chore: import upstream snapshot with attribution

paddle/phi/kernels/gpu/multiplex_grad_kernel.cu

@@ -0,0 +1,71 @@
+// 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/multiplex_grad_kernel.h"
+
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/common/memory_utils.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/tensor_utils.h"
+#include "paddle/phi/kernels/funcs/eigen/common.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+void MultiplexGradKernel(const Context& dev_ctx,
+                         const DenseTensor& ids,
+                         const DenseTensor& out_grad,
+                         std::vector<DenseTensor*> ins_grad) {
+  size_t idx = -1UL;
+  for (size_t i = 0; i < ins_grad.size(); i++) {
+    if (ins_grad[i]) {
+      dev_ctx.template Alloc<T>(ins_grad[i]);
+      auto t = EigenVector<T>::Flatten(*ins_grad[i]);
+      t.device(*dev_ctx.eigen_device()) = t.constant(static_cast<T>(0));
+      idx = i;
+    }
+  }
+  if (idx == -1UL) return;
+
+  auto rows = ins_grad[idx]->dims()[0];
+  auto cols = ins_grad[idx]->numel() / rows;
+  DenseTensor index_t_cpu;
+  Copy(dev_ctx, ids, CPUPlace(), true, &index_t_cpu);
+  auto* index = index_t_cpu.data<int32_t>();
+  auto stream = dev_ctx.stream();
+  for (auto i = 0; i < rows; i++) {
+    size_t k = static_cast<size_t>(index[i]);
+    if (ins_grad[k]) {
+      memory_utils::Copy(dev_ctx.GetPlace(),
+                         ins_grad[k]->data<T>() + i * cols,
+                         dev_ctx.GetPlace(),
+                         out_grad.data<T>() + i * cols,
+                         cols * sizeof(T),
+                         stream);
+    }
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(multiplex_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::MultiplexGradKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t,
+                   phi::complex64,
+                   phi::complex128) {}