chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/slice_kernel.cc

@@ -0,0 +1,282 @@
+// Copyright (c) 2022 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/slice_kernel.h"
+
+#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/funcs/slice_utils.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void SliceKernel(const Context& dev_ctx,
+                 const DenseTensor& input,
+                 const std::vector<int64_t>& axes,
+                 const IntArray& starts_t,
+                 const IntArray& ends_t,
+                 const std::vector<int64_t>& infer_flags,
+                 const std::vector<int64_t>& decrease_axis,
+                 DenseTensor* out) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  // Step 1: Get the accurate attribute value of starts and ends
+  std::vector<int64_t> starts = starts_t.GetData();
+  std::vector<int64_t> ends = ends_t.GetData();
+  PADDLE_ENFORCE_EQ(
+      starts.size(),
+      axes.size(),
+      common::errors::InvalidArgument(
+          "The size of starts must be equal to the size of axes."));
+  PADDLE_ENFORCE_EQ(ends.size(),
+                    axes.size(),
+                    common::errors::InvalidArgument(
+                        "The size of ends must be equal to the size of axes."));
+
+  // Step 2: Compute output
+  auto in_dims = input.dims();
+  auto out_dims = out->dims();
+  auto slice_dims = out_dims;
+  bool is_same = true;
+  if (in_dims.size() == out_dims.size()) {
+    for (int i = 0; i < in_dims.size(); i++) {
+      if (in_dims[i] != out_dims[i]) {
+        is_same = false;
+        break;
+      } else {
+        continue;
+      }
+    }
+    if (is_same) {
+      Copy<Context>(dev_ctx, input, dev_ctx.GetPlace(), false, out);
+      return;
+    }
+  }
+
+  // 2.1 Infer output dims
+  for (size_t i = 0; i < axes.size(); ++i) {
+    // when start == -1 && end == start+1
+    if (starts[i] == -1 && ends[i] == 0 && infer_flags[i] == -1) {
+      auto ret = std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
+      if (ret != decrease_axis.end()) {
+        ends[i] = in_dims[axes[i]];
+      }
+    }
+  }
+
+  funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
+  slice_dims = funcs::GetSliceDims<int64_t>(
+      in_dims, axes, starts, ends, nullptr, nullptr);
+  out_dims = funcs::GetDecreasedDims(slice_dims, decrease_axis);
+
+  out->Resize(out_dims);
+
+  // 2.2 Get output
+  size_t shape_size = in_dims.size();
+  // the slice XPU kernel require that the length of `start`, `end` must be
+  // equal
+  // to the dims size of input tensor, therefore, if shape_size >
+  // axes.size(), the `starts_extension` and `ends_extension` is necessary.
+  std::vector<int64_t> starts_extension(shape_size, 0);
+  std::vector<int64_t> ends_extension(shape_size, 0);
+  if (shape_size > axes.size()) {
+    for (size_t i = 0; i < shape_size; ++i) {
+      ends_extension[i] = in_dims[i];
+    }
+    for (size_t i = 0; i < axes.size(); ++i) {
+      starts_extension[axes[i]] = starts[i];
+      ends_extension[axes[i]] = ends[i];
+    }
+  } else {
+    for (size_t i = 0; i < axes.size(); ++i) {
+      starts_extension[i] = starts[i];
+      ends_extension[i] = ends[i];
+    }
+  }
+
+  // prepare shape on XPU
+  std::vector<int64_t> shape(shape_size, 0);
+  for (size_t i = 0; i < shape_size; ++i) {
+    shape[i] = in_dims[i];
+  }
+
+  dev_ctx.template Alloc<T>(out);
+  for (size_t i = 0; i < shape_size; ++i) {
+    if (starts_extension[i] == ends_extension[i] || shape[i] == 0) {
+      return;
+    }
+  }
+
+  int r = xpu::slice<XPUType>(dev_ctx.x_context(),
+                              reinterpret_cast<const XPUType*>(input.data<T>()),
+                              reinterpret_cast<XPUType*>(out->data<T>()),
+                              shape,
+                              starts_extension,
+                              ends_extension);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "slice");
+}
+
+#ifdef PADDLE_WITH_XPU_FFT
+template <>
+void SliceKernel<phi::complex64, XPUContext>(
+    const XPUContext& dev_ctx,
+    const DenseTensor& input,
+    const std::vector<int64_t>& axes,
+    const IntArray& starts_t,
+    const IntArray& ends_t,
+    const std::vector<int64_t>& infer_flags,
+    const std::vector<int64_t>& decrease_axis,
+    DenseTensor* out) {
+  using T = phi::complex64;
+  if (out->numel() == 0) {
+    dev_ctx.template Alloc<T>(out);
+    return;
+  }
+  // Step 1: Get the accurate attribute value of starts and ends
+  std::vector<int64_t> starts = starts_t.GetData();
+  std::vector<int64_t> ends = ends_t.GetData();
+  PADDLE_ENFORCE_EQ(
+      starts.size(),
+      axes.size(),
+      common::errors::InvalidArgument(
+          "The size of starts must be equal to the size of axes."));
+  PADDLE_ENFORCE_EQ(ends.size(),
+                    axes.size(),
+                    common::errors::InvalidArgument(
+                        "The size of ends must be equal to the size of axes."));
+
+  // Step 2: Compute output
+  auto in_dims = input.dims();
+  auto out_dims = out->dims();
+  auto slice_dims = out_dims;
+  bool is_same = true;
+  if (in_dims.size() == out_dims.size()) {
+    for (int i = 0; i < in_dims.size(); i++) {
+      if (in_dims[i] != out_dims[i]) {
+        is_same = false;
+        break;
+      } else {
+        continue;
+      }
+    }
+    if (is_same) {
+      Copy<XPUContext>(dev_ctx, input, dev_ctx.GetPlace(), false, out);
+      return;
+    }
+  }
+
+  // 2.1 Infer output dims
+  for (size_t i = 0; i < axes.size(); ++i) {
+    // when start == -1 && end == start+1
+    if (starts[i] == -1 && ends[i] == 0 && infer_flags[i] == -1) {
+      auto ret = std::find(decrease_axis.begin(), decrease_axis.end(), axes[i]);
+      if (ret != decrease_axis.end()) {
+        ends[i] = in_dims[axes[i]];
+      }
+    }
+  }
+
+  funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
+  slice_dims = funcs::GetSliceDims<int64_t>(
+      in_dims, axes, starts, ends, nullptr, nullptr);
+  out_dims = funcs::GetDecreasedDims(slice_dims, decrease_axis);
+
+  out->Resize(out_dims);
+
+  // 2.2 Get output
+  size_t shape_size = in_dims.size();
+  // the slice XPU kernel require that the length of `start`, `end` must be
+  // equal
+  // to the dims size of input tensor, therefore, if shape_size >
+  // axes.size(), the `starts_extension` and `ends_extension` is necessary.
+  std::vector<int64_t> starts_extension(shape_size, 0);
+  std::vector<int64_t> ends_extension(shape_size, 0);
+  if (shape_size > axes.size()) {
+    for (size_t i = 0; i < shape_size; ++i) {
+      ends_extension[i] = in_dims[i];
+    }
+    for (size_t i = 0; i < axes.size(); ++i) {
+      starts_extension[axes[i]] = starts[i];
+      ends_extension[axes[i]] = ends[i];
+    }
+  } else {
+    for (size_t i = 0; i < axes.size(); ++i) {
+      starts_extension[i] = starts[i];
+      ends_extension[i] = ends[i];
+    }
+  }
+
+  // prepare shape on XPU
+  std::vector<int64_t> shape(shape_size, 0);
+  for (size_t i = 0; i < shape_size; ++i) {
+    shape[i] = in_dims[i];
+  }
+
+  dev_ctx.template Alloc<T>(out);
+  for (size_t i = 0; i < shape_size; ++i) {
+    if (starts_extension[i] == ends_extension[i] || shape[i] == 0) {
+      return;
+    }
+  }
+
+  // The current complex number implementation uses separate real/imaginary
+  // parts,resulting in redundant operations and performance
+  // penalties.Optimization should address this in future iterations.
+  const DenseTensor real = Real<T, XPUContext>(dev_ctx, input);
+  const DenseTensor imag = Imag<T, XPUContext>(dev_ctx, input);
+  DenseTensor real_out, imag_out;
+  real_out.Resize(out->dims());
+  imag_out.Resize(out->dims());
+  dev_ctx.template Alloc<float>(&real_out);
+  dev_ctx.template Alloc<float>(&imag_out);
+  int r = xpu::slice<float>(dev_ctx.x_context(),
+                            real.data<float>(),
+                            real_out.data<float>(),
+                            shape,
+                            starts_extension,
+                            ends_extension);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "slice");
+  r = xpu::slice<float>(dev_ctx.x_context(),
+                        imag.data<float>(),
+                        imag_out.data<float>(),
+                        shape,
+                        starts_extension,
+                        ends_extension);
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "slice");
+  phi::ComplexKernel<float>(dev_ctx, real_out, imag_out, out);
+}
+#endif
+}  // namespace phi
+
+PD_REGISTER_KERNEL(slice,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::SliceKernel,
+                   float,
+                   phi::float16,
+                   phi::bfloat16,
+#ifdef PADDLE_WITH_XPU_FFT
+                   phi::complex64,
+#endif
+                   double,
+                   uint8_t,
+                   int8_t,
+                   int16_t,
+                   int32_t,
+                   int64_t) {
+}