838 lines
31 KiB
Python
838 lines
31 KiB
Python
"""
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Copyright 2023-2024 SGLang Team
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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"""Meta data for requests and batches"""
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import logging
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from dataclasses import dataclass
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from typing import List, Optional, Union
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import torch
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import torch.distributed as dist
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from flashinfer.sampling import top_k_top_p_sampling_from_probs
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from vllm.distributed import get_tensor_model_parallel_group
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import sglang.srt.sampling.penaltylib as penaltylib
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from sglang.global_config import global_config
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from sglang.srt.constrained import RegexGuide
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from sglang.srt.constrained.jump_forward import JumpForwardMap
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from sglang.srt.mem_cache.base_prefix_cache import BasePrefixCache
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from sglang.srt.mem_cache.chunk_cache import ChunkCache
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from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool
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INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
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# Put some global args for easy access
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global_server_args_dict = {
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"disable_flashinfer": False,
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"disable_flashinfer_sampling": False,
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"attention_reduce_in_fp32": False,
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"enable_mla": False,
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}
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logger = logging.getLogger(__name__)
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class BaseFinishReason:
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def __init__(self, is_error: bool = False):
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self.is_error = is_error
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def __str__(self):
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raise NotImplementedError("Subclasses must implement this method")
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class FINISH_MATCHED_TOKEN(BaseFinishReason):
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def __init__(self, matched: Union[int, List[int]]):
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super().__init__()
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self.matched = matched
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def __str__(self) -> str:
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return f"FINISH_MATCHED_TOKEN: {self.matched}"
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class FINISH_LENGTH(BaseFinishReason):
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def __init__(self, length: int):
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super().__init__()
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self.length = length
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def __str__(self) -> str:
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return f"FINISH_LENGTH: {self.length}"
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class FINISH_MATCHED_STR(BaseFinishReason):
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def __init__(self, matched: str):
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super().__init__()
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self.matched = matched
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def __str__(self) -> str:
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return f"FINISH_MATCHED_STR: {self.matched}"
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class FINISH_ABORT(BaseFinishReason):
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def __init__(self):
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super().__init__(is_error=True)
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def __str__(self) -> str:
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return "FINISH_ABORT"
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class Req:
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"""Store all inforamtion of a request."""
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def __init__(self, rid, origin_input_text, origin_input_ids):
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# Input and output info
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self.rid = rid
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self.origin_input_text = origin_input_text
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self.origin_input_ids_unpadded = origin_input_ids # Before image padding
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self.origin_input_ids = origin_input_ids
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self.output_ids = [] # Each decode stage's output ids
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self.fill_ids = None # fill_ids = origin_input_ids + output_ids
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# Memory info
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self.req_pool_idx = None
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# For incremental decoding
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# ----- | --------- read_ids -------|
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# ----- | surr_ids |
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# xxxxx | xxxxxxxxxxx | xxxxxxxxxxx |
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# ----- ^ ----------- ^ ----------- ^
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# ----- 1 ----------- 2 ----------- 3
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# 1: surr_offset
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# 2: read_offset
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# 3: last token
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self.vid = 0 # version id to sync decode status with in detokenizer_manager
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self.decoded_text = ""
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self.surr_offset = None # Surrounding offset to defeat the cleanup algorithm
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self.read_offset = None
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# The number of decoded tokens for token usage report. Note that
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# this does not include the jump forward tokens.
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self.completion_tokens_wo_jump_forward = 0
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# For vision input
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self.pixel_values = None
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self.image_size = None
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self.image_offset = None
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self.pad_value = None
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# Prefix info
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self.extend_input_len = 0
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self.prefix_indices = []
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self.last_node = None
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# Sampling parameters
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self.sampling_params = None
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self.stream = False
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# Check finish
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self.tokenizer = None
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self.finished_reason = None
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# Logprobs
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self.return_logprob = False
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self.embedding = None
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self.logprob_start_len = 0
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self.top_logprobs_num = 0
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self.normalized_prompt_logprob = None
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self.input_token_logprobs = None
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self.input_top_logprobs = None
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self.output_token_logprobs = []
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self.output_top_logprobs = []
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# The tokens is prefilled but need to be considered as decode tokens
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# and should be updated for the decode logprobs
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self.last_update_decode_tokens = 0
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# Constrained decoding
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self.regex_fsm: RegexGuide = None
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self.regex_fsm_state: int = 0
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self.jump_forward_map: JumpForwardMap = None
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# whether request reached finished condition
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def finished(self) -> bool:
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return self.finished_reason is not None
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def init_next_round_input(self, tree_cache: Optional[BasePrefixCache] = None):
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self.fill_ids = self.origin_input_ids + self.output_ids
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if tree_cache is not None:
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self.prefix_indices, self.last_node = tree_cache.match_prefix(
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rid=self.rid, key=self.adjust_max_prefix_ids()
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)
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self.extend_input_len = len(self.fill_ids) - len(self.prefix_indices)
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def adjust_max_prefix_ids(self):
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self.fill_ids = self.origin_input_ids + self.output_ids
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input_len = len(self.fill_ids)
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max_prefix_len = input_len
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if self.sampling_params.max_new_tokens > 0:
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# Need at least one token to compute logits
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max_prefix_len = min(max_prefix_len, input_len - 1)
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if self.return_logprob:
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max_prefix_len = min(max_prefix_len, self.logprob_start_len)
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if self.normalized_prompt_logprob is None:
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# Need at least two tokens to compute normalized logprob
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max_prefix_len = min(max_prefix_len, input_len - 2)
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return self.fill_ids[:max_prefix_len]
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# Based on https://github.com/vllm-project/vllm/blob/7a64d24aad69e4d2548aa0bf528d9fe63428ab01/vllm/transformers_utils/detokenizer.py#L194-L313
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def init_incremental_detokenize(self):
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first_iter = self.surr_offset is None or self.read_offset is None
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if first_iter:
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self.read_offset = len(self.origin_input_ids_unpadded)
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self.surr_offset = max(
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self.read_offset - INIT_INCREMENTAL_DETOKENIZATION_OFFSET, 0
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)
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all_ids = self.origin_input_ids_unpadded + self.output_ids
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return all_ids[self.surr_offset :], self.read_offset - self.surr_offset
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def get_next_inc_detokenization(self):
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if self.tokenizer is None:
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return False, ""
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read_ids, read_offset = self.init_incremental_detokenize()
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surr_ids = read_ids[:read_offset]
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surr_text = self.tokenizer.decode(
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surr_ids,
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skip_special_tokens=self.sampling_params.skip_special_tokens,
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spaces_between_special_tokens=self.sampling_params.spaces_between_special_tokens,
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)
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new_text = self.tokenizer.decode(
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read_ids,
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skip_special_tokens=self.sampling_params.skip_special_tokens,
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spaces_between_special_tokens=self.sampling_params.spaces_between_special_tokens,
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)
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if len(new_text) > len(surr_text) and not new_text.endswith("�"):
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return True, new_text[len(surr_text) :]
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return False, ""
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def check_finished(self):
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if self.finished():
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return
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if len(self.output_ids) >= self.sampling_params.max_new_tokens:
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self.finished_reason = FINISH_LENGTH(
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length=self.sampling_params.max_new_tokens
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)
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return
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last_token_id = self.output_ids[-1]
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matched_eos = last_token_id in self.sampling_params.stop_token_ids
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if self.tokenizer is not None:
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matched_eos |= last_token_id == self.tokenizer.eos_token_id
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if matched_eos and not self.sampling_params.ignore_eos:
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self.finished_reason = FINISH_MATCHED_TOKEN(matched=last_token_id)
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return
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if len(self.sampling_params.stop_strs) > 0:
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tail_str = self.tokenizer.decode(
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self.output_ids[-(self.sampling_params.stop_str_max_len + 1) :]
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)
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for stop_str in self.sampling_params.stop_strs:
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if stop_str in tail_str or stop_str in self.decoded_text:
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self.finished_reason = FINISH_MATCHED_STR(matched=stop_str)
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return
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def jump_forward_and_retokenize(self, jump_forward_str, next_state):
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if self.origin_input_text is None:
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# Recovering text can only use unpadded ids
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self.origin_input_text = self.tokenizer.decode(
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self.origin_input_ids_unpadded
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)
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all_text = self.origin_input_text + self.decoded_text + jump_forward_str
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all_ids = self.tokenizer.encode(all_text)
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prompt_tokens = len(self.origin_input_ids_unpadded)
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if all_ids[prompt_tokens - 1] != self.origin_input_ids_unpadded[-1]:
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# TODO(lsyin): fix token fusion
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logger.warning(
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"Token fusion between input and output, try to avoid this by removing the space at the end of the input."
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)
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return False
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old_output_ids = self.output_ids
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self.output_ids = all_ids[prompt_tokens:]
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self.decoded_text = self.decoded_text + jump_forward_str
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self.surr_offset = prompt_tokens
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self.read_offset = len(all_ids)
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# NOTE: A trick to reduce the surrouding tokens decoding overhead
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for i in range(0, INIT_INCREMENTAL_DETOKENIZATION_OFFSET):
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surr_text_ = self.tokenizer.decode(
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all_ids[self.read_offset - i : self.read_offset]
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)
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if not surr_text_.endswith("�"):
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self.surr_offset = self.read_offset - i
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break
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self.regex_fsm_state = next_state
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if self.return_logprob:
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# For fast-forward part's logprobs
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k = 0
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for i, old_id in enumerate(old_output_ids):
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if old_id == self.output_ids[i]:
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k = k + 1
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else:
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break
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self.output_token_logprobs = self.output_token_logprobs[:k]
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self.output_top_logprobs = self.output_top_logprobs[:k]
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self.logprob_start_len = prompt_tokens + k
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self.last_update_decode_tokens = len(self.output_ids) - k
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return True
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def __repr__(self):
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return f"rid(n={self.rid}, " f"input_ids={self.origin_input_ids}, "
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@dataclass
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class ScheduleBatch:
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"""Store all inforamtion of a batch."""
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# Request, memory pool, and cache
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reqs: List[Req]
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req_to_token_pool: ReqToTokenPool
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token_to_kv_pool: BaseTokenToKVPool
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tree_cache: BasePrefixCache
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# Batched arguments to model runner
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input_ids: torch.Tensor = None
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req_pool_indices: torch.Tensor = None
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seq_lens: torch.Tensor = None
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position_ids_offsets: torch.Tensor = None
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out_cache_loc: torch.Tensor = None
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extend_num_tokens: int = None
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# For mixed chunekd prefill
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prefix_lens_cpu: List[int] = None
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# For processing logprobs
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return_logprob: bool = False
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top_logprobs_nums: List[int] = None
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# Batched sampling params
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temperatures: torch.Tensor = None
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top_ps: torch.Tensor = None
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top_ks: torch.Tensor = None
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penalizer_orchestrator: penaltylib.BatchedPenalizerOrchestrator = None
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logit_bias: torch.Tensor = None
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@classmethod
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def init_new(cls, reqs, req_to_token_pool, token_to_kv_pool, tree_cache):
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return_logprob = any(req.return_logprob for req in reqs)
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return cls(
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reqs=reqs,
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req_to_token_pool=req_to_token_pool,
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token_to_kv_pool=token_to_kv_pool,
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tree_cache=tree_cache,
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return_logprob=return_logprob,
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)
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def batch_size(self):
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return len(self.reqs) if self.reqs is not None else 0
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def is_empty(self):
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return len(self.reqs) == 0
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def has_stream(self) -> bool:
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# Return whether batch has at least 1 streaming request
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return any(r.stream for r in self.reqs)
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def alloc_req_slots(self, num_reqs):
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req_pool_indices = self.req_to_token_pool.alloc(num_reqs)
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if req_pool_indices is None:
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raise RuntimeError(
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"Out of memory. "
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"Please set a smaller number for `--max-running-requests`."
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)
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return req_pool_indices
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def alloc_token_slots(self, num_tokens: int):
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out_cache_loc = self.token_to_kv_pool.alloc(num_tokens)
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if out_cache_loc is None:
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if self.tree_cache is not None:
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self.tree_cache.evict(num_tokens, self.token_to_kv_pool.free)
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out_cache_loc = self.token_to_kv_pool.alloc(num_tokens)
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if out_cache_loc is None:
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logger.error("Prefill out of memory. Try to lower your batch size.")
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if self.tree_cache is not None:
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self.tree_cache.pretty_print()
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exit(1)
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return out_cache_loc
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def batch_sampling_params(self, vocab_size):
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device = "cuda"
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bs, reqs = self.batch_size(), self.reqs
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self.temperatures = torch.tensor(
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[r.sampling_params.temperature for r in reqs],
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dtype=torch.float,
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device=device,
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).view(-1, 1)
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self.top_ps = torch.tensor(
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[r.sampling_params.top_p for r in reqs], dtype=torch.float, device=device
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)
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self.top_ks = torch.tensor(
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[r.sampling_params.top_k for r in reqs], dtype=torch.int, device=device
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)
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# Each penalizers will do nothing if they evaluate themselves as not required by looking at
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# the sampling_params of the requests (See {_is_required()} of each penalizers). So this
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# should not add hefty computation overhead other than simple checks.
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#
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# While we choose not to even create the class instances if they are not required, this
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# could add additional complexity to the {ScheduleBatch} class, especially we need to
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# handle {filter_batch()} and {merge()} cases as well.
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self.penalizer_orchestrator = penaltylib.BatchedPenalizerOrchestrator(
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vocab_size=vocab_size,
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batch=self,
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device=device,
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Penalizers={
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penaltylib.BatchedFrequencyPenalizer,
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penaltylib.BatchedMinNewTokensPenalizer,
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penaltylib.BatchedPresencePenalizer,
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penaltylib.BatchedRepetitionPenalizer,
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},
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)
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# Handle logit bias but only allocate when needed
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self.logit_bias = None
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def prepare_for_extend(self, vocab_size: int):
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bs = self.batch_size()
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reqs = self.reqs
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input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
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extend_num_tokens = sum(len(ids) for ids in input_ids)
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seq_lens = []
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# Allocate memory
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req_pool_indices_cpu = self.alloc_req_slots(bs)
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out_cache_loc = self.alloc_token_slots(extend_num_tokens)
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pt = 0
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for i, req in enumerate(reqs):
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req.req_pool_idx = req_pool_indices_cpu[i]
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pre_len, seq_len = len(req.prefix_indices), len(req.fill_ids)
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ext_len = seq_len - pre_len
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seq_lens.append(seq_len)
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if pre_len > 0:
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self.req_to_token_pool.req_to_token[req.req_pool_idx][
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:pre_len
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] = req.prefix_indices
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self.req_to_token_pool.req_to_token[req.req_pool_idx][pre_len:seq_len] = (
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out_cache_loc[pt : pt + ext_len]
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)
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pt += ext_len
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# Set fields
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with torch.device("cuda"):
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self.input_ids = torch.tensor(sum(input_ids, []), dtype=torch.int32)
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self.req_pool_indices = torch.tensor(req_pool_indices_cpu)
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self.seq_lens = torch.tensor(seq_lens, dtype=torch.int32)
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self.position_ids_offsets = torch.zeros((bs,), dtype=torch.int64)
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self.extend_num_tokens = extend_num_tokens
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self.out_cache_loc = out_cache_loc
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self.top_logprobs_nums = [r.top_logprobs_num for r in reqs]
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self.prefix_lens_cpu = [len(r.prefix_indices) for r in reqs]
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self.batch_sampling_params(vocab_size)
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def mix_with_running(self, running_batch: "ScheduleBatch"):
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# NOTE: prefix_indices is what has been cached, but we don't cache each decode step
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prefix_lens_cpu = [len(r.prefix_indices) for r in self.reqs]
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prefix_lens_cpu.extend(
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[
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len(r.origin_input_ids) + len(r.output_ids) - 1
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for r in running_batch.reqs
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]
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)
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for req in running_batch.reqs:
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req.fill_ids = req.origin_input_ids + req.output_ids
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req.extend_input_len = 1
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input_ids = torch.cat([self.input_ids, running_batch.input_ids])
|
|
out_cache_loc = torch.cat([self.out_cache_loc, running_batch.out_cache_loc])
|
|
extend_num_tokens = self.extend_num_tokens + running_batch.batch_size()
|
|
self.merge(running_batch)
|
|
self.input_ids = input_ids
|
|
self.out_cache_loc = out_cache_loc
|
|
self.extend_num_tokens = extend_num_tokens
|
|
self.prefix_lens_cpu = prefix_lens_cpu
|
|
|
|
def check_decode_mem(self):
|
|
bs = self.batch_size()
|
|
if self.token_to_kv_pool.available_size() >= bs:
|
|
return True
|
|
|
|
self.tree_cache.evict(bs, self.token_to_kv_pool.free)
|
|
|
|
if self.token_to_kv_pool.available_size() >= bs:
|
|
return True
|
|
|
|
return False
|
|
|
|
def retract_decode(self):
|
|
sorted_indices = [i for i in range(len(self.reqs))]
|
|
|
|
# TODO(lsyin): improve retraction policy for radix cache
|
|
sorted_indices.sort(
|
|
key=lambda i: (
|
|
len(self.reqs[i].output_ids),
|
|
-len(self.reqs[i].origin_input_ids),
|
|
),
|
|
reverse=True,
|
|
)
|
|
|
|
retracted_reqs = []
|
|
seq_lens_cpu = self.seq_lens.cpu().numpy()
|
|
while (
|
|
self.token_to_kv_pool.available_size()
|
|
< len(sorted_indices) * global_config.retract_decode_steps
|
|
):
|
|
if len(sorted_indices) == 1:
|
|
# Corner case: only one request left
|
|
assert (
|
|
self.token_to_kv_pool.available_size() > 0
|
|
), "No space left for only one request"
|
|
break
|
|
|
|
idx = sorted_indices.pop()
|
|
req = self.reqs[idx]
|
|
retracted_reqs.append(req)
|
|
|
|
if isinstance(self.tree_cache, ChunkCache):
|
|
# ChunkCache does not have eviction
|
|
token_indices = self.req_to_token_pool.req_to_token[req.req_pool_idx][
|
|
: seq_lens_cpu[idx]
|
|
]
|
|
self.token_to_kv_pool.free(token_indices)
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
del self.tree_cache.entries[req.rid]
|
|
else:
|
|
# TODO: apply more fine-grained retraction
|
|
last_uncached_pos = len(req.prefix_indices)
|
|
token_indices = self.req_to_token_pool.req_to_token[req.req_pool_idx][
|
|
last_uncached_pos : seq_lens_cpu[idx]
|
|
]
|
|
self.token_to_kv_pool.free(token_indices)
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
|
|
# release the last node
|
|
self.tree_cache.dec_lock_ref(req.last_node)
|
|
|
|
# NOTE(lsyin): we should use the newly evictable memory instantly.
|
|
residual_size = (
|
|
len(sorted_indices) * global_config.retract_decode_steps
|
|
- self.token_to_kv_pool.available_size()
|
|
)
|
|
residual_size = max(0, residual_size)
|
|
self.tree_cache.evict(residual_size, self.token_to_kv_pool.free)
|
|
|
|
req.prefix_indices = []
|
|
req.last_node = None
|
|
req.extend_input_len = 0
|
|
|
|
# For incremental logprobs
|
|
req.last_update_decode_tokens = 0
|
|
req.logprob_start_len = 10**9
|
|
|
|
self.filter_batch(sorted_indices)
|
|
|
|
# Reqs in batch are filtered
|
|
total_decoded_tokens = sum(len(r.output_ids) for r in self.reqs)
|
|
total_max_new_tokens = sum(r.sampling_params.max_new_tokens for r in self.reqs)
|
|
|
|
new_estimate_ratio = (
|
|
total_decoded_tokens + global_config.retract_decode_steps * len(self.reqs)
|
|
) / total_max_new_tokens
|
|
new_estimate_ratio = min(1.0, new_estimate_ratio)
|
|
|
|
return retracted_reqs, new_estimate_ratio
|
|
|
|
def check_for_jump_forward(self, model_runner):
|
|
jump_forward_reqs = []
|
|
filter_indices = [i for i in range(len(self.reqs))]
|
|
|
|
for i, req in enumerate(self.reqs):
|
|
if req.jump_forward_map is not None:
|
|
jump_forward_bytes = req.jump_forward_map.jump_forward_byte(
|
|
req.regex_fsm_state
|
|
)
|
|
if jump_forward_bytes is not None and len(jump_forward_bytes) > 1:
|
|
suffix_bytes = []
|
|
continuation_range = range(0x80, 0xC0)
|
|
cur_state = req.regex_fsm_state
|
|
while (
|
|
len(jump_forward_bytes)
|
|
and jump_forward_bytes[0][0] in continuation_range
|
|
):
|
|
# continuation bytes
|
|
byte_edge = jump_forward_bytes.pop(0)
|
|
suffix_bytes.append(byte_edge[0])
|
|
cur_state = byte_edge[1]
|
|
|
|
suffix_tokens = [f"<0x{hex(b)[2:].upper()}>" for b in suffix_bytes]
|
|
suffix_ids = req.tokenizer.convert_tokens_to_ids(suffix_tokens)
|
|
|
|
# Current ids, for cache and revert
|
|
cur_all_ids = tuple(req.origin_input_ids + req.output_ids)[:-1]
|
|
cur_output_ids = req.output_ids
|
|
|
|
req.output_ids.extend(suffix_ids)
|
|
decode_res, new_text = req.get_next_inc_detokenization()
|
|
if not decode_res:
|
|
req.output_ids = cur_output_ids
|
|
continue
|
|
|
|
(
|
|
jump_forward_str,
|
|
next_state,
|
|
) = req.jump_forward_map.jump_forward_symbol(cur_state)
|
|
|
|
# Make the incrementally decoded text part of jump_forward_str
|
|
# so that the UTF-8 will not corrupt
|
|
jump_forward_str = new_text + jump_forward_str
|
|
if not req.jump_forward_and_retokenize(
|
|
jump_forward_str, next_state
|
|
):
|
|
req.output_ids = cur_output_ids
|
|
continue
|
|
|
|
# The decode status has diverged from detokenizer_manager
|
|
req.vid += 1
|
|
|
|
# insert the old request into tree_cache
|
|
self.tree_cache.cache_finished_req(req, cur_all_ids)
|
|
|
|
# re-applying image padding
|
|
if req.pixel_values is not None:
|
|
(
|
|
req.origin_input_ids,
|
|
req.image_offset,
|
|
) = model_runner.model.pad_input_ids(
|
|
req.origin_input_ids_unpadded,
|
|
req.pad_value,
|
|
req.pixel_values.shape,
|
|
req.image_size,
|
|
)
|
|
|
|
jump_forward_reqs.append(req)
|
|
filter_indices.remove(i)
|
|
|
|
self.filter_batch(filter_indices)
|
|
|
|
return jump_forward_reqs
|
|
|
|
def prepare_for_decode(self, input_ids=None):
|
|
if input_ids is None:
|
|
input_ids = [
|
|
r.output_ids[-1] if r.output_ids else r.origin_input_ids[-1]
|
|
for r in self.reqs
|
|
]
|
|
else:
|
|
self.penalizer_orchestrator.cumulate_input_tokens(input_ids)
|
|
|
|
self.input_ids = torch.tensor(input_ids, dtype=torch.int32, device="cuda")
|
|
self.seq_lens.add_(1)
|
|
|
|
# Alloc mem
|
|
bs = self.batch_size()
|
|
self.out_cache_loc = self.alloc_token_slots(bs)
|
|
|
|
self.req_to_token_pool.req_to_token[
|
|
self.req_pool_indices, self.seq_lens - 1
|
|
] = self.out_cache_loc
|
|
|
|
def filter_batch(self, unfinished_indices: List[int]):
|
|
if unfinished_indices is None or len(unfinished_indices) == 0:
|
|
# Filter out all requests
|
|
self.reqs = []
|
|
return
|
|
|
|
if len(unfinished_indices) == len(self.reqs):
|
|
# No need to filter
|
|
return
|
|
|
|
self.reqs = [self.reqs[i] for i in unfinished_indices]
|
|
new_indices = torch.tensor(unfinished_indices, dtype=torch.int32, device="cuda")
|
|
self.seq_lens = self.seq_lens[new_indices]
|
|
self.input_ids = None
|
|
self.req_pool_indices = self.req_pool_indices[new_indices]
|
|
self.position_ids_offsets = self.position_ids_offsets[new_indices]
|
|
self.out_cache_loc = None
|
|
self.top_logprobs_nums = [self.top_logprobs_nums[i] for i in unfinished_indices]
|
|
self.return_logprob = any(req.return_logprob for req in self.reqs)
|
|
|
|
self.penalizer_orchestrator.filter(unfinished_indices, new_indices)
|
|
|
|
for item in [
|
|
"temperatures",
|
|
"top_ps",
|
|
"top_ks",
|
|
"logit_bias",
|
|
]:
|
|
self_val = getattr(self, item, None)
|
|
if self_val is not None: # logit_bias can be None
|
|
setattr(self, item, self_val[new_indices])
|
|
|
|
def merge(self, other: "ScheduleBatch"):
|
|
# Penalizer orchestrator must be merged before Batch.reqs is merged. This is because
|
|
# orchestrator.merge() depends on Batch.reqs during preparation of each penalizers, so it
|
|
# needs to be called with pre-merged Batch.reqs.
|
|
self.penalizer_orchestrator.merge(other.penalizer_orchestrator)
|
|
|
|
self.reqs.extend(other.reqs)
|
|
|
|
self.req_pool_indices = torch.concat(
|
|
[self.req_pool_indices, other.req_pool_indices]
|
|
)
|
|
self.seq_lens = torch.concat([self.seq_lens, other.seq_lens])
|
|
self.position_ids_offsets = torch.concat(
|
|
[self.position_ids_offsets, other.position_ids_offsets]
|
|
)
|
|
self.out_cache_loc = None
|
|
self.top_logprobs_nums.extend(other.top_logprobs_nums)
|
|
self.return_logprob = any(req.return_logprob for req in self.reqs)
|
|
|
|
for item in [
|
|
"temperatures",
|
|
"top_ps",
|
|
"top_ks",
|
|
]:
|
|
self_val = getattr(self, item, None)
|
|
other_val = getattr(other, item, None)
|
|
setattr(self, item, torch.concat([self_val, other_val]))
|
|
|
|
# logit_bias can be None
|
|
if self.logit_bias is not None or other.logit_bias is not None:
|
|
vocab_size = (
|
|
self.logit_bias.shape[1]
|
|
if self.logit_bias is not None
|
|
else other.logit_bias.shape[1]
|
|
)
|
|
if self.logit_bias is None:
|
|
self.logit_bias = torch.zeros(
|
|
(len(self.reqs), vocab_size), dtype=torch.float32, device="cuda"
|
|
)
|
|
if other.logit_bias is None:
|
|
other.logit_bias = torch.zeros(
|
|
(len(other.reqs), vocab_size), dtype=torch.float32, device="cuda"
|
|
)
|
|
self.logit_bias = torch.concat([self.logit_bias, other.logit_bias])
|
|
|
|
def sample(self, logits: torch.Tensor):
|
|
# TODO(lsyin): move this into a part of layer and run with CUDA Graph
|
|
# Post process logits
|
|
logits = logits.contiguous()
|
|
logits.div_(self.temperatures)
|
|
if self.logit_bias is not None:
|
|
logits.add_(self.logit_bias)
|
|
|
|
has_regex = any(req.regex_fsm is not None for req in self.reqs)
|
|
if has_regex:
|
|
allowed_mask = torch.empty_like(logits[0], dtype=torch.bool)
|
|
for i, req in enumerate(self.reqs):
|
|
if req.regex_fsm is not None:
|
|
allowed_mask.zero_()
|
|
allowed_mask[
|
|
req.regex_fsm.get_next_instruction(req.regex_fsm_state).tokens
|
|
] = 1
|
|
logits[i].masked_fill_(~allowed_mask, float("-inf"))
|
|
|
|
logits = self.penalizer_orchestrator.apply(logits)
|
|
|
|
probs = torch.softmax(logits, dim=-1)
|
|
|
|
if not global_server_args_dict["disable_flashinfer_sampling"]:
|
|
max_top_k_round, batch_size = 32, probs.shape[0]
|
|
uniform_samples = torch.rand(
|
|
(max_top_k_round, batch_size), device=probs.device
|
|
)
|
|
batch_next_token_ids, success = top_k_top_p_sampling_from_probs(
|
|
probs, uniform_samples, self.top_ks, self.top_ps
|
|
)
|
|
else:
|
|
# Here we provide a slower fallback implementation.
|
|
batch_next_token_ids, success = top_k_top_p_sampling_from_probs_torch(
|
|
probs, self.top_ks, self.top_ps
|
|
)
|
|
|
|
if not torch.all(success):
|
|
logger.warning(f"Sampling failed. Fallback to top_k=1 strategy. {logits=}")
|
|
probs = probs.masked_fill(torch.isnan(probs), 0.0)
|
|
argmax_ids = torch.argmax(probs, dim=-1)
|
|
batch_next_token_ids = torch.where(
|
|
success, batch_next_token_ids, argmax_ids
|
|
)
|
|
|
|
if has_regex:
|
|
batch_next_token_ids_cpu = batch_next_token_ids.cpu().numpy()
|
|
for i, req in enumerate(self.reqs):
|
|
if req.regex_fsm is not None:
|
|
req.regex_fsm_state = req.regex_fsm.get_next_state(
|
|
req.regex_fsm_state, batch_next_token_ids_cpu[i]
|
|
)
|
|
|
|
self.penalizer_orchestrator.cumulate_output_tokens(batch_next_token_ids)
|
|
|
|
return batch_next_token_ids
|
|
|
|
|
|
def top_k_top_p_sampling_from_probs_torch(
|
|
probs: torch.Tensor, top_ks: torch.Tensor, top_ps: torch.Tensor
|
|
):
|
|
"""A top-k and top-k sampling implementation with native pytorch operations."""
|
|
probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
|
|
probs_sum = torch.cumsum(probs_sort, dim=-1)
|
|
probs_sort[(probs_sum - probs_sort) > top_ps.view(-1, 1)] = 0.0
|
|
probs_sort[
|
|
torch.arange(0, probs.shape[-1], device=probs.device).view(1, -1)
|
|
>= top_ks.view(-1, 1)
|
|
] = 0.0
|
|
probs_sort.div_(probs_sort.max(dim=-1, keepdim=True)[0])
|
|
try:
|
|
sampled_index = torch.multinomial(probs_sort, num_samples=1)
|
|
except RuntimeError as e:
|
|
logger.warning(f"Sampling error: {e}")
|
|
batch_next_token_ids = torch.zeros(
|
|
(probs_sort.shape[0],), dtype=torch.int32, device=probs.device
|
|
)
|
|
success = torch.zeros(probs.shape[0], dtype=torch.bool, device=probs.device)
|
|
return batch_next_token_ids, success
|
|
|
|
batch_next_token_ids = torch.gather(probs_idx, dim=1, index=sampled_index).view(-1)
|
|
success = torch.ones(probs.shape[0], dtype=torch.bool, device=probs.device)
|
|
return batch_next_token_ids, success
|