wehub-resource-sync
100 lines
3.3 KiB
Plaintext
100 lines
3.3 KiB
Plaintext
// Copyright (c) 2021 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/dot_kernel.h"
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#include "paddle/common/enforce.h"
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#include "paddle/phi/backends/gpu/gpu_context.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/blas/blas.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/full_kernel.h"
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#include "paddle/phi/kernels/funcs/eigen/eigen_function.h"
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namespace phi {
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template <typename T, typename Context>
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void DotKernel(const Context& dev_ctx,
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const DenseTensor& x,
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const DenseTensor& y,
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DenseTensor* out) {
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if (x.numel() == 0 || y.numel() == 0) {
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// x[2, 1], y[2, 0], out[2]
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Full<T, Context>(dev_ctx, out->dims(), 0, out);
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return;
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}
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if (out->numel() <= 0) {
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return;
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}
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auto x_data = x.data<T>();
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auto y_data = y.data<T>();
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dev_ctx.template Alloc<T>(out);
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auto out_data = out->data<T>();
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if (out->dims().size() == 0) {
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#ifdef PADDLE_WITH_CUDA
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if constexpr (std::is_same_v<T, int> || std::is_same_v<T, int64_t>) {
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auto eigen_out = EigenScalar<T>::From(*out);
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auto eigen_x = EigenVector<T>::Flatten(x);
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auto eigen_y = EigenVector<T>::Flatten(y);
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auto& dev = *dev_ctx.eigen_device();
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eigen_out.device(dev) = (eigen_x * eigen_y).sum();
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} else {
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PADDLE_ENFORCE_LE_INT_MAX(x.numel(), "dot CUDOT n");
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PADDLE_ENFORCE_LE_INT_MAX(x.strides()[0], "dot CUDOT incx");
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const int n = static_cast<int>(x.numel());
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int incx = static_cast<int>(x.strides()[0]);
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int incy = static_cast<int>(x.strides()[0]);
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if (n == 1) {
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incx = 1;
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incy = 1;
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}
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auto blas = funcs::GetBlas<GPUContext, T>(dev_ctx);
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blas.CUDOT(n, x_data, incx, y_data, incy, out_data);
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}
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#else
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auto eigen_out = EigenScalar<T>::From(*out);
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auto eigen_x = EigenVector<T>::Flatten(x);
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auto eigen_y = EigenVector<T>::Flatten(y);
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auto& dev = *dev_ctx.eigen_device();
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eigen_out.device(dev) = (eigen_x * eigen_y).sum();
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#endif
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} else {
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auto eigen_out = EigenVector<T>::From(*out);
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auto eigen_x = EigenMatrix<T>::From(x);
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auto eigen_y = EigenMatrix<T>::From(y);
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auto& dev = *dev_ctx.eigen_device();
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eigen_out.device(dev) = (eigen_x * eigen_y).sum(Eigen::DSizes<int, 1>(1));
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}
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}
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} // namespace phi
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using complex64 = phi::complex64;
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using complex128 = phi::complex128;
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PD_REGISTER_KERNEL(dot,
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GPU,
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ALL_LAYOUT,
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phi::DotKernel,
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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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complex64,
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complex128,
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phi::float16,
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phi::bfloat16) {}
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