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提交 74a8e6b0 编写于 作者: S sneaxiy
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merge develop 

fix conflict
test=develop
上级 ae6f46a1 b37fb7a6
隐藏空白更改
内联 并排
Showing with 1026 addition and 1239 deletion
CMakeLists.txt
浏览文件 @ 74a8e6b0
......@@ -208,10 +208,10 @@ include(external/xxhash) # download xxhash
include(external/dlpack)
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
if (NOT WIN32)
# there is no official support of warpctc, nccl, cupti in windows
include(external/warpctc) # download, build, install warpctc
# there is no official support of nccl, cupti in windows
include(cupti)
include(external/gzstream)
endif (NOT WIN32)
......
cmake/external/warpctc.cmake
浏览文件 @ 74a8e6b0
......@@ -26,25 +26,33 @@ SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
# Used in unit test test_WarpCTCLayer
SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib"
CACHE PATH "Warp-ctc Library Directory" FORCE)
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" )
IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR WIN32)
SET(USE_OMP OFF)
ELSE()
SET(USE_OMP ON)
ENDIF()
IF(WIN32)
SET(WARPCTC_REPOSITORY "https://github.com/wopeizl/warp-ctc.git")
ELSE()
SET(WARPCTC_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git")
ENDIF()
ExternalProject_Add(
extern_warpctc
${EXTERNAL_PROJECT_LOG_ARGS}
GIT_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git"
GIT_REPOSITORY ${WARPCTC_REPOSITORY}
PREFIX ${WARPCTC_SOURCES_DIR}
UPDATE_COMMAND ""
CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
-DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
-DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
-DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
-DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
-DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
-DWITH_GPU=${WITH_GPU}
-DWITH_OMP=${USE_OMP}
......@@ -59,6 +67,18 @@ ExternalProject_Add(
-DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
-DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
)
IF(WIN32)
IF(NOT EXISTS "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}")
add_custom_command(TARGET extern_warpctc POST_BUILD
COMMAND cmake -E copy ${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} ${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}
)
ENDIF()
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
else(WIN32)
SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
CACHE FILEPATH "Warp-ctc Library" FORCE)
ENDIF(WIN32)
MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.
......
cmake/operators.cmake
浏览文件 @ 74a8e6b0
......@@ -84,7 +84,7 @@ function(op_library TARGET)
endif()
if (WIN32)
# remove windows unsupported op, because windows has no nccl, no warpctc such ops.
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
return()
endif()
......
paddle/fluid/API.spec
浏览文件 @ 74a8e6b0
......@@ -376,7 +376,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ 74a8e6b0
......@@ -16,100 +16,25 @@ limitations under the License. */
#include <functional>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/framework/ngraph_bridge.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/operators/ngraph/ngraph_ops.h"
#include "paddle/fluid/platform/enforce.h"
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace framework {
static std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<OperatorBase>& op, const std::string name,
const VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = var_map.at(name);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
"op %s name %s expects one associated var", op->Type(),
name);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
return nullptr;
}
}
static std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<OperatorBase>& op, const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, name, op->Inputs(), ngb_node_map);
}
static std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string name,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, name, op->Outputs(), ngb_node_map);
}
static void SetOutputNode(
const std::shared_ptr<OperatorBase>& op, const std::string name,
std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = op->Outputs().at(name);
if (var_names.size() == 1) {
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
PADDLE_THROW("name %s has more than 1 var_names.", name);
}
}
static bool HasOutput(const std::shared_ptr<OperatorBase>& op,
const std::string name) {
auto& outputs = op->Outputs();
if (outputs.find(name) == outputs.end()) return false;
return outputs.at(name).size() > 0;
}
template <typename T>
static void BuildBinaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = GetInputNode(op, "X", ngb_node_map);
auto y = GetInputNode(op, "Y", ngb_node_map);
auto out = std::make_shared<T>(x, y);
SetOutputNode(op, "Out", out, ngb_node_map);
}
template <typename T>
static void BuildUnaryNode(
const std::shared_ptr<OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = GetInputNode(op, "X", ngb_node_map);
auto out = std::make_shared<T>(input);
SetOutputNode(op, "Out", out, ngb_node_map);
}
std::map<std::string,
std::function<void(const std::shared_ptr<OperatorBase>&,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {{"relu", BuildUnaryNode<ngraph::op::Relu>},
{"tanh", BuildUnaryNode<ngraph::op::Tanh>}};
NgraphBridge::NG_NODE_MAP = {
{"mul", paddle::operators::ngraphs::BuildMulNode},
{"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
{"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
{"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>}};
void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
auto& op_type = op->Type();
......
paddle/fluid/framework/ngraph_operator.cc
浏览文件 @ 74a8e6b0
......@@ -278,7 +278,8 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphEngine::backend_ =
ngraph::runtime::Backend::create("CPU");
void NgraphEngine::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
op->RuntimeInferShape(scope_, place_);
RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_);
op->RuntimeInferShape(scope_, place_, ctx);
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
auto* var = scope_.FindVar(var_name);
......
paddle/fluid/framework/operator.cc
浏览文件 @ 74a8e6b0
......@@ -137,6 +137,23 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
}
}
RuntimeContext::RuntimeContext(const VariableNameMap& innames,
const VariableNameMap& outnames,
const Scope& scope) {
for (auto& var_name_item : innames) {
std::vector<Variable*>& input_vars = inputs[var_name_item.first];
for (auto& var_name : var_name_item.second) {
input_vars.push_back(scope.FindVar(var_name));
}
}
for (auto& var_name_item : outnames) {
std::vector<Variable*>& output_vars = outputs[var_name_item.first];
for (auto& var_name : var_name_item.second) {
output_vars.push_back(scope.FindVar(var_name));
}
}
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(4) << place << " " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
......@@ -412,11 +429,48 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
return var != nullptr;
}
const Variable* ExecutionContext::InputVar(const std::string& name) const {
auto it = ctx_.inputs.find(name);
if (it == ctx_.inputs.end()) return nullptr;
PADDLE_ENFORCE_LE(it->second.size(), 1UL,
"Operator %s's input %s should contain only one variable.",
op_.Type(), name);
return it->second.empty() ? nullptr : it->second[0];
}
const Variable* ExecutionContext::LegacyInputVar(
const std::string& name) const {
auto ipt = op_.Input(name);
return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
}
Variable* ExecutionContext::OutputVar(const std::string& name) const {
auto it = ctx_.outputs.find(name);
if (it == ctx_.outputs.end()) return nullptr;
PADDLE_ENFORCE_LE(it->second.size(), 1UL,
"Operator %s's output %s should contain only one variable.",
op_.Type(), name);
return it->second.empty() ? nullptr : it->second[0];
}
Variable* ExecutionContext::LegacyOutputVar(const std::string& name) const {
auto opt = op_.Output(name);
return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
}
template <>
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
return Input<LoDTensor>(name);
}
template <>
const Tensor* ExecutionContext::LegacyInput<Tensor>(
const std::string& name) const {
return LegacyInput<LoDTensor>(name);
}
template <>
const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
const std::string& name) const {
......@@ -441,6 +495,11 @@ Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
return Output<LoDTensor>(name);
}
template <>
Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const {
return LegacyOutput<LoDTensor>(name);
}
template <>
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
const std::string& name) const {
......@@ -477,23 +536,22 @@ bool OpSupportGPU(const std::string& op_type) {
class RuntimeInferShapeContext : public InferShapeContext {
public:
RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
const RuntimeContext& ctx)
: op_(op), scope_(scope), ctx_(ctx) {}
bool HasInput(const std::string& name) const override {
// has only one input
const auto& ins = op_.Inputs();
const auto& ins = ctx_.inputs;
auto it = ins.find(name);
if (it == ins.end()) {
return false;
}
const auto& in = it->second;
if (in.size() == 0 || in[0] == kEmptyVarName) {
return false;
}
if (in.size() == 0) return false;
PADDLE_ENFORCE_EQ(in.size(), 1UL,
"Input %s should not have more than one inputs", name);
return scope_.FindVar(in[0]) != nullptr;
return in[0] != nullptr;
}
bool HasOutput(const std::string& name) const override {
......@@ -678,6 +736,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
private:
const OperatorBase& op_;
const Scope& scope_;
const RuntimeContext& ctx_;
};
static void CheckTensorNANOrInf(const std::string& name,
......@@ -696,15 +755,15 @@ static void CheckTensorNANOrInf(const std::string& name,
}
void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
const platform::Place& place,
const RuntimeContext& ctx) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
this->InferShape(&infer_shape_ctx);
}
void OperatorWithKernel::RunImpl(const Scope& scope,
const platform::Place& place) const {
RuntimeInferShapeContext infer_shape_ctx(*this, scope);
this->InferShape(&infer_shape_ctx);
RuntimeContext ctx(Inputs(), Outputs(), scope);
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto* dev_ctx = pool.Get(place);
......@@ -718,15 +777,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
OpKernelMap& kernels = kernels_iter->second;
// TODO(dzhwinter) : kernel fallback mechanism will be added when all the
// transform functions are ready.
// for (auto& candidate : kKernelPriority) {
// Do selection
// }
auto expected_kernel_key =
this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
auto expected_kernel_key = this->GetExpectedKernelType(
ExecutionContext(*this, scope, *dev_ctx, ctx));
VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
auto kernel_iter = kernels.find(expected_kernel_key);
......@@ -748,7 +800,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
// do data transformScope &transfer_scope;
std::vector<std::string> transfered_inplace_vars;
auto* transfer_scope =
TryTransferData(scope, expected_kernel_key, &transfered_inplace_vars);
PrepareData(scope, expected_kernel_key, &transfered_inplace_vars, &ctx);
// exec scope is the scope that kernel actually executed on.
const Scope& exec_scope =
......@@ -758,7 +810,11 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
dev_ctx = pool.Get(expected_kernel_key.place_);
}
kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
this->InferShape(&infer_shape_ctx);
// TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
// not Scope. Imperative mode only pass inputs and get outputs.
kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx, ctx));
if (!transfered_inplace_vars.empty()) {
// there is inplace variable has been transfered.
......@@ -782,6 +838,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
}
}
}
void OperatorWithKernel::TransferInplaceVarsBack(
const Scope& scope, const std::vector<std::string>& inplace_vars,
const Scope& transfer_scope) const {
......@@ -797,13 +854,19 @@ void OperatorWithKernel::TransferInplaceVarsBack(
}
}
Scope* OperatorWithKernel::TryTransferData(
Scope* OperatorWithKernel::PrepareData(
const Scope& scope, const OpKernelType& expected_kernel_key,
std::vector<std::string>* transfered_inplace_vars) const {
std::vector<std::string>* transfered_inplace_vars,
RuntimeContext* ctx) const {
Scope* new_scope = nullptr;
for (auto& var_name_item : Inputs()) {
for (auto& var_name : var_name_item.second) {
std::vector<Variable*>& input_vars = ctx->inputs[var_name_item.first];
for (size_t i = 0; i < var_name_item.second.size(); ++i) {
auto& var_name = var_name_item.second[i];
auto* var = scope.FindVar(var_name);
input_vars[i] = var;
// Only tensor can be tranfer to another device.
if (var == nullptr || !VarIsTensor(*var)) {
continue;
......@@ -851,6 +914,7 @@ Scope* OperatorWithKernel::TryTransferData(
}
auto* trans_var = new_scope->Var(var_name);
input_vars[i] = trans_var;
Tensor out;
TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);
......
paddle/fluid/framework/operator.h
浏览文件 @ 74a8e6b0
......@@ -70,6 +70,15 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
class OperatorBase;
class ExecutionContext;
class RuntimeContext {
public:
RuntimeContext(const VariableNameMap& innames,
const VariableNameMap& outnames, const Scope& scope);
VariableValueMap inputs;
VariableValueMap outputs;
};
/**
* OperatorBase has the basic elements that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -129,7 +138,8 @@ class OperatorBase {
void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
virtual void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const {}
const platform::Place& place,
const RuntimeContext& ctx) const {}
protected:
std::string type_;
......@@ -156,8 +166,9 @@ class OperatorBase {
class ExecutionContext {
public:
ExecutionContext(const OperatorBase& op, const Scope& scope,
const platform::DeviceContext& device_context)
: op_(op), scope_(scope), device_context_(device_context) {}
const platform::DeviceContext& device_context,
const RuntimeContext& ctx)
: op_(op), scope_(scope), device_context_(device_context), ctx_(ctx) {}
const OperatorBase& op() const { return op_; }
......@@ -180,15 +191,9 @@ class ExecutionContext {
return op_.Outputs(name).size();
}
const Variable* InputVar(const std::string& name) const {
auto ipt = op_.Input(name);
return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
}
const Variable* InputVar(const std::string& name) const;
Variable* OutputVar(const std::string& name) const {
auto opt = op_.Output(name);
return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
}
Variable* OutputVar(const std::string& name) const;
const std::vector<const Variable*> MultiInputVar(
const std::string& name) const {
......@@ -227,6 +232,22 @@ class ExecutionContext {
return var == nullptr ? nullptr : var->GetMutable<T>();
}
template <typename T>
const T* LegacyInput(const std::string& name) const {
auto* var = LegacyInputVar(name);
return var == nullptr ? nullptr : &var->Get<T>();
}
template <typename T>
T* LegacyOutput(const std::string& name) const {
auto var = LegacyOutputVar(name);
return var == nullptr ? nullptr : var->GetMutable<T>();
}
const Variable* LegacyInputVar(const std::string& name) const;
Variable* LegacyOutputVar(const std::string& name) const;
template <typename T>
const std::vector<const T*> MultiInput(const std::string& name) const {
auto names = op_.Inputs(name);
......@@ -286,6 +307,7 @@ class ExecutionContext {
const OperatorBase& op_;
const Scope& scope_;
const platform::DeviceContext& device_context_;
const RuntimeContext& ctx_;
};
inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
......@@ -303,6 +325,10 @@ inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
template <>
const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const;
template <>
const Tensor* ExecutionContext::LegacyInput<Tensor>(
const std::string& name) const;
template <>
const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
const std::string& name) const;
......@@ -310,6 +336,9 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
template <>
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
template <>
Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const;
template <>
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
const std::string& name) const;
......@@ -362,8 +391,8 @@ class OperatorWithKernel : public OperatorBase {
OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
}
void RuntimeInferShape(const Scope& scope,
const platform::Place& place) const override;
void RuntimeInferShape(const Scope& scope, const platform::Place& place,
const RuntimeContext& ctx) const override;
protected:
virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
......@@ -383,9 +412,10 @@ class OperatorWithKernel : public OperatorBase {
*
* * transfered_inplace_vars is a output vector.
*/
Scope* TryTransferData(
const Scope& scope, const OpKernelType& expected_kernel_key,
std::vector<std::string>* transfered_inplace_vars) const;
Scope* PrepareData(const Scope& scope,
const OpKernelType& expected_kernel_key,
std::vector<std::string>* transfered_inplace_vars,
RuntimeContext* ctx) const;
void TransferInplaceVarsBack(const Scope& scope,
const std::vector<std::string>& inplace_vars,
......
paddle/fluid/framework/type_defs.h
浏览文件 @ 74a8e6b0
......@@ -28,8 +28,11 @@ class OperatorBase;
class OpDesc;
class InferShapeContext;
class BlockDesc;
class Variable;
using VariableNameMap = std::map<std::string, std::vector<std::string>>;
// TODO(panyx0718): Replace vector with something like gtl::Vector.
using VariableValueMap = std::map<std::string, std::vector<Variable*>>;
// The order should be as same as framework.proto
using Attribute =
......
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 74a8e6b0
......@@ -64,9 +64,7 @@ endif()
set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
if (NOT WIN32)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
endif()
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
if (WITH_GPU)
......
paddle/fluid/operators/beam_search_decode_op.cc
浏览文件 @ 74a8e6b0
......@@ -122,7 +122,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
auto& dev_ctx = *pool.Get(dev_place);
framework::ExecutionContext ctx(*this, scope, dev_ctx);
framework::RuntimeContext run_ctx(Inputs(), Outputs(), scope);
framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx);
const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");
......
paddle/fluid/operators/controlflow/parallel_do_op.cc 已删除 100644 → 0
浏览文件 @ ae6f46a1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
static constexpr char kInputs[] = "inputs";
static constexpr char kParameters[] = "parameters";
static constexpr char kPlaces[] = "places";
static constexpr char kOutputs[] = "outputs";
static constexpr char kParallelScopes[] = "parallel_scopes";
static constexpr char kParallelBlock[] = "sub_block";
static constexpr char kUseNCCL[] = "use_nccl";
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
static void SplitTensorAndMoveTensorToScopes(
const framework::Scope &scope, std::vector<framework::Scope *> *sub_scopes,
const std::vector<platform::Place> &places,
const std::vector<std::string> &names) {
size_t num_sub_scopes = 0;
for (auto &argu : names) {
const auto &tensor =
detail::Ref(scope.FindVar(argu),
"Cannot find variable %s in the parent scope", argu)
.Get<LoDTensor>();
auto lod_tensors = tensor.SplitLoDTensor(places);
for (auto &lod : lod_tensors) {
VLOG(3) << lod.dims();
}
if (num_sub_scopes == 0) {
num_sub_scopes = lod_tensors.size();
} else {
PADDLE_ENFORCE_EQ(num_sub_scopes, lod_tensors.size());
}
PADDLE_ENFORCE_NE(num_sub_scopes, 0);
if (sub_scopes->size() == 0) {
sub_scopes->reserve(num_sub_scopes);
for (size_t i = 0; i < num_sub_scopes; ++i) {
sub_scopes->emplace_back(&scope.NewScope());
}
}
for (size_t i = 0; i < lod_tensors.size(); ++i) {
*detail::Ref(sub_scopes->at(i)->Var(argu),
"Cannot find variable in the sub-scope", argu)
.GetMutable<LoDTensor>() = lod_tensors[i];
}
}
}
inline void CopyOrShare(const framework::Variable &src,
const platform::Place &dst_place,
framework::Variable *dst) {
if (src.IsType<LoDTensor>()) {
if (src.Get<LoDTensor>().place() == dst_place) {
dst->GetMutable<LoDTensor>()->ShareDataWith(src.Get<LoDTensor>());
dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
} else {
TensorCopy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
}
} else if (src.IsType<SelectedRows>()) {
auto &src_sr = src.Get<SelectedRows>();
auto *dst_sr = dst->GetMutable<SelectedRows>();
dst_sr->set_height(src_sr.height());
if (src_sr.value().place() == dst_place) {
dst_sr->mutable_value()->ShareDataWith(src_sr.value());
dst_sr->set_rows(src_sr.rows());
} else {
TensorCopy(src_sr.value(), dst_place, dst_sr->mutable_value());
}
} else {
PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s",
framework::ToTypeName(src.Type()));
}
}
void WaitOnPlace(const platform::Place place) {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
dev_ctx.Wait();
}
void WaitOnPlaces(const std::vector<platform::Place> places) {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
for (auto &place : places) {
auto &dev_ctx = *pool.Get(place);
dev_ctx.Wait();
}
}
class ParallelDoOp : public framework::OperatorBase {
public:
ParallelDoOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(place);
auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
auto *program = block->Program();
auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
auto &sub_scopes = *scope.FindVar(Output(kParallelScopes))
->GetMutable<std::vector<framework::Scope *>>();
// split input
SplitTensorAndMoveTensorToScopes(scope, &sub_scopes, places,
Inputs(kInputs));
// copy parameter
for (auto &param : Inputs(kParameters)) {
PADDLE_ENFORCE(scope.FindVar(param)->IsType<LoDTensor>(),
"Only support parameter type as LoDTensor");
auto &src = scope.FindVar(param)->Get<LoDTensor>();
auto *sub_scope0 = sub_scopes[0];
auto *dst0 = sub_scope0->Var(param)->GetMutable<LoDTensor>();
dst0->ShareDataWith(src);
for (size_t i = 1; i < sub_scopes.size(); ++i) {
auto &place = places[i];
auto *sub_scope = sub_scopes[i];
auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
framework::TensorCopy(src, place, dst);
}
}
WaitOnPlaces(places);
std::vector<std::future<void>> workers;
workers.reserve(places.size());
for (size_t place_idx = 0; place_idx < sub_scopes.size(); ++place_idx) {
auto &place = places[place_idx];
auto *cur_scope = sub_scopes[place_idx];
workers.emplace_back(framework::Async([program, cur_scope, place, block] {
framework::Executor executor(place);
executor.Run(*program, cur_scope, block->ID(),
false /*create_local_scope*/);
}));
}
for (auto &worker : workers) {
worker.wait();
}
WaitOnPlaces(places);
// merge output
for (auto &o_name : Outputs(kOutputs)) {
std::vector<const framework::LoDTensor *> lod_tensors;
lod_tensors.reserve(sub_scopes.size());
for (auto *sub_scope : sub_scopes) {
lod_tensors.emplace_back(&sub_scope->FindVar(o_name)->Get<LoDTensor>());
}
auto *lod_tensor_to_be_merged =
scope.FindVar(o_name)->GetMutable<LoDTensor>();
lod_tensor_to_be_merged->MergeLoDTensor(lod_tensors, dev_ctx.GetPlace());
}
WaitOnPlaces(places);
}
};
class ParallelDoOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput(kInputs, "").AsDuplicable();
AddInput(kParameters, "").AsDuplicable();
AddInput(kPlaces, "");
AddOutput(kOutputs, "").AsDuplicable();
AddOutput(kParallelScopes, "");
AddAttr<framework::BlockDesc *>(kParallelBlock, "");
AddAttr<bool>(kUseNCCL, "true if we use nccl on backward")
.SetDefault(false);
AddComment(R"DOC(
ParallelDo Operator.
)DOC");
}
};
class ParallelDoGradOp : public framework::OperatorBase {
public:
ParallelDoGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: framework::OperatorBase(type, inputs, outputs, attrs) {}
private:
void RunImpl(const framework::Scope &scope,
const platform::Place &place) const override {
auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
auto *program = block->Program();
auto &sub_scopes = scope.FindVar(Input(kParallelScopes))
->Get<std::vector<framework::Scope *>>();
auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
// feed output@grad
SplitTensorAndMoveTensorToScopes(
scope, const_cast<std::vector<framework::Scope *> *>(&sub_scopes),
places, Inputs(framework::GradVarName(kOutputs)));
WaitOnPlaces(places);
// exe run
std::vector<std::future<void>> workers;
for (size_t i = 0; i < sub_scopes.size(); ++i) {
auto &place = places[i];
auto *cur_scope = sub_scopes[i];
// execute
workers.emplace_back(framework::Async([program, cur_scope, place, block] {
framework::Executor executor(place);
executor.Run(*program, cur_scope, block->ID(),
false /*create_local_scope*/);
}));
}
for (auto &worker : workers) {
worker.wait();
}
WaitOnPlaces(places);
// NCCL allreduce op will be added by backward,
// so no need to explicitly accumulate grad
if (!(Attr<bool>(kUseNCCL))) {
AccumulateGrad(scope, place, sub_scopes, places);
} else {
for (auto &place : places) {
PADDLE_ENFORCE(platform::is_gpu_place(place),
"NCCL only supports cuda place");
}
}
for (auto &s : Outputs(framework::GradVarName(kParameters))) {
if (s == framework::kEmptyVarName) {
continue;
}
VLOG(3) << "Moving " << s;
CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
}
WaitOnPlaces(places);
}
void AccumulateGrad(const framework::Scope &scope,
const platform::Place &place,
const std::vector<framework::Scope *> &sub_scopes,
const platform::PlaceList &places) const {
for (auto &s : Outputs(framework::GradVarName(kParameters))) {
if (s == framework::kEmptyVarName) {
continue;
}
VLOG(3) << "Accumulating " << s;
if (s == framework::kEmptyVarName) continue;
std::string tmp_name;
auto *tmp = sub_scopes[0]->Var(&tmp_name);
for (size_t i = 1; i < sub_scopes.size(); ++i) {
CopyOrShare(*sub_scopes[i]->FindVar(s), places[0], tmp);
WaitOnPlaces(places);
auto sum_op = framework::OpRegistry::CreateOp(
"sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
framework::AttributeMap{{"use_mkldnn", {false}}});
VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
sum_op->Run(*sub_scopes[0], places[0]);
WaitOnPlace(places[0]);
}
CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
}
WaitOnPlaces(places);
}
};
std::ostream &operator<<(std::ostream &sout,
const std::vector<std::string> &strs) {
std::copy(strs.begin(), strs.end(),
std::ostream_iterator<std::string>(sout, ","));
return sout;
}
class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
virtual std::unique_ptr<framework::OpDesc> Apply() const {
auto *grad = new framework::OpDesc();
grad->SetType("parallel_do_grad");
for (auto &input_param : this->InputNames()) {
VLOG(3) << input_param;
grad->SetInput(input_param, this->Input(input_param));
if (input_param != kPlaces) {
grad->SetOutput(framework::GradVarName(input_param),
this->InputGrad(input_param, false));
}
}
auto *g_block = this->grad_block_[0];
// All variable name that needed by gradient operators
std::unordered_set<std::string> all_inputs_in_grad_blocks;
for (size_t i = 0; i < g_block->OpSize(); ++i) {
auto *op = g_block->Op(i);
for (auto &var_name : op->InputArgumentNames()) {
all_inputs_in_grad_blocks.insert(var_name);
}
}
for (auto &output_param : this->OutputNames()) {
if (output_param == kParallelScopes) {
grad->SetInput(output_param, this->Output(output_param));
grad->SetInput(framework::GradVarName(output_param),
this->Output(output_param));
} else {
grad->SetInput(output_param, this->Output(output_param));
std::vector<std::string> og_names;
for (auto &og_name : this->OutputGrad(output_param)) {
if (all_inputs_in_grad_blocks.count(og_name) != 0) {
// there are some gradient operators who need the OG. So make this
// OG as an input of parallel.do
og_names.push_back(og_name);
}
// else, there is no operator who need the OG. Do not use this OG as
// an input
}
grad->SetInput(framework::GradVarName(output_param), og_names);
}
}
grad->SetInput("Communicator", {"nccl_com__do_not_change_"});
grad->SetAttrMap(this->Attrs());
grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
return std::unique_ptr<framework::OpDesc>(grad);
}
};
class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInputs(kParameters));
PADDLE_ENFORCE(ctx->HasInputs(kInputs));
PADDLE_ENFORCE(ctx->HasInputs(kOutputs));
ctx->SetOutputsDim(framework::GradVarName(kParameters),
ctx->GetInputsDim(kParameters));
auto i_dims = ctx->GetInputsDim(kInputs);
auto ig_names = ctx->Outputs(framework::GradVarName(kInputs));
for (size_t i = 0; i < ig_names.size(); ++i) {
auto &ig_name = ig_names[i];
if (ig_name == framework::kEmptyVarName) {
continue;
}
ctx->SetDims({ig_name}, {i_dims[i]});
}
auto p_dims = ctx->GetInputsDim(kParameters);
auto pg_names = ctx->Outputs(framework::GradVarName(kParameters));
for (size_t i = 0; i < pg_names.size(); ++i) {
auto &pg_name = pg_names[i];
if (pg_name == framework::kEmptyVarName) {
continue;
}
ctx->SetDims({pg_name}, {p_dims[i]});
}
}
};
class ParallelDoGradOpVarTypeInference : public framework::VarTypeInference {
public:
void operator()(const framework::OpDesc &op_desc,
framework::BlockDesc *block) const override {
framework::BlockDesc *sub_block =
boost::get<framework::BlockDesc *>(op_desc.GetAttr(kParallelBlock));
for (auto &out_vars : op_desc.Outputs()) {
for (auto &out_var : out_vars.second) {
auto &var = block->FindRecursiveOrCreateVar(out_var);
auto sub_var = sub_block->FindRecursiveOrCreateVar(out_var);
if (sub_var.GetType() != var.GetType()) {
var.SetType(sub_var.GetType());
}
}
}
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(parallel_do, paddle::operators::ParallelDoOp,
paddle::operators::ParallelDoOpProtoMaker,
paddle::operators::ParallelDoGradOpDescMaker);
REGISTER_OPERATOR(parallel_do_grad, paddle::operators::ParallelDoGradOp,
paddle::operators::ParallelDoGradOpShapeInference,
paddle::operators::ParallelDoGradOpVarTypeInference);
paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc
浏览文件 @ 74a8e6b0
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <nccl.h>
#endif
#include <sys/time.h>
#include <limits>
#include <thread> // NOLINT
#include "paddle/fluid/framework/data_type.h"
......@@ -31,7 +32,12 @@ namespace distributed {
class IOBufWriter {
public:
static void Append(butil::IOBuf* iobuf, int k, const char* v, int64_t vlen) {
static void Append(const std::string& varname, butil::IOBuf* iobuf, int k,
const char* v, int64_t vlen) {
if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
}
iobuf->append(reinterpret_cast<char*>(&k), 4);
iobuf->append(reinterpret_cast<char*>(&vlen), 8);
iobuf->append(v, vlen);
......@@ -87,6 +93,10 @@ class IOBufWriter {
int k, const char* v, int64_t vlen,
bool in_cuda_pinned, void (*destroy)(void*),
void* user_data) {
if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
}
#ifdef PADDLE_WITH_BRPC_RDMA
IOBufWriter::AppendRdmaZeroCopy(varname, iobuf, k, v, vlen, in_cuda_pinned,
destroy, user_data);
......@@ -134,7 +144,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
request->set_type(::sendrecv::NCCL_ID);
const ncclUniqueId& uid = var->Get<ncclUniqueId>();
// TODO(gongwb): use append_zero to avoid data copy.
IOBufWriter::Append(iobuf,
IOBufWriter::Append(name, iobuf,
sendrecv::VariableMessage::kSerializedFieldNumber,
uid.internal, NCCL_UNIQUE_ID_BYTES);
return;
......@@ -149,7 +159,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
// FIXME(gongwb): it seems that can use zero copy.
if (var_is_not_stable) {
IOBufWriter::Append(
iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
name, iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
static_cast<const char*>(payload->ptr()), payload->memory_size());
} else {
if (platform::is_gpu_place(ctx.GetPlace())) {
......@@ -171,9 +181,11 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
if (var->IsType<framework::SelectedRows>()) {
auto* slr = var->GetMutable<framework::SelectedRows>();
PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
IOBufWriter::Append(iobuf, ::sendrecv::VariableMessage::kRowsFieldNumber,
IOBufWriter::Append(name, iobuf,
::sendrecv::VariableMessage::kRowsFieldNumber,
reinterpret_cast<const char*>(slr->rows().data()),
static_cast<int64_t>(rows_memory_size));
}
......
paddle/fluid/operators/distributed/grpc_serde.cc
浏览文件 @ 74a8e6b0
......@@ -15,6 +15,7 @@ limitations under the License. */
#ifdef PADDLE_WITH_CUDA
#include <nccl.h>
#endif
#include <limits>
#include <thread> // NOLINT
#include "google/protobuf/io/coded_stream.h"
......@@ -102,6 +103,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
payload->memory_size());
if (payload->memory_size() >= std::numeric_limits<int>::max()) {
LOG(FATAL) << "AppendZeroCopy varname:" << name
<< ", vlen:" << payload->memory_size();
}
// steal reference of tensor data
::grpc::Slice slices[4]; // metadata, tensor, rows meta, rows
int num_slices = 2; // only SelectedRows have rows buffer
......@@ -115,7 +120,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
if (var->IsType<framework::SelectedRows>()) {
auto* slr = var->GetMutable<framework::SelectedRows>();
ProtoEncodeHelper e2(static_cast<char*>(buf), 128);
PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
e2.WriteVarlengthBeginning(VarMsg::kRowsFieldNumber, rows_memory_size);
slices[2] = ::grpc::Slice(e2.size());
memcpy(const_cast<uint8_t*>(slices[2].begin()), e2.data(), e2.size());
......
paddle/fluid/operators/distributed/sendrecvop_utils.h
浏览文件 @ 74a8e6b0
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <iostream>
#include <string>
#include <typeindex>
#include <vector>
#include "paddle/fluid/framework/data_type.h"
......@@ -23,9 +24,8 @@ limitations under the License. */
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/platform/port.h"
#include "paddle/fluid/operators/distributed/send_recv.pb.h"
#include "paddle/fluid/platform/port.h"
namespace paddle {
namespace operators {
......@@ -83,6 +83,11 @@ inline framework::proto::VarType::Type ToVarType(
}
}
template <template <typename> class T, typename Elem>
std::string VectorElemName(const T<Elem>& arg) {
return typeid(Elem).name();
}
} // namespace distributed
} // namespace operators
} // namespace paddle
paddle/fluid/operators/distributed/variable_response.cc
浏览文件 @ 74a8e6b0
......@@ -118,7 +118,7 @@ bool VariableResponse::CopyLodTensorData(
VLOG(6) << "Tensor.memory_size = " << tensor->memory_size()
<< ", Buffer Size = " << length;
PADDLE_ENFORCE_EQ(tensor->memory_size(), length);
PADDLE_ENFORCE_EQ(tensor->memory_size(), static_cast<unsigned int>(length));
return ReadRaw(input, ctx, tensor->place(), tensor_data, length);
}
......
paddle/fluid/operators/ngraph/ngraph_ops.h 0 → 100644
浏览文件 @ 74a8e6b0
/* 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. */
/*
* This file contains the list of the ngraph operators for Paddle.
*
* ATTENTION: It requires some C++11 features, for lower version C++ or C, we
* might release another API.
*/
#pragma once
#include "ops/binary_unnary_op.h"
#include "ops/mul_op.h"
paddle/fluid/operators/ngraph/ops/binary_unnary_op.h 0 → 100644
浏览文件 @ 74a8e6b0
/* 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
template <typename T>
static void BuildBinaryNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
auto out = std::make_shared<T>(x, y);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
template <typename T>
static void BuildUnaryNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto out = std::make_shared<T>(input);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/mul_op.h 0 → 100644
浏览文件 @ 74a8e6b0
/*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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
static void BuildMulNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
auto x_reshape = x;
auto y_reshape = y;
if (x->get_shape().size() > 2) {
auto x_2d = paddle::platform::FlattenTo2d(x->get_shape(), x_num_col_dims);
x_reshape = paddle::platform::NgReshaper(x, x_2d);
}
if (y->get_shape().size() > 2) {
auto y_2d = paddle::platform::FlattenTo2d(y->get_shape(), y_num_col_dims);
y_reshape = paddle::platform::NgReshaper(y, y_2d);
}
std::shared_ptr<ngraph::Node> out =
std::make_shared<ngraph::op::Dot>(x_reshape, y_reshape);
auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
if (dummy_out && dummy_out->get_shape() != out->get_shape()) {
out = paddle::platform::NgReshaper(out, dummy_out->get_shape());
}
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
static void BuildMulGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
bool is_dx = paddle::platform::HasOutput(op, "X@GRAD") ? true : false;
bool is_dy = paddle::platform::HasOutput(op, "Y@GRAD") ? true : false;
auto x_shape = x->get_shape();
auto y_shape = y->get_shape();
auto x_reshape = x;
auto y_reshape = y;
if (x_shape.size() > 2) {
auto x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_num_col_dims);
x_reshape = paddle::platform::NgReshaper(x, x_2d_shape);
}
if (y_shape.size() > 2) {
auto y_2d_shape = paddle::platform::FlattenTo2d(y_shape, y_num_col_dims);
y_reshape = paddle::platform::NgReshaper(y, y_2d_shape);
}
auto x_reshape_shape = x_reshape->get_shape();
std::reverse(x_reshape_shape.begin(), x_reshape_shape.end());
auto x_transpose = std::make_shared<ngraph::op::Reshape>(
x_reshape, ngraph::AxisVector{1, 0}, x_reshape_shape);
auto y_reshape_shape = y_reshape->get_shape();
std::reverse(y_reshape_shape.begin(), y_reshape_shape.end());
auto y_transpose = std::make_shared<ngraph::op::Reshape>(
y_reshape, ngraph::AxisVector{1, 0}, y_reshape_shape);
if (is_dx) {
if (dout->get_shape().size() > 2) {
auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
}
auto dx = std::make_shared<ngraph::op::Dot>(dout, y_transpose);
if (dx->get_shape() == x_shape) {
paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
} else {
auto dx_reshape = paddle::platform::NgReshaper(dx, x_shape);
paddle::platform::SetOutputNode(op, "X@GRAD", dx_reshape, ngb_node_map);
}
}
if (is_dy) {
if (dout->get_shape().size() > 2) {
auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
}
auto dy = std::make_shared<ngraph::op::Dot>(x_transpose, dout);
if (dy->get_shape() == y_shape) {
paddle::platform::SetOutputNode(op, "Y@GRAD", dy, ngb_node_map);
} else {
auto dy_reshape = paddle::platform::NgReshaper(dy, y_shape);
paddle::platform::SetOutputNode(op, "Y@GRAD", dy_reshape, ngb_node_map);
}
}
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/optimizers/adam_op.cc
浏览文件 @ 74a8e6b0
......@@ -109,6 +109,11 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
"(float, default 1.0e-8) "
"Constant for numerical stability")
.SetDefault(1.0e-8f);
AddAttr<bool>(
"lazy_mode",
"(bool, default false) "
"only update the parameter that has gradient in sparse update")
.SetDefault(false);
AddComment(R"DOC(
Adam Optimizer.
......
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @ 74a8e6b0
......@@ -177,12 +177,13 @@ struct SparseAdamFunctor {
const int64_t* rows_;
int64_t row_numel_;
int64_t row_count_;
bool lazy_mode_;
SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
const T* beta2_pow, const T* mom1, T* mom1_out,
const T* mom2, T* mom2_out, const T* lr, const T* grad,
const T* param, T* param_out, const int64_t* rows,
int64_t row_numel, int64_t row_count)
int64_t row_numel, int64_t row_count, bool lazy_mode)
: beta1_(beta1),
beta2_(beta2),
epsilon_(epsilon),
......@@ -198,13 +199,10 @@ struct SparseAdamFunctor {
param_out_(param_out),
rows_(rows),
row_numel_(row_numel),
row_count_(row_count) {}
inline HOSTDEVICE void operator()(size_t i) const {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
row_count_(row_count),
lazy_mode_(lazy_mode) {}
inline HOSTDEVICE void adam_update(size_t i, T g) const {
// The following code is the same as dense
T mom1 = moment1_[i];
T mom2 = moment2_[i];
......@@ -225,6 +223,17 @@ struct SparseAdamFunctor {
moment2_out_[i] = mom2;
param_out_[i] = p;
}
inline HOSTDEVICE void operator()(size_t i) const {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
if (lazy_mode_ && row_idx < 0) {
return;
} else {
T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
adam_update(i, g);
}
}
};
template <typename DeviceContext, typename T>
......@@ -241,6 +250,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
using paddle::framework::LoDTensor;
using paddle::operators::detail::Ref;
bool lazy_mode = ctx.Attr<bool>("lazy_mode");
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
......@@ -352,11 +362,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
mom2_out.template mutable_data<T>(ctx.GetPlace()),
lr.template data<T>(), grad_data, param.template data<T>(),
param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
grad_merge.rows().size());
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
param.numel());
for_range(functor);
grad_merge.rows().size(), lazy_mode);
VLOG(3) << "lazy_mode :" << lazy_mode;
if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
size_t row_count = grad_merge.rows().size();
std::vector<int64_t> cpu_rows(grad_merge.rows());
for (size_t row_index = 0; row_index < row_count; ++row_index) {
for (size_t offset = 0; offset < row_numel; ++offset) {
size_t i = cpu_rows[row_index] * row_numel + offset;
functor.adam_update(i, grad_data[row_index * row_numel + offset]);
}
}
} else {
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
param.numel());
for_range(functor);
}
} else {
PADDLE_THROW("Variable type not supported by adam_op");
}
......
paddle/fluid/operators/transpose_mkldnn_op.cc 0 → 100644
浏览文件 @ 74a8e6b0
/* 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/data_layout_transform.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using framework::DataLayout;
template <typename T>
class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
public:
void Compute(const paddle::framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
"It must use CPUPlace.");
const bool is_test = ctx.Attr<bool>("is_test");
PADDLE_ENFORCE(
is_test == true,
"ConvTransposeMKLDNN works only for inference!. Set is_test = True");
auto& dev_ctx =
ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
const auto& mkldnn_engine = dev_ctx.GetEngine();
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
int ndims = axis.size();
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
const T* input_data = input->data<T>();
if (ndims == 1) {
output->ShareDataWith(*input);
return;
}
std::vector<int> nchw_axis(ndims, 0);
for (size_t i = 0; i < nchw_axis.size(); ++i) {
nchw_axis[i] = i;
}
std::vector<int> nchw_tz = paddle::framework::vectorize2int(input->dims());
std::string data_format = ctx.Attr<std::string>("data_format");
auto src_md =
input->format() != mkldnn::memory::format::nchw
? platform::MKLDNNMemDesc(nchw_tz, platform::MKLDNNGetDataType<T>(),
input->format())
: Axis2MemoryDesc(nchw_tz, nchw_axis);
this->TransposeKernel(ctx.GetPlace(), Axis2MemoryDesc(nchw_tz, axis),
src_md, output, input_data, nchw_tz, mkldnn_engine);
}
protected:
mkldnn::memory::desc Axis2MemoryDesc(std::vector<int>& nchw_tz,
std::vector<int>& axis) const {
mkldnn_memory_desc_t mem_fmt;
mem_fmt.primitive_kind = mkldnn_memory;
mem_fmt.ndims = axis.size();
for (unsigned int i = 0; i < nchw_tz.size(); ++i) {
mem_fmt.dims[i] = nchw_tz[i]; // logical dimensions (nchw format,
// regardless physical layout)
}
mem_fmt.data_type = mkldnn_f32;
mem_fmt.format = mkldnn_blocked;
unsigned int total_stride = 1;
for (int i = nchw_tz.size() - 1; i >= 0; --i) {
mem_fmt.layout_desc.blocking.padding_dims[i] =
nchw_tz[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][axis[i]] = total_stride;
mem_fmt.layout_desc.blocking.strides[1][axis[i]] = 1;
total_stride *= nchw_tz[axis[i]];
}
mem_fmt.layout_desc.blocking.offset_padding = 0; // no initial offset
return mem_fmt;
}
void TransposeKernel(platform::Place place, mkldnn::memory::desc md_o,
mkldnn::memory::desc md_i, Tensor* output,
const T* data_i, std::vector<int>& nchw_dims,
const mkldnn::engine& eng) const {
// Make Memory primitive descriptors
auto mpd_o = mkldnn::memory::primitive_desc(md_o, eng);
auto mpd_i = mkldnn::memory::primitive_desc(md_i, eng);
auto data_o = output->mutable_data<T>(
place, paddle::memory::Allocator::kDefault, mpd_o.get_size());
auto src = mkldnn::memory(mpd_i, (T*)(data_i));
auto dst = mkldnn::memory(mpd_o, data_o);
auto r = mkldnn::reorder(src, dst);
mkldnn::stream(mkldnn::stream::kind::eager).submit({r}).wait();
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(transpose2, MKLDNN, ::paddle::platform::CPUPlace,
ops::TransposeMKLDNNOpKernel<float>);
REGISTER_OP_KERNEL(transpose, MKLDNN, ::paddle::platform::CPUPlace,
ops::TransposeMKLDNNOpKernel<float>);
paddle/fluid/operators/transpose_op.cc
浏览文件 @ 74a8e6b0
......@@ -16,6 +16,10 @@ limitations under the License. */
#include <string>
#include <vector>
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
......@@ -53,11 +57,32 @@ class TransposeOp : public framework::OperatorWithKernel {
}
ctx->SetOutputDim("Out", out_dims);
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.GetPlace(), layout_, library_);
}
};
class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddAttr<bool>("is_test",
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true.")
.SetDefault(false);
AddInput(
"X",
"(Tensor) The input tensor, tensors with rank up to 6 are supported.");
......@@ -67,6 +92,16 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
"(vector<int>) A list of values, and the size of the list should be "
"the same with the input tensor rank. This operator permutes the input "
"tensor's axes according to the values given.");
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
AddComment(R"DOC(
Transpose Operator.
......@@ -144,8 +179,18 @@ class Transpose2Op : public TransposeOp {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
ctx.device_context());
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
library_ = framework::LibraryType::kMKLDNN;
layout_ = framework::DataLayout::kMKLDNN;
}
#endif
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.GetPlace(), layout_, library_);
}
};
......
paddle/fluid/platform/dynload/CMakeLists.txt
浏览文件 @ 74a8e6b0
......@@ -16,9 +16,7 @@ if (CUPTI_FOUND)
list(APPEND CUDA_SRCS cupti.cc)
endif(CUPTI_FOUND)
nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
if (NOT WIN32)
cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
endif(NOT WIN32)
if (WITH_MKLML)
cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
endif()
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 74a8e6b0
......@@ -34,7 +34,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
using cudnn_func = decltype(&::__name); \
std::call_once(cudnn_dso_flag, []() { \
cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 74a8e6b0
......@@ -201,6 +201,8 @@ void* GetCurandDsoHandle() {
void* GetWarpCTCDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib");
#elif defined(_WIN32)
return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "warpctc.dll");
#else
return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so");
#endif
......
paddle/fluid/platform/dynload/dynamic_loader.h
浏览文件 @ 74a8e6b0
......@@ -18,6 +18,12 @@ namespace paddle {
namespace platform {
namespace dynload {
#ifndef _WIN32
#define DECLARE_TYPE(__name, ...) decltype(__name(__VA_ARGS__))
#else
#define DECLARE_TYPE(__name, ...) decltype(auto)
#endif
void* GetCublasDsoHandle();
void* GetCUDNNDsoHandle();
void* GetCUPTIDsoHandle();
......
paddle/fluid/platform/dynload/mklml.h
浏览文件 @ 74a8e6b0
......@@ -34,7 +34,7 @@ extern void* mklml_dso_handle;
#define DYNAMIC_LOAD_MKLML_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
using mklmlFunc = decltype(&::__name); \
std::call_once(mklml_dso_flag, []() { \
mklml_dso_handle = paddle::platform::dynload::GetMKLMLDsoHandle(); \
......
paddle/fluid/platform/dynload/tensorrt.h
浏览文件 @ 74a8e6b0
......@@ -33,7 +33,7 @@ extern void* tensorrt_dso_handle;
#define DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
using tensorrt_func = decltype(__name(args...)) (*)(Args...); \
std::call_once(tensorrt_dso_flag, []() { \
tensorrt_dso_handle = \
......
paddle/fluid/platform/dynload/warpctc.h
浏览文件 @ 74a8e6b0
......@@ -34,7 +34,7 @@ extern void* warpctc_dso_handle;
#define DYNAMIC_LOAD_WARPCTC_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) { \
using warpctcFunc = decltype(&::__name); \
std::call_once(warpctc_dso_flag, []() { \
warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
......
paddle/fluid/platform/ngraph_helper.h 0 → 100644
浏览文件 @ 74a8e6b0
/* 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. */
#ifdef PADDLE_WITH_NGRAPH
#pragma once
#include <functional>
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
namespace paddle {
namespace platform {
static ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
std::multiplies<size_t>());
auto x2 = std::accumulate(std::begin(sh) + num, std::end(sh), 1,
std::multiplies<size_t>());
size_t x1_l = static_cast<size_t>(x1);
size_t x2_l = static_cast<size_t>(x2);
return ngraph::Shape{x1_l, x2_l};
}
static std::shared_ptr<ngraph::Node> NgReshaper(
std::shared_ptr<ngraph::Node> input, ngraph::Shape shape) {
std::vector<size_t> input_order(input->get_shape().size());
std::iota(std::begin(input_order), std::end(input_order), 0);
return std::make_shared<ngraph::op::Reshape>(
input, ngraph::AxisVector(input_order), shape);
}
static std::shared_ptr<ngraph::Node> GetNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm, const paddle::framework::VariableNameMap& var_map,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = var_map.at(prm);
PADDLE_ENFORCE_EQ(var_names.size(), 1,
"op %s prm %s expects one associated var", op->Type(), prm);
if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
return (*ngb_node_map)[var_names[0]];
} else {
return nullptr;
}
}
static std::shared_ptr<ngraph::Node> GetInputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Inputs(), ngb_node_map);
}
static std::shared_ptr<ngraph::Node> GetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
return GetNode(op, prm, op->Outputs(), ngb_node_map);
}
static void SetOutputNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm, std::shared_ptr<ngraph::Node> node,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto& var_names = op->Outputs().at(prm);
if (var_names.size() == 1) {
(*ngb_node_map)[var_names[0]] = node;
} else if (var_names.size() == 0) {
(*ngb_node_map)[""] = node;
} else {
PADDLE_THROW("prm %s has more than 1 var_names.", prm);
}
}
static bool HasOutput(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
const std::string prm) {
auto& outputs = op->Outputs();
if (outputs.find(prm) == outputs.end()) return false;
return outputs.at(prm).size() > 0;
}
} // namespace platform
} // namespace paddle
#endif
paddle/fluid/platform/port.h
浏览文件 @ 74a8e6b0
......@@ -55,7 +55,6 @@ static void *dlsym(void *handle, const char *symbol_name) {
static void *dlopen(const char *filename, int flag) {
std::string file_name(filename);
file_name.replace(0, file_name.size() - 1, '/', '\\');
HMODULE hModule = LoadLibrary(file_name.c_str());
if (!hModule) {
throw std::runtime_error(file_name + " not found.");
......
paddle/scripts/paddle_build.sh
浏览文件 @ 74a8e6b0
......@@ -509,11 +509,11 @@ function assert_api_spec_approvals() {
if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
# NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
echo "You must have at least 2 approvals for the api change! ${API_FILE}"
exit 1
echo "You must have panyx0718 approval for the api change! ${API_FILE}"
exit 1
fi
fi
done
......@@ -521,11 +521,11 @@ function assert_api_spec_approvals() {
HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then
APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
if [ "${APPROVALS}" == "FALSE" ]; then
echo "You must have at least 2 approvals for the const_cast"
exit 1
echo "You must have panyx0718 approval for the const_cast"
exit 1
fi
fi
......
python/paddle/fluid/__init__.py
浏览文件 @ 74a8e6b0
......@@ -102,6 +102,13 @@ def __bootstrap__():
import sys
import os
import platform
if os.name == 'nt':
third_lib_path = os.path.abspath(os.path.dirname(
__file__)) + os.sep + '..' + os.sep + 'libs'
os.environ['path'] += ';' + third_lib_path
sys.path.append(third_lib_path)
from . import core
in_test = 'unittest' in sys.modules
......@@ -128,13 +135,12 @@ def __bootstrap__():
'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
'eager_delete_tensor_gb', 'fast_eager_deletion_mode',
'allocator_strategy', 'reader_queue_speed_test_mode',
'print_sub_graph_dir', 'pe_profile_fname'
'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir'
]
if 'Darwin' not in sysstr:
read_env_flags.append('use_pinned_memory')
if os.name != 'nt':
read_env_flags.append('warpctc_dir')
read_env_flags.append('cpu_deterministic')
if core.is_compiled_with_dist():
......
python/paddle/fluid/backward.py
浏览文件 @ 74a8e6b0
......@@ -249,69 +249,6 @@ def serialize_op_decs(op_desc):
return proto.__str__()
def _callback_lookup_(op):
"""
Only used in _append_backward_ops_
Build and returns a callback function for certain op. For example
parallel_do: AllReduce
:param op:
:return: callback function
"""
if op.type == 'parallel_do' and op.attr('use_nccl'):
all_vars = op.block.vars
param_names = set(op.input('parameters'))
param_names = [
name for name in param_names
if all_vars[name].stop_gradient is False
]
param_grad_names = [n + "@GRAD" for n in param_names]
class ParallelDoCallBack(object):
def __init__(self, param_grad_names, parallel_scopes_name):
self.has_inserted_nccl_init = False
self.param_grad_names = param_grad_names
self.parallel_scopes_name = parallel_scopes_name
def __call__(self, block, context):
if not self.has_inserted_nccl_init:
op_desc = _create_op_desc_(
"ncclInit",
{"parallel_scopes": self.parallel_scopes_name},
{"Communicator": ['nccl_com__do_not_change_']}, {})
block.program.global_block().desc.append_op().copy_from(
op_desc)
self.has_inserted_nccl_init = True
current_op_desc = context["__current_op_desc__"]
for o_param in current_op_desc.output_names():
for o_argu in current_op_desc.output(o_param):
if o_argu in self.param_grad_names:
allreduce_out_name = o_argu + "__nccl_all_reduce__"
op_desc = _create_op_desc_(
"ncclReduce",
{
"X": [o_argu],
"Communicator":
['nccl_com__do_not_change_']
},
{"Out": [allreduce_out_name]},
{"reduction": "ncclSum",
"root": 0}, )
block.desc.append_op().copy_from(op_desc)
op_desc = _create_op_desc_(
"assign", {"X": [allreduce_out_name]},
{"Out": [o_argu]}, {})
block.desc.append_op().copy_from(op_desc)
return ParallelDoCallBack(param_grad_names,
op.output("parallel_scopes"))
else:
return None
def _append_backward_ops_(block,
ops,
target_block,
......@@ -349,17 +286,8 @@ def _append_backward_ops_(block,
sub_block = program.block(op._block_attr_id("sub_block"))
grad_sub_block = program._create_block()
grad_sub_block._set_forward_block_idx(sub_block.idx)
cb = _callback_lookup_(op)
if cb is not None:
if callbacks is None:
new_callbacks = [cb]
else:
new_callbacks = callbacks + [_callback_lookup_(op)]
_append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
no_grad_dict, grad_to_var, new_callbacks)
else:
_append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
no_grad_dict, grad_to_var, callbacks)
_append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
no_grad_dict, grad_to_var, callbacks)
program._rollback()
grad_sub_block_list.append(grad_sub_block.desc)
......@@ -424,9 +352,6 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
# infer_shape and infer_type
op_desc.infer_var_type(block.desc)
op_desc.infer_shape(block.desc)
# ncclInit dones't need to set data_type
if op_desc.type() == 'ncclInit':
continue
for arg in op_desc.output_arg_names():
if arg in new_vars:
_infer_var_data_type_(arg, block)
......
python/paddle/fluid/framework.py
浏览文件 @ 74a8e6b0
......@@ -16,6 +16,7 @@ from __future__ import print_function
import collections
import contextlib
import os
import re
import six
import sys
......@@ -27,11 +28,18 @@ from .proto import framework_pb2
try:
from . import core
except ImportError as e:
raise ImportError(
"""NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
if you encounters \"libmkldnn.so not found\" errors. If you have python
installed in other directory, replace \"/usr/local/lib\" with your own
directory. The original error is: \n""" + cpt.get_exception_message(e))
if os.name == 'nt':
raise ImportError(
"""NOTE: You may need to run \"set PATH=c:\python27\lib:%PATH%\"
if you encounters \"mkldnn.dll not found\" errors. If you have python
installed in other directory, replace \"c:\python27\lib" with your own
directory. The original error is: \n""" + cpt.get_exception_message(e))
else:
raise ImportError(
"""NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
if you encounters \"libmkldnn.so not found\" errors. If you have python
installed in other directory, replace \"/usr/local/lib\" with your own
directory. The original error is: \n""" + cpt.get_exception_message(e))
except Exception as e:
raise e
from . import unique_name
......@@ -563,8 +571,8 @@ class Operator(object):
OP_WITHOUT_KERNEL_SET = {
'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
'checkpoint_notify', 'gen_nccl_id'
}
def __init__(self,
......
python/paddle/fluid/layers/control_flow.py
浏览文件 @ 74a8e6b0
......@@ -226,156 +226,6 @@ class BlockGuard(object):
return True
class ParallelDo(object):
"""
ParallelDo is used to represent multi-thread data parallel processing.
Its vanilla implementation can be shown as the following (:math:`|` means
single thread and :math:`||||` means multiple threads)
.. code-block:: text
In the forward pass
| Split input onto different devices
| Copy parameter onto different devices
|||| Compute forward pass in parallel
| Merge output from different devices
In the backward pass
| Split output@grad onto different devices
|||| Compute backward pass in parallel
| accumulate param@grad from different devices to the first device
| Merge input@grad from different devices
| Copy param@grad to the place of parallel_do_op
Examples:
.. code-block:: python
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# ParallelDo version & Single-thread version
if thread_num > 1:
places = fluid.layers.get_places(thread_num)
pd = fluid.layers.control_flow.ParallelDo(places)
with pd.do():
images = pd.read_input(images)
label = pd.read_input(label)
predict = cnn_model(images)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
pd.write_output(avg_cost)
avg_cost = pd()
avg_cost = fluid.layers.mean(avg_cost)
else:
predict = cnn_model(images)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
.. warning::
It will be soon deprecated, please use ParallelExecutor instead.
"""
def __init__(self, places, use_nccl=False, name=None):
warnings.warn(
"API ParallelDo is deprecated since 0.15.0. Please use ParallelExecutor instead.",
Warning)
self.helper = LayerHelper("parallel_do", name=name)
self.inputs = []
self.places = places
self.outputs = []
self.status = StaticRNN.BEFORE_RNN_BLOCK
self.use_nccl = use_nccl
def do(self):
return BlockGuardWithCompletion(self)
def parent_block(self):
prog = self.helper.main_program
parent_idx = prog.current_block().parent_idx
assert parent_idx >= 0
parent_block = prog.block(parent_idx)
return parent_block
def __call__(self, *args, **kwargs):
if self.status != StaticRNN.AFTER_RNN_BLOCK:
raise ValueError("RNN output can only be retrieved after rnn block")
if len(self.outputs) == 0:
raise ValueError("RNN has no output")
elif len(self.outputs) == 1:
return self.outputs[0]
else:
return self.outputs
def read_input(self, var):
self.inputs.append(var)
return var
def write_output(self, var):
self.outputs.append(var)
def get_parameters(self):
main_program = self.helper.main_program
current_block = main_program.current_block()
parent_block = self.parent_block()
local_inputs = set()
params = list()
for var in self.inputs:
local_inputs.add(var.name)
for op in current_block.ops:
for iname in op.input_names:
for in_var_name in op.input(iname):
if in_var_name not in local_inputs:
params.append(in_var_name)
for oname in op.output_names:
for out_var_name in op.output(oname):
local_inputs.add(out_var_name)
params = list(set(params))
return [parent_block.var(name) for name in params]
def _complete_op(self):
main_program = self.helper.main_program
current_block = main_program.current_block()
parent_block = self.parent_block()
step_scope = parent_block.create_var(
type=core.VarDesc.VarType.STEP_SCOPES)
self.outputs = [
parent_block.create_var(
name=o.name,
shape=o.shape,
dtype=o.dtype,
lod_level=o.lod_level,
persistable=o.persistable,
stop_gradient=o.stop_gradient) for o in self.outputs
]
inputs = [parent_block.var(i.name) for i in self.inputs]
outputs = [parent_block.var(o.name) for o in self.outputs]
parent_block.append_op(
type='parallel_do',
inputs={
'inputs': inputs,
'parameters': self.get_parameters(),
'places': self.places
},
outputs={'outputs': outputs,
'parallel_scopes': [step_scope]},
attrs={'sub_block': current_block,
'use_nccl': self.use_nccl})
class BlockGuardWithCompletion(BlockGuard):
"""
BlockGuardWithCompletion class.
......@@ -384,9 +234,8 @@ class BlockGuardWithCompletion(BlockGuard):
"""
def __init__(self, rnn):
if not (isinstance(rnn, StaticRNN) or isinstance(rnn, ParallelDo)):
raise TypeError(
"BlockGuardWithCompletion takes a StaticRNN or ParallelDo")
if not isinstance(rnn, StaticRNN):
raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
super(BlockGuardWithCompletion, self).__init__(rnn.helper.main_program)
self.rnn = rnn
......
python/paddle/fluid/optimizer.py
浏览文件 @ 74a8e6b0
......@@ -641,9 +641,14 @@ class AdamOptimizer(Optimizer):
beta1 (float): The exponential decay rate for the 1st moment estimates.
beta2 (float): The exponential decay rate for the 2nd moment estimates.
epsilon (float): a small float value for numerical stability.
regularization: A Regularizer, such as
fluid.regularizer.L2DecayRegularizer.
regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
name: A optional name prefix.
lazy_mode(bool: false): The official Adam algorithm has two moving-average accumulators
the accumulators are updated at every step. Every element of the two moving-average is updated
in both dense mode and sparse mode. If the size of parameter is very large, then the update
may be very slow. The lazy mode only update the element that has gradient is the current
mini-batch, so it will be much more faster. But this mode has different semantics with the
original Adam algorithm and may lead to different result.
Examples:
.. code-block:: python
......@@ -663,7 +668,8 @@ class AdamOptimizer(Optimizer):
beta2=0.999,
epsilon=1e-8,
regularization=None,
name=None):
name=None,
lazy_mode=False):
assert learning_rate is not None
assert beta1 is not None
assert beta2 is not None
......@@ -676,6 +682,7 @@ class AdamOptimizer(Optimizer):
self._beta1 = beta1
self._beta2 = beta2
self._epsilon = epsilon
self._lazy_mode = lazy_mode
def _create_accumulators(self, block, parameters):
assert isinstance(block, framework.Block)
......@@ -729,7 +736,8 @@ class AdamOptimizer(Optimizer):
attrs={
"beta1": self._beta1,
"beta2": self._beta2,
"epsilon": self._epsilon
"epsilon": self._epsilon,
"lazy_mode": self._lazy_mode
})
return adam_op
......
python/paddle/fluid/tests/book/notest_understand_sentiment.py
浏览文件 @ 74a8e6b0
......@@ -15,7 +15,6 @@
from __future__ import print_function
from paddle.fluid.layers.device import get_places
from paddle.fluid.layers.control_flow import ParallelDo
import unittest
import paddle.fluid as fluid
import paddle
......@@ -147,22 +146,7 @@ def train(word_dict,
cost, acc_out, prediction = net_method(
data, label, input_dim=dict_dim, class_dim=class_dim)
else:
places = get_places()
pd = ParallelDo(places)
with pd.do():
cost, acc, _ = net_method(
pd.read_input(data),
pd.read_input(label),
input_dim=dict_dim,
class_dim=class_dim)
pd.write_output(cost)
pd.write_output(acc)
cost, acc = pd()
cost = fluid.layers.mean(cost)
acc_out = fluid.layers.mean(acc)
prediction = None
assert save_dirname is None
raise NotImplementedError()
adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
adagrad.minimize(cost)
......
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @ 74a8e6b0
......@@ -25,7 +25,6 @@ import numpy
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
from paddle.fluid.layers.control_flow import ParallelDo
BATCH_SIZE = 64
......@@ -82,19 +81,7 @@ def train(nn_type,
net_conf = conv_net
if parallel:
places = get_places()
pd = ParallelDo(places)
with pd.do():
img_ = pd.read_input(img)
label_ = pd.read_input(label)
prediction, avg_loss, acc = net_conf(img_, label_)
for o in [avg_loss, acc]:
pd.write_output(o)
avg_loss, acc = pd()
# get mean loss and acc through every devices.
avg_loss = fluid.layers.mean(avg_loss)
acc = fluid.layers.mean(acc)
raise NotImplementedError()
else:
prediction, avg_loss, acc = net_conf(img, label)
......@@ -273,7 +260,7 @@ def inject_all_tests():
for use_cuda in (False, True):
if use_cuda and not core.is_compiled_with_cuda():
continue
for parallel in (False, True):
for parallel in (False, ):
for nn_type in ('mlp', 'conv'):
inject_test_method(use_cuda, parallel, nn_type, True)
......
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @ 74a8e6b0
......@@ -17,7 +17,6 @@ from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
from paddle.fluid.layers.control_flow import ParallelDo
import unittest
import os
import numpy as np
......@@ -84,18 +83,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
avg_cost, predict_word = __network__(
[first_word, second_word, third_word, forth_word, next_word])
else:
places = get_places()
pd = ParallelDo(places)
with pd.do():
avg_cost, predict_word = __network__(
list(
map(pd.read_input, [
first_word, second_word, third_word, forth_word,
next_word
])))
pd.write_output(avg_cost)
avg_cost = fluid.layers.mean(pd())
raise NotImplementedError()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
......@@ -262,7 +250,7 @@ def inject_test_method(use_cuda, is_sparse, is_parallel):
for use_cuda in (False, True):
for is_sparse in (False, True):
for is_parallel in (False, True):
for is_parallel in (False, ):
inject_test_method(use_cuda, is_sparse, is_parallel)
if __name__ == '__main__':
......
python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.py 已删除 100644 → 0
浏览文件 @ ae6f46a1
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import math
import sys
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
from paddle.fluid.layers.control_flow import ParallelDo
# need to fix random seed and training data to compare the loss
# value accurately calculated by the default and the memory optimization
# version.
fluid.default_startup_program().random_seed = 111
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
device_type = 'CPU'
use_nccl = False
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
device_type = 'CUDA'
use_nccl = False
place = fluid.CUDAPlace(0)
places = get_places(device_count=0, device_type=device_type)
pd = ParallelDo(places, use_nccl=use_nccl)
with pd.do():
x_ = pd.read_input(x)
y_ = pd.read_input(y)
y_predict = fluid.layers.fc(input=x_, size=1, act=None)
cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
avg_cost = fluid.layers.mean(x=cost)
pd.write_output(avg_cost)
cost = pd()
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
sgd_optimizer.minimize(avg_cost)
fluid.memory_optimize(fluid.default_main_program(), print_log=True)
# fluid.release_memory(fluid.default_main_program())
BATCH_SIZE = 200
# fix the order of training data
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE, drop_last=False)
# train_reader = paddle.batch(
# paddle.reader.shuffle(
# paddle.dataset.uci_housing.train(), buf_size=500),
# batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
avg_loss_value, = exe.run(fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
print(avg_loss_value[0])
if math.isnan(float(avg_loss_value)):
sys.exit("got NaN loss, training failed.")
exit(1)
python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py 0 → 100644
浏览文件 @ 74a8e6b0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh
class TestNGRAPHReluDim2(TestRelu):
def setUp(self):
super(TestNGRAPHReluDim2, self).setUp()
class TestNGRAPHTanhDim2(TestTanh):
def setUp(self):
super(TestNGRAPHTanhDim2, self).setUp()
class TestNGRAPHReluDim4(TestRelu):
def setUp(self):
super(TestNGRAPHReluDim4, self).setUp()
x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
# The same reason with TestAbs
x[np.abs(x) < 0.005] = 0.02
out = np.maximum(x, 0)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.outputs = {'Out': out}
class TestNGRAPHTanhDim4(TestTanh):
def setUp(self):
super(TestNGRAPHTanhDim4, self).setUp()
self.inputs = {
'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
}
self.outputs = {'Out': np.tanh(self.inputs['X'])}
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.py 0 → 100644
浏览文件 @ 74a8e6b0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from paddle.fluid.tests.unittests.test_mul_op import TestMulOp, TestMulOp2, TestFP16MulOp1, TestFP16MulOp2
class TestNGRAPHMulOp(TestMulOp):
def init_dtype_type(self):
pass
class TestNGRAPHMulOp2(TestMulOp2):
def init_dtype_type(self):
pass
class TestNGRAPHFP16MulOp1(TestFP16MulOp1):
def init_dtype_type(self):
pass
class TestNGRAPHFP16MulOp2(TestFP16MulOp2):
def init_dtype_type(self):
pass
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_adam_op.py
浏览文件 @ 74a8e6b0
......@@ -194,7 +194,8 @@ def adam_step(inputs, attributes):
return param_out, moment1_out, moment2_out
def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad,
lazy_mode):
'''
Simulate one step of the adam optimizer
:param inputs: dict of inputs
......@@ -218,19 +219,30 @@ def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
moment2_out = np.zeros(shape=[height, row_numel])
param_out = np.zeros(shape=[height, row_numel])
for idx, row_id in enumerate(rows):
def update_row(row_id, update_value):
moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
) * np_grad[idx]
) * update_value
moment2_out[row_id] = beta2 * moment2[row_id] + (
1 - beta2) * np.square(np_grad[idx])
1 - beta2) * np.square(update_value)
lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
np.sqrt(moment2_out[row_id]) + epsilon))
if lazy_mode:
for idx, row_id in enumerate(rows):
update_row(row_id, np_grad[idx])
else:
for row_id in range(param_out.shape[0]):
update_value = np.zeros(np_grad[0].shape).astype("float32")
if row_id in rows:
update_value = np_grad[rows.index(row_id)]
update_row(row_id, update_value)
return param_out, moment1_out, moment2_out
class TestSparseAdamOp(unittest.TestCase):
def setup(self, scope, place):
def setup(self, scope, place, lazy_mode):
beta1 = 0.78
beta2 = 0.836
epsilon = 1e-4
......@@ -248,6 +260,7 @@ class TestSparseAdamOp(unittest.TestCase):
'Beta2Pow': np.array([beta2**10]).astype("float32"),
"LearningRate": np.full((1), 2.0).astype("float32")
}
self.init_output = np.full((height, row_numel), 0.0).astype("float32")
self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
grad_selected_rows = scope.var('Grad').get_selected_rows()
......@@ -262,19 +275,21 @@ class TestSparseAdamOp(unittest.TestCase):
self.sparse_inputs = ["Grad"]
param_out, mom1, mom2 = adam_step_sparse(
self.dense_inputs, self.attrs, height, rows, row_numel, np_array)
param_out, mom1, mom2 = adam_step_sparse(self.dense_inputs, self.attrs,
height, rows, row_numel,
np_array, lazy_mode)
self.outputs = {
"ParamOut": param_out,
"Moment1Out": mom1,
"Moment2Out": mom2
}
def check_with_place(self, place):
def check_with_place(self, place, lazy_mode):
scope = core.Scope()
self.setup(scope, place)
self.setup(scope, place, lazy_mode)
op_args = dict()
op_args['lazy_mode'] = lazy_mode
for key, np_array in self.dense_inputs.items():
var = scope.var(key).get_tensor()
var.set(np_array, place)
......@@ -283,7 +298,7 @@ class TestSparseAdamOp(unittest.TestCase):
op_args[s] = s
for s in self.outputs:
var = scope.var(s).get_tensor()
var.set(self.outputs[s], place)
var.set(self.init_output, place)
op_args[s] = s
for k in self.attrs:
op_args[k] = self.attrs[k]
......@@ -297,20 +312,17 @@ class TestSparseAdamOp(unittest.TestCase):
actual = np.array(out_var)
actual = actual.reshape([actual.size])
np_array = np_array.reshape([np_array.size])
for idx, row_id in enumerate(self.rows):
j = 0
while j < self.row_numel:
pos = row_id * self.row_numel + j
self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
0.00001)
j += 1
def test_sparse_sgd(self):
for i in range(np_array.size):
self.assertLess((actual[i] - np_array[i]), 0.00001)
def test_sparse_adam(self):
places = [core.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(core.CUDAPlace(0))
for place in places:
self.check_with_place(place)
for lazy_mode in (True, False):
self.check_with_place(place, lazy_mode)
if __name__ == "__main__":
......
python/paddle/fluid/tests/unittests/test_parallel_op.py 已删除 100644 → 0
浏览文件 @ ae6f46a1
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
from paddle.fluid.layers.device import get_places
from paddle.fluid.layers.control_flow import ParallelDo
import paddle.fluid.profiler as profiler
import numpy
import six
class BaseParallelForTest(unittest.TestCase):
def run_test(self, callback, feed, fetch):
"""
Run the unittest for parallel.for
Args:
callback(callable): A callable function returns a generator. There
are two yields in the generator function. The first yield
returns the data layers, and the second yield returns the loss.
The modified data variables will be sent back during the first
yield.
feed(dict): The executor feeding dictionary.
fetch(list|basestr): The fetch name lists.
Returns:
None
Raises:
AssertionError when the computation of cpu, parallel.for in cpu,
gpu, parallel.for in gpu are different.
"""
cpu = fluid.CPUPlace()
result_cpu = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=cpu,
use_parallel=False)
result_cpu_parallel = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=cpu,
use_parallel=True)
if fluid.core.is_compiled_with_cuda():
gpu = fluid.CUDAPlace(0)
result_gpu = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=gpu,
use_parallel=False,
use_gpu=True)
result_gpu_parallel = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=gpu,
use_parallel=True,
use_gpu=True)
result_gpu_nccl = self._run_test_impl_(
callback=callback,
feed=feed,
fetch=fetch,
place=gpu,
use_parallel=True,
use_nccl=True,
use_gpu=True)
self._assert_same_(fetch, result_cpu, result_cpu_parallel,
result_gpu, result_gpu_parallel, result_gpu_nccl)
else:
self._assert_same_(fetch, result_cpu, result_cpu_parallel)
def _run_test_impl_(self,
callback,
feed,
fetch,
place,
use_parallel=False,
use_nccl=False,
use_gpu=False):
"""
Run a single test, returns the fetch values
Args:
place(Place): the computation place.
use_parallel(bool): Whether use parallel.for or not.
Returns:
Fetched numpy arrays.
"""
if isinstance(fetch, six.string_types):
fetch = [fetch]
main = fluid.Program()
startup = fluid.Program()
# Fix seed
main.random_seed = 10
startup.random_seed = 10
with fluid.program_guard(main, startup):
generator = callback()
# Automatically insert parallel do if use_parallel = True
if use_parallel:
thread_num = fluid.core.get_cuda_device_count(
) if use_gpu else 8
places = get_places(thread_num)
pd = ParallelDo(places, use_nccl=use_nccl)
data = next(generator)
if isinstance(data, fluid.framework.Variable):
data = [data]
with pd.do():
ins = list(map(pd.read_input, data))
if len(ins) == 1:
ins = ins[0]
loss = generator.send(ins) # patch input
pd.write_output(loss)
loss = pd()
else:
data = next(generator)
loss = generator.send(data)
self.assertIsNotNone(loss)
avg_loss = fluid.layers.mean(loss)
fluid.backward.append_backward(loss=avg_loss)
exe = fluid.Executor(place)
exe.run(startup)
if use_gpu:
profile_type = 'GPU'
else:
profile_type = 'CPU'
with profiler.profiler(profile_type, 'total', '/tmp/profiler'):
return exe.run(main, feed=feed, fetch_list=fetch)
def _assert_same_(self, fetch, *args):
"""
Assert the return values of `run_test` are same.
Args:
fetch: Fetch list. Used for print error message
*args: The fetch result lists of each situations.
Returns:
None
Raises:
AssertionError
"""
def _impl_(a, b, fetch_id, item_id):
item_str = [
'CPU', 'ParallelCPU', 'GPU', 'ParallelGPU', 'ParallelGPUNCCL'
]
flag = numpy.allclose(a, b, rtol=0.1, atol=1e-3)
self.assertTrue(flag,
"The {0} are different in {1}, {2} vs {3}".format(
fetch[fetch_id], item_str[item_id], a, b))
for i, items in enumerate(zip(*args)):
self.assertGreater(len(items), 0)
for j in range(1, len(items)):
_impl_(items[0], items[j], fetch_id=i, item_id=j)
class ParallelOpTest(BaseParallelForTest):
@staticmethod
def __network__():
x = fluid.layers.data(shape=[784], dtype='float32', name='img')
x = yield x
hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden = fluid.layers.batch_norm(input=hidden)
loss = fluid.layers.mean(hidden)
yield loss
def test_simple_fc(self):
self.run_test(
callback=self.__network__,
feed={
'img': numpy.random.random(size=(51, 784)).astype('float32')
},
fetch=['fc1.w@GRAD'])
def test_fc_with_tiny_data(self):
self.run_test(
callback=self.__network__,
feed={'img': numpy.random.random(size=(1, 784)).astype('float32')},
fetch=['fc1.w@GRAD'])
class ParallelOpTestMultipleInput(BaseParallelForTest):
@staticmethod
def __network__():
x = fluid.layers.data(
shape=[784], dtype='float32', name='img1', stop_gradient=False)
y = fluid.layers.data(
shape=[784], dtype='float32', name='img2', stop_gradient=False)
yield [x, y]
x = x + y
hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
hidden2 = fluid.layers.fc(input=hidden1, size=200, param_attr='fc2.w')
hidden3 = fluid.layers.fc(input=hidden2, size=200, param_attr='fc3.w')
loss = fluid.layers.mean(hidden3)
yield loss
def test_simple_fc(self):
self.run_test(
callback=self.__network__,
feed={
'img1': numpy.random.random(size=(51, 784)).astype('float32'),
'img2': numpy.random.random(size=(51, 784)).astype('float32')
},
fetch=['fc1.w@GRAD', 'fc2.w@GRAD', 'fc3.w@GRAD'])
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_transpose_mkldnn_op.py 0 → 100644
浏览文件 @ 74a8e6b0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
from test_transpose_op import TestTransposeOp
class TestTransposeMKLDNN(TestTransposeOp):
def init_op_type(self):
self.op_type = "transpose2"
self.use_mkldnn = True
self.is_test = True
return
def test_check_grad(self):
return
def test_check_grad_no_input(self):
return
def test_check_grad_no_filter(self):
return
class TestCase0MKLDNN(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (3, )
self.axis = (0, )
class TestCase1a(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (3, 4, 5)
self.axis = (0, 2, 1)
class TestCase1b(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (3, 4, 5)
self.axis = (2, 1, 0)
class TestCase2(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (2, 3, 4, 5)
self.axis = (0, 2, 3, 1)
class TestCase3(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (2, 3, 4, 5, 6)
self.axis = (4, 2, 3, 1, 0)
class TestCase4(TestTransposeMKLDNN):
def initTestCase(self):
self.shape = (2, 3, 4, 5, 6, 1)
self.axis = (4, 2, 3, 1, 0, 5)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_transpose_op.py
浏览文件 @ 74a8e6b0
......@@ -21,15 +21,24 @@ from op_test import OpTest
class TestTransposeOp(OpTest):
def setUp(self):
self.init_op_type()
self.initTestCase()
self.op_type = "transpose2"
self.inputs = {'X': np.random.random(self.shape).astype("float32")}
self.attrs = {'axis': list(self.axis)}
self.attrs = {
'axis': list(self.axis),
'use_mkldnn': self.use_mkldnn,
'is_test': self.is_test,
}
self.outputs = {
'XShape': np.random.random(self.shape).astype("float32"),
'Out': self.inputs['X'].transpose(self.axis)
}
def init_op_type(self):
self.op_type = "transpose2"
self.use_mkldnn = False
self.is_test = False
def test_check_output(self):
self.check_output(no_check_set=['XShape'])
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ 74a8e6b0
......@@ -35,11 +35,10 @@ dtype_to_size = {
}
SUB_BLOCK_OPS = [
"while", "while_grad", "parallel_do", "parallel_do_grad",
"conditional_block", "conditional_block_grad"
"while", "while_grad", "conditional_block", "conditional_block_grad"
]
SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
SUB_BLOCK_PAIR = [("while", "while_grad"),
("conditional_block", "conditional_block_grad")]
PRINT_LOG = False
......
python/setup.py.in
浏览文件 @ 74a8e6b0
......@@ -160,10 +160,11 @@ if '${WITH_FLUID_ONLY}'== 'OFF':
# put all thirdparty libraries in paddle.libs
libs_path='${PADDLE_BINARY_DIR}/python/paddle/libs'
if os.name != 'nt':
package_data['paddle.libs']= []
package_data['paddle.libs']=['libwarpctc' + ext_name]
shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
package_data['paddle.libs']= []
package_data['paddle.libs']=[('libwarpctc' if os.name != 'nt' else 'warpctc') + ext_name]
shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
if '${WITH_MKL}' == 'ON':
shutil.copy('${MKLML_LIB}', libs_path)
shutil.copy('${MKLML_IOMP_LIB}', libs_path)
......
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