wehub-resource-sync
52 lines
1.7 KiB
C++
52 lines
1.7 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/core/kernel_registry.h"
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namespace paddle {
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namespace custom_kernel {
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// Here we use dot <CPU, ANY, INT8> for test
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// This test will fail when this kernel is supported in framework
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template <typename T, typename Context>
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void DotKernel(const Context& dev_ctx,
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const phi::DenseTensor& x,
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const phi::DenseTensor& y,
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phi::DenseTensor* out) {
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auto const *x_ptr = x.data<T>(), *x_ptr_ = &x_ptr[0];
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auto const *y_ptr = y.data<T>(), *y_ptr_ = &y_ptr[0];
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T* z = dev_ctx.template Alloc<T>(out);
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// Loop over the total N elements of both operands while sum-reducing every
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// B pairs along the way where B is the dimension of the least ordered axis
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auto&& d = x.dims();
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auto const N = x.numel();
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auto const B = d[d.size() - 1];
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for (int j = 0; j < N / B; j++) {
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T ss = 0;
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for (int i = 0; i < B; i++) ss += (*x_ptr_++) * (*y_ptr_++);
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z[j] = ss;
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}
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}
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} // namespace custom_kernel
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} // namespace paddle
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PD_REGISTER_BUILTIN_KERNEL(
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dot, CPU, ALL_LAYOUT, paddle::custom_kernel::DotKernel, int8_t) {
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kernel->OutputAt(0).SetDataType(phi::DataType::INT8);
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}
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