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108 lines
4.3 KiB
Plaintext
108 lines
4.3 KiB
Plaintext
// Copyright (c) 2025 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/extension.h"
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#ifndef PD_BUILD_STATIC_OP
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#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
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#endif
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__global__ void System2GroupKernel(int* group_ids,
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int* group_lens,
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int* group_lens_without_encoder,
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int* dec_group_num,
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const int* seq_lens_this_time,
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const int* seq_lens_encoder,
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const int* system_ids,
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const int bsz,
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const int max_bsz) {
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const int ti = threadIdx.x;
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if (ti < bsz) {
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if (seq_lens_this_time[ti] <=
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0) { // 终止位置不参与分组，encoder需要是一个特定的system
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// id，在seqs2seqs里处理
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return;
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}
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int group_id = system_ids[ti];
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int group_len_now = atomicAdd(&group_lens[group_id], 1);
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if (seq_lens_encoder[ti] <= 0) { // is decoder
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atomicAdd(dec_group_num, 1);
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atomicAdd(&group_lens_without_encoder[group_id], 1);
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}
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group_ids[group_id * max_bsz + group_len_now] = ti;
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}
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}
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std::vector<paddle::Tensor> System2Group(
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const paddle::Tensor& system_ids,
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const paddle::Tensor& seq_lens_this_time,
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const paddle::Tensor& seq_lens_encoder) {
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auto cu_stream = seq_lens_this_time.stream();
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const int bsz = seq_lens_this_time.shape()[0];
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const int max_bsz = seq_lens_encoder.shape()[0];
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auto group_ids = paddle::full({bsz, max_bsz},
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-1,
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paddle::DataType::INT32,
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seq_lens_this_time.place());
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auto group_lens = paddle::full(
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{bsz, 1}, 0, paddle::DataType::INT32, seq_lens_this_time.place());
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auto group_lens_without_encoder = paddle::full(
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{bsz, 1}, 0, paddle::DataType::INT32, seq_lens_this_time.place());
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auto dec_group_num = paddle::full(
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{1}, 0, paddle::DataType::INT32, seq_lens_this_time.place());
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const int blockSize = (bsz + 32 - 1) / 32 * 32;
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System2GroupKernel<<<1, blockSize, 0, cu_stream>>>(
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group_ids.data<int>(),
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group_lens.data<int>(),
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group_lens_without_encoder.data<int>(),
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dec_group_num.data<int>(),
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seq_lens_this_time.data<int>(),
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seq_lens_encoder.data<int>(),
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system_ids.data<int>(),
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bsz,
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max_bsz);
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return {group_ids, group_lens, group_lens_without_encoder, dec_group_num};
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}
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std::vector<std::vector<int64_t>> System2GroupInferShape(
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const std::vector<int64_t>& system_ids_shape,
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const std::vector<int64_t>& seq_lens_this_time_shape,
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const std::vector<int64_t>& seq_lens_encoder_shape) {
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int64_t bsz = seq_lens_this_time_shape[0];
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int64_t max_bsz = seq_lens_encoder_shape[0];
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return {{bsz, max_bsz}, {bsz, 1}, {bsz, 1}, {1}};
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}
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std::vector<paddle::DataType> System2GroupInferDtype(
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const paddle::DataType& system_ids_dtype,
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const paddle::DataType& seq_lens_this_time_dtype,
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const paddle::DataType& seq_lens_encoder_dtype) {
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return {seq_lens_this_time_dtype,
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seq_lens_this_time_dtype,
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seq_lens_this_time_dtype,
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seq_lens_this_time_dtype};
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}
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PD_BUILD_STATIC_OP(system2group)
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.Inputs({"system_ids", "seq_lens_this_time", "seq_lens_encoder"})
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.Outputs({"group_ids",
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"group_lens",
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"group_lens_without_encoder",
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"dec_group_num"})
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.SetKernelFn(PD_KERNEL(System2Group))
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.SetInferShapeFn(PD_INFER_SHAPE(System2GroupInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(System2GroupInferDtype));
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