chore: import upstream snapshot with attribution
This commit is contained in:
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 "paddle/phi/kernels/set_value_kernel.h"
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#include <chrono>
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#include <iostream>
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#include <type_traits>
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#include "paddle/phi/backends/gpu/gpu_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/math_function.h"
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#include "paddle/phi/kernels/funcs/slice_utils.h"
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#include "paddle/phi/kernels/strided_copy_kernel.h"
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namespace phi {
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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& 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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if (in.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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auto in_dims = in.dims();
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auto meta = in.meta();
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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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funcs::CheckAndUpdateSliceAttrs(
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in_dims, axes, &starts_local, &ends_local, &steps_local);
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std::vector<int64_t> output_dims = vectorize<int64_t>(in.dims());
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std::vector<int64_t> output_stride = vectorize<int64_t>(in.strides());
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int64_t output_offset = static_cast<int64_t>(in.offset());
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for (size_t i = 0; i < axes.size(); ++i) {
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int64_t axis_size = in.dims()[axes[i]];
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if (axis_size < 0) {
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continue;
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}
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int64_t step_size = std::abs(steps_local[i]);
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auto out_dim =
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(std::abs(ends_local[i] - starts_local[i]) + step_size - 1) / step_size;
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output_offset += static_cast<int64_t>(
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starts_local[i] * output_stride[axes[i]] * SizeOf(out->dtype()));
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output_dims[axes[i]] = out_dim;
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output_stride[axes[i]] *= steps_local[i];
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}
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// generate new shape
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std::vector<int64_t> new_out_shape;
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std::vector<int64_t> new_out_stride;
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funcs::GetDecreasedDimsAndStrides(output_dims,
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output_stride,
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decrease_axes,
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none_axes,
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&new_out_shape,
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&new_out_stride);
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if (product(make_ddim(new_out_shape)) <= 0) {
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// 0-size tensor, no need to copy
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out->ResetHolder(in.Holder());
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out->ShareInplaceVersionCounterWith(in);
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return;
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}
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funcs::CheckIsDimsMatch(make_ddim(new_out_shape), value.dims());
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if (new_out_shape.empty()) new_out_shape.push_back(1);
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DenseTensor expand_tensor;
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if (value.numel() == 1) {
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expand_tensor = value;
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expand_tensor.Resize({1});
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} else if (product(value.dims()) == product(make_ddim(new_out_shape))) {
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expand_tensor = value;
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if (value.dims() != make_ddim(new_out_shape)) {
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expand_tensor.Resize(new_out_shape);
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}
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} else {
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auto value_dims = vectorize<int64_t>(value.dims());
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DenseTensor value_tensor = Empty<T>(dev_ctx, IntArray{value_dims});
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value_tensor = value;
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auto it = value_dims.begin();
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while (it != value_dims.end() && *it == 1) {
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it = value_dims.erase(it);
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}
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if (value_dims.empty()) value_dims.push_back(1);
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auto v_dims = make_ddim(value_dims);
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auto out_dims = make_ddim(new_out_shape);
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value_tensor.Resize(v_dims);
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if (funcs::CheckIsLastDimsMatch(v_dims, out_dims)) {
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expand_tensor = value_tensor;
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} else {
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expand_tensor = Empty<T>(dev_ctx, IntArray{new_out_shape});
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ExpandKernel<T, Context>(
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dev_ctx, value_tensor, IntArray{new_out_shape}, &expand_tensor);
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}
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}
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out->ResetHolder(in.Holder());
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out->ShareInplaceVersionCounterWith(in);
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if (starts_local.empty() && ends_local.empty() && steps_local.empty()) {
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if (expand_tensor.numel() != out->numel()) {
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ExpandKernel<T, Context>(
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dev_ctx, expand_tensor, IntArray{new_out_shape}, out);
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} else {
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Copy<Context>(dev_ctx, expand_tensor, dev_ctx.GetPlace(), false, out);
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}
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} else {
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StridedCopyKernel<T, Context>(dev_ctx,
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expand_tensor,
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new_out_shape,
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new_out_stride,
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output_offset,
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out);
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}
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out->set_meta(meta);
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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& in,
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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, complex64>::value &&
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!std::is_same<T, complex128>::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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in,
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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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GPU,
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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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GPU,
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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::float16,
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phi::bfloat16,
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phi::complex64,
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phi::complex128) {}
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