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未验证 提交 30881647 编写于 作者: G gouzil 提交者： GitHub
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[static op generation] pool2d, pool3d (#54070)

上级 0a10cf40
隐藏空白更改
内联 并排
Showing with 179 addition and 721 deletion
paddle/fluid/operators/generator/get_expected_kernel_func.cc
浏览文件 @ 30881647
......@@ -61,6 +61,20 @@ static bool ReduceOpHasOptimizedOneDNNKernel(
return true;
}
// only poolop
bool CanMKLDNNSupportPool(const framework::ExecutionContext& ctx) {
if (ctx.Attr<bool>("adaptive") == false) return true;
// oneDNN is supporting only unchangable in size pool window
auto src_tz = phi::vectorize(ctx.Input<phi::DenseTensor>("X")->dims());
if (!ctx.HasAttr("ksize")) {
return false;
}
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
// Fast but not exhustive check
return ((src_tz[src_tz.size() - 1] % ksize[1] == 0) &&
(src_tz[src_tz.size() - 2] % ksize[0] == 0));
}
phi::KernelKey GetCheckFiniteAndUnscaleExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr) {
......@@ -136,6 +150,31 @@ phi::KernelKey GetAssignExpectedKernelType(
ctx.device_context().GetPlace());
}
phi::KernelKey GetPoolExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr) {
auto data_type = op_ptr->OperatorWithKernel::IndicateVarDataType(ctx, "X");
// NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
op_ptr->SetDnnFallback(!CanMKLDNNSupportPool(ctx));
// NOTE(jiahongyu) END: Above codes originally enclosed by PADDLE_WITH_MKLDNN
return phi::KernelKey(data_type, ctx.GetPlace());
}
phi::KernelKey GetPoolDoubleGradExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr) {
auto data_type =
op_ptr->OperatorWithKernel::IndicateVarDataType(ctx, "grad_x@GRAD");
// NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
op_ptr->SetDnnFallback(!CanMKLDNNSupportPool(ctx));
// NOTE(jiahongyu) END: Above codes originally enclosed by PADDLE_WITH_MKLDNN
return phi::KernelKey(data_type, ctx.GetPlace());
}
phi::KernelKey GetSgdExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr) {
......
paddle/fluid/operators/generator/get_expected_kernel_func.h
浏览文件 @ 30881647
......@@ -40,6 +40,14 @@ phi::KernelKey GetAssignExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr);
phi::KernelKey GetPoolExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr);
phi::KernelKey GetPoolDoubleGradExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr);
phi::KernelKey GetSgdExpectedKernelType(
const framework::ExecutionContext& ctx,
const framework::OperatorWithKernel* op_ptr);
......
paddle/fluid/operators/pool_op.cc 已删除 100644 → 0
浏览文件 @ 0a10cf40
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/pool_op.h"
#include <unordered_map>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/unary.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
bool CanMKLDNNSupportPool(const framework::ExecutionContext& ctx) {
if (ctx.Attr<bool>("adaptive") == false) return true;
// oneDNN is supporting only unchangable in size pool window
auto src_tz = phi::vectorize(ctx.Input<phi::DenseTensor>("X")->dims());
if (!ctx.HasAttr("ksize")) {
return false;
}
std::vector<int> ksize = ctx.Attr<std::vector<int>>("ksize");
// Fast but not exhustive check
return ((src_tz[src_tz.size() - 1] % ksize[1] == 0) &&
(src_tz[src_tz.size() - 2] % ksize[0] == 0));
}
phi::KernelKey PoolOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
// NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
this->SetDnnFallback(!CanMKLDNNSupportPool(ctx));
// NOTE(jiahongyu) END: Above codes originally enclosed by PADDLE_WITH_MKLDNN
return phi::KernelKey(data_type, ctx.GetPlace());
}
phi::KernelKey PoolOp::GetKernelTypeForVar(
const std::string& var_name,
const phi::DenseTensor& tensor,
const phi::KernelKey& expected_kernel_type) const {
#ifdef PADDLE_WITH_MKLDNN
if ((expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
(tensor.layout() != phi::DataLayout::ONEDNN)) {
auto attrs = Attrs();
auto ar = paddle::framework::AttrReader(attrs);
const std::string data_format = ar.Get<std::string>("data_format");
auto dl = phi::StringToDataLayout(data_format);
// Some models may have intentionally set "AnyLayout" for pool
// op. Treat this as NCHW (default data_format value)
if (dl != phi::DataLayout::kAnyLayout) {
return phi::KernelKey(tensor.place(), dl, expected_kernel_type.dtype());
}
}
#endif
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
phi::KernelKey PoolOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
auto input_data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
// NOTE(jiahongyu): Below codes originally enclosed by PADDLE_WITH_MKLDNN
this->SetDnnFallback(!CanMKLDNNSupportPool(ctx));
// NOTE(jiahongyu): Above codes originally enclosed by PADDLE_WITH_MKLDNN
return phi::KernelKey(input_data_type, ctx.GetPlace());
}
phi::KernelKey PoolOpGrad::GetKernelTypeForVar(
const std::string& var_name,
const phi::DenseTensor& tensor,
const phi::KernelKey& expected_kernel_type) const {
#ifdef PADDLE_WITH_MKLDNN
if ((expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
(tensor.layout() != phi::DataLayout::ONEDNN)) {
auto attrs = Attrs();
auto ar = paddle::framework::AttrReader(attrs);
const std::string data_format = ar.Get<std::string>("data_format");
return phi::KernelKey(tensor.place(),
phi::StringToDataLayout(data_format),
expected_kernel_type.dtype());
}
#endif
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
void Pool2dOpMaker::Make() {
AddInput(
"X",
"(phi::DenseTensor) The input tensor of pooling operator. "
"The format of input tensor is NCHW, where N is batch size, C is the "
"number of channels, H is the height of the feature, "
"and W is the width of the feature.");
AddOutput("Out",
"(phi::DenseTensor) The output tensor of pooling operator. "
"The format of output tensor is also NCHW, "
"where N is batch size, C is the number of channels, "
"H is the height of the feature, "
"and W is the width of the feature.");
AddAttr<std::string>("pooling_type",
"(string), pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>("ksize",
"(vector<int>) The pooling window "
"size(height, width) of the pooling operator. "
"If global_pooling = true, ksize and paddings will "
"be ignored.")
.SupportTensor();
AddAttr<bool>(
"global_pooling",
"(bool) Whether to use the global pooling. "
"If global_pooling = true, kernel size and paddings will be ignored. "
"Default False.")
.SetDefault(false);
AddAttr<std::vector<int>>("strides",
"(vector<int>, default {1, 1}), strides(height, "
"width) of pooling operator.")
.SetDefault({1, 1});
// TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, default {0,0}), paddings(height_top, height_bottom, "
"width_left, wifth_right) of pooling operator."
"If global_pooling = true, paddings and kernel size will be ignored.")
.SetDefault({0, 0});
AddAttr<bool>(
"exclusive",
"(bool) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The default is True. "
"Default True.")
.SetDefault(true);
AddAttr<bool>(
"adaptive",
"(bool) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value. "
"Default False.")
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool) Whether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used. Default False")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("NCHW");
AddAttr<std::string>(
"padding_algorithm",
"(string, default \"EXPLICIT\") An optional string from: \"EXPLICIT\","
"\"SAME\",\"VALID\". Set to \"EXPLICIT\" for explicit padding. "
"Set to \"SAME\" or \"VALID\" for algorithm of padding. ")
.SetDefault("EXPLICIT");
// TODO(dzhwinter): need to registered layout transform function
AddAttr<bool>(
"use_cudnn",
"(bool) Only used in cudnn kernel, need install cudnn. Default False")
.SetDefault(false)
.AsExtra();
AddComment(R"DOC(
This operation calculates the pooling output based on
the input, pooling_type and pool_size, pool_stride, pool_padding parameters.
Input(X) and Output(Out) are in NCHW or NHWC format, where N is batch size, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Parameters(pool_size, pool_stride, pool_padding) hold two integer elements.
These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
For pool_padding = "SAME":
$$
H_{out} = \\frac{(H_{in} + strides[0] - 1)}{strides[0]}
$$
$$
W_{out} = \\frac{(W_{in} + strides[1] - 1)}{strides[1]}
$$
For pool_padding = "VALID":
$$
H_{out} = \\frac{(H_{in} - ksize[0] + strides[0])}{strides[0]}
$$
$$
W_{out} = \\frac{(W_{in} - ksize[1] + strides[1])}{strides[1]}
$$
For ceil_mode = false:
$$
H_{out} = \\frac{(H_{in} - ksize[0] + pad_height_top + pad_height_bottom}{strides[0]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[1] + pad_width_left + pad_width_right}{strides[1]} + 1
$$
For ceil_mode = true:
$$
H_{out} = \\frac{(H_{in} - ksize[0] + pad_height_top + pad_height_bottom + strides[0] - 1)}{strides[0]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[1] + pad_width_left + pad_width_right + strides[1] - 1)}{strides[1]} + 1
$$
For exclusive = false:
$$
hstart = i * strides[0] - pad_height_top
$$
$$
hend = hstart + ksize[0]
$$
$$
wstart = j * strides[1] - pad_width_left
$$
$$
wend = wstart + ksize[1]
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{ksize[0] * ksize[1]}
$$
For exclusive = true:
$$
hstart = max(0, i * strides[0] - pad_height_top)
$$
$$
hend = min(H, hstart + ksize[0])
$$
$$
wstart = max(0, j * strides[1] - pad_width_left)
$$
$$
wend = min(W, wstart + ksize[1])
$$
$$
Output(i ,j) = \\frac{sum(Input[hstart:hend, wstart:wend])}{(hend - hstart) * (wend - wstart)}
$$
)DOC");
}
template <typename T>
class Pool2dOpGradGradMaker : public framework::SingleGradOpMaker<T> {
public:
using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
protected:
void Apply(GradOpPtr<T> grad_op) const override {
grad_op->SetType("pool2d_double_grad");
grad_op->SetInput("X", this->OutputGrad(framework::GradVarName("X")));
grad_op->SetOutput("Out", this->InputGrad(framework::GradVarName("Out")));
grad_op->SetAttrMap(this->Attrs());
}
};
class PoolOpInferVarType : public framework::PassInDtypeAndVarTypeToOutput {
protected:
std::unordered_map<std::string, std::string>& GetInputOutputWithSameType()
const override {
static std::unordered_map<std::string, std::string> m{{"X", /*->*/ "Out"}};
return m;
}
};
void Pool3dOpMaker::Make() {
AddInput("X",
"(phi::DenseTensor) The input tensor of pooling operator. "
"The format of input tensor is NCDHW or NDHWC, where N is batch "
"size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of "
"the feature, respectively.");
AddOutput("Out",
"(phi::DenseTensor) The output tensor of pooling operator."
"The format of output tensor is also NCDHW or NDHWC, "
"where N is batch size, C is "
"the number of channels, and D, H and W is the depth, height and "
"width of the feature, respectively.");
AddAttr<std::string>("pooling_type",
"(string) Pooling type, can be \"max\" for max-pooling "
"and \"avg\" for average-pooling.")
.InEnum({"max", "avg"});
AddAttr<std::vector<int>>(
"ksize",
"(vector<int>) The pooling window size(depth, height, "
"width) of pooling operator. "
"If global_pooling = true, ksize and paddings will "
"be ignored.");
AddAttr<bool>(
"global_pooling",
"(bool) Whether to use the global pooling. "
"If global_pooling = true, kernel size and paddings will be ignored. "
"Default False")
.SetDefault(false);
AddAttr<std::vector<int>>(
"strides",
"(vector<int>, default {1,1,1}) Strides(depth, height, "
"width) of the pooling operator.")
.SetDefault({1, 1, 1}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, default {0,0,0}), paddings(pad_depth_front, "
"pad_depth_back, "
"pad_height_top, pad_height_bottom, pad_width_left, pad_width_right"
") of pooling operator. "
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
// TypedAttrChecker don't support vector type.)
AddAttr<bool>(
"exclusive",
"(bool) When true, will exclude the zero-padding in the "
"averaging calculating, otherwise, include the zero-padding. Note, it "
"is only used when pooling_type is avg. The default is True. "
"Default True")
.SetDefault(true);
AddAttr<bool>(
"adaptive",
"(bool) When true, will perform adaptive pooling instead, "
"output shape in H and W dimensions will be same as ksize, input data "
"will be divided into grids specify by ksize averagely and perform "
"pooling in each grid area to get output pooling value. "
"Default False")
.SetDefault(false);
AddAttr<bool>(
"ceil_mode",
"(bool) Whether to use the ceil function to calculate "
"output height and width. False is the default. If it is set to False, "
"the floor function will be used. Default False")
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCDHW) Only used in "
"An optional string from: \"NDHWC\", \"NCDHW\". "
"Defaults to \"NDHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("NCDHW");
AddAttr<std::string>(
"padding_algorithm",
"(string, default \"EXPLICIT\") An optional string from: \"EXPLICIT\","
"\"SAME\",\"VALID\". Set to \"EXPLICIT\" for explicit padding. "
"Set to \"SAME\" or \"VALID\" for algorithm of padding. ")
.SetDefault("EXPLICIT");
AddAttr<bool>(
"use_cudnn",
"(bool) Only used in cudnn kernel, need install cudnn. Default False")
.SetDefault(false)
.AsExtra();
AddComment(R"DOC(
This operation calculates the output based on
the input, pooling_type, pool_size, pool_stride, and pool_padding parameters.
Input(X) and output(Out) are in NCDHW or NDHWC format, where N is batch
size, C is the number of channels, and D, H and W are the depth, height and
width of the feature, respectively. Parameters(pool_size, pool_stride, pool_padding)
hold three integer elements. These three elements represent depth, height and
width, respectively. The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
For pool_padding = "SAME":
$$
D_{out} = \\frac{(D_{in} + strides[0] - 1)}{strides[0]}
$$
$$
H_{out} = \\frac{(H_{in} + strides[1] - 1)}{strides[1]}
$$
$$
W_{out} = \\frac{(W_{in} + strides[2] - 1)}{strides[2]}
$$
For pool_padding = "VALID":
$$
D_{out} = \\frac{(D_{in} - ksize[0] + strides[0])}{strides[0]}
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + strides[1])}{strides[1]}
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + strides[2])}{strides[2]}
$$
For ceil_mode = false:
$$
D_{out} = \\frac{(D_{in} - ksize[0] + pad_depth_front + pad_depth_back)}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + pad_height_top + pad_height_bottom)}{strides[1]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + pad_width_left + pad_width_right)}{strides[2]} + 1
$$
For ceil_mode = true:
$$
D_{out} = \\frac{(D_{in} - ksize[0] + pad_depth_front + pad_depth_back + strides[0] -1)}{strides[0]} + 1
$$
$$
H_{out} = \\frac{(H_{in} - ksize[1] + pad_height_top + pad_height_bottom + strides[1] -1)}{strides[1]} + 1
$$
$$
W_{out} = \\frac{(W_{in} - ksize[2] + pad_width_left + pad_width_right + strides[2] -1)}{strides[2]} + 1
$$
For exclusive = false:
$$
dstart = i * strides[0] - pad_depth_front
$$
$$
dend = dstart + ksize[0]
$$
$$
hstart = j * strides[1] - pad_height_top
$$
$$
hend = hstart + ksize[1]
$$
$$
wstart = k * strides[2] - pad_width_left
$$
$$
wend = wstart + ksize[2]
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{ksize[0] * ksize[1] * ksize[2]}
$$
For exclusive = true:
$$
dstart = max(0, i * strides[0] - pad_depth_front)
$$
$$
dend = min(D, dstart + ksize[0])
$$
$$
hstart = max(0, j * strides[1] - pad_height_top)
$$
$$
hend = min(H, hstart + ksize[1])
$$
$$
wstart = max(0, k * strides[2] - pad_width_left)
$$
$$
wend = min(W, wstart + ksize[2])
$$
$$
Output(i ,j, k) = \\frac{sum(Input[dstart:dend, hstart:hend, wstart:wend])}{(dend - dstart) * (hend - hstart) * (wend - wstart)}
$$
)DOC");
}
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
DECLARE_INFER_SHAPE_FUNCTOR(pool2d,
Pool2dInferShapeFunctor,
PD_INFER_META(phi::Pool2DInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool2d_grad,
Pool2dGradInferShapeFunctor,
PD_INFER_META(phi::UnchangedInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool2d_double_grad,
Pool2dDoubleGradInferShapeFunctor,
PD_INFER_META(phi::Pool2DInferMeta));
REGISTER_OPERATOR(
pool2d,
ops::PoolOp,
ops::Pool2dOpMaker,
ops::PoolOpInferVarType,
paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>,
Pool2dInferShapeFunctor);
REGISTER_OPERATOR(pool2d_grad,
ops::PoolOpGrad,
ops::Pool2dOpGradGradMaker<paddle::framework::OpDesc>,
ops::Pool2dOpGradGradMaker<paddle::imperative::OpBase>,
Pool2dGradInferShapeFunctor);
REGISTER_OPERATOR(pool2d_double_grad,
ops::PoolOp,
Pool2dDoubleGradInferShapeFunctor);
DECLARE_INFER_SHAPE_FUNCTOR(pool3d,
Pool3dInferShapeFunctor,
PD_INFER_META(phi::PoolInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(pool3d_grad,
Pool3dGradInferShapeFunctor,
PD_INFER_META(phi::UnchangedInferMeta));
REGISTER_OPERATOR(
pool3d,
ops::PoolOp,
ops::Pool3dOpMaker,
ops::PoolOpInferVarType,
paddle::framework::DefaultGradOpMaker<paddle::framework::OpDesc, true>,
paddle::framework::DefaultGradOpMaker<paddle::imperative::OpBase, true>,
Pool3dInferShapeFunctor);
REGISTER_OPERATOR(pool3d_grad, ops::PoolOpGrad, Pool3dGradInferShapeFunctor);
paddle/fluid/operators/pool_op.h 已删除 100644 → 0
浏览文件 @ 0a10cf40
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class PoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
phi::KernelKey GetKernelTypeForVar(
const std::string& var_name,
const phi::DenseTensor& tensor,
const phi::KernelKey& expected_kernel_type) const override;
};
class PoolOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
phi::KernelKey GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
phi::KernelKey GetKernelTypeForVar(
const std::string& var_name,
const phi::DenseTensor& tensor,
const phi::KernelKey& expected_kernel_type) const override;
};
class Pool2dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
class Pool3dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override;
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/unity_build_rule.cmake
浏览文件 @ 30881647
......@@ -200,7 +200,6 @@ register_unity_group(
cc
partial_sum_op.cc
pixel_shuffle_op.cc
pool_op.cc
pool_with_index_op.cc
positive_negative_pair_op.cc
prelu_op.cc
......
paddle/phi/api/yaml/op_compat.yaml
浏览文件 @ 30881647
......@@ -1887,17 +1887,38 @@
out : Out
- op : pool2d
backward : pool2d_grad
attrs:
kernel_size: ksize
backward : pool2d_grad, pool2d_double_grad
inputs :
{x : X}
outputs :
{out : Out}
attrs :
{kernel_size : ksize}
int_array:
kernel_size :
data_type : int
support_tensor : true
get_expected_kernel_type :
pool2d : GetPoolExpectedKernelType
pool2d_grad : GetPoolExpectedKernelType
pool2d_double_grad : GetPoolDoubleGradExpectedKernelType
extra :
attrs : [bool use_mkldnn = false, bool use_quantizer = false,
str mkldnn_data_type = "float32", bool is_test = false]
str mkldnn_data_type = "float32", bool is_test = false, bool use_cudnn = false]
- op : pool3d
backward : pool3d_grad
inputs :
{x : X}
outputs :
{out : Out}
attrs :
{kernel_size : ksize}
get_expected_kernel_type :
pool3d : GetPoolExpectedKernelType
pool3d_grad : GetPoolExpectedKernelType
extra :
attrs : [bool use_mkldnn = false]
attrs : [bool use_mkldnn = false, bool use_cudnn = false]
- op : pow
backward : pow_grad, pow_double_grad, pow_triple_grad
......
paddle/phi/api/yaml/static_backward.yaml
浏览文件 @ 30881647
......@@ -123,6 +123,40 @@
func : max_grad
composite: max_grad(x, out, out_grad, axis, keepdim, reduce_all, x_grad)
- backward_op : pool2d_double_grad
forward : pool2d_grad(Tensor x, Tensor out, Tensor grad_out, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm) -> Tensor(grad_x)
args : (Tensor grad_x_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(grad_out_grad)
infer_meta :
func : Pool2DInferMeta
param : [grad_x_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
kernel :
func : pool2d_double_grad
param : [grad_x_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
- backward_op : pool2d_grad
forward : pool2d(Tensor x, IntArray kernel_size, int[] strides = {1,1}, int[] paddings = {0,0}, bool ceil_mode = false, bool exclusive = true, str data_format = "NCHW", str pooling_type = "", bool global_pooling = false, bool adaptive = false, str padding_algorithm = "EXPLICIT") -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, IntArray kernel_size, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pool2d_grad
param : [x, out, out_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
backward : pool2d_double_grad
- backward_op : pool3d_grad
forward : pool3d(Tensor x, int[] kernel_size, int[] strides = {1,1,1}, int[] paddings = {0,0,0}, bool ceil_mode = false, bool exclusive = true, str data_format = "NCDHW", str pooling_type = "", bool global_pooling = false, bool adaptive = false, str padding_algorithm = "EXPLICIT") -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int[] ksize, int[] strides, int[] paddings, bool ceil_mode, bool exclusive, str data_format, str pooling_type, bool global_pooling, bool adaptive, str padding_algorithm)
output : Tensor(x_grad)
infer_meta :
func : UnchangedInferMeta
param: [x]
kernel :
func : pool3d_grad
param : [x, out, out_grad, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
- backward_op : relu6_grad
forward : relu6 (Tensor x, float threshold = 6.0f) -> Tensor(out)
args : (Tensor out, Tensor out_grad)
......
paddle/phi/api/yaml/static_ops.yaml
浏览文件 @ 30881647
......@@ -351,6 +351,28 @@
func : p_recv_array
param : [peer, dtype, out_shape]
- op : pool2d
args : (Tensor x, IntArray kernel_size, int[] strides = {1,1}, int[] paddings = {0,0}, bool ceil_mode = false, bool exclusive = true, str data_format = "NCHW", str pooling_type = "", bool global_pooling = false, bool adaptive = false, str padding_algorithm = "EXPLICIT")
output : Tensor(out)
infer_meta :
func : Pool2DInferMeta
param : [x, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
kernel :
func : pool2d
param : [x, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
backward : pool2d_grad
- op : pool3d
args : (Tensor x, int[] kernel_size, int[] strides = {1,1,1}, int[] paddings = {0,0,0}, bool ceil_mode = false, bool exclusive = true, str data_format = "NCDHW", str pooling_type = "", bool global_pooling = false, bool adaptive = false, str padding_algorithm = "EXPLICIT")
output : Tensor(out)
infer_meta :
func : PoolInferMeta
param : [x, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
kernel :
func : pool3d
param : [x, kernel_size, strides, paddings, ceil_mode, exclusive, data_format, pooling_type, global_pooling, adaptive, padding_algorithm]
backward : pool3d_grad
- op : randint
args : (int low, int high, IntArray shape = {}, DataType dtype = DataType::INT64, int seed = 0)
output : Tensor(out)
......
paddle/phi/kernels/onednn/pool_grad_kernel.cc
浏览文件 @ 30881647
......@@ -71,6 +71,26 @@ void Pool2dGradKernel(const Context& dev_ctx,
dx->set_mem_desc(diff_src_memory->get_desc());
}
phi::KernelKey PoolOpGradGetKernelTypeForVar(
const GetKernelTypeForVarContext* ctx) {
const DenseTensor& tensor = ctx->GetTensor();
const KernelKey& expected_kernel_type = ctx->GetKernelKey();
#ifdef PADDLE_WITH_MKLDNN
if ((expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
(tensor.layout() != phi::DataLayout::ONEDNN)) {
const AttributeMap& attrs = ctx->GetAttrs();
auto it = attrs.find("data_format");
const std::string data_format = PADDLE_GET_CONST(std::string, it->second);
return phi::KernelKey(tensor.place(),
phi::StringToDataLayout(data_format),
expected_kernel_type.dtype());
}
#endif
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
} // namespace phi
PD_REGISTER_KERNEL(pool2d_grad,
......@@ -78,4 +98,6 @@ PD_REGISTER_KERNEL(pool2d_grad,
ONEDNN,
phi::Pool2dGradKernel,
float,
phi::dtype::bfloat16) {}
phi::dtype::bfloat16) {
kernel->get_kerneltype_forvar_fn_ = phi::PoolOpGradGetKernelTypeForVar;
}
paddle/phi/kernels/onednn/pool_kernel.cc
浏览文件 @ 30881647
......@@ -70,6 +70,29 @@ void Pool2dKernel(const Context& dev_ctx,
out->set_mem_desc(dst_memory->get_desc());
}
phi::KernelKey PoolOpGetKernelTypeForVar(
const GetKernelTypeForVarContext* ctx) {
const phi::DenseTensor& tensor = ctx->GetTensor();
const phi::KernelKey& expected_kernel_type = ctx->GetKernelKey();
#ifdef PADDLE_WITH_MKLDNN
if ((expected_kernel_type.layout() == phi::DataLayout::ONEDNN) &&
(tensor.layout() != phi::DataLayout::ONEDNN)) {
const AttributeMap& attrs = ctx->GetAttrs();
auto it = attrs.find("data_format");
const std::string data_format = PADDLE_GET_CONST(std::string, it->second);
auto dl = phi::StringToDataLayout(data_format);
// Some models may have intentionally set "AnyLayout" for pool
// op. Treat this as NCHW (default data_format value)
if (dl != phi::DataLayout::kAnyLayout) {
return phi::KernelKey(tensor.place(), dl, expected_kernel_type.dtype());
}
}
#endif
return phi::KernelKey(
tensor.place(), tensor.layout(), expected_kernel_type.dtype());
}
} // namespace phi
PD_REGISTER_KERNEL(pool2d,
......@@ -79,4 +102,6 @@ PD_REGISTER_KERNEL(pool2d,
float,
int8_t,
uint8_t,
phi::dtype::bfloat16) {}
phi::dtype::bfloat16) {
kernel->get_kerneltype_forvar_fn_ = phi::PoolOpGetKernelTypeForVar;
}
paddle/phi/ops/compat/pool_sig.cc 已删除 100644 → 0
浏览文件 @ 0a10cf40
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/phi/core/compat/op_utils.h"
namespace phi {
KernelSignature Pool2dOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature("pool2d",
{"X"},
{"ksize",
"strides",
"paddings",
"ceil_mode",
"exclusive",
"data_format",
"pooling_type",
"global_pooling",
"adaptive",
"padding_algorithm"},
{"Out"});
}
KernelSignature Pool2dGradOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature("pool2d_grad",
{"X", "Out", "Out@GRAD"},
{"ksize",
"strides",
"paddings",
"ceil_mode",
"exclusive",
"data_format",
"pooling_type",
"global_pooling",
"adaptive",
"padding_algorithm"},
{"X@GRAD"});
}
KernelSignature Pool2dDoubleGradOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature("pool2d_double_grad",
{"X"},
{"ksize",
"strides",
"paddings",
"ceil_mode",
"exclusive",
"data_format",
"pooling_type",
"global_pooling",
"adaptive",
"padding_algorithm"},
{"Out"});
}
KernelSignature Pool3dOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature("pool3d",
{"X"},
{"ksize",
"strides",
"paddings",
"ceil_mode",
"exclusive",
"data_format",
"pooling_type",
"global_pooling",
"adaptive",
"padding_algorithm"},
{"Out"});
}
KernelSignature Pool3dGradOpArgumentMapping(
const ArgumentMappingContext& ctx UNUSED) {
return KernelSignature("pool3d_grad",
{"X", "Out", "Out@GRAD"},
{"ksize",
"strides",
"paddings",
"ceil_mode",
"exclusive",
"data_format",
"pooling_type",
"global_pooling",
"adaptive",
"padding_algorithm"},
{"X@GRAD"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(pool2d, phi::Pool2dOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(pool2d_grad, phi::Pool2dGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(pool2d_double_grad,
phi::Pool2dDoubleGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(pool3d, phi::Pool3dOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(pool3d_grad, phi::Pool3dGradOpArgumentMapping);
test/cpp/fluid/mkldnn/CMakeLists.txt
浏览文件 @ 30881647
......@@ -41,7 +41,7 @@ cc_test_old(
recurrent_op_helper
recurrent_op
op_registry
pool_op
generated_static_op
crop_op
activation_op
generated_op
......
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