wehub-resource-sync
218 lines
8.2 KiB
Plaintext
218 lines
8.2 KiB
Plaintext
// 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/temporal_shift_grad_kernel.h"
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#include <cstdint>
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#include "paddle/common/enforce.h"
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#include "paddle/common/layout.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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namespace phi {
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template <typename T, typename IndexT>
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__global__ void KeTemporalShiftBwNCHW(const T* output_grad,
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T* input_grad,
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const IndexT ntchw,
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const IndexT tchw,
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const IndexT chw,
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const IndexT hw,
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const int t,
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const IndexT c1,
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const IndexT c2) {
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IndexT tid = static_cast<IndexT>(blockIdx.x) * blockDim.x + threadIdx.x;
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IndexT stride = static_cast<IndexT>(blockDim.x) * gridDim.x;
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IndexT src_it = 0;
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for (; tid < ntchw; tid += stride) {
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IndexT it = (tid % tchw) / chw;
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IndexT ic = (tid % chw) / hw;
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if (ic < c1) {
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src_it = it + 1;
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} else if (ic < c2) {
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src_it = it - 1;
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} else {
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src_it = it;
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}
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if (src_it >= 0 && src_it < t) {
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input_grad[tid] = output_grad[tid + (src_it - it) * chw];
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} else {
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input_grad[tid] = 0;
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}
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}
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}
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template <typename T, typename IndexT>
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__global__ void KeTemporalShiftBwNHWC(const T* output_grad,
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T* input_grad,
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const IndexT nthwc,
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const IndexT thwc,
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const IndexT hwc,
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const int t,
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const IndexT c,
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const IndexT c1,
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const IndexT c2) {
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IndexT tid = static_cast<IndexT>(blockIdx.x) * blockDim.x + threadIdx.x;
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IndexT stride = static_cast<IndexT>(blockDim.x) * gridDim.x;
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IndexT src_it = 0;
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for (; tid < nthwc; tid += stride) {
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IndexT it = (tid % thwc) / hwc;
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IndexT ic = tid % c;
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if (ic < c1) {
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src_it = it + 1;
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} else if (ic < c2) {
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src_it = it - 1;
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} else {
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src_it = it;
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}
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if (src_it >= 0 && src_it < t) {
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input_grad[tid] = output_grad[tid + (src_it - it) * hwc];
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} else {
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input_grad[tid] = 0;
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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 TemporalShiftGradKernel(const Context& dev_ctx,
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const DenseTensor& out_grad,
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int seg_num,
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float shift_ratio,
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const std::string& data_format_str,
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DenseTensor* x_grad) {
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if (x_grad && x_grad->numel() == 0) {
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dev_ctx.template Alloc<T>(x_grad);
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return;
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}
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auto* input_grad = x_grad;
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auto* output_grad = &out_grad;
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int t = seg_num;
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const DataLayout data_layout = StringToDataLayout(data_format_str);
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const int64_t nt = output_grad->dims()[0];
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const int64_t c = (data_layout == DataLayout::NCHW ? output_grad->dims()[1]
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: output_grad->dims()[3]);
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const int64_t h = (data_layout == DataLayout::NCHW ? output_grad->dims()[2]
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: output_grad->dims()[1]);
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const int64_t w = (data_layout == DataLayout::NCHW ? output_grad->dims()[3]
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: output_grad->dims()[2]);
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const int64_t hw = h * w;
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const int64_t chw = c * hw;
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const int64_t tchw = t * chw;
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const int64_t ntchw = nt * chw;
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const int64_t c1 = static_cast<int64_t>(c * shift_ratio);
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const int64_t c2 = static_cast<int64_t>(c * 2 * shift_ratio);
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DDim in_grad_dims =
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(data_layout == DataLayout::NCHW ? make_ddim({nt, c, h, w})
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: make_ddim({nt, h, w, c}));
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const T* output_grad_data = output_grad->data<T>();
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input_grad->Resize(in_grad_dims);
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T* input_grad_data = dev_ctx.template Alloc<T>(input_grad);
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int64_t pixelNum = nt * chw;
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int64_t threads = 1024;
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int64_t grid = (pixelNum + threads - 1) / threads;
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int64_t blocks_per_sm = dev_ctx.GetMaxPhysicalThreadCount() / threads;
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grid = std::min(dev_ctx.GetSMCount() * blocks_per_sm, grid);
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PADDLE_ENFORCE_LE_UINT32_MAX(grid, "grid");
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PADDLE_ENFORCE_LE_UINT32_MAX(threads, "threads");
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const uint32_t grid_32 = static_cast<uint32_t>(grid);
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const uint32_t threads_32 = static_cast<uint32_t>(threads);
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if (data_layout == DataLayout::NCHW) {
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if (output_grad->numel() < std::numeric_limits<int32_t>::max()) {
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PADDLE_ENFORCE_LE_INT_MAX(ntchw, "ntchw");
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PADDLE_ENFORCE_LE_INT_MAX(tchw, "tchw");
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PADDLE_ENFORCE_LE_INT_MAX(chw, "chw");
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PADDLE_ENFORCE_LE_INT_MAX(hw, "hw");
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PADDLE_ENFORCE_LE_INT_MAX(c1, "c1");
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PADDLE_ENFORCE_LE_INT_MAX(c2, "c2");
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KeTemporalShiftBwNCHW<T, int32_t>
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<<<grid_32, threads_32, 0, dev_ctx.stream()>>>(
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output_grad_data,
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input_grad_data,
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static_cast<int32_t>(ntchw),
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static_cast<int32_t>(tchw),
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static_cast<int32_t>(chw),
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static_cast<int32_t>(hw),
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t,
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static_cast<int32_t>(c1),
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static_cast<int32_t>(c2));
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} else {
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KeTemporalShiftBwNCHW<T, int64_t>
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<<<grid_32, threads_32, 0, dev_ctx.stream()>>>(output_grad_data,
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input_grad_data,
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ntchw,
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tchw,
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chw,
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hw,
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t,
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c1,
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c2);
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}
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} else {
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if (output_grad->numel() < std::numeric_limits<int32_t>::max()) {
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PADDLE_ENFORCE_LE_INT_MAX(ntchw, "ntchw");
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PADDLE_ENFORCE_LE_INT_MAX(tchw, "tchw");
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PADDLE_ENFORCE_LE_INT_MAX(chw, "chw");
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PADDLE_ENFORCE_LE_INT_MAX(c, "c");
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PADDLE_ENFORCE_LE_INT_MAX(c1, "c1");
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PADDLE_ENFORCE_LE_INT_MAX(c2, "c2");
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KeTemporalShiftBwNHWC<T, int32_t>
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<<<grid_32, threads_32, 0, dev_ctx.stream()>>>(
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output_grad_data,
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input_grad_data,
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static_cast<int32_t>(ntchw),
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static_cast<int32_t>(tchw),
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static_cast<int32_t>(chw),
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t,
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static_cast<int32_t>(c),
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static_cast<int32_t>(c1),
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static_cast<int32_t>(c2));
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} else {
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KeTemporalShiftBwNHWC<T, int64_t>
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<<<grid_32, threads_32, 0, dev_ctx.stream()>>>(output_grad_data,
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input_grad_data,
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ntchw,
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tchw,
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chw,
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t,
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c,
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c1,
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c2);
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}
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(temporal_shift_grad,
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GPU,
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ALL_LAYOUT,
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phi::TemporalShiftGradKernel,
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float,
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double,
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phi::float16,
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phi::bfloat16) {}
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