585 lines
26 KiB
Python
585 lines
26 KiB
Python
"""AR CPU 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 Any, Optional, Union
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import numpy as np
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import torch
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from vllm.forward_context import BatchDescriptor
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from vllm.logger import init_logger
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from vllm.v1.core.sched.output import SchedulerOutput
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from vllm.v1.outputs import AsyncModelRunnerOutput
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from vllm.v1.structured_output.utils import apply_grammar_bitmask
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from vllm.v1.utils import record_function_or_nullcontext
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from vllm.v1.worker.gpu_model_runner import (
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EMPTY_MODEL_RUNNER_OUTPUT,
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AsyncGPUModelRunnerOutput,
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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.worker.ubatch_utils import UBatchSlices
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from vllm_omni.engine import AdditionalInformationPayload, PromptEmbedsPayload
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from vllm_omni.outputs import OmniModelRunnerOutput
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from vllm_omni.worker.cpu_model_runner import OmniCPUModelRunner
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logger = init_logger(__name__)
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class CPUARModelRunner(OmniCPUModelRunner):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.input_ids = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
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def _preprocess(
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self,
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scheduler_output: "SchedulerOutput",
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num_scheduled_tokens_np: np.ndarray,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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ubatch_slices: Optional[UBatchSlices] = None,
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num_tokens_after_padding: Optional[torch.Tensor] = None,
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) -> tuple[
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int,
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int,
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Optional[torch.Tensor],
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Optional[torch.Tensor],
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Optional[torch.Tensor],
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torch.Tensor,
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Optional[IntermediateTensors],
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dict[str, Any],
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]:
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num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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if ubatch_slices:
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assert num_tokens_after_padding is not None
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num_input_tokens = int(num_tokens_after_padding[0].item() * 2)
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self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
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elif ubatch_slices is None:
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num_input_tokens = self._get_num_input_tokens(num_scheduled_tokens)
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num_pad, num_tokens_after_padding = self.get_dp_padding(num_input_tokens)
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num_input_tokens += num_pad
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# _prepare_inputs may reorder the batch, so we must gather multi
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# modal outputs after that to ensure the correct order
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if (
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self.supports_mm_inputs
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and get_pp_group().is_first_rank
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and not self.model_config.is_encoder_decoder
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):
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# Run the multimodal encoder if any.
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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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# NOTE(woosuk): To unify token ids and soft tokens (vision
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# embeddings), we always use embeddings (rather than token ids)
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# as input to the multimodal model, even when the input is text.
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inputs_embeds_scheduled = self.model.get_input_embeddings(
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input_ids=self.input_ids.gpu[:num_scheduled_tokens],
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multimodal_embeddings=mm_embeds or None,
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)
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# TODO(woosuk): Avoid the copy. Optimize.
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self.inputs_embeds.gpu[:num_scheduled_tokens].copy_(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;
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# 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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)
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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[
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num_computed_tokens : num_computed_tokens + overlay_len
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].to(dtype=self.dtype, device=self.device, 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].copy_(
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src
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)
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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 (
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not hasattr(self, "_forward_additional_information")
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or self._forward_additional_information is None
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):
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self._forward_additional_information = {}
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# Process tensors (slice by scheduled prompt range) and
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# 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[
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num_computed_tokens : num_computed_tokens
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+ overlay_len
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].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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)
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except Exception:
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# Fallback: move whole tensor if slicing fails
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seg = v.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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)
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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)
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else seg.clone()
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)
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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.gpu[:num_input_tokens]
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inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
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model_kwargs = {
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**self._init_model_kwargs(num_scheduled_tokens),
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**self._extract_mm_kwargs(scheduler_output),
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}
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elif self.enable_prompt_embeds and get_pp_group().is_first_rank:
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# Get the input embeddings for the tokens that are not input embeds,
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# then put them into the appropriate positions.
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# TODO(qthequartermasterman): Since even when prompt embeds are
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# enabled, (a) not all requests will use prompt embeds, and (b)
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# after the initial prompt is processed, the rest of the generated
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# tokens will be token ids, it is not desirable to have the
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# embedding layer outside of the CUDA graph all the time. The v0
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# engine avoids this by "double compiling" the CUDA graph, once
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# with input_ids and again with inputs_embeds, for all num_tokens.
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# If a batch only has token ids, then including the embedding layer
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# in the CUDA graph will be more performant (like in the else case
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# below).
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token_ids_idx = (
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self.is_token_ids.gpu[:num_scheduled_tokens]
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.nonzero(as_tuple=False)
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.squeeze(1)
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)
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# Some tokens ids may need to become embeds
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if token_ids_idx.numel() > 0:
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token_ids = self.input_ids.gpu[token_ids_idx]
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tokens_to_embeds = self.model.get_input_embeddings(input_ids=token_ids)
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self.inputs_embeds.gpu[token_ids_idx] = tokens_to_embeds
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inputs_embeds = self.inputs_embeds.gpu[:num_input_tokens]
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model_kwargs = self._init_model_kwargs(num_input_tokens)
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input_ids = None
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else:
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# For text-only models, we use token ids as input.
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# While it is possible to use embeddings as input just like the
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# multimodal models, it is not desirable for performance since
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# then the embedding layer is not included in the CUDA graph.
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input_ids = self.input_ids.gpu[:num_input_tokens]
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inputs_embeds = None
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model_kwargs = self._init_model_kwargs(num_input_tokens)
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if self.uses_mrope:
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positions = self.mrope_positions.gpu[:, :num_input_tokens]
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else:
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positions = self.positions.gpu[:num_input_tokens]
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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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)
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if (
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self.model_config.is_encoder_decoder
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and scheduler_output.scheduled_encoder_inputs
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):
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encoder_inputs = self._extract_encoder_inputs(scheduler_output)
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model_kwargs.update(encoder_inputs)
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return (
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num_scheduled_tokens,
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num_input_tokens,
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num_tokens_after_padding,
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input_ids,
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inputs_embeds,
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positions,
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intermediate_tensors,
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model_kwargs,
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)
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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, AsyncModelRunnerOutput, IntermediateTensors]:
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with record_function_or_nullcontext("Preprocess"):
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with self.synchronize_input_prep():
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# Update persistent batch states.
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self._update_states(scheduler_output)
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# Decode per-request prompt_embeds / additional_hidden_states payloads
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# (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(
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nr, "request_id", None
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)
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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(
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getattr(payload_pe, "dtype", "float32")
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)
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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(
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self.requests[req_id],
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"prompt_embeds_cpu",
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pe_cpu,
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)
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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(
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payload_info, AdditionalInformationPayload
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):
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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(
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getattr(
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entry, "tensor_dtype", "float32"
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)
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)
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arr = np.frombuffer(
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entry.tensor_data, dtype=dt
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)
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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(
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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(
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f"Error decoding prompt_embeds / additional_information: {e}"
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)
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pass
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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 ModelRunnerOutput if no work to do.
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return EMPTY_MODEL_RUNNER_OUTPUT
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return self.kv_connector_no_forward(
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scheduler_output, self.vllm_config
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)
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if self.cache_config.kv_sharing_fast_prefill:
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assert not self.input_batch.num_prompt_logprobs, (
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"--kv-sharing-fast-prefill produces incorrect "
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"logprobs for prompt tokens, tokens, please disable "
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"it when the requests need prompt logprobs"
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)
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# Prepare the decoder inputs.
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(
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attn_metadata,
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logits_indices,
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spec_decode_metadata,
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num_scheduled_tokens_np,
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spec_decode_common_attn_metadata,
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max_query_len,
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ubatch_slices,
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num_tokens_after_padding,
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) = self._prepare_inputs(scheduler_output)
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(
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num_scheduled_tokens,
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num_input_tokens,
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num_tokens_across_dp,
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input_ids,
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inputs_embeds,
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positions,
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intermediate_tensors,
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model_kwargs,
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) = self._preprocess(
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scheduler_output,
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num_scheduled_tokens_np,
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intermediate_tensors,
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ubatch_slices,
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num_tokens_after_padding,
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)
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uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
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num_scheduled_tokens == self.input_batch.num_reqs * max_query_len
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)
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batch_descriptor = BatchDescriptor(
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num_tokens=num_input_tokens, uniform_decode=uniform_decode
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)
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cudagraph_runtime_mode, batch_descriptor = (
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self.cudagraph_dispatcher.dispatch(batch_descriptor)
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)
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# This is currently to get around the assert in the DPMetadata
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# where it wants `num_tokens_across_dp` to align with `num_tokens`
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if ubatch_slices is not None:
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num_input_tokens = ubatch_slices[0].num_tokens
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# Run the model.
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# Use persistent buffers for CUDA graphs.
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with (
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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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cudagraph_runtime_mode=cudagraph_runtime_mode,
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batch_descriptor=batch_descriptor,
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ubatch_slices=ubatch_slices,
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),
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record_function_or_nullcontext("Forward"),
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self.maybe_get_kv_connector_output(scheduler_output) as kv_connector_output,
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):
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model_kwargs_extra = {}
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# Only pass additional_information for the prefill part
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if (
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hasattr(self, "_forward_additional_information")
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and self._forward_additional_information is not None
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and isinstance(self._forward_additional_information, dict)
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):
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model_kwargs_extra["additional_information"] = (
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self._forward_additional_information
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)
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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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**model_kwargs,
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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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with record_function_or_nullcontext("Postprocess"):
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if self.use_aux_hidden_state_outputs:
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# True when EAGLE 3 is used.
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hidden_states, aux_hidden_states = model_output
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else:
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# Common case.
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hidden_states = model_output
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aux_hidden_states = None
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hidden_states, multimodal_outputs = self.extract_multimodal_outputs(
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hidden_states
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)
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if not self.broadcast_pp_output:
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# Common case.
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if not get_pp_group().is_last_rank:
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# Return the intermediate tensors.
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assert isinstance(hidden_states, IntermediateTensors)
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hidden_states.kv_connector_output = kv_connector_output
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return hidden_states
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if self.is_pooling_model:
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# Return the pooling output.
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output = self._pool(
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hidden_states, num_scheduled_tokens, num_scheduled_tokens_np
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)
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output.kv_connector_output = kv_connector_output
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return output
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sample_hidden_states = hidden_states[logits_indices]
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logits = self.model.compute_logits(sample_hidden_states)
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else:
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# Rare case.
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assert not self.is_pooling_model
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if not get_pp_group().is_last_rank:
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all_gather_tensors = {
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"residual": not is_residual_scattered_for_sp(
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self.vllm_config, num_input_tokens
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)
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}
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get_pp_group().send_tensor_dict(
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hidden_states.tensors,
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all_gather_group=get_tp_group(),
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all_gather_tensors=all_gather_tensors,
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)
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logits = None
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else:
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sample_hidden_states = hidden_states[logits_indices]
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logits = self.model.compute_logits(sample_hidden_states)
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model_output_broadcast_data = {}
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if logits is not None:
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model_output_broadcast_data["logits"] = logits.contiguous()
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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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)
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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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apply_grammar_bitmask(
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scheduler_output, self.input_batch, logits, self.device
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)
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with record_function_or_nullcontext("Sample"):
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sampler_output = self._sample(logits, spec_decode_metadata)
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def propose_draft_token_ids(sampled_token_ids):
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assert spec_decode_common_attn_metadata is not None
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with record_function_or_nullcontext("Draft"):
|
|
self._draft_token_ids = self.propose_draft_token_ids(
|
|
scheduler_output,
|
|
sampled_token_ids,
|
|
self.input_batch.sampling_metadata,
|
|
hidden_states,
|
|
sample_hidden_states,
|
|
aux_hidden_states,
|
|
spec_decode_metadata,
|
|
spec_decode_common_attn_metadata,
|
|
)
|
|
|
|
use_padded_batch_for_eagle = (
|
|
self.speculative_config
|
|
and self.speculative_config.use_eagle()
|
|
and not self.speculative_config.disable_padded_drafter_batch
|
|
)
|
|
effective_drafter_max_model_len = self.max_model_len
|
|
if effective_drafter_max_model_len is None:
|
|
effective_drafter_max_model_len = self.model_config.max_model_len
|
|
if (
|
|
self.speculative_config
|
|
and self.speculative_config.draft_model_config is not None
|
|
and self.speculative_config.draft_model_config.max_model_len is not None
|
|
):
|
|
effective_drafter_max_model_len = (
|
|
self.speculative_config.draft_model_config.max_model_len
|
|
)
|
|
input_fits_in_drafter = spec_decode_common_attn_metadata and (
|
|
spec_decode_common_attn_metadata.seq_lens.max()
|
|
+ self.speculative_config.num_speculative_tokens
|
|
<= effective_drafter_max_model_len
|
|
)
|
|
if use_padded_batch_for_eagle and input_fits_in_drafter:
|
|
# EAGLE speculative decoding can use the GPU sampled tokens
|
|
# as inputs, and does not need to wait for bookkeeping to finish.
|
|
propose_draft_token_ids(sampler_output.sampled_token_ids)
|
|
|
|
with record_function_or_nullcontext("Bookkeep"):
|
|
(
|
|
num_nans_in_logits,
|
|
logprobs_lists,
|
|
valid_sampled_token_ids,
|
|
prompt_logprobs_dict,
|
|
req_ids_output_copy,
|
|
req_id_to_index_output_copy,
|
|
invalid_req_indices,
|
|
) = self._bookkeeping_sync(
|
|
scheduler_output,
|
|
sampler_output,
|
|
logits,
|
|
hidden_states,
|
|
num_scheduled_tokens,
|
|
)
|
|
|
|
if (
|
|
self.speculative_config
|
|
and not use_padded_batch_for_eagle
|
|
and input_fits_in_drafter
|
|
):
|
|
# ngram and other speculative decoding methods use the sampled
|
|
# tokens on the CPU, so they are run after bookkeeping.
|
|
propose_draft_token_ids(valid_sampled_token_ids)
|
|
|
|
with record_function_or_nullcontext("EPLB"):
|
|
self.eplb_step()
|
|
|
|
# Convert to per-request tensors on CPU
|
|
hidden_states_cpu = hidden_states.detach().to("cpu").contiguous()
|
|
pooler_output: list[Optional[torch.Tensor]] = []
|
|
prev_logits_index = 0
|
|
for logits_index in logits_indices:
|
|
pooler_output.append(
|
|
hidden_states_cpu[prev_logits_index : logits_index + 1]
|
|
)
|
|
prev_logits_index = logits_index + 1
|
|
|
|
output = OmniModelRunnerOutput(
|
|
req_ids=req_ids_output_copy,
|
|
req_id_to_index=req_id_to_index_output_copy,
|
|
sampled_token_ids=valid_sampled_token_ids,
|
|
logprobs=logprobs_lists,
|
|
prompt_logprobs_dict=prompt_logprobs_dict,
|
|
pooler_output=(
|
|
pooler_output
|
|
if self.vllm_config.model_config.engine_output_type != "text"
|
|
else None
|
|
),
|
|
kv_connector_output=kv_connector_output,
|
|
num_nans_in_logits=num_nans_in_logits,
|
|
)
|
|
|
|
if not self.use_async_scheduling:
|
|
return output
|
|
|
|
return AsyncGPUModelRunnerOutput(
|
|
model_runner_output=output,
|
|
sampled_token_ids=sampler_output.sampled_token_ids,
|
|
invalid_req_indices=invalid_req_indices,
|
|
async_output_copy_stream=self.async_output_copy_stream,
|
|
)
|