chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/c_embedding_grad_kernel.cc

@@ -0,0 +1,101 @@
+// 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/c_embedding_grad_kernel.h"
+
+#include "glog/logging.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+
+namespace phi {
+
+template <typename TIds, typename TData>
+void UpdateEmbedding(const TIds* ids,
+                     size_t ids_len,
+                     int64_t start_idx,
+                     TData* table,
+                     int64_t height,
+                     int64_t width,
+                     const TData* out) {
+  for (size_t i = 0; i < ids_len; i++) {
+    TIds id = ids[i];
+    int64_t local = id - start_idx;
+
+    if (local >= 0 && local < height) {
+      for (int64_t w = 0; w < width; w++) {
+        table[local * width + w] += out[i * width + w];
+      }
+    }
+  }
+}
+
+template <typename T, typename Context>
+void CEmbeddingGradKernel(const Context& dev_ctx,
+                          const DenseTensor& w,
+                          const DenseTensor& ids,
+                          const DenseTensor& out_grad,
+                          int64_t start_index,
+                          DenseTensor* w_grad) {
+  w_grad->Resize(w.dims());
+  T* table_grad_data = dev_ctx.template Alloc<T>(w_grad);
+
+  size_t table_t_mem_size = w.numel() * sizeof(w_grad->dtype());
+  size_t table_grad_t_mem_size = w_grad->numel() * sizeof(w_grad->dtype());
+
+  VLOG(10) << "table_dims:" << w.dims()
+           << ", table_t memory_size:" << table_t_mem_size
+           << ", table_grad_t memory_size:" << table_grad_t_mem_size
+           << ", start_index:" << start_index;
+
+  memset(table_grad_data, 0, table_grad_t_mem_size);
+  const T* d_output_data = out_grad.data<T>();
+
+  const int64_t height = w.dims()[0];
+  const int64_t width = w.dims()[1];
+
+  const auto& index_type = ids.dtype();
+  if (index_type == DataType::INT32) {
+    UpdateEmbedding(ids.data<int32_t>(),
+                    ids.numel(),
+                    start_index,
+                    table_grad_data,
+                    height,
+                    width,
+                    d_output_data);
+  } else if (index_type == DataType::INT64) {
+    UpdateEmbedding(ids.data<int64_t>(),
+                    ids.numel(),
+                    start_index,
+                    table_grad_data,
+                    height,
+                    width,
+                    d_output_data);
+  } else {
+    PADDLE_THROW(common::errors::Unavailable(
+        "CPU c_embedding ids only support int32 or int64."));
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(c_embedding_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::CEmbeddingGradKernel,
+                   float,
+                   double,
+                   phi::float16,
+                   phi::complex64,
+                   phi::complex128) {}