提交 23433def 编写于 作者: Y Yancey1989

Merge branch 'develop' of github.com:PaddlePaddle/Paddle into overlap_memcpy_with_dist

......@@ -87,7 +87,7 @@ cc_library(executor SRCS executor.cc DEPS op_registry device_context scope
framework_proto glog lod_rank_table feed_fetch_method)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS multi_devices_graph_builder threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
cc_library(parallel_executor SRCS parallel_executor.cc DEPS graph_builder_factory threaded_ssa_graph_executor scope_buffered_ssa_graph_executor)
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)
......
......@@ -7,6 +7,7 @@ cc_library(rpc_op_handle SRCS rpc_op_handle.cc DEPS framework_proto scope place
cc_library(ssa_graph SRCS ssa_graph.cc DEPS var_handle op_handle_base)
cc_library(ssa_graph_builder SRCS ssa_graph_builder.cc DEPS ssa_graph)
cc_library(ssa_graph_printer SRCS ssa_graph_printer.cc DEPS ssa_graph_builder)
cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
......@@ -28,6 +29,9 @@ cc_library(gather_op_handle SRCS gather_op_handle.cc DEPS op_handle_base scope d
cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
scale_loss_grad_op_handle rpc_op_handle ${multi_devices_graph_builder_deps} reduce_op_handle broadcast_op_handle)
cc_library(graph_builder_factory SRCS graph_builder_factory.cc DEPS multi_devices_graph_builder ssa_graph_printer)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ssa_graph framework_proto)
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
simple_threadpool device_context)
......
......@@ -59,8 +59,8 @@ struct BroadcastOpHandle : public OpHandleBase {
void RunImpl() override;
private:
const std::vector<Scope *> &local_scopes_;
const std::vector<platform::Place> &places_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
#ifdef PADDLE_WITH_CUDA
const platform::NCCLContextMap *nccl_ctxs_;
#endif
......
......@@ -14,6 +14,8 @@
#pragma once
#include <string>
namespace paddle {
namespace framework {
namespace details {
......@@ -29,6 +31,8 @@ struct BuildStrategy {
ReduceStrategy reduce_{ReduceStrategy::kAllReduce};
GradientScaleStrategy gradient_scale_{GradientScaleStrategy::kCoeffNumDevice};
std::string debug_graphviz_path_{""};
};
} // namespace details
......
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/graph_builder_factory.h"
#include <fstream>
#include "paddle/fluid/framework/details/multi_devices_graph_builder.h"
#include "paddle/fluid/framework/details/ssa_graph_printer.h"
namespace paddle {
namespace framework {
namespace details {
std::unique_ptr<SSAGraphBuilder> SSAGraphBuilderFactory::Create() {
std::unique_ptr<SSAGraphBuilder> res(
#ifdef PADDLE_WITH_CUDA
new MultiDevSSAGraphBuilder(places_, loss_var_name_, param_names_,
local_scopes_, nccl_ctxs_, strategy_)
#else
new MultiDevSSAGraphBuilder(places_, loss_var_name_, param_names_,
local_scopes_, strategy_)
#endif
); // NOLINT
if (!strategy_.debug_graphviz_path_.empty()) {
std::unique_ptr<std::ostream> fout(
new std::ofstream(strategy_.debug_graphviz_path_));
PADDLE_ENFORCE(fout->good());
std::unique_ptr<GraphvizSSAGraphPrinter> graphviz_printer(
new GraphvizSSAGraphPrinter());
res.reset(new SSAGraghBuilderWithPrinter(
std::move(fout), std::move(graphviz_printer), std::move(res)));
}
return res;
}
} // namespace details
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
#include "paddle/fluid/platform/place.h"
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/nccl_helper.h"
#endif
namespace paddle {
namespace framework {
class Scope;
namespace details {
class SSAGraphBuilderFactory {
public:
SSAGraphBuilderFactory(const std::vector<platform::Place>& places,
const std::string& loss_var_name,
const std::unordered_set<std::string>& param_names,
const std::vector<Scope*>& local_scopes,
const BuildStrategy& strategy)
: places_(places),
loss_var_name_(loss_var_name),
param_names_(param_names),
local_scopes_(local_scopes),
strategy_(strategy) {}
#ifdef PADDLE_WITH_CUDA
void SetNCCLContextMap(platform::NCCLContextMap* nccl_ctxs) {
nccl_ctxs_ = nccl_ctxs;
}
#endif
std::unique_ptr<SSAGraphBuilder> Create();
private:
std::vector<platform::Place> places_;
std::string loss_var_name_;
std::unordered_set<std::string> param_names_;
std::vector<Scope*> local_scopes_;
BuildStrategy strategy_;
#ifdef PADDLE_WITH_CUDA
platform::NCCLContextMap* nccl_ctxs_;
#endif
};
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -30,10 +30,6 @@
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#endif
DEFINE_string(ssa_graph_path, "/tmp/ssa_graph.dot",
"the ssa graph path only print with GLOG_v=10,"
"default /tmp/graph.dot");
namespace paddle {
namespace framework {
namespace details {
......@@ -149,6 +145,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(
std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
const ProgramDesc &program) const {
VLOG(3) << "Building ....";
std::unordered_map<std::string, VarDesc *> all_vars;
for (auto *var : program.Block(0).AllVars()) {
all_vars[var->Name()] = var;
......@@ -315,11 +312,6 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
*/
AddOutputToLeafOps(&result);
if (VLOG_IS_ON(10)) {
std::ofstream fout(FLAGS_ssa_graph_path);
PrintGraphviz(*graph, fout);
}
return std::unique_ptr<SSAGraph>(graph);
}
......
......@@ -48,7 +48,7 @@ class MultiDevSSAGraphBuilder : public SSAGraphBuilder {
std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const override;
int GetRemoteVarDevice(const std::string &var_name) const {
int GetRemoteVarDeviceId(const std::string &var_name) const override {
auto got = remote_vars_devices_.find(var_name);
if (got != remote_vars_devices_.end()) {
return got->second;
......
......@@ -41,8 +41,8 @@ struct NCCLAllReduceOpHandle : public OpHandleBase {
void RunImpl() override;
private:
const std::vector<Scope *> &local_scopes_;
const std::vector<platform::Place> &places_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
const platform::NCCLContextMap &nccl_ctxs_;
};
......
......@@ -32,8 +32,8 @@ namespace framework {
namespace details {
struct ReduceOpHandle : public OpHandleBase {
const std::vector<Scope *> &local_scopes_;
const std::vector<platform::Place> &places_;
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
#ifdef PADDLE_WITH_CUDA
const platform::NCCLContextMap *nccl_ctxs_;
......
......@@ -73,64 +73,6 @@ void SSAGraphBuilder::CreateOpOutput(SSAGraph *graph, OpHandleBase *op_handle,
op_handle->AddOutput(var);
}
template <typename Callback>
void IterAllVar(const SSAGraph &graph, Callback callback) {
for (auto &each : graph.vars_) {
for (auto &pair1 : each) {
for (auto &pair2 : pair1.second) {
callback(*pair2);
}
}
}
for (auto &var : graph.dep_vars_) {
callback(*var);
}
}
void SSAGraphBuilder::PrintGraphviz(const SSAGraph &graph, std::ostream &sout) {
size_t var_id = 0;
std::unordered_map<const VarHandleBase *, size_t> vars;
sout << "digraph G {\n";
IterAllVar(graph, [&](const VarHandleBase &var) {
auto *var_ptr = &var;
auto *var_handle_ptr = dynamic_cast<const VarHandle *>(var_ptr);
auto *dummy_ptr = dynamic_cast<const DummyVarHandle *>(var_ptr);
size_t cur_var_id = var_id++;
vars[var_ptr] = cur_var_id;
if (var_handle_ptr) {
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name_
<< "\\n"
<< var_handle_ptr->place_ << "\\n"
<< var_handle_ptr->version_ << "\"]" << std::endl;
} else if (dummy_ptr) {
sout << "var_" << cur_var_id << " [label=\"dummy\"]" << std::endl;
}
});
size_t op_id = 0;
for (auto &op : graph.ops_) {
std::string op_name = "op_" + std::to_string(op_id++);
sout << op_name << " [label=\"" << op->Name() << "\", shape=rect]"
<< std::endl;
for (auto in : op->Inputs()) {
std::string var_name = "var_" + std::to_string(vars[in]);
sout << var_name << " -> " << op_name << std::endl;
}
for (auto out : op->Outputs()) {
std::string var_name = "var_" + std::to_string(vars[out]);
sout << op_name << " -> " << var_name << std::endl;
}
}
sout << "}\n";
}
void SSAGraphBuilder::AddOutputToLeafOps(SSAGraph *graph) {
for (auto &op : graph->ops_) {
if (!op->Outputs().empty()) {
......
......@@ -30,6 +30,7 @@ class SSAGraphBuilder {
SSAGraphBuilder() {}
virtual ~SSAGraphBuilder() {}
virtual std::unique_ptr<SSAGraph> Build(const ProgramDesc &program) const = 0;
virtual int GetRemoteVarDeviceId(const std::string &var_name) const = 0;
DISABLE_COPY_AND_ASSIGN(SSAGraphBuilder);
......@@ -55,8 +56,6 @@ class SSAGraphBuilder {
const platform::Place &place, size_t place_offset);
static void AddOutputToLeafOps(SSAGraph *graph);
static void PrintGraphviz(const SSAGraph &graph, std::ostream &sout);
};
} // namespace details
} // namespace framework
......
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/framework/details/ssa_graph_printer.h"
#include <string>
#include "paddle/fluid/framework/details/ssa_graph.h"
namespace paddle {
namespace framework {
namespace details {
template <typename Callback>
static inline void IterAllVar(const SSAGraph &graph, Callback callback) {
for (auto &each : graph.vars_) {
for (auto &pair1 : each) {
for (auto &pair2 : pair1.second) {
callback(*pair2);
}
}
}
for (auto &var : graph.dep_vars_) {
callback(*var);
}
}
void GraphvizSSAGraphPrinter::Print(const SSAGraph &graph,
std::ostream &sout) const {
size_t var_id = 0;
std::unordered_map<const VarHandleBase *, size_t> vars;
sout << "digraph G {\n";
IterAllVar(graph, [&](const VarHandleBase &var) {
auto *var_ptr = &var;
auto *var_handle_ptr = dynamic_cast<const VarHandle *>(var_ptr);
auto *dummy_ptr = dynamic_cast<const DummyVarHandle *>(var_ptr);
size_t cur_var_id = var_id++;
vars[var_ptr] = cur_var_id;
if (var_handle_ptr) {
sout << "var_" << cur_var_id << " [label=\"" << var_handle_ptr->name_
<< "\\n"
<< var_handle_ptr->place_ << "\\n"
<< var_handle_ptr->version_ << "\"]" << std::endl;
} else if (dummy_ptr) {
sout << "var_" << cur_var_id << " [label=\"dummy\"]" << std::endl;
}
});
size_t op_id = 0;
for (auto &op : graph.ops_) {
std::string op_name = "op_" + std::to_string(op_id++);
sout << op_name << " [label=\"" << op->Name() << "\", shape=rect]"
<< std::endl;
for (auto in : op->Inputs()) {
std::string var_name = "var_" + std::to_string(vars[in]);
sout << var_name << " -> " << op_name << std::endl;
}
for (auto out : op->Outputs()) {
std::string var_name = "var_" + std::to_string(vars[out]);
sout << op_name << " -> " << var_name << std::endl;
}
}
sout << "}\n";
}
} // namespace details
} // namespace framework
} // namespace paddle
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <iosfwd>
#include "paddle/fluid/framework/details/ssa_graph_builder.h"
namespace paddle {
namespace framework {
namespace details {
class SSAGraph;
class SSAGraphPrinter {
public:
virtual ~SSAGraphPrinter() {}
virtual void Print(const SSAGraph& graph, std::ostream& sout) const = 0;
};
class GraphvizSSAGraphPrinter : public SSAGraphPrinter {
public:
void Print(const SSAGraph& graph, std::ostream& sout) const override;
};
class SSAGraghBuilderWithPrinter : public SSAGraphBuilder {
public:
SSAGraghBuilderWithPrinter(std::ostream& sout,
std::unique_ptr<SSAGraphPrinter>&& printer,
std::unique_ptr<SSAGraphBuilder>&& builder)
: printer_(std::move(printer)),
builder_(std::move(builder)),
stream_ref_(sout) {}
SSAGraghBuilderWithPrinter(std::unique_ptr<std::ostream>&& sout,
std::unique_ptr<SSAGraphPrinter>&& printer,
std::unique_ptr<SSAGraphBuilder>&& builder)
: printer_(std::move(printer)),
builder_(std::move(builder)),
stream_ptr_(std::move(sout)),
stream_ref_(*stream_ptr_) {}
std::unique_ptr<SSAGraph> Build(const ProgramDesc& program) const override {
auto graph = builder_->Build(program);
printer_->Print(*graph, stream_ref_);
return graph;
}
private:
std::unique_ptr<SSAGraphPrinter> printer_;
std::unique_ptr<SSAGraphBuilder> builder_;
std::unique_ptr<std::ostream> stream_ptr_;
std::ostream& stream_ref_;
};
} // namespace details
} // namespace framework
} // namespace paddle
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "paddle/fluid/platform/nccl_helper.h"
#endif
#include "paddle/fluid/framework/details/graph_builder_factory.h"
#include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
#include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -101,23 +102,23 @@ ParallelExecutor::ParallelExecutor(
var_infos.back().persistable_ = var->Persistable();
}
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
#ifdef PADDLE_WITH_CUDA
builder_.reset(new details::MultiDevSSAGraphBuilder(
member_->places_, loss_var_name, params, member_->local_scopes_,
member_->nccl_ctxs_.get(), build_strategy));
#else
builder_.reset(new details::MultiDevSSAGraphBuilder(
// Step 3. Convert main_program to SSA form and dependency graph. Also, insert
// ncclOp
details::SSAGraphBuilderFactory builder_factory(
member_->places_, loss_var_name, params, member_->local_scopes_,
build_strategy));
build_strategy);
#ifdef PADDLE_WITH_CUDA
builder_factory.SetNCCLContextMap(member_->nccl_ctxs_.get());
#endif
auto graph = builder_->Build(main_program);
builder_.reset(builder_factory.Create().get());
if (builder_.get() == nullptr) {
VLOG(3) << "builder is null.";
}
member_->executor_.reset(new details::ThreadedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, places, std::move(graph)));
exec_strategy, member_->local_scopes_, places,
builder_->Build(main_program)));
member_->executor_.reset(new details::ScopeBufferedSSAGraphExecutor(
exec_strategy, member_->local_scopes_, std::move(var_infos),
......@@ -155,8 +156,8 @@ void ParallelExecutor::BCastParamsToGPUs(
auto &nccl_ctx = member_->nccl_ctxs_->at(place);
if (builder_.get() != nullptr &&
builder_->GetRemoteVarDevice(var) != -1) {
int place_id = builder_->GetRemoteVarDevice(var);
builder_->GetRemoteVarDeviceId(var) != -1) {
int place_id = builder_->GetRemoteVarDeviceId(var);
platform::dynload::ncclBcast(buffer, numel, data_type, place_id,
nccl_ctx.comm_, nccl_ctx.stream());
} else {
......
......@@ -70,7 +70,7 @@ class ParallelExecutor {
private:
ParallelExecutorPrivate *member_;
std::unique_ptr<details::MultiDevSSAGraphBuilder> builder_;
std::unique_ptr<details::SSAGraphBuilder> builder_;
};
} // namespace framework
......
......@@ -15,5 +15,102 @@ limitations under the License. */
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace framework {}
namespace framework {
extern size_t SizeOfType(std::type_index type);
void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_LE(
numel() * SizeOfType(type()), memory_size(),
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
}
size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
void* Tensor::mutable_data(platform::Place place, std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
PADDLE_ENFORCE_GE(numel(), 0,
"When calling this method, the Tensor's numel must be "
"equal or larger than zero. "
"Please check Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
boost::get<platform::CPUPlace>(place), size, type));
} else if (platform::is_gpu_place(place) ||
platform::is_cuda_pinned_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(
"CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode.");
}
#else
if (platform::is_gpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
boost::get<platform::CUDAPlace>(place), size, type));
} else if (platform::is_cuda_pinned_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPinnedPlace>(
boost::get<platform::CUDAPinnedPlace>(place), size, type));
}
}
#endif
offset_ = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
void* Tensor::mutable_data(platform::Place place) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing.");
return mutable_data(place, holder_->type());
}
Tensor& Tensor::ShareDataWith(const Tensor& src) {
src.check_memory_size();
*this = src;
return *this;
}
Tensor Tensor::Slice(int begin_idx, int end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx, 0,
"The start row index must be greater than 0.");
PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is out of bound.");
PADDLE_ENFORCE_LT(
begin_idx, end_idx,
"The start row index must be lesser than the end row index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
}
Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
return *this;
}
const DDim& Tensor::dims() const { return dims_; }
int64_t Tensor::numel() const { return product(dims_); }
} // namespace framework
} // namespace paddle
......@@ -54,26 +54,24 @@ class Tensor {
/*! Return a pointer to mutable memory block. */
template <typename T>
inline T* data();
T* data();
/*! Return a pointer to constant memory block. */
template <typename T>
inline const T* data() const;
const T* data() const;
inline bool IsInitialized() const;
inline void switch_place(platform::Place new_place);
bool IsInitialized() const;
/**
* @brief Return a pointer to mutable memory block.
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place);
T* mutable_data(platform::Place place);
inline void* mutable_data(platform::Place place, std::type_index type);
void* mutable_data(platform::Place place, std::type_index type);
inline void* mutable_data(platform::Place place);
void* mutable_data(platform::Place place);
/**
* @brief Return a pointer to mutable memory block.
......@@ -84,19 +82,19 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place);
T* mutable_data(DDim dims, platform::Place place);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
const DDim& dims() const;
/*! Return the numel of the memory block. */
inline int64_t numel() const;
int64_t numel() const;
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
Tensor& Resize(const DDim& dims);
/*! The internal of two tensors share the same memory block. */
inline Tensor& ShareDataWith(const Tensor& src);
Tensor& ShareDataWith(const Tensor& src);
/**
* @brief Return a sub-tensor of the given tensor.
......@@ -106,7 +104,7 @@ class Tensor {
* @param[in] end_idx The index of the end row(exclusive) to slice.
* The index number begins from 0.
*/
inline Tensor Slice(int begin_idx, int end_idx) const;
Tensor Slice(int begin_idx, int end_idx) const;
platform::Place place() const {
PADDLE_ENFORCE_NOT_NULL(
......@@ -123,11 +121,11 @@ class Tensor {
// memory size returns the holding memory size in byte.
size_t memory_size() const;
inline void check_memory_size() const;
void check_memory_size() const;
inline DataLayout layout() const { return layout_; }
DataLayout layout() const { return layout_; }
inline void set_layout(const DataLayout layout) { layout_ = layout; }
void set_layout(const DataLayout layout) { layout_ = layout; }
private:
/**
......@@ -210,15 +208,6 @@ class Tensor {
size_t offset_;
};
inline void Tensor::switch_place(platform::Place new_place) {
if (holder_->place() == new_place) {
return;
}
// TODO(tonyyang-svail): do memcpy here.
PADDLE_THROW("Not Implemented");
}
} // namespace framework
} // namespace paddle
......
......@@ -20,21 +20,6 @@ limitations under the License. */
namespace paddle {
namespace framework {
extern size_t SizeOfType(std::type_index type);
inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_LE(
numel() * SizeOfType(type()), memory_size(),
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
}
inline size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
}
template <typename T>
inline const T* Tensor::data() const {
check_memory_size();
......@@ -73,88 +58,6 @@ inline T* Tensor::mutable_data(platform::Place place) {
return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
}
inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
PADDLE_ENFORCE_GE(numel(), 0,
"When calling this method, the Tensor's numel must be "
"equal or larger than zero. "
"Please check Tensor::Resize has been called first.");
int64_t size = numel() * SizeOfType(type);
/* some versions of boost::variant don't have operator!= */
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
boost::get<platform::CPUPlace>(place), size, type));
} else if (platform::is_gpu_place(place) ||
platform::is_cuda_pinned_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(
"CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode.");
}
#else
if (platform::is_gpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
boost::get<platform::CUDAPlace>(place), size, type));
} else if (platform::is_cuda_pinned_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPinnedPlace>(
boost::get<platform::CUDAPinnedPlace>(place), size, type));
}
}
#endif
offset_ = 0;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
inline void* Tensor::mutable_data(platform::Place place) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing.");
return mutable_data(place, holder_->type());
}
inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
src.check_memory_size();
*this = src;
return *this;
}
inline Tensor Tensor::Slice(int begin_idx, int end_idx) const {
check_memory_size();
PADDLE_ENFORCE_GE(begin_idx, 0,
"The start row index must be greater than 0.");
PADDLE_ENFORCE_LE(end_idx, dims_[0], "The end row index is out of bound.");
PADDLE_ENFORCE_LT(
begin_idx, end_idx,
"The start row index must be lesser than the end row index.");
if (dims_[0] == 1) {
return *this;
} else {
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
dst.set_layout(layout_);
DDim dst_dims = dims_;
dst_dims[0] = end_idx - begin_idx;
dst.Resize(dst_dims);
dst.offset_ = offset_ + begin_idx * base * SizeOfType(type());
return dst;
}
}
inline Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
return *this;
}
inline const DDim& Tensor::dims() const { return dims_; }
inline int64_t Tensor::numel() const { return product(dims_); }
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
......
......@@ -553,6 +553,12 @@ All parameter, weight, gradient are variables in Paddle.
[](BuildStrategy &self,
BuildStrategy::GradientScaleStrategy strategy) {
self.gradient_scale_ = strategy;
})
.def_property(
"debug_graphviz_path",
[](const BuildStrategy &self) { return self.debug_graphviz_path_; },
[](BuildStrategy &self, const std::string &path) {
self.debug_graphviz_path_ = path;
});
pe.def(py::init<const std::vector<platform::Place> &,
......
......@@ -1182,12 +1182,12 @@ def conv2d(input,
- Input:
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
- Output:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
......
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