chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/contiguous_kernel.cc

@@ -0,0 +1,143 @@
+/* Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "paddle/phi/kernels/contiguous_kernel.h"
+
+#include <cstdint>
+#include <vector>
+
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/complex_kernel.h"
+#include "paddle/phi/kernels/xpu/xpu_api_wrapper.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ContiguousKernel(const Context& dev_ctx,
+                      const DenseTensor& input,
+                      DenseTensor* out) {
+  DenseTensorMeta meta = input.meta();
+  meta.strides = meta.calc_strides(meta.dims);
+  meta.offset = 0;
+  out->set_meta(meta);
+
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+
+  // use XPUCopyTypeTrait to deal with double and int16_t copy instead of
+  // XPUTypeTrait
+  using XPUType = typename XPUCopyTypeTrait<T>::Type;
+
+  int r = 0;
+  auto input_data = reinterpret_cast<const XPUType*>(input.data<T>());
+  auto output_data = reinterpret_cast<XPUType*>(dev_ctx.template Alloc<T>(out));
+  if (input.numel() == 1) {
+    r = xpu::copy<XPUType>(dev_ctx.x_context(), input_data, output_data, 1);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
+  } else {
+    r = xpu::as_strided<XPUType>(dev_ctx.x_context(),
+                                 input_data,
+                                 output_data,
+                                 vectorize<int64_t>(input.dims()),
+                                 vectorize<int64_t>(input.strides()),
+                                 0);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "as_strided");
+  }
+}
+
+#ifdef PADDLE_WITH_XPU_FFT
+template <typename T>
+typename std::enable_if<std::is_same<T, phi::complex64>::value ||
+                        std::is_same<T, phi::complex128>::value>::type
+ComplexContiguousKernelImpl(const XPUContext& dev_ctx,
+                            const DenseTensor& input,
+                            DenseTensor* out) {
+  DenseTensorMeta meta = input.meta();
+  meta.strides = meta.calc_strides(meta.dims);
+  meta.offset = 0;
+  out->set_meta(meta);
+
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+
+  // For strided complex tensors, avoid using Real/Imag kernels that assume
+  // contiguous complex layout. Instead, materialize bytes with
+  // as_strided<int8_t> to preserve both real/imag parts and handle large
+  // strides safely.
+  dev_ctx.template Alloc<T>(out);
+  auto bytes_shape = vectorize<int64_t>(input.dims());
+  auto bytes_strides = vectorize<int64_t>(input.strides());
+  const int64_t bytes_per_elem = static_cast<int64_t>(sizeof(T));
+  for (auto& s : bytes_strides) {
+    s *= bytes_per_elem;
+  }
+  bytes_shape.push_back(bytes_per_elem);
+  bytes_strides.push_back(1);
+
+  const auto* input_bytes = reinterpret_cast<const int8_t*>(input.data<T>());
+  auto* output_bytes = reinterpret_cast<int8_t*>(out->data<T>());
+
+  int r = 0;
+  if (input.numel() == 1) {
+    r = xpu::copy<int8_t>(
+        dev_ctx.x_context(), input_bytes, output_bytes, bytes_per_elem);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "copy");
+  } else {
+    r = xpu::as_strided<int8_t>(dev_ctx.x_context(),
+                                input_bytes,
+                                output_bytes,
+                                bytes_shape,
+                                bytes_strides,
+                                0);
+    PADDLE_ENFORCE_XDNN_SUCCESS(r, "as_strided");
+  }
+}
+template <>
+void ContiguousKernel<phi::complex64, XPUContext>(const XPUContext& dev_ctx,
+                                                  const DenseTensor& input,
+                                                  DenseTensor* out) {
+  ComplexContiguousKernelImpl<phi::complex64>(dev_ctx, input, out);
+}
+
+template <>
+void ContiguousKernel<phi::complex128, XPUContext>(const XPUContext& dev_ctx,
+                                                   const DenseTensor& input,
+                                                   DenseTensor* out) {
+  ComplexContiguousKernelImpl<phi::complex128>(dev_ctx, input, out);
+}
+#endif
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(contiguous,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::ContiguousKernel,
+                   bool,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int32_t,
+                   int64_t,
+                   float,
+                   double,
+#ifdef PADDLE_WITH_XPU_FFT
+                   phi::complex64,
+                   phi::complex128,
+#endif
+                   phi::float16,
+                   phi::bfloat16) {
+}