wehub-resource-sync
61 lines
2.1 KiB
C++
61 lines
2.1 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/prod_kernel.h"
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#include "paddle/phi/backends/xpu/enforce_xpu.h"
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#include "paddle/phi/backends/xpu/xpu_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/xpu/reduce.h"
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namespace phi {
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template <typename T, typename Context>
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void ProdKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const IntArray& dims,
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bool keep_dim,
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bool reduce_all,
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DenseTensor* out) {
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if (x.numel() == 0) {
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Full<T, Context>(dev_ctx, out->dims(), 1, out);
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return;
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}
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reduce_all = recompute_reduce_all(x, dims, reduce_all);
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using XPUType = typename XPUTypeTrait<T>::Type;
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auto f = [](xpu::Context* xpu_ctx,
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const T* x,
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T* y,
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const std::vector<int64_t>& xdims,
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const std::vector<int64_t>& reduce_dims) {
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return xpu::reduce_prod<XPUType>(xpu_ctx,
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reinterpret_cast<const XPUType*>(x),
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reinterpret_cast<XPUType*>(y),
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xdims,
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reduce_dims);
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};
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int r = XPUReduce<Context, T>(
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dev_ctx, x, dims.GetData(), keep_dim, reduce_all, out, f);
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PADDLE_ENFORCE_XDNN_SUCCESS(r, "reduce_prod");
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}
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} // namespace phi
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PD_REGISTER_KERNEL(
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prod, XPU, ALL_LAYOUT, phi::ProdKernel, float, int, int64_t) {}
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