927 lines
45 KiB
Python
927 lines
45 KiB
Python
from typing import TYPE_CHECKING, Any, cast
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import numpy as np
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import torch
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from vllm.config import CUDAGraphMode
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from vllm.distributed.parallel_state import get_pp_group
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from vllm.forward_context import set_forward_context
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from vllm.logger import init_logger
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from vllm.model_executor.layers.rotary_embedding import MRotaryEmbedding
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from vllm.model_executor.models.interfaces import supports_mrope
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from vllm.model_executor.models.interfaces_base import VllmModelForPooling
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from vllm.sampling_params import SamplingType
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from vllm.utils.import_utils import LazyLoader
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from vllm.utils.math_utils import cdiv
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm.v1.worker.gpu_input_batch import CachedRequestState
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from vllm.v1.worker.gpu_model_runner import GPUModelRunner, IntermediateTensors, PerLayerAttnMetadata
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if TYPE_CHECKING:
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from vllm.v1.core.sched.output import SchedulerOutput
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else:
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xgr = LazyLoader("xgr", globals(), "xgrammar")
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xgr_torch_compile = LazyLoader(
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"xgr_torch_compile",
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globals(),
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"xgrammar.kernels.apply_token_bitmask_inplace_torch_compile",
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)
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logger = init_logger(__name__)
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class OmniGPUModelRunner(GPUModelRunner):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._omni_per_req_additional_information: dict[str, dict] | None = None
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self._omni_num_scheduled_tokens_np: np.ndarray | None = None
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self._omni_last_model_output: object | None = None
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def _init_mrope_positions(self, req_state: CachedRequestState):
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"""Initialize M-RoPE positions for multimodal inputs.
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Extracts multimodal feature metadata (image grids, video grids,
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audio features) and computes M-RoPE positions for proper positional
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encoding of multimodal tokens.
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Args:
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req_state: Cached request state containing multimodal features
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Raises:
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AssertionError: If the model does not support M-RoPE
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"""
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image_grid_thw = []
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video_grid_thw = []
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second_per_grid_ts = []
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audio_feature_lengths = []
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use_audio_in_video = False
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for mm_feature in req_state.mm_features:
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mm_item = mm_feature.data
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if mm_item is None:
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continue
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mm_input = mm_item.get_data()
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if (t := mm_input.get("image_grid_thw")) is not None:
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image_grid_thw.append(t.tolist())
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if (t := mm_input.get("video_grid_thw")) is not None:
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video_grid_thw.append(t.tolist())
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if (t := mm_input.get("second_per_grid_ts")) is not None:
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second_per_grid_ts.append(t)
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if (t := mm_input.get("audio_feature_lengths")) is not None:
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audio_feature_lengths.append(t)
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# Check for use_audio_in_video
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use_audio_in_video_value = mm_input.get("use_audio_in_video")
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if use_audio_in_video_value is not None:
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use_audio_in_video = bool(use_audio_in_video_value.item())
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if supports_mrope(self.model):
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req_state.mrope_positions, req_state.mrope_position_delta = self.model.get_mrope_input_positions(
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req_state.prompt_token_ids,
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hf_config=self.model_config.hf_config,
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image_grid_thw=image_grid_thw,
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video_grid_thw=video_grid_thw,
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second_per_grid_ts=second_per_grid_ts,
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audio_feature_lengths=audio_feature_lengths,
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use_audio_in_video=use_audio_in_video,
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)
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else:
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req_state.mrope_positions, req_state.mrope_position_delta = MRotaryEmbedding.get_input_positions_tensor(
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req_state.prompt_token_ids,
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hf_config=self.model_config.hf_config,
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image_grid_thw=image_grid_thw,
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video_grid_thw=video_grid_thw,
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second_per_grid_ts=second_per_grid_ts,
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audio_feature_lengths=audio_feature_lengths,
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use_audio_in_video=use_audio_in_video,
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)
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def _update_states(self, scheduler_output: "SchedulerOutput") -> None:
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"""Update the cached states and the persistent batch with the scheduler
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output.
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The updated states are used by the `_prepare_inputs` function to create
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the input GPU tensors for the model.
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The SamplingMetadata is updated and copied to the GPU if there is a
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new/resumed/paused/finished request in the batch.
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"""
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# Remove finished requests from the cached states.
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for req_id in scheduler_output.finished_req_ids:
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self.requests.pop(req_id, None)
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# Remove the finished requests from the persistent batch.
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# NOTE(woosuk): There could be an edge case where finished_req_ids and
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# scheduled_req_ids overlap. This happens when a request is aborted and
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# then resubmitted with the same ID. In this case, we treat them as two
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# distinct requests - clearing the cached states for the first request
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# and handling the second as a new request.
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for req_id in scheduler_output.finished_req_ids:
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self.input_batch.remove_request(req_id)
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# Free the cached encoder outputs.
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for mm_hash in scheduler_output.free_encoder_mm_hashes:
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self.encoder_cache.pop(mm_hash, None)
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# Remove the unscheduled requests from the persistent batch.
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# NOTE(woosuk): The unscheduled requests are either preempted requests
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# or running requests that are not scheduled in this step. We remove
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# them from the persistent batch but keep their cached states since
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# they will be scheduled again sometime in the future.
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scheduled_req_ids = scheduler_output.num_scheduled_tokens.keys()
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cached_req_ids = self.input_batch.req_id_to_index.keys()
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unscheduled_req_ids = cached_req_ids - scheduled_req_ids
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# NOTE(woosuk): The persistent batch optimization assumes that
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# consecutive batches contain mostly the same requests. If batches
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# have low request overlap (e.g., alternating between two distinct
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# sets of requests), this optimization becomes very inefficient.
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for req_id in unscheduled_req_ids:
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self.input_batch.remove_request(req_id)
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reqs_to_add: list[CachedRequestState] = []
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# Add new requests to the cached states.
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for new_req_data in scheduler_output.scheduled_new_reqs:
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req_id = new_req_data.req_id
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sampling_params = new_req_data.sampling_params
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pooling_params = new_req_data.pooling_params
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if sampling_params and sampling_params.sampling_type == SamplingType.RANDOM_SEED:
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generator = torch.Generator(device=self.device)
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generator.manual_seed(sampling_params.seed)
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else:
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generator = None
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if self.is_pooling_model:
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assert pooling_params is not None
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task = pooling_params.task
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assert task is not None, "You did not set `task` in the API"
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model = cast(VllmModelForPooling, self.get_model())
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to_update = model.pooler.get_pooling_updates(task)
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to_update.apply(pooling_params)
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req_state = CachedRequestState(
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req_id=req_id,
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prompt_token_ids=new_req_data.prompt_token_ids,
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prompt_embeds=new_req_data.prompt_embeds,
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mm_features=new_req_data.mm_features,
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sampling_params=sampling_params,
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pooling_params=pooling_params,
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generator=generator,
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block_ids=new_req_data.block_ids,
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num_computed_tokens=new_req_data.num_computed_tokens,
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output_token_ids=[],
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lora_request=new_req_data.lora_request,
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)
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self.requests[req_id] = req_state
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# If prompt embeddings are provided, decode and attach to inter_data
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try:
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if getattr(new_req_data, "prompt_embeds", None) is not None:
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payload = new_req_data.prompt_embeds
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dtype = getattr(np, payload.dtype)
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arr = np.frombuffer(payload.data, dtype=dtype)
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arr = arr.reshape(payload.shape)
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pe_cpu = torch.from_numpy(arr)
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# Store temporarily on CPU; later moved to device in builder
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setattr(self.requests[req_id], "prompt_embeds_cpu", pe_cpu)
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# Also replace payload with Tensor for user visibility in
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# scheduler_output
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try:
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new_req_data.prompt_embeds = pe_cpu # type: ignore[assignment]
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except Exception:
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pass
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except Exception as e:
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logger.error(f"Error decoding prompt embeds: {e}")
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# Decode additional_information payloads (dictionary)
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try:
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if getattr(new_req_data, "additional_information", None) is not None:
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payload_info = new_req_data.additional_information
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info_dict = {}
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if isinstance(payload_info, dict):
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info_dict = payload_info
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else:
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from vllm_omni.engine import AdditionalInformationPayload
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if isinstance(payload_info, AdditionalInformationPayload):
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for k, entry in payload_info.entries.items():
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if entry.tensor_data is not None:
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dt = np.dtype(getattr(entry, "tensor_dtype", "float32"))
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arr = np.frombuffer(entry.tensor_data, dtype=dt)
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arr = arr.reshape(entry.tensor_shape)
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info_dict[k] = torch.from_numpy(arr.copy())
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else:
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info_dict[k] = entry.list_data
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if info_dict:
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setattr(
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self.requests[req_id],
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"additional_information_cpu",
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info_dict,
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)
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except Exception as e:
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logger.error(f"Error decoding additional information: {e}")
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pass
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# Only relevant for models using M-RoPE (e.g, Qwen2-VL)
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if self.uses_mrope:
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self._init_mrope_positions(req_state)
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reqs_to_add.append(self.requests[req_id])
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# Update the states of the running/resumed requests.
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is_last_rank = get_pp_group().is_last_rank
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req_data = scheduler_output.scheduled_cached_reqs
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for i, req_id in enumerate(req_data.req_ids):
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req_state = self.requests[req_id]
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num_computed_tokens = req_data.num_computed_tokens[i]
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new_block_ids = req_data.new_block_ids[i]
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resumed_from_preemption = req_data.resumed_from_preemption[i]
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# Update the cached states.
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req_state.num_computed_tokens = num_computed_tokens
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if not is_last_rank:
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# When using PP, the scheduler sends the sampled tokens back,
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# because there's no direct communication between the first-
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# stage worker and the last-stage worker.
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new_token_ids = req_data.new_token_ids[i]
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# Add the sampled token(s) from the previous step (if any).
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# This doesn't include "unverified" tokens like spec tokens.
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num_new_tokens = num_computed_tokens + len(new_token_ids) - req_state.num_tokens
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if num_new_tokens == 1:
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# Avoid slicing list in most common case.
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req_state.output_token_ids.append(new_token_ids[-1])
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elif num_new_tokens > 0:
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req_state.output_token_ids.extend(new_token_ids[-num_new_tokens:])
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# Update the block IDs.
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if not resumed_from_preemption:
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if new_block_ids is not None:
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# Append the new blocks to the existing block IDs.
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for block_ids, new_ids in zip(req_state.block_ids, new_block_ids):
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block_ids.extend(new_ids)
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else:
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assert new_block_ids is not None
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# The request is resumed from preemption.
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# Replace the existing block IDs with the new ones.
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req_state.block_ids = new_block_ids
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req_index = self.input_batch.req_id_to_index.get(req_id)
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if req_index is None:
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# The request is not in the persistent batch.
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# The request was either preempted and resumed later, or was not
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# scheduled in the previous step and needs to be added again.
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reqs_to_add.append(req_state)
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continue
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# Update the persistent batch.
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self.input_batch.num_computed_tokens_cpu[req_index] = num_computed_tokens
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if new_block_ids is not None:
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self.input_batch.block_table.append_row(new_block_ids, req_index)
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# For the last rank, we don't need to update the token_ids_cpu
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# because the sampled tokens are already cached.
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if not is_last_rank:
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# Add new_token_ids to token_ids_cpu.
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start_token_index = num_computed_tokens
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end_token_index = num_computed_tokens + len(new_token_ids)
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self.input_batch.token_ids_cpu[req_index, start_token_index:end_token_index] = new_token_ids
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self.input_batch.num_tokens_no_spec[req_index] = end_token_index
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self.input_batch.num_tokens[req_index] = end_token_index
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# Add spec_token_ids to token_ids_cpu.
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spec_token_ids = scheduler_output.scheduled_spec_decode_tokens.get(req_id, ())
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if spec_token_ids:
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num_spec_tokens = len(spec_token_ids)
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start_index = self.input_batch.num_tokens_no_spec[req_index]
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end_token_index = start_index + num_spec_tokens
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self.input_batch.token_ids_cpu[req_index, start_index:end_token_index] = spec_token_ids
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# NOTE(woosuk): `num_tokens` here may include spec tokens.
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self.input_batch.num_tokens[req_index] += num_spec_tokens
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# Add the new or resumed requests to the persistent batch.
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# The smaller empty indices are filled first.
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for request in reqs_to_add:
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self.input_batch.add_request(request)
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# Condense the batched states if there are gaps left by removed requests
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self.input_batch.condense()
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# Allow attention backend to reorder the batch, potentially
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self._may_reorder_batch(scheduler_output)
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# Refresh batch metadata with any pending updates.
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self.input_batch.refresh_metadata()
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@torch.inference_mode()
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def extract_multimodal_outputs(self, hidden_states: torch.Tensor | list[torch.Tensor]) -> dict:
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if hasattr(self.model, "have_multimodal_outputs") and self.model.have_multimodal_outputs:
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text_hidden_states = hidden_states.text_hidden_states
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multimodal_outputs = hidden_states.multimodal_outputs
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elif isinstance(hidden_states, torch.Tensor):
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text_hidden_states = hidden_states
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multimodal_outputs = {}
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elif isinstance(hidden_states, list):
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text_hidden_states = hidden_states[0]
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multimodal_outputs = {}
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else:
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raise ValueError(f"Invalid hidden states type: {type(hidden_states)}")
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return text_hidden_states, multimodal_outputs
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@torch.inference_mode()
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def _dummy_run(
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self,
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num_tokens: int,
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cudagraph_runtime_mode: CUDAGraphMode | None = None,
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force_attention: bool = False,
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uniform_decode: bool = False,
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allow_microbatching: bool = True,
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skip_eplb: bool = False,
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is_profile: bool = False,
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create_mixed_batch: bool = False,
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remove_lora: bool = True,
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activate_lora: bool = False,
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is_graph_capturing: bool = False,
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) -> tuple[torch.Tensor, torch.Tensor]:
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"""
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Run a dummy forward pass to warm up/profile run or capture the
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CUDA graph for the model.
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Args:
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num_tokens: Number of tokens to run the dummy forward pass.
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cudagraph_runtime_mode: used to control the behavior.
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- if not set will determine the cudagraph mode based on using
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the self.cudagraph_dispatcher.
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- CUDAGraphMode.NONE: No cudagraph, for warm up and profile run
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- CUDAGraphMode.PIECEWISE: Piecewise cudagraph.
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- CUDAGraphMode.FULL: Full cudagraph, attention metadata is
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needed.
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force_attention: If True, always create attention metadata. Used to
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warm up attention backend when mode is NONE.
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uniform_decode: If True, the batch is a uniform decode batch.
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skip_eplb: If True, skip EPLB state update.
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is_profile: If True, this is a profile run.
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create_mixed_batch: If True, create a mixed batch with both decode
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(1 token) and prefill (multiple tokens) requests.
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remove_lora: If False, dummy LoRAs are not destroyed after the run
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activate_lora: If False, dummy_run is performed without LoRAs.
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"""
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assert cudagraph_runtime_mode is None or cudagraph_runtime_mode.valid_runtime_modes()
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# If cudagraph_mode.decode_mode() == FULL and
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# cudagraph_mode.separate_routine(). This means that we are using
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# different graphs and/or modes for mixed prefill-decode batches vs.
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# uniform decode batches. A uniform decode batch means that all
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# requests have identical query length, except a potential virtual
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# request (shorter) in the batch account for padding.
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# Uniform decode batch could either be common pure decode, where
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# max_query_len == 1, or speculative decode, where
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# max_query_len == 1 + num_spec_decode_tokens.
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# When setting max_query_len = 1, we switch to and capture the optimized
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# routine of FA2 for pure decode, i.e., Flashdecode + an optimization
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# for GQA/MQA.
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max_query_len = self.uniform_decode_query_len if uniform_decode else num_tokens
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# Set num_scheduled_tokens based on num_tokens and max_num_seqs
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# for dummy run with LoRA so that the num_reqs collectively
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# has num_tokens in total.
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assert num_tokens <= self.scheduler_config.max_num_batched_tokens
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max_num_reqs = self.scheduler_config.max_num_seqs
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if create_mixed_batch:
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assert not uniform_decode
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# Create mixed batch:
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# first half decode tokens, second half one prefill
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num_decode_tokens = min(max_num_reqs - 1, num_tokens // 2)
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num_prefill_tokens = num_tokens - num_decode_tokens
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num_reqs = num_decode_tokens + 1
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# Create decode requests (1 token each) followed by prefill request
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num_scheduled_tokens_list = [1] * num_decode_tokens + [num_prefill_tokens]
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# Note: Overriding max_query_len to be the prefill tokens
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max_query_len = num_prefill_tokens
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elif uniform_decode:
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assert not create_mixed_batch
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num_reqs = min(max_num_reqs, cdiv(num_tokens, max_query_len))
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num_scheduled_tokens_list = [max_query_len] * num_reqs
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if num_tokens % max_query_len != 0:
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num_scheduled_tokens_list[-1] = num_tokens % max_query_len
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else:
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num_reqs = min(num_tokens, max_num_reqs)
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min_tokens_per_req = num_tokens // num_reqs
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num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
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num_scheduled_tokens_list[-1] += num_tokens % num_reqs
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assert sum(num_scheduled_tokens_list) == num_tokens
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assert len(num_scheduled_tokens_list) == num_reqs
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num_scheduled_tokens = np.array(num_scheduled_tokens_list, dtype=np.int32)
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num_tokens_unpadded = int(num_scheduled_tokens.sum())
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|
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num_sampled_tokens = np.ones(num_reqs, dtype=np.int32)
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|
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_cudagraph_mode, batch_desc, ubatch_slices, num_tokens_across_dp = self._determine_batch_execution_and_padding(
|
|
num_tokens=num_tokens_unpadded,
|
|
num_reqs=num_reqs,
|
|
num_scheduled_tokens_np=num_scheduled_tokens,
|
|
max_num_scheduled_tokens=max_query_len,
|
|
use_cascade_attn=False,
|
|
allow_microbatching=allow_microbatching,
|
|
force_eager=is_profile or (cudagraph_runtime_mode == CUDAGraphMode.NONE),
|
|
# `force_uniform_decode` is used for cudagraph capture; because for
|
|
# capturing mixed prefill-decode batches, we sometimes use
|
|
# num_tokens == num_reqs which looks like a uniform decode batch to the
|
|
# dispatcher; but we actually want to capture a piecewise cudagraph
|
|
force_uniform_decode=uniform_decode,
|
|
# `force_has_lora` is used for cudagraph capture; because LoRA is
|
|
# activated later in the context manager, but we need to know the
|
|
# LoRA state when determining the batch descriptor for capture
|
|
force_has_lora=activate_lora,
|
|
)
|
|
|
|
if cudagraph_runtime_mode is None:
|
|
cudagraph_runtime_mode = _cudagraph_mode
|
|
else:
|
|
assert cudagraph_runtime_mode == _cudagraph_mode, (
|
|
f"Cudagraph runtime mode mismatch in dummy_run. "
|
|
f"Expected {_cudagraph_mode}, but got {cudagraph_runtime_mode}."
|
|
)
|
|
|
|
num_tokens_padded = batch_desc.num_tokens
|
|
num_reqs_padded = batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
|
|
|
|
attn_metadata: PerLayerAttnMetadata | None = None
|
|
|
|
# If force_attention is True, we always capture attention. Otherwise,
|
|
# it only happens for cudagraph_runtime_mode=FULL.
|
|
if force_attention or cudagraph_runtime_mode == CUDAGraphMode.FULL:
|
|
if create_mixed_batch:
|
|
# In the mixed batch mode (used for FI warmup), we use
|
|
# shorter sequence lengths to run faster.
|
|
# TODO(luka) better system for describing dummy batches
|
|
seq_lens = [1] * num_decode_tokens + [num_prefill_tokens + 1]
|
|
else:
|
|
seq_lens = max_query_len # type: ignore[assignment]
|
|
self.seq_lens.np[:num_reqs] = seq_lens
|
|
self.seq_lens.np[num_reqs:] = 0
|
|
self.seq_lens.copy_to_gpu()
|
|
|
|
cum_num_tokens, _ = self._get_cumsum_and_arange(num_scheduled_tokens)
|
|
self.query_start_loc.np[1 : num_reqs + 1] = cum_num_tokens
|
|
self.query_start_loc.copy_to_gpu()
|
|
|
|
attn_metadata, _ = self._build_attention_metadata(
|
|
num_tokens=num_tokens_unpadded,
|
|
num_reqs=num_reqs_padded,
|
|
max_query_len=max_query_len,
|
|
ubatch_slices=ubatch_slices,
|
|
for_cudagraph_capture=is_graph_capturing,
|
|
)
|
|
|
|
with self.maybe_dummy_run_with_lora(
|
|
self.lora_config,
|
|
num_scheduled_tokens,
|
|
num_sampled_tokens,
|
|
activate_lora,
|
|
remove_lora,
|
|
):
|
|
# Make sure padding doesn't exceed max_num_tokens
|
|
assert num_tokens_padded <= self.max_num_tokens
|
|
model_kwargs = self._init_model_kwargs(num_tokens_padded)
|
|
if self.supports_mm_inputs and not self.model_config.is_encoder_decoder:
|
|
input_ids = None
|
|
inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
|
|
model_kwargs = {
|
|
**model_kwargs,
|
|
**self._dummy_mm_kwargs(num_reqs),
|
|
}
|
|
elif self.enable_prompt_embeds:
|
|
input_ids = None
|
|
inputs_embeds = self.inputs_embeds.gpu[:num_tokens_padded]
|
|
model_kwargs = self._init_model_kwargs(num_tokens_padded)
|
|
else:
|
|
input_ids = self.input_ids.gpu[:num_tokens_padded]
|
|
inputs_embeds = None
|
|
|
|
if self.uses_mrope:
|
|
positions = self.mrope_positions.gpu[:, :num_tokens_padded]
|
|
elif self.uses_xdrope_dim > 0:
|
|
positions = self.xdrope_positions.gpu[:, :num_tokens_padded]
|
|
else:
|
|
positions = self.positions.gpu[:num_tokens_padded]
|
|
|
|
if get_pp_group().is_first_rank:
|
|
intermediate_tensors = None
|
|
else:
|
|
if self.intermediate_tensors is None:
|
|
self.intermediate_tensors = self.model.make_empty_intermediate_tensors(
|
|
batch_size=self.max_num_tokens,
|
|
dtype=self.model_config.dtype,
|
|
device=self.device,
|
|
)
|
|
|
|
intermediate_tensors = self.sync_and_slice_intermediate_tensors(num_tokens_padded, None, False)
|
|
|
|
if ubatch_slices is not None:
|
|
# Adjust values to reflect a single ubatch.
|
|
# TODO(sage,lucas): this is cruft that should be addressed in
|
|
# the padding refactor.
|
|
num_tokens_padded = ubatch_slices[0].num_tokens
|
|
if num_tokens_across_dp is not None:
|
|
num_tokens_across_dp[:] = num_tokens_padded
|
|
|
|
with (
|
|
self.maybe_randomize_inputs(input_ids),
|
|
set_forward_context(
|
|
attn_metadata,
|
|
self.vllm_config,
|
|
num_tokens=num_tokens_padded,
|
|
num_tokens_across_dp=num_tokens_across_dp,
|
|
cudagraph_runtime_mode=cudagraph_runtime_mode,
|
|
batch_descriptor=batch_desc,
|
|
ubatch_slices=ubatch_slices,
|
|
),
|
|
):
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
**model_kwargs,
|
|
)
|
|
|
|
if self.use_aux_hidden_state_outputs:
|
|
hidden_states, _ = outputs
|
|
else:
|
|
hidden_states = outputs
|
|
hidden_states, multimodal_outputs = self.extract_multimodal_outputs(hidden_states)
|
|
if self.speculative_config and self.speculative_config.use_eagle():
|
|
assert isinstance(self.drafter, EagleProposer)
|
|
use_cudagraphs = (
|
|
cudagraph_runtime_mode.has_mode(CUDAGraphMode.PIECEWISE)
|
|
and not self.speculative_config.enforce_eager
|
|
)
|
|
|
|
# Note(gnovack) - We need to disable cudagraphs for one of the two
|
|
# lora cases when cudagraph_specialize_lora is enabled. This is a
|
|
# short term mitigation for issue mentioned in
|
|
# https://github.com/vllm-project/vllm/issues/28334
|
|
if self.compilation_config.cudagraph_specialize_lora and activate_lora:
|
|
use_cudagraphs = False
|
|
|
|
self.drafter.dummy_run(
|
|
num_tokens,
|
|
use_cudagraphs=use_cudagraphs,
|
|
is_graph_capturing=is_graph_capturing,
|
|
)
|
|
|
|
# This is necessary to avoid blocking DP.
|
|
# For dummy runs, we typically skip EPLB since we don't have any real
|
|
# requests to process.
|
|
# However, in DP settings, there may be cases when some DP ranks do
|
|
# not have any requests to process, so they're executing dummy batches.
|
|
# In such cases, we still have to trigger EPLB to make sure
|
|
# ranks execute the rearrangement in synchronization.
|
|
if not skip_eplb:
|
|
self.eplb_step(is_dummy=True, is_profile=is_profile)
|
|
|
|
logit_indices = np.cumsum(num_scheduled_tokens) - 1
|
|
logit_indices_device = torch.from_numpy(logit_indices).to(self.device, non_blocking=True)
|
|
return hidden_states, hidden_states[logit_indices_device]
|
|
|
|
def _decode_and_store_request_payloads(self, scheduler_output: "SchedulerOutput") -> None:
|
|
"""Decode per-request prompt_embeds and additional_information for newly
|
|
scheduled requests and store them to CPU in the request state.
|
|
This version avoids hard dependency on payload classes by duck-typing."""
|
|
try:
|
|
new_reqs = getattr(scheduler_output, "scheduled_new_reqs", [])
|
|
if not new_reqs:
|
|
return
|
|
for nr in new_reqs:
|
|
req_id = getattr(nr, "req_id", None) or getattr(nr, "request_id", None)
|
|
if req_id is None:
|
|
continue
|
|
# prompt_embeds
|
|
payload_pe = getattr(nr, "prompt_embeds", None)
|
|
pe_cpu = None
|
|
if payload_pe is not None:
|
|
if isinstance(payload_pe, torch.Tensor):
|
|
pe_cpu = payload_pe.detach().to("cpu").contiguous()
|
|
else:
|
|
# Try duck-typing a payload with data/shape/dtype
|
|
data = getattr(payload_pe, "data", None)
|
|
shape = getattr(payload_pe, "shape", None)
|
|
if data is not None and shape is not None:
|
|
dt = np.dtype(getattr(payload_pe, "dtype", "float32"))
|
|
arr = np.frombuffer(data, dtype=dt)
|
|
arr = arr.reshape(shape)
|
|
pe_cpu = torch.from_numpy(arr.copy())
|
|
if pe_cpu is not None and req_id in self.requests:
|
|
setattr(self.requests[req_id], "prompt_embeds_cpu", pe_cpu)
|
|
# additional_information
|
|
payload_info = getattr(nr, "additional_information", None)
|
|
if payload_info is not None:
|
|
info_dict = {}
|
|
if isinstance(payload_info, dict):
|
|
info_dict = payload_info
|
|
else:
|
|
# Try duck-typing a payload with entries, each entry may have
|
|
# tensor_data/tensor_dtype/tensor_shape or list_data
|
|
entries = getattr(payload_info, "entries", None)
|
|
if isinstance(entries, dict):
|
|
for k, entry in entries.items():
|
|
tensor_data = getattr(entry, "tensor_data", None)
|
|
if tensor_data is not None:
|
|
dt = np.dtype(getattr(entry, "tensor_dtype", "float32"))
|
|
arr = np.frombuffer(tensor_data, dtype=dt)
|
|
arr = arr.reshape(getattr(entry, "tensor_shape", ()))
|
|
info_dict[k] = torch.from_numpy(arr.copy())
|
|
else:
|
|
info_dict[k] = getattr(entry, "list_data", None)
|
|
if info_dict and req_id in self.requests:
|
|
setattr(self.requests[req_id], "additional_information_cpu", info_dict)
|
|
except Exception as e:
|
|
logger.error(f"Error decoding prompt_embeds / additional_information: {e}")
|
|
|
|
def _gather_runtime_additional_information(self) -> list[dict]:
|
|
"""Gather per-request additional_information stored in request state in batch order."""
|
|
per_req_runtime_info = []
|
|
for req_id in self.input_batch.req_ids:
|
|
req_state = self.requests.get(req_id)
|
|
info = getattr(req_state, "additional_information_cpu", None) if req_state is not None else None
|
|
if info and isinstance(info, dict):
|
|
per_req_runtime_info.append(info)
|
|
if "thinker_reply_part_per_request" in info:
|
|
q = info["thinker_reply_part_per_request"]
|
|
if hasattr(q, "shape"):
|
|
logger.debug(f"[OMNI] req={req_id} has thinker_reply_part_per_request queue shape: {q.shape}")
|
|
else:
|
|
per_req_runtime_info.append({})
|
|
return per_req_runtime_info
|
|
|
|
def _compute_request_token_spans(self, num_scheduled_tokens_np) -> list[tuple[int, int]]:
|
|
"""Compute (start, end) token spans for each request within the flattened step sequence."""
|
|
req_token_spans: list[tuple[int, int]] = []
|
|
for req_index in range(len(self.input_batch.req_ids)):
|
|
start_offset = int(self.query_start_loc.cpu[req_index])
|
|
sched_tokens = int(num_scheduled_tokens_np[req_index])
|
|
req_token_spans.append((start_offset, start_offset + sched_tokens))
|
|
return req_token_spans
|
|
|
|
def _build_model_kwargs_extra(self) -> dict:
|
|
"""Build extra keyword arguments passed to the model for this step, including:
|
|
- runtime_additional_information: per-request additional information stored in request state
|
|
"""
|
|
model_kwargs_extra: dict[str, object] = {}
|
|
try:
|
|
model_kwargs_extra["runtime_additional_information"] = self._gather_runtime_additional_information()
|
|
except Exception as e:
|
|
logger.error(f"[OMNI DEBUG] Error building model_kwargs_extra: {e}")
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
return model_kwargs_extra
|
|
|
|
def _process_additional_information_updates(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
multimodal_outputs: object,
|
|
num_scheduled_tokens_np: np.ndarray,
|
|
) -> None:
|
|
"""Process model-provided per-request additional_information updates and merge into request state."""
|
|
try:
|
|
# execute the custom postprocess function
|
|
# TODO(Peiqi): do we have a more elegant way to do this?
|
|
if hasattr(self.model, "has_postprocess") and self.model.has_postprocess:
|
|
for req_index, req_id in enumerate(self.input_batch.req_ids):
|
|
req_state = self.requests.get(req_id)
|
|
req_infos = (
|
|
getattr(req_state, "additional_information_cpu", None) if req_state is not None else None
|
|
)
|
|
start_offset = int(self.query_start_loc.cpu[req_index])
|
|
sched_tokens = int(num_scheduled_tokens_np[req_index])
|
|
s, e = start_offset, start_offset + sched_tokens
|
|
# only consider to store data into update dict.
|
|
hidden_states_slice = hidden_states[s:e]
|
|
update_dict = self.model.postprocess(hidden_states_slice, **req_infos)
|
|
self._merge_additional_information_update(req_id, update_dict)
|
|
except Exception as e:
|
|
logger.error(
|
|
f"Error merging for requests:{self.input_batch.req_ids} "
|
|
f"additional information update: {e}, with the multimodal_outputs "
|
|
f"as {multimodal_outputs}"
|
|
)
|
|
import traceback
|
|
|
|
traceback.print_exc()
|
|
|
|
def _collect_additional_information_for_prefill(
|
|
self,
|
|
num_scheduled_tokens_np: np.ndarray,
|
|
) -> dict[str, dict]:
|
|
"""Overlay per-request prompt_embeds for the prefill portion and collect
|
|
additional_information slices for this step. Returns a map req_id -> dict."""
|
|
for req_index, req_id in enumerate(self.input_batch.req_ids):
|
|
req_state = self.requests[req_id]
|
|
pe_cpu = getattr(req_state, "prompt_embeds_cpu", None)
|
|
num_computed_tokens = int(self.input_batch.num_computed_tokens_cpu[req_index])
|
|
prompt_len = len(req_state.prompt_token_ids)
|
|
prompt_remaining = max(0, prompt_len - num_computed_tokens)
|
|
sched_tokens = int(num_scheduled_tokens_np[req_index])
|
|
overlay_len = min(sched_tokens, prompt_remaining)
|
|
if overlay_len <= 0:
|
|
continue
|
|
if overlay_len > 0 and pe_cpu is not None:
|
|
src = pe_cpu[num_computed_tokens : num_computed_tokens + overlay_len].to(
|
|
dtype=self.dtype, device=self.device, non_blocking=True
|
|
)
|
|
start_offset = int(self.query_start_loc.cpu[req_index])
|
|
self.inputs_embeds[start_offset : start_offset + overlay_len].copy_(src)
|
|
|
|
def _preprocess(
|
|
self,
|
|
scheduler_output: "SchedulerOutput",
|
|
num_input_tokens: int,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
):
|
|
"""Align with v0.12 preprocess and omni's additional information handling."""
|
|
num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
|
|
is_first_rank = get_pp_group().is_first_rank
|
|
is_encoder_decoder = self.model_config.is_encoder_decoder
|
|
|
|
# _prepare_inputs may reorder the batch, so we must gather multi
|
|
# modal outputs after that to ensure the correct order
|
|
ec_connector_output = None
|
|
|
|
if self.supports_mm_inputs and is_first_rank and not is_encoder_decoder:
|
|
# Run the multimodal encoder if any.
|
|
with self.maybe_get_ec_connector_output(
|
|
scheduler_output,
|
|
encoder_cache=self.encoder_cache,
|
|
) as ec_connector_output:
|
|
self._execute_mm_encoder(scheduler_output)
|
|
mm_embeds, is_mm_embed = self._gather_mm_embeddings(scheduler_output)
|
|
|
|
# NOTE(woosuk): To unify token ids and soft tokens (vision
|
|
# embeddings), we always use embeddings (rather than token ids)
|
|
# as input to the multimodal model, even when the input is text.
|
|
inputs_embeds_scheduled = self.model.embed_input_ids(
|
|
self.input_ids.gpu[:num_scheduled_tokens],
|
|
multimodal_embeddings=mm_embeds,
|
|
is_multimodal=is_mm_embed,
|
|
)
|
|
|
|
# TODO(woosuk): Avoid the copy. Optimize.
|
|
self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(inputs_embeds_scheduled)
|
|
|
|
input_ids = self.input_ids.gpu[:num_input_tokens]
|
|
inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
|
|
model_kwargs = {
|
|
**self._init_model_kwargs(num_scheduled_tokens),
|
|
**self._extract_mm_kwargs(scheduler_output),
|
|
}
|
|
elif self.enable_prompt_embeds and is_first_rank:
|
|
# Get the input embeddings for the tokens that are not input embeds,
|
|
# then put them into the appropriate positions.
|
|
# TODO(qthequartermasterman): Since even when prompt embeds are
|
|
# enabled, (a) not all requests will use prompt embeds, and (b)
|
|
# after the initial prompt is processed, the rest of the generated
|
|
# tokens will be token ids, it is not desirable to have the
|
|
# embedding layer outside of the CUDA graph all the time. The v0
|
|
# engine avoids this by "double compiling" the CUDA graph, once
|
|
# with input_ids and again with inputs_embeds, for all num_tokens.
|
|
# If a batch only has token ids, then including the embedding layer
|
|
# in the CUDA graph will be more performant (like in the else case
|
|
# below).
|
|
token_ids_idx = self.is_token_ids.gpu[:num_scheduled_tokens].nonzero(as_tuple=False).squeeze(1)
|
|
# Some tokens ids may need to become embeds
|
|
if token_ids_idx.numel() > 0:
|
|
token_ids = self.input_ids.gpu[token_ids_idx]
|
|
tokens_to_embeds = self.model.embed_input_ids(input_ids=token_ids)
|
|
self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds
|
|
|
|
inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
|
|
model_kwargs = self._init_model_kwargs(num_input_tokens)
|
|
input_ids = self.input_ids.gpu[:num_input_tokens]
|
|
else:
|
|
# For text-only models, we use token ids as input.
|
|
# While it is possible to use embeddings as input just like the
|
|
# multimodal models, it is not desirable for performance since
|
|
# then the embedding layer is not included in the CUDA graph.
|
|
input_ids = self.input_ids.gpu[:num_input_tokens]
|
|
inputs_embeds = None
|
|
model_kwargs = self._init_model_kwargs(num_input_tokens)
|
|
|
|
if self.uses_mrope:
|
|
positions = self.mrope_positions.gpu[:, :num_input_tokens]
|
|
elif self.uses_xdrope_dim > 0:
|
|
positions = self.xdrope_positions.gpu[:, :num_input_tokens]
|
|
else:
|
|
positions = self.positions.gpu[:num_input_tokens]
|
|
|
|
if is_first_rank:
|
|
intermediate_tensors = None
|
|
else:
|
|
assert intermediate_tensors is not None
|
|
intermediate_tensors = self.sync_and_slice_intermediate_tensors(
|
|
num_input_tokens, intermediate_tensors, True
|
|
)
|
|
|
|
if is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
|
|
# Run the encoder, just like we do with other multimodal inputs.
|
|
# For an encoder-decoder model, our processing here is a bit
|
|
# simpler, because the outputs are just passed to the decoder.
|
|
# We are not doing any prompt replacement. We also will only
|
|
# ever have a single encoder input.
|
|
encoder_outputs = self._execute_mm_encoder(scheduler_output)
|
|
model_kwargs.update({"encoder_outputs": encoder_outputs})
|
|
|
|
req_ids = self.input_batch.req_ids
|
|
num_scheduled_tokens_np = np.array(
|
|
[scheduler_output.num_scheduled_tokens[rid] for rid in req_ids],
|
|
dtype=np.int32,
|
|
)
|
|
self._omni_num_scheduled_tokens_np = num_scheduled_tokens_np
|
|
|
|
# Note: only prefill need collect additional_information for now.
|
|
# Decode don't need per_req_additional_information anymore.
|
|
if inputs_embeds is not None:
|
|
# Prefill: overlay prompt_embeds and collect additional_information
|
|
self._collect_additional_information_for_prefill(num_scheduled_tokens_np)
|
|
|
|
if hasattr(self.model, "has_preprocess") and self.model.has_preprocess:
|
|
# Overlay custom prompt_embeds per request for the prompt portion;
|
|
# collect additional_information (tensor/list) for prefill portion only
|
|
for req_index, req_id in enumerate(self.input_batch.req_ids):
|
|
req_state = self.requests.get(req_id)
|
|
req_infos = getattr(req_state, "additional_information_cpu", None) if req_state is not None else None
|
|
|
|
start_offset = int(self.query_start_loc.cpu[req_index])
|
|
sched_tokens = int(num_scheduled_tokens_np[req_index])
|
|
s, e = start_offset, start_offset + sched_tokens
|
|
span_len = int(e) - int(s)
|
|
|
|
# call the custom process function
|
|
req_input_ids, req_embeds, update_dict = self.model.preprocess(
|
|
input_ids=input_ids[s:e], input_embeds=inputs_embeds[s:e], **req_infos
|
|
)
|
|
# TODO(Peiqi): the merge stage could move out from the critical path
|
|
self._merge_additional_information_update(req_id, update_dict)
|
|
|
|
# update the inputs_embeds and input_ids
|
|
seg_len = min(span_len, req_embeds.shape[0])
|
|
inputs_embeds[s : s + seg_len] = req_embeds[:seg_len]
|
|
if isinstance(req_input_ids, torch.Tensor) and req_input_ids.numel() == seg_len:
|
|
input_ids[s : s + seg_len] = req_input_ids
|
|
|
|
return (
|
|
input_ids,
|
|
inputs_embeds,
|
|
positions,
|
|
intermediate_tensors,
|
|
model_kwargs,
|
|
ec_connector_output,
|
|
)
|
|
|
|
def _model_forward(
|
|
self,
|
|
input_ids: torch.Tensor | None = None,
|
|
positions: torch.Tensor | None = None,
|
|
intermediate_tensors: IntermediateTensors | None = None,
|
|
inputs_embeds: torch.Tensor | None = None,
|
|
**model_kwargs: dict[str, Any],
|
|
):
|
|
"""Inject omni-specific kwargs into forward and cache model output"""
|
|
model_kwargs_extra = self._build_model_kwargs_extra()
|
|
|
|
runtime_info = model_kwargs_extra.get("runtime_additional_information", [])
|
|
if runtime_info:
|
|
for i, info in enumerate(runtime_info):
|
|
if info:
|
|
logger.debug(f"[OMNI] req[{i}] runtime_additional_information keys: {list(info.keys())}")
|
|
|
|
model_output = super()._model_forward(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
**model_kwargs,
|
|
**model_kwargs_extra,
|
|
)
|
|
# Cache model output so later sample_tokens can consume multimodal results.
|
|
self._omni_last_model_output = model_output
|
|
return model_output
|
|
|
|
def _merge_additional_information_update(self, req_id: str, upd: dict) -> None:
|
|
req_state = self.requests.get(req_id)
|
|
if req_state is None:
|
|
return
|
|
existing = getattr(req_state, "additional_information_cpu", {})
|
|
if not isinstance(existing, dict):
|
|
existing = {}
|
|
merged = dict(existing)
|
|
for k, v in upd.items():
|
|
if isinstance(v, torch.Tensor):
|
|
merged[k] = v.detach().to("cpu").contiguous()
|
|
elif isinstance(v, list):
|
|
merged[k] = [
|
|
(item.detach().to("cpu").contiguous() if isinstance(item, torch.Tensor) else item) for item in v
|
|
]
|
|
else:
|
|
merged[k] = v
|
|
setattr(req_state, "additional_information_cpu", merged)
|