wehub-resource-sync
144 lines
5.0 KiB
C++
144 lines
5.0 KiB
C++
/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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/contiguous_kernel.h"
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#include <cstdint>
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#include <vector>
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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/complex_kernel.h"
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#include "paddle/phi/kernels/xpu/xpu_api_wrapper.h"
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namespace phi {
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template <typename T, typename Context>
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void ContiguousKernel(const Context& dev_ctx,
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const DenseTensor& input,
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DenseTensor* out) {
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DenseTensorMeta meta = input.meta();
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meta.strides = meta.calc_strides(meta.dims);
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meta.offset = 0;
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out->set_meta(meta);
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if (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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// use XPUCopyTypeTrait to deal with double and int16_t copy instead of
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// XPUTypeTrait
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using XPUType = typename XPUCopyTypeTrait<T>::Type;
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int r = 0;
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auto input_data = reinterpret_cast<const XPUType*>(input.data<T>());
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auto output_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
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if (input.numel() == 1) {
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r = xpu::copy<XPUType>(dev_ctx.x_context(), input_data, output_data, 1);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
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} else {
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r = xpu::as_strided<XPUType>(dev_ctx.x_context(),
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input_data,
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output_data,
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vectorize<int64_t>(input.dims()),
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vectorize<int64_t>(input.strides()),
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0);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "as_strided");
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}
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}
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#ifdef PADDLE_WITH_XPU_FFT
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template <typename T>
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typename std::enable_if<std::is_same<T, phi::complex64>::value ||
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std::is_same<T, phi::complex128>::value>::type
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ComplexContiguousKernelImpl(const XPUContext& dev_ctx,
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const DenseTensor& input,
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DenseTensor* out) {
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DenseTensorMeta meta = input.meta();
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meta.strides = meta.calc_strides(meta.dims);
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meta.offset = 0;
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out->set_meta(meta);
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if (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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// For strided complex tensors, avoid using Real/Imag kernels that assume
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// contiguous complex layout. Instead, materialize bytes with
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// as_strided<int8_t> to preserve both real/imag parts and handle large
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// strides safely.
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dev_ctx.template Alloc<T>(out);
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auto bytes_shape = vectorize<int64_t>(input.dims());
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auto bytes_strides = vectorize<int64_t>(input.strides());
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const int64_t bytes_per_elem = static_cast<int64_t>(sizeof(T));
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for (auto& s : bytes_strides) {
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s *= bytes_per_elem;
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}
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bytes_shape.push_back(bytes_per_elem);
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bytes_strides.push_back(1);
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const auto* input_bytes = reinterpret_cast<const int8_t*>(input.data<T>());
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auto* output_bytes = reinterpret_cast<int8_t*>(out->data<T>());
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int r = 0;
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if (input.numel() == 1) {
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r = xpu::copy<int8_t>(
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dev_ctx.x_context(), input_bytes, output_bytes, bytes_per_elem);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
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} else {
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r = xpu::as_strided<int8_t>(dev_ctx.x_context(),
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input_bytes,
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output_bytes,
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bytes_shape,
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bytes_strides,
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0);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "as_strided");
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}
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}
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template <>
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void ContiguousKernel<phi::complex64, XPUContext>(const XPUContext& dev_ctx,
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const DenseTensor& input,
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DenseTensor* out) {
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ComplexContiguousKernelImpl<phi::complex64>(dev_ctx, input, out);
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}
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template <>
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void ContiguousKernel<phi::complex128, XPUContext>(const XPUContext& dev_ctx,
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const DenseTensor& input,
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DenseTensor* out) {
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ComplexContiguousKernelImpl<phi::complex128>(dev_ctx, input, out);
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}
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#endif
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} // namespace phi
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PD_REGISTER_KERNEL(contiguous,
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XPU,
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ALL_LAYOUT,
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phi::ContiguousKernel,
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bool,
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uint8_t,
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int8_t,
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int16_t,
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int32_t,
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int64_t,
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float,
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double,
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#ifdef PADDLE_WITH_XPU_FFT
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phi::complex64,
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phi::complex128,
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#endif
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phi::float16,
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phi::bfloat16) {
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}
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