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提交 0f254465 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'ups/develop' into fea/jit/rnn

上级 35620513 d93349a1
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AUTHORS.md
浏览文件 @ 0f254465
......@@ -25,6 +25,7 @@
| kexinzhao | Ke-Xin Zhao |
| kuke | Yi-Bing Liu |
| lcy-seso | Ying Cao |
| cjld | Dun Liang |
| lipeng-unisound | Peng Li |
| liuyuan | Yuan Liu |
| livc | Zhao Li |
......
CMakeLists.txt
浏览文件 @ 0f254465
......@@ -130,6 +130,21 @@ if (APPLE OR WIN32)
"Disable MKL for building on mac and windows" FORCE)
endif()
if (WIN32)
set(WITH_AVX OFF CACHE STRING
"Disable AVX when compiling for Windows" FORCE)
set(WITH_DSO OFF CACHE STRING
"Disable DSO when compiling for Windows" FORCE)
set(WITH_MKL OFF CACHE STRING
"Disable MKL when compiling for Windows" FORCE)
set(WITH_DISTRIBUTE OFF CACHE STRING
"Disable DISTRIBUTE when compiling for Windows" FORCE)
set(WITH_C_API OFF CACHE STRING
"Disable C_API when compiling for Windows" FORCE)
set(WITH_FLUID_ONLY ON CACHE STRING
"Enable FLUID_ONLY when compiling for Windows" FORCE)
endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
......@@ -190,11 +205,11 @@ include(external/pybind11) # download pybind11
include(external/cares)
include(external/cub)
include(external/xxhash) # download xxhash
if (NOT WIN32)
# there is no official support of snappystream, warpctc, nccl, cupti in windows
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
if (NOT WIN32)
# there is no official support of warpctc, nccl, cupti in windows
include(external/warpctc) # download, build, install warpctc
include(cupti)
endif (NOT WIN32)
......
cmake/external/gtest.cmake
浏览文件 @ 0f254465
......@@ -50,7 +50,11 @@ IF(WITH_TESTING)
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR}
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DBUILD_GMOCK=ON
......
cmake/external/snappy.cmake
浏览文件 @ 0f254465
......@@ -24,7 +24,11 @@ set(SNAPPY_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy)
set(SNAPPY_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy)
set(SNAPPY_INCLUDE_DIR "${SNAPPY_INSTALL_DIR}/include" CACHE PATH "snappy include directory." FORCE)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
if (WIN32)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/snappy.lib")
else(WIN32)
set(SNAPPY_LIBRARIES "${SNAPPY_INSTALL_DIR}/lib/libsnappy.a")
endif (WIN32)
ExternalProject_Add(
extern_snappy
......@@ -34,8 +38,12 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=${SNAPPY_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
......
cmake/external/snappystream.cmake
浏览文件 @ 0f254465
......@@ -18,15 +18,19 @@ ENDIF()
include (ExternalProject)
# NOTE: snappy is needed when linking with recordio
set(SNAPPYSTREAM_SOURCES_DIR ${THIRD_PARTY_PATH}/snappy_stream)
set(SNAPPYSTREAM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/snappy_stream)
set(SNAPPYSTREAM_INCLUDE_DIR "${SNAPPYSTREAM_INSTALL_DIR}/include" CACHE PATH "snappy stream include directory." FORCE)
set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
if(WIN32)
# Fix me, VS2015 come without VLA support
set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/snappystream.lib")
MESSAGE(WARNING, "In windows, snappystream has no compile support for windows,
please build it manually and put it at " ${SNAPPYSTREAM_INSTALL_DIR})
else(WIN32)
set(SNAPPYSTREAM_LIBRARIES "${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
ExternalProject_Add(
ExternalProject_Add(
extern_snappystream
GIT_REPOSITORY "https://github.com/hoxnox/snappystream.git"
GIT_TAG "0.2.8"
......@@ -34,8 +38,12 @@ ExternalProject_Add(
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${SNAPPY_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=${SNAPPY_INSTALL_DIR}/lib
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
......@@ -47,7 +55,8 @@ ExternalProject_Add(
-DCMAKE_INSTALL_LIBDIR:PATH=${SNAPPYSTREAM_INSTALL_DIR}/lib
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
DEPENDS snappy
)
)
endif(WIN32)
add_library(snappystream STATIC IMPORTED GLOBAL)
set_property(TARGET snappystream PROPERTY IMPORTED_LOCATION ${SNAPPYSTREAM_LIBRARIES})
......
cmake/generic.cmake
浏览文件 @ 0f254465
......@@ -351,6 +351,9 @@ function(cc_test TARGET_NAME)
cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_executable(${TARGET_NAME} ${cc_test_SRCS})
target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
if(WIN32)
target_link_libraries(${TARGET_NAME} shlwapi)
endif(WIN32)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main lod_tensor memory gtest gflags glog)
add_test(NAME ${TARGET_NAME}
COMMAND ${TARGET_NAME} ${cc_test_ARGS}
......
cmake/operators.cmake
浏览文件 @ 0f254465
......@@ -84,9 +84,7 @@ function(op_library TARGET)
endif()
if (WIN32)
# remove windows unsupported op, because windows has no nccl, no warpctc such ops.
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op" "hierarchical_sigmoid_op"
"crf_decoding_op" "select_op" "lstmp_op" "gru_op" "fusion_gru_op" "lstm_op" "fusion_lstm_op" "cumsum_op"
"fusion_seqconv_eltadd_relu_op" "channel_send_op" "channel_create_op" "channel_close_op" "channel_recv_op")
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......
cmake/simd.cmake
浏览文件 @ 0f254465
......@@ -57,43 +57,46 @@ int main()
return 0;
}" SSE3_FOUND)
# Check AVX
set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
# disable AVX by default on windows
if(NOT WIN32)
# Check AVX
set(CMAKE_REQUIRED_FLAGS ${AVX_FLAG})
set(AVX_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256 a = _mm256_set_ps (-1.0f, 2.0f, -3.0f, 4.0f, -1.0f, 2.0f, -3.0f, 4.0f);
__m256 b = _mm256_set_ps (1.0f, 2.0f, 3.0f, 4.0f, 1.0f, 2.0f, 3.0f, 4.0f);
__m256 result = _mm256_add_ps (a, b);
return 0;
}" AVX_FOUND)
}" AVX_FOUND)
# Check AVX 2
set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
# Check AVX 2
set(CMAKE_REQUIRED_FLAGS ${AVX2_FLAG})
set(AVX2_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
__m256i result = _mm256_abs_epi32 (a);
return 0;
}" AVX2_FOUND)
}" AVX2_FOUND)
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
# Check AVX512F
set(CMAKE_REQUIRED_FLAGS ${AVX512F_FLAG})
set(AVX512F_FOUND_EXITCODE 1 CACHE STRING "Result from TRY_RUN" FORCE)
CHECK_CXX_SOURCE_RUNS("
#include <immintrin.h>
int main()
{
__m512i a = _mm512_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4,
13, -5, 6, -7, 9, 2, -6, 3);
__m512i result = _mm512_abs_epi32 (a);
return 0;
}" AVX512F_FOUND)
}" AVX512F_FOUND)
endif(NOT WIN32)
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_RETAINED})
mark_as_advanced(MMX_FOUND SSE2_FOUND SSE3_FOUND AVX_FOUND AVX2_FOUND AVX512F_FOUND)
paddle/fluid/API.spec
浏览文件 @ 0f254465
......@@ -103,6 +103,7 @@ paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 's
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)
paddle.fluid.layers.layer_norm ArgSpec(args=['input', 'scale', 'shift', 'begin_norm_axis', 'epsilon', 'param_attr', 'bias_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(True, True, 1, 1e-05, None, None, None, None))
paddle.fluid.layers.group_norm ArgSpec(args=['input', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW', None))
paddle.fluid.layers.softmax_with_cross_entropy ArgSpec(args=['logits', 'label', 'soft_label', 'ignore_index', 'numeric_stable_mode', 'return_softmax'], varargs=None, keywords=None, defaults=(False, -100, False, False))
paddle.fluid.layers.smooth_l1 ArgSpec(args=['x', 'y', 'inside_weight', 'outside_weight', 'sigma'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.one_hot ArgSpec(args=['input', 'depth'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -3,13 +3,9 @@ add_subdirectory(platform)
add_subdirectory(framework)
add_subdirectory(operators)
add_subdirectory(string)
add_subdirectory(pybind)
if (NOT WIN32)
add_subdirectory(recordio)
endif(NOT WIN32)
add_subdirectory(pybind)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
add_subdirectory(train)
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -31,9 +31,7 @@ function(windows_symbolic TARGET)
endfunction()
add_subdirectory(ir)
if (NOT WIN32)
add_subdirectory(details)
endif (NOT WIN32)
# ddim lib
proto_library(framework_proto SRCS framework.proto)
......@@ -68,11 +66,7 @@ if(WITH_GPU)
else()
cc_test(mixed_vector_test SRCS mixed_vector_test.cc DEPS place memory device_context tensor)
endif()
if (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
else()
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto version)
endif (NOT WIN32)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio version)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
......@@ -122,13 +116,8 @@ cc_test(op_proto_maker_test SRCS op_proto_maker_test.cc DEPS op_proto_maker)
cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto)
cc_library(shape_inference SRCS shape_inference.cc DEPS ddim attribute device_context)
if (NOT WIN32)
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor profiler)
else()
cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope glog
shape_inference data_transform lod_tensor)
endif(NOT WIN32)
cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry device_context)
......@@ -183,12 +172,10 @@ else()
cc_test(test_naive_executor SRCS naive_executor_test.cc DEPS naive_executor elementwise_add_op)
endif()
if (NOT WIN32)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS
threaded_ssa_graph_executor scope_buffered_ssa_graph_executor
graph build_strategy
fast_threaded_ssa_graph_executor)
endif() # NOT WIN32
cc_library(prune SRCS prune.cc DEPS framework_proto)
cc_test(prune_test SRCS prune_test.cc DEPS op_info prune recurrent_op device_context)
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
浏览文件 @ 0f254465
......@@ -13,9 +13,9 @@
// limitations under the License.
#pragma once
#include <ThreadPool.h>
#include <string>
#include <vector>
#include "ThreadPool.h"
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
#include "paddle/fluid/framework/details/execution_strategy.h"
......
paddle/fluid/framework/eigen.h
浏览文件 @ 0f254465
......@@ -13,11 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
// logging.h and windows.h conflict
#define GLOG_NO_ABBREVIATED_SEVERITIES
// solve static linking error in windows
// https://github.com/google/glog/issues/301
#define GOOGLE_GLOG_DLL_DECL
#include "paddle/fluid/framework/tensor.h"
#include "unsupported/Eigen/CXX11/Tensor"
......
paddle/fluid/framework/op_registry.h
浏览文件 @ 0f254465
......@@ -23,11 +23,6 @@ limitations under the License. */
#include <unordered_map>
#include <unordered_set>
#if defined(_WIN32)
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#define GOOGLE_GLOG_DLL_DECL
#endif
#include "glog/logging.h" // For VLOG()
#include "paddle/fluid/framework/attribute.h"
#include "paddle/fluid/framework/details/op_registry.h"
......
paddle/fluid/framework/operator.cc
浏览文件 @ 0f254465
......@@ -11,8 +11,6 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include <gflags/gflags.h>
#include <glog/logging.h>
......
paddle/fluid/framework/operator.h
浏览文件 @ 0f254465
......@@ -20,8 +20,6 @@ limitations under the License. */
#include <tuple>
#include <unordered_map>
#include <vector>
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include "glog/logging.h" // For VLOG
#include "paddle/fluid/framework/attribute.h"
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -35,4 +35,4 @@ function(inference_analysis_test TARGET)
endif()
endfunction(inference_analysis_test)
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS paddle_inference_api)
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc EXTRA_DEPS reset_tensor_array paddle_inference_api)
paddle/fluid/inference/api/api_impl.h
浏览文件 @ 0f254465
......@@ -14,12 +14,6 @@ limitations under the License. */
#pragma once
// logging.h and windows.h conflict
#define GLOG_NO_ABBREVIATED_SEVERITIES
// solve static linking error in windows
// https://github.com/google/glog/issues/301
#define GOOGLE_GLOG_DLL_DECL
#include <glog/logging.h>
#include <map>
#include <memory>
......
paddle/fluid/inference/tensorrt/convert/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -18,7 +18,7 @@ nv_test(test_trt_activation_op SRCS test_activation_op.cc activation_op.cc
nv_test(test_trt_conv_op SRCS test_conv2d_op.cc conv2d_op.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine conv_op conv_transpose_op SERIAL)
nv_test(test_trt_pool2d_op SRCS test_pool2d_op.cc pool2d_op.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op SERIAL)
DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine pool_op tensorrt_plugin SERIAL)
nv_test(test_trt_elementwise_op SRCS test_elementwise_op.cc elementwise_op.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OPERATOR_DEPS} tensorrt_engine tensorrt_plugin
elementwise_add_op elementwise_mul_op SERIAL)
......
paddle/fluid/inference/tensorrt/convert/pool2d_op.cc
浏览文件 @ 0f254465
......@@ -13,25 +13,57 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h"
namespace paddle {
namespace inference {
namespace tensorrt {
void DealCeilMode(const nvinfer1::Dims &input_shape, std::vector<int> ksize,
std::vector<int> strides, std::vector<int> paddings,
nvinfer1::DimsHW *pre_pad, nvinfer1::DimsHW *post_pad,
int input_dims) {
int input_height = input_shape.d[input_dims - 2];
int input_width = input_shape.d[input_dims - 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;
}
}
/*
* Pool2dOp, IPoolingLayer in TRT. This Layer doesn't has weights.
*/
class Pool2dOpConverter : public OpConverter {
public:
void operator()(const framework::proto::OpDesc& op,
const framework::Scope& scope, bool test_mode) override {
VLOG(3)
void operator()(const framework::proto::OpDesc &op,
const framework::Scope &scope, bool test_mode) override {
VLOG(40)
<< "convert a fluid pool2d op to tensorrt pool2d layer without bias";
framework::OpDesc op_desc(op, nullptr);
// Declare inputs
PADDLE_ENFORCE_EQ(op_desc.Input("X").size(), 1);
PADDLE_ENFORCE_EQ(op_desc.Output("Out").size(), 1);
auto* input1 = engine_->GetITensor(op_desc.Input("X")[0]);
auto *input1 = engine_->GetITensor(op_desc.Input("X")[0]);
nvinfer1::Dims input_shape = input1->getDimensions();
int input_dims = input_shape.nbDims;
PADDLE_ENFORCE_EQ(input_dims, 3UL);
bool global_pooling = boost::get<bool>(op_desc.GetAttr("global_pooling"));
std::string pool_type =
......@@ -44,23 +76,6 @@ class Pool2dOpConverter : public OpConverter {
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) {
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;
}
PADDLE_ENFORCE_EQ(input1->getDimensions().nbDims, 3UL);
nvinfer1::PoolingType nv_pool_type = nvinfer1::PoolingType::kMAX;
if (pool_type == "max") {
nv_pool_type = nvinfer1::PoolingType::kMAX;
......@@ -70,42 +85,63 @@ 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;
nvinfer1::DimsHW nv_ksize(ksize[0], ksize[1]);
nvinfer1::DimsHW nv_strides(strides[0], strides[1]);
nvinfer1::DimsHW nv_paddings(paddings[0], paddings[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;
}
nvinfer1::ILayer *layer = nullptr;
if (floor_w_output_size != ceil_w_output_size) {
post_pad.w() = strides[1] - 1;
if (global_pooling == true) {
nv_ksize.d[0] = input_shape.d[input_dims - 2];
nv_ksize.d[1] = input_shape.d[input_dims - 1];
auto *layer = TRT_ENGINE_ADD_LAYER(
engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
nv_pool_type, nv_ksize);
PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
auto output_name = op_desc.Output("Out")[0];
layer->setName(("pool2d (Output: " + output_name + ")").c_str());
layer->getOutput(0)->setName(output_name.c_str());
engine_->SetITensor(output_name, layer->getOutput(0));
if (test_mode) {
engine_->DeclareOutput(output_name);
}
return;
}
auto* layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor*>(input1), pre_pad,
if (pool_type == "max") {
nvinfer1::DimsHW pre_pad(paddings[0], paddings[1]);
nvinfer1::DimsHW post_pad(paddings[0], paddings[1]);
if (ceil_mode) {
// If ceil mode is true, we will pad the appropriate size to the input.
DealCeilMode(input_shape, ksize, strides, paddings, &pre_pad, &post_pad,
input_dims);
auto *pad_layer = TRT_ENGINE_ADD_LAYER(
engine_, Padding, *const_cast<nvinfer1::ITensor *>(input1), pre_pad,
post_pad);
input1 = layer->getOutput(0);
PADDLE_ENFORCE_NOT_NULL(
pad_layer, "pad layer in poolOp converter could not be created.");
input1 = pad_layer->getOutput(0);
}
auto* layer = TRT_ENGINE_ADD_LAYER(engine_, Pooling,
*const_cast<nvinfer1::ITensor*>(input1),
auto *pool_layer = TRT_ENGINE_ADD_LAYER(
engine_, Pooling, *const_cast<nvinfer1::ITensor *>(input1),
nv_pool_type, nv_ksize);
PADDLE_ENFORCE_NOT_NULL(layer, "pool layer could not be created.");
layer->setStride(nv_strides);
layer->setPadding(nv_paddings);
PADDLE_ENFORCE_NOT_NULL(pool_layer, "pool layer could not be created.");
pool_layer->setStride(nv_strides);
pool_layer->setPadding(nv_paddings);
layer = pool_layer;
} else {
// Average pooling needs to exclude the padding pixels from the average
// mean.
// It is not supported well by TRT, we use a plugin here.
std::vector<int> input_shape_v;
for (int i = 0; i < input_dims; i++) {
input_shape_v.push_back(input_shape.d[i]);
}
plugin::AvgPoolPlugin *plugin = new plugin::AvgPoolPlugin(
ceil_mode, ksize, strides, paddings, input_shape_v);
auto *avg_pool_layer = engine_->AddPlugin(&input1, 1, plugin);
layer = avg_pool_layer;
}
auto output_name = op_desc.Output("Out")[0];
layer->setName(("pool2d (Output: " + output_name + ")").c_str());
......
paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc
浏览文件 @ 0f254465
......@@ -20,20 +20,21 @@ namespace paddle {
namespace inference {
namespace tensorrt {
void test_pool2d(bool global_pooling, bool ceil_mode) {
void test_pool2d(bool global_pooling, bool ceil_mode,
std::string pool_type = "max") {
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, 13, 14));
validator.DeclInputVar("pool2d-X", nvinfer1::Dims3(3, 6, 7));
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));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 4));
else
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 6, 6));
validator.DeclOutputVar("pool2d-Out", nvinfer1::Dims3(3, 3, 3));
// Prepare Op description
framework::OpDesc desc;
......@@ -41,10 +42,10 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
desc.SetInput("X", {"pool2d-X"});
desc.SetOutput("Out", {"pool2d-Out"});
std::vector<int> ksize({3, 3});
std::vector<int> ksize({2, 2});
std::vector<int> strides({2, 2});
std::vector<int> paddings({0, 0});
std::string pooling_t = "max";
std::string pooling_t = pool_type;
desc.SetAttr("pooling_type", pooling_t);
desc.SetAttr("ksize", ksize);
......@@ -63,7 +64,8 @@ void test_pool2d(bool global_pooling, bool ceil_mode) {
TEST(Pool2dOpConverter, normal) { test_pool2d(false, false); }
TEST(Pool2dOpConverter, test_global_pooling) { test_pool2d(true, false); }
TEST(Pool2dOpConverter, test_ceil_mode) { test_pool2d(false, true); }
TEST(Pool2dOpConverter, max_ceil_test) { test_pool2d(false, true); }
TEST(Pool2dOpConverter, avg_ceil_test) { test_pool2d(false, true, "avg"); }
} // namespace tensorrt
} // namespace inference
......
paddle/fluid/inference/tensorrt/plugin/CMakeLists.txt
浏览文件 @ 0f254465
nv_library(tensorrt_plugin
SRCS trt_plugin.cc split_op_plugin.cu elementwise_op_plugin.cu prelu_op_plugin.cu
avg_pool_op_plugin.cu
DEPS enforce tensorrt_engine)
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.cu 0 → 100644
浏览文件 @ 0f254465
// 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/inference/tensorrt/plugin/avg_pool_op_plugin.h"
#include "paddle/fluid/operators/math/pooling.h"
namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {
nvinfer1::Dims AvgPoolPlugin::getOutputDimensions(
int index, const nvinfer1::Dims* inputDims, int nbInputs) {
assert(nbInputs == 1);
assert(index == 0);
assert(inputDims[0].nbDims == 3);
nvinfer1::Dims const& input_dims = inputDims[0];
nvinfer1::Dims output_dims = input_dims;
output_dims.d[1] = output_shape_[1];
output_dims.d[2] = output_shape_[2];
return output_dims;
}
int AvgPoolPlugin::enqueue(int batchSize, const void* const* inputs,
void** outputs, void* workspace,
cudaStream_t stream) {
auto const& input_dims = this->getInputDims(0);
int input_size = 0;
float const* idata = reinterpret_cast<float const*>(inputs[0]);
float** odatas = reinterpret_cast<float**>(outputs);
paddle::operators::math::AvgPool<float> pool_process;
paddle::operators::math::Pool2dDirectCUDAFunctor<
paddle::operators::math::AvgPool<float>, float>
pool2d_forward;
std::vector<int> input_shape = input_shape_;
std::vector<int> output_shape = output_shape_;
input_shape.insert(input_shape.begin(), batchSize);
output_shape.insert(output_shape.begin(), batchSize);
pool2d_forward(idata, input_shape, output_shape, ksize_, strides_, paddings_,
pool_process, true, odatas[0], stream);
return cudaGetLastError() != cudaSuccess;
}
} // namespace plugin
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tensorrt/plugin/avg_pool_op_plugin.h 0 → 100644
浏览文件 @ 0f254465
// 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 <cassert>
#include <vector>
#include "paddle/fluid/inference/tensorrt/plugin/trt_plugin.h"
namespace paddle {
namespace inference {
namespace tensorrt {
namespace plugin {
class AvgPoolPlugin : public PluginTensorRT {
private:
bool ceil_mode_;
std::vector<int> ksize_;
std::vector<int> strides_;
std::vector<int> paddings_;
std::vector<int> input_shape_;
std::vector<int> output_shape_;
protected:
size_t getSerializationSize() override {
return SerializedSize(ceil_mode_) + SerializedSize(ksize_) +
SerializedSize(strides_) + SerializedSize(paddings_) +
SerializedSize(input_shape_) + getBaseSerializationSize();
}
// TRT will call this func when we need to serialize the configuration of
// tensorrt.
// It should not be called by users.
void serialize(void *buffer) override {
serializeBase(buffer);
SerializeValue(&buffer, ceil_mode_);
SerializeValue(&buffer, ksize_);
SerializeValue(&buffer, strides_);
SerializeValue(&buffer, paddings_);
SerializeValue(&buffer, input_shape_);
}
public:
AvgPoolPlugin(bool ceil_mode, std::vector<int> ksize,
std::vector<int> strides, std::vector<int> paddings,
std::vector<int> input_shape)
: ceil_mode_(ceil_mode),
ksize_(ksize),
strides_(strides),
paddings_(paddings),
input_shape_(input_shape) {
int output_h, output_w;
output_shape_ = input_shape_;
if (!ceil_mode_) {
output_h =
(input_shape[1] - ksize_[0] + 2 * paddings_[0]) / strides_[0] + 1;
output_w =
(input_shape[2] - ksize_[1] + 2 * paddings_[1]) / strides_[1] + 1;
} else {
output_h =
(input_shape[1] - ksize_[0] + 2 * paddings_[0] + strides_[0] - 1) /
strides_[0] +
1;
output_w =
(input_shape[2] - ksize_[1] + 2 * paddings_[1] + strides_[1] - 1) /
strides_[1] +
1;
}
output_shape_[1] = output_h;
output_shape_[2] = output_w;
}
// It was used for tensorrt deserialization.
// It should not be called by users.
AvgPoolPlugin(void const *serialData, size_t serialLength) {
deserializeBase(serialData, serialLength);
DeserializeValue(&serialData, &serialLength, &ceil_mode_);
DeserializeValue(&serialData, &serialLength, &ksize_);
DeserializeValue(&serialData, &serialLength, &strides_);
DeserializeValue(&serialData, &serialLength, &paddings_);
DeserializeValue(&serialData, &serialLength, &input_shape_);
}
AvgPoolPlugin *clone() const override {
return new AvgPoolPlugin(ceil_mode_, ksize_, strides_, paddings_,
input_shape_);
}
const char *getPluginType() const override { return "avg_pool"; }
int getNbOutputs() const override { return 1; }
nvinfer1::Dims getOutputDimensions(int index, const nvinfer1::Dims *inputs,
int nbInputDims) override;
int initialize() override { return 0; }
int enqueue(int batchSize, const void *const *inputs, void **outputs,
void *workspace, cudaStream_t stream) override;
};
} // namespace plugin
} // namespace tensorrt
} // namespace inference
} // namespace paddle
paddle/fluid/memory/allocation/best_fit_allocator_test.cc
浏览文件 @ 0f254465
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/memory/allocation/best_fit_allocator.h"
#include <random>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
......
paddle/fluid/memory/allocation/best_fit_allocator_test.cu
浏览文件 @ 0f254465
......@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <random>
#include <thread> // NOLINT
#include <vector>
#include "gtest/gtest.h"
......
paddle/fluid/memory/allocation/cpu_allocator.h
浏览文件 @ 0f254465
......@@ -15,6 +15,12 @@
#pragma once
#include "paddle/fluid/memory/allocation/allocator.h"
#ifdef _WIN32
#define posix_memalign_free _aligned_free
#define posix_memalign(p, a, s) \
(((*(p)) = _aligned_malloc((s), (a))), *(p) ? 0 : errno)
#endif
namespace paddle {
namespace memory {
namespace allocation {
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -22,9 +22,7 @@ if(WITH_DISTRIBUTE)
add_subdirectory(distributed_ops)
endif()
if (NOT WIN32)
add_subdirectory(reader)
endif()
add_subdirectory(reader)
if (NOT WIN32)
add_subdirectory(nccl)
......@@ -34,29 +32,39 @@ if (WITH_GPU AND TENSORRT_FOUND)
add_subdirectory(tensorrt)
endif()
register_operators(EXCLUDES warpctc_op conv_fusion_op)
# warpctc_cudnn need cudnn 7 above
SET(OP_HEADER_DEPS xxhash)
if (WITH_GPU)
SET(OP_HEADER_DEPS ${OP_HEADER_DEPS} cub)
endif()
register_operators(EXCLUDES warpctc_op conv_fusion_op DEPS ${OP_HEADER_DEPS})
# warpctc_op needs cudnn 7 above
if (WITH_GPU AND NOT WIN32)
if (${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale SRCS warpctc_op.cc warpctc_op.cu.cc)
else()
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
endif()
# conv_fusion_op needs cudnn 7 above
if (NOT ${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
op_library(conv_fusion_op)
file(APPEND ${pybind_file} "USE_CUDA_ONLY_OP(conv2d_fusion);\n")
endif()
else()
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
endif()
set(COMMON_OP_DEPS "")
set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} xxhash selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor dynload_warpctc sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
if (NOT WIN32)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
endif()
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
if (WITH_GPU)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv cub)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} depthwise_conv)
endif()
# FIXME(typhoonzero): operator deps may not needed.
......
paddle/fluid/operators/conv_fusion_op.cu.cc
浏览文件 @ 0f254465
......@@ -22,6 +22,7 @@ DECLARE_bool(cudnn_exhaustive_search);
namespace paddle {
namespace operators {
#if CUDNN_VERSION >= 7001
using Tensor = framework::Tensor;
using ScopedTensorDescriptor = platform::ScopedTensorDescriptor;
using ScopedFilterDescriptor = platform::ScopedFilterDescriptor;
......@@ -178,10 +179,13 @@ class CUDNNConvFusionOpKernel : public framework::OpKernel<T> {
workspace_handle.RunFunc(cudnn_func, workspace_size_in_bytes);
}
};
#endif
} // namespace operators
} // namespace paddle
#if CUDNN_VERSION >= 7001
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(conv2d_fusion, ops::CUDNNConvFusionOpKernel<float>,
ops::CUDNNConvFusionOpKernel<double>);
#endif
paddle/fluid/operators/group_norm_op.cc 0 → 100644
浏览文件 @ 0f254465
/* 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/operators/group_norm_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
class GroupNormOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mean"),
"Output(Mean) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Variance"),
"Output(Variance) of GroupNormOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto channel_num = x_dim[1];
auto batch_size = x_dim[0];
auto groups = ctx->Attrs().Get<int>("groups");
PADDLE_ENFORCE_LE(
groups, channel_num,
"'groups' must be less equal than the number of channels.");
PADDLE_ENFORCE_GE(groups, 1, "'groups' must be greater equal than 1.");
if (ctx->HasInput("Scale")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], channel_num);
}
if (ctx->HasInput("Bias")) {
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], channel_num);
}
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->SetOutputDim("Mean", {batch_size, groups});
ctx->SetOutputDim("Variance", {batch_size, groups});
ctx->ShareLoD("X", "Y");
}
};
class GroupNormOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "The input tensor.");
AddInput("Scale",
"Scale is a 1-dimensional tensor of size C"
"that is applied to the output.")
.AsDispensable();
AddInput("Bias",
"Bias is a 1-dimensional tensor of size C "
"that is applied to the output")
.AsDispensable();
AddOutput("Y", "Result after normalization.");
AddOutput("Mean", "Mean of each group.").AsIntermediate();
AddOutput("Variance", "Variance of each group.").AsIntermediate();
AddAttr<float>("epsilon",
"Constant for numerical stability [default 1e-5].")
.SetDefault(1e-5)
.AddCustomChecker([](const float &epsilon) {
PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 1.0f,
"'epsilon' should be between 0.0 and 1.0.");
});
AddAttr<int>("groups", "The number of groups that divided from channels.")
.AddCustomChecker([](const int &groups) {
PADDLE_ENFORCE_GT(groups, 0, "'groups' should be greater than zero.");
});
AddComment(R"DOC(
Group Normalization
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`_
)DOC");
}
};
class GroupNormGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
// check input
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mean"),
"Input(Mean) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Variance"),
"Input(Variance) of GroupNormOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) of GroupNormOp should not be null.");
// check output
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
if (ctx->HasOutput(framework::GradVarName("Scale"))) {
ctx->SetOutputDim(framework::GradVarName("Scale"),
ctx->GetInputDim("Scale"));
}
if (ctx->HasOutput(framework::GradVarName("Bias"))) {
ctx->SetOutputDim(framework::GradVarName("Bias"),
ctx->GetInputDim("Bias"));
}
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
const auto *var = ctx.InputVar(framework::GradVarName("Y"));
if (var == nullptr) {
PADDLE_THROW("can't find Y@GRAD");
}
const Tensor *t = nullptr;
if (var->IsType<Tensor>()) {
t = &var->Get<Tensor>();
} else if (var->IsType<LoDTensor>()) {
t = &var->Get<LoDTensor>();
}
if (t == nullptr) {
PADDLE_THROW("can't find Y@GRAD");
}
return framework::OpKernelType(framework::ToDataType(t->type()),
ctx.GetPlace());
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(group_norm, ops::GroupNormOp, ops::GroupNormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(group_norm_grad, ops::GroupNormGradOp);
REGISTER_OP_CPU_KERNEL(
group_norm, ops::GroupNormKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
group_norm_grad,
ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GroupNormGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/group_norm_op.cu 0 → 100644
浏览文件 @ 0f254465
/* 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 <cub/cub.cuh>
#include "paddle/fluid/operators/group_norm_op.h"
namespace paddle {
namespace operators {
template <typename T>
__global__ void GroupNormForwardGetMeanAndVar(const T* x, int N, int C,
int imsize, int groups,
int group_size, T* mean, T* var) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = 0, x_var = 0;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T val = x[(bid * C + ccid) * imsize + imid];
x_mean += val;
x_var += val * val;
}
x_mean /= number * imsize;
x_var /= number * imsize;
__shared__ T s_mem[2];
if (threadIdx.x == 0) {
s_mem[0] = s_mem[1] = 0;
}
__syncthreads();
paddle::platform::CudaAtomicAdd(&s_mem[0], x_mean);
paddle::platform::CudaAtomicAdd(&s_mem[1], x_var);
__syncthreads();
if (threadIdx.x == 0) {
paddle::platform::CudaAtomicAdd(&mean[bid * groups + gid], s_mem[0]);
paddle::platform::CudaAtomicAdd(&var[bid * groups + gid], s_mem[1]);
}
}
template <typename T>
__global__ void GroupNormForward(const T* x, const T* mean, const T* var,
const T* scale, const T* bias, int N, int C,
int imsize, int groups, int group_size,
T epsilon, T* y, T* real_var) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
x_var = x_var - x_mean * x_mean;
T var_inv = 1.0 / sqrt(x_var + epsilon);
if (cid == 0 && threadIdx.x == 0) real_var[bid * groups + gid] = x_var;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T val = x[(bid * C + ccid) * imsize + imid];
val = (val - x_mean) * var_inv;
if (scale) val *= scale[gid * group_size + cid];
if (bias) val += bias[gid * group_size + cid];
y[(bid * C + ccid) * imsize + imid] = val;
}
}
template <typename T>
class GroupNormKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* var = ctx.Output<Tensor>("Variance");
const auto groups = ctx.Attr<int>("groups");
const auto x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
Tensor temp_var;
temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, mean, static_cast<T>(0));
set_zero(dev_ctx, &temp_var, static_cast<T>(0));
auto* x_data = x->data<T>();
auto* y_data = y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
auto* temp_var_data = temp_var.data<T>();
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
const T* bias_data = nullptr;
if (bias) bias_data = bias->data<T>();
int imsize = x_dims[2] * x_dims[3];
int block_size = std::min(512, imsize);
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
GroupNormForwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, x_dims[0], x_dims[1], imsize, groups, group_size, mean_data,
temp_var_data);
GroupNormForward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, temp_var_data, scale_data, bias_data, x_dims[0],
x_dims[1], imsize, groups, group_size, epsilon, y_data, var_data);
}
};
template <typename T>
__global__ void GroupNormBackwardGetMeanAndVar(
const T* x, const T* mean, const T* var, const T* scale, const T* d_y,
int N, int C, int imsize, int groups, int group_size, T epsilon, T* d_x,
T* d_mean, T* d_var, T* d_scale, T* d_bias) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
T var_inv = 1.0 / sqrt(x_var + epsilon);
T d_var_inv = 0, d_x_mean = 0;
T d_mean_data = 0, d_var_data = 0, d_scale_data = 0, d_bias_data = 0;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T tmp = x[(bid * C + ccid) * imsize + imid];
T val = (tmp - x_mean) * var_inv;
T dval = d_y[(bid * C + ccid) * imsize + imid];
if (d_bias) d_bias_data += dval;
if (d_scale) d_scale_data += val * dval;
if (scale) dval = dval * scale[ccid];
d_var_data += (tmp - x_mean) * dval;
T d_tmp = dval * var_inv;
if (d_x) d_x[(bid * C + ccid) * imsize + imid] = d_tmp;
d_mean_data -= d_tmp;
}
__shared__ T s_mem[4];
if (threadIdx.x == 0) {
s_mem[0] = s_mem[1] = 0;
if (d_scale) s_mem[2] = 0;
if (d_bias) s_mem[3] = 0;
}
__syncthreads();
paddle::platform::CudaAtomicAdd(&s_mem[0], d_mean_data);
paddle::platform::CudaAtomicAdd(&s_mem[1], d_var_data);
if (d_scale) paddle::platform::CudaAtomicAdd(&s_mem[2], d_scale_data);
if (d_bias) paddle::platform::CudaAtomicAdd(&s_mem[3], d_bias_data);
__syncthreads();
if (threadIdx.x == 0) {
paddle::platform::CudaAtomicAdd(&d_mean[bid * groups + gid], s_mem[0]);
paddle::platform::CudaAtomicAdd(&d_var[bid * groups + gid], s_mem[1]);
if (d_scale) paddle::platform::CudaAtomicAdd(&d_scale[ccid], s_mem[2]);
if (d_bias) paddle::platform::CudaAtomicAdd(&d_bias[ccid], s_mem[3]);
}
}
template <typename T>
__global__ void GroupNormBackward(const T* x, const T* mean, const T* var,
const T* d_mean, const T* d_var, int N, int C,
int imsize, int groups, int group_size,
T epsilon, T* d_x) {
int gid = blockIdx.y;
int cid = blockIdx.x;
int bid = blockIdx.z;
int number = min(group_size, static_cast<int>(C - gid * group_size));
int ccid = gid * group_size + cid;
if (ccid >= C) return;
T x_mean = mean[bid * groups + gid];
T x_var = var[bid * groups + gid];
T d_x_mean = d_mean[bid * groups + gid];
T d_var_inv = d_var[bid * groups + gid];
T d_x_var =
-1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
d_x_mean -= 2 * d_x_var * x_mean;
d_x_var /= number * imsize;
d_x_mean /= number * imsize;
for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
T tmp = x[(bid * C + ccid) * imsize + imid];
if (d_x)
d_x[(bid * C + ccid) * imsize + imid] += d_x_mean + tmp * 2 * d_x_var;
}
}
template <typename T>
class GroupNormGradKernel<platform::CUDADeviceContext, T>
: public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* x = ctx.Input<Tensor>("X");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto groups = ctx.Attr<int>("groups");
// init output
auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto& x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
T* d_x_data = nullptr;
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
d_x_data = d_x->data<T>();
}
math::SetConstant<platform::CUDADeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
Tensor temp_var;
temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, &temp_var, static_cast<T>(0));
T* temp_var_data = temp_var.data<T>();
Tensor temp_mean;
temp_mean.mutable_data<T>(var->dims(), ctx.GetPlace());
set_zero(dev_ctx, &temp_mean, static_cast<T>(0));
T* temp_mean_data = temp_mean.data<T>();
auto* x_data = x->data<T>();
auto* y_data = d_y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
T* d_scale_data = nullptr;
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_scale, static_cast<T>(0));
d_scale_data = d_scale->data<T>();
}
T* d_bias_data = nullptr;
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_bias, static_cast<T>(0));
d_bias_data = d_bias->data<T>();
}
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
int imsize = x_dims[2] * x_dims[3];
int block_size = std::min(512, imsize);
dim3 grid(group_size, groups, x_dims[0]);
dim3 threads(block_size, 1, 1);
GroupNormBackwardGetMeanAndVar<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, var_data, scale_data, y_data, x_dims[0], x_dims[1],
imsize, groups, group_size, epsilon, d_x_data, temp_mean_data,
temp_var_data, d_scale_data, d_bias_data);
GroupNormBackward<T><<<grid, threads, 0, dev_ctx.stream()>>>(
x_data, mean_data, var_data, temp_mean_data, temp_var_data, x_dims[0],
x_dims[1], imsize, groups, group_size, epsilon, d_x_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
group_norm,
ops::GroupNormKernel<paddle::platform::CUDADeviceContext, float>,
ops::GroupNormKernel<paddle::platform::CUDADeviceContext, double>);
REGISTER_OP_CUDA_KERNEL(
group_norm_grad,
ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::GroupNormGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/group_norm_op.h 0 → 100644
浏览文件 @ 0f254465
/* 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.
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 <algorithm>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DataLayout = framework::DataLayout;
template <typename DeviceContext, typename T>
class GroupNormKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* scale = ctx.Input<Tensor>("Scale");
auto* bias = ctx.Input<Tensor>("Bias");
auto* x = ctx.Input<Tensor>("X");
auto* y = ctx.Output<Tensor>("Y");
auto* mean = ctx.Output<Tensor>("Mean");
auto* var = ctx.Output<Tensor>("Variance");
const auto groups = ctx.Attr<int>("groups");
const auto x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
y->mutable_data<T>(ctx.GetPlace());
mean->mutable_data<T>(ctx.GetPlace());
var->mutable_data<T>(ctx.GetPlace());
auto* x_data = x->data<T>();
auto* y_data = y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
const T* bias_data = nullptr;
if (bias) bias_data = bias->data<T>();
int imsize = x_dims[2] * x_dims[3];
auto* iter_x_data = x_data;
auto* iter_y_data = y_data;
for (int bid = 0; bid < x_dims[0]; bid++)
for (int gid = 0; gid < groups; gid++) {
T x_mean = 0, x_var = 0;
int number = std::min(group_size,
static_cast<int>(x_dims[1] - gid * group_size));
auto* tmp = iter_x_data;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize; imid++, iter_x_data++) {
x_mean += iter_x_data[0];
x_var += iter_x_data[0] * iter_x_data[0];
}
}
x_mean /= number * imsize;
x_var /= number * imsize;
x_var = x_var - x_mean * x_mean;
T var_inv = 1.0 / sqrt(x_var + epsilon);
mean_data[bid * groups + gid] = x_mean;
var_data[bid * groups + gid] = x_var;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize; imid++, tmp++, iter_y_data++) {
T val = (tmp[0] - x_mean) * var_inv;
if (scale_data) val *= scale_data[gid * group_size + cid];
if (bias_data) val += bias_data[gid * group_size + cid];
iter_y_data[0] = val;
}
}
}
}
};
template <typename DeviceContext, typename T>
class GroupNormGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
const float epsilon = ctx.Attr<float>("epsilon");
auto* x = ctx.Input<Tensor>("X");
auto* mean = ctx.Input<Tensor>("Mean");
auto* var = ctx.Input<Tensor>("Variance");
auto* scale = ctx.Input<Tensor>("Scale");
auto* d_y = ctx.Input<Tensor>(framework::GradVarName("Y"));
const auto groups = ctx.Attr<int>("groups");
// init output
auto* d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* d_scale = ctx.Output<Tensor>(framework::GradVarName("Scale"));
auto* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias"));
const auto& x_dims = x->dims();
const int group_size = (x_dims[1] - 1) / groups + 1;
// TODO(liangdun): need to check d_x is null
math::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
T* d_x_data = nullptr;
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_x, static_cast<T>(0));
d_x_data = d_x->data<T>();
}
auto* x_data = x->data<T>();
auto* y_data = d_y->data<T>();
auto* mean_data = mean->data<T>();
auto* var_data = var->data<T>();
T* d_scale_data = nullptr;
if (d_scale) {
d_scale->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_scale, static_cast<T>(0));
d_scale_data = d_scale->data<T>();
}
T* d_bias_data = nullptr;
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
set_zero(dev_ctx, d_bias, static_cast<T>(0));
d_bias_data = d_bias->data<T>();
}
const T* scale_data = nullptr;
if (scale) scale_data = scale->data<T>();
int imsize = x_dims[2] * x_dims[3];
auto* iter_x_data = x_data;
auto* iter_d_x_data = d_x_data;
auto* iter_y_data = y_data;
for (int bid = 0; bid < x_dims[0]; bid++)
for (int gid = 0; gid < groups; gid++) {
T x_mean = mean_data[bid * groups + gid];
T x_var = var_data[bid * groups + gid];
T var_inv = 1.0 / sqrt(x_var + epsilon);
int number = std::min(group_size,
static_cast<int>(x_dims[1] - gid * group_size));
auto* tmp = iter_x_data;
auto* tmp2 = iter_d_x_data;
T d_var_inv = 0, d_x_mean = 0;
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize;
imid++, tmp++, iter_y_data++, iter_d_x_data++) {
T val = (tmp[0] - x_mean) * var_inv;
T dval = iter_y_data[0];
if (d_bias_data) d_bias_data[gid * group_size + cid] += dval;
if (d_scale_data)
d_scale_data[gid * group_size + cid] += val * dval;
if (scale_data) dval = scale_data[gid * group_size + cid] * dval;
d_var_inv += (tmp[0] - x_mean) * dval;
T d_tmp = dval * var_inv;
if (d_x_data) iter_d_x_data[0] += d_tmp;
d_x_mean -= d_tmp;
}
}
T d_x_var =
-1.0 / (2 * (x_var + epsilon) * sqrt(x_var + epsilon)) * d_var_inv;
d_x_mean -= 2 * d_x_var * x_mean;
d_x_var /= number * imsize;
d_x_mean /= number * imsize;
iter_d_x_data = tmp2;
if (d_x_data) {
for (int cid = 0; cid < number; cid++) {
for (int imid = 0; imid < imsize;
imid++, iter_x_data++, iter_d_x_data++) {
iter_d_x_data[0] += d_x_mean;
iter_d_x_data[0] += iter_x_data[0] * 2 * d_x_var;
}
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @ 0f254465
......@@ -111,7 +111,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
auto pre_out_mat = EigenMatrix<T>::From(*pre_out);
auto pre_out_grad_mat = EigenMatrix<T>::From(pre_out_grad);
auto out_grad_mat = EigenMatrix<T>::From(*out_grad);
Eigen::array<int, 2> bcast({{1, static_cast<int>(pre_out_grad.dims()[1])}});
Eigen::array<int, 2> bcast{1, static_cast<int>(pre_out_grad.dims()[1])};
// softrelu derivative
pre_out_grad_mat.device(place) =
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 0f254465
if (NOT WIN32)
add_subdirectory(detail)
endif(NOT WIN32)
add_subdirectory(detail)
function(math_library TARGET)
# math_library is a function to create math library.
......@@ -43,10 +41,8 @@ math_library(depthwise_conv)
math_library(im2col)
math_library(sampler)
if (NOT WIN32) # windows do not support avx functions yet.
math_library(gru_compute DEPS activation_functions math_function)
math_library(lstm_compute DEPS activation_functions)
endif (NOT WIN32)
math_library(gru_compute DEPS activation_functions math_function)
math_library(lstm_compute DEPS activation_functions)
cc_library(blas SRCS blas.cc DEPS cblas framework_proto device_context)
math_library(math_function DEPS blas)
......@@ -58,9 +54,9 @@ math_library(sequence_padding)
math_library(sequence_pooling DEPS math_function)
math_library(sequence_scale)
math_library(softmax DEPS math_function)
if (NOT WIN32)
math_library(matrix_bit_code)
endif (NOT WIN32)
math_library(matrix_bit_code)
math_library(unpooling)
math_library(vol2col)
......@@ -76,13 +72,12 @@ if(WITH_GPU)
endif()
cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
if (NOT WIN32)
set(JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc jit_kernel_layer_norm.cc)
set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce)
if(WITH_XBYAK)
set(JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc jit_kernel_layer_norm.cc)
set(JIT_KERNEL_DEPS cpu_info cblas gflags enforce)
if(WITH_XBYAK)
list(APPEND JIT_KERNEL_SRCS jit_gen.cc jit_code.cc)
list(APPEND JIT_KERNEL_DEPS xbyak)
endif()
cc_library(jit_kernel SRCS ${JIT_KERNEL_SRCS} DEPS ${JIT_KERNEL_DEPS})
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
endif (NOT WIN32)
endif()
cc_library(jit_kernel SRCS ${JIT_KERNEL_SRCS} DEPS ${JIT_KERNEL_DEPS})
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/detail/activation_functions.h
浏览文件 @ 0f254465
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h>
#include <string>
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/hostdevice.h"
......
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @ 0f254465
......@@ -67,7 +67,7 @@ inline constexpr size_t FindLastSet(size_t x) {
: (std::is_same<size_t, unsigned long>::value // NOLINT
? (x ? 8 * sizeof(x) - __builtin_clzl(x) : 0)
: (x ? 8 * sizeof(x) - __builtin_clzll(x) : 0));
}
#else
// windows don't have built-in clz, ctz function
template <typename T>
......@@ -92,7 +92,6 @@ inline int clz(const T& value) {
inline size_t FindLastSet(size_t x) { return sizeof(size_t) * 8 - clz(x); }
#endif // !_WIN32
}
struct SimpleCode {
SimpleCode(size_t code, size_t num_classes) : c_(code + num_classes) {}
......
paddle/fluid/operators/math/pooling.cu
浏览文件 @ 0f254465
......@@ -153,6 +153,37 @@ __global__ void KernelMaxPool2DGrad(
}
}
template <typename PoolProcess, typename T>
void Pool2dDirectCUDAFunctor<PoolProcess, T>::operator()(
const T* input, const std::vector<int>& input_shape,
const std::vector<int>& output_shape, const std::vector<int>& ksize,
const std::vector<int>& strides, const std::vector<int>& paddings,
PoolProcess pool_compute, bool exclusive, T* output, cudaStream_t stream) {
const int batch_size = input_shape[0];
const int input_channels = input_shape[1];
const int input_height = input_shape[2];
const int input_width = input_shape[3];
const int output_channels = output_shape[1];
const int output_height = output_shape[2];
const int output_width = output_shape[3];
const int ksize_height = ksize[0];
const int ksize_width = ksize[1];
const int stride_height = strides[0];
const int stride_width = strides[1];
const int padding_height = paddings[0];
const int padding_width = paddings[1];
int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KernelPool2D<PoolProcess, T><<<grid, threads, 0, stream>>>(
nthreads, input, input_channels, input_height, input_width, output_height,
output_width, ksize_height, ksize_width, stride_height, stride_width,
padding_height, padding_width, pool_compute, exclusive, output);
}
/*
* All tensors are in NCHW format.
* Ksize, strides, paddings are two elements. These two elements represent
......@@ -291,6 +322,11 @@ class MaxPool2dGradFunctor<platform::CUDADeviceContext, T> {
}
};
template class Pool2dDirectCUDAFunctor<paddle::operators::math::MaxPool<float>,
float>;
template class Pool2dDirectCUDAFunctor<paddle::operators::math::AvgPool<float>,
float>;
template class MaxPool2dGradFunctor<platform::CUDADeviceContext, float>;
template class MaxPool2dGradFunctor<platform::CUDADeviceContext, double>;
......
paddle/fluid/operators/math/pooling.h
浏览文件 @ 0f254465
......@@ -82,6 +82,19 @@ class AvgPoolGrad {
* This is different from average pooling. So we rewrite the max_pool_grad:
* MaxPool2dGradFunctor, MaxPool3dGradFunctor.
*/
#ifdef PADDLE_WITH_CUDA
template <typename PoolProcess, typename T>
class Pool2dDirectCUDAFunctor {
public:
void operator()(const T* input, const std::vector<int>& input_shape,
const std::vector<int>& output_shape,
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, PoolProcess pool_compute,
bool exclusive, T* output, cudaStream_t stream);
};
#endif
template <typename DeviceContext, typename PoolProcess, typename T>
class Pool2dFunctor {
public:
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 0f254465
......@@ -35,10 +35,10 @@ class ROIAlignOp : public framework::OperatorWithKernel {
"The format of input tensor is NCHW.");
PADDLE_ENFORCE(rois_dims.size() == 2,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …].");
"given as [[x1, y1, x2, y2], ...].");
PADDLE_ENFORCE(rois_dims[1] == 4,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …].");
"given as [[x1, y1, x2, y2], ...].");
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
......@@ -103,7 +103,7 @@ class ROIAlignOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …]. "
"given as [[x1, y1, x2, y2], ...]. "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
AddOutput("Out",
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 0f254465
......@@ -40,10 +40,10 @@ class ROIPoolOp : public framework::OperatorWithKernel {
"The format of input tensor is NCHW.");
PADDLE_ENFORCE(rois_dims.size() == 2,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …].");
"given as [[x1, y1, x2, y2], ...].");
PADDLE_ENFORCE(rois_dims[1] == kROISize,
"ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …].");
"given as [[x1, y1, x2, y2], ...].");
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
......@@ -110,7 +110,7 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
"(LoDTensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D LoDTensor of shape (num_rois, 4)"
"given as [[x1, y1, x2, y2], …]. "
"given as [[x1, y1, x2, y2], ...]. "
"Where batch_id is the id of the data, "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
......
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 0f254465
if (NOT WIN32)
proto_library(profiler_proto SRCS profiler.proto DEPS framework_proto)
py_proto_compile(profiler_py_proto SRCS profiler.proto)
......@@ -6,11 +5,19 @@ add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch _
add_dependencies(profiler_py_proto profiler_py_proto_init)
if (NOT WIN32)
add_custom_command(TARGET profiler_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
else(NOT WIN32)
string(REPLACE "/" "\\" proto_dstpath "${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler/")
add_custom_command(TARGET profiler_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMAND copy /Y *.py ${proto_dstpath}
COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif(NOT WIN32)
if(WITH_GPU)
......@@ -60,12 +67,9 @@ cc_test(init_test SRCS init_test.cc DEPS device_context)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
if (NOT WIN32)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto framework_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
cc_test(profiler_test SRCS profiler_test.cc DEPS profiler)
endif(NOT WIN32)
nv_test(float16_gpu_test SRCS float16_test.cu DEPS lod_tensor)
cc_test(float16_test SRCS float16_test.cc DEPS lod_tensor)
......
paddle/fluid/platform/cpu_helper.cc
浏览文件 @ 0f254465
......@@ -29,6 +29,13 @@ namespace platform {
void SetNumThreads(int num_threads) {
#ifdef PADDLE_USE_OPENBLAS
// windows has no support for openblas multi-thread
// please refer to: https://github.com/PaddlePaddle/Paddle/issues/7234
#ifdef _WIN32
if (num_threads > 1) {
num_threads = 1;
}
#endif
int real_num_threads = num_threads > 1 ? num_threads : 1;
openblas_set_num_threads(real_num_threads);
#elif defined(PADDLE_WITH_MKLML)
......
paddle/fluid/platform/device_tracer.h
浏览文件 @ 0f254465
......@@ -13,17 +13,11 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#if !defined(_WIN32)
#include <sys/time.h>
#else
#include <windows.h>
#endif // !_WIN32
#include <time.h>
#include <chrono> // NOLINT
#include <string>
#include "paddle/fluid/platform/dynload/cupti.h"
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/platform/profiler.pb.h"
namespace paddle {
......@@ -32,15 +26,11 @@ namespace platform {
///////////////////////
// WARN: Under Development. Don't depend on it yet.
//////////////////////
#if !defined(_WIN32)
inline uint64_t PosixInNsec() {
struct timeval tv;
gettimeofday(&tv, nullptr);
return 1000 * (static_cast<uint64_t>(tv.tv_sec) * 1000000 + tv.tv_usec);
}
#else
inline uint64_t PosixInNsec() { return static_cast<uint64_t>(0); }
#endif // !_WIN32
// DeviceTracer performs the following tasks:
// 1. Register cuda callbacks for various events: kernel, memcpy, etc.
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 0f254465
......@@ -13,8 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include <glog/logging.h>
#include <cudnn.h>
......
paddle/fluid/platform/enforce.h
浏览文件 @ 0f254465
......@@ -18,12 +18,6 @@ limitations under the License. */
#include <cxxabi.h> // for __cxa_demangle
#endif // __GNUC__
#if defined(_WIN32)
#define NOMINMAX // msvc max/min macro conflict with std::min/max
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#define GOOGLE_GLOG_DLL_DECL
#endif
#ifdef PADDLE_WITH_CUDA
#include <cublas_v2.h>
#include <cudnn.h>
......@@ -127,14 +121,14 @@ struct EOFException : public std::exception {
#define UNLIKELY(condition) __builtin_expect(static_cast<bool>(condition), 0)
#else
// there is no equivalent intrinsics in msvc.
#define UNLIKELY(condition) (condition == 0)
#define UNLIKELY(condition) (condition)
#endif
#if !defined(_WIN32)
#define LIKELY(condition) __builtin_expect(static_cast<bool>(condition), 1)
#else
// there is no equivalent intrinsics in msvc.
#define LIKELY(condition) (condition != 0)
#define LIKELY(condition) (condition)
#endif
template <typename... Args>
......@@ -248,7 +242,6 @@ inline void throw_on_error(T e) {
throw_on_error(e, "");
}
#if !defined(_WIN32)
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
......@@ -272,17 +265,6 @@ inline void throw_on_error(T e) {
#define PADDLE_ENFORCE(...) ::paddle::platform::throw_on_error(__VA_ARGS__);
#endif // REPLACE_ENFORCE_GLOG
#else // !_WIN32
// disable enforce, caused by the varardic macro exception error
#define PADDLE_THROW(x) \
do { \
throw std::make_exception_ptr( \
std::runtime_error("Windows disable the enforce.")); \
} while (false)
#define PADDLE_ENFORCE(x, ...) x
#endif // !_WIN32
#define PADDLE_THROW_EOF() \
do { \
throw ::paddle::platform::EOFException("There is no next data.", __FILE__, \
......@@ -302,20 +284,6 @@ inline void throw_on_error(T e) {
* extra messages is also supported, for example:
* PADDLE_ENFORCE(a, b, "some simple enforce failed between %d numbers", 2)
*/
#if !defined(_WIN32)
#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
#define PADDLE_ENFORCE_NOT_NULL(__VAL, ...) \
do { \
if (UNLIKELY(nullptr == (__VAL))) { \
......@@ -335,27 +303,19 @@ inline void throw_on_error(T e) {
paddle::string::Sprintf("" __VA_ARGS__)); \
} \
} while (0)
#else
#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) ((__VAL0) == (__VAL1))
#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) ((__VAL0) != (__VAL1))
#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) ((__VAL0) > (__VAL1))
#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) ((__VAL0) >= (__VAL1))
#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) ((__VAL0) < (__VAL1))
#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) ((__VAL0) <= (__VAL1))
#define __PADDLE_BINARY_COMPARE(__VAL0, __VAL1, __CMP, __INV_CMP, ...) \
do { \
if (!((__VAL0)__CMP(__VAL1))) { \
PADDLE_THROW("Windows disable the enforce. Enforce failed."); \
} \
} while (0)
#define PADDLE_ENFORCE_NOT_NULL(__VAL1, ...) \
do { \
if (nullptr == (__VAL1)) { \
PADDLE_THROW("Windows disable the enforce. Enforce failed"); \
} \
} while (0)
#endif // !_WIN32
#define PADDLE_ENFORCE_EQ(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, ==, !=, __VA_ARGS__)
#define PADDLE_ENFORCE_NE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, !=, ==, __VA_ARGS__)
#define PADDLE_ENFORCE_GT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >, <=, __VA_ARGS__)
#define PADDLE_ENFORCE_GE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, >=, <, __VA_ARGS__)
#define PADDLE_ENFORCE_LT(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <, >=, __VA_ARGS__)
#define PADDLE_ENFORCE_LE(__VAL0, __VAL1, ...) \
__PADDLE_BINARY_COMPARE(__VAL0, __VAL1, <=, >, __VA_ARGS__)
} // namespace platform
} // namespace paddle
paddle/fluid/platform/init.cc
浏览文件 @ 0f254465
......@@ -117,13 +117,6 @@ void InitDevices(bool init_p2p, const std::vector<int> devices) {
places.emplace_back(platform::CPUPlace());
platform::DeviceContextPool::Init(places);
// windows has no support for openblas multi-thread
#ifdef _WIN32
if (FLAGS_paddle_num_threads > 1) {
FLAGS_paddle_num_threads = 1;
}
#endif
#ifndef PADDLE_WITH_MKLDNN
platform::SetNumThreads(FLAGS_paddle_num_threads);
#endif
......
paddle/fluid/platform/init.h
浏览文件 @ 0f254465
......@@ -16,9 +16,6 @@ limitations under the License. */
#include <string>
#include <vector>
#define GLOG_NO_ABBREVIATED_SEVERITIES
#define GOOGLE_GLOG_DLL_DECL
#include "gflags/gflags.h"
#include "glog/logging.h"
......
paddle/fluid/platform/port.h
浏览文件 @ 0f254465
......@@ -17,6 +17,7 @@
#include <cstdio>
#include <stdexcept>
#include <time.h>
#include <memory>
#include <string>
......@@ -27,8 +28,13 @@
#include <dlfcn.h> // dladdr
#include <execinfo.h> // backtrace
#include <sys/stat.h>
#include <sys/time.h>
#include <algorithm> // std::accumulate
#else
#define NOMINMAX // msvc max/min macro conflict with std::min/max
// solve static linking error in windows
// https://github.com/google/glog/issues/301
#define GOOGLE_GLOG_DLL_DECL
#include <io.h> // _popen, _pclose
#include <stdio.h>
#include <windows.h>
......@@ -57,6 +63,25 @@ static void *dlopen(const char *filename, int flag) {
return reinterpret_cast<void *>(hModule);
}
static int gettimeofday(struct timeval *tp, void *tzp) {
time_t clock;
struct tm tm;
SYSTEMTIME wtm;
GetLocalTime(&wtm);
tm.tm_year = wtm.wYear - 1900;
tm.tm_mon = wtm.wMonth - 1;
tm.tm_mday = wtm.wDay;
tm.tm_hour = wtm.wHour;
tm.tm_min = wtm.wMinute;
tm.tm_sec = wtm.wSecond;
tm.tm_isdst = -1;
clock = mktime(&tm);
tp->tv_sec = clock;
tp->tv_usec = wtm.wMilliseconds * 1000;
return (0);
}
#endif // !_WIN32
static void ExecShellCommand(const std::string &cmd, std::string *message) {
......@@ -132,11 +157,13 @@ static void MkDir(const char *path) {
}
}
#else
CreateDirectory(path, NULL);
BOOL return_value = CreateDirectory(path, NULL);
if (!return_value) {
auto errorno = GetLastError();
if (errorno != ERROR_ALREADY_EXISTS) {
throw std::runtime_error(path_error);
}
}
#endif // !_WIN32
}
......
paddle/fluid/platform/profiler.cc
浏览文件 @ 0f254465
......@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/platform/port.h"
#include <sys/time.h>
#include <algorithm>
#include <iomanip>
#include <limits>
......
paddle/fluid/platform/profiler.h
浏览文件 @ 0f254465
......@@ -69,7 +69,6 @@ void PushEvent(const std::string& name, const DeviceContext* dev_ctx);
void PopEvent(const std::string& name, const DeviceContext* dev_ctx);
#if !defined(_WIN32)
struct RecordEvent {
// dev_ctx can be set to nullptr if device is cpu.
RecordEvent(const std::string& name, const DeviceContext* dev_ctx);
......@@ -106,15 +105,6 @@ struct RecordBlock {
std::string name_;
uint64_t start_ns_;
};
#else
// windows do not support profiler temporarily.
struct RecordEvent {
RecordEvent(const std::string& name, const DeviceContext* dev_ctx) {}
};
struct RecordBlock {
explicit RecordBlock(int block_id) {}
};
#endif
// Return the event list of all threads. Assumed the returned value calls
// event_lists, event_lists[i][j] represents the j-th Event of i-th thread.
......
paddle/fluid/platform/stream_callback_manager.h
浏览文件 @ 0f254465
......@@ -45,16 +45,15 @@ class StreamCallbackManager {
inline void AddCallback(Callback &&callback) const {
auto *stream_callback_context =
new StreamCallbackContext(this, std::forward<Callback>(callback));
PADDLE_ENFORCE(
#if CUDA_VERSION >= 10000
cudaLaunchHostFunc(stream_, StreamCallbackManager::StreamCallbackFunc,
stream_callback_context)
#else
cudaStreamAddCallback(stream_,
PADDLE_ENFORCE(cudaLaunchHostFunc(stream_,
StreamCallbackManager::StreamCallbackFunc,
stream_callback_context, 0)
stream_callback_context)); // NOLINT
#else
PADDLE_ENFORCE(cudaStreamAddCallback(
stream_, StreamCallbackManager::StreamCallbackFunc,
stream_callback_context, 0)); // NOLINT
#endif
); // NOLINT
}
void Wait() const { thread_pool_.reset(new ThreadPool(1)); }
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 0f254465
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method pass_builder)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc)
if(NOT WIN32)
list(APPEND PYBIND_DEPS parallel_executor profiler)
list(APPEND PYBIND_SRCS recordio.cc)
endif(NOT WIN32)
set(PYBIND_DEPS pybind python proto_desc memory executor prune feed_fetch_method pass_builder parallel_executor profiler)
set(PYBIND_SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc)
if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 0f254465
......@@ -21,13 +21,6 @@ limitations under the License. */
#include <utility>
#include <vector>
#if defined(_WIN32)
#define NOMINMAX
#define GLOG_NO_ABBREVIATED_SEVERITIES // msvc conflict logging with windows.h
#define GOOGLE_GLOG_DLL_DECL
#include <Windows.h>
#endif
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/feed_fetch_method.h"
#include "paddle/fluid/framework/framework.pb.h"
......@@ -36,9 +29,7 @@ limitations under the License. */
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/lod_tensor_array.h"
#include "paddle/fluid/framework/op_registry.h"
#ifndef _WIN32
#include "paddle/fluid/framework/parallel_executor.h"
#endif
#include "paddle/fluid/framework/prune.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/selected_rows.h"
......@@ -46,6 +37,7 @@ limitations under the License. */
#include "paddle/fluid/memory/allocation/allocator_strategy.h"
#include "paddle/fluid/operators/activation_op.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/init.h"
#include "paddle/fluid/platform/place.h"
......@@ -95,6 +87,9 @@ bool IsCompiledWithDIST() {
}
PYBIND11_PLUGIN(core) {
// Not used, just make sure cpu_info.cc is linked.
paddle::platform::CpuTotalPhysicalMemory();
paddle::memory::allocation::UseAllocatorStrategyGFlag();
py::module m("core", "C++ core of PaddlePaddle");
......@@ -359,22 +354,16 @@ All parameter, weight, gradient are variables in Paddle.
return self.GetMutable<platform::Communicator>();
},
py::return_value_policy::reference)
#endif
#ifndef _WIN32
.def("get_reader",
[](Variable &self) -> framework::ReaderHolder * {
PADDLE_ENFORCE(self.IsType<framework::ReaderHolder>());
return self.GetMutable<framework::ReaderHolder>();
},
py::return_value_policy::reference)
#endif
; // NOLINT
py::return_value_policy::reference);
#if !defined(_WIN32)
py::class_<framework::ReaderHolder>(m, "Reader", "")
.def("reset", &framework::ReaderHolder::ResetAll);
#endif
using LoDTensorBlockingQueue =
::paddle::operators::reader::LoDTensorBlockingQueue;
......@@ -643,7 +632,6 @@ All parameter, weight, gradient are variables in Paddle.
#endif
#endif
#ifndef _WIN32
py::enum_<platform::ProfilerState>(m, "ProfilerState", py::arithmetic())
.value("kDisabled", platform::ProfilerState::kDisabled)
.value("kCPU", platform::ProfilerState::kCPU)
......@@ -664,7 +652,6 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
#endif
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
......@@ -693,7 +680,6 @@ All parameter, weight, gradient are variables in Paddle.
.def("remove_pass",
[](ir::PassBuilder &self, size_t idx) { self.RemovePass(idx); });
#ifndef _WIN32
// -- python binds for parallel executor.
py::class_<ParallelExecutor> pe(m, "ParallelExecutor");
py::class_<ExecutionStrategy> exec_strategy(pe, "ExecutionStrategy", R"DOC(
......@@ -921,7 +907,6 @@ All parameter, weight, gradient are variables in Paddle.
});
BindRecordIOWriter(&m);
#endif
return m.ptr();
}
} // namespace pybind
......
python/paddle/fluid/__init__.py
浏览文件 @ 0f254465
......@@ -115,9 +115,8 @@ def __bootstrap__():
'use_pinned_memory', 'check_nan_inf', 'benchmark', 'eager_delete_scope',
'use_mkldnn', 'use_ngraph', 'initial_cpu_memory_in_mb',
'init_allocated_mem', 'free_idle_memory', 'paddle_num_threads',
"dist_threadpool_size", 'cpu_deterministic', 'eager_delete_tensor_gb',
'allocator_strategy', 'reader_queue_speed_test_mode',
'print_sub_graph_dir'
"dist_threadpool_size", 'eager_delete_tensor_gb', 'allocator_strategy',
'reader_queue_speed_test_mode', 'print_sub_graph_dir'
]
if os.name != 'nt':
read_env_flags.append('warpctc_dir')
......
python/paddle/fluid/contrib/inferencer.py
浏览文件 @ 0f254465
......@@ -15,15 +15,13 @@
from __future__ import print_function
import contextlib
import os
from .. import core
from .. import executor
from .. import framework
from .. import io
if os.name != 'nt':
from .. import parallel_executor
from .. import parallel_executor
from .. import unique_name
from .trainer import check_and_get_place
......
python/paddle/fluid/contrib/trainer.py
浏览文件 @ 0f254465
......@@ -28,8 +28,7 @@ from .. import framework
from .. import io
# optimizer is same as the parameter of Trainer.__init__. Rename it to opt_module
from .. import optimizer as opt_module
if os.name != 'nt':
from .. import parallel_executor
from .. import parallel_executor
from ..transpiler import distribute_transpiler
__all__ = [
......
python/paddle/fluid/layers/io.py
浏览文件 @ 0f254465
......@@ -347,10 +347,8 @@ def _copy_reader_create_op_(block, op):
return new_op
if os.name != 'nt':
@templatedoc(op_type='create_recordio_file_reader')
def open_recordio_file(filename,
@templatedoc(op_type='create_recordio_file_reader')
def open_recordio_file(filename,
shapes,
lod_levels,
dtypes,
......@@ -406,8 +404,8 @@ if os.name != 'nt':
startup_var.desc.set_dtypes(dtypes)
startup_var.persistable = True
main_prog_var = _copy_reader_var_(
default_main_program().current_block(), startup_var)
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
startup_var)
if pass_num > 1:
main_prog_var = multi_pass(reader=main_prog_var, pass_num=pass_num)
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 0f254465
......@@ -85,6 +85,7 @@ __all__ = [
'row_conv',
'multiplex',
'layer_norm',
'group_norm',
'softmax_with_cross_entropy',
'smooth_l1',
'one_hot',
......@@ -343,10 +344,8 @@ def embedding(input,
return tmp
if os.name != 'nt':
@templatedoc(op_type="lstm")
def dynamic_lstm(input,
@templatedoc(op_type="lstm")
def dynamic_lstm(input,
size,
h_0=None,
c_0=None,
......@@ -963,10 +962,8 @@ def linear_chain_crf(input, label, param_attr=None):
return log_likelihood
if os.name != 'nt':
@templatedoc()
def crf_decoding(input, param_attr, label=None):
@templatedoc()
def crf_decoding(input, param_attr, label=None):
"""
${comment}
......@@ -992,11 +989,9 @@ if os.name != 'nt':
dtype=helper.input_dtype())
helper.append_op(
type='crf_decoding',
inputs={
"Emission": [input],
inputs={"Emission": [input],
"Transition": transition,
"Label": label
},
"Label": label},
outputs={"ViterbiPath": [viterbi_path]})
return viterbi_path
......@@ -2553,6 +2548,84 @@ def layer_norm(input,
return helper.append_activation(layer_norm_out)
@templatedoc()
def group_norm(input,
groups,
epsilon=1e-05,
param_attr=None,
bias_attr=None,
act=None,
data_layout='NCHW',
name=None):
"""
**Group Normalization Layer**
Refer to `Group Normalization <https://arxiv.org/abs/1803.08494>`
Args:
input(Variable): The input tensor variable.
groups(int): The number of groups that divided from channels.
epsilon(float): The small value added to the variance to prevent
division by zero.
param_attr(ParamAttr|None): The parameter attribute for the learnable
scale :math:`g`. If it is set to False, no scale will be added to the output units.
If it is set to None, the bias is initialized one. Default: None.
bias_attr(ParamAttr|None): The parameter attribute for the learnable
bias :math:`b`. If it is set to False, no bias will be added to the output units.
If it is set to None, the bias is initialized zero. Default: None.
act(str): Activation to be applied to the output of group normalizaiton.
data_layout(string|NCHW): Only NCHW is supported.
name (str): The name of this layer. It is optional.
Returns:
Variable: A tensor variable which is the result after applying group normalization on the input.
Examples:
>>> data = fluid.layers.data(name='data', shape=[8, 32, 32],
>>> dtype='float32')
>>> x = fluid.layers.group_norm(input=data, groups=4)
"""
helper = LayerHelper('group_norm', **locals())
dtype = helper.input_dtype()
# create intput and parameters
inputs = {'X': input}
input_shape = input.shape
if data_layout != 'NCHW':
raise ValueError("unsupported data layout:" + data_layout)
param_shape = [input_shape[1]]
if param_attr:
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
default_initializer=Constant(1.0))
inputs['Scale'] = scale
if bias_attr:
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
inputs['Bias'] = bias
# create output
mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
group_norm_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="group_norm",
inputs=inputs,
outputs={
"Y": group_norm_out,
"Mean": mean_out,
"Variance": variance_out,
},
attrs={"epsilon": epsilon,
"groups": groups})
return helper.append_activation(group_norm_out)
def conv2d_transpose(input,
num_filters,
output_size=None,
......@@ -5593,14 +5666,8 @@ def label_smooth(label,
return smooth_label
if os.name != 'nt':
@templatedoc()
def roi_pool(input,
rois,
pooled_height=1,
pooled_width=1,
spatial_scale=1.0):
@templatedoc()
def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
"""
${comment}
......@@ -7702,6 +7769,15 @@ def logical_and(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_and(x=left, y=right)
"""
return _logical_op(
......@@ -7721,6 +7797,15 @@ def logical_or(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_or(x=left, y=right)
"""
return _logical_op(
......@@ -7740,6 +7825,15 @@ def logical_xor(x, y, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
right = fluid.layers.data(
name='right', shape=[1], dtype='int32')
result = fluid.layers.logical_xor(x=left, y=right)
"""
return _logical_op(
......@@ -7758,6 +7852,13 @@ def logical_not(x, out=None, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
left = fluid.layers.data(
name='left', shape=[1], dtype='int32')
result = fluid.layers.logical_not(x=left)
"""
return _logical_op(
......@@ -7777,6 +7878,13 @@ def clip(x, min, max, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
input = fluid.layers.data(
name='data', shape=[1], dtype='float32')
reward = fluid.layers.clip(x=input, min=-1.0, max=1.0)
"""
helper = LayerHelper("clip", **locals())
......@@ -7809,6 +7917,13 @@ def clip_by_norm(x, max_norm, name=None):
Returns:
out(${out_type}): ${out_comment}
Examples:
.. code-block:: python
input = fluid.layers.data(
name='data', shape=[1], dtype='float32')
reward = fluid.layers.clip_by_norm(x=input, max_norm=1.0)
"""
helper = LayerHelper("clip_by_norm", **locals())
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 0f254465
......@@ -100,12 +100,12 @@ Examples:
>>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""
if os.name != 'nt':
__all__ += ['cumsum']
__all__ += ['cumsum']
_cum_sum_ = generate_layer_fn('cumsum')
_cum_sum_ = generate_layer_fn('cumsum')
def cumsum(x, axis=None, exclusive=None, reverse=None):
def cumsum(x, axis=None, exclusive=None, reverse=None):
locals_var = locals().keys()
kwargs = dict()
for name in locals_var:
......@@ -114,12 +114,13 @@ if os.name != 'nt':
kwargs[name] = val
return _cum_sum_(**kwargs)
cumsum.__doc__ = _cum_sum_.__doc__ + """
Examples:
cumsum.__doc__ = _cum_sum_.__doc__ + """
Examples:
>>> data = fluid.layers.data(name="input", shape=[32, 784])
>>> result = fluid.layers.cumsum(data, axis=0)
"""
"""
__all__ += ['thresholded_relu']
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 0f254465
......@@ -23,6 +23,12 @@ if(NOT WITH_DISTRIBUTE)
LIST(REMOVE_ITEM TEST_OPS test_dist_text_classification)
endif(NOT WITH_DISTRIBUTE)
if (NOT ${WITH_GPU})
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
elseif(${CUDNN_MAJOR_VERSION} VERSION_LESS 7)
LIST(REMOVE_ITEM TEST_OPS test_conv2d_fusion_op)
endif()
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list(REMOVE_ITEM TEST_OPS test_modified_huber_loss_op) # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5184
list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
......@@ -75,10 +81,12 @@ list(REMOVE_ITEM TEST_OPS test_dist_se_resnext)
list(REMOVE_ITEM TEST_OPS test_dist_transformer)
list(REMOVE_ITEM TEST_OPS test_parallel_executor_transformer)
list(REMOVE_ITEM TEST_OPS test_image_classification_resnet)
list(REMOVE_ITEM TEST_OPS test_interpolate_op)
foreach(TEST_OP ${TEST_OPS})
py_test_modules(${TEST_OP} MODULES ${TEST_OP})
endforeach(TEST_OP)
py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR} SERIAL)
py_test_modules(test_interpolate_op MODULES test_interpolate_op SERIAL)
if(WITH_DISTRIBUTE)
py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)
......
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 0f254465
......@@ -381,7 +381,7 @@ class OpTest(unittest.TestCase):
outs.sort(key=len)
checker(outs)
def __assert_is_close(self, numeric_grads, analytic_grads, names,
def _assert_is_close(self, numeric_grads, analytic_grads, names,
max_relative_error, msg_prefix):
for a, b, name in six.moves.zip(numeric_grads, analytic_grads, names):
......@@ -451,7 +451,7 @@ class OpTest(unittest.TestCase):
analytic_grads = self._get_gradient(inputs_to_check, place,
output_names, no_grad_set)
self.__assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
self._assert_is_close(numeric_grads, analytic_grads, inputs_to_check,
max_relative_error,
"Gradient Check On %s" % str(place))
......
python/paddle/fluid/tests/unittests/test_group_norm_op.py 0 → 100644
浏览文件 @ 0f254465
# 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.
from __future__ import print_function
import unittest
import numpy as np
from operator import mul
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test import OpTest
from testsuite import create_op
def group_norm_naive(x, scale, bias, epsilon, groups):
N, C, H, W = x.shape
G = groups
x = x.reshape((N * G, -1))
mean = np.mean(x, axis=1, keepdims=True)
var = np.var(x, axis=1, keepdims=True)
output = (x - mean) / np.sqrt(var + epsilon)
output = output.reshape((N, C, H, W)) * scale.reshape(
(-1, 1, 1)) + bias.reshape((-1, 1, 1))
return output, mean.reshape((N, G)), var.reshape((N, G))
class TestGroupNormOp(OpTest):
def setUp(self):
self.op_type = "group_norm"
self.data_format = "NCHW"
self.dtype = np.float32
self.shape = (2, 4, 3, 3)
self.attrs = {'epsilon': 1e-5, 'groups': 2}
self.compare_between_place = False
self.init_test_case()
input = np.random.random(self.shape).astype(self.dtype)
scale = np.random.random([self.shape[1]]).astype(self.dtype)
bias = np.random.random([self.shape[1]]).astype(self.dtype)
output, mean, var = group_norm_naive(
input, scale, bias, self.attrs['epsilon'], self.attrs['groups'])
self.inputs = {
'X': OpTest.np_dtype_to_fluid_dtype(input),
'Scale': OpTest.np_dtype_to_fluid_dtype(scale),
'Bias': OpTest.np_dtype_to_fluid_dtype(bias)
}
self.outputs = {'Y': output, 'Mean': mean, 'Variance': var}
def test_check_output(self):
atol = 1e-4
place = core.CPUPlace()
self.check_output_with_place(place, atol=atol)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=atol)
def do_compare_between_place(self):
if not core.is_compiled_with_cuda(): return
place = core.CPUPlace()
place2 = core.CUDAPlace(0)
self.scope = core.Scope()
op_inputs = self.inputs if hasattr(self, "inputs") else dict()
op_outputs = self.outputs if hasattr(self, "outputs") else dict()
op_attrs = self.attrs if hasattr(self, "attrs") else dict()
self.op = create_op(self.scope, self.op_type, op_inputs, op_outputs,
op_attrs)
inputs_to_check = set(['X', 'Scale', 'Bias'])
output_names = 'Y'
cpu_grads = self._get_gradient(inputs_to_check, place, output_names,
None)
gpu_grads = self._get_gradient(inputs_to_check, place2, output_names,
None)
self._assert_is_close(cpu_grads, gpu_grads, inputs_to_check, 0.005,
"Gradient Check On %s" % str(place))
def test_check_grad(self):
if self.compare_between_place:
self.do_compare_between_place()
return
place = core.CPUPlace()
self.check_grad_with_place(
place, set(['X', 'Scale', 'Bias']), 'Y', max_relative_error=0.01)
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
self.check_grad_with_place(
place,
set(['X', 'Scale', 'Bias']),
'Y',
max_relative_error=0.01)
def init_test_case(self):
pass
class TestGroupNormOp1(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 1
class TestGroupNormOp2(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 4
class TestGroupNormOpBigEps1(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 1
self.attrs['epsilon'] = 0.5
class TestGroupNormOpBigEps2(TestGroupNormOp):
def init_test_case(self):
self.attrs['groups'] = 4
self.attrs['epsilon'] = 0.5
class TestGroupNormOpBigEps3(TestGroupNormOp):
def init_test_case(self):
self.attrs['epsilon'] = 0.5
class TestGroupNormOpLargeData(TestGroupNormOp):
def init_test_case(self):
self.shape = (2, 32, 64, 64)
self.attrs['groups'] = 8
self.compare_between_place = True
if __name__ == '__main__':
unittest.main()
python/requirements.txt
浏览文件 @ 0f254465
requests==2.9.2
numpy>=1.12,<=1.14 #TODO:change to ">=1.12" when numpy fix bug in 1.15 and higher version
numpy>=1.12
protobuf==3.1
recordio>=0.1.0
matplotlib==2.2.3 # TODO: let python3 paddlepaddle package use latest matplotlib
......
tools/manylinux1/Dockerfile.x64
浏览文件 @ 0f254465
......@@ -36,17 +36,21 @@ RUN cd /opt && wget -q --no-check-certificate https://github.com/google/protobuf
tar xzf protobuf-cpp-3.1.0.tar.gz && \
cd protobuf-3.1.0 && ./configure && make -j4 && make install && cd .. && rm -f protobuf-cpp-3.1.0.tar.gz
RUN wget -O /root/requirements.txt https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt
RUN wget https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/python/requirements.txt -O /root/requirements.txt
RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install -r /root/requirements.txt && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install -r /root/requirements.txt && \
go get github.com/Masterminds/glide && \
rm -rf /root/requirements.txt
RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27mu/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs2/lib:${LD_LIBRARY_PATH} /opt/python/cp27-cp27m/bin/pip install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
LD_LIBRARY_PATH=/opt/_internal/cpython-3.5.1/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.5.1/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
RUN wget -O /opt/swig-2.0.12.tar.gz https://cytranet.dl.sourceforge.net/project/swig/swig/swig-2.0.12/swig-2.0.12.tar.gz && \
cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz
......
tools/manylinux1/build_scripts/build.sh
浏览文件 @ 0f254465
......@@ -9,12 +9,12 @@ set -ex
# remove others to expedite build and reduce docker image size. The original
# manylinux docker image project builds many python versions.
# NOTE We added back 3.5.1, since auditwheel requires python 3.3+
CPYTHON_VERSIONS="2.7.11 3.5.1"
CPYTHON_VERSIONS="3.7.0 3.6.0 3.5.1 2.7.11"
# openssl version to build, with expected sha256 hash of .tar.gz
# archive
OPENSSL_ROOT=openssl-1.0.2l
OPENSSL_HASH=ce07195b659e75f4e1db43552860070061f156a98bb37b672b101ba6e3ddf30c
OPENSSL_ROOT=openssl-1.1.0i
OPENSSL_HASH=ebbfc844a8c8cc0ea5dc10b86c9ce97f401837f3fa08c17b2cdadc118253cf99
EPEL_RPM_HASH=e5ed9ecf22d0c4279e92075a64c757ad2b38049bcf5c16c4f2b75d5f6860dc0d
DEVTOOLS_HASH=a8ebeb4bed624700f727179e6ef771dafe47651131a00a78b342251415646acc
PATCHELF_HASH=d9afdff4baeacfbc64861454f368b7f2c15c44d245293f7587bbf726bfe722fb
......@@ -25,7 +25,7 @@ AUTOCONF_HASH=954bd69b391edc12d6a4a51a2dd1476543da5c6bbf05a95b59dc0dd6fd4c2969
# Dependencies for compiling Python that we want to remove from
# the final image after compiling Python
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel"
PYTHON_COMPILE_DEPS="zlib-devel bzip2-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel libffi-devel"
# Libraries that are allowed as part of the manylinux1 profile
MANYLINUX1_DEPS="glibc-devel libstdc++-devel glib2-devel libX11-devel libXext-devel libXrender-devel mesa-libGL-devel libICE-devel libSM-devel ncurses-devel freetype-devel libpng-devel"
......@@ -61,7 +61,7 @@ yum -y install bzip2 make git patch unzip bison yasm diffutils \
wget -q https://cmake.org/files/v3.5/cmake-3.5.2.tar.gz && tar xzf cmake-3.5.2.tar.gz && \
cd cmake-3.5.2 && ./bootstrap && \
make -j4 && make install && cd .. && rm cmake-3.5.2.tar.gz
make -j8 && make install && cd .. && rm cmake-3.5.2.tar.gz
# Install newest autoconf
......@@ -77,11 +77,13 @@ mkdir -p /opt/python
build_cpythons $CPYTHON_VERSIONS
PY35_BIN=/opt/python/cp35-cp35m/bin
PY36_BIN=/opt/python/cp36-cp36m/bin
PY37_BIN=/opt/python/cp37-cp37m/bin
# NOTE Since our custom manylinux image builds pythons with shared
# libpython, we need to add libpython's dir to LD_LIBRARY_PATH before running
# python.
ORIGINAL_LD_LIBRARY_PATH="${LD_LIBRARY_PATH}"
LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib"
LD_LIBRARY_PATH="${ORIGINAL_LD_LIBRARY_PATH}:$(dirname ${PY35_BIN})/lib:$(dirname ${PY36_BIN})/lib:$(dirname ${PY37_BIN})/lib"
# Our openssl doesn't know how to find the system CA trust store
# (https://github.com/pypa/manylinux/issues/53)
......@@ -119,9 +121,8 @@ ln -s $PY35_BIN/auditwheel /usr/local/bin/auditwheel
# final image
yum -y erase wireless-tools gtk2 libX11 hicolor-icon-theme \
avahi freetype bitstream-vera-fonts \
${PYTHON_COMPILE_DEPS} > /dev/null 2>&1
yum -y install ${MANYLINUX1_DEPS}
yum -y clean all > /dev/null 2>&1
${PYTHON_COMPILE_DEPS} > /dev/null 2>&1 || true
yum -y install ${MANYLINUX1_DEPS} && yum -y clean all > /dev/null 2>&1 || true
yum list installed
# we don't need libpython*.a, and they're many megabytes
find /opt/_internal -name '*.a' -print0 | xargs -0 rm -f
......
tools/manylinux1/build_scripts/build_utils.sh
浏览文件 @ 0f254465
......@@ -52,9 +52,17 @@ function do_cpython_build {
# NOTE --enable-shared for generating libpython shared library needed for
# linking of some of the nupic.core test executables.
if [ $(lex_pyver $py_ver) -ge $(lex_pyver 3.7) ]; then
# NOTE python 3.7 should be installed via make altinstall rather than
# make install, and we should specify the location of ssl
CFLAGS="-Wformat" ./configure --prefix=${prefix} --with-openssl=/usr/local/ssl --enable-shared $unicode_flags > /dev/null
make -j8 > /dev/null
make altinstall > /dev/null
else
CFLAGS="-Wformat" ./configure --prefix=${prefix} --enable-shared $unicode_flags > /dev/null
make -j2 > /dev/null
make -j8 > /dev/null
make install > /dev/null
fi
popd
echo "ZZZ looking for libpython"
find / -name 'libpython*.so*'
......@@ -64,6 +72,9 @@ function do_cpython_build {
if [ -e ${prefix}/bin/python3 ]; then
ln -s python3 ${prefix}/bin/python
fi
if [ -e ${prefix}/bin/python3.7 ]; then
ln -s python3.7 ${prefix}/bin/python
fi
# NOTE Make libpython shared library visible to python calls below
LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/python get-pip.py
LD_LIBRARY_PATH="${prefix}/lib" ${prefix}/bin/pip install wheel
......
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