chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/embedding_kernel.cc

@@ -0,0 +1,130 @@
+// 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/embedding_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/common/data_type.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/embedding_util.h"
+#include "paddle/phi/kernels/p_norm_kernel.h"
+
+namespace phi {
+
+template <typename T, typename Context>
+struct EmbeddingCPUFunctor {
+  EmbeddingCPUFunctor(const Context& dev_ctx,
+                      const DenseTensor& input,
+                      const DenseTensor& weight,
+                      int64_t padding_idx,
+                      DenseTensor* out)
+      : dev_ctx_(dev_ctx),
+        input_(input),
+        weight_(weight),
+        out_(out),
+        padding_idx_(padding_idx) {}
+
+  template <typename IdT>
+  void apply() {
+    auto ids = CopyIdsToVector<IdT, int64_t>(input_);
+    auto ids_numel = static_cast<int64_t>(ids.size());
+
+    int64_t row_number = weight_.dims()[0];
+    int64_t row_width = weight_.dims()[1];
+
+    auto* table = weight_.data<T>();
+
+    dev_ctx_.template Alloc<T>(out_);
+    auto* output = out_->data<T>();
+
+    for (int64_t i = 0; i < ids_numel; ++i) {
+      if (padding_idx_ == kNoPadding && ids[i] != padding_idx_) {
+        PADDLE_ENFORCE_LT(
+            ids[i],
+            row_number,
+            common::errors::InvalidArgument(
+                "Variable value (input) of OP(embedding) "
+                "expected >= 0 and < %ld, but got %ld. Please check input "
+                "value.",
+                row_number,
+                ids[i]));
+        PADDLE_ENFORCE_GE(
+            ids[i],
+            0,
+            common::errors::InvalidArgument(
+                "Variable value (input) of OP(embedding) "
+                "expected >= 0 and < %ld, but got %ld. Please check input "
+                "value.",
+                row_number,
+                ids[i]));
+      }
+    }
+
+#if defined(_OPENMP) && !defined(PADDLE_WITH_CUDA)
+#pragma omp parallel for
+#endif
+
+    for (int64_t i = 0; i < ids_numel; ++i) {
+      if (padding_idx_ != kNoPadding && ids[i] == padding_idx_) {
+        memset(output + i * row_width, 0, row_width * sizeof(T));
+      } else {
+        memcpy(output + i * row_width,
+               table + ids[i] * row_width,
+               row_width * sizeof(T));
+      }
+    }
+  }
+
+ private:
+  const Context& dev_ctx_;
+  const DenseTensor& input_;
+  const DenseTensor& weight_;
+  DenseTensor* out_;
+  int64_t padding_idx_;
+};
+
+template <typename T, typename Context>
+void EmbeddingKernel(const Context& dev_ctx,
+                     const DenseTensor& input,
+                     const DenseTensor& weight,
+                     int64_t padding_idx,
+                     DenseTensor* out) {
+  EmbeddingCPUFunctor<T, Context> functor(
+      dev_ctx, input, weight, padding_idx, out);
+
+  if (input.dtype() == DataType::INT32) {
+    functor.template apply<int>();
+  } else if (input.dtype() == DataType::INT64) {
+    functor.template apply<int64_t>();
+  } else {
+    PADDLE_THROW(common::errors::Unimplemented(
+        "embedding input only support int32 and int64, but get %s",
+        input.dtype()));
+  }
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(embedding,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::EmbeddingKernel,
+                   float,
+                   double,
+                   int8_t,
+                   phi::float16,
+                   phi::bfloat16,
+                   phi::complex64,
+                   phi::complex128) {}