chore: import upstream snapshot with attribution
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wehub-resource-sync
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// Copyright (c) 2022 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 <type_traits>
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#include "paddle/phi/kernels/set_value_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/common/int_array.h"
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#include "paddle/phi/common/scalar.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/tensor_utils.h"
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#include "paddle/phi/kernels/empty_kernel.h"
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#include "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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#include "paddle/phi/kernels/funcs/slice_utils.h"
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namespace phi {
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template <typename T, typename Context, size_t RANK>
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void SetValueImpl(const Context& dev_ctx,
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const DenseTensor& in,
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const DenseTensor& value,
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const IntArray& starts,
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const IntArray& ends,
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const IntArray& steps,
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const std::vector<int64_t>& axes,
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const std::vector<int64_t>& decrease_axes,
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const std::vector<int64_t>& none_axes,
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DenseTensor* out) {
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auto in_dims = in.dims();
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std::vector<int64_t> starts_local = starts.GetData();
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std::vector<int64_t> ends_local = ends.GetData();
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std::vector<int64_t> steps_local = steps.GetData();
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if (starts_local.empty() && ends_local.empty() && steps_local.empty() &&
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axes.empty() && decrease_axes.empty() && none_axes.empty() &&
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value.numel() == 1) {
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ExpandKernel<T, Context>(
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dev_ctx, value, IntArray{vectorize<int64_t>(in.dims())}, out);
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return;
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}
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funcs::CheckAndUpdateSliceAttrs(
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in_dims, axes, &starts_local, &ends_local, &steps_local);
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auto slice_dims = funcs::GetSliceDims(
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in_dims, axes, starts_local, ends_local, &steps_local);
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auto decrease_slice_dims = funcs::GetDecreasedDims(slice_dims, decrease_axes);
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auto slice_dims_for_assign = decrease_slice_dims;
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if (!none_axes.empty()) {
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std::vector<int64_t> slice_dims_with_none;
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size_t none_axes_cur = 0, decrease_axes_cur = 0;
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for (int i = 0; i < slice_dims.size(); ++i) {
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while (none_axes_cur < none_axes.size() &&
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none_axes[none_axes_cur] <= i) {
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slice_dims_with_none.push_back(1);
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none_axes_cur++;
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}
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if (decrease_axes_cur < decrease_axes.size() &&
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decrease_axes[decrease_axes_cur] == i) {
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decrease_axes_cur++;
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} else {
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slice_dims_with_none.push_back(slice_dims[i]);
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}
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}
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while (none_axes_cur < none_axes.size()) {
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slice_dims_with_none.push_back(1);
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none_axes_cur++;
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}
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slice_dims_for_assign = make_ddim(slice_dims_with_none);
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}
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funcs::CheckIsDimsMatch(slice_dims_for_assign, value.dims());
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auto value_shape = vectorize<int64_t>(value.dims());
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DenseTensor value_tensor = Empty<T>(dev_ctx, IntArray{value_shape});
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value_tensor = value;
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auto it = value_shape.begin();
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while (it != value_shape.end() && *it == 1) {
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it = value_shape.erase(it);
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}
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if (value_shape.empty()) value_shape.push_back(1);
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value_tensor.Resize(value_shape);
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auto expand_shape = vectorize<int64_t>(slice_dims_for_assign);
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for (size_t i = 0; i < expand_shape.size(); i++) {
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if (expand_shape[i] == 0) expand_shape[i] = 1;
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}
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if (expand_shape.empty()) expand_shape.push_back(1);
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DenseTensor expand_tensor = Empty<T>(dev_ctx, IntArray{expand_shape});
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auto place = dev_ctx.GetPlace();
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auto& eigen_place = *dev_ctx.eigen_device();
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Copy(dev_ctx, in, place, false, out);
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ExpandKernel<T, Context>(
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dev_ctx, value_tensor, IntArray{expand_shape}, &expand_tensor);
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expand_tensor.Resize(slice_dims);
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auto out_e = EigenTensor<T, RANK>::From(*out);
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auto value_e = EigenTensor<T, RANK>::From(expand_tensor);
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auto starts_indices = Eigen::DSizes<int64_t, RANK>();
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auto ends_indices = Eigen::DSizes<int64_t, RANK>();
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auto strides_indices = Eigen::DSizes<int64_t, RANK>();
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for (size_t i = 0; i < RANK; ++i) {
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starts_indices[i] = 0;
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ends_indices[i] = slice_dims[i];
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strides_indices[i] = 1;
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}
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for (size_t i = 0; i < axes.size(); i++) {
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int axis_index = axes[i];
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starts_indices[axis_index] = starts_local[i];
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ends_indices[axis_index] = ends_local[i];
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strides_indices[axis_index] = steps_local[i];
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if (starts_local[i] ==
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ends_local[i]) { // slice is empty, data will not be changed
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return;
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}
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}
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out_e.stridedSlice(starts_indices, ends_indices, strides_indices)
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.device(eigen_place) = value_e;
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}
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template <typename T, typename Context>
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void SetTensorValueKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& value,
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const IntArray& starts,
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const IntArray& ends,
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const IntArray& steps,
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const std::vector<int64_t>& axes,
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const std::vector<int64_t>& decrease_axes,
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const std::vector<int64_t>& none_axes,
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DenseTensor* out) {
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if (x.numel() == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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const int rank = x.dims().size();
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switch (rank) {
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#define CASE_RANK(__RK) \
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case __RK: \
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SetValueImpl<T, Context, __RK>(dev_ctx, \
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x, \
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value, \
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starts, \
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ends, \
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steps, \
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axes, \
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decrease_axes, \
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none_axes, \
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out); \
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break;
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CASE_RANK(1)
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CASE_RANK(2)
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CASE_RANK(3)
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CASE_RANK(4)
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CASE_RANK(5)
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CASE_RANK(6)
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#undef CASE_RANK
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default:
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PADDLE_THROW(errors::InvalidArgument(
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"The rank of input should be less than 7, but received %d.", rank));
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}
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}
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template <typename T, typename Context>
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void SetValueKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& starts,
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const IntArray& ends,
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const IntArray& steps,
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const std::vector<int64_t>& axes,
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const std::vector<int64_t>& decrease_axes,
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const std::vector<int64_t>& none_axes,
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const std::vector<int64_t>& shape,
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const std::vector<Scalar>& values,
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DenseTensor* out) {
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std::vector<T> assign_values;
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assign_values.reserve(values.size());
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for (const auto& val : values) {
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assign_values.push_back(val.to<T>());
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}
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bool is_full_set_one_value = false;
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std::vector<int64_t> starts_local = starts.GetData();
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std::vector<int64_t> ends_local = ends.GetData();
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std::vector<int64_t> steps_local = steps.GetData();
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if (starts_local.empty() && ends_local.empty() && steps_local.empty() &&
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shape.size() == 1 && shape[0] == 1 && assign_values.size() == 1) {
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is_full_set_one_value = true;
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}
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if (is_full_set_one_value && std::is_same<T, float>::value) {
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dev_ctx.template Alloc<T>(out);
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funcs::set_constant(dev_ctx, out, static_cast<float>(assign_values[0]));
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return;
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}
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DenseTensor value_tensor = Empty<T>(dev_ctx, shape);
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TensorFromVector(assign_values, dev_ctx, &value_tensor);
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value_tensor.Resize(shape);
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SetTensorValueKernel<T, Context>(dev_ctx,
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x,
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value_tensor,
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starts,
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ends,
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steps,
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axes,
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decrease_axes,
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none_axes,
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out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(set_value,
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CPU,
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ALL_LAYOUT,
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phi::SetValueKernel,
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float,
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double,
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int,
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int64_t,
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bool,
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int16_t,
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uint8_t,
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int8_t,
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phi::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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PD_REGISTER_KERNEL(set_value_with_tensor,
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CPU,
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ALL_LAYOUT,
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phi::SetTensorValueKernel,
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float,
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double,
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int,
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int64_t,
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bool,
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int16_t,
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uint8_t,
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int8_t,
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phi::bfloat16,
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phi::float16,
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phi::complex64,
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phi::complex128) {}
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