wehub-resource-sync
116 lines
4.0 KiB
C++
116 lines
4.0 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/index_put_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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#include "paddle/phi/kernels/expand_kernel.h"
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#include "paddle/phi/kernels/funcs/index_put_utils.h"
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#include "paddle/phi/kernels/xpu/index_put_xpu_utils.h"
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namespace phi {
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template <typename T, typename Context>
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void IndexPutKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const std::vector<const DenseTensor*>& indices,
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const DenseTensor& value,
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bool accumulate,
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DenseTensor* out) {
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if (out && out->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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PADDLE_ENFORCE_EQ(
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x.dtype(),
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value.dtype(),
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common::errors::InvalidArgument(
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"The data type of tensor value must be same to the data type "
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"of tensor x."));
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PADDLE_ENFORCE_EQ(
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indices.empty(),
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false,
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common::errors::InvalidArgument("Indices cannot be empty."));
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const int64_t total_dims = x.dims().size();
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PADDLE_ENFORCE_LE(
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total_dims,
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6,
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errors::InvalidArgument("Dims of input tensor should be less than 7."));
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// All bool indices are converted to integers currently
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std::vector<DenseTensor> tmp_args;
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std::vector<const DenseTensor*> int_indices_v =
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funcs::DealWithBoolIndices<T, Context>(dev_ctx, indices, &tmp_args);
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if (int_indices_v.empty()) {
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if (!out->initialized()) {
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Copy(dev_ctx, x, dev_ctx.GetPlace(), false, out);
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}
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return;
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}
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto out_data = dev_ctx.template Alloc<T>(out);
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auto bd_dims = funcs::BroadCastTensorsDims(int_indices_v);
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DenseTensor res_indices(DataType::INT64);
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// Broadcast and merge indices
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XPUDealWithIndices<Context>(dev_ctx, int_indices_v, bd_dims, &res_indices);
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auto index_shape = vectorize<int64_t>(res_indices.dims());
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auto x_shape = vectorize<int64_t>(x.dims());
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const T* value_data = value.data<T>();
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// Broadcast value
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auto value_shape = vectorize<int64_t>(value.dims());
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int64_t value_rank = bd_dims.size() + (x_shape.size() - int_indices_v.size());
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std::vector<int64_t> value_shape_bd(value_rank);
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std::copy(index_shape.begin(), index_shape.end() - 1, value_shape_bd.begin());
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std::copy(x_shape.begin() + int_indices_v.size(),
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x_shape.end(),
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value_shape_bd.begin() + index_shape.size() - 1);
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DenseTensor value_bd(value.dtype());
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if (value_shape != value_shape_bd) {
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value_bd.Resize(value_shape_bd);
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ExpandKernel<T, Context>(
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dev_ctx, value, IntArray(value_shape_bd), &value_bd);
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value_data = value_bd.data<T>();
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}
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int r = xpu::index_put<XPUType, int64_t>(
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dev_ctx.x_context(),
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reinterpret_cast<const XPUType*>(x.data<T>()),
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reinterpret_cast<const XPUType*>(value_data),
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res_indices.data<int64_t>(),
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reinterpret_cast<XPUType*>(out_data),
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x_shape,
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index_shape,
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accumulate);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "index_put");
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if (dev_ctx.x_context()->xpu_stream) {
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dev_ctx.Wait();
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(index_put,
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XPU,
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ALL_LAYOUT,
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phi::IndexPutKernel,
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float,
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phi::float16,
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phi::bfloat16,
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int,
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int64_t) {}
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