wehub-resource-sync
120 lines
4.1 KiB
C++
120 lines
4.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "paddle/phi/kernels/bincount_kernel.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/math_function.h"
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namespace phi {
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template <typename Context, typename T, typename InputT>
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void BincountInner(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& weights,
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int64_t minlength,
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DenseTensor* out) {
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const DenseTensor* input = &x;
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DenseTensor* output = out;
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const InputT* input_data = input->data<InputT>();
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auto input_numel = input->numel();
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if (input_data == nullptr) {
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DDim out_dim{minlength};
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output->Resize(out_dim);
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// Since minlength may >0 , so fill with 0.
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Full<int64_t, Context>(dev_ctx, output->dims(), 0, output);
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return;
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}
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PADDLE_ENFORCE_GE(
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*std::min_element(input_data, input_data + input_numel),
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static_cast<InputT>(0),
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common::errors::InvalidArgument(
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"The elements in input tensor must be non-negative ints"));
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int64_t output_size = static_cast<int64_t>(*std::max_element(
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input_data, input_data + input_numel)) +
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1L;
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output_size = std::max(output_size, minlength);
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DDim out_dim{output_size};
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output->Resize(out_dim);
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bool has_weights = weights.is_initialized();
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if (has_weights) {
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const T* weights_data = weights->data<T>();
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if (weights->dtype() == DataType::FLOAT32) {
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float* output_data = dev_ctx.template Alloc<float>(output);
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funcs::SetConstant<Context, float>()(
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dev_ctx, output, static_cast<float>(0));
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for (int64_t i = 0; i < input_numel; i++) {
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output_data[input_data[i]] += static_cast<float>(weights_data[i]);
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}
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} else {
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double* output_data = dev_ctx.template Alloc<double>(output);
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funcs::SetConstant<Context, double>()(
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dev_ctx, output, static_cast<double>(0));
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for (int64_t i = 0; i < input_numel; i++) {
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output_data[input_data[i]] += static_cast<double>(weights_data[i]);
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}
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}
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} else {
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int64_t* output_data = dev_ctx.template Alloc<int64_t>(output);
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funcs::SetConstant<Context, int64_t>()(
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dev_ctx, output, static_cast<int64_t>(0));
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for (int64_t i = 0; i < input_numel; i++) {
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output_data[input_data[i]] += 1L;
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}
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}
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}
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template <typename T, typename Context>
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void BincountKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const optional<DenseTensor>& weights,
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const Scalar& minlength,
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DenseTensor* out) {
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int64_t int_minlength = minlength.to<int64_t>();
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PADDLE_ENFORCE_GE(int_minlength,
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0,
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common::errors::InvalidArgument(
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"The minlength should be greater than or equal to 0."
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"But received minlength is %d",
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int_minlength));
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if (x.dtype() == DataType::INT32) {
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BincountInner<Context, T, int>(dev_ctx, x, weights, int_minlength, out);
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} else if (x.dtype() == DataType::INT64) {
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BincountInner<Context, T, int64_t>(dev_ctx, x, weights, int_minlength, out);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(bincount,
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CPU,
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ALL_LAYOUT,
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phi::BincountKernel,
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float,
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double,
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int,
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int64_t) {
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kernel->OutputAt(0).SetDataType(phi::DataType::UNDEFINED);
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}
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