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2e9e53ff7e
* remove max_num_batched_tokens in parallel config * remove max_num_seqs * update test case * fix test * fix --------- Co-authored-by: Jiang-Jia-Jun <163579578+Jiang-Jia-Jun@users.noreply.github.com>
1246 lines
57 KiB
Python
1246 lines
57 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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from paddle import 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.forward_meta import ForwardMeta
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from fastdeploy.model_executor.graph_optimization.utils import (
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profile_run_guard,
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sot_warmup_guard,
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)
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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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)
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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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)
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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, SpeculativeSampler
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from fastdeploy.model_executor.model_loader import get_model_loader
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from fastdeploy.model_executor.ops.gcu import set_value_by_flags_and_idx
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from fastdeploy.model_executor.pre_and_post_process import (
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post_process,
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pre_process,
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rebuild_padding,
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)
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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 GCUModelRunner(ModelRunnerBase):
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""" """
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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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):
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super().__init__(fd_config=fd_config, device=device)
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self.enable_mm = self.model_config.enable_mm
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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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# Cuda Graph
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self.graph_opt_level = self.graph_opt_config.graph_opt_level
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self.use_cudagraph = self.graph_opt_config.use_cudagraph
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self.cudagraph_capture_sizes = list(reversed(self.graph_opt_config.cudagraph_capture_sizes))
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self.sot_warmup_sizes = self.graph_opt_config.sot_warmup_sizes
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# Initialize share inputs
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self._init_share_inputs(self.scheduler_config.max_num_seqs)
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self.infer_seed_increment = paddle.full(
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shape=[self.scheduler_config.max_num_seqs, 1],
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fill_value=4,
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dtype="int64",
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).cpu()
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self.restore_chunked_prefill_request = dict()
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# Initialize attention Backend
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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(self.parallel_config.engine_worker_queue_port)
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def exist_prefill(self):
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"""
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Check whether prefill stage exist
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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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raise NotImplementedError("NgramProposer is not support by GCUModelRunner.")
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elif self.speculative_method == "mtp":
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raise NotImplementedError("MTPProposer is not support by GCUModelRunner.")
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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, (
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"guided_backend is None, use " "--guided-decoding-backend to specify the backend at server startup."
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)
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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 (
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self.guided_backend.get_logits_processor(schemata_key=schemata_key),
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schemata_key,
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)
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def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests: int = None):
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"""
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Process inputs for prefill tasks and insert it to share_inputs buffer
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req_dict: A list of Request dict
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num_running_requests: batch_size
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"""
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if req_dicts[-1].disaggregate_info is not None and req_dicts[-1].disaggregate_info["role"] == "prefill":
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os.environ["PREFILL_NODE_ONE_STEP_STOP"] = "1"
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def get_attr_from_request(request, attr, default_value=None):
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res = request.get(attr, default_value)
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if res is not None:
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return res
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else:
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return default_value
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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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prefill_tokens = []
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if (
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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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):
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logits_info, schemata_key = self._init_logits_processor(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[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 + 1] = request.prompt_token_ids[-1]
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self.share_inputs["input_ids"][idx : idx + 1, 0] = request.prompt_token_ids[0]
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self.share_inputs["prompt_ids"][idx : idx + 1, :length] = np.array(request.prompt_token_ids)
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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.seq_lens_this_time_buffer[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 + 1] = length
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self.share_inputs["prompt_lens"][idx : idx + 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] = paddle.to_tensor(
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request.draft_token_ids[0:num_prefill_send_token],
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dtype="int64",
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)
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self.seq_lens_this_time_buffer[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 + 1, :length] = np.array(request.prompt_token_ids)
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self.share_inputs["prompt_ids"][idx : idx + 1, :length] = np.array(request.prompt_token_ids)
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# Use chunked prefill
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if self.cache_config.enable_chunked_prefill:
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request.set("chunk_idx", 1)
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logger.info(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["input_ids"][idx, :token_chunk_size] = np.array(
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request.prompt_token_ids[:token_chunk_size]
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)
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self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
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self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
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self.seq_lens_this_time_buffer[idx : idx + 1] = token_chunk_size
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self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = token_chunk_size
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self.share_inputs["seq_lens_encoder"][idx : idx + 1] = token_chunk_size
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self.share_inputs["prompt_lens"][idx : idx + 1] = token_chunk_size
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else:
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self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
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self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
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self.seq_lens_this_time_buffer[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["prompt_lens"][idx : idx + 1] = length
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self.share_inputs["eos_token_id"][:] = np.array(request.eos_token_ids, dtype="int64").reshape(-1, 1)
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self.share_inputs["top_p"][idx : idx + 1] = get_attr_from_request(request, "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["top_k_list"][idx] = request.get("top_k", 0)
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self.share_inputs["min_p"][idx : idx + 1] = request.get("min_p", 0.0)
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self.share_inputs["min_p_list"][idx] = request.get("min_p", 0.0)
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self.share_inputs["temperature"][idx : idx + 1] = get_attr_from_request(request, "temperature", 0.95)
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self.share_inputs["penalty_score"][idx : idx + 1] = get_attr_from_request(
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request, "repetition_penalty", 1.0
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)
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self.share_inputs["frequency_score"][idx : idx + 1] = get_attr_from_request(
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request, "frequency_penalty", 0.0
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)
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self.share_inputs["presence_score"][idx : idx + 1] = get_attr_from_request(
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request, "presence_penalty", 0.0
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)
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self.share_inputs["min_dec_len"][idx : idx + 1] = request.get("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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)
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self.share_inputs["stop_flags"][idx : idx + 1] = False
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self.share_inputs["first_token_ids"][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 + 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 + 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"][idx : idx + 1, :encoder_block_num] = np.array(
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request.block_tables, dtype="int32"
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)
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if request.get("bad_words_token_ids") is not None and len(request.get("bad_words_token_ids")) > 0:
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bad_words_len = len(request.get("bad_words_token_ids"))
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self.share_inputs["bad_tokens_len"][idx : idx + 1] = bad_words_len
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self.share_inputs["bad_tokens"][idx : idx + 1, :bad_words_len] = np.array(
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request.get("bad_words_token_ids"), dtype="int64"
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)
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else:
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self.share_inputs["bad_tokens_len"][idx : idx + 1] = 1
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self.share_inputs["bad_tokens"][idx : idx + 1, :] = np.array([-1], dtype="int64")
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if request.get("stop_token_ids") is not None and request.get("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, 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(request.stop_seqs_len, dtype="int32")
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self.share_inputs["stop_seqs"][:stop_seqs_num, : len(request.get("stop_token_ids")[0])] = np.array(
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request.get("stop_token_ids"), dtype="int64"
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)
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self.sampler.apply_logits_processor(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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self.share_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer
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def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode_len: int):
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"""Set dummy prefill inputs to share_inputs"""
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max_dec_len = expected_decode_len + 1
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full_length = min(
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num_tokens // batch_size,
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self.parallel_config.max_model_len - max_dec_len,
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)
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input_length = int(full_length * self.cache_config.kv_cache_ratio)
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block_num = (
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input_length + self.cache_config.block_size - 1
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) // self.cache_config.block_size + self.cache_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 + 1, :input_length] = np.array([5] * input_length)
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self.share_inputs["prompt_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
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self.share_inputs["eos_token_id"][:] = np.array(
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[2] * self.model_config.eos_tokens_lens, dtype="int64"
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).reshape(-1, 1)
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self.seq_lens_this_time_buffer[idx : idx + 1] = input_length
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self.share_inputs["step_seq_lens_encoder"][idx : idx + 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["prompt_lens"][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["min_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["temperature"][idx : idx + 1] = 1
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self.share_inputs["first_token_ids"][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] = 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(
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idx * block_num, (idx + 1) * block_num, 1
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)
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self.share_inputs["seq_lens_this_time"] = self.seq_lens_this_time_buffer
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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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)
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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.model_config.pad_token_id,
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dtype="int64",
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)
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self.share_inputs["prompt_ids"] = paddle.full(
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[max_num_seqs, self.parallel_config.max_model_len],
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self.model_config.pad_token_id,
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dtype="int64",
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)
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self.share_inputs["eos_token_id"] = paddle.full([self.model_config.eos_tokens_lens, 1], 0, dtype="int64")
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self.share_inputs["top_p"] = paddle.full([max_num_seqs, 1], self.model_config.top_p, dtype="float32")
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self.share_inputs["top_k"] = paddle.full([max_num_seqs, 1], 0, dtype="int64")
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self.share_inputs["top_k_list"] = [0] * max_num_seqs
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self.share_inputs["min_p"] = paddle.full([max_num_seqs, 1], 0.0, dtype="float32")
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self.share_inputs["min_p_list"] = [0.0] * max_num_seqs
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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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)
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self.share_inputs["penalty_score"] = paddle.full(
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[max_num_seqs, 1], self.model_config.penalty_score, dtype="float32"
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)
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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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)
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self.share_inputs["presence_score"] = paddle.full(
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[max_num_seqs, 1], self.model_config.presence_score, dtype="float32"
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)
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self.share_inputs["min_dec_len"] = paddle.full([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"
|
|
)
|
|
self.share_inputs["min_length"] = paddle.full([max_num_seqs, 1], self.model_config.min_length, dtype="int64")
|
|
self.share_inputs["max_length"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.max_model_len, dtype="int64"
|
|
)
|
|
self.seq_lens_this_time_buffer = paddle.full(max_num_seqs, 0, dtype="int32")
|
|
self.share_inputs["seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["seq_lens_decoder"] = paddle.full([max_num_seqs, 1], 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["prompt_lens"] = paddle.full([max_num_seqs, 1], 0, dtype="int64")
|
|
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()
|
|
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([max_num_seqs, self.model_config.vocab_size], -1, dtype="int64")
|
|
self.share_inputs["bad_tokens_len"] = paddle.full([max_num_seqs], 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").cpu()
|
|
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["batch_id_per_token"] = 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")
|
|
# Declare AttentionBackend buffers
|
|
self.share_inputs["decoder_batch_ids"] = None
|
|
self.share_inputs["decoder_tile_ids_per_batch"] = None
|
|
self.share_inputs["decoder_num_blocks_cpu"] = None # Pinning Memory
|
|
self.share_inputs["max_len_tensor_cpu"] = None # CPU
|
|
self.share_inputs["encoder_batch_ids"] = None
|
|
self.share_inputs["encoder_tile_ids_per_batch"] = None
|
|
self.share_inputs["encoder_num_blocks_x_cpu"] = None # CPU
|
|
self.share_inputs["kv_batch_ids"] = None
|
|
self.share_inputs["kv_tile_ids_per_batch"] = None
|
|
self.share_inputs["kv_num_blocks_x_cpu"] = None # CPU
|
|
self.share_inputs["max_len_kv_cpu"] = None # CPU
|
|
|
|
# Initialize rotary position embedding
|
|
tmp_position_ids = paddle.arange(self.parallel_config.max_model_len).reshape((1, -1))
|
|
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.cache_config.block_size - 1
|
|
) // self.cache_config.block_size + self.cache_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.cache_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,
|
|
batch_id_per_token,
|
|
cu_seqlens_q,
|
|
cu_seqlens_k,
|
|
output_cum_offsets,
|
|
output_padding_offset,
|
|
) = pre_process(
|
|
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["batch_id_per_token"].copy_(batch_id_per_token, 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)
|
|
|
|
# Update bad tokens len
|
|
max_bad_tokens_len = paddle.max(self.share_inputs["bad_tokens_len"])
|
|
|
|
# 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"],
|
|
top_k_list=self.share_inputs["top_k_list"],
|
|
min_p=self.share_inputs["min_p"],
|
|
min_p_list=self.share_inputs["min_p_list"],
|
|
seed=self.share_inputs["infer_seed"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
pre_token_ids=self.share_inputs["pre_ids"],
|
|
prompt_ids=self.share_inputs["prompt_ids"],
|
|
prompt_lens=self.share_inputs["prompt_lens"],
|
|
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"][:, :max_bad_tokens_len],
|
|
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"""
|
|
# 1. Load original model
|
|
model_loader = get_model_loader(load_config=self.fd_config.load_config)
|
|
self.model = model_loader.load_model(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)
|
|
|
|
# 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"],
|
|
decoder_num_blocks_cpu=self.share_inputs["decoder_num_blocks_cpu"],
|
|
max_len_tensor_cpu=self.share_inputs["max_len_tensor_cpu"],
|
|
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"],
|
|
batch_id_per_token=self.share_inputs["batch_id_per_token"],
|
|
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"],
|
|
encoder_batch_ids=self.share_inputs["encoder_batch_ids"],
|
|
encoder_tile_ids_per_batch=self.share_inputs["encoder_tile_ids_per_batch"],
|
|
encoder_num_blocks_x_cpu=self.share_inputs["encoder_num_blocks_x_cpu"],
|
|
kv_batch_ids=self.share_inputs["kv_batch_ids"],
|
|
kv_tile_ids_per_batch=self.share_inputs["kv_tile_ids_per_batch"],
|
|
kv_num_blocks_x_cpu=self.share_inputs["kv_num_blocks_x_cpu"],
|
|
max_len_kv_cpu=self.share_inputs["max_len_kv_cpu"],
|
|
)
|
|
|
|
# Update Batch type for cuda graph
|
|
self.forward_meta.step_use_cudagraph = self.use_cudagraph and (
|
|
not ((self.share_inputs["seq_lens_this_time"] > 1).sum() > 0)
|
|
)
|
|
|
|
# Initialzie attention meta data
|
|
for attn_backend in self.attn_backends:
|
|
attn_backend.init_attention_metadata(self.forward_meta)
|
|
|
|
def initialize_kv_cache(self, profile: bool = False) -> None:
|
|
"""
|
|
Initialize kv cache
|
|
"""
|
|
cache_kvs = {}
|
|
max_block_num = self.num_gcu_blocks
|
|
|
|
# Get kv cache dtype
|
|
cache_type = self.parallel_config.dtype
|
|
|
|
kv_cache_quant_type = 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_type = "uint8"
|
|
kv_cache_quant_type = self.quant_config.kv_cache_quant_type
|
|
|
|
# Get kv cache shape
|
|
kv_cache_shape = self.attn_backends[0].get_kv_cache_shape(
|
|
max_num_blocks=max_block_num, kv_cache_quant_type=kv_cache_quant_type
|
|
)
|
|
# local_rank = self.local_rank % self.parallel_config.tensor_parallel_size
|
|
|
|
if not profile and (self.cache_config.enable_prefix_caching or self.parallel_config.splitwise_role != "mixed"):
|
|
raise NotImplementedError("prefix_caching is not support by GCUModelRunner.")
|
|
else:
|
|
for i in range(self.model_config.num_hidden_layers):
|
|
|
|
cache_kvs[f"key_caches_{i}"] = paddle.full(
|
|
shape=kv_cache_shape,
|
|
fill_value=0,
|
|
dtype=cache_type,
|
|
)
|
|
cache_kvs[f"value_caches_{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
|
|
|
|
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_size
|
|
self.model_config.kv_num_heads = max(
|
|
1,
|
|
int(self.model_config.num_key_value_heads) // self.parallel_config.tensor_parallel_size,
|
|
)
|
|
head_dim = self.model_config.head_dim
|
|
|
|
# Initialize AttentionBackend buffers
|
|
encoder_block_shape_q = 64
|
|
decoder_block_shape_q = 16
|
|
decoder_step_token_num = self.speculative_config.num_speculative_tokens + 1
|
|
group_size = np.ceil(num_heads / self.model_config.kv_num_heads)
|
|
|
|
decode_max_tile_size = self.scheduler_config.max_num_seqs * np.ceil(
|
|
(decoder_step_token_num * group_size) / decoder_block_shape_q
|
|
)
|
|
encode_max_tile_size = self.scheduler_config.max_num_seqs * np.ceil(
|
|
(self.model_config.max_model_len * group_size) / encoder_block_shape_q
|
|
)
|
|
kv_max_tile_size = self.scheduler_config.max_num_seqs * np.ceil(
|
|
self.model_config.max_model_len / self.fd_config.cache_config.block_size
|
|
)
|
|
self.share_inputs["decoder_batch_ids"] = paddle.full([int(decode_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["decoder_tile_ids_per_batch"] = paddle.full([int(decode_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["decoder_num_blocks_cpu"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
self.share_inputs["max_len_tensor_cpu"] = paddle.full([8], 0, dtype="int32").cpu()
|
|
|
|
self.share_inputs["encoder_batch_ids"] = paddle.full([int(encode_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["encoder_tile_ids_per_batch"] = paddle.full([int(encode_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["encoder_num_blocks_x_cpu"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
|
|
self.share_inputs["kv_batch_ids"] = paddle.full([int(kv_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["kv_tile_ids_per_batch"] = paddle.full([int(kv_max_tile_size)], 0, dtype="int32")
|
|
self.share_inputs["kv_num_blocks_x_cpu"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
self.share_inputs["max_len_kv_cpu"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
|
|
# 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,
|
|
encoder_block_shape_q=encoder_block_shape_q,
|
|
decoder_block_shape_q=decoder_block_shape_q,
|
|
)
|
|
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 _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
|
|
in_capturing: Is cuda graph in capturing state
|
|
"""
|
|
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. Padding inputs for cuda graph
|
|
self.forward_meta.step_use_cudagraph = in_capturing and self.forward_meta.step_use_cudagraph
|
|
self.padding_cudagraph_inputs()
|
|
|
|
# 3. Run model
|
|
model_output = self.model(
|
|
ids_remove_padding=self.share_inputs["ids_remove_padding"],
|
|
forward_meta=self.forward_meta,
|
|
)
|
|
|
|
hidden_states = rebuild_padding(
|
|
model_output,
|
|
self.share_inputs["cu_seqlens_q"],
|
|
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(hidden_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_size > 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_size > 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),
|
|
enable_thinking=(self.share_inputs["enable_thinking"] if self.enable_mm else None),
|
|
think_end_id=(self.model_config.think_end_id if self.enable_mm else -1),
|
|
need_think_end=(self.share_inputs["need_think_end"] if self.enable_mm else None),
|
|
reasoning_index=(self.share_inputs["reasoning_index"] if self.enable_mm else None),
|
|
)
|
|
|
|
post_process(
|
|
sampler_output=sampler_output,
|
|
model_output=model_output_data,
|
|
share_inputs=self.share_inputs,
|
|
block_size=self.parallel_config.block_size,
|
|
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
|
|
|
|
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.cache_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["input_ids"][idx, :token_chunk_size] = np.array(
|
|
task.prompt_token_ids[start_idx : start_idx + token_chunk_size]
|
|
)
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["prompt_lens"][idx : idx + 1] += token_chunk_size
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 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.scheduler_config.max_num_batched_tokens,
|
|
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")
|
|
|
|
@sot_warmup_guard(True)
|
|
def sot_warmup(self) -> None:
|
|
start_time = time.perf_counter()
|
|
for batch_size in self.sot_warmup_sizes:
|
|
self._dummy_run(
|
|
num_tokens=self.scheduler_config.max_num_batched_tokens,
|
|
batch_size=batch_size,
|
|
)
|
|
logger.info(f"SOT warmup the model with the batch size:{batch_size}")
|
|
logger.info(f"SOT warmup took {time.perf_counter() - start_time} 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.cache_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,
|
|
num_running_requests: int = 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'.
|
|
num_running_requests: batch_size
|
|
intermediate_tensors:
|
|
"""
|
|
# 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
|
|
model_output = self.model(
|
|
ids_remove_padding=self.share_inputs["ids_remove_padding"],
|
|
forward_meta=self.forward_meta,
|
|
)
|
|
|
|
hidden_states = rebuild_padding(
|
|
model_output,
|
|
self.share_inputs["cu_seqlens_q"],
|
|
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(hidden_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_size > 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_size > 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),
|
|
enable_thinking=(self.share_inputs["enable_thinking"] if self.enable_mm else None),
|
|
think_end_id=(self.model_config.think_end_id if self.enable_mm else -1),
|
|
need_think_end=(self.share_inputs["need_think_end"] if self.enable_mm else None),
|
|
reasoning_index=(self.share_inputs["reasoning_index"] if self.enable_mm 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,
|
|
share_inputs=self.share_inputs,
|
|
block_size=self.parallel_config.block_size,
|
|
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
|
|
|
|
self._update_chunked_prefill(model_forward_batch)
|
|
self._add_cache(model_forward_batch)
|
|
self.seq_lens_this_time_buffer.copy_(self.share_inputs["seq_lens_this_time"], False)
|
|
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")
|
|
|
|
@profile_run_guard(True)
|
|
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.
|
|
self.num_gcu_blocks = self.parallel_config.total_block_num
|
|
self.initialize_kv_cache(profile=True)
|
|
|
|
# 1. Profile with multimodal encoder & encoder cache
|
|
|
|
# 2. Dummy run
|
|
self._dummy_run(
|
|
num_tokens=self.scheduler_config.max_num_batched_tokens,
|
|
batch_size=min(self.scheduler_config.max_num_seqs, 3),
|
|
)
|
|
|
|
# 3. gc
|
|
self.clear_cache()
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.clear_dummy_input()
|
|
# paddle.device.cuda.synchronize()
|
|
|
|
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_gcu_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_gcu_blocks - 1,
|
|
int(self.num_gcu_blocks * self.cache_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"),
|
|
}
|
|
)
|
|
|
|
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
|
|
"""
|
|
"""
|
|
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
|
|
num_layers = (
|
|
self.model_config.num_hidden_layers + self.speculative_config.num_gpu_block_expand_ratio
|
|
if self.speculative_method in ["mtp"]
|
|
else self.model_config.num_hidden_layers
|
|
)
|
|
required_memory = byte_of_dtype * 2 * (self.cache_config.block_size * hidden_dim) * num_layers # k + v
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return required_memory
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|
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|
def not_need_stop(self) -> bool:
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"""Stop decoding if the tensor meets the termination condition"""
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return self.share_inputs["not_need_stop"][0]
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|
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def clear_cache(self):
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"""Clear cached data from shared inputs and forward metadata"""
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|
self.share_inputs.pop("caches", None)
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if self.forward_meta is not None:
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self.forward_meta.clear_caches()
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|
|
|
def clear_parameters(self, pid):
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|
""" " Dynamic model loader use to clear parameters use for RL"""
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|
self.dynamic_weight_manager.clear_parameters(pid)
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|
self.clear_cache()
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|
paddle.device.cuda.empty_cache()
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|
self.dynamic_weight_manager._log_memory("dynamic weight manager clear all memory")
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|
|
|
def update_parameters(self, pid):
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|
""" " Dynamic model loader use to update parameters use for RL"""
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|
self.dynamic_weight_manager.update_parameters(pid)
|
|
self.initialize_kv_cache()
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|
self.dynamic_weight_manager._log_memory("dynamic weight manager update all memory")
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|
|
|
def padding_cudagraph_inputs(self) -> None:
|
|
"""
|
|
Clean buffers used for the CUDA graph when replaying the CUDA graph with the padded batch.
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|
In FastDeploy, almost all input tensors have a buffer. So, just keep the buffer clean when replaying the CUDA graph with the padded batch.
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|
"""
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|
# In init_attention_metadata, the decode buffer has already been cleared
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|
return
|