tzhouam
53b86439fc
- Removed unnecessary imports of numpy and torch within the request scheduling logic. - Simplified the handling of auxiliary hidden states by initializing it to None when not present. - Optimized CPU tensor conversion for text hidden states to improve performance.
579 lines
27 KiB
Python
579 lines
27 KiB
Python
"""AR GPU Model Runner for vLLM-omni.
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Exposes per-request hidden representations via ModelRunnerOutput.pooler_output
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and also outputs sampled tokens.
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"""
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from __future__ import annotations
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from typing import Optional, Union, Any, List
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import numpy as np
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import torch
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from vllm import envs
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from vllm.v1.worker.gpu_model_runner import (
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EMPTY_MODEL_RUNNER_OUTPUT,
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IntermediateTensors,
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get_pp_group,
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get_tp_group,
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has_kv_transfer_group,
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set_forward_context,
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)
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.multimodal.inputs import MultiModalKwargs
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from vllm.v1.attention.backends.utils import CommonAttentionMetadata
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from vllm.v1.spec_decode.eagle import EagleProposer
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from vllm_omni.engine import PromptEmbedsPayload, AdditionalInformationPayload
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from vllm_omni.outputs import OmniModelRunnerOutput
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from vllm_omni.worker.gpu_model_runner import OmniGPUModelRunner
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class ARModelRunner(OmniGPUModelRunner):
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"""Autoregressive GPU model runner that returns hidden states per request.
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This runner follows the same preparation and forward path as GPUModelRunner
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(inputs assembly, multi-modal handling, TP/PP/DP integration, CUDA graphs),
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and additionally performs lightweight sampling so that sampled tokens are
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available in outputs. Hidden representations are taken at the same indices
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that GPUModelRunner would use for sampling/logits (i.e. `logits_indices`).
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.input_ids = torch.zeros(self.max_num_tokens,
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dtype=torch.int64,
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device=self.device)
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@torch.inference_mode()
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def execute_model(
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self,
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scheduler_output: "SchedulerOutput",
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intermediate_tensors: Optional[IntermediateTensors] = None,
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) -> Union[OmniModelRunnerOutput, IntermediateTensors]:
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# Update internal state with the new schedule
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self._update_states(scheduler_output)
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# Decode per-request prompt_embeds / additional_hidden_states payloads (if present) into CPU tensors
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try:
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new_reqs = getattr(scheduler_output, "scheduled_new_reqs", [])
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if new_reqs:
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for nr in new_reqs:
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req_id = getattr(nr, "req_id", None) or getattr(nr, "request_id", None)
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if req_id is None:
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continue
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# prompt_embeds
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payload_pe = getattr(nr, "prompt_embeds", None)
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if payload_pe is not None:
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if isinstance(payload_pe, torch.Tensor):
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pe_cpu = payload_pe.detach().to("cpu").contiguous()
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elif isinstance(payload_pe, PromptEmbedsPayload):
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dt = np.dtype(getattr(payload_pe, "dtype", "float32"))
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arr = np.frombuffer(payload_pe.data, dtype=dt)
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arr = arr.reshape(payload_pe.shape)
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pe_cpu = torch.from_numpy(arr)
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else:
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pe_cpu = None
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if pe_cpu is not None and req_id in self.requests:
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setattr(self.requests[req_id], "prompt_embeds_cpu", pe_cpu)
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# additional_information
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payload_info = getattr(nr, "additional_information", None)
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if payload_info is not None:
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info_dict = {}
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if isinstance(payload_info, dict):
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# Already decoded
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info_dict = payload_info
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elif 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)
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else:
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info_dict[k] = entry.list_data
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if info_dict and req_id in self.requests:
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setattr(self.requests[req_id], "additional_information_cpu", info_dict)
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except Exception:
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pass
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# If there's no work to do, either return empty output or kv-only path
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if not scheduler_output.total_num_scheduled_tokens:
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if not has_kv_transfer_group():
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return EMPTY_MODEL_RUNNER_OUTPUT
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return self.kv_connector_no_forward(scheduler_output,
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self.vllm_config)
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# Prepare decoder inputs and attention metadata
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(attn_metadata, attention_cuda_graphs, logits_indices,
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spec_decode_metadata, num_scheduled_tokens_np,
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spec_decode_common_attn_metadata) = self._prepare_inputs(
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scheduler_output)
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# Determine number of input tokens for this iteration
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num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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if (self.use_cuda_graph
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and num_scheduled_tokens <= self.cudagraph_batch_sizes[-1]):
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num_input_tokens = self.vllm_config.pad_for_cudagraph(
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num_scheduled_tokens)
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else:
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tp_size = self.vllm_config.parallel_config.tensor_parallel_size
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if (self.compilation_config.pass_config.enable_sequence_parallelism
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and tp_size > 1):
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from vllm.utils import round_up # lazy local import
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num_input_tokens = round_up(num_scheduled_tokens, tp_size)
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else:
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num_input_tokens = num_scheduled_tokens
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# DP padding
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num_pad, num_tokens_across_dp = self.get_dp_padding(num_input_tokens)
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num_input_tokens += num_pad
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# Multimodal handling (encode and gather embeddings if needed)
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if self.is_multimodal_model:
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self._execute_mm_encoder(scheduler_output)
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mm_embeds = self._gather_mm_embeddings(scheduler_output)
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else:
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mm_embeds = []
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# Always assemble inputs_embeds on first PP rank; overlay per-request prompt_embeds and collect additional_hidden_states for prefill only
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if get_pp_group().is_first_rank:
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inputs_embeds_scheduled = self.model.get_input_embeddings(
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input_ids=self.input_ids[:num_scheduled_tokens],
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multimodal_embeddings=(mm_embeds or None)
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if self.is_multimodal_model else None,
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)
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# Copy into persistent buffer to enable CUDA Graph capture
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self.inputs_embeds[:num_scheduled_tokens].copy_(
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inputs_embeds_scheduled)
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# Reset per-step additional information collector
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if hasattr(self, "_forward_additional_information"):
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self._forward_additional_information = None
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# Overlay custom prompt_embeds per request for the prompt portion; collect additional_information (tensor/list) for prefill portion only
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for req_index, req_id in enumerate(self.input_batch.req_ids):
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req_state = self.requests[req_id]
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pe_cpu = getattr(req_state, "prompt_embeds_cpu", None)
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addi_cpu = getattr(req_state, "additional_information_cpu", None)
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num_computed_tokens = int(
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self.input_batch.num_computed_tokens_cpu[req_index])
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prompt_len = len(req_state.prompt_token_ids)
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prompt_remaining = max(0, prompt_len - num_computed_tokens)
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sched_tokens = int(num_scheduled_tokens_np[req_index])
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overlay_len = min(sched_tokens, prompt_remaining)
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if overlay_len <= 0:
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continue
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if pe_cpu is not None:
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src = pe_cpu[num_computed_tokens:
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num_computed_tokens + overlay_len].to(
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dtype=self.dtype,
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device=self.device,
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non_blocking=True)
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start_offset = int(self.query_start_loc_cpu[req_index])
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self.inputs_embeds[start_offset:start_offset + overlay_len] \
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.copy_(src)
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# For additional_information: handle arbitrary keys
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if addi_cpu is not None and isinstance(addi_cpu, dict):
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# Lazy init collector dict
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if not hasattr(self, "_forward_additional_information") or \
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self._forward_additional_information is None:
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self._forward_additional_information = {}
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# Process tensors (slice by scheduled prompt range) and lists (append per-request)
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for k, v in addi_cpu.items():
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if isinstance(v, torch.Tensor):
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# Slice along token dimension for prefill part
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try:
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seg = v[num_computed_tokens:
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num_computed_tokens + overlay_len].to(
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dtype=self.dtype,
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device=self.device,
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non_blocking=True)
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except Exception:
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# Fallback: move whole tensor if slicing fails
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seg = v.to(dtype=self.dtype,
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device=self.device,
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non_blocking=True)
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prev_val = self._forward_additional_information.get(k)
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self._forward_additional_information[k] = (
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torch.cat([prev_val, seg], dim=0)
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if isinstance(prev_val, torch.Tensor) else seg.clone())
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elif isinstance(v, list):
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prev_val = self._forward_additional_information.get(k)
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if prev_val is None:
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self._forward_additional_information[k] = [v]
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elif isinstance(prev_val, list):
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self._forward_additional_information[k].append(v)
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else:
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# Mixed types: wrap existing into list
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self._forward_additional_information[k] = [prev_val, v]
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input_ids = self.input_ids[:num_input_tokens] # preserved for APIs
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inputs_embeds = self.inputs_embeds[:num_input_tokens]
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model_mm_kwargs = (self._extract_mm_kwargs(scheduler_output)
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if self.is_multimodal_model else {})
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else:
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input_ids = self.input_ids[:num_input_tokens]
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inputs_embeds = None
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model_mm_kwargs = {}
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# Positions/mRoPE
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if self.uses_mrope:
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positions = self.mrope_positions[:, :num_input_tokens]
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else:
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positions = self.positions[:num_input_tokens]
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# Handle pipeline-parallel intermediate tensors
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if get_pp_group().is_first_rank:
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intermediate_tensors = None
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else:
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intermediate_tensors = self.sync_and_slice_intermediate_tensors(
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num_input_tokens, intermediate_tensors, True)
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# Some attention backends only support CUDA Graphs in pure decode.
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skip_cuda_graphs = self.full_cuda_graph and not attention_cuda_graphs
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# Forward pass
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with set_forward_context(
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attn_metadata,
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self.vllm_config,
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num_tokens=num_input_tokens,
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num_tokens_across_dp=num_tokens_across_dp,
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skip_cuda_graphs=skip_cuda_graphs,
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), self.maybe_get_kv_connector_output(
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scheduler_output) as kv_connector_output:
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model_kwargs_extra = {}
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# Only pass additional_information for the prefill part
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if hasattr(self, "_forward_additional_information") and \
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self._forward_additional_information is not None and \
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isinstance(self._forward_additional_information, dict):
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model_kwargs_extra["additional_information"] = self._forward_additional_information
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model_output = self.model(
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input_ids=input_ids,
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positions=positions,
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intermediate_tensors=intermediate_tensors,
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inputs_embeds=inputs_embeds,
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**MultiModalKwargs.as_kwargs(
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model_mm_kwargs,
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device=self.device,
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),
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sampling_metadata=self.input_batch.sampling_metadata,
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logits_index=logits_indices,
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sampler=self.sampler,
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**model_kwargs_extra,
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)
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if self.use_aux_hidden_state_outputs:
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hidden_states, _aux_hidden_states = model_output
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else:
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hidden_states = model_output
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_aux_hidden_states = None
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text_hidden_states, multimodal_outputs = (
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self.extract_multimodal_outputs(hidden_states))
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# Mid PP stages return intermediate tensors unmodified
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if not get_pp_group().is_last_rank:
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assert isinstance(text_hidden_states, IntermediateTensors)
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text_hidden_states.kv_connector_output = kv_connector_output
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return text_hidden_states
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# Broadcast PP output for external_launcher (torchrun)
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broadcast_pp_output = \
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self.parallel_config.distributed_executor_backend \
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== "external_launcher" and len(get_pp_group().ranks) > 0
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if not get_pp_group().is_last_rank:
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assert isinstance(text_hidden_states, IntermediateTensors)
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if not broadcast_pp_output:
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text_hidden_states.kv_connector_output = kv_connector_output
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return text_hidden_states
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get_pp_group().send_tensor_dict(text_hidden_states.tensors,
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all_gather_group=get_tp_group())
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logits = None
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else:
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if self.input_batch.pooling_params:
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return self._pool(text_hidden_states, num_scheduled_tokens,
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num_scheduled_tokens_np, kv_connector_output)
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sample_hidden_states = text_hidden_states[logits_indices]
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logits = self.model.compute_logits(sample_hidden_states, None)
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if broadcast_pp_output:
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model_output_broadcast_data = {
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"logits": logits.contiguous(),
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} if logits is not None else {}
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model_output_broadcast_data = get_pp_group().broadcast_tensor_dict(
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model_output_broadcast_data, src=len(get_pp_group().ranks) - 1)
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assert model_output_broadcast_data is not None
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logits = model_output_broadcast_data["logits"]
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# Apply structured output bitmasks if present
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if scheduler_output.grammar_bitmask is not None:
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self.apply_grammar_bitmask(scheduler_output, logits)
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# Sample the next token and get logprobs if needed (with spec decode)
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sampling_metadata = self.input_batch.sampling_metadata
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if spec_decode_metadata is None:
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sampler_output = self.sampler(
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logits=logits,
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sampling_metadata=sampling_metadata,
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)
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else:
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assert logits is not None
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bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
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sampler_output = self.sampler(
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logits=bonus_logits,
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sampling_metadata=sampling_metadata,
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)
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bonus_token_ids = sampler_output.sampled_token_ids
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target_logits = logits[spec_decode_metadata.target_logits_indices]
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output_token_ids = self.rejection_sampler(
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spec_decode_metadata,
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None,
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target_logits,
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bonus_token_ids,
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sampling_metadata,
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)
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sampler_output.sampled_token_ids = output_token_ids
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num_nans_in_logits = {}
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if envs.VLLM_COMPUTE_NANS_IN_LOGITS:
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num_nans_in_logits = self._get_nans_in_logits(logits)
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# Handle partial prefill: discard sampled tokens and rewind RNG
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discard_sampled_tokens_req_indices = []
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for i, req_id in enumerate(self.input_batch.req_ids):
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req_state = self.requests[req_id]
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seq_len = (req_state.num_computed_tokens +
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scheduler_output.num_scheduled_tokens[req_id])
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if seq_len < req_state.num_tokens:
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generator = self.input_batch.generators.get(i)
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if generator is not None:
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generator.set_offset(generator.get_offset() - 4)
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discard_sampled_tokens_req_indices.append(i)
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# Move CPU sync parts
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logprobs_tensors = sampler_output.logprobs_tensors
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logprobs_lists = logprobs_tensors.tolists() \
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if logprobs_tensors is not None else None
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# Prompt logprobs if needed
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prompt_logprobs_dict = self._get_prompt_logprobs_dict(
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text_hidden_states[:num_scheduled_tokens],
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scheduler_output,
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)
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# Parse valid sampled tokens
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sampled_token_ids = sampler_output.sampled_token_ids
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max_gen_len = sampled_token_ids.shape[-1]
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if max_gen_len == 1:
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valid_sampled_token_ids = sampled_token_ids.tolist()
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else:
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valid_sampled_token_ids = self.rejection_sampler.parse_output(
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sampled_token_ids,
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self.input_batch.vocab_size,
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)
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for i in discard_sampled_tokens_req_indices:
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valid_sampled_token_ids[i].clear()
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# Cache sampled tokens
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for req_idx, sampled_ids in enumerate(valid_sampled_token_ids):
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if not sampled_ids:
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continue
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start_idx = self.input_batch.num_tokens_no_spec[req_idx]
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end_idx = start_idx + len(sampled_ids)
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self.input_batch.token_ids_cpu[req_idx, start_idx:end_idx] = sampled_ids
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self.input_batch.num_tokens_no_spec[req_idx] = end_idx
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self.input_batch.num_tokens[req_idx] = end_idx
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req_id = self.input_batch.req_ids[req_idx]
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req_state = self.requests[req_id]
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req_state.output_token_ids.extend(sampled_ids)
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# Speculative decoding draft tokens if configured
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if not self.speculative_config:
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spec_token_ids = None
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else:
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assert spec_decode_common_attn_metadata is not None
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spec_token_ids = self.propose_draft_token_ids(
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scheduler_output,
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valid_sampled_token_ids,
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sampling_metadata,
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text_hidden_states,
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sample_hidden_states,
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_aux_hidden_states,
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spec_decode_metadata,
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spec_decode_common_attn_metadata,
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)
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# Convert to per-request tensors on CPU
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text_hidden_states_cpu = text_hidden_states.detach().to("cpu").contiguous()
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pooler_output: list[Optional[torch.Tensor]] = []
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prev_logits_index = 0
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for logits_index in logits_indices:
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pooler_output.append(text_hidden_states_cpu[prev_logits_index:logits_index+1])
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prev_logits_index = logits_index + 1
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|
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self.eplb_step()
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return OmniModelRunnerOutput(
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req_ids=self.input_batch.req_ids,
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req_id_to_index=self.input_batch.req_id_to_index,
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sampled_token_ids=valid_sampled_token_ids,
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spec_token_ids=spec_token_ids,
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logprobs=logprobs_lists,
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prompt_logprobs_dict=prompt_logprobs_dict,
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pooler_output=pooler_output if self.vllm_config.model_config.engine_output_type != "text" else None,
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kv_connector_output=kv_connector_output,
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num_nans_in_logits=num_nans_in_logits,
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)
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@torch.inference_mode()
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def extract_multimodal_outputs(self, hidden_states: Union[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 = {}
|
|
elif isinstance(hidden_states, List):
|
|
text_hidden_states = hidden_states[0]
|
|
multimodal_outputs = {}
|
|
else:
|
|
raise ValueError(f"Invalid hidden states type: {type(hidden_states)}")
|
|
return text_hidden_states, multimodal_outputs
|
|
|
|
@torch.inference_mode()
|
|
def _dummy_run(
|
|
self,
|
|
num_tokens: int,
|
|
capture_attn_cudagraph: bool = False,
|
|
skip_eplb: bool = False,
|
|
is_profile: bool = False,
|
|
) -> tuple[torch.Tensor, torch.Tensor]:
|
|
|
|
# Padding for DP
|
|
num_pad, num_tokens_across_dp = self.get_dp_padding(num_tokens)
|
|
num_tokens += num_pad
|
|
|
|
# Set num_scheduled_tokens based on num_tokens and max_num_seqs
|
|
# for dummy run with LoRA so that the num_reqs collectively
|
|
# has num_tokens in total.
|
|
assert num_tokens <= self.scheduler_config.max_num_batched_tokens
|
|
max_num_reqs = self.scheduler_config.max_num_seqs
|
|
num_reqs = min(num_tokens, max_num_reqs)
|
|
min_tokens_per_req = num_tokens // num_reqs
|
|
num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
|
|
num_scheduled_tokens_list[-1] += num_tokens % num_reqs
|
|
assert sum(num_scheduled_tokens_list) == num_tokens
|
|
assert len(num_scheduled_tokens_list) == num_reqs
|
|
num_scheduled_tokens = np.array(num_scheduled_tokens_list,
|
|
dtype=np.int32)
|
|
|
|
attn_metadata: Optional[dict[str, Any]] = None
|
|
if capture_attn_cudagraph:
|
|
attn_metadata = {}
|
|
|
|
# Make sure max_model_len is used at the graph capture time.
|
|
self.seq_lens_np[:num_reqs] = self.max_model_len
|
|
self.seq_lens_np[num_reqs:] = 0
|
|
self.seq_lens[:num_reqs].copy_(self.seq_lens_cpu[:num_reqs],
|
|
non_blocking=True)
|
|
|
|
for kv_cache_group_id, kv_cache_group_spec in enumerate(
|
|
self.kv_cache_config.kv_cache_groups):
|
|
common_attn_metadata = CommonAttentionMetadata(
|
|
query_start_loc=self.query_start_loc[:num_reqs + 1],
|
|
query_start_loc_cpu=self.query_start_loc_cpu[:num_reqs +
|
|
1],
|
|
seq_lens=self.seq_lens[:num_reqs],
|
|
seq_lens_cpu=self.seq_lens_cpu[:num_reqs],
|
|
num_computed_tokens_cpu=self.input_batch.
|
|
num_computed_tokens_cpu_tensor[:num_reqs],
|
|
num_reqs=num_reqs,
|
|
num_actual_tokens=num_tokens,
|
|
max_query_len=num_tokens,
|
|
block_table_tensor=self.input_batch.block_table[
|
|
kv_cache_group_id].get_device_tensor()[:num_reqs],
|
|
slot_mapping=self.input_batch.
|
|
block_table[kv_cache_group_id].slot_mapping[:num_tokens],
|
|
causal=True)
|
|
|
|
for attn_group in self.attn_groups[kv_cache_group_id]:
|
|
attn_metadata_i = attn_group.metadata_builder\
|
|
.build_for_cudagraph_capture(common_attn_metadata)
|
|
for layer_name in kv_cache_group_spec.layer_names:
|
|
attn_metadata[layer_name] = attn_metadata_i
|
|
|
|
with self.maybe_dummy_run_with_lora(self.lora_config,
|
|
num_scheduled_tokens):
|
|
if self.is_multimodal_model:
|
|
input_ids = None
|
|
inputs_embeds = self.inputs_embeds[:num_tokens]
|
|
model_mm_kwargs = self._dummy_mm_kwargs(num_reqs)
|
|
else:
|
|
input_ids = self.input_ids[:num_tokens]
|
|
inputs_embeds = None
|
|
model_mm_kwargs = {}
|
|
|
|
if self.uses_mrope:
|
|
positions = self.mrope_positions[:, :num_tokens]
|
|
else:
|
|
positions = self.positions[:num_tokens]
|
|
|
|
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, None, False)
|
|
|
|
with self.maybe_randomize_inputs(input_ids), set_forward_context(
|
|
attn_metadata,
|
|
self.vllm_config,
|
|
num_tokens=num_tokens,
|
|
num_tokens_across_dp=num_tokens_across_dp):
|
|
outputs = self.model(
|
|
input_ids=input_ids,
|
|
positions=positions,
|
|
intermediate_tensors=intermediate_tensors,
|
|
inputs_embeds=inputs_embeds,
|
|
**MultiModalKwargs.as_kwargs(
|
|
model_mm_kwargs,
|
|
device=self.device,
|
|
),
|
|
)
|
|
|
|
if self.use_aux_hidden_state_outputs:
|
|
hidden_states, _ = outputs
|
|
else:
|
|
hidden_states = outputs
|
|
|
|
if self.speculative_config and self.speculative_config.use_eagle():
|
|
assert isinstance(self.drafter, EagleProposer)
|
|
self.drafter.dummy_run(num_tokens)
|
|
|
|
# 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
|
|
hidden_states, multimodal_outputs = self.extract_multimodal_outputs(hidden_states)
|
|
return hidden_states, hidden_states[logit_indices]
|
|
|