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未验证 提交 1eb96eec 编写于 作者: F From00 提交者： GitHub
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Move conv-transpose OPs to phi (#40675) 

* Move conv-transpose OPs to phi

* Fix CI errors

* Fix CI errors
上级 b77e20ac
隐藏空白更改
内联 并排
Showing with 3294 addition and 2275 deletion
paddle/fluid/framework/ir/mkldnn/mkldnn_conv_bn_fuse_pass_tester.cc
浏览文件 @ 1eb96eec
......@@ -28,7 +28,7 @@
USE_OP_ITSELF(batch_norm);
USE_OP_DEVICE_KERNEL(batch_norm, MKLDNN);
USE_OP(conv2d_transpose);
USE_OP_ITSELF(conv2d_transpose);
USE_OP_DEVICE_KERNEL(conv2d_transpose, MKLDNN);
USE_OP_ITSELF(elementwise_add);
USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN);
......
paddle/fluid/inference/tensorrt/convert/test_conv2d_op.cc
浏览文件 @ 1eb96eec
......@@ -17,7 +17,7 @@ limitations under the License. */
#include "paddle/fluid/inference/tensorrt/convert/ut_helper.h"
USE_OP_ITSELF(conv2d);
USE_OP(conv2d_transpose);
USE_OP_ITSELF(conv2d_transpose);
namespace paddle {
namespace inference {
......
paddle/fluid/operators/conv_transpose_cudnn_op.cu 已删除 100644 → 0
浏览文件 @ b77e20ac
/* 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/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/memory/memory.h"
#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/operators/conv_miopen_helper.h"
#else
#include "paddle/fluid/operators/conv_cudnn_helper.h"
#endif
#include "paddle/fluid/operators/conv_transpose_op.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/padding.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int D>
static void DataTranspose(const framework::ExecutionContext& ctx,
const Tensor* input, Tensor* output,
const std::vector<int>& axis, int flag = 0) {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
phi::funcs::Transpose<platform::CUDADeviceContext, T, D> transpose;
auto in_dims = input->dims();
std::vector<int64_t> input_transpose_vec;
for (size_t i = 0; i < axis.size(); ++i) {
if (flag == 0)
input_transpose_vec.push_back(in_dims[axis[i]]);
else
input_transpose_vec.push_back(in_dims[i]);
}
framework::DDim input_transpose_dims(phi::make_ddim(input_transpose_vec));
output->mutable_data<T>(input_transpose_dims, ctx.GetPlace());
transpose(dev_ctx, *input, output, axis);
}
template <typename T>
class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto* input = ctx.Input<Tensor>("Input");
auto* filter = ctx.Input<Tensor>("Filter");
auto* output = ctx.Output<Tensor>("Output");
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
// cudnn v5 does not support dilations
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
const T* filter_data = filter->data<T>();
const std::string data_layout_str = ctx.Attr<std::string>("data_format");
const paddle::platform::DataLayout data_layout =
(data_layout_str != "NHWC" ? platform::DataLayout::kNCHW
: platform::DataLayout::kNHWC);
// if channel_last, transpose to channel_first
Tensor input_transpose;
std::vector<int> input_vec = phi::vectorize<int>(input->dims());
std::vector<int> output_vec = phi::vectorize<int>(output->dims());
if (data_layout == platform::DataLayout::kNHWC) {
if (strides.size() == 2U) {
std::vector<int> axis = {0, 3, 1, 2};
for (size_t i = 0; i < axis.size(); ++i) {
input_vec[i] = input->dims()[axis[i]];
output_vec[i] = output->dims()[axis[i]];
}
DataTranspose<T, 4>(ctx, input, &input_transpose, axis);
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 4, 1, 2, 3};
for (size_t i = 0; i < axis.size(); ++i) {
input_vec[i] = input->dims()[axis[i]];
output_vec[i] = output->dims()[axis[i]];
}
DataTranspose<T, 5>(ctx, input, &input_transpose, axis);
}
} else {
input_transpose = *input;
}
// update padding and dilation
auto in_dims = input_transpose.dims();
auto filter_dims = filter->dims();
framework::DDim in_data_dims;
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = phi::funcs::IsSymmetricPadding(paddings, data_dim);
std::vector<int> input_pad(input_transpose.dims().size() * 2, 0);
Tensor transformed_input;
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_input_shape_vec(data_dim + 2);
new_input_shape_vec[0] = input_transpose.dims()[0];
new_input_shape_vec[1] = input_transpose.dims()[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings[2 * i] - paddings[2 * i + 1]);
padding_common[i] = std::min(paddings[2 * i], paddings[2 * i + 1]);
new_input_shape_vec[i + 2] =
input_transpose.dims()[i + 2] + padding_diff[i];
input_pad[2 * i + 4] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] = paddings[2 * i + 1] - padding_common[i];
}
framework::DDim new_input_shape(phi::make_ddim(new_input_shape_vec));
transformed_input.Resize(new_input_shape);
auto& dev_ctx =
ctx.template device_context<paddle::platform::CUDADeviceContext>();
transformed_input =
ctx.AllocateTmpTensor<T, paddle::platform::CUDADeviceContext>(
new_input_shape, dev_ctx);
const int rank = input_transpose.dims().size();
T pad_value(0.0);
switch (rank) {
case 4: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 4>(
dev_ctx, input_pad, input_transpose, pad_value,
&transformed_input);
} break;
case 5: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 5>(
dev_ctx, input_pad, input_transpose, pad_value,
&transformed_input);
} break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor."));
}
} else {
transformed_input = input_transpose;
if (paddings.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[2 * i];
}
}
}
std::vector<int64_t> starts(data_dim, 0);
std::vector<int64_t> ends(data_dim, 0);
std::vector<int64_t> axes(data_dim, 0);
for (size_t i = 0; i < data_dim; ++i) {
starts[i] = input_pad[2 * i + 4] * (strides[i] + 1);
ends[i] = starts[i] + output_vec[i + 2];
axes[i] = i + 2;
}
const T* input_data = transformed_input.data<T>();
input_vec = phi::vectorize<int>(transformed_input.dims());
std::vector<int> transformed_output_vec = output_vec;
for (size_t i = 0; i < data_dim; ++i) {
transformed_output_vec[i + 2] =
output_vec[i + 2] +
(input_pad[2 * i + 4] + input_pad[2 * i + 5]) * strides[i] -
2 * padding_common[i] + paddings[2 * i] + paddings[2 * i + 1];
}
Tensor transformed_output;
if (!is_sys_pad) {
DDim transformed_output_shape(phi::make_ddim(transformed_output_vec));
transformed_output.mutable_data<T>(transformed_output_shape,
ctx.GetPlace());
} else {
output->mutable_data<T>(ctx.GetPlace());
transformed_output.ShareDataWith(*output);
transformed_output.Resize(phi::make_ddim(transformed_output_vec));
}
T* transformed_output_data = transformed_output.data<T>();
platform::DataLayout layout;
int iwo_groups = groups;
int c_groups = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups = 1;
c_groups = groups;
groups = 1;
#endif
if (strides.size() == 2U) {
layout = platform::DataLayout::kNCHW;
} else {
layout = platform::DataLayout::kNCDHW;
}
size_t workspace_size = 0;
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t algo{};
#else
cudnnConvolutionBwdDataAlgo_t algo{};
#endif
// ------------------- cudnn conv algorithm ---------------------
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
auto layout_tensor = GetCudnnTensorFormat(layout);
bool deterministic = FLAGS_cudnn_deterministic;
auto dtype = platform::CudnnDataType<T>::type;
// ------------------- cudnn descriptors ---------------------
ConvArgs args{&transformed_output,
filter,
&transformed_input,
strides,
padding_common,
dilations,
dtype};
args.handle = handle;
args.idesc.set(transformed_output, iwo_groups);
args.wdesc.set(*filter, layout_tensor, iwo_groups);
args.odesc.set(transformed_input, iwo_groups);
args.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search::GetWorkspaceSize(args));
algo = search::Find<T>(
args, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
algo = search::Find<T>(
args, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size =
std::max(workspace_size, search::GetWorkspaceSize(args, algo));
#endif
// ------------------- cudnn conv transpose forward ---------------------
int input_offset =
transformed_input.numel() / transformed_input.dims()[0] / groups;
int output_offset =
transformed_output.numel() / transformed_output.dims()[0] / groups;
int filter_offset = filter->numel() / groups;
ScalingParamType<T> alpha = 1.0f;
ScalingParamType<T> beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args.odesc.desc(),
input_data + input_offset * g, args.wdesc.desc(),
filter_data + filter_offset * g, args.cdesc.desc(), algo, &beta,
args.idesc.desc(), transformed_output_data + output_offset * g,
cudnn_workspace, workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args.wdesc.desc(),
filter_data + filter_offset * g, args.odesc.desc(),
input_data + input_offset * g, args.cdesc.desc(), algo,
cudnn_workspace, workspace_size, &beta, args.idesc.desc(),
transformed_output_data + output_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
if (!is_sys_pad && strides.size() == 2U) {
Slice<paddle::platform::CUDADeviceContext, T, 4>(
ctx, &transformed_output, output, starts, ends, axes);
} else if (!is_sys_pad && strides.size() == 3U) {
Slice<paddle::platform::CUDADeviceContext, T, 5>(
ctx, &transformed_output, output, starts, ends, axes);
}
if (data_layout == platform::DataLayout::kNHWC) {
Tensor output_transpose;
Tensor output_nchw;
output_nchw.ShareDataWith(*output);
output_nchw.Resize(phi::make_ddim(output_vec));
if (strides.size() == 2U) {
std::vector<int> axis = {0, 2, 3, 1};
DataTranspose<T, 4>(ctx, &output_nchw, &output_transpose, axis);
*output = output_transpose;
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 2, 3, 4, 1};
DataTranspose<T, 5>(ctx, &output_nchw, &output_transpose, axis);
*output = output_transpose;
}
}
}
};
template <typename T>
class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto input = ctx.Input<Tensor>("Input");
auto filter = ctx.Input<Tensor>("Filter");
auto output_grad = ctx.Input<Tensor>(framework::GradVarName("Output"));
auto input_grad = ctx.Output<Tensor>(framework::GradVarName("Input"));
auto filter_grad = ctx.Output<Tensor>(framework::GradVarName("Filter"));
const T* filter_data = filter->data<T>();
std::vector<int> strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
// cudnn v5 does not support dilations
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
int user_workspace_size = ctx.Attr<int>("workspace_size_MB");
const std::string data_layout_str = ctx.Attr<std::string>("data_format");
const paddle::platform::DataLayout data_layout =
(data_layout_str != "NHWC" ? platform::DataLayout::kNCHW
: platform::DataLayout::kNHWC);
// if channel_last, transpose to channel_first
Tensor input_transpose;
Tensor output_grad_transpose;
std::vector<int> input_vec = phi::vectorize<int>(input->dims());
std::vector<int> output_vec = phi::vectorize<int>(output_grad->dims());
if (data_layout == platform::DataLayout::kNHWC) {
if (strides.size() == 2U) {
std::vector<int> axis = {0, 3, 1, 2};
for (size_t i = 0; i < axis.size(); ++i) {
input_vec[i] = input->dims()[axis[i]];
output_vec[i] = output_grad->dims()[axis[i]];
}
DataTranspose<T, 4>(ctx, input, &input_transpose, axis);
DataTranspose<T, 4>(ctx, output_grad, &output_grad_transpose, axis);
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 4, 1, 2, 3};
for (size_t i = 0; i < axis.size(); ++i) {
input_vec[i] = input->dims()[axis[i]];
output_vec[i] = output_grad->dims()[axis[i]];
}
DataTranspose<T, 5>(ctx, input, &input_transpose, axis);
DataTranspose<T, 5>(ctx, output_grad, &output_grad_transpose, axis);
}
} else {
input_transpose = *input;
output_grad_transpose = *output_grad;
}
// update padding and dilation
auto in_dims = input_transpose.dims();
auto filter_dims = filter->dims();
framework::DDim in_data_dims;
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = phi::funcs::IsSymmetricPadding(paddings, data_dim);
std::vector<int> input_pad(input_transpose.dims().size() * 2, 0);
Tensor transformed_output_grad;
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_output_grad_shape_vec(data_dim + 2);
new_output_grad_shape_vec[0] = output_grad_transpose.dims()[0];
new_output_grad_shape_vec[1] = output_grad_transpose.dims()[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings[2 * i] - paddings[2 * i + 1]);
padding_common[i] = std::min(paddings[2 * i], paddings[2 * i + 1]);
new_output_grad_shape_vec[i + 2] =
output_grad_transpose.dims()[i + 2] + padding_diff[i];
input_pad[2 * i + 4] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] = paddings[2 * i + 1] - padding_common[i];
}
framework::DDim new_output_grad_shape(
phi::make_ddim(new_output_grad_shape_vec));
transformed_output_grad.Resize(new_output_grad_shape);
auto& dev_ctx =
ctx.template device_context<paddle::platform::CUDADeviceContext>();
transformed_output_grad =
ctx.AllocateTmpTensor<T, paddle::platform::CUDADeviceContext>(
new_output_grad_shape, dev_ctx);
const int rank = input_transpose.dims().size();
T pad_value(0.0);
switch (rank) {
case 4: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 4>(
dev_ctx, input_pad, output_grad_transpose, pad_value,
&transformed_output_grad);
} break;
case 5: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 5>(
dev_ctx, input_pad, output_grad_transpose, pad_value,
&transformed_output_grad);
} break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D input Tensor."));
}
} else {
transformed_output_grad = output_grad_transpose;
if (paddings.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[2 * i];
}
}
}
const T* input_data = input_transpose.data<T>();
const T* output_grad_data = transformed_output_grad.data<T>();
output_vec = phi::vectorize<int>(transformed_output_grad.dims());
// ------------------- cudnn descriptors ---------------------
platform::DataLayout layout;
if (strides.size() == 2U) {
layout = platform::DataLayout::kNCHW;
} else {
layout = platform::DataLayout::kNCDHW;
}
int iwo_groups = groups;
int c_groups = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups = 1;
c_groups = groups;
groups = 1;
#endif
auto dtype = platform::CudnnDataType<T>::type;
ConvArgs args1{&transformed_output_grad,
filter,
&input_transpose,
strides,
padding_common,
dilations,
dtype};
ConvArgs args2{&transformed_output_grad,
filter,
&input_transpose,
strides,
padding_common,
dilations,
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t data_algo{};
miopenConvBwdWeightsAlgorithm_t filter_algo{};
#else
cudnnConvolutionFwdAlgo_t data_algo{};
cudnnConvolutionBwdFilterAlgo_t filter_algo{};
#endif
auto layout_tensor = GetCudnnTensorFormat(layout);
size_t workspace_size = 0;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
auto handle = dev_ctx.cudnn_handle();
bool deterministic = FLAGS_cudnn_deterministic;
T* input_grad_data = nullptr;
T* filter_grad_data = nullptr;
if (input_grad) {
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
args1.handle = handle;
args1.idesc.set(transformed_output_grad, iwo_groups);
args1.wdesc.set(*filter, layout_tensor, iwo_groups);
args1.odesc.set(input_transpose, iwo_groups);
args1.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1));
data_algo = search1::Find<T>(
args1, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search1 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search1::Find<T>(
args1, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
#endif
}
if (filter_grad) {
filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
args2.handle = handle;
args2.idesc.set(transformed_output_grad, iwo_groups);
args2.wdesc.set(*filter_grad, layout_tensor, iwo_groups);
args2.odesc.set(input_transpose, iwo_groups);
args2.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_groups);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
filter_algo = search2::Find<T>(
args2, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search2 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search2::Find<T>(
args2, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, filter_algo));
#endif
}
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
int input_offset = input->numel() / input->dims()[0] / groups;
int output_grad_offset = transformed_output_grad.numel() /
transformed_output_grad.dims()[0] / groups;
int filter_offset = filter->numel() / groups;
ScalingParamType<T> alpha = 1.0f;
ScalingParamType<T> beta = 0.0f;
auto workspace_handle = dev_ctx.cudnn_workspace_handle();
if (input_grad) {
// Because beta is zero, it is unnecessary to reset input_grad.
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args1.idesc.desc(),
output_grad_data + output_grad_offset * g, args1.wdesc.desc(),
filter_data + filter_offset * g, args1.cdesc.desc(),
data_algo, &beta, args1.odesc.desc(),
input_grad_data + input_offset * g, cudnn_workspace,
workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cudnnConvolutionForward(
handle, &alpha, args1.idesc.desc(),
output_grad_data + output_grad_offset * g, args1.wdesc.desc(),
filter_data + filter_offset * g, args1.cdesc.desc(), data_algo,
cudnn_workspace, workspace_size, &beta, args1.odesc.desc(),
input_grad_data + input_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
if (data_layout == platform::DataLayout::kNHWC) {
Tensor input_grad_transpose;
Tensor input_grad_nchw;
input_grad_nchw.ShareDataWith(*input_grad);
input_grad_nchw.Resize(phi::make_ddim(input_vec));
if (strides.size() == 2U) {
std::vector<int> axis = {0, 2, 3, 1};
DataTranspose<T, 4>(ctx, &input_grad_nchw, &input_grad_transpose,
axis);
*input_grad = input_grad_transpose;
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 2, 3, 4, 1};
DataTranspose<T, 5>(ctx, &input_grad_nchw, &input_grad_transpose,
axis);
*input_grad = input_grad_transpose;
}
}
}
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
// Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args2.odesc.desc(),
input_data + input_offset * g, args2.idesc.desc(),
output_grad_data + output_grad_offset * g, args2.cdesc.desc(),
filter_algo, &beta, args2.wdesc.desc(),
filter_grad_data + filter_offset * g, cudnn_workspace,
workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args2.idesc.desc(),
output_grad_data + output_grad_offset * g, args2.odesc.desc(),
input_data + input_offset * g, args2.cdesc.desc(),
filter_algo, cudnn_workspace, workspace_size, &beta,
args2.wdesc.desc(), filter_grad_data + filter_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
}
}
};
/*
* Inputs: I, W, dO, ddI, ddW
* Outputs: ddO, dW, dI
* ddo = conv_bp_data(W, ddI) + conv_bp_data(ddW, I)
* dW = conv_bp_filter(dO, ddI)
* dI = conv(dO, ddW)
*/
template <typename T>
class CUDNNConvTransposeDoubleGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
PADDLE_ENFORCE_EQ(
platform::is_gpu_place(ctx.GetPlace()), true,
paddle::platform::errors::PreconditionNotMet("It must use CUDAPlace."));
auto X = ctx.Input<Tensor>("Input");
auto W = ctx.Input<Tensor>("Filter");
auto dO = ctx.Input<Tensor>("DOutput");
auto ddX = ctx.Input<Tensor>("DDInput");
auto ddW = ctx.Input<Tensor>("DDFilter");
auto ddO = ctx.Output<Tensor>("DDOutput");
auto dW = ctx.Output<Tensor>("DFilter");
auto dX = ctx.Output<Tensor>("DInput");
if (ddO) {
ddO->mutable_data<T>(ctx.GetPlace());
phi::funcs::SetConstant<platform::CUDADeviceContext, T> set_zero;
set_zero(dev_ctx, ddO, static_cast<T>(0));
}
if (dW) {
dW->mutable_data<T>(ctx.GetPlace());
}
if (dX) {
dX->mutable_data<T>(ctx.GetPlace());
}
const T* dy = dO->data<T>();
const T* w = W->data<T>();
const T* ddx = nullptr;
const T* ddw = nullptr;
T *dw, *dx, *ddy;
dw = dx = ddy = nullptr;
T* transformed_dx = nullptr;
const std::vector<int>& strides = ctx.Attr<std::vector<int>>("strides");
std::vector<int> dilations = ctx.Attr<std::vector<int>>("dilations");
int groups = ctx.Attr<int>("groups");
bool deterministic = FLAGS_cudnn_deterministic;
std::vector<int> paddings = ctx.Attr<std::vector<int>>("paddings");
std::string padding_algorithm = ctx.Attr<std::string>("padding_algorithm");
const std::string data_format = ctx.Attr<std::string>("data_format");
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
// transform Tensors to channel first-----------
Tensor transformed_X_channel(X->type());
Tensor transformed_dO_channel(dO->type());
Tensor transformed_ddX_channel(X->type());
Tensor transformed_ddO_channel(dO->type());
Tensor transformed_dX_channel(X->type());
if (channel_last) {
ResizeToChannelFirst<platform::CUDADeviceContext, T>(
ctx, X, &transformed_X_channel);
TransToChannelFirst<platform::CUDADeviceContext, T>(
ctx, X, &transformed_X_channel);
ResizeToChannelFirst<platform::CUDADeviceContext, T>(
ctx, dO, &transformed_dO_channel);
TransToChannelFirst<platform::CUDADeviceContext, T>(
ctx, dO, &transformed_dO_channel);
if (ddX) {
ResizeToChannelFirst<platform::CUDADeviceContext, T>(
ctx, ddX, &transformed_ddX_channel);
TransToChannelFirst<platform::CUDADeviceContext, T>(
ctx, ddX, &transformed_ddX_channel);
}
if (ddO) {
ResizeToChannelFirst<platform::CUDADeviceContext, T>(
ctx, ddO, &transformed_ddO_channel);
}
if (dX) {
ResizeToChannelFirst<platform::CUDADeviceContext, T>(
ctx, dX, &transformed_dX_channel);
transformed_dX_channel.mutable_data<T>(ctx.GetPlace());
}
} else {
transformed_X_channel = *X;
transformed_dO_channel = *dO;
if (ddX) {
transformed_ddX_channel = *ddX;
}
if (dX) {
transformed_dX_channel = *dX;
}
}
std::vector<int> output_vec =
phi::vectorize<int>(transformed_dO_channel.dims());
auto in_dims = transformed_X_channel.dims();
auto filter_dims = W->dims();
framework::DDim in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = phi::funcs::IsSymmetricPadding(paddings, data_dim);
Tensor transformed_X(X->type());
Tensor transformed_ddX(X->type());
Tensor transformed_dO(dO->type());
std::vector<int> padding_common(data_dim, 0);
std::vector<int> input_pad(X->dims().size() * 2, 0);
if (!is_sys_pad) {
// get pad
std::vector<int> padding_diff(data_dim);
std::vector<int> new_input_shape_vec(data_dim + 2);
std::vector<int> new_output_grad_shape_vec(data_dim + 2);
new_input_shape_vec[0] = transformed_X_channel.dims()[0];
new_input_shape_vec[1] = transformed_X_channel.dims()[1];
new_output_grad_shape_vec[0] = transformed_dO_channel.dims()[0];
new_output_grad_shape_vec[1] = transformed_dO_channel.dims()[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings[2 * i] - paddings[2 * i + 1]);
padding_common[i] = std::min(paddings[2 * i], paddings[2 * i + 1]);
new_input_shape_vec[i + 2] =
transformed_X_channel.dims()[i + 2] + padding_diff[i];
new_output_grad_shape_vec[i + 2] =
transformed_dO_channel.dims()[i + 2] + padding_diff[i];
input_pad[2 * i + 4] = paddings[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] = paddings[2 * i + 1] - padding_common[i];
}
framework::DDim new_input_shape(phi::make_ddim(new_input_shape_vec));
transformed_X.Resize(new_input_shape);
transformed_ddX.Resize(new_input_shape);
framework::DDim new_output_grad_shape(
phi::make_ddim(new_output_grad_shape_vec));
transformed_dO.Resize(new_output_grad_shape);
transformed_dO =
ctx.AllocateTmpTensor<T, paddle::platform::CUDADeviceContext>(
new_output_grad_shape, dev_ctx);
transformed_X =
ctx.AllocateTmpTensor<T, paddle::platform::CUDADeviceContext>(
new_input_shape, dev_ctx);
if (ddX) {
transformed_ddX =
ctx.AllocateTmpTensor<T, paddle::platform::CUDADeviceContext>(
new_input_shape, dev_ctx);
}
// pad for input
const int rank = X->dims().size();
T pad_value(0.0);
switch (rank) {
case 4: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 4>(
dev_ctx, input_pad, transformed_X_channel, pad_value,
&transformed_X);
if (dO) {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 4>(
dev_ctx, input_pad, transformed_dO_channel, pad_value,
&transformed_dO);
}
if (ddX) {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 4>(
dev_ctx, input_pad, transformed_ddX_channel, pad_value,
&transformed_ddX);
}
} break;
case 5: {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 5>(
dev_ctx, input_pad, transformed_X_channel, pad_value,
&transformed_X);
if (ddX) {
phi::funcs::PadFunction<paddle::platform::CUDADeviceContext, T, 5>(
dev_ctx, input_pad, transformed_ddX_channel, pad_value,
&transformed_ddX);
}
} break;
default:
PADDLE_THROW(platform::errors::InvalidArgument(
"ConvOp only support tensors with 4 or 5 dimensions."));
}
} else {
transformed_X = transformed_X_channel;
transformed_dO = transformed_dO_channel;
if (ddX) {
transformed_ddX = transformed_ddX_channel;
}
if (paddings.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings[2 * i];
}
}
}
std::vector<int64_t> starts(data_dim, 0);
std::vector<int64_t> ends(data_dim, 0);
std::vector<int64_t> axes(data_dim, 0);
for (size_t i = 0; i < data_dim; ++i) {
starts[i] = input_pad[2 * i + 4] * (strides[i] + 1);
ends[i] = starts[i] + output_vec[i + 2];
axes[i] = i + 2;
}
std::vector<int> transformed_output_vec = output_vec;
for (size_t i = 0; i < data_dim; ++i) {
transformed_output_vec[i + 2] =
output_vec[i + 2] +
(input_pad[2 * i + 4] + input_pad[2 * i + 5]) * strides[i] -
2 * padding_common[i] + paddings[2 * i] + paddings[2 * i + 1];
}
if (!is_sys_pad) {
DDim transformed_output_shape(phi::make_ddim(transformed_output_vec));
transformed_ddO_channel.mutable_data<T>(transformed_output_shape,
ctx.GetPlace());
} else {
ddO->mutable_data<T>(ctx.GetPlace());
transformed_ddO_channel = *ddO;
transformed_ddO_channel.Resize(phi::make_ddim(transformed_output_vec));
}
const T* x = transformed_X.data<T>();
int iwo_group = groups;
int c_group = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_group = 1;
c_group = groups;
groups = 1;
#endif
auto dtype = platform::CudnnDataType<T>::type;
auto handle = dev_ctx.cudnn_handle();
ConvArgs args1{&transformed_ddO_channel,
W,
&transformed_ddX,
strides,
padding_common,
dilations,
dtype};
ConvArgs args2{&transformed_ddO_channel, ddW, &transformed_X, strides,
padding_common, dilations, dtype};
ConvArgs args3{&transformed_dO,
dW,
&transformed_ddX_channel,
strides,
padding_common,
dilations,
dtype};
ConvArgs args4{
&transformed_dO, ddW, &transformed_dX_channel, strides, padding_common,
dilations, dtype};
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t bwd_algo1 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvBwdDataAlgorithm_t bwd_algo2 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvFwdAlgorithm_t data_algo =
static_cast<miopenConvFwdAlgorithm_t>(0);
miopenConvBwdWeightsAlgorithm_t filter_algo =
static_cast<miopenConvBwdWeightsAlgorithm_t>(0);
#else
cudnnConvolutionBwdDataAlgo_t bwd_algo1 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionBwdDataAlgo_t bwd_algo2 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionFwdAlgo_t data_algo =
static_cast<cudnnConvolutionFwdAlgo_t>(0);
cudnnConvolutionBwdFilterAlgo_t filter_algo =
static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
#endif
auto layout = GetCudnnTensorFormat(platform::DataLayout::kNCHW);
// ddo = conv(ddI, W) + conv(I, ddW)
size_t workspace_size = 0;
T* transformed_ddy_channel = nullptr;
if (ddO) {
ddy = ddO->data<T>();
transformed_ddy_channel = transformed_ddO_channel.data<T>();
if (ddX) {
args1.handle = handle;
args1.idesc.set(transformed_ddO_channel, iwo_group);
args1.wdesc.set(*W, layout, iwo_group);
args1.odesc.set(transformed_ddX, iwo_group);
args1.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search1 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
bwd_algo1 = search1::Find<T>(
args1, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search1 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo1 = search1::Find<T>(
args1, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size = search1::GetWorkspaceSize(args1, bwd_algo1);
#endif
}
if (ddW) {
ddw = ddW->data<T>();
args2.handle = handle;
args2.idesc.set(transformed_ddO_channel, iwo_group);
args2.wdesc.set(*ddW, layout, iwo_group);
args2.odesc.set(transformed_X, iwo_group);
args2.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search2 = SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2));
bwd_algo2 = search2::Find<T>(
args2, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search2 = SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo2 = search2::Find<T>(
args2, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size = std::max(workspace_size,
search2::GetWorkspaceSize(args2, bwd_algo2));
#endif
}
}
if (dW && ddX) {
dw = dW->data<T>();
args3.handle = handle;
args3.idesc.set(transformed_dO, iwo_group);
args3.wdesc.set(*dW, layout, iwo_group);
args3.odesc.set(transformed_ddX_channel, iwo_group);
args3.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search3 = SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo = search3::Find<T>(
args3, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search3 = SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search3::Find<T>(
args3, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size = std::max(workspace_size,
search3::GetWorkspaceSize(args3, filter_algo));
#endif
}
if (ddW && dX) {
transformed_dx = transformed_dX_channel.data<T>();
args4.handle = handle;
args4.idesc.set(transformed_dO, iwo_group);
args4.wdesc.set(*ddW, layout, iwo_group);
args4.odesc.set(transformed_dX_channel, iwo_group);
args4.cdesc.set(dtype, padding_common, strides, dilations,
platform::AllowTF32Cudnn(), c_group);
#ifdef PADDLE_WITH_HIP
using search4 = SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo = search4::Find<T>(
args4, false, deterministic, workspace_size,
ctx.template device_context<platform::CUDADeviceContext>());
#else
using search4 = SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search4::Find<T>(
args4, false, deterministic,
ctx.template device_context<platform::CUDADeviceContext>());
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
#endif
}
int i_n, i_c, i_d, i_h, i_w;
GetNCDHW(transformed_X.dims(), platform::DataLayout::kNCHW, &i_n, &i_c,
&i_d, &i_h, &i_w);
int o_n, o_c, o_d, o_h, o_w;
GetNCDHW(transformed_dO.dims(), platform::DataLayout::kNCHW, &o_n, &o_c,
&o_d, &o_h, &o_w);
int group_offset_in =
transformed_X.numel() / transformed_X.dims()[0] / groups;
int group_offset_out =
transformed_dO.numel() / transformed_dO.dims()[0] / groups;
int group_offset_filter = W->numel() / groups;
ScalingParamType<T> alpha = 1.0f;
ScalingParamType<T> beta = 0.0f;
auto wkspace_handle = dev_ctx.cudnn_workspace_handle();
if (ddO) {
if (ddX) {
ddx = transformed_ddX.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args1.odesc.desc(),
ddx + i * group_offset_in, args1.wdesc.desc(),
w + i * group_offset_filter, args1.cdesc.desc(),
bwd_algo1, &beta, args1.idesc.desc(),
transformed_ddy_channel + i * group_offset_out,
workspace_ptr, workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args1.wdesc.desc(),
w + i * group_offset_filter, args1.odesc.desc(),
ddx + i * group_offset_in, args1.cdesc.desc(),
bwd_algo1, workspace_ptr, workspace_size, &beta,
args1.idesc.desc(),
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
}
if (ddW) {
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
// MIOPEN ONLY support beta to be 0.0f
Tensor conv_x_ddw(dO->type());
conv_x_ddw.Resize(transformed_ddO_channel.dims());
T* conv_x_ddw_data = conv_x_ddw.mutable_data<T>(ctx.GetPlace());
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionBackwardData(
handle, &alpha, args2.odesc.desc(),
x + i * group_offset_in, args2.wdesc.desc(),
ddw + i * group_offset_filter, args2.cdesc.desc(),
bwd_algo2, &beta, args2.idesc.desc(),
conv_x_ddw_data + i * group_offset_out, workspace_ptr,
workspace_size));
},
workspace_size);
PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::miopenOpTensor(
handle, miopenTensorOpAdd, &alpha, args2.idesc.desc(),
transformed_ddy_channel + i * group_offset_out, &alpha,
args2.idesc.desc(), conv_x_ddw_data + i * group_offset_out, &beta,
args2.idesc.desc(),
transformed_ddy_channel + i * group_offset_out));
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionBackwardData(
handle, &alpha, args2.wdesc.desc(),
ddw + i * group_offset_filter, args2.odesc.desc(),
x + i * group_offset_in, args2.cdesc.desc(), bwd_algo2,
workspace_ptr, workspace_size, &alpha,
args2.idesc.desc(),
transformed_ddy_channel + i * group_offset_out));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
}
if ((!is_sys_pad) && (!channel_last)) {
if (strides.size() == 2U) {
Slice<paddle::platform::CUDADeviceContext, T, 4>(
ctx, &transformed_ddO_channel, ddO, starts, ends, axes);
} else if (!is_sys_pad && strides.size() == 3U) {
Slice<paddle::platform::CUDADeviceContext, T, 5>(
ctx, &transformed_ddO_channel, ddO, starts, ends, axes);
}
} else if ((!is_sys_pad) && (channel_last)) {
if (strides.size() == 2U) {
Slice<paddle::platform::CUDADeviceContext, T, 4>(
ctx, &transformed_ddO_channel, &transformed_ddO_channel, starts,
ends, axes);
} else if (!is_sys_pad && strides.size() == 3U) {
Slice<paddle::platform::CUDADeviceContext, T, 5>(
ctx, &transformed_ddO_channel, &transformed_ddO_channel, starts,
ends, axes);
}
TransToChannelLast<paddle::platform::CUDADeviceContext, T>(
ctx, &transformed_ddO_channel, ddO);
}
}
T* transformed_dy_channel = transformed_dO.data<T>();
if (dW && ddX) {
ddx = transformed_ddX_channel.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionBackwardWeights(
handle, &alpha, args3.odesc.desc(),
ddx + i * group_offset_in, args3.idesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.cdesc.desc(), filter_algo, &beta,
args3.wdesc.desc(), dw + i * group_offset_filter,
workspace_ptr, workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, args3.idesc.desc(),
transformed_dy_channel + i * group_offset_out,
args3.odesc.desc(), ddx + i * group_offset_in,
args3.cdesc.desc(), filter_algo, workspace_ptr,
workspace_size, &beta, args3.wdesc.desc(),
dw + i * group_offset_filter));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
}
if (dX && ddW) {
ddw = ddW->data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::miopenConvolutionForward(
handle, &alpha, args4.idesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.wdesc.desc(), ddw + i * group_offset_filter,
args4.cdesc.desc(), data_algo, &beta, args4.odesc.desc(),
transformed_dx + i * group_offset_in, workspace_ptr,
workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
platform::dynload::cudnnConvolutionForward(
handle, &alpha, args4.idesc.desc(),
transformed_dy_channel + i * group_offset_out,
args4.wdesc.desc(), ddw + i * group_offset_filter,
args4.cdesc.desc(), data_algo, workspace_ptr,
workspace_size, &beta, args4.odesc.desc(),
transformed_dx + i * group_offset_in));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
if (channel_last) {
TransToChannelLast<paddle::platform::CUDADeviceContext, T>(
ctx, &transformed_dX_channel, dX);
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
namespace plat = paddle::platform;
#ifdef PADDLE_WITH_HIP
// MIOPEN do not support double
REGISTER_OP_KERNEL(conv2d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeOpKernel<plat::float16>,
ops::CUDNNConvTransposeOpKernel<float>);
REGISTER_OP_KERNEL(conv2d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeGradOpKernel<plat::float16>,
ops::CUDNNConvTransposeGradOpKernel<float>);
REGISTER_OP_KERNEL(
conv2d_transpose_grad_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvTransposeDoubleGradOpKernel<float>,
paddle::operators::CUDNNConvTransposeDoubleGradOpKernel<plat::float16>);
REGISTER_OP_KERNEL(conv3d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeOpKernel<plat::float16>,
ops::CUDNNConvTransposeOpKernel<float>);
REGISTER_OP_KERNEL(conv3d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeGradOpKernel<plat::float16>,
ops::CUDNNConvTransposeGradOpKernel<float>);
#else
REGISTER_OP_KERNEL(conv2d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeOpKernel<plat::float16>,
ops::CUDNNConvTransposeOpKernel<float>,
ops::CUDNNConvTransposeOpKernel<double>);
REGISTER_OP_KERNEL(conv2d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeGradOpKernel<plat::float16>,
ops::CUDNNConvTransposeGradOpKernel<float>,
ops::CUDNNConvTransposeGradOpKernel<double>);
REGISTER_OP_KERNEL(
conv2d_transpose_grad_grad, CUDNN, plat::CUDAPlace,
paddle::operators::CUDNNConvTransposeDoubleGradOpKernel<float>,
paddle::operators::CUDNNConvTransposeDoubleGradOpKernel<double>,
paddle::operators::CUDNNConvTransposeDoubleGradOpKernel<plat::float16>);
REGISTER_OP_KERNEL(conv3d_transpose, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeOpKernel<plat::float16>,
ops::CUDNNConvTransposeOpKernel<float>,
ops::CUDNNConvTransposeOpKernel<double>);
REGISTER_OP_KERNEL(conv3d_transpose_grad, CUDNN, ::paddle::platform::CUDAPlace,
ops::CUDNNConvTransposeGradOpKernel<plat::float16>,
ops::CUDNNConvTransposeGradOpKernel<float>,
ops::CUDNNConvTransposeGradOpKernel<double>);
#endif
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @ 1eb96eec
......@@ -13,13 +13,17 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/conv_transpose_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/op_version_registry.h"
#include "paddle/fluid/platform/cudnn_workspace_helper.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/binary.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
......@@ -29,165 +33,6 @@ namespace operators {
using DataLayout = framework::DataLayout;
void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "ConvTranspose");
OP_INOUT_CHECK(ctx->HasInput("Filter"), "Input", "Filter", "ConvTranspose");
OP_INOUT_CHECK(ctx->HasOutput("Output"), "Output", "Output", "ConvTranspose");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> output_size =
ctx->Attrs().Get<std::vector<int>>("output_size");
std::vector<int> output_padding =
ctx->Attrs().Get<std::vector<int>>("output_padding");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
int groups = ctx->Attrs().Get<int>("groups");
std::string padding_algorithm =
ctx->Attrs().Get<std::string>("padding_algorithm");
const std::string data_layout_str =
ctx->Attrs().Get<std::string>("data_format");
const DataLayout data_layout =
ctx->IsRunMKLDNNKernel() ? DataLayout::kNCHW
: framework::StringToDataLayout(data_layout_str);
PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
platform::errors::InvalidArgument(
"Input of Op(conv_transpose) should be 4-D or "
"5-D Tensor. But received: %u-D Tensor, "
"the shape of input is [%s]",
in_dims.size(), in_dims));
PADDLE_ENFORCE_EQ(
in_dims.size(), filter_dims.size(),
platform::errors::InvalidArgument(
"The input's dimension size and filter's dimension size of "
"Op (conv_transpose) should be equal. But received: the shape of "
"input is [%s], the dimension size of input is [%d], the shape "
"of filter is [%s], the dimension size of filter is [%d]. ",
in_dims, in_dims.size(), filter_dims, filter_dims.size()));
int stride_size = strides.size();
for (int i = 0; i < stride_size; ++i) {
PADDLE_ENFORCE_GT(
strides[i], 0,
platform::errors::InvalidArgument(
"The stride of Op(Conv) should be larget than 0, but received "
"stride is %d.",
strides[i]));
}
int in_sub_stride_size = in_dims.size() - stride_size;
PADDLE_ENFORCE_EQ(
in_dims.size() - strides.size(), 2U,
platform::errors::InvalidArgument(
"The input's dimension size minus Attr(stride)'s size must "
"be euqal to 2 for Op(conv_transpose). But received: [%d], the "
"input's dimension size is [%d], the shape of input "
"is [%s], the Attr(stride)'s size is [%d].",
in_sub_stride_size, in_dims.size(), in_dims, strides.size()));
if (output_size.size())
PADDLE_ENFORCE_EQ(
output_size.size(), strides.size(),
platform::errors::InvalidArgument(
"The Attr(output_size) and Attr(stride) of Op(conv_transpose) "
"should be the same."));
if (output_padding.size())
PADDLE_ENFORCE_EQ(
output_padding.size(), strides.size(),
platform::errors::InvalidArgument(
"The Attr(output_padding) and Attr(stride) of Op(conv_transpose) "
"should be the same."));
const int64_t C =
(data_layout != DataLayout::kNHWC ? in_dims[1]
: in_dims[in_dims.size() - 1]);
PADDLE_ENFORCE_EQ(
C, filter_dims[0],
platform::errors::InvalidArgument(
"The number of input channels should be equal to filter channels "
"for Op(conv_transpose). But received: the input's channels is "
"[%d], the shape of input is [%s], the filter's channels is [%d], "
"the shape of filter is [%s]. The data_format is %s."
"The error may come from wrong data_format setting.",
C, in_dims, filter_dims[0], filter_dims, data_layout_str));
framework::DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
std::vector<int64_t> output_shape({in_dims[0]});
if (data_layout != DataLayout::kNHWC) {
output_shape.push_back(filter_dims[1] * groups);
}
const int offset = (data_layout != DataLayout::kNHWC ? 2 : 1);
for (size_t i = 0; i < strides.size(); ++i) {
auto filter_extent = dilations[i] * (filter_dims[i + 2] - 1) + 1;
auto infer_shape = (ctx->IsRuntime() || in_dims[i + offset] > 0)
? (in_dims[i + offset] - 1) * strides[i] -
paddings[2 * i] - paddings[2 * i + 1] +
filter_extent
: -1;
if (output_size.size()) {
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_GE(
output_size[i], infer_shape,
platform::errors::InvalidArgument(
"output_size of Op(ConvTransposeOp) should not be "
"less than the infered output size. But received output_size = "
"[%s], whose dim %d is less than the infered output size [%s]",
phi::make_ddim(output_size).to_str(), i, infer_shape));
PADDLE_ENFORCE_LT(
output_size[i], infer_shape + strides[i],
platform::errors::InvalidArgument(
"output_size of Op(ConvTransposeOp) should be less "
"than infered size + stride. But received output_size = [%s], "
"whose dim %d is not less than the infered output size (%d) + "
"stride (%d) = %d",
phi::make_ddim(output_size).to_str(), i, infer_shape,
strides[i], infer_shape + strides[i]));
}
output_shape.push_back(output_size[i]);
} else if (output_padding.size()) {
if (ctx->IsRuntime()) {
PADDLE_ENFORCE_GE(
output_padding[i], 0,
platform::errors::InvalidArgument(
"output_padding of Op(ConvTransposeOp) should not be "
"less than the 0. But received output_padding = "
"[%s], whose dim %d is less than 0",
phi::make_ddim(output_padding).to_str(), i));
PADDLE_ENFORCE_LT(
output_padding[i], std::max(strides[i], dilations[i]),
platform::errors::InvalidArgument(
"output_padding of Op(ConvTransposeOp) should be less "
"than either stride or dilation. But received output_size = "
"[%s], "
"whose dim %d is not less than either stride (%d) or "
"dilation (%d)",
phi::make_ddim(output_size).to_str(), i, strides[i],
dilations[i]));
}
output_shape.push_back((infer_shape + output_padding[i]));
} else {
output_shape.push_back(infer_shape);
}
}
if (data_layout == DataLayout::kNHWC) {
output_shape.push_back(filter_dims[1] * groups);
}
ctx->SetOutputDim("Output", phi::make_ddim(output_shape));
}
framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_{framework::LibraryType::kPlain};
......@@ -217,7 +62,7 @@ framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
}
framework::OpKernelType ConvTransposeOp::GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const std::string& var_name, const framework::Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const {
#ifdef PADDLE_WITH_MKLDNN
// Only input require reshaping, weights and
......@@ -493,17 +338,6 @@ Example:
)DOC");
}
void ConvTransposeOpGrad::InferShape(framework::InferShapeContext* ctx) const {
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);
}
}
framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
bool use_cudnn =
......@@ -587,24 +421,6 @@ class ConvTransposeDoubleGradMaker : public framework::SingleGradOpMaker<T> {
}
};
void ConvTransposeOpDoubleGrad::InferShape(
framework::InferShapeContext* ctx) const {
auto x_dims = ctx->GetInputDim("Input");
auto w_dims = ctx->GetInputDim("Filter");
auto do_dims = ctx->GetInputDim("DOutput");
if (ctx->HasOutput("DDOutput") &&
(ctx->HasInput("DDInput") || (ctx->HasInput("DDFilter")))) {
ctx->SetOutputDim("DDOutput", do_dims);
}
if (ctx->HasOutput("DFilter") && ctx->HasInput("DDInput")) {
ctx->SetOutputDim("DFilter", w_dims);
}
if (ctx->HasOutput("DInput") && ctx->HasInput("DDFilter")) {
ctx->SetOutputDim("DInput", x_dims);
}
}
framework::OpKernelType ConvTransposeOpDoubleGrad::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
bool use_cudnn =
......@@ -635,59 +451,57 @@ framework::OpKernelType ConvTransposeOpDoubleGrad::GetExpectedKernelType(
namespace ops = paddle::operators;
// conv2d_transpose
DECLARE_INFER_SHAPE_FUNCTOR(conv2d_transpose, Conv2dTranposeInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(conv2d_transpose_grad,
Conv2dTranposeGradInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeGradInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(
conv2d_transpose_grad_grad, Conv2dTranposeDoubleGradInferShapeFunctor,
PD_INFER_META(phi::Conv2dTransposeDoubleGradInferMeta));
REGISTER_OPERATOR(conv2d_transpose, ops::ConvTransposeOp,
ops::Conv2DTransposeOpMaker,
ops::ConvTransposeGradOpMaker<paddle::framework::OpDesc>,
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(
conv2d_transpose_grad, ops::ConvTransposeOpGrad,
ops::ConvTransposeDoubleGradMaker<paddle::framework::OpDesc>,
ops::ConvTransposeDoubleGradMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(conv2d_transpose_grad_grad, ops::ConvTransposeOpDoubleGrad);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>,
Conv2dTranposeInferShapeFunctor);
REGISTER_OPERATOR(conv2d_transpose_grad, ops::ConvTransposeOpGrad,
ops::ConvTransposeDoubleGradMaker<paddle::framework::OpDesc>,
ops::ConvTransposeDoubleGradMaker<paddle::imperative::OpBase>,
Conv2dTranposeGradInferShapeFunctor);
REGISTER_OPERATOR(conv2d_transpose_grad_grad, ops::ConvTransposeOpDoubleGrad,
Conv2dTranposeDoubleGradInferShapeFunctor);
// conv3d_transpose
DECLARE_INFER_SHAPE_FUNCTOR(conv3d_transpose, Conv3dTranposeInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(conv3d_transpose_grad,
Conv3dTranposeGradInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeGradInferMeta));
REGISTER_OPERATOR(conv3d_transpose, ops::ConvTransposeOp,
ops::Conv3DTransposeOpMaker,
ops::ConvTransposeGradOpMaker<paddle::framework::OpDesc>,
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(conv3d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
conv3d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>,
Conv3dTranposeInferShapeFunctor);
REGISTER_OPERATOR(conv3d_transpose_grad, ops::ConvTransposeOpGrad,
Conv3dTranposeGradInferShapeFunctor);
// depthwise conv2d_transpose
DECLARE_INFER_SHAPE_FUNCTOR(depthwise_conv2d_transpose,
DepthWiseConv2dTranposeInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeInferMeta));
DECLARE_INFER_SHAPE_FUNCTOR(depthwise_conv2d_transpose_grad,
DepthWiseConv2dTranposeGradInferShapeFunctor,
PD_INFER_META(phi::ConvTransposeGradInferMeta));
REGISTER_OPERATOR(depthwise_conv2d_transpose, ops::ConvTransposeOp,
ops::Conv2DTransposeOpMaker,
ops::ConvTransposeGradOpMaker<paddle::framework::OpDesc>,
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OPERATOR(depthwise_conv2d_transpose_grad, ops::ConvTransposeOpGrad);
REGISTER_OP_CPU_KERNEL(
depthwise_conv2d_transpose,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
depthwise_conv2d_transpose_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUDeviceContext,
double>);
ops::ConvTransposeGradOpMaker<paddle::imperative::OpBase>,
DepthWiseConv2dTranposeInferShapeFunctor);
REGISTER_OPERATOR(depthwise_conv2d_transpose_grad, ops::ConvTransposeOpGrad,
DepthWiseConv2dTranposeGradInferShapeFunctor);
REGISTER_OP_VERSION(conv_transpose)
.AddCheckpoint(
......
paddle/fluid/operators/conv_transpose_op.cu 已删除 100644 → 0
浏览文件 @ b77e20ac
/* 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/conv_transpose_op.h"
#include "paddle/phi/kernels/gpu/depthwise_conv.h"
namespace ops = paddle::operators;
using CUDA = paddle::platform::CUDADeviceContext;
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T>
class DepthwiseConvTransposeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const std::string data_layout_str =
context.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
const Tensor* input = context.Input<Tensor>("Input");
Tensor filter = *context.Input<Tensor>("Filter");
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
int groups = context.Attr<int>("groups");
PADDLE_ENFORCE_EQ(
groups, filter.dims()[0],
platform::errors::InvalidArgument(
"groups should be error to the 1st dimension of filter. But "
"received groups is %d and filter dimension[0] is %d",
groups, filter.dims()[0]));
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
std::string padding_algorithm =
context.Attr<std::string>("padding_algorithm");
for (auto v : dilations) {
PADDLE_ENFORCE_EQ(v, 1, platform::errors::InvalidArgument(
"dilations should be 1 in depthwise conv. "
"But received dilations is %d",
v));
}
auto in_dims = input->dims();
auto filter_dims = filter.dims();
framework::DDim in_data_dims;
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
output->mutable_data<T>(context.GetPlace());
auto& dev_ctx = context.template device_context<DeviceContext>();
phi::funcs::SetConstant<DeviceContext, T> set_zero;
set_zero(dev_ctx, output, static_cast<T>(0));
math::DepthwiseConvInputGradFunctor<phi::GPUContext, T>
depthwiseConvInputGrad;
depthwiseConvInputGrad(
static_cast<const typename framework::ConvertToPhiContext<
DeviceContext>::TYPE&>(dev_ctx),
*output, filter, *input, strides,
std::vector<int>{paddings[0], paddings[2], paddings[1], paddings[3]},
dilations, output, data_layout);
}
};
template <typename DeviceContext, typename T>
class DepthwiseConvTransposeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const std::string data_layout_str =
context.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
const Tensor* input = context.Input<Tensor>("Input");
const Tensor* output_grad =
context.Input<Tensor>(framework::GradVarName("Output"));
Tensor* input_grad =
context.Output<Tensor>(framework::GradVarName("Input"));
Tensor* filter_grad =
context.Output<Tensor>(framework::GradVarName("Filter"));
Tensor filter = *context.Input<Tensor>("Filter");
if (!input_grad && !filter_grad) return;
auto& dev_ctx = context.template device_context<DeviceContext>();
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
std::string padding_algorithm =
context.Attr<std::string>("padding_algorithm");
auto in_dims = input->dims();
auto filter_dims = filter.dims();
framework::DDim in_data_dims;
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
if (input_grad) {
math::DepthwiseConvFunctor<phi::GPUContext, T> depthwiseConv;
depthwiseConv(
static_cast<const typename framework::ConvertToPhiContext<
DeviceContext>::TYPE&>(dev_ctx),
*output_grad, filter, strides,
std::vector<int>{paddings[0], paddings[2], paddings[1], paddings[3]},
dilations, input_grad, data_layout);
}
if (filter_grad) {
phi::funcs::SetConstant<DeviceContext, T> set_zero;
filter_grad->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, filter_grad, static_cast<T>(0));
math::DepthwiseConvFilterGradFunctor<phi::GPUContext, T>
depthwiseConvFilterGrad;
depthwiseConvFilterGrad(
static_cast<const typename framework::ConvertToPhiContext<
DeviceContext>::TYPE&>(dev_ctx),
*output_grad, *input, strides,
std::vector<int>{paddings[0], paddings[2], paddings[1], paddings[3]},
dilations, filter_grad, data_layout);
}
}
};
} // namespace operators
} // namespace paddle
// conv2d
REGISTER_OP_CUDA_KERNEL(conv2d_transpose,
ops::GemmConvTransposeKernel<CUDA, float>,
ops::GemmConvTransposeKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(conv2d_transpose_grad,
ops::GemmConvTransposeGradKernel<CUDA, float>,
ops::GemmConvTransposeGradKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(conv2d_transpose_grad_grad,
ops::GemmConvTransposeGradKernel<CUDA, float>,
ops::GemmConvTransposeGradKernel<CUDA, double>);
// conv3d
REGISTER_OP_CUDA_KERNEL(conv3d_transpose,
ops::GemmConvTransposeKernel<CUDA, float>,
ops::GemmConvTransposeKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(conv3d_transpose_grad,
ops::GemmConvTransposeGradKernel<CUDA, float>,
ops::GemmConvTransposeGradKernel<CUDA, double>);
// depthwise conv2d
REGISTER_OP_CUDA_KERNEL(depthwise_conv2d_transpose,
ops::DepthwiseConvTransposeKernel<CUDA, float>,
ops::DepthwiseConvTransposeKernel<CUDA, double>);
REGISTER_OP_CUDA_KERNEL(depthwise_conv2d_transpose_grad,
ops::DepthwiseConvTransposeGradKernel<CUDA, float>,
ops::DepthwiseConvTransposeGradKernel<CUDA, double>);
paddle/fluid/operators/conv_transpose_op.h
浏览文件 @ 1eb96eec
......@@ -13,72 +13,14 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/conv_op.h"
#include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/math/concat_and_split.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/vol2col.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/fluid/framework/op_kernel_type.h"
#include "paddle/fluid/framework/op_proto_maker.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using DDim = framework::DDim;
template <typename DeviceContext, typename T, size_t D>
static void Slice(const framework::ExecutionContext& context,
const Tensor* input, Tensor* out,
const std::vector<int64_t>& begin_vec,
const std::vector<int64_t>& end_vec,
const std::vector<int64_t>& axes_vec) {
auto& place =
*context.template device_context<DeviceContext>().eigen_device();
auto in_dims = input->dims();
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
auto extents = Eigen::DSizes<Eigen::DenseIndex, D>();
for (size_t i = 0; i < D; ++i) {
offsets[i] = 0;
extents[i] = in_dims[i];
}
std::vector<int64_t> out_shape_vec = phi::vectorize(in_dims);
for (size_t i = 0; i < axes_vec.size(); ++i) {
offsets[axes_vec[i]] = begin_vec[i];
extents[axes_vec[i]] = end_vec[i] - begin_vec[i];
out_shape_vec[axes_vec[i]] = end_vec[i] - begin_vec[i];
}
framework::DDim out_dims(phi::make_ddim(out_shape_vec));
out->mutable_data<T>(out_dims, context.GetPlace());
auto in_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*input);
auto out_t =
framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*out, out_dims);
EigenSlice<std::decay_t<decltype(place)>, T, D>::Eval(place, out_t, in_t,
offsets, extents);
out->Resize(out_dims);
}
template <typename DeviceContext, typename T, size_t D>
static void Slice(const framework::ExecutionContext& context,
const Tensor* input, Tensor* out, int64_t begin_idx,
int64_t end_idx, int64_t axes) {
std::vector<int64_t> begin_vec = {begin_idx};
std::vector<int64_t> end_vec = {end_idx};
std::vector<int64_t> axes_vec = {axes};
Slice<DeviceContext, T, D>(context, input, out, begin_vec, end_vec, axes_vec);
}
// Define Op classes in .h file so that other conv transpose
// operator implementations can reuse the code.
class Conv2DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -94,21 +36,19 @@ class Conv3DTransposeOpMaker : public framework::OpProtoAndCheckerMaker {
class ConvTransposeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
framework::OpKernelType GetKernelTypeForVar(
const std::string& var_name, const Tensor& tensor,
const std::string& var_name, const framework::Tensor& tensor,
const framework::OpKernelType& expected_kernel_type) const override;
};
class ConvTransposeOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
......@@ -118,464 +58,11 @@ class ConvTransposeOpGrad : public framework::OperatorWithKernel {
class ConvTransposeOpDoubleGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override;
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override;
};
template <typename DeviceContext, typename T>
class GemmConvTransposeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const std::string data_layout_str =
context.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
const Tensor* input = context.Input<Tensor>("Input");
// The filter will be reshaped, so it should not be constant pointer
Tensor filter = *context.Input<Tensor>("Filter");
Tensor* output = context.Output<Tensor>("Output");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
int groups = context.Attr<int>("groups");
std::string padding_algorithm =
context.Attr<std::string>("padding_algorithm");
auto in_dims = input->dims();
auto filter_dims = filter.dims();
auto out_dims = output->dims();
const int batch_size = static_cast<int>(input->dims()[0]);
framework::DDim in_data_dims;
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
// input_shape_vec: {n, c, h, w} or {n, c, d, h, w} for channel_first
// input_shape_vec: {n, h, w, c} or {n, d, h, w, c} for channel_last
std::vector<int64_t> input_shape_vec = phi::vectorize(input->dims());
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec = phi::vectorize(filter.dims());
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// col_shape_vec: {o_c/g, k_h, k_w, h, w} or {o_c/g, k_d, k_h, k_w, d, h, w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
if (data_layout != framework::DataLayout::kNHWC) {
col_shape_vec[0] = out_dims[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 2];
}
} else {
col_shape_vec[0] = out_dims[out_dims.size() - 1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 1];
}
}
DDim col_shape(phi::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (o_c/g * k_h * k_w, h * w) or (o_c/g * k_d * k_h * k_w, d * h * w)
DDim col_matrix_shape = phi::flatten_to_2d(col_shape, data_dim + 1);
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
// output size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// output size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
DDim output_shape =
phi::slice_ddim(output->dims(), 1, output->dims().size());
// input matrix size: (i_c, h * w) or (i_c, d * h * w) for channel_first
// input matrix size: (h * w, i_c) or (d * h * w, i_c) for channel_last
DDim input_matrix_shape;
if (data_layout != framework::DataLayout::kNHWC) {
input_matrix_shape = {in_dims[1], col_matrix_shape[1]};
} else {
input_matrix_shape = {col_matrix_shape[1], in_dims[in_dims.size() - 1]};
}
// filter size: (i_c, o_c/g * k_h * k_w) or (i_c, o_c/g * k_d * k_h * k_w)
DDim filter_matrix_shape;
if (data_layout != framework::DataLayout::kNHWC) {
filter_matrix_shape = {in_dims[1], col_matrix_shape[0]};
} else {
filter_matrix_shape = {in_dims[in_dims.size() - 1], col_matrix_shape[0]};
}
filter.Resize(filter_matrix_shape);
output->mutable_data<T>(context.GetPlace());
phi::funcs::SetConstant<DeviceContext, T> set_zero;
auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx);
set_zero(dev_ctx, output, static_cast<T>(0));
int in_step =
(data_layout != framework::DataLayout::kNHWC
? static_cast<int>(in_dims[1]) / groups
: static_cast<int>(in_dims[in_dims.size() - 1]) / groups);
int out_step =
(data_layout != framework::DataLayout::kNHWC
? static_cast<int>(out_dims[1]) / groups
: static_cast<int>(out_dims[out_dims.size() - 1]) / groups);
math::Col2ImFunctor<math::ColFormat::kCFO, DeviceContext, T> col2im;
math::Col2VolFunctor<DeviceContext, T> col2vol;
math::ConcatFunctor<DeviceContext, T> concat_functor;
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on input)
size_t D = input->dims().size();
for (int i = 0; i < batch_size; i++) {
// batch with size (i_c, h * w) or (i_c, d * h * w) for channel_first
// batch with size (h * w, i_c) or (d * h * w, i_c) for channel_last
Tensor input_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
// output size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// output size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
Tensor output_batch = output->Slice(i, i + 1).Resize(output_shape);
std::vector<Tensor> output_batch_vec;
for (int g = 0; g < groups; g++) {
int64_t start = g * in_step;
int64_t end = (g + 1) * in_step;
int axes = (data_layout != framework::DataLayout::kNHWC ? 0 : 1);
Tensor filter_slice = filter.Slice(g * in_step, (g + 1) * in_step);
Tensor in_slice, out_slice;
// col_matrix = filter_slice * input_slice
// of shape (o_c/g * k_h * k_w, h * w)
// or (o_c/g * k_d * k_h * k_w, d * h * w)
if (data_layout != framework::DataLayout::kNHWC) {
in_slice = input_batch.Slice(g * in_step, (g + 1) * in_step);
out_slice = output_batch.Slice(g * out_step, (g + 1) * out_step);
blas.MatMul(filter_slice, true, in_slice, false, static_cast<T>(1.0),
&col_matrix, static_cast<T>(0.0));
} else {
Slice<DeviceContext, T, 2>(context, &input_batch, &in_slice, start,
end, axes);
start = g * out_step;
end = (g + 1) * out_step;
axes = D - 2;
if (D == 4U) {
Slice<DeviceContext, T, 3>(context, &output_batch, &out_slice,
start, end, axes);
} else if (D == 5U) {
Slice<DeviceContext, T, 4>(context, &output_batch, &out_slice,
start, end, axes);
}
blas.MatMul(filter_slice, true, in_slice, true, static_cast<T>(1.0),
&col_matrix, static_cast<T>(0.0));
}
if (data_dim == 2U) {
// col2im: col_matrix -> dy
// from (o_c/g * k_h * k_w, h * w) to (o_c/g, o_h, o_w) or (o_h, o_w,
// o_c/g)
col2im(dev_ctx, col, dilations, strides,
std::vector<int>{paddings[0], paddings[2], paddings[1],
paddings[3]},
&out_slice, data_layout);
} else if (data_dim == 3U) {
// col2vol: col_matrix -> dy
// from (o_c/g * k_d * k_h * k_w, d * h * w) to (o_c/g, o_d, o_h, o_w)
// or (o_d, o_h, o_w, o_c/g)
col2vol(dev_ctx, col, dilations, strides, paddings, &out_slice,
data_layout);
}
if (data_layout == framework::DataLayout::kNHWC) {
output_batch_vec.push_back(out_slice);
}
}
if (data_layout == framework::DataLayout::kNHWC) {
concat_functor(dev_ctx, output_batch_vec, static_cast<int>(D - 2),
&output_batch);
}
}
}
};
template <typename DeviceContext, typename T>
class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const std::string data_layout_str =
context.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
const Tensor* input = context.Input<Tensor>("Input");
const Tensor* output_grad =
context.Input<Tensor>(framework::GradVarName("Output"));
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
Tensor filter = *context.Input<Tensor>("Filter");
Tensor* input_grad =
context.Output<Tensor>(framework::GradVarName("Input"));
Tensor* filter_grad =
context.Output<Tensor>(framework::GradVarName("Filter"));
if ((!input_grad) && (!filter_grad)) return;
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
std::vector<int> dilations = context.Attr<std::vector<int>>("dilations");
int groups = context.Attr<int>("groups");
std::string padding_algorithm =
context.Attr<std::string>("padding_algorithm");
auto in_dims = input->dims();
auto filter_dims = filter.dims();
auto out_grad_dims = output_grad->dims();
const int batch_size = static_cast<int>(input->dims()[0]);
framework::DDim in_data_dims;
if (data_layout != framework::DataLayout::kNHWC) {
in_data_dims = phi::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = phi::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
// input_shape_vec: {n, c, h, w} or {n, c, d, h, w} for channel_first
// input_shape_vec: {n, h, w, c} or {n, d, h, w, c} for channel_last
std::vector<int64_t> input_shape_vec = phi::vectorize(input->dims());
// filter_shape_vec: {i_c, o_c, k_h, k_w} or {i_c, o_c, k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec = phi::vectorize(filter.dims());
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// col_shape_vec: {o_c, k_h, k_w, h, w} or {o_c, k_d, k_h, k_w, d, h, w} for
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
if (data_layout != framework::DataLayout::kNHWC) {
col_shape_vec[0] = out_grad_dims[1];
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 2];
}
} else {
col_shape_vec[0] = out_grad_dims[out_grad_dims.size() - 1];
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 1];
}
}
DDim col_shape(phi::make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (o_c * k_h * k_w, h * w) or (o_c * k_d * k_h * k_w, d * h * w)
DDim col_matrix_shape = phi::flatten_to_2d(col_shape, data_dim + 1);
// output size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// output size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
DDim output_shape =
phi::slice_ddim(output_grad->dims(), 1, output_grad->dims().size());
// input matrix size: (i_c, h * w) or (i_c, d * h * w) for channel_first
// input matrix size: (h * w, i_c) or (d * h * w, i_c) for channel_last
DDim input_matrix_shape;
if (data_layout != framework::DataLayout::kNHWC) {
input_matrix_shape = {in_dims[1], col_matrix_shape[1]};
} else {
input_matrix_shape = {col_matrix_shape[1], in_dims[in_dims.size() - 1]};
}
// filter size: (i_c, o_c/g * k_h * k_w) or (i_c, o_c/g * k_d * k_h * k_w)
DDim filter_matrix_shape;
if (data_layout != framework::DataLayout::kNHWC) {
filter_matrix_shape = {in_dims[1], col_matrix_shape[0] / groups};
} else {
filter_matrix_shape = {in_dims[in_dims.size() - 1],
col_matrix_shape[0] / groups};
}
filter.Resize(filter_matrix_shape);
int in_step =
(data_layout != framework::DataLayout::kNHWC
? static_cast<int>(in_dims[1]) / groups
: static_cast<int>(in_dims[in_dims.size() - 1]) / groups);
int col_step = static_cast<int>(col_matrix_shape[0]) / groups;
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
auto& dev_ctx = context.template device_context<DeviceContext>();
auto blas = phi::funcs::GetBlas<DeviceContext, T>(dev_ctx);
if (input_grad || filter_grad) {
Tensor col;
col.mutable_data<T>(col_shape, context.GetPlace());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor col_matrix;
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
Tensor filter_grad_;
phi::funcs::SetConstant<DeviceContext, T> set_zero;
math::Im2ColFunctor<math::ColFormat::kCFO, DeviceContext, T> im2col;
math::Vol2ColFunctor<DeviceContext, T> vol2col;
math::ConcatFunctor<DeviceContext, T> concat_functor;
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, input_grad, static_cast<T>(0));
}
if (filter_grad) { // filter_grad_ size (i_c, o_c/g, k_h, k_w)
filter_grad->mutable_data<T>(context.GetPlace());
set_zero(dev_ctx, filter_grad, static_cast<T>(0));
filter_grad_ = *filter_grad;
filter_grad_.Resize(filter_matrix_shape);
}
size_t D = input->dims().size();
for (int i = 0; i < batch_size; i++) {
// batch with size (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for
// channel_first
// batch with size (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for
// channel_last
Tensor output_grad_batch =
output_grad->Slice(i, i + 1).Resize(output_shape);
if (data_dim == 2U) {
// im2col: dy -> col matrix
// from (o_c, o_h, o_w) to (o_c * k_h * k_w, i_h * i_w) for
// channel_first
// from (o_h, o_w, o_c) to (o_c * k_h * k_w, i_h * i_w) for
// channel_last
im2col(dev_ctx, output_grad_batch, dilations, strides,
std::vector<int>{paddings[0], paddings[2], paddings[1],
paddings[3]},
&col, data_layout);
} else if (data_dim == 3U) {
// vol2col: dy -> col_matrix
// from (o_c, o_d, o_h, o_w) to (o_c * k_d * k_h * k_w, i_d * i_h *
// i_w) for channel_first
// from (o_d, o_h, o_w, o_c) to (i_d * i_h * i_w, o_c * k_d * k_h *
// k_w) for channel_last
vol2col(dev_ctx, output_grad_batch, dilations, strides, paddings,
&col, data_layout);
}
if (input_grad) {
// batch with size (i_c, i_h, i_w) or (i_h, i_w, i_c)
Tensor input_grad_batch =
input_grad->Slice(i, i + 1).Resize(input_matrix_shape);
// gemm: dx = filter * dy
// (i_c, o_c * k_h * k_w) * (o_c * k_h * k_w, i_h * i_w) -> (i_c, i_h
// * i_w)
// or
// (i_c, o_c * k_d * k_h * k_w) * (o_c * k_d * k_h * k_w, i_d * i_h *
// i_w) -> (i_c,
// i_d, i_h, i_w)
// gemm: dx = dy^T * filter^T for channel_last
std::vector<Tensor> input_grad_batch_vec;
for (int g = 0; g < groups; g++) {
// input_grad_slice: (i_c/g, i_h * i_w) or (i_c/g, i_d * i_h * i_w)
// for channel_first
// input_grad_slice: (i_h * i_w, i_c/g) or (i_d * i_h * i_w, i_c/g)
// for channel_last
// filter_slice: (i_c/g, o_c/g * k_h * k_w)
Tensor filter_slice = filter.Slice(g * in_step, (g + 1) * in_step);
// col_matrix_slice: (o_c/g * k_h * k_w, h * w) or (o_c/g * k_d *
// k_h * k_w, d * h * w)
Tensor col_matrix_slice =
col_matrix.Slice(g * col_step, (g + 1) * col_step);
if (data_layout != framework::DataLayout::kNHWC) {
Tensor input_grad_slice =
input_grad_batch.Slice(g * in_step, (g + 1) * in_step);
blas.MatMul(filter_slice, false, col_matrix_slice, false,
static_cast<T>(1.0), &input_grad_slice,
static_cast<T>(0.0));
} else {
Tensor input_grad_slice;
Slice<DeviceContext, T, 2>(context, &input_grad_batch,
&input_grad_slice, g * in_step,
(g + 1) * in_step, 1);
blas.MatMul(col_matrix_slice, true, filter_slice, true,
static_cast<T>(1.0), &input_grad_slice,
static_cast<T>(0.0));
DDim input_grad_slice_shape;
if (data_dim == 2U) {
input_grad_slice_shape = {in_dims[1], in_dims[2], in_step};
} else {
input_grad_slice_shape = {in_dims[1], in_dims[2], in_dims[3],
in_step};
}
input_grad_slice =
input_grad_slice.Resize(input_grad_slice_shape);
input_grad_batch_vec.push_back(input_grad_slice);
}
}
if (data_layout == framework::DataLayout::kNHWC) {
concat_functor(dev_ctx, input_grad_batch_vec,
static_cast<int>(D - 2), &input_grad_batch);
}
}
if (filter_grad) {
// input batch: (i_c, i_h * i_w) or (i_h, i_w * i_c)
Tensor in_batch = input->Slice(i, i + 1).Resize(input_matrix_shape);
// gemm: d_filter = x * dy^T
// (i_c, i_h * i_w) * (i_h * i_w, o_c * k_h * k_w) -> (i_c, o_c * k_h
// * k_w)
// or
// (i_c, i_d * i_h * i_w) * (i_d * i_h * i_w, o_c * k_d * k_h * k_w)
// -> (i_c, o_c * k_d *
// k_h * k_w)
// gemm: d_filter = x^T * dy^T for channel_last
for (int g = 0; g < groups; g++) {
Tensor filter_grad_slice =
filter_grad_.Slice(g * in_step, (g + 1) * in_step);
Tensor col_matrix_slice =
col_matrix.Slice(g * col_step, (g + 1) * col_step);
if (data_layout != framework::DataLayout::kNHWC) {
Tensor in_batch_slice =
in_batch.Slice(g * in_step, (g + 1) * in_step);
blas.MatMul(in_batch_slice, false, col_matrix_slice, true,
static_cast<T>(1.0), &filter_grad_slice,
static_cast<T>(1.0));
} else {
Tensor in_batch_slice;
Slice<DeviceContext, T, 2>(context, &in_batch, &in_batch_slice,
g * in_step, (g + 1) * in_step, 1);
blas.MatMul(in_batch_slice, true, col_matrix_slice, true,
static_cast<T>(1.0), &filter_grad_slice,
static_cast<T>(1.0));
}
}
}
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/fluid/operators/conv_transpose_op_npu.cc
浏览文件 @ 1eb96eec
......@@ -13,11 +13,15 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/conv_transpose_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/npu/npu_op_runner.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using NPUDeviceContext = platform::NPUDeviceContext;
template <typename T>
......@@ -55,8 +59,8 @@ class Conv2DTransposeNPUKernel : public framework::OpKernel<T> {
filter_data_dims = phi::slice_ddim(filter_dims, 2, in_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&padding, &dilation, padding_algorithm,
in_data_dims, stride, ksize);
phi::UpdatePaddingAndDilation(&padding, &dilation, padding_algorithm,
in_data_dims, stride, ksize);
// construct NPU attr
std::vector<int> strides(4, 1);
......@@ -137,8 +141,8 @@ class Conv2DTransposeGradNPUKernel : public framework::OpKernel<T> {
framework::DDim filter_data_dims =
phi::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
phi::UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
std::vector<int> strides_vec(4, 1);
std::vector<int> dilations_vec(4, 1);
......
paddle/fluid/operators/conv_transpose_op_xpu.cc
浏览文件 @ 1eb96eec
......@@ -8,15 +8,22 @@ 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/conv_transpose_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/device/device_wrapper.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#ifdef PADDLE_WITH_XPU
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
// target_len == 2 || target_len == 4
inline std::vector<int> vector_extend(const std::vector<int>& src,
int target_len) {
......@@ -61,8 +68,8 @@ class Conv2DTransposeXPUKernel : public framework::OpKernel<T> {
framework::DDim filter_data_dims =
phi::slice_ddim(filter.dims(), 2, filter.dims().size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
phi::UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
const int batch_size = static_cast<int>(input->dims()[0]);
const int img_yc = static_cast<int>(input->dims()[1]);
......@@ -135,8 +142,8 @@ class Conv2DTransposeGradXPUKernel : public framework::OpKernel<T> {
framework::DDim filter_data_dims =
phi::slice_ddim(filter.dims(), 2, filter.dims().size());
std::vector<int> ksize = phi::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
phi::UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
const int batch_size = static_cast<int>(input->dims()[0]);
const int img_yc = static_cast<int>(input->dims()[1]);
......
paddle/phi/infermeta/backward.cc
浏览文件 @ 1eb96eec
......@@ -64,6 +64,45 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
}
}
void ConvTransposeGradInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const MetaTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* dx,
MetaTensor* dfilter) {
GeneralBinaryGradInferMeta(x, filter, dx, dfilter);
}
void Conv2dTransposeDoubleGradInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const MetaTensor& dout,
const MetaTensor& ddx,
const MetaTensor& ddfilter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* dx,
MetaTensor* dfilter,
MetaTensor* ddout) {
GeneralBinaryGradInferMeta(x, filter, dx, dfilter);
if (ddout) {
ddout->share_meta(dout);
}
}
void GatherNdGradInferMeta(const MetaTensor& x,
const MetaTensor& index,
const MetaTensor& out_grad,
......
paddle/phi/infermeta/backward.h
浏览文件 @ 1eb96eec
......@@ -37,6 +37,37 @@ void BilinearTensorProductGradInferMeta(const MetaTensor& x,
MetaTensor* dweight,
MetaTensor* dbias);
void ConvTransposeGradInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const MetaTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* dx,
MetaTensor* dfilter);
void Conv2dTransposeDoubleGradInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const MetaTensor& dout,
const MetaTensor& ddx,
const MetaTensor& ddfilter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* dx,
MetaTensor* dfilter,
MetaTensor* ddout);
void GatherNdGradInferMeta(const MetaTensor& x,
const MetaTensor& index,
const MetaTensor& out_grad,
......
paddle/phi/infermeta/binary.cc
浏览文件 @ 1eb96eec
......@@ -17,8 +17,10 @@ limitations under the License. */
#include <algorithm>
#include <vector>
#include "paddle/phi/common/data_type.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/common_shape.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
......@@ -312,51 +314,6 @@ void CompareAllInferMeta(const MetaTensor& x,
out->set_dtype(DataType::BOOL);
}
void CrossInferMeta(const MetaTensor& x,
const MetaTensor& y,
int axis,
MetaTensor* out) {
auto x_dim = x.dims();
auto y_dim = y.dims();
auto dim = axis;
bool dims_match = phi::funcs::CheckDims(x_dim, y_dim);
PADDLE_ENFORCE_EQ(
dims_match,
true,
phi::errors::InvalidArgument("The 'shape' of Input(X) should be equal to "
"the 'shape' of Input(Y). But received "
"Input(X).dimensions = [%s], "
"Input(Y).dimensions = [%s]",
x_dim,
y_dim));
if (dim != DDim::kMaxRank) {
PADDLE_ENFORCE_EQ(
dim < x_dim.size() && dim >= (0 - x_dim.size()),
true,
phi::errors::OutOfRange(
"Attr(dim) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dim) = %d.",
x_dim.size(),
x_dim.size() - 1,
dim));
if (dim < 0) {
dim += x_dim.size();
}
PADDLE_ENFORCE_EQ(x_dim[dim] == 3 && y_dim[dim] == 3,
true,
phi::errors::InvalidArgument(
"Input(X/Y).dims()[dim] should be equal to 3."
"But received Input(X/Y).dims()[dim] = %d.",
x_dim[dim]));
}
out->set_dims(x_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
out->share_lod(x);
}
void ConvInferMeta(const MetaTensor& input,
const MetaTensor& filter,
const std::vector<int>& strides,
......@@ -512,6 +469,241 @@ void ConvInferMeta(const MetaTensor& input,
out->set_dtype(input.dtype());
}
void ConvTransposeInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* out,
MetaConfig config) {
auto x_dims = x.dims();
auto filter_dims = filter.dims();
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
const DataLayout data_layout =
config.is_run_mkldnn_kernel
? DataLayout::kNCHW
: paddle::framework::StringToDataLayout(data_format);
PADDLE_ENFORCE_EQ(
x_dims.size() == 4 || x_dims.size() == 5,
true,
errors::InvalidArgument("Input of Op(conv_transpose) should be 4-D or "
"5-D Tensor. But received: %u-D Tensor, "
"the shape of input is [%s]",
x_dims.size(),
x_dims));
PADDLE_ENFORCE_EQ(
x_dims.size(),
filter_dims.size(),
errors::InvalidArgument(
"The input's dimension size and filter's dimension size of "
"Op (conv_transpose) should be equal. But received: the shape of "
"input is [%s], the dimension size of input is [%d], the shape "
"of filter is [%s], the dimension size of filter is [%d]. ",
x_dims,
x_dims.size(),
filter_dims,
filter_dims.size()));
int stride_size = strides.size();
for (int i = 0; i < stride_size; ++i) {
PADDLE_ENFORCE_GT(
strides[i],
0,
errors::InvalidArgument(
"The stride of Op(Conv) should be larget than 0, but received "
"stride is %d.",
strides[i]));
}
int in_sub_stride_size = x_dims.size() - stride_size;
PADDLE_ENFORCE_EQ(
x_dims.size() - strides.size(),
2U,
errors::InvalidArgument(
"The input's dimension size minus Attr(stride)'s size must "
"be euqal to 2 for Op(conv_transpose). But received: [%d], the "
"input's dimension size is [%d], the shape of input "
"is [%s], the Attr(stride)'s size is [%d].",
in_sub_stride_size,
x_dims.size(),
x_dims,
strides.size()));
if (output_size.size())
PADDLE_ENFORCE_EQ(
output_size.size(),
strides.size(),
errors::InvalidArgument(
"The Attr(output_size) and Attr(stride) of Op(conv_transpose) "
"should be the same."));
if (output_padding.size())
PADDLE_ENFORCE_EQ(
output_padding.size(),
strides.size(),
errors::InvalidArgument(
"The Attr(output_padding) and Attr(stride) of Op(conv_transpose) "
"should be the same."));
const int64_t C =
(data_layout != DataLayout::kNHWC ? x_dims[1]
: x_dims[x_dims.size() - 1]);
PADDLE_ENFORCE_EQ(
C,
filter_dims[0],
errors::InvalidArgument(
"The number of input channels should be equal to filter channels "
"for Op(conv_transpose). But received: the input's channels is "
"[%d], the shape of input is [%s], the filter's channels is [%d], "
"the shape of filter is [%s]. The data_format is %s."
"The error may come from wrong data_format setting.",
C,
x_dims,
filter_dims[0],
filter_dims,
data_format));
DDim x_data_dims;
if (data_layout != DataLayout::kNHWC) {
x_data_dims = slice_ddim(x_dims, 2, x_dims.size());
} else {
x_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
}
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, x_data_dims, strides, ksize);
std::vector<int64_t> output_shape({x_dims[0]});
if (data_layout != DataLayout::kNHWC) {
output_shape.push_back(filter_dims[1] * groups);
}
const int offset = (data_layout != DataLayout::kNHWC ? 2 : 1);
for (size_t i = 0; i < strides.size(); ++i) {
auto filter_extent = dilations_[i] * (filter_dims[i + 2] - 1) + 1;
auto infer_shape = (config.is_runtime || x_dims[i + offset] > 0)
? (x_dims[i + offset] - 1) * strides[i] -
paddings_[2 * i] - paddings_[2 * i + 1] +
filter_extent
: -1;
if (output_size.size()) {
if (config.is_runtime) {
PADDLE_ENFORCE_GE(
output_size[i],
infer_shape,
errors::InvalidArgument(
"output_size of Op(ConvTransposeOp) should not be "
"less than the infered output size. But received output_size = "
"[%s], whose dim %d is less than the infered output size [%s]",
make_ddim(output_size).to_str(),
i,
infer_shape));
PADDLE_ENFORCE_LT(
output_size[i],
infer_shape + strides[i],
errors::InvalidArgument(
"output_size of Op(ConvTransposeOp) should be less "
"than infered size + stride. But received output_size = [%s], "
"whose dim %d is not less than the infered output size (%d) + "
"stride (%d) = %d",
make_ddim(output_size).to_str(),
i,
infer_shape,
strides[i],
infer_shape + strides[i]));
}
output_shape.push_back(output_size[i]);
} else if (output_padding.size()) {
if (config.is_runtime) {
PADDLE_ENFORCE_GE(
output_padding[i],
0,
errors::InvalidArgument(
"output_padding of Op(ConvTransposeOp) should not be "
"less than the 0. But received output_padding = "
"[%s], whose dim %d is less than 0",
make_ddim(output_padding).to_str(),
i));
PADDLE_ENFORCE_LT(
output_padding[i],
std::max(strides[i], dilations_[i]),
errors::InvalidArgument(
"output_padding of Op(ConvTransposeOp) should be less "
"than either stride or dilation. But received output_size = "
"[%s], "
"whose dim %d is not less than either stride (%d) or "
"dilation (%d)",
make_ddim(output_size).to_str(),
i,
strides[i],
dilations_[i]));
}
output_shape.push_back((infer_shape + output_padding[i]));
} else {
output_shape.push_back(infer_shape);
}
}
if (data_layout == DataLayout::kNHWC) {
output_shape.push_back(filter_dims[1] * groups);
}
out->set_dims(make_ddim(output_shape));
out->set_dtype(x.dtype());
}
void CrossInferMeta(const MetaTensor& x,
const MetaTensor& y,
int axis,
MetaTensor* out) {
auto x_dim = x.dims();
auto y_dim = y.dims();
auto dim = axis;
bool dims_match = phi::funcs::CheckDims(x_dim, y_dim);
PADDLE_ENFORCE_EQ(
dims_match,
true,
phi::errors::InvalidArgument("The 'shape' of Input(X) should be equal to "
"the 'shape' of Input(Y). But received "
"Input(X).dimensions = [%s], "
"Input(Y).dimensions = [%s]",
x_dim,
y_dim));
if (dim != DDim::kMaxRank) {
PADDLE_ENFORCE_EQ(
dim < x_dim.size() && dim >= (0 - x_dim.size()),
true,
phi::errors::OutOfRange(
"Attr(dim) is out of range, It's expected "
"to be in range of [-%d, %d]. But received Attr(dim) = %d.",
x_dim.size(),
x_dim.size() - 1,
dim));
if (dim < 0) {
dim += x_dim.size();
}
PADDLE_ENFORCE_EQ(x_dim[dim] == 3 && y_dim[dim] == 3,
true,
phi::errors::InvalidArgument(
"Input(X/Y).dims()[dim] should be equal to 3."
"But received Input(X/Y).dims()[dim] = %d.",
x_dim[dim]));
}
out->set_dims(x_dim);
out->set_dtype(x.dtype());
out->set_layout(x.layout());
out->share_lod(x);
}
void DistInferMeta(const MetaTensor& x,
const MetaTensor& y,
float p,
......
paddle/phi/infermeta/binary.h
浏览文件 @ 1eb96eec
......@@ -83,6 +83,19 @@ void ConvInferMeta(const MetaTensor& input,
MetaTensor* out,
MetaConfig config = MetaConfig());
void ConvTransposeInferMeta(const MetaTensor& x,
const MetaTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
MetaTensor* out,
MetaConfig config = MetaConfig());
void CrossInferMeta(const MetaTensor& x,
const MetaTensor& y,
int axis,
......
paddle/phi/kernels/conv_transpose_grad_kernel.h 0 → 100644
浏览文件 @ 1eb96eec
// 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.
#pragma once
#include <string>
#include <vector>
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void Conv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter);
template <typename T, typename Context>
void Conv2dTransposeDoubleGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const DenseTensor& ddx,
const DenseTensor& ddfilter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter,
DenseTensor* ddout);
template <typename T, typename Context>
void Conv3dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter);
template <typename T, typename Context>
void DepthwiseConv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter);
} // namespace phi
paddle/phi/kernels/conv_transpose_kernel.h 0 → 100644
浏览文件 @ 1eb96eec
// 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.
#pragma once
#include <string>
#include <vector>
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void Conv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out);
template <typename T, typename Context>
void Conv3dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out);
template <typename T, typename Context>
void DepthwiseConv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out);
} // namespace phi
paddle/phi/kernels/cpu/conv_transpose_grad_kernel.cc 0 → 100644
浏览文件 @ 1eb96eec
// 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/kernels/conv_transpose_grad_kernel.h"
#include "paddle/phi/kernels/impl/conv_transpose_grad_kernel_impl.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void DepthwiseConv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
ConvTransposeGradRawKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
dx,
dfilter);
}
} // namespace phi
PD_REGISTER_KERNEL(conv2d_transpose_grad,
CPU,
ALL_LAYOUT,
phi::Conv2dTransposeGradKernel,
float,
double) {}
PD_REGISTER_KERNEL(conv3d_transpose_grad,
CPU,
ALL_LAYOUT,
phi::Conv3dTransposeGradKernel,
float,
double) {}
PD_REGISTER_KERNEL(depthwise_conv2d_transpose_grad,
CPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeGradKernel,
float,
double) {}
paddle/phi/kernels/cpu/conv_transpose_kernel.cc 0 → 100644
浏览文件 @ 1eb96eec
// 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/kernels/conv_transpose_kernel.h"
#include "paddle/phi/kernels/impl/conv_transpose_kernel_impl.h"
#include "paddle/phi/core/kernel_registry.h"
namespace phi {
template <typename T, typename Context>
void DepthwiseConv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
ConvTransposeRawKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
} // namespace phi
PD_REGISTER_KERNEL(conv2d_transpose,
CPU,
ALL_LAYOUT,
phi::Conv2dTransposeKernel,
float,
double) {}
PD_REGISTER_KERNEL(conv3d_transpose,
CPU,
ALL_LAYOUT,
phi::Conv3dTransposeKernel,
float,
double) {}
PD_REGISTER_KERNEL(depthwise_conv2d_transpose,
CPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeKernel,
float,
double) {}
paddle/phi/kernels/funcs/slice.h
浏览文件 @ 1eb96eec
......@@ -123,5 +123,56 @@ DenseTensor Slice(const Context& dev_ctx,
return ret;
}
// Use in conv_transpose kernel
template <typename Context, typename T, size_t D>
static void Slice(const Context& ctx,
const DenseTensor* input,
DenseTensor* out,
const std::vector<int64_t>& begin_vec,
const std::vector<int64_t>& end_vec,
const std::vector<int64_t>& axes_vec) {
auto& place = *ctx.eigen_device();
auto in_dims = input->dims();
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
auto extents = Eigen::DSizes<Eigen::DenseIndex, D>();
for (size_t i = 0; i < D; ++i) {
offsets[i] = 0;
extents[i] = in_dims[i];
}
std::vector<int64_t> out_shape_vec = vectorize(in_dims);
for (size_t i = 0; i < axes_vec.size(); ++i) {
offsets[axes_vec[i]] = begin_vec[i];
extents[axes_vec[i]] = end_vec[i] - begin_vec[i];
out_shape_vec[axes_vec[i]] = end_vec[i] - begin_vec[i];
}
DDim out_dims(make_ddim(out_shape_vec));
out->Resize(out_dims);
ctx.template Alloc<T>(out);
auto in_t =
EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(*input);
auto out_t = EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
*out, out_dims);
funcs::EigenSlice<std::decay_t<decltype(place)>, T, D>::Eval(
place, out_t, in_t, offsets, extents);
out->Resize(out_dims);
}
template <typename Context, typename T, size_t D>
static void Slice(const Context& ctx,
const DenseTensor* input,
DenseTensor* out,
int64_t begin_idx,
int64_t end_idx,
int64_t axes) {
std::vector<int64_t> begin_vec = {begin_idx};
std::vector<int64_t> end_vec = {end_idx};
std::vector<int64_t> axes_vec = {axes};
Slice<Context, T, D>(ctx, input, out, begin_vec, end_vec, axes_vec);
}
} // namespace funcs
} // namespace phi
paddle/phi/kernels/gpu/conv_transpose_grad_kernel.cu 0 → 100644
浏览文件 @ 1eb96eec
// 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/kernels/conv_transpose_grad_kernel.h"
#include "paddle/phi/kernels/impl/conv_transpose_grad_kernel_impl.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/gpu/depthwise_conv.h"
namespace phi {
template <typename T, typename Context>
void Conv2dTransposeDoubleGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const DenseTensor& ddx,
const DenseTensor& ddfilter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter,
DenseTensor* ddout) {
ConvTransposeGradRawKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
dx,
dfilter);
}
template <typename T, typename Context>
void DepthwiseConv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_format);
DenseTensor filter_ = filter;
if (!dx && !dfilter) {
return;
}
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
auto x_dims = x.dims();
auto filter_dims = filter_.dims();
DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
} else {
in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
}
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
if (dx) {
paddle::operators::math::DepthwiseConvFunctor<Context, T> depthwiseConv;
depthwiseConv(ctx,
dout,
filter_,
strides,
std::vector<int>{
paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
dilations_,
dx,
data_layout);
}
if (dfilter) {
funcs::SetConstant<Context, T> set_zero;
ctx.template Alloc<T>(dfilter);
set_zero(ctx, dfilter, static_cast<T>(0));
paddle::operators::math::DepthwiseConvFilterGradFunctor<Context, T>
depthwiseConvFilterGrad;
depthwiseConvFilterGrad(
ctx,
dout,
x,
strides,
std::vector<int>{
paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
dilations_,
dfilter,
data_layout);
}
}
} // namespace phi
PD_REGISTER_KERNEL(conv2d_transpose_grad,
GPU,
ALL_LAYOUT,
phi::Conv2dTransposeGradKernel,
float,
double) {}
PD_REGISTER_KERNEL(conv2d_transpose_grad_grad,
GPU,
ALL_LAYOUT,
phi::Conv2dTransposeDoubleGradKernel,
float,
double) {}
PD_REGISTER_KERNEL(conv3d_transpose_grad,
GPU,
ALL_LAYOUT,
phi::Conv3dTransposeGradKernel,
float,
double) {}
PD_REGISTER_KERNEL(depthwise_conv2d_transpose_grad,
GPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeGradKernel,
float,
double) {}
paddle/phi/kernels/gpu/conv_transpose_kernel.cu 0 → 100644
浏览文件 @ 1eb96eec
// 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/kernels/conv_transpose_kernel.h"
#include "paddle/phi/kernels/impl/conv_transpose_kernel_impl.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/gpu/depthwise_conv.h"
namespace phi {
template <typename T, typename Context>
void DepthwiseConv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_format);
DenseTensor filter_ = filter;
ctx.template Alloc<T>(out);
PADDLE_ENFORCE_EQ(
groups,
filter_.dims()[0],
errors::InvalidArgument(
"groups should be error to the 1st dimension of filter_. But "
"received groups is %d and filter dimension[0] is %d",
groups,
filter_.dims()[0]));
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
for (auto v : dilations_) {
PADDLE_ENFORCE_EQ(
v,
1,
errors::InvalidArgument("dilations should be 1 in depthwise conv. "
"But received dilations is %d",
v));
}
auto x_dims = x.dims();
auto filter_dims = filter_.dims();
DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
} else {
in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
}
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
ctx.template Alloc<T>(out);
funcs::SetConstant<Context, T> set_zero;
set_zero(ctx, out, static_cast<T>(0));
paddle::operators::math::DepthwiseConvInputGradFunctor<Context, T>
depthwiseConvInputGrad;
depthwiseConvInputGrad(
ctx,
*out,
filter,
x,
strides,
std::vector<int>{paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
dilations_,
out,
data_layout);
}
} // namespace phi
PD_REGISTER_KERNEL(conv2d_transpose,
GPU,
ALL_LAYOUT,
phi::Conv2dTransposeKernel,
float,
double) {}
PD_REGISTER_KERNEL(conv3d_transpose,
GPU,
ALL_LAYOUT,
phi::Conv3dTransposeKernel,
float,
double) {}
PD_REGISTER_KERNEL(depthwise_conv2d_transpose,
GPU,
ALL_LAYOUT,
phi::DepthwiseConv2dTransposeKernel,
float,
double) {}
paddle/phi/kernels/gpudnn/conv_transpose_grad_kernel.cu 0 → 100644
浏览文件 @ 1eb96eec
/* 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/kernels/conv_transpose_grad_kernel.h"
#include <algorithm>
#include "paddle/phi/backends/dynload/cudnn.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/batch_norm_utils.h"
#include "paddle/phi/kernels/funcs/math_function.h"
#include "paddle/phi/kernels/funcs/padding.h"
#include "paddle/phi/kernels/funcs/slice.h"
#include "paddle/phi/kernels/transpose_kernel.h"
#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/operators/conv_miopen_helper.h"
#include "paddle/fluid/platform/device/gpu/rocm/miopen_helper.h"
#else
#include "paddle/fluid/operators/conv_cudnn_helper.h"
#include "paddle/fluid/platform/device/gpu/cuda/cudnn_helper.h"
#endif
namespace phi {
using GPUDNNDataLayout = paddle::platform::DataLayout;
template <typename T, typename Context>
void ConvTransposeGradRawGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
const T* filter_data = filter.data<T>();
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ =
dilations; // cudnn v5 does not support dilations
const GPUDNNDataLayout data_layout =
(data_format != "NHWC" ? GPUDNNDataLayout::kNCHW
: GPUDNNDataLayout::kNHWC);
// if channel_last, transpose to channel_first
DenseTensor x_transpose;
DenseTensor dout_transpose;
std::vector<int> x_vec = vectorize<int>(x.dims());
std::vector<int> out_vec = vectorize<int>(dout.dims());
if (data_layout == GPUDNNDataLayout::kNHWC) {
if (strides.size() == 2U) {
std::vector<int> axis = {0, 3, 1, 2};
for (size_t i = 0; i < axis.size(); ++i) {
x_vec[i] = x.dims()[axis[i]];
out_vec[i] = dout.dims()[axis[i]];
}
x_transpose = Transpose<T, Context>(ctx, x, axis);
dout_transpose = Transpose<T, Context>(ctx, dout, axis);
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 4, 1, 2, 3};
for (size_t i = 0; i < axis.size(); ++i) {
x_vec[i] = x.dims()[axis[i]];
out_vec[i] = dout.dims()[axis[i]];
}
x_transpose = Transpose<T, Context>(ctx, x, axis);
dout_transpose = Transpose<T, Context>(ctx, dout, axis);
}
} else {
x_transpose = x;
dout_transpose = dout;
}
// update padding and dilation
auto x_dims = x_transpose.dims();
auto filter_dims = filter.dims();
DDim x_data_dims;
x_data_dims = slice_ddim(x_dims, 2, x_dims.size());
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, x_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = funcs::IsSymmetricPadding(paddings_, data_dim);
std::vector<int> x_pad(x_dims.size() * 2, 0);
DenseTensor transformed_dout;
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_dout_shape_vec(data_dim + 2);
new_dout_shape_vec[0] = dout_transpose.dims()[0];
new_dout_shape_vec[1] = dout_transpose.dims()[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings_[2 * i] - paddings_[2 * i + 1]);
padding_common[i] = std::min(paddings_[2 * i], paddings_[2 * i + 1]);
new_dout_shape_vec[i + 2] =
dout_transpose.dims()[i + 2] + padding_diff[i];
x_pad[2 * i + 4] = paddings_[2 * i] - padding_common[i];
x_pad[2 * i + 4 + 1] = paddings_[2 * i + 1] - padding_common[i];
}
transformed_dout.Resize(make_ddim(new_dout_shape_vec));
ctx.template Alloc<T>(&transformed_dout);
const int rank = x_transpose.dims().size();
T pad_value(0.0);
switch (rank) {
case 4: {
funcs::PadFunction<Context, T, 4>(
ctx, x_pad, dout_transpose, pad_value, &transformed_dout);
} break;
case 5: {
funcs::PadFunction<Context, T, 5>(
ctx, x_pad, dout_transpose, pad_value, &transformed_dout);
} break;
default:
PADDLE_THROW(errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D x DenseTensor."));
}
} else {
transformed_dout = dout_transpose;
if (paddings_.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[2 * i];
}
}
}
const T* x_data = x_transpose.data<T>();
const T* dout_data = transformed_dout.data<T>();
out_vec = vectorize<int>(transformed_dout.dims());
// ------------------- cudnn descriptors ---------------------
GPUDNNDataLayout layout;
if (strides.size() == 2U) {
layout = GPUDNNDataLayout::kNCHW;
} else {
layout = GPUDNNDataLayout::kNCDHW;
}
int iwo_groups = groups;
int c_groups = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups = 1;
c_groups = groups;
groups = 1;
#endif
auto dtype = paddle::platform::CudnnDataType<T>::type;
paddle::operators::ConvArgs args1{&transformed_dout,
&filter,
&x_transpose,
strides,
padding_common,
dilations_,
dtype};
paddle::operators::ConvArgs args2{&transformed_dout,
&filter,
&x_transpose,
strides,
padding_common,
dilations_,
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvFwdAlgorithm_t data_algo{};
miopenConvBwdWeightsAlgorithm_t filter_algo{};
#else
cudnnConvolutionFwdAlgo_t data_algo{};
cudnnConvolutionBwdFilterAlgo_t filter_algo{};
#endif
auto layout_tensor = paddle::platform::GetCudnnTensorFormat(layout);
size_t workspace_size = 0;
auto handle = ctx.cudnn_handle();
bool deterministic = FLAGS_cudnn_deterministic;
T* dx_data = nullptr;
T* dfilter_data = nullptr;
if (dx) {
dx_data = ctx.template Alloc<T>(dx);
args1.handle = handle;
args1.idesc.set(transformed_dout, iwo_groups);
args1.wdesc.set(filter, layout_tensor, iwo_groups);
args1.odesc.set(x_transpose, iwo_groups);
args1.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_groups);
#ifdef PADDLE_WITH_HIP
using search1 =
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = std::max(workspace_size, search1::GetWorkspaceSize(args1));
data_algo =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search1::GetWorkspaceSize(args1, data_algo));
#endif
}
if (dfilter) {
dfilter_data = ctx.template Alloc<T>(dfilter);
args2.handle = handle;
args2.idesc.set(transformed_dout, iwo_groups);
args2.wdesc.set(*dfilter, layout_tensor, iwo_groups);
args2.odesc.set(x_transpose, iwo_groups);
args2.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_groups);
#ifdef PADDLE_WITH_HIP
using search2 =
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size = std::max(workspace_size, search2::GetWorkspaceSize(args2));
filter_algo =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2, filter_algo));
#endif
}
// ------------------- cudnn conv backward data ---------------------
// FIxME(typhoonzero): template type T may not be the same as cudnn call.
int x_offset = x.numel() / x.dims()[0] / groups;
int dout_offset =
transformed_dout.numel() / transformed_dout.dims()[0] / groups;
int filter_offset = filter.numel() / groups;
paddle::operators::ScalingParamType<T> alpha = 1.0f;
paddle::operators::ScalingParamType<T> beta = 0.0f;
auto workspace_handle = ctx.cudnn_workspace_handle();
if (dx) {
// Because beta is zero, it is unnecessary to reset dx.
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
dynload::miopenConvolutionForward(handle,
&alpha,
args1.idesc.desc(),
dout_data + dout_offset * g,
args1.wdesc.desc(),
filter_data + filter_offset * g,
args1.cdesc.desc(),
data_algo,
&beta,
args1.odesc.desc(),
dx_data + x_offset * g,
cudnn_workspace,
workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(
dynload::cudnnConvolutionForward(handle,
&alpha,
args1.idesc.desc(),
dout_data + dout_offset * g,
args1.wdesc.desc(),
filter_data + filter_offset * g,
args1.cdesc.desc(),
data_algo,
cudnn_workspace,
workspace_size,
&beta,
args1.odesc.desc(),
dx_data + x_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
if (data_layout == GPUDNNDataLayout::kNHWC) {
DenseTensor dx_transpose;
DenseTensor dx_nchw;
dx_nchw.ShareDataWith(*dx);
dx_nchw.Resize(make_ddim(x_vec));
if (strides.size() == 2U) {
std::vector<int> axis = {0, 2, 3, 1};
dx_transpose = Transpose<T, Context>(ctx, dx_nchw, axis);
*dx = dx_transpose;
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 2, 3, 4, 1};
dx_transpose = Transpose<T, Context>(ctx, dx_nchw, axis);
*dx = dx_transpose;
}
}
}
// ------------------- cudnn conv backward filter ---------------------
if (dfilter) {
// Because beta is zero, it is unnecessary to reset dfilter.
// Gradient with respect to the filter
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenConvolutionBackwardWeights(
handle,
&alpha,
args2.odesc.desc(),
x_data + x_offset * g,
args2.idesc.desc(),
dout_data + dout_offset * g,
args2.cdesc.desc(),
filter_algo,
&beta,
args2.wdesc.desc(),
dfilter_data + filter_offset * g,
cudnn_workspace,
workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionBackwardFilter(
handle,
&alpha,
args2.idesc.desc(),
dout_data + dout_offset * g,
args2.odesc.desc(),
x_data + x_offset * g,
args2.cdesc.desc(),
filter_algo,
cudnn_workspace,
workspace_size,
&beta,
args2.wdesc.desc(),
dfilter_data + filter_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
}
}
template <typename T, typename Context>
void Conv2dTransposeGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings_,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations_,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
ConvTransposeGradRawGPUDNNKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings_,
padding_algorithm,
groups,
dilations_,
data_format,
dx,
dfilter);
}
/*
* Inputs: I, filter, dout, ddI, ddfilter
* Outputs: ddout, dfilter, dI
* ddo = conv_bp_data(filter, ddI) + conv_bp_data(ddfilter, I)
* dfilter = conv_bp_filter(dout, ddI)
* dI = conv(dout, ddfilter)
*/
template <typename T, typename Context>
void Conv2dTransposeDoubleGradGPUDNNKernel(
const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const DenseTensor& ddx,
const DenseTensor& ddfilter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter,
DenseTensor* ddout) {
if (dx) {
ctx.template Alloc<T>(dx);
}
if (dfilter) {
ctx.template Alloc<T>(dfilter);
}
if (ddout) {
ctx.template Alloc<T>(ddout);
funcs::SetConstant<Context, T> set_zero;
set_zero(ctx, ddout, static_cast<T>(0));
}
const T* filter_ = filter.data<T>();
const T* dout_ = dout.data<T>();
const T* ddx_ = nullptr;
const T* ddfilter_ = nullptr;
T* dx_ = nullptr;
T* dfilter_ = nullptr;
T* ddout_ = nullptr;
T* transformed_dx_ = nullptr;
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
bool deterministic = FLAGS_cudnn_deterministic;
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
// transform DenseTensors to channel first-----------
DenseTensor transformed_x_channel(x.type());
DenseTensor transformed_dout_channel(dout.type());
DenseTensor transformed_ddx_channel(x.type());
DenseTensor transformed_dx_channel(x.type());
DenseTensor transformed_ddout_channel(dout.type());
if (channel_last) {
ResizeToChannelFirst<Context, T>(ctx, &x, &transformed_x_channel);
TransToChannelFirst<Context, T>(ctx, &x, &transformed_x_channel);
ResizeToChannelFirst<Context, T>(ctx, &dout, &transformed_dout_channel);
TransToChannelFirst<Context, T>(ctx, &dout, &transformed_dout_channel);
ResizeToChannelFirst<Context, T>(ctx, &ddx, &transformed_ddx_channel);
TransToChannelFirst<Context, T>(ctx, &ddx, &transformed_ddx_channel);
if (dx) {
ResizeToChannelFirst<Context, T>(ctx, dx, &transformed_dx_channel);
ctx.template Alloc<T>(&transformed_dx_channel);
}
if (ddout) {
ResizeToChannelFirst<Context, T>(ctx, ddout, &transformed_ddout_channel);
}
} else {
transformed_x_channel = x;
transformed_dout_channel = dout;
transformed_ddx_channel = ddx;
if (dx) {
transformed_dx_channel = *dx;
}
}
std::vector<int> out_vec = vectorize<int>(transformed_dout_channel.dims());
auto x_dims = transformed_x_channel.dims();
auto filter_dims = filter.dims();
DDim x_data_dims = slice_ddim(x_dims, 2, x_dims.size());
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, x_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = funcs::IsSymmetricPadding(paddings_, data_dim);
DenseTensor transformed_x(x.type());
DenseTensor transformed_ddx(x.type());
DenseTensor transformed_dout(dout.type());
std::vector<int> padding_common(data_dim, 0);
std::vector<int> input_pad(x.dims().size() * 2, 0);
if (!is_sys_pad) {
// get pad
std::vector<int> padding_diff(data_dim);
std::vector<int> new_input_shape_vec(data_dim + 2);
std::vector<int> new_output_grad_shape_vec(data_dim + 2);
new_input_shape_vec[0] = transformed_x_channel.dims()[0];
new_input_shape_vec[1] = transformed_x_channel.dims()[1];
new_output_grad_shape_vec[0] = transformed_dout_channel.dims()[0];
new_output_grad_shape_vec[1] = transformed_dout_channel.dims()[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings_[2 * i] - paddings_[2 * i + 1]);
padding_common[i] = std::min(paddings_[2 * i], paddings_[2 * i + 1]);
new_input_shape_vec[i + 2] =
transformed_x_channel.dims()[i + 2] + padding_diff[i];
new_output_grad_shape_vec[i + 2] =
transformed_dout_channel.dims()[i + 2] + padding_diff[i];
input_pad[2 * i + 4] = paddings_[2 * i] - padding_common[i];
input_pad[2 * i + 4 + 1] = paddings_[2 * i + 1] - padding_common[i];
}
DDim new_input_shape(make_ddim(new_input_shape_vec));
transformed_x.Resize(new_input_shape);
transformed_ddx.Resize(new_input_shape);
transformed_dout.Resize(make_ddim(new_output_grad_shape_vec));
ctx.template Alloc<T>(&transformed_x);
ctx.template Alloc<T>(&transformed_ddx);
ctx.template Alloc<T>(&transformed_dout);
// pad for input
const int rank = x.dims().size();
T pad_value(0.0);
switch (rank) {
case 4: {
funcs::PadFunction<Context, T, 4>(
ctx, input_pad, transformed_x_channel, pad_value, &transformed_x);
funcs::PadFunction<Context, T, 4>(ctx,
input_pad,
transformed_dout_channel,
pad_value,
&transformed_dout);
funcs::PadFunction<Context, T, 4>(ctx,
input_pad,
transformed_ddx_channel,
pad_value,
&transformed_ddx);
} break;
case 5: {
funcs::PadFunction<Context, T, 5>(
ctx, input_pad, transformed_x_channel, pad_value, &transformed_x);
funcs::PadFunction<Context, T, 5>(ctx,
input_pad,
transformed_ddx_channel,
pad_value,
&transformed_ddx);
} break;
default:
PADDLE_THROW(errors::InvalidArgument(
"ConvOp only support tensors with 4 or 5 dimensions."));
}
} else {
transformed_x = transformed_x_channel;
transformed_dout = transformed_dout_channel;
transformed_ddx = transformed_ddx_channel;
if (paddings_.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[2 * i];
}
}
}
std::vector<int64_t> starts(data_dim, 0);
std::vector<int64_t> ends(data_dim, 0);
std::vector<int64_t> axes(data_dim, 0);
for (size_t i = 0; i < data_dim; ++i) {
starts[i] = input_pad[2 * i + 4] * (strides[i] + 1);
ends[i] = starts[i] + out_vec[i + 2];
axes[i] = i + 2;
}
std::vector<int> transformed_out_vec = out_vec;
for (size_t i = 0; i < data_dim; ++i) {
transformed_out_vec[i + 2] =
out_vec[i + 2] +
(input_pad[2 * i + 4] + input_pad[2 * i + 5]) * strides[i] -
2 * padding_common[i] + paddings_[2 * i] + paddings_[2 * i + 1];
}
if (!is_sys_pad) {
transformed_ddout_channel.Resize(make_ddim(transformed_out_vec));
ctx.template Alloc<T>(&transformed_ddout_channel);
} else {
ctx.template Alloc<T>(ddout);
transformed_ddout_channel = *ddout;
transformed_ddout_channel.Resize(make_ddim(transformed_out_vec));
}
const T* x_ = transformed_x.data<T>();
int iwo_group = groups;
int c_group = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_group = 1;
c_group = groups;
groups = 1;
#endif
auto dtype = paddle::platform::CudnnDataType<T>::type;
auto handle = ctx.cudnn_handle();
paddle::operators::ConvArgs args1{&transformed_ddout_channel,
&filter,
&transformed_ddx,
strides,
padding_common,
dilations_,
dtype};
paddle::operators::ConvArgs args2{&transformed_ddout_channel,
&ddfilter,
&transformed_x,
strides,
padding_common,
dilations_,
dtype};
paddle::operators::ConvArgs args3{&transformed_dout,
dfilter,
&transformed_ddx_channel,
strides,
padding_common,
dilations_,
dtype};
paddle::operators::ConvArgs args4{&transformed_dout,
&ddfilter,
&transformed_dx_channel,
strides,
padding_common,
dilations_,
dtype};
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t bwd_algo1 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvBwdDataAlgorithm_t bwd_algo2 =
static_cast<miopenConvBwdDataAlgorithm_t>(0);
miopenConvFwdAlgorithm_t data_algo = static_cast<miopenConvFwdAlgorithm_t>(0);
miopenConvBwdWeightsAlgorithm_t filter_algo =
static_cast<miopenConvBwdWeightsAlgorithm_t>(0);
#else
cudnnConvolutionBwdDataAlgo_t bwd_algo1 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionBwdDataAlgo_t bwd_algo2 =
static_cast<cudnnConvolutionBwdDataAlgo_t>(0);
cudnnConvolutionFwdAlgo_t data_algo =
static_cast<cudnnConvolutionFwdAlgo_t>(0);
cudnnConvolutionBwdFilterAlgo_t filter_algo =
static_cast<cudnnConvolutionBwdFilterAlgo_t>(0);
#endif
auto layout = paddle::platform::GetCudnnTensorFormat(GPUDNNDataLayout::kNCHW);
// ddo = conv(ddI, filter) + conv(I, ddfilter)
size_t workspace_size = 0;
T* transformed_ddout_channel_ = nullptr;
if (ddout) {
ddout_ = ddout->data<T>();
transformed_ddout_channel_ = transformed_ddout_channel.data<T>();
args1.handle = handle;
args1.idesc.set(transformed_ddout_channel, iwo_group);
args1.wdesc.set(filter, layout, iwo_group);
args1.odesc.set(transformed_ddx, iwo_group);
args1.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_group);
#ifdef PADDLE_WITH_HIP
using search1 =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = search1::GetWorkspaceSize(args1);
bwd_algo1 =
search1::Find<T>(args1, false, deterministic, workspace_size, ctx);
#else
using search1 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo1 = search1::Find<T>(args1, false, deterministic, ctx);
workspace_size = search1::GetWorkspaceSize(args1, bwd_algo1);
#endif
ddfilter_ = ddfilter.data<T>();
args2.handle = handle;
args2.idesc.set(transformed_ddout_channel, iwo_group);
args2.wdesc.set(ddfilter, layout, iwo_group);
args2.odesc.set(transformed_x, iwo_group);
args2.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_group);
#ifdef PADDLE_WITH_HIP
using search2 =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search2::GetWorkspaceSize(args2));
bwd_algo2 =
search2::Find<T>(args2, false, deterministic, workspace_size, ctx);
#else
using search2 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
bwd_algo2 = search2::Find<T>(args2, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search2::GetWorkspaceSize(args2, bwd_algo2));
#endif
}
if (dfilter) {
dfilter_ = dfilter->data<T>();
args3.handle = handle;
args3.idesc.set(transformed_dout, iwo_group);
args3.wdesc.set(*dfilter, layout, iwo_group);
args3.odesc.set(transformed_ddx_channel, iwo_group);
args3.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_group);
#ifdef PADDLE_WITH_HIP
using search3 =
paddle::operators::SearchAlgorithm<miopenConvBwdWeightsAlgorithm_t>;
workspace_size = std::max(workspace_size, search3::GetWorkspaceSize(args3));
filter_algo =
search3::Find<T>(args3, false, deterministic, workspace_size, ctx);
#else
using search3 =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdFilterAlgoPerf_t>;
filter_algo = search3::Find<T>(args3, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search3::GetWorkspaceSize(args3, filter_algo));
#endif
}
if (dx) {
transformed_dx_ = transformed_dx_channel.data<T>();
args4.handle = handle;
args4.idesc.set(transformed_dout, iwo_group);
args4.wdesc.set(ddfilter, layout, iwo_group);
args4.odesc.set(transformed_dx_channel, iwo_group);
args4.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_group);
#ifdef PADDLE_WITH_HIP
using search4 =
paddle::operators::SearchAlgorithm<miopenConvFwdAlgorithm_t>;
workspace_size = std::max(workspace_size, search4::GetWorkspaceSize(args4));
data_algo =
search4::Find<T>(args4, false, deterministic, workspace_size, ctx);
#else
using search4 =
paddle::operators::SearchAlgorithm<cudnnConvolutionFwdAlgoPerf_t>;
data_algo = search4::Find<T>(args4, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search4::GetWorkspaceSize(args4, data_algo));
#endif
}
int i_n, i_c, i_d, i_h, i_w;
paddle::operators::GetNCDHW(transformed_x.dims(),
GPUDNNDataLayout::kNCHW,
&i_n,
&i_c,
&i_d,
&i_h,
&i_w);
int o_n, o_c, o_d, o_h, o_w;
paddle::operators::GetNCDHW(transformed_dout.dims(),
GPUDNNDataLayout::kNCHW,
&o_n,
&o_c,
&o_d,
&o_h,
&o_w);
int group_offset_in =
transformed_x.numel() / transformed_x.dims()[0] / groups;
int group_offset_out =
transformed_dout.numel() / transformed_dout.dims()[0] / groups;
int group_offset_filter = filter.numel() / groups;
paddle::operators::ScalingParamType<T> alpha = 1.0f;
paddle::operators::ScalingParamType<T> beta = 0.0f;
auto wkspace_handle = ctx.cudnn_workspace_handle();
if (ddout) {
ddx_ = transformed_ddx.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenConvolutionBackwardData(
handle,
&alpha,
args1.odesc.desc(),
ddx_ + i * group_offset_in,
args1.wdesc.desc(),
filter_ + i * group_offset_filter,
args1.cdesc.desc(),
bwd_algo1,
&beta,
args1.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out,
workspace_ptr,
workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionBackwardData(
handle,
&alpha,
args1.wdesc.desc(),
filter_ + i * group_offset_filter,
args1.odesc.desc(),
ddx_ + i * group_offset_in,
args1.cdesc.desc(),
bwd_algo1,
workspace_ptr,
workspace_size,
&beta,
args1.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
// MIOPEN ONLY support beta to be 0.0f
DenseTensor conv_x_ddfilter(dout.type());
conv_x_ddfilter.Resize(transformed_ddout_channel.dims());
T* conv_x_ddfilter_data = ctx.template Alloc<T>(&conv_x_ddfilter);
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenConvolutionBackwardData(
handle,
&alpha,
args2.odesc.desc(),
x_ + i * group_offset_in,
args2.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args2.cdesc.desc(),
bwd_algo2,
&beta,
args2.idesc.desc(),
conv_x_ddfilter_data + i * group_offset_out,
workspace_ptr,
workspace_size));
},
workspace_size);
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenOpTensor(
handle,
miopenTensorOpAdd,
&alpha,
args2.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out,
&alpha,
args2.idesc.desc(),
conv_x_ddfilter_data + i * group_offset_out,
&beta,
args2.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out));
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionBackwardData(
handle,
&alpha,
args2.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args2.odesc.desc(),
x_ + i * group_offset_in,
args2.cdesc.desc(),
bwd_algo2,
workspace_ptr,
workspace_size,
&alpha,
args2.idesc.desc(),
transformed_ddout_channel_ + i * group_offset_out));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
if ((!is_sys_pad) && (!channel_last)) {
if (strides.size() == 2U) {
funcs::Slice<Context, T, 4>(
ctx, &transformed_ddout_channel, ddout, starts, ends, axes);
} else if (!is_sys_pad && strides.size() == 3U) {
funcs::Slice<Context, T, 5>(
ctx, &transformed_ddout_channel, ddout, starts, ends, axes);
}
} else if ((!is_sys_pad) && (channel_last)) {
if (strides.size() == 2U) {
funcs::Slice<Context, T, 4>(ctx,
&transformed_ddout_channel,
&transformed_ddout_channel,
starts,
ends,
axes);
} else if (!is_sys_pad && strides.size() == 3U) {
funcs::Slice<Context, T, 5>(ctx,
&transformed_ddout_channel,
&transformed_ddout_channel,
starts,
ends,
axes);
}
TransToChannelLast<Context, T>(ctx, &transformed_ddout_channel, ddout);
}
}
T* transformed_dout_channel_ = transformed_dout.data<T>();
if (dfilter) {
ddx_ = transformed_ddx_channel.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(
dynload::miopenConvolutionBackwardWeights(
handle,
&alpha,
args3.odesc.desc(),
ddx_ + i * group_offset_in,
args3.idesc.desc(),
transformed_dout_channel_ + i * group_offset_out,
args3.cdesc.desc(),
filter_algo,
&beta,
args3.wdesc.desc(),
dfilter_ + i * group_offset_filter,
workspace_ptr,
workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionBackwardFilter(
handle,
&alpha,
args3.idesc.desc(),
transformed_dout_channel_ + i * group_offset_out,
args3.odesc.desc(),
ddx_ + i * group_offset_in,
args3.cdesc.desc(),
filter_algo,
workspace_ptr,
workspace_size,
&beta,
args3.wdesc.desc(),
dfilter_ + i * group_offset_filter));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
}
if (dx) {
ddfilter_ = ddfilter.data<T>();
for (int i = 0; i < groups; i++) {
#ifdef PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenConvolutionForward(
handle,
&alpha,
args4.idesc.desc(),
transformed_dout_channel_ + i * group_offset_out,
args4.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args4.cdesc.desc(),
data_algo,
&beta,
args4.odesc.desc(),
transformed_dx_ + i * group_offset_in,
workspace_ptr,
workspace_size));
},
workspace_size);
#else // PADDLE_WITH_HIP
wkspace_handle.RunFunc(
[&](void* workspace_ptr) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionForward(
handle,
&alpha,
args4.idesc.desc(),
transformed_dout_channel_ + i * group_offset_out,
args4.wdesc.desc(),
ddfilter_ + i * group_offset_filter,
args4.cdesc.desc(),
data_algo,
workspace_ptr,
workspace_size,
&beta,
args4.odesc.desc(),
transformed_dx_ + i * group_offset_in));
},
workspace_size);
#endif // PADDLE_WITH_HIP
}
if (channel_last) {
TransToChannelLast<Context, T>(ctx, &transformed_dx_channel, dx);
}
}
}
template <typename T, typename Context>
void Conv3dTransposeGradGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings_,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations_,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
ConvTransposeGradRawGPUDNNKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings_,
padding_algorithm,
groups,
dilations_,
data_format,
dx,
dfilter);
}
} // namespace phi
using float16 = phi::dtype::float16;
#ifdef PADDLE_WITH_HIP
// MIOPEN do not support double
PD_REGISTER_KERNEL(conv2d_transpose_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeGradGPUDNNKernel,
float,
float16) {}
PD_REGISTER_KERNEL(conv2d_transpose_grad_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeDoubleGradGPUDNNKernel,
float,
float16) {}
PD_REGISTER_KERNEL(conv3d_transpose_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv3dTransposeGradGPUDNNKernel,
float,
float16) {}
#else
PD_REGISTER_KERNEL(conv2d_transpose_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeGradGPUDNNKernel,
float,
double,
float16) {}
PD_REGISTER_KERNEL(conv2d_transpose_grad_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeDoubleGradGPUDNNKernel,
float,
double,
float16) {}
PD_REGISTER_KERNEL(conv3d_transpose_grad,
GPUDNN,
ALL_LAYOUT,
phi::Conv3dTransposeGradGPUDNNKernel,
float,
double,
float16) {}
#endif
paddle/phi/kernels/gpudnn/conv_transpose_kernel.cu 0 → 100644
浏览文件 @ 1eb96eec
/* 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/kernels/conv_transpose_kernel.h"
#include <algorithm>
#include "paddle/phi/backends/dynload/cudnn.h"
#include "paddle/phi/common/float16.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/core/kernel_registry.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/padding.h"
#include "paddle/phi/kernels/funcs/slice.h"
#include "paddle/phi/kernels/transpose_kernel.h"
#ifdef PADDLE_WITH_HIP
#include "paddle/fluid/operators/conv_miopen_helper.h"
#include "paddle/fluid/platform/device/gpu/rocm/miopen_helper.h"
#else
#include "paddle/fluid/operators/conv_cudnn_helper.h"
#include "paddle/fluid/platform/device/gpu/cuda/cudnn_helper.h"
#endif
namespace phi {
using GPUDNNDataLayout = paddle::platform::DataLayout;
template <typename T, typename Context>
void ConvTransposeRawGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ =
dilations; // cudnn v5 does not support dilations
const T* filter_data = filter.data<T>();
const GPUDNNDataLayout data_layout =
(data_format != "NHWC" ? GPUDNNDataLayout::kNCHW
: GPUDNNDataLayout::kNHWC);
std::vector<int> x_vec = vectorize<int>(x.dims());
std::vector<int> out_vec = vectorize<int>(out->dims());
// if channel_last, transpose to channel_first
DenseTensor x_transpose;
if (data_layout == GPUDNNDataLayout::kNHWC) {
if (strides.size() == 2U) {
std::vector<int> axis = {0, 3, 1, 2};
for (size_t i = 0; i < axis.size(); ++i) {
x_vec[i] = x.dims()[axis[i]];
out_vec[i] = out->dims()[axis[i]];
}
x_transpose = Transpose<T, Context>(ctx, x, axis);
} else if (strides.size() == 3U) {
std::vector<int> axis = {0, 4, 1, 2, 3};
for (size_t i = 0; i < axis.size(); ++i) {
x_vec[i] = x.dims()[axis[i]];
out_vec[i] = out->dims()[axis[i]];
}
x_transpose = Transpose<T, Context>(ctx, x, axis);
}
} else {
x_transpose = x;
}
// update padding and dilation
auto x_dims = x_transpose.dims();
auto filter_dims = filter.dims();
DDim x_data_dims;
x_data_dims = slice_ddim(x_dims, 2, x_dims.size());
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, x_data_dims, strides, ksize);
int data_dim = strides.size(); // 2d or 3d
bool is_sys_pad = funcs::IsSymmetricPadding(paddings_, data_dim);
std::vector<int> x_pad(x_dims.size() * 2, 0);
DenseTensor transformed_x;
std::vector<int> padding_common(data_dim, 0);
if (!is_sys_pad) {
std::vector<int> padding_diff(data_dim);
std::vector<int> new_x_shape_vec(data_dim + 2);
new_x_shape_vec[0] = x_dims[0];
new_x_shape_vec[1] = x_dims[1];
for (size_t i = 0; i < data_dim; ++i) {
padding_diff[i] = std::abs(paddings_[2 * i] - paddings_[2 * i + 1]);
padding_common[i] = std::min(paddings_[2 * i], paddings_[2 * i + 1]);
new_x_shape_vec[i + 2] = x_dims[i + 2] + padding_diff[i];
x_pad[2 * i + 4] = paddings_[2 * i] - padding_common[i];
x_pad[2 * i + 4 + 1] = paddings_[2 * i + 1] - padding_common[i];
}
DDim new_x_shape(make_ddim(new_x_shape_vec));
transformed_x.Resize(new_x_shape);
ctx.template Alloc<T>(&transformed_x);
const int rank = x_dims.size();
T pad_value(0.0);
switch (rank) {
case 4: {
funcs::PadFunction<Context, T, 4>(
ctx, x_pad, x_transpose, pad_value, &transformed_x);
} break;
case 5: {
funcs::PadFunction<Context, T, 5>(
ctx, x_pad, x_transpose, pad_value, &transformed_x);
} break;
default:
PADDLE_THROW(errors::InvalidArgument(
"Op(ConvTranspose) only supports 4-D or 5-D x DenseTensor."));
}
} else {
transformed_x = x_transpose;
if (paddings_.size() == data_dim) {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[i];
}
} else {
for (size_t i = 0; i < data_dim; ++i) {
padding_common[i] = paddings_[2 * i];
}
}
}
std::vector<int64_t> starts(data_dim, 0);
std::vector<int64_t> ends(data_dim, 0);
std::vector<int64_t> axes(data_dim, 0);
for (size_t i = 0; i < data_dim; ++i) {
starts[i] = x_pad[2 * i + 4] * (strides[i] + 1);
ends[i] = starts[i] + out_vec[i + 2];
axes[i] = i + 2;
}
const T* x_data = transformed_x.data<T>();
x_vec = vectorize<int>(transformed_x.dims());
std::vector<int> transformed_out_vec = out_vec;
for (size_t i = 0; i < data_dim; ++i) {
transformed_out_vec[i + 2] =
out_vec[i + 2] + (x_pad[2 * i + 4] + x_pad[2 * i + 5]) * strides[i] -
2 * padding_common[i] + paddings_[2 * i] + paddings_[2 * i + 1];
}
DenseTensor transformed_out;
if (!is_sys_pad) {
transformed_out.Resize(make_ddim(transformed_out_vec));
ctx.template Alloc<T>(&transformed_out);
} else {
ctx.template Alloc<T>(out);
transformed_out.ShareDataWith(*out);
transformed_out.Resize(make_ddim(transformed_out_vec));
}
T* transformed_out_data = transformed_out.data<T>();
GPUDNNDataLayout layout;
int iwo_groups = groups;
int c_groups = 1;
#if defined(PADDLE_WITH_HIP) || CUDNN_VERSION_MIN(7, 0, 1)
iwo_groups = 1;
c_groups = groups;
groups = 1;
#endif
if (strides.size() == 2U) {
layout = GPUDNNDataLayout::kNCHW;
} else {
layout = GPUDNNDataLayout::kNCDHW;
}
size_t workspace_size = 0;
#ifdef PADDLE_WITH_HIP
miopenConvBwdDataAlgorithm_t algo{};
#else
cudnnConvolutionBwdDataAlgo_t algo{};
#endif
// ------------------- cudnn conv algorithm ---------------------
auto handle = ctx.cudnn_handle();
auto layout_tensor = paddle::platform::GetCudnnTensorFormat(layout);
bool deterministic = FLAGS_cudnn_deterministic;
auto dtype = paddle::platform::CudnnDataType<T>::type;
// ------------------- cudnn descriptors ---------------------
paddle::operators::ConvArgs args{&transformed_out,
&filter,
&transformed_x,
strides,
padding_common,
dilations_,
dtype};
args.handle = handle;
args.idesc.set(transformed_out, iwo_groups);
args.wdesc.set(filter, layout_tensor, iwo_groups);
args.odesc.set(transformed_x, iwo_groups);
args.cdesc.set(dtype,
padding_common,
strides,
dilations_,
paddle::platform::AllowTF32Cudnn(),
c_groups);
#ifdef PADDLE_WITH_HIP
using search =
paddle::operators::SearchAlgorithm<miopenConvBwdDataAlgorithm_t>;
workspace_size = std::max(workspace_size, search::GetWorkspaceSize(args));
algo = search::Find<T>(args, false, deterministic, workspace_size, ctx);
#else
using search =
paddle::operators::SearchAlgorithm<cudnnConvolutionBwdDataAlgoPerf_t>;
algo = search::Find<T>(args, false, deterministic, ctx);
workspace_size =
std::max(workspace_size, search::GetWorkspaceSize(args, algo));
#endif
// ------------------- cudnn conv transpose forward ---------------------
int x_offset = transformed_x.numel() / transformed_x.dims()[0] / groups;
int out_offset = transformed_out.numel() / transformed_out.dims()[0] / groups;
int filter_offset = filter.numel() / groups;
paddle::operators::ScalingParamType<T> alpha = 1.0f;
paddle::operators::ScalingParamType<T> beta = 0.0f;
auto workspace_handle = ctx.cudnn_workspace_handle();
for (int g = 0; g < groups; g++) {
#ifdef PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::miopenConvolutionBackwardData(
handle,
&alpha,
args.odesc.desc(),
x_data + x_offset * g,
args.wdesc.desc(),
filter_data + filter_offset * g,
args.cdesc.desc(),
algo,
&beta,
args.idesc.desc(),
transformed_out_data + out_offset * g,
cudnn_workspace,
workspace_size));
};
#else // PADDLE_WITH_HIP
auto cudnn_func = [&](void* cudnn_workspace) {
PADDLE_ENFORCE_GPU_SUCCESS(dynload::cudnnConvolutionBackwardData(
handle,
&alpha,
args.wdesc.desc(),
filter_data + filter_offset * g,
args.odesc.desc(),
x_data + x_offset * g,
args.cdesc.desc(),
algo,
cudnn_workspace,
workspace_size,
&beta,
args.idesc.desc(),
transformed_out_data + out_offset * g));
};
#endif // PADDLE_WITH_HIP
workspace_handle.RunFunc(cudnn_func, workspace_size);
}
if (!is_sys_pad && strides.size() == 2U) {
funcs::Slice<Context, T, 4>(ctx, &transformed_out, out, starts, ends, axes);
} else if (!is_sys_pad && strides.size() == 3U) {
funcs::Slice<Context, T, 5>(ctx, &transformed_out, out, starts, ends, axes);
}
if (data_layout == GPUDNNDataLayout::kNHWC) {
DenseTensor out_transpose;
DenseTensor out_nchw;
out_nchw.ShareDataWith(*out);
out_nchw.Resize(make_ddim(out_vec));
if (strides.size() == 2U) {
out_transpose = Transpose<T, Context>(ctx, out_nchw, {0, 2, 3, 1});
} else if (strides.size() == 3U) {
out_transpose = Transpose<T, Context>(ctx, out_nchw, {0, 2, 3, 4, 1});
}
*out = out_transpose;
}
}
template <typename T, typename Context>
void Conv2dTransposeGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
ConvTransposeRawGPUDNNKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
template <typename T, typename Context>
void Conv3dTransposeGPUDNNKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
ConvTransposeRawGPUDNNKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
} // namespace phi
using float16 = phi::dtype::float16;
#ifdef PADDLE_WITH_HIP
// MIOPEN do not support double
PD_REGISTER_KERNEL(conv2d_transpose,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeGPUDNNKernel,
float,
float16) {}
PD_REGISTER_KERNEL(conv3d_transpose,
GPUDNN,
ALL_LAYOUT,
phi::Conv3dTransposeGPUDNNKernel,
float,
float16) {}
#else
PD_REGISTER_KERNEL(conv2d_transpose,
GPUDNN,
ALL_LAYOUT,
phi::Conv2dTransposeGPUDNNKernel,
float,
double,
float16) {}
PD_REGISTER_KERNEL(conv3d_transpose,
GPUDNN,
ALL_LAYOUT,
phi::Conv3dTransposeGPUDNNKernel,
float,
double,
float16) {}
#endif
paddle/phi/kernels/impl/conv_transpose_grad_kernel_impl.h 0 → 100644
浏览文件 @ 1eb96eec
// 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.
#pragma once
#include "paddle/phi/kernels/conv_transpose_grad_kernel.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/vol2col.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
#include "paddle/phi/kernels/funcs/slice.h"
namespace phi {
template <typename T, typename Context>
void ConvTransposeGradRawKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_format);
// For filter, we do not use const pointer because we will do reshape,
// but we should avoid modifying its value.
DenseTensor filter_ = filter;
if ((!dx) && (!dfilter)) {
return;
}
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
auto x_dims = x.dims();
auto filter_dims = filter_.dims();
auto dout_dims = dout.dims();
const int batch_size = static_cast<int>(x.dims()[0]);
DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
} else {
in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
}
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
// x_shape_vec: {n, c, h, w} or {n, c, d, h, w} for channel_first
// x_shape_vec: {n, h, w, c} or {n, d, h, w, c} for channel_last
std::vector<int64_t> x_shape_vec = vectorize(x.dims());
// filter_shape_vec: {i_c, o_c, k_h, k_w} or {i_c, o_c, k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec = vectorize(filter_.dims());
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// col_shape_vec: {o_c, k_h, k_w, h, w} or {o_c, k_d, k_h, k_w, d, h, w} for
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
if (data_layout != DataLayout::kNHWC) {
col_shape_vec[0] = dout_dims[1];
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = x_shape_vec[j + 2];
}
} else {
col_shape_vec[0] = dout_dims[dout_dims.size() - 1];
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = x_shape_vec[j + 1];
}
}
DDim col_shape(make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (o_c * k_h * k_w, h * w) or (o_c * k_d * k_h * k_w, d * h * w)
DDim col_matrix_shape = flatten_to_2d(col_shape, data_dim + 1);
// output size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// output size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
DDim output_shape = slice_ddim(dout.dims(), 1, dout.dims().size());
// x matrix size: (i_c, h * w) or (i_c, d * h * w) for channel_first
// x matrix size: (h * w, i_c) or (d * h * w, i_c) for channel_last
DDim x_matrix_shape;
if (data_layout != DataLayout::kNHWC) {
x_matrix_shape = {x_dims[1], col_matrix_shape[1]};
} else {
x_matrix_shape = {col_matrix_shape[1], x_dims[x_dims.size() - 1]};
}
// filter size: (i_c, o_c/g * k_h * k_w) or (i_c, o_c/g * k_d * k_h * k_w)
DDim filter_matrix_shape;
if (data_layout != DataLayout::kNHWC) {
filter_matrix_shape = {x_dims[1], col_matrix_shape[0] / groups};
} else {
filter_matrix_shape = {x_dims[x_dims.size() - 1],
col_matrix_shape[0] / groups};
}
filter_.Resize(filter_matrix_shape);
int in_step = (data_layout != DataLayout::kNHWC
? static_cast<int>(x_dims[1]) / groups
: static_cast<int>(x_dims[x_dims.size() - 1]) / groups);
int col_step = static_cast<int>(col_matrix_shape[0]) / groups;
// convolution transpose grad on x:
// im2col + gemm (similar to conv-forward)
// x need to compute gradient
auto blas = funcs::GetBlas<Context, T>(ctx);
if (dx || dfilter) {
DenseTensor col;
col.Resize(col_shape);
ctx.template Alloc<T>(&col);
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
DenseTensor col_matrix;
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
DenseTensor dfilter_;
funcs::SetConstant<Context, T> set_zero;
paddle::operators::math::
Im2ColFunctor<paddle::operators::math::ColFormat::kCFO, Context, T>
im2col;
paddle::operators::math::Vol2ColFunctor<Context, T> vol2col;
funcs::ConcatFunctor<Context, T> concat_functor;
if (dx) {
ctx.template Alloc<T>(dx);
set_zero(ctx, dx, static_cast<T>(0));
}
if (dfilter) { // dfilter_ size (i_c, o_c/g, k_h, k_w)
ctx.template Alloc<T>(dfilter);
set_zero(ctx, dfilter, static_cast<T>(0));
dfilter_ = *dfilter;
dfilter_.Resize(filter_matrix_shape);
}
size_t D = x.dims().size();
for (int i = 0; i < batch_size; i++) {
// batch with size (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for
// channel_first
// batch with size (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for
// channel_last
DenseTensor dout_batch = dout.Slice(i, i + 1).Resize(output_shape);
if (data_dim == 2U) {
// im2col: dy -> col matrix
// from (o_c, o_h, o_w) to (o_c * k_h * k_w, i_h * i_w) for
// channel_first
// from (o_h, o_w, o_c) to (o_c * k_h * k_w, i_h * i_w) for
// channel_last
im2col(ctx,
dout_batch,
dilations_,
strides,
std::vector<int>{
paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
&col,
data_layout);
} else if (data_dim == 3U) {
// vol2col: dy -> col_matrix
// from (o_c, o_d, o_h, o_w) to (o_c * k_d * k_h * k_w, i_d * i_h *
// i_w) for channel_first
// from (o_d, o_h, o_w, o_c) to (i_d * i_h * i_w, o_c * k_d * k_h *
// k_w) for channel_last
vol2col(
ctx, dout_batch, dilations_, strides, paddings_, &col, data_layout);
}
if (dx) {
// batch with size (i_c, i_h, i_w) or (i_h, i_w, i_c)
DenseTensor dx_batch = dx->Slice(i, i + 1).Resize(x_matrix_shape);
// gemm: dx = filter * dy
// (i_c, o_c * k_h * k_w) * (o_c * k_h * k_w, i_h * i_w) -> (i_c, i_h
// * i_w)
// or
// (i_c, o_c * k_d * k_h * k_w) * (o_c * k_d * k_h * k_w, i_d * i_h *
// i_w) -> (i_c,
// i_d, i_h, i_w)
// gemm: dx = dy^T * filter^T for channel_last
std::vector<DenseTensor> dx_batch_vec;
for (int g = 0; g < groups; g++) {
// dx_slice: (i_c/g, i_h * i_w) or (i_c/g, i_d * i_h * i_w)
// for channel_first
// dx_slice: (i_h * i_w, i_c/g) or (i_d * i_h * i_w, i_c/g)
// for channel_last
// filter_slice: (i_c/g, o_c/g * k_h * k_w)
DenseTensor filter_slice =
filter_.Slice(g * in_step, (g + 1) * in_step);
// col_matrix_slice: (o_c/g * k_h * k_w, h * w) or (o_c/g * k_d *
// k_h * k_w, d * h * w)
DenseTensor col_matrix_slice =
col_matrix.Slice(g * col_step, (g + 1) * col_step);
if (data_layout != DataLayout::kNHWC) {
DenseTensor dx_slice =
dx_batch.Slice(g * in_step, (g + 1) * in_step);
blas.MatMul(filter_slice,
false,
col_matrix_slice,
false,
static_cast<T>(1.0),
&dx_slice,
static_cast<T>(0.0));
} else {
DenseTensor dx_slice;
funcs::Slice<Context, T, 2>(
ctx, &dx_batch, &dx_slice, g * in_step, (g + 1) * in_step, 1);
blas.MatMul(col_matrix_slice,
true,
filter_slice,
true,
static_cast<T>(1.0),
&dx_slice,
static_cast<T>(0.0));
DDim dx_slice_shape;
if (data_dim == 2U) {
dx_slice_shape = {x_dims[1], x_dims[2], in_step};
} else {
dx_slice_shape = {x_dims[1], x_dims[2], x_dims[3], in_step};
}
dx_slice = dx_slice.Resize(dx_slice_shape);
dx_batch_vec.push_back(dx_slice);
}
}
if (data_layout == DataLayout::kNHWC) {
concat_functor(ctx, dx_batch_vec, static_cast<int>(D - 2), &dx_batch);
}
}
if (dfilter) {
// x batch: (i_c, i_h * i_w) or (i_h, i_w * i_c)
DenseTensor in_batch = x.Slice(i, i + 1).Resize(x_matrix_shape);
// gemm: d_filter = x * dy^T
// (i_c, i_h * i_w) * (i_h * i_w, o_c * k_h * k_w) -> (i_c, o_c * k_h
// * k_w)
// or
// (i_c, i_d * i_h * i_w) * (i_d * i_h * i_w, o_c * k_d * k_h * k_w)
// -> (i_c, o_c * k_d *
// k_h * k_w)
// gemm: d_filter = x^T * dy^T for channel_last
for (int g = 0; g < groups; g++) {
DenseTensor dfilter_slice =
dfilter_.Slice(g * in_step, (g + 1) * in_step);
DenseTensor col_matrix_slice =
col_matrix.Slice(g * col_step, (g + 1) * col_step);
if (data_layout != DataLayout::kNHWC) {
DenseTensor in_batch_slice =
in_batch.Slice(g * in_step, (g + 1) * in_step);
blas.MatMul(in_batch_slice,
false,
col_matrix_slice,
true,
static_cast<T>(1.0),
&dfilter_slice,
static_cast<T>(1.0));
} else {
DenseTensor in_batch_slice;
funcs::Slice<Context, T, 2>(ctx,
&in_batch,
&in_batch_slice,
g * in_step,
(g + 1) * in_step,
1);
blas.MatMul(in_batch_slice,
true,
col_matrix_slice,
true,
static_cast<T>(1.0),
&dfilter_slice,
static_cast<T>(1.0));
}
}
}
}
}
}
template <typename T, typename Context>
void Conv2dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
ConvTransposeGradRawKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
dx,
dfilter);
}
template <typename T, typename Context>
void Conv3dTransposeGradKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const DenseTensor& dout,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* dx,
DenseTensor* dfilter) {
ConvTransposeGradRawKernel<T, Context>(ctx,
x,
filter,
dout,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
dx,
dfilter);
}
} // namespace phi
paddle/phi/kernels/impl/conv_transpose_kernel_impl.h 0 → 100644
浏览文件 @ 1eb96eec
// 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.
#pragma once
#include "paddle/phi/kernels/conv_transpose_kernel.h"
#include "paddle/fluid/operators/math/im2col.h"
#include "paddle/fluid/operators/math/vol2col.h"
#include "paddle/phi/common/layout.h"
#include "paddle/phi/core/ddim.h"
#include "paddle/phi/kernels/cpu/conv_util.h"
#include "paddle/phi/kernels/funcs/blas/blas.h"
#include "paddle/phi/kernels/funcs/concat_and_split_functor.h"
#include "paddle/phi/kernels/funcs/slice.h"
namespace phi {
template <typename T, typename Context>
void ConvTransposeRawKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
const DataLayout data_layout =
paddle::framework::StringToDataLayout(data_format);
// The filter will be reshaped, so it should not be constant
DenseTensor filter_ = filter;
std::vector<int> paddings_ = paddings;
std::vector<int> dilations_ = dilations;
auto x_dims = x.dims();
auto filter_dims = filter_.dims();
auto out_dims = out->dims();
const int batch_size = static_cast<int>(x.dims()[0]);
DDim in_data_dims;
if (data_layout != DataLayout::kNHWC) {
in_data_dims = slice_ddim(x_dims, 2, x_dims.size());
} else {
in_data_dims = slice_ddim(x_dims, 1, x_dims.size() - 1);
}
DDim filter_data_dims = slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(
&paddings_, &dilations_, padding_algorithm, in_data_dims, strides, ksize);
// x_shape_vec: {n, c, h, w} or {n, c, d, h, w} for channel_first
// x_shape_vec: {n, h, w, c} or {n, d, h, w, c} for channel_last
std::vector<int64_t> x_shape_vec = vectorize(x.dims());
// filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
std::vector<int64_t> filter_shape_vec = vectorize(filter_.dims());
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// col_shape_vec: {o_c/g, k_h, k_w, h, w} or {o_c/g, k_d, k_h, k_w, d, h, w}
size_t data_dim = filter_shape_vec.size() - 2;
std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
if (data_layout != DataLayout::kNHWC) {
col_shape_vec[0] = out_dims[1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = x_shape_vec[j + 2];
}
} else {
col_shape_vec[0] = out_dims[out_dims.size() - 1] / groups;
for (size_t j = 0; j < data_dim; ++j) {
col_shape_vec[j + 1] = filter_shape_vec[j + 2];
col_shape_vec[j + 1 + data_dim] = x_shape_vec[j + 1];
}
}
DDim col_shape(make_ddim(col_shape_vec));
// use col_matrix_shape in the gemm calculation
// size: (o_c/g * k_h * k_w, h * w) or (o_c/g * k_d * k_h * k_w, d * h * w)
DDim col_matrix_shape = flatten_to_2d(col_shape, data_dim + 1);
DenseTensor col;
col.Resize(col_shape);
ctx.template Alloc<T>(&col);
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
DenseTensor col_matrix;
col_matrix.ShareDataWith(col);
col_matrix.Resize(col_matrix_shape);
// out size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// out size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
DDim out_shape = slice_ddim(out->dims(), 1, out->dims().size());
// x matrix size: (i_c, h * w) or (i_c, d * h * w) for channel_first
// x matrix size: (h * w, i_c) or (d * h * w, i_c) for channel_last
DDim x_matrix_shape;
if (data_layout != DataLayout::kNHWC) {
x_matrix_shape = {x_dims[1], col_matrix_shape[1]};
} else {
x_matrix_shape = {col_matrix_shape[1], x_dims[x_dims.size() - 1]};
}
// filter size: (i_c, o_c/g * k_h * k_w) or (i_c, o_c/g * k_d * k_h * k_w)
DDim filter_matrix_shape;
if (data_layout != DataLayout::kNHWC) {
filter_matrix_shape = {x_dims[1], col_matrix_shape[0]};
} else {
filter_matrix_shape = {x_dims[x_dims.size() - 1], col_matrix_shape[0]};
}
filter_.Resize(filter_matrix_shape);
ctx.template Alloc<T>(out);
funcs::SetConstant<Context, T> set_zero;
auto blas = funcs::GetBlas<Context, T>(ctx);
set_zero(ctx, out, static_cast<T>(0));
int in_step = (data_layout != DataLayout::kNHWC
? static_cast<int>(x_dims[1]) / groups
: static_cast<int>(x_dims[x_dims.size() - 1]) / groups);
int out_step =
(data_layout != DataLayout::kNHWC
? static_cast<int>(out_dims[1]) / groups
: static_cast<int>(out_dims[out_dims.size() - 1]) / groups);
paddle::operators::math::
Col2ImFunctor<paddle::operators::math::ColFormat::kCFO, Context, T>
col2im;
paddle::operators::math::Col2VolFunctor<Context, T> col2vol;
funcs::ConcatFunctor<Context, T> concat_functor;
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on x)
size_t D = x.dims().size();
for (int i = 0; i < batch_size; i++) {
// batch with size (i_c, h * w) or (i_c, d * h * w) for channel_first
// batch with size (h * w, i_c) or (d * h * w, i_c) for channel_last
DenseTensor x_batch = x.Slice(i, i + 1).Resize(x_matrix_shape);
// out size: (o_c, o_h, o_w) or (o_c, o_d, o_h, o_w) for channel_first
// out size: (o_h, o_w, o_c) or (o_d, o_h, o_w, o_c) for channel_last
DenseTensor out_batch = out->Slice(i, i + 1).Resize(out_shape);
std::vector<DenseTensor> out_batch_vec;
for (int g = 0; g < groups; g++) {
int64_t start = g * in_step;
int64_t end = (g + 1) * in_step;
int axes = (data_layout != DataLayout::kNHWC ? 0 : 1);
DenseTensor filter_slice = filter_.Slice(g * in_step, (g + 1) * in_step);
DenseTensor in_slice, out_slice;
// col_matrix = filter_slice * x_slice
// of shape (o_c/g * k_h * k_w, h * w)
// or (o_c/g * k_d * k_h * k_w, d * h * w)
if (data_layout != DataLayout::kNHWC) {
in_slice = x_batch.Slice(g * in_step, (g + 1) * in_step);
out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
blas.MatMul(filter_slice,
true,
in_slice,
false,
static_cast<T>(1.0),
&col_matrix,
static_cast<T>(0.0));
} else {
funcs::Slice<Context, T, 2>(ctx, &x_batch, &in_slice, start, end, axes);
start = g * out_step;
end = (g + 1) * out_step;
axes = D - 2;
if (D == 4U) {
funcs::Slice<Context, T, 3>(
ctx, &out_batch, &out_slice, start, end, axes);
} else if (D == 5U) {
funcs::Slice<Context, T, 4>(
ctx, &out_batch, &out_slice, start, end, axes);
}
blas.MatMul(filter_slice,
true,
in_slice,
true,
static_cast<T>(1.0),
&col_matrix,
static_cast<T>(0.0));
}
if (data_dim == 2U) {
// col2im: col_matrix -> dy from (o_c/g * k_h * k_w, h * w) to (o_c/g,
// o_h, o_w) or (o_h, o_w, o_c/g)
col2im(ctx,
col,
dilations_,
strides,
std::vector<int>{
paddings_[0], paddings_[2], paddings_[1], paddings_[3]},
&out_slice,
data_layout);
} else if (data_dim == 3U) {
// col2vol: col_matrix -> dy from (o_c/g * k_d * k_h * k_w, d * h * w)
// to (o_c/g, o_d, o_h, o_w) or (o_d, o_h, o_w, o_c/g)
col2vol(
ctx, col, dilations_, strides, paddings_, &out_slice, data_layout);
}
if (data_layout == DataLayout::kNHWC) {
out_batch_vec.push_back(out_slice);
}
}
if (data_layout == DataLayout::kNHWC) {
concat_functor(ctx, out_batch_vec, static_cast<int>(D - 2), &out_batch);
}
}
}
template <typename T, typename Context>
void Conv2dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
ConvTransposeRawKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
template <typename T, typename Context>
void Conv3dTransposeKernel(const Context& ctx,
const DenseTensor& x,
const DenseTensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& output_padding,
const std::vector<int>& output_size,
const std::string& padding_algorithm,
int groups,
const std::vector<int>& dilations,
const std::string& data_format,
DenseTensor* out) {
ConvTransposeRawKernel<T, Context>(ctx,
x,
filter,
strides,
paddings,
padding_algorithm,
groups,
dilations,
data_format,
out);
}
} // namespace phi
paddle/phi/ops/compat/conv_transpose_sig.cc 0 → 100644
浏览文件 @ 1eb96eec
// 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 Conv2dTransposeOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("conv2d_transpose",
{"Input", "Filter"},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{"Output"});
}
KernelSignature Conv2dTransposeGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("conv2d_transpose_grad",
{"Input", "Filter", GradVarName("Output")},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{GradVarName("Input"), GradVarName("Filter")});
}
KernelSignature Conv2dTransposeDoubleGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("conv2d_transpose_grad_grad",
{"Input", "Filter", "DOutput", "DDInput", "DDFilter"},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{"DInput", "DFilter", "DDOutput"});
}
KernelSignature Conv3dTransposeOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("conv3d_transpose",
{"Input", "Filter"},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{"Output"});
}
KernelSignature Conv3dTransposeGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("conv3d_transpose_grad",
{"Input", "Filter", GradVarName("Output")},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{GradVarName("Input"), GradVarName("Filter")});
}
KernelSignature DepthwiseConv2dTransposeOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("depthwise_conv2d_transpose",
{"Input", "Filter"},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{"Output"});
}
KernelSignature DepthwiseConv2dTransposeGradOpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("depthwise_conv2d_transpose_grad",
{"Input", "Filter", GradVarName("Output")},
{"strides",
"paddings",
"output_padding",
"output_size",
"padding_algorithm",
"groups",
"dilations",
"data_format"},
{GradVarName("Input"), GradVarName("Filter")});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(conv2d_transpose,
phi::Conv2dTransposeOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(conv2d_transpose_grad,
phi::Conv2dTransposeGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(conv2d_transpose_grad_grad,
phi::Conv2dTransposeDoubleGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(conv3d_transpose,
phi::Conv3dTransposeOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(conv3d_transpose_grad,
phi::Conv3dTransposeGradOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(depthwise_conv2d_transpose,
phi::DepthwiseConv2dTransposeOpArgumentMapping);
PD_REGISTER_ARG_MAPPING_FN(depthwise_conv2d_transpose_grad,
phi::DepthwiseConv2dTransposeGradOpArgumentMapping);
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