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提交 9a9d50a6 编写于 作者: Q Qiao Longfei 提交者： GitHub
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Refactoring InferShape (#3946) 

* init Infershape

* add static InferShape interface

* refactor add-op infershape

* add AttrReader

* add all maker's infershape

* add all InferShape

* add python infer api

* add VarDesc interface

* add python VarDesc and OpDesc interface

* update python code

* use infershape function to do shape inference

* clean code

* do not use pointer

* refine code of op_proto_maker

* add get_dims to VarDesc

* refine the code

* remove the dependency from operator to op registry

* remove OpProtoAndCheckerMaker from operator

* restore complete_add_op

* add shape_infer_impl.h

* code optimization

* remove const return value

* add fake BlockDesc class

* optimize code

* remove infer function in op_info

* move InferShapeContextImpl to operator.h

* optimize the interface of InferShapeContextBase

* add temperary interface of new infershape

* change add_op, clip_op, conv2d_op and activation_op

* change all operators InferShape

* fix SetDim

* update cos_sim_op

* update crop_op

* update lookup_table_op

* allocate tensor when call GetDim in InferShapeContext

* update modified_huber_loss_op

* update rowwise_add_op

* update mean_op

* update sequence_avg_pool_op

* typo

* remove old InferShape interface

* can compile

* fix or unit test

* clean code

* clean code

* remove const before InferShapeContext

* change InferenceContextBase to pointer

* rename RunTime to Runtime, code clean
上级 86351037
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Showing with 975 addition and 864 deletion
paddle/framework/attribute.h
浏览文件 @ 9a9d50a6
......@@ -45,6 +45,21 @@ inline AttrType AttrTypeID() {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
class AttrReader {
public:
explicit AttrReader(const AttributeMap& attrs) : attrs_(attrs) {}
template <typename T>
inline const T& Get(const std::string& name) const {
PADDLE_ENFORCE(attrs_.count(name) != 0, "%s should be in AttributeMap",
name);
return boost::get<T>(attrs_.at(name));
}
private:
const AttributeMap& attrs_;
};
// check whether a value(attribute) fit a certain limit
template <typename T>
class GreaterThanChecker {
......
paddle/framework/op_registry_test.cc
浏览文件 @ 9a9d50a6
......@@ -174,4 +174,4 @@ TEST(OpRegistry, CustomChecker) {
op->Run(scope, dev_ctx);
int test_attr = op->Attr<int>("test_attr");
ASSERT_EQ(test_attr, 4);
}
\ No newline at end of file
}
paddle/framework/operator.cc
浏览文件 @ 9a9d50a6
......@@ -14,7 +14,6 @@ limitations under the License. */
#include "paddle/framework/operator.h"
#include <algorithm>
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace framework {
......@@ -33,6 +32,24 @@ ExecutionContext::GetEigenDevice<platform::GPUPlace, Eigen::GpuDevice>() const {
}
#endif
const Tensor* GetTensorFromVar(const Variable* var) {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
Tensor* GetTensorFromVar(Variable* var) {
if (var->IsType<LoDTensor>()) {
return var->GetMutable<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input must be LoDTensor or Tensor.");
return var->GetMutable<Tensor>();
}
std::string OperatorBase::Input(const std::string& name) const {
auto& ins = Inputs(name);
PADDLE_ENFORCE_LE(ins.size(), 1UL,
......
paddle/framework/operator.h
浏览文件 @ 9a9d50a6
......@@ -24,6 +24,7 @@ limitations under the License. */
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/shape_inference.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/place.h"
......@@ -56,6 +57,9 @@ class OperatorBase;
class InferShapeContext;
class ExecutionContext;
extern const Tensor* GetTensorFromVar(const Variable* var);
extern Tensor* GetTensorFromVar(Variable* var);
/**
* OperatorBase has the basic element that Net will call to do computation.
* Only CreateOperator from OpRegistry will new Operator directly. User
......@@ -262,15 +266,6 @@ class InferShapeContext {
return res;
}
const Tensor* GetTensorFromVar(const Variable* var) const {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input(%s) must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const {
PADDLE_ENFORCE_LT(i, InputSize(in));
......@@ -340,6 +335,78 @@ class ExecutionContext : public InferShapeContext {
const platform::DeviceContext& device_context_;
};
class RuntimeInferShapeContext : public InferShapeContextBase {
public:
RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
: op_(op), scope_(scope) {}
bool HasInput(const std::string& name) const {
auto ipt = op_.Input(name);
auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
return var != nullptr;
}
bool HasOutput(const std::string& name) const {
auto ipt = op_.Output(name);
auto* var = ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
return var != nullptr;
}
DDim GetInputDim(const std::string& name) const {
return GetDim(op_.Input(name));
}
void SetInputDim(const std::string& name, const DDim& dim) {
SetDim(op_.Input(name), dim);
}
DDim GetOutputDim(const std::string& name) const {
return GetDim(op_.Output(name));
}
void SetOutputDim(const std::string& name, const DDim& dim) {
SetDim(op_.Output(name), dim);
}
AttrReader Attrs() const { return AttrReader(op_.Attrs()); }
const std::vector<std::string>& Inputs(const std::string& name) const {
return op_.Inputs(name);
}
const std::vector<std::string>& Outputs(const std::string& name) const {
return op_.Outputs(name);
}
private:
template <bool Allocate>
Tensor* GetTensor(const std::string& name) const {
Tensor* t = nullptr;
auto* var = scope_.FindVar(name);
if (!var->IsType<LoDTensor>() && !var->IsType<Tensor>()) {
if (Allocate) {
t = var->GetMutable<LoDTensor>();
} else {
PADDLE_THROW("Variable(%s) should be tensor", name);
}
} else {
t = GetTensorFromVar(scope_.FindVar(name));
}
return t;
}
DDim GetDim(const std::string& name) const {
return GetTensor<false>(name)->dims();
}
void SetDim(const std::string& name, const DDim& dim) {
GetTensor<true>(name)->Resize(dim);
}
const OperatorBase& op_;
const Scope& scope_;
};
class OpKernel {
public:
/**
......@@ -383,8 +450,10 @@ class OperatorWithKernel : public OperatorBase {
const VariableNameMap& outputs, const AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
// runtime infershape
void InferShape(const Scope& scope) const override {
InferShape(InferShapeContext(*this, scope));
auto c = RuntimeInferShapeContext(*this, scope);
InferShape(&c);
}
void Run(const Scope& scope,
......@@ -406,7 +475,7 @@ class OperatorWithKernel : public OperatorBase {
}
protected:
virtual void InferShape(const InferShapeContext& ctx) const = 0;
virtual void InferShape(InferShapeContextBase* ctx) const = 0;
};
} // namespace framework
......
paddle/framework/operator_test.cc
浏览文件 @ 9a9d50a6
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "paddle/framework/operator.h"
#include "gtest/gtest.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
......@@ -114,7 +115,7 @@ class OpWithKernelTest : public OperatorWithKernel {
using OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {}
void InferShape(framework::InferShapeContextBase* ctx) const override {}
};
template <typename T1, typename T2>
......
paddle/framework/shape_inference.h 0 → 100644
浏览文件 @ 9a9d50a6
/* Copyright (c) 2016 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. */
#pragma once
#include "paddle/framework/ddim.h"
namespace paddle {
namespace framework {
class InferShapeContextBase {
public:
virtual ~InferShapeContextBase() {}
virtual bool HasInput(const std::string &name) const = 0;
virtual bool HasOutput(const std::string &name) const = 0;
virtual framework::DDim GetInputDim(const std::string &name) const = 0;
std::vector<framework::DDim> GetInputsDim(const std::string &name) const {
const std::vector<std::string> &names = Inputs(name);
return GetDims(names);
}
virtual void SetInputDim(const std::string &name,
const framework::DDim &dim) = 0;
void SetInputsDim(const std::string &name,
const std::vector<framework::DDim> &dims) {
auto &names = Inputs(name);
SetDims(names, dims);
}
virtual framework::DDim GetOutputDim(const std::string &name) const = 0;
std::vector<framework::DDim> GetOutputsDim(const std::string &name) const {
const std::vector<std::string> &names = Outputs(name);
return GetDims(names);
}
virtual void SetOutputDim(const std::string &name, const DDim &dim) = 0;
void SetOutputsDim(const std::string &name,
const std::vector<framework::DDim> &dims) {
auto &names = Outputs(name);
SetDims(names, dims);
}
virtual AttrReader Attrs() const = 0;
virtual const std::vector<std::string> &Inputs(
const std::string &name) const = 0;
virtual const std::vector<std::string> &Outputs(
const std::string &name) const = 0;
// TODO(qiao) implement this function
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const {}
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;
virtual void SetDim(const std::string &name, const framework::DDim &dim) = 0;
std::vector<framework::DDim> GetDims(
const std::vector<std::string> &names) const {
std::vector<framework::DDim> ret;
ret.reserve(names.size());
std::transform(
names.begin(), names.end(), std::back_inserter(ret),
[this](const std::string &name) { return this->GetDim(name); });
return ret;
}
void SetDims(const std::vector<std::string> &names,
const std::vector<framework::DDim> &dims) {
size_t length = names.size();
PADDLE_ENFORCE_EQ(length, dims.size());
for (size_t i = 0; i < length; ++i) {
SetDim(names[i], dims[i]);
}
}
};
} // namespace framework
} // namespace paddle
paddle/operators/accuracy_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,25 +22,23 @@ class AccuracyOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Inference"),
"Input(Inference) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Accuracy"),
"Output(Accuracy) of AccuracyOp should not be null.");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Inference"),
"Input(Inference) of AccuracyOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Label"),
"Input(Label) of AccuracyOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Accuracy"),
"Output(Accuracy) of AccuracyOp should not be null.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *label = ctx.Input<framework::Tensor>("Label");
auto inference_dim = ctx->GetInputDim("Inference");
auto label_dim = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
PADDLE_ENFORCE_EQ(label_dim.size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference_dim[0], label_dim[0],
"inference size must be the same as label size");
ctx.Output<framework::Tensor>("Accuracy")->Resize({1});
ctx.ShareLoD("Inference", /*->*/ "Accuracy");
ctx->SetOutputDim("Accuracy", {1});
ctx->ShareLoD("Inference", /*->*/ "Accuracy");
}
};
......
paddle/operators/activation_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,10 +22,9 @@ class ActivationOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>("Y")->Resize(
ctx.Input<framework::Tensor>("X")->dims());
ctx.ShareLoD("X", /*->*/ "Y");
void InferShape(framework::InferShapeContextBase *ctx) const override {
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......@@ -34,9 +33,8 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<framework::Tensor>("Y")->dims());
void InferShape(framework::InferShapeContextBase *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Y"));
}
};
......
paddle/operators/add_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,25 +22,23 @@ class AddOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of AddOp should not be null.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of AddOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Two input of Add Op's dimension must be same.");
ctx.Output<framework::Tensor>("Out")->Resize(
ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim("Out", x_dims);
}
};
class AddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
AddOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of add op");
AddInput("Y", "The second input of add op");
......@@ -58,7 +56,7 @@ class AddOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
void InferShape(framework::InferShapeContextBase* ctx) const override {}
};
} // namespace operators
......
paddle/operators/clip_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,24 +22,24 @@ class ClipOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ClipOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ClipOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto max = Attr<float>("max");
auto min = Attr<float>("min");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ClipOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ClipOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto max = ctx->Attrs().Get<float>("max");
auto min = ctx->Attrs().Get<float>("min");
PADDLE_ENFORCE_LT(min, max, "max should be greater than min.");
ctx.Output<Tensor>("Out")->Resize(x_dims);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename AttrType>
class ClipOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ClipOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
ClipOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor)The input of clip op."
......@@ -61,14 +61,13 @@ class ClipOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
void InferShape(framework::InferShapeContextBase* 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");
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/concat_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,31 +24,30 @@ class ConcatOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ConcatOp should not be null.");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ConcatOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
auto ins = ctx->GetInputsDim("X");
size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
size_t n = ins.size();
PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
auto out_dims = ins[0]->dims();
auto out_dims = ins[0];
size_t in_zero_dims_size = out_dims.size();
for (size_t i = 1; i < n; i++) {
for (size_t j = 0; j < in_zero_dims_size; j++) {
if (j == axis) {
out_dims[axis] += ins[i]->dims()[j];
out_dims[axis] += ins[i][j];
continue;
}
PADDLE_ENFORCE_EQ(out_dims[j], ins[i]->dims()[j],
PADDLE_ENFORCE_EQ(out_dims[j], ins[i][j],
"Input tensors should have the same "
"elements except the specify axis.")
}
}
out->Resize(out_dims);
ctx->SetOutputDim("Out", out_dims);
}
};
......
paddle/operators/cond_op.cc
浏览文件 @ 9a9d50a6
......@@ -215,7 +215,7 @@ class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Sample dependent Cond Operator:
Given Cond[i] as a 1/0 vector to indicate true/false
The equation is:
The equation is:
Out[i] = subnet_t[i], if Cond[i] == true
Out[i] = subnet_t[i], if Cond[i] == false
)DOC");
......
paddle/operators/conv2d_op.cc
浏览文件 @ 9a9d50a6
......@@ -27,27 +27,25 @@ class Conv2DOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Input"),
"Input(Input) of Conv2DOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Filter"),
"Input(Filter) of Conv2DOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Output"),
"Output(Output) of Conv2DOp should not be null.");
auto in = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto out = ctx.Output<framework::Tensor>("Output");
std::vector<int> strides = Attr<std::vector<int>>("strides");
std::vector<int> paddings = Attr<std::vector<int>>("paddings");
int groups = Attr<int>("groups");
int input_channels = in->dims()[1];
int output_channels = filter->dims()[0];
PADDLE_ENFORCE_EQ(in->dims().size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter->dims().size(), 4,
"Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter->dims()[1] * groups,
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of Conv2DOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of Conv2DOp should not be null.");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int groups = ctx->Attrs().Get<int>("groups");
int input_channels = in_dims[1];
int output_channels = filter_dims[0];
PADDLE_ENFORCE_EQ(in_dims.size(), 4, "Conv2DOp input should be 4-D.");
PADDLE_ENFORCE_EQ(filter_dims.size(), 4, "Conv2DOp filter should be 4-D.");
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
PADDLE_ENFORCE_EQ(
......@@ -55,17 +53,17 @@ class Conv2DOp : public framework::OperatorWithKernel {
"The number of output channels should be divided by groups.");
auto output_height =
outputSize(in->dims()[2], filter->dims()[2], paddings[0], strides[0]);
outputSize(in_dims[2], filter_dims[2], paddings[0], strides[0]);
auto output_width =
outputSize(in->dims()[3], filter->dims()[3], paddings[1], strides[1]);
out->Resize(
{in->dims()[0], filter->dims()[0], output_height, output_width});
outputSize(in_dims[3], filter_dims[3], paddings[1], strides[1]);
ctx->SetOutputDim(
"Output", {in_dims[0], filter_dims[0], output_height, output_width});
}
};
class Conv2DOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
Conv2DOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
......@@ -108,14 +106,15 @@ class Conv2DOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto in = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto d_in = ctx.Output<framework::Tensor>(framework::GradVarName("Input"));
auto d_filter =
ctx.Output<framework::Tensor>(framework::GradVarName("Filter"));
if (d_in) d_in->Resize(in->dims());
if (d_filter) d_filter->Resize(filter->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
if (ctx->HasOutput(framework::GradVarName("Input"))) {
ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
}
}
};
......
paddle/operators/cos_sim_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,22 +24,22 @@ class CosSimOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase* ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("XNorm"),
"Output(XNorm) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("YNorm"),
"Output(YNorm) of CosSimOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of CosSimOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of CosSimOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of CosSimOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("XNorm"),
"Output(XNorm) of CosSimOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("YNorm"),
"Output(YNorm) of CosSimOp should not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
......@@ -54,16 +54,16 @@ class CosSimOp : public framework::OperatorWithKernel {
" just 1 (which will be broadcasted to match Input(X)).");
// resize tensor
ctx.Output<framework::Tensor>("Out")->Resize({x_dims[0], 1});
ctx.Output<framework::Tensor>("XNorm")->Resize({x_dims[0], 1});
ctx.Output<framework::Tensor>("YNorm")->Resize({y_dims[0], 1});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", {x_dims[0], 1});
ctx->SetOutputDim("XNorm", {x_dims[0], 1});
ctx->SetOutputDim("YNorm", {y_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
CosSimOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The 1st input of cos_sim op.");
AddInput("Y", "The 2nd input of cos_sim op.");
......@@ -98,27 +98,23 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase* ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
"Input(XNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
"Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Out"),
"Input(Out) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("XNorm"), "Input(XNorm) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("YNorm"), "Input(YNorm) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Out"), "Input(Out) must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>("Out")->dims();
auto out_grad_dims =
ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto xnorm_dims = ctx->GetInputDim("XNorm");
auto ynorm_dims = ctx->GetInputDim("YNorm");
auto out_dims = ctx->GetInputDim("Out");
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
......@@ -143,10 +139,14 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
"Shape of Input(Out@Grad) must be [X.Dim(0), 1].");
// resize tensor
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/crop_op.cc
浏览文件 @ 9a9d50a6
......@@ -25,16 +25,14 @@ class CropOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of CropOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of CropOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto *y = ctx.Input<Tensor>("Y");
auto *out = ctx.Output<Tensor>("Out");
if (y == nullptr) {
auto shape = Attr<std::vector<int>>("shape");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of CropOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of CropOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
if (!ctx->HasInput("Y")) {
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE_EQ(
int64_t(shape.size()), x_dim.size(),
"Shape size should be equal to dimention size of input tensor.");
......@@ -42,19 +40,20 @@ class CropOp : public framework::OperatorWithKernel {
for (size_t i = 0; i < shape.size(); ++i) {
tensor_shape[i] = static_cast<int64_t>(shape[i]);
}
out->Resize(framework::make_ddim(tensor_shape));
ctx->SetOutputDim("Out", framework::make_ddim(tensor_shape));
} else {
PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y->dims()),
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y_dim),
"Tensor rank of both CropOp's "
"inputs must be same.");
out->Resize(y->dims());
ctx->SetOutputDim("Out", y_dim);
}
}
};
class CropOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
CropOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -78,12 +77,12 @@ class CropOpMaker : public framework::OpProtoAndCheckerMaker {
Crop Operator.
Crop input into output, as specified by offsets and shape.
There are two ways to set shape:
There are two ways to set shape:
1. referenc input: crop input X as shape as reference input.
The dimension of reference input should
The dimension of reference input should
be as same as input X.
2. shape list: crop input X by shape described by a list<int>.
The size of shape list should be as same as
The size of shape list should be as same as
dimension size of input X.
The input should be a k-D tensor(k > 0 and k < 7). As an example:
......@@ -94,15 +93,15 @@ Given:
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
and
and
offsets = [0, 1]
and
shape = [2, 2]
then we get
then we get
Out = [[1, 2],
[3, 4]]
......@@ -116,14 +115,14 @@ class CropOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
if (x_grad != nullptr) {
x_grad->Resize(x_dims);
void InferShape(framework::InferShapeContextBase* 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 x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
......
paddle/operators/cross_entropy_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,33 +22,30 @@ class CrossEntropyOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
"Output(Y) should be not null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
if (ctx.Attr<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
if (ctx->Attrs().Get<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"If Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"If Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
ctx.Output<Tensor>("Y")->Resize({x->dims()[0], 1});
ctx.ShareLoD("X", /*->*/ "Y");
ctx->SetOutputDim("Y", {x_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......@@ -57,50 +54,45 @@ class CrossEntropyGradientOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
"Input(Y@GRAD) shoudl be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x = ctx.Input<Tensor>("X");
auto label = ctx.Input<Tensor>("Label");
auto dy = ctx.Input<Tensor>(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dy->dims().size(), 2,
"Input(Y@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 2,
"Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x->dims()[0], label->dims()[0],
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) shoudl be not null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Output(X@GRAD) should be not null.");
auto x_dims = ctx->GetInputDim("X");
auto label_dims = ctx->GetInputDim("Label");
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
PADDLE_ENFORCE_EQ(label_dims.size(), 2, "Input(Label)'s rank should be 2.");
PADDLE_ENFORCE_EQ(x_dims[0], label_dims[0],
"The 1st dimension of Input(X) and Input(Label) should "
"be equal.");
PADDLE_ENFORCE_EQ(x->dims()[0], dy->dims()[0],
PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
"The 1st dimension of Input(X) and Input(Y@Grad) should "
"be equal.");
PADDLE_ENFORCE_EQ(dy->dims()[1], 1,
PADDLE_ENFORCE_EQ(dy_dims[1], 1,
"The 2nd dimension of Input(Y@Grad) should be 1.");
if (ctx.Attr<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x->dims()[1], label->dims()[1],
if (ctx->Attrs().Get<bool>("softLabel")) {
PADDLE_ENFORCE_EQ(x_dims[1], label_dims[1],
"When Attr(softLabel) == true, the 2nd dimension of "
"Input(X) and Input(Label) should be equal.");
} else {
PADDLE_ENFORCE_EQ(label->dims()[1], 1,
PADDLE_ENFORCE_EQ(label_dims[1], 1,
"When Attr(softLabel) == false, the 2nd dimension of "
"Input(Label) should be 1.");
}
auto dx = ctx.Output<Tensor>(framework::GradVarName("X"));
dx->Resize(x->dims());
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
class CrossEntropyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CrossEntropyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
CrossEntropyOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor, default Tensor<float>), a 2-D tensor with shape N x D, "
......
paddle/operators/dropout_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,25 +24,25 @@ class DropoutOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<float>("dropout_prob"), 1);
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize(dims);
if (ctx.Attr<bool>("is_training")) {
ctx.Output<Tensor>("Mask")->Resize(dims);
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_GE(ctx->Attrs().Get<float>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<float>("dropout_prob"), 1);
auto x_dims = ctx->GetInputDim("X");
ctx->SetOutputDim("Out", x_dims);
if (ctx->Attrs().Get<bool>("is_training") == 1) {
ctx->SetOutputDim("Mask", x_dims);
}
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
};
template <typename AttrType>
class DropoutOpMaker : public framework::OpProtoAndCheckerMaker {
public:
DropoutOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
DropoutOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddAttr<AttrType>("dropout_prob", "Probability of setting units to zero.")
.SetDefault(.5f);
......@@ -70,27 +70,26 @@ class DropoutOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Attr<bool>("is_training"),
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Mask"), "Mask must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE_GE(ctx.Attr<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx.Attr<AttrType>("dropout_prob"), 1);
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE_EQ(ctx->Attrs().Get<bool>("is_training"), 1,
"GradOp is only callable when is_training is true");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput("Mask"), "Mask must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
PADDLE_ENFORCE_GE(ctx->Attrs().Get<AttrType>("dropout_prob"), 0);
PADDLE_ENFORCE_LE(ctx->Attrs().Get<AttrType>("dropout_prob"), 1);
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(x_dims, out_dims,
"Dimensions of Input(X) and Out@Grad must be the same.");
auto mask_dims = ctx.Input<Tensor>("Mask")->dims();
auto mask_dims = ctx->GetInputDim("Mask");
PADDLE_ENFORCE_EQ(x_dims, mask_dims,
"Dimensions of Input(X) and Mask must be the same.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
......
paddle/operators/elementwise_op.h
浏览文件 @ 9a9d50a6
......@@ -202,21 +202,20 @@ class ElementwiseOp : public framework::OperatorWithKernel {
protected:
using Tensor = framework::Tensor;
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of elementwise op should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of elementwise op should not be null");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of elementwise op should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto y_dim = ctx.Input<Tensor>("Y")->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of elementwise op should not be null");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of elementwise op should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of elementwise op should not be null.");
auto x_dim = ctx->GetInputDim("X");
auto y_dim = ctx->GetInputDim("Y");
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.")
ctx.Output<framework::Tensor>("Out")->Resize(x_dim);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -234,7 +233,7 @@ must be small or equal to X's dimensions.
)DOC");
AddAttr<int>("axis",
R"DOC(
When the shape(Y) does not equal the shape(X),Y will be broadcasted
When the shape(Y) does not equal the shape(X),Y will be broadcasted
to match the shape of X and axis should be dimension index Y in X
)DOC")
.SetDefault(-1)
......@@ -244,7 +243,7 @@ to match the shape of X and axis should be dimension index Y in X
comment_ = R"DOC(
Limited elementwise {name} operator.The equation is: Out = {equation}.
1. The shape of Y should be same with X or
2. Y's shape is a subset of X.
2. Y's shape is a subset of X.
Y will be broadcasted to match the shape of X and axis should be dimension index Y in X.
example:
......@@ -284,27 +283,26 @@ class ElementwiseOpGrad : public framework::OperatorWithKernel {
using Tensor = framework::Tensor;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) 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 y_dims = ctx->GetInputDim("Y");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Rank of first input must >= rank of second input.")
if (x_grad) {
x_grad->Resize(x_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (y_grad) {
y_grad->Resize(y_dims);
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,15 +22,13 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of FillZerosLikeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
"Output(Y) of FillZerosLikeOp should not be null.");
ctx.Output<framework::Tensor>("Y")->Resize(
ctx.Input<framework::Tensor>("X")->dims());
ctx.ShareLoD("X", /*->*/ "Y");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of FillZerosLikeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of FillZerosLikeOp should not be null.");
ctx->SetOutputDim("Y", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Y");
}
};
......
paddle/operators/gather_op.cc
浏览文件 @ 9a9d50a6
......@@ -23,19 +23,19 @@ class GatherOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
"Input(Index) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of GatherOp should not be null.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of GatherOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Index"),
"Input(Index) of GatherOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GatherOp should not be null.");
int batch_size = ctx.Input<Tensor>("Index")->dims()[0];
int batch_size = ctx->GetInputDim("Index")[0];
PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
framework::DDim output_dims(ctx->GetInputDim("X"));
output_dims[0] = batch_size;
ctx.Output<framework::Tensor>("Out")->Resize(output_dims);
ctx->SetOutputDim("Out", output_dims);
}
};
......@@ -44,23 +44,20 @@ class GatherGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto X_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
X_grad->Resize(X->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
class GatherOpMaker : public framework::OpProtoAndCheckerMaker {
public:
GatherOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
GatherOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The source input of gather op");
AddInput("Index", "The index input of gather op");
AddOutput("Out", "The output of add op");
AddComment(R"DOC(
Gather Operator by selecting from the first axis,
Gather Operator by selecting from the first axis,
Out = X[Index]
)DOC");
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 9a9d50a6
......@@ -43,13 +43,10 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto dims = ctx->Attrs().Get<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
......@@ -57,7 +54,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
}
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
tensor->Resize(framework::make_ddim(temp));
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
};
......
paddle/operators/lookup_table_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,27 +22,26 @@ class LookupTableOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("W"),
"Input(W) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ids"),
"Input(Ids) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of LookupTableOp should not be null.");
auto table_t = ctx.Input<Tensor>("W");
auto ids_t = ctx.Input<Tensor>("Ids");
auto output_t = ctx.Output<framework::Tensor>("Out");
output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
ctx.ShareLoD("Ids", /*->*/ "Out");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("W"),
"Input(W) of LookupTableOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Ids"),
"Input(Ids) of LookupTableOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of LookupTableOp should not be null.");
auto table_dims = ctx->GetInputDim("W");
auto ids_dims = ctx->GetInputDim("Ids");
ctx->SetOutputDim("Out", {ids_dims[0], table_dims[1]});
ctx->ShareLoD("Ids", /*->*/ "Out");
}
};
class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LookupTableOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LookupTableOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("W",
"An input represents embedding tensors,"
......@@ -66,11 +65,9 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table = context.Input<Tensor>("W");
auto d_table =
context.Output<framework::Tensor>(framework::GradVarName("W"));
d_table->Resize(table->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto table_dims = ctx->GetInputDim("W");
ctx->SetOutputDim(framework::GradVarName("W"), table_dims);
}
};
......
paddle/operators/lstm_unit_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,37 +22,36 @@ class LstmUnitOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("C_prev"),
"Input(C_prev) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("C"),
"Output(C) of LSTM should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("H"),
"Output(H) of LSTM should not be null.");
auto *x = ctx.Input<framework::Tensor>("X");
auto *c_prev = ctx.Input<framework::Tensor>("C_prev");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE(x->dims()[0] == c_prev->dims()[0],
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("C_prev"),
"Input(C_prev) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("C"),
"Output(C) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("H"),
"Output(H) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto c_prev_dims = ctx->GetInputDim("C_prev");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
PADDLE_ENFORCE(x_dims[0] == c_prev_dims[0],
"Batch size of inputs and states must be equal");
PADDLE_ENFORCE(x->dims()[1] == c_prev->dims()[1] * 4,
PADDLE_ENFORCE(x_dims[1] == c_prev_dims[1] * 4,
"Dimension of FC should equal to prev state * 4");
int b_size = c_prev->dims()[0]; // batch size
int s_dim = c_prev->dims()[1]; // state dim
ctx.Output<framework::LoDTensor>("C")->Resize({b_size, s_dim});
ctx.Output<framework::LoDTensor>("H")->Resize({b_size, s_dim});
int b_size = c_prev_dims[0]; // batch size
int s_dim = c_prev_dims[1]; // state dim
ctx->SetOutputDim("C", {b_size, s_dim});
ctx->SetOutputDim("H", {b_size, s_dim});
}
};
template <typename AttrType>
class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LstmUnitOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
LstmUnitOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "FC input before the non-linear activation.");
AddInput(
......@@ -63,11 +62,11 @@ class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(Lstm-Unit Operator
Equation:
Equation:
i, f, o, j = split(X)
C = C_prev * sigm(f + forget_bias) + sigm(i) * tanh(j)
H = C * sigm(o)
)DOC");
AddAttr<AttrType>("forget_bias", "The forget bias of Lstm Unit.")
.SetDefault(0.0);
......@@ -79,15 +78,14 @@ class LstmUnitGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("C")),
"Input(C@GRAD) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("H")),
"Input(H@GRAD) should not be null");
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
ctx.Output<framework::LoDTensor>(framework::GradVarName("C_prev"))
->Resize(ctx.Input<Tensor>("C_prev")->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("C")),
"Input(C@GRAD) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("H")),
"Input(H@GRAD) should not be null");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("C_prev"),
ctx->GetInputDim("C_prev"));
}
};
......
paddle/operators/mean_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,18 +22,18 @@ class MeanOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MeanOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MeanOp should not be null.");
ctx.Output<framework::Tensor>("Out")->Resize({1});
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MeanOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MeanOp should not be null.");
ctx->SetOutputDim("Out", {1});
}
};
class MeanOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MeanOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The input of mean op");
AddOutput("Out", "The output of mean op").NotInGradient();
......@@ -47,9 +47,8 @@ class MeanGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
......
paddle/operators/minus_op.cc
浏览文件 @ 9a9d50a6
......@@ -26,22 +26,22 @@ class MinusOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MinusOp should not be null.");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of MinusOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of MinusOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MinusOp should not be null.");
auto *left_tensor = ctx.Input<framework::Tensor>("X");
auto *right_tensor = ctx.Input<framework::Tensor>("Y");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(
left_tensor->numel(), right_tensor->numel(),
x_dims, y_dims,
"Minus operator must take two tensor with same num of elements");
ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/operators/modified_huber_loss_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,20 +22,19 @@ class ModifiedHuberLossOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
PADDLE_ENFORCE_NOT_NULL(context.InputVar("X"), "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(context.InputVar("Y"), "Y must be initialized.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
"The shape of X and Y must be the same.");
PADDLE_ENFORCE_EQ(x->dims().size(), 2, "The tensor rank of X must be 2.");
PADDLE_ENFORCE_EQ(x->dims()[1], 1, "The 2nd dimension of X must be 1.");
PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "The tensor rank of X must be 2.");
PADDLE_ENFORCE_EQ(x_dims[1], 1, "The 2nd dimension of X must be 1.");
context.Output<framework::Tensor>("IntermediateVal")->Resize(x->dims());
context.Output<framework::Tensor>("Out")->Resize({x->dims()[0], 1});
ctx->SetOutputDim("IntermediateVal", x_dims);
ctx->SetOutputDim("Out", {x_dims[0], 1});
}
};
......@@ -75,27 +74,28 @@ class ModifiedHuberLossGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
auto* x = context.Input<Tensor>("X");
auto* y = context.Input<Tensor>("Y");
auto* intermediate_val = context.Input<Tensor>("IntermediateVal");
auto* out_grad = context.Input<Tensor>(framework::GradVarName("Out"));
auto* x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
PADDLE_ENFORCE_NOT_NULL(x, "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(y, "Y must be initialized.");
PADDLE_ENFORCE_NOT_NULL(intermediate_val,
"Intermediate value must not be null.");
PADDLE_ENFORCE_NOT_NULL(out_grad, "Input(Out@Grad) must not be null.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("IntermediateVal"),
"Intermediate value must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@Grad) must not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto intermediate_dims = ctx->GetInputDim("IntermediateVal");
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_EQ(
intermediate_val->dims(), x->dims(),
intermediate_dims, x_dims,
"The shape of X and intermediate value must be the same.");
PADDLE_ENFORCE_EQ(out_grad->dims(), x->dims(),
PADDLE_ENFORCE_EQ(out_grad_dims, x_dims,
"The shape of Input(Out@Grad) and X must be the same.");
if (x_grad) x_grad->Resize(x->dims());
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/mul_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,27 +24,23 @@ class MulOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MulOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
int x_num_col_dims = Attr<int>("x_num_col_dims");
int y_num_col_dims = Attr<int>("y_num_col_dims");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MulOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
int x_num_col_dims = ctx->Attrs().Get<int>("x_num_col_dims");
int y_num_col_dims = ctx->Attrs().Get<int>("y_num_col_dims");
PADDLE_ENFORCE(x_dims.size() > x_num_col_dims,
"The rank of input tensor X(%s) should be larger than "
"`mul_op`'s `x_num_col_dims`.",
ctx.op().Input("X"));
"The rank of input tensor X should be larger than "
"`mul_op`'s `x_num_col_dims`.");
PADDLE_ENFORCE(y_dims.size() > y_num_col_dims,
"The rank of input tensor Y(%s) should be larger than "
"`mul_op`'s `y_num_col_dims`.",
ctx.op().Input("Y"));
"The rank of input tensor Y should be larger than "
"`mul_op`'s `y_num_col_dims`.");
auto x_mat_dims = framework::flatten_to_2d(x_dims, x_num_col_dims);
auto y_mat_dims = framework::flatten_to_2d(y_dims, y_num_col_dims);
......@@ -52,24 +48,23 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's height.");
ctx.Output<framework::Tensor>("Out")->Resize(
{x_mat_dims[0], y_mat_dims[1]});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", {x_mat_dims[0], y_mat_dims[1]});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class MulOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
MulOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of mul op");
AddInput("Y", "The second input of mul op");
AddOutput("Out", "The output of mul op");
AddAttr<int>(
"x_num_col_dims",
R"DOC(mul_op can take tensors with more than two dimensions as input `X`,
in that case, tensors will be reshaped to a matrix. The matrix's first
dimension(column length) will be the product of tensor's last
R"DOC(mul_op can take tensors with more than two dimensions as input `X`,
in that case, tensors will be reshaped to a matrix. The matrix's first
dimension(column length) will be the product of tensor's last
`num_col_dims` dimensions, and the matrix's second dimension(row length)
will be the product of tensor's first `rank - num_col_dims` dimensions.
)DOC")
......@@ -100,16 +95,14 @@ class MulOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) 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 y_dims = ctx->GetInputDim("Y");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_mat_dims =
framework::flatten_to_2d(x_dims, Attr<int>("x_num_col_dims"));
......@@ -125,8 +118,15 @@ class MulOpGrad : public framework::OperatorWithKernel {
"The second dimension of Out@GRAD must equal to the second "
"dimension of the second operand.");
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, y_dims);
}
}
};
......
paddle/operators/multiplex_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,41 +24,38 @@ class MultiplexOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ids"),
"Input(Ids) shouldn't be null.");
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ids"), "Input(Ids) shouldn't be null.");
PADDLE_ENFORCE(!ctx->Inputs("X").empty(),
"MultiInput(X) shouldn't be empty.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) shouldn't be null.");
auto ids_dim = ctx.Input<Tensor>("Ids")->dims();
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) shouldn't be null.");
auto ids_dim = ctx->GetInputDim("Ids");
PADDLE_ENFORCE(
ids_dim.size() == 2 && ids_dim[1] == 1,
"The index tensor must be a vector with size batchSize x 1.");
auto ins = ctx.MultiInput<Tensor>("X");
auto *out = ctx.Output<Tensor>("Out");
auto num_ins = ins.size();
auto ins_dims = ctx->GetInputsDim("X");
auto num_ins = ins_dims.size();
PADDLE_ENFORCE(num_ins > 1,
"multiplex operator should have more than "
"one candidate input tensors.");
auto in_dim = ins[0]->dims();
auto in_dim = ins_dims[0];
PADDLE_ENFORCE(in_dim.size() >= 2,
"The rank of candidate tensors must be not less than 2.");
for (size_t i = 1; i < num_ins; i++) {
auto dim = ins[i]->dims();
auto dim = ins_dims[i];
PADDLE_ENFORCE(in_dim == dim,
"All the candidate tensors must have the same size.");
}
out->Resize(in_dim);
ctx->SetOutputDim("Out", in_dim);
}
};
class MultiplexOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MultiplexOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
MultiplexOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ids", "The index tensor of multiplex operator.");
AddInput("X", "The candidate tensors of multiplex operator.")
......@@ -88,21 +85,19 @@ class MultiplexGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) should not be null.");
PADDLE_ENFORCE(!ctx.MultiOutputVar(framework::GradVarName("X")).empty(),
void InferShape(framework::InferShapeContextBase* 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.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
auto d_ins = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
auto ins = ctx.MultiInput<Tensor>("X");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null.");
std::vector<framework::DDim> d_ins;
auto ins = ctx->GetInputsDim("X");
// No need to compute gradient for Input(Ids)
for (size_t i = 0; i < ins.size(); i++) {
if (d_ins[i]) {
d_ins[i]->Resize(ins[i]->dims());
}
d_ins.push_back(ins[i]);
}
ctx->SetOutputsDim(framework::GradVarName("X"), d_ins);
}
};
......
paddle/operators/pad_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,14 +24,13 @@ class PadOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of PadOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<std::vector<int>>("paddings");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of PadOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx->GetInputDim("X");
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.");
......@@ -39,19 +38,18 @@ class PadOp : public framework::OperatorWithKernel {
for (int i = 0; i < x_dim.size(); ++i) {
out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
}
ctx.Output<framework::Tensor>("Out")->Resize(
framework::make_ddim(out_dims));
ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
if (out_dims[0] == x_dim[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
PadOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
......@@ -68,15 +66,15 @@ Given:
X = [[1, 2],
[3, 4]]
and
and
paddings = [0, 1, 1, 2]
and
pad_value = 0
then we get
pad_value = 0
then we get
Out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
......@@ -101,14 +99,14 @@ class PadOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_g = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
if (x_g != nullptr) {
x_g->Resize(x_dims);
void InferShape(framework::InferShapeContextBase* 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 x_grad_name = framework::GradVarName("X");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
}
};
......
paddle/operators/prelu_op.cc
浏览文件 @ 9a9d50a6
......@@ -26,19 +26,14 @@ class PReluOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
auto *in = ctx.Input<framework::Tensor>("X");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Alpha"),
"Input(Alpha) should not be null");
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
PADDLE_ENFORCE(alpha->numel() == 1, "Size of weight Alpha must be one.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) should not be null");
auto *out = ctx.Output<framework::Tensor>("Out");
out->Resize(in->dims());
ctx.ShareLoD("X", /*->*/ "Out");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasInput("Alpha"), "Input(Alpha) should not be null");
PADDLE_ENFORCE(product(ctx->GetInputDim("Alpha")) == 1,
"Size of weight Alpha must be one.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -68,19 +63,13 @@ class PReluGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto *dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *x = ctx.Input<framework::Tensor>("X");
auto *dalpha =
ctx.Output<framework::Tensor>(framework::GradVarName("Alpha"));
auto *alpha = ctx.Input<framework::Tensor>("Alpha");
dx->Resize(x->dims());
dalpha->Resize(alpha->dims());
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->SetOutputDim(framework::GradVarName("Alpha"),
ctx->GetInputDim("Alpha"));
}
};
......
paddle/operators/rank_loss_op.cc
浏览文件 @ 9a9d50a6
......@@ -25,22 +25,21 @@ class RankLossOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase *ctx) const override {
// input check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) shouldn't be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
"Input(Left) shouldn't be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
"Input(Right) shouldn't be null");
auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
auto left_dims = ctx.Input<framework::Tensor>("Left")->dims();
auto right_dims = ctx.Input<framework::Tensor>("Right")->dims();
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null");
PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null");
PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null");
auto label_dims = ctx->GetInputDim("Label");
auto left_dims = ctx->GetInputDim("Left");
auto right_dims = ctx->GetInputDim("Right");
PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
"All inputs must have the same size");
PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be row vector with size batch_size x 1.");
ctx.Output<framework::Tensor>("Out")->Resize(label_dims);
ctx->SetOutputDim("Out", label_dims);
}
};
......@@ -91,25 +90,22 @@ class RankLossGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input(Label) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Left"),
"Input(Left) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Right"),
"Input(Right) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("Left")->dims();
auto *left_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Left"));
auto *right_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("Right"));
if (left_grad) {
left_grad->Resize(dims);
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Left"), "Input(Left) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput("Right"), "Input(Right) shouldn't be null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx->GetInputDim("Left");
auto left_grad_name = framework::GradVarName("Left");
auto right_grad_name = framework::GradVarName("Right");
if (ctx->HasOutput(left_grad_name)) {
ctx->SetOutputDim(left_grad_name, dims);
}
if (right_grad) {
right_grad->Resize(dims);
if (ctx->HasOutput(right_grad_name)) {
ctx->SetOutputDim(right_grad_name, dims);
}
}
};
......
paddle/operators/reshape_op.cc
浏览文件 @ 9a9d50a6
......@@ -26,14 +26,14 @@ class ReshapeOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
void InferShape(framework::InferShapeContextBase *ctx) const override {
// input check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ReshapeOp should not be null.");
auto shape = ctx.Attr<std::vector<int>>("shape");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
for (auto dim : shape) {
PADDLE_ENFORCE(dim > 0, "Each dimension of shape must be positive.");
......@@ -41,8 +41,8 @@ class ReshapeOp : public framework::OperatorWithKernel {
// capacity check
int64_t capacity =
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
auto *in = ctx.Input<framework::Tensor>("X");
int64_t in_size = framework::product(in->dims());
auto x_dims = ctx->GetInputDim("X");
int64_t in_size = framework::product(x_dims);
PADDLE_ENFORCE_EQ(capacity, in_size,
"The size of Input(X) mismatches with Attr(shape).");
// resize output
......@@ -50,11 +50,11 @@ class ReshapeOp : public framework::OperatorWithKernel {
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto out_dims = framework::make_ddim(shape_int64);
ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
if (shape[0] == in->dims()[0]) {
ctx->SetOutputDim("Out", out_dims);
if (shape[0] == x_dims[0]) {
// Only pass LoD when the first dimension is equal between
// output and input.
ctx.ShareLoD("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");
}
}
};
......@@ -76,7 +76,7 @@ Given a 2-D tensor X with 2 rows and 2 columns
[[1, 2], [3, 4]]
with target shape = [1, 4], the reshape operator will transform
with target shape = [1, 4], the reshape operator will transform
the tensor X into a 1-D tensor:
[1, 2, 3, 4]
......@@ -94,13 +94,11 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("X")->dims();
auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
d_in->Resize(dims);
void InferShape(framework::InferShapeContextBase *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"));
}
};
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,16 +24,16 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"),
"Input(b) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of RowwiseAddOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto b_dims = ctx.Input<Tensor>("b")->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("b"),
"Input(b) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of RowwiseAddOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto b_dims = ctx->GetInputDim("b");
PADDLE_ENFORCE_GT(
x_dims.size(), b_dims.size(),
"The rank of input `X` must be larger than the one of input `b`.");
......@@ -43,16 +43,17 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
PADDLE_ENFORCE_EQ(ctx.OutputSize("Out"), 1, "The output size must be 1");
ctx.Output<framework::Tensor>("Out")->Resize(x_dims);
ctx.ShareLoD("X", /*->*/ "Out");
PADDLE_ENFORCE_EQ(ctx->Outputs("Out").size(), 1,
"The output size must be 1");
ctx->SetOutputDim("Out", x_dims);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker {
public:
RowwiseAddOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
RowwiseAddOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The left input of row-wise add op, must be matrix");
AddInput("b", "The right input of row-wise add op, must be vector");
......@@ -69,25 +70,29 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto b_dims = ctx.Input<Tensor>("b")->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X should not be null");
PADDLE_ENFORCE(ctx->HasInput("b"), "b 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 b_dims = ctx->GetInputDim("b");
PADDLE_ENFORCE_GT(
x_dims.size(), b_dims.size(),
"The rank of input `X` must be larger than the one of input `b`.");
int num_col_dims = x_dims.size() - b_dims.size();
int64_t num_col_dims = x_dims.size() - b_dims.size();
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
auto *dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *db = ctx.Output<framework::Tensor>(framework::GradVarName("b"));
if (dx) dx->Resize(x_dims);
if (db) db->Resize(b_dims);
auto x_grad_name = framework::GradVarName("X");
auto b_grad_name = framework::GradVarName("b");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, x_dims);
}
if (ctx->HasOutput(b_grad_name)) {
ctx->SetOutputDim(b_grad_name, b_dims);
}
}
};
......
paddle/operators/scale_op.cc
浏览文件 @ 9a9d50a6
......@@ -26,16 +26,13 @@ class ScaleOp : public framework::OperatorWithKernel {
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ScaleOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ScaleOp should not be null.");
auto *in = ctx.Input<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
out->Resize(in->dims());
ctx.ShareLoD("X", /*->*/ "Out");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ScaleOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ScaleOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......
paddle/operators/scatter_op.cc
浏览文件 @ 9a9d50a6
......@@ -23,29 +23,30 @@ class ScatterOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ref"),
"Input(Ref) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
"Input(Index) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Updates"),
"Input(Updates) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ScatterOp should not be null.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Ref"),
"Input(Ref) of ScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Index"),
"Input(Index) of ScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Updates"),
"Input(Updates) of ScatterOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ScatterOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Index")->dims().size(), 1,
auto updates_dims = ctx->GetInputDim("Updates");
auto ref_dims = ctx->GetInputDim("Ref");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Index").size(), 1,
"Update Index should be 1-D.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Ref")->dims().size(),
ctx.Input<Tensor>("Updates")->dims().size(),
PADDLE_ENFORCE_EQ(ref_dims.size(), updates_dims.size(),
"Reference and Updates should have the same shape size");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Updates")->dims()[0],
ctx.Input<Tensor>("Index")->dims()[0],
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Updates")[0],
ctx->GetInputDim("Index")[0],
"Updates and Index should have same batch-size.");
framework::DDim data_dim(ctx.Input<Tensor>("Updates")->dims());
for (int i = 1; i < data_dim.size(); ++i)
PADDLE_ENFORCE_EQ(data_dim[i], ctx.Input<Tensor>("Updates")->dims()[i]);
ctx.Output<framework::Tensor>("Out")->Resize(
ctx.Input<Tensor>("Ref")->dims());
framework::DDim data_dim(updates_dims);
for (int i = 1; i < data_dim.size(); ++i) {
PADDLE_ENFORCE_EQ(data_dim[i], updates_dims[i]);
}
ctx->SetOutputDim("Out", ref_dims);
}
};
......@@ -54,22 +55,17 @@ class ScatterGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *dUpdates =
ctx.Output<framework::Tensor>(framework::GradVarName("Updates"));
auto *Updates = ctx.Input<Tensor>("Updates");
auto *dRef = ctx.Output<framework::Tensor>(framework::GradVarName("Ref"));
auto *Ref = ctx.Input<Tensor>("Ref");
dRef->Resize(Ref->dims());
dUpdates->Resize(Updates->dims());
void InferShape(framework::InferShapeContextBase* ctx) const override {
ctx->SetOutputDim(framework::GradVarName("Updates"),
ctx->GetInputDim("Updates"));
ctx->SetOutputDim(framework::GradVarName("Ref"), ctx->GetInputDim("Ref"));
}
};
class ScatterOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ScatterOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
ScatterOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Ref", "The source input of scatter op");
AddInput("Index",
......@@ -77,13 +73,14 @@ class ScatterOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Updates", "The updated value of updates op");
AddOutput("Out", "The output of add op");
AddComment(R"DOC(
Scatter Operator by selecting from the first axis,
Scatter Operator by selecting from the first axis,
Out = Ref
Out[Index] = Ref[Index] + Updates
)DOC");
}
};
} // namespace operators
} // namespace paddle
......
paddle/operators/sequence_pool_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,23 +22,12 @@ class SequencePoolOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SequencePoolOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SequencePoolOp should not be null.");
auto* x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
auto lod = x->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
dims[0] = lod[0].size() - 1;
ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceAvgPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceAvgPoolOp should not be null.");
ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
}
};
......@@ -61,17 +50,17 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
SequencePoolOp pools features of all time-steps of each instance.
For a mini-batch of 3 variable lengths sentences, containing 2, 3, and 2 time-steps:
Assume X is a [7,M,N] float LoDTensor, and X->lod()[0] = [0, 2, 5, 7].
Besides, for the sake of simplicity, we assume M=1 and N=1,
Besides, for the sake of simplicity, we assume M=1 and N=1,
and the value of X = [[1, 3], [2, 4, 6], [5, 1]].
Thus, Out is a [3,1,1] float LoDTensor, but Out->lod() is nullptr.
And for different strategy, the value of Out is as follows:
And for different strategy, the value of Out is as follows:
- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
- SUM: [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
- SQRT: [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
- SQRT: [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
- MAX: [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
- LAST: [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
......@@ -85,22 +74,18 @@ class SequencePoolGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Gradient of Out should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"The input X should not be null.");
auto og_dims =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<framework::LoDTensor>("X")->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
auto og_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
"The rank of output grad must equal to Input(X).");
for (int64_t i = 1; i < og_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
}
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
};
......
paddle/operators/sequence_pool_op.h
浏览文件 @ 9a9d50a6
......@@ -46,16 +46,27 @@ class SequencePoolKernel : public framework::OpKernel {
int strategy = context.Attr<int>("strategy");
auto dims = in->dims();
auto lod = in->lod()[0];
auto lod = in->lod();
int64_t w = in->numel() / dims[0];
// InferShape by lod
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
dims[0] = lod[0].size() - 1;
out->Resize({dims});
auto lod_level_0 = lod[0];
out->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod.size()) - 1; ++i) {
Tensor in_t =
in->Slice<T>(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
for (int i = 0; i < static_cast<int>(lod_level_0.size()) - 1; ++i) {
Tensor in_t = in->Slice<T>(static_cast<int>(lod_level_0[i]),
static_cast<int>(lod_level_0[i + 1]));
Tensor out_t = out->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[i + 1] - lod[i]);
int64_t h = static_cast<int64_t>(lod_level_0[i + 1] - lod_level_0[i]);
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t);
......
paddle/operators/sgd_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,19 +22,18 @@ class SGDOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("param"),
"Input(param) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("grad"),
"Input(grad) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("param_out"),
"Output(param_out) of SGDOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("param")->dims(),
ctx.Input<Tensor>("grad")->dims(),
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("param"),
"Input(param) of SGDOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("grad"),
"Input(grad) of SGDOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("param_out"),
"Output(param_out) of SGDOp should not be null.");
auto param_dim = ctx->GetInputDim("param");
PADDLE_ENFORCE_EQ(param_dim, ctx->GetInputDim("grad"),
"Two input of SGD Op's dimension must be same.");
ctx.Output<framework::Tensor>("param_out")
->Resize(ctx.Input<Tensor>("param")->dims());
ctx->SetOutputDim("param_out", param_dim);
}
};
......
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,33 +22,28 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Y must be initialized.");
auto* x = ctx.Input<framework::Tensor>("X");
auto* y = ctx.Input<framework::Tensor>("Y");
PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
"The shape of X and Y must be the same.");
PADDLE_ENFORCE_GE(x->dims().size(), 2,
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"The tensor rank of X must be at least 2.");
auto* inside_weight = ctx.Input<framework::Tensor>("InsideWeight");
if (inside_weight) {
auto* outside_weight = ctx.Input<framework::Tensor>("OutsideWeight");
PADDLE_ENFORCE_NOT_NULL(outside_weight,
"If weights are provided, must specify both "
"inside and outside weights.");
PADDLE_ENFORCE_EQ(inside_weight->dims(), x->dims(),
if (ctx->HasInput("InsideWeight")) {
PADDLE_ENFORCE(ctx->HasInput("OutsideWeight"),
"If weights are provided, must specify both "
"inside and outside weights.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("InsideWeight"), x_dims,
"The shape of InsideWeight must be same as X.");
PADDLE_ENFORCE_EQ(outside_weight->dims(), x->dims(),
PADDLE_ENFORCE_EQ(ctx->GetInputDim("OutsideWeight"), x_dims,
"The shape of OutsideWeight must be same as X.");
}
auto* diff = ctx.Output<framework::Tensor>("Diff");
auto* out = ctx.Output<framework::Tensor>("Out");
diff->Resize(x->dims());
ctx->SetOutputDim("Diff", x_dims);
// loss is a two-rank tensor
out->Resize({x->dims()[0], 1});
ctx->SetOutputDim("Out", {x_dims[0], 1});
}
};
......@@ -99,12 +94,9 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
auto in_dims = ctx.Input<framework::Tensor>("X")->dims();
auto out_dims =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->dims();
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto in_dims = ctx->GetInputDim("X");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
PADDLE_ENFORCE_GE(out_dims.size(), 2,
"The tensor rank of Input(Out@Grad) should be 2.");
......@@ -114,8 +106,14 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"The 2nd dimension of Input(Out@Grad) must be 1.");
if (x_grad) x_grad->Resize(in_dims);
if (y_grad) y_grad->Resize(in_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) {
ctx->SetOutputDim(x_grad_name, in_dims);
}
if (ctx->HasOutput(y_grad_name)) {
ctx->SetOutputDim(y_grad_name, in_dims);
}
}
};
......
paddle/operators/softmax_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,22 +22,23 @@ class SoftmaxOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SoftmaxOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
"Output(Y) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Y"),
"Output(Y) of SoftmaxOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE(x_dims.size() == 2UL,
"The input of softmax op must be a matrix.");
ctx.Output<framework::Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim("Y", x_dims);
}
};
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
SoftmaxOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input tensor of softmax. "
......@@ -68,16 +69,15 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should be not null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Y")),
"Input(Y@GRAD) should be not null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Y")->dims(),
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should be not null.");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
"Input(Y@GRAD) should be not null.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("Y"),
ctx->GetInputDim(framework::GradVarName("Y")),
"Input(Y) and its gradients should have a same shape.");
ctx.Output<framework::Tensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
......
paddle/operators/split_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,40 +24,42 @@ class SplitOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// infershape
auto *in = ctx.Input<framework::Tensor>("X");
auto outs = ctx.MultiOutput<framework::Tensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
size_t num = static_cast<size_t>(ctx.Attr<int>("num"));
std::vector<int> sections =
static_cast<std::vector<int>>(ctx.Attr<std::vector<int>>("sections"));
const size_t n = outs.size();
void InferShape(framework::InferShapeContextBase *ctx) const override {
auto in_dims = ctx->GetInputDim("X");
auto outs_names = ctx->Outputs("Out");
size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
size_t num = static_cast<size_t>(ctx->Attrs().Get<int>("num"));
std::vector<int> sections = static_cast<std::vector<int>>(
ctx->Attrs().Get<std::vector<int>>("sections"));
const size_t outs_number = outs_names.size();
std::vector<framework::DDim> outs_dims;
outs_dims.reserve(outs_number);
if (num > 0) {
int64_t in_axis_dim = in->dims()[axis];
int64_t in_axis_dim = in_dims[axis];
PADDLE_ENFORCE_EQ(in_axis_dim % num, 0,
"tensor split does not result"
" in an equal division");
size_t out_axis_dim = in_axis_dim / num;
for (size_t i = 0; i < n; ++i) {
auto dim = in->dims();
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = out_axis_dim;
outs[i]->Resize(dim);
outs_dims.push_back(dim);
}
} else if (sections.size() > 0) {
PADDLE_ENFORCE_EQ(sections.size(), n,
PADDLE_ENFORCE_EQ(sections.size(), outs_number,
"tensor split sections size"
"should be equal to output size.");
for (size_t i = 0; i < n; ++i) {
auto dim = in->dims();
for (size_t i = 0; i < outs_number; ++i) {
auto dim = in_dims;
dim[axis] = sections[i];
outs[i]->Resize(dim);
outs_dims.push_back(dim);
}
} else {
PADDLE_ENFORCE_NOT_NULL(nullptr, "split operator should",
" specify indices or sections.");
}
ctx->SetOutputsDim("Out", outs_dims);
}
};
......
paddle/operators/squared_l2_distance_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,24 +22,19 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"),
"Input(X) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Y"),
"Input(Y) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("sub_result"),
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"),
"Input(Y) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("sub_result"),
"Output(sub_result) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SquaredL2DistanceOp should not be null.");
auto* x = ctx.Input<Tensor>("X");
auto x_dims = x->dims();
auto* y = ctx.Input<Tensor>("Y");
auto y_dims = y->dims();
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims),
"Tensor rank of both SquaredL2DistanceOp's "
......@@ -47,17 +42,16 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
int rank = framework::arity(x_dims);
PADDLE_ENFORCE_GE(rank, 2, "Tensor rank should be at least equal to 2.");
PADDLE_ENFORCE_EQ(x->numel() / x_dims[0], y->numel() / y_dims[0],
PADDLE_ENFORCE_EQ(product(x_dims) / x_dims[0], product(y_dims) / y_dims[0],
"Product of dimensions expcet the first dimension of "
"input and target must be equal.");
PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0],
"First dimension of target must be equal to input "
"or to 1.");
ctx.Output<framework::Tensor>("sub_result")
->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output<framework::Tensor>("Out")->Resize({x_dims[0], 1});
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("sub_result", {x_dims[0], product(x_dims) / x_dims[0]});
ctx->SetOutputDim("Out", {x_dims[0], 1});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
......@@ -92,22 +86,22 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Gradient of Out should not be null");
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"Gradient of Out should not be null");
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
"First dimension of output gradient and "
"input value must be equal.");
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"Second dimension of output gradient "
"must be 1.");
auto* x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
if (ctx->HasOutput(x_grad_name)) ctx->SetOutputDim(x_grad_name, x_dims);
if (ctx->HasOutput(y_grad_name)) ctx->SetOutputDim(y_grad_name, y_dims);
}
};
......
paddle/operators/sum_op.cc
浏览文件 @ 9a9d50a6
......@@ -21,31 +21,27 @@ class SumOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) of SumOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of SumOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
int N = ins.size();
auto in_dim = ins[0]->dims();
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto x_dims = ctx->GetInputsDim("X");
PADDLE_ENFORCE(!x_dims.empty(), "Input(X) of SumOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SumOp should not be null.");
auto in_dim = x_dims[0];
size_t N = x_dims.size();
PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
for (int i = 1; i < N; i++) {
auto dim = ins[i]->dims();
for (size_t i = 1; i < N; i++) {
auto dim = x_dims[i];
PADDLE_ENFORCE(in_dim == dim, "Input tensors must have same shape");
}
out->Resize(in_dim);
ctx.ShareLoD("X", /*->*/ "Out");
ctx->SetOutputDim("Out", in_dim);
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class SumOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SumOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
SumOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "the input tensors of sum operator.").AsDuplicable();
AddOutput("Out", "the output tensor of sum operator.");
......@@ -63,13 +59,16 @@ class SumGradOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto outputs =
ctx.MultiOutput<framework::Tensor>(framework::GradVarName("X"));
auto dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
for (auto output : outputs) {
output->Resize(dims);
void InferShape(framework::InferShapeContextBase* ctx) const override {
auto out_grad_dims = ctx->GetInputDim(framework::GradVarName("Out"));
auto x_grad_names = ctx->Outputs(framework::GradVarName("X"));
size_t x_length = x_grad_names.size();
std::vector<framework::DDim> x_grad_dims;
x_grad_dims.reserve(x_length);
for (size_t i = 0; i < x_length; ++i) {
x_grad_dims.push_back(out_grad_dims);
}
ctx->SetOutputsDim(framework::GradVarName("X"), x_grad_dims);
}
};
......
paddle/operators/top_k_op.cc
浏览文件 @ 9a9d50a6
......@@ -22,26 +22,26 @@ class TopkOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Indices"),
"Output(Indices) of TopkOp should not be null.");
void InferShape(framework::InferShapeContextBase *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of TopkOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of TopkOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Indices"),
"Output(Indices) of TopkOp should not be null.");
auto *input = ctx.Input<framework::Tensor>("X");
const int k = static_cast<int>(ctx.Attr<int>("k"));
auto input_dims = ctx->GetInputDim("X");
const int k = static_cast<int>(ctx->Attrs().Get<int>("k"));
PADDLE_ENFORCE_GE(k, 1, "k must >= 1");
PADDLE_ENFORCE_GE(input->dims().size(), 1, "input must have >= 1d shape");
PADDLE_ENFORCE_GE(input->dims()[input->dims().size() - 1], k,
PADDLE_ENFORCE_GE(input_dims.size(), 1, "input must have >= 1d shape");
PADDLE_ENFORCE_GE(input_dims[input_dims.size() - 1], k,
"input must have >= k columns");
framework::DDim dims = input->dims();
framework::DDim dims = input_dims;
dims[dims.size() - 1] = k;
ctx.Output<framework::Tensor>("Out")->Resize(dims);
ctx.Output<framework::Tensor>("Indices")->Resize(dims);
ctx->SetOutputDim("Out", dims);
ctx->SetOutputDim("Indices", dims);
}
};
......
paddle/operators/transpose_op.cc
浏览文件 @ 9a9d50a6
......@@ -24,12 +24,11 @@ class TransposeOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
auto x_dims = ctx->GetInputDim("X");
std::vector<int> axis = ctx->Attrs().Get<std::vector<int>>("axis");
size_t x_rank = x_dims.size();
size_t axis_size = axis.size();
......@@ -51,14 +50,14 @@ class TransposeOp : public framework::OperatorWithKernel {
for (size_t i = 0; i < axis_size; i++) {
out_dims[i] = x_dims[axis[i]];
}
ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
ctx->SetOutputDim("Out", out_dims);
}
};
class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TransposeOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
TransposeOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
......@@ -79,7 +78,7 @@ For example:
[3, 4, 5]])
>> axis = [1, 0]
>> output = input.transpose(axis)
>> output
>> output
array([[0, 3],
[1, 4],
[2, 5]])
......@@ -94,14 +93,15 @@ class TransposeOpGrad : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
if (x_grad) x_grad->Resize(x_dims);
void InferShape(framework::InferShapeContextBase* 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");
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
}
}
};
......
paddle/operators/uniform_random_op.cc
浏览文件 @ 9a9d50a6
......@@ -23,18 +23,18 @@ namespace operators {
template <typename T>
class CPUUniformRandomKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(context.GetPlace());
unsigned int seed = static_cast<unsigned int>(context.Attr<int>("seed"));
void Compute(const framework::ExecutionContext& ctx) const override {
auto* tensor = ctx.Output<framework::Tensor>("Out");
T* data = tensor->mutable_data<T>(ctx.GetPlace());
unsigned int seed = static_cast<unsigned int>(ctx.Attr<int>("seed"));
std::minstd_rand engine;
if (seed == 0) {
seed = std::random_device()();
}
engine.seed(seed);
std::uniform_real_distribution<T> dist(
static_cast<T>(context.Attr<float>("min")),
static_cast<T>(context.Attr<float>("max")));
static_cast<T>(ctx.Attr<float>("min")),
static_cast<T>(ctx.Attr<float>("max")));
int64_t size = tensor->numel();
for (int64_t i = 0; i < size; ++i) {
data[i] = dist(engine);
......@@ -47,21 +47,20 @@ class UniformRandomOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of UniformRandomOp should not be null.");
void InferShape(framework::InferShapeContextBase* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of UniformRandomOp should not be null.");
PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
PADDLE_ENFORCE(
ctx->Attrs().Get<float>("min") < ctx->Attrs().Get<float>("max"),
"uniform_random's min must less then max");
auto dims = Attr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.push_back(static_cast<int64_t>(dim));
}
tensor->Resize(framework::make_ddim(temp));
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
};
......
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