chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/shuffle_batch_grad_kernel.cc

@@ -0,0 +1,60 @@
+// 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 <memory>
+
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/shuffle_batch_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void ShuffleBatchGradKernel(const Context& dev_ctx,
+                            const DenseTensor& shuffleidx,
+                            const DenseTensor& out_grad,
+                            int startup_seed,
+                            DenseTensor* x_grad) {
+  auto embed_size = out_grad.dims()[out_grad.dims().size() - 1];
+  auto elem_size = 1;
+  for (auto i = 0; i < out_grad.dims().size() - 1; i++)
+    elem_size *= static_cast<int>(out_grad.dims()[i]);
+
+  std::vector<int> idx_vec_grad(elem_size);
+  auto* shuffleidx_data = shuffleidx.data<int64_t>();
+  for (int i = 0; i < static_cast<int>(idx_vec_grad.size()); i++) {
+    idx_vec_grad[shuffleidx_data[i]] = i;
+  }
+
+  // copy data according to idx_vec_grad
+  auto* out_grad_data = out_grad.data<T>();
+  auto* x_grad_data = dev_ctx.template Alloc<T>(x_grad);
+
+  for (auto i = 0; i < elem_size; i++) {
+    memcpy(x_grad_data + idx_vec_grad[i] * embed_size,
+           out_grad_data + i * embed_size,
+           embed_size * sizeof(T));
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(shuffle_batch_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::ShuffleBatchGradKernel,
+                   float,
+                   double,
+                   int32_t,
+                   int64_t) {}