chore: import upstream snapshot with attribution

paddle/phi/kernels/xpu/stack_grad_kernel.cc

@@ -0,0 +1,114 @@
+// 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/stack_grad_kernel.h"
+#include "paddle/phi/backends/xpu/enforce_xpu.h"
+#include "paddle/phi/common/memory_utils.h"
+#include "paddle/phi/core/ddim.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_utils.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void StackGradKernel(const Context& dev_ctx,
+                     const DenseTensor& out_grad,
+                     int axis,
+                     std::vector<DenseTensor*> x_grad) {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+  auto og_dims = out_grad.dims();
+  int rank = og_dims.size();
+  if (axis < 0) {
+    axis += rank;
+  }
+  int64_t n_slices = og_dims[axis];
+
+  struct ValidSlice {
+    DenseTensor* dx;
+    DDim final_dims;
+  };
+  std::vector<ValidSlice> valid_slices;
+  valid_slices.reserve(x_grad.size());
+
+  for (size_t i = 0; i < x_grad.size(); ++i) {
+    DenseTensor* dx_i = x_grad[i];
+    if (dx_i == nullptr || dx_i->numel() == 0) {
+      continue;
+    }
+    ValidSlice vs;
+    vs.dx = dx_i;
+    vs.final_dims = dx_i->dims();
+    valid_slices.push_back(vs);
+  }
+
+  if (valid_slices.empty()) {
+    return;
+  }
+
+  int64_t needed_slices = static_cast<int64_t>(valid_slices.size());
+
+  PADDLE_ENFORCE_LE(
+      needed_slices,
+      n_slices,
+      common::errors::InvalidArgument(
+          "Number of valid slices (%ld) exceeds out_grad's dimension (%ld) "
+          "along axis %d in stack_grad kernel. Mismatch between forward and "
+          "backward shapes.",
+          needed_slices,
+          n_slices,
+          axis));
+
+  std::vector<int64_t> partial_shape = vectorize<int64_t>(og_dims);
+  partial_shape[axis] = needed_slices;
+
+  std::vector<XPUType*> dx_ptrs;
+  dx_ptrs.reserve(needed_slices);
+
+  std::vector<int64_t> dx_dims_list;
+  dx_dims_list.reserve(needed_slices);
+
+  for (auto& vs : valid_slices) {
+    dev_ctx.template Alloc<T>(vs.dx);
+    dx_ptrs.push_back(reinterpret_cast<XPUType*>(vs.dx->template data<T>()));
+    dx_dims_list.push_back(1);
+  }
+
+  const XPUType* og_data =
+      reinterpret_cast<const XPUType*>(out_grad.template data<T>());
+
+  int r = xpu::split<XPUType>(
+      dev_ctx.x_context(), og_data, dx_ptrs, partial_shape, dx_dims_list, axis);
+
+  PADDLE_ENFORCE_XDNN_SUCCESS(r, "split in stack_grad op");
+
+  for (auto& vs : valid_slices) {
+    vs.dx->Resize(vs.final_dims);
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(stack_grad,
+                   XPU,
+                   ALL_LAYOUT,
+                   phi::StackGradKernel,
+                   float,
+                   phi::float16,
+                   phi::bfloat16,
+                   int64_t,
+                   int,
+                   int16_t,
+                   int8_t,
+                   uint8_t) {}