2035 lines
75 KiB
Python
2035 lines
75 KiB
Python
from __future__ import annotations
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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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#
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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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"""
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Store information about requests and batches.
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The following is the flow of data structures for a batch:
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ScheduleBatch -> ModelWorkerBatch -> ForwardBatch
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- ScheduleBatch is managed by `scheduler.py::Scheduler`.
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It contains high-level scheduling data. Most of the data is on the CPU.
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- ModelWorkerBatch is managed by `tp_worker.py::TpModelWorker`.
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It is a subset of `ScheduleBatch` that only contains data related to the model forward on GPU.
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It will be transformed from CPU scheduler to GPU model runner.
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- ForwardBatch is managed by `model_runner.py::ModelRunner`.
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It contains low-level tensor data. Most of the data consists of GPU tensors.
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TODO(lmzheng): ModelWorkerBatch seems a bit redundant and we consider removing it in the future.
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"""
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import copy
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import dataclasses
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import logging
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import threading
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from enum import Enum, auto
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from http import HTTPStatus
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from itertools import chain
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from typing import TYPE_CHECKING, Any, List, Optional, Set, Tuple, Union
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import numpy as np
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import torch
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import triton
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import triton.language as tl
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from sglang.global_config import global_config
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from sglang.srt.constrained.base_grammar_backend import BaseGrammarObject
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from sglang.srt.disaggregation.base import BaseKVSender
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from sglang.srt.disaggregation.decode_schedule_batch_mixin import (
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ScheduleBatchDisaggregationDecodeMixin,
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)
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from sglang.srt.distributed.parallel_state import get_tensor_model_parallel_rank
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from sglang.srt.layers.moe import is_tbo_enabled
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from sglang.srt.mem_cache.allocator import (
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BaseTokenToKVPoolAllocator,
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SWATokenToKVPoolAllocator,
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)
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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, SWAChunkCache
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from sglang.srt.mem_cache.lora_radix_cache import LoRAKey, LoRARadixCache
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from sglang.srt.mem_cache.memory_pool import ReqToTokenPool
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from sglang.srt.mem_cache.swa_radix_cache import SWARadixCache
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from sglang.srt.metrics.collector import TimeStats
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from sglang.srt.model_executor.forward_batch_info import CaptureHiddenMode, ForwardMode
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from sglang.srt.sampling.sampling_batch_info import SamplingBatchInfo
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from sglang.srt.sampling.sampling_params import SamplingParams
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from sglang.srt.server_args import ServerArgs
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from sglang.srt.utils import flatten_nested_list, support_triton
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if TYPE_CHECKING:
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.speculative.eagle_utils import EagleDraftInput, EagleVerifyInput
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from sglang.srt.speculative.spec_info import SpeculativeAlgorithm
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INIT_INCREMENTAL_DETOKENIZATION_OFFSET = 5
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GLOBAL_SERVER_ARGS_KEYS = [
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"attention_backend",
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"mm_attention_backend",
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"debug_tensor_dump_inject",
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"debug_tensor_dump_output_folder",
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"chunked_prefill_size",
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"device",
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"disable_chunked_prefix_cache",
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"disable_flashinfer_cutlass_moe_fp4_allgather",
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"disable_radix_cache",
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"enable_dp_lm_head",
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"flashinfer_mxfp4_moe_precision",
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"enable_flashinfer_allreduce_fusion",
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"moe_dense_tp_size",
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"ep_dispatch_algorithm",
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"ep_num_redundant_experts",
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"enable_nan_detection",
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"flashinfer_mla_disable_ragged",
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"max_micro_batch_size",
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"disable_shared_experts_fusion",
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"sampling_backend",
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"speculative_accept_threshold_single",
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"speculative_accept_threshold_acc",
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"torchao_config",
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"triton_attention_reduce_in_fp32",
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"num_reserved_decode_tokens",
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"weight_loader_disable_mmap",
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"enable_multimodal",
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"enable_symm_mem",
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"quantization",
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"enable_custom_logit_processor",
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"disaggregation_mode",
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]
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# Put some global args for easy access
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global_server_args_dict = {k: getattr(ServerArgs, k) for k in GLOBAL_SERVER_ARGS_KEYS}
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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 to_json(self):
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raise NotImplementedError()
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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 to_json(self):
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return {
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"type": "stop", # to match OpenAI API's return value
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"matched": self.matched,
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}
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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 to_json(self):
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return {
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"type": "stop", # to match OpenAI API's return value
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"matched": self.matched,
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}
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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 to_json(self):
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return {
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"type": "length", # to match OpenAI API's return value
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"length": self.length,
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}
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class FINISH_ABORT(BaseFinishReason):
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def __init__(self, message=None, status_code=None, err_type=None):
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super().__init__(is_error=True)
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self.message = message or "Aborted"
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self.status_code = status_code
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self.err_type = err_type
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def to_json(self):
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return {
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"type": "abort",
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"message": self.message,
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"status_code": self.status_code,
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"err_type": self.err_type,
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}
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class Modality(Enum):
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IMAGE = auto()
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MULTI_IMAGES = auto()
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VIDEO = auto()
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AUDIO = auto()
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@staticmethod
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def from_str(modality_str: str):
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try:
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return Modality[modality_str.upper()]
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except KeyError:
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raise ValueError(
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f"Invalid modality string: {modality_str}. Valid modalities are: {[m.name for m in Modality]}"
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)
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@staticmethod
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def all():
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return [Modality.IMAGE, Modality.VIDEO, Modality.AUDIO]
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@dataclasses.dataclass
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class MultimodalDataItem:
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"""
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One MultimodalDataItem contains all inputs for one modality.
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For example, if there are 3 images and 1 audio inputs, there will be 2 MultimodalDataItem.
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One for images and one for audio.
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We put the common fields first and the model-specific fields in model_specific_data.
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"""
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modality: Modality
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hash: int = None
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pad_value: int = None
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offsets: Optional[list] = None
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# the raw features returned by processor, e.g. pixel_values or audio_features
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feature: Union[torch.Tensor, np.ndarray] = None
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# the precomputed embeddings, passed as final encoder embeddings
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# One and only one of the feature and precomputed_embeddings will be empty
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precomputed_embeddings: Optional[Union[torch.Tensor, np.ndarray]] = None
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# Model-specific data stored in a dictionary
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model_specific_data: dict[str, Any] = dataclasses.field(default_factory=dict)
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def __getattr__(self, name: str):
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if (
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"model_specific_data" in self.__dict__
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and name in self.__dict__["model_specific_data"]
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):
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return self.__dict__["model_specific_data"][name]
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else:
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raise AttributeError(
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f"'{self.__class__.__name__}' object has no attribute '{name}'"
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)
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def __setitem__(self, key: str, value: Any):
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if key in self.__dict__:
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self.__dict__[key] = value
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else:
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self.model_specific_data[key] = value
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def set(self, key: str, value: Any):
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self.__setitem__(key, value)
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@staticmethod
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def is_empty_list(l):
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if l is None:
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return True
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return len([item for item in flatten_nested_list(l) if item is not None]) == 0
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def set_pad_value(self):
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"""
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Set the pad value after first hashing the data
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"""
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from sglang.srt.managers.mm_utils import hash_feature
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if self.hash is None:
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if self.feature is not None:
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hashed_feature = self.feature
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else:
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hashed_feature = self.precomputed_embeddings
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self.hash = hash_feature(hashed_feature)
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assert self.hash is not None
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self.pad_value = self.hash % (1 << 30)
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def is_modality(self, modality: Modality) -> bool:
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return self.modality == modality
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def is_audio(self):
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return self.modality == Modality.AUDIO
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def is_image(self):
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return self.modality in [Modality.IMAGE, Modality.MULTI_IMAGES]
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def is_video(self):
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return self.modality == Modality.VIDEO
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def is_valid(self) -> bool:
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return self.is_image() or self.is_video() or self.is_audio()
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def validate(self):
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...
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# TODO
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@staticmethod
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def from_dict(obj: dict):
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kwargs = dict(obj)
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modality = kwargs.pop("modality")
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if isinstance(modality, str):
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modality = Modality[modality]
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ret = MultimodalDataItem(modality=modality, **kwargs)
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ret.validate()
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return ret
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def merge(self, other):
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self.feature += other.feature
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self.offsets += other.offsets
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self.hash = hash((self.hash, other.hash))
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self.set_pad_value()
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@dataclasses.dataclass
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class MultimodalInputs:
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"""The multimodal data related inputs."""
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# items of data
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mm_items: List[MultimodalDataItem]
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image_pad_len: Optional[list] = None
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num_image_tokens: Optional[int] = None
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# image
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im_token_id: Optional[int] = None
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im_start_id: Optional[int] = None
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im_end_id: Optional[int] = None
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slice_start_id: Optional[int] = None
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slice_end_id: Optional[int] = None
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# video
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video_token_id: Optional[int] = None
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# audio
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audio_token_id: Optional[int] = None
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audio_start_id: Optional[int] = None
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audio_end_id: Optional[int] = None
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# QWen2-VL related
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mrope_positions: Optional[torch.Tensor] = None
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mrope_position_delta: Optional[torch.Tensor] = None
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@staticmethod
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def from_dict(obj: dict):
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ret = MultimodalInputs(
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mm_items=obj["mm_items"],
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)
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assert isinstance(ret.mm_items, list)
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ret.mm_items = [item for item in ret.mm_items if item.is_valid()]
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for item in ret.mm_items:
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item.set_pad_value()
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optional_args = [
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"mrope_positions",
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"mrope_position_delta",
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"im_token_id",
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"im_start_id",
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"im_end_id",
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"video_token_id",
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"slice_start_id",
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"slice_end_id",
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"audio_start_id",
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"audio_end_id",
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"audio_token_id",
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]
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for arg in optional_args:
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if arg in obj:
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setattr(ret, arg, obj[arg])
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return ret
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def contains_image_inputs(self) -> bool:
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return any(item.is_image() for item in self.mm_items)
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def contains_video_inputs(self) -> bool:
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return any(item.is_video() for item in self.mm_items)
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def contains_audio_inputs(self) -> bool:
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return any(item.is_audio() for item in self.mm_items)
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def contains_mm_input(self) -> bool:
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return any(True for item in self.mm_items if item.is_valid())
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def merge(self, other: MultimodalInputs):
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"""
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merge image inputs when requests are being merged
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"""
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# args needed to be merged
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optional_args = [
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"mm_items",
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"image_pad_len",
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]
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for arg in optional_args:
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self_arg = getattr(self, arg, None)
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if self_arg is not None:
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setattr(self, arg, self_arg + getattr(other, arg))
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mrope_positions = self.mrope_positions
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if mrope_positions is not None:
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if other.mrope_positions is None:
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self.mrope_positions = mrope_positions
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else:
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self.mrope_positions = torch.cat(
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[self.mrope_positions, other.mrope_positions], dim=1
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)
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mrope_position_delta = self.mrope_position_delta
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if mrope_position_delta is not None:
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if other.mrope_position_delta is None:
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self.mrope_position_delta = mrope_position_delta
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else:
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self.mrope_position_delta = torch.cat(
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[self.mrope_position_delta, other.mrope_position_delta], dim=0
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)
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for key, val in other.__dict__.items():
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if "_id" in key:
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# set token_ids
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if getattr(self, key, None) is None:
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setattr(self, key, getattr(other, key, None))
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# other args would be kept intact
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class Req:
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"""The input and output status of a request."""
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def __init__(
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self,
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rid: str,
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origin_input_text: str,
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origin_input_ids: List[int],
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sampling_params: SamplingParams,
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return_logprob: bool = False,
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top_logprobs_num: int = 0,
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token_ids_logprob: List[int] = None,
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stream: bool = False,
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origin_input_ids_unpadded: Optional[Tuple[int]] = None,
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lora_id: Optional[str] = None,
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input_embeds: Optional[List[List[float]]] = None,
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token_type_ids: List[int] = None,
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session_id: Optional[str] = None,
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custom_logit_processor: Optional[str] = None,
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return_hidden_states: bool = False,
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eos_token_ids: Optional[Set[int]] = None,
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bootstrap_host: Optional[str] = None,
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bootstrap_port: Optional[int] = None,
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bootstrap_room: Optional[int] = None,
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data_parallel_rank: Optional[int] = None,
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vocab_size: Optional[int] = None,
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):
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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 = (
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origin_input_ids_unpadded
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if origin_input_ids_unpadded
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else origin_input_ids # Before image padding
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)
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self.origin_input_ids = origin_input_ids
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# Each decode stage's output ids
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self.output_ids = []
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# fill_ids = origin_input_ids + output_ids. Updated if chunked.
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self.fill_ids = []
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self.session_id = session_id
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self.input_embeds = input_embeds
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# for corss-endoder model
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self.token_type_ids = token_type_ids
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# The length of KV that have been removed in local attention chunked prefill
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self.evicted_seqlen_local = 0
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# Sampling info
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if isinstance(sampling_params.custom_params, dict):
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sampling_params = copy.copy(sampling_params)
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sampling_params.custom_params = sampling_params.custom_params | {
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"__req__": self
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}
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self.sampling_params = sampling_params
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self.custom_logit_processor = custom_logit_processor
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self.return_hidden_states = return_hidden_states
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self.lora_id = lora_id
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# Memory pool info
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self.req_pool_idx: Optional[int] = None
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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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# Whether this request has finished output
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self.finished_output = None
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# If we want to abort the request in the middle of the event loop, set this to true
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# Note: We should never set finished_reason in the middle, the req will get filtered and never respond
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self.to_abort = False
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# This carries the error message for `.to_abort` and will be attached to the finished_reason at the end of the event loop
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self.to_abort_message: str = None
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self.stream = stream
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self.eos_token_ids = eos_token_ids
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self.vocab_size = vocab_size
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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.surr_offset = None # Surrounding offset to defeat the cleanup algorithm
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self.read_offset = None
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self.decoded_text = ""
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# For multimodal inputs
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self.multimodal_inputs: Optional[MultimodalInputs] = None
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# Prefix info
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# The indices to kv cache for the shared prefix.
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self.prefix_indices: torch.Tensor = []
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# Number of tokens to run prefill.
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self.extend_input_len = 0
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# The relative logprob_start_len in an extend batch
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self.extend_logprob_start_len = 0
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self.last_node: Any = None
|
|
self.last_host_node: Any = None
|
|
self.host_hit_length = 0
|
|
# The node to lock until for swa radix tree lock ref
|
|
self.swa_uuid_for_lock: Optional[int] = None
|
|
|
|
# Whether or not if it is chunked. It increments whenever
|
|
# it is chunked, and decrement whenever chunked request is
|
|
# processed.
|
|
self.is_chunked = 0
|
|
|
|
# For retraction
|
|
self.is_retracted = False
|
|
|
|
# Incremental streamining
|
|
self.send_token_offset: int = 0
|
|
self.send_decode_id_offset: int = 0
|
|
# TODO (Byron): send_output_token_logprobs_offset and send_decode_id_offset can be different in disaggregation mode
|
|
# because the decode server does not have the first output token logprobs
|
|
self.send_output_token_logprobs_offset: int = 0
|
|
|
|
# Logprobs (arguments)
|
|
self.return_logprob = return_logprob
|
|
# Start index to compute logprob from.
|
|
self.logprob_start_len = 0
|
|
self.top_logprobs_num = top_logprobs_num
|
|
self.token_ids_logprob = token_ids_logprob
|
|
self.temp_scaled_logprobs = False
|
|
self.top_p_normalized_logprobs = False
|
|
|
|
# Logprobs (return values)
|
|
# True means the input logprob has been already sent to detokenizer.
|
|
self.input_logprob_sent: bool = False
|
|
self.input_token_logprobs_val: Optional[List[float]] = None
|
|
self.input_token_logprobs_idx: Optional[List[int]] = None
|
|
self.input_top_logprobs_val: Optional[List[float]] = None
|
|
self.input_top_logprobs_idx: Optional[List[int]] = None
|
|
self.input_token_ids_logprobs_val: Optional[List[float]] = None
|
|
self.input_token_ids_logprobs_idx: Optional[List[int]] = None
|
|
# Temporary holder to store input_token_logprobs.
|
|
self.input_token_logprobs: Optional[List[Tuple[int]]] = None
|
|
self.temp_input_top_logprobs_val: Optional[List[torch.Tensor]] = None
|
|
self.temp_input_top_logprobs_idx: Optional[List[int]] = None
|
|
self.temp_input_token_ids_logprobs_val: Optional[List[float]] = None
|
|
self.temp_input_token_ids_logprobs_idx: Optional[List[int]] = None
|
|
|
|
if return_logprob:
|
|
# shape: (bs, 1)
|
|
self.output_token_logprobs_val = []
|
|
self.output_token_logprobs_idx = []
|
|
# shape: (bs, k)
|
|
self.output_top_logprobs_val = []
|
|
self.output_top_logprobs_idx = []
|
|
self.output_token_ids_logprobs_val = []
|
|
self.output_token_ids_logprobs_idx = []
|
|
else:
|
|
self.output_token_logprobs_val = self.output_token_logprobs_idx = (
|
|
self.output_top_logprobs_val
|
|
) = self.output_top_logprobs_idx = self.output_token_ids_logprobs_val = (
|
|
self.output_token_ids_logprobs_idx
|
|
) = None
|
|
self.hidden_states: List[List[float]] = []
|
|
self.hidden_states_tensor = None # Note: use tensor instead of list to transfer hidden_states when PD + MTP
|
|
|
|
# Embedding (return values)
|
|
self.embedding = None
|
|
|
|
# Constrained decoding
|
|
self.grammar: Optional[BaseGrammarObject] = None
|
|
self.grammar_wait_ct = 0
|
|
|
|
# The number of cached tokens that were already cached in the KV cache
|
|
self.cached_tokens = 0
|
|
self.already_computed = 0
|
|
|
|
# The number of verification forward passes in the speculative decoding.
|
|
# This is used to compute the average acceptance length per request.
|
|
self.spec_verify_ct = 0
|
|
|
|
# For metrics
|
|
self.time_stats: TimeStats = TimeStats()
|
|
self.has_log_time_stats: bool = False
|
|
self.queue_time_start = None
|
|
self.queue_time_end = None
|
|
|
|
# For disaggregation
|
|
self.bootstrap_host: str = bootstrap_host
|
|
self.bootstrap_port: Optional[int] = bootstrap_port
|
|
self.bootstrap_room: Optional[int] = bootstrap_room
|
|
self.disagg_kv_sender: Optional[BaseKVSender] = None
|
|
|
|
# For data parallel rank routing
|
|
self.data_parallel_rank: Optional[int] = data_parallel_rank
|
|
|
|
# the start index of the sent kv cache
|
|
# We want to send it chunk by chunk for chunked prefill.
|
|
# After every chunk forward, we do the following:
|
|
# kv_send(req.input_ids[req.start_send_idx:len(req.fill_ids)])
|
|
# start_send_idx = len(req.fill_ids)
|
|
self.start_send_idx: int = 0
|
|
|
|
# For overlap schedule, we delay the kv transfer until `process_batch_result_disagg_prefill` rather than `process_prefill_chunk` in non-overlap
|
|
# This is because kv is not ready in `process_prefill_chunk`.
|
|
# We use `tmp_end_idx` to store the end index of the kv cache to send.
|
|
self.tmp_end_idx: int = -1
|
|
self.metadata_buffer_index: int = -1
|
|
|
|
@property
|
|
def seqlen(self):
|
|
return len(self.origin_input_ids) + len(self.output_ids)
|
|
|
|
def extend_image_inputs(self, image_inputs):
|
|
if self.multimodal_inputs is None:
|
|
self.multimodal_inputs = image_inputs
|
|
else:
|
|
self.multimodal_inputs.merge(image_inputs)
|
|
|
|
def finished(self) -> bool:
|
|
# Whether request reached finished condition
|
|
return self.finished_reason is not None
|
|
|
|
def init_next_round_input(
|
|
self,
|
|
tree_cache: Optional[BasePrefixCache] = None,
|
|
):
|
|
self.fill_ids = self.origin_input_ids + self.output_ids
|
|
if tree_cache is not None:
|
|
if isinstance(tree_cache, LoRARadixCache):
|
|
(
|
|
self.prefix_indices,
|
|
self.last_node,
|
|
self.last_host_node,
|
|
self.host_hit_length,
|
|
) = tree_cache.match_prefix_with_lora_id(
|
|
key=LoRAKey(
|
|
lora_id=self.lora_id, token_ids=self.adjust_max_prefix_ids()
|
|
),
|
|
)
|
|
else:
|
|
(
|
|
self.prefix_indices,
|
|
self.last_node,
|
|
self.last_host_node,
|
|
self.host_hit_length,
|
|
) = tree_cache.match_prefix(
|
|
key=self.adjust_max_prefix_ids(),
|
|
)
|
|
self.extend_input_len = len(self.fill_ids) - len(self.prefix_indices)
|
|
|
|
def adjust_max_prefix_ids(self):
|
|
self.fill_ids = self.origin_input_ids + self.output_ids
|
|
input_len = len(self.fill_ids)
|
|
|
|
# FIXME: To work around some bugs in logprob computation, we need to ensure each
|
|
# request has at least one token. Later, we can relax this requirement and use `input_len`.
|
|
max_prefix_len = input_len - 1
|
|
|
|
if self.sampling_params.max_new_tokens > 0:
|
|
# Need at least one token to compute logits
|
|
max_prefix_len = min(max_prefix_len, input_len - 1)
|
|
|
|
if self.return_logprob:
|
|
max_prefix_len = min(max_prefix_len, self.logprob_start_len)
|
|
|
|
max_prefix_len = max(max_prefix_len, 0)
|
|
return self.fill_ids[:max_prefix_len]
|
|
|
|
# Based on https://github.com/vllm-project/vllm/blob/7a64d24aad69e4d2548aa0bf528d9fe63428ab01/vllm/transformers_utils/detokenizer.py#L194-L313
|
|
def init_incremental_detokenize(self):
|
|
first_iter = self.surr_offset is None or self.read_offset is None
|
|
|
|
if first_iter:
|
|
self.read_offset = len(self.origin_input_ids_unpadded)
|
|
self.surr_offset = max(
|
|
self.read_offset - INIT_INCREMENTAL_DETOKENIZATION_OFFSET, 0
|
|
)
|
|
|
|
all_ids = self.origin_input_ids_unpadded + self.output_ids
|
|
return all_ids[self.surr_offset :], self.read_offset - self.surr_offset
|
|
|
|
def check_finished(self):
|
|
if self.finished():
|
|
return
|
|
|
|
if self.to_abort:
|
|
self.finished_reason = FINISH_ABORT(
|
|
message=self.to_abort_message,
|
|
)
|
|
return
|
|
|
|
if len(self.output_ids) >= self.sampling_params.max_new_tokens:
|
|
self.finished_reason = FINISH_LENGTH(
|
|
length=self.sampling_params.max_new_tokens
|
|
)
|
|
return
|
|
|
|
if self.grammar is not None:
|
|
if self.grammar.is_terminated():
|
|
self.finished_reason = FINISH_MATCHED_TOKEN(matched=self.output_ids[-1])
|
|
return
|
|
|
|
last_token_id = self.output_ids[-1]
|
|
|
|
if not self.sampling_params.ignore_eos:
|
|
matched_eos = False
|
|
|
|
# Check stop token ids
|
|
if self.sampling_params.stop_token_ids:
|
|
matched_eos = last_token_id in self.sampling_params.stop_token_ids
|
|
if self.eos_token_ids:
|
|
matched_eos |= last_token_id in self.eos_token_ids
|
|
if self.tokenizer is not None:
|
|
matched_eos |= last_token_id == self.tokenizer.eos_token_id
|
|
if self.tokenizer.additional_stop_token_ids:
|
|
matched_eos |= (
|
|
last_token_id in self.tokenizer.additional_stop_token_ids
|
|
)
|
|
if matched_eos:
|
|
self.finished_reason = FINISH_MATCHED_TOKEN(matched=last_token_id)
|
|
return
|
|
|
|
if last_token_id > self.vocab_size or last_token_id < 0:
|
|
if self.sampling_params.stop_token_ids:
|
|
self.output_ids[-1] = next(iter(self.sampling_params.stop_token_ids))
|
|
if self.eos_token_ids:
|
|
self.output_ids[-1] = next(iter(self.eos_token_ids))
|
|
self.finished_reason = FINISH_MATCHED_STR(matched="NaN happened")
|
|
return
|
|
|
|
# Check stop strings
|
|
if len(self.sampling_params.stop_strs) > 0:
|
|
tail_str = self.tokenizer.decode(
|
|
self.output_ids[-(self.sampling_params.stop_str_max_len + 1) :]
|
|
)
|
|
|
|
for stop_str in self.sampling_params.stop_strs:
|
|
if stop_str in tail_str or stop_str in self.decoded_text:
|
|
self.finished_reason = FINISH_MATCHED_STR(matched=stop_str)
|
|
return
|
|
|
|
def reset_for_retract(self):
|
|
self.prefix_indices = []
|
|
self.last_node = None
|
|
self.swa_uuid_for_lock = None
|
|
self.extend_input_len = 0
|
|
self.is_retracted = True
|
|
self.input_token_logprobs = None
|
|
self.temp_input_top_logprobs_val = None
|
|
self.temp_input_top_logprobs_idx = None
|
|
self.extend_logprob_start_len = 0
|
|
self.is_chunked = 0
|
|
self.req_pool_idx = None
|
|
self.already_computed = 0
|
|
|
|
def offload_kv_cache(self, req_to_token_pool, token_to_kv_pool_allocator):
|
|
token_indices = req_to_token_pool.req_to_token[
|
|
self.req_pool_idx, : self.seqlen - 1
|
|
]
|
|
self.kv_cache_cpu = token_to_kv_pool_allocator.get_cpu_copy(token_indices)
|
|
|
|
def load_kv_cache(self, req_to_token_pool, token_to_kv_pool_allocator):
|
|
token_indices = req_to_token_pool.req_to_token[
|
|
self.req_pool_idx, : self.seqlen - 1
|
|
]
|
|
token_to_kv_pool_allocator.load_cpu_copy(self.kv_cache_cpu, token_indices)
|
|
del self.kv_cache_cpu
|
|
|
|
def log_time_stats(self):
|
|
# If overlap schedule, we schedule one decode batch ahead so this gets called twice.
|
|
if self.has_log_time_stats is True:
|
|
return
|
|
|
|
if self.bootstrap_room is not None:
|
|
prefix = f"Req Time Stats(rid={self.rid}, bootstrap_room={self.bootstrap_room}, input len={len(self.origin_input_ids)}, output len={len(self.output_ids)}, type={self.time_stats.get_type().value})"
|
|
else:
|
|
prefix = f"Req Time Stats(rid={self.rid}, input len={len(self.origin_input_ids)}, output len={len(self.output_ids)}, type={self.time_stats.get_type().value})"
|
|
logger.info(f"{prefix}: {self.time_stats}")
|
|
self.has_log_time_stats = True
|
|
|
|
def set_finish_with_abort(self, error_msg: str):
|
|
if get_tensor_model_parallel_rank() == 0:
|
|
logger.error(f"{error_msg}, {self.rid=}")
|
|
self.multimodal_inputs = None
|
|
self.grammar = None
|
|
self.origin_input_ids = [0] # set it to one token to skip the long prefill
|
|
self.return_logprob = False
|
|
self.finished_reason = FINISH_ABORT(
|
|
error_msg, HTTPStatus.BAD_REQUEST, "BadRequestError"
|
|
)
|
|
|
|
def __repr__(self):
|
|
return (
|
|
f"Req(rid={self.rid}, "
|
|
f"input_ids={self.origin_input_ids}, output_ids={self.output_ids}, "
|
|
f"{self.grammar=}, "
|
|
f"{self.sampling_params=})"
|
|
)
|
|
|
|
|
|
# Batch id
|
|
bid = 0
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
|
|
"""Store all information of a batch on the scheduler."""
|
|
|
|
# Request, memory pool, and cache
|
|
reqs: List[Req]
|
|
req_to_token_pool: ReqToTokenPool = None
|
|
token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator = None
|
|
tree_cache: BasePrefixCache = None
|
|
is_hybrid: bool = False
|
|
|
|
# Batch configs
|
|
model_config: ModelConfig = None
|
|
forward_mode: ForwardMode = None
|
|
enable_overlap: bool = False
|
|
# Tell whether the current running batch is full so that we can skip
|
|
# the check of whether to prefill new requests.
|
|
# This is an optimization to reduce the overhead of the prefill check.
|
|
batch_is_full: bool = False
|
|
|
|
# Events
|
|
launch_done: Optional[threading.Event] = None
|
|
|
|
# For chunked prefill in PP
|
|
chunked_req: Optional[Req] = None
|
|
|
|
# Sampling info
|
|
sampling_info: SamplingBatchInfo = None
|
|
next_batch_sampling_info: SamplingBatchInfo = None
|
|
|
|
# Batched arguments to model runner
|
|
input_ids: torch.Tensor = None # shape: [b], int64
|
|
input_embeds: torch.Tensor = None # shape: [b, hidden_size], float32
|
|
token_type_ids: torch.Tensor = None # shape: [b], int64
|
|
req_pool_indices: torch.Tensor = None # shape: [b], int64
|
|
seq_lens: torch.Tensor = None # shape: [b], int64
|
|
# The output locations of the KV cache
|
|
out_cache_loc: torch.Tensor = None # shape: [b], int64
|
|
output_ids: torch.Tensor = None # shape: [b], int64
|
|
|
|
# For multimodal inputs
|
|
multimodal_inputs: Optional[List] = None
|
|
|
|
# The sum of all sequence lengths
|
|
seq_lens_sum: int = None
|
|
# The original sequence lengths, Qwen-1M related
|
|
orig_seq_lens: torch.Tensor = None # shape: [b], int32
|
|
|
|
# For DP attention
|
|
global_num_tokens: Optional[List[int]] = None
|
|
global_num_tokens_for_logprob: Optional[List[int]] = None
|
|
is_extend_in_batch: bool = False
|
|
can_run_dp_cuda_graph: bool = False
|
|
tbo_split_seq_index: Optional[int] = None
|
|
global_forward_mode: Optional[ForwardMode] = None
|
|
|
|
# For processing logprobs
|
|
return_logprob: bool = False
|
|
top_logprobs_nums: Optional[List[int]] = None
|
|
token_ids_logprobs: Optional[List[List[int]]] = None
|
|
|
|
# For logits and logprob post processing
|
|
temp_scaled_logprobs: bool = False
|
|
top_p_normalized_logprobs: bool = False
|
|
|
|
# For extend and mixed chunekd prefill
|
|
prefix_lens: List[int] = None
|
|
extend_lens: List[int] = None
|
|
extend_num_tokens: Optional[int] = None
|
|
decoding_reqs: List[Req] = None
|
|
extend_logprob_start_lens: List[int] = None
|
|
# It comes empty list if logprob is not required.
|
|
extend_input_logprob_token_ids: Optional[torch.Tensor] = None
|
|
|
|
# For encoder-decoder architectures
|
|
encoder_cached: Optional[List[bool]] = None
|
|
encoder_lens: Optional[torch.Tensor] = None
|
|
encoder_lens_cpu: Optional[List[int]] = None
|
|
encoder_out_cache_loc: Optional[torch.Tensor] = None
|
|
|
|
# Stream
|
|
has_stream: bool = False
|
|
|
|
# Has grammar
|
|
has_grammar: bool = False
|
|
|
|
# Device
|
|
device: str = "cuda"
|
|
|
|
# Speculative decoding
|
|
spec_algorithm: SpeculativeAlgorithm = None
|
|
spec_info: Optional[Union[EagleDraftInput, EagleVerifyInput]] = None
|
|
|
|
# Whether to return hidden states
|
|
return_hidden_states: bool = False
|
|
|
|
# Whether this batch is prefill-only (no token generation needed)
|
|
is_prefill_only: bool = False
|
|
|
|
# hicache pointer for synchronizing data loading from CPU to GPU
|
|
hicache_consumer_index: int = 0
|
|
|
|
@classmethod
|
|
def init_new(
|
|
cls,
|
|
reqs: List[Req],
|
|
req_to_token_pool: ReqToTokenPool,
|
|
token_to_kv_pool_allocator: BaseTokenToKVPoolAllocator,
|
|
tree_cache: BasePrefixCache,
|
|
model_config: ModelConfig,
|
|
enable_overlap: bool,
|
|
spec_algorithm: SpeculativeAlgorithm,
|
|
chunked_req: Optional[Req] = None,
|
|
):
|
|
return_logprob = any(req.return_logprob for req in reqs)
|
|
|
|
is_hybrid = False
|
|
if isinstance(token_to_kv_pool_allocator, SWATokenToKVPoolAllocator):
|
|
assert (
|
|
tree_cache is None
|
|
or isinstance(tree_cache, SWARadixCache)
|
|
or isinstance(tree_cache, SWAChunkCache)
|
|
), "SWARadixCache or SWAChunkCache is required for SWATokenToKVPoolAllocator"
|
|
is_hybrid = True
|
|
|
|
return cls(
|
|
reqs=reqs,
|
|
req_to_token_pool=req_to_token_pool,
|
|
token_to_kv_pool_allocator=token_to_kv_pool_allocator,
|
|
tree_cache=tree_cache,
|
|
is_hybrid=is_hybrid,
|
|
model_config=model_config,
|
|
enable_overlap=enable_overlap,
|
|
return_logprob=return_logprob,
|
|
has_stream=any(req.stream for req in reqs),
|
|
has_grammar=any(req.grammar for req in reqs),
|
|
device=req_to_token_pool.device,
|
|
spec_algorithm=spec_algorithm,
|
|
return_hidden_states=any(req.return_hidden_states for req in reqs),
|
|
is_prefill_only=all(
|
|
req.sampling_params.max_new_tokens == 0 for req in reqs
|
|
),
|
|
chunked_req=chunked_req,
|
|
)
|
|
|
|
def batch_size(self):
|
|
return len(self.reqs)
|
|
|
|
def is_empty(self):
|
|
return len(self.reqs) == 0
|
|
|
|
def alloc_req_slots(self, num_reqs: int):
|
|
req_pool_indices = self.req_to_token_pool.alloc(num_reqs)
|
|
if req_pool_indices is None:
|
|
raise RuntimeError(
|
|
"alloc_req_slots runs out of memory. "
|
|
"Please set a smaller number for `--max-running-requests`. "
|
|
f"{self.req_to_token_pool.available_size()=}, "
|
|
f"{num_reqs=}, "
|
|
)
|
|
return req_pool_indices
|
|
|
|
def alloc_token_slots(self, num_tokens: int, backup_state: bool = False):
|
|
self._evict_tree_cache_if_needed(num_tokens)
|
|
|
|
if backup_state:
|
|
state = self.token_to_kv_pool_allocator.backup_state()
|
|
|
|
out_cache_loc = self.token_to_kv_pool_allocator.alloc(num_tokens)
|
|
if out_cache_loc is None:
|
|
phase_str = "Prefill" if self.forward_mode.is_extend() else "Decode"
|
|
error_msg = (
|
|
f"{phase_str} out of memory. Try to lower your batch size.\n"
|
|
f"Try to allocate {num_tokens} tokens.\n"
|
|
f"{self._available_and_evictable_str()}"
|
|
)
|
|
logger.error(error_msg)
|
|
if self.tree_cache is not None:
|
|
self.tree_cache.pretty_print()
|
|
raise RuntimeError(error_msg)
|
|
|
|
if backup_state:
|
|
return out_cache_loc, state
|
|
else:
|
|
return out_cache_loc
|
|
|
|
def alloc_paged_token_slots_extend(
|
|
self,
|
|
prefix_lens: torch.Tensor,
|
|
seq_lens: torch.Tensor,
|
|
last_loc: torch.Tensor,
|
|
extend_num_tokens: int,
|
|
backup_state: bool = False,
|
|
):
|
|
# Over estimate the number of tokens: assume each request needs a new page.
|
|
num_tokens = (
|
|
extend_num_tokens
|
|
+ len(seq_lens) * self.token_to_kv_pool_allocator.page_size
|
|
)
|
|
self._evict_tree_cache_if_needed(num_tokens)
|
|
|
|
if backup_state:
|
|
state = self.token_to_kv_pool_allocator.backup_state()
|
|
|
|
out_cache_loc = self.token_to_kv_pool_allocator.alloc_extend(
|
|
prefix_lens, seq_lens, last_loc, extend_num_tokens
|
|
)
|
|
if out_cache_loc is None:
|
|
error_msg = (
|
|
f"Prefill out of memory. Try to lower your batch size.\n"
|
|
f"Try to allocate {extend_num_tokens} tokens.\n"
|
|
f"{self._available_and_evictable_str()}"
|
|
)
|
|
logger.error(error_msg)
|
|
raise RuntimeError(error_msg)
|
|
|
|
if backup_state:
|
|
return out_cache_loc, state
|
|
else:
|
|
return out_cache_loc
|
|
|
|
def alloc_paged_token_slots_decode(
|
|
self,
|
|
seq_lens: torch.Tensor,
|
|
last_loc: torch.Tensor,
|
|
backup_state: bool = False,
|
|
):
|
|
# Over estimate the number of tokens: assume each request needs a new page.
|
|
num_tokens = len(seq_lens) * self.token_to_kv_pool_allocator.page_size
|
|
self._evict_tree_cache_if_needed(num_tokens)
|
|
|
|
if backup_state:
|
|
state = self.token_to_kv_pool_allocator.backup_state()
|
|
|
|
out_cache_loc = self.token_to_kv_pool_allocator.alloc_decode(seq_lens, last_loc)
|
|
if out_cache_loc is None:
|
|
error_msg = (
|
|
f"Decode out of memory. Try to lower your batch size.\n"
|
|
f"Try to allocate {len(seq_lens)} tokens.\n"
|
|
f"{self._available_and_evictable_str()}"
|
|
)
|
|
logger.error(error_msg)
|
|
raise RuntimeError(error_msg)
|
|
|
|
if backup_state:
|
|
return out_cache_loc, state
|
|
else:
|
|
return out_cache_loc
|
|
|
|
def prepare_encoder_info_extend(self, input_ids: List[int], seq_lens: List[int]):
|
|
self.encoder_lens_cpu = []
|
|
self.encoder_cached = []
|
|
|
|
for req in self.reqs:
|
|
im = req.multimodal_inputs
|
|
if im is None or im.num_image_tokens is None:
|
|
# No image input
|
|
self.encoder_lens_cpu.append(0)
|
|
self.encoder_cached.append(True)
|
|
else:
|
|
self.encoder_lens_cpu.append(im.num_image_tokens)
|
|
self.encoder_cached.append(
|
|
self.forward_mode.is_decode()
|
|
or len(req.prefix_indices) >= im.num_image_tokens
|
|
)
|
|
|
|
self.encoder_lens = torch.tensor(self.encoder_lens_cpu, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
|
|
# Strip encoder infos
|
|
pt = 0
|
|
decoder_out_cache_loc = []
|
|
encoder_out_cache_loc = []
|
|
for i, req in enumerate(self.reqs):
|
|
encoder_len = self.encoder_lens_cpu[i]
|
|
seq_lens[i] -= encoder_len
|
|
|
|
if len(req.prefix_indices) < encoder_len:
|
|
# NOTE: the encoder part should be considered as a whole
|
|
assert len(req.prefix_indices) == 0
|
|
input_ids[i] = input_ids[i][encoder_len:]
|
|
encoder_out_cache_loc.append(self.out_cache_loc[pt : pt + encoder_len])
|
|
decoder_out_cache_loc.append(
|
|
self.out_cache_loc[pt + encoder_len : pt + req.extend_input_len]
|
|
)
|
|
self.extend_lens[i] -= encoder_len
|
|
self.extend_num_tokens -= encoder_len
|
|
else:
|
|
decoder_out_cache_loc.append(
|
|
self.out_cache_loc[pt : pt + req.extend_input_len]
|
|
)
|
|
self.prefix_lens[i] -= encoder_len
|
|
|
|
pt += req.extend_input_len
|
|
|
|
# Reassign
|
|
self.input_ids = torch.tensor(sum(input_ids, []), dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
self.seq_lens = torch.tensor(seq_lens, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
|
|
if not decoder_out_cache_loc:
|
|
self.out_cache_loc = torch.zeros(0, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
else:
|
|
self.out_cache_loc = torch.cat(decoder_out_cache_loc)
|
|
|
|
if not encoder_out_cache_loc:
|
|
self.encoder_out_cache_loc = torch.zeros(0, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
else:
|
|
self.encoder_out_cache_loc = torch.cat(encoder_out_cache_loc)
|
|
|
|
assert (
|
|
len(self.out_cache_loc) == self.extend_num_tokens
|
|
), f"Expected {len(self.out_cache_loc)}, got {self.extend_num_tokens}"
|
|
|
|
def prepare_for_extend(self):
|
|
self.forward_mode = ForwardMode.EXTEND
|
|
|
|
# Allocate req slots
|
|
bs = len(self.reqs)
|
|
req_pool_indices = self.alloc_req_slots(bs)
|
|
|
|
# Init tensors
|
|
reqs = self.reqs
|
|
input_ids = [r.fill_ids[len(r.prefix_indices) :] for r in reqs]
|
|
extend_num_tokens = sum(len(ids) for ids in input_ids)
|
|
seq_lens = [len(r.fill_ids) for r in reqs]
|
|
orig_seq_lens = [max(len(r.fill_ids), len(r.origin_input_ids)) for r in reqs]
|
|
prefix_lens = [len(r.prefix_indices) for r in reqs]
|
|
extend_lens = [r.extend_input_len for r in reqs]
|
|
|
|
token_type_ids = [
|
|
r.token_type_ids for r in reqs if r.token_type_ids is not None
|
|
]
|
|
|
|
req_pool_indices_tensor = torch.tensor(req_pool_indices, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
input_ids_tensor = torch.tensor(
|
|
list(chain.from_iterable(input_ids)), dtype=torch.int64
|
|
).to(self.device, non_blocking=True)
|
|
seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
orig_seq_lens_tensor = torch.tensor(orig_seq_lens, dtype=torch.int32).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
prefix_lens_tensor = torch.tensor(
|
|
prefix_lens, dtype=torch.int64, device=self.device
|
|
)
|
|
|
|
token_type_ids_tensor = None
|
|
if len(token_type_ids) > 0:
|
|
token_type_ids_tensor = torch.tensor(
|
|
sum(token_type_ids, []), dtype=torch.int64
|
|
).to(self.device, non_blocking=True)
|
|
|
|
extend_lens_tensor = seq_lens_tensor - prefix_lens_tensor
|
|
|
|
# Copy prefix and do some basic check
|
|
input_embeds = []
|
|
extend_input_logprob_token_ids = []
|
|
multimodal_inputs = []
|
|
|
|
for i, (req, seq_len, pre_len) in enumerate(zip(reqs, seq_lens, prefix_lens)):
|
|
req.req_pool_idx = req_pool_indices[i]
|
|
assert seq_len - pre_len == req.extend_input_len
|
|
|
|
if pre_len > 0:
|
|
self.req_to_token_pool.write(
|
|
(req.req_pool_idx, slice(0, pre_len)), req.prefix_indices
|
|
)
|
|
if isinstance(self.tree_cache, SWAChunkCache):
|
|
self.tree_cache.evict_swa(
|
|
req, pre_len, self.model_config.attention_chunk_size
|
|
)
|
|
|
|
# If input_embeds are available, store them
|
|
if req.input_embeds is not None:
|
|
# If req.input_embeds is already a list, append its content directly
|
|
input_embeds.extend(req.input_embeds) # Use extend to avoid nesting
|
|
|
|
multimodal_inputs.append(req.multimodal_inputs)
|
|
|
|
req.cached_tokens += pre_len - req.already_computed
|
|
req.already_computed = seq_len
|
|
req.is_retracted = False
|
|
|
|
# Compute the relative logprob_start_len in an extend batch
|
|
if req.logprob_start_len >= pre_len:
|
|
req.extend_logprob_start_len = min(
|
|
req.logprob_start_len - pre_len,
|
|
req.extend_input_len,
|
|
req.seqlen - 1,
|
|
)
|
|
else:
|
|
req.extend_logprob_start_len = 0
|
|
|
|
if self.return_logprob:
|
|
# Find input logprob token ids.
|
|
# First, find a global index within origin_input_ids and slide it by 1
|
|
# to compute input logprobs. It is because you need the next token
|
|
# to compute input logprobs. E.g., (chunk size 2)
|
|
#
|
|
# input_logprobs = [1, 2, 3, 4]
|
|
# fill_ids = [1, 2]
|
|
# extend_input_logprob_token_id = [2, 3]
|
|
#
|
|
# Note that it can also overflow. In this case, we pad it with 0.
|
|
# input_logprobs = [1, 2, 3, 4]
|
|
# fill_ids = [3, 4]
|
|
# extend_input_logprob_token_id = [4, 0]
|
|
global_start_idx, global_end_idx = (
|
|
len(req.prefix_indices),
|
|
len(req.fill_ids),
|
|
)
|
|
# Apply logprob_start_len
|
|
if global_start_idx < req.logprob_start_len:
|
|
global_start_idx = req.logprob_start_len
|
|
|
|
logprob_token_ids = req.origin_input_ids[
|
|
global_start_idx + 1 : global_end_idx + 1
|
|
]
|
|
extend_input_logprob_token_ids.extend(logprob_token_ids)
|
|
|
|
# We will need req.extend_input_len - req.extend_logprob_start_len number of
|
|
# tokens, and logprob_token_ids is for input logprob, so pad the rest of them by 0.
|
|
extend_input_logprob_token_ids.extend(
|
|
[0]
|
|
* (
|
|
req.extend_input_len
|
|
- req.extend_logprob_start_len
|
|
- len(logprob_token_ids)
|
|
)
|
|
)
|
|
|
|
if self.return_logprob:
|
|
extend_input_logprob_token_ids = torch.tensor(
|
|
extend_input_logprob_token_ids
|
|
)
|
|
else:
|
|
extend_input_logprob_token_ids = None
|
|
|
|
# Allocate memory
|
|
if self.token_to_kv_pool_allocator.page_size == 1:
|
|
out_cache_loc = self.alloc_token_slots(extend_num_tokens)
|
|
else:
|
|
last_loc = get_last_loc(
|
|
self.req_to_token_pool.req_to_token,
|
|
req_pool_indices_tensor,
|
|
prefix_lens_tensor,
|
|
)
|
|
out_cache_loc = self.alloc_paged_token_slots_extend(
|
|
prefix_lens_tensor, seq_lens_tensor, last_loc, extend_num_tokens
|
|
)
|
|
|
|
# Set fields
|
|
self.input_ids = input_ids_tensor
|
|
self.req_pool_indices = req_pool_indices_tensor
|
|
self.seq_lens = seq_lens_tensor
|
|
self.orig_seq_lens = orig_seq_lens_tensor
|
|
self.out_cache_loc = out_cache_loc
|
|
self.input_embeds = (
|
|
torch.tensor(input_embeds).to(self.device, non_blocking=True)
|
|
if input_embeds
|
|
else None
|
|
)
|
|
for mm_input in multimodal_inputs:
|
|
if mm_input is None:
|
|
continue
|
|
for mm_item in mm_input.mm_items:
|
|
pixel_values = getattr(mm_item, "feature", None)
|
|
if isinstance(pixel_values, torch.Tensor):
|
|
mm_item.feature = pixel_values.to(self.device, non_blocking=True)
|
|
self.multimodal_inputs = multimodal_inputs
|
|
self.token_type_ids = token_type_ids_tensor
|
|
self.seq_lens_sum = sum(seq_lens)
|
|
|
|
if self.return_logprob:
|
|
self.top_logprobs_nums = [r.top_logprobs_num for r in reqs]
|
|
self.token_ids_logprobs = [r.token_ids_logprob for r in reqs]
|
|
|
|
self.extend_logprob_start_lens = [r.extend_logprob_start_len for r in reqs]
|
|
self.extend_num_tokens = extend_num_tokens
|
|
self.prefix_lens = prefix_lens
|
|
self.extend_lens = extend_lens
|
|
self.extend_input_logprob_token_ids = extend_input_logprob_token_ids
|
|
|
|
# Write to req_to_token_pool
|
|
if support_triton(global_server_args_dict.get("attention_backend")):
|
|
# TODO: some tensors can be reused for ForwardBatchInfo (e.g., extend_lens, cumsum_start)
|
|
|
|
write_req_to_token_pool_triton[(bs,)](
|
|
self.req_to_token_pool.req_to_token,
|
|
req_pool_indices_tensor,
|
|
prefix_lens_tensor,
|
|
seq_lens_tensor,
|
|
extend_lens_tensor,
|
|
out_cache_loc,
|
|
self.req_to_token_pool.req_to_token.shape[1],
|
|
)
|
|
else:
|
|
pt = 0
|
|
for i in range(bs):
|
|
self.req_to_token_pool.write(
|
|
(req_pool_indices[i], slice(prefix_lens[i], seq_lens[i])),
|
|
out_cache_loc[pt : pt + extend_lens[i]],
|
|
)
|
|
pt += extend_lens[i]
|
|
|
|
if self.model_config.is_encoder_decoder:
|
|
self.prepare_encoder_info_extend(input_ids, seq_lens)
|
|
|
|
# Build sampling info
|
|
self.sampling_info = SamplingBatchInfo.from_schedule_batch(
|
|
self,
|
|
self.model_config.vocab_size,
|
|
)
|
|
|
|
def prepare_for_split_prefill(self):
|
|
self.prepare_for_extend()
|
|
# For split prefill, we need to set the forward mode to SPLIT_PREFILL
|
|
self.forward_mode = ForwardMode.SPLIT_PREFILL
|
|
|
|
def mix_with_running(self, running_batch: "ScheduleBatch"):
|
|
self.forward_mode = ForwardMode.MIXED
|
|
running_bs = running_batch.batch_size()
|
|
|
|
for req in running_batch.reqs:
|
|
req.fill_ids = req.origin_input_ids + req.output_ids
|
|
req.extend_input_len = 1
|
|
|
|
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])
|
|
|
|
self.merge_batch(running_batch)
|
|
self.input_ids = input_ids
|
|
self.out_cache_loc = out_cache_loc
|
|
|
|
# For overlap scheduler, the output_ids has one step delay
|
|
delta = 0 if self.enable_overlap else -1
|
|
|
|
# NOTE: prefix_indices is what has been cached, but we don't cache each decode step
|
|
self.prefix_lens.extend(
|
|
[
|
|
len(r.origin_input_ids) + len(r.output_ids) + delta
|
|
for r in running_batch.reqs
|
|
]
|
|
)
|
|
self.extend_lens.extend([1] * running_bs)
|
|
self.extend_num_tokens += running_bs
|
|
# TODO (lianmin): Revisit this. It should be seq_len - 1
|
|
self.extend_logprob_start_lens.extend([0] * running_bs)
|
|
|
|
def new_page_count_next_decode(self):
|
|
page_size = self.token_to_kv_pool_allocator.page_size
|
|
if page_size == 1:
|
|
return len(self.reqs)
|
|
# In the decoding phase, the length of a request's KV cache should be
|
|
# the total length of the request minus 1
|
|
return (
|
|
sum(1 for req in self.reqs if req.seqlen % page_size == 0)
|
|
if self.enable_overlap
|
|
else sum(1 for req in self.reqs if (req.seqlen - 1) % page_size == 0)
|
|
)
|
|
|
|
def check_decode_mem(self, buf_multiplier=1):
|
|
num_tokens = (
|
|
self.new_page_count_next_decode()
|
|
* buf_multiplier
|
|
* self.token_to_kv_pool_allocator.page_size
|
|
)
|
|
|
|
self._evict_tree_cache_if_needed(num_tokens)
|
|
return self._is_available_size_sufficient(num_tokens)
|
|
|
|
def retract_decode(self, server_args: ServerArgs):
|
|
"""Retract the decoding requests when there is not enough memory."""
|
|
sorted_indices = list(range(len(self.reqs)))
|
|
|
|
# TODO(lsyin): improve retraction policy for radix cache
|
|
# For spec decoding, filter_batch API can only filter
|
|
# requests from the back, so we can only retract from the back.
|
|
# TODO(sang): Clean up finish path and support better retract
|
|
# policy.
|
|
if not server_args.speculative_algorithm:
|
|
sorted_indices.sort(
|
|
key=lambda i: (
|
|
len(self.reqs[i].output_ids),
|
|
-len(self.reqs[i].origin_input_ids),
|
|
),
|
|
reverse=True,
|
|
)
|
|
|
|
def get_required_tokens(num_reqs: int):
|
|
headroom_for_spec_decode = 0
|
|
if server_args.speculative_algorithm:
|
|
headroom_for_spec_decode += (
|
|
num_reqs
|
|
* server_args.speculative_eagle_topk
|
|
* server_args.speculative_num_steps
|
|
+ num_reqs * server_args.speculative_num_draft_tokens
|
|
)
|
|
return (
|
|
num_reqs * global_config.retract_decode_steps + headroom_for_spec_decode
|
|
)
|
|
|
|
def _get_available_size():
|
|
if self.is_hybrid:
|
|
return min(
|
|
self.token_to_kv_pool_allocator.full_available_size(),
|
|
self.token_to_kv_pool_allocator.swa_available_size(),
|
|
)
|
|
else:
|
|
return self.token_to_kv_pool_allocator.available_size()
|
|
|
|
retracted_reqs = []
|
|
seq_lens_cpu = self.seq_lens.cpu().numpy()
|
|
first_iter = True
|
|
while (
|
|
_get_available_size() < get_required_tokens(len(sorted_indices))
|
|
or first_iter
|
|
):
|
|
if len(sorted_indices) == 1:
|
|
# Corner case: only one request left
|
|
if self.is_hybrid:
|
|
full_available_size = (
|
|
self.token_to_kv_pool_allocator.full_available_size()
|
|
)
|
|
swa_available_size = (
|
|
self.token_to_kv_pool_allocator.swa_available_size()
|
|
)
|
|
assert (
|
|
full_available_size > 0 and swa_available_size > 0
|
|
), f"No space left for only one request in SWA mode {full_available_size=}, {swa_available_size=}"
|
|
else:
|
|
assert (
|
|
self.token_to_kv_pool_allocator.available_size() > 0
|
|
), f"No space left for only one request, {self.token_to_kv_pool_allocator.available_size()=}"
|
|
break
|
|
|
|
first_iter = False
|
|
idx = sorted_indices.pop()
|
|
req = self.reqs[idx]
|
|
retracted_reqs.append(req)
|
|
|
|
if server_args.disaggregation_mode == "decode":
|
|
req.offload_kv_cache(
|
|
self.req_to_token_pool, self.token_to_kv_pool_allocator
|
|
)
|
|
|
|
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_allocator.free(token_indices)
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
else:
|
|
# TODO: apply more fine-grained retraction
|
|
last_uncached_pos = (
|
|
len(req.prefix_indices) // server_args.page_size
|
|
) * server_args.page_size
|
|
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_allocator.free(token_indices)
|
|
self.req_to_token_pool.free(req.req_pool_idx)
|
|
|
|
# release the last node
|
|
if self.is_hybrid:
|
|
self.tree_cache.dec_lock_ref(req.last_node, req.swa_uuid_for_lock)
|
|
else:
|
|
self.tree_cache.dec_lock_ref(req.last_node)
|
|
|
|
# NOTE(lsyin): we should use the newly evictable memory instantly.
|
|
num_tokens = len(sorted_indices) * global_config.retract_decode_steps
|
|
self._evict_tree_cache_if_needed(num_tokens)
|
|
|
|
req.reset_for_retract()
|
|
|
|
if len(retracted_reqs) == 0:
|
|
# Corner case: only one request left
|
|
raise ValueError(
|
|
"Failed to retract any request. No space left for only one request."
|
|
)
|
|
|
|
self.filter_batch(keep_indices=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 prepare_encoder_info_decode(self):
|
|
# Reset the encoder cached status
|
|
self.encoder_cached = [True] * len(self.reqs)
|
|
|
|
def prepare_for_idle(self):
|
|
self.forward_mode = ForwardMode.IDLE
|
|
self.input_ids = torch.empty(0, dtype=torch.int64, device=self.device)
|
|
self.seq_lens = torch.empty(0, dtype=torch.int64, device=self.device)
|
|
self.orig_seq_lens = torch.empty(0, dtype=torch.int32, device=self.device)
|
|
self.out_cache_loc = torch.empty(0, dtype=torch.int64, device=self.device)
|
|
self.req_pool_indices = torch.empty(0, dtype=torch.int32, device=self.device)
|
|
self.seq_lens_sum = 0
|
|
self.extend_num_tokens = 0
|
|
self.sampling_info = SamplingBatchInfo.from_schedule_batch(
|
|
self,
|
|
self.model_config.vocab_size,
|
|
)
|
|
|
|
def prepare_for_decode(self):
|
|
self.forward_mode = ForwardMode.DECODE
|
|
bs = len(self.reqs)
|
|
|
|
if self.spec_algorithm.is_eagle():
|
|
# if spec decoding is used, the decode batch is prepared inside
|
|
# `forward_batch_speculative_generation` after running draft models.
|
|
return
|
|
|
|
if self.sampling_info.penalizer_orchestrator.is_required:
|
|
if self.enable_overlap:
|
|
# TODO: this can be slow, optimize this.
|
|
delayed_output_ids = torch.tensor(
|
|
[
|
|
(
|
|
req.output_ids[-1]
|
|
if len(req.output_ids)
|
|
else req.origin_input_ids[-1]
|
|
)
|
|
for req in self.reqs
|
|
],
|
|
dtype=torch.int64,
|
|
device=self.device,
|
|
)
|
|
self.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
|
|
delayed_output_ids
|
|
)
|
|
else:
|
|
self.sampling_info.penalizer_orchestrator.cumulate_output_tokens(
|
|
self.output_ids.to(torch.int64)
|
|
)
|
|
|
|
# Update fields
|
|
self.input_ids = self.output_ids
|
|
self.output_ids = None
|
|
|
|
if self.model_config.is_encoder_decoder:
|
|
locs = self.encoder_lens + self.seq_lens
|
|
self.prepare_encoder_info_decode()
|
|
else:
|
|
locs = self.seq_lens.clone()
|
|
|
|
if self.enable_overlap:
|
|
# Do not use in-place operations in the overlap mode
|
|
self.seq_lens = self.seq_lens + 1
|
|
self.orig_seq_lens = self.orig_seq_lens + 1
|
|
else:
|
|
# A faster in-place version
|
|
self.seq_lens.add_(1)
|
|
self.orig_seq_lens.add_(1)
|
|
self.seq_lens_sum += bs
|
|
|
|
# free memory
|
|
if isinstance(self.tree_cache, SWAChunkCache):
|
|
for req in self.reqs:
|
|
self.tree_cache.evict_swa(
|
|
req, req.seqlen - 1, self.model_config.attention_chunk_size
|
|
)
|
|
|
|
# Allocate memory
|
|
if self.token_to_kv_pool_allocator.page_size == 1:
|
|
self.out_cache_loc = self.alloc_token_slots(bs)
|
|
else:
|
|
last_loc = self.req_to_token_pool.req_to_token[
|
|
self.req_pool_indices, self.seq_lens - 2
|
|
]
|
|
self.out_cache_loc = self.alloc_paged_token_slots_decode(
|
|
self.seq_lens, last_loc
|
|
)
|
|
|
|
self.req_to_token_pool.write(
|
|
(self.req_pool_indices, locs), self.out_cache_loc.to(torch.int32)
|
|
)
|
|
|
|
def filter_batch(
|
|
self,
|
|
chunked_req_to_exclude: Optional[Union[Req, List[Req]]] = None,
|
|
keep_indices: Optional[List[int]] = None,
|
|
):
|
|
if keep_indices is None:
|
|
if isinstance(chunked_req_to_exclude, Req):
|
|
chunked_req_to_exclude = [chunked_req_to_exclude]
|
|
elif chunked_req_to_exclude is None:
|
|
chunked_req_to_exclude = []
|
|
keep_indices = [
|
|
i
|
|
for i in range(len(self.reqs))
|
|
if not self.reqs[i].finished()
|
|
and self.reqs[i] not in chunked_req_to_exclude
|
|
]
|
|
|
|
if keep_indices is None or len(keep_indices) == 0:
|
|
# Filter out all requests
|
|
self.reqs = []
|
|
return
|
|
|
|
if len(keep_indices) == len(self.reqs):
|
|
# No need to filter
|
|
return
|
|
|
|
keep_indices_device = torch.tensor(keep_indices, dtype=torch.int64).to(
|
|
self.device, non_blocking=True
|
|
)
|
|
|
|
if self.model_config.is_encoder_decoder:
|
|
self.encoder_lens = self.encoder_lens[keep_indices_device]
|
|
self.encoder_lens_cpu = [self.encoder_lens_cpu[i] for i in keep_indices]
|
|
|
|
self.reqs = [self.reqs[i] for i in keep_indices]
|
|
if self.multimodal_inputs is not None:
|
|
self.multimodal_inputs = [self.multimodal_inputs[i] for i in keep_indices]
|
|
self.req_pool_indices = self.req_pool_indices[keep_indices_device]
|
|
self.seq_lens = self.seq_lens[keep_indices_device]
|
|
self.orig_seq_lens = self.orig_seq_lens[keep_indices_device]
|
|
self.out_cache_loc = None
|
|
self.seq_lens_sum = self.seq_lens.sum().item()
|
|
self.output_ids = self.output_ids[keep_indices_device]
|
|
self.return_logprob = any(req.return_logprob for req in self.reqs)
|
|
if self.return_logprob:
|
|
self.top_logprobs_nums = [self.top_logprobs_nums[i] for i in keep_indices]
|
|
self.token_ids_logprobs = [self.token_ids_logprobs[i] for i in keep_indices]
|
|
else:
|
|
self.top_logprobs_nums = None
|
|
self.token_ids_logprobs = None
|
|
|
|
self.has_stream = any(req.stream for req in self.reqs)
|
|
self.has_grammar = any(req.grammar for req in self.reqs)
|
|
|
|
self.sampling_info.filter_batch(keep_indices, keep_indices_device)
|
|
if self.spec_info:
|
|
self.spec_info.filter_batch(keep_indices_device)
|
|
|
|
def merge_batch(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.sampling_info.merge_batch(other.sampling_info)
|
|
|
|
# Encoder-decoder infos
|
|
if self.model_config.is_encoder_decoder:
|
|
self.encoder_lens = torch.cat([self.encoder_lens, other.encoder_lens])
|
|
self.encoder_lens_cpu.extend(other.encoder_lens_cpu)
|
|
self.req_pool_indices = torch.cat(
|
|
[self.req_pool_indices, other.req_pool_indices]
|
|
)
|
|
self.seq_lens = torch.cat([self.seq_lens, other.seq_lens])
|
|
self.orig_seq_lens = torch.cat([self.orig_seq_lens, other.orig_seq_lens])
|
|
self.out_cache_loc = None
|
|
self.seq_lens_sum += other.seq_lens_sum
|
|
if self.output_ids is not None:
|
|
self.output_ids = torch.cat([self.output_ids, other.output_ids])
|
|
if self.return_logprob and other.return_logprob:
|
|
self.top_logprobs_nums.extend(other.top_logprobs_nums)
|
|
self.token_ids_logprobs.extend(other.token_ids_logprobs)
|
|
elif self.return_logprob:
|
|
self.top_logprobs_nums.extend([0] * len(other.reqs))
|
|
self.token_ids_logprobs.extend([None] * len(other.reqs))
|
|
elif other.return_logprob:
|
|
self.top_logprobs_nums = [0] * len(self.reqs) + other.top_logprobs_nums
|
|
self.token_ids_logprobs = [None] * len(self.reqs) + other.token_ids_logprobs
|
|
self.reqs.extend(other.reqs)
|
|
if self.multimodal_inputs is not None:
|
|
self.multimodal_inputs.extend(other.multimodal_inputs)
|
|
|
|
self.return_logprob |= other.return_logprob
|
|
self.has_stream |= other.has_stream
|
|
self.has_grammar |= other.has_grammar
|
|
self.return_hidden_states |= other.return_hidden_states
|
|
|
|
if self.spec_info:
|
|
self.spec_info.merge_batch(other.spec_info)
|
|
|
|
def get_model_worker_batch(
|
|
self, seq_lens_cpu_cache: Optional[torch.Tensor] = None
|
|
) -> ModelWorkerBatch:
|
|
if self.forward_mode.is_decode_or_idle():
|
|
extend_seq_lens = extend_prefix_lens = extend_logprob_start_lens = None
|
|
else:
|
|
extend_seq_lens = self.extend_lens
|
|
extend_prefix_lens = self.prefix_lens
|
|
extend_logprob_start_lens = self.extend_logprob_start_lens
|
|
|
|
if self.sampling_info:
|
|
if self.has_grammar:
|
|
self.sampling_info.grammars = [req.grammar for req in self.reqs]
|
|
else:
|
|
self.sampling_info.grammars = None
|
|
|
|
seq_lens_cpu = (
|
|
seq_lens_cpu_cache
|
|
if seq_lens_cpu_cache is not None
|
|
else self.seq_lens.cpu()
|
|
)
|
|
|
|
global bid
|
|
bid += 1
|
|
return ModelWorkerBatch(
|
|
bid=bid,
|
|
forward_mode=self.forward_mode,
|
|
input_ids=self.input_ids,
|
|
req_pool_indices=self.req_pool_indices,
|
|
seq_lens=self.seq_lens,
|
|
orig_seq_lens=self.orig_seq_lens,
|
|
out_cache_loc=self.out_cache_loc,
|
|
seq_lens_cpu=seq_lens_cpu,
|
|
seq_lens_sum=self.seq_lens_sum,
|
|
return_logprob=self.return_logprob,
|
|
top_logprobs_nums=self.top_logprobs_nums,
|
|
token_ids_logprobs=self.token_ids_logprobs,
|
|
global_num_tokens=self.global_num_tokens,
|
|
global_num_tokens_for_logprob=self.global_num_tokens_for_logprob,
|
|
is_extend_in_batch=self.is_extend_in_batch,
|
|
can_run_dp_cuda_graph=self.can_run_dp_cuda_graph,
|
|
tbo_split_seq_index=self.tbo_split_seq_index,
|
|
global_forward_mode=self.global_forward_mode,
|
|
extend_num_tokens=self.extend_num_tokens,
|
|
extend_seq_lens=extend_seq_lens,
|
|
extend_prefix_lens=extend_prefix_lens,
|
|
extend_logprob_start_lens=extend_logprob_start_lens,
|
|
multimodal_inputs=self.multimodal_inputs,
|
|
encoder_cached=self.encoder_cached,
|
|
encoder_lens=self.encoder_lens,
|
|
encoder_lens_cpu=self.encoder_lens_cpu,
|
|
encoder_out_cache_loc=self.encoder_out_cache_loc,
|
|
lora_ids=[req.lora_id for req in self.reqs],
|
|
sampling_info=self.sampling_info,
|
|
input_embeds=self.input_embeds,
|
|
token_type_ids=self.token_type_ids,
|
|
spec_algorithm=self.spec_algorithm,
|
|
spec_info=self.spec_info,
|
|
hicache_consumer_index=self.hicache_consumer_index,
|
|
capture_hidden_mode=(
|
|
CaptureHiddenMode.FULL
|
|
if self.return_hidden_states
|
|
else (
|
|
getattr(
|
|
self.spec_info, "capture_hidden_mode", CaptureHiddenMode.NULL
|
|
)
|
|
if self.spec_info
|
|
else CaptureHiddenMode.NULL
|
|
)
|
|
),
|
|
extend_input_logprob_token_ids=self.extend_input_logprob_token_ids,
|
|
launch_done=self.launch_done,
|
|
)
|
|
|
|
def copy(self):
|
|
# Only contain fields that will be used by process_batch_result
|
|
return ScheduleBatch(
|
|
reqs=self.reqs,
|
|
model_config=self.model_config,
|
|
forward_mode=self.forward_mode,
|
|
out_cache_loc=self.out_cache_loc,
|
|
return_logprob=self.return_logprob,
|
|
decoding_reqs=self.decoding_reqs,
|
|
spec_algorithm=self.spec_algorithm,
|
|
global_num_tokens=self.global_num_tokens,
|
|
global_num_tokens_for_logprob=self.global_num_tokens_for_logprob,
|
|
can_run_dp_cuda_graph=self.can_run_dp_cuda_graph,
|
|
is_extend_in_batch=self.is_extend_in_batch,
|
|
is_prefill_only=self.is_prefill_only,
|
|
)
|
|
|
|
def _evict_tree_cache_if_needed(self, num_tokens: int):
|
|
if isinstance(self.tree_cache, (SWAChunkCache, ChunkCache)):
|
|
return
|
|
|
|
if self.is_hybrid:
|
|
full_available_size = self.token_to_kv_pool_allocator.full_available_size()
|
|
swa_available_size = self.token_to_kv_pool_allocator.swa_available_size()
|
|
|
|
if full_available_size < num_tokens or swa_available_size < num_tokens:
|
|
if self.tree_cache is not None:
|
|
full_num_tokens = max(0, num_tokens - full_available_size)
|
|
swa_num_tokens = max(0, num_tokens - swa_available_size)
|
|
self.tree_cache.evict(full_num_tokens, swa_num_tokens)
|
|
else:
|
|
if self.token_to_kv_pool_allocator.available_size() < num_tokens:
|
|
if self.tree_cache is not None:
|
|
self.tree_cache.evict(num_tokens)
|
|
|
|
def _is_available_size_sufficient(self, num_tokens: int) -> bool:
|
|
if self.is_hybrid:
|
|
return (
|
|
self.token_to_kv_pool_allocator.full_available_size() >= num_tokens
|
|
and self.token_to_kv_pool_allocator.swa_available_size() >= num_tokens
|
|
)
|
|
else:
|
|
return self.token_to_kv_pool_allocator.available_size() >= num_tokens
|
|
|
|
def _available_and_evictable_str(self) -> str:
|
|
if self.is_hybrid:
|
|
full_available_size = self.token_to_kv_pool_allocator.full_available_size()
|
|
swa_available_size = self.token_to_kv_pool_allocator.swa_available_size()
|
|
full_evictable_size = self.tree_cache.full_evictable_size()
|
|
swa_evictable_size = self.tree_cache.swa_evictable_size()
|
|
return (
|
|
f"Available full tokens: {full_available_size + full_evictable_size} ({full_available_size=} + {full_evictable_size=})\n"
|
|
f"Available swa tokens: {swa_available_size + swa_evictable_size} ({swa_available_size=} + {swa_evictable_size=})\n"
|
|
f"Full LRU list evictable size: {self.tree_cache.full_lru_list_evictable_size()}\n"
|
|
f"SWA LRU list evictable size: {self.tree_cache.swa_lru_list_evictable_size()}\n"
|
|
)
|
|
else:
|
|
available_size = self.token_to_kv_pool_allocator.available_size()
|
|
evictable_size = self.tree_cache.evictable_size()
|
|
return f"Available tokens: {available_size + evictable_size} ({available_size=} + {evictable_size=})\n"
|
|
|
|
def __str__(self):
|
|
return (
|
|
f"ScheduleBatch(forward_mode={self.forward_mode.name if self.forward_mode else 'None'}, "
|
|
f"#req={(len(self.reqs))})"
|
|
)
|
|
|
|
|
|
@dataclasses.dataclass
|
|
class ModelWorkerBatch:
|
|
# The batch id
|
|
bid: int
|
|
# The forward mode
|
|
forward_mode: ForwardMode
|
|
# The input ids
|
|
input_ids: torch.Tensor
|
|
# The indices of requests in the req_to_token_pool
|
|
req_pool_indices: torch.Tensor
|
|
# The sequence length
|
|
seq_lens: torch.Tensor
|
|
# The indices of output tokens in the token_to_kv_pool_allocator
|
|
out_cache_loc: torch.Tensor
|
|
# The sequence length tensor on CPU
|
|
seq_lens_cpu: Optional[torch.Tensor]
|
|
seq_lens_sum: int
|
|
|
|
# For logprob
|
|
return_logprob: bool
|
|
top_logprobs_nums: Optional[List[int]]
|
|
token_ids_logprobs: Optional[List[List[int]]]
|
|
|
|
# For DP attention
|
|
global_num_tokens: Optional[List[int]]
|
|
global_num_tokens_for_logprob: Optional[List[int]]
|
|
is_extend_in_batch: bool
|
|
can_run_dp_cuda_graph: bool
|
|
tbo_split_seq_index: Optional[int]
|
|
global_forward_mode: Optional[ForwardMode]
|
|
|
|
# For extend
|
|
extend_num_tokens: Optional[int]
|
|
extend_seq_lens: Optional[List[int]]
|
|
extend_prefix_lens: Optional[List[int]]
|
|
extend_logprob_start_lens: Optional[List[int]]
|
|
extend_input_logprob_token_ids: Optional[torch.Tensor]
|
|
|
|
# For multimodal
|
|
multimodal_inputs: Optional[List[MultimodalInputs]]
|
|
|
|
# For encoder-decoder
|
|
encoder_cached: Optional[List[bool]]
|
|
encoder_lens: Optional[torch.Tensor]
|
|
encoder_lens_cpu: Optional[List[int]]
|
|
encoder_out_cache_loc: Optional[torch.Tensor]
|
|
|
|
# For LoRA
|
|
lora_ids: Optional[List[str]]
|
|
|
|
# Sampling info
|
|
sampling_info: SamplingBatchInfo
|
|
|
|
# The original sequence lengths, Qwen-1M related
|
|
orig_seq_lens: Optional[torch.Tensor] = None
|
|
|
|
# The input Embeds
|
|
input_embeds: Optional[torch.Tensor] = None
|
|
|
|
# For corss-encoder model
|
|
token_type_ids: Optional[torch.Tensor] = None
|
|
|
|
# Speculative decoding
|
|
spec_algorithm: SpeculativeAlgorithm = None
|
|
spec_info: Optional[Union[EagleVerifyInput, EagleDraftInput]] = None
|
|
# If set, the output of the batch contains the hidden states of the run.
|
|
capture_hidden_mode: CaptureHiddenMode = None
|
|
hicache_consumer_index: int = 0
|
|
|
|
# Overlap event
|
|
launch_done: Optional[threading.Event] = None
|
|
|
|
|
|
@triton.jit
|
|
def write_req_to_token_pool_triton(
|
|
req_to_token_ptr, # [max_batch, max_context_len]
|
|
req_pool_indices,
|
|
pre_lens,
|
|
seq_lens,
|
|
extend_lens,
|
|
out_cache_loc,
|
|
req_to_token_ptr_stride: tl.constexpr,
|
|
):
|
|
BLOCK_SIZE: tl.constexpr = 512
|
|
pid = tl.program_id(0)
|
|
|
|
req_pool_index = tl.load(req_pool_indices + pid)
|
|
pre_len = tl.load(pre_lens + pid)
|
|
seq_len = tl.load(seq_lens + pid)
|
|
|
|
# NOTE: This can be slow for large bs
|
|
cumsum_start = tl.cast(0, tl.int64)
|
|
for i in range(pid):
|
|
cumsum_start += tl.load(extend_lens + i)
|
|
|
|
num_loop = tl.cdiv(seq_len - pre_len, BLOCK_SIZE)
|
|
for i in range(num_loop):
|
|
offset = tl.arange(0, BLOCK_SIZE) + i * BLOCK_SIZE
|
|
mask = offset < (seq_len - pre_len)
|
|
value = tl.load(out_cache_loc + cumsum_start + offset, mask=mask)
|
|
tl.store(
|
|
req_to_token_ptr
|
|
+ req_pool_index * req_to_token_ptr_stride
|
|
+ offset
|
|
+ pre_len,
|
|
value,
|
|
mask=mask,
|
|
)
|
|
|
|
|
|
def get_last_loc(
|
|
req_to_token: torch.Tensor,
|
|
req_pool_indices_tensor: torch.Tensor,
|
|
prefix_lens_tensor: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
if (
|
|
global_server_args_dict["attention_backend"] != "ascend"
|
|
and global_server_args_dict["attention_backend"] != "torch_native"
|
|
):
|
|
impl = get_last_loc_triton
|
|
else:
|
|
impl = get_last_loc_torch
|
|
|
|
return impl(req_to_token, req_pool_indices_tensor, prefix_lens_tensor)
|
|
|
|
|
|
def get_last_loc_torch(
|
|
req_to_token: torch.Tensor,
|
|
req_pool_indices_tensor: torch.Tensor,
|
|
prefix_lens_tensor: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
return torch.where(
|
|
prefix_lens_tensor > 0,
|
|
req_to_token[req_pool_indices_tensor, prefix_lens_tensor - 1],
|
|
torch.full_like(prefix_lens_tensor, -1),
|
|
)
|
|
|
|
|
|
@triton.jit
|
|
def get_last_loc_kernel(
|
|
req_to_token,
|
|
req_pool_indices_tensor,
|
|
prefix_lens_tensor,
|
|
result,
|
|
num_tokens,
|
|
req_to_token_stride,
|
|
BLOCK_SIZE: tl.constexpr,
|
|
):
|
|
pid = tl.program_id(0)
|
|
offset = tl.arange(0, BLOCK_SIZE) + pid * BLOCK_SIZE
|
|
mask = offset < num_tokens
|
|
|
|
prefix_lens = tl.load(prefix_lens_tensor + offset, mask=mask, other=0)
|
|
req_pool_indices = tl.load(req_pool_indices_tensor + offset, mask=mask, other=0)
|
|
|
|
token_mask = prefix_lens > 0
|
|
token_index = req_pool_indices * req_to_token_stride + (prefix_lens - 1)
|
|
tokens = tl.load(req_to_token + token_index, mask=token_mask, other=-1)
|
|
|
|
tl.store(result + offset, tokens, mask=mask)
|
|
|
|
|
|
def get_last_loc_triton(
|
|
req_to_token: torch.Tensor,
|
|
req_pool_indices_tensor: torch.Tensor,
|
|
prefix_lens_tensor: torch.Tensor,
|
|
) -> torch.Tensor:
|
|
BLOCK_SIZE = 256
|
|
num_tokens = prefix_lens_tensor.shape[0]
|
|
result = torch.empty_like(prefix_lens_tensor)
|
|
grid = (triton.cdiv(num_tokens, BLOCK_SIZE),)
|
|
|
|
get_last_loc_kernel[grid](
|
|
req_to_token,
|
|
req_pool_indices_tensor,
|
|
prefix_lens_tensor,
|
|
result,
|
|
num_tokens,
|
|
req_to_token.stride(0),
|
|
BLOCK_SIZE,
|
|
)
|
|
return result
|