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272 lines
11 KiB
C++
272 lines
11 KiB
C++
// 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 <vector>
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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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template <typename T>
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void RebuildPaddingCPUImpl(T *output_data,
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const T *input_data,
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const int *cu_seqlens_q_data,
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const int *seq_len_this_time_data,
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const int *seq_lens_decoder_data,
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const int *seq_lens_encoder_data,
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int max_input_length,
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int dim_embed,
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const int elem_nums) {
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for (int i = 0; i < elem_nums; ++i) {
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const int bi = i / dim_embed;
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const int bias_idx = i % dim_embed;
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int seq_id = 0;
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if (seq_len_this_time_data[bi] == 0) {
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continue;
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}
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if (seq_lens_decoder_data[bi] == 0 && seq_lens_encoder_data[bi] == 0) {
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continue;
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}
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if (seq_lens_encoder_data[bi] > 0) {
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seq_id = seq_lens_encoder_data[bi] - 1;
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}
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const int ori_token_idx = cu_seqlens_q_data[bi] + seq_id;
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const int src_offset = ori_token_idx * dim_embed + bias_idx;
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output_data[i] = input_data[src_offset];
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}
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}
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template <typename T>
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void RebuildAppendPaddingCPUImpl(T *output_data,
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const T *input_data,
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const int *cu_seqlens_q_data,
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const int *seq_len_this_time_data,
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const int *seq_lens_decoder_data,
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const int *seq_lens_encoder_data,
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const int *output_padding_offset_data,
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const int max_input_length,
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const int dim_embed,
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const int64_t output_elem_nums) {
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for (int i = 0; i < output_elem_nums; ++i) {
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int out_token_id = i / dim_embed;
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int ori_token_id =
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out_token_id + output_padding_offset_data[out_token_id];
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int bi = ori_token_id / max_input_length;
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if (seq_len_this_time_data[bi] == 0 ||
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(seq_lens_decoder_data[bi] == 0 &&
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seq_lens_encoder_data[bi] == 0)) {
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continue;
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}
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int seq_id = 0;
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if (seq_lens_encoder_data[bi] > 0) {
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seq_id = seq_lens_encoder_data[bi] - 1;
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}
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int input_token_id = cu_seqlens_q_data[bi] + seq_id;
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int bias_idx = i % dim_embed;
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int src_offset = input_token_id * dim_embed + bias_idx;
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output_data[i] = input_data[src_offset];
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}
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}
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std::vector<paddle::Tensor> RebuildPaddingCPU(
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const paddle::Tensor &tmp_out,
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const paddle::Tensor &cu_seqlens_q,
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const paddle::Tensor &seq_len_this_time,
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const paddle::Tensor &seq_lens_decoder,
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const paddle::Tensor &seq_lens_encoder,
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const paddle::optional<paddle::Tensor> &output_padding_offset,
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int max_input_length) {
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auto tmp_out_cpu = tmp_out.copy_to(paddle::CPUPlace(), true);
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auto cu_seqlens_q_cpu = cu_seqlens_q.copy_to(paddle::CPUPlace(), true);
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auto seq_len_this_time_cpu =
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seq_len_this_time.copy_to(paddle::CPUPlace(), true);
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auto seq_lens_decoder_cpu =
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seq_lens_decoder.copy_to(paddle::CPUPlace(), true);
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auto seq_lens_encoder_cpu =
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seq_lens_encoder.copy_to(paddle::CPUPlace(), true);
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paddle::optional<paddle::Tensor> output_padding_offset_cpu;
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if (output_padding_offset) {
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output_padding_offset_cpu =
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output_padding_offset->copy_to(paddle::CPUPlace(), true);
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}
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int token_num = tmp_out_cpu.shape()[0];
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int dim_embed = tmp_out_cpu.shape()[1];
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int bsz = cu_seqlens_q_cpu.shape()[0] - 1;
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paddle::Tensor out;
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if (output_padding_offset_cpu) {
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int need_delete_token_num = 0;
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for (int i = 0; i < bsz; ++i) {
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if (seq_lens_encoder_cpu.data<int>()[i] > 0) {
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need_delete_token_num +=
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seq_lens_encoder_cpu.data<int>()[i] - 1;
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}
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}
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int output_token_num = token_num - need_delete_token_num;
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out = paddle::full({output_token_num, dim_embed},
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0,
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tmp_out_cpu.dtype(),
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paddle::CPUPlace());
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} else {
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out = paddle::full(
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{bsz, dim_embed}, 0, tmp_out_cpu.dtype(), paddle::CPUPlace());
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}
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const int *cu_seqlens_q_data = cu_seqlens_q_cpu.data<int>();
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const int *seq_len_this_time_data = seq_len_this_time_cpu.data<int>();
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const int *seq_lens_decoder_data = seq_lens_decoder_cpu.data<int>();
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const int *seq_lens_encoder_data = seq_lens_encoder_cpu.data<int>();
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int elem_nums = out.numel();
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if (output_padding_offset_cpu) {
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const int *output_padding_offset_data =
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output_padding_offset_cpu->data<int>();
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switch (tmp_out_cpu.dtype()) {
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case paddle::DataType::FLOAT32:
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RebuildAppendPaddingCPUImpl<float>(out.data<float>(),
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tmp_out_cpu.data<float>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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output_padding_offset_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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case paddle::DataType::FLOAT16:
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RebuildAppendPaddingCPUImpl<paddle::float16>(
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out.data<paddle::float16>(),
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tmp_out_cpu.data<paddle::float16>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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output_padding_offset_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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case paddle::DataType::BFLOAT16:
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RebuildAppendPaddingCPUImpl<paddle::bfloat16>(
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out.data<paddle::bfloat16>(),
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tmp_out_cpu.data<paddle::bfloat16>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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output_padding_offset_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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default:
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PD_THROW(
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"Unsupported data type for rebuild_padding_cpu. "
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"Only float32, float16, and bfloat16 are supported.");
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}
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} else {
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switch (tmp_out_cpu.dtype()) {
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case paddle::DataType::FLOAT32:
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RebuildPaddingCPUImpl<float>(out.data<float>(),
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tmp_out_cpu.data<float>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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case paddle::DataType::FLOAT16:
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RebuildPaddingCPUImpl<paddle::float16>(
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out.data<paddle::float16>(),
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tmp_out_cpu.data<paddle::float16>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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case paddle::DataType::BFLOAT16:
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RebuildPaddingCPUImpl<paddle::bfloat16>(
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out.data<paddle::bfloat16>(),
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tmp_out_cpu.data<paddle::bfloat16>(),
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cu_seqlens_q_data,
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seq_len_this_time_data,
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seq_lens_decoder_data,
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seq_lens_encoder_data,
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max_input_length,
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dim_embed,
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elem_nums);
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break;
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default:
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PD_THROW(
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"Unsupported data type for rebuild_padding_cpu. "
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"Only float32, float16, and bfloat16 are supported.");
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}
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}
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return {out};
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}
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std::vector<std::vector<int64_t>> RebuildPaddingInferShape(
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const std::vector<int64_t> &tmp_out_shape,
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const std::vector<int64_t> &cu_seqlens_q_shape,
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const std::vector<int64_t> &seq_len_this_time_shape,
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const std::vector<int64_t> &seq_lens_decoder_shape,
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const std::vector<int64_t> &seq_lens_encoder_shape,
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const paddle::optional<std::vector<int64_t>> &output_padding_offset_shape) {
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int64_t dim_embed = tmp_out_shape[1];
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if (output_padding_offset_shape) {
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return {{-1, dim_embed}};
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} else {
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int64_t bsz = cu_seqlens_q_shape[0] - 1;
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return {{bsz, dim_embed}};
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}
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}
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std::vector<paddle::DataType> RebuildPaddingInferDtype(
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const paddle::DataType &tmp_out_dtype,
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const paddle::DataType &cu_seqlens_q_dtype,
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const paddle::DataType &seq_len_this_time_dtype,
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const paddle::DataType &seq_lens_decoder_dtype,
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const paddle::DataType &seq_lens_encoder_dtype,
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const paddle::optional<paddle::DataType> &output_padding_offset_dtype) {
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return {tmp_out_dtype};
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}
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PD_BUILD_STATIC_OP(rebuild_padding_cpu)
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.Inputs({"tmp_out",
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"cu_seqlens_q",
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"seq_len_this_time",
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"seq_lens_decoder",
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"seq_lens_encoder",
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paddle::Optional("output_padding_offset")})
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.Outputs({"out"})
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.Attrs({"max_input_length: int"})
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.SetKernelFn(PD_KERNEL(RebuildPaddingCPU))
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.SetInferShapeFn(PD_INFER_SHAPE(RebuildPaddingInferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(RebuildPaddingInferDtype));
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