wehub-resource-sync
134 lines
4.8 KiB
C++
134 lines
4.8 KiB
C++
// 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/expand_as_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/core/kernel_registry.h"
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#define MAX_RANK_SUPPORTED 8
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namespace phi {
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template <typename Context, typename T>
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void ExpandAs(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<int64_t>& target_shape_,
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DenseTensor* out) {
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto vec_in_dims = vectorize<int64_t>(x.dims());
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std::vector<int64_t> target_shape(target_shape_.begin(), target_shape_.end());
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auto diff = target_shape.size() - vec_in_dims.size();
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vec_in_dims.insert(vec_in_dims.begin(), diff, 1);
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for (size_t i = 0; i < vec_in_dims.size(); ++i) {
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if (target_shape[i] == 0) {
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dev_ctx.template Alloc<T>(out);
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return;
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}
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if (vec_in_dims[i] != 1) {
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PADDLE_ENFORCE_EQ(
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vec_in_dims[i],
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target_shape[i],
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common::errors::InvalidArgument(
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"The value (%d) of the non-singleton dimension does not match"
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" the corresponding value (%d) in "
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"target tensor for expand_as_v2 op.",
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vec_in_dims[i],
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target_shape[i]));
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}
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}
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if (target_shape.size() == 0) {
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DDim out_dims = make_ddim(target_shape);
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out->Resize(out_dims);
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dev_ctx.template Alloc<T>(out);
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int r = xpu::copy<XPUType>(dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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reinterpret_cast<XPUType*>(out->data<T>()),
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x.numel());
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
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return;
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}
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DDim out_dims = make_ddim(target_shape);
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out->Resize(out_dims);
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dev_ctx.template Alloc<T>(out);
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auto& x_shape = vec_in_dims;
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auto out_shape = vectorize<int64_t>(out_dims);
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int r = 0;
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if (std::is_same<T, bool>::value) {
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auto x_data = reinterpret_cast<const int8_t*>(x.data<T>());
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auto out_data = reinterpret_cast<int8_t*>(out->data<T>());
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r = xpu::broadcast<int8_t>(
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dev_ctx.x_context(), x_data, out_data, x_shape, out_shape);
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} else {
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auto x_data = reinterpret_cast<const XPUType*>(x.data<T>());
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auto out_data = reinterpret_cast<XPUType*>(out->data<T>());
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r = xpu::broadcast<XPUType>(
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dev_ctx.x_context(), x_data, out_data, x_shape, out_shape);
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}
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "broadcast");
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}
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template <typename T, typename Context>
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void ExpandAsKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& y,
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const std::vector<int64_t>& target_shape,
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DenseTensor* out) {
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if (x.numel() == 0 || (y.get_ptr() && y.get_ptr()->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 rank = x.dims().size();
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auto target_rank = target_shape.size();
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PADDLE_ENFORCE_GE(target_rank,
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rank,
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common::errors::InvalidArgument(
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"The rank (%d) of the input 'target_tensor' for "
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"expand_as_v2 op must be greater than or equal to "
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"the rank (%d) of the input 'x'.",
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target_rank,
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rank));
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PADDLE_ENFORCE_GE(
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rank,
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0,
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common::errors::InvalidArgument("The rank (%d) of the input 'x' for "
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"expand_as_v2 op must be positive.",
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rank));
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PADDLE_ENFORCE_LE(target_rank,
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MAX_RANK_SUPPORTED,
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common::errors::InvalidArgument(
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"The rank (%d) of the input 'target_tensor' for "
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"expand_as_v2 op must be less than or equal to %d.",
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target_rank,
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MAX_RANK_SUPPORTED));
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ExpandAs<Context, T>(dev_ctx, x, target_shape, out);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(expand_as,
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XPU,
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ALL_LAYOUT,
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phi::ExpandAsKernel,
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double,
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float,
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phi::bfloat16,
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phi::float16,
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bool,
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int,
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int64_t) {}
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