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PaddleDetection
提交 d0e3b240 编写于 作者: Q Qiao Longfei
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-dist-sparse-decay 

test=develop
上级 c3b9edf9 223cc89f
隐藏空白更改
内联 并排
Showing with 1239 addition and 197 deletion
cmake/FindJeMalloc.cmake
浏览文件 @ d0e3b240
......@@ -19,3 +19,10 @@ find_package_handle_standard_args(jemalloc DEFAULT_MSG JEMALLOC_LIBRARIES JEMALL
mark_as_advanced(
JEMALLOC_LIBRARIES
JEMALLOC_INCLUDE_DIR)
if (JEMALLOC_FOUND)
add_library(jemalloc::jemalloc UNKNOWN IMPORTED)
set_target_properties(jemalloc::jemalloc PROPERTIES
IMPORTED_LOCATION ${JEMALLOC_LIBRARIES}
INTERFACE_INCLUDE_DIRECTORIES "${JEMALLOC_INCLUDE_DIR}")
endif()
cmake/cuda.cmake
浏览文件 @ d0e3b240
......@@ -2,9 +2,11 @@ if(NOT WITH_GPU)
return()
endif()
set(paddle_known_gpu_archs "30 35 50 52 60 61 70 75")
set(paddle_known_gpu_archs "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs7 "30 35 50 52")
set(paddle_known_gpu_archs8 "30 35 50 52 60 61")
set(paddle_known_gpu_archs9 "30 35 50 52 60 61 70")
set(paddle_known_gpu_archs10 "30 35 50 52 60 61 70 75")
######################################################################################
# A function for automatic detection of GPUs installed (if autodetection is enabled)
......@@ -155,6 +157,16 @@ elseif (${CUDA_VERSION} LESS 9.0) # CUDA 8.x
# warning for now.
list(APPEND CUDA_NVCC_FLAGS "-Wno-deprecated-gpu-targets")
add_definitions("-DPADDLE_CUDA_BINVER=\"80\"")
elseif (${CUDA_VERSION} LESS 10.0) # CUDA 9.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs9})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
add_definitions("-DPADDLE_CUDA_BINVER=\"90\"")
elseif (${CUDA_VERSION} LESS 11.0) # CUDA 10.x
set(paddle_known_gpu_archs ${paddle_known_gpu_archs10})
list(APPEND CUDA_NVCC_FLAGS "-D_MWAITXINTRIN_H_INCLUDED")
list(APPEND CUDA_NVCC_FLAGS "-D__STRICT_ANSI__")
add_definitions("-DPADDLE_CUDA_BINVER=\"100\"")
endif()
include_directories(${CUDA_INCLUDE_DIRS})
......
cmake/external/boost.cmake
浏览文件 @ d0e3b240
......@@ -23,11 +23,8 @@ set(BOOST_PROJECT "extern_boost")
# checked that the devtools package of CentOS 6 installs boost 1.41.0.
# So we use 1.41.0 here.
set(BOOST_VER "1.41.0")
if((NOT DEFINED BOOST_TAR) OR (NOT DEFINED BOOST_URL))
message(STATUS "use pre defined download url")
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
endif()
set(BOOST_TAR "boost_1_41_0" CACHE STRING "" FORCE)
set(BOOST_URL "http://paddlepaddledeps.cdn.bcebos.com/${BOOST_TAR}.tar.gz" CACHE STRING "" FORCE)
MESSAGE(STATUS "BOOST_TAR: ${BOOST_TAR}, BOOST_URL: ${BOOST_URL}")
......
cmake/external/mkldnn.cmake
浏览文件 @ d0e3b240
......@@ -55,7 +55,7 @@ ExternalProject_Add(
${MKLDNN_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
DEPENDS ${MKLDNN_DEPENDS}
GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git"
GIT_REPOSITORY "https://github.com/intel/mkl-dnn.git"
GIT_TAG "830a10059a018cd2634d94195140cf2d8790a75a"
PREFIX ${MKLDNN_SOURCES_DIR}
UPDATE_COMMAND ""
......
cmake/external/mklml.cmake
浏览文件 @ d0e3b240
......@@ -16,6 +16,12 @@ IF(NOT ${WITH_MKLML})
return()
ENDIF(NOT ${WITH_MKLML})
IF(APPLE)
MESSAGE(WARNING "Mac is not supported with MKLML in Paddle yet. Force WITH_MKLML=OFF.")
SET(WITH_MKLML OFF CACHE STRING "Disable MKLML package in MacOS" FORCE)
return()
ENDIF()
INCLUDE(ExternalProject)
SET(MKLML_DST_DIR "mklml")
SET(MKLML_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
......@@ -23,32 +29,24 @@ SET(MKLML_INSTALL_DIR ${MKLML_INSTALL_ROOT}/${MKLML_DST_DIR})
SET(MKLML_ROOT ${MKLML_INSTALL_DIR})
SET(MKLML_INC_DIR ${MKLML_ROOT}/include)
SET(MKLML_LIB_DIR ${MKLML_ROOT}/lib)
if(WIN32)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
SET(TIME_VERSION "2019.0.1.20181227")
IF(WIN32)
SET(MKLML_VER "mklml_win_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
SET(MKLML_LIB ${MKLML_LIB_DIR}/mklml.lib)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.lib)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/mklml.dll)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5md.dll)
else()
ELSE()
SET(MKLML_VER "mklml_lnx_${TIME_VERSION}" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
SET(MKLML_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
SET(MKLML_SHARED_LIB ${MKLML_LIB_DIR}/libmklml_intel.so)
SET(MKLML_SHARED_IOMP_LIB ${MKLML_LIB_DIR}/libiomp5.so)
endif()
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${MKLML_ROOT}/lib")
IF((NOT DEFINED MKLML_VER) OR (NOT DEFINED MKLML_URL))
MESSAGE(STATUS "use pre defined download url")
if(WIN32)
SET(MKLML_VER "mklml_win_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "https://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.zip" CACHE STRING "" FORCE)
elseif(APPLE)
SET(MKLML_VER "mklml_mac_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
else()
SET(MKLML_VER "mklml_lnx_2019.0.1.20180928" CACHE STRING "" FORCE)
SET(MKLML_URL "http://paddlepaddledeps.cdn.bcebos.com/${MKLML_VER}.tgz" CACHE STRING "" FORCE)
ENDIF()
endif()
ENDIF()
SET(MKLML_PROJECT "extern_mklml")
MESSAGE(STATUS "MKLML_VER: ${MKLML_VER}, MKLML_URL: ${MKLML_URL}")
......
cmake/generic.cmake
浏览文件 @ d0e3b240
......@@ -117,7 +117,7 @@ function(common_link TARGET_NAME)
endif()
if (WITH_JEMALLOC)
target_link_libraries(${TARGET_NAME} ${JEMALLOC_LIBRARIES})
target_link_libraries(${TARGET_NAME} jemalloc::jemalloc)
endif()
endfunction()
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -94,4 +94,4 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS
graph_viz_pass multi_devices_graph_pass
multi_devices_graph_print_pass multi_devices_graph_check_pass
fuse_elewise_add_act_pass multi_batch_merge_pass
memory_optimize_pass)
memory_optimize_pass lock_free_optimize_pass)
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ d0e3b240
......@@ -232,3 +232,4 @@ USE_PASS(analysis_var_pass);
USE_PASS(sequential_execution_pass);
USE_PASS(all_reduce_deps_pass);
USE_PASS(modify_op_lock_and_record_event_pass);
USE_PASS(lock_free_optimize_pass);
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -31,6 +31,7 @@ cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(lock_free_optimize_pass base)
pass_library(fc_fuse_pass inference)
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
......
paddle/fluid/framework/ir/lock_free_optimize_pass.cc 0 → 100644
浏览文件 @ d0e3b240
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/ir/lock_free_optimize_pass.h"
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace framework {
namespace ir {
const char kSumGradOpName[] = "sum";
// TODO(minqiyang): only support sgd at current time, please add
// other optimizers later.
const char kOptimizerType[] = "sgd";
std::unique_ptr<ir::Graph> LockFreeOptimizePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
// We could collect all weights' name from SGD, where
// W1 <- SGD(W0, Grad0)
std::unordered_set<std::string> weight_var_set;
for (auto* node : graph->Nodes()) {
if (IsOpNamed(node, kOptimizerType)) {
auto& param_out_vars = node->Op()->Output("ParamOut");
PADDLE_ENFORCE(param_out_vars.size() == 1u);
weight_var_set.insert(param_out_vars[0]);
}
}
// find all grad's merge op via weight name, where
// Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
std::unordered_set<ir::Node*> grad_sum_op_set;
for (ir::Node* node : graph->Nodes()) {
if (IsOpNamed(node, kSumGradOpName)) {
for (ir::Node* output : node->outputs) {
// strip the last grad suffix @GRAD
std::string var_name = output->Name();
const std::string suffix(kGradVarSuffix);
if (var_name != suffix && var_name.size() > suffix.size() &&
var_name.substr(var_name.size() - suffix.size()) == suffix) {
// if so then strip them off
var_name = var_name.substr(0, var_name.size() - suffix.size());
if (weight_var_set.find(var_name) != weight_var_set.end()) {
grad_sum_op_set.insert(node);
break;
}
}
}
}
}
// get the forward op and backward op pairs, where
// out <- forward(X, W)
// Grad1 <- backward(out, X')
// Grad0 <- SUM(Grad1, Grad2, Grad3 ...)
// W0 <- SGD(W1, Grad0)
for (ir::Node* node : grad_sum_op_set) {
for (ir::Node* merged_grad_var : node->outputs) {
// find the optimizers connected with sum op
if (IsVarNameEndsWith(merged_grad_var, kGradVarSuffix) &&
merged_grad_var->outputs.size() == 1u) {
ir::Node* opt_node = merged_grad_var->outputs[0];
VLOG(3) << "Found opt node " << opt_node->Name();
// find the backward op connected with sum op
for (ir::Node* unmerged_grad_var : node->inputs) {
if (IsVarNameContains(unmerged_grad_var, kGradVarSuffix) &&
unmerged_grad_var->inputs.size() == 1u) {
ir::Node* backward_op = unmerged_grad_var->inputs[0];
VLOG(3) << "Found backward_op " << backward_op->Name();
// find the forward op related to the backward op
ir::Node* forward_op =
FindForwardOpViaBackwardOp(graph.get(), backward_op);
VLOG(3) << "Found forward_op " << forward_op->Name();
PADDLE_ENFORCE(forward_op);
Node* new_optimizer_node = CreateNewSGDNode(
graph.get(), forward_op, backward_op, node, opt_node);
PADDLE_ENFORCE(new_optimizer_node);
}
}
}
}
}
// Remove the sum_op and its' outputs and connected Optimizers
for (Node* sum_op : grad_sum_op_set) {
for (Node* sum_op_output : sum_op->outputs) {
for (Node* optimize_op : sum_op_output->outputs) {
if (optimize_op->NodeType() == Node::Type::kOperation &&
optimize_op->Name() == kOptimizerType) {
VLOG(3) << "remove optimize_op: " << optimize_op->Name() << "_"
<< optimize_op->id();
graph->RemoveNode(optimize_op);
}
}
VLOG(3) << "remove sum_op_output: " << sum_op_output->Name() << "_"
<< sum_op_output->id();
graph->RemoveNode(sum_op_output);
}
VLOG(3) << "remove sum_op: " << sum_op->Name() << "_" << sum_op->id();
graph->RemoveNode(sum_op);
}
for (auto* node : graph->Nodes()) {
for (Node* output_node : node->outputs) {
if (output_node->Name() == "sgd") {
VLOG(3) << "Node link to SGD: " << node->Name() << "_" << node->id()
<< " --> " << output_node->Name() << "_" << output_node->id();
for (Node* input_node : node->inputs) {
VLOG(3) << "SGD Input link: " << input_node->Name() << "_"
<< input_node->id() << " --> " << node->Name() << "_"
<< node->id();
}
}
}
}
return graph;
}
ir::Node* LockFreeOptimizePass::CreateNewSGDNode(
ir::Graph* graph, ir::Node* forward_node, ir::Node* backward_node,
ir::Node* grad_sum_node, ir::Node* optimize_node) const {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(forward_node);
PADDLE_ENFORCE(backward_node);
PADDLE_ENFORCE(grad_sum_node);
PADDLE_ENFORCE(optimize_node);
// find the grad var node between the grad sum node and backward_node
std::vector<ir::Node*> grad_vars =
FindConnectedNode(backward_node, grad_sum_node);
ir::Node* grad_node = nullptr;
for (ir::Node* node : grad_vars) {
if (!ir::IsControlDepVar(*node)) {
grad_node = node;
}
}
PADDLE_ENFORCE(grad_node);
// create a new SGD node
OpDesc* old_desc = optimize_node->Op();
// keep with the same block between new optimizer and the old one
OpDesc new_desc(*old_desc, old_desc->Block());
new_desc.SetInput("Param", old_desc->Input("Param"));
new_desc.SetInput("LearningRate", old_desc->Input("LearningRate"));
new_desc.SetInput("Grad", std::vector<std::string>({grad_node->Name()}));
new_desc.SetOutput("ParamOut", old_desc->Output("ParamOut"));
std::vector<std::string> op_role_vars = boost::get<std::vector<std::string>>(
new_desc.GetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName()));
// replace the second op role var, because the grad name was
// changed in new optimizer
op_role_vars.pop_back();
op_role_vars.push_back(grad_node->Name());
new_desc.SetAttr(framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(),
op_role_vars);
new_desc.SetType(kOptimizerType);
// set backward op's op role var, this will be used to
// set device_id in multi_device_pass
backward_node->Op()->SetAttr(
framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), op_role_vars);
// backward_node->Op()->SetAttr(
// framework::OpProtoAndCheckerMaker::OpRoleVarAttrName(), {});
// keep with the same output nodes between new optimizer and the
// old one
Node* sgd_node = graph->CreateOpNode(&new_desc);
// change all outputs of the optimize_node to the new one
ReplaceAllDownstreamNode(optimize_node, sgd_node);
// find connected node between forward node and optimize node
// and replace the optimize node to new sgd node
std::vector<ir::Node*> forward_opt_connected_nodes =
FindConnectedNode(forward_node, optimize_node);
for (ir::Node* node : forward_opt_connected_nodes) {
ReplaceUpstreamNode(node, optimize_node, sgd_node);
}
// find connected node between backward node and optimize node
// and replace the optimize node to new sgd node
std::vector<ir::Node*> backward_opt_connected_nodes =
FindConnectedNode(backward_node, optimize_node);
for (ir::Node* node : backward_opt_connected_nodes) {
ReplaceUpstreamNode(node, optimize_node, sgd_node);
}
// SGD must have only one param and LR in
PADDLE_ENFORCE(old_desc->Input("LearningRate").size() == 1u);
PADDLE_ENFORCE(old_desc->Input("Param").size() == 1u);
// LR and weight nodes should be copied
for (Node* upstream_node : optimize_node->inputs) {
if (upstream_node->Name() == old_desc->Input("LearningRate")[0] ||
upstream_node->Name() == old_desc->Input("Param")[0]) {
ReplaceUpstreamNode(upstream_node, optimize_node, sgd_node);
}
}
VLOG(3) << "Create new opt node" << sgd_node->Name() << "_" << sgd_node->id();
return sgd_node;
}
std::vector<ir::Node*> LockFreeOptimizePass::FindConnectedNode(
ir::Node* upstream_node, ir::Node* downstream_node) const {
std::vector<ir::Node*> result;
for (ir::Node* out_node : upstream_node->outputs) {
for (ir::Node* in_node : downstream_node->inputs) {
if (in_node == out_node) {
result.push_back(in_node);
}
}
}
return result;
}
void LockFreeOptimizePass::ReplaceUpstreamNode(
ir::Node* upstream_node, ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const {
PADDLE_ENFORCE(upstream_node);
PADDLE_ENFORCE(old_optimizer_node);
PADDLE_ENFORCE(new_optimizer_node);
// Remove the old_optimizer_node from upstream_node's outputs vector
auto& output_node_vec = upstream_node->outputs;
for (auto output_node_iter = output_node_vec.begin();
output_node_iter != output_node_vec.end();) {
if (*output_node_iter == old_optimizer_node) {
output_node_vec.erase(output_node_iter);
break;
} else {
++output_node_iter;
}
}
// Add the new_optimizer_node to upstream_node's outputs vector
output_node_vec.emplace_back(new_optimizer_node);
new_optimizer_node->inputs.emplace_back(upstream_node);
}
void LockFreeOptimizePass::ReplaceAllDownstreamNode(
ir::Node* old_optimizer_node, ir::Node* new_optimizer_node) const {
PADDLE_ENFORCE(old_optimizer_node);
PADDLE_ENFORCE(new_optimizer_node);
for (ir::Node* downstream_node : old_optimizer_node->outputs) {
// Remove the old_optimizer_node from downstream_node's inputs vector
auto& input_node_vec = downstream_node->inputs;
for (auto input_node_iter = input_node_vec.begin();
input_node_iter != input_node_vec.end();) {
if (*input_node_iter == old_optimizer_node) {
input_node_vec.erase(input_node_iter);
break;
} else {
++input_node_iter;
}
}
// Add the new_optimizer_node to downstream_node's inputs vector
input_node_vec.emplace_back(new_optimizer_node);
new_optimizer_node->outputs.emplace_back(downstream_node);
}
}
ir::Node* LockFreeOptimizePass::FindForwardOpViaBackwardOp(
ir::Graph* graph, ir::Node* backward_node) const {
PADDLE_ENFORCE(graph);
PADDLE_ENFORCE(backward_node);
// strip the suffix _grad of backward_node's name
std::string forward_op_name = backward_node->Name();
const std::string suffix("_grad");
if (forward_op_name != suffix && forward_op_name.size() > suffix.size() &&
forward_op_name.substr(forward_op_name.size() - suffix.size()) ==
suffix) {
// if so then strip them off
forward_op_name =
forward_op_name.substr(0, forward_op_name.size() - suffix.size());
} else {
LOG(WARNING) << "Illegal backward node's name " << backward_node->Name()
<< " id " << backward_node->id();
return nullptr;
}
for (ir::Node* node : graph->Nodes()) {
if (node->Name() == forward_op_name) {
if (node->outputs.size() == 0u) {
// if forward_node has no output, then it has NO grad op
continue;
}
// check whether all inputs of the backward_op that ends_with @GRAD
// comes from the output of forward_op is the input of the backward_op
bool is_related_forward_node = true;
for (ir::Node* backward_input : backward_node->inputs) {
if (IsVarNameEndsWith(backward_input, kGradVarSuffix)) {
bool meets_correct_output = false;
for (ir::Node* forward_output : node->outputs) {
if (forward_output->Name() + kGradVarSuffix ==
backward_input->Name()) {
meets_correct_output = true;
break;
}
}
if (!meets_correct_output) {
is_related_forward_node = false;
break;
}
}
}
if (is_related_forward_node) {
return node;
}
}
}
return nullptr;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(lock_free_optimize_pass,
paddle::framework::ir::LockFreeOptimizePass);
paddle/fluid/framework/ir/lock_free_optimize_pass.h 0 → 100644
浏览文件 @ d0e3b240
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#ifndef PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
#define PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
#include <string>
#include <vector>
#include <boost/algorithm/string/predicate.hpp>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class Node;
/*
* Remove the sum op of all gradients of the backward op.
* And remove the dependecies of the optimizer related to the
* same backward op.
*
* Before this pass:
*
* forward_op1 forward_op2
* | |
* grad_op1 grad_op2
* \ /
* \ /
* sum_op
* |
* sgd_op
*
* After this pass:
* forward_op1 forward_op2
* | |
* grad_op1 grad_op2
* | |
* sgd_op1 sgd_op2
*
* sgd_op1 and sgd_op2 will update the same weight which holds the same
* memory, so we could benefits from the acceleration
*/
class LockFreeOptimizePass : public Pass {
public:
virtual ~LockFreeOptimizePass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const;
private:
// Create a new sgd node via current optimizer node
ir::Node* CreateNewSGDNode(ir::Graph* graph, ir::Node* forward_node,
ir::Node* backward_node, ir::Node* grad_sum_node,
ir::Node* optimize_node) const;
// Replace the input weight's optimizers
void ReplaceUpstreamNode(ir::Node* upstream_node,
ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const;
// Replace the output weight's optimizers
void ReplaceAllDownstreamNode(ir::Node* old_optimizer_node,
ir::Node* new_optimizer_node) const;
// Find all weight variables in graph
bool FindAllWeightVars(ir::Graph* graph) const;
// Find the forward_op node via the backward_op node
ir::Node* FindForwardOpViaBackwardOp(ir::Graph* graph,
ir::Node* backward_node) const;
std::vector<ir::Node*> FindConnectedNode(ir::Node* upstream_node,
ir::Node* downstream_node) const;
inline bool IsOpNamed(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kOperation && node->Name() == name;
}
inline bool IsVarNamed(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable && node->Name() == name;
}
inline bool IsVarNameEndsWith(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable &&
boost::algorithm::ends_with(node->Name(), name);
}
inline bool IsVarNameContains(ir::Node* node, const std::string& name) const {
PADDLE_ENFORCE(node);
return node->NodeType() == Node::Type::kVariable &&
node->Name().find(name) != std::string::npos;
}
inline bool IsControlDepFrom(ir::Node* ctrl_dep_node, ir::Node* node) const {
PADDLE_ENFORCE(ctrl_dep_node);
PADDLE_ENFORCE(node);
return IsControlDepVar(*ctrl_dep_node) &&
ctrl_dep_node->inputs.size() >= 1u &&
ctrl_dep_node->inputs[0] == node;
}
};
} // namespace ir
} // namespace framework
} // namespace paddle
#endif // PADDLE_FLUID_FRAMEWORK_IR_LOCK_FREE_OPTIMIZE_PASS_H_
paddle/fluid/framework/scope.cc
浏览文件 @ d0e3b240
......@@ -87,11 +87,12 @@ Variable* Scope::Var(const std::string& name) {
}
Variable* Scope::Var(std::string* name) {
auto new_name = string::Sprintf("%p.%d", this, vars_.size());
SCOPE_VARS_WRITER_LOCK
auto new_name = std::to_string(reinterpret_cast<uintptr_t>(this)) + "." +
std::to_string(vars_.size());
if (name != nullptr) {
*name = new_name;
}
SCOPE_VARS_WRITER_LOCK
return VarInternal(new_name);
}
......
paddle/fluid/framework/var_type_traits.cc
浏览文件 @ d0e3b240
......@@ -105,13 +105,15 @@ struct VarIdToTypeIndexMapHolder {
} // namespace detail
const std::type_index &ToTypeIndex(int var_id) {
const std::type_index &VarTraitIdToTypeIndex(int var_id) {
return detail::VarIdToTypeIndexMapHolder::ToTypeIndex(var_id);
}
const char *ToTypeName(int var_id) { return ToTypeIndex(var_id).name(); }
const char *ToTypeName(int var_id) {
return VarTraitIdToTypeIndex(var_id).name();
}
int ToTypeId(const std::type_index &type) {
int TypeIndexToVarTraitId(const std::type_index &type) {
return detail::VarIdToTypeIndexMapHolder::ToTypeId(type);
}
......
paddle/fluid/framework/var_type_traits.h
浏览文件 @ d0e3b240
......@@ -66,8 +66,8 @@ namespace paddle {
namespace framework {
const char *ToTypeName(int var_id);
const std::type_index &ToTypeIndex(int var_id);
int ToTypeId(const std::type_index &type);
const std::type_index &VarTraitIdToTypeIndex(int var_id);
int TypeIndexToVarTraitId(const std::type_index &type);
namespace detail {
......
paddle/fluid/framework/var_type_traits_test.cc
浏览文件 @ d0e3b240
......@@ -45,10 +45,11 @@ struct TypeIndexChecker {
constexpr auto kId = VarTypeTrait<Type>::kId;
std::type_index actual_type(typeid(Type));
EXPECT_EQ(std::string(ToTypeName(kId)), std::string(actual_type.name()));
EXPECT_EQ(ToTypeIndex(kId), actual_type);
EXPECT_EQ(ToTypeId(actual_type), kId);
EXPECT_EQ(ToTypeIndex(ToTypeId(actual_type)), actual_type);
EXPECT_EQ(ToTypeId(ToTypeIndex(kId)), kId);
EXPECT_EQ(VarTraitIdToTypeIndex(kId), actual_type);
EXPECT_EQ(TypeIndexToVarTraitId(actual_type), kId);
EXPECT_EQ(VarTraitIdToTypeIndex(TypeIndexToVarTraitId(actual_type)),
actual_type);
EXPECT_EQ(TypeIndexToVarTraitId(VarTraitIdToTypeIndex(kId)), kId);
EXPECT_TRUE(var_id_set->count(kId) == 0); // NOLINT
EXPECT_TRUE(type_index_set->count(actual_type) == 0); // NOLINT
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ d0e3b240
......@@ -80,8 +80,8 @@ void TestWord2vecPrediction(const std::string& model_path) {
i++) {
LOG(INFO) << "data: " << static_cast<float*>(outputs.front().data.data())[i]
<< " result: " << result[i];
PADDLE_ENFORCE(static_cast<float*>(outputs.front().data.data())[i],
result[i]);
EXPECT_NEAR(static_cast<float*>(outputs.front().data.data())[i], result[i],
1e-3);
}
}
......
paddle/fluid/inference/analysis/passes/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -7,4 +7,5 @@ set(analysis_deps ${analysis_deps}
ir_graph_build_pass
ir_analysis_pass
analysis_passes
subgraph_detector
CACHE INTERNAL "")
paddle/fluid/operators/jit/benchmark.cc
浏览文件 @ d0e3b240
......@@ -190,6 +190,26 @@ void BenchGRUKernel() {
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void BenchSeqPoolKernel() {
std::vector<jit::SeqPoolType> pool_types = {
jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt};
for (auto type : pool_types) {
for (int w : TestSizes()) {
jit::seq_pool_attr_t attr(w, type);
for (int h : TestSizes()) {
attr.h = h;
std::vector<T> x(h * w), y(w);
RandomVec<T>(h * w, x.data(), -2.f, 2.f);
const T* x_data = x.data();
T* y_data = y.data();
BenchAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType>(attr, x_data,
y_data, &attr);
}
}
}
}
// Benchmark all jit kernels including jitcode, mkl and refer.
// To use this tool, run command: ./benchmark [options...]
// Options:
......@@ -228,4 +248,7 @@ int main(int argc, char* argv[]) {
BenchGRUKernel<jit::kGRUH1, T, PlaceType>();
BenchGRUKernel<jit::kGRUHtPart1, T, PlaceType>();
BenchGRUKernel<jit::kGRUHtPart2, T, PlaceType>();
// seq pool function
BenchSeqPoolKernel<jit::kSeqPool, T, PlaceType>();
}
paddle/fluid/operators/jit/gen/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -26,3 +26,4 @@ USE_JITKERNEL_GEN(kGRUH1)
USE_JITKERNEL_GEN(kGRUHtPart1)
USE_JITKERNEL_GEN(kGRUHtPart2)
USE_JITKERNEL_GEN(kNCHW16CMulNC)
USE_JITKERNEL_GEN(kSeqPool)
paddle/fluid/operators/jit/gen/seqpool.cc 0 → 100644
浏览文件 @ d0e3b240
/* 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/jit/gen/seqpool.h"
#include "paddle/fluid/operators/jit/gen/act.h" // for exp_float_consts ones
#include "paddle/fluid/operators/jit/registry.h"
#include "paddle/fluid/platform/cpu_info.h"
namespace paddle {
namespace operators {
namespace jit {
namespace gen {
void SeqPoolJitCode::genCode() {
constexpr int block = YMM_FLOAT_BLOCK;
constexpr int max_num_regs = 8;
const int num_block = w_ / block;
const int num_groups = num_block / max_num_regs;
int rest_num_regs = num_block % max_num_regs;
mov(reg32_int_h, dword[param_attr]);
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(exp_float_consts));
vmovups(xmm_t(1), ptr[reg_tmp + OFFSET_EXP_ONE]);
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
fild(dword[param_attr]);
fstp(dword[reg_tmp]);
vmovss(xmm_t(0), ptr[reg_tmp]);
if (type_ == SeqPoolType::kSqrt) {
vsqrtps(xmm_t(0), xmm_t(0));
}
vdivps(xmm_t(1), xmm_t(1), xmm_t(0));
vmovss(ptr[reg_tmp], xmm_t(1));
}
const int group_len = max_num_regs * block * sizeof(float);
for (int g = 0; g < num_groups; ++g) {
pool_height<ymm_t>(g * group_len, block, max_num_regs);
}
if (rest_num_regs > 0) {
pool_height<ymm_t>(num_groups * group_len, block, rest_num_regs);
}
// part of rest_w * height
const int rest = w_ % block;
pool_height_of_rest_width(rest, (w_ - rest) * sizeof(float), max_num_regs);
ret();
}
class SeqPoolCreator : public JitCodeCreator<seq_pool_attr_t> {
public:
bool UseMe(const seq_pool_attr_t& attr) const override {
return platform::MayIUse(platform::avx);
}
size_t CodeSize(const seq_pool_attr_t& attr) const override {
return 96 +
((attr.w / YMM_FLOAT_BLOCK + 4 /* for rest */) *
4 /* load, mul and save */ +
256) *
8;
}
std::unique_ptr<GenBase> CreateJitCode(
const seq_pool_attr_t& attr) const override {
PADDLE_ENFORCE_GT(attr.w, 0);
PADDLE_ENFORCE_GT(attr.h, 0);
return make_unique<SeqPoolJitCode>(attr, CodeSize(attr));
}
};
} // namespace gen
} // namespace jit
} // namespace operators
} // namespace paddle
namespace gen = paddle::operators::jit::gen;
REGISTER_JITKERNEL_GEN(kSeqPool, gen::SeqPoolCreator);
paddle/fluid/operators/jit/gen/seqpool.h 0 → 100644
浏览文件 @ d0e3b240
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License. */
#pragma once
#include <string>
#include "glog/logging.h"
#include "paddle/fluid/operators/jit/gen/jitcode.h"
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace operators {
namespace jit {
namespace gen {
class SeqPoolJitCode : public JitCode {
public:
explicit SeqPoolJitCode(const seq_pool_attr_t& attr,
size_t code_size = 256 * 1024,
void* code_ptr = nullptr)
: JitCode(code_size, code_ptr), w_(attr.w), type_(attr.type) {
if (!(type_ == SeqPoolType::kSum || type_ == SeqPoolType::kAvg ||
type_ == SeqPoolType::kSqrt)) {
LOG(FATAL) << "Only support sum pool yet ";
}
fp_h_[0] = 1.f;
this->genCode();
}
virtual const char* name() const {
std::string base = "SeqPoolJitCode";
if (type_ == SeqPoolType::kSum) {
base += "_Sum";
} else if (type_ == SeqPoolType::kAvg) {
base += "_Avg";
} else if (type_ == SeqPoolType::kSqrt) {
base += "_Sqrt";
}
base += ("_W" + std::to_string(w_));
return base.c_str();
}
void genCode() override;
protected:
template <typename JMM>
void pool_height(int w_offset, int block, int max_num_regs) {
int offset = w_offset;
for (int i = 0; i < max_num_regs; ++i) {
vmovups(JMM(i), ptr[param_src + offset]);
offset += sizeof(float) * block;
}
cmp(reg32_int_h, 1);
Label l_next_h, l_h_done;
jle(l_h_done, T_NEAR);
mov(reg_h_i, 1);
mov(reg_tmp, param_src);
add(reg_tmp, w_ * sizeof(float) + w_offset);
L(l_next_h);
{
mov(reg_ptr_src_i, reg_tmp);
for (int i = 0; i < max_num_regs; ++i) {
vmovups(JMM(i + max_num_regs), ptr[reg_ptr_src_i]);
// sum anyway
vaddps(JMM(i), JMM(i), JMM(i + max_num_regs));
add(reg_ptr_src_i, sizeof(float) * block);
}
inc(reg_h_i);
add(reg_tmp, w_ * sizeof(float));
cmp(reg_h_i, reg32_int_h);
jl(l_next_h, T_NEAR);
}
L(l_h_done);
// save right now
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
vbroadcastss(JMM(max_num_regs), ptr[reg_tmp]);
}
offset = w_offset;
for (int i = 0; i < max_num_regs; ++i) {
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
vmulps(JMM(i), JMM(i), JMM(max_num_regs));
}
vmovups(ptr[param_dst + offset], JMM(i));
offset += sizeof(float) * block;
}
}
void pool_height_of_rest_width(int rest, int w_offset, int max_num_regs) {
const int rest_used_num_regs = load_rest(rest, w_offset, 0);
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
cmp(reg32_int_h, 1);
Label l_next_h, l_h_done;
jle(l_h_done, T_NEAR);
mov(reg_h_i, 1);
mov(reg_tmp, param_src);
add(reg_tmp, w_ * sizeof(float) + w_offset);
L(l_next_h);
{
int reg_idx = 0;
mov(reg_ptr_src_i, reg_tmp);
if (has_block4) {
vmovups(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
add(reg_ptr_src_i, sizeof(float) * 4);
reg_idx++;
}
if (has_block2) {
vmovups(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
add(reg_ptr_src_i, sizeof(float) * 2);
reg_idx++;
}
if (has_block1) {
vmovss(xmm_t(reg_idx + max_num_regs), ptr[reg_ptr_src_i]);
reg_idx++;
}
PADDLE_ENFORCE_EQ(reg_idx, rest_used_num_regs,
"All heights should use same regs");
for (int i = 0; i < reg_idx; ++i) {
vaddps(xmm_t(i), xmm_t(i), xmm_t(i + max_num_regs));
}
inc(reg_h_i);
add(reg_tmp, w_ * sizeof(float));
cmp(reg_h_i, reg32_int_h);
jl(l_next_h, T_NEAR);
}
L(l_h_done);
// save right now
if (type_ == SeqPoolType::kAvg || type_ == SeqPoolType::kSqrt) {
mov(reg_tmp, reinterpret_cast<size_t>(fp_h_));
vbroadcastss(xmm_t(max_num_regs), ptr[reg_tmp]);
for (int i = 0; i < rest_used_num_regs; ++i) {
vmulps(xmm_t(i), xmm_t(i), xmm_t(max_num_regs));
}
}
save_rest(rest, w_offset);
}
// return the number of used regs, use start from reg 0
int load_rest(int rest, int w_offset, const int num_shift_regs,
const int reg_start = 0) {
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
int reg_idx = reg_start;
if (has_block4) {
vmovups(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
w_offset += sizeof(float) * 4;
reg_idx++;
}
if (has_block2) {
vmovq(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
w_offset += sizeof(float) * 2;
reg_idx++;
}
if (has_block1) {
vmovss(xmm_t(reg_idx + num_shift_regs), ptr[param_src + w_offset]);
reg_idx++;
}
return reg_idx;
}
// use reg start from 0
void save_rest(int rest, int w_offset, int reg_start = 0) {
const bool has_block4 = rest / 4 > 0;
const bool has_block2 = (rest % 4) / 2 > 0;
const bool has_block1 = (rest % 2) == 1;
int reg_idx = reg_start;
if (has_block4) {
vmovups(ptr[param_dst + w_offset], xmm_t(reg_idx));
w_offset += sizeof(float) * 4;
reg_idx++;
}
if (has_block2) {
vmovq(ptr[param_dst + w_offset], xmm_t(reg_idx));
w_offset += sizeof(float) * 2;
reg_idx++;
}
if (has_block1) {
vmovss(ptr[param_dst + w_offset], xmm_t(reg_idx));
}
}
private:
float ALIGN32_BEG fp_h_[1] ALIGN32_END;
int w_;
SeqPoolType type_;
reg64_t param_src{abi_param1};
reg64_t param_dst{abi_param2};
reg64_t param_attr{abi_param3};
reg64_t reg_tmp{rax};
reg32_t reg32_int_h{r8d};
reg32_t reg32_fp_h{r9d};
reg64_t reg_h_i{r10};
reg64_t reg_ptr_src_i{r11};
};
} // namespace gen
} // namespace jit
} // namespace operators
} // namespace paddle
paddle/fluid/operators/jit/helper.cc
浏览文件 @ d0e3b240
......@@ -26,6 +26,7 @@ namespace jit {
const char* to_string(KernelType kt) {
switch (kt) {
ONE_CASE(kNone);
ONE_CASE(kVMul);
ONE_CASE(kVAdd);
ONE_CASE(kVAddRelu);
......@@ -45,12 +46,26 @@ const char* to_string(KernelType kt) {
ONE_CASE(kCRFDecoding);
ONE_CASE(kLayerNorm);
ONE_CASE(kNCHW16CMulNC);
ONE_CASE(kSeqPool);
default:
PADDLE_THROW("Not support type: %d, or forget to add it.", kt);
return "NOT JITKernel";
}
return nullptr;
}
const char* to_string(SeqPoolType tp) {
switch (tp) {
ONE_CASE(kNonePoolType);
ONE_CASE(kSum);
ONE_CASE(kAvg);
ONE_CASE(kSqrt);
default:
PADDLE_THROW("Not support type: %d, or forget to add it.", tp);
return "NOT PoolType";
}
return nullptr;
}
#undef ONE_CASE
KernelType to_kerneltype(const std::string& act) {
......
paddle/fluid/operators/jit/helper.h
浏览文件 @ d0e3b240
......@@ -119,6 +119,7 @@ typename KernelTuples::func_type Get(
}
const char* to_string(KernelType kt);
const char* to_string(SeqPoolType kt);
KernelType to_kerneltype(const std::string& act);
......@@ -134,6 +135,11 @@ inline std::ostream& operator<<(std::ostream& os, const gru_attr_t& attr) {
<< "],act_cand[" << to_string(attr.act_cand) << "]";
return os;
}
inline std::ostream& operator<<(std::ostream& os, const seq_pool_attr_t& attr) {
os << "height_size[" << attr.h << "],width_size[" << attr.w << "],pool_type["
<< to_string(attr.type) << "]";
return os;
}
} // namespace jit
} // namespace operators
......
paddle/fluid/operators/jit/kernel_base.h
浏览文件 @ d0e3b240
......@@ -41,8 +41,16 @@ typedef enum {
kCRFDecoding,
kLayerNorm,
kNCHW16CMulNC,
kSeqPool,
} KernelType;
typedef enum {
kNonePoolType = 0,
kSum = 1,
kAvg,
kSqrt,
} SeqPoolType;
template <typename T>
struct XYZNTuples {
typedef T data_type;
......@@ -112,6 +120,21 @@ struct GRUTuples {
typedef void (*func_type)(gru_t*, const gru_attr_t*);
};
typedef struct seq_pool_attr_s {
int h, w; // h should always be the first one
SeqPoolType type;
seq_pool_attr_s() = default;
explicit seq_pool_attr_s(int width, SeqPoolType pool_type, int height = 1)
: h(height), w(width), type(pool_type) {}
} seq_pool_attr_t;
template <typename T>
struct SeqPoolTuples {
typedef T data_type;
typedef seq_pool_attr_t attr_type;
typedef void (*func_type)(const T*, T*, const seq_pool_attr_t*);
};
template <typename T>
struct CRFDecodingTuples {
typedef T data_type;
......
paddle/fluid/operators/jit/kernel_key.cc
浏览文件 @ d0e3b240
......@@ -42,6 +42,13 @@ size_t JitCodeKey<gru_attr_t>(const gru_attr_t& attr) {
(static_cast<int>(attr.act_cand) << act_type_shift);
}
template <>
size_t JitCodeKey<seq_pool_attr_t>(const seq_pool_attr_t& attr) {
size_t key = attr.w;
constexpr int pool_type_shift = 3;
return (key << pool_type_shift) + static_cast<int>(attr.type);
}
} // namespace jit
} // namespace operators
} // namespace paddle
paddle/fluid/operators/jit/more/mkl/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -9,3 +9,4 @@ USE_JITKERNEL_MORE(kVScal, mkl)
USE_JITKERNEL_MORE(kVExp, mkl)
USE_JITKERNEL_MORE(kVSigmoid, mkl)
USE_JITKERNEL_MORE(kVTanh, mkl)
USE_JITKERNEL_MORE(kSeqPool, mkl)
paddle/fluid/operators/jit/more/mkl/mkl.cc
浏览文件 @ d0e3b240
......@@ -72,6 +72,26 @@ void VExp<double>(const double* x, double* y, int n) {
platform::dynload::vdExp(n, x, y);
}
template <>
void VCopy<float>(const float* x, float* y, int n) {
platform::dynload::cblas_scopy(n, x, 1, y, 1);
}
template <>
void VCopy<double>(const double* x, double* y, int n) {
platform::dynload::cblas_dcopy(n, x, 1, y, 1);
}
template <>
void VAXPY<float>(float a, const float* x, float* y, int n) {
platform::dynload::cblas_saxpy(n, a, x, 1, y, 1);
}
template <>
void VAXPY<double>(double a, const double* x, double* y, int n) {
platform::dynload::cblas_daxpy(n, a, x, 1, y, 1);
}
// TODO(TJ): tuning me carefully on AVX, AVX2 and AVX512
template <>
bool VMulKernel<float>::UseMe(const int& d) const {
......@@ -103,6 +123,16 @@ bool VTanhKernel<float>::UseMe(const int& d) const {
return d > 7;
}
template <>
bool SeqPoolKernel<float>::UseMe(const seq_pool_attr_t& attr) const {
return true;
}
template <>
bool SeqPoolKernel<double>::UseMe(const seq_pool_attr_t& attr) const {
return true;
}
#define AWALYS_USE_ME_WITH_DOUBLE(func) \
template <> \
bool func##Kernel<double>::UseMe(const int& d) const { \
......@@ -135,5 +165,6 @@ REGISTER_MKL_KERNEL(kVScal, VScal);
REGISTER_MKL_KERNEL(kVExp, VExp);
REGISTER_MKL_KERNEL(kVSigmoid, VSigmoid);
REGISTER_MKL_KERNEL(kVTanh, VTanh);
REGISTER_MKL_KERNEL(kSeqPool, SeqPool);
#undef REGISTER_MKL_KERNEL
paddle/fluid/operators/jit/more/mkl/mkl.h
浏览文件 @ d0e3b240
......@@ -14,6 +14,7 @@
#pragma once
#include <cmath>
#include <type_traits>
#include "paddle/fluid/operators/jit/kernel_base.h"
......@@ -35,6 +36,12 @@ void VScal(const T* a, const T* x, T* y, int n);
template <typename T>
void VExp(const T* x, T* y, int n);
template <typename T>
void VCopy(const T* x, T* y, int n);
template <typename T>
void VAXPY(T a, const T* x, T* y, int n);
template <typename T>
void VSigmoid(const T* x, T* y, int n) {
const T min = SIGMOID_THRESHOLD_MIN;
......@@ -60,6 +67,23 @@ void VTanh(const T* x, T* y, int n) {
}
}
template <typename T>
void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
VCopy<T>(x, y, attr->w);
for (int h = 1; h != attr->h; ++h) {
VAXPY<T>(static_cast<T>(1), x + h * attr->w, y, attr->w);
}
if (attr->type == SeqPoolType::kAvg || attr->type == SeqPoolType::kSqrt) {
T scalar = static_cast<T>(1);
if (attr->type == SeqPoolType::kAvg) {
scalar = scalar / static_cast<T>(attr->h);
} else {
scalar = scalar / std::sqrt(static_cast<T>(attr->h));
}
VScal<T>(&scalar, y, y, attr->w);
}
}
#define DECLARE_MKL_KERNEL(name, tuples) \
template <typename T> \
class name##Kernel : public KernelMore<tuples<T>> { \
......@@ -81,6 +105,8 @@ DECLARE_MKL_KERNEL(VExp, XYNTuples);
DECLARE_MKL_KERNEL(VSigmoid, XYNTuples);
DECLARE_MKL_KERNEL(VTanh, XYNTuples);
DECLARE_MKL_KERNEL(SeqPool, SeqPoolTuples);
#undef DECLARE_MKL_KERNEL
} // namespace mkl
......
paddle/fluid/operators/jit/refer/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -26,3 +26,4 @@ USE_JITKERNEL_REFER(kGRUHtPart2)
USE_JITKERNEL_REFER(kCRFDecoding)
USE_JITKERNEL_REFER(kLayerNorm)
USE_JITKERNEL_REFER(kNCHW16CMulNC)
USE_JITKERNEL_REFER(kSeqPool)
paddle/fluid/operators/jit/refer/refer.cc
浏览文件 @ d0e3b240
......@@ -47,4 +47,6 @@ REGISTER_REFER_KERNEL(kLayerNorm, LayerNorm);
REGISTER_REFER_KERNEL(kNCHW16CMulNC, NCHW16CMulNC);
REGISTER_REFER_KERNEL(kSeqPool, SeqPool);
#undef REGISTER_REFER_KERNEL
paddle/fluid/operators/jit/refer/refer.h
浏览文件 @ d0e3b240
......@@ -332,6 +332,28 @@ void NCHW16CMulNC(const T* x, const T* y, T* z, int height, int width) {
}
}
template <typename T>
void SeqPool(const T* x, T* y, const seq_pool_attr_t* attr) {
for (int w = 0; w < attr->w; ++w) {
const T* src = x + w;
T* dst = y + w;
*dst = static_cast<T>(0);
for (int h = 0; h < attr->h; ++h) {
*dst = *dst + *src;
src += attr->w;
}
}
if (attr->type == SeqPoolType::kAvg || attr->type == SeqPoolType::kSqrt) {
T scalar = static_cast<T>(1);
if (attr->type == SeqPoolType::kAvg) {
scalar = scalar / static_cast<T>(attr->h);
} else {
scalar = scalar / std::sqrt(static_cast<T>(attr->h));
}
VScal<T>(&scalar, y, y, attr->w);
}
}
#define DECLARE_REFER_KERNEL(name, tuples) \
template <typename T> \
class name##Kernel : public ReferKernel<tuples<T>> { \
......@@ -370,6 +392,8 @@ DECLARE_REFER_KERNEL(LayerNorm, LayerNormTuples);
DECLARE_REFER_KERNEL(NCHW16CMulNC, NCHW16CMulNCTuples);
DECLARE_REFER_KERNEL(SeqPool, SeqPoolTuples);
#undef DECLARE_REFER_KERNEL
} // namespace refer
......
paddle/fluid/operators/jit/test.cc
浏览文件 @ d0e3b240
......@@ -211,6 +211,24 @@ struct TestFuncWithRefer<jit::GRUTuples<T>, std::vector<T>, std::vector<T>,
}
};
template <typename T>
struct TestFuncWithRefer<jit::SeqPoolTuples<T>, std::vector<T>,
std::vector<T>> {
void operator()(const typename jit::SeqPoolTuples<T>::func_type tgt,
const std::vector<T>& x, const std::vector<T>& yref,
const typename jit::SeqPoolTuples<T>::attr_type& attr) {
EXPECT_TRUE(tgt != nullptr);
EXPECT_EQ(x.size() % yref.size(), 0);
int w = yref.size();
std::vector<T> y(w);
const T* x_data = x.data();
const T* yref_data = yref.data();
T* y_data = y.data();
tgt(x_data, y_data, &attr);
ExpectEQ<T>(y_data, yref_data, w);
}
};
template <paddle::operators::jit::KernelType KT, typename KernelTuples,
typename PlaceType, typename... Args>
void TestAllImpls(const typename KernelTuples::attr_type& attr, Args... args) {
......@@ -415,6 +433,31 @@ void TestGRUKernel() {
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestSeqPoolKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
std::vector<jit::SeqPoolType> pool_types = {
jit::SeqPoolType::kSum, jit::SeqPoolType::kAvg, jit::SeqPoolType::kSqrt};
for (auto type : pool_types) {
for (int w : TestSizes()) {
jit::seq_pool_attr_t attr(w, type);
for (int h : TestSizes()) {
attr.h = h;
auto ref = jit::GetRefer<KT, jit::SeqPoolTuples<T>>();
EXPECT_TRUE(ref != nullptr);
std::vector<T> x(h * w), yref(w);
RandomVec<T>(h * w, x.data(), -2.f, 2.f);
const T* x_data = x.data();
T* yref_data = yref.data();
ref(x_data, yref_data, &attr);
VLOG(10) << attr;
TestAllImpls<KT, jit::SeqPoolTuples<T>, PlaceType, std::vector<T>,
std::vector<T>>(attr, x, yref, attr);
}
}
}
}
template <paddle::operators::jit::KernelType KT, typename T, typename PlaceType>
void TestNCHW16CMulNCKernel() {
VLOG(10) << "===== Test JITKernel " << jit::to_string(KT);
......@@ -569,6 +612,12 @@ TEST(JITKernel, kGRUHtPart2) {
TestGRUKernel<jit::kGRUHtPart2, double, paddle::platform::CPUPlace>();
}
TEST(JITKernel, kSeqPool) {
namespace jit = paddle::operators::jit;
TestSeqPoolKernel<jit::kSeqPool, float, paddle::platform::CPUPlace>();
TestSeqPoolKernel<jit::kSeqPool, double, paddle::platform::CPUPlace>();
}
TEST(JITKernel, kNCHW16CMulNC) {
namespace jit = paddle::operators::jit;
TestNCHW16CMulNCKernel<jit::kNCHW16CMulNC, float,
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ d0e3b240
......@@ -51,7 +51,7 @@ math_library(pooling)
math_library(selected_rows_functor DEPS selected_rows math_function blas)
math_library(sequence2batch)
math_library(sequence_padding)
math_library(sequence_pooling DEPS math_function)
math_library(sequence_pooling DEPS math_function jit_kernel_helper)
math_library(sequence_scale)
math_library(softmax DEPS math_function)
......
paddle/fluid/operators/math/blas_impl.cu.h
浏览文件 @ d0e3b240
......@@ -62,27 +62,19 @@ struct CUBlas<float> {
cudaDataType_t Atype, int lda, const void *B,
cudaDataType_t Btype, int ldb, const float *beta, void *C,
cudaDataType_t Ctype, int ldc) {
// Because the gcc 4.8 doesn't expand template parameter pack that
// appears in a lambda-expression, I can not use template parameter pack
// here.
auto cublas_call = [&]() {
// Because the gcc 4.8 doesn't expand template parameter pack that
// appears in a lambda-expression, I can not use template parameter pack
// here.
#if CUDA_VERSION >= 8000
VLOG(5) << "use_tensor_op_math: "
<< (platform::TensorCoreAvailable() ? "True" : "False");
VLOG(5) << "use_tensor_op_math: "
<< (dev_ctx->tensor_core_available() ? "True" : "False");
dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasSgemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc));
handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
beta, C, Ctype, ldc));
});
#else
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
PADDLE_THROW("cublasSgemmEx is supported on cuda >= 8.0");
#endif
}
};
......@@ -170,32 +162,24 @@ struct CUBlas<platform::float16> {
cudaDataType_t Btype, int ldb, const void *beta, void *C,
cudaDataType_t Ctype, int ldc,
cudaDataType_t computeType) {
auto cublas_call = [&]() {
#if CUDA_VERSION >= 8000
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
#if CUDA_VERSION >= 9000
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
bool use_tensor_op_math = dev_ctx->tensor_core_available();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
#endif // CUDA_VERSION >= 9000
dev_ctx->TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
dev_ctx->cublas_handle(), transa, transb, m, n, k, alpha, A, Atype,
lda, B, Btype, ldb, beta, C, Ctype, ldc, computeType, algo));
handle, transa, transb, m, n, k, alpha, A, Atype, lda, B, Btype, ldb,
beta, C, Ctype, ldc, computeType, algo));
});
#else
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
#endif
};
#if CUDA_VERSION >= 9000
// NOTES: To use Tensor Core, we should change the cublas config,
// but the cublas may be hold by multi-thread.
dev_ctx->CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
#else
cublas_call();
PADDLE_THROW("cublasGemmEx is supported on cuda >= 8.0");
#endif
}
};
......@@ -223,9 +207,10 @@ void Blas<platform::CUDADeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
CUDA_R_32F, N);
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, N);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
lda, &beta, C, N);
});
#if CUDA_VERSION >= 8000
}
......@@ -266,9 +251,12 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N, CUDA_R_32F);
#else
// CUDA 7.5 does not support cublasGemmEx, hence we fall back to use hgemm
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &h_alpha, h_B, ldb, h_A, lda,
&h_beta, h_C, N);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K,
&h_alpha, h_B, ldb, h_A, lda, &h_beta, h_C,
N);
});
#endif // CUDA_VERSION >= 8000
}
......@@ -292,8 +280,10 @@ void Blas<platform::CUDADeviceContext>::GEMM(bool transA, bool transB, int M,
} else {
#endif // CUDA_VERSION >= 8000
CUBlas<T>::GEMM(context_.cublas_handle(), cuTransB, cuTransA, N, M, K,
&alpha, B, ldb, A, lda, &beta, C, ldc);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A,
lda, &beta, C, ldc);
});
#if CUDA_VERSION >= 8000
}
......@@ -311,16 +301,19 @@ inline void Blas<platform::CUDADeviceContext>::GEMM(
cublasOperation_t cuTransA = transA ? CUBLAS_OP_T : CUBLAS_OP_N;
cublasOperation_t cuTransB = transB ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<platform::float16>::GEMM(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, A, lda, &beta, C,
ldc);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<platform::float16>::GEMM(handle, cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, A, lda, &beta, C, ldc);
});
}
template <>
template <typename T>
void Blas<platform::CUDADeviceContext>::AXPY(int n, T alpha, const T *x,
T *y) const {
CUBlas<T>::AXPY(context_.cublas_handle(), n, &alpha, x, 1, y, 1);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::AXPY(handle, n, &alpha, x, 1, y, 1);
});
}
template <>
......@@ -330,8 +323,9 @@ void Blas<platform::CUDADeviceContext>::GEMV(bool trans_a, int M, int N,
T beta, T *C) const {
cublasOperation_t cuTransA = !trans_a ? CUBLAS_OP_T : CUBLAS_OP_N;
CUBlas<T>::GEMV(context_.cublas_handle(), cuTransA, N, M, &alpha, A, N, B, 1,
&beta, C, 1);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMV(handle, cuTransA, N, M, &alpha, A, N, B, 1, &beta, C, 1);
});
}
template <>
......@@ -353,28 +347,28 @@ void Blas<platform::CUDADeviceContext>::BatchedGEMM(
#if CUDA_VERSION >= 9010
if (FLAGS_enable_cublas_tensor_op_math && std::is_same<T, float>::value) {
auto cublas_call = [&]() {
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
bool use_tensor_op_math = platform::TensorCoreAvailable();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
bool use_tensor_op_math = context_.tensor_core_available();
if (use_tensor_op_math) {
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
}
VLOG(5) << "use_tensor_op_math: "
<< (use_tensor_op_math ? "True" : "False");
context_.TensorCoreCublasCallIfAvailable([&](cublasHandle_t handle) {
PADDLE_ENFORCE(platform::dynload::cublasGemmStridedBatchedEx(
context_.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B,
CUDA_R_32F, ldb, strideB, A, CUDA_R_32F, lda, strideA, &beta, C,
CUDA_R_32F, ldc, strideC, batchCount, CUDA_R_32F, algo));
};
auto &dev_ctx = const_cast<platform::CUDADeviceContext &>(context_);
dev_ctx.CublasCall(cublas_call, CUBLAS_TENSOR_OP_MATH);
handle, cuTransB, cuTransA, N, M, K, &alpha, B, CUDA_R_32F, ldb,
strideB, A, CUDA_R_32F, lda, strideA, &beta, C, CUDA_R_32F, ldc,
strideC, batchCount, CUDA_R_32F, algo));
});
} else {
#endif // CUDA_VERSION >= 9010
CUBlas<T>::GEMM_STRIDED_BATCH(context_.cublas_handle(), cuTransB, cuTransA,
N, M, K, &alpha, B, ldb, strideB, A, lda,
strideA, &beta, C, ldc, strideC, batchCount);
context_.CublasCall([&](cublasHandle_t handle) {
CUBlas<T>::GEMM_STRIDED_BATCH(handle, cuTransB, cuTransA, N, M, K, &alpha,
B, ldb, strideB, A, lda, strideA, &beta, C,
ldc, strideC, batchCount);
});
#if CUDA_VERSION >= 9010
}
......
paddle/fluid/operators/math/sequence_pooling.cc
浏览文件 @ d0e3b240
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <string>
#include "paddle/fluid/operators/jit/kernels.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/sequence_pooling.h"
......@@ -239,15 +240,33 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
last_pool(context, input, output);
return;
}
if (pooltype == "FIRST") {
math::FirstSeqPoolFunctor<T> first_pool;
first_pool(context, input, output);
return;
}
auto lod = input.lod()[0];
if (pooltype == "SUM") {
auto place = context.GetPlace();
PADDLE_ENFORCE(platform::is_cpu_place(place));
const T* src = input.data<T>();
T* dst = output->mutable_data<T>(place);
jit::seq_pool_attr_t attr(
static_cast<int>(input.numel() / input.dims()[0]),
jit::SeqPoolType::kSum);
auto seqpool =
jit::Get<jit::kSeqPool, jit::SeqPoolTuples<T>, platform::CPUPlace>(
attr);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
attr.h = static_cast<int>(lod[i + 1] - lod[i]);
seqpool(src, dst, &attr);
dst += attr.w;
src += attr.h * attr.w;
}
return;
}
auto& place = *context.eigen_device();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
input.Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
......@@ -258,15 +277,6 @@ class SequencePoolFunctor<platform::CPUDeviceContext, T> {
auto out_e = EigenVector<T>::Flatten(out_t);
if (pooltype == "AVERAGE") {
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "SUM") {
if (h > 0) {
const T* in_data = in_t.data<T>();
T* out_data = out_t.mutable_data<T>(context.GetPlace());
blas.VCOPY(w, in_data, out_data);
for (int64_t r = 1; r != h; ++r) {
blas.AXPY(w, 1., in_data + r * w, out_data);
}
}
} else if (pooltype == "SQRT") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
std::sqrt(static_cast<T>(h));
......
paddle/fluid/operators/ngraph/ops/binary_unnary_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -48,4 +47,3 @@ static void BuildUnaryNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -58,4 +57,3 @@ std::shared_ptr<ngraph::Node> ElementwiseScalar(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/fill_constant_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -58,4 +57,3 @@ void BuildFillConstantNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/mean_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <functional>
......@@ -65,4 +64,3 @@ void BuildMeanGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/mul_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -131,4 +130,3 @@ static void BuildMulGradNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/scale_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -38,4 +37,3 @@ void BuildScaleNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/top_k_op.h
浏览文件 @ d0e3b240
......@@ -12,7 +12,6 @@ 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
......@@ -48,4 +47,3 @@ void BuildTopKNode(
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/platform/cuda_helper.h 0 → 100644
浏览文件 @ d0e3b240
// Copyright (c) 2019 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 <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/macros.h"
#if CUDA_VERSION < 9000
enum cublasMath_t { CUBLAS_DEFAULT_MATH = 0 };
#endif
namespace paddle {
namespace platform {
class CublasHandleHolder {
public:
CublasHandleHolder(cudaStream_t stream, cublasMath_t math_type) {
PADDLE_ENFORCE(dynload::cublasCreate(&handle_));
PADDLE_ENFORCE(dynload::cublasSetStream(handle_, stream));
#if CUDA_VERSION >= 9000
if (math_type == CUBLAS_TENSOR_OP_MATH) {
PADDLE_ENFORCE(
dynload::cublasSetMathMode(handle_, CUBLAS_TENSOR_OP_MATH));
}
#endif
}
~CublasHandleHolder() { PADDLE_ENFORCE(dynload::cublasDestroy(handle_)); }
template <typename Callback>
inline void Call(Callback &&callback) const {
std::lock_guard<std::mutex> guard(mtx_);
callback(handle_);
}
private:
DISABLE_COPY_AND_ASSIGN(CublasHandleHolder);
cublasHandle_t handle_;
mutable std::mutex mtx_;
};
} // namespace platform
} // namespace paddle
paddle/fluid/platform/device_context.cc
浏览文件 @ d0e3b240
......@@ -245,8 +245,15 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
eigen_stream_.reset(new EigenCudaStreamDevice());
eigen_stream_->Reinitialize(&stream_, place);
eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get()));
PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_));
PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_));
cublas_handle_.reset(new CublasHandleHolder(stream_, CUBLAS_DEFAULT_MATH));
if (TensorCoreAvailable()) {
#if CUDA_VERSION >= 9000
cublas_tensor_core_handle_.reset(
new CublasHandleHolder(stream_, CUBLAS_TENSOR_OP_MATH));
#endif
}
if (dynload::HasCUDNN()) {
cudnn_holder_.reset(new CudnnHolder(&stream_, place));
}
......@@ -306,7 +313,8 @@ CUDADeviceContext::~CUDADeviceContext() {
SetDeviceId(place_.device);
Wait();
WaitStreamCallback();
PADDLE_ENFORCE(dynload::cublasDestroy(cublas_handle_));
cublas_handle_.reset();
cublas_tensor_core_handle_.reset();
eigen_stream_.reset();
eigen_device_.reset();
PADDLE_ENFORCE(cudaStreamDestroy(stream_));
......@@ -335,8 +343,8 @@ Eigen::GpuDevice* CUDADeviceContext::eigen_device() const {
return eigen_device_.get();
}
cublasHandle_t CUDADeviceContext::cublas_handle() const {
return cublas_handle_;
bool CUDADeviceContext::tensor_core_available() const {
return cublas_tensor_core_handle_ != nullptr;
}
cudnnHandle_t CUDADeviceContext::cudnn_handle() const {
......
paddle/fluid/platform/device_context.h
浏览文件 @ d0e3b240
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/platform/temporary_allocator.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cuda_helper.h"
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/gpu_info.h"
......@@ -209,39 +210,6 @@ class CudnnWorkspaceHandle {
std::unique_ptr<std::lock_guard<std::mutex>> guard_;
};
#if CUDA_VERSION >= 9000
class ScopedCublasMathMode {
public:
ScopedCublasMathMode(cublasHandle_t handle, cublasMath_t new_math_mode)
: handle_(handle) {
need_reset = false;
PADDLE_ENFORCE(
platform::dynload::cublasGetMathMode(handle_, &old_math_mode_),
"Failed to get old cublas math mode");
if (old_math_mode_ != new_math_mode) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, new_math_mode),
"Failed to set old cublas math mode");
need_reset = true;
}
}
~ScopedCublasMathMode() {
if (need_reset) {
PADDLE_ENFORCE(
platform::dynload::cublasSetMathMode(handle_, old_math_mode_),
"Failed to set old cublas math mode");
}
}
private:
cublasHandle_t handle_;
cublasMath_t old_math_mode_;
bool need_reset;
};
#endif
class CUDADeviceContext : public DeviceContext {
public:
explicit CUDADeviceContext(CUDAPlace place);
......@@ -262,8 +230,25 @@ class CUDADeviceContext : public DeviceContext {
/*! \brief Return eigen device in the device context. */
Eigen::GpuDevice* eigen_device() const;
/*! \brief Return cublas handle in the device context. */
cublasHandle_t cublas_handle() const;
/*! \brief Call cublas function safely. */
template <typename Callback>
inline void CublasCall(Callback&& callback) const {
cublas_handle_->Call(std::forward<Callback>(callback));
}
/*! \brief Check whether tensor core is supported */
bool tensor_core_available() const;
/*! \brief Call cublas function with Tensor Core safely. If
Tensor Core is not available, use DEFAULT_MATH instead. */
template <typename Callback>
inline void TensorCoreCublasCallIfAvailable(Callback&& callback) const {
if (cublas_tensor_core_handle_) {
cublas_tensor_core_handle_->Call(std::forward<Callback>(callback));
} else {
cublas_handle_->Call(std::forward<Callback>(callback));
}
}
/*! \brief Return cudnn handle in the device context. */
cudnnHandle_t cudnn_handle() const;
......@@ -282,7 +267,6 @@ class CUDADeviceContext : public DeviceContext {
template <typename Callback>
void RecordEvent(cudaEvent_t ev, Callback callback) {
std::lock_guard<std::mutex> guard(mtx_);
callback();
PADDLE_ENFORCE(cudaEventRecord(ev, stream_));
}
......@@ -294,18 +278,6 @@ class CUDADeviceContext : public DeviceContext {
void WaitStreamCallback() const { callback_manager_->Wait(); }
#if CUDA_VERSION >= 9000
/*! \brief CublasCall may need to change cublas's config,
* but the cublas may be hold by multi-thread, so we should
* add lock here. */
template <typename Callback>
void CublasCall(Callback callback, cublasMath_t new_math) {
std::lock_guard<std::mutex> guard(cublas_mtx_);
ScopedCublasMathMode scoped_cublas_math(cublas_handle_, new_math);
callback();
}
#endif
private:
CUDAPlace place_;
......@@ -313,7 +285,9 @@ class CUDADeviceContext : public DeviceContext {
std::unique_ptr<EigenCudaStreamDevice> eigen_stream_;
std::unique_ptr<CudnnHolder> cudnn_holder_;
cudaStream_t stream_;
cublasHandle_t cublas_handle_;
std::unique_ptr<CublasHandleHolder> cublas_handle_;
std::unique_ptr<CublasHandleHolder> cublas_tensor_core_handle_;
int compute_capability_;
int runtime_version_;
......@@ -321,12 +295,10 @@ class CUDADeviceContext : public DeviceContext {
int multi_process_;
int max_threads_per_mp_;
mutable std::mutex mtx_;
// StreamCallbackManager is thread-safe
std::unique_ptr<StreamCallbackManager> callback_manager_;
mutable std::mutex cublas_mtx_;
DISABLE_COPY_AND_ASSIGN(CUDADeviceContext);
};
template <>
......
paddle/fluid/platform/device_context_test.cu
浏览文件 @ d0e3b240
......@@ -43,9 +43,6 @@ TEST(Device, CUDADeviceContext) {
ASSERT_NE(nullptr, gpu_device);
cudnnHandle_t cudnn_handle = device_context->cudnn_handle();
ASSERT_NE(nullptr, cudnn_handle);
cublasHandle_t cublas_handle = device_context->cublas_handle();
ASSERT_NE(nullptr, cublas_handle);
ASSERT_NE(nullptr, device_context->stream());
delete device_context;
}
}
......
python/paddle/fluid/__init__.py
浏览文件 @ d0e3b240
......@@ -155,7 +155,7 @@ def __bootstrap__():
'fraction_of_gpu_memory_to_use', 'cudnn_deterministic',
'enable_cublas_tensor_op_math', 'conv_workspace_size_limit',
'cudnn_exhaustive_search', 'memory_optimize_debug', 'selected_gpus',
'cudnn_exhaustive_search_times', 'sync_nccl_allreduce'
'sync_nccl_allreduce'
]
core.init_gflags([sys.argv[0]] +
......
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