wehub-resource-sync
102 lines
3.8 KiB
C++
102 lines
3.8 KiB
C++
// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/unsqueeze_kernel.h"
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#include "glog/logging.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/all_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/unsqueeze.h"
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COMMON_DECLARE_bool(use_stride_kernel);
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namespace phi {
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template <typename Context>
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void UnsqueezeStridedKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const IntArray& axes_arr,
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DenseTensor* out) {
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if (!FLAGS_use_stride_kernel) {
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PADDLE_THROW(common::errors::Fatal(
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"FLAGS_use_stride_kernel is closed. Strided kernel "
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"be called, something wrong has happened!"));
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}
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std::vector<int64_t> axes = axes_arr.GetData();
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std::vector<int64_t> input_dims = vectorize<int64_t>(input.dims());
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std::vector<int64_t> input_stride = vectorize<int64_t>(input.strides());
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if (input.Holder() == out->Holder() && input.meta() == out->meta()) {
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input_dims = vectorize<int64_t>(out->dims());
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for (int64_t i = static_cast<int64_t>(axes.size() - 1); i >= 0; --i) {
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axes[i] = static_cast<int64_t>(axes[i] < 0 ? axes[i] + input_dims.size()
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: axes[i]);
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axes[i] = axes[i] < 0 ? 0 : axes[i];
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input_dims.erase(input_dims.begin() + axes[i]);
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}
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}
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std::vector<int64_t> output_dims = input_dims;
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std::vector<int64_t> output_stride = input_stride;
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for (int64_t item : axes) {
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item =
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static_cast<int64_t>(item < 0 ? item + output_dims.size() + 1 : item);
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item = item < 0 ? 0 : item;
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int64_t stride = static_cast<size_t>(item) >= output_dims.size()
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? 1
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: output_dims[item] * output_stride[item];
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output_dims.insert(output_dims.begin() + item, 1);
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output_stride.insert(output_stride.begin() + item, stride);
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}
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auto meta = out->meta();
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auto tmp_dim = DDim(output_dims.data(), static_cast<int>(output_dims.size()));
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// if (product(meta.dims) > 0 && meta.dims != tmp_dim) {
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// PADDLE_THROW(
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// common::errors::Fatal("Unsqueeze kernel stride compute diff, infer
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// shape"
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// "is %s, but compute is %s.",
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// meta.dims,
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// tmp_dim));
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// }
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meta.dims = tmp_dim;
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meta.strides =
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DDim(output_stride.data(), static_cast<int>(output_stride.size()));
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meta.offset = input.offset();
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out->set_meta(meta);
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out->ResetHolder(input.Holder());
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out->ShareInplaceVersionCounterWith(input);
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}
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template <typename Context>
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void UnsqueezeWithXShapeStridedKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& axes,
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DenseTensor* out,
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DenseTensor* xshape) {
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UnsqueezeStridedKernel<Context>(dev_ctx, x, axes, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(unsqueeze,
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STRIDED,
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phi::UnsqueezeStridedKernel) {}
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PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(
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unsqueeze_with_xshape, STRIDED, phi::UnsqueezeWithXShapeStridedKernel) {}
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