wehub-resource-sync
63 lines
2.4 KiB
Plaintext
63 lines
2.4 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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#include "paddle/phi/kernels/gpu/shuffle_channel_kernel.h"
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#include "paddle/phi/backends/gpu/gpu_primitives.h"
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#include "paddle/phi/core/dense_tensor.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/kernels/gpu/shuffle_channel.h"
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namespace phi {
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template <typename T, typename Context>
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void ShuffleChannelOpCUDAKernel(const Context& dev_ctx,
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const DenseTensor& x,
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int group,
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DenseTensor* out) {
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auto input_dims = x.dims();
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auto num = input_dims[0];
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auto channel = input_dims[1];
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auto height = input_dims[2];
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auto weight = input_dims[3];
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auto feature_map_size = channel * height * weight;
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auto sp_sz = height * weight;
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int group_row = group;
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int group_column = channel / group_row;
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// count is the product of NCHW same as numel()
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int64_t count = num * group_column * group_row * sp_sz;
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int blocks = NumBlocks(out->numel());
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int threads = kNumCUDAThreads;
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const T* input_data = x.data<T>();
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T* output_data = dev_ctx.template Alloc<T>(out);
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ShuffleChannel<T><<<blocks, threads, 0, dev_ctx.stream()>>>(count,
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feature_map_size,
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output_data,
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input_data,
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group_row,
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group_column,
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sp_sz);
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}
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} // namespace phi
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PD_REGISTER_KERNEL(shuffle_channel,
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GPU,
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ALL_LAYOUT,
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phi::ShuffleChannelOpCUDAKernel,
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float,
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double) {}
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