chore: import upstream snapshot with attribution

paddle/phi/kernels/cpu/bincount_kernel.cc

@@ -0,0 +1,119 @@
+// 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/bincount_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
+#include "paddle/phi/kernels/funcs/math_function.h"
+
+namespace phi {
+
+template <typename Context, typename T, typename InputT>
+void BincountInner(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const optional<DenseTensor>& weights,
+                   int64_t minlength,
+                   DenseTensor* out) {
+  const DenseTensor* input = &x;
+  DenseTensor* output = out;
+  const InputT* input_data = input->data<InputT>();
+
+  auto input_numel = input->numel();
+
+  if (input_data == nullptr) {
+    DDim out_dim{minlength};
+    output->Resize(out_dim);
+    // Since minlength may >0 , so fill with 0.
+    Full<int64_t, Context>(dev_ctx, output->dims(), 0, output);
+    return;
+  }
+
+  PADDLE_ENFORCE_GE(
+      *std::min_element(input_data, input_data + input_numel),
+      static_cast<InputT>(0),
+      common::errors::InvalidArgument(
+          "The elements in input tensor must be non-negative ints"));
+
+  int64_t output_size = static_cast<int64_t>(*std::max_element(
+                            input_data, input_data + input_numel)) +
+                        1L;
+  output_size = std::max(output_size, minlength);
+
+  DDim out_dim{output_size};
+  output->Resize(out_dim);
+
+  bool has_weights = weights.is_initialized();
+
+  if (has_weights) {
+    const T* weights_data = weights->data<T>();
+    if (weights->dtype() == DataType::FLOAT32) {
+      float* output_data = dev_ctx.template Alloc<float>(output);
+      funcs::SetConstant<Context, float>()(
+          dev_ctx, output, static_cast<float>(0));
+      for (int64_t i = 0; i < input_numel; i++) {
+        output_data[input_data[i]] += static_cast<float>(weights_data[i]);
+      }
+    } else {
+      double* output_data = dev_ctx.template Alloc<double>(output);
+      funcs::SetConstant<Context, double>()(
+          dev_ctx, output, static_cast<double>(0));
+      for (int64_t i = 0; i < input_numel; i++) {
+        output_data[input_data[i]] += static_cast<double>(weights_data[i]);
+      }
+    }
+
+  } else {
+    int64_t* output_data = dev_ctx.template Alloc<int64_t>(output);
+    funcs::SetConstant<Context, int64_t>()(
+        dev_ctx, output, static_cast<int64_t>(0));
+    for (int64_t i = 0; i < input_numel; i++) {
+      output_data[input_data[i]] += 1L;
+    }
+  }
+}
+
+template <typename T, typename Context>
+void BincountKernel(const Context& dev_ctx,
+                    const DenseTensor& x,
+                    const optional<DenseTensor>& weights,
+                    const Scalar& minlength,
+                    DenseTensor* out) {
+  int64_t int_minlength = minlength.to<int64_t>();
+  PADDLE_ENFORCE_GE(int_minlength,
+                    0,
+                    common::errors::InvalidArgument(
+                        "The minlength should be greater than or equal to 0."
+                        "But received minlength is %d",
+                        int_minlength));
+
+  if (x.dtype() == DataType::INT32) {
+    BincountInner<Context, T, int>(dev_ctx, x, weights, int_minlength, out);
+  } else if (x.dtype() == DataType::INT64) {
+    BincountInner<Context, T, int64_t>(dev_ctx, x, weights, int_minlength, out);
+  }
+}
+}  // namespace phi
+
+PD_REGISTER_KERNEL(bincount,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::BincountKernel,
+                   float,
+                   double,
+                   int,
+                   int64_t) {
+  kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
+}