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Paddle
提交 8ed02339 编写于 作者: M minqiyang
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into accelerate_ddpg 

test=develop
上级 68b86d66 9c6a0203
展开全部 隐藏空白更改
内联 并排
Showing with 2800 addition and 211 deletion
paddle/contrib/float16/float16_transpiler.py
浏览文件 @ 8ed02339
......@@ -60,7 +60,7 @@ class Float16Transpiler:
raise TypeError("place should be as CPUPlace/CUDAPlace type")
if scope is None:
scope = global_scope()
if not isinstance(scope, core.Scope):
if not isinstance(scope, core._Scope):
raise TypeError("scope should be as Scope type or None")
self.scope = scope
......
paddle/fluid/API.spec
浏览文件 @ 8ed02339
......@@ -351,6 +351,23 @@ paddle.fluid.contrib.QuantizeTranspiler.__init__ ArgSpec(args=['self', 'weight_b
paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.QuantizeTranspiler.freeze_program ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.contrib.build_compressor ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.__init__ ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None))
paddle.fluid.contrib.CompressPass.add_strategy ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.CompressPass.apply ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.ImitationGraph.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.ImitationGraph.all_parameters ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.__init__ ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.MagnitudePruner.__init__ ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.MagnitudePruner.prune ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.RatioPruner.__init__ ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.RatioPruner.prune ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.contrib.load_persistables_for_increment ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.load_persistables_for_inference ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None)
paddle.fluid.contrib.convert_dist_to_sparse_program ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None)
......@@ -447,11 +464,7 @@ paddle.fluid.unique_name.switch ArgSpec(args=['new_generator'], varargs=None, ke
paddle.fluid.unique_name.guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None)
paddle.fluid.recordio_writer.convert_reader_to_recordio_file ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.recordio_writer.convert_reader_to_recordio_files ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None))
paddle.fluid.Scope.__init__ __init__(self: paddle.fluid.core.Scope) -> None
paddle.fluid.Scope.drop_kids drop_kids(self: paddle.fluid.core.Scope) -> None
paddle.fluid.Scope.find_var find_var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.fluid.Scope.new_scope new_scope(self: paddle.fluid.core.Scope) -> paddle.fluid.core.Scope
paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope
paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None)
paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None)
paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 8ed02339
......@@ -48,10 +48,10 @@ if(WITH_GPU)
nv_library(tensor SRCS tensor.cc .tensor_util.cu DEPS place memory data_type device_context)
add_dependencies(tensor tensor_util)
else()
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS place memory data_type device_context )
endif(WIN32)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context)
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS place memory data_type device_context )
endif()
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
......@@ -84,6 +84,7 @@ cc_library(threadpool SRCS threadpool.cc DEPS enforce)
cc_test(threadpool_test SRCS threadpool_test.cc DEPS threadpool)
cc_library(scope SRCS scope.cc DEPS glog threadpool xxhash)
cc_library(scope_pool SRCS scope_pool.cc DEPS scope)
cc_test(scope_test SRCS scope_test.cc DEPS scope)
cc_library(data_device_transform SRCS data_device_transform.cc DEPS tensor)
......
paddle/fluid/framework/attribute.h
浏览文件 @ 8ed02339
......@@ -165,7 +165,7 @@ template <typename T>
class GreaterThanChecker {
public:
explicit GreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
void operator()(T& value) const {
void operator()(const T& value) const {
PADDLE_ENFORCE(value > lower_bound_, "larger_than check fails.");
}
......@@ -177,7 +177,7 @@ template <typename T>
class EqualGreaterThanChecker {
public:
explicit EqualGreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
void operator()(T& value) const {
void operator()(const T& value) const {
PADDLE_ENFORCE_GE(value, lower_bound_, "equal_larger_than check fails.");
}
......@@ -193,7 +193,7 @@ class DefaultValueSetter {
public:
explicit DefaultValueSetter(T default_value)
: default_value_(default_value) {}
void operator()(T& value) const { value = default_value_; } // NOLINT
void operator()(T* value) const { *value = default_value_; }
private:
T default_value_;
......@@ -203,7 +203,7 @@ template <typename T>
class EnumInContainer {
public:
explicit EnumInContainer(const std::unordered_set<T>& c) : container_(c) {}
void operator()(T& val) const {
void operator()(const T& val) const {
PADDLE_ENFORCE(container_.find(val) != container_.end(),
"Value %s is not in enum container %s", val,
ContainerDebugString());
......@@ -232,7 +232,8 @@ class EnumInContainer {
// an attribute can have more than one limits
template <typename T>
class TypedAttrChecker {
typedef std::function<void(T&)> ValueChecker;
typedef std::function<void(T*)> DefaultValueChecker;
typedef std::function<void(const T&)> ValueChecker;
public:
explicit TypedAttrChecker(const std::string& attr_name)
......@@ -268,17 +269,17 @@ class TypedAttrChecker {
return *this;
}
void operator()(AttributeMap& attr_map) const { // NOLINT
if (!attr_map.count(attr_name_)) {
void operator()(AttributeMap* attr_map) const {
if (!attr_map->count(attr_name_)) {
// user do not set this attr
PADDLE_ENFORCE(!default_value_setter_.empty(),
"Attribute '%s' is required!", attr_name_);
// default_value_setter_ has no more than one element
T val;
(default_value_setter_[0])(val);
attr_map[attr_name_] = val;
(default_value_setter_[0])(&val);
(*attr_map)[attr_name_] = val;
}
Attribute& attr = attr_map.at(attr_name_);
Attribute& attr = attr_map->at(attr_name_);
ExtractAttribute<T> extract_attr(attr_name_);
T* attr_value = extract_attr(attr);
for (const auto& checker : value_checkers_) {
......@@ -289,12 +290,12 @@ class TypedAttrChecker {
private:
std::string attr_name_;
std::vector<ValueChecker> value_checkers_;
std::vector<ValueChecker> default_value_setter_;
std::vector<DefaultValueChecker> default_value_setter_;
};
// check whether op's all attributes fit their own limits
class OpAttrChecker {
typedef std::function<void(AttributeMap&)> AttrChecker;
typedef std::function<void(AttributeMap*)> AttrChecker;
public:
template <typename T>
......@@ -304,7 +305,7 @@ class OpAttrChecker {
return *(checker.target<TypedAttrChecker<T>>());
}
void Check(AttributeMap& attr_map) const { // NOLINT
void Check(AttributeMap* attr_map) const {
for (const auto& checker : attr_checkers_) {
checker(attr_map);
}
......
paddle/fluid/framework/details/multi_devices_graph_pass.cc
浏览文件 @ 8ed02339
......@@ -355,7 +355,9 @@ std::unique_ptr<ir::Graph> MultiDevSSAGraphBuilder::ApplyImpl(
BuildStrategy::GradientScaleStrategy::kCustomized) {
// TODO(paddle-dev): Why is there no input for this op_handle?
auto loss_grad_name = node->Op()->OutputArgumentNames()[0];
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0]);
auto out_dtype = all_vars_.at(loss_grad_name)->GetDataType();
CreateScaleLossGradOp(&result, loss_grad_name, node->outputs[0],
out_dtype);
}
// This assumes the backward generating code will ensure IsScaleLossOp
// is true only for the op that scale the final scalar loss.
......@@ -658,13 +660,13 @@ int MultiDevSSAGraphBuilder::GetVarDeviceID(
void MultiDevSSAGraphBuilder::CreateScaleLossGradOp(
ir::Graph *result, const std::string &loss_grad_name,
ir::Node *out_var_node) const {
ir::Node *out_var_node, proto::VarType::Type dtype) const {
for (size_t i = 0; i < places_.size(); ++i) {
// Insert ScaleCost OpHandle
auto *dev_ctx = platform::DeviceContextPool::Instance().Get(places_[i]);
auto *op_handle = new ScaleLossGradOpHandle(
result->CreateEmptyNode("scale_loss_grad", ir::Node::Type::kOperation),
local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx);
local_scopes_.size(), local_scopes_[i], places_[i], dev_ctx, dtype);
result->Get<GraphOps>(kGraphOps).emplace_back(op_handle);
// FIXME: Currently ScaleLossGradOp only use device_count as scale
......
paddle/fluid/framework/details/multi_devices_graph_pass.h
浏览文件 @ 8ed02339
......@@ -68,7 +68,8 @@ class MultiDevSSAGraphBuilder : public ir::Pass {
void CreateScaleLossGradOp(ir::Graph *result,
const std::string &loss_grad_name,
ir::Node *out_var_node) const;
ir::Node *out_var_node,
proto::VarType::Type dtype) const;
VarHandle *CreateReduceOp(ir::Graph *result, const std::string &og,
int dst_dev_id) const;
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.cc
浏览文件 @ 8ed02339
......@@ -22,39 +22,66 @@ namespace details {
ScaleLossGradOpHandle::ScaleLossGradOpHandle(ir::Node *node, size_t num_dev,
Scope *scope,
platform::Place place,
platform::DeviceContext *dev_ctx)
platform::DeviceContext *dev_ctx,
proto::VarType::Type dtype)
: OpHandleBase(node),
coeff_(static_cast<float>(1.0 / num_dev)),
scope_(scope),
place_(place) {
place_(place),
out_dtype_(dtype) {
this->SetDeviceContext(place_, dev_ctx);
}
ScaleLossGradOpHandle::~ScaleLossGradOpHandle() {}
struct ScaleLossGradFunctor {
float coeff_;
Tensor *out_;
platform::Place place_;
OpHandleBase *op_handle_;
proto::VarType::Type out_dtype_;
platform::DeviceContext *ctx_;
ScaleLossGradFunctor(float coeff, Tensor *out, platform::Place place,
OpHandleBase *op_handle, proto::VarType::Type dtype,
platform::DeviceContext *ctx)
: coeff_(coeff), out_(out), place_(place), out_dtype_(dtype), ctx_(ctx) {}
template <typename OutT>
void apply() const {
auto *out_data = out_->mutable_data<OutT>(place_);
if (platform::is_cpu_place(place_)) {
*out_data = static_cast<OutT>(coeff_);
} else {
#ifdef PADDLE_WITH_CUDA
OutT cast_coeff = static_cast<OutT>(coeff_);
auto stream = static_cast<platform::CUDADeviceContext *>(ctx_)->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), out_data,
platform::CPUPlace(), &cast_coeff, SizeOfType(out_dtype_),
stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
#endif
}
}
};
void ScaleLossGradOpHandle::RunImpl() {
// Doesn't wait any event
std::string var_name = static_cast<VarHandle *>(this->outputs_[0])->name_;
auto &local_scope = *scope_->FindVar(kLocalExecScopeName)->Get<Scope *>();
float *tmp = local_scope.FindVar(var_name)
->GetMutable<LoDTensor>()
->mutable_data<float>(make_ddim({1}), place_);
auto *tensor = local_scope.FindVar(var_name)->GetMutable<LoDTensor>();
tensor->Resize(make_ddim({1}));
if (platform::is_cpu_place(place_)) {
*tmp = coeff_;
} else {
#ifdef PADDLE_WITH_CUDA
this->RunAndRecordEvent([&] {
auto stream = static_cast<platform::CUDADeviceContext *>(
this->dev_ctxes_.at(place_))
->stream();
memory::Copy(boost::get<platform::CUDAPlace>(place_), tmp,
platform::CPUPlace(), &coeff_, sizeof(float), stream);
VLOG(10) << place_ << "RUN Scale loss grad op";
});
ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_,
this->dev_ctxes_.at(place_));
this->RunAndRecordEvent([&] { framework::VisitDataType(out_dtype_, func); });
#else
ScaleLossGradFunctor func(coeff_, tensor, place_, this, out_dtype_, nullptr);
framework::VisitDataType(out_dtype_, func);
#endif
}
}
std::string ScaleLossGradOpHandle::Name() const { return "Scale LossGrad"; }
......
paddle/fluid/framework/details/scale_loss_grad_op_handle.h
浏览文件 @ 8ed02339
......@@ -26,8 +26,8 @@ namespace details {
struct ScaleLossGradOpHandle : public OpHandleBase {
ScaleLossGradOpHandle(ir::Node *node, size_t num_dev, Scope *scope,
platform::Place place,
platform::DeviceContext *context);
platform::Place place, platform::DeviceContext *context,
proto::VarType::Type dtype);
~ScaleLossGradOpHandle() final;
......@@ -40,6 +40,7 @@ struct ScaleLossGradOpHandle : public OpHandleBase {
float coeff_;
Scope *scope_;
platform::Place place_;
proto::VarType::Type out_dtype_;
};
} // namespace details
......
paddle/fluid/framework/ngraph_bridge.cc
浏览文件 @ 8ed02339
......@@ -31,10 +31,12 @@ std::map<std::string,
std::shared_ptr<std::unordered_map<
std::string, std::shared_ptr<ngraph::Node>>>)>>
NgraphBridge::NG_NODE_MAP = {
{"fill_constant", paddle::operators::ngraphs::BuildFillConstantNode},
{"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>}};
{"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>},
{"top_k", paddle::operators::ngraphs::BuildTopKNode}};
void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
auto& op_type = op->Type();
......
paddle/fluid/framework/op_desc.cc
浏览文件 @ 8ed02339
......@@ -643,7 +643,7 @@ void OpDesc::CheckAttrs() {
// not by users.
return;
}
checker->Check(attrs_);
checker->Check(&attrs_);
}
void OpDesc::InferShape(const BlockDesc &block) const {
......
paddle/fluid/framework/op_proto_maker.cc
浏览文件 @ 8ed02339
......@@ -82,6 +82,10 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto,
AddAttr<std::string>(OpNamescopeAttrName(), "Operator name with namesope.")
.SetDefault("");
AddAttr<std::vector<std::string>>(OpCreationCallstackAttrName(),
"Callstack for Op Creatation.")
.SetDefault({});
Validate();
}
......
paddle/fluid/framework/op_proto_maker.h
浏览文件 @ 8ed02339
......@@ -47,6 +47,7 @@ class OpProtoAndCheckerMaker {
static const char *OpRoleAttrName() { return "op_role"; }
static const char *OpRoleVarAttrName() { return "op_role_var"; }
static const char *OpNamescopeAttrName() { return "op_namescope"; }
static const char *OpCreationCallstackAttrName() { return "op_callstack"; }
void operator()(proto::OpProto *proto, OpAttrChecker *attr_checker);
......
paddle/fluid/framework/op_registry.cc
浏览文件 @ 8ed02339
......@@ -24,7 +24,7 @@ std::unique_ptr<OperatorBase> OpRegistry::CreateOp(
const VariableNameMap& outputs, AttributeMap attrs) {
auto& info = OpInfoMap::Instance().Get(type);
if (info.Checker() != nullptr) {
info.Checker()->Check(attrs);
info.Checker()->Check(&attrs);
}
auto op = info.Creator()(type, inputs, outputs, attrs);
return std::unique_ptr<OperatorBase>(op);
......
paddle/fluid/framework/operator.cc
浏览文件 @ 8ed02339
......@@ -16,10 +16,15 @@ limitations under the License. */
#include <glog/logging.h>
#include <algorithm>
#include <sstream>
#include <string>
#include <vector>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/fluid/framework/data_transform.h"
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/shape_inference.h"
#include "paddle/fluid/framework/transfer_scope_cache.h"
......@@ -157,27 +162,59 @@ RuntimeContext::RuntimeContext(const VariableNameMap& innames,
}
void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
VLOG(4) << place << " " << DebugStringEx(&scope);
if (platform::is_gpu_place(place)) {
try {
if (VLOG_IS_ON(4)) {
VLOG(4) << place << " " << DebugStringEx(&scope);
}
if (platform::is_gpu_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("Cannot run operator on place %s", place);
PADDLE_THROW("Cannot run operator on place %s", place);
#else
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
auto dev_id = boost::get<platform::CUDAPlace>(place).device;
platform::SetDeviceId(dev_id);
#endif
}
}
// The profile has a process-wide mutex, results in serious performance issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
} else {
RunImpl(scope, place);
// The profile has a process-wide mutex, results in serious performance
// issue
// in concurrency scenerio. Here use an `if` to fix this issue.
// Please not remove the `if`, ask @Superjomn if there are any concern.
if (platform::IsProfileEnabled()) {
platform::DeviceContextPool& pool =
platform::DeviceContextPool::Instance();
platform::RecordEvent record_event(Type(), pool.Get(place));
RunImpl(scope, place);
} else {
RunImpl(scope, place);
}
if (VLOG_IS_ON(3)) {
VLOG(3) << place << " " << DebugStringEx(&scope);
}
} catch (platform::EnforceNotMet exception) {
if (Attrs().count("sub_block") != 0) {
throw exception;
}
auto& callstack = Attr<std::vector<std::string>>(
OpProtoAndCheckerMaker::OpCreationCallstackAttrName());
if (callstack.empty()) {
throw exception;
}
std::ostringstream sout;
sout << "Invoke operator " << Type() << " error.\n";
sout << "Python Callstacks: \n";
for (auto& line : callstack) {
sout << line;
}
sout << "C++ Callstacks: \n";
sout << exception.err_str_;
exception.err_str_ = sout.str();
throw exception;
} catch (...) {
std::rethrow_exception(std::current_exception());
}
VLOG(3) << place << " " << DebugStringEx(&scope);
}
bool OperatorBase::HasInputs(const std::string& name) const {
......@@ -1057,8 +1094,8 @@ proto::VarType::Type OperatorWithKernel::IndicateDataType(
t = &(var->Get<SelectedRows>().value());
}
if (t != nullptr) {
PADDLE_ENFORCE(t->IsInitialized(), "Input %s is not initialized: %s",
ipt_name, DebugString());
PADDLE_ENFORCE(t->IsInitialized(), "Input %s is not initialized",
ipt_name);
int tmp = static_cast<int>(t->type());
PADDLE_ENFORCE(
tmp == data_type || data_type == -1,
......
paddle/fluid/framework/scope_pool.cc 0 → 100644
浏览文件 @ 8ed02339
// 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/scope_pool.h"
#include "paddle/fluid/framework/threadpool.h"
namespace paddle {
namespace framework {
ScopePool &ScopePool::Instance() { // NOLINT
static ScopePool pool;
return pool;
}
void ScopePool::DeleteScope(Scope *scope) { delete scope; }
void ScopePool::Insert(std::unique_ptr<Scope> &&s) {
std::lock_guard<std::mutex> guard(mtx_);
scopes_.insert(s.release());
}
void ScopePool::Remove(Scope *s) {
size_t has_scope;
{
std::lock_guard<std::mutex> guard(mtx_);
has_scope = scopes_.erase(s);
}
PADDLE_ENFORCE(has_scope > 0, "Delete non-existing global scope");
DeleteScope(s);
}
ScopePool::~ScopePool() { Clear(); }
void ScopePool::Clear() {
std::lock_guard<std::mutex> guard(mtx_);
for (auto *s : scopes_) {
DeleteScope(s);
}
scopes_.clear();
}
} // namespace framework
} // namespace paddle
paddle/fluid/platform/create_tensor_with_allocationptr.h → paddle/fluid/framework/scope_pool.h
浏览文件 @ 8ed02339
......@@ -13,30 +13,34 @@
// limitations under the License.
#pragma once
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/temporary_allocator.h"
#include <mutex> // NOLINT
#include <unordered_set>
#include "paddle/fluid/framework/scope.h"
namespace paddle {
namespace platform {
template <typename T>
paddle::framework::Tensor GetTensor(
memory::allocation::AllocationPtr temp_allocation_ptr,
const framework::DDim &dim) {
auto &deleter = temp_allocation_ptr.get_deleter();
auto *allocation_ptr = temp_allocation_ptr.release();
auto shared_allocation =
std::shared_ptr<memory::allocation::Allocation>(allocation_ptr, deleter);
PADDLE_ENFORCE(dynamic_cast<TemporaryAllocation *>(allocation_ptr) != nullptr,
"The AllocationPtr must be TemporaryAllocation.");
PADDLE_ENFORCE_EQ(allocation_ptr->size(),
framework::product(dim) * sizeof(T));
paddle::framework::Tensor temp_tensor(std::type_index(typeid(T)));
temp_tensor.Resize(dim);
temp_tensor.ResetHolder(std::move(shared_allocation));
return temp_tensor;
}
} // namespace platform
namespace framework {
class ScopePool {
public:
static ScopePool &Instance(); // NOLINT
void Insert(std::unique_ptr<Scope> &&s);
void Remove(Scope *s);
void Clear();
~ScopePool();
private:
ScopePool() = default;
static void DeleteScope(Scope *scope);
std::unordered_set<Scope *> scopes_;
std::mutex mtx_;
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/tensor.cc
浏览文件 @ 8ed02339
......@@ -28,8 +28,7 @@ void Tensor::check_memory_size() const {
"or maybe the required data-type mismatches the data already stored.");
}
Tensor::Tensor(std::type_index type)
: type_(framework::ToDataType(type)), offset_(0) {}
Tensor::Tensor(const proto::VarType::Type& dtype) : type_(dtype), offset_(0) {}
size_t Tensor::memory_size() const {
return holder_ == nullptr ? 0UL : holder_->size() - offset_;
......
paddle/fluid/framework/tensor.h
浏览文件 @ 8ed02339
......@@ -69,7 +69,7 @@ class Tensor {
public:
Tensor() : type_(proto::VarType::FP32), offset_(0) {}
explicit Tensor(std::type_index type);
explicit Tensor(const proto::VarType::Type&);
/*! Return a pointer to mutable memory block. */
template <typename T>
......
paddle/fluid/framework/tensor_util.h
浏览文件 @ 8ed02339
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "paddle/fluid/framework/framework.pb.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/temporary_allocator.h"
namespace paddle {
namespace framework {
......@@ -151,5 +152,26 @@ void TensorToVector(const Tensor& src, std::vector<T>* dst) {
src_ptr, size);
}
template <typename T>
paddle::framework::Tensor GetTensor(
memory::allocation::AllocationPtr temp_allocation_ptr,
const framework::DDim& dim) {
auto& deleter = temp_allocation_ptr.get_deleter();
auto* allocation_ptr = temp_allocation_ptr.release();
auto shared_allocation =
std::shared_ptr<memory::allocation::Allocation>(allocation_ptr, deleter);
PADDLE_ENFORCE(
dynamic_cast<platform::TemporaryAllocation*>(allocation_ptr) != nullptr,
"The AllocationPtr must be TemporaryAllocation.");
PADDLE_ENFORCE_EQ(allocation_ptr->size(),
framework::product(dim) * sizeof(T));
paddle::framework::Tensor temp_tensor(
framework::ToDataType(std::type_index(typeid(T))));
temp_tensor.Resize(dim);
temp_tensor.ResetHolder(std::move(shared_allocation));
return temp_tensor;
}
} // namespace framework
} // namespace paddle
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 8ed02339
......@@ -231,11 +231,14 @@ bool AnalysisPredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx =
static_cast<const platform::CUDADeviceContext *>(pool.Get(place_));
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
inputs[i].data.length(), dev_ctx->stream());
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
......
paddle/fluid/inference/api/api_impl.cc
浏览文件 @ 8ed02339
......@@ -208,11 +208,14 @@ bool NativePaddlePredictor::SetFeed(const std::vector<PaddleTensor> &inputs,
inputs[i].data.length());
} else {
#ifdef PADDLE_WITH_CUDA
platform::DeviceContextPool &pool =
platform::DeviceContextPool::Instance();
auto *dev_ctx =
static_cast<const platform::CUDADeviceContext *>(pool.Get(place_));
auto dst_gpu_place = boost::get<platform::CUDAPlace>(place_);
memory::Copy(dst_gpu_place, static_cast<void *>(input_ptr),
platform::CPUPlace(), inputs[i].data.data(),
inputs[i].data.length(),
0); // stream 0 for sync copy
inputs[i].data.length(), dev_ctx->stream());
#else
PADDLE_THROW("Not compile with CUDA, should not reach here.");
#endif
......
paddle/fluid/inference/tests/api/CMakeLists.txt
浏览文件 @ 8ed02339
......@@ -75,6 +75,11 @@ set(LAC_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/lac")
download_model_and_data(${LAC_INSTALL_DIR} "lac_model.tar.gz" "lac_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_lac ${LAC_INSTALL_DIR} analyzer_lac_tester.cc)
# MM DNN
set(MM_DNN_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/mm_dnn")
download_model_and_data(${MM_DNN_INSTALL_DIR} "MM_DNN_model.tar.gz" "MM_DNN_data.txt.tar.gz")
inference_analysis_api_test(test_analyzer_mm_dnn ${MM_DNN_INSTALL_DIR} analyzer_mm_dnn_tester.cc)
# text_classification
set(TEXT_CLASSIFICATION_INSTALL_DIR "${INFERENCE_DEMO_INSTALL_DIR}/text_classification")
download_model_and_data(${TEXT_CLASSIFICATION_INSTALL_DIR} "text-classification-Senta.tar.gz" "text_classification_data.txt.tar.gz")
......@@ -103,6 +108,10 @@ inference_analysis_api_test_with_refer_result(test_analyzer_mobilenet_transpose
inference_analysis_api_test_with_fake_data(test_analyzer_resnet50
"${INFERENCE_DEMO_INSTALL_DIR}/resnet50" analyzer_resnet50_tester.cc "resnet50_model.tar.gz")
# seq_pool1
inference_analysis_api_test_with_fake_data(test_analyzer_seq_pool1
"${INFERENCE_DEMO_INSTALL_DIR}/seq_pool1" analyzer_seq_pool1_tester.cc "seq_pool1.tar.gz")
# mobilenet with depthwise_conv op
inference_analysis_api_test_with_fake_data(test_analyzer_mobilenet_depthwise_conv
"${INFERENCE_DEMO_INSTALL_DIR}/mobilenet_depthwise_conv" analyzer_resnet50_tester.cc "mobilenet_model.tar.gz")
......
paddle/fluid/inference/tests/api/analyzer_mm_dnn_tester.cc 0 → 100644
浏览文件 @ 8ed02339
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
using contrib::AnalysisConfig;
struct DataRecord {
std::vector<std::vector<int64_t>> query_data_all, title_data_all;
std::vector<size_t> lod1, lod2;
size_t batch_iter{0};
size_t batch_size{1};
size_t num_samples; // total number of samples
DataRecord() = default;
explicit DataRecord(const std::string &path, int batch_size = 1)
: batch_size(batch_size) {
Load(path);
}
DataRecord NextBatch() {
DataRecord data;
size_t batch_end = batch_iter + batch_size;
// NOTE skip the final batch, if no enough data is provided.
if (batch_end <= query_data_all.size()) {
data.query_data_all.assign(query_data_all.begin() + batch_iter,
query_data_all.begin() + batch_end);
data.title_data_all.assign(title_data_all.begin() + batch_iter,
title_data_all.begin() + batch_end);
// Prepare LoDs
data.lod1.push_back(0);
data.lod2.push_back(0);
CHECK(!data.query_data_all.empty());
CHECK(!data.title_data_all.empty());
CHECK_EQ(data.query_data_all.size(), data.title_data_all.size());
for (size_t j = 0; j < data.query_data_all.size(); j++) {
// calculate lod
data.lod1.push_back(data.lod1.back() + data.query_data_all[j].size());
data.lod2.push_back(data.lod2.back() + data.title_data_all[j].size());
}
}
batch_iter += batch_size;
return data;
}
void Load(const std::string &path) {
std::ifstream file(path);
std::string line;
int num_lines = 0;
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
split(line, '\t', &data);
// load query data
std::vector<int64_t> query_data;
split_to_int64(data[0], ' ', &query_data);
// load title data
std::vector<int64_t> title_data;
split_to_int64(data[1], ' ', &title_data);
query_data_all.push_back(std::move(query_data));
title_data_all.push_back(std::move(title_data));
}
num_samples = num_lines;
}
};
void PrepareInputs(std::vector<PaddleTensor> *input_slots, DataRecord *data,
int batch_size) {
PaddleTensor lod_query_tensor, lod_title_tensor;
lod_query_tensor.name = "left";
lod_title_tensor.name = "right";
auto one_batch = data->NextBatch();
int size1 = one_batch.lod1[one_batch.lod1.size() - 1]; // token batch size
int size2 = one_batch.lod2[one_batch.lod2.size() - 1]; // token batch size
lod_query_tensor.shape.assign({size1, 1});
lod_query_tensor.lod.assign({one_batch.lod1});
lod_title_tensor.shape.assign({size2, 1});
lod_title_tensor.lod.assign({one_batch.lod2});
// assign data
TensorAssignData<int64_t>(&lod_query_tensor, one_batch.query_data_all);
TensorAssignData<int64_t>(&lod_title_tensor, one_batch.title_data_all);
// Set inputs.
input_slots->assign({lod_query_tensor, lod_title_tensor});
for (auto &tensor : *input_slots) {
tensor.dtype = PaddleDType::INT64;
}
}
void SetConfig(contrib::AnalysisConfig *cfg) {
cfg->model_dir = FLAGS_infer_model;
cfg->use_gpu = false;
cfg->device = 0;
cfg->specify_input_name = true;
cfg->enable_ir_optim = true;
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
std::vector<PaddleTensor> input_slots;
int epoch = FLAGS_test_all_data ? data.num_samples / FLAGS_batch_size : 1;
LOG(INFO) << "number of samples: " << epoch * FLAGS_batch_size;
for (int bid = 0; bid < epoch; ++bid) {
PrepareInputs(&input_slots, &data, FLAGS_batch_size);
(*inputs).emplace_back(input_slots);
}
}
// Easy for profiling independently.
TEST(Analyzer_MM_DNN, profile) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, FLAGS_num_threads);
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
PADDLE_ENFORCE_EQ(outputs.size(), 2UL);
for (auto &output : outputs) {
size_t size = GetSize(output);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(output.data.data());
// output is probability, which is in (-1, 1).
for (size_t i = 0; i < size; i++) {
EXPECT_GT(result[i], -1);
EXPECT_LT(result[i], 1);
}
}
}
}
// Check the fuse status
TEST(Analyzer_MM_DNN, fuse_statis) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
}
// Compare result of NativeConfig and AnalysisConfig
TEST(Analyzer_MM_DNN, compare) {
contrib::AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareNativeAndAnalysis(
reinterpret_cast<const PaddlePredictor::Config *>(&cfg), input_slots_all);
}
// Compare Deterministic result
TEST(Analyzer_MM_DNN, compare_determine) {
AnalysisConfig cfg;
SetConfig(&cfg);
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
CompareDeterministic(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all);
}
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/analyzer_seq_pool1_tester.cc 0 → 100644
浏览文件 @ 8ed02339
/* 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 <fstream>
#include <iostream>
#include "paddle/fluid/inference/tests/api/tester_helper.h"
namespace paddle {
namespace inference {
namespace analysis {
void SetConfig(AnalysisConfig *cfg) {
cfg->param_file = FLAGS_infer_model + "/params";
cfg->prog_file = FLAGS_infer_model + "/model";
cfg->use_gpu = false;
cfg->device = 0;
cfg->enable_ir_optim = true;
cfg->specify_input_name = true;
cfg->SetCpuMathLibraryNumThreads(FLAGS_paddle_num_threads);
}
void SetInput(std::vector<std::vector<PaddleTensor>> *inputs) {
std::vector<std::string> feed_names = {
"slot10000_embed", "slot10001_embed", "slot10004_embed",
"slot10005_embed", "slot10008_embed", "slot10009_embed",
"slot10012_embed", "slot10013_embed", "slot10108_embed",
"slot13324_embed", "slot13325_embed", "slot13326_embed",
"slot13327_embed", "slot13328_embed", "slot13329_embed",
"slot13330_embed", "slot13331_embed", "slot15501_embed",
"slot15502_embed", "slot15503_embed", "slot15504_embed",
"slot15505_embed", "slot15506_embed", "slot15507_embed",
"slot15508_embed", "slot15516_embed", "slot15519_embed",
"slot15523_embed", "slot15531_embed", "slot15533_embed",
"slot15548_embed", "slot15564_embed", "slot15565_embed",
"slot15566_embed", "slot15570_embed", "slot15571_embed",
"slot15572_embed", "slot15573_embed", "slot15574_embed",
"slot15575_embed", "slot15576_embed", "slot15577_embed",
"slot15579_embed", "slot15581_embed", "slot15582_embed",
"slot15583_embed", "slot15584_embed", "slot5016_embed",
"slot5021_embed", "slot6002_embed", "slot6003_embed",
"slot6004_embed", "slot6005_embed", "slot6006_embed",
"slot6007_embed", "slot6008_embed", "slot6009_embed",
"slot6011_embed", "slot6014_embed", "slot6015_embed",
"slot6023_embed", "slot6024_embed", "slot6025_embed",
"slot6027_embed", "slot6029_embed", "slot6031_embed",
"slot6034_embed", "slot6035_embed", "slot6036_embed",
"slot6037_embed", "slot6039_embed", "slot6048_embed",
"slot6050_embed", "slot6058_embed", "slot6059_embed",
"slot6060_embed", "slot6066_embed", "slot6067_embed",
"slot6068_embed", "slot6069_embed", "slot6070_embed",
"slot6071_embed", "slot6072_embed", "slot6073_embed",
"slot6182_embed", "slot6183_embed", "slot6184_embed",
"slot6185_embed", "slot6186_embed", "slot6188_embed",
"slot6189_embed", "slot6190_embed", "slot6201_embed",
"slot6202_embed", "slot6203_embed", "slot6247_embed",
"slot6248_embed", "slot6250_embed", "slot6251_embed",
"slot6807_embed", "slot6808_embed", "slot6809_embed",
"slot6810_embed", "slot6811_embed", "slot6812_embed",
"slot6813_embed", "slot6814_embed", "slot6815_embed",
"slot6816_embed", "slot6817_embed", "slot6818_embed",
"slot6819_embed", "slot6820_embed", "slot6822_embed",
"slot6823_embed", "slot6826_embed", "slot7002_embed",
"slot7003_embed", "slot7004_embed", "slot7005_embed",
"slot7006_embed", "slot7008_embed", "slot7009_embed",
"slot7010_embed", "slot7011_embed", "slot7013_embed",
"slot7014_embed", "slot7015_embed", "slot7016_embed",
"slot7017_embed", "slot7019_embed", "slot7100_embed",
"slot7506_embed", "slot7507_embed", "slot7514_embed",
"slot7515_embed", "slot7516_embed"};
SetFakeImageInput(inputs, FLAGS_infer_model, true, "model", "params",
&feed_names);
}
// Easy for profiling independently.
void profile(bool use_mkldnn = false) {
AnalysisConfig cfg;
SetConfig(&cfg);
if (use_mkldnn) {
cfg.EnableMKLDNN();
}
std::vector<PaddleTensor> outputs;
std::vector<std::vector<PaddleTensor>> input_slots_all;
SetInput(&input_slots_all);
TestPrediction(reinterpret_cast<const PaddlePredictor::Config *>(&cfg),
input_slots_all, &outputs, FLAGS_num_threads);
}
TEST(Analyzer_seq_pool1, profile) { profile(); }
// Check the fuse status
TEST(Analyzer_seq_pool1, fuse_statis) {
AnalysisConfig cfg;
SetConfig(&cfg);
int num_ops;
auto predictor = CreatePaddlePredictor<AnalysisConfig>(cfg);
auto fuse_statis = GetFuseStatis(
static_cast<AnalysisPredictor *>(predictor.get()), &num_ops);
LOG(INFO) << "num_ops: " << num_ops;
EXPECT_EQ(num_ops, 314);
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/tests/api/tester_helper.h
浏览文件 @ 8ed02339
......@@ -132,7 +132,8 @@ std::unordered_map<std::string, int> GetFuseStatis(PaddlePredictor *predictor,
void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
const std::string &dirname, bool is_combined = true,
std::string model_filename = "model",
std::string params_filename = "params") {
std::string params_filename = "params",
const std::vector<std::string> *feed_names = nullptr) {
// Set fake_image_data
PADDLE_ENFORCE_EQ(FLAGS_test_all_data, 0, "Only have single batch of data.");
std::vector<std::vector<int64_t>> feed_target_shapes = GetFeedTargetShapes(
......@@ -146,26 +147,32 @@ void SetFakeImageInput(std::vector<std::vector<PaddleTensor>> *inputs,
os << "}\n";
}
LOG(INFO) << os.str();
int dim1 = feed_target_shapes[0][1];
int dim2 = feed_target_shapes[0][2];
int dim3 = feed_target_shapes[0][3];
PaddleTensor input;
std::vector<int> shape({FLAGS_batch_size, dim1, dim2, dim3});
input.shape = shape;
input.dtype = PaddleDType::FLOAT32;
// fill input data, for profile easily, do not use random data here.
size_t size = FLAGS_batch_size * dim1 * dim2 * dim3;
input.data.Resize(size * sizeof(float));
float *input_data = static_cast<float *>(input.data.data());
for (size_t i = 0; i < size; i++) {
*(input_data + i) = static_cast<float>(i) / size;
if (feed_names) {
PADDLE_ENFORCE_EQ(feed_names->size(), feed_target_shapes.size());
}
std::vector<PaddleTensor> input_slots(feed_target_shapes.size());
for (size_t i = 0; i < feed_target_shapes.size(); ++i) {
const auto &feed_shape = feed_target_shapes[i];
auto &input = input_slots[i];
std::vector<int> shape({FLAGS_batch_size});
for (size_t s = 1; s < feed_shape.size(); ++s) {
shape.push_back(static_cast<int>(feed_shape[s]));
}
if (feed_names) {
input.name = (*feed_names)[i];
}
input.shape = shape;
input.dtype = PaddleDType::FLOAT32;
size_t len = std::accumulate(shape.begin(), shape.end(), 1,
[](int a, int b) { return a * b; });
input.data.Resize(len * sizeof(float));
input.lod.assign({{0, static_cast<size_t>(FLAGS_batch_size)}});
float *input_data = static_cast<float *>(input.data.data());
// fill input data, for profile easily, do not use random data here.
for (size_t j = 0; j < len; ++j) {
*(input_data + j) = static_cast<float>(j) / len;
}
}
std::vector<PaddleTensor> input_slots;
input_slots.assign({input});
(*inputs).emplace_back(input_slots);
}
......
paddle/fluid/operators/conv_op.h
浏览文件 @ 8ed02339
......@@ -18,11 +18,11 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/tensor_util.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/depthwise_conv.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/vol2col.h"
#include "paddle/fluid/platform/create_tensor_with_allocationptr.h"
namespace paddle {
namespace operators {
......@@ -161,10 +161,7 @@ class GemmConvKernel : public framework::OpKernel<T> {
auto tmp_allocation_ptr =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
framework::product(col_shape) * sizeof(T));
Tensor tep_tensor =
platform::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
col.ShareDataWith(tep_tensor);
col = framework::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
......@@ -299,10 +296,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
auto tmp_allocation_ptr =
platform::DeviceTemporaryAllocator::Instance().Get(dev_ctx).Allocate(
framework::product(col_shape) * sizeof(T));
Tensor tep_tensor =
platform::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
col.ShareDataWith(tep_tensor);
col = framework::GetTensor<T>(std::move(tmp_allocation_ptr), col_shape);
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
}
......
paddle/fluid/operators/dequantize_mkldnn_op.cc 0 → 100644
浏览文件 @ 8ed02339
/* 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 "mkldnn.hpp"
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/dequantize_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
namespace paddle {
namespace operators {
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using platform::to_void_cast;
using Tensor = framework::Tensor;
using framework::DataLayout;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
template <typename T>
class DeQuantOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("Input");
auto scale_data = ctx.Attr<float>("Scale");
auto* output = ctx.Output<Tensor>("Output");
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& engine = dev_ctx.GetEngine();
const T* input_data = input->data<T>();
float* output_data = output->mutable_data<float>(ctx.GetPlace());
std::vector<float> reorder_scale = {1.0f / scale_data};
std::vector<primitive> pipeline;
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
mkldnn::memory::data_type src_dt =
paddle::framework::ToMKLDNNDataType(input->type());
mkldnn::memory::format src_fmt = input->format();
mkldnn::primitive_attr attri;
int mask = 0;
attri.set_output_scales(mask, reorder_scale);
auto src_md = platform::MKLDNNMemDesc({src_tz}, src_dt, src_fmt);
auto src_pd = mkldnn::memory::primitive_desc(src_md, engine);
auto src_memory =
std::make_shared<mkldnn::memory>(src_pd, to_void_cast<T>(input_data));
std::shared_ptr<primitive::at> src_memory_p =
std::shared_ptr<primitive::at>(new primitive::at(*src_memory));
auto dst_md = platform::MKLDNNMemDesc({dst_tz}, memory::data_type::f32,
memory::format::nchw);
auto dst_pd = mkldnn::memory::primitive_desc(dst_md, engine);
auto dst_memory = mkldnn::memory(dst_pd, to_void_cast<float>(output_data));
auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
new reorder::primitive_desc(src_pd, dst_pd, attri));
auto reorder_p = std::shared_ptr<reorder>(
new reorder(*reorder_pd, *src_memory_p, dst_memory));
pipeline.push_back(*reorder_p);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_format(GetMKLDNNFormat(dst_memory));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_KERNEL(dequantize, MKLDNN, ::paddle::platform::CPUPlace,
ops::DeQuantOpKernel<uint8_t>, ops::DeQuantOpKernel<int8_t>);
paddle/fluid/operators/dequantize_op.cc 0 → 100644
浏览文件 @ 8ed02339
/* 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 "paddle/fluid/operators/dequantize_op.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
framework::OpKernelType DeQuantOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_ = framework::LibraryType::kMKLDNN;
framework::DataLayout layout_ = framework::DataLayout::kMKLDNN;
return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_);
}
void DeQuantOpMaker::Make() {
AddInput("Input", "input data");
AddOutput("Output", "output data");
AddAttr<float>("Scale", "scale data").SetDefault({1.0f});
AddComment(R"DOC(This op will dequantize data from INT8 to FP32)DOC");
}
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(dequantize, ops::DeQuantOp, ops::DeQuantOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
paddle/fluid/operators/dequantize_op.h 0 → 100644
浏览文件 @ 8ed02339
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::OpKernelType;
using framework::Tensor;
class DeQuantOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Output", ctx->GetInputDim("Input"));
ctx->ShareLoD("Input", /*->*/ "Output");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class DeQuantOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
class DeQuantGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/density_prior_box_op.cu
浏览文件 @ 8ed02339
......@@ -142,12 +142,13 @@ class DensityPriorBoxOpCUDAKernel : public framework::OpKernel<T> {
vars->mutable_data<T>(ctx.GetPlace());
framework::Tensor d_temp;
framework::TensorCopySync(h_temp, ctx.GetPlace(), &d_temp);
framework::TensorCopy(h_temp, ctx.GetPlace(), &d_temp);
// At least use 32 threads, at most 512 threads.
// blockx is multiple of 32.
int blockx = std::min(
static_cast<long>(((feature_width * num_priors + 31) >> 5) << 5), 512L);
static_cast<int64_t>(((feature_width * num_priors + 31) >> 5) << 5),
512L);
int gridx = (feature_width * num_priors + blockx - 1) / blockx;
dim3 threads(blockx, 1);
dim3 grids(gridx, feature_height);
......
paddle/fluid/operators/elementwise/elementwise_div_op.cu
浏览文件 @ 8ed02339
......@@ -12,18 +12,23 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/elementwise/elementwise_div_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
elementwise_div,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivKernel<paddle::platform::CUDADeviceContext, int64_t>);
REGISTER_OP_CUDA_KERNEL(
elementwise_div_grad,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseDivGradKernel<paddle::platform::CUDADeviceContext,
......
paddle/fluid/operators/elementwise/elementwise_mul_op.cu
浏览文件 @ 8ed02339
......@@ -12,19 +12,21 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/elementwise/elementwise_mul_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
elementwise_mul,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulKernel<paddle::platform::CUDADeviceContext, int64_t>);
elementwise_mul, ops::ElementwiseMulKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseMulKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(
elementwise_mul_grad,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, double>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext, int>,
ops::ElementwiseMulGradKernel<paddle::platform::CUDADeviceContext,
int64_t>);
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, float>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, double>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, int64_t>,
ops::ElementwiseMulGradKernel<plat::CUDADeviceContext, plat::float16>);
paddle/fluid/operators/fill_zeros_like_op.cu.cc
浏览文件 @ 8ed02339
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/fluid/operators/fill_zeros_like_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
......@@ -22,4 +23,6 @@ REGISTER_OP_CUDA_KERNEL(
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, int64_t>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, float>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, double>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>,
ops::FillZerosLikeKernel<paddle::platform::CUDADeviceContext, bool>);
paddle/fluid/operators/math/concat_and_split.cu
浏览文件 @ 8ed02339
......@@ -131,8 +131,9 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
int in_col = input[0].numel() / in_row;
int out_row = in_row, out_col = 0;
std::vector<T*> inputs_data(in_num);
std::vector<const T*> inputs_data;
std::vector<int> inputs_col(in_num + 1);
inputs_data.reserve(in_num);
inputs_col[0] = 0;
bool sameShape = true;
......@@ -143,7 +144,7 @@ class ConcatFunctor<platform::CUDADeviceContext, T> {
}
out_col += t_cols;
inputs_col[i + 1] = out_col;
inputs_data[i] = const_cast<T*>(input[i].data<T>());
inputs_data.emplace_back(input[i].data<T>());
}
// computation
......
paddle/fluid/operators/math/selected_rows_functor.cc
浏览文件 @ 8ed02339
......@@ -12,6 +12,7 @@ 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 <algorithm>
#include <set>
#include <unordered_map>
......@@ -252,23 +253,26 @@ elementwise_add_to(const DeviceContext& ctx, BlasT<DeviceContext, T>* blas,
template <typename T>
struct MergeAdd<platform::CPUDeviceContext, T> {
framework::SelectedRows operator()(const platform::CPUDeviceContext& context,
const framework::SelectedRows& input) {
const framework::SelectedRows& input,
const bool sorted_result = false) {
framework::SelectedRows out;
(*this)(context, input, &out);
(*this)(context, input, &out, sorted_result);
return out;
}
void operator()(const platform::CPUDeviceContext& context,
const framework::SelectedRows& input,
framework::SelectedRows* output) {
framework::SelectedRows* output,
const bool sorted_result = false) {
std::vector<const framework::SelectedRows*> inputs;
inputs.push_back(&input);
(*this)(context, inputs, output);
(*this)(context, inputs, output, sorted_result);
}
void operator()(const platform::CPUDeviceContext& context,
const std::vector<const framework::SelectedRows*>& inputs,
framework::SelectedRows* output) {
framework::SelectedRows* output,
const bool sorted_result = false) {
if (inputs.size() == 0) {
VLOG(3) << "no input! return";
return;
......@@ -301,6 +305,9 @@ struct MergeAdd<platform::CPUDeviceContext, T> {
}
std::vector<int64_t> merge_rows(merged_row_set.begin(),
merged_row_set.end());
if (sorted_result) {
std::sort(merge_rows.begin(), merge_rows.end());
}
std::unordered_map<int64_t, size_t> rows_to_id;
for (size_t i = 0; i < merge_rows.size(); ++i) {
rows_to_id[merge_rows[i]] = i;
......
paddle/fluid/operators/math/selected_rows_functor.cu
浏览文件 @ 8ed02339
......@@ -266,7 +266,8 @@ __global__ void MergeAddKernel(const T* input, const int64_t* input_rows,
template <typename T>
struct MergeAdd<platform::CUDADeviceContext, T> {
framework::SelectedRows operator()(const platform::CUDADeviceContext& context,
const framework::SelectedRows& input) {
const framework::SelectedRows& input,
const bool sorted_result = false) {
framework::SelectedRows out;
(*this)(context, input, &out);
return out;
......@@ -274,7 +275,8 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const framework::SelectedRows& input,
framework::SelectedRows* output) {
framework::SelectedRows* output,
const bool sorted_result = false) {
framework::Vector<int64_t> input_rows(input.rows());
if (input_rows.size() == 0) {
return;
......@@ -312,7 +314,8 @@ struct MergeAdd<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& context,
const std::vector<const framework::SelectedRows*>& inputs,
framework::SelectedRows* output) {
framework::SelectedRows* output,
const bool sorted_result = false) {
if (inputs.size() == 0) {
VLOG(3) << "no input! return";
return;
......
paddle/fluid/operators/math/selected_rows_functor.h
浏览文件 @ 8ed02339
......@@ -81,13 +81,16 @@ struct MergeAdd {
// unary functor, merge by adding duplicated rows in
// the input SelectedRows object.
framework::SelectedRows operator()(const DeviceContext& context,
const framework::SelectedRows& input);
const framework::SelectedRows& input,
const bool sorted_result = false);
void operator()(const DeviceContext& context,
const framework::SelectedRows& input,
framework::SelectedRows* output);
framework::SelectedRows* output,
const bool sorted_result = false);
void operator()(const DeviceContext& context,
const std::vector<const framework::SelectedRows*>& inputs,
framework::SelectedRows* output);
framework::SelectedRows* output,
const bool sorted_result = false);
};
enum class ScatterOps { ASSIGN, ADD, SUB, SUBBY, MUL, DIV, DIVBY };
......
paddle/fluid/operators/metrics/accuracy_op.cu
浏览文件 @ 8ed02339
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <thrust/reduce.h>
#include "paddle/fluid/operators/metrics/accuracy_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
#include "paddle/fluid/platform/float16.h"
#include "paddle/fluid/platform/gpu_info.h"
namespace paddle {
......@@ -94,6 +95,7 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
// FIXME(typhoonzero): types of T is for inference data.
// label data is always int64
REGISTER_OP_CUDA_KERNEL(accuracy,
paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
paddle::operators::AccuracyOpCUDAKernel<double>,
paddle::operators::AccuracyOpCUDAKernel<paddle::platform::float16>);
paddle/fluid/operators/ngraph/ngraph_ops.h
浏览文件 @ 8ed02339
......@@ -22,4 +22,6 @@ limitations under the License. */
#pragma once
#include "ops/binary_unnary_op.h"
#include "ops/fill_constant_op.h"
#include "ops/mul_op.h"
#include "ops/top_k_op.h"
paddle/fluid/operators/ngraph/ops/binary_unnary_op.h
浏览文件 @ 8ed02339
......@@ -45,7 +45,6 @@ static void BuildUnaryNode(
auto out = std::make_shared<T>(input);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
......
paddle/fluid/operators/ngraph/ops/fill_constant_op.h 0 → 100644
浏览文件 @ 8ed02339
/*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 <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildFillConstantNode(
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());
auto vsp = op_attrs.Get<std::vector<int64_t>>("shape");
ngraph::Shape shape;
for (auto& sp : vsp) {
shape.push_back(sp);
}
float value = op_attrs.Get<float>("value");
ngraph::element::Type ng_dtype;
auto data_type = static_cast<paddle::framework::proto::VarType::Type>(
op_attrs.Get<int>("dtype"));
if (data_type == paddle::framework::proto::VarType::FP32) {
ng_dtype = ngraph::element::f32;
} else if (data_type == paddle::framework::proto::VarType::FP64) {
ng_dtype = ngraph::element::f64;
} else if (data_type == paddle::framework::proto::VarType::INT64) {
ng_dtype = ngraph::element::i64;
} else if (data_type == paddle::framework::proto::VarType::INT32) {
ng_dtype = ngraph::element::i32;
} else if (data_type == paddle::framework::proto::VarType::BOOL) {
ng_dtype = ngraph::element::boolean;
} else {
PADDLE_THROW("unsupported data type: %s", data_type);
}
auto out = ngraph::op::Constant::create(ng_dtype, shape, {value});
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/ngraph/ops/top_k_op.h 0 → 100644
浏览文件 @ 8ed02339
/*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 {
void BuildTopKNode(
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 k = op_attrs.Get<int>("k");
auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto top_k = std::make_shared<ngraph::op::TopK>(
input, input->get_shape().size() - 1, ngraph::element::i64, k);
std::shared_ptr<ngraph::Node> indices =
std::make_shared<ngraph::op::GetOutputElement>(top_k, 0);
std::shared_ptr<ngraph::Node> out =
std::make_shared<ngraph::op::GetOutputElement>(top_k, 1);
auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
if (dummy_out && dummy_out->get_element_type() != out->get_element_type()) {
out = std::make_shared<ngraph::op::Convert>(out,
dummy_out->get_element_type());
}
paddle::platform::SetOutputNode(op, "Indices", indices, ngb_node_map);
paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/optimizers/adam_op.h
浏览文件 @ 8ed02339
......@@ -157,8 +157,11 @@ struct AdamFunctor<T, CPUAdam> {
}
};
template <typename T, typename Flavour>
struct SparseAdamFunctor;
template <typename T>
struct SparseAdamFunctor {
struct SparseAdamFunctor<T, GPUAdam> {
T beta1_;
T beta2_;
T epsilon_;
......@@ -236,6 +239,106 @@ struct SparseAdamFunctor {
}
};
template <typename T>
struct SparseAdamFunctor<T, CPUAdam> {
T beta1_;
T beta2_;
T epsilon_;
const T* beta1_pow_;
const T* beta2_pow_;
const T* moment1_;
T* moment1_out_;
const T* moment2_;
T* moment2_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_;
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, bool lazy_mode)
: beta1_(beta1),
beta2_(beta2),
epsilon_(epsilon),
beta1_pow_(beta1_pow),
beta2_pow_(beta2_pow),
moment1_(mom1),
moment1_out_(mom1_out),
moment2_(mom2),
moment2_out_(mom2_out),
lr_(lr),
grad_(grad),
param_(param),
param_out_(param_out),
rows_(rows),
row_numel_(row_numel),
row_count_(row_count) {}
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];
T lr = *lr_;
T beta1_pow = *beta1_pow_;
T beta2_pow = *beta2_pow_;
T p = param_[i];
// Calculation
lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
mom1 = beta1_ * mom1 + (1 - beta1_) * g;
mom2 = beta2_ * mom2 + (1 - beta2_) * g * g;
p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
// Write back to global memory
moment1_out_[i] = mom1;
moment2_out_[i] = mom2;
param_out_[i] = p;
}
inline void operator()(size_t numel) const {
// lr could be reuse
T lr = *lr_;
T beta1_pow = *beta1_pow_;
T beta2_pow = *beta2_pow_;
lr *= sqrt(1 - beta2_pow) / (1 - beta1_pow);
size_t row_count = numel / row_numel_;
for (size_t i = 0U, j = 0U; i != row_count; ++i) {
if (i == *(rows_ + j)) {
for (size_t k = 0U; k != row_numel_; ++k) {
T g = grad_[j * row_numel_ + k];
adam_update(i * row_numel_ + k, g);
}
++j;
} else {
for (size_t k = 0U; k != row_numel_; ++k) {
T mom1 = moment1_[i * row_numel_ + k];
T mom2 = moment2_[i * row_numel_ + k];
T p = param_[i * row_numel_ + k];
mom1 = beta1_ * mom1;
mom2 = beta2_ * mom2;
p -= lr * (mom1 / (sqrt(mom2) + epsilon_));
// Write back to global memory
moment1_out_[i * row_numel_ + k] = mom1;
moment2_out_[i * row_numel_ + k] = mom2;
param_out_[i * row_numel_ + k] = p;
}
}
}
}
};
template <typename DeviceContext, typename T>
class AdamOpKernel : public framework::OpKernel<T> {
public:
......@@ -331,7 +434,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
.Var()
->GetMutable<framework::SelectedRows>();
merge_func(ctx.template device_context<DeviceContext>(), grad,
grad_merge_var);
grad_merge_var, true);
grad_merge_ptr = grad_merge_var;
}
......@@ -347,32 +450,46 @@ class AdamOpKernel : public framework::OpKernel<T> {
} else {
#endif
rows = grad_merge.rows().data();
#if defined(PADDLE_WITH_CUDA)
}
#endif
auto row_numel = grad_tensor.numel() / grad_merge.rows().size();
SparseAdamFunctor<T> functor(
beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(), mom1.template data<T>(),
mom1_out.template mutable_data<T>(ctx.GetPlace()),
mom2.template data<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(), 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]);
if (platform::is_cpu_place(ctx.GetPlace())) {
SparseAdamFunctor<T, CPUAdam> functor(
beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(), mom1.template data<T>(),
mom1_out.template mutable_data<T>(ctx.GetPlace()),
mom2.template data<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(), lazy_mode);
if (lazy_mode) {
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 {
functor(param.numel());
}
} else {
} else if (platform::is_gpu_place(ctx.GetPlace())) {
SparseAdamFunctor<T, GPUAdam> functor(
beta1, beta2, epsilon, beta1_pow.template data<T>(),
beta2_pow.template data<T>(), mom1.template data<T>(),
mom1_out.template mutable_data<T>(ctx.GetPlace()),
mom2.template data<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(), lazy_mode);
// FIXME(minqiyang): remove BinarySearch in GPU later
platform::ForRange<DeviceContext> for_range(
static_cast<const DeviceContext&>(ctx.device_context()),
param.numel());
......
paddle/fluid/operators/optimizers/momentum_op.cu
浏览文件 @ 8ed02339
......@@ -14,8 +14,11 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/optimizers/momentum_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
momentum, ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>);
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext, double>,
ops::MomentumOpKernel<paddle::platform::CUDADeviceContext,
paddle::platform::float16>);
paddle/fluid/operators/optimizers/momentum_op.h
浏览文件 @ 8ed02339
......@@ -237,7 +237,8 @@ class SparseMomentumFunctor<T, UseNesterov> {
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
......@@ -282,7 +283,8 @@ class SparseMomentumFunctor<T, NoNesterov> {
inline HOSTDEVICE void operator()(size_t i) {
auto row_idx =
math::BinarySearch<int64_t>(rows_, row_height_, i / row_numel_);
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_] : 0;
T g = row_idx >= 0 ? g_[row_idx * row_numel_ + i % row_numel_]
: static_cast<T>(0);
// put memory access in register
const T p = p_[i];
const T lr = lr_[0];
......
paddle/fluid/operators/quantize_mkldnn_op.cc 0 → 100644
浏览文件 @ 8ed02339
/* 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 "mkldnn.hpp"
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/operators/quantize_op.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/mkldnn_reuse.h"
namespace paddle {
namespace operators {
using mkldnn::memory;
using mkldnn::primitive;
using mkldnn::reorder;
using platform::to_void_cast;
using Tensor = framework::Tensor;
using framework::DataLayout;
using mkldnn::stream;
using platform::GetMKLDNNFormat;
template <typename T>
class QuantOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<Tensor>("Input");
auto scale_data = ctx.Attr<float>("Scale");
auto* output = ctx.Output<Tensor>("Output");
auto& dev_ctx =
ctx.template device_context<platform::MKLDNNDeviceContext>();
const auto& engine = dev_ctx.GetEngine();
std::vector<primitive> pipeline;
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims());
std::vector<int> dst_tz = paddle::framework::vectorize2int(output->dims());
const T* input_data = input->data<T>();
mkldnn::primitive_attr attri;
int mask = 0;
attri.set_output_scales(mask, {scale_data});
auto src_md = platform::MKLDNNMemDesc({src_tz}, memory::data_type::f32,
input->format());
auto src_pd = mkldnn::memory::primitive_desc(src_md, engine);
auto src_memory =
std::make_shared<mkldnn::memory>(src_pd, to_void_cast<T>(input_data));
std::shared_ptr<primitive::at> src_memory_p =
std::shared_ptr<primitive::at>(new primitive::at(*src_memory));
bool is_negative = ctx.Attr<bool>("is_negative_input");
std::shared_ptr<mkldnn::memory::primitive_desc> dst_pd;
std::shared_ptr<mkldnn::memory> dst_memory;
if (is_negative) {
platform::ConvMKLDNNHandler::SetDstMemory<int8_t>(
ctx, output, dst_tz, engine, dst_pd, dst_memory);
} else {
platform::ConvMKLDNNHandler::SetDstMemory<uint8_t>(
ctx, output, dst_tz, engine, dst_pd, dst_memory);
}
auto reorder_pd = std::shared_ptr<reorder::primitive_desc>(
new reorder::primitive_desc(src_pd, *dst_pd, attri));
auto reorder_p = std::shared_ptr<reorder>(
new reorder(*reorder_pd, *src_memory_p, *dst_memory));
pipeline.push_back(*reorder_p);
stream(stream::kind::eager).submit(pipeline).wait();
output->set_layout(DataLayout::kMKLDNN);
output->set_format(GetMKLDNNFormat(*dst_memory));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
// TODO(Xiaoli) Support FP32->S8 quantization.
REGISTER_OP_KERNEL(quantize, MKLDNN, ::paddle::platform::CPUPlace,
ops::QuantOpKernel<float>);
paddle/fluid/operators/quantize_op.cc 0 → 100644
浏览文件 @ 8ed02339
/* 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 "paddle/fluid/operators/quantize_op.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
framework::OpKernelType QuantOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_ = framework::LibraryType::kMKLDNN;
framework::DataLayout layout_ = framework::DataLayout::kMKLDNN;
return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_);
}
void QuantOpMaker::Make() {
AddInput("Input", "input data");
AddOutput("Output", "output data");
AddAttr<bool>("is_negative_input",
"(bool, default false) Only used in mkldnn INT8 kernel")
.SetDefault(false);
AddAttr<float>("Scale", "scale data").SetDefault({1.0f});
AddComment(R"DOC(This op will quantize data from FP32 to INT8)DOC");
}
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(quantize, ops::QuantOp, ops::QuantOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
paddle/fluid/operators/quantize_op.h 0 → 100644
浏览文件 @ 8ed02339
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using framework::OpKernelType;
using framework::Tensor;
class QuantOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim("Output", ctx->GetInputDim("Input"));
ctx->ShareLoD("Input", /*->*/ "Output");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
class QuantOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/sequence_ops/sequence_mask_op.h
浏览文件 @ 8ed02339
......@@ -52,7 +52,7 @@ class SequenceMaskOpMaker : public framework::OpProtoAndCheckerMaker {
"The maximum length of the sequence. If maxlen < 0, maxlen "
"= max(Input(X)).")
.SetDefault(-1)
.AddCustomChecker([](int &v) {
.AddCustomChecker([](const int &v) {
PADDLE_ENFORCE(v < 0 || v >= 1,
"Attr(maxlen) must be less than 0 or larger than 1");
});
......
paddle/fluid/operators/split_lod_tensor_op.cc
浏览文件 @ 8ed02339
......@@ -63,7 +63,7 @@ class SplitLoDTensorOp : public framework::OperatorBase {
}
auto *mask_data = cpu_mask->data<bool>();
std::vector<std::vector<CopyRange>> copy_ranges(mask_dim[0]);
std::vector<std::vector<CopyRange>> copy_ranges(2);
// set out_true/out_false lod
for (size_t t = 0; t < 2; t++) {
......
paddle/fluid/operators/top_k_op.cc
浏览文件 @ 8ed02339
......@@ -21,7 +21,7 @@ class TopkOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of TopkOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
......@@ -44,12 +44,25 @@ class TopkOp : public framework::OperatorWithKernel {
ctx->ShareLoD("X", "Out");
ctx->ShareLoD("X", "Indices");
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
ctx.device_context(), layout_, library_);
}
};
class TopkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor) The input of Topk op");
AddInput("K",
"(Tensor) Number of top elements to look for along "
"the last dimension (along each row for matrices).")
.AsDispensable();
AddOutput("Out", "(Tensor) The output tensor of Topk op");
AddOutput("Indices", "(Tensor) The indices of Topk elements of input");
AddComment(R"DOC(
......
paddle/fluid/operators/top_k_op.cu
浏览文件 @ 8ed02339
......@@ -16,6 +16,7 @@ limitations under the License. */
#include "paddle/fluid/operators/top_k_op.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cuda_device_function.h"
#include "paddle/fluid/platform/float16.h"
namespace paddle {
namespace operators {
......@@ -150,7 +151,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if (k < MaxLength - (*beam)) {
topk[k] = topk[k + *beam];
} else {
topk[k].set(-INFINITY, -1);
topk[k].set(-static_cast<T>(INFINITY), -1);
}
}
if (!(*is_empty)) {
......@@ -160,7 +161,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
}
*max = topk[MaxLength - 1];
if ((*max).v == -1) *is_empty = true;
if ((*max).v == -static_cast<T>(1)) *is_empty = true;
*beam = 0;
}
}
......@@ -181,7 +182,7 @@ __device__ __forceinline__ void ThreadGetTopK(Pair<T> topk[], int* beam,
if (k < MaxLength - *beam) {
topk[k] = topk[k + *beam];
} else {
topk[k].set(-INFINITY, -1);
topk[k].set(-static_cast<T>(INFINITY), -1);
}
}
if (!(*is_empty)) {
......@@ -278,7 +279,7 @@ __global__ void KeMatrixTopK(T* output, int output_stride, int64_t* indices,
bool firststep = true;
for (int j = 0; j < MaxLength; j++) {
topk[j].set(-INFINITY, -1);
topk[j].set(-static_cast<T>(INFINITY), -1);
}
while (top_num) {
ThreadGetTopK<T, MaxLength, BlockSize>(
......@@ -326,6 +327,17 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
auto* indices = ctx.Output<Tensor>("Indices");
size_t k = static_cast<int>(ctx.Attr<int>("k"));
auto* k_t = ctx.Input<Tensor>("K");
if (k_t) {
Tensor k_host;
framework::TensorCopySync(*k_t, platform::CPUPlace(), &k_host);
k = k_host.data<int>()[0];
framework::DDim output_dims = output->dims();
output_dims[output_dims.size() - 1] = k;
output->Resize(output_dims);
indices->Resize(output_dims);
}
const T* input_data = input->data<T>();
T* output_data = output->mutable_data<T>(ctx.GetPlace());
// FIXME(typhoonzero): data is always converted to type T?
......@@ -362,5 +374,7 @@ class TopkOpCUDAKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_CUDA_KERNEL(top_k, paddle::operators::TopkOpCUDAKernel<float>,
paddle::operators::TopkOpCUDAKernel<double>);
REGISTER_OP_CUDA_KERNEL(
top_k, paddle::operators::TopkOpCUDAKernel<float>,
paddle::operators::TopkOpCUDAKernel<double>,
paddle::operators::TopkOpCUDAKernel<paddle::platform::float16>);
paddle/fluid/operators/top_k_op.h
浏览文件 @ 8ed02339
......@@ -37,8 +37,16 @@ class TopkKernel : public framework::OpKernel<T> {
auto* input = ctx.Input<Tensor>("X");
auto* output = ctx.Output<Tensor>("Out");
auto* indices = ctx.Output<Tensor>("Indices");
// k is determined by Attr
const size_t k = static_cast<int>(ctx.Attr<int>("k"));
size_t k = static_cast<int>(ctx.Attr<int>("k"));
auto* k_t = ctx.Input<Tensor>("K");
if (k_t) {
k = k_t->data<int>()[0];
framework::DDim output_dims = output->dims();
output_dims[output_dims.size() - 1] = k;
output->Resize(output_dims);
indices->Resize(output_dims);
}
T* output_data = output->mutable_data<T>(ctx.GetPlace());
int64_t* indices_data = indices->mutable_data<int64_t>(ctx.GetPlace());
......
paddle/fluid/platform/device_context.cc
浏览文件 @ 8ed02339
......@@ -256,10 +256,11 @@ CUDADeviceContext::CUDADeviceContext(CUDAPlace place)
LOG_FIRST_N(WARNING, 1) << "Please NOTE: device: " << place_.device
<< ", CUDA Capability: " << compute_capability_
<< ", Driver Version: " << driver_version_ / 1000
<< ", Driver API Version: " << driver_version_ / 1000
<< "." << (driver_version_ % 100) / 10
<< ", Runtime Version: " << runtime_version_ / 1000
<< "." << (runtime_version_ % 100) / 10;
<< ", Runtime API Version: "
<< runtime_version_ / 1000 << "."
<< (runtime_version_ % 100) / 10;
size_t cudnn_dso_ver = dynload::cudnnGetVersion();
LOG_FIRST_N(WARNING, 1) << "device: " << place_.device
<< ", cuDNN Version: " << cudnn_dso_ver / 1000 << "."
......
paddle/fluid/platform/device_context.h
浏览文件 @ 8ed02339
......@@ -41,7 +41,28 @@ limitations under the License. */
namespace paddle {
namespace platform {
/*! \brief device temporary allocator singleton */
/*! \brief device temporary allocator singleton.
*
* Some operator needs temporary memory during computation, for example,
* conv_gemm, which needs use col to store the result of im2col. If we
* create a stack memory which is used by CUDA Kernel, before the
* Computation(...) returns, we should add ctx->Wait(), because the
* execution of CUDA is async, if there doesn't have ctx->Wait(),
* the temporary memory will be released before the CUDA Kernel uses
* it.
*
* DeviceTemporaryAllocator is a singleton, which contains a
* `TemporaryAllocator` for each <Place, Stream>. And the TemporaryAllocator
* contains a temp_allocation_queue which is used to store the temporary
* allocations. The allocation, which is allocated by TemporaryAllocator,
* is a unique_ptr, and when it is not held by any variable, it will be
* pushed into the temp_allocation_queue. There are two opportunities to free
* the allocations of temp_allocation_queue:
* - when the Stream calls cudaStreamSynchronize;
* - when the allocation size of opportunities exceeds a certain threshold
* (defined by FLAGS_limit_of_temporary_allocation).
*
* */
class DeviceTemporaryAllocator {
public:
static DeviceTemporaryAllocator& Instance() {
......
paddle/fluid/platform/mkldnn_reuse.h
浏览文件 @ 8ed02339
......@@ -15,6 +15,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout_transform.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/mkldnn_helper.h"
#include "paddle/fluid/platform/place.h"
......@@ -181,6 +182,21 @@ class MKLDNNHandler {
return dims2str(operand_dims) + suffix;
}
template <typename M>
static void SetDstMemory(
const framework::ExecutionContext& ctx, framework::Tensor* output,
std::vector<int> dst_tz, const mkldnn::engine& engine,
std::shared_ptr<mkldnn::memory::primitive_desc>& dst_pd, // NOLINT
std::shared_ptr<mkldnn::memory>& dst_memory) { // NOLINT
M* output_data = output->mutable_data<M>(ctx.GetPlace());
auto dst_md = platform::MKLDNNMemDesc(
{dst_tz}, paddle::framework::ToMKLDNNDataType(
framework::DataTypeTrait<M>::DataType),
mkldnn::memory::format::nhwc);
dst_pd.reset(new mkldnn::memory::primitive_desc(dst_md, engine));
dst_memory.reset(new mkldnn::memory(*dst_pd, to_void_cast<M>(output_data)));
}
protected:
static std::string dims2str(const mkldnn::memory::dims& operand_dims) {
std::string dstr = "";
......
paddle/fluid/platform/nccl_helper.h
浏览文件 @ 8ed02339
......@@ -23,6 +23,7 @@
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/float16.h"
#define NCCL_ID_VARNAME "NCCLID"
......@@ -38,6 +39,8 @@ inline ncclDataType_t ToNCCLDataType(framework::proto::VarType::Type type) {
return ncclInt;
} else if (type == framework::proto::VarType::INT64) {
return ncclInt64;
} else if (type == framework::proto::VarType::FP16) {
return ncclFloat16;
} else {
PADDLE_THROW("Not supported");
}
......
paddle/fluid/platform/temporary_allocator.h
浏览文件 @ 8ed02339
......@@ -29,6 +29,19 @@ class TemporaryAllocation : public memory::allocation::Allocation {
memory::allocation::AllocationPtr underlying_allocation_;
};
/*! \brief the TemporaryAllocator is used to alloc the temporary allocation
* which used by CUDA's async operation.
*
* The TemporaryAllocator contains a temp_allocation_queue which
* is used to store the temporary allocations. The allocation, which is
* allocated by TemporaryAllocator, is a unique_ptr, and when it is not held
* by any variable, it will be pushed into the temp_allocation_queue.
*
* There is one opportunity to free the allocations of temp_allocation_queue:
* - when the allocation size of opportunities exceeds a certain threshold
* (defined by FLAGS_limit_of_temporary_allocation).
*
* */
class TemporaryAllocator : public memory::allocation::Allocator {
public:
explicit TemporaryAllocator(platform::Place place);
......
paddle/fluid/platform/temporary_allocator_test.cc
浏览文件 @ 8ed02339
......@@ -14,8 +14,7 @@
#include "paddle/fluid/platform/temporary_allocator.h"
#include <gtest/gtest.h>
#include "paddle/fluid/framework/tensor.h"
#include "paddle/fluid/platform/create_tensor_with_allocationptr.h"
#include "paddle/fluid/framework/tensor_util.h"
DECLARE_double(limit_of_temporary_allocation);
namespace paddle {
......@@ -47,6 +46,7 @@ TEST(temporary_allocator, temporary_allocator) {
TEST(temporary_allocator, add_callback) {
#ifdef PADDLE_WITH_CUDA
const double limit = FLAGS_limit_of_temporary_allocation;
FLAGS_limit_of_temporary_allocation = 10;
platform::CUDAPlace gpu_place(0);
TemporaryAllocator gpu_alloc(gpu_place);
......@@ -63,7 +63,7 @@ TEST(temporary_allocator, add_callback) {
});
{ gpu_alloc.Allocate(100); }
PADDLE_ENFORCE(deleted);
FLAGS_limit_of_temporary_allocation = -1;
FLAGS_limit_of_temporary_allocation = limit;
#endif
}
......@@ -75,8 +75,8 @@ TEST(temporary_allocator, create_tensor_with_allocationptr) {
auto allocation = cpu_alloc.Allocate(memory_size);
void* address = allocation->ptr();
int numel = memory_size / sizeof(float);
framework::Tensor tensor =
GetTensor<float>(std::move(allocation), framework::make_ddim({numel}));
framework::Tensor tensor = framework::GetTensor<float>(
std::move(allocation), framework::make_ddim({numel}));
PADDLE_ENFORCE_EQ(address, tensor.data<float>());
PADDLE_ENFORCE_EQ(tensor.numel(), numel);
}
......@@ -90,8 +90,8 @@ TEST(temporary_allocator, create_tensor_with_allocationptr) {
auto allocation = gpu_alloc.Allocate(memory_size);
void* address = allocation->ptr();
int numel = memory_size / sizeof(float);
framework::Tensor tensor =
GetTensor<float>(std::move(allocation), framework::make_ddim({numel}));
framework::Tensor tensor = framework::GetTensor<float>(
std::move(allocation), framework::make_ddim({numel}));
PADDLE_ENFORCE_EQ(address, tensor.data<float>());
PADDLE_ENFORCE_EQ(tensor.numel(), numel);
}
......@@ -116,7 +116,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr2) {
{
auto allocation = cpu_alloc.Allocate(memory_size);
address = allocation->ptr();
framework::Tensor tensor = GetTensor<float>(
framework::Tensor tensor = framework::GetTensor<float>(
std::move(allocation), framework::make_ddim({numel}));
PADDLE_ENFORCE_EQ(address, tensor.data<float>());
PADDLE_ENFORCE_EQ(tensor.numel(), numel);
......@@ -138,7 +138,7 @@ TEST(temporary_allocator, create_tensor_with_allocationptr2) {
{
auto allocation = gpu_alloc.Allocate(memory_size);
address = allocation->ptr();
framework::Tensor tensor = GetTensor<float>(
framework::Tensor tensor = framework::GetTensor<float>(
std::move(allocation), framework::make_ddim({numel}));
PADDLE_ENFORCE_EQ(address, tensor.data<float>());
PADDLE_ENFORCE_EQ(tensor.numel(), numel);
......
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 8ed02339
set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler layer)
set(PYBIND_DEPS pybind python proto_desc memory executor async_executor prune feed_fetch_method pass_builder parallel_executor profiler layer scope_pool)
if(WITH_PYTHON)
list(APPEND PYBIND_DEPS py_func_op)
endif()
......
paddle/fluid/pybind/const_value.cc
浏览文件 @ 8ed02339
......@@ -49,6 +49,9 @@ void BindConstValue(pybind11::module* m) {
op_proto_and_checker_maker.def(
"kOpNameScopeAttrName",
framework::OpProtoAndCheckerMaker::OpNamescopeAttrName);
op_proto_and_checker_maker.def(
"kOpCreationCallstackAttrName",
framework::OpProtoAndCheckerMaker::OpCreationCallstackAttrName);
}
} // namespace pybind
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ 8ed02339
......@@ -32,6 +32,7 @@ limitations under the License. */
#include "paddle/fluid/framework/parallel_executor.h"
#include "paddle/fluid/framework/prune.h"
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/scope_pool.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/version.h"
#include "paddle/fluid/imperative/layer.h"
......@@ -117,6 +118,9 @@ PYBIND11_MODULE(core, m) {
return paddle::operators::AppendPythonCallableObjectAndReturnId(py_obj);
});
m.add_object("_cleanup",
py::capsule([]() { ScopePool::Instance().Clear(); }));
py::class_<imperative::VarBase, PyVarBase>(m, "VarBase", R"DOC()DOC")
.def(py::init<>())
.def("_run_backward",
......@@ -454,7 +458,7 @@ All parameter, weight, gradient are variables in Paddle.
},
py::return_value_policy::copy);
py::class_<Scope>(m, "Scope", R"DOC(
py::class_<Scope>(m, "_Scope", R"DOC(
Scope is an association of a name to Variable. All variables belong to Scope.
Variables in a parent scope can be retrieved from local scope.
......@@ -474,17 +478,26 @@ All parameter, weight, gradient are variables in Paddle.
param.set(param_array, place)
)DOC")
.def("_remove_from_pool",
[](Scope &self) { ScopePool::Instance().Remove(&self); })
.def("var",
[](Scope &self, const std::string &name) -> Variable * {
return self.Var(name);
},
py::return_value_policy::reference)
.def("find_var", &Scope::FindVar, py::return_value_policy::reference)
.def(py::init<>())
.def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); },
py::return_value_policy::reference)
.def("drop_kids", &Scope::DropKids);
m.def("Scope",
[]() -> Scope * {
auto *s = new Scope();
ScopePool::Instance().Insert(std::unique_ptr<Scope>(s));
return s;
},
py::return_value_policy::reference);
//! @note: Be careful! PyBind will return std::string as an unicode, not
//! Python str. If you want a str object, you should cast them in Python.
m.def("get_all_op_protos", []() -> std::vector<py::bytes> {
......
paddle/scripts/installation_validate.py 0 → 100644
浏览文件 @ 8ed02339
# 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.
import paddle.fluid as fluid
import paddle as pd
print(pd.__version__)
paddle/scripts/paddle_build.sh
浏览文件 @ 8ed02339
......@@ -79,6 +79,7 @@ function cmake_gen() {
PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/2.7/bin/python2.7
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/2.7/include/python2.7
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/2.7/lib/libpython2.7.dylib"
pip install --user -r ${PADDLE_ROOT}/python/requirements.txt
else
exit 1
fi
......@@ -91,6 +92,7 @@ function cmake_gen() {
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.5/include/python3.5m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.5/lib/libpython3.5m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
pip3.5 install --user -r ${PADDLE_ROOT}/python/requirements.txt
else
exit 1
fi
......@@ -103,6 +105,7 @@ function cmake_gen() {
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.6/include/python3.6m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.6/lib/libpython3.6m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
pip3.6 install --user -r ${PADDLE_ROOT}/python/requirements.txt
else
exit 1
fi
......@@ -115,6 +118,7 @@ function cmake_gen() {
-DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.7/include/python3.7m/
-DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.7/lib/libpython3.7m.dylib"
WITH_FLUID_ONLY=${WITH_FLUID_ONLY:-ON}
pip3.7 install --user -r ${PADDLE_ROOT}/python/requirements.txt
else
exit 1
fi
......@@ -441,7 +445,9 @@ EOF
# make install should also be test when unittest
make install -j 8
if [ "$1" == "cp27-cp27m" ]; then
set -e
pip install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
python ${PADDLE_ROOT}/paddle/scripts/installation_validate.py
elif [ "$1" == "cp35-cp35m" ]; then
pip3.5 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
elif [ "$1" == "cp36-cp36m" ]; then
......
python/paddle/fluid/__init__.py
浏览文件 @ 8ed02339
......@@ -46,7 +46,7 @@ from . import transpiler
from . import distribute_lookup_table
from .param_attr import ParamAttr, WeightNormParamAttr
from .data_feeder import DataFeeder
from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope, _Scope
from .transpiler import DistributeTranspiler, \
memory_optimize, release_memory, DistributeTranspilerConfig
from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
......
python/paddle/fluid/contrib/__init__.py
浏览文件 @ 8ed02339
......@@ -22,6 +22,8 @@ from . import op_frequence
from .op_frequence import *
from . import quantize
from .quantize import *
from . import slim
from .slim import *
from . import utils
from .utils import *
......@@ -30,4 +32,5 @@ __all__ += decoder.__all__
__all__ += memory_usage_calc.__all__
__all__ += op_frequence.__all__
__all__ += quantize.__all__
__all__ += slim.__all__
__all__ += utils.__all__
python/paddle/fluid/contrib/slim/__init__.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 .core import *
from .graph import *
from .prune import *
__all__ = [
'build_compressor',
'CompressPass',
'ImitationGraph',
'SensitivePruneStrategy',
'MagnitudePruner',
'RatioPruner',
]
python/paddle/fluid/contrib/slim/core/__init__.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 . import config
from .config import *
from . import compress_pass
from .compress_pass import *
from . import strategy
from .strategy import *
from . import pass_builder
from .pass_builder import *
__all__ = config.__all__ + compress_pass.__all__ + strategy.__all__ + pass_builder.__all__
python/paddle/fluid/contrib/slim/core/compress_pass.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 ....core import CPUPlace
from ..graph import get_executor
__all__ = ['Context', 'CompressPass']
class Context(object):
"""
The context in the process of compression.
Args:
exe: The executor used to execute graph.
graph: The graph to be compressed.
scope: The scope used to execute graph.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self, exe, graph, scope, program_exe=None):
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.exe = exe
self.graph = graph
self.scope = scope
self.program_exe = program_exe
class CompressPass(object):
"""
The pass used to compress model.
Args:
place: The device used in compression.
data_reader: The data_reader used to run graph.
data_feeder: The data_feeder used to run graph.
scope: The scope used to run graph.
metrics: The metrics for evaluating model.
epoch: The total epoches of trainning in compression.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self,
place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
program_exe=None):
self.strategies = []
self.place = CPUPlace() if place is None else place
self.data_reader = data_reader
self.data_feeder = data_feeder
self.scope = scope
self.metrics = metrics
self.epoch = epoch
self.program_exe = program_exe
def add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def apply(self, graph):
"""
Compress a model.
Args:
graph: The target graph to be compressed.
"""
self.executor = get_executor(graph, self.place)
context = Context(
self.executor, graph, self.scope, program_exe=self.program_exe)
for strategy in self.strategies:
strategy.on_compress_begin(context)
for epoch in range(self.epoch):
for strategy in self.strategies:
strategy.on_epoch_begin(context)
for data in self.data_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
fetches = None
if self.metrics:
fetches = self.metrics.values()
feed = None
if self.data_feeder:
feed = self.data_feeder.feed(data)
results = self.executor.run(graph,
fetches=fetches,
scope=self.scope,
feed=feed)
if results:
print("results: {}".format(
zip(self.metrics.keys(), results)))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
for strategy in self.strategies:
strategy.on_epoch_end(context)
context.epoch_id += 1
for strategy in self.strategies:
strategy.on_compress_end(context)
python/paddle/fluid/contrib/slim/core/config.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import inspect
import funcsigs
import yaml
from collections import OrderedDict
from ..prune import *
from .compress_pass import *
from .strategy import *
__all__ = ['ConfigFactory']
"""This factory is used to create instances by loading and parsing configure file with yaml format.
"""
class ConfigFactory(object):
def __init__(self, config):
"""Init a factory from configure file."""
self.instances = {}
self.version = None
self._parse_config(config)
def get_compress_pass(self):
"""
Get compress pass from factory.
"""
return self.instance('compress_pass')
def instance(self, name):
"""
Get instance from factory.
"""
if name in self.instances:
return self.instances[name]
else:
return None
def _new_instance(self, name, attrs):
if name not in self.instances:
class_ = globals()[attrs['class']]
sig = funcsigs.signature(class_.__init__)
keys = [
param.name for param in sig.parameters.values()
if (param.kind == param.POSITIONAL_OR_KEYWORD)
][1:]
keys = set(attrs.keys()).intersection(set(keys))
args = {}
for key in keys:
value = attrs[key]
if isinstance(value, str) and value in self.instances:
value = self.instances[value]
args[key] = value
self.instances[name] = class_(**args)
return self.instances.get(name)
def _parse_config(self, config):
assert config
with open(config, 'r') as config_file:
key_values = self._ordered_load(config_file)
for key in key_values:
# parse version
if key == 'version' and self.version is None:
self.version = int(key_values['version'])
assert self.version == int(key_values['version'])
# parse pruners
if key == 'pruners' or key == 'strategies':
instances = key_values[key]
for name in instances:
self._new_instance(name, instances[name])
if key == 'compress_pass':
compress_pass = self._new_instance(key, key_values[key])
for name in key_values[key]['strategies']:
strategy = self.instance(name)
compress_pass.add_strategy(strategy)
if key == 'include':
for config_file in key_values[key]:
self._parse_config(config_file.strip())
def _ordered_load(self,
stream,
Loader=yaml.Loader,
object_pairs_hook=OrderedDict):
"""
See: https://stackoverflow.com/questions/5121931/in-python-how-can-you-load-yaml-mappings-as-ordereddicts
"""
class OrderedLoader(Loader):
pass
def construct_mapping(loader, node):
loader.flatten_mapping(node)
return object_pairs_hook(loader.construct_pairs(node))
OrderedLoader.add_constructor(
yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, construct_mapping)
return yaml.load(stream, OrderedLoader)
python/paddle/fluid/contrib/slim/core/pass_builder.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 .compress_pass import CompressPass
from .config import ConfigFactory
__all__ = ['build_compressor']
def build_compressor(place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
config=None):
if config is not None:
factory = ConfigFactory(config)
comp_pass = factory.get_compress_pass()
else:
comp_pass = CompressPass()
comp_pass.place = place
comp_pass.data_reader = data_reader
comp_pass.data_feeder = data_feeder
comp_pass.scope = scope
comp_pass.metrics = metrics
comp_pass.epoch = epoch
return comp_pass
python/paddle/fluid/contrib/slim/core/strategy.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
__all__ = ['Strategy']
class Strategy(object):
"""
Base class for all strategies.
"""
def __init__(self, start_epoch=0, end_epoch=10):
"""
Args:
start_epoch: The first epoch to apply the strategy.
end_epoch: The last epoch to apply the strategy.
"""
self.start_epoch = start_epoch
self.end_epoch = end_epoch
def on_compress_begin(self, context):
pass
def on_epoch_begin(self, context):
pass
def on_epoch_end(self, context):
pass
def on_batch_begin(self, context):
pass
def on_batch_end(self, context):
pass
def on_compress_end(self, context):
pass
python/paddle/fluid/contrib/slim/demo/filter_prune/config.yaml 0 → 100644
浏览文件 @ 8ed02339
version: 1.0
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/demo/filter_prune/demo.py 0 → 100644
浏览文件 @ 8ed02339
# 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.
import paddle.fluid as fluid
import paddle
import os
import sys
from paddle.fluid.contrib.slim import CompressPass
from paddle.fluid.contrib.slim import build_compressor
from paddle.fluid.contrib.slim import ImitationGraph
class LinearModel(object):
def __init__(slef):
pass
def train(self):
train_program = fluid.Program()
startup_program = fluid.Program()
startup_program.random_seed = 10
with fluid.program_guard(train_program, startup_program):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=predict, label=y)
avg_cost = fluid.layers.mean(cost)
eval_program = train_program.clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
eval_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=1)
place = fluid.CPUPlace()
train_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
eval_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(startup_program)
train_metrics = {"loss": avg_cost.name}
eval_metrics = {"loss": avg_cost.name}
graph = ImitationGraph(train_program)
config = './config.yaml'
comp_pass = build_compressor(
place,
data_reader=train_reader,
data_feeder=train_feeder,
scope=fluid.global_scope(),
metrics=train_metrics,
epoch=1,
config=config)
comp_pass.apply(graph)
if __name__ == "__main__":
model = LinearModel()
model.train()
python/paddle/fluid/contrib/slim/graph/__init__.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 . import executor
from .executor import *
from . import graph
from .graph import *
from . import graph_pass
from .graph_pass import *
__all__ = executor.__all__
__all__ += graph.__all__
__all__ += graph_pass.__all__
python/paddle/fluid/contrib/slim/graph/executor.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
import abc
from abc import abstractmethod
from .... import executor
from .graph import IRGraph, ImitationGraph
__all__ = ['get_executor']
class GraphExecutor(object):
__metaclass__ = abc.ABCMeta
def __init__(self, place):
self.place = place
@abstractmethod
def run(self, graph, feches=None, feed=None):
pass
class IRGraphExecutor(GraphExecutor):
def run(self, grah, fetches, feed=None):
pass
class ImitationGraphExecutor(GraphExecutor):
def __init__(self, place):
super(ImitationGraphExecutor, self).__init__(place)
self.exe = executor.Executor(place)
def run(self, graph, scope=None, fetches=None, feed=None):
assert isinstance(graph, ImitationGraph)
fetch_list = None
if fetches:
fetch_list = [
graph.program.global_block().var(name) for name in fetches
]
results = self.exe.run(graph.program,
scope=scope,
fetch_list=fetch_list,
feed=feed)
return results
def get_executor(graph, place):
if isinstance(graph, ImitationGraph):
return ImitationGraphExecutor(place)
if isinstance(graph, IRGraph):
return IRGraphExecutor(place)
python/paddle/fluid/contrib/slim/graph/graph.py 0 → 100644
浏览文件 @ 8ed02339
# 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 ....framework import Program
__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
class Graph(object):
"""
Base class for all graph.
"""
def __init__(self):
pass
def all_parameters(self):
"""
Return all the parameters in current graph.
"""
pass
class ImitationGraph(Graph):
def __init__(self, program=None):
super(ImitationGraph, self).__init__()
self.program = Program() if program is None else program
def all_parameters(self):
return self.program.global_block().all_parameters()
class IRGraph(Graph):
pass
python/paddle/fluid/contrib/slim/graph/graph_pass.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
__all__ = ['GraphPass', 'PruneParameterPass']
class GraphPass(object):
"""
Base class for all graph pass.
"""
def __init__(self):
pass
def apply(self, graph):
pass
class PruneParameterPass(GraphPass):
"""
Generate a graph for pruning parameters from target graph.
"""
def __init__(self, pruned_params, thresholds):
super(PruneParameterPass, self).__init__()
self.pruned_params = pruned_params
self.thresholds = thresholds
self.default_threshold = thresholds['*']
def apply(self, graph):
pass
python/paddle/fluid/contrib/slim/prune/__init__.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 . import pruner
from .pruner import *
from . import prune_strategy
from .prune_strategy import *
__all__ = pruner.__all__
__all__ += prune_strategy.__all__
python/paddle/fluid/contrib/slim/prune/prune_strategy.py 0 → 100644
浏览文件 @ 8ed02339
# 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 ..core.strategy import Strategy
from ....framework import Program, program_guard
from .... import layers
import numpy as np
__all__ = ['SensitivePruneStrategy', 'PruneStrategy']
class SensitivePruneStrategy(Strategy):
def __init__(self,
pruner=None,
start_epoch=0,
end_epoch=10,
delta_rate=0.20,
acc_loss_threshold=0.2,
sensitivities=None):
super(SensitivePruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.delta_rate = delta_rate
self.acc_loss_threshold = acc_loss_threshold
self.sensitivities = sensitivities
class PruneStrategy(Strategy):
"""
The strategy that pruning weights by threshold or ratio iteratively.
"""
def __init__(self,
pruner,
mini_batch_pruning_frequency=1,
start_epoch=0,
end_epoch=10):
super(PruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.mini_batch_pruning_frequency = mini_batch_pruning_frequency
def _triger(self, context):
return (context.batch_id % self.mini_batch_pruning_frequency == 0 and
self.start_epoch <= context.epoch_id < self.end_epoch)
def on_batch_end(self, context):
if self._triger(context):
prune_program = Program()
with program_guard(prune_program):
for param in context.graph.all_parameters():
prune_program.global_block().clone_variable(param)
p = prune_program.global_block().var(param.name)
zeros_mask = self.pruner.prune(p)
pruned_param = p * zeros_mask
layers.assign(input=pruned_param, output=param)
context.program_exe.run(prune_program, scope=context.scope)
python/paddle/fluid/contrib/slim/prune/pruner.py 0 → 100644
浏览文件 @ 8ed02339
# 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.
import numpy as np
from .... import layers
__all__ = ['Pruner', 'MagnitudePruner', 'RatioPruner']
class Pruner(object):
"""
Base class of all pruners.
"""
def __init__(self):
pass
def prune(self, param):
pass
class MagnitudePruner(Pruner):
"""
Pruner used to pruning a parameter by threshold.
"""
def __init__(self, threshold):
self.threshold = threshold
def prune(self, param, threshold=None):
if threshold is None:
thres = layers.fill_constant(
shape=[1], dtype='float32', value=self.threshold)
else:
thres = threshold
zeros_mask = layers.less_than(x=param, y=thres)
return zeros_mask
class RatioPruner(Pruner):
"""
Pruner used to pruning a parameter by ratio.
"""
def __init__(self, ratios=None):
"""
Args:
ratios: dict with pair (paramer_name, pruned_ratio).
"""
self.ratios = ratios
def prune(self, param, ratio=None):
"""
Args:
ratio: `ratio=40%` means pruning (1 - 40%) weights to zero.
"""
if ratio is None:
rat = self.ratios[
param.name] if param.name in self.ratios else self.ratios['*']
else:
rat = ratio
if rat < 1.0:
k = max(int(rat * np.prod(param.shape)), 1)
param_vec = layers.reshape(x=param, shape=[1, -1])
param_topk, _ = layers.topk(param_vec, k=k)
threshold = layers.slice(
param_topk, axes=[1], starts=[-1], ends=[k])
threshold = layers.reshape(x=threshold, shape=[1])
zeros_mask = layers.less_than(x=param, y=threshold)
else:
zeros_mask = layers.ones(param.shape)
return zeros_mask
python/paddle/fluid/contrib/slim/unitest/__init__.py 0 → 100644
浏览文件 @ 8ed02339
# 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.
python/paddle/fluid/contrib/slim/unitest/configs/config.yaml 0 → 100644
浏览文件 @ 8ed02339
version: 1.0
include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_2'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/unitest/configs/pruners.yaml 0 → 100644
浏览文件 @ 8ed02339
version: 1.0
pruners:
pruner_2:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/unitest/configs/pruners_0.yaml 0 → 100644
浏览文件 @ 8ed02339
version: 1.0
pruners:
pruner_3:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/unitest/test_factory.py 0 → 100644
浏览文件 @ 8ed02339
# 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 paddle.fluid.contrib.slim import ConfigFactory
import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./unitest/configs/config.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
pruner = factory.instance('pruner_2')
self.assertEquals(pruner.ratios['*'], 0.7)
strategy = factory.instance('strategy_1')
pruner = strategy.pruner
self.assertEquals(pruner.ratios['*'], 0.7)
compress_pass = factory.get_compress_pass()
self.assertEquals(compress_pass.epoch, 100)
strategy = compress_pass.strategies[0]
self.assertEquals(strategy.delta_rate, 0.2)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/data_feeder.py
浏览文件 @ 8ed02339
......@@ -44,6 +44,8 @@ class DataToLoDTensorConverter(object):
self.dtype = 'int64'
elif dtype == core.VarDesc.VarType.FP64:
self.dtype = 'float64'
elif dtype == core.VarDesc.VarType.FP16:
self.dtype = 'float16'
elif dtype == core.VarDesc.VarType.INT32:
self.dtype = 'int32'
elif dtype == core.VarDesc.VarType.UINT8:
......
python/paddle/fluid/executor.py
浏览文件 @ 8ed02339
......@@ -191,7 +191,7 @@ def _fetch_var(name, scope=None, return_numpy=True):
assert isinstance(name, str)
if scope is None:
scope = global_scope()
assert isinstance(scope, core.Scope)
assert isinstance(scope, core._Scope)
var = scope.find_var(name)
assert var is not None, (
......
python/paddle/fluid/framework.py
浏览文件 @ 8ed02339
......@@ -20,6 +20,7 @@ import os
import re
import six
import sys
import traceback
import numpy as np
......@@ -604,6 +605,10 @@ class Operator(object):
if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
del op_attrs[role_var_name]
callstack_var_name = op_maker.kOpCreationCallstackAttrName()
op_attrs[callstack_var_name] = list(
reversed(traceback.format_stack()))[1:]
if len(self.desc.type()) != 0:
return
if type is None:
......
python/paddle/fluid/initializer.py
浏览文件 @ 8ed02339
......@@ -18,6 +18,7 @@ from . import framework
import numpy as np
import contextlib
from .core import VarDesc
from . import unique_name
__all__ = [
'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
......@@ -207,16 +208,39 @@ class UniformInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16:
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
op = block._prepend_op(
type="uniform_random",
outputs={"Out": var},
outputs={"Out": out_var},
attrs={
"shape": var.shape,
"dtype": int(var.dtype),
"dtype": out_dtype,
"min": self._low,
"max": self._high,
"seed": self._seed
})
if var.dtype == VarDesc.VarType.FP16:
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
var.op = op
return op
......@@ -261,17 +285,39 @@ class NormalInitializer(Initializer):
# Initialization Ops should be prepended and not appended
if self._seed == 0:
self._seed = block.program.random_seed
# to be compatible of fp16 initalizers
if var.dtype == VarDesc.VarType.FP16:
out_dtype = VarDesc.VarType.FP32
out_var = block.create_var(
name=unique_name.generate(".".join(['gaussian_random', 'tmp'])),
shape=var.shape,
dtype=out_dtype,
type=VarDesc.VarType.LOD_TENSOR,
persistable=False)
else:
out_dtype = var.dtype
out_var = var
op = block._prepend_op(
type="gaussian_random",
outputs={"Out": var},
outputs={"Out": out_var},
attrs={
"shape": var.shape,
"dtype": int(var.dtype),
"dtype": out_dtype,
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed,
"use_mkldnn": False
})
if var.dtype == VarDesc.VarType.FP16:
block.append_op(
type="cast",
inputs={"X": out_var},
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
var.op = op
return op
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 8ed02339
......@@ -2801,6 +2801,10 @@ def batch_norm(input,
helper = LayerHelper('batch_norm', **locals())
dtype = helper.input_dtype()
# use fp32 for bn parameter
if dtype == core.VarDesc.VarType.FP16:
dtype = core.VarDesc.VarType.FP32
input_shape = input.shape
if data_layout == 'NCHW':
channel_num = input_shape[1]
......@@ -2835,7 +2839,7 @@ def batch_norm(input,
trainable=False,
do_model_average=do_model_average_for_mean_and_var),
shape=param_shape,
dtype=input.dtype)
dtype=dtype)
mean.stop_gradient = True
variance = helper.create_parameter(
......@@ -2845,7 +2849,7 @@ def batch_norm(input,
trainable=False,
do_model_average=do_model_average_for_mean_and_var),
shape=param_shape,
dtype=input.dtype)
dtype=dtype)
variance.stop_gradient = True
# create output
......@@ -4526,7 +4530,7 @@ def topk(input, k, name=None):
Args:
input(Variable): The input variable which can be a vector or Tensor with
higher rank.
k(int): The number of top elements to look for along the last dimension
k(int | Variable): The number of top elements to look for along the last dimension
of input.
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
......@@ -4549,12 +4553,18 @@ def topk(input, k, name=None):
helper = LayerHelper("top_k", **locals())
values = helper.create_variable_for_type_inference(dtype=input.dtype)
indices = helper.create_variable_for_type_inference(dtype="int64")
inputs = {"X": [input]}
attrs = None
if isinstance(k, Variable):
inputs['K'] = k
else:
attrs = {'k': k}
helper.append_op(
type="top_k",
inputs={"X": [input]},
inputs=inputs,
outputs={"Out": [values],
"Indices": [indices]},
attrs={"k": k})
attrs=attrs)
values.stop_gradient = True
indices.stop_gradient = True
return values, indices
......@@ -7943,7 +7953,7 @@ def unstack(x, axis=0, num=None):
num = x.shape[axis]
outs = []
for _ in num:
for _ in range(num):
outs.append(helper.create_variable_for_type_inference(x.dtype))
helper.append_op(
......
python/paddle/fluid/tests/unittests/ngraph/test_fill_constant_ngraph_op.py 0 → 100644
浏览文件 @ 8ed02339
# 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_fill_constant_op import TestFillConstantOp1, TestFillConstantOp2, TestFillConstantOpWithSelectedRows
class TestNGRAPHFillConstantOp1(TestFillConstantOp1):
def setUp(self):
super(TestNGRAPHFillConstantOp1, self).setUp()
class TestNGRAPHFillConstantOp2(TestFillConstantOp2):
def setUp(self):
super(TestNGRAPHFillConstantOp2, self).setUp()
class TestNGRAPHFillConstantOpWithSelectedRows(
TestFillConstantOpWithSelectedRows):
def setUp(self):
super(TestFillConstantOpWithSelectedRows, self).setUp()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/ngraph/test_top_k_ngraph_op.py 0 → 100644
浏览文件 @ 8ed02339
# 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_top_k_op import TestTopkOp, TestTopkOp3d, TestTopkOp2, TestTopkOp3, TestTopkOp4
class TestNGRAPHTopkOp(TestTopkOp):
def setUp(self):
super(TestNGRAPHTopkOp, self).setUp()
class TestNGRAPHTopkOp2(TestTopkOp2):
def setUp(self):
super(TestNGRAPHTopkOp2, self).setUp()
class TestNGRAPHTopkOp3(TestTopkOp3):
def setUp(self):
super(TestNGRAPHTopkOp3, self).setUp()
class TestNGRAPHTopkOp4(TestTopkOp4):
def setUp(self):
super(TestNGRAPHTopkOp4, self).setUp()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @ 8ed02339
......@@ -368,6 +368,8 @@ class OpTest(unittest.TestCase):
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
return [place]
else:
return []
else:
return []
places = [fluid.CPUPlace()]
......
python/paddle/fluid/tests/unittests/test_accuracy_op.py
浏览文件 @ 8ed02339
......@@ -22,8 +22,10 @@ from op_test import OpTest
class TestAccuracyOp(OpTest):
def setUp(self):
self.op_type = "accuracy"
self.dtype = np.float32
self.init_dtype()
n = 8192
infer = np.random.random((n, 1)).astype("float32")
infer = np.random.random((n, 1)).astype(self.dtype)
indices = np.random.randint(0, 2, (n, 1))
label = np.random.randint(0, 2, (n, 1))
self.inputs = {'Out': infer, 'Indices': indices, "Label": label}
......@@ -34,14 +36,25 @@ class TestAccuracyOp(OpTest):
num_correct += 1
break
self.outputs = {
'Accuracy': np.array([num_correct / float(n)]).astype("float32"),
'Accuracy': np.array([num_correct / float(n)]).astype(self.dtype),
'Correct': np.array([num_correct]).astype("int32"),
'Total': np.array([n]).astype("int32")
}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestAccuracyOpFp16(TestAccuracyOp):
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
self.check_output(atol=1e-3)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_dequantize_mkldnn_op.py 0 → 100644
浏览文件 @ 8ed02339
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import unittest
import numpy as np
from op_test import OpTest
class TestDeQuantizeOp(OpTest):
def setUp(self):
self.op_type = 'dequantize'
self.scale = 2.0
self.input_size = [1, 1, 5, 5] #Naive nChw16c
self.data_type = 'int8'
self.set_scale()
self.set_data_type()
if self.data_type == 'int8':
input = (np.random.randint(0, 100, self.input_size) - 50
).astype(self.data_type)
output = (input * (1 / self.scale)).astype('float')
else:
input = (np.random.randint(0, 100,
self.input_size)).astype(self.data_type)
output = (input * (1 / self.scale)).astype('float')
self.inputs = {'Input': OpTest.np_dtype_to_fluid_dtype(input)}
self.outputs = {'Output': output}
self.attrs = {'Scale': self.scale, }
def test_check_output(self):
self.check_output()
def set_scale(self):
pass
def set_data_type(OpTest):
pass
class TestDeQuantizeOp1(TestDeQuantizeOp):
def set_scale(self):
self.scale = 1.5
def set_data_type(self):
self.data_type = 'int8'
class TestDeQuantizeOp2(TestDeQuantizeOp):
def set_scale(self):
self.scale = 0.8
def set_data_type(self):
self.data_type = 'uint8'
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_div_op.py
浏览文件 @ 8ed02339
......@@ -21,14 +21,16 @@ from op_test import OpTest
class ElementwiseDivOp(OpTest):
def setUp(self):
self.op_type = "elementwise_div"
self.dtype = np.float32
self.init_dtype()
""" Warning
CPU gradient check error!
'X': np.random.random((32,84)).astype("float32"),
'Y': np.random.random((32,84)).astype("float32")
"""
self.inputs = {
'X': np.random.uniform(0.1, 1, [13, 17]).astype("float32"),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype("float32")
'X': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype),
'Y': np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
}
self.outputs = {'Out': np.divide(self.inputs['X'], self.inputs['Y'])}
......@@ -46,6 +48,9 @@ class ElementwiseDivOp(OpTest):
self.check_grad(
['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
def init_dtype(self):
pass
class TestElementwiseDivOp_scalar(ElementwiseDivOp):
def setUp(self):
......@@ -126,5 +131,21 @@ class TestElementwiseDivOp_broadcast_3(ElementwiseDivOp):
}
class TestElementwiseDivOpFp16(ElementwiseDivOp):
def init_dtype(self):
self.dtype = np.float16
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=1)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=1, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=1, no_grad_set=set('Y'))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_elementwise_mul_op.py
浏览文件 @ 8ed02339
......@@ -135,5 +135,10 @@ class TestElementwiseMulOp_broadcast_3(ElementwiseMulOp):
}
class TestElementwiseMulOpFp16(ElementwiseMulOp):
def init_dtype(self):
self.dtype = np.float16
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_fill_zeros_like_op.py
浏览文件 @ 8ed02339
......@@ -22,12 +22,22 @@ from op_test import OpTest
class TestFillZerosLikeOp(OpTest):
def setUp(self):
self.op_type = "fill_zeros_like"
self.inputs = {'X': np.random.random((219, 232)).astype("float32")}
self.dtype = np.float32
self.init_dtype()
self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)}
self.outputs = {'Out': np.zeros_like(self.inputs["X"])}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestFillZerosLikeOpFp16(TestFillZerosLikeOp):
def init_dtype(self):
self.dtype = np.float16
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_momentum_op.py
浏览文件 @ 8ed02339
......@@ -24,11 +24,13 @@ from op_test import OpTest
class TestMomentumOp1(OpTest):
def setUp(self):
self.op_type = "momentum"
self.dtype = np.float32
self.init_dtype()
param = np.random.random((123, 321)).astype("float32")
grad = np.random.random((123, 321)).astype("float32")
velocity = np.zeros((123, 321)).astype("float32")
learning_rate = np.array([0.001]).astype("float32")
param = np.random.random((123, 321)).astype(self.dtype)
grad = np.random.random((123, 321)).astype(self.dtype)
velocity = np.zeros((123, 321)).astype(self.dtype)
learning_rate = np.array([0.001]).astype(self.dtype)
mu = 0.0001
use_nesterov = False
......@@ -50,10 +52,21 @@ class TestMomentumOp1(OpTest):
self.outputs = {'ParamOut': param_out, 'VelocityOut': velocity_out}
def init_dtype(self):
pass
def test_check_output(self):
self.check_output()
class TestMomentumOpFp16(TestMomentumOp1):
def init_dtype(self):
self.dtype = np.float16
def test_check_output(self):
self.check_output(atol=1e-3)
class TestMomentumOp2(OpTest):
'''Test Momentum with default values for attributes
'''
......
python/requirements.txt
浏览文件 @ 8ed02339
此差异已折叠。
python/setup.py.in
浏览文件 @ 8ed02339
此差异已折叠。
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