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paddlepaddle--paddle/paddle/fluid/operators/reshape_op.cc
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wehub-resource-sync e25996e7db chore: import upstream snapshot with attribution
2026-07-13 12:40:42 +08:00

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <string>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/phi_utils.h"
// only can include the headers in paddle/phi/api dirs
#include "paddle/fluid/prim/api/composite_backward/composite_backward_api.h"
#include "paddle/fluid/prim/utils/static/composite_grad_desc_maker.h"
#include "paddle/phi/backends/cpu/cpu_context.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/unary.h"
namespace paddle {
namespace framework {
class InferShapeContext;
class OpDesc;
} // namespace framework
namespace imperative {
class OpBase;
} // namespace imperative
} // namespace paddle
namespace paddle {
namespace operators {
class ReshapeOp : public framework::OperatorWithKernel {
public:
ReshapeOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_EQ(ctx->HasInput("X"),
true,
common::errors::InvalidArgument(
"Input(X) of ReshapeOp should not be null."));
PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"),
true,
common::errors::InvalidArgument(
"Output(Out) of ReshapeOp should not be null."));
if (ctx->IsRuntime()) {
auto *x_var =
PADDLE_GET(framework::Variable *, ctx->GetInputVarPtrs("X")[0]);
auto *out_var =
PADDLE_GET(framework::Variable *, ctx->GetOutputVarPtrs("Out")[0]);
// inplace, can not to run infer shape.
if (x_var == out_var) {
return;
}
}
if (ctx->HasInputs("ShapeTensor")) {
// top priority shape
auto ShapeTensor = ctx->Inputs("ShapeTensor");
PADDLE_ENFORCE_GT(
ShapeTensor.size(),
0,
common::errors::InvalidArgument(
"When `shape` in ReshapeOp is a list or tuple "
"which contains Tensor, the shape's size can't be zero. "
"But received shape's size is %d.",
ShapeTensor.size()));
auto infer_shape = ctx->Attrs().Get<std::vector<int>>("shape");
const int64_t copy_dim_val = 0;
auto in_dims = ctx->GetInputDim("X");
for (size_t i = 0; i < infer_shape.size(); ++i) {
if (infer_shape[i] == copy_dim_val) {
PADDLE_ENFORCE_LT(
static_cast<int>(i),
in_dims.size(),
common::errors::InvalidArgument(
"The index of 0 in `shape` must be less than "
"the input tensor X's dimensions. But received shape[%d] "
"= 0, X's dimensions = %d, X's shape = [%s].",
i,
in_dims.size(),
in_dims));
infer_shape[i] = static_cast<int>(in_dims[static_cast<int>(i)]);
}
}
auto infer_out_dims = common::make_ddim(infer_shape);
ctx->SetOutputDim("Out", infer_out_dims);
return;
}
const std::vector<int> &shape = ctx->Attrs().Get<std::vector<int>>("shape");
if (ctx->HasInput("Shape") && shape.empty()) {
auto shape_dims = ctx->GetInputDim("Shape");
int num_ele = 1;
for (int i = 0; i < shape_dims.size(); ++i) {
num_ele *= static_cast<int>(shape_dims[i]);
}
auto vec_dims = std::vector<int>(num_ele, -1);
auto out_dims = common::make_ddim(vec_dims);
ctx->SetOutputDim("Out", out_dims);
ctx->ShareLoD("X", /*->*/ "Out");
return;
}
if (ctx->HasInput("Shape") && !shape.empty() && ctx->IsRuntime()) {
// If true, set the shape of Output(Out) according to Input(Shape) in
// ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel.
ctx->ShareLoD("X", /*->*/ "Out");
return;
}
auto x_dims = ctx->GetInputDim("X");
auto out_dims = ValidateShape(shape, x_dims);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", /*->*/ "Out");
}
}
static phi::DDim ValidateShape(const std::vector<int> shape,
const phi::DDim &in_dims) {
const int64_t in_size = common::product(in_dims);
auto in_dims_vec = common::vectorize(in_dims);
std::vector<int64_t> output_shape(shape.size(), 0);
int64_t capacity = 1;
int unk_dim_idx = -1;
for (size_t i = 0; i < shape.size(); ++i) {
if (shape[i] == -1) {
// only one dimension can be set to -1, whose size will be inferred.
PADDLE_ENFORCE_EQ(
unk_dim_idx,
-1,
common::errors::InvalidArgument(
"Only one dimension value of 'shape' in ReshapeOp can "
"be -1. But received shape = [%s], shape[%d] is also -1.",
common::make_ddim(shape),
i));
unk_dim_idx = static_cast<int>(i);
output_shape[i] = shape[i];
} else if (shape[i] == 0) {
if (in_size == 0) {
// zero-sized tensor case
// index i could be < in_dims.size(): such as [3, 2, 0] -> [0, 0] is
// [0, 0], [3, 2, 0] -> [10, 0] is [10, 0]; index i could be >=
// in_dims.size(): such as [3, 2, 0] -> [1, 3, 0, 0] is [1, 3, 0, 0]
output_shape[i] = 0;
} else {
// in other cases 0 means keep in_dims[i] unchanged
// index i must be < in_dims.size(): such as [3, 2, 1] -> [0, 0]
// is [3, 2] or [3, 2, 1] -> [3, 2, 0] is [3, 2, 1]
PADDLE_ENFORCE_LT(
static_cast<int>(i),
in_dims.size(),
common::errors::InvalidArgument(
"The index of 0 in `shape` must be less than "
"the input tensor X's dimensions. "
"But received shape = [%s], shape[%d] = 0, X's shape = [%s], "
"X's dimensions = %d.",
phi::make_ddim(shape),
i,
in_dims,
in_dims.size()));
output_shape[i] = in_dims[static_cast<int>(i)];
}
capacity *= output_shape[i];
} else {
PADDLE_ENFORCE_GT(
shape[i],
0,
common::errors::InvalidArgument(
"Each dimension value of 'shape' in ReshapeOp must not "
"be negative except one unknown dimension. "
"But received shape = [%s], shape[%d] = %d.",
common::make_ddim(shape),
i,
shape[i]));
output_shape[i] = shape[i];
capacity *= output_shape[i];
}
}
if (capacity == 0) {
PADDLE_ENFORCE_EQ(in_size,
0,
common::errors::InvalidArgument(
"Only Zero-Size Tensor'shape can contain 0"));
PADDLE_ENFORCE_EQ(unk_dim_idx,
-1,
common::errors::InvalidArgument(
"can not reshape %s to %s, because the unspecified "
"dimension %i can be any number and is ambiguous",
in_dims,
common::make_ddim(shape),
unk_dim_idx));
}
bool no_negative = std::all_of(in_dims_vec.cbegin(),
in_dims_vec.cend(),
[](int64_t i) { return i >= 0; });
if (unk_dim_idx != -1) {
// in compile time, no_negative may be False.
if (no_negative) {
output_shape[unk_dim_idx] = in_size / capacity;
PADDLE_ENFORCE_EQ(
output_shape[unk_dim_idx] * capacity,
in_size,
common::errors::InvalidArgument(
"The 'shape' attribute in ReshapeOp is invalid. "
"The input tensor X'size must be divisible by known "
"capacity of 'shape'. "
"But received X's shape = [%s], X's size = %d, "
"'shape' is [%s], known capacity of 'shape' is %d.",
in_dims,
in_size,
common::make_ddim(shape),
capacity));
} else {
// such as [-1, 8, 3]->[-1, 8], out_shape will remain [-1, 8]
output_shape[unk_dim_idx] = -1;
}
} else {
if (no_negative) {
PADDLE_ENFORCE_EQ(
capacity,
in_size,
common::errors::InvalidArgument(
"The 'shape' in ReshapeOp is invalid. "
"The input tensor X'size must be equal to the capacity of "
"'shape'. "
"But received X's shape = [%s], X's size = %d, 'shape' is "
"[%s], the capacity of 'shape' is %d.",
in_dims,
in_size,
common::make_ddim(shape),
capacity));
}
}
return common::make_ddim(output_shape);
}
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
phi::KernelKey GetKernelTypeForVar(
const std::string &var_name,
const DenseTensor &tensor,
const phi::KernelKey &expected_kernel_type) const override {
if (var_name == "ShapeTensor") {
return phi::KernelKey(phi::Backend::ALL_BACKEND,
expected_kernel_type.layout(),
expected_kernel_type.dtype());
}
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
};
class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X", "(Tensor). The input tensor of reshape operator.");
AddInput("Shape",
"(Tensor<int32>, optional). Target shape of reshape operator. "
"It has a higher priority than Attr(shape) but a lower priority "
"than Input(ShapeTensor). The Attr(shape) still should be "
"set correctly to guarantee shape inference in compile time.")
.AsDispensable();
AddInput(
"ShapeTensor",
"(vector<Tensor<int32>>, optional). Target shape of reshape operator. "
"It has the highest priority compare with Input(Shape) and "
"Attr(shape)."
"The shape of the element in vector must be [1].")
.AsDuplicable()
.AsDispensable();
AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
AddAttr<std::vector<int>>(
"shape",
"(std::vector<int>) Target shape of reshape operator."
"It has the lowest priority compare with Input(Shape) and "
" Input(ShapeTensor).")
.SetDefault({});
AddComment(R"DOC(
Reshape Operator.
Reshape Input(X) into the shape specified by Attr(shape) or Input(Shape). The
data in Input(X) are unchanged.
Examples:
1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
the value of this dimension is inferred from the total element number of
Input(X) and remaining dimensions.
3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
unchanged. In this case, besides -1, 0 means the actual dimension value is going
to be copied from the corresponding dimension of Input(X).
Note:
1. One and only one dimension in Attr(shape) can be set -1. In this case,
the actual dimension value will be inferred from the total element number of
Input(X) and remaining dimensions.
2. More than one dimensions in Attr(shape) can be set to 0, which means the real
dimension value will be copied from Input(X) at runtime. Note that the index of
0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape
[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
Attr(shape) still should be set correctly to guarantee shape inference in
compile-time.
)DOC");
}
};
class ReshapeGradOp : public framework::OperatorWithKernel {
public:
ReshapeGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("X"),
true,
common::errors::InvalidArgument("Input(X) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput(framework::GradVarName("Out")),
true,
common::errors::InvalidArgument("Input(Out@GRAD) shouldn't be null."));
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type =
framework::OperatorWithKernel::IndicateVarDataType(ctx, "X");
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
};
// FIXME(zcd): reshape2 adds an intermediate output(XShape) based on reshape,
// the XShape is used to carry the shape and lod of X which will be used in
// reshape_grad, in this way, the framework can reuse the memory of X
// immediately the reshape_op is finished.
// Considering compatibility issues, we could not fix reshape_op
class Reshape2Op : public ReshapeOp {
public:
Reshape2Op(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: ReshapeOp(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
if (ctx->HasOutput("XShape")) {
const auto &x_dims = ctx->GetInputDim("X");
std::vector<int64_t> xshape_dims(x_dims.size() + 1);
xshape_dims[0] = 0;
for (int i = 0; i < x_dims.size(); ++i) {
xshape_dims[i + 1] = x_dims[i];
}
ctx->SetOutputDim("XShape", common::make_ddim(xshape_dims));
ctx->ShareLoD("X", /*->*/ "XShape");
}
ReshapeOp::InferShape(ctx);
}
};
class Reshape2OpMaker : public ReshapeOpMaker {
public:
void Make() override {
ReshapeOpMaker::Make();
AddOutput("XShape",
"XShape is just used to store the shape and lod of X, which will "
"be used in FlattenGradOp.")
.AsIntermediate();
AddAttr<bool>(
"use_quantizer",
"(bool, default false) "
"This parameter is no longer used. Use 'mkldnn_data_type' instead.")
.SetDefault(false);
AddAttr<std::string>(
"mkldnn_data_type",
"(string, default \"float32\"). Data type of mkldnn kernel")
.SetDefault("float32")
.InEnum({"float32", "int8", "bfloat16"})
.AsExtra();
}
};
template <typename T>
class Reshape2GradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("reshape2_grad");
grad_op->SetInput("XShape", this->Output("XShape"));
grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
grad_op->SetAttrMap(this->Attrs());
}
};
class Reshape2CompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
using prim::CompositeGradOpMakerBase::CompositeGradOpMakerBase;
public:
void Apply() override {
// We prefer to use x.shape instead of using xshape, this is different from
// PHI definition.
paddle::Tensor x = this->GetSingleForwardInput("X");
paddle::Tensor out_grad = this->GetSingleOutputGrad("Out");
paddle::Tensor dx = this->GetSingleInputGrad("X");
auto *dx_ptr = this->GetOutputPtr(&dx);
std::string dx_name = this->GetOutputName(dx);
VLOG(6) << "Running reshape2_grad composite func";
prim::reshape_grad<prim::DescTensor>(x, out_grad, dx_ptr);
this->RecoverOutputName(dx, dx_name);
}
};
template <typename T>
class Reshape2DoubleGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("reshape2_grad_grad");
grad_op->SetInput("DOut", this->Input(framework::GradVarName("Out")));
grad_op->SetInput("DDX", this->OutputGrad(framework::GradVarName("X")));
grad_op->SetOutput("DDOut", this->InputGrad(framework::GradVarName("Out")));
grad_op->SetAttrMap(this->Attrs());
}
};
class Reshape2GradOp : public framework::OperatorWithKernel {
public:
Reshape2GradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("XShape"),
true,
common::errors::InvalidArgument("Input(XShape) shouldn't be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput(framework::GradVarName("Out")),
true,
common::errors::InvalidArgument("Input(Out@GRAD) shouldn't be null."));
// Construct MetaTensor for InferMeta Func
using CompatMetaTensor = framework::CompatMetaTensor;
CompatMetaTensor xshape(ctx->GetInputVarPtrs("XShape")[0],
ctx->IsRuntime());
CompatMetaTensor out_grad(
ctx->GetInputVarPtrs(framework::GradVarName("Out"))[0],
ctx->IsRuntime());
CompatMetaTensor dx(ctx->GetOutputVarPtrs(framework::GradVarName("X"))[0],
ctx->IsRuntime());
phi::KernelWithXShapeInferMeta(xshape, out_grad, &dx);
}
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
auto input_data_type = framework::OperatorWithKernel::IndicateVarDataType(
ctx, framework::GradVarName("Out"));
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
phi::KernelKey GetKernelTypeForVar(
const std::string &var_name,
const DenseTensor &tensor,
const phi::KernelKey &expected_kernel_type) const override {
if (var_name == "ShapeTensor") {
return phi::KernelKey(phi::Backend::ALL_BACKEND,
expected_kernel_type.layout(),
expected_kernel_type.dtype());
}
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
};
class Reshape2DoubleGradOp : public framework::OperatorWithKernel {
public:
Reshape2DoubleGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext &ctx) const override {
return phi::KernelKey(OperatorWithKernel::IndicateVarDataType(ctx, "DDX"),
ctx.GetPlace());
}
phi::KernelKey GetKernelTypeForVar(
const std::string &var_name,
const DenseTensor &tensor,
const phi::KernelKey &expected_kernel_type) const override {
if (var_name == "ShapeTensor") {
return phi::KernelKey(phi::Backend::ALL_BACKEND,
expected_kernel_type.layout(),
expected_kernel_type.dtype());
}
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
};
class Reshape2InferVarType : public framework::VarTypeInference {
public:
void operator()(framework::InferVarTypeContext *ctx) const override {
ctx->SyncTypeAndDataType("X", "Out");
}
};
DECLARE_INPLACE_OP_INFERER(ReshapeOpInplaceInferer, {"X", "Out"});
DECLARE_INPLACE_OP_INFERER(ReshapeGradInplaceInferer,
{framework::GradVarName("Out"),
framework::GradVarName("X")});
DECLARE_INPLACE_OP_INFERER(ReshapeDoubleGradInplaceInferer, {"DDX", "DDOut"});
DECLARE_NO_NEED_BUFFER_VARS_INFERER(ReshapeDoubleGradOpNoNeedBufferVarInferer,
"DOut");
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(
reshape,
ops::ReshapeOp,
ops::ReshapeOpMaker,
paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>,
ops::ReshapeOpInplaceInferer);
REGISTER_OPERATOR(reshape_grad,
ops::ReshapeGradOp,
ops::ReshapeGradInplaceInferer);
REGISTER_OPERATOR(reshape2,
ops::Reshape2Op,
ops::Reshape2OpMaker,
ops::Reshape2GradMaker<paddle::framework::OpDesc>,
ops::Reshape2GradMaker<paddle::imperative::OpBase>,
ops::Reshape2InferVarType,
ops::Reshape2CompositeGradOpMaker,
ops::ReshapeOpInplaceInferer);
REGISTER_OPERATOR(reshape2_grad,
ops::Reshape2GradOp,
ops::Reshape2DoubleGradMaker<paddle::framework::OpDesc>,
ops::Reshape2DoubleGradMaker<paddle::imperative::OpBase>,
ops::ReshapeGradInplaceInferer);
DECLARE_INFER_SHAPE_FUNCTOR(reshape2_grad_grad,
Reshape2DoubleGradInferShapeFunctor,
PD_INFER_META(phi::ReshapeDoubleGradInferMeta));
REGISTER_OPERATOR(reshape2_grad_grad,
ops::Reshape2DoubleGradOp,
ops::ReshapeDoubleGradInplaceInferer,
ops::ReshapeDoubleGradOpNoNeedBufferVarInferer,
Reshape2DoubleGradInferShapeFunctor);
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