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提交 63651c19 编写于 作者: S sneaxiy
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fix grad desc maker 

test=develop
上级 a0f4fefb
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Showing with 473 addition and 426 deletion
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @ 63651c19
......@@ -335,6 +335,7 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
var_name);
ref_cnts[i].emplace(var_name, result.size());
last_live_ops_of_vars[i].emplace(var_name, std::move(result));
break;
}
// Seldomly, all preceding trying failed.
......
paddle/fluid/operators/bpr_loss_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/bpr_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -127,6 +128,23 @@ neural networks>(https://arxiv.org/abs/1511.06939)
)DOC");
}
};
class BprLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("bpr_loss_grad");
op->SetInput("X", Input("X"));
op->SetInput("Label", Input("Label"));
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
......@@ -134,7 +152,7 @@ namespace ops = paddle::operators;
using CPUCtx = paddle::platform::CPUDeviceContext;
REGISTER_OPERATOR(bpr_loss, ops::BprLossOp, ops::BprLossOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::BprLossGradDescMaker);
REGISTER_OPERATOR(bpr_loss_grad, ops::BprLossGradientOp);
REGISTER_OP_CPU_KERNEL(bpr_loss, ops::BprLossOpKernel<CPUCtx, float>,
ops::BprLossOpKernel<CPUCtx, double>);
......
paddle/fluid/operators/controlflow/CMakeLists.txt
浏览文件 @ 63651c19
include(operators)
register_operators(DEPS naive_executor)
cc_library(while_op_helper SRCS while_op_helper.cc DEPS operator)
cc_library(loop_op_helper SRCS loop_op_helper.cc DEPS operator)
file(APPEND ${pybind_file} "USE_OP(less_than);\nUSE_OP(logical_and);\nUSE_NO_KERNEL_OP(read_from_array);\n")
paddle/fluid/operators/controlflow/while_op.cc
浏览文件 @ 63651c19
......@@ -18,28 +18,21 @@
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/var_type.h"
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
#include "paddle/fluid/operators/controlflow/loop_op_helper.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
namespace paddle {
namespace operators {
static constexpr char kCondition[] = "Condition";
static constexpr char kStepScopes[] = "StepScopes";
static constexpr char kX[] = "X";
static constexpr char kXGRAD[] = "X@GRAD";
static constexpr char kOutputs[] = "Out";
using StepScopeVar = std::vector<framework::Scope *>;
using LoDTensor = framework::LoDTensor;
namespace { // NOLINT
static std::string GetSkipEagerDeletionVarsDebugString(
const std::vector<std::string> &vars) {
std::string str = "Skip " + std::to_string(vars.size()) +
" var(s) in eager deletion mode: ";
for (auto &var : vars) {
str.append(var);
str.push_back(' ');
}
return str;
}
} // NOLINT
class WhileOp : public framework::OperatorBase {
public:
WhileOp(const std::string &type, const framework::VariableNameMap &inputs,
......
paddle/fluid/operators/controlflow/while_op_helper.cc 已删除 100644 → 0
浏览文件 @ a0f4fefb
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/controlflow/while_op_helper.h"
#include <string>
#include <unordered_set>
#include <utility>
#include "paddle/fluid/framework/program_desc.h"
namespace paddle {
namespace operators {
// OpVariant is a wrapper class of OpDesc and OperatorBase
// So that API would be the same.
class OpVariant {
struct InputsVisitor
: public boost::static_visitor<const framework::VariableNameMap *> {
template <typename OpType>
const framework::VariableNameMap *operator()(const OpType *op) const {
return &(op->Inputs());
}
};
struct OutputsVisitor
: public boost::static_visitor<const framework::VariableNameMap *> {
template <typename OpType>
const framework::VariableNameMap *operator()(const OpType *op) const {
return &(op->Outputs());
}
};
struct AttributeMapVisitor
: public boost::static_visitor<const framework::AttributeMap *> {
const framework::AttributeMap *operator()(
const framework::OpDesc *op) const {
return &(op->GetAttrMap());
}
const framework::AttributeMap *operator()(
const framework::OperatorBase *op) const {
return &(op->Attrs());
}
};
struct RawPointerVisitor : public boost::static_visitor<const void *> {
template <typename OpType>
const void *operator()(const OpType *op) const {
return op;
}
};
public:
OpVariant(const framework::OperatorBase *op) : op_(op) {} // NOLINT
OpVariant(const framework::OpDesc *op) : op_(op) {} // NOLINT
const framework::VariableNameMap &Inputs() const {
return *boost::apply_visitor(InputsVisitor(), op_);
}
const framework::VariableNameMap &Outputs() const {
return *boost::apply_visitor(OutputsVisitor(), op_);
}
const framework::AttributeMap &Attrs() const {
return *boost::apply_visitor(AttributeMapVisitor(), op_);
}
template <typename AttrType>
const AttrType &Attr(const std::string &name) const {
auto &attrs = Attrs();
auto it = attrs.find(name);
PADDLE_ENFORCE(it != attrs.end(), "Cannot find attribute %s", name);
return boost::get<AttrType>(it->second);
}
bool operator==(const OpVariant &other) const {
return RawPointer() == other.RawPointer();
}
const void *RawPointer() const {
return boost::apply_visitor(RawPointerVisitor(), op_);
}
int which() const { return static_cast<int>(op_.which()); }
struct Hasher {
size_t operator()(const OpVariant &op) const {
return reinterpret_cast<size_t>(op.RawPointer());
}
};
private:
const boost::variant<const framework::OperatorBase *,
const framework::OpDesc *>
op_;
};
static std::string GetDebugString(const std::vector<std::string> &names) {
if (names.empty()) return "";
std::string ret = names[0];
for (size_t i = 1; i < names.size(); ++i) {
ret += (" " + names[i]);
}
return ret;
}
// Set skip variables of while_op and while_grad_op
// These variables should be skipped when eager deletion enables.
// It is because:
// 1. while_grad_op needs some variables defined in while_op.
// 2. while_grad_op needs variables from the previous time step.
static void SetSkipVars(const OpVariant &op, std::vector<std::string> attr) {
auto &attrs = const_cast<framework::AttributeMap &>(op.Attrs());
VLOG(2) << "Prepare to skip " << attr.size()
<< " var(s): " << GetDebugString(attr);
attrs[kSkipEagerDeletionVars] = std::move(attr);
}
// Check whether the forward while_op and while_grad_op match
// The program may have many while_ops.
static bool IsMatchedWhileOpAndWhileGradOp(const OpVariant &fwd_op,
const OpVariant &grad_op) {
return fwd_op.Inputs().at(kX) == grad_op.Inputs().at(kX) &&
fwd_op.Outputs().at(kOutputs) == grad_op.Inputs().at(kOutputs);
}
// Test whether the variable is skippable in forward while_op
// The variable is skippable in while_op when the variable used in while_grad
// is not from grad_block.
static bool IsSkippableVar(const std::string &name,
framework::BlockDesc *grad_block) {
return name != framework::kEmptyVarName && !grad_block->HasVar(name);
}
static void ModifyWhileOpAndWhileGradOpAttr(const OpVariant &fwd_op,
const OpVariant &bwd_op) {
auto *grad_block = bwd_op.Attr<framework::BlockDesc *>(kStepBlock);
// Find all skippable variables in forward while_op
std::unordered_set<std::string> forward_skip_vars;
for (auto *op_desc : grad_block->AllOps()) {
for (auto &in_arg_name : op_desc->InputArgumentNames()) {
if (IsSkippableVar(in_arg_name, grad_block)) {
forward_skip_vars.insert(in_arg_name);
}
}
for (auto &out_arg_name : op_desc->OutputArgumentNames()) {
if (IsSkippableVar(out_arg_name, grad_block)) {
forward_skip_vars.insert(out_arg_name);
}
}
}
SetSkipVars(fwd_op, std::vector<std::string>(forward_skip_vars.begin(),
forward_skip_vars.end()));
// Find all skippable variables in while_grad_op
// The skipped variables are those which would be used across time steps.
auto &fwd_input = fwd_op.Inputs().at(kX);
auto &in_grads = bwd_op.Outputs().at(framework::GradVarName(kX));
PADDLE_ENFORCE_EQ(
fwd_input.size(), in_grads.size(),
"Backward input gradient number does not match forward input number.");
std::unordered_set<std::string> backward_skip_vars;
for (size_t i = 0; i < in_grads.size(); ++i) {
if (in_grads[i] == framework::kEmptyVarName) {
continue;
}
backward_skip_vars.insert(in_grads[i]);
backward_skip_vars.insert(framework::GradVarName(fwd_input[i]));
}
SetSkipVars(bwd_op, std::vector<std::string>(backward_skip_vars.begin(),
backward_skip_vars.end()));
}
// Find all while_ops and while_grad_ops in the graph or program
// The while_grad_op and while_op may located in different blocks
// So we should traverse all blocks in the program and find them out.
static void FindAllWhileAndWhileGradOp(std::vector<OpVariant> *while_ops,
std::vector<OpVariant> *while_grad_ops) {
PADDLE_ENFORCE_GE(while_ops->size(), while_grad_ops->size());
if (while_ops->empty()) return;
const auto *program =
while_ops->front().Attr<framework::BlockDesc *>(kStepBlock)->Program();
for (size_t i = 1; i < program->Size(); ++i) {
auto &block = program->Block(i);
for (size_t j = 0; j < block.OpSize(); ++j) {
auto *op = block.Op(j);
if (op->Type() == "while") {
while_ops->emplace_back(op);
} else if (op->Type() == "while_grad") {
while_grad_ops->emplace_back(op);
}
}
}
PADDLE_ENFORCE_GE(while_ops->size(), while_grad_ops->size(),
"There are extra while_grad ops in the graph or program");
}
static void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl(
std::vector<OpVariant> *while_ops, std::vector<OpVariant> *while_grad_ops) {
FindAllWhileAndWhileGradOp(while_ops, while_grad_ops);
VLOG(2) << "Found while op num: " << while_ops->size()
<< ", while grad op num: " << while_grad_ops->size();
if (while_grad_ops->empty()) {
return;
}
std::unordered_set<OpVariant, OpVariant::Hasher> while_op_set(
while_ops->begin(), while_ops->end());
for (auto &bwd_op : *while_grad_ops) {
const OpVariant *matched_fwd_op = nullptr;
for (auto &fwd_op : while_op_set) {
if (IsMatchedWhileOpAndWhileGradOp(fwd_op, bwd_op)) {
PADDLE_ENFORCE(matched_fwd_op == nullptr,
"Found multiple matched while ops");
matched_fwd_op = &fwd_op;
}
}
PADDLE_ENFORCE_NOT_NULL(matched_fwd_op,
"Cannot find matched forward while op.");
ModifyWhileOpAndWhileGradOpAttr(*matched_fwd_op, bwd_op);
while_op_set.erase(*matched_fwd_op);
}
}
void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(
int block_id,
const std::vector<std::unique_ptr<framework::OperatorBase>> &all_ops) {
// If block_id is not 0, returns
// This is because all while_ops and while_grad_ops in the whole program
// would be processed when block_id is 0 (i.e. when Executor::Run() or
// ParallelExecutor constructs).
// What's more, all while_ops and while_grad_ops must be processed when
// block_id is zero. If not, while_op may run first and erase variables
// used in while_grad_op, and in this moment, while_grad_ops may be not
// constructed yet.
if (block_id != 0) return;
std::vector<OpVariant> fwd_ops, bwd_ops;
for (auto &op : all_ops) {
if (op->Type() == "while") {
fwd_ops.emplace_back(op.get());
} else if (op->Type() == "while_grad") {
bwd_ops.emplace_back(op.get());
}
}
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl(&fwd_ops, &bwd_ops);
}
void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(
const std::vector<framework::OperatorBase *> &while_ops,
const std::vector<framework::OperatorBase *> &while_grad_ops) {
std::vector<OpVariant> fwd_ops, bwd_ops;
fwd_ops.reserve(while_ops.size());
for (auto *op : while_ops) {
fwd_ops.emplace_back(op);
}
bwd_ops.reserve(while_grad_ops.size());
for (auto *op : while_grad_ops) {
bwd_ops.emplace_back(op);
}
PrepareSafeEagerDeletionOnWhileOpAndWhileGradOpImpl(&fwd_ops, &bwd_ops);
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/controlflow/while_op_helper.h 已删除 100644 → 0
浏览文件 @ a0f4fefb
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/platform/variant.h"
namespace paddle {
namespace operators {
static constexpr char kStepBlock[] = "sub_block";
static constexpr char kCondition[] = "Condition";
static constexpr char kStepScopes[] = "StepScopes";
static constexpr char kX[] = "X";
static constexpr char kXGRAD[] = "X@GRAD";
static constexpr char kOutputs[] = "Out";
static constexpr char kSkipEagerDeletionVars[] = "skip_eager_deletion_vars";
void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(
int block_id,
const std::vector<std::unique_ptr<framework::OperatorBase>> &all_ops);
void PrepareSafeEagerDeletionOnWhileOpAndWhileGradOp(
const std::vector<framework::OperatorBase *> &while_ops,
const std::vector<framework::OperatorBase *> &while_grad_ops);
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/roi_perspective_transform_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include <algorithm>
#include <memory>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -568,13 +569,31 @@ class ROIPerspectiveTransformOpMaker
}
};
class ROIPerspectiveTransformGradDescMaker
: public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_perspective_transform_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_perspective_transform, ops::ROIPerspectiveTransformOp,
ops::ROIPerspectiveTransformOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIPerspectiveTransformGradDescMaker);
REGISTER_OPERATOR(roi_perspective_transform_grad,
ops::ROIPerspectiveTransformGradOp);
REGISTER_OP_CPU_KERNEL(roi_perspective_transform,
......
paddle/fluid/operators/gaussian_random_batch_size_like_op.cc
浏览文件 @ 63651c19
......@@ -65,11 +65,17 @@ by input arguments.
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
GaussianRandomBatchSizeLikeNoNeedBufferVarsInference, "Input");
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(
REGISTER_OPERATOR(
gaussian_random_batch_size_like,
paddle::operators::GaussianRandomBatchSizeLikeOp,
paddle::operators::GaussianRandomBatchSizeLikeOpMaker);
paddle::operators::GaussianRandomBatchSizeLikeOpMaker,
paddle::framework::EmptyGradOpMaker,
paddle::operators::GaussianRandomBatchSizeLikeNoNeedBufferVarsInference);
// Kernels are registered in gaussian_random_op.cc and gaussian_random_op.cu
paddle/fluid/operators/im2sequence_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/im2sequence_op.h"
#include <memory>
#include <string>
#include <vector>
......@@ -146,12 +147,28 @@ class Im2SequenceGradOp : public framework::OperatorWithKernel {
}
};
class Im2SequenceGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("im2sequence_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(im2sequence, ops::Im2SequenceOp, ops::Im2SequenceOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::Im2SequenceGradDescMaker);
REGISTER_OPERATOR(im2sequence_grad, ops::Im2SequenceGradOp);
REGISTER_OP_CPU_KERNEL(
im2sequence,
......
paddle/fluid/operators/interpolate_op.cc
浏览文件 @ 63651c19
......@@ -194,21 +194,43 @@ class InterpolateOpGrad : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.GetPlace());
}
};
class InterpolateGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType(ForwardOp().Type() + "_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(InterpolateGradNoNeedBufferVarsInference,
"X");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(bilinear_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad);
ops::InterpolateGradDescMaker);
REGISTER_OPERATOR(bilinear_interp_grad, ops::InterpolateOpGrad,
ops::InterpolateGradNoNeedBufferVarsInference);
REGISTER_OPERATOR(nearest_interp, ops::InterpolateOp, ops::InterpolateOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad);
ops::InterpolateGradDescMaker);
REGISTER_OPERATOR(nearest_interp_grad, ops::InterpolateOpGrad,
ops::InterpolateGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(bilinear_interp, ops::InterpolateKernel<float>,
ops::InterpolateKernel<double>,
ops::InterpolateKernel<uint8_t>);
......
paddle/fluid/operators/l1_norm_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/l1_norm_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -62,12 +63,28 @@ $$Out = \sum{|X|}$$
}
};
class L1NormGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("l1_norm_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(l1_norm, ops::L1NormOp, ops::L1NormOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::L1NormGradDescMaker);
REGISTER_OPERATOR(l1_norm_grad, ops::L1NormGradOp);
REGISTER_OP_CPU_KERNEL(
l1_norm, ops::L1NormKernel<paddle::platform::CPUDeviceContext, float>);
......
paddle/fluid/operators/label_smooth_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/label_smooth_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -105,10 +106,23 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
}
};
class LabelSmoothGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("label_smooth_grad");
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
......@@ -117,7 +131,7 @@ class LabelSmoothGradOp : public framework::OperatorWithKernel {
namespace ops = paddle::operators;
REGISTER_OPERATOR(label_smooth, ops::LabelSmoothOp, ops::LabelSmoothOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LabelSmoothGradDescMaker);
REGISTER_OPERATOR(label_smooth_grad, ops::LabelSmoothGradOp);
REGISTER_OP_CPU_KERNEL(
label_smooth,
......
paddle/fluid/operators/linear_chain_crf_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/linear_chain_crf_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -250,14 +251,46 @@ class LinearChainCRFGradOp : public framework::OperatorWithKernel {
}
};
class LinearChainCRFGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("linear_chain_crf_grad");
op->SetAttrMap(Attrs());
op->SetInput("Emission", Input("Emission"));
op->SetInput("Transition", Input("Transition"));
op->SetInput("Label", Input("Label"));
op->SetInput("Alpha", Output("Alpha"));
op->SetInput("EmissionExps", Output("EmissionExps"));
op->SetInput("TransitionExps", Output("TransitionExps"));
op->SetInput(framework::GradVarName("LogLikelihood"),
OutputGrad("LogLikelihood"));
op->SetOutput(framework::GradVarName("Emission"), InputGrad("Emission"));
op->SetOutput(framework::GradVarName("Transition"),
InputGrad("Transition"));
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(
LinearChainCRFGradNoNeedBufferVarsInference, "Transition", "Emission");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(linear_chain_crf, ops::LinearChainCRFOp,
ops::LinearChainCRFOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp);
ops::LinearChainCRFOpMaker, ops::LinearChainCRFGradDescMaker);
REGISTER_OPERATOR(linear_chain_crf_grad, ops::LinearChainCRFGradOp,
ops::LinearChainCRFGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(
linear_chain_crf,
ops::LinearChainCRFOpKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/log_loss_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/log_loss_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -100,12 +101,29 @@ class LogLossGradOp : public framework::OperatorWithKernel {
}
};
class LogLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("log_loss_grad");
op->SetInput("Predicted", Input("Predicted"));
op->SetInput("Labels", Input("Labels"));
op->SetInput(framework::GradVarName("Loss"), OutputGrad("Loss"));
op->SetOutput(framework::GradVarName("Predicted"), InputGrad("Predicted"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(log_loss, ops::LogLossOp, ops::LogLossOpMaker<float>,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LogLossGradDescMaker);
REGISTER_OPERATOR(log_loss_grad, ops::LogLossGradOp);
REGISTER_OP_CPU_KERNEL(
log_loss, ops::LogLossKernel<paddle::platform::CPUDeviceContext, float>);
......
paddle/fluid/operators/lstm_op.cc
浏览文件 @ 63651c19
......@@ -264,12 +264,51 @@ class LSTMGradOp : public framework::OperatorWithKernel {
}
};
class LSTMGradOpDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("lstm_grad");
op->SetAttrMap(Attrs());
op->SetInput("Input", Input("Input"));
op->SetOutput(framework::GradVarName("Input"), InputGrad("Input"));
if (ForwardOp().Inputs().count("H0") > 0) {
op->SetInput("H0", Input("H0"));
op->SetOutput(framework::GradVarName("H0"), InputGrad("H0"));
}
if (ForwardOp().Inputs().count("C0") > 0) {
op->SetInput("C0", Input("C0"));
op->SetOutput(framework::GradVarName("C0"), InputGrad("C0"));
}
op->SetInput("Weight", Input("Weight"));
op->SetOutput(framework::GradVarName("Weight"), InputGrad("Weight"));
op->SetInput("Bias", Input("Bias"));
op->SetOutput(framework::GradVarName("Bias"), InputGrad("Bias"));
op->SetInput("Cell", Output("Cell"));
op->SetInput("Hidden", Output("Hidden"));
op->SetInput(framework::GradVarName("Hidden"), OutputGrad("Hidden"));
op->SetInput("BatchGate", Output("BatchGate"));
op->SetInput("BatchCellPreAct", Output("BatchCellPreAct"));
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(lstm, ops::LSTMOp, ops::LSTMOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::LSTMGradOpDescMaker);
REGISTER_OPERATOR(lstm_grad, ops::LSTMGradOp);
REGISTER_OP_CPU_KERNEL(
lstm, ops::LSTMKernel<paddle::platform::CPUDeviceContext, float>,
......
paddle/fluid/operators/margin_rank_loss_op.cc
浏览文件 @ 63651c19
......@@ -94,8 +94,6 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X1"), "Input(X1) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("X2"), "Input(X2) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Activated"),
......@@ -106,13 +104,31 @@ class MarginRankLossGradOp : public framework::OperatorWithKernel {
}
};
class MarginRankLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("margin_rank_loss_grad");
op->SetInput("Activated", Output("Activated"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetInput("Label", Input("Label"));
op->SetOutput(framework::GradVarName("X1"), InputGrad("X1"));
op->SetOutput(framework::GradVarName("X2"), InputGrad("X2"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(margin_rank_loss, ops::MarginRankLossOp,
ops::MarginRankLossOpMaker<float>,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::MarginRankLossGradDescMaker);
REGISTER_OPERATOR(margin_rank_loss_grad, ops::MarginRankLossGradOp);
REGISTER_OP_CPU_KERNEL(
margin_rank_loss,
......
paddle/fluid/operators/mean_op.cc
浏览文件 @ 63651c19
......@@ -61,7 +61,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
auto input_data_type = ctx.Input<Tensor>("X")->type();
auto input_data_type =
ctx.Input<Tensor>(framework::GradVarName("Out"))->type();
return framework::OpKernelType(input_data_type, ctx.GetPlace());
}
};
......@@ -81,13 +82,16 @@ class MeanGradMaker : public framework::SingleGradOpDescMaker {
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(MeanGradNoNeedBufferVarsInference, "X");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(mean, ops::MeanOp, ops::MeanOpMaker, ops::MeanOpInferVarType,
ops::MeanGradMaker);
REGISTER_OPERATOR(mean_grad, ops::MeanGradOp);
REGISTER_OPERATOR(mean_grad, ops::MeanGradOp,
ops::MeanGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(
mean, ops::MeanKernel<paddle::platform::CPUDeviceContext, float>,
ops::MeanKernel<paddle::platform::CPUDeviceContext, double>);
......
paddle/fluid/operators/multiplex_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/multiplex_op.h"
#include <vector>
namespace paddle {
namespace operators {
......@@ -111,28 +112,47 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(!ctx->Inputs("X").empty(), "Input(X) should not be null.");
PADDLE_ENFORCE(!ctx->Outputs(framework::GradVarName("X")).empty(),
"Output(X@Grad) should not be null.");
auto& dxs = ctx->Outputs(framework::GradVarName("X"));
PADDLE_ENFORCE(!dxs.empty(), "Output(X@Grad) should not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
auto dout_dim = ctx->GetInputDim(framework::GradVarName("Out"));
ctx->SetOutputsDim(framework::GradVarName("X"),
std::vector<framework::DDim>(dxs.size(), dout_dim));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.MultiInput<Tensor>("X")[0]->type(),
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.device_context());
}
};
class MultiplexGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("multiplex_grad");
op->SetInput("Ids", Input("Ids"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X", false));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(multiplex, ops::MultiplexOp, ops::MultiplexOpMaker,
paddle::framework::DefaultGradOpDescMaker<false>);
ops::MultiplexGradDescMaker);
REGISTER_OPERATOR(multiplex_grad, ops::MultiplexGradOp);
REGISTER_OP_CPU_KERNEL(
multiplex,
......
paddle/fluid/operators/multiplex_op.cu
浏览文件 @ 63651c19
......@@ -53,20 +53,25 @@ class MultiplexGradGPUKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<Tensor>(framework::GradVarName("Out"));
auto ins = ctx.MultiInput<Tensor>("X");
auto* ids = ctx.Input<Tensor>("Ids");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
size_t idx = -1UL;
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(*ctx.template device_context<Place>().eigen_device()) =
t.constant(static_cast<T>(0));
idx = i;
}
}
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
if (idx == -1UL) return;
auto rows = d_ins[idx]->dims()[0];
auto cols = d_ins[idx]->numel() / rows;
// copy index to cpu
Tensor index_t_cpu;
TensorCopySync(*ids, platform::CPUPlace(), &index_t_cpu);
......
paddle/fluid/operators/multiplex_op.h
浏览文件 @ 63651c19
......@@ -52,20 +52,25 @@ class MultiplexGradCPUKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const {
auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* ids = ctx.Input<framework::Tensor>("Ids");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto d_ins =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
size_t idx = -1UL;
for (size_t i = 0; i < d_ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->mutable_data<T>(ctx.GetPlace());
auto t = framework::EigenVector<T>::Flatten(*d_ins[i]);
t.device(*ctx.template device_context<DeviceContext>().eigen_device()) =
t.constant(static_cast<T>(0));
idx = i;
}
}
auto rows = ins[0]->dims()[0];
auto cols = ins[0]->numel() / rows;
if (idx == -1UL) return;
auto rows = d_ins[idx]->dims()[0];
auto cols = d_ins[idx]->numel() / rows;
auto* index = ids->data<int32_t>();
platform::CPUPlace place = boost::get<platform::CPUPlace>(ctx.GetPlace());
for (auto i = 0; i < rows; i++) {
......
paddle/fluid/operators/pad_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pad_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -29,7 +30,7 @@ class PadOp : public framework::OperatorWithKernel {
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
"Size of paddings should be equal to 2 * dimension size "
"of input tensor.");
......@@ -99,13 +100,20 @@ class PadOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx->GetInputDim("X");
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
for (int i = 0; i < dout_dims.size(); ++i) {
dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
}
auto x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto& paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
for (int i = 0; i < dout_dims.size(); ++i) {
dout_dims[i] -= (paddings[i * 2] + paddings[i * 2 + 1]);
}
ctx->SetOutputDim(x_grad_name, dout_dims);
}
}
};
......@@ -117,7 +125,6 @@ class PadOpGradMaker : public framework::SingleGradOpDescMaker {
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* bind = new framework::OpDesc();
bind->SetInput("X", Input("X"));
bind->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
bind->SetOutput(framework::GradVarName("X"), InputGrad("X"));
bind->SetAttrMap(Attrs());
......
paddle/fluid/operators/psroi_pool_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/psroi_pool_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -154,12 +155,29 @@ class PSROIPoolGradOp : public framework::OperatorWithKernel {
}
};
class PSROIPoolGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("psroi_pool_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(psroi_pool, ops::PSROIPoolOp, ops::PSROIPoolOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::PSROIPoolGradDescMaker);
REGISTER_OPERATOR(psroi_pool_grad, ops::PSROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
psroi_pool,
......
paddle/fluid/operators/rank_loss_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/rank_loss_op.h"
#include <memory>
#include <string>
namespace paddle {
......@@ -116,6 +117,25 @@ class RankLossGradOp : public framework::OperatorWithKernel {
}
};
class RankLossGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("rank_loss_grad");
op->SetInput("Label", Input("Label"));
op->SetInput("Left", Input("Left"));
op->SetInput("Right", Input("Right"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("Left"), InputGrad("Left"));
op->SetOutput(framework::GradVarName("Right"), InputGrad("Right"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
......
paddle/fluid/operators/recurrent_op.cc
浏览文件 @ 63651c19
......@@ -15,24 +15,24 @@ limitations under the License. */
#include <vector>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/controlflow/loop_op_helper.h"
namespace paddle {
namespace operators {
constexpr char kInputs[] = "inputs";
constexpr char kInitialStates[] = "initial_states";
constexpr char kParameters[] = "parameters";
constexpr char kOutputs[] = "outputs";
constexpr char kStepScopes[] = "step_scopes";
constexpr char kExStates[] = "ex_states";
constexpr char kStates[] = "states";
constexpr char kStepBlock[] = "sub_block";
constexpr char kReverse[] = "reverse";
constexpr char kIsTrain[] = "is_train";
#define GRAD_SUFFIX "@GRAD"
constexpr char kInputGrads[] = "inputs" GRAD_SUFFIX;
constexpr char kOutputGrads[] = "outputs" GRAD_SUFFIX;
constexpr char kParamGrads[] = "parameters" GRAD_SUFFIX;
constexpr char kInitStateGrads[] = "initial_states" GRAD_SUFFIX;
using recurrent::kInputs;
using recurrent::kInitialStates;
using recurrent::kParameters;
using recurrent::kOutputs;
using recurrent::kStepScopes;
using recurrent::kExStates;
using recurrent::kStates;
using recurrent::kReverse;
using recurrent::kIsTrain;
using recurrent::kInputGrads;
using recurrent::kOutputGrads;
using recurrent::kParamGrads;
using recurrent::kInitStateGrads;
using StepScopeVar = std::vector<framework::Scope *>;
......@@ -249,6 +249,9 @@ class RecurrentOp : public RecurrentBase {
framework::Executor executor(place);
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto &keep_vars = Attr<std::vector<std::string>>(kSkipEagerDeletionVars);
VLOG(2) << GetSkipEagerDeletionVarsDebugString(keep_vars);
auto *program = block->Program();
for (size_t i = 0; i < seq_len; ++i) {
......@@ -283,8 +286,7 @@ class RecurrentOp : public RecurrentBase {
// Every inputs are linked now, execute!
executor.Run(*program, &cur_scope, block->ID(),
false /*create_local_scope*/, true /*create_vars*/,
std::vector<std::string>() /*skip_ref_cnt_vars*/,
true /*force_disable_gc*/);
keep_vars);
// get device context from pool
platform::DeviceContextPool &pool =
......@@ -341,6 +343,9 @@ class RecurrentGradOp : public RecurrentBase {
auto *block = Attr<framework::BlockDesc *>(kStepBlock);
auto *program = block->Program();
auto &keep_vars = Attr<std::vector<std::string>>(kSkipEagerDeletionVars);
VLOG(2) << GetSkipEagerDeletionVarsDebugString(keep_vars);
// get device context from pool
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
......@@ -401,8 +406,7 @@ class RecurrentGradOp : public RecurrentBase {
// Run step block with cur_scope
executor.Run(*program, &cur_scope, block->ID(),
false /*create_local_scope*/, true /*create_vars*/,
std::vector<std::string>() /*skip_ref_cnt_vars*/,
true /*force_disable_gc*/);
keep_vars);
VLOG(5) << "executor.Run finished ";
......@@ -579,6 +583,10 @@ if reverse is True
o o o o
)DOC").SetDefault(false);
AddAttr<bool>(kIsTrain, "").SetDefault(true);
AddAttr<std::vector<std::string>>(kSkipEagerDeletionVars,
"Skip vars that would "
"be used in backward ops")
.SetDefault(std::vector<std::string>());
AddComment(R"DOC(
Static Length Recurrent Operator.
......@@ -614,7 +622,11 @@ class RecurrentGradOpDescMaker : public framework::SingleGradOpDescMaker {
this->OutputGrad(output_param));
}
}
grad->SetAttrMap(this->Attrs());
auto attrs = this->Attrs();
attrs.insert({kSkipEagerDeletionVars, std::vector<std::string>()});
grad->SetAttrMap(attrs);
grad->SetBlockAttr(kStepBlock, grad_block_[0]);
return std::unique_ptr<framework::OpDesc>(grad);
......
paddle/fluid/operators/roi_align_op.cc
浏览文件 @ 63651c19
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/roi_align_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -147,12 +148,29 @@ Thus avoid the misaligned problem.
}
};
class ROIAlignGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_align_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_align, ops::ROIAlignOp, ops::ROIAlignOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIAlignGradDescMaker);
REGISTER_OPERATOR(roi_align_grad, ops::ROIAlignGradOp);
REGISTER_OP_CPU_KERNEL(
roi_align,
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 63651c19
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/roi_pool_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -158,12 +159,30 @@ https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
}
};
class ROIPoolGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("roi_pool_grad");
op->SetInput("X", Input("X"));
op->SetInput("ROIs", Input("ROIs"));
op->SetInput("Argmax", Output("Argmax"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(roi_pool, ops::ROIPoolOp, ops::ROIPoolOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ROIPoolGradDescMaker);
REGISTER_OPERATOR(roi_pool_grad, ops::ROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
roi_pool,
......
paddle/fluid/operators/scatter_op.cc
浏览文件 @ 63651c19
......@@ -63,13 +63,15 @@ class ScatterGradOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("Updates"),
ctx->GetInputDim("Updates"));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("X"),
ctx->GetInputDim(framework::GradVarName("Out")));
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
return framework::OpKernelType(
ctx.Input<Tensor>(framework::GradVarName("Out"))->type(),
ctx.device_context());
}
};
......@@ -95,12 +97,34 @@ $$
}
};
class ScatterGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("scatter_grad");
op->SetInput("Ids", Input("Ids"));
op->SetInput("Updates", Input("Updates"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetOutput(framework::GradVarName("Updates"), InputGrad("Updates"));
op->SetAttrMap(Attrs());
return op;
}
};
DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(ScatterGradNoNeedBufferVarsInference,
"Updates");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(scatter, ops::ScatterOp, ops::ScatterOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp);
ops::ScatterGradDescMaker);
REGISTER_OPERATOR(scatter_grad, ops::ScatterGradOp,
ops::ScatterGradNoNeedBufferVarsInference);
REGISTER_OP_CPU_KERNEL(scatter, ops::ScatterOpKernel<float>);
REGISTER_OP_CPU_KERNEL(scatter_grad, ops::ScatterGradientOpKernel<float>);
paddle/fluid/operators/shuffle_channel_op.cc
浏览文件 @ 63651c19
......@@ -10,6 +10,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/shuffle_channel_op.h"
#include <memory>
namespace paddle {
namespace operators {
......@@ -91,13 +92,28 @@ class ShuffleChannelGradOp : public framework::OperatorWithKernel {
}
};
class ShuffleChannelGradDescMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
op->SetType("shuffle_channel_grad");
op->SetInput("X", Input("X"));
op->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
op->SetAttrMap(Attrs());
return op;
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(shuffle_channel, ops::ShuffleChannelOp,
ops::ShuffleChannelOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::ShuffleChannelOpMaker, ops::ShuffleChannelGradDescMaker);
REGISTER_OPERATOR(shuffle_channel_grad, ops::ShuffleChannelGradOp);
......
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