Skip to content

P
Paddle

BaiXuePrincess / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
提交 33473890 编写于 作者: S sneaxiy
浏览文件
操作

Merge develop 

test=develop
上级 2c836ff9 278debab
展开全部 隐藏空白更改
内联 并排
Showing with 2767 addition and 1344 deletion
paddle/fluid/API.spec
浏览文件 @ 33473890
......@@ -134,7 +134,7 @@ paddle.fluid.layers.sampled_softmax_with_cross_entropy (ArgSpec(args=['logits',
paddle.fluid.layers.hsigmoid (ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name', 'path_table', 'path_code', 'is_custom', 'is_sparse'], varargs=None, keywords=None, defaults=(None, None, None, None, None, False, False)), ('document', '80641ee6810b1cdc3fd6e14fc89ecc9d'))
paddle.fluid.layers.beam_search (ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'is_accumulated', 'name', 'return_parent_idx'], varargs=None, keywords=None, defaults=(0, True, None, False)), ('document', 'b350b9a30a18e7efd7e1bb740eef6996'))
paddle.fluid.layers.row_conv (ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None)), ('document', '17485788fffe4e2d36dc58c2ac8d174e'))
paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None), ('document', '013795af319e2e86d3506741941078ee'))
paddle.fluid.layers.multiplex (ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None), ('document', '2c4d1ae83da6ed35e3b36ba1b3b51d23'))
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)), ('document', 'de6a906950bae9f3c245cb744d22b94e'))
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)), ('document', '419c3a24a83cc89219a029cf4092788b'))
paddle.fluid.layers.spectral_norm (ArgSpec(args=['weight', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '3f536aafba30d793287b52d231baff1b'))
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 33473890
......@@ -195,8 +195,7 @@ 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)
cc_test(var_type_inference_test SRCS var_type_inference_test.cc DEPS op_registry
proto_desc)
cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS op_registry proto_desc op_info memory_optimize_helper)
cc_test(inplace_op_inference_test SRCS inplace_op_inference_test.cc DEPS inplace_op_pass op_registry proto_desc op_info memory_optimize_helper pass_builder)
cc_library(selected_rows SRCS selected_rows.cc DEPS tensor)
cc_test(selected_rows_test SRCS selected_rows_test.cc DEPS selected_rows)
......
paddle/fluid/framework/data_layout_transform.cc
浏览文件 @ 33473890
......@@ -134,6 +134,11 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
out_layout =
out_layout == DataLayout::kAnyLayout ? DataLayout::kNCHW : out_layout;
auto& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(
pool.Get(expected_kernel_type.place_));
auto& cpu_engine = dev_ctx->GetEngine();
std::vector<int> in_tz = paddle::framework::vectorize2int(in.dims());
std::vector<int> out_tz = in_tz;
......@@ -142,25 +147,29 @@ void TransDataLayoutFromMKLDNN(const OpKernelType& kernel_type_for_var,
"Input tensor type is not supported: %s", in.type());
memory::data_type out_type = in_type;
auto in_format = platform::MKLDNNFormatForSize(in_tz.size(), in.format());
auto out_format =
platform::MKLDNNFormatForSize(in_tz.size(), ToMKLDNNFormat(out_layout));
// output tensor has the same dims as input. Reorder don't change dims
out->Resize(in.dims());
// tempory mem pd fr out , to make reorder
auto out_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(out->dims()),
mkldnn::memory::format::blocked, out_type);
if (in.get_mkldnn_prim_desc() != out_mem_pd) {
if (in_format != out_format) {
void* in_data = GetDataFromTensor(in, in_type);
auto out_data = out->mutable_data(expected_kernel_type.place_, in.type());
auto in_memory = memory(in.get_mkldnn_prim_desc(), in_data);
auto out_memory = memory(out_mem_pd, out_data);
auto in_memory =
memory({{{in_tz}, in_type, in_format}, cpu_engine}, in_data);
auto out_memory =
memory({{{out_tz}, out_type, out_format}, cpu_engine}, out_data);
platform::Reorder(in_memory, out_memory);
} else {
out->ShareDataWith(in);
}
out->set_layout(out_layout);
// reset format since the out tensor will be feed to non-MKLDNN OPkernel
out->set_format(memory::format::format_undef);
#endif
}
......
paddle/fluid/framework/data_transform.cc
浏览文件 @ 33473890
......@@ -51,31 +51,13 @@ void TransformData(const OpKernelType &expected_kernel_type,
#ifdef PADDLE_WITH_MKLDNN
// Case1 - transform from Non-MKLDNN OPKernel to MKLDNN OPKernel
// Just set layout/format. No real transform occur
auto out_format = platform::MKLDNNFormatForSize(in.dims().size(),
ToMKLDNNFormat(lin));
out.ShareDataWith(input_tensor);
// TODO(jczaja): Remove that once all mkldnn ops
// are modified to work with mkldnn_blocked
auto mkldnn_fmt = [&](int rank) {
switch (rank) {
case 5:
return mkldnn::memory::format::ncdhw;
case 4:
return mkldnn::memory::format::nchw;
case 3:
return mkldnn::memory::format::ncw;
case 2:
return mkldnn::memory::format::nc;
case 1:
return mkldnn::memory::format::x;
default:
return mkldnn::memory::format::blocked;
}
};
auto out_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(out.dims()),
mkldnn_fmt(out.dims().size()));
out.set_mkldnn_prim_desc(out_mem_pd);
out.set_layout(DataLayout::kMKLDNN);
out.set_format(out_format);
#endif
} else {
// Case2 - transfrom from MKLDNN OPKernel to Non-MKLDNN OPKernel
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 33473890
......@@ -10,7 +10,10 @@ cc_library(fetch_barrier_op_handle SRCS fetch_barrier_op_handle.cc DEPS framewor
cc_library(multi_devices_helper SRCS multi_devices_helper.cc DEPS graph graph_helper)
cc_library(multi_devices_graph_print_pass SRCS multi_devices_graph_print_pass.cc DEPS multi_devices_helper)
cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc DEPS multi_devices_helper)
cc_library(alloc_continuous_space_for_grad_pass SRCS alloc_continuous_space_for_grad_pass.cc DEPS graph graph_helper)
cc_library(fuse_adam_op_pass SRCS fuse_adam_op_pass.cc fuse_optimizer_op_pass.cc DEPS graph graph_helper)
cc_library(fuse_sgd_op_pass SRCS fuse_sgd_op_pass.cc fuse_optimizer_op_pass.cc DEPS graph graph_helper)
cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
......@@ -104,5 +107,7 @@ 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
fuse_relu_depthwise_conv_pass
memory_optimize_pass lock_free_optimize_pass alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass)
fuse_relu_depthwise_conv_pass
memory_optimize_pass lock_free_optimize_pass
alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass
fuse_adam_op_pass fuse_sgd_op_pass)
paddle/fluid/framework/details/alloc_continuous_space_for_grad_pass.cc
浏览文件 @ 33473890
......@@ -21,6 +21,7 @@
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
DEFINE_uint32(fuse_parameter_memory_size, 0, // 0 KB
"fuse_parameter_memory_size is up limited memory size "
"of one group parameters' gradient which is the input "
......@@ -105,20 +106,29 @@ class AllocContinuousSpaceForGradPass : public ir::Pass {
auto ele_dtype = iter->second->Var()->GetDataType();
if (dtype == kDefaultDtype) {
dtype = ele_dtype;
PADDLE_ENFORCE_NE(ele_dtype, kDefaultDtype);
PADDLE_ENFORCE_NE(ele_dtype, kDefaultDtype,
"The data type should not be bool.");
}
PADDLE_ENFORCE_EQ(ele_dtype, dtype);
PADDLE_ENFORCE_EQ(ele_dtype, dtype,
"The data type of input is not consistent.");
}
// Create the fused variable name.
// Create a FusedVarsSet to avoid duplicating names for fused_var in other
// pass.
if (!result.Has(kFusedVars)) {
result.Set(kFusedVars, new FusedVars);
}
const std::string prefix(kFusedVarNamePrefix);
// The fused_var_name should be unique.
auto fused_var_name = prefix + "GRAD@" + params_grads[0].second;
// the kFusedGrads is used be fuse_optimizer_op_pass.
result.Set(kFusedGrads, new FusedGrads);
// the fused_var_name should be unique, so it appends
// params_grads.begin()->second.
auto fused_var_name = std::string(kFusedVarNamePrefix) + "@GRAD@" +
params_grads.begin()->second;
result.Get<FusedGrads>(kFusedGrads) = fused_var_name;
auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
PADDLE_ENFORCE_EQ(fused_var_set.count(fused_var_name), 0);
PADDLE_ENFORCE_EQ(fused_var_set.count(fused_var_name), 0,
"%s is duplicate in FusedVars.", fused_var_name);
fused_var_set.insert(fused_var_name);
InitFusedVarsAndAllocSpaceForVars(places, local_scopes, vars,
......@@ -295,17 +305,6 @@ class AllocContinuousSpaceForGradPass : public ir::Pass {
return type == proto::VarType::LOD_TENSOR;
}
void AppendAllocSpaceForVarsOp(const std::vector<std::string> &params_name,
const std::vector<std::string> &grads_name,
const std::string &fused_var_name,
BlockDesc *global_block) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", params_name);
op_desc->SetOutput("Output", grads_name);
op_desc->SetOutput("FusedOutput", {fused_var_name});
}
void RecordParamsAndGrads(ir::Node *node,
ParamsAndGrads *params_grads) const {
try {
......@@ -358,6 +357,7 @@ class AllocContinuousSpaceForGradPass : public ir::Pass {
}
}
// Alloc continuous space for vars.
std::vector<std::string> grads_name;
std::vector<std::string> params_name;
grads_name.reserve(params_grads.size());
......@@ -370,7 +370,6 @@ class AllocContinuousSpaceForGradPass : public ir::Pass {
AppendAllocSpaceForVarsOp(params_name, grads_name, fused_var_name,
program_desc.MutableBlock(0));
// Run Only Once Programs
for (size_t i = 0; i < local_scopes.size(); ++i) {
for (auto &op_desc : program_desc.Block(0).AllOps()) {
auto op = OpRegistry::CreateOp(*op_desc);
......@@ -378,6 +377,17 @@ class AllocContinuousSpaceForGradPass : public ir::Pass {
}
}
}
void AppendAllocSpaceForVarsOp(const std::vector<std::string> &params_name,
const std::vector<std::string> &grads_name,
const std::string &fused_var_name,
BlockDesc *global_block) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", params_name);
op_desc->SetOutput("Output", grads_name);
op_desc->SetOutput("FusedOutput", {fused_var_name});
}
};
} // namespace details
......
paddle/fluid/framework/details/broadcast_op_handle.cc
浏览文件 @ 33473890
......@@ -27,20 +27,17 @@ void BroadcastOpHandle::RunImpl() {
if (places_.size() == 1) return;
// The input and output may have dummy vars.
VarHandle *in_var_handle;
{
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
PADDLE_ENFORCE_EQ(in_var_handles.size(), 1UL,
"The number of input should be one.");
in_var_handle = in_var_handles[0];
}
auto in_var_handles = DynamicCast<VarHandle>(inputs_);
auto out_var_handles = DynamicCast<VarHandle>(outputs_);
PADDLE_ENFORCE_EQ(in_var_handles.size(), 1UL,
"The number of input should be one.");
PADDLE_ENFORCE_EQ(
out_var_handles.size(), places_.size(),
"The number of output should equal to the number of places.");
VarHandle *in_var_handle = in_var_handles[0];
WaitInputVarGenerated();
std::vector<const Scope *> var_scopes;
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 33473890
......@@ -17,7 +17,6 @@ limitations under the License. */
#include <glog/logging.h>
#include <memory>
#include <utility>
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/details/multi_devices_graph_pass.h"
#include "paddle/fluid/framework/details/multi_devices_graph_print_pass.h"
......@@ -82,23 +81,43 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
AppendPass("inplace_pass");
}
if (strategy.fuse_elewise_add_act_ops_) {
if (strategy_.fuse_elewise_add_act_ops_) {
VLOG(10) << "Add fuse_elewise_add_act_pass";
AppendPass("fuse_elewise_add_act_pass");
}
// for single card training, fuse_all_reduce_ops is unnecessary.
// alloc_continuous_space_for_grad_pass should be before of MultiDevPass.
if (strategy.fuse_all_reduce_ops_) {
if (strategy_.fuse_all_reduce_ops_) {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
}
if (strategy_.fuse_all_optimizer_ops_) {
if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce ||
strategy_.is_distribution_) {
VLOG(3)
<< "Currently, fuse_all_optimizer_ops only works under AllReduce "
"mode.";
strategy_.fuse_all_optimizer_ops_ = false;
} else {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
// NOTE: fuse_all_xx_ops will count the number of xx operator first,
// if the number is zero, fuse_all_reduce_ops will do nothing.
// Currently, only one type of optimization algorithm can be fused.
VLOG(10) << "Add fuse_adam_op_pass";
AppendPass("fuse_adam_op_pass");
VLOG(10) << "Add fuse_sgd_op_pass";
AppendPass("fuse_sgd_op_pass");
}
}
// Add a graph viz pass to record a graph.
if (!strategy.debug_graphviz_path_.empty()) {
auto viz_pass = AppendPass("graph_viz_pass");
const std::string graph_path = string::Sprintf(
"%s%s", strategy.debug_graphviz_path_.c_str(), "_fused_graph");
"%s%s", strategy_.debug_graphviz_path_.c_str(), "_fused_graph");
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
......@@ -118,14 +137,14 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// the de-fact IR, any reuse on Graph is meaningless.
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy.memory_optimize_) {
if (strategy_.memory_optimize_) {
VLOG(10) << "Add memory_optimize_pass";
AppendPass("memory_optimize_pass");
}
AppendMultiDevPass(strategy);
AppendMultiDevPass(strategy_);
if (strategy.fuse_all_reduce_ops_) {
if (strategy_.fuse_all_reduce_ops_) {
// NOTE: fuse_all_reduce_ops will count the number of all_reduce operator
// first, if the number is zero, fuse_all_reduce_ops will do nothing.
VLOG(10) << "Add fuse_all_reduce_op_pass";
......@@ -151,7 +170,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
AppendPass("all_reduce_deps_pass");
}
if (SeqOnlyAllReduceOps(strategy)) {
if (SeqOnlyAllReduceOps(strategy_)) {
VLOG(10) << "Add all_reduce_deps_pass";
AppendPass("all_reduce_deps_pass");
}
......@@ -165,7 +184,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Convert graph to run on multi-devices.
void AppendMultiDevPass(const BuildStrategy &strategy) {
ir::Pass *multi_devices_pass = nullptr;
if (strategy_.is_distribution_) {
if (strategy.is_distribution_) {
VLOG(10) << "Add dist_multi_devices_pass";
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
......@@ -235,17 +254,22 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
#endif
} else if (pass->Type() == "fuse_all_reduce_op_pass") {
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass" ||
pass->Type() == "fuse_adam_op_pass" ||
pass->Type() == "fuse_sgd_op_pass" ||
pass->Type() == "fuse_all_reduce_op_pass") {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
pass->Erase(kLocalScopes);
pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
&local_scopes);
if (pass->Type() == "fuse_all_reduce_op_pass") {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
platform::NCCLContextMap *nctx = use_cuda ? nccl_ctxs : nullptr;
pass->Erase(kNCCLCtxs);
pass->SetNotOwned<platform::NCCLContextMap>(kNCCLCtxs, nctx);
#endif
}
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass") {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
......@@ -294,4 +318,6 @@ USE_PASS(inplace_pass);
USE_PASS(lock_free_optimize_pass);
USE_PASS(alloc_continuous_space_for_grad_pass);
USE_PASS(graph_to_program_pass);
USE_PASS(fuse_adam_op_pass);
USE_PASS(fuse_sgd_op_pass);
USE_PASS(fuse_all_reduce_op_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 33473890
......@@ -18,7 +18,6 @@
#include <string>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/framework/scope.h"
......@@ -76,6 +75,8 @@ struct BuildStrategy {
bool fuse_elewise_add_act_ops_{false};
bool fuse_all_optimizer_ops_{false};
bool fuse_all_reduce_ops_{false};
bool fuse_relu_depthwise_conv_{false};
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
浏览文件 @ 33473890
......@@ -31,9 +31,10 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
local_scopes_(local_scopes),
places_(places),
graph_(graph),
fetch_ctxs_(places),
pool_(strategy.num_threads_),
prepare_pool_(1), // add one more thread for generate op_deps
fetch_ctxs_(places) {
// add one more thread for generate op_deps
prepare_pool_(1) {
for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
int dep = static_cast<int>(op->NotReadyInputSize());
op_deps_.emplace(op, dep);
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
浏览文件 @ 33473890
......@@ -14,7 +14,9 @@
#pragma once
#include <ThreadPool.h>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/blocking_queue.h"
#include "paddle/fluid/framework/details/exception_holder.h"
......@@ -37,6 +39,8 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
const ir::Graph &Graph() const override;
private:
// Note(zcd): the ThreadPool should be placed last so that ThreadPool should
// be destroyed first.
ExecutionStrategy strategy_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
......@@ -45,21 +49,22 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
std::unordered_map<OpHandleBase *, int> op_deps_;
std::vector<OpHandleBase *> bootstrap_ops_;
::ThreadPool pool_;
::ThreadPool prepare_pool_;
platform::DeviceContextPool fetch_ctxs_;
std::atomic<int> remaining_;
std::future<
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
atomic_op_deps_;
ExceptionHolder exception_;
::ThreadPool pool_;
::ThreadPool prepare_pool_;
void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q);
void PrepareAtomicOpDeps();
std::future<
std::unique_ptr<std::unordered_map<OpHandleBase *, std::atomic<int>>>>
atomic_op_deps_;
ExceptionHolder exception_;
};
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/fuse_adam_op_pass.cc 0 → 100644
浏览文件 @ 33473890
// 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.
#include "paddle/fluid/framework/details/fuse_adam_op_pass.h"
#include <algorithm>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace details {
const std::string FuseAdamOpPass::GetOpType() const { return "adam"; }
const std::vector<std::string> FuseAdamOpPass::GetAuxiliaryVarNames() const {
return {"Param", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
}
void FuseAdamOpPass::FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
FuseAdamOps(aux_var_set, fused_vars_name, adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta1Pow"), fused_vars_name.at("Beta1Pow"),
adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta2Pow"), fused_vars_name.at("Beta2Pow"),
adam_ops, graph);
}
void FuseAdamOpPass::FuseAdamOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(adam_ops.size(), static_cast<size_t>(0));
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
adam_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float beta1 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta1"));
float beta2 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta2"));
float epsilon = boost::get<float>(adam_ops[0]->Op()->GetAttr("epsilon"));
bool lazy_mode = boost::get<bool>(adam_ops[0]->Op()->GetAttr("lazy_mode"));
int64_t min_row_size_to_use_multithread = boost::get<int64_t>(
adam_ops[0]->Op()->GetAttr("min_row_size_to_use_multithread"));
for (auto &adam_op : adam_ops) {
PADDLE_ENFORCE_EQ(beta1,
boost::get<float>(adam_op->Op()->GetAttr("beta1")));
PADDLE_ENFORCE_EQ(beta2,
boost::get<float>(adam_op->Op()->GetAttr("beta2")));
PADDLE_ENFORCE_EQ(epsilon,
boost::get<float>(adam_op->Op()->GetAttr("epsilon")));
PADDLE_ENFORCE_EQ(lazy_mode,
boost::get<bool>(adam_op->Op()->GetAttr("lazy_mode")));
PADDLE_ENFORCE_EQ(min_row_size_to_use_multithread,
boost::get<int64_t>(adam_op->Op()->GetAttr(
"min_row_size_to_use_multithread")));
PADDLE_ENFORCE_EQ(op_role, boost::get<int>(adam_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
VLOG(10) << "Insert adam to graph ";
OpDesc adam_desc(adam_ops[0]->Op()->Block());
adam_desc.SetType("adam");
adam_desc.SetInput("Param", {fused_vars_name.at("Param")});
adam_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
adam_desc.SetInput("Moment1", {fused_vars_name.at("Moment1")});
adam_desc.SetInput("Moment2", {fused_vars_name.at("Moment2")});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
adam_desc.SetInput("LearningRate", adam_ops[0]->Op()->Input("LearningRate"));
adam_desc.SetInput("Beta1Pow", adam_ops[0]->Op()->Input("Beta1Pow"));
adam_desc.SetInput("Beta2Pow", adam_ops[0]->Op()->Input("Beta2Pow"));
adam_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
adam_desc.SetAttr("beta1", beta1);
adam_desc.SetAttr("beta2", beta2);
adam_desc.SetAttr("epsilon", epsilon);
adam_desc.SetAttr("lazy_mode", lazy_mode);
adam_desc.SetAttr("min_row_size_to_use_multithread",
min_row_size_to_use_multithread);
adam_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto adam_node = graph->CreateOpNode(&adam_desc);
InserInputAndOutputForOptOps(adam_ops, adam_node);
}
void FuseAdamOpPass::FuseScaleOps(const std::vector<std::string> &beta_name,
const std::string &fused_var_name,
const std::vector<ir::Node *> &adam_ops,
ir::Graph *graph) const {
PADDLE_ENFORCE_EQ(beta_name.size(), adam_ops.size());
const std::string scale_op_name = "scale";
// Get the scale_ops of dealing the adam's beta var.
std::vector<ir::Node *> scale_ops;
scale_ops.reserve(beta_name.size());
for (size_t i = 0; i < adam_ops.size(); ++i) {
auto &beta_1_pow_name = beta_name[i];
auto beta_pow_iter = std::find_if(
adam_ops[i]->inputs.begin(), adam_ops[i]->inputs.end(),
[&beta_name, &beta_1_pow_name](ir::Node *var_node) -> bool {
return var_node->Var() && var_node->Var()->Name() == beta_1_pow_name;
});
PADDLE_ENFORCE(beta_pow_iter != adam_ops[i]->inputs.end());
auto beta_pow_node = *beta_pow_iter;
auto scale_op_iter = std::find_if(
beta_pow_node->outputs.begin(), beta_pow_node->outputs.end(),
[&scale_op_name](ir::Node *op_node) -> bool {
return op_node->Op() && op_node->Op()->Type() == scale_op_name;
});
PADDLE_ENFORCE(scale_op_iter != beta_pow_node->outputs.end());
scale_ops.emplace_back(*scale_op_iter);
}
PADDLE_ENFORCE_EQ(scale_ops.size(), beta_name.size());
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
scale_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float scale = boost::get<float>(scale_ops[0]->Op()->GetAttr("scale"));
float bias = boost::get<float>(scale_ops[0]->Op()->GetAttr("bias"));
bool bias_after_scale =
boost::get<bool>(scale_ops[0]->Op()->GetAttr("bias_after_scale"));
for (auto &scale_op : scale_ops) {
PADDLE_ENFORCE_EQ(scale,
boost::get<float>(scale_op->Op()->GetAttr("scale")));
PADDLE_ENFORCE_EQ(bias, boost::get<float>(scale_op->Op()->GetAttr("bias")));
PADDLE_ENFORCE_EQ(
bias_after_scale,
boost::get<bool>(scale_op->Op()->GetAttr("bias_after_scale")));
PADDLE_ENFORCE_EQ(op_role, boost::get<int>(scale_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
VLOG(10) << "Insert fused scale to graph.";
OpDesc scale_desc(scale_ops[0]->Op()->Block());
scale_desc.SetType("scale");
scale_desc.SetInput("X", {fused_var_name});
scale_desc.SetOutput("Out", {fused_var_name});
scale_desc.SetAttr("scale", scale);
scale_desc.SetAttr("bias", bias);
scale_desc.SetAttr("bias_after_scale", bias_after_scale);
scale_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto scale_node = graph->CreateOpNode(&scale_desc);
for (auto scale_op : scale_ops) {
// set inputs
scale_node->inputs.insert(scale_node->inputs.begin(),
scale_op->inputs.begin(), scale_op->inputs.end());
for (auto &input : scale_op->inputs) {
std::replace(input->outputs.begin(), input->outputs.end(), scale_op,
scale_node);
}
// set outputs
scale_node->outputs.insert(scale_node->outputs.begin(),
scale_op->outputs.begin(),
scale_op->outputs.end());
for (auto &output : scale_op->outputs) {
std::replace(output->inputs.begin(), output->inputs.end(), scale_op,
scale_node);
}
}
// Delete scale_ops
for (auto &scale_op : scale_ops) {
graph->RemoveNode(scale_op);
}
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(fuse_adam_op_pass, paddle::framework::details::FuseAdamOpPass)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kLocalScopes);
paddle/fluid/framework/details/fuse_adam_op_pass.h 0 → 100644
浏览文件 @ 33473890
// 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 <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace details {
class FuseAdamOpPass : public FuseOptimizerOpPass {
private:
virtual const std::string GetOpType() const;
virtual const std::vector<std::string> GetAuxiliaryVarNames() const;
// Fuse Adam Ops and Scale Ops which are used to update "Beta1Pow", "Beta2Pow"
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const;
void FuseAdamOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const;
void FuseScaleOps(const std::vector<std::string> &aux_var_set,
const std::string &fused_var_name,
const std::vector<ir::Node *> &adam_ops,
ir::Graph *graph) const;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_optimizer_op_pass.cc 0 → 100644
浏览文件 @ 33473890
// 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.
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include <algorithm>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace details {
void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
ir::Graph &result = *graph;
auto &places = Get<const std::vector<platform::Place>>(kPlaces);
auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
const std::string fuse_op_type = GetOpType();
const std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
// Step 1: Get the specified op and auxiliary variables.
std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(result);
std::unordered_map<std::string, std::vector<std::string>> aux_var_set;
std::vector<ir::Node *> opt_ops;
for (auto &node : topo_nodes) {
GetSpecifiedOpsAndVars(fuse_op_type, aux_var_names, node, &opt_ops,
&aux_var_set);
}
VLOG(10) << "Find " << fuse_op_type << " operators: " << opt_ops.size();
if (opt_ops.size() == 0) {
return;
}
if (result.Has(kFusedOptType)) {
VLOG(10)
<< "Currently only support fusing one type optimizer op. Has fused "
<< result.Get<FusedOptType>(kFusedOptType);
return;
} else {
result.Set(kFusedOptType, new FusedOptType);
}
result.Get<FusedOptType>(kFusedOptType) = fuse_op_type;
// Step 2: Insert fused_var_name to FusedVars, and the FusedVars need be
// initialized in scopes before execution.
if (!result.Has(kFusedVars)) {
result.Set(kFusedVars, new FusedVars);
}
std::unordered_map<std::string, std::string> fused_vars_name;
fused_vars_name.reserve(aux_var_names.size() + 1);
auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
const std::string prefix(kFusedVarNamePrefix);
// NOTE: the fused_var_name should be unique.
for (auto &var_name : aux_var_names) {
auto fused_var_name = prefix + "_" + fuse_op_type + "_" + var_name + "_" +
aux_var_set[var_name][0];
VLOG(10) << fused_var_name;
fused_vars_name.emplace(var_name, fused_var_name);
PADDLE_ENFORCE_EQ(fused_var_set.count(fused_var_name), 0);
fused_var_set.insert(fused_var_name);
}
// Step 3: Get the fused Gradient's name
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
if (!result.Has(kFusedGrads)) {
PADDLE_THROW(
"The alloc_continuous_space_for_grad_pass should be called before this "
"pass.");
}
auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
auto &fused_vars = result.Get<FusedVars>(kFusedVars);
auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
fused_vars_name.emplace("Grad", fused_grad);
// Step 4: Sort the parameters and auxiliary variables according
// to parameters' name to make variables' name correspond correctly.
PADDLE_ENFORCE(result.Has(kParamsAndGrads), "Does't find kParamsAndGrads.");
PADDLE_ENFORCE_EQ(params_grads.size(), aux_var_set.begin()->second.size(),
"The size of params_grads and aux_var_set are not equal.");
SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
// Step 5: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
// Moment1, Moment2, Beta1Pow, Beta2Pow) of all the optimizer ops separately.
InitFusedVarsAndAllocSpaceForVars(places, local_scopes, aux_var_names,
aux_var_set, fused_vars_name);
// Step 6: Fuse optimizer Ops and Scale Ops
FuseOptimizerOps(aux_var_set, fused_vars_name, opt_ops, &result);
// Step 7: Remove optimizer Ops
for (auto &opt_op : opt_ops) {
graph->RemoveNode(opt_op);
}
}
void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &aux_var_names,
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name) const {
VLOG(10) << "Init FusedVars.";
// Alloc parameters and auxiliary vars in the respective scope.
size_t idx = local_scopes.size();
for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
++iter, --idx) {
auto &scope = *iter;
for (auto &var_name : aux_var_names) {
auto fused_var_name = fused_vars_name.at(var_name);
VLOG(10) << "Init " << fused_var_name;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has exist in scope[%d]", fused_var_name, idx);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
}
}
ProgramDesc program_desc;
auto *global_block = program_desc.MutableBlock(0);
for (auto &var_name : aux_var_names) {
AppendAllocContinuousSpace(aux_var_set.at(var_name),
fused_vars_name.at(var_name), true,
global_block);
}
for (size_t i = 0; i < local_scopes.size(); ++i) {
for (auto &op_desc : global_block->AllOps()) {
auto op = OpRegistry::CreateOp(*op_desc);
op->Run(*local_scopes[i], places[i]);
}
}
}
void FuseOptimizerOpPass::SortParametersAndAuxVars(
const std::vector<std::pair<std::string, std::string>> &params_grads,
std::unordered_map<std::string, std::vector<std::string>> *aux_vars_set,
std::vector<ir::Node *> *ops) const {
PADDLE_ENFORCE_NE(aux_vars_set->count("Param"), static_cast<size_t>(0));
auto &param_vec = aux_vars_set->at("Param");
std::vector<size_t> param_sort_idx;
param_sort_idx.reserve(param_vec.size());
for (auto &p_g : params_grads) {
auto iter = std::find(param_vec.begin(), param_vec.end(), p_g.first);
PADDLE_ENFORCE(iter != param_vec.end());
auto idx = std::distance(param_vec.begin(), iter);
param_sort_idx.emplace_back(idx);
}
for (auto &aux_vars : *aux_vars_set) {
std::vector<std::string> sorted_vars;
sorted_vars.reserve(aux_vars.second.size());
for (size_t i = 0; i < aux_vars.second.size(); ++i) {
sorted_vars.emplace_back(aux_vars.second.at(param_sort_idx[i]));
}
std::swap(aux_vars.second, sorted_vars);
std::stringstream out;
for (auto &var_name : aux_vars.second) {
out << var_name << " ";
}
VLOG(10) << aux_vars.first << ": " << out.str();
}
std::vector<ir::Node *> sorted_ops;
sorted_ops.reserve(ops->size());
for (size_t i = 0; i < ops->size(); ++i) {
sorted_ops.emplace_back(ops->at(param_sort_idx[i]));
}
std::swap(*ops, sorted_ops);
}
void FuseOptimizerOpPass::GetSpecifiedOpsAndVars(
const std::string &op_type, const std::vector<std::string> &aux_vars_name,
ir::Node *node, std::vector<ir::Node *> *ops,
std::unordered_map<std::string, std::vector<std::string>> *aux_args_name)
const {
if (node->Op()->Type() != op_type) return;
for (auto &var_n : aux_vars_name) {
auto arg_names = node->Op()->Input(var_n);
PADDLE_ENFORCE_EQ(arg_names.size(), static_cast<size_t>(1));
(*aux_args_name)[var_n].emplace_back(arg_names[0]);
VLOG(10) << var_n << ", " << arg_names[0];
}
ops->emplace_back(node);
}
void FuseOptimizerOpPass::AppendAllocContinuousSpace(
const std::vector<std::string> &args, const std::string &out_arg,
bool copy_data, BlockDesc *global_block) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", args);
op_desc->SetOutput("Output", args);
op_desc->SetOutput("FusedOutput", {out_arg});
op_desc->SetAttr("copy_data", copy_data);
op_desc->SetAttr("check_name", true);
}
void FuseOptimizerOpPass::InserInputAndOutputForOptOps(
const std::vector<ir::Node *> &opt_ops, ir::Node *opt_node) const {
std::unordered_set<ir::Node *> inputs;
std::unordered_set<ir::Node *> outputs;
for (auto opt_op : opt_ops) {
// set inputs
inputs.insert(opt_op->inputs.begin(), opt_op->inputs.end());
for (auto &input : opt_op->inputs) {
replace(input->outputs.begin(), input->outputs.end(), opt_op, opt_node);
}
// set outputs
outputs.insert(opt_op->outputs.begin(), opt_op->outputs.end());
for (auto &output : opt_op->outputs) {
replace(output->inputs.begin(), output->inputs.end(), opt_op, opt_node);
}
}
opt_node->inputs.insert(opt_node->inputs.begin(), inputs.begin(),
inputs.end());
opt_node->outputs.insert(opt_node->outputs.begin(), outputs.begin(),
outputs.end());
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_optimizer_op_pass.h 0 → 100644
浏览文件 @ 33473890
// 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 <memory>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace details {
class FuseOptimizerOpPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const override;
protected:
virtual void SortParametersAndAuxVars(
const std::vector<std::pair<std::string, std::string>> &params_grads,
std::unordered_map<std::string, std::vector<std::string>> *aux_var_set,
std::vector<ir::Node *> *ops) const;
void InserInputAndOutputForOptOps(const std::vector<ir::Node *> &opt_ops,
ir::Node *opt_node) const;
private:
virtual const std::string GetOpType() const = 0;
virtual const std::vector<std::string> GetAuxiliaryVarNames() const = 0;
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const = 0;
void GetSpecifiedOpsAndVars(
const std::string &op_type, const std::vector<std::string> &aux_vars_name,
ir::Node *node, std::vector<ir::Node *> *ops,
std::unordered_map<std::string, std::vector<std::string>> *aux_args_name)
const;
void AppendAllocContinuousSpace(const std::vector<std::string> &args,
const std::string &out_arg, bool copy_data,
BlockDesc *global_block) const;
void InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &aux_var_names,
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name)
const;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fuse_sgd_op_pass.cc 0 → 100644
浏览文件 @ 33473890
// 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.
#include "paddle/fluid/framework/details/fuse_sgd_op_pass.h"
#include <algorithm>
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace details {
const std::string FuseSgdOpPass::GetOpType() const { return "sgd"; }
const std::vector<std::string> FuseSgdOpPass::GetAuxiliaryVarNames() const {
return {"Param"};
}
void FuseSgdOpPass::FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const {
FuseSgdOps(aux_var_set, fused_vars_name, sgd_ops, graph);
}
void FuseSgdOpPass::FuseSgdOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(sgd_ops.size(), static_cast<size_t>(0));
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
int op_role = boost::get<int>(
sgd_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
VLOG(10) << "Insert sgd to graph ";
// Add fused scale
OpDesc Sgd_desc(sgd_ops[0]->Op()->Block());
Sgd_desc.SetType("sgd");
Sgd_desc.SetInput("Param", {fused_vars_name.at("Param")});
Sgd_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
Sgd_desc.SetInput("LearningRate", sgd_ops[0]->Op()->Input("LearningRate"));
// NOTE: multi_devices_pass requires that every op should have a role.
Sgd_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto sgd_node = graph->CreateOpNode(&Sgd_desc);
InserInputAndOutputForOptOps(sgd_ops, sgd_node);
}
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(fuse_sgd_op_pass, paddle::framework::details::FuseSgdOpPass)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kLocalScopes);
paddle/fluid/framework/details/fuse_sgd_op_pass.h 0 → 100644
浏览文件 @ 33473890
// 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 <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
namespace paddle {
namespace framework {
namespace details {
class FuseSgdOpPass : public FuseOptimizerOpPass {
private:
virtual const std::string GetOpType() const;
virtual const std::vector<std::string> GetAuxiliaryVarNames() const;
// Fuse Sgd Ops
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const;
void FuseSgdOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/fused_all_reduce_op_handle.cc
浏览文件 @ 33473890
......@@ -24,6 +24,19 @@ namespace paddle {
namespace framework {
namespace details {
// Note(zcd): Addresses should be aligned, otherwise, the results may have
// diff.
static size_t Alignment(size_t size, const platform::Place &place) {
// Allow to allocate the minimum chunk size is 4 KB.
size_t alignment = 1 << 12;
if (platform::is_gpu_place(place)) {
// Allow to allocate the minimum chunk size is 256 B.
alignment = 1 << 8;
}
size_t remaining = size % alignment;
return remaining == 0 ? size : size + (alignment - remaining);
}
typedef std::vector<std::vector<std::pair<std::string, const LoDTensor *>>>
GradientAndLoDTensor;
......@@ -111,10 +124,11 @@ void FusedAllReduceOpHandle::RunImpl() {
return grad1.second->data<void>() < grad2.second->data<void>();
});
size_t size_of_dtype = framework::SizeOfType(dtype);
for (size_t k = 1; k < g_tensor.size(); ++k) {
const void *cur_address = g_tensor.at(k - 1).second->data<void>();
int64_t len = g_tensor.at(k - 1).second->numel();
auto offset = len * framework::SizeOfType(dtype);
auto offset = Alignment(len * size_of_dtype, places_[0]);
void *infer_next_address = reinterpret_cast<void *>(
reinterpret_cast<uintptr_t>(cur_address) + offset);
const void *next_address = g_tensor.at(k).second->data<void>();
......@@ -228,18 +242,21 @@ void FusedAllReduceOpHandle::GetDTypeAndNumel(
const std::vector<std::pair<std::string, const LoDTensor *>> &grad_tensor,
proto::VarType::Type *dtype, int64_t *numel) const {
*numel = 0;
size_t size_of_dtype = 0;
for (size_t i = 0; i < grad_tensor.size(); ++i) {
// Get element number
int64_t len = grad_tensor.at(i).second->numel();
PADDLE_ENFORCE_GT(len, 0);
*numel += len;
// Get dtype
auto ele_type = grad_tensor.at(i).second->type();
if (i == 0) {
*dtype = ele_type;
size_of_dtype = framework::SizeOfType(ele_type);
}
PADDLE_ENFORCE_EQ(ele_type, *dtype);
// Get element number
int64_t len = grad_tensor.at(i).second->numel();
PADDLE_ENFORCE_GT(len, 0);
// Alignment(len)
*numel += Alignment(len * size_of_dtype, places_[0]) / size_of_dtype;
}
}
......
paddle/fluid/framework/details/inplace_op_pass.cc
浏览文件 @ 33473890
......@@ -156,7 +156,6 @@ void InplacePass::ApplyImpl(ir::Graph* graph) const {
continue;
TryInplaceOpInputOutput(op, graph);
}
// graph->ResolveHazard(var_nodes_);
}
void InplacePass::InplaceModifyDesc(const std::string& var,
......@@ -168,7 +167,7 @@ void InplacePass::InplaceModifyDesc(const std::string& var,
auto* op_desc = op->Op();
op_desc->RenameInput(var, cache_var);
op_desc->RenameOutput(var, cache_var);
if (op_desc->Block()->HasVar(var)) op_desc->Block()->RemoveVar(var);
op_desc->Flush();
}
}
......@@ -265,8 +264,6 @@ void InplacePass::WithdrawModify(const NodeSwapQueue& nodes,
void InplacePass::TryInplaceOpInputOutput(ir::Node* op,
ir::Graph* graph) const {
VLOG(4) << "Try to inplace op " << op->Name();
// PADDLE_ENFORCE(op->Op() != nullptr && op->Op()->Block() != nullptr,
// "op_desc is nullptr");
// some pre-requirments need to meet if the op want to inplaced.
PADDLE_ENFORCE(op->Op() != nullptr, "op_desc is nullptr");
......@@ -446,19 +443,20 @@ bool GraphView::CheckDeps(ir::Node* var, ir::Node* current_op) const {
// check if op2 depends on op1's output
bool GraphView::CheckOpDeps(ir::Node* op1, ir::Node* op2) const {
auto print_op = [&](ir::Node* op, const char* name) {
std::ostringstream os;
os << " " << name << " : " << op->Name() << " ";
os << "Input args : ";
for (auto& arg : op->inputs) os << arg->Name() << " ";
os << "Output args : ";
for (auto& arg : op->outputs) os << arg->Name() << " ";
os << "Level : " << op_level_.at(op);
VLOG(4) << os.str();
};
print_op(op1, "OP1");
print_op(op2, "OP2");
if (VLOG_IS_ON(4)) {
auto print_op = [&](ir::Node* op, const char* name) {
std::ostringstream os;
os << " " << name << " : " << op->Name() << " ";
os << "Input args : ";
for (auto& arg : op->inputs) os << arg->Name() << " ";
os << "Output args : ";
for (auto& arg : op->outputs) os << arg->Name() << " ";
os << "Level : " << op_level_.at(op);
VLOG(4) << os.str();
};
print_op(op1, "OP1");
print_op(op2, "OP2");
}
if (op1 == op2) return true;
if (op_level_.at(op1) >= op_level_.at(op2)) return false;
......
paddle/fluid/framework/details/memory_optimize_helper_test.cc
浏览文件 @ 33473890
......@@ -142,16 +142,15 @@ TEST(OrderedSet, FindBestFitNode) {
for (auto& node : nodes) {
pool.Insert(node.get());
}
// FIXME(liuwei1031) this API has changed,
// disable these tests temporarily
// FindNextBestFitNode
// auto* n = nodes[0].get();
// auto* cache = pool.FindBestFitNode(n);
// PADDLE_ENFORCE(cache->Name() == "a");
// cache = pool.FindNextBestFitNode(n, cache);
// PADDLE_ENFORCE(cache->Name() == "c");
// cache = pool.FindNextBestFitNode(n, cache);
// PADDLE_ENFORCE(cache->Name() == "b");
auto* n = nodes[0].get();
auto* cache = pool.FindBestFitNode(n);
ASSERT_TRUE(cache->Name() == "a" || cache->Name() == "c");
auto* cache_b = pool.FindNextBestFitNode(n, cache);
ASSERT_TRUE(cache_b->Name() != cache->Name());
ASSERT_TRUE(cache_b->Name() == "a" || cache_b->Name() == "c");
cache = pool.FindNextBestFitNode(n, cache_b);
ASSERT_TRUE(cache == nullptr);
}
} // namespace details
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 33473890
......@@ -20,7 +20,6 @@
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
......@@ -34,6 +33,10 @@ namespace framework {
class Scope;
namespace details {
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
class MultiDevSSAGraphBuilderBase : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const override;
......
paddle/fluid/framework/details/multi_devices_helper.h
浏览文件 @ 33473890
......@@ -20,7 +20,6 @@
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/details/var_handle.h"
......@@ -41,22 +40,25 @@ namespace details {
// `std::vector<VarHandle*>` is the version of varaibles.
typedef std::vector<std::unordered_map<std::string, std::vector<VarHandle *>>>
GraphVars;
const char kGraphVars[] = "vars";
// aux variables to represent dependency. Useful to resolve data hazard.
typedef std::unordered_set<VarHandleBase *> GraphDepVars;
const char kGraphDepVars[] = "dep_vars";
constexpr char kGraphVars[] = "vars";
constexpr char kNCCLCtxs[] = "nccl_ctxs";
constexpr char kLossVarName[] = "loss_var_name";
constexpr char kPlaces[] = "places";
constexpr char kLocalScopes[] = "local_scopes";
constexpr char kStrategy[] = "strategy";
constexpr char kNRanks[] = "nranks";
constexpr char kNCCLCtxs[] = "nccl_ctxs";
// aux variables to represent dependency. Useful to resolve data hazard.
typedef std::unordered_set<VarHandleBase *> GraphDepVars;
constexpr char kGraphDepVars[] = "dep_vars";
typedef std::unordered_set<std::string> FusedVars;
constexpr char kFusedVars[] = "fused_vars";
constexpr char kFusedVarNamePrefix[] = "@FUSEDVAR@";
typedef std::string FusedOptType;
constexpr char kFusedOptType[] = "fused_opt_type";
typedef std::string FusedGrads;
constexpr char kFusedGrads[] = "fused_gradients";
typedef std::vector<std::pair<std::string, std::string>> ParamsAndGrads;
constexpr char kParamsAndGrads[] = "params_grads";
......@@ -65,8 +67,6 @@ typedef std::vector<std::vector<std::pair<std::string, std::string>>>
GroupGradsAndParams;
constexpr char kGroupGradsAndParams[] = "group_grads_params";
constexpr char kFusedVarNamePrefix[] = "@FUSEDVAR@";
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 33473890
......@@ -24,13 +24,13 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor(
const ExecutionStrategy &strategy, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places, ir::Graph *graph)
: graph_(graph),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr),
prepare_pool_(1),
local_scopes_(local_scopes),
places_(places),
fetch_ctxs_(places),
strategy_(strategy) {
strategy_(strategy),
prepare_pool_(1),
pool_(strategy.num_threads_ >= 2 ? new ::ThreadPool(strategy.num_threads_)
: nullptr) {
PrepareOpDeps();
CopyOpDeps();
}
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.h
浏览文件 @ 33473890
......@@ -63,13 +63,20 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
details::OpHandleBase *op);
private:
// Note(zcd): the ThreadPool should be placed last so that ThreadPool should
// be destroyed first.
ir::Graph *graph_;
std::unique_ptr<::ThreadPool> pool_;
::ThreadPool prepare_pool_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
platform::DeviceContextPool fetch_ctxs_;
ExceptionHolder exception_holder_;
std::unique_ptr<OpDependentData> op_deps_;
std::future<std::unique_ptr<OpDependentData>> op_deps_futures_;
ExecutionStrategy strategy_;
// use std::list because clear(), push_back, and for_each are O(1)
std::list<std::future<void>> run_op_futures_;
::ThreadPool prepare_pool_;
std::unique_ptr<::ThreadPool> pool_;
void InsertPendingOp(std::unordered_map<OpHandleBase *, size_t> *pending_ops,
OpHandleBase *op_instance) const;
......@@ -88,14 +95,6 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
void PrepareOpDeps();
void CopyOpDeps();
private:
std::future<std::unique_ptr<OpDependentData>> op_deps_futures_;
ExecutionStrategy strategy_;
std::unique_ptr<OpDependentData> op_deps_;
// use std::list because clear(), push_back, and for_each are O(1)
std::list<std::future<void>> run_op_futures_;
};
} // namespace details
......
paddle/fluid/framework/inplace_op_inference_test.cc
浏览文件 @ 33473890
......@@ -12,9 +12,14 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include <iostream>
#include <iterator>
#include <memory>
#include <string>
#include <vector>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/details/inplace_op_pass.h"
#include "paddle/fluid/framework/ir/pass_builder.h"
#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
......@@ -165,118 +170,147 @@ REGISTER_OPERATOR(multi_out_grad, f::NOP, f::MultiOutGradInplaceInToOut,
namespace paddle {
namespace framework {
// TEST(InferInplace, SingleOpInplaceInToOut) {
// ProgramDesc prog;
// auto* op = prog.MutableBlock(0)->AppendOp();
// op->SetType("single_op");
// op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
// op->SetOutput("Out", {"test2_out"});
//
// prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
// prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_out");
// prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 128, 128});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// EXPECT_EQ(in_to_outs.size(), 1ul);
// auto it = in_to_outs.begin();
// EXPECT_EQ(it->first, "test2_a");
// EXPECT_EQ(it->second, "test2_out");
// }
//
// TEST(InferInplace, SingleGradOpInplaceInToOut) {
// ProgramDesc prog;
// auto* op = prog.MutableBlock(0)->AppendOp();
// op->SetType("single_op_grad");
// op->SetInput(GradVarName("Out"), {"test2_out"});
// op->SetOutput(GradVarName("X"), {"test2_a", "test2_b", "test2_c"});
//
// prog.MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_a")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("test2_out");
// prog.MutableBlock(0)->Var("test2_out")->SetShape({32, 16, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// EXPECT_EQ(in_to_outs.size(), 1ul);
// auto it = in_to_outs.begin();
// EXPECT_EQ(it->first, "test2_out");
// EXPECT_EQ(it->second, "test2_a");
// }
//
// TEST(InferInplace, MultiOutInplaceInToOut) {
// ProgramDesc prog;
// auto* op = prog.MutableBlock(0)->AppendOp();
// op->SetType("multi_out_op");
// op->SetInput("X", {"a0", "a1"});
// op->SetInput("Y", {"b0"});
// op->SetInput("Z", {"c0", "c1"});
// op->SetOutput("Out", {"o0"});
// op->SetOutput("YOut", {"y0"});
// op->SetOutput("ZOut", {"z0"});
//
// prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("o0");
// prog.MutableBlock(0)->Var("y0");
// prog.MutableBlock(0)->Var("z0");
// prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
// EXPECT_EQ(in_to_outs.size(), 3ul);
// std::unordered_map<std::string, std::string> expects = {
// {"a0", "o0"}, {"b0", "y0"}, {"c0", "z0"},
// };
// EXPECT_TRUE(expects == in_to_outs);
// }
//
// TEST(InferInplace, MultiGradInplaceInToOut) {
// ProgramDesc prog;
// auto* op = prog.MutableBlock(0)->AppendOp();
// op->SetType("multi_out_grad");
// op->SetInput(GradVarName("Out"), {"o0"});
// op->SetInput(GradVarName("YOut"), {"y0"});
// op->SetInput(GradVarName("ZOut"), {"z0"});
// op->SetOutput(GradVarName("X"), {"a0", "a1"});
// op->SetOutput(GradVarName("Y"), {"b0"});
// op->SetOutput(GradVarName("Z"), {"c0", "c1"});
//
// prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
// prog.MutableBlock(0)->Var("o0");
// prog.MutableBlock(0)->Var("y0");
// prog.MutableBlock(0)->Var("z0");
// prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
// prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
//
// auto& infer_inplace = OpInfoMap::Instance().Get(op->Type()).infer_inplace_;
// auto in_to_outs = infer_inplace(*op);
//
// EXPECT_EQ(in_to_outs.size(), 3ul);
// std::unordered_map<std::string, std::string> expects = {
// {"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
// };
// EXPECT_TRUE(expects == in_to_outs);
// }
void FakeSuccData(ProgramDesc* prog) { // NOLINT
prog->MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
prog->MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_out");
prog->MutableBlock(0)->Var("test2_out")->SetShape({64, 32, 128, 128});
}
void FakeNoInplaceData(ProgramDesc* prog) { // NOLINT
prog->MutableBlock(0)->Var("test2_a")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_a")->SetShape({32, 64, 128, 128});
prog->MutableBlock(0)->Var("test2_b")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_c")->SetType(proto::VarType::LOD_TENSOR);
prog->MutableBlock(0)->Var("test2_out");
prog->MutableBlock(0)->Var("test2_out")->SetShape({64, 31, 128, 128});
}
ir::Node* GetNodeFromGraph(ir::Graph* g, std::string name) {
ir::Node* op_node = nullptr;
for (auto& item : g->Nodes()) {
if (item->Name() == name) {
op_node = item;
break;
}
}
return op_node;
}
std::unique_ptr<ir::Graph> test_SingleOpInplaceInToOut(
std::unique_ptr<ir::Graph> g) {
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
ir::Node* op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_NE(op_node, nullptr);
pass->Apply(g.get());
return g;
}
TEST(InferInplace, SingleOpInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("single_op");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
FakeSuccData(&prog);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
g = test_SingleOpInplaceInToOut(std::move(g));
auto op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_EQ(op_node->outputs[0]->Name(), "test2_a");
}
TEST(InferInplace, SingleOpInplaceInToOutNoInplace) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("single_op");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
FakeNoInplaceData(&prog);
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
g = test_SingleOpInplaceInToOut(std::move(g));
auto op_node = GetNodeFromGraph(g.get(), "single_op");
EXPECT_EQ(op_node->outputs[0]->Name(), "test2_out");
}
TEST(InferInplace, MultiOutInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("multi_out_op");
op->SetInput("X", {"a0", "a1"});
op->SetInput("Y", {"b0"});
op->SetInput("Z", {"c0", "c1"});
op->SetOutput("Out", {"o0"});
op->SetOutput("YOut", {"y0"});
op->SetOutput("ZOut", {"z0"});
prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 16, 1024, 1024});
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
pass->Apply(g.get());
auto op_node = GetNodeFromGraph(g.get(), "multi_out_op");
ASSERT_TRUE(op_node != nullptr);
EXPECT_EQ(op_node->outputs[0]->Name(), "a0");
EXPECT_EQ(op_node->outputs[1]->Name(), "b0");
EXPECT_EQ(op_node->outputs[2]->Name(), "c0");
}
TEST(InferInplace, MultiGradInplaceInToOut) {
ProgramDesc prog;
auto* op = prog.MutableBlock(0)->AppendOp();
op->SetType("multi_out_grad");
op->SetInput(GradVarName("Out"), {"o0"});
op->SetInput(GradVarName("YOut"), {"y0"});
op->SetInput(GradVarName("ZOut"), {"z0"});
op->SetOutput(GradVarName("X"), {"a0", "a1"});
op->SetOutput(GradVarName("Y"), {"b0"});
op->SetOutput(GradVarName("Z"), {"c0", "c1"});
prog.MutableBlock(0)->Var("a0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("b0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c0")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("c1")->SetType(proto::VarType::LOD_TENSOR);
prog.MutableBlock(0)->Var("o0");
prog.MutableBlock(0)->Var("y0");
prog.MutableBlock(0)->Var("z0");
prog.MutableBlock(0)->Var("a0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("b0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("c0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("o0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("y0")->SetShape({32, 16, 1024, 1024});
prog.MutableBlock(0)->Var("z0")->SetShape({32, 15, 1024, 1024});
std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
std::unique_ptr<details::InplacePass> pass(new details::InplacePass());
pass->Apply(g.get());
auto op_node = GetNodeFromGraph(g.get(), "multi_out_grad");
ASSERT_TRUE(op_node != nullptr);
EXPECT_EQ(op_node->outputs[0]->Name(), "o0");
EXPECT_EQ(op_node->outputs[2]->Name(), "y0");
EXPECT_EQ(op_node->outputs[3]->Name(), "c0");
std::unordered_map<std::string, std::string> expects = {
{"o0", "a0"}, {"y0", "b0"}, {"z0", "c0"},
};
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/operator.cc
浏览文件 @ 33473890
......@@ -56,8 +56,8 @@ proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
}
}
static DDim GetDims(const Scope& scope, const std::string& name,
bool get_actual_dim = false) {
static DDim GetDimsDebug(const Scope& scope, const std::string& name,
bool get_actual_dim = false) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
return DDim({-1});
......@@ -65,9 +65,9 @@ static DDim GetDims(const Scope& scope, const std::string& name,
if (var->IsType<LoDTensor>()) {
const LoDTensor& tensor = var->Get<LoDTensor>();
// if (UNLIKELY(!tensor.IsInitialized())) {
// return DDim({-1});
// }
if (UNLIKELY(!tensor.IsInitialized())) {
return DDim({-1});
}
return tensor.dims();
} else if (var->IsType<SelectedRows>()) {
if (get_actual_dim) {
......@@ -123,7 +123,7 @@ static int GetRowSize(const Scope& scope, const std::string& name) {
return -1;
}
static LoD GetLoD(const Scope& scope, const std::string& name) {
static LoD GetLoDDebug(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
auto default_lod = LoD({{}});
......@@ -133,9 +133,9 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
if (var->IsType<LoDTensor>()) {
const LoDTensor& tensor = var->Get<LoDTensor>();
// if (UNLIKELY(!tensor.IsInitialized())) {
// return default_lod;
// }
if (UNLIKELY(!tensor.IsInitialized())) {
return default_lod;
}
return tensor.lod();
} else {
return default_lod;
......@@ -274,8 +274,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
}
std::string dtype = GetDtype(*scope, var_name);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
ss << "(" << GetLoDDebug(*scope, var_name) << ")";
}
}
if (i != input.second.size() - 1) {
......@@ -305,8 +305,8 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
}
std::string dtype = GetDtype(*scope, output.second[i]);
ss << ":" << dtype;
ss << "[" << GetDims(*scope, var_name, true) << "]";
ss << "(" << GetLoD(*scope, var_name) << ")";
ss << "[" << GetDimsDebug(*scope, var_name, true) << "]";
ss << "(" << GetLoDDebug(*scope, var_name) << ")";
}
}
if (i != output.second.size() - 1) {
......
paddle/fluid/framework/operator.h
浏览文件 @ 33473890
......@@ -365,6 +365,9 @@ class ExecutionContext {
auto shared_allocation = std::shared_ptr<memory::allocation::Allocation>(
allocation_ptr, deleter);
PADDLE_ENFORCE(
dynamic_cast<platform::TemporaryAllocation*>(allocation_ptr) != nullptr,
"The AllocationPtr must be TemporaryAllocation.");
PADDLE_ENFORCE_GE(allocation_ptr->size(),
framework::product(dim) * sizeof(T));
......
paddle/fluid/framework/tensor.cc
浏览文件 @ 33473890
......@@ -70,7 +70,7 @@ Tensor& Tensor::ShareDataWith(const Tensor& src) {
return *this;
}
Tensor Tensor::Slice(int begin_idx, int end_idx) const {
Tensor Tensor::Slice(int64_t begin_idx, int64_t end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx, 0,
"The start row index must be greater than 0.");
......
paddle/fluid/framework/tensor.h
浏览文件 @ 33473890
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <cstring>
#include <memory>
#include <typeindex>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/ddim.h"
......@@ -27,10 +28,6 @@ limitations under the License. */
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_utils.h"
#endif
namespace paddle {
namespace framework {
......@@ -41,34 +38,10 @@ class Tensor {
#ifdef PADDLE_WITH_MKLDNN
public:
// TODO(jczaja): This is depracted and will be removed
inline mkldnn::memory::format format() const {
if (layout_ == DataLayout::kMKLDNN) {
return static_cast<mkldnn::memory::format>(mem_pd_.desc().data.format);
} else {
return mkldnn::memory::format::format_undef;
}
}
inline mkldnn::memory::format format() const { return format_; }
// TODO(jczaja): This is depracted and will be removed
inline void set_format(
const mkldnn::memory::format fmt,
mkldnn::memory::data_type data_type = mkldnn::memory::f32) {
mem_pd_ = paddle::platform::create_prim_desc_from_format(
paddle::framework::vectorize2int(dims()), fmt, data_type);
layout_ = DataLayout::kMKLDNN;
}
inline mkldnn::memory::primitive_desc get_mkldnn_prim_desc() const {
return mem_pd_;
}
inline void set_mkldnn_prim_desc(
const mkldnn::memory::primitive_desc& mem_pd) {
// Internally MKL-DNN is just copying (increasing reference counter)
// to shared_ptr. So asignment should be quite cheap
mem_pd_ = mem_pd;
layout_ = DataLayout::kMKLDNN;
inline void set_format(const mkldnn::memory::format format) {
format_ = format;
}
protected:
......@@ -76,9 +49,12 @@ class Tensor {
* @brief the detail format of memory block which have layout as kMKLDNN
*
* @note MKLDNN lib support various memory format like nchw, nhwc, nChw8C,
* nChw16c, etc. For a MKLDNN memory block, we store memory descriptor
* nChw16c, etc. For a MKLDNN memory block, layout will be set as
* DataLayout::kMKLDNN meanwhile detail memory format will be kept in
* this field.
*/
mutable mkldnn::memory::primitive_desc mem_pd_;
mkldnn::memory::format format_ = mkldnn::memory::format::format_undef;
#endif
public:
......@@ -157,7 +133,7 @@ class Tensor {
* @param[in] end_idx The index of the end row(exclusive) to slice.
* The index number begins from 0.
*/
Tensor Slice(int begin_idx, int end_idx) const;
Tensor Slice(int64_t begin_idx, int64_t end_idx) const;
platform::Place place() const {
PADDLE_ENFORCE_NOT_NULL(
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 33473890
......@@ -44,11 +44,6 @@ void TensorCopy(const Tensor& src, const platform::Place& dst_place,
<< dst_place;
return;
}
#ifdef PADDLE_WITH_MKLDNN
if (src.layout() == DataLayout::kMKLDNN) {
dst->set_mkldnn_prim_desc(src.get_mkldnn_prim_desc());
}
#endif
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
boost::get<platform::CPUPlace>(src_place), src_ptr, size);
}
......
paddle/fluid/memory/allocation/CMakeLists.txt
浏览文件 @ 33473890
......@@ -4,7 +4,6 @@ cc_library(best_fit_allocator SRCS best_fit_allocator.cc DEPS allocator)
cc_library(locked_allocator SRCS locked_allocator.cc DEPS allocator)
cc_library(buffered_allocator SRCS buffered_allocator.cc DEPS allocator)
cc_library(legacy_allocator SRCS legacy_allocator.cc DEPS allocator buddy_allocator profiler)
cc_library(zero_size_allocator SRCS zero_size_allocator.cc DEPS allocator)
cc_test(buffered_allocator_test SRCS buffered_allocator_test.cc DEPS best_fit_allocator locked_allocator buffered_allocator cpu_allocator)
if (WITH_GPU)
......@@ -38,20 +37,30 @@ else ()
set(AllocatorFacadeDeps)
endif()
list(APPEND AllocatorFacadeDeps cpu_allocator locked_allocator best_fit_allocator aligned_allocator auto_increment_allocator conditional_allocator retry_allocator buffered_allocator legacy_allocator zero_size_allocator)
cc_library(aligned_allocator SRCS aligned_allocator.cc DEPS allocator)
cc_library(auto_increment_allocator SRCS auto_increment_allocator.cc DEPS allocator)
cc_library(zero_size_allocator SRCS zero_size_allocator.cc DEPS allocator)
cc_library(conditional_allocator SRCS conditional_allocator.cc DEPS allocator)
cc_library(allocator_strategy SRCS allocator_strategy.cc DEPS gflags ${AllocatorFacadeDeps})
cc_library(allocator_facade SRCS allocator_facade.cc DEPS allocator_strategy)
cc_library(allocator_strategy SRCS allocator_strategy.cc DEPS gflags)
cc_library(allocator_facade SRCS allocator_facade.cc DEPS
${AllocatorFacadeDeps}
cpu_allocator
locked_allocator
best_fit_allocator
aligned_allocator
auto_increment_allocator
zero_size_allocator
conditional_allocator
retry_allocator
buffered_allocator
allocator_strategy
legacy_allocator
)
nv_test(allocation_and_eigen_test SRCS allocation_and_eigen_test.cu DEPS allocator_facade)
cc_test(retry_allocator_test SRCS retry_allocator_test.cc DEPS retry_allocator best_fit_allocator locked_allocator cpu_allocator)
cc_test(naive_best_fit_allocator_facade_test SRCS naive_best_fit_allocator_facade_test.cc DEPS allocator_facade)
cc_test(allocator_facade_abs_flags_test SRCS allocator_facade_abs_flags_test.cc DEPS allocator_facade)
cc_test(allocator_facade_frac_flags_test SRCS allocator_facade_frac_flags_test.cc DEPS allocator_facade)
paddle/fluid/memory/allocation/aligned_allocator.h
浏览文件 @ 33473890
......@@ -94,8 +94,6 @@ class AlignedAllocator : public ThinAlignedAllocator {
underlying_allocator_->Allocate(size + kAlignment, attr);
return new AlignedAllocation<kAlignment>(std::move(raw_allocation), size);
}
void FreeImpl(Allocation* allocation) override { delete allocation; }
};
} // namespace allocation
......
paddle/fluid/memory/allocation/allocator.cc
浏览文件 @ 33473890
......@@ -27,24 +27,16 @@ bool Allocator::IsAllocThreadSafe() const { return false; }
AllocationPtr Allocator::Allocate(size_t size, Allocator::Attr attr) {
auto ptr = AllocateImpl(size, attr);
ptr->RegisterDecoratedAllocator(this);
ptr->set_allocator(this);
return AllocationPtr(ptr);
}
void Allocator::FreeImpl(Allocation* allocation) {
Allocator* allocator = allocation->TopDecoratedAllocator();
allocator->Free(allocation);
}
void Allocator::Free(Allocation* allocation) {
allocation->PopDecoratedAllocator();
FreeImpl(allocation);
}
void Allocator::Free(Allocation* allocation) { delete allocation; }
const char* BadAlloc::what() const noexcept { return msg_.c_str(); }
void AllocationDeleter::operator()(Allocation* allocation) const {
Allocator* allocator = allocation->TopDecoratedAllocator();
auto* allocator = allocation->allocator();
allocator->Free(allocation);
}
......
paddle/fluid/memory/allocation/allocator.h
浏览文件 @ 33473890
......@@ -46,56 +46,13 @@ class Allocator;
// NOTE: this is the base class of Allocation. Each allocator can use its own
// allocation object.
// NOTE: the `Allocation::ptr()` could be nullptr, if the allocation size is 0
/**
* Allocation is returned by Allocator::Allocate() method.
*
* An allocator may be decorated by another allocator. For example, we can
* decorate
* a RetryAllocator to any allocator to perform allocation retry when first
* allocation request fails.
*
* Explanations of Allocator design is as follows:
*
* Suppose we have an allocator which is decorated by several allocators:
*
* A(1) <- A(2) <- A(3) <- ... <- A(n)
*
* , and the public allocator is A(1).
*
* The allocation process would be:
*
* A(n).Allocate() -> ... -> A(2).Allocate() -> A(1).Allocate()
*
* , and the free process would be:
*
* A(1).Free() -> A(2).Free() -> ... -> A(n).Free()
*
* Therefore, we should record the allocator chain when allocating, so
* that we can free the allocation in the reverse order of allocator chain.
* The field `decorated_allocators_` is used to record this chain.
*
* Another example is that we want to add additional fields in Allocation,
* e.g., something what is done in AlignedAllocator, etc.
* In this case, we should declare a derived class of Allocation, which
* contains an underlying Allocation allocated by the underlying allocator.
* Therefore, `decorated_allocators_` of the new Allocation object would
* be a new chain, differing from the underlying Allocation object.
*/
class Allocation {
public:
Allocation(void* ptr, size_t size, platform::Place place)
: ptr_(ptr), size_(size), place_(place) {
// NOTE(zjl): Since decorated_allocators_ is usually a small vector
// We reserve a small buffer to it to prevent frequent heap allocation
// Not quite sure whether we need something like gtl vector.
decorated_allocators_.reserve(8);
}
: allocator_(nullptr), ptr_(ptr), size_(size), place_(place) {}
Allocation(const Allocation& o) = delete;
Allocation& operator=(const Allocation& o) = delete;
Allocation(Allocation&& o) = delete;
Allocation& operator=(Allocation&& o) = delete;
// Returns the holding pointer.
// NOTE: For performance consideration, it is better not to make this method
......@@ -117,31 +74,17 @@ class Allocation {
const platform::Place& place() const { return place_; }
virtual ~Allocation();
private:
const std::vector<Allocator*>& DecoratedAllocators() const {
return decorated_allocators_;
}
inline void RegisterDecoratedAllocator(Allocator* allocator) {
decorated_allocators_.push_back(allocator);
}
Allocator* allocator() { return allocator_; }
inline void PopDecoratedAllocator() { decorated_allocators_.pop_back(); }
void set_allocator(Allocator* allocator) { allocator_ = allocator; }
inline Allocator* TopDecoratedAllocator() {
return decorated_allocators_.back();
}
virtual ~Allocation();
private:
Allocator* allocator_;
void* ptr_;
size_t size_;
platform::Place place_;
std::vector<Allocator*> decorated_allocators_;
friend class Allocator;
friend class AllocationDeleter;
};
using AllocationPtr = std::unique_ptr<Allocation, AllocationDeleter>;
......@@ -191,12 +134,9 @@ class Allocator {
// True if the `Allocate` is thread safe.
virtual bool IsAllocThreadSafe() const;
// This function should not be called outside
void Free(Allocation* allocation);
protected:
virtual void Free(Allocation* allocation);
virtual Allocation* AllocateImpl(size_t size, Allocator::Attr attr) = 0;
virtual void FreeImpl(Allocation* allocation);
private:
friend class AllocationDeleter;
......
paddle/fluid/memory/allocation/allocator_facade.cc
浏览文件 @ 33473890
......@@ -49,17 +49,6 @@ namespace paddle {
namespace memory {
namespace allocation {
static inline std::shared_ptr<Allocator> WrapRetryAllocator(
std::shared_ptr<Allocator> allocator, int64_t retry_time) {
if (retry_time > 0) {
auto* retry_allocator =
new RetryAllocator(std::move(allocator), retry_time);
allocator.reset(retry_allocator);
}
return allocator;
}
// TODO(yy): Dirty code here. This class should be configurable in runtime.
class CPUManagedAllocator : public Allocator {
public:
......@@ -123,10 +112,14 @@ class ChunkedAllocator : public Allocator {
std::shared_ptr<Allocator> CreateAllocatorWithChunk() {
chunks_.emplace_back(raw_allocator_->Allocate(max_chunk_size_));
auto* allocation = chunks_.back().get();
std::shared_ptr<Allocator> allocator(new LockedAllocator(
std::shared_ptr<Allocator>(new BestFitAllocator(allocation))));
std::unique_ptr<Allocator> allocator(new LockedAllocator(
std::unique_ptr<Allocator>(new BestFitAllocator(allocation))));
allocator = WrapRetryAllocator(allocator, retry_time_);
if (retry_time_ > 0) {
auto* retry_allocator =
new RetryAllocator(std::move(allocator), retry_time_);
allocator.reset(retry_allocator);
}
return std::make_shared<AlignedAllocator<64u>>(std::move(allocator));
}
......@@ -197,23 +190,13 @@ class AllocatorFacadePrivate {
~AllocatorFacadePrivate() = default;
AllocatorFacadePrivate() {
auto strategy = GetAllocatorStrategy();
switch (strategy) {
case AllocatorStrategy::kLegacy: {
InitLegacyAllocator();
break;
}
case AllocatorStrategy::kNaiveBestFit: {
InitCPUAllocator();
InitCUDAAllocator();
InitCUDAPinnedAllocator();
WrapZeroSizeAllocator();
break;
}
default: {
PADDLE_THROW("Unsupported allocator strategy: %d",
static_cast<int>(strategy));
}
if (GetAllocatorStrategy() == AllocatorStrategy::kLegacy) {
InitLegacyAllocator();
} else {
InitCPUAllocator();
InitCUDAAllocator();
InitCUDAPinnedAllocator();
WrapZeroSizeAllocator();
}
}
......@@ -271,7 +254,8 @@ AllocatorFacade& AllocatorFacade::Instance() {
std::shared_ptr<Allocation> AllocatorFacade::AllocShared(
const platform::Place& place, size_t size, Allocator::Attr attr) {
return std::shared_ptr<Allocation>(Alloc(place, size, attr));
return std::shared_ptr<Allocation>(Alloc(place, size, attr).release(),
AllocationDeleter());
}
AllocationPtr AllocatorFacade::Alloc(const platform::Place& place, size_t size,
......
paddle/fluid/memory/allocation/allocator_strategy.cc
浏览文件 @ 33473890
......@@ -19,22 +19,16 @@
DEFINE_string(
allocator_strategy, "legacy",
"The allocation strategy. Legacy means the original allocator of Fluid."
"naive_best_fit means the experimental best fit allocator. "
"allocator. Enum in [legacy, naive_best_fit].");
"New means the experimental allocators of Fluid. in [legacy, new]");
namespace paddle {
namespace memory {
namespace allocation {
static AllocatorStrategy GetStrategyFromFlag() {
if (FLAGS_allocator_strategy == "legacy") {
return AllocatorStrategy::kLegacy;
} else if (FLAGS_allocator_strategy == "naive_best_fit") {
return AllocatorStrategy::kNaiveBestFit;
} else {
PADDLE_THROW("Unsupported allocator strategy: %s",
FLAGS_allocator_strategy);
}
return FLAGS_allocator_strategy == "legacy"
? AllocatorStrategy::kLegacy
: AllocatorStrategy::kNaiveBestFit;
}
AllocatorStrategy GetAllocatorStrategy() {
......
paddle/fluid/memory/allocation/best_fit_allocator.cc
浏览文件 @ 33473890
......@@ -109,7 +109,7 @@ size_t BestFitAllocator::NumFreeChunks() const {
}
return num;
}
void BestFitAllocator::FreeImpl(Allocation* allocation) {
void BestFitAllocator::Free(Allocation* allocation) {
auto* bf_allocation = dynamic_cast<BestFitAllocation*>(allocation);
PADDLE_ENFORCE_NOT_NULL(bf_allocation,
"The input allocation is not BestFitAllocation.");
......
paddle/fluid/memory/allocation/best_fit_allocator.h
浏览文件 @ 33473890
......@@ -119,7 +119,7 @@ class BestFitAllocator : public Allocator {
void InsertFreeNode(const ListIt& it);
protected:
void FreeImpl(Allocation* allocation) override;
void Free(Allocation* allocation) override;
Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override;
private:
......
paddle/fluid/memory/allocation/buffered_allocator.cc
浏览文件 @ 33473890
......@@ -22,11 +22,11 @@ namespace paddle {
namespace memory {
namespace allocation {
BufferedAllocator::BufferedAllocator(std::shared_ptr<Allocator> allocator)
BufferedAllocator::BufferedAllocator(std::unique_ptr<Allocator> &&allocator)
: underlying_allocator_(std::move(allocator)) {
PADDLE_ENFORCE_NOT_NULL(
underlying_allocator_,
"Underlying allocator of BufferedAllocator must not be null");
"Underlying allocator of BufferedAllocator must be unmanaged");
if (underlying_allocator_->IsAllocThreadSafe()) {
mtx_.reset(new std::mutex());
}
......@@ -41,19 +41,19 @@ void BufferedAllocator::FreeCache(size_t size) {
while (!allocations_.empty()) { // free the largest
auto it = --allocations_.end();
cur += it->second->size();
underlying_allocator_->Free(it->second.release());
delete it->second.release();
allocations_.erase(it);
if (cur >= size) return;
}
}
bool BufferedAllocator::IsAllocThreadSafe() const { return mtx_ != nullptr; }
void BufferedAllocator::FreeImpl(Allocation *allocation) {
bool BufferedAllocator::IsAllocThreadSafe() const {
return this->underlying_allocator_->IsAllocThreadSafe();
}
void BufferedAllocator::Free(Allocation *allocation) {
platform::LockGuardPtr<std::mutex> guard(mtx_);
allocations_.emplace(allocation->size(), AllocationPtr(allocation));
}
Allocation *BufferedAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
{
platform::LockGuardPtr<std::mutex> guard(mtx_);
......@@ -61,15 +61,17 @@ Allocation *BufferedAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
if (it != allocations_.end() && it->first < size * 2) {
AllocationPtr result(std::move(it->second));
allocations_.erase(it);
return result.release();
return new AllocationWithUnderlying(std::move(result));
}
}
try {
return underlying_allocator_->Allocate(size, attr).release();
return new AllocationWithUnderlying(
underlying_allocator_->Allocate(size, attr));
} catch (BadAlloc &) {
FreeCache(size);
return underlying_allocator_->Allocate(size, attr).release();
return new AllocationWithUnderlying(
underlying_allocator_->Allocate(size, attr));
}
}
......
paddle/fluid/memory/allocation/buffered_allocator.h
浏览文件 @ 33473890
......@@ -31,7 +31,7 @@ namespace allocation {
// underlying_allocator_
class BufferedAllocator : public Allocator {
public:
explicit BufferedAllocator(std::shared_ptr<Allocator> allocator);
explicit BufferedAllocator(std::unique_ptr<Allocator> &&allocator);
~BufferedAllocator();
......@@ -44,11 +44,11 @@ class BufferedAllocator : public Allocator {
void FreeCache(size_t size);
protected:
void FreeImpl(Allocation *allocation) override;
void Free(Allocation *allocation) override;
Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override;
private:
std::shared_ptr<Allocator> underlying_allocator_;
std::unique_ptr<Allocator> underlying_allocator_;
std::multimap<size_t, AllocationPtr> allocations_;
std::unique_ptr<std::mutex> mtx_;
};
......
paddle/fluid/memory/allocation/buffered_allocator_test.cc
浏览文件 @ 33473890
......@@ -14,6 +14,7 @@
#include "paddle/fluid/memory/allocation/buffered_allocator.h"
#include <gtest/gtest.h>
#include <memory>
#include <utility>
#include "paddle/fluid/memory/allocation/best_fit_allocator.h"
#include "paddle/fluid/memory/allocation/cpu_allocator.h"
......@@ -65,7 +66,7 @@ class StubAllocator : public Allocator {
size_t GetFreeCount() const { return destruct_count_; }
protected:
void FreeImpl(Allocation *allocation) override {
void Free(Allocation *allocation) override {
auto *alloc = dynamic_cast<StubAllocation *>(allocation);
PADDLE_ENFORCE_NOT_NULL(alloc);
if (alloc->ptr()) delete[] static_cast<uint8_t *>(alloc->ptr());
......
paddle/fluid/memory/allocation/cpu_allocator.cc
浏览文件 @ 33473890
......@@ -20,27 +20,25 @@ namespace paddle {
namespace memory {
namespace allocation {
CPUAllocation::CPUAllocation(void *ptr, size_t size)
: Allocation(ptr, size, platform::CPUPlace()) {}
bool CPUAllocator::IsAllocThreadSafe() const { return true; }
void CPUAllocator::FreeImpl(Allocation *allocation) {
void *p = allocation->ptr();
#ifdef _WIN32
_aligned_free(p);
#else
free(p);
#endif
void CPUAllocator::Free(Allocation *allocation) {
PADDLE_ENFORCE_NOT_NULL(dynamic_cast<CPUAllocation *>(allocation));
free(allocation->ptr());
delete allocation;
}
Allocation *CPUAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
void *p;
#ifdef _WIN32
p = _aligned_malloc(size, kAlignment);
#else
PADDLE_ENFORCE_EQ(posix_memalign(&p, kAlignment, size), 0, "Alloc %ld error!",
size);
#endif
return new Allocation(p, size, platform::CPUPlace());
void *ptr;
auto status = posix_memalign(&ptr, kAlignment, size);
if (UNLIKELY(status) != 0) {
throw BadAlloc(string::Sprintf("Cannot allocate cpu memory %d. Errno is %d",
size, status));
}
return new CPUAllocation(ptr, size);
}
} // namespace allocation
} // namespace memory
......
paddle/fluid/memory/allocation/cpu_allocator.h
浏览文件 @ 33473890
......@@ -31,13 +31,19 @@ namespace allocation {
//
// NOTE(yy): It is no need to use `BestFitAllocator` in CPU. We can import
// an open-sourced allocator into Paddle.
class CPUAllocator;
class CPUAllocation : public Allocation {
public:
CPUAllocation(void* ptr, size_t size);
};
class CPUAllocator : public Allocator {
public:
constexpr static size_t kAlignment = 4096UL;
constexpr static size_t kAlignment = 64u;
bool IsAllocThreadSafe() const override;
protected:
void FreeImpl(Allocation* allocation) override;
void Free(Allocation* allocation) override;
Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override;
};
} // namespace allocation
......
paddle/fluid/memory/allocation/cuda_allocator.cc
浏览文件 @ 33473890
......@@ -23,14 +23,15 @@ namespace paddle {
namespace memory {
namespace allocation {
bool CUDAAllocator::IsAllocThreadSafe() const { return true; }
void CUDAAllocator::FreeImpl(Allocation* allocation) {
void CUDAAllocator::Free(Allocation* allocation) {
platform::CUDADeviceGuard guard(place_.device);
PADDLE_ENFORCE_EQ(boost::get<platform::CUDAPlace>(allocation->place()),
auto* cuda_allocation = dynamic_cast<CUDAAllocation*>(allocation);
PADDLE_ENFORCE_NOT_NULL(cuda_allocation);
PADDLE_ENFORCE_EQ(boost::get<platform::CUDAPlace>(cuda_allocation->place()),
place_);
PADDLE_ENFORCE(cudaFree(allocation->ptr()));
delete allocation;
}
Allocation* CUDAAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
platform::CUDADeviceGuard guard(place_.device);
void* ptr;
......@@ -40,9 +41,8 @@ Allocation* CUDAAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
"Cannot allocate %d on GPU %d, cuda status %d, %s", size, place_.device,
status, cudaGetErrorString(status)));
}
return new Allocation(ptr, size, platform::Place(place_));
return new CUDAAllocation(ptr, size, platform::Place(place_));
}
} // namespace allocation
} // namespace memory
} // namespace paddle
paddle/fluid/memory/allocation/cuda_allocator.h
浏览文件 @ 33473890
......@@ -20,6 +20,13 @@ namespace paddle {
namespace memory {
namespace allocation {
// CUDA System allocator and allocation.
// Just a flag type.
class CUDAAllocation : public Allocation {
public:
using Allocation::Allocation;
};
class CUDAAllocator : public Allocator {
public:
explicit CUDAAllocator(const platform::CUDAPlace& place) : place_(place) {}
......@@ -28,7 +35,7 @@ class CUDAAllocator : public Allocator {
bool IsAllocThreadSafe() const override;
protected:
void FreeImpl(Allocation* allocation) override;
void Free(Allocation* allocation) override;
Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override;
private:
......
paddle/fluid/memory/allocation/legacy_allocator.cc
浏览文件 @ 33473890
......@@ -134,22 +134,26 @@ size_t Used<platform::CPUPlace>(const platform::CPUPlace &place) {
}
#ifdef PADDLE_WITH_CUDA
class GPUBuddyAllocatorList {
public:
GPUBuddyAllocatorList()
: allocators_(platform::GetCUDADeviceCount()),
flags_(platform::GetCUDADeviceCount()) {
allocation::GPUMemMonitor.Initialize(allocators_.size());
}
BuddyAllocator *GetGPUBuddyAllocator(int gpu_id) {
static std::once_flag init_flag;
static detail::BuddyAllocator **a_arr = nullptr;
static std::vector<int> devices;
std::call_once(init_flag, [gpu_id]() {
devices = platform::GetSelectedDevices();
int gpu_num = devices.size();
BuddyAllocator *Get(size_t dev_id) {
PADDLE_ENFORCE(dev_id < flags_.size(), "Invalid device id %s", dev_id);
std::call_once(flags_[dev_id], [this, dev_id] {
allocation::GPUMemMonitor.Initialize(devices.size());
a_arr = new BuddyAllocator *[gpu_num];
for (size_t i = 0; i < devices.size(); ++i) {
int dev_id = devices[i];
a_arr[i] = nullptr;
platform::SetDeviceId(dev_id);
allocators_[dev_id] = new BuddyAllocator(
std::unique_ptr<detail::SystemAllocator>(
new detail::GPUAllocator(dev_id)),
platform::GpuMinChunkSize(), platform::GpuMaxChunkSize());
a_arr[i] = new BuddyAllocator(std::unique_ptr<detail::SystemAllocator>(
new detail::GPUAllocator(dev_id)),
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
VLOG(10) << "\n\nNOTE:\n"
<< "You can set GFlags environment variable "
......@@ -163,19 +167,13 @@ class GPUBuddyAllocatorList {
<< FLAGS_initial_gpu_memory_in_mb
<< ". Current 'FLAGS_reallocate_gpu_memory_in_mb' value is "
<< FLAGS_reallocate_gpu_memory_in_mb << "\n\n";
});
return allocators_[dev_id];
}
private:
std::vector<BuddyAllocator *> allocators_;
std::vector<std::once_flag> flags_;
};
}
});
BuddyAllocator *GetGPUBuddyAllocator(int gpu_id) {
static GPUBuddyAllocatorList allocators;
platform::SetDeviceId(gpu_id);
return allocators.Get(gpu_id);
auto pos = std::distance(devices.begin(),
std::find(devices.begin(), devices.end(), gpu_id));
return a_arr[pos];
}
#endif
......@@ -194,7 +192,7 @@ void *Alloc<platform::CUDAPlace>(const platform::CUDAPlace &place,
#ifdef PADDLE_WITH_CUDA
auto *buddy_allocator = GetGPUBuddyAllocator(place.device);
auto *ptr = buddy_allocator->Alloc(size);
if (ptr == nullptr && size > 0) {
if (ptr == nullptr) {
int cur_dev = platform::GetCurrentDeviceId();
platform::SetDeviceId(place.device);
size_t avail, total;
......@@ -349,7 +347,7 @@ Allocation *LegacyAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
return tmp_alloc;
}
void LegacyAllocator::FreeImpl(Allocation *allocation) {
void LegacyAllocator::Free(Allocation *allocation) {
boost::apply_visitor(
legacy::FreeVisitor(allocation->ptr(), allocation->size()),
allocation->place());
......
paddle/fluid/memory/allocation/legacy_allocator.h
浏览文件 @ 33473890
......@@ -73,7 +73,7 @@ class LegacyAllocator : public Allocator {
protected:
Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override;
void FreeImpl(Allocation *allocation) override;
void Free(Allocation *allocation) override;
private:
platform::Place place_;
......
paddle/fluid/memory/allocation/locked_allocator.cc
浏览文件 @ 33473890
......@@ -17,7 +17,6 @@
#include <utility>
#include "paddle/fluid/memory/allocation/allocation_with_underlying.h"
#include "paddle/fluid/platform/lock_guard_ptr.h"
namespace paddle {
namespace memory {
namespace allocation {
......@@ -25,24 +24,26 @@ namespace allocation {
bool LockedAllocator::IsAllocThreadSafe() const { return true; }
LockedAllocator::LockedAllocator(
std::shared_ptr<Allocator> underlying_allocator)
std::unique_ptr<Allocator> &&underlying_allocator)
: underlying_allocator_(std::move(underlying_allocator)) {
PADDLE_ENFORCE_NOT_NULL(underlying_allocator_);
if (!underlying_allocator_->IsAllocThreadSafe()) {
mtx_.reset(new std::mutex());
}
}
void LockedAllocator::FreeImpl(Allocation *allocation) {
platform::LockGuardPtr<std::mutex> guard(mtx_);
underlying_allocator_->Free(allocation);
void LockedAllocator::Free(Allocation *allocation) {
{
platform::LockGuardPtr<std::mutex> guard(mtx_);
reinterpret_cast<AllocationWithUnderlying *>(allocation)
->allocation_.reset(); // Destroy inner allocation
}
delete allocation;
}
Allocation *LockedAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
platform::LockGuardPtr<std::mutex> guard(mtx_);
return underlying_allocator_->Allocate(size, attr).release();
return new AllocationWithUnderlying(
underlying_allocator_->Allocate(size, attr));
}
} // namespace allocation
} // namespace memory
} // namespace paddle
paddle/fluid/memory/allocation/locked_allocator.h
浏览文件 @ 33473890
......@@ -24,15 +24,15 @@ namespace allocation {
// A allocator to make underlying allocator thread safe.
class LockedAllocator : public Allocator {
public:
explicit LockedAllocator(std::shared_ptr<Allocator> underlying_allocator);
explicit LockedAllocator(std::unique_ptr<Allocator> &&underlying_allocator);
bool IsAllocThreadSafe() const override;
protected:
void FreeImpl(Allocation *allocation) override;
void Free(Allocation *allocation) override;
Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override;
private:
std::shared_ptr<Allocator> underlying_allocator_;
std::unique_ptr<Allocator> underlying_allocator_;
std::unique_ptr<std::mutex> mtx_;
};
......
paddle/fluid/memory/allocation/naive_best_fit_allocator_facade_test.cc 已删除 100644 → 0
浏览文件 @ 2c836ff9
// 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 <gflags/gflags.h>
#include <gtest/gtest.h>
#include "paddle/fluid/memory/allocation/allocator_facade.h"
#ifdef PADDLE_WITH_CUDA
DECLARE_double(fraction_of_gpu_memory_to_use);
DECLARE_double(fraction_of_cuda_pinned_memory_to_use);
DECLARE_int64(gpu_allocator_retry_time);
#endif
DECLARE_string(allocator_strategy);
namespace paddle {
namespace memory {
namespace allocation {
TEST(allocator, allocator) {
#ifdef PADDLE_WITH_CUDA
FLAGS_fraction_of_gpu_memory_to_use = 0.01;
FLAGS_gpu_allocator_retry_time = 500;
FLAGS_fraction_of_cuda_pinned_memory_to_use = 0.5;
#endif
FLAGS_allocator_strategy = "naive_best_fit";
auto &instance = AllocatorFacade::Instance();
platform::Place place;
size_t size = 1024;
{
place = platform::CPUPlace();
size = 1024;
auto cpu_allocation = instance.Alloc(place, size);
ASSERT_NE(cpu_allocation, nullptr);
ASSERT_NE(cpu_allocation->ptr(), nullptr);
ASSERT_EQ(cpu_allocation->place(), place);
ASSERT_EQ(cpu_allocation->size(), size);
}
#ifdef PADDLE_WITH_CUDA
{
place = platform::CUDAPlace(0);
size = 1024;
auto gpu_allocation = instance.Alloc(place, size);
ASSERT_NE(gpu_allocation, nullptr);
ASSERT_NE(gpu_allocation->ptr(), nullptr);
ASSERT_EQ(gpu_allocation->place(), place);
ASSERT_GE(gpu_allocation->size(), size);
}
{
// Allocate 2GB gpu memory
place = platform::CUDAPlace(0);
size = 2 * static_cast<size_t>(1 << 30);
auto gpu_allocation = instance.Alloc(place, size);
ASSERT_NE(gpu_allocation, nullptr);
ASSERT_NE(gpu_allocation->ptr(), nullptr);
ASSERT_EQ(gpu_allocation->place(), place);
ASSERT_GE(gpu_allocation->size(), size);
}
{
place = platform::CUDAPinnedPlace();
size = (1 << 20);
auto cuda_pinned_allocation =
instance.Alloc(platform::CUDAPinnedPlace(), 1 << 20);
ASSERT_NE(cuda_pinned_allocation, nullptr);
ASSERT_NE(cuda_pinned_allocation->ptr(), nullptr);
ASSERT_EQ(cuda_pinned_allocation->place(), place);
ASSERT_GE(cuda_pinned_allocation->size(), size);
}
#endif
}
} // namespace allocation
} // namespace memory
} // namespace paddle
paddle/fluid/memory/allocation/pinned_allocator.cc
浏览文件 @ 33473890
......@@ -20,15 +20,20 @@ namespace paddle {
namespace memory {
namespace allocation {
bool CPUPinnedAllocator::IsAllocThreadSafe() const { return true; }
void CPUPinnedAllocator::FreeImpl(Allocation *allocation) {
void CPUPinnedAllocator::Free(Allocation *allocation) {
PADDLE_ENFORCE_NOT_NULL(dynamic_cast<CPUPinnedAllocation *>(allocation));
PADDLE_ENFORCE(cudaFreeHost(allocation->ptr()));
delete allocation;
}
Allocation *CPUPinnedAllocator::AllocateImpl(size_t size,
Allocator::Attr attr) {
// PADDLE_ENFORCE_EQ(
// attr, kCrossDevice,
// "CPUPinnedAllocator should be used for Cross-Device Communication");
void *ptr;
PADDLE_ENFORCE(cudaHostAlloc(&ptr, size, cudaHostAllocPortable));
return new Allocation(ptr, size, platform::CUDAPinnedPlace());
return new CPUPinnedAllocation(ptr, size);
}
} // namespace allocation
} // namespace memory
......
paddle/fluid/memory/allocation/pinned_allocator.h
浏览文件 @ 33473890
......@@ -20,12 +20,18 @@ namespace memory {
namespace allocation {
// Allocator uses `cudaHostAlloc`
class CPUPinnedAllocation : public Allocation {
public:
CPUPinnedAllocation(void *ptr, size_t size)
: Allocation(ptr, size, platform::CUDAPinnedPlace()) {}
};
class CPUPinnedAllocator : public Allocator {
public:
bool IsAllocThreadSafe() const override;
protected:
void FreeImpl(Allocation *allocation) override;
void Free(Allocation *allocation) override;
Allocation *AllocateImpl(size_t size, Allocator::Attr attr) override;
};
......
paddle/fluid/memory/allocation/retry_allocator.cc
浏览文件 @ 33473890
......@@ -18,15 +18,25 @@ namespace paddle {
namespace memory {
namespace allocation {
void RetryAllocator::FreeImpl(Allocation* allocation) {
bool RetryAllocator::IsAllocThreadSafe() const {
return underlying_allocator_->IsAllocThreadSafe();
}
void RetryAllocator::Free(Allocation* allocation) {
// Delete underlying allocation first.
underlying_allocator_->Free(allocation);
cv_.notify_all();
reinterpret_cast<AllocationWithUnderlying*>(allocation)->allocation_.reset();
{
// notify all waited allocators, they can try to allocate memory after free.
std::lock_guard<std::mutex> lock(mutex_);
cv_.notify_all();
}
delete allocation;
}
Allocation* RetryAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
auto alloc_func = [&, this]() {
return underlying_allocator_->Allocate(size, attr).release();
return new AllocationWithUnderlying(
underlying_allocator_->Allocate(size, attr));
};
// In fact, we can unify the code of allocation success and failure
// But it would add lock even when allocation success at the first time
......
paddle/fluid/memory/allocation/retry_allocator.h
浏览文件 @ 33473890
......@@ -25,25 +25,32 @@ namespace paddle {
namespace memory {
namespace allocation {
class RetryAllocator;
class RetryAllocator : public Allocator {
public:
RetryAllocator(std::shared_ptr<Allocator> allocator, size_t retry_ms)
RetryAllocator(std::unique_ptr<Allocator>&& allocator, size_t retry_ms)
: underlying_allocator_(std::move(allocator)), retry_time_(retry_ms) {
EnforceCheck();
}
bool IsAllocThreadSafe() const override;
private:
void EnforceCheck() {
PADDLE_ENFORCE_NOT_NULL(
underlying_allocator_,
"UnderlyingAllocator of RetryAllocator must not be null");
underlying_allocator_.get(),
"UnderlyingAllocator of RetryAllocator must be UnmanagedAllocator");
PADDLE_ENFORCE(underlying_allocator_->IsAllocThreadSafe(),
"UnderlyingAllocator of RetryAllocator must be thread-safe");
}
bool IsAllocThreadSafe() const override { return true; }
protected:
void FreeImpl(Allocation* allocation) override;
void Free(Allocation* allocation) override;
Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override;
private:
std::shared_ptr<Allocator> underlying_allocator_;
std::unique_ptr<Allocator> underlying_allocator_;
std::chrono::milliseconds retry_time_;
std::mutex mutex_;
std::condition_variable cv_;
......@@ -51,6 +58,8 @@ class RetryAllocator : public Allocator {
// For debug, We can add an atomic integer to record how many memory sizes are
// waited to allocate
// std::atomic<size_t> waited_allocate_size_{0};
friend class RetryAllocation;
};
} // namespace allocation
......
paddle/fluid/memory/allocation/zero_size_allocator.cc
浏览文件 @ 33473890
......@@ -24,20 +24,11 @@ bool ZeroSizeAllocator::IsAllocThreadSafe() const {
Allocation *ZeroSizeAllocator::AllocateImpl(size_t size, Allocator::Attr attr) {
if (size == 0) {
return new Allocation(nullptr, 0, place_);
return new ZeroSizeAllocation(place_);
} else {
return underlying_allocator_->Allocate(size, attr).release();
}
}
void ZeroSizeAllocator::FreeImpl(Allocation *allocation) {
if (allocation->size() == 0) {
delete allocation;
} else {
underlying_allocator_->Free(allocation);
}
}
} // namespace allocation
} // namespace memory
} // namespace paddle
paddle/fluid/memory/allocation/zero_size_allocator.h
浏览文件 @ 33473890
......@@ -24,6 +24,12 @@ namespace allocation {
// The allocator handles the request's size is zero. Allocator will always
// return an allocation even the request size is zero. However, the
// allocation.ptr() is nullptr
class ZeroSizeAllocation : public Allocation {
public:
explicit ZeroSizeAllocation(const platform::Place& p)
: Allocation(nullptr, 0, p) {}
};
class ZeroSizeAllocator : public Allocator {
public:
ZeroSizeAllocator(std::shared_ptr<Allocator> underlying_allocator,
......@@ -34,7 +40,6 @@ class ZeroSizeAllocator : public Allocator {
protected:
Allocation* AllocateImpl(size_t size, Allocator::Attr attr) override;
void FreeImpl(Allocation* allocation) override;
private:
std::shared_ptr<Allocator> underlying_allocator_;
......
paddle/fluid/operators/alloc_continuous_space_op.cc
浏览文件 @ 33473890
......@@ -65,7 +65,8 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
// Get numel and dtype
size_t numel = 0;
auto dtype = kDefaultDtype;
GetMemSizeAndDtype(in_tensors, in_var_names, &numel, &dtype);
GetMemSizeAndDtype(in_tensors, in_var_names, &numel, &dtype,
context.GetPlace());
// Alloc the continuous space
auto fused_tensor = context.Output<framework::LoDTensor>("FusedOutput");
......@@ -74,14 +75,18 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
// Init the continuous space
auto out_tensors = context.MultiOutput<framework::LoDTensor>("Output");
int64_t offset = 0;
size_t offset = 0;
size_t size_of_dtype = framework::SizeOfType(dtype);
if (context.Attr<bool>("copy_data")) {
for (size_t i = 0; i < in_var_names.size(); ++i) {
int64_t len = out_tensors[i]->numel();
auto sub_tensor = fused_tensor->Slice(offset, offset + len);
offset += len;
framework::TensorCopy(*out_tensors[i], context.GetPlace(), dev_ctx,
size_t len = static_cast<size_t>(in_tensors[i]->numel());
auto sub_tensor = fused_tensor->Slice(
static_cast<int64_t>(offset), static_cast<int64_t>(offset + len));
framework::TensorCopy(*in_tensors[i], context.GetPlace(), dev_ctx,
&sub_tensor);
offset +=
Alignment(len * size_of_dtype, context.GetPlace()) / size_of_dtype;
}
} else if (context.Attr<bool>("set_constant")) {
math::SetConstant<DeviceContext, T> set_constant;
......@@ -92,11 +97,13 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
// Make the outputs point to the continuous space.
offset = 0;
for (size_t i = 0; i < out_tensors.size(); ++i) {
int64_t len = out_tensors[i]->numel();
size_t len = static_cast<size_t>(out_tensors[i]->numel());
auto dim = out_tensors[i]->dims();
out_tensors[i]
->ShareDataWith(fused_tensor->Slice(offset, offset + len))
->ShareDataWith(fused_tensor->Slice(
static_cast<int64_t>(offset), static_cast<int64_t>(offset + len)))
.Resize(dim);
len = Alignment(len * size_of_dtype, context.GetPlace()) / size_of_dtype;
offset += len;
VLOG(10) << "alloc_space_for_vars: output(" << out_var_names[i]
<< ") ,dim:(" << dim << ")"
......@@ -104,12 +111,28 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
}
}
private:
// Note(zcd): Addresses should be aligned, otherwise, the results may have
// diff.
size_t Alignment(size_t size, const platform::Place &place) const {
// Allow to allocate the minimum chunk size is 4 KB.
size_t alignment = 1 << 12;
if (platform::is_gpu_place(place)) {
// Allow to allocate the minimum chunk size is 256 B.
alignment = 1 << 8;
}
size_t remaining = size % alignment;
return remaining == 0 ? size : size + (alignment - remaining);
}
void GetMemSizeAndDtype(
const std::vector<const framework::LoDTensor *> &lod_tensors,
const std::vector<std::string> var_names, size_t *numel,
framework::proto::VarType::Type *dtype) const {
framework::proto::VarType::Type *dtype,
const platform::Place &place) const {
PADDLE_ENFORCE_EQ(lod_tensors.size(), var_names.size());
*numel = 0;
size_t size_of_dtype = 0;
for (size_t i = 0; i < var_names.size(); ++i) {
PADDLE_ENFORCE(lod_tensors[i]->IsInitialized(), "%s is not initialized.",
var_names[i]);
......@@ -119,6 +142,7 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_NE(p_dtype, kDefaultDtype, "%s's type should not be %s.",
var_names[i], kDefaultDtype);
*dtype = p_dtype;
size_of_dtype = framework::SizeOfType(p_dtype);
}
PADDLE_ENFORCE_EQ(p_dtype, *dtype, "Input vars is not equal.");
......@@ -126,7 +150,8 @@ class AllocContinuousSpaceKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_GT(size, 0);
VLOG(10) << "alloc_space_for_vars: input(" << var_names[i] << ") ,dim:("
<< lod_tensors[i]->dims() << ")";
*numel += size;
*numel += Alignment(static_cast<size_t>(size) * size_of_dtype, place) /
size_of_dtype;
}
}
};
......
paddle/fluid/operators/bpr_loss_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/bpr_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -127,6 +128,23 @@ neural networks>(https://arxiv.org/abs/1511.06939)
)DOC");
}
};
class BprLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("bpr_loss_grad");
op->SetInput("X", Input("X"));
op->SetInput("Label", Input("Label"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
......@@ -134,7 +152,7 @@ namespace ops = paddle::operators;
using CPUCtx = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(bpr_loss, ops::BprLossOp, ops::BprLossOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::BprLossGradDescMaker);
REGISTER_OPERATOR(bpr_loss_grad, ops::BprLossGradientOp);
REGISTER_OP_CPU_KERNEL(bpr_loss, ops::BprLossOpKernel<CPUCtx, float>,
ops::BprLossOpKernel<CPUCtx, double>);
......
paddle/fluid/operators/detection/roi_perspective_transform_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <memory>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -568,13 +569,31 @@ class ROIPerspectiveTransformOpMaker
}
};
class ROIPerspectiveTransformGradDescMaker
: public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_perspective_transform_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_perspective_transform, ops::ROIPerspectiveTransformOp,
ops::ROIPerspectiveTransformOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIPerspectiveTransformGradDescMaker);
REGISTER_OPERATOR(roi_perspective_transform_grad,
ops::ROIPerspectiveTransformGradOp);
REGISTER_OP_CPU_KERNEL(roi_perspective_transform,
......
paddle/fluid/operators/elementwise/mkldnn/elementwise_add_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -77,7 +77,8 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel<T> {
} else {
functor.RunMidWise(n, pre, post);
}
z->set_mkldnn_prim_desc(x->get_mkldnn_prim_desc());
z->set_layout(DataLayout::kMKLDNN);
z->set_format(x->format());
} else {
PADDLE_ENFORCE(x->layout() == DataLayout::kMKLDNN &&
x->format() != memory::format::format_undef,
......@@ -115,8 +116,7 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel<T> {
auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_pd);
// create mkldnn memory for dst
auto dst_mem_pd = sum_pd.dst_primitive_desc();
memory dst_memory = memory(dst_mem_pd, z_data);
memory dst_memory = memory(sum_pd.dst_primitive_desc(), z_data);
std::vector<primitive::at> inputs;
inputs.push_back(srcs[0]);
......@@ -129,7 +129,9 @@ class EltwiseAddMKLDNNKernel : public framework::OpKernel<T> {
pipeline.push_back(sum_prim);
stream(stream::kind::eager).submit(pipeline).wait();
z->set_mkldnn_prim_desc(dst_mem_pd);
z->set_layout(DataLayout::kMKLDNN);
z->set_format(
(memory::format)dst_memory.get_primitive_desc().desc().data.format);
}
}
};
......@@ -150,19 +152,24 @@ class EltwiseAddMKLDNNGradKernel : public ElemwiseGradKernel<T> {
auto* out = dout;
auto *x = dout, *y = dout;
auto set_mkldnn_format = [](Tensor* in, const Tensor* out) {
in->set_layout(DataLayout::kMKLDNN);
in->set_format(out->format());
};
if (dx != nullptr && dy != nullptr && dx->dims() == dy->dims()) {
if (dx->dims() == dy->dims()) {
auto blas = math::GetBlas<paddle::platform::CPUDeviceContext, T>(ctx);
if (dx) {
blas.VCOPY(dout->numel(), dout->data<T>(),
dx->mutable_data<T>(ctx.GetPlace()));
dx->set_mkldnn_prim_desc(dout->get_mkldnn_prim_desc());
set_mkldnn_format(dx, dout);
}
if (dy) {
blas.VCOPY(dout->numel(), dout->data<T>(),
dy->mutable_data<T>(ctx.GetPlace()));
dy->set_mkldnn_prim_desc(dout->get_mkldnn_prim_desc());
set_mkldnn_format(dy, dout);
}
}
} else {
......
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
浏览文件 @ 33473890
......@@ -65,11 +65,17 @@ by input arguments.
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
GaussianRandomBatchSizeLikeNoNeedBufferVarsInference, "Input");
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(
REGISTER_OPERATOR(
gaussian_random_batch_size_like,
paddle::operators::GaussianRandomBatchSizeLikeOp,
paddle::operators::GaussianRandomBatchSizeLikeOpMaker);
paddle::operators::GaussianRandomBatchSizeLikeOpMaker,
paddle::framework::EmptyGradOpMaker,
paddle::operators::GaussianRandomBatchSizeLikeNoNeedBufferVarsInference);
// Kernels are registered in gaussian_random_op.cc and gaussian_random_op.cu
paddle/fluid/operators/im2sequence_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/im2sequence_op.h"
#include <memory>
#include <string>
#include <vector>
......@@ -146,12 +147,28 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel {
}
};
class Im2SequenceGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("im2sequence_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(im2sequence, ops::Im2SequenceOp, ops::Im2SequenceOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::Im2SequenceGradDescMaker);
REGISTER_OPERATOR(im2sequence_grad, ops::Im2SequenceGradOp);
REGISTER_OP_CPU_KERNEL(
im2sequence,
......
paddle/fluid/operators/interpolate_op.cc
浏览文件 @ 33473890
......@@ -10,6 +10,7 @@
limitations under the License. */
#include "paddle/fluid/operators/interpolate_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
......@@ -194,21 +195,46 @@ class InterpolateOpGrad : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.GetPlace());
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.GetPlace());
}
};
class InterpolateGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType(ForwardOp().Type() + "_grad");
op->SetInput("X", Input("X"));
if (ForwardOp().Inputs().count("OutSize") > 0) {
op->SetInput("OutSize", Input("OutSize"));
}
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(InterpolateGradNoNeedBufferVarsInference,
"X");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(bilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad);
ops::InterpolateGradDescMaker);
REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad,
ops::InterpolateGradNoNeedBufferVarsInference);
REGISTER_OPERATOR(nearest_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad);
ops::InterpolateGradDescMaker);
REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad,
ops::InterpolateGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::InterpolateKernel<float>,
ops::InterpolateKernel<double>,
ops::InterpolateKernel<uint8_t>);
......
paddle/fluid/operators/l1_norm_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/l1_norm_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -62,12 +63,28 @@ $$Out = \sum{|X|}$$
}
};
class L1NormGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("l1_norm_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(l1_norm, ops::L1NormOp, ops::L1NormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::L1NormGradDescMaker);
REGISTER_OPERATOR(l1_norm_grad, ops::L1NormGradOp);
REGISTER_OP_CPU_KERNEL(
l1_norm, ops::L1NormKernel<paddle::platform::CPUDeviceContext, float>);
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/label_smooth_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -105,10 +106,23 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
}
};
class LabelSmoothGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("label_smooth_grad");
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
......@@ -117,7 +131,7 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OPERATOR(label_smooth, ops::LabelSmoothOp, ops::LabelSmoothOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LabelSmoothGradDescMaker);
REGISTER_OPERATOR(label_smooth_grad, ops::LabelSmoothGradOp);
REGISTER_OP_CPU_KERNEL(
label_smooth,
......
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/linear_chain_crf_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -250,14 +251,46 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
}
};
class LinearChainCRFGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("linear_chain_crf_grad");
op->SetAttrMap(Attrs());
op->SetInput("Emission", Input("Emission"));
op->SetInput("Transition", Input("Transition"));
op->SetInput("Label", Input("Label"));
op->SetInput("Alpha", Output("Alpha"));
op->SetInput("EmissionExps", Output("EmissionExps"));
op->SetInput("TransitionExps", Output("TransitionExps"));
op->SetInput(framework::GradVarName("LogLikelihood"),
OutputGrad("LogLikelihood"));
op->SetOutput(framework::GradVarName("Emission"), InputGrad("Emission"));
op->SetOutput(framework::GradVarName("Transition"),
InputGrad("Transition"));
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
LinearChainCRFGradNoNeedBufferVarsInference, "Transition", "Emission");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(linear_chain_crf, ops::LinearChainCRFOp,
ops::LinearChainCRFOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp);
ops::LinearChainCRFOpMaker, ops::LinearChainCRFGradDescMaker);
REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp,
ops::LinearChainCRFGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(
linear_chain_crf,
ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/log_loss_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/log_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -100,12 +101,29 @@ class LogLossGradOp : public framework::OperatorWithKernel {
}
};
class LogLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("log_loss_grad");
op->SetInput("Predicted", Input("Predicted"));
op->SetInput("Labels", Input("Labels"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetOutput(framework::GradVarName("Predicted"), InputGrad("Predicted"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(log_loss, ops::LogLossOp, ops::LogLossOpMaker<float>,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LogLossGradDescMaker);
REGISTER_OPERATOR(log_loss_grad, ops::LogLossGradOp);
REGISTER_OP_CPU_KERNEL(
log_loss, ops::LogLossKernel<paddle::platform::CPUDeviceContext, float>);
......
paddle/fluid/operators/lstm_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/lstm_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -264,12 +265,51 @@ class LSTMGradOp : public framework::OperatorWithKernel {
}
};
class LSTMGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("lstm_grad");
op->SetAttrMap(Attrs());
op->SetInput("Input", Input("Input"));
op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
if (ForwardOp().Inputs().count("H0") > 0) {
op->SetInput("H0", Input("H0"));
op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
}
if (ForwardOp().Inputs().count("C0") > 0) {
op->SetInput("C0", Input("C0"));
op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
}
op->SetInput("Weight", Input("Weight"));
op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
op->SetInput("Bias", Input("Bias"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
op->SetInput("Cell", Output("Cell"));
op->SetInput("Hidden", Output("Hidden"));
op->SetInput(framework::GradVarName("Hidden"), OutputGrad("Hidden"));
op->SetInput("BatchGate", Output("BatchGate"));
op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lstm, ops::LSTMOp, ops::LSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LSTMGradOpDescMaker);
REGISTER_OPERATOR(lstm_grad, ops::LSTMGradOp);
REGISTER_OP_CPU_KERNEL(
lstm, ops::LSTMKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/margin_rank_loss_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/margin_rank_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -94,8 +95,6 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Activated"),
......@@ -106,13 +105,31 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
}
};
class MarginRankLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("margin_rank_loss_grad");
op->SetInput("Activated", Output("Activated"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetInput("Label", Input("Label"));
op->SetOutput(framework::GradVarName("X1"), InputGrad("X1"));
op->SetOutput(framework::GradVarName("X2"), InputGrad("X2"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(margin_rank_loss, ops::MarginRankLossOp,
ops::MarginRankLossOpMaker<float>,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::MarginRankLossGradDescMaker);
REGISTER_OPERATOR(margin_rank_loss_grad, ops::MarginRankLossGradOp);
REGISTER_OP_CPU_KERNEL(
margin_rank_loss,
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 33473890
......@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/mean_op.h"
#include <memory>
#include <string>
#include <unordered_map>
namespace paddle {
namespace operators {
......@@ -61,7 +64,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = ctx.Input<Tensor>("X")->type();
auto input_data_type =
ctx.Input<Tensor>(framework::GradVarName("Out"))->type();
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
......@@ -81,13 +85,16 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(MeanGradNoNeedBufferVarsInference, "X");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanOpInferVarType,
ops::MeanGradMaker);
REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
REGISTER_OPERATOR(mean_grad, ops::MeanGradOp,
ops::MeanGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(
mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,
ops::MeanKernel<paddle::platform::CPUDeviceContext, double>);
......
paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -96,7 +96,8 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
std::vector<int> src_tz = framework::vectorize2int(x->dims());
auto src_format = x->format();
auto src_format =
src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format();
const std::string key = gethash(src_tz, algorithm);
const std::string key_src_data =
......@@ -126,8 +127,10 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
if (p_fwd == nullptr) {
// create mkldnn memory for input X
auto src_md = platform::MKLDNNMemDesc(
src_tz, platform::MKLDNNGetDataType<T>(), src_format);
auto src_memory = std::shared_ptr<memory>(
new memory(x->get_mkldnn_prim_desc(), to_void_cast(x_data)));
new memory({src_md, mkldnn_engine}, to_void_cast(x_data)));
// save src_memory to be referred in backward path
dev_ctx.SetBlob(key_src_mem, src_memory);
......@@ -174,7 +177,8 @@ void eltwise_forward(const framework::ExecutionContext &ctx,
pipeline.push_back(*p_fwd);
stream(stream::kind::eager).submit(pipeline).wait();
y->set_mkldnn_prim_desc(dst_memory->get_primitive_desc());
y->set_layout(DataLayout::kMKLDNN);
y->set_format(GetMKLDNNFormat(*dst_memory));
}
template <typename T>
......@@ -192,6 +196,9 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());
auto diff_y_format =
diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format();
const std::string key = gethash(diff_dst_tz, algorithm);
const std::string key_src_data =
key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
......@@ -203,8 +210,8 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem";
const std::string key_fwd_pd =
key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd";
const std::string key_with_layouts = key + std::to_string(*p_src_layout) +
"-" + std::to_string(diff_y->format());
const std::string key_with_layouts =
key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format);
const std::string key_diff_src_mem =
key_with_layouts + "@eltwise_diff_src_mem";
const std::string key_diff_dst_mem =
......@@ -227,8 +234,10 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
if (p_grad == nullptr) {
// create mkldnn memory for input diff_y
auto diff_dst_md = platform::MKLDNNMemDesc(
diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
auto diff_dst_memory = std::shared_ptr<memory>(
new memory(diff_y->get_mkldnn_prim_desc(), to_void_cast(diff_y_data)));
new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data)));
dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory);
// retrieve eltwise primitive desc from device context
......@@ -272,7 +281,8 @@ void eltwise_grad(const framework::ExecutionContext &ctx,
pipeline.push_back(*p_grad);
stream(stream::kind::eager).submit(pipeline).wait();
diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc());
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format(GetMKLDNNFormat(*diff_src_memory));
}
template <typename T, mkldnn::algorithm algorithm>
......
paddle/fluid/operators/mkldnn/batch_norm_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -206,14 +206,17 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
if (fuse_with_relu) flags |= mkldnn::fuse_bn_relu;
// create mkldnn memory from input x tensor
mkldnn::memory::format input_format =
platform::MKLDNNFormatForSize(src_tz.size(), x->format());
// keys for backward pass
const std::string key = BatchNormMKLDNNHandler::GetHash(
src_tz, epsilon, flags, global_stats, x->format(),
src_tz, epsilon, flags, global_stats, input_format,
ctx.op().Output("SavedMean"));
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
auto user_src_md = x->get_mkldnn_prim_desc().desc();
auto user_src_md = platform::MKLDNNMemDesc(
{src_tz}, platform::MKLDNNGetDataType<T>(), input_format);
// create primitive descriptor for batch norm forward
using bn_fwd_types = bn_type_traits<mkldnn::batch_normalization_forward>;
......@@ -227,8 +230,8 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
BatchNormMKLDNNHandler handler(batch_norm_fwd_pd, dev_ctx, mkldnn_engine,
key);
auto src_memory = handler.AcquireSrcMemory(x->get_mkldnn_prim_desc(),
to_void_cast(x_data));
auto src_memory =
handler.AcquireSrcMemory(user_src_md, to_void_cast(x_data));
// crate mkldnn memory for weights(scale/shift)
auto scaleshift_memory =
......@@ -262,7 +265,8 @@ class BatchNormMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
variance_memory, false);
}
y->set_mkldnn_prim_desc(dst_memory->get_primitive_desc());
y->set_layout(DataLayout::kMKLDNN);
y->set_format(platform::GetMKLDNNFormat(*dst_memory));
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(*batch_norm_p);
......@@ -332,6 +336,9 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
using bn_bwd_types = bn_type_traits<mkldnn::batch_normalization_backward>;
mkldnn::memory::format dst_format =
platform::MKLDNNFormatForSize(src_tz.size(), diff_y->format());
mkldnn::memory::format input_format =
platform::MKLDNNFormatForSize(src_tz.size(), x->format());
......@@ -339,14 +346,14 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
// keys from forward pass
const std::string key = BatchNormMKLDNNHandler::GetHash(
src_tz, epsilon, flags, false, x->format(),
src_tz, epsilon, flags, false, input_format,
ctx.op().Input("SavedMean"));
const std::string key_batch_norm_fwd_pd = key + "@bn_fwd_pd";
// keys for primitives reuse
const std::string key_with_hash =
key + BatchNormMKLDNNHandler::GetHash(src_tz, epsilon, flags, false,
x->format());
input_format);
const std::string key_batch_norm_bwd_p =
key_with_hash + "@batch_norm_bwd_p";
const std::string key_batch_norm_src_mem_p =
......@@ -366,8 +373,9 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
primitive reorder_diff_dst;
bool is_diff_dst_reordered = false;
auto user_diff_dst_memory =
memory(diff_y->get_mkldnn_prim_desc(), to_void_cast(diff_y_data));
auto user_diff_dst_memory = memory(
{{{diff_dst_tz}, memory::data_type::f32, dst_format}, mkldnn_engine},
to_void_cast(diff_y_data));
// MKLDNN requires a single piece of memory for scale and shift/bias data
const size_t scaleshift_size = 2 * ic;
......@@ -451,7 +459,10 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
dev_ctx.SetBlob(key_batch_norm_diff_dst_mem_p, diff_dst_memory);
// set layout/format of output tensors
diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc());
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc()
.desc()
.data.format);
} else {
// primitives already exist
UpdateMemoryData(dev_ctx, key_batch_norm_src_mem_p, to_void_cast(x_data));
......@@ -476,7 +487,10 @@ class BatchNormMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
}
// set layout/format of output tensors
diff_x->set_mkldnn_prim_desc(diff_src_memory->get_primitive_desc());
diff_x->set_layout(DataLayout::kMKLDNN);
diff_x->set_format((memory::format)diff_src_memory->get_primitive_desc()
.desc()
.data.format);
}
// execute optional reorder and batch_norm backward primitive
......
paddle/fluid/operators/mkldnn/concat_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -210,7 +210,8 @@ class ConcatMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
stream(stream::kind::eager).submit({*concat_p}).wait();
output->set_mkldnn_prim_desc(concat_pd->dst_primitive_desc());
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetDstMemFormat(*concat_pd));
}
};
} // namespace operators
......
paddle/fluid/operators/mkldnn/conv_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -96,8 +96,12 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto* bias = ctx.HasInput("Bias") ? ctx.Input<Tensor>("Bias") : nullptr;
auto* output = ctx.Output<Tensor>("Output");
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN);
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN);
PADDLE_ENFORCE(input->layout() == DataLayout::kMKLDNN &&
input->format() != memory::format::format_undef,
"Wrong layout/format set for Input tensor");
PADDLE_ENFORCE(filter->layout() == DataLayout::kMKLDNN &&
filter->format() != memory::format::format_undef,
"Wrong layout/format set for Filter tensor");
PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 5,
"Input must be with 4 or 5 dimensions, i.e. NCHW or NCDHW");
PADDLE_ENFORCE(filter->dims().size() == 4 || filter->dims().size() == 5,
......@@ -144,19 +148,14 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std::vector<primitive> pipeline;
// For convolution with groups we need to recreate primitive descriptor
// as Paddle tensor is not having group dims while mkldnn treats
// group as another dimensions
mkldnn::memory::primitive_desc user_weights_mpd =
filter->get_mkldnn_prim_desc();
if (g > 1) {
mkldnn::memory::format weights_format =
GetWeightsFormat(filter->format(), g, is_conv3d);
auto user_weights_md = platform::MKLDNNMemDesc(
{weights_tz}, platform::MKLDNNGetDataType<T>(), weights_format);
user_weights_mpd =
mkldnn::memory::primitive_desc(user_weights_md, mkldnn_engine);
}
auto src_format = input->format();
mkldnn::memory::format weights_format =
GetWeightsFormat(filter->format(), g, is_conv3d);
auto user_src_md = platform::MKLDNNMemDesc(
{src_tz}, platform::MKLDNNGetDataType<T>(), src_format);
auto user_weights_md = platform::MKLDNNMemDesc(
{weights_tz}, platform::MKLDNNGetDataType<T>(), weights_format);
/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
......@@ -166,7 +165,7 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto chosen_memory_format =
platform::data_format_to_memory_format(data_format);
mkldnn::memory::format weights_format = mkldnn::memory::format::any;
weights_format = mkldnn::memory::format::any;
// Check the format for user's special output
if (chosen_memory_format != mkldnn::memory::format::any) {
if (is_conv3d) {
......@@ -206,10 +205,10 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
platform::ConvMKLDNNHandler handler(conv_pd, dev_ctx, mkldnn_engine, key);
// create mkldnn memory from input tensors (data/weights)
auto user_src_memory_p = handler.AcquireSrcMemory(
input->get_mkldnn_prim_desc(), to_void_cast<T>(input_data));
auto user_src_memory_p =
handler.AcquireSrcMemory(user_src_md, to_void_cast<T>(input_data));
auto user_weights_memory_p = handler.AcquireWeightsMemory(
user_weights_mpd, to_void_cast<T>(filter_data));
user_weights_md, to_void_cast<T>(filter_data));
// create reorder primitive if the input format is not the preferred one
auto src_memory_p =
......@@ -282,7 +281,8 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
pipeline.push_back(*conv_p);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_mkldnn_prim_desc(dst_memory_p->get_primitive_desc());
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(*dst_memory_p));
}
void ComputeINT8(const paddle::framework::ExecutionContext& ctx) const {
const bool is_test = ctx.Attr<bool>("is_test");
......@@ -948,8 +948,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
// push primitive to stream and wait until it's executed
pipeline.push_back(*conv_bwd_weights_p);
auto filter_grad_mpd = diff_weights_memory_p->get_primitive_desc();
filter_grad->set_mkldnn_prim_desc(filter_grad_mpd);
filter_grad->set_layout(DataLayout::kMKLDNN);
filter_grad->set_format(GetMKLDNNFormat(*diff_weights_memory_p));
}
if (input_grad) {
......@@ -972,7 +972,8 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
pipeline.push_back(*conv_bwd_data_p);
input_grad->set_mkldnn_prim_desc(diff_src_memory_p->get_primitive_desc());
input_grad->set_layout(DataLayout::kMKLDNN);
input_grad->set_format(GetMKLDNNFormat(*diff_src_memory_p));
}
stream(stream::kind::eager).submit(pipeline).wait();
}
......
paddle/fluid/operators/mkldnn/conv_transpose_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -221,7 +221,8 @@ class ConvTransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
pipeline.push_back(*conv_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
output->set_mkldnn_prim_desc(dst_memory_p->get_primitive_desc());
output->set_layout(DataLayout::kMKLDNN);
output->set_format(platform::GetMKLDNNFormat(*dst_memory_p));
}
private:
......
paddle/fluid/operators/mkldnn/gaussian_random_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -42,12 +42,8 @@ class GaussianMKLDNNKernel : public paddle::framework::OpKernel<T> {
// The format of output is set as the mkldnn's format
// TODO(@mozga-intel) The format of matrix sets inside the another layers.
// TODO(jczaja): Remove this hack after checking performance on block layout
auto tensor_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(tensor->dims()),
mkldnn::memory::format::oihw);
tensor->set_mkldnn_prim_desc(tensor_mem_pd);
tensor->set_layout(DataLayout::kMKLDNN);
tensor->set_format(mkldnn::memory::format::oihw);
}
};
} // namespace operators
......
paddle/fluid/operators/mkldnn/lrn_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -81,7 +81,10 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
auto e_mid = framework::EigenTensor<T, 4>::From(*mid);
e_mid = e_mid.constant(k);
auto src_md = x->get_mkldnn_prim_desc().desc();
auto dims = paddle::framework::vectorize2int(x->dims());
auto src_md = paddle::platform::MKLDNNMemDesc(
dims, mkldnn::memory::data_type::f32, x->format());
auto forward_desc = mkldnn::lrn_forward::desc{mkldnn::prop_kind::forward,
mkldnn::lrn_across_channels,
......@@ -91,7 +94,7 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
beta,
k};
auto src_memory_pd = x->get_mkldnn_prim_desc();
auto src_memory_pd = mkldnn::memory::primitive_desc{src_md, mkldnn_engine};
if (!is_test) {
const std::string key = ctx.op().Output("Out");
......@@ -108,15 +111,16 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
src_memory->set_data_handle(
static_cast<void*>(const_cast<T*>(input_data)));
auto dst_memory_pd = forward_pd->dst_primitive_desc();
auto dst_memory =
mkldnn::memory(dst_memory_pd, static_cast<void*>(output_data));
auto dst_memory = mkldnn::memory(forward_pd->dst_primitive_desc(),
static_cast<void*>(output_data));
auto workspace_memory = insert_to_context<mkldnn::memory>(
key_workspace_memory, dev_ctx,
forward_pd->workspace_primitive_desc());
run_primitive(*forward_pd, *src_memory, *workspace_memory, dst_memory);
out->set_mkldnn_prim_desc(dst_memory_pd);
out->set_layout(framework::DataLayout::kMKLDNN);
out->set_format(platform::GetMKLDNNFormat(dst_memory));
} else {
auto forward_pd =
mkldnn::lrn_forward::primitive_desc{forward_desc, mkldnn_engine};
......@@ -124,12 +128,13 @@ class LRNMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
src_memory_pd, static_cast<void*>(const_cast<T*>(input_data))};
auto workspace_memory =
mkldnn::memory{forward_pd.workspace_primitive_desc()};
auto dst_memory_pd = forward_pd.dst_primitive_desc();
auto dst_memory = mkldnn::memory(forward_pd.dst_primitive_desc(),
static_cast<void*>(output_data));
run_primitive(forward_pd, src_memory, workspace_memory, dst_memory);
out->set_mkldnn_prim_desc(dst_memory_pd);
out->set_layout(framework::DataLayout::kMKLDNN);
out->set_format(platform::GetMKLDNNFormat(dst_memory));
}
}
};
......
paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -158,14 +158,6 @@ class SoftmaxMKLDNNKernel : public paddle::framework::OpKernel<T> {
auto softmax_p =
handler.AcquireSoftmax(softmax_dst_memory_p, softmax_src_memory_p);
// We cannot use softmax_dst_memory_p to get prim desc as
// it contains flattened dims (2D) while output tensor can
// have 2,3,4+ dims
auto output_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(output->dims()),
mkldnn::memory::format::blocked);
output->set_mkldnn_prim_desc(output_mem_pd);
std::vector<primitive> pipeline{
*(static_cast<softmax_forward::primitive*>(softmax_p.get()))};
stream(stream::kind::eager).submit(pipeline).wait();
......
paddle/fluid/operators/mkldnn/sum_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -106,12 +106,12 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
memory::desc(dst_tz, memory::data_type::f32, memory::format::any);
auto sum_pd = sum::primitive_desc(dst_md, scales, srcs_mpd);
auto dst_mem_pd = sum_pd.dst_primitive_desc();
std::shared_ptr<memory> dst_mem;
if (in_place) {
dst_mem.reset(new memory(dst_mem_pd));
dst_mem.reset(new memory(sum_pd.dst_primitive_desc()));
} else {
dst_mem.reset(new memory(dst_mem_pd, output_data));
dst_mem.reset(new memory(sum_pd.dst_primitive_desc(), output_data));
}
std::vector<mkldnn::primitive::at> inputs;
for (size_t i = 0; i < srcs_mem.size(); ++i) {
......@@ -136,7 +136,8 @@ class SumMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
if (in_place) pipeline.push_back(reorder_prim);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_mkldnn_prim_desc(dst_mem_pd);
output->set_layout(DataLayout::kMKLDNN);
output->set_format(output_format);
} else { // Fallback to naive version
// TODO(@mozga-intel) Add MKLDNN SelectedRows & LoDTensorArray support
SumKernel<CPUDeviceContext, T> reference_kernel;
......
paddle/fluid/operators/mkldnn/transpose_mkldnn_op.cc
浏览文件 @ 33473890
......@@ -52,7 +52,7 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
mkldnn_engine, key);
auto transpose_src_memory_p = handler.AcquireSrcMemory(
input->get_mkldnn_prim_desc(), platform::to_void_cast<T>(input_data));
input->format(), platform::to_void_cast<T>(input_data));
auto transpose_dst_memory_p =
handler.AcquireDstMemory(output, ctx.GetPlace());
auto transpose_p = handler.AcquireTranspose(transpose_dst_memory_p,
......@@ -62,14 +62,8 @@ class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
pipeline.push_back(*transpose_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
// Transpose did change logical dimensions of Tensor, but reorder does not.
// Reorder does change only physical layout eg. format , strides
// so we need to create new primitive descriptor with changed logical layout
// so it match output shape
auto output_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(output->dims()),
mkldnn::memory::format::blocked);
output->set_mkldnn_prim_desc(output_mem_pd);
output->set_layout(DataLayout::kNCHW);
output->set_format(mkldnn::memory::format::format_undef);
}
};
......@@ -134,9 +128,8 @@ class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
platform::TransposeMKLDNNHandler handler(nchw_tz, reversed_axis, dev_ctx,
mkldnn_engine, key);
auto transpose_src_memory_p =
handler.AcquireSrcMemory(out_grad->get_mkldnn_prim_desc(),
platform::to_void_cast<T>(out_grad_data));
auto transpose_src_memory_p = handler.AcquireSrcMemory(
out_grad->format(), platform::to_void_cast<T>(out_grad_data));
auto transpose_dst_memory_p =
handler.AcquireDstMemory(x_grad, ctx.GetPlace());
auto transpose_p = handler.AcquireTranspose(transpose_dst_memory_p,
......@@ -145,15 +138,6 @@ class TransposeMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std::vector<mkldnn::primitive> pipeline;
pipeline.push_back(*transpose_p);
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
// Transpose did change logical dimensions of Tensor, but reorder does not.
// Reorder does change only physical layout eg. format , strides
// so we need to create new primitive descriptor with changed logical layout
// so it match output shape
auto x_grad_mem_pd = paddle::platform::create_prim_desc_from_dims(
paddle::framework::vectorize2int(x_grad->dims()),
mkldnn::memory::format::blocked);
x_grad->set_mkldnn_prim_desc(x_grad_mem_pd);
}
};
......
paddle/fluid/operators/multiplex_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,8 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/multiplex_op.h"
#include <memory>
#include <vector>
namespace paddle {
namespace operators {
......@@ -111,28 +113,47 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null.");
PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(),
"Output(X@Grad) should not be null.");
auto& dxs = ctx->Outputs(framework::GradVarName("X"));
PADDLE_ENFORCE(!dxs.empty(), "Output(X@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
ctx->SetOutputsDim(framework::GradVarName("X"),
std::vector<framework::DDim>(dxs.size(), dout_dim));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.MultiInput<Tensor>("X")[0]->type(),
ctx.device_context());
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.device_context());
}
};
class MultiplexGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("multiplex_grad");
op->SetInput("Ids", Input("Ids"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
paddle::framework::DefaultGradOpDescMaker<false>);
ops::MultiplexGradDescMaker);
REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
REGISTER_OP_CPU_KERNEL(
multiplex,
......
paddle/fluid/operators/multiplex_op.cu
浏览文件 @ 33473890
......@@ -53,20 +53,25 @@ class MultiplexGradGPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
size_t idx = -1UL;
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(*ctx.template device_context<Place>().eigen_device()) =
t.constant(static_cast<T>(0));
idx = i;
}
}
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
if (idx == -1UL) return;
auto rows = d_ins[idx]->dims()[0];
auto cols = d_ins[idx]->numel() / rows;
// copy index to cpu
Tensor index_t_cpu;
TensorCopySync(*ids, platform::CPUPlace(), &index_t_cpu);
......
paddle/fluid/operators/multiplex_op.h
浏览文件 @ 33473890
......@@ -52,20 +52,25 @@ class MultiplexGradCPUKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* ids = ctx.Input<framework::Tensor>("Ids");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
size_t idx = -1UL;
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(*ctx.template device_context<DeviceContext>().eigen_device()) =
t.constant(static_cast<T>(0));
idx = i;
}
}
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
if (idx == -1UL) return;
auto rows = d_ins[idx]->dims()[0];
auto cols = d_ins[idx]->numel() / rows;
auto* index = ids->data<int32_t>();
platform::CPUPlace place = boost::get<platform::CPUPlace>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
......
paddle/fluid/operators/pad_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pad_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -29,7 +30,7 @@ class PadOp : public framework::OperatorWithKernel {
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
"Size of paddings should be equal to 2 * dimension size "
"of input tensor.");
......@@ -99,13 +100,20 @@ class PadOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx->GetInputDim("X");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
for (int i = 0; i < dout_dims.size(); ++i) {
dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
}
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
for (int i = 0; i < dout_dims.size(); ++i) {
dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
}
ctx->SetOutputDim(x_grad_name, dout_dims);
}
}
};
......@@ -117,7 +125,6 @@ class PadOpGradMaker : public framework::SingleGradOpDescMaker {
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* bind = new framework::OpDesc();
bind->SetInput("X", Input("X"));
bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
bind->SetOutput(framework::GradVarName("X"), InputGrad("X"));
bind->SetAttrMap(Attrs());
......
paddle/fluid/operators/psroi_pool_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/psroi_pool_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -154,12 +155,29 @@ class PSROIPoolGradOp : public framework::OperatorWithKernel {
}
};
class PSROIPoolGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("psroi_pool_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(psroi_pool, ops::PSROIPoolOp, ops::PSROIPoolOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::PSROIPoolGradDescMaker);
REGISTER_OPERATOR(psroi_pool_grad, ops::PSROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
psroi_pool,
......
paddle/fluid/operators/rank_loss_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/rank_loss_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -116,6 +117,25 @@ class RankLossGradOp : public framework::OperatorWithKernel {
}
};
class RankLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("rank_loss_grad");
op->SetInput("Label", Input("Label"));
op->SetInput("Left", Input("Left"));
op->SetInput("Right", Input("Right"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("Left"), InputGrad("Left"));
op->SetOutput(framework::GradVarName("Right"), InputGrad("Right"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 33473890
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/roi_align_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -147,12 +148,29 @@ Thus avoid the misaligned problem.
}
};
class ROIAlignGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_align_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_align, ops::ROIAlignOp, ops::ROIAlignOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIAlignGradDescMaker);
REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp);
REGISTER_OP_CPU_KERNEL(
roi_align,
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/roi_pool_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -158,12 +159,30 @@ https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
}
};
class ROIPoolGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_pool_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput("Argmax", Output("Argmax"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_pool, ops::ROIPoolOp, ops::ROIPoolOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIPoolGradDescMaker);
REGISTER_OPERATOR(roi_pool_grad, ops::ROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
roi_pool,
......
paddle/fluid/operators/scatter_op.cc
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/scatter_op.h"
#include <memory>
#include "paddle/fluid/framework/ddim.h"
namespace paddle {
......@@ -63,14 +64,16 @@ class ScatterGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("Updates"),
ctx->GetInputDim("Updates"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context());
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.device_context());
}
};
......@@ -95,12 +98,34 @@ $$
}
};
class ScatterGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("scatter_grad");
op->SetInput("Ids", Input("Ids"));
op->SetInput("Updates", Input("Updates"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Updates"), InputGrad("Updates"));
op->SetAttrMap(Attrs());
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ScatterGradNoNeedBufferVarsInference,
"Updates");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(scatter, ops::ScatterOp, ops::ScatterOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp);
ops::ScatterGradDescMaker);
REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp,
ops::ScatterGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(scatter, ops::ScatterOpKernel<float>);
REGISTER_OP_CPU_KERNEL(scatter_grad, ops::ScatterGradientOpKernel<float>);
paddle/fluid/operators/shuffle_channel_op.cc
浏览文件 @ 33473890
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -91,13 +92,28 @@ class ShuffleChannelGradOp : public framework::OperatorWithKernel {
}
};
class ShuffleChannelGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("shuffle_channel_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(shuffle_channel, ops::ShuffleChannelOp,
ops::ShuffleChannelOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ShuffleChannelOpMaker, ops::ShuffleChannelGradDescMaker);
REGISTER_OPERATOR(shuffle_channel_grad, ops::ShuffleChannelGradOp);
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ 33473890
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout_transform.h"
......@@ -39,45 +40,6 @@ class MKLDNNHandler {
return this->AcquireMemory(md, ptr, "@user_src_mem_p");
}
// TODO(jczaja): extract common part and make AcquireMemory
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
const mkldnn::memory::primitive_desc& mpd, void* ptr) {
auto local_key = key_ + "@user_src_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
mem_p = std::make_shared<mkldnn::memory>(mpd, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
std::shared_ptr<mkldnn::memory> AcquireWeightsMemory(
const mkldnn::memory::primitive_desc& mpd, void* ptr) {
auto local_key = key_ + "@user_weights_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
mem_p = std::make_shared<mkldnn::memory>(mpd, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
std::shared_ptr<mkldnn::memory> AcquireWeightsMemory(
const mkldnn::memory::desc& md, void* ptr,
user_function custom_func = {}) {
......@@ -315,7 +277,37 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
mkldnn::engine engine, const std::string& base_key)
: platform::MKLDNNHandler(dev_ctx, engine, base_key),
dims_(dims),
axis_(axis) {}
axis_(axis),
logical_axis_(dims.size(), 0) {}
std::shared_ptr<mkldnn::memory> AcquireSrcMemory(
const mkldnn::memory::format& fmt, void* ptr) {
auto local_key = key_ + "@user_src_mem_p";
auto mem_p =
std::static_pointer_cast<mkldnn::memory>(dev_ctx_.GetBlob(local_key));
PADDLE_ENFORCE((mem_p != nullptr) || (is_reusing_ == false),
" find mem primitive in device context");
if (mem_p == nullptr) {
// Make memory descriptor using input format, unless it
// cannot be trusted (nchw) then make up memory fmt manually
for (size_t i = 0; i < logical_axis_.size(); ++i) {
logical_axis_[i] = i;
}
auto src_md = fmt != mkldnn::memory::format::nchw
? platform::MKLDNNMemDesc(
dims_, platform::MKLDNNGetDataType<float>(), fmt)
: Axis2MemoryDesc(dims_, logical_axis_);
mem_p = std::make_shared<mkldnn::memory>(
mkldnn::memory::primitive_desc{src_md, engine_}, ptr);
dev_ctx_.SetBlob(local_key, mem_p);
} else {
mem_p->set_data_handle(ptr);
// Mark that reusing happenned. All primitives from operator instance
// should be reused or none of them. So we check consistency
is_reusing_ = true;
}
return mem_p;
}
std::shared_ptr<mkldnn::memory> AcquireDstMemory(framework::Tensor* output,
platform::Place place) {
......@@ -400,6 +392,7 @@ class TransposeMKLDNNHandler : public MKLDNNHandler {
private:
std::vector<int> dims_;
std::vector<int> axis_;
std::vector<int> logical_axis_;
};
template <class forward_t, class backward_data_t, class backward_weights_t>
......
paddle/fluid/platform/mkldnn_utils.h 已删除 100644 → 0
浏览文件 @ 2c836ff9
/* 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 <mkldnn.h>
#include <string>
namespace paddle {
namespace platform {
inline mkldnn::memory::primitive_desc create_prim_desc_from_dims(
const std::vector<int>& ltz, mkldnn::memory::format fmt,
mkldnn::memory::data_type data_type = mkldnn::memory::data_type::f32) {
mkldnn_memory_desc_t mem_fmt;
mem_fmt.primitive_kind = mkldnn_memory;
mem_fmt.ndims = ltz.size();
for (unsigned int i = 0; i < ltz.size(); ++i) {
mem_fmt.dims[i] = ltz[i]; // logical dimensions (nchw format,
// regardless physical layout)
}
mem_fmt.data_type = static_cast<mkldnn_data_type_t>(data_type);
mem_fmt.format = static_cast<mkldnn_memory_format_t>(fmt);
unsigned int total_stride = 1;
for (int i = ltz.size() - 1; i >= 0; --i) {
mem_fmt.layout_desc.blocking.padding_dims[i] =
ltz[i]; // logical dimensions (nchw format, regardless physical
// layout)
mem_fmt.layout_desc.blocking.block_dims[i] = 1;
mem_fmt.layout_desc.blocking.offset_padding_to_data[i] = 0; // no offset
mem_fmt.layout_desc.blocking.strides[0][i] = total_stride;
mem_fmt.layout_desc.blocking.strides[1][i] = 1;
total_stride *= ltz[i];
}
mem_fmt.layout_desc.blocking.offset_padding = 0; // no initial offset
auto& pool = platform::DeviceContextPool::Instance();
auto place = paddle::platform::CPUPlace();
auto* dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
auto& cpu_engine = dev_ctx->GetEngine();
return mkldnn::memory::primitive_desc(mem_fmt, cpu_engine);
}
inline mkldnn::memory::primitive_desc create_prim_desc_from_format(
const std::vector<int>& ltz, const mkldnn::memory::format format,
const mkldnn::memory::data_type data_type) {
auto md = mkldnn::memory::desc({ltz}, data_type, format);
auto& pool = platform::DeviceContextPool::Instance();
auto place = paddle::platform::CPUPlace();
auto dev_ctx = dynamic_cast<platform::MKLDNNDeviceContext*>(pool.Get(place));
PADDLE_ENFORCE_NOT_NULL(dev_ctx, "Could not get valid device");
auto& cpu_engine = dev_ctx->GetEngine();
return mkldnn::memory::primitive_desc(md, cpu_engine);
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/temporary_allocator.cc
浏览文件 @ 33473890
......@@ -14,6 +14,7 @@
#include "paddle/fluid/platform/temporary_allocator.h"
#include <memory>
#include <utility>
#include "paddle/fluid/memory/allocation/allocator_facade.h"
DEFINE_int64(limit_of_tmp_allocation, -1,
......@@ -30,31 +31,38 @@ namespace paddle {
namespace platform {
namespace alloc = memory::allocation;
TemporaryAllocation::TemporaryAllocation(
alloc::AllocationPtr &&underlying_allocation)
: Allocation(underlying_allocation->ptr(), underlying_allocation->size(),
underlying_allocation->place()),
underlying_allocation_(std::move(underlying_allocation)) {}
TemporaryAllocator::TemporaryAllocator(platform::Place place) : place_(place) {
temp_mem_map_.reset(new std::multimap<size_t, alloc::Allocation *>());
temp_mem_map_.reset(new std::multimap<size_t, TemporaryAllocation *>());
}
bool TemporaryAllocator::IsAllocThreadSafe() const { return true; }
void TemporaryAllocator::Release(const std::function<void()> &callback) {
std::unique_ptr<std::multimap<size_t, alloc::Allocation *>> t_allocations;
std::unique_ptr<std::multimap<size_t, TemporaryAllocation *>> t_allocations;
{
std::unique_lock<std::mutex> lock(mtx_);
callback();
t_allocations.swap(temp_mem_map_);
temp_mem_map_.reset(new std::multimap<size_t, alloc::Allocation *>());
temp_mem_map_.reset(new std::multimap<size_t, TemporaryAllocation *>());
wait_delete_mem_ = 0;
}
alloc::AllocationDeleter deleter;
for (auto tmp : *t_allocations) {
VLOG(10) << "Delete temporary allocation " << tmp.second->ptr()
<< " size: " << tmp.second->size();
deleter(tmp.second);
delete tmp.second;
}
}
void TemporaryAllocator::FreeImpl(alloc::Allocation *temp_allocation) {
void TemporaryAllocator::Free(alloc::Allocation *allocation) {
auto *temp_allocation = dynamic_cast<TemporaryAllocation *>(allocation);
PADDLE_ENFORCE_NOT_NULL(temp_allocation);
if (platform::is_gpu_place(temp_allocation->place())) {
PADDLE_ENFORCE(platform::is_same_place(temp_allocation->place(), place_),
"The place should be the same.");
......@@ -78,7 +86,7 @@ void TemporaryAllocator::FreeImpl(alloc::Allocation *temp_allocation) {
}
VLOG(10) << "Delete temporary allocation " << temp_allocation->ptr()
<< " size: " << temp_allocation->size();
alloc::AllocationDeleter()(temp_allocation);
delete temp_allocation;
}
size_t TemporaryAllocator::TemporaryAllocationQueueSize() {
......@@ -113,9 +121,11 @@ alloc::Allocation *TemporaryAllocator::AllocateImpl(
}
// If not find the the available allocation, get allocation from
// AllocatorFacadeInstance.
auto temp_mem = alloc::AllocatorFacade::Instance().Alloc(place_, size, attr);
auto raw_allocation =
alloc::AllocatorFacade::Instance().Alloc(place_, size, attr);
auto temp_mem = new TemporaryAllocation(std::move(raw_allocation));
VLOG(10) << "Alloc temporary allocation: " << temp_mem->ptr() << ": " << size;
return temp_mem.release();
return temp_mem;
}
} // namespace platform
......
paddle/fluid/platform/temporary_allocator.h
浏览文件 @ 33473890
......@@ -23,6 +23,14 @@
namespace paddle {
namespace platform {
class TemporaryAllocation : public memory::allocation::Allocation {
public:
explicit TemporaryAllocation(
memory::allocation::AllocationPtr &&underlying_allocation);
memory::allocation::AllocationPtr underlying_allocation_;
};
/*! \brief the TemporaryAllocator is used to alloc the temporary allocation
* which used by CUDA's async operation.
*
......@@ -49,7 +57,7 @@ class TemporaryAllocator : public memory::allocation::Allocator {
void SetCallback(const std::function<void()> &callback);
protected:
void FreeImpl(memory::allocation::Allocation *allocation) override;
void Free(memory::allocation::Allocation *allocation) override;
memory::allocation::Allocation *AllocateImpl(
size_t size, memory::allocation::Allocator::Attr attr) override;
......@@ -58,8 +66,8 @@ class TemporaryAllocator : public memory::allocation::Allocator {
platform::Place place_;
// When the allocation is not held by any variable, it should be placed
// to temp_mem_map immediately.
std::unique_ptr<std::multimap<size_t, memory::allocation::Allocation *>>
temp_mem_map_{nullptr};
std::unique_ptr<std::multimap<size_t, TemporaryAllocation *>> temp_mem_map_{
nullptr};
std::mutex mtx_;
size_t wait_delete_mem_{0};
std::function<void()> callback_;
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 33473890
......@@ -328,7 +328,6 @@ PYBIND11_MODULE(core, m) {
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
self.mutable_data<float>(place);
})
.def("_clear", &Tensor::clear)
.def("set", PyCPUTensorSetFromArray<float>)
.def("set", PyCPUTensorSetFromArray<int>)
.def("set", PyCPUTensorSetFromArray<double>)
......@@ -1287,6 +1286,15 @@ All parameter, weight, gradient are variables in Paddle.
it will save GPU memory and may make the execution faster.
This options is only available in GPU devices.
Default False)DOC")
.def_property("fuse_all_optimizer_ops",
[](const BuildStrategy &self) {
return self.fuse_all_optimizer_ops_;
},
[](BuildStrategy &self, bool b) {
PADDLE_ENFORCE(!self.IsFinalized(),
"BuildStrategy is finlaized.");
self.fuse_all_optimizer_ops_ = b;
})
.def_property(
"sync_batch_norm",
[](const BuildStrategy &self) { return self.sync_batch_norm_; },
......
paddle/fluid/string/printf.h
浏览文件 @ 33473890
......@@ -105,12 +105,14 @@ void Printf(const char* fmt, const Args&... args) {
Fprintf(std::cout, fmt, args...);
}
inline std::string HumanReadableSize(double f_size) {
template <typename T>
std::string HumanReadableSize(T size) {
size_t i = 0;
double f_size = static_cast<double>(size);
double orig = f_size;
const std::vector<std::string> units(
{"B", "kB", "MB", "GB", "TB", "PB", "EB", "ZB", "YB"});
while (f_size >= 1024) {
while (f_size > 1024) {
f_size /= 1024;
i++;
}
......
python/paddle/fluid/__init__.py
浏览文件 @ 33473890
......@@ -34,7 +34,7 @@ from . import io
from . import evaluator
from . import initializer
from . import layers
from . import imperative
from . import dygraph
from . import contrib
from . import nets
from . import optimizer
......@@ -71,7 +71,7 @@ __all__ = framework.__all__ + executor.__all__ + \
'initializer',
'layers',
'contrib',
'imperative',
'dygraph',
'transpiler',
'nets',
'optimizer',
......
python/paddle/fluid/contrib/model_stat.py 0 → 100644
浏览文件 @ 33473890
# 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.
'''
Example:
>>from paddle.fluid.contrib.model_stat import summary
>>main_program = ...
>>summary(main_program)
+-----+------------+----------------+----------------+---------+------------+
| No. | TYPE | INPUT | OUTPUT | PARAMs | FLOPs |
+-----+------------+----------------+----------------+---------+------------+
| 0 | conv2d | (3, 200, 200) | (64, 100, 100) | 9408 | 188160000 |
| 1 | batch_norm | (64, 100, 100) | (64, 100, 100) | 256 | 640000 |
| 2 | relu | (64, 100, 100) | (64, 100, 100) | 0 | 640000 |
| 3 | pool2d | (64, 100, 100) | (64, 50, 50) | 0 | 1440000 |
...
| 176 | conv2d | (512, 7, 7) | (512, 7, 7) | 2359296 | 231211008 |
| 177 | relu | (512, 7, 7) | (512, 7, 7) | 0 | 25088 |
| 178 | conv2d | (512, 7, 7) | (2048, 7, 7) | 1048576 | 102760448 |
| 179 | relu | (2048, 7, 7) | (2048, 7, 7) | 0 | 100352 |
| 180 | pool2d | (2048, 7, 7) | (2048, 1, 1) | 0 | 100352 |
+-----+------------+----------------+----------------+---------+------------+
Total PARAMs: 48017344(0.0480G)
Total FLOPs: 11692747751(11.69G)
'''
from collections import OrderedDict
from prettytable import PrettyTable
def summary(main_prog):
'''
It can summary model's PARAMS, FLOPs until now.
It support common operator like conv, fc, pool, relu, sigmoid, bn etc.
Args:
main_prog: main program
Returns:
print summary on terminal
'''
collected_ops_list = []
for one_b in main_prog.blocks:
block_vars = one_b.vars
for one_op in one_b.ops:
op_info = OrderedDict()
spf_res = _summary_model(block_vars, one_op)
if spf_res is None:
continue
# TODO: get the operator name
op_info['type'] = one_op.type
op_info['input_shape'] = spf_res[0][1:]
op_info['out_shape'] = spf_res[1][1:]
op_info['PARAMs'] = spf_res[2]
op_info['FLOPs'] = spf_res[3]
collected_ops_list.append(op_info)
summary_table, total = _format_summary(collected_ops_list)
_print_summary(summary_table, total)
def _summary_model(block_vars, one_op):
'''
Compute operator's params and flops.
Args:
block_vars: all vars of one block
one_op: one operator to count
Returns:
in_data_shape: one operator's input data shape
out_data_shape: one operator's output data shape
params: one operator's PARAMs
flops: : one operator's FLOPs
'''
if one_op.type in ['conv2d', 'depthwise_conv2d']:
k_arg_shape = block_vars[one_op.input("Filter")[0]].shape
in_data_shape = block_vars[one_op.input("Input")[0]].shape
out_data_shape = block_vars[one_op.output("Output")[0]].shape
c_out, c_in, k_h, k_w = k_arg_shape
_, c_out_, h_out, w_out = out_data_shape
assert c_out == c_out_, 'shape error!'
k_groups = one_op.attr("groups")
kernel_ops = k_h * k_w * (c_in / k_groups)
bias_ops = 0 if one_op.input("Bias") == [] else 1
params = c_out * (kernel_ops + bias_ops)
flops = h_out * w_out * c_out * (kernel_ops + bias_ops)
# base nvidia paper, include mul and add
flops = 2 * flops
elif one_op.type == 'pool2d':
in_data_shape = block_vars[one_op.input("X")[0]].shape
out_data_shape = block_vars[one_op.output("Out")[0]].shape
_, c_out, h_out, w_out = out_data_shape
k_size = one_op.attr("ksize")
params = 0
flops = h_out * w_out * c_out * (k_size[0] * k_size[1])
elif one_op.type == 'mul':
k_arg_shape = block_vars[one_op.input("Y")[0]].shape
in_data_shape = block_vars[one_op.input("X")[0]].shape
out_data_shape = block_vars[one_op.output("Out")[0]].shape
# TODO: fc has mul ops
# add attr to mul op, tell us whether it belongs to 'fc'
# this's not the best way
if 'fc' not in one_op.output("Out")[0]:
return None
k_in, k_out = k_arg_shape
# bias in sum op
params = k_in * k_out + 1
flops = k_in * k_out
elif one_op.type in ['sigmoid', 'tanh', 'relu', 'leaky_relu', 'prelu']:
in_data_shape = block_vars[one_op.input("X")[0]].shape
out_data_shape = block_vars[one_op.output("Out")[0]].shape
params = 0
if one_op.type == 'prelu':
params = 1
flops = 1
for one_dim in in_data_shape:
flops *= one_dim
elif one_op.type == 'batch_norm':
in_data_shape = block_vars[one_op.input("X")[0]].shape
out_data_shape = block_vars[one_op.output("Y")[0]].shape
_, c_in, h_out, w_out = in_data_shape
# gamma, beta
params = c_in * 2
# compute mean and std
flops = h_out * w_out * c_in * 2
else:
return None
return in_data_shape, out_data_shape, params, flops
def _format_summary(collected_ops_list):
'''
Format summary report.
Args:
collected_ops_list: the collected operator with summary
Returns:
summary_table: summary report format
total: sum param and flops
'''
summary_table = PrettyTable(
["No.", "TYPE", "INPUT", "OUTPUT", "PARAMs", "FLOPs"])
summary_table.align = 'r'
total = {}
total_params = []
total_flops = []
for i, one_op in enumerate(collected_ops_list):
# notice the order
table_row = [
i,
one_op['type'],
one_op['input_shape'],
one_op['out_shape'],
int(one_op['PARAMs']),
int(one_op['FLOPs']),
]
summary_table.add_row(table_row)
total_params.append(int(one_op['PARAMs']))
total_flops.append(int(one_op['FLOPs']))
total['params'] = total_params
total['flops'] = total_flops
return summary_table, total
def _print_summary(summary_table, total):
'''
Print all the summary on terminal.
Args:
summary_table: summary report format
total: sum param and flops
'''
parmas = total['params']
flops = total['flops']
print(summary_table)
print('Total PARAMs: {}({:.4f}M)'.format(
sum(parmas), sum(parmas) / (10**6)))
print('Total FLOPs: {}({:.2f}G)'.format(sum(flops), sum(flops) / 10**9))
print(
"Notice: \n now supported ops include [Conv, DepthwiseConv, FC(mul), BatchNorm, Pool, Activation(sigmoid, tanh, relu, leaky_relu, prelu)]"
)
python/paddle/fluid/contrib/slim/graph/graph_wrapper.py
浏览文件 @ 33473890
......@@ -204,6 +204,10 @@ class GraphWrapper(object):
"""
super(GraphWrapper, self).__init__()
self.program = Program() if program is None else program
self.persistables = {}
for var in self.program.list_vars():
if var.persistable:
self.persistables[var.name] = var
self.compiled_graph = None
self.in_nodes = OrderedDict(in_nodes)
self.out_nodes = OrderedDict(out_nodes)
......@@ -467,7 +471,12 @@ class GraphWrapper(object):
path(str): The path to save the persistables.
exe(framework.Executor): The executor used to save the persistables.
"""
io.save_persistables(exe.exe, path, main_program=self.program)
# update persistables from program
for var in self.program.list_vars():
if var.persistable and var.name not in self.persistables:
self.persistables[var.name] = var
io.save_vars(exe.exe, path, vars=self.persistables.values())
def load_persistables(self, path, exe):
"""
......@@ -481,7 +490,7 @@ class GraphWrapper(object):
return os.path.exists(os.path.join(path, var.name))
io.load_vars(
exe.exe, path, main_program=self.program, predicate=if_exist)
exe.exe, path, vars=self.persistables.values(), predicate=if_exist)
def update_param_shape(self, scope):
"""
......
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ 33473890
......@@ -26,6 +26,17 @@ __all__ = [
]
def _init_var_node(var_node, value, scope, place):
assert isinstance(value,
np.ndarray), 'The type of value should be numpy array.'
assert scope is not None, \
'The scope cannot be set None.'
assert place is not None, \
'The place cannot be set None.'
tensor = scope.var(var_node.name()).get_tensor()
tensor.set(value, place)
class QuantizationTransformPass(object):
def __init__(self,
scope=None,
......@@ -88,14 +99,14 @@ class QuantizationTransformPass(object):
assert activation_quantize_type != 'channel_wise_abs_max', "The activation quantization type does not support 'channel_wise_abs_max'."
if activation_quantize_type not in quant_type:
raise ValueError(
"Unknown activation_quantize_type : '%s'. It can only be ",
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
str(activation_quantize_type))
"Unknown activation_quantize_type : '%s'. It can only be "
"'abs_max' or 'range_abs_max' or 'moving_average_abs_max'." %
(str(activation_quantize_type)))
if weight_quantize_type not in quant_type:
raise ValueError(
"Unknown weight_quantize_type: '%s'. It can only be ",
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'.",
str(weight_quantize_type))
"Unknown weight_quantize_type: '%s'. It can only be "
"'abs_max' or 'channel_wise_abs_max' or 'range_abs_max' or 'moving_average_abs_max'."
% (str(weight_quantize_type)))
self._activation_quantize_type = activation_quantize_type
self._weight_quantize_type = weight_quantize_type
......@@ -121,8 +132,6 @@ class QuantizationTransformPass(object):
"""
assert isinstance(graph,
IrGraph), 'graph must be the instance of IrGraph.'
#sequential_execution = core.get_pass('sequential_execution_pass')
#sequential_execution.apply(graph.graph)
self._is_test = graph.is_test()
# marked the variable which has been dequantized.
dequantized_vars = collections.OrderedDict()
......@@ -203,9 +212,12 @@ class QuantizationTransformPass(object):
var_type=core.VarDesc.VarType.LOD_TENSOR,
shape=[1],
var_dtype=core.VarDesc.VarType.INT64)
self._init_var_node(
global_step_in, np.zeros(
[1], dtype='int64'))
_init_var_node(
global_step_in,
np.zeros(
[1], dtype='int64'),
self._scope,
self._place)
global_step_out = graph.create_var_node_from_desc(
global_step_in.var())
# The attribute of `op_role` is needed by ParallelExecutor.
......@@ -284,7 +296,12 @@ class QuantizationTransformPass(object):
var_dtype=var_node.dtype())
data_type = 'float64' if var_node.dtype(
) == core.VarDesc.VarType.FP64 else 'float32'
self._init_var_node(scale_in_node, np.array([0.001], dtype=data_type))
_init_var_node(
scale_in_node,
np.array(
[0.001], dtype=data_type),
self._scope,
self._place)
scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
inputs = {'X': var_node, 'InScale': scale_in_node}
......@@ -299,9 +316,13 @@ class QuantizationTransformPass(object):
var_dtype=var_node.dtype())
data_type = 'float64' if var_node.dtype(
) == core.VarDesc.VarType.FP64 else 'float32'
self._init_var_node(
scales_node, np.zeros(
[self._window_size], dtype=data_type))
_init_var_node(
scales_node,
np.zeros(
[self._window_size], dtype=data_type),
self._scope,
self._place)
inputs['Iter'] = self._global_step
outputs['OutScales'] = scales_node
attrs = {
......@@ -343,7 +364,12 @@ class QuantizationTransformPass(object):
var_dtype=var_node.dtype())
data_type = 'float64' if var_node.dtype(
) == core.VarDesc.VarType.FP64 else 'float32'
self._init_var_node(scale_in_node, np.array([0.001], dtype=data_type))
_init_var_node(
scale_in_node,
np.array(
[0.001], dtype=data_type),
self._scope,
self._place)
scale_out_node = graph.create_var_node_from_desc(scale_in_node.var())
ins = {'X': var_node, 'InScale': scale_in_node}
......@@ -356,13 +382,23 @@ class QuantizationTransformPass(object):
shape=[1])
data_type = 'float64' if var_node.dtype(
) == core.VarDesc.VarType.FP64 else 'float32'
self._init_var_node(scale_in_node, np.ones([1], dtype=data_type))
_init_var_node(
scale_in_node,
np.ones(
[1], dtype=data_type),
self._scope,
self._place)
accum_in_node = graph.create_persistable_node(
name=unique_name.generate('accum'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
var_dtype=var_node.dtype(),
shape=[1])
self._init_var_node(accum_in_node, np.ones([1], dtype=data_type))
_init_var_node(
accum_in_node,
np.ones(
[1], dtype=data_type),
self._scope,
self._place)
state_out_node = graph.create_var_node_from_desc(state_in_node.var(
))
accum_out_node = graph.create_var_node_from_desc(accum_in_node.var(
......@@ -482,16 +518,6 @@ class QuantizationTransformPass(object):
graph.link_to(dequant_op_node, dequant_var_node)
return dequant_var_node
def _init_var_node(self, var_node, value):
assert isinstance(
value, np.ndarray), 'The type of value should be numpy array.'
assert self._scope is not None, \
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert self._place is not None, \
'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
tensor = self._scope.var(var_node.name()).get_tensor()
tensor.set(value, self._place)
def _quantized_var_name(self, var_name):
"""
Return quantized variable name for the input `var_name`.
......@@ -594,8 +620,8 @@ class QuantizationFreezePass(object):
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
else:
scale_v = self._to_node(op_node.outputs,
op_node.output('OutScale')[0])
scale_v = graph._find_node_by_name(
op_node.outputs, op_node.output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
ops = graph.all_op_nodes()
......@@ -627,8 +653,8 @@ class QuantizationFreezePass(object):
return graph
def _remove_fake_quant_and_dequant_op(self, graph, op_node):
k = self._to_node(op_node.outputs, op_node.output('Out')[0])
v = self._to_node(op_node.inputs, op_node.input('X')[0])
k = graph._find_node_by_name(op_node.outputs, op_node.output('Out')[0])
v = graph._find_node_by_name(op_node.inputs, op_node.input('X')[0])
if v.node not in self._op_input_rename_map:
self._op_input_rename_map[k.node] = v
else:
......@@ -663,8 +689,8 @@ class QuantizationFreezePass(object):
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = self._to_node(op_node.outputs,
op_node.output_arg_names()[0])
output_var_node = graph._find_node_by_name(
op_node.outputs, op_node.output_arg_names()[0])
weight_scale_node = graph.create_persistable_node(
name=unique_name.generate('channel_scale'),
var_type=core.VarDesc.VarType.LOD_TENSOR,
......@@ -672,7 +698,9 @@ class QuantizationFreezePass(object):
var_dtype=output_var_node.dtype())
data_type = 'float64' if output_var_node.dtype(
) == core.VarDesc.VarType.FP64 else 'float32'
self._init_var_node(weight_scale_node, channel_scale.astype(data_type))
_init_var_node(weight_scale_node,
channel_scale.astype(data_type), self._scope,
self._place)
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.type(),
......@@ -724,8 +752,8 @@ class QuantizationFreezePass(object):
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = self._to_node(op_node.outputs,
op_node.output_arg_names()[0])
output_var_node = graph._find_node_by_name(
op_node.outputs, op_node.output_arg_names()[0])
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.type(),
......@@ -746,24 +774,6 @@ class QuantizationFreezePass(object):
self._op_output_rename_map[output_var_node.node] = dequant_var_node
return dequant_var_node
def _init_var_node(self, var_node, value):
assert isinstance(
value, np.ndarray), 'The type of value should be numpy array.'
assert self._scope is not None, \
'The scope cannot be set None when activation_quantize_type equals to range_abs_max.'
assert self._place is not None, \
'The place cannot be set None when activation_quantize_type equals to range_abs_max.'
tensor = self._scope.var(var_node.name()).get_tensor()
tensor.set(value, self._place)
def _to_node(self, nodes, node_name):
target_node = None
for n in nodes:
if n.name() == node_name:
target_node = n
assert target_node is not None, "Cannot find the target node in the giving set."
return target_node
def _load_var(self, name):
return np.array(self._scope.find_var(name).get_tensor())
......
python/paddle/fluid/framework.py
浏览文件 @ 33473890
此差异已折叠。
python/paddle/fluid/initializer.py
浏览文件 @ 33473890
此差异已折叠。
python/paddle/fluid/layer_helper.py
浏览文件 @ 33473890
此差异已折叠。
python/paddle/fluid/layers/nn.py
浏览文件 @ 33473890
此差异已折叠。
python/paddle/fluid/optimizer.py
浏览文件 @ 33473890
此差异已折叠。
python/setup.py.in
浏览文件 @ 33473890
此差异已折叠。
tools/print_signatures.py
浏览文件 @ 33473890
此差异已折叠。
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或