wehub-resource-sync
138 lines
5.1 KiB
C++
138 lines
5.1 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 <climits>
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#include "paddle/phi/kernels/unique_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/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/unique_functor.h"
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namespace phi {
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template <typename T, typename Context>
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void UniqueKernel(const Context& dev_ctx,
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const DenseTensor& x,
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bool return_index,
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bool return_inverse,
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bool return_counts,
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const std::vector<int>& axis,
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DataType dtype,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* counts) {
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bool is_sorted = true;
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UniqueRawKernel<T, Context>(dev_ctx,
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x,
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return_index,
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return_inverse,
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return_counts,
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axis,
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dtype,
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is_sorted,
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out,
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indices,
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index,
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counts);
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}
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template <typename T, typename Context>
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void UniqueRawKernel(const Context& dev_ctx,
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const DenseTensor& x,
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bool return_index,
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bool return_inverse,
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bool return_counts,
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const std::vector<int>& axis,
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DataType dtype,
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bool is_sorted,
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DenseTensor* out,
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DenseTensor* indices,
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DenseTensor* index,
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DenseTensor* counts) {
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if (dtype == DataType::INT32) {
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PADDLE_ENFORCE_LE(
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x.numel(),
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INT_MAX,
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common::errors::InvalidArgument(
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"The number of elements in Input(X) should be less than or "
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"equal to INT_MAX, but received num is %d. Please set `dtype` to "
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"int64.",
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x.numel()));
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}
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if (!is_sorted) {
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VisitDataType(dtype,
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funcs::UniqueOpFunctor<Context, T>(dev_ctx, out, index, &x));
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return;
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}
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if (axis.empty()) {
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VisitDataTypeTiny(
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dtype,
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funcs::UniqueFlattenedTensorFunctor<Context, T>(dev_ctx,
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x,
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out,
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indices,
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index,
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counts,
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return_index,
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return_inverse,
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return_counts));
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} else {
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int axis_value = axis[0];
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axis_value = (axis_value == -1) ? (x.dims().size() - 1) : axis_value;
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VisitDataTypeTiny(dtype,
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funcs::UniqueDimFunctor<Context, T>(dev_ctx,
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x,
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out,
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indices,
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index,
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counts,
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axis_value,
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return_index,
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return_inverse,
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return_counts));
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(unique,
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CPU,
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ALL_LAYOUT,
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phi::UniqueKernel,
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float,
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double,
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int32_t,
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int64_t) {
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kernel->OutputAt(1).SetDataType(phi::DataType::UNDEFINED);
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kernel->OutputAt(2).SetDataType(phi::DataType::UNDEFINED);
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kernel->OutputAt(3).SetDataType(phi::DataType::UNDEFINED);
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}
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PD_REGISTER_KERNEL(unique_raw,
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CPU,
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ALL_LAYOUT,
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phi::UniqueRawKernel,
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float,
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double,
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int32_t,
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int64_t) {
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kernel->OutputAt(1).SetDataType(phi::DataType::UNDEFINED);
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kernel->OutputAt(2).SetDataType(phi::DataType::UNDEFINED);
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kernel->OutputAt(3).SetDataType(phi::DataType::UNDEFINED);
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}
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