mirror of
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836 lines
35 KiB
Python
836 lines
35 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 random
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import time
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from typing import Dict, List, Optional
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import numpy as np
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import paddle
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import paddle.nn as nn
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from fastdeploy.config import FDConfig
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from fastdeploy.engine.request import Request
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from fastdeploy.model_executor.forward_meta import ForwardMeta, XPUForwardMeta
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from fastdeploy.model_executor.layers.attention import get_attention_backend
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from fastdeploy.model_executor.layers.attention.base_attention_backend import \
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AttentionBackend
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from fastdeploy.model_executor.layers.rotary_embedding import get_rope
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from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
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from fastdeploy.model_executor.layers.sample.sampler import Sampler
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from fastdeploy.model_executor.model_loader import get_model_from_loader
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from fastdeploy.utils import get_logger
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from fastdeploy.worker.model_runner_base import ModelRunnerBase
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from fastdeploy.worker.output import ModelOutputData, ModelRunnerOutput
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logger = get_logger("xpu_model_runner", "xpu_model_runner.log")
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def xpu_pre_process(
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max_len: int,
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input_ids: paddle.Tensor,
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seq_lens_this_time: int,
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share_inputs: Dict,
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use_speculate_method: 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) -> XPUForwardMeta:
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"""
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"""
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cum_offsets_now = paddle.cumsum(max_len - seq_lens_this_time)
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token_num = paddle.sum(seq_lens_this_time)
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from fastdeploy.model_executor.ops.xpu import (adjust_batch,
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get_infer_param,
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get_padding_offset)
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(
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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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) = get_padding_offset(input_ids, cum_offsets_now, token_num,
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seq_lens_this_time)
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share_inputs["ids_remove_padding"] = None # set this after adjust batch
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share_inputs["cum_offsets"] = cum_offsets
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share_inputs["batch_id_per_token"] = batch_id_per_token
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share_inputs["cu_seqlens_q"] = cu_seqlens_q
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share_inputs["cu_seqlens_k"] = cu_seqlens_k
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xpu_forward_meta = XPUForwardMeta(
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input_ids=share_inputs["input_ids"],
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ids_remove_padding=share_inputs["ids_remove_padding"],
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rotary_embs=share_inputs["rope_emb"],
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attn_backend=None,
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seq_lens_encoder=share_inputs["seq_lens_encoder"],
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seq_lens_decoder=share_inputs["seq_lens_decoder"],
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seq_lens_this_time=share_inputs["seq_lens_this_time"],
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cum_offsets=share_inputs["cum_offsets"],
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batch_id_per_token=share_inputs["batch_id_per_token"],
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cu_seqlens_q=share_inputs["cu_seqlens_q"],
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cu_seqlens_k=share_inputs["cu_seqlens_k"],
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block_tables=share_inputs["block_tables"],
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caches=share_inputs["caches"]
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)
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# Get xpu extra param
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(
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xpu_forward_meta.encoder_batch_map,
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xpu_forward_meta.decoder_batch_map,
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xpu_forward_meta.encoder_batch_idx,
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xpu_forward_meta.decoder_batch_idx,
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xpu_forward_meta.encoder_seq_lod,
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xpu_forward_meta.decoder_context_len,
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xpu_forward_meta.decoder_context_len_cache,
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xpu_forward_meta.encoder_batch_map_cpu,
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xpu_forward_meta.decoder_batch_map_cpu,
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xpu_forward_meta.encoder_batch_idx_cpu,
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xpu_forward_meta.decoder_batch_idx_cpu,
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xpu_forward_meta.encoder_seq_lod_cpu,
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xpu_forward_meta.decoder_context_len_cpu,
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xpu_forward_meta.decoder_context_len_cache_cpu,
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xpu_forward_meta.enc_batch,
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xpu_forward_meta.dec_batch,
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xpu_forward_meta.total_enc_len,
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) = get_infer_param(seq_lens_encoder, seq_lens_decoder)
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# Adjust batch
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adjusted_input = adjust_batch(
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ids_remove_padding.reshape([-1, 1]),
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cum_offsets,
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xpu_forward_meta.encoder_seq_lod,
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xpu_forward_meta.encoder_batch_idx,
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xpu_forward_meta.decoder_batch_idx,
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xpu_forward_meta.encoder_seq_lod_cpu,
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xpu_forward_meta.encoder_batch_idx_cpu,
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xpu_forward_meta.decoder_batch_idx_cpu,
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xpu_forward_meta.enc_batch,
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xpu_forward_meta.dec_batch,
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None, # output_padding_offset
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-1, # max_input_length
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)
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adjusted_input = adjusted_input.squeeze(1)
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share_inputs["ids_remove_padding"] = adjusted_input
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xpu_forward_meta.ids_remove_padding = adjusted_input
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return xpu_forward_meta
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def xpu_process_output(
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forward_output,
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cum_offsets: paddle.Tensor,
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xpu_forward_meta: XPUForwardMeta,
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) -> paddle.Tensor:
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"""
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"""
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from fastdeploy.model_executor.ops.xpu import gather_next_token
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hiddden_states = gather_next_token(
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forward_output,
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cum_offsets,
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xpu_forward_meta.encoder_seq_lod,
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xpu_forward_meta.encoder_batch_map,
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xpu_forward_meta.decoder_batch_map,
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xpu_forward_meta.encoder_seq_lod_cpu,
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xpu_forward_meta.encoder_batch_map_cpu,
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xpu_forward_meta.decoder_batch_map_cpu,
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xpu_forward_meta.enc_batch,
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xpu_forward_meta.dec_batch,
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None, # output_padding_offset
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-1, # max_input_length
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)
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return hiddden_states
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def xpu_post_process(sampled_token_ids: paddle.Tensor,
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model_output: ModelOutputData,
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skip_save_output: bool) -> None:
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"""
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"""
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from fastdeploy.model_executor.ops.xpu import (save_output,
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set_stop_value_multi_ends,
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update_inputs)
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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,
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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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set_stop_value_multi_ends(sampled_token_ids, 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, False) # multi ends
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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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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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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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save_output(
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sampled_token_ids,
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model_output.not_need_stop,
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model_output.mp_rank,
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False, # use_ep
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)
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def step_paddle(share_inputs: Dict[str, paddle.Tensor], block_size: int,
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enc_dec_block_num: int) -> None:
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"""
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TODO(gongshaotian): normalization name
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"""
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from fastdeploy.model_executor.ops.xpu import step_paddle
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step_paddle(
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share_inputs["stop_flags"],
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share_inputs["seq_lens_this_time"],
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share_inputs["step_seq_lens_encoder"],
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share_inputs["seq_lens_encoder"],
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share_inputs["seq_lens_decoder"],
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share_inputs["block_tables"],
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share_inputs["encoder_block_lens"],
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share_inputs["is_block_step"],
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share_inputs["step_block_list"],
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share_inputs["step_lens"],
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share_inputs["recover_block_list"],
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share_inputs["recover_lens"],
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share_inputs["need_block_list"],
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share_inputs["need_block_len"],
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share_inputs["used_list_len"],
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share_inputs["free_list"],
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share_inputs["free_list_len"],
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share_inputs["input_ids"],
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share_inputs["pre_ids"],
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share_inputs["step_idx"],
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share_inputs["next_tokens"],
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share_inputs["first_token_ids"],
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block_size,
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enc_dec_block_num,
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)
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class XPUModelRunner(ModelRunnerBase):
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""" """
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def __init__(self, fd_config: FDConfig, device: str, rank: int,
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local_rank: int):
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super().__init__(fd_config=fd_config, device=device)
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self.rank = rank
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self.local_rank = local_rank
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# Sampler
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self.sampler = Sampler()
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# Lazy initialize kv cache after model loading
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# self.kv_caches: list[paddle.Tensor] = []
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# Cuda Graph
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self.use_cudagraph = False
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self.input_ids = paddle.zeros(self.parallel_config.max_num_seqs,
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dtype='int32')
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# Initialize share inputs
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self._init_share_inputs(self.fd_config.parallel_config.max_num_seqs)
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self.infer_seed_increment = paddle.full(
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shape=[self.parallel_config.max_num_seqs, 1],
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fill_value=4,
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dtype="int64")
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# Initialize attention Backend
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# Note(gonshaotian): Currently, all attention layers share one attention backend instance.
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# In the future, we will expand it as a list.
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self.attn_backends: list[AttentionBackend] = []
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self.initialize_attn_backend()
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# Forward meta store the global meta information of the forward
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self.forward_meta: ForwardMeta = None
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def process_prefill_inputs(self, req_dicts: List[Request]):
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""" Process inputs for prefill tasks and update share_inputs buffer """
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req_len = len(req_dicts)
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for i in range(req_len):
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request = req_dicts[i]
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idx = request.idx
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length = request.prompt_token_ids_len
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self.share_inputs["input_ids"][idx:idx + 1, :length] = np.array(
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request.prompt_token_ids)
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if len(request.eos_token_ids
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) < self.parallel_config.eos_tokens_lens:
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request.eos_token_ids.append(request.eos_token_ids[0])
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self.share_inputs["eos_token_id"][:] = np.array(
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request.eos_token_ids, dtype="int64").reshape(-1, 1)
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self.share_inputs["pre_ids"][idx:idx + 1] = -1
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self.share_inputs["top_p"][idx:idx + 1] = request.get("top_p", 0.7)
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self.share_inputs["top_k"][idx:idx + 1] = request.get("top_k", 0)
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self.share_inputs["temperature"][idx:idx + 1] = request.get(
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"temperature", 0.95)
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self.share_inputs["penalty_score"][idx:idx + 1] = request.get(
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"repetition_penalty", 1.0)
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self.share_inputs["frequency_score"][idx:idx + 1] = request.get(
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"frequency_penalty", 0.0)
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self.share_inputs["presence_score"][idx:idx + 1] = request.get(
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"presence_penalty", 0.0)
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self.share_inputs["seq_lens_this_time"][idx:idx + 1] = length
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self.share_inputs["step_seq_lens_encoder"][idx:idx + 1] = length
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self.share_inputs["seq_lens_encoder"][idx:idx + 1] = length
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self.share_inputs["seq_lens_decoder"][idx:idx + 1] = 0
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self.share_inputs["step_idx"][idx:idx + 1] = 0
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self.share_inputs["min_dec_len"][idx:idx + 1] = request.get(
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"min_tokens", 1)
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self.share_inputs["max_dec_len"][idx:idx + 1] = request.get(
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"max_tokens", self.model_config.max_model_len)
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self.share_inputs["stop_flags"][idx:idx + 1] = False
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self.share_inputs["first_token_ids"][
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idx:idx + 1] = self.share_inputs["input_ids"][idx:idx + 1, :1]
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self.share_inputs["ori_seq_lens_encoder"][idx:idx + 1] = length
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if request.get("seed") is not None:
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self.share_inputs["infer_seed"][idx:idx +
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1] = request.get("seed")
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encoder_block_num = len(request.get("block_tables"))
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self.share_inputs["encoder_block_lens"][idx:idx +
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1] = encoder_block_num
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self.share_inputs["block_tables"][idx:idx + 1, :] = -1
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self.share_inputs["block_tables"][
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idx:idx + 1, :encoder_block_num] = np.array(
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request.block_tables, dtype="int32")
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if request.get("stop_token_ids") is not None and request.get(
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"stop_seqs_len") is not None:
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stop_seqs_num = len(request.get("stop_seqs_len"))
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for i in range(stop_seqs_num,
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self.model_config.max_stop_seqs_num):
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request.stop_seqs_len.append(0)
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self.share_inputs["stop_seqs_len"][:] = np.array(
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request.stop_seqs_len, dtype="int32")
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self.share_inputs["stop_seqs"][:stop_seqs_num, :len(
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request.get("stop_token_ids")[0])] = np.array(
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request.get("stop_token_ids"), dtype="int64")
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self.share_inputs["not_need_stop"][0] = True
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def _init_share_inputs(self, max_num_seqs: int):
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"""Initialize all share buffers for model inputs.
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Note: In the future, we may abandon share buffers.
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"""
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self.MAX_INFER_SEED = 9223372036854775806
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self.share_inputs = {}
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self.share_inputs["pre_ids"] = paddle.full(
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[max_num_seqs, self.parallel_config.max_model_len],
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-1,
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dtype='int64')
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self.share_inputs["input_ids"] = paddle.full(
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[max_num_seqs, self.parallel_config.max_model_len],
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self.parallel_config.pad_token_id,
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dtype='int64')
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self.share_inputs["eos_token_id"] = paddle.full(
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[self.parallel_config.eos_tokens_lens, 1], 0, dtype='int64')
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self.share_inputs["top_p"] = paddle.full([max_num_seqs, 1],
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self.model_config.top_p,
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dtype='float32')
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self.share_inputs["top_k"] = paddle.full([max_num_seqs, 1],
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0,
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dtype='int64')
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self.share_inputs["temperature"] = paddle.full(
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[max_num_seqs, 1], self.model_config.temperature, dtype='float32')
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self.share_inputs["penalty_score"] = paddle.full(
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[max_num_seqs, 1],
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self.model_config.penalty_score,
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dtype='float32')
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self.share_inputs["frequency_score"] = paddle.full(
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[max_num_seqs, 1],
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self.model_config.frequency_score,
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dtype='float32')
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self.share_inputs["presence_score"] = paddle.full(
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[max_num_seqs, 1],
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self.model_config.presence_score,
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dtype='float32')
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self.share_inputs["min_dec_len"] = paddle.full(
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[max_num_seqs, 1], self.model_config.min_length, dtype='int64')
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self.share_inputs["max_dec_len"] = paddle.full(
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[max_num_seqs, 1], self.model_config.max_model_len, dtype='int64')
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self.share_inputs["min_length"] = paddle.full(
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[max_num_seqs, 1], self.model_config.min_length, dtype='int64')
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self.share_inputs["max_length"] = paddle.full(
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[max_num_seqs, 1], self.model_config.max_model_len, dtype='int64')
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self.share_inputs["seq_lens_this_time"] = paddle.full(max_num_seqs,
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0,
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dtype='int32')
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self.share_inputs["seq_lens_encoder"] = paddle.full([max_num_seqs, 1],
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0,
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dtype='int32')
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self.share_inputs["seq_lens_decoder"] = paddle.full([max_num_seqs, 1],
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0,
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dtype='int32')
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self.share_inputs["step_seq_lens_encoder"] = paddle.full(
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[max_num_seqs, 1], 0, dtype='int32')
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self.share_inputs["step_idx"] = paddle.full([max_num_seqs, 1],
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0,
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dtype='int64')
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self.share_inputs["not_need_stop"] = paddle.full(
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[1], False,
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dtype='bool').cpu() # TODO(gongshaotian): move to pinnd memory
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self.share_inputs["stop_flags"] = paddle.full([max_num_seqs, 1],
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True,
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dtype='bool')
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self.share_inputs["stop_nums"] = paddle.full([1],
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max_num_seqs,
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dtype='int64')
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self.share_inputs["bad_tokens"] = paddle.full([1], -1, dtype='int64')
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self.share_inputs["next_tokens"] = paddle.full([max_num_seqs, 1],
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-1,
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dtype='int64')
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self.share_inputs["is_block_step"] = paddle.full([max_num_seqs],
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False,
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dtype='bool')
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self.share_inputs["encoder_block_lens"] = paddle.full([max_num_seqs],
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0,
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dtype='int32')
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self.share_inputs["step_block_list"] = paddle.full([max_num_seqs],
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-1,
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dtype='int32')
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self.share_inputs["step_lens"] = paddle.full([1], 0, dtype='int32')
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self.share_inputs["recover_block_list"] = paddle.full([max_num_seqs],
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-1,
|
|
dtype='int32')
|
|
self.share_inputs["recover_lens"] = paddle.full([1], 0, dtype='int32')
|
|
self.share_inputs["need_block_list"] = paddle.full([max_num_seqs],
|
|
-1,
|
|
dtype='int32')
|
|
self.share_inputs["need_block_len"] = paddle.full([1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["used_list_len"] = paddle.full([max_num_seqs],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["infer_seed"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int64')
|
|
self.share_inputs["first_token_ids"] = paddle.full([max_num_seqs, 1],
|
|
-1,
|
|
dtype='int64')
|
|
self.share_inputs["ori_seq_lens_encoder"] = paddle.full(
|
|
[max_num_seqs, 1], 0, dtype='int32')
|
|
self.share_inputs["system_lens"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["system_ids"] = paddle.full([max_num_seqs, 1],
|
|
-1,
|
|
dtype='int32')
|
|
|
|
# Initialize rotary position embedding
|
|
tmp_position_ids = paddle.arange(
|
|
self.parallel_config.max_model_len).reshape((1, -1))
|
|
# TODO(gongshaotian): move to models
|
|
self.share_inputs["rope_emb"] = get_rope(
|
|
rotary_dim=self.model_config.head_dim,
|
|
position_ids=tmp_position_ids,
|
|
base=self.model_config.rope_theta,
|
|
model_config=self.model_config)
|
|
|
|
# Set block tables
|
|
pre_max_block_num = (
|
|
self.parallel_config.max_model_len +
|
|
self.parallel_config.block_size - 1
|
|
) // self.parallel_config.block_size + self.parallel_config.enc_dec_block_num
|
|
self.share_inputs["block_tables"] = paddle.full(
|
|
[max_num_seqs, pre_max_block_num], -1, dtype='int32')
|
|
|
|
# Initialize free list
|
|
free_list = list(
|
|
range(
|
|
self.parallel_config.total_block_num - 1,
|
|
int(self.parallel_config.total_block_num *
|
|
self.parallel_config.kv_cache_ratio) - 1, -1))
|
|
self.free_list_len = len(free_list)
|
|
self.share_inputs["free_list"] = paddle.to_tensor(free_list,
|
|
dtype="int32")
|
|
self.share_inputs["free_list_len"] = paddle.full([1],
|
|
self.free_list_len,
|
|
dtype="int32")
|
|
|
|
# Initialize stop seqs
|
|
self.share_inputs["stop_seqs_len"] = paddle.full(
|
|
[self.model_config.max_stop_seqs_num], 0, dtype="int32")
|
|
self.share_inputs["stop_seqs"] = paddle.full([
|
|
self.model_config.max_stop_seqs_num,
|
|
self.model_config.stop_seqs_max_len
|
|
],
|
|
-1,
|
|
dtype="int32")
|
|
|
|
def _prepare_inputs(self) -> None:
|
|
""" prepare the model inputs """
|
|
self.forward_meta = xpu_pre_process(
|
|
self.parallel_config.max_model_len,
|
|
self.share_inputs["input_ids"],
|
|
self.share_inputs["seq_lens_this_time"],
|
|
self.share_inputs,
|
|
use_speculate_method=False,
|
|
draft_tokens=None,
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
)
|
|
self.forward_meta.attn_backend = self.attn_backends[0]
|
|
self.initialize_attention_backend()
|
|
|
|
# Get sampling metadata
|
|
self.sampling_metadata = SamplingMetadata(
|
|
temperature=self.share_inputs["temperature"],
|
|
top_p=self.share_inputs["top_p"],
|
|
top_k=self.share_inputs["top_k"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
pre_token_ids=self.share_inputs["pre_ids"],
|
|
frequency_penalties=self.share_inputs["frequency_score"],
|
|
presence_penalties=self.share_inputs["presence_score"],
|
|
repetition_penalties=self.share_inputs["penalty_score"],
|
|
min_dec_lens=self.share_inputs["min_dec_len"],
|
|
bad_words_token_ids=self.share_inputs["bad_tokens"],
|
|
eos_token_ids=self.share_inputs["eos_token_id"],
|
|
)
|
|
|
|
def load_model(self) -> None:
|
|
""" load or download model """
|
|
logger.info(
|
|
f"Starting to load model {self.model_config.architectures[0]}")
|
|
time_before_load = time.perf_counter()
|
|
# 1. Load original model
|
|
self.model = get_model_from_loader(fd_config=self.fd_config)
|
|
|
|
# 2. Load lora model
|
|
|
|
# 3. Load drafter model(for speculative decoding)
|
|
|
|
time_after_load = time.perf_counter()
|
|
logger.info(
|
|
f"Model loading took {time_after_load - time_before_load} seconds")
|
|
|
|
def get_model(self) -> nn.Layer:
|
|
""" get current model """
|
|
return self.model
|
|
|
|
def initialize_attention_backend(self):
|
|
"""
|
|
Initialize attention meta data
|
|
"""
|
|
# Initialzie attention meta data
|
|
for attn_backend in self.attn_backends:
|
|
attn_backend.init_attention_metadata(self.forward_meta)
|
|
|
|
def initialize_kv_cache(self) -> None:
|
|
"""
|
|
Initialize kv cache
|
|
"""
|
|
cache_kvs = {}
|
|
max_block_num = self.num_gpu_blocks
|
|
|
|
cache_type = self.parallel_config.dtype
|
|
|
|
if (self.quant_config
|
|
and hasattr(self.quant_config, "kv_cache_quant_type")
|
|
and self.quant_config.kv_cache_quant_type is not None):
|
|
cache_type = 'uint8'
|
|
|
|
kv_cache_shape = self.attn_backends[0].get_kv_cache_shape(
|
|
max_num_blocks=max_block_num)
|
|
|
|
for i in range(self.model_config.num_hidden_layers):
|
|
cache_kvs["key_caches_{}".format(i)] = paddle.full(
|
|
shape=kv_cache_shape,
|
|
fill_value=0,
|
|
dtype=cache_type,
|
|
)
|
|
cache_kvs["value_caches_{}".format(i)] = paddle.full(
|
|
shape=kv_cache_shape,
|
|
fill_value=0,
|
|
dtype=cache_type,
|
|
)
|
|
self.share_inputs["caches"] = list(cache_kvs.values())
|
|
for value in cache_kvs.values():
|
|
del value
|
|
paddle.device.xpu.empty_cache()
|
|
|
|
def initialize_attn_backend(self) -> None:
|
|
"""
|
|
Initialize attention backends and forward metadata
|
|
"""
|
|
assert len(self.attn_backends) == 0
|
|
|
|
# TODO(gongshaotian): Get rank from config
|
|
num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_size
|
|
self.model_config.kv_num_heads = int(
|
|
self.model_config.num_key_value_heads
|
|
) // self.parallel_config.tensor_parallel_size
|
|
head_dim = self.model_config.head_dim
|
|
|
|
# Get the attention backend
|
|
attn_cls = get_attention_backend()
|
|
attn_backend = attn_cls(self.fd_config,
|
|
kv_num_heads=self.model_config.kv_num_heads,
|
|
num_heads=num_heads,
|
|
head_dim=head_dim)
|
|
if attn_backend is None:
|
|
raise NotImplementedError(
|
|
"Attention backend which you specified is not supported, please set FD_ATTENTION_BACKEND correctly."
|
|
)
|
|
self.attn_backends.append(attn_backend)
|
|
|
|
def capture_model(self) -> None:
|
|
"""
|
|
Trigger CUDA Graph capture for all shapes in 'CudaGraphConfig.cudagraph_capture_sizes'
|
|
"""
|
|
logger.warn("XPU not support cuda graph currently")
|
|
pass
|
|
|
|
def prefill_finished(self):
|
|
"""
|
|
check whether prefill stage finished
|
|
"""
|
|
if int(paddle.max(self.share_inputs['seq_lens_encoder'])) != 0:
|
|
return 1
|
|
else:
|
|
return 0
|
|
|
|
def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int):
|
|
""" Set dummy prefill inputs to share_inputs """
|
|
full_length = min(num_tokens // batch_size,
|
|
self.parallel_config.max_model_len - 10)
|
|
input_length = int(full_length - 512)
|
|
block_num = (
|
|
input_length + self.parallel_config.block_size - 1
|
|
) // self.parallel_config.block_size + self.parallel_config.enc_dec_block_num
|
|
|
|
for i in range(batch_size):
|
|
idx = i
|
|
self.share_inputs["input_ids"][idx:idx +
|
|
1, :input_length] = np.array(
|
|
[5] * input_length)
|
|
self.share_inputs["eos_token_id"][:] = np.array(
|
|
[2], dtype="int64").reshape(-1, 1)
|
|
self.share_inputs["seq_lens_this_time"][idx:idx + 1] = input_length
|
|
self.share_inputs["step_seq_lens_encoder"][idx:idx +
|
|
1] = input_length
|
|
self.share_inputs["seq_lens_encoder"][idx:idx + 1] = input_length
|
|
self.share_inputs["seq_lens_decoder"][idx:idx + 1] = 0
|
|
self.share_inputs["step_idx"][idx:idx + 1] = 0
|
|
self.share_inputs["max_dec_len"][idx:idx + 1] = 10
|
|
self.share_inputs["stop_flags"][idx:idx + 1] = False
|
|
|
|
self.share_inputs["first_token_ids"][
|
|
idx:idx + 1] = self.share_inputs["input_ids"][idx:idx + 1, :1]
|
|
self.share_inputs["ori_seq_lens_encoder"][idx:idx +
|
|
1] = input_length
|
|
|
|
self.share_inputs["infer_seed"][idx:idx + 1] = random.randint(
|
|
0, 922337203685477580)
|
|
self.share_inputs["encoder_block_lens"][idx:idx + 1] = block_num
|
|
self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(idx * block_num, \
|
|
(idx + 1) * block_num, 1)
|
|
|
|
def _dummy_run(self,
|
|
num_tokens: paddle.Tensor,
|
|
batch_size: paddle.Tensor,
|
|
in_capturing: bool = False) -> paddle.Tensor:
|
|
"""
|
|
Use dummy inputs to run before formal execution.
|
|
Args:
|
|
num_tokens: Expected number of tokens generated
|
|
"""
|
|
self._dummy_prefill_inputs(num_tokens, batch_size)
|
|
|
|
while True:
|
|
self.execute_model(None, True)
|
|
|
|
if int((self.share_inputs['seq_lens_this_time'] > 0).sum()) == 0:
|
|
break
|
|
|
|
def execute_model(
|
|
self,
|
|
model_forward_batch: Optional[List[Request]] = None,
|
|
is_dummy_run: bool = False,
|
|
) -> Optional[ModelRunnerOutput]:
|
|
"""
|
|
The Entrance of model execute.
|
|
Args:
|
|
model_forward_batch: 'Request' contains information related to prompt and is an abstract
|
|
class at the server level, which is too granular for ModelRunner.
|
|
We plan to replace it with 'ModelForwardBatch'.
|
|
intermediate_tensors:
|
|
"""
|
|
# 1. Prepare inputs of model and decoder.
|
|
self._prepare_inputs()
|
|
|
|
# 2. Padding inputs for cuda grph
|
|
|
|
# 3. Execute model
|
|
model_output = self.model(self.share_inputs["ids_remove_padding"],
|
|
self.forward_meta)
|
|
|
|
hiddden_states = xpu_process_output(model_output,
|
|
self.share_inputs["cum_offsets"],
|
|
self.forward_meta)
|
|
|
|
# 4. Compute logits, Sample
|
|
logits = self.model.compute_logits(hiddden_states)
|
|
|
|
sampler_output = self.sampler(logits, self.sampling_metadata)
|
|
|
|
# 5. Speculative decode
|
|
|
|
# 6. Post Process
|
|
model_output_data = ModelOutputData(
|
|
next_tokens=self.share_inputs["next_tokens"],
|
|
stop_flags=self.share_inputs["stop_flags"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
max_dec_len=self.share_inputs["max_dec_len"],
|
|
pre_ids=self.share_inputs["pre_ids"],
|
|
seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
|
|
eos_token_id=self.share_inputs["eos_token_id"],
|
|
not_need_stop=self.share_inputs["not_need_stop"],
|
|
input_ids=self.share_inputs["input_ids"],
|
|
stop_nums=self.share_inputs["stop_nums"],
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
is_block_step=self.share_inputs["is_block_step"],
|
|
msg_queue_id=self.parallel_config.msg_queue_id,
|
|
mp_rank=self.local_rank,
|
|
use_ep=self.parallel_config.use_ep,
|
|
# 投机解码
|
|
full_hidden_states=None,
|
|
draft_tokens=None,
|
|
actual_draft_token_num=None,
|
|
accept_tokens=None,
|
|
accept_num=None,
|
|
)
|
|
xpu_post_process(sampled_token_ids=sampler_output.sampled_token_ids,
|
|
model_output=model_output_data,
|
|
skip_save_output=is_dummy_run)
|
|
|
|
# 7. Updata 'infer_seed' and step_paddle()
|
|
self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
|
|
self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
|
|
step_paddle(self.share_inputs, self.parallel_config.block_size,
|
|
self.parallel_config.enc_dec_block_num)
|
|
|
|
return None
|
|
|
|
def prepare_profile(self) -> None:
|
|
"""Prepare the profile run by setting the block number and initializing the KV cache."""
|
|
paddle.device.xpu.empty_cache()
|
|
self.num_gpu_blocks = self.parallel_config.total_block_num
|
|
self.initialize_kv_cache()
|
|
|
|
def profile_run(self) -> None:
|
|
"""Execute a forward pass with dummy inputs to profile the memory usage of the model."""
|
|
|
|
self._dummy_run(num_tokens=int(
|
|
self.parallel_config.max_num_batched_tokens),
|
|
batch_size=min(self.parallel_config.max_num_seqs, 1))
|
|
|
|
def clear_block_table(self) -> None:
|
|
"""
|
|
Clear the block tables and kv cache after profiling.
|
|
"""
|
|
del self.share_inputs["caches"]
|
|
if self.forward_meta is not None:
|
|
del self.forward_meta.caches
|
|
paddle.device.xpu.empty_cache()
|
|
|
|
def cal_theortical_kvcache(self):
|
|
"""
|
|
Calculate the total block memory required at the model level
|
|
TODO(gongshaotian): Move to Attention Backend
|
|
"""
|
|
"""
|
|
Byte of dtype:
|
|
- default(bf16): 2
|
|
- cache_int8: 1
|
|
- cache_int4:
|
|
"""
|
|
cache_quant_dtype = None
|
|
if (self.quant_config
|
|
and hasattr(self.quant_config, "kv_cache_quant_type")
|
|
and self.quant_config.kv_cache_quant_type is not None):
|
|
cache_quant_dtype = self.quant_config.kv_cache_quant_type
|
|
|
|
if cache_quant_dtype is not None: # int8, int8_zp, fp8, fp8_zp
|
|
byte_of_dtype = 1
|
|
else: # default
|
|
byte_of_dtype = 2
|
|
|
|
hidden_dim = self.model_config.head_dim * self.model_config.kv_num_heads
|
|
required_memory = (
|
|
byte_of_dtype * 2 * # k + v
|
|
(self.parallel_config.block_size * hidden_dim) *
|
|
self.model_config.num_hidden_layers)
|
|
return required_memory
|
|
|
|
def update_share_input_block_num(self, num_gpu_blocks: int) -> None:
|
|
"""
|
|
Set a globally unified block number and update the model's shared input.
|
|
Args:
|
|
num_gpu_blocks:
|
|
"""
|
|
self.num_gpu_blocks = num_gpu_blocks
|
|
|
|
# Reset block table and kv cache with global block num
|
|
self.initialize_kv_cache()
|
|
|
|
# Reset free list
|
|
free_list = list(
|
|
range(
|
|
self.num_gpu_blocks - 1,
|
|
int(self.num_gpu_blocks * self.parallel_config.kv_cache_ratio)
|
|
- 1, -1))
|
|
self.free_list_len = len(free_list)
|
|
self.share_inputs.update({
|
|
"free_list":
|
|
paddle.to_tensor(free_list, dtype="int32"),
|
|
"free_list_len":
|
|
paddle.full([1], self.free_list_len, dtype="int32"),
|
|
})
|
|
|
|
def not_need_stop(self) -> bool:
|
|
""" """
|
|
return self.share_inputs["not_need_stop"][0]
|