wehub-resource-sync
89 lines
2.9 KiB
Plaintext
89 lines
2.9 KiB
Plaintext
// Copyright (c) 2024 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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#pragma once
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#include "paddle/phi/kernels/gpu/cvm_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/funcs/eigen/common.h"
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#include "paddle/phi/kernels/impl/cvm_kernel_impl.h"
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namespace phi {
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template <typename T>
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__global__ void CvmComputeKernel(const bool use_cvm,
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const int64_t item_width,
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const T* X,
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T* Y,
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int64_t numel) {
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CUDA_KERNEL_LOOP(i, numel) {
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if (use_cvm) {
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if (i % item_width == 0) {
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Y[i] = log(X[i] + 1);
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} else if (i % item_width == 1) {
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Y[i] = log(X[i] + 1) - log(X[i - 1] + 1);
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} else {
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Y[i] = X[i];
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}
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} else {
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Y[i] = X[i / (item_width - 2) * item_width + i % (item_width - 2) + 2];
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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 CVMCUDAKernel(const Context& dev_ctx,
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const DenseTensor& x_in,
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const DenseTensor& cvm,
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bool use_cvm,
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DenseTensor* out) {
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const auto* x = &x_in;
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const T* x_data = x->data<T>();
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auto batch_size = x->dims()[0];
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auto numel = x->numel();
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auto item_size = numel / batch_size;
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auto* y = out;
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T* y_data = dev_ctx.template Alloc<T>(y);
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// for Input X do not have Lod Information.
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auto stream = dev_ctx.stream();
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if (x->NumLevels() == 0) {
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CvmComputeKernel<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) /
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PADDLE_CUDA_NUM_THREADS,
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PADDLE_CUDA_NUM_THREADS,
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0,
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stream>>>(
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use_cvm, item_size, x_data, y_data, y->numel());
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} else {
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auto lod = x->lod()[0];
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PADDLE_ENFORCE_EQ(
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batch_size,
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lod[lod.size() - 1],
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common::errors::PreconditionNotMet(
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"Input(X)'s dim[0] must be equal to last element of lod"));
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CvmComputeKernel<<<(numel + PADDLE_CUDA_NUM_THREADS - 1) /
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PADDLE_CUDA_NUM_THREADS,
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PADDLE_CUDA_NUM_THREADS,
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0,
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stream>>>(
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use_cvm, item_size, x_data, y_data, y->numel());
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(cvm, GPU, ALL_LAYOUT, phi::CVMCUDAKernel, float, double) {}
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