wehub-resource-sync
211 lines
7.3 KiB
C++
211 lines
7.3 KiB
C++
/* 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/infermeta/sparse/binary.h"
|
|
|
|
namespace phi {
|
|
namespace sparse {
|
|
|
|
inline void GetOutShape(const DDim& x_dims,
|
|
const std::vector<int>& kernel_sizes,
|
|
const std::vector<int>& paddings,
|
|
const std::vector<int>& dilations,
|
|
const std::vector<int>& strides,
|
|
DDim* out_dims) {
|
|
const bool is2D = out_dims->size() == 4 ? true : false;
|
|
if (is2D) {
|
|
PADDLE_ENFORCE_EQ(x_dims.size(),
|
|
4,
|
|
common::errors::InvalidArgument(
|
|
"the shape of x should be (N, H, W, C)"));
|
|
PADDLE_ENFORCE_EQ(kernel_sizes.size(),
|
|
4,
|
|
common::errors::InvalidArgument(
|
|
"the shape of kernel should be (H, W, C, OC)"));
|
|
|
|
// infer out shape
|
|
(*out_dims)[0] = x_dims[0];
|
|
(*out_dims)[3] = kernel_sizes[3];
|
|
for (int i = 1; i < 3; i++) {
|
|
(*out_dims)[i] = (x_dims[i] + 2 * paddings[i - 1] -
|
|
dilations[i - 1] * (kernel_sizes[i - 1] - 1) - 1) /
|
|
strides[i - 1] +
|
|
1;
|
|
}
|
|
} else {
|
|
PADDLE_ENFORCE_EQ(x_dims.size(),
|
|
5,
|
|
common::errors::InvalidArgument(
|
|
"the shape of x should be (N, D, H, W, C)"));
|
|
PADDLE_ENFORCE_EQ(kernel_sizes.size(),
|
|
5,
|
|
common::errors::InvalidArgument(
|
|
"the shape of kernel should be (D, H, W, C, OC)"));
|
|
|
|
// infer out shape
|
|
(*out_dims)[0] = x_dims[0];
|
|
(*out_dims)[4] = kernel_sizes[4];
|
|
for (int i = 1; i < 4; i++) {
|
|
(*out_dims)[i] = (x_dims[i] + 2 * paddings[i - 1] -
|
|
dilations[i - 1] * (kernel_sizes[i - 1] - 1) - 1) /
|
|
strides[i - 1] +
|
|
1;
|
|
}
|
|
}
|
|
}
|
|
|
|
inline void ResetSubmKernelSizeAndStrides(const DDim& kernel_dims,
|
|
std::vector<int>* paddings,
|
|
std::vector<int>* strides) {
|
|
for (uint64_t i = 0; i < paddings->size(); i++) {
|
|
(*paddings)[i] = kernel_dims[i] / 2; // NOLINT
|
|
(*strides)[i] = 1;
|
|
}
|
|
}
|
|
|
|
void Conv3dInferMeta(const MetaTensor& x,
|
|
const MetaTensor& kernel,
|
|
const std::vector<int>& paddings,
|
|
const std::vector<int>& dilations,
|
|
const std::vector<int>& strides,
|
|
const int groups,
|
|
const bool subm,
|
|
const std::string& key,
|
|
MetaTensor* out,
|
|
MetaTensor* rulebook,
|
|
MetaTensor* counter) {
|
|
const auto& x_dims = x.dims();
|
|
const bool is2D = x_dims.size() == 4 ? true : false;
|
|
const auto& kernel_dims = kernel.dims();
|
|
|
|
int rank = is2D ? 4 : 5;
|
|
std::vector<int> out_dims_vec(rank, 1);
|
|
DDim out_dims = make_ddim(out_dims_vec);
|
|
|
|
std::vector<int> kernel_sizes(kernel_dims.size());
|
|
for (int i = 0; i < kernel_dims.size(); i++) {
|
|
kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
|
|
}
|
|
|
|
std::vector<int> subm_paddings(paddings), subm_strides(strides);
|
|
if (subm) {
|
|
// the out shape of subm_conv is same as input shape
|
|
// reset the padding=kernel_size/2 and strides=1
|
|
ResetSubmKernelSizeAndStrides(kernel.dims(), &subm_paddings, &subm_strides);
|
|
}
|
|
|
|
GetOutShape(
|
|
x_dims, kernel_sizes, subm_paddings, dilations, subm_strides, &out_dims);
|
|
|
|
out->set_dtype(x.dtype());
|
|
out->set_dims(out_dims);
|
|
out->set_layout(x.layout());
|
|
|
|
rulebook->set_dtype(DataType::INT32);
|
|
rulebook->set_layout(DataLayout::NCHW);
|
|
rulebook->set_dims({1});
|
|
|
|
counter->set_dtype(DataType::INT32);
|
|
counter->set_layout(DataLayout::NCHW);
|
|
counter->set_dims({1});
|
|
}
|
|
|
|
void Conv3dImplicitGemmInferMeta(const MetaTensor& x,
|
|
const MetaTensor& kernel,
|
|
const std::vector<int>& paddings,
|
|
const std::vector<int>& dilations,
|
|
const std::vector<int>& strides,
|
|
const int groups,
|
|
const bool subm,
|
|
const std::string& key,
|
|
MetaTensor* out) {
|
|
const auto& x_dims = x.dims();
|
|
const bool is2D = x_dims.size() == 4 ? true : false;
|
|
const auto& kernel_dims = kernel.dims();
|
|
|
|
int rank = is2D ? 4 : 5;
|
|
std::vector<int> out_dims_vec(rank, 1);
|
|
DDim out_dims = make_ddim(out_dims_vec);
|
|
|
|
std::vector<int> kernel_sizes(kernel_dims.size());
|
|
for (int i = 0; i < kernel_dims.size(); i++) {
|
|
kernel_sizes[i] = static_cast<int>(kernel_dims[i]);
|
|
}
|
|
|
|
std::vector<int> subm_paddings(paddings), subm_strides(strides);
|
|
if (subm) {
|
|
// the out shape of subm_conv is same as input shape
|
|
// reset the padding=kernel_size/2 and strides=1
|
|
ResetSubmKernelSizeAndStrides(kernel.dims(), &subm_paddings, &subm_strides);
|
|
}
|
|
|
|
GetOutShape(
|
|
x_dims, kernel_sizes, subm_paddings, dilations, subm_strides, &out_dims);
|
|
|
|
out->set_dtype(x.dtype());
|
|
out->set_dims(out_dims);
|
|
out->set_layout(x.layout());
|
|
}
|
|
|
|
inline const std::vector<int> PoolResetKernel(
|
|
const std::vector<int>& kernel_sizes,
|
|
const int in_channels,
|
|
const int out_channels) {
|
|
std::vector<int> res(kernel_sizes);
|
|
res.resize(5);
|
|
res[3] = in_channels;
|
|
res[4] = out_channels;
|
|
return res;
|
|
}
|
|
|
|
void Pool3dInferMeta(const MetaTensor& x,
|
|
const std::vector<int>& kernel_sizes,
|
|
const std::vector<int>& paddings,
|
|
const std::vector<int>& dilations,
|
|
const std::vector<int>& strides,
|
|
MetaTensor* out,
|
|
MetaTensor* rulebook,
|
|
MetaTensor* counter) {
|
|
const auto& x_dims = x.dims();
|
|
DDim out_dims = {1, 1, 1, 1, 1};
|
|
|
|
const std::vector<int>& real_kernel_sizes = PoolResetKernel(
|
|
kernel_sizes, static_cast<int>(x_dims[4]), static_cast<int>(x_dims[4]));
|
|
GetOutShape(
|
|
x_dims, real_kernel_sizes, paddings, dilations, strides, &out_dims);
|
|
out->set_dtype(x.dtype());
|
|
out->set_dims(out_dims);
|
|
out->set_layout(x.layout());
|
|
|
|
rulebook->set_dtype(DataType::INT32);
|
|
rulebook->set_layout(DataLayout::NCHW);
|
|
rulebook->set_dims({1});
|
|
|
|
counter->set_dtype(DataType::INT32);
|
|
counter->set_layout(DataLayout::NCHW);
|
|
counter->set_dims({1});
|
|
}
|
|
|
|
void SparseCooTensorInferMeta(const MetaTensor& values,
|
|
const MetaTensor& indices,
|
|
const std::vector<int64_t>& shape,
|
|
MetaTensor* out) {
|
|
out->set_dims(make_ddim(shape));
|
|
out->set_dtype(values.dtype());
|
|
out->set_layout(values.layout());
|
|
}
|
|
|
|
} // namespace sparse
|
|
} // namespace phi
|