mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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59071268b6
* Use PEP 563 in attention.py and fix conflict * merge commit * Change what was left out last time
1269 lines
58 KiB
Python
1269 lines
58 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 os
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import time
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from typing import 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 paddleformers.utils.log import logger
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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.guided_decoding import get_guided_backend
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from fastdeploy.model_executor.guided_decoding.base_guided_decoding import \
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LogitsProcessorBase
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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 (
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Sampler, SpeculativeSampler)
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from fastdeploy.model_executor.model_loader import get_model_from_loader
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from fastdeploy.model_executor.ops.gpu import (set_value_by_flags_and_idx,
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share_external_data)
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from fastdeploy.model_executor.pre_and_post_process import (post_process,
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pre_process,
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rebuild_padding,
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step_cuda)
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from fastdeploy.platforms import current_platform
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if not current_platform.is_dcu():
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from fastdeploy.spec_decode import MTPProposer, NgramProposer
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from fastdeploy.model_executor.forward_meta import ForwardMeta
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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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class GPUModelRunner(ModelRunnerBase):
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def __init__(
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self,
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fd_config: FDConfig,
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device: str, # logic device
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device_id: int, # physical device id
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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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self.device_id = device_id
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self.speculative_method = self.fd_config.speculative_config.method
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self.speculative_decoding = self.speculative_method is not None
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self.enable_logprob = fd_config.model_config.enable_logprob
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self.guided_backend = None
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if self.fd_config.parallel_config.guided_decoding_backend != "off":
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self.guided_backend = get_guided_backend(fd_config=self.fd_config)
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# Sampler
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if not self.speculative_decoding:
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self.sampler = Sampler()
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else:
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self.sampler = SpeculativeSampler(fd_config)
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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 = self.graph_opt_config.use_cudagraph
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self.cudagraph_capture_sizes = list(
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reversed(self.graph_opt_config.cudagraph_capture_sizes))
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# Initialize share inputs
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self._init_share_inputs(self.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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self.restore_chunked_prefill_request = dict()
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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.attn_metadatas: list[AttentionMetadata] = []
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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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# Postprocess Env params
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os.environ["INFERENCE_MSG_QUEUE_ID"] = str(
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self.local_rank +
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int(self.parallel_config.engine_worker_queue_port))
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def prefill_finished(self):
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"""
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Check whether prefill stage finished
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"""
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if int(paddle.max(self.share_inputs['seq_lens_encoder'])) != 0:
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return 1
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else:
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return 0
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def _init_speculative_proposer(self):
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"""
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Init speculative proposer
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"""
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if self.speculative_method == "ngram":
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self.proposer = NgramProposer(self.fd_config)
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elif self.speculative_method == "mtp":
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self.proposer = MTPProposer(self.fd_config, self.get_model(),
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self.local_rank, self.device_id,
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self.share_inputs)
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else:
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self.proposer = None
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def _init_logits_processor(self, request):
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"""
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init logits processor for guided decoding
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"""
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assert self.guided_backend is not None, "guided_backend is None, use "\
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"--guided-decoding-backend to specify the backend at server startup."
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if request.guided_json is not None:
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schemata_key = ("json", request.guided_json)
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elif request.guided_regex is not None:
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schemata_key = ("regex", request.guided_regex)
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elif request.guided_grammar is not None:
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schemata_key = ("grammar", request.guided_grammar)
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elif request.structural_tag is not None:
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schemata_key = ("structural_tag", request.structural_tag)
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return self.guided_backend.get_logits_processor(
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schemata_key=schemata_key), schemata_key
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def insert_prefill_inputs(self, req_dicts: List[Request]):
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"""
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Process inputs for prefill tasks and insert it to share_inputs buffer
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TODO(gongshaotian): Refactor this func
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"""
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# NOTE(luotingdan): Lazy initialize kv cache
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if "caches" not in self.share_inputs:
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self.initialize_kv_cache()
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# NOTE(luotingdan): Set environment variable of prefill node
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if req_dicts[-1].disaggregate_info is not None and req_dicts[
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-1].disaggregate_info["role"] == "prefill":
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os.environ['PREFILL_NODE_ONE_STEP_STOP'] = "1"
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top_k_reqs = []
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top_p_reqs = []
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max_num_seqs = self.parallel_config.max_num_seqs
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top_p_buffer = 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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top_k_buffer = paddle.full([max_num_seqs, 1],
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0,
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dtype='int64')
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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 = len(request.prompt_token_ids)
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assert length > 0, "The prompt requested must not be empty."
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if sampling_params := request.sampling_params:
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if sampling_params.top_p < 1:
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top_p_reqs.append(idx)
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top_k = sampling_params.top_k
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if top_k > 0:
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top_k_reqs.append(idx)
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prefill_tokens = []
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if (request.guided_json is not None
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or request.guided_regex is not None
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or request.structural_tag is not None
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or request.guided_grammar is not None):
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logits_info, schemata_key = self._init_logits_processor(
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request)
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request.logits_processor, request.logits_cached = logits_info
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request.schemata_key = schemata_key
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# Is Decode Node
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if req_dicts[i].disaggregate_info is not None and req_dicts[
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i].disaggregate_info["role"] == "decode":
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prefill_tokens.append(request.prompt_token_ids[0])
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self.share_inputs["pre_ids"][idx:idx +
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1] = request.prompt_token_ids[-1]
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self.share_inputs["input_ids"][idx:idx + 1,
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0] = request.prompt_token_ids[0]
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self.share_inputs['seq_lens_encoder'][idx:idx + 1] = 0
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self.share_inputs['seq_lens_decoder'][idx:idx + 1] = length
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self.share_inputs['seq_lens_this_time'][idx:idx + 1] = 1
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self.share_inputs['step_seq_lens_encoder'][idx:idx + 1] = 0
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self.share_inputs['step_seq_lens_decoder'][idx:idx +
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1] = length
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self.share_inputs['step_idx'][idx:idx + 1] = 1
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if self.speculative_decoding:
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num_prefill_send_token = self.speculative_config.num_speculative_tokens + 1
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self.share_inputs['draft_tokens'][idx:idx + 1, 0:num_prefill_send_token] =\
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paddle.to_tensor(request.draft_token_ids[0:num_prefill_send_token], dtype="int64")
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self.share_inputs['seq_lens_this_time'][
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idx:idx + 1] = num_prefill_send_token
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else:
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self.share_inputs["pre_ids"][idx:idx + 1] = -1
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self.share_inputs["step_idx"][idx:idx + 1] = 0
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self.share_inputs["input_ids"][idx:idx +
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1, :length] = np.array(
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request.prompt_token_ids)
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# Use chunked prefill
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if self.parallel_config.enable_chunked_prefill:
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request.set("chunk_idx", 1)
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logger.info(
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f"prefill_chunk_info: {request.prefill_chunk_info}")
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token_chunk_size = request.prefill_chunk_info[0]
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self.share_inputs["seq_lens_this_time"][
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idx:idx + 1] = token_chunk_size
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self.share_inputs['input_ids'][
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idx, :token_chunk_size] = np.array(
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request.prompt_token_ids[:token_chunk_size])
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self.share_inputs['step_seq_lens_encoder'][
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idx:idx + 1] = token_chunk_size
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self.share_inputs['seq_lens_encoder'][idx:idx +
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1] = token_chunk_size
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self.share_inputs['seq_lens_decoder'][
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idx:idx + 1] = request.get("seq_lens_decoder", 0)
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self.share_inputs['step_seq_lens_decoder'][
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idx:idx + 1] = request.get("seq_lens_decoder", 0)
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else:
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self.share_inputs['seq_lens_decoder'][
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idx:idx + 1] = request.get("seq_lens_decoder", 0)
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self.share_inputs['step_seq_lens_decoder'][
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idx:idx + 1] = request.get("seq_lens_decoder", 0)
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self.share_inputs['seq_lens_this_time'][idx:idx +
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1] = length
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self.share_inputs['step_seq_lens_encoder'][idx:idx +
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1] = length
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self.share_inputs['seq_lens_encoder'][idx:idx + 1] = length
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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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top_p_buffer[idx:idx + 1] = request.get("top_p", 1.0)
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top_k_buffer[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["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_length)
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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.sampler.apply_logits_processor(
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idx, request.get("logits_processor"), prefill_tokens)
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self.share_inputs["not_need_stop"][0] = True
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if self.speculative_method in ["mtp"]:
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self.proposer.insert_prefill_inputs(req_dicts)
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if len(top_k_reqs) == 0:
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self.share_inputs["top_k"] = None
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else:
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self.share_inputs["top_k"] = top_k_buffer
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if len(top_p_reqs) == 0:
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self.share_inputs["top_p"] = None
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else:
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self.share_inputs["top_p"] = top_p_buffer
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def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int,
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expected_decode_len: int):
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""" Set dummy prefill inputs to share_inputs """
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# NOTE(gongshaotian): The maximum decoding length is equal to the expected decoded tokens plus the eos token
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max_dec_len = expected_decode_len + 1
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full_length = min(num_tokens // batch_size,
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self.parallel_config.max_model_len - max_dec_len)
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input_length = int(full_length * self.parallel_config.kv_cache_ratio)
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block_num = (
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input_length + self.parallel_config.block_size - 1
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) // self.parallel_config.block_size + self.parallel_config.enc_dec_block_num
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for i in range(batch_size):
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idx = i
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self.share_inputs["input_ids"][idx:idx +
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1, :input_length] = np.array(
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[5] * input_length)
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self.share_inputs["eos_token_id"][:] = np.array(
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[2], dtype="int64").reshape(-1, 1)
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self.share_inputs["seq_lens_this_time"][idx:idx + 1] = input_length
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self.share_inputs["step_seq_lens_encoder"][idx:idx +
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1] = input_length
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self.share_inputs["seq_lens_encoder"][idx:idx + 1] = input_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["max_dec_len"][idx:idx + 1] = max_dec_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 +
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1] = input_length
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self.share_inputs["encoder_block_lens"][idx:idx + 1] = block_num
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self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(idx * block_num, \
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(idx + 1) * block_num, 1)
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def _init_share_inputs(self, max_num_seqs: int):
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"""
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Initialize all share buffers for model inputs.
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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_length, 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_length, 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,
|
|
dtype='int32')
|
|
self.share_inputs["step_seq_lens_encoder"] = paddle.full(
|
|
[max_num_seqs, 1], 0, dtype='int32')
|
|
self.share_inputs["step_seq_lens_decoder"] = paddle.full(
|
|
[max_num_seqs, 1], 0, dtype='int32')
|
|
self.share_inputs["step_idx"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int64')
|
|
self.share_inputs["not_need_stop"] = paddle.full(
|
|
[1], False,
|
|
dtype='bool').cpu() # TODO(gongshaotian): move to pinnd memory
|
|
self.share_inputs["stop_flags"] = paddle.full([max_num_seqs, 1],
|
|
True,
|
|
dtype='bool')
|
|
self.share_inputs["stop_nums"] = paddle.full([1],
|
|
max_num_seqs,
|
|
dtype='int64')
|
|
|
|
self.share_inputs["bad_tokens"] = paddle.full([1], -1, dtype='int64')
|
|
self.share_inputs["next_tokens"] = paddle.full([max_num_seqs, 1],
|
|
-1,
|
|
dtype='int64')
|
|
self.share_inputs["is_block_step"] = paddle.full([max_num_seqs],
|
|
False,
|
|
dtype='bool')
|
|
self.share_inputs["encoder_block_lens"] = paddle.full([max_num_seqs],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["step_block_list"] = paddle.full([max_num_seqs],
|
|
-1,
|
|
dtype='int32')
|
|
self.share_inputs["step_lens"] = paddle.full([1], 0, dtype='int32')
|
|
self.share_inputs["recover_block_list"] = paddle.full([max_num_seqs],
|
|
-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')
|
|
|
|
self.share_inputs["ids_remove_padding"] = paddle.full(
|
|
[max_num_seqs * self.parallel_config.max_model_len],
|
|
0,
|
|
dtype='int64')
|
|
self.share_inputs["cum_offsets"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["padding_offset"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["cu_seqlens_q"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["cu_seqlens_k"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
# AttentionBackend buffers
|
|
self.share_inputs["decoder_batch_ids"] = paddle.full([max_num_seqs, 1],
|
|
0,
|
|
dtype='int32')
|
|
self.share_inputs["decoder_tile_ids_per_batch"] = paddle.full(
|
|
[max_num_seqs, 1], 0, 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.max_block_num - 1,
|
|
int(self.parallel_config.max_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")
|
|
if self.speculative_decoding:
|
|
max_draft_token_num = self.speculative_config.num_speculative_tokens
|
|
self.share_inputs["input_ids_cpu"] = paddle.full(
|
|
shape=[max_num_seqs, self.parallel_config.max_model_len],
|
|
fill_value=1,
|
|
dtype='int64').cpu()
|
|
self.share_inputs['accept_tokens'] = paddle.full(
|
|
shape=[max_num_seqs, max_draft_token_num + 1],
|
|
fill_value=0,
|
|
dtype="int64")
|
|
self.share_inputs['accept_num'] = paddle.full(shape=[max_num_seqs],
|
|
fill_value=0,
|
|
dtype='int32')
|
|
self.share_inputs['draft_tokens'] = paddle.full(
|
|
shape=[max_num_seqs, max_draft_token_num + 1],
|
|
fill_value=0,
|
|
dtype="int64")
|
|
|
|
self.share_inputs['actual_draft_token_num'] = paddle.full(
|
|
shape=[max_num_seqs],
|
|
fill_value=max_draft_token_num,
|
|
dtype="int32")
|
|
self.share_inputs["output_cum_offsets"] = paddle.full(
|
|
shape=[max_num_seqs, 1], fill_value=0, dtype='int32')
|
|
self.share_inputs["output_padding_offset"] = paddle.full(
|
|
shape=[max_num_seqs * (max_draft_token_num + 1)],
|
|
fill_value=0,
|
|
dtype="int32")
|
|
|
|
def _prepare_inputs(self) -> None:
|
|
""" Prepare the model inputs """
|
|
# Remove padding
|
|
(
|
|
ids_remove_padding,
|
|
cum_offsets,
|
|
padding_offset,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
output_cum_offsets,
|
|
output_padding_offset,
|
|
) = pre_process(
|
|
self.parallel_config.max_model_len, self.share_inputs["input_ids"],
|
|
self.share_inputs["seq_lens_this_time"], self.speculative_decoding,
|
|
self.share_inputs["draft_tokens"] if self.speculative_decoding else
|
|
None, self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["seq_lens_decoder"])
|
|
|
|
self.share_inputs["ids_remove_padding"].copy_(ids_remove_padding,
|
|
False)
|
|
self.share_inputs["cum_offsets"].copy_(cum_offsets, False)
|
|
self.share_inputs["padding_offset"].copy_(padding_offset, False)
|
|
self.share_inputs["cu_seqlens_q"].copy_(cu_seqlens_q, False)
|
|
self.share_inputs["cu_seqlens_k"].copy_(cu_seqlens_k, False)
|
|
|
|
# For speculative decoding
|
|
if self.speculative_decoding:
|
|
self.share_inputs["output_cum_offsets"].copy_(
|
|
output_cum_offsets, False)
|
|
self.share_inputs["output_padding_offset"].copy_(
|
|
output_padding_offset, False)
|
|
|
|
# Initialize forward meta data
|
|
self.initialize_forward_meta()
|
|
|
|
# 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"],
|
|
max_num_logprobs=20 if self.enable_logprob else None,
|
|
)
|
|
|
|
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)
|
|
# 1.1 Load RL dynamic model
|
|
if self.fd_config.load_config.dynamic_load_weight:
|
|
from fastdeploy.rl.dynamic_weight_manager import \
|
|
DynamicWeightManager
|
|
self.dynamic_weight_manager = DynamicWeightManager(
|
|
self.fd_config, self.model)
|
|
|
|
# 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")
|
|
|
|
# 4. Init proposer for speculative method
|
|
self._init_speculative_proposer()
|
|
|
|
def get_model(self) -> nn.Layer:
|
|
""" Get current model """
|
|
return self.model
|
|
|
|
def initialize_forward_meta(self):
|
|
"""
|
|
Initialize forward meta and attention meta data
|
|
"""
|
|
# Initialize forward meta
|
|
self.forward_meta = ForwardMeta(
|
|
input_ids=self.share_inputs["input_ids"],
|
|
ids_remove_padding=self.share_inputs["ids_remove_padding"],
|
|
rotary_embs=self.share_inputs["rope_emb"],
|
|
attn_backend=self.attn_backends[0],
|
|
decoder_batch_ids=self.share_inputs["decoder_batch_ids"],
|
|
decoder_tile_ids_per_batch=self.share_inputs["decoder_tile_ids_per_batch"],
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
|
|
cum_offsets=self.share_inputs["cum_offsets"],
|
|
padding_offset=self.share_inputs["padding_offset"],
|
|
cu_seqlens_q=self.share_inputs["cu_seqlens_q"],
|
|
cu_seqlens_k=self.share_inputs["cu_seqlens_k"],
|
|
block_tables=self.share_inputs["block_tables"],
|
|
caches=self.share_inputs["caches"]
|
|
)
|
|
|
|
# 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
|
|
|
|
# Get kv cache dtype
|
|
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'
|
|
|
|
# Get kv cache shape
|
|
kv_cache_shape = self.attn_backends[0].get_kv_cache_shape(
|
|
max_num_blocks=max_block_num)
|
|
local_rank = self.local_rank % self.parallel_config.tensor_parallel_degree
|
|
|
|
if not self.parallel_config.do_profile and (
|
|
self.parallel_config.enable_prefix_caching \
|
|
or self.parallel_config.splitwise_role != "mixed"):
|
|
cache_kvs_list = []
|
|
for i in range(self.model_config.num_layers):
|
|
key_cache = paddle.empty(shape=[], dtype=cache_type)
|
|
key_cache_name = f"key_caches_{i}_rank{local_rank}.device{self.device_id}"
|
|
val_cache_name = f"value_caches_{i}_rank{local_rank}.device{self.device_id}"
|
|
key_cache = share_external_data(key_cache, key_cache_name,
|
|
kv_cache_shape)
|
|
cache_kvs_list.append(key_cache)
|
|
value_cache = paddle.empty(shape=[], dtype=cache_type)
|
|
value_cache = share_external_data(value_cache, val_cache_name,
|
|
kv_cache_shape)
|
|
cache_kvs_list.append(value_cache)
|
|
|
|
self.share_inputs["caches"] = cache_kvs_list
|
|
|
|
else:
|
|
for i in range(self.model_config.num_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.cuda.empty_cache()
|
|
|
|
def initialize_attn_backend(self) -> None:
|
|
"""
|
|
Initialize attention backends
|
|
"""
|
|
assert len(self.attn_backends) == 0
|
|
|
|
num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_degree
|
|
self.model_config.kv_num_heads = int(
|
|
self.model_config.num_key_value_heads
|
|
) // self.parallel_config.tensor_parallel_degree
|
|
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)
|
|
|
|
self.attn_backends.append(attn_backend)
|
|
|
|
def _dummy_run(self,
|
|
num_tokens: paddle.Tensor,
|
|
batch_size: paddle.Tensor,
|
|
expected_decode_len: int = 1,
|
|
in_capturing: bool = False) -> paddle.Tensor:
|
|
"""
|
|
Use dummy inputs to run before formal execution.
|
|
Args:
|
|
num_tokens:
|
|
expected_decode_len: Expected number of tokens generated
|
|
"""
|
|
self._dummy_prefill_inputs(num_tokens=num_tokens,
|
|
batch_size=batch_size,
|
|
expected_decode_len=expected_decode_len)
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.dummy_prefill_inputs(
|
|
num_tokens=num_tokens,
|
|
batch_size=batch_size,
|
|
expected_decode_len=expected_decode_len)
|
|
while True:
|
|
|
|
# 1. Initialize forward meta and attention meta data
|
|
self._prepare_inputs()
|
|
|
|
# 2. Prepare lora
|
|
|
|
# 3. Run model
|
|
is_decode_batch = not ((self.share_inputs["seq_lens_this_time"]
|
|
> 1).sum() > 0)
|
|
self.forward_meta.step_use_cudagraph = is_decode_batch and in_capturing
|
|
self.forward_meta.is_decode_batch = is_decode_batch
|
|
model_output = self.model(
|
|
ids_remove_padding=self.share_inputs["ids_remove_padding"],
|
|
forward_meta=self.forward_meta)
|
|
|
|
hiddden_states = rebuild_padding(
|
|
model_output,
|
|
self.share_inputs["cum_offsets"],
|
|
self.share_inputs["seq_lens_this_time"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["output_padding_offset"]
|
|
if self.speculative_decoding else
|
|
None, # speculative decoding requires
|
|
self.parallel_config.max_model_len,
|
|
)
|
|
|
|
# 4. Execute spec decode
|
|
logits = self.model.compute_logits(hiddden_states)
|
|
|
|
if not self.speculative_decoding:
|
|
set_value_by_flags_and_idx(
|
|
self.share_inputs["pre_ids"],
|
|
self.share_inputs["input_ids"],
|
|
self.share_inputs["seq_lens_this_time"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.share_inputs["step_idx"],
|
|
self.share_inputs["stop_flags"],
|
|
)
|
|
sampler_output = self.sampler(logits,
|
|
self.sampling_metadata)
|
|
if self.parallel_config.tensor_parallel_degree > 1:
|
|
paddle.distributed.broadcast(sampler_output.sampled_token_ids, 0)
|
|
else:
|
|
self.sampler(logits, self.sampling_metadata,
|
|
self.parallel_config.max_model_len,
|
|
self.share_inputs)
|
|
sampler_output = None
|
|
if self.parallel_config.tensor_parallel_degree > 1:
|
|
paddle.distributed.broadcast(
|
|
self.share_inputs["accept_tokens"], 0)
|
|
paddle.distributed.broadcast(
|
|
self.share_inputs["accept_num"], 0)
|
|
paddle.distributed.broadcast(self.share_inputs["step_idx"],
|
|
0)
|
|
paddle.distributed.broadcast(
|
|
self.share_inputs["stop_flags"], 0)
|
|
|
|
# 5. 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"],
|
|
full_hidden_states=model_output,
|
|
msg_queue_id=self.parallel_config.msg_queue_id,
|
|
mp_rank=self.local_rank,
|
|
use_ep=self.parallel_config.use_ep,
|
|
draft_tokens=self.share_inputs["draft_tokens"]
|
|
if self.speculative_decoding else None,
|
|
actual_draft_token_num=self.
|
|
share_inputs["actual_draft_token_num"]
|
|
if self.speculative_decoding else None,
|
|
accept_tokens=self.share_inputs["accept_tokens"]
|
|
if self.speculative_decoding else None,
|
|
accept_num=self.share_inputs["accept_num"]
|
|
if self.speculative_decoding else None)
|
|
|
|
post_process(sampler_output=sampler_output,
|
|
model_output=model_output_data,
|
|
speculative_decoding=self.speculative_decoding,
|
|
skip_save_output=True)
|
|
|
|
if self.speculative_decoding:
|
|
if self.speculative_method == "mtp":
|
|
self.proposer.run(full_hidden_states=model_output)
|
|
else:
|
|
self.proposer.run(share_inputs=self.share_inputs)
|
|
|
|
# 7. Updata 'infer_seed' and step_cuda()
|
|
self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
|
|
self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
|
|
step_cuda(self.share_inputs, self.parallel_config.block_size,
|
|
self.parallel_config.enc_dec_block_num,
|
|
self.speculative_config,
|
|
self.parallel_config.enable_prefix_caching)
|
|
|
|
if int((self.share_inputs['seq_lens_this_time'] > 0).sum()) == 0:
|
|
break
|
|
|
|
def _update_chunked_prefill(self, tasks):
|
|
"""
|
|
Update chunked prefill related parameters
|
|
"""
|
|
if not self.parallel_config.enable_chunked_prefill:
|
|
return
|
|
|
|
for task in tasks:
|
|
if task.get("prefill_chunk_info", None) is None:
|
|
continue
|
|
|
|
if task.chunk_idx > len(task.prefill_chunk_info):
|
|
continue
|
|
self.restore_chunked_prefill_request[task.request_id] = task
|
|
|
|
for id, task in list(self.restore_chunked_prefill_request.items()):
|
|
idx = task.idx
|
|
logger.debug(
|
|
f"{task.request_id} chunked prefill {task.chunk_idx}/{len(task.prefill_chunk_info)}"
|
|
)
|
|
start_idx = sum(task.prefill_chunk_info[:task.chunk_idx])
|
|
if task.chunk_idx == len(task.prefill_chunk_info):
|
|
self.share_inputs["seq_lens_this_time"][idx:idx + 1] = 1
|
|
self.share_inputs['seq_lens_encoder'][idx:idx + 1] = 0
|
|
self.share_inputs["step_idx"][idx:idx + 1] = 1
|
|
self.share_inputs["seq_lens_decoder"][
|
|
idx:idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
|
|
del self.restore_chunked_prefill_request[task.request_id]
|
|
else:
|
|
token_chunk_size = task.prefill_chunk_info[task.chunk_idx]
|
|
|
|
self.share_inputs["seq_lens_this_time"][idx:idx +
|
|
1] = token_chunk_size
|
|
self.share_inputs['input_ids'][
|
|
idx, :token_chunk_size] = np.array(
|
|
task.prompt_token_ids[start_idx:start_idx +
|
|
token_chunk_size])
|
|
self.share_inputs['seq_lens_encoder'][idx:idx +
|
|
1] = token_chunk_size
|
|
self.share_inputs["step_idx"][idx:idx + 1] = 0
|
|
self.share_inputs["seq_lens_decoder"][
|
|
idx:idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
|
|
if self.speculative_decoding and self.proposer.is_chunk_prefill_enabled(
|
|
):
|
|
self.proposer.update_task_chunk_prefill(task)
|
|
task.chunk_idx += 1
|
|
|
|
def capture_model(self) -> None:
|
|
"""
|
|
Trigger CUDA Graph capture for all shapes in cuda graph capture list
|
|
"""
|
|
if not self.use_cudagraph:
|
|
logger.info(
|
|
"Skipping CUDA graph capture. Please check GraphOptimizationConfig"
|
|
)
|
|
return
|
|
time_before_capture = time.perf_counter()
|
|
expected_decode_len = 1
|
|
capture_sizes = self.cudagraph_capture_sizes.copy()
|
|
for batch_size in sorted(capture_sizes, reverse=True):
|
|
self._dummy_run(num_tokens=self.parallel_config.max_model_len,
|
|
batch_size=batch_size,
|
|
in_capturing=True,
|
|
expected_decode_len=expected_decode_len)
|
|
logger.info(
|
|
f"Warm up the model with the batch size:{batch_size}, num tokens:{expected_decode_len}"
|
|
)
|
|
|
|
time_after_capture = time.perf_counter()
|
|
logger.info(
|
|
f"Cuda Graph capturing took {time_after_capture - time_before_capture} seconds"
|
|
)
|
|
|
|
def _get_skip_idx(self,
|
|
model_forward_batch: Optional[List[Request]] = None):
|
|
"""
|
|
Get the index of the request that needs to be skipped during execution.
|
|
Args:
|
|
model_forward_batch: A list of requests to be executed by this runner.
|
|
Returns:
|
|
A list of indices corresponding to the requests that need to be skipped.
|
|
"""
|
|
skip_idx_list = []
|
|
if not self.parallel_config.enable_chunked_prefill or self.guided_backend is None:
|
|
return skip_idx_list
|
|
|
|
for task in model_forward_batch:
|
|
if task.get("prefill_chunk_info",
|
|
None) is None or task.chunk_idx >= len(
|
|
task.prefill_chunk_info):
|
|
continue
|
|
skip_idx_list.append(task.idx)
|
|
|
|
for task in self.restore_chunked_prefill_request.values():
|
|
if task.idx in skip_idx_list or task.chunk_idx >= len(
|
|
task.prefill_chunk_info):
|
|
continue
|
|
skip_idx_list.append(task.idx)
|
|
|
|
return skip_idx_list
|
|
|
|
def execute_model(
|
|
self,
|
|
model_forward_batch: Optional[List[Request]] = None,
|
|
) -> 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:
|
|
"""
|
|
# NOTE(wufeisheng): If `not_need_stop`` is False, it means the current worker is in an idle state.
|
|
# This logic is not used in TP (Tensor Parallelism) mode. However, in EP (Expert Parallelism) mode,
|
|
# when there is data on other runner, the current runner is required to execute part of the model.
|
|
if not self.not_need_stop():
|
|
self._execute_empty_input()
|
|
return None
|
|
|
|
# 1. Prepare inputs of model and sampler.
|
|
skip_idx_list = self._get_skip_idx(model_forward_batch)
|
|
self._prepare_inputs()
|
|
self.sampler.pre_process(skip_idx_list)
|
|
|
|
# 2. Padding inputs for cuda graph
|
|
|
|
# 3. Execute model
|
|
# TODO(gongshaotian): Use seq_lens_encoder to set is_decode_batch
|
|
is_decode_batch = not ((self.share_inputs["seq_lens_this_time"]
|
|
> 1).sum() > 0)
|
|
self.forward_meta.step_use_cudagraph = self.use_cudagraph and is_decode_batch
|
|
self.forward_meta.is_decode_batch = is_decode_batch
|
|
model_output = self.model(
|
|
ids_remove_padding=self.share_inputs["ids_remove_padding"],
|
|
forward_meta=self.forward_meta)
|
|
|
|
hiddden_states = rebuild_padding(
|
|
model_output,
|
|
self.share_inputs["cum_offsets"],
|
|
self.share_inputs["seq_lens_this_time"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["output_padding_offset"]
|
|
if self.speculative_decoding else None,
|
|
self.parallel_config.max_model_len,
|
|
)
|
|
|
|
# 4. Compute logits, Sample
|
|
logits = self.model.compute_logits(hiddden_states)
|
|
|
|
if not self.speculative_decoding:
|
|
set_value_by_flags_and_idx(
|
|
self.share_inputs["pre_ids"],
|
|
self.share_inputs["input_ids"],
|
|
self.share_inputs["seq_lens_this_time"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.share_inputs["step_idx"],
|
|
self.share_inputs["stop_flags"],
|
|
)
|
|
sampler_output = self.sampler(
|
|
logits,
|
|
self.sampling_metadata,
|
|
skip_idx_list,
|
|
)
|
|
if self.parallel_config.tensor_parallel_degree > 1:
|
|
paddle.distributed.broadcast(sampler_output.sampled_token_ids, 0)
|
|
|
|
else:
|
|
self.sampler(logits, self.sampling_metadata,
|
|
self.parallel_config.max_model_len, self.share_inputs)
|
|
sampler_output = None
|
|
if self.parallel_config.tensor_parallel_degree > 1:
|
|
paddle.distributed.broadcast(
|
|
self.share_inputs["accept_tokens"], 0)
|
|
paddle.distributed.broadcast(self.share_inputs["accept_num"],
|
|
0)
|
|
paddle.distributed.broadcast(self.share_inputs["step_idx"], 0)
|
|
paddle.distributed.broadcast(self.share_inputs["stop_flags"],
|
|
0)
|
|
|
|
# 5. 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"],
|
|
full_hidden_states=model_output,
|
|
msg_queue_id=self.parallel_config.msg_queue_id,
|
|
mp_rank=self.local_rank,
|
|
use_ep=self.parallel_config.use_ep,
|
|
draft_tokens=self.share_inputs["draft_tokens"]
|
|
if self.speculative_decoding else None,
|
|
actual_draft_token_num=self.share_inputs["actual_draft_token_num"]
|
|
if self.speculative_decoding else None,
|
|
accept_tokens=self.share_inputs["accept_tokens"]
|
|
if self.speculative_decoding else None,
|
|
accept_num=self.share_inputs["accept_num"]
|
|
if self.speculative_decoding else None)
|
|
|
|
if self.speculative_config.method in ["mtp"] and \
|
|
self.parallel_config.splitwise_role == "prefill":
|
|
skip_save_output = True
|
|
else:
|
|
skip_save_output = False
|
|
post_process(sampler_output=sampler_output,
|
|
model_output=model_output_data,
|
|
save_each_rank=self.parallel_config.use_ep,
|
|
speculative_decoding=self.speculative_decoding,
|
|
skip_save_output=skip_save_output)
|
|
|
|
# 6. Speculative decode
|
|
if self.speculative_decoding:
|
|
if self.speculative_method == "mtp":
|
|
self.proposer.run(full_hidden_states=model_output)
|
|
else:
|
|
self.proposer.run(share_inputs=self.share_inputs)
|
|
|
|
# 7. Updata 'infer_seed' and step_cuda()
|
|
self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
|
|
self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
|
|
step_cuda(
|
|
self.share_inputs,
|
|
self.parallel_config.block_size,
|
|
self.parallel_config.enc_dec_block_num,
|
|
self.speculative_config,
|
|
self.parallel_config.enable_prefix_caching,
|
|
)
|
|
|
|
self._update_chunked_prefill(model_forward_batch)
|
|
self._add_cache(model_forward_batch)
|
|
return None
|
|
|
|
def _add_cache(self, model_forward_batch) -> None:
|
|
"""
|
|
Add cache for guided decoding.
|
|
"""
|
|
if self.guided_backend is None:
|
|
return
|
|
|
|
for request in model_forward_batch:
|
|
logits_cached = request.get("logits_cached", None)
|
|
if logits_cached is None or logits_cached:
|
|
continue
|
|
|
|
request.logits_cached = True
|
|
if isinstance(request.logits_processor, LogitsProcessorBase):
|
|
self.guided_backend.add_cache(request.schemata_key,
|
|
request.logits_processor)
|
|
else:
|
|
self.guided_backend.add_cache(
|
|
request.schemata_key, request.logits_processor.result())
|
|
|
|
def _execute_empty_input(self) -> None:
|
|
"""
|
|
In certain scenarios, such as during EP,
|
|
the runner needs to execute partial modules of the model without input data.
|
|
This requires the model to implement the `empty_input_forward` method.
|
|
"""
|
|
if hasattr(self.model, "empty_input_forward"):
|
|
self.model.empty_input_forward()
|
|
else:
|
|
raise ValueError(
|
|
f"{type(self.model)} has no attribute 'empty_input_forward")
|
|
|
|
def profile_run(self) -> None:
|
|
""" Execute a forward pass with dummy inputs to profile the memory usage of the model """
|
|
|
|
# Initialize kv cache for profile run. After profile run kv cache will be reset.
|
|
# TODO(gongshaotian): Optimize the management logic of kvcache
|
|
self.num_gpu_blocks = self.parallel_config.max_block_num
|
|
self.initialize_kv_cache()
|
|
|
|
# 1. Profile with multimodal encoder & encoder cache
|
|
|
|
# 2. Dummy run
|
|
self._dummy_run(num_tokens=self.parallel_config.max_num_batched_tokens,
|
|
batch_size=min(self.parallel_config.max_num_seqs, 3))
|
|
|
|
# 3. gc
|
|
self.clear_cache()
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.clear_dummy_input()
|
|
|
|
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
|
|
if not (self.parallel_config.enable_prefix_caching \
|
|
or self.parallel_config.splitwise_role != "mixed"):
|
|
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"),
|
|
})
|
|
|
|
self.parallel_config.do_profile = False
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.update_block_num(num_gpu_blocks)
|
|
|
|
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
|
|
# NOTE(liuzichang): Implement multi-layer MTP architecture in the future
|
|
num_layers = self.model_config.num_layers + \
|
|
self.speculative_config.num_gpu_block_expand_ratio if \
|
|
self.speculative_method in [
|
|
"mtp"
|
|
] else self.model_config.num_layers
|
|
required_memory = (
|
|
byte_of_dtype * 2 * # k + v
|
|
(self.parallel_config.block_size * hidden_dim) * num_layers)
|
|
return required_memory
|
|
|
|
def not_need_stop(self) -> bool:
|
|
""" Stop decoding if the tensor meets the termination condition """
|
|
return self.share_inputs["not_need_stop"][0]
|
|
|
|
def clear_cache(self):
|
|
""" Clear cached data from shared inputs and forward metadata """
|
|
self.share_inputs.pop("caches", None)
|
|
if self.forward_meta is not None:
|
|
self.forward_meta.clear_caches()
|
|
|
|
def clear_parameters(self, pid):
|
|
"""" Dynamic model loader use to clear parameters use for RL """
|
|
self.dynamic_weight_manager.clear_parameters(pid)
|
|
self.clear_cache()
|
|
paddle.device.cuda.empty_cache()
|
|
self.dynamic_weight_manager._log_memory(
|
|
"dynamic weight manager clear all memory")
|
|
|
|
def update_parameters(self, pid):
|
|
"""" Dynamic model loader use to update parameters use for RL """
|
|
self.dynamic_weight_manager.update_parameters(pid)
|
|
self.initialize_kv_cache()
|
|
self.dynamic_weight_manager._log_memory(
|
|
"dynamic weight manager update all memory")
|