mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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778 lines
27 KiB
Python
778 lines
27 KiB
Python
"""
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# 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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"""
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import queue
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from typing import Dict, Optional
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import numpy as np
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import paddle
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from fastdeploy import envs
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from fastdeploy.config import SpeculativeConfig
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from fastdeploy.platforms import current_platform
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if current_platform.is_iluvatar():
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from fastdeploy.model_executor.ops.iluvatar import (
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get_padding_offset,
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save_output,
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set_stop_value_multi_ends,
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step_paddle,
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update_inputs,
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)
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elif current_platform.is_gcu():
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from fastdeploy.model_executor.ops.gcu import (
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get_padding_offset,
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save_output,
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set_stop_value_multi_ends,
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update_inputs,
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)
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elif current_platform.is_dcu():
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from fastdeploy.model_executor.ops.gpu import (
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get_padding_offset,
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save_output,
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set_stop_value_multi_ends,
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step_paddle,
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update_inputs,
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)
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elif current_platform.is_maca():
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from fastdeploy.model_executor.ops.gpu import (
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get_padding_offset,
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save_output,
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set_stop_value_multi_ends,
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step_paddle,
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update_inputs,
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update_inputs_v1,
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)
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elif current_platform.is_intel_hpu():
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pass
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else:
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from fastdeploy.model_executor.ops.gpu import (
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get_padding_offset,
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save_output,
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save_output_topk,
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set_stop_value_multi_ends,
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speculate_get_output_padding_offset,
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speculate_get_padding_offset,
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speculate_get_seq_lens_output,
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speculate_save_output,
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speculate_save_output_topk,
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speculate_set_value_by_flags_and_idx,
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speculate_step_paddle,
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speculate_step_system_cache,
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speculate_update,
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step_paddle,
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step_system_cache,
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update_inputs,
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step_reschedule,
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update_inputs_v1,
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speculate_step_reschedule,
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limit_thinking_content_length_v1,
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limit_thinking_content_length_v2,
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speculate_limit_thinking_content_length_v1,
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speculate_limit_thinking_content_length_v2,
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)
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from fastdeploy.output.stream_transfer_data import DecoderState, StreamTransferData
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from fastdeploy.worker.output import ModelOutputData, ModelRunnerOutput, SamplerOutput
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DISABLE_RECOVER = envs.FD_DISABLED_RECOVER == "1"
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def limit_thinking_content_length(
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limit_strategy: str,
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sampled_token_ids: paddle.Tensor,
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max_think_lens: paddle.Tensor,
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step_idx: paddle.Tensor,
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limit_think_status: paddle.Tensor,
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think_end_id: int,
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line_break_id: int = None,
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):
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if limit_strategy == "</think>":
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# for ernie4_5_vl
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limit_thinking_content_length_v1(
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sampled_token_ids,
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max_think_lens,
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step_idx,
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limit_think_status,
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think_end_id,
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)
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elif limit_strategy == "\n</think>\n\n":
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# for ernie_x1
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assert line_break_id > 0
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limit_thinking_content_length_v2(
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sampled_token_ids,
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max_think_lens,
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step_idx,
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limit_think_status,
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think_end_id,
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line_break_id,
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)
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else:
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raise NotImplementedError(f"Not support {limit_strategy=} for limit thinking content length.")
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def speculate_limit_thinking_content_length(
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limit_strategy: str,
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accept_tokens: paddle.Tensor,
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max_think_lens: paddle.Tensor,
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step_idx: paddle.Tensor,
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limit_think_status: paddle.Tensor,
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accept_num: paddle.Tensor,
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seq_lens_decoder: paddle.Tensor,
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think_end_id: int,
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line_break_id: int = None,
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):
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if limit_strategy == "</think>":
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# for ernie4_5_vl
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speculate_limit_thinking_content_length_v1(
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accept_tokens,
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max_think_lens,
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step_idx,
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limit_think_status,
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accept_num,
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seq_lens_decoder,
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think_end_id,
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)
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elif limit_strategy == "\n</think>\n\n":
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# for ernie_x1
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assert line_break_id > 0
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speculate_limit_thinking_content_length_v2(
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accept_tokens,
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max_think_lens,
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step_idx,
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limit_think_status,
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accept_num,
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seq_lens_decoder,
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think_end_id,
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line_break_id,
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)
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else:
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raise NotImplementedError(f"Not support {limit_strategy=} for limit thinking content length.")
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def pre_process(
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input_ids: paddle.Tensor,
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seq_lens_this_time: int,
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speculative_decoding: bool,
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draft_tokens: Optional[paddle.Tensor] = None,
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seq_lens_encoder: Optional[paddle.Tensor] = None,
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seq_lens_decoder: Optional[paddle.Tensor] = None,
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):
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"""
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Preprocessing before embedding.
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Args:
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input_ids:
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seq_lens_this_time:
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speculative_decoding:
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draft_tokens:
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seq_lens_encoder:
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Return:
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ids_remove_padding:
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cum_offsets:
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batch_id_per_token:
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cu_seqlens_q:
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cu_seqlens_k:
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"""
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# Remove padding
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max_len = input_ids.shape[1]
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cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time, dtype="int32")
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token_num = paddle.sum(seq_lens_this_time)
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output_padding_offset = None
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output_cum_offsets = None
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if speculative_decoding:
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(
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ids_remove_padding,
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batch_id_per_token,
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cu_seqlens_q,
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cu_seqlens_k,
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) = speculate_get_padding_offset(
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input_ids,
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draft_tokens,
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cum_offsets_now,
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token_num,
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seq_lens_this_time,
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seq_lens_encoder,
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)
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seq_lens_output = speculate_get_seq_lens_output(
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seq_lens_this_time,
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seq_lens_encoder,
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seq_lens_decoder,
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)
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if isinstance(seq_lens_output, list):
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seq_lens_output = seq_lens_output[0]
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output_token_num = paddle.sum(seq_lens_output)
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output_cum_offsets_tmp = paddle.cumsum(max_len - seq_lens_output, dtype="int32")
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output_padding_offset, output_cum_offsets = speculate_get_output_padding_offset(
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output_cum_offsets_tmp,
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output_token_num,
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seq_lens_output,
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max_len,
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)
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else:
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(
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ids_remove_padding,
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batch_id_per_token,
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cu_seqlens_q,
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cu_seqlens_k,
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) = get_padding_offset(input_ids, cum_offsets_now, token_num, seq_lens_this_time)
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return (
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ids_remove_padding,
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batch_id_per_token,
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cu_seqlens_q,
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cu_seqlens_k,
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output_cum_offsets,
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output_padding_offset,
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)
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def _build_stream_transfer_data(output_tokens: np.ndarray):
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"""Split output_tokens and output"""
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output_tokens = output_tokens.reshape([-1]).numpy()
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output_tokens_lists = np.split(output_tokens, output_tokens.shape[0])
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stream_transfer_datas = []
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for bid, output_token_per_sample in enumerate(output_tokens_lists):
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stream_transfer_data = StreamTransferData(
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decoder_state=DecoderState.TEXT, tokens=output_token_per_sample, batch_id=bid
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)
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stream_transfer_datas.append(stream_transfer_data)
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return stream_transfer_datas
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def post_process_normal(
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sampler_output: SamplerOutput,
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model_output: ModelOutputData,
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share_inputs: Dict[str, paddle.Tensor],
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block_size: int = 64,
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save_each_rank: bool = False,
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skip_save_output: bool = False,
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async_output_queue: queue.Queue = None,
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think_end_id: int = -1,
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line_break_id: int = -1,
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) -> ModelRunnerOutput:
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"""Post-processing steps after completing a single token generation."""
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if think_end_id > 0:
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limit_thinking_content_length(
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limit_strategy=envs.FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR,
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sampled_token_ids=sampler_output.sampled_token_ids,
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max_think_lens=share_inputs["max_think_lens"],
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step_idx=share_inputs["step_idx"],
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limit_think_status=share_inputs["limit_think_status"],
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think_end_id=think_end_id,
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line_break_id=line_break_id,
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)
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# 1. Set stop value
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paddle.assign(
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paddle.where(
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model_output.stop_flags,
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model_output.step_idx,
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model_output.step_idx + 1,
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),
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model_output.step_idx,
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)
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length_cond = paddle.greater_equal(model_output.step_idx, model_output.max_dec_len)
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paddle.assign(
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paddle.logical_or(model_output.stop_flags, length_cond),
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model_output.stop_flags,
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)
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if current_platform.is_cuda() or current_platform.is_iluvatar() or current_platform.is_dcu():
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set_stop_value_multi_ends(
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sampler_output.sampled_token_ids,
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model_output.stop_flags,
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model_output.seq_lens_this_time,
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model_output.eos_token_id,
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model_output.next_tokens,
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model_output.pre_ids,
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model_output.step_idx,
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model_output.stop_token_ids,
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model_output.stop_seqs_len,
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False,
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) # multi ends
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elif current_platform.is_maca():
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set_stop_value_multi_ends(
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sampler_output.sampled_token_ids,
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model_output.stop_flags,
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model_output.seq_lens_this_time,
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model_output.eos_token_id,
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model_output.next_tokens,
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model_output.pre_ids,
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model_output.step_idx,
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model_output.stop_token_ids,
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model_output.stop_seqs_len,
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False,
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) # multi ends
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else:
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set_stop_value_multi_ends(
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sampler_output.sampled_token_ids,
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model_output.stop_flags,
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model_output.seq_lens_this_time,
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model_output.eos_token_id,
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model_output.next_tokens,
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False,
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)
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# 2. Update the input buffer of the model
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with paddle.framework._no_check_dy2st_diff():
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if envs.ENABLE_V1_KVCACHE_SCHEDULER:
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update_inputs_v1(
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model_output.stop_flags,
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model_output.not_need_stop,
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model_output.seq_lens_this_time,
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model_output.seq_lens_encoder,
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model_output.seq_lens_decoder,
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share_inputs["step_seq_lens_decoder"],
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share_inputs["prompt_lens"],
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sampler_output.sampled_token_ids,
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model_output.input_ids,
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share_inputs["block_tables"],
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model_output.stop_nums,
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model_output.next_tokens,
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model_output.is_block_step,
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block_size,
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)
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else:
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update_inputs(
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model_output.stop_flags,
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model_output.not_need_stop,
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model_output.seq_lens_this_time,
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model_output.seq_lens_encoder,
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model_output.seq_lens_decoder,
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model_output.input_ids,
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model_output.stop_nums,
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sampler_output.sampled_token_ids,
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model_output.is_block_step,
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)
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# 3. Transmit the model's output and stop generation signal via message queue.
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# In the future, we will abandon this approach.
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if not skip_save_output:
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if sampler_output.logprobs_tensors is None:
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if envs.FD_USE_GET_SAVE_OUTPUT_V1:
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if save_each_rank or model_output.mp_rank == 0:
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output = _build_stream_transfer_data(sampler_output.sampled_token_ids)
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async_output_queue.put(output)
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else:
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save_output(
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sampler_output.sampled_token_ids,
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model_output.not_need_stop,
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model_output.mp_rank,
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save_each_rank,
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)
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else:
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save_output_topk(
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sampler_output.sampled_token_ids,
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sampler_output.logprobs_tensors.logprob_token_ids,
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sampler_output.logprobs_tensors.logprobs,
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sampler_output.logprobs_tensors.selected_token_ranks,
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model_output.not_need_stop,
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model_output.mp_rank,
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)
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def post_process_specualate(
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sampler_output: SamplerOutput,
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model_output: ModelOutputData,
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share_inputs: Dict[str, paddle.Tensor],
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save_each_rank: bool = False,
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skip_save_output: bool = False,
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think_end_id: int = -1,
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line_break_id: int = -1,
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):
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if think_end_id > 0:
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speculate_limit_thinking_content_length(
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limit_strategy=envs.FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR,
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accept_tokens=share_inputs["accept_tokens"],
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max_think_lens=share_inputs["max_think_lens"],
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step_idx=share_inputs["step_idx"],
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limit_think_status=share_inputs["limit_think_status"],
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accept_num=share_inputs["accept_num"],
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seq_lens_decoder=share_inputs["seq_lens_decoder"],
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think_end_id=think_end_id,
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line_break_id=line_break_id,
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)
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speculate_update(
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model_output.seq_lens_encoder,
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model_output.seq_lens_decoder,
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model_output.not_need_stop,
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model_output.draft_tokens,
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model_output.actual_draft_token_num,
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model_output.accept_tokens,
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model_output.accept_num,
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model_output.stop_flags,
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model_output.seq_lens_this_time,
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model_output.is_block_step,
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model_output.stop_nums,
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)
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if not skip_save_output:
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if sampler_output.logprobs_tensors is None:
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speculate_save_output(
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model_output.accept_tokens,
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model_output.accept_num,
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model_output.not_need_stop,
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model_output.seq_lens_decoder,
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model_output.prompt_lens,
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model_output.mp_rank,
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save_each_rank,
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envs.ENABLE_V1_KVCACHE_SCHEDULER,
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)
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else:
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speculate_save_output_topk(
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sampler_output.sampled_token_ids,
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sampler_output.logprobs_tensors.logprob_token_ids,
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sampler_output.logprobs_tensors.logprobs,
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sampler_output.logprobs_tensors.selected_token_ranks,
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sampler_output.token_num_per_batch,
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sampler_output.cu_batch_token_offset,
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model_output.not_need_stop,
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3, # mtype
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model_output.mp_rank,
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)
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# Update pre_ids through accept tokens
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speculate_set_value_by_flags_and_idx(
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model_output.pre_ids,
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model_output.accept_tokens,
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model_output.accept_num,
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model_output.stop_flags,
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model_output.seq_lens_this_time,
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model_output.seq_lens_encoder,
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model_output.seq_lens_decoder,
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model_output.step_idx,
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)
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def post_process(
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sampler_output: SamplerOutput,
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model_output: ModelOutputData,
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share_inputs: Dict[str, paddle.Tensor],
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block_size: int = 64,
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save_each_rank: bool = False,
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speculative_decoding: bool = False,
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skip_save_output: bool = False,
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async_output_queue: queue.Queue = None,
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think_end_id: int = -1,
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line_break_id: int = -1,
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) -> None:
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"""Post-processing steps after completing a single token generation."""
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if speculative_decoding:
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post_process_specualate(
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sampler_output,
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model_output,
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share_inputs,
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save_each_rank,
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skip_save_output,
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think_end_id,
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line_break_id,
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)
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else:
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post_process_normal(
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sampler_output,
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model_output,
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share_inputs,
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block_size,
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save_each_rank,
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skip_save_output,
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async_output_queue,
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think_end_id,
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line_break_id,
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)
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def step_cuda(
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share_inputs: Dict[str, paddle.Tensor],
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block_size: int,
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enc_dec_block_num: int,
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speculative_config: SpeculativeConfig,
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enable_prefix_caching: bool = False,
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) -> None:
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"""
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TODO(gongshaotian): normalization name
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"""
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|
if speculative_config.method is not None:
|
|
if DISABLE_RECOVER:
|
|
speculate_step_reschedule(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
share_inputs["accept_num"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
speculative_config.num_speculative_tokens,
|
|
)
|
|
else:
|
|
if enable_prefix_caching:
|
|
speculate_step_system_cache(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["step_seq_lens_decoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
share_inputs["accept_num"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
speculative_config.num_speculative_tokens,
|
|
)
|
|
else:
|
|
speculate_step_paddle(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
share_inputs["accept_num"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
speculative_config.num_speculative_tokens,
|
|
)
|
|
else:
|
|
if DISABLE_RECOVER:
|
|
step_reschedule(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
)
|
|
else:
|
|
if enable_prefix_caching:
|
|
step_system_cache(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["step_seq_lens_decoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
)
|
|
else:
|
|
step_paddle(
|
|
share_inputs["stop_flags"],
|
|
share_inputs["seq_lens_this_time"],
|
|
share_inputs["step_seq_lens_encoder"],
|
|
share_inputs["seq_lens_encoder"],
|
|
share_inputs["seq_lens_decoder"],
|
|
share_inputs["block_tables"],
|
|
share_inputs["encoder_block_lens"],
|
|
share_inputs["is_block_step"],
|
|
share_inputs["step_block_list"],
|
|
share_inputs["step_lens"],
|
|
share_inputs["recover_block_list"],
|
|
share_inputs["recover_lens"],
|
|
share_inputs["need_block_list"],
|
|
share_inputs["need_block_len"],
|
|
share_inputs["used_list_len"],
|
|
share_inputs["free_list"],
|
|
share_inputs["free_list_len"],
|
|
share_inputs["input_ids"],
|
|
share_inputs["pre_ids"],
|
|
share_inputs["step_idx"],
|
|
share_inputs["next_tokens"],
|
|
share_inputs["first_token_ids"],
|
|
block_size,
|
|
enc_dec_block_num,
|
|
)
|
|
|
|
|
|
def rebuild_padding(
|
|
tmp_out: paddle.Tensor,
|
|
cu_seqlens_q: paddle.Tensor,
|
|
seq_len_this_time: paddle.Tensor,
|
|
seq_lens_decoder: paddle.Tensor,
|
|
seq_lens_encoder: paddle.Tensor,
|
|
output_padding_offset: Optional[paddle.Tensor] = None,
|
|
max_input_length: Optional[int] = None,
|
|
first_token_out: Optional[paddle.Tensor] = None,
|
|
enable_logprob: Optional[bool] = False,
|
|
):
|
|
"""
|
|
Args:
|
|
Returns:
|
|
"""
|
|
if current_platform.is_cuda():
|
|
from fastdeploy.model_executor.ops.gpu import rebuild_padding
|
|
|
|
hidden_states = rebuild_padding(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
first_token_out,
|
|
max_input_length,
|
|
enable_logprob,
|
|
)
|
|
elif current_platform.is_dcu():
|
|
from fastdeploy.model_executor.ops.gpu import rebuild_padding
|
|
|
|
hidden_states = rebuild_padding(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
max_input_length,
|
|
)
|
|
elif current_platform.is_iluvatar():
|
|
from fastdeploy.model_executor.ops.iluvatar import rebuild_padding
|
|
|
|
hidden_states = rebuild_padding(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
first_token_out,
|
|
max_input_length,
|
|
enable_logprob,
|
|
)
|
|
elif current_platform.is_gcu():
|
|
from fastdeploy.model_executor.ops.gcu import rebuild_padding
|
|
|
|
hidden_states = rebuild_padding(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
max_input_length,
|
|
)
|
|
elif current_platform.is_cpu():
|
|
from fastdeploy.model_executor.ops.cpu import rebuild_padding_cpu
|
|
|
|
hidden_states = rebuild_padding_cpu(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
max_input_length,
|
|
)
|
|
elif current_platform.is_maca():
|
|
from fastdeploy.model_executor.ops.gpu import rebuild_padding
|
|
|
|
hidden_states = rebuild_padding(
|
|
tmp_out,
|
|
cu_seqlens_q,
|
|
seq_len_this_time,
|
|
seq_lens_decoder,
|
|
seq_lens_encoder,
|
|
output_padding_offset,
|
|
max_input_length,
|
|
)
|
|
else:
|
|
raise RuntimeError("Not supported platform")
|
|
return hidden_states
|