paddle/fluid/operators/detection/gpc.cc 0 → 100644
浏览文件 @ 6253b152
此差异已折叠。
paddle/fluid/operators/detection/gpc.h 0 → 100644
浏览文件 @ 6253b152
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// 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.
/***************************************************************************
*
* Copyright (c) 2015 Baidu.com, Inc. All Rights Reserved
*
**************************************************************************/
/**
* @file include/gpc.h
* @author huhan02(com@baidu.com)
* @date 2015/12/18 13:52:10
* @brief
*
* @modified by sunyipeng
* @email sunyipeng@baidu.com
* @date 2018/6/12
**/
#ifndef PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_ // GPC_H_
#define PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_ // GPC_H_
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
namespace gpc {
typedef enum { // Set operation type
GPC_DIFF, // Difference
GPC_INT, // Intersection
GPC_XOR, // Exclusive or
GPC_UNION // Union
} gpc_op;
typedef struct { // Polygon vertex structure
double x; // Vertex x component
double y; // vertex y component
} gpc_vertex;
typedef struct { // Vertex list structure
int num_vertices; // Number of vertices in list
gpc_vertex *vertex; // Vertex array pointer
} gpc_vertex_list;
typedef struct { // Polygon set structure
int num_contours; // Number of contours in polygon
int *hole; // Hole external contour flags
gpc_vertex_list *contour; // Contour array pointer
} gpc_polygon;
typedef struct { // Tristrip set structure
int num_strips; // Number of tristrips
gpc_vertex_list *strip; // Tristrip array pointer
} gpc_tristrip;
typedef enum { LEFT, RIGHT } gpc_left_right;
typedef enum { ABOVE, BELOW } gpc_above_below;
typedef enum { CLIP, SUBJ } gpc_clip_subj;
typedef enum { /* Edge intersection classes */
NUL, /* Empty non-intersection */
EMX, /* External maximum */
ELI, /* External left intermediate */
TED, /* Top edge */
ERI, /* External right intermediate */
RED, /* Right edge */
IMM, /* Internal maximum and minimum */
IMN, /* Internal minimum */
EMN, /* External minimum */
EMM, /* External maximum and minimum */
LED, /* Left edge */
ILI, /* Internal left intermediate */
BED, /* Bottom edge */
IRI, /* Internal right intermediate */
IMX, /* Internal maximum */
FUL /* Full non-intersection */
} vertex_type;
typedef enum { /* Horizontal edge states */
NH, /* No horizontal edge */
BH, /* Bottom horizontal edge */
TH /* Top horizontal edge */
} h_state;
typedef enum { /* Edge bundle state */
UNBUNDLED, /* Isolated edge not within a bundle */
BUNDLE_HEAD, /* Bundle head node */
BUNDLE_TAIL /* Passive bundle tail node */
} bundle_state;
typedef struct v_shape { /* Internal vertex list datatype */
double x; /* X coordinate component */
double y; /* Y coordinate component */
struct v_shape *next; /* Pointer to next vertex in list */
} vertex_node;
typedef struct p_shape { /* Internal contour / tristrip type */
int active; /* Active flag / vertex count */
int hole; /* Hole / external contour flag */
vertex_node *v[2]; /* Left and right vertex list ptrs */
struct p_shape *next; /* Pointer to next polygon contour */
struct p_shape *proxy; /* Pointer to actual structure used */
} polygon_node;
typedef struct edge_shape {
gpc_vertex vertex; /* Piggy-backed contour vertex data */
gpc_vertex bot; /* Edge lower (x, y) coordinate */
gpc_vertex top; /* Edge upper (x, y) coordinate */
double xb; /* Scanbeam bottom x coordinate */
double xt; /* Scanbeam top x coordinate */
double dx; /* Change in x for a unit y increase */
int type; /* Clip / subject edge flag */
int bundle[2][2]; /* Bundle edge flags */
int bside[2]; /* Bundle left / right indicators */
bundle_state bstate[2]; /* Edge bundle state */
polygon_node *outp[2]; /* Output polygon / tristrip pointer */
struct edge_shape *prev; /* Previous edge in the AET */
struct edge_shape *next; /* Next edge in the AET */
struct edge_shape *pred; /* Edge connected at the lower end */
struct edge_shape *succ; /* Edge connected at the upper end */
struct edge_shape *next_bound; /* Pointer to next bound in LMT */
} edge_node;
inline bool gpc_eq(float a, float b) { return (fabs(a - b) <= 1e-6); }
inline bool gpc_prev_index(float a, float b) { return (fabs(a - b) <= 1e-6); }
inline int gpc_prev_index(int i, int n) { return ((i - 1 + n) % n); }
inline int gpc_next_index(int i, int n) { return ((i + 1) % n); }
inline int gpc_optimal(gpc_vertex *v, int i, int n) {
return (v[(i + 1) % n].y != v[i].y || v[(i - 1 + n) % n].y != v[i].y);
}
inline int gpc_fwd_min(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y > v[i].vertex.y &&
v[(i - 1 + n) % n].vertex.y >= v[i].vertex.y);
}
inline int gpc_not_fmax(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y > v[i].vertex.y);
}
inline int gpc_rev_min(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y >= v[i].vertex.y &&
v[(i - 1 + n) % n].vertex.y > v[i].vertex.y);
}
inline int gpc_not_rmax(edge_node *v, int i, int n) {
return (v[(i - 1 + n) % n].vertex.y > v[i].vertex.y);
}
// inline void gpc_p_edge(edge_node *d, edge_node *e, int p, double i, double j)
// {
inline void gpc_p_edge(edge_node *d, edge_node *e, int p) {
d = e;
do {
d = d->prev;
} while (!d->outp[p]);
// i = d->bot.x + d->dx * (j - d->bot.y);
}
// inline void gpc_n_edge(edge_node *d, edge_node *e, int p, double i, double j)
// {
inline void gpc_n_edge(edge_node *d, edge_node *e, int p) {
d = e;
do {
d = d->next;
} while (!d->outp[p]);
// i = d->bot.x + d->dx * (j - d->bot.y);
}
template <typename T>
void gpc_malloc(T *&p, int b, char *s) {
if (b > 0) {
p = (T *)malloc(b);
if (!p) {
fprintf(stderr, "gpc malloc failure: %s\n", s);
exit(0);
}
} else {
p = NULL;
}
}
template <typename T>
void gpc_free(T *&p) {
if (p) {
free(p);
p = NULL;
}
}
/*
===========================================================================
Public Function Prototypes
===========================================================================
*/
void add_vertex(vertex_node **t, double x, double y);
void gpc_vertex_create(edge_node *e, int p, int s, double x, double y);
/*
void gpc_read_polygon(FILE *infile_ptr, int read_hole_flags,
gpc_polygon *polygon);
void gpc_write_polygon(FILE *outfile_ptr, int write_hole_flags,
gpc_polygon *polygon);
*/
void gpc_add_contour(gpc_polygon *polygon, gpc_vertex_list *contour, int hole);
void gpc_polygon_clip(gpc_op set_operation, gpc_polygon *subject_polygon,
gpc_polygon *clip_polygon, gpc_polygon *result_polygon);
void gpc_tristrip_clip(gpc_op set_operation, gpc_polygon *subject_polygon,
gpc_polygon *clip_polygon,
gpc_tristrip *result_tristrip);
void gpc_polygon_to_tristrip(gpc_polygon *polygon, gpc_tristrip *tristrip);
void gpc_free_polygon(gpc_polygon *polygon);
void gpc_free_tristrip(gpc_tristrip *tristrip);
} // namespace gpc
#endif // PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_
/* vim: set expandtab ts=4 sw=4 sts=4 tw=100: */
paddle/fluid/operators/detection/multiclass_nms_op.cc
浏览文件 @ 6253b152
......@@ -9,10 +9,11 @@ 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/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/poly_util.h"
namespace paddle {
namespace operators {
......@@ -20,9 +21,6 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
constexpr int64_t kOutputDim = 6;
constexpr int64_t kBBoxSize = 4;
class MultiClassNMSOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -42,10 +40,15 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
"The rank of Input(BBoxes) must be 3.");
PADDLE_ENFORCE_EQ(score_dims.size(), 3,
"The rank of Input(Scores) must be 3.");
PADDLE_ENFORCE_EQ(box_dims[2], 4,
"The 2nd dimension of Input(BBoxes) must be 4, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax]");
PADDLE_ENFORCE(box_dims[2] == 4 || box_dims[2] == 8 || box_dims[2] == 16 ||
box_dims[2] == 24 || box_dims[2] == 32,
"The 2nd dimension of Input(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16");
PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2],
"The 1st dimensiong of Input(BBoxes) must be equal to "
"3rd dimension of Input(Scores), which represents the "
......@@ -53,7 +56,7 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
ctx->SetOutputDim("Out", {box_dims[1], 6});
ctx->SetOutputDim("Out", {box_dims[1], box_dims[2] + 2});
}
protected:
......@@ -128,6 +131,21 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
}
}
template <class T>
T PolyIoU(const T* box1, const T* box2, const size_t box_size,
const bool normalized) {
T bbox1_area = PolyArea<T>(box1, box_size, normalized);
T bbox2_area = PolyArea<T>(box2, box_size, normalized);
T inter_area = PolyOverlapArea<T>(box1, box2, box_size, normalized);
if (bbox1_area == 0 || bbox2_area == 0 || inter_area == 0) {
// If coordinate values are is invalid
// if area size <= 0, return 0.
return T(0.);
} else {
return inter_area / (bbox1_area + bbox2_area - inter_area);
}
}
template <typename T>
class MultiClassNMSKernel : public framework::OpKernel<T> {
public:
......@@ -137,6 +155,8 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
// The total boxes for each instance.
int64_t num_boxes = bbox.dims()[0];
// 4: [xmin ymin xmax ymax]
// 8: [x1 y1 x2 y2 x3 y3 x4 y4]
// 16, 24, or 32: [x1 y1 x2 y2 ... xn yn], n = 8, 12 or 16
int64_t box_size = bbox.dims()[1];
std::vector<T> scores_data(num_boxes);
......@@ -154,8 +174,19 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
for (size_t k = 0; k < selected_indices->size(); ++k) {
if (keep) {
const int kept_idx = (*selected_indices)[k];
T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
T overlap = T(0.);
// 4: [xmin ymin xmax ymax]
if (box_size == 4) {
overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
bbox_data + kept_idx * box_size, true);
}
// 8: [x1 y1 x2 y2 x3 y3 x4 y4] or 16, 24, 32
if (box_size == 8 || box_size == 16 || box_size == 24 ||
box_size == 32) {
overlap =
PolyIoU<T>(bbox_data + idx * box_size,
bbox_data + kept_idx * box_size, box_size, true);
}
keep = overlap <= adaptive_threshold;
} else {
break;
......@@ -228,7 +259,9 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
void MultiClassOutput(const Tensor& scores, const Tensor& bboxes,
const std::map<int, std::vector<int>>& selected_indices,
Tensor* outs) const {
int predict_dim = scores.dims()[1];
int64_t predict_dim = scores.dims()[1];
int64_t box_size = bboxes.dims()[1];
int64_t out_dim = bboxes.dims()[1] + 2;
auto* scores_data = scores.data<T>();
auto* bboxes_data = bboxes.data<T>();
auto* odata = outs->data<T>();
......@@ -240,11 +273,11 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
const std::vector<int>& indices = it.second;
for (size_t j = 0; j < indices.size(); ++j) {
int idx = indices[j];
const T* bdata = bboxes_data + idx * kBBoxSize;
odata[count * kOutputDim] = label; // label
odata[count * kOutputDim + 1] = sdata[idx]; // score
// xmin, ymin, xmax, ymax
std::memcpy(odata + count * kOutputDim + 2, bdata, 4 * sizeof(T));
const T* bdata = bboxes_data + idx * box_size;
odata[count * out_dim] = label; // label
odata[count * out_dim + 1] = sdata[idx]; // score
// xmin, ymin, xmax, ymax or multi-points coordinates
std::memcpy(odata + count * out_dim + 2, bdata, box_size * sizeof(T));
count++;
}
}
......@@ -261,6 +294,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
int64_t class_num = score_dims[1];
int64_t predict_dim = score_dims[2];
int64_t box_dim = boxes->dims()[2];
int64_t out_dim = boxes->dims()[2] + 2;
std::vector<std::map<int, std::vector<int>>> all_indices;
std::vector<size_t> batch_starts = {0};
......@@ -283,7 +317,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
T* od = outs->mutable_data<T>({1}, ctx.GetPlace());
od[0] = -1;
} else {
outs->mutable_data<T>({num_kept, kOutputDim}, ctx.GetPlace());
outs->mutable_data<T>({num_kept, out_dim}, ctx.GetPlace());
for (int64_t i = 0; i < batch_size; ++i) {
Tensor ins_score = scores->Slice(i, i + 1);
ins_score.Resize({class_num, predict_dim});
......@@ -311,10 +345,11 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("BBoxes",
"(Tensor) A 3-D Tensor with shape [N, M, 4] represents the "
"(Tensor) A 3-D Tensor with shape "
"[N, M, 4 or 8 16 24 32] represents the "
"predicted locations of M bounding bboxes, N is the batch size. "
"Each bounding box has four coordinate values and the layout is "
"[xmin, ymin, xmax, ymax].");
"[xmin, ymin, xmax, ymax], when box size equals to 4.");
AddInput("Scores",
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"predicted confidence predictions. N is the batch size, C is the "
......@@ -351,8 +386,12 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out",
"(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], No is the total "
"number of detections in this mini-batch. For each instance, "
"[label, confidence, xmin, ymin, xmax, ymax] or "
"(LoDTensor) A 2-D LoDTensor with shape [No, 10] represents the "
"detections. Each row has 10 values: "
"[label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the "
"total number of detections in this mini-batch."
"For each instance, "
"the offsets in first dimension are called LoD, the number of "
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected bbox.");
......
paddle/fluid/operators/detection/poly_util.cc 0 → 100644
浏览文件 @ 6253b152
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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. */
#ifndef POLY_UTIL_CC_
#define POLY_UTIL_CC_
#include "paddle/fluid/operators/detection/poly_util.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using gpc::gpc_polygon_clip;
using gpc::gpc_free_polygon;
template <class T>
void Array2PointVec(const T*& box, const size_t box_size,
std::vector<Point_<T>>& vec) {
size_t pts_num = box_size / 2;
vec.resize(pts_num);
for (size_t i = 0; i < pts_num; i++) {
vec.at(i).x = box[2 * i];
vec.at(i).y = box[2 * i + 1];
}
}
template <class T>
void Array2Poly(const T*& box, const size_t box_size, gpc::gpc_polygon& poly) {
size_t pts_num = box_size / 2;
poly.num_contours = 1;
poly.hole = (int*)malloc(sizeof(int));
poly.hole[0] = 0;
poly.contour = (gpc::gpc_vertex_list*)malloc(sizeof(gpc::gpc_vertex_list));
poly.contour->num_vertices = pts_num;
poly.contour->vertex =
(gpc::gpc_vertex*)malloc(sizeof(gpc::gpc_vertex) * pts_num);
for (size_t i = 0; i < pts_num; ++i) {
poly.contour->vertex[i].x = box[2 * i];
poly.contour->vertex[i].y = box[2 * i + 1];
}
}
template <class T>
void PointVec2Poly(const std::vector<Point_<T>>& vec, gpc::gpc_polygon& poly) {
int pts_num = vec.size();
poly.num_contours = 1;
poly.hole = (int*)malloc(sizeof(int));
poly.hole[0] = 0;
poly.contour = (gpc::gpc_vertex_list*)malloc(sizeof(gpc::gpc_vertex_list));
poly.contour->num_vertices = pts_num;
poly.contour->vertex =
(gpc::gpc_vertex*)malloc(sizeof(gpc::gpc_vertex) * pts_num);
for (size_t i = 0; i < pts_num; ++i) {
poly.contour->vertex[i].x = vec[i].x;
poly.contour->vertex[i].y = vec[i].y;
}
}
template <class T>
void Poly2PointVec(const gpc::gpc_vertex_list& contour,
std::vector<Point_<T>>& vec) {
int pts_num = contour.num_vertices;
vec.resize(pts_num);
for (int i = 0; i < pts_num; i++) {
vec.at(i).x = contour.vertex[i].x;
vec.at(i).y = contour.vertex[i].y;
}
}
template <class T>
T GetContourArea(std::vector<Point_<T>>& vec) {
size_t pts_num = vec.size();
if (pts_num < 3) return T(0.);
T area = T(0.);
for (size_t i = 0; i < pts_num; ++i) {
area += vec[i].x * vec[(i + 1) % pts_num].y -
vec[i].y * vec[(i + 1) % pts_num].x;
}
return std::fabs(area / 2.0);
}
template <class T>
T PolyArea(const T* box, const size_t box_size, const bool normalized) {
// If coordinate values are is invalid
// if area size <= 0, return 0.
std::vector<Point_<T>> vec;
Array2PointVec<T>(box, box_size, vec);
return GetContourArea<T>(vec);
}
template <class T>
T PolyOverlapArea(const T* box1, const T* box2, const size_t box_size,
const bool normalized) {
gpc::gpc_polygon poly1;
gpc::gpc_polygon poly2;
Array2Poly<T>(box1, box_size, poly1);
Array2Poly<T>(box2, box_size, poly2);
gpc::gpc_polygon respoly;
gpc::gpc_op op = gpc::GPC_INT;
gpc::gpc_polygon_clip(op, &poly2, &poly1, &respoly);
T inter_area = T(0.);
int contour_num = respoly.num_contours;
for (int i = 0; i < contour_num; ++i) {
std::vector<Point_<T>> resvec;
Poly2PointVec<T>(respoly.contour[i], resvec);
// inter_area += std::fabs(cv::contourArea(resvec)) + 0.5f *
// (cv::arcLength(resvec, true));
inter_area += GetContourArea<T>(resvec);
}
gpc::gpc_free_polygon(&poly1);
gpc::gpc_free_polygon(&poly2);
gpc::gpc_free_polygon(&respoly);
return inter_area;
}
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/detection/poly_util.h 0 → 100644
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paddle/fluid/operators/detection/polygon_box_transform_op.cc
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......@@ -41,9 +41,9 @@ class PolygonBoxTransformCPUKernel : public framework::OpKernel<T> {
for (int id_w = 0; id_w < width; ++id_w) {
id = id_n * height * width + width * id_h + id_w;
if (id_n % 2 == 0) {
out_data[id] = id_w - in_data[id];
out_data[id] = id_w * 4 - in_data[id];
} else {
out_data[id] = id_h - in_data[id];
out_data[id] = id_h * 4 - in_data[id];
}
}
}
......
paddle/fluid/operators/detection/polygon_box_transform_op.cu
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......@@ -32,9 +32,9 @@ __global__ void PolygonBoxTransformKernel(const int n, const int h, const int w,
if (id_n < n && id_h < h && id_w < w) {
int id = id_n * h * w + w * id_h + id_w;
if (id_n % 2 == 0) {
output[id] = id_w - input[id];
output[id] = id_w * 4 - input[id];
} else {
output[id] = id_h - input[id];
output[id] = id_h * 4 - input[id];
}
}
}
......
paddle/fluid/operators/distributed/CMakeLists.txt
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......@@ -20,7 +20,7 @@ if(WITH_GRPC)
DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc scope profiler math_function SERIAL)
cc_test(rpc_server_test SRCS rpc_server_test.cc
DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_sparse_table_op SERIAL)
cc_test(varhandle_test SRCS varhandle_test.cc)
cc_test(varhandle_test SRCS varhandle_test.cc DEPS profiler)
return()
endif()
......
paddle/fluid/operators/distributed/grpc_serde.cc
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......@@ -36,6 +36,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg,
const std::string& out_name) {
platform::RecordRPCEvent record_event("serial", &ctx);
// Default DestroyCallback does nothing, When using GPU
// the CPU buffer need to be freed.
DestroyCallback destroy_callback = [](void* backing) {};
......@@ -147,6 +148,7 @@ void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
const framework::Scope* scope,
framework::Variable** var) {
platform::RecordRPCEvent record_event("deserial", &ctx);
operators::distributed::GRPCVariableResponse resp(scope, &ctx);
PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!");
*var = resp.GetVar();
......
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paddle/fluid/operators/gather.h
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