wehub-resource-sync
111 lines
3.8 KiB
C++
111 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/slice_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/slice_utils.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 SliceStridedKernel(const Context& dev_ctx,
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const DenseTensor& input,
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const std::vector<int64_t>& axes,
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const IntArray& starts_arr,
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const IntArray& ends_arr,
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const std::vector<int64_t>& infer_flags,
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const std::vector<int64_t>& decrease_axis,
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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> starts = starts_arr.GetData();
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std::vector<int64_t> ends = ends_arr.GetData();
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const auto& in_dims = input.dims();
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auto new_axes = axes;
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for (auto& item : new_axes) {
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if (item < 0) {
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item = std::max(int64_t(0), item + int64_t(in_dims.size()));
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}
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}
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// axis = 0, dim_value = 3, st[0]=0, ed[0]=4
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// The step seems to be regarded as 1 here
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funcs::CheckAndUpdateSliceAttrs<int64_t>(
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in_dims, new_axes, &starts, &ends, nullptr, nullptr);
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std::vector<int64_t> output_dims = vectorize<int64_t>(input.dims());
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std::vector<int64_t> output_stride = vectorize<int64_t>(input.strides());
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int64_t output_offset = static_cast<int64_t>(input.offset());
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for (size_t i = 0; i < new_axes.size(); ++i) {
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output_offset = static_cast<int64_t>(
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output_offset +
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starts[i] * output_stride[new_axes[i]] * SizeOf(out->dtype()));
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output_dims[new_axes[i]] = std::abs(ends[i] - starts[i]);
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}
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std::vector<uint8_t> decrease_flag(output_dims.size(), 0);
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if (!decrease_axis.empty()) {
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for (auto axis : decrease_axis) {
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decrease_flag[axis] = 1;
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}
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std::vector<int64_t> new_shape;
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std::vector<int64_t> new_stride;
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for (size_t i = 0; i < output_dims.size(); ++i) {
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if (decrease_flag[i] == 0) {
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new_shape.push_back(output_dims[i]);
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new_stride.push_back(output_stride[i]);
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}
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}
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output_dims = new_shape;
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output_stride = new_stride;
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}
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auto meta = out->meta();
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meta.offset = output_offset;
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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("Slice kernel stride compute diff, infer shape
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// is
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// "
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// "%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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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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} // namespace phi
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PD_REGISTER_KERNEL_FOR_ALL_BACKEND_DTYPE(slice,
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STRIDED,
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phi::SliceStridedKernel) {}
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