600 lines
27 KiB
Python
600 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 copy import copy
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from typing import Any, NamedTuple
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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.ec_transfer import get_ec_transfer, has_ec_transfer
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from vllm.distributed.kv_transfer import get_kv_transfer_group, has_kv_transfer_group
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from vllm.distributed.parallel_state import get_pp_group, get_tp_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.fused_moe.routed_experts_capturer import (
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RoutedExpertsCapturer,
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)
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from vllm.v1.core.sched.output import GrammarOutput, SchedulerOutput
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from vllm.v1.outputs import AsyncModelRunnerOutput, make_empty_encoder_model_runner_output
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from vllm.v1.spec_decode.eagle import EagleProposer
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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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)
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from vllm.v1.worker.ubatch_utils import maybe_create_ubatch_slices
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from vllm.v1.worker.utils import is_residual_scattered_for_sp
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from vllm_omni.distributed.omni_connectors.kv_transfer_manager import OmniKVTransferManager
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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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logger = init_logger(__name__)
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class ExecuteModelState(NamedTuple):
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scheduler_output: SchedulerOutput
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logits: torch.Tensor | None
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spec_decode_metadata: Any
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spec_decode_common_attn_metadata: Any
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hidden_states: torch.Tensor
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sample_hidden_states: torch.Tensor
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aux_hidden_states: list[torch.Tensor] | None
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ec_connector_output: Any
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cudagraph_stats: Any
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multimodal_outputs: Any
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class GPUARModelRunner(OmniGPUModelRunner):
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"""Autoregressive GPU model runner that returns hidden states per request.
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Follows the v0.12 two-phase execute/sample flow from GPUModelRunner, and
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reuses Omni hooks for additional_information / multimodal outputs. This
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class only overrides sample_tokens to expose hidden states + multimodal
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outputs per request while keeping Async output semantics.
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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 = self._make_buffer(self.max_num_tokens, dtype=torch.int32)
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# each model stage has their own hidden size
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self.hidden_size = self.model_config.hf_text_config.hidden_size
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self.inputs_embeds = self._make_buffer(self.max_num_tokens, self.hidden_size, dtype=self.dtype, numpy=False)
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# Initialize KV cache manager (preserve vllm_config fallback behavior)
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self.kv_transfer_manager = OmniKVTransferManager.from_vllm_config(self.vllm_config, self.model_config)
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def _make_buffer(self, *size, dtype, numpy=True):
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# Prevent ray from pinning the buffer due to large size
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from vllm_omni.distributed.ray_utils.utils import (
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calculate_total_bytes,
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maybe_disable_pin_memory_for_ray,
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)
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total_bytes = calculate_total_bytes(size, dtype)
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# Use the context manager to temporarily disable pinning if needed
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with maybe_disable_pin_memory_for_ray(self, total_bytes):
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return super()._make_buffer(*size, dtype=dtype, numpy=numpy)
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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: IntermediateTensors | None = None,
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) -> OmniModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors | None:
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if self.execute_model_state is not None:
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raise RuntimeError("State error: sample_tokens() must be called after execute_model() returns None.")
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# [Omni] Handle KV transfer BEFORE updating states (which removes finished requests)
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self.kv_extracted_req_ids = self.kv_transfer_manager.handle_finished_requests_kv_transfer(
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finished_reqs=getattr(scheduler_output, "finished_requests_needing_kv_transfer", {}),
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kv_caches=self.kv_caches,
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block_size=self.cache_config.block_size,
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cache_dtype=str(self.cache_config.cache_dtype),
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request_id_resolver=self._resolve_global_request_id,
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)
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if self.vllm_config.model_config.enable_return_routed_experts:
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capturer = RoutedExpertsCapturer.get_instance()
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if capturer is not None:
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capturer.clear_buffer() # noqa
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else:
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logger.error("RoutedExpertsCapturer not initialized.")
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if scheduler_output.preempted_req_ids and has_kv_transfer_group():
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get_kv_transfer_group().handle_preemptions(scheduler_output.preempted_req_ids)
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num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
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with (
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record_function_or_nullcontext("gpu_model_runner: preprocess"),
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self.synchronize_input_prep(),
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):
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# Update persistent batch states.
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self._update_states(scheduler_output)
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if has_ec_transfer() and get_ec_transfer().is_producer:
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with self.maybe_get_ec_connector_output(
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scheduler_output,
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encoder_cache=self.encoder_cache,
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) as ec_connector_output:
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self._execute_mm_encoder(scheduler_output)
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return make_empty_encoder_model_runner_output(scheduler_output)
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if not num_scheduled_tokens:
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if (
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self.parallel_config.distributed_executor_backend == "external_launcher"
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and self.parallel_config.data_parallel_size > 1
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):
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# this is a corner case when both external launcher
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# and DP are enabled, num_scheduled_tokens could be
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# 0, and has_unfinished_requests in the outer loop
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# returns True. before returning early here we call
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# dummy run to ensure coordinate_batch_across_dp
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# is called into to avoid out of sync issues.
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self._dummy_run(1)
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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(scheduler_output, self.vllm_config)
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if self.cache_config.kv_sharing_fast_prefill:
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assert not self.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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num_reqs = self.input_batch.num_reqs
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req_ids = self.input_batch.req_ids
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tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
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num_scheduled_tokens_np = np.array(tokens, dtype=np.int32)
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max_num_scheduled_tokens = int(num_scheduled_tokens_np.max())
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num_tokens_unpadded = scheduler_output.total_num_scheduled_tokens
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logits_indices, spec_decode_metadata = self._prepare_inputs(
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scheduler_output,
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num_scheduled_tokens_np,
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)
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cascade_attn_prefix_lens = None
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# Disable cascade attention when using microbatching (DBO)
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if self.cascade_attn_enabled and not self.parallel_config.use_ubatching:
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# Pre-compute cascade attention prefix lengths
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cascade_attn_prefix_lens = self._compute_cascade_attn_prefix_lens(
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num_scheduled_tokens_np,
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self.input_batch.num_computed_tokens_cpu[:num_reqs],
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scheduler_output.num_common_prefix_blocks,
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)
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(
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cudagraph_mode,
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batch_desc,
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should_ubatch,
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num_tokens_across_dp,
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cudagraph_stats,
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) = self._determine_batch_execution_and_padding(
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num_tokens=num_tokens_unpadded,
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num_reqs=num_reqs,
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num_scheduled_tokens_np=num_scheduled_tokens_np,
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max_num_scheduled_tokens=max_num_scheduled_tokens,
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use_cascade_attn=cascade_attn_prefix_lens is not None,
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num_encoder_reqs=len(scheduler_output.scheduled_encoder_inputs),
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)
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logger.debug(
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"Running batch with cudagraph_mode: %s, batch_descriptor: %s, "
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"should_ubatch: %s, num_tokens_across_dp: %s",
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cudagraph_mode,
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batch_desc,
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should_ubatch,
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num_tokens_across_dp,
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)
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num_tokens_padded = batch_desc.num_tokens
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num_reqs_padded = batch_desc.num_reqs if batch_desc.num_reqs is not None else num_reqs
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ubatch_slices, ubatch_slices_padded = maybe_create_ubatch_slices(
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should_ubatch,
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num_scheduled_tokens_np,
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num_tokens_padded,
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num_reqs_padded,
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self.parallel_config.num_ubatches,
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)
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logger.debug(
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"ubatch_slices: %s, ubatch_slices_padded: %s",
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ubatch_slices,
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ubatch_slices_padded,
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)
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pad_attn = cudagraph_mode == CUDAGraphMode.FULL
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use_spec_decode = len(scheduler_output.scheduled_spec_decode_tokens) > 0
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ubatch_slices_attn = ubatch_slices_padded if pad_attn else ubatch_slices
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attn_metadata, spec_decode_common_attn_metadata = self._build_attention_metadata(
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num_tokens=num_tokens_unpadded,
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num_tokens_padded=num_tokens_padded if pad_attn else None,
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num_reqs=num_reqs,
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num_reqs_padded=num_reqs_padded if pad_attn else None,
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max_query_len=max_num_scheduled_tokens,
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ubatch_slices=ubatch_slices_attn,
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logits_indices=logits_indices,
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use_spec_decode=use_spec_decode,
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num_scheduled_tokens=scheduler_output.num_scheduled_tokens,
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cascade_attn_prefix_lens=cascade_attn_prefix_lens,
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)
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(
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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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ec_connector_output,
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) = self._preprocess(scheduler_output, num_tokens_padded, intermediate_tensors)
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# Set cudagraph mode to none if calc_kv_scales is true.
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# KV scales calculation involves dynamic operations that are incompatible
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# with CUDA graph capture.
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if self.calculate_kv_scales:
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cudagraph_mode = CUDAGraphMode.NONE
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# Mark KV scales as calculated after the first forward pass
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self.calculate_kv_scales = False
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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_tokens_padded,
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num_tokens_across_dp=num_tokens_across_dp,
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cudagraph_runtime_mode=cudagraph_mode,
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batch_descriptor=batch_desc,
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ubatch_slices=ubatch_slices_padded,
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),
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record_function_or_nullcontext("gpu_model_runner: 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_output = self._model_forward(
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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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)
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with record_function_or_nullcontext("gpu_model_runner: 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(model_output)
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if multimodal_outputs is not None:
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keys_or_type = (
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list(multimodal_outputs.keys())
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if isinstance(multimodal_outputs, dict)
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else type(multimodal_outputs)
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)
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logger.debug(f"[AR] execute_model: multimodal_outputs keys = {keys_or_type}")
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else:
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logger.debug("[AR] execute_model: multimodal_outputs is None")
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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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self.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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return self._pool(
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hidden_states,
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num_scheduled_tokens,
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num_scheduled_tokens_np,
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kv_connector_output,
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)
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sample_hidden_states = hidden_states[logits_indices]
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# Try with sampling_metadata first; fall back to without for models that don't support it
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try:
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logits = self.model.compute_logits(
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sample_hidden_states, sampling_metadata=self.input_batch.sampling_metadata
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)
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except TypeError:
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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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sample_hidden_states = hidden_states[logits_indices]
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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(self.vllm_config, num_tokens_padded)
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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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# Try with sampling_metadata first; fall back to without for models that don't support it
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try:
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logits = self.model.compute_logits(
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sample_hidden_states, sampling_metadata=self.input_batch.sampling_metadata
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)
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except TypeError:
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logits = self.model.compute_logits(sample_hidden_states)
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model_output_broadcast_data: dict[str, Any] = {}
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if logits is not None:
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model_output_broadcast_data["logits"] = logits.contiguous()
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broadcasted = 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 broadcasted is not None
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logits = broadcasted["logits"]
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self.execute_model_state = ExecuteModelState(
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scheduler_output,
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logits,
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spec_decode_metadata,
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spec_decode_common_attn_metadata,
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hidden_states,
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sample_hidden_states,
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aux_hidden_states,
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ec_connector_output,
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cudagraph_stats,
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multimodal_outputs,
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)
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self.kv_connector_output = kv_connector_output
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return None
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@torch.inference_mode()
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def sample_tokens(
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self,
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grammar_output: GrammarOutput | None,
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) -> OmniModelRunnerOutput | AsyncModelRunnerOutput | IntermediateTensors:
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kv_connector_output = self.kv_connector_output
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self.kv_connector_output = None
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kv_extracted_req_ids = getattr(self, "kv_extracted_req_ids", None)
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self.kv_extracted_req_ids = None
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if self.execute_model_state is None:
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# Nothing to do (PP non-final rank case), output isn't used.
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if not kv_connector_output:
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return None # type: ignore[return-value]
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# In case of PP with kv transfer, we need to pass through the
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# kv_connector_output
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if kv_connector_output.is_empty():
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return EMPTY_MODEL_RUNNER_OUTPUT
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output = copy(EMPTY_MODEL_RUNNER_OUTPUT)
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output.kv_connector_output = kv_connector_output
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return output
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# Unpack ephemeral state.
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(
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scheduler_output,
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logits,
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spec_decode_metadata,
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spec_decode_common_attn_metadata,
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hidden_states,
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sample_hidden_states,
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aux_hidden_states,
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ec_connector_output,
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cudagraph_stats,
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multimodal_outputs,
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) = self.execute_model_state
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self.execute_model_state = None
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# Apply structured output bitmasks if present.
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if grammar_output is not None:
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apply_grammar_bitmask(scheduler_output, grammar_output, self.input_batch, logits)
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with record_function_or_nullcontext("gpu_model_runner: sample"):
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sampler_output = self._sample(logits, spec_decode_metadata)
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self._draft_token_ids = None
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self._draft_token_req_ids = None
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self.input_batch.prev_sampled_token_ids = None
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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("gpu_model_runner: draft"):
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self._draft_token_ids = self.propose_draft_token_ids(
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scheduler_output,
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sampled_token_ids,
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self.input_batch.sampling_metadata,
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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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self._copy_draft_token_ids_to_cpu(scheduler_output)
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spec_config = self.speculative_config
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propose_drafts_after_bookkeeping = False
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if spec_config is not None:
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input_fits_in_drafter = spec_decode_common_attn_metadata is not None and (
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spec_decode_common_attn_metadata.max_seq_len + self.num_spec_tokens
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<= self.effective_drafter_max_model_len
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)
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if spec_config.use_eagle() and not spec_config.disable_padded_drafter_batch:
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# EAGLE speculative decoding can use the GPU sampled tokens
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# as inputs, and does not need to wait for bookkeeping to finish.
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assert isinstance(self.drafter, EagleProposer)
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sampled_token_ids = sampler_output.sampled_token_ids
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if input_fits_in_drafter:
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propose_draft_token_ids(sampled_token_ids)
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elif self.valid_sampled_token_count_event is not None:
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assert spec_decode_common_attn_metadata is not None
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next_token_ids, valid_sampled_tokens_count = self.drafter.prepare_next_token_ids_padded(
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spec_decode_common_attn_metadata,
|
|
sampled_token_ids,
|
|
self.requests,
|
|
self.input_batch,
|
|
self.discard_request_mask.gpu,
|
|
)
|
|
self._copy_valid_sampled_token_count(next_token_ids, valid_sampled_tokens_count)
|
|
# Since we couldn't run the drafter,
|
|
# just use zeros for the draft tokens.
|
|
self._draft_token_ids = torch.zeros(1, device=self.device, dtype=torch.int32).expand(
|
|
len(self.input_batch.req_ids), self.num_spec_tokens
|
|
)
|
|
self._copy_draft_token_ids_to_cpu(scheduler_output, zeros_only=True)
|
|
else:
|
|
propose_drafts_after_bookkeeping = input_fits_in_drafter
|
|
|
|
with record_function_or_nullcontext("gpu_model_runner: 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,
|
|
scheduler_output.total_num_scheduled_tokens,
|
|
spec_decode_metadata,
|
|
)
|
|
|
|
if propose_drafts_after_bookkeeping:
|
|
# 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("gpu_model_runner: eplb"):
|
|
self.eplb_step()
|
|
|
|
hidden_states_cpu = hidden_states.detach().to("cpu").contiguous()
|
|
num_scheduled_tokens_np = getattr(self, "_omni_num_scheduled_tokens_np", None)
|
|
if num_scheduled_tokens_np is None:
|
|
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._process_additional_information_updates(
|
|
hidden_states, multimodal_outputs, num_scheduled_tokens_np, scheduler_output
|
|
)
|
|
|
|
pooler_output: list[dict[str, object]] = []
|
|
for rid in req_ids_output_copy:
|
|
idx = req_id_to_index_output_copy[rid]
|
|
start = int(self.query_start_loc.cpu[idx])
|
|
sched = int(num_scheduled_tokens_np[idx])
|
|
end = start + sched
|
|
hidden_slice = hidden_states_cpu[start:end]
|
|
payload: dict[str, object] = {"hidden": hidden_slice}
|
|
if isinstance(multimodal_outputs, dict) and multimodal_outputs:
|
|
mm_payload: dict[str, object] = {}
|
|
for k, v in multimodal_outputs.items():
|
|
try:
|
|
if isinstance(v, torch.Tensor) and v.shape[0] == hidden_states_cpu.shape[0]:
|
|
mm_payload[k] = v.detach().to("cpu")[start:end].contiguous()
|
|
elif isinstance(v, dict):
|
|
sub_dict: dict[str, torch.Tensor] = {}
|
|
for sk, sv in v.items():
|
|
if isinstance(sv, torch.Tensor) and sv.shape[0] == hidden_states_cpu.shape[0]:
|
|
sub_dict[str(sk)] = sv.detach().to("cpu")[start:end].contiguous()
|
|
if sub_dict:
|
|
mm_payload[k] = sub_dict
|
|
elif isinstance(v, list):
|
|
element = v[0]
|
|
if isinstance(element, torch.Tensor):
|
|
element = element.detach().to("cpu").contiguous()
|
|
mm_payload[k] = element
|
|
except Exception as e:
|
|
logger.error(f"Error in merge multimodal outputs: {e}")
|
|
if mm_payload:
|
|
payload.update(mm_payload)
|
|
pooler_output.append(payload)
|
|
with record_function_or_nullcontext("gpu_model_runner: ModelRunnerOutput"):
|
|
if self.model_config.enable_return_routed_experts:
|
|
capturer = RoutedExpertsCapturer.get_instance()
|
|
if capturer is not None:
|
|
capturer.save_captured_experts(indices=self.slot_mapping) # noqa
|
|
else:
|
|
logger.error("RoutedExpertsCapturer not initialized.")
|
|
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,
|
|
ec_connector_output=ec_connector_output if self.supports_mm_inputs else None,
|
|
num_nans_in_logits=num_nans_in_logits,
|
|
cudagraph_stats=cudagraph_stats,
|
|
)
|
|
output.kv_extracted_req_ids = kv_extracted_req_ids
|
|
|
|
if not self.use_async_scheduling:
|
|
return output
|
|
with record_function_or_nullcontext("gpu_model_runner: AsyncGPUModelRunnerOutput"):
|
|
async_output = AsyncGPUModelRunnerOutput(
|
|
model_runner_output=output,
|
|
sampled_token_ids=sampler_output.sampled_token_ids,
|
|
logprobs_tensors=sampler_output.logprobs_tensors,
|
|
invalid_req_indices=invalid_req_indices,
|
|
async_output_copy_stream=self.async_output_copy_stream,
|
|
vocab_size=self.input_batch.vocab_size,
|
|
)
|
|
with record_function_or_nullcontext("gpu_model_runner: set_async_sampled_token_ids"):
|
|
# Save ref of sampled_token_ids CPU tensor if the batch contains
|
|
# any requests with sampling params that require output ids.
|
|
self.input_batch.set_async_sampled_token_ids(
|
|
async_output.sampled_token_ids_cpu,
|
|
async_output.async_copy_ready_event,
|
|
)
|
|
|
|
return async_output
|
|
|
|
def _resolve_global_request_id(self, req_id: str) -> str:
|
|
"""Resolve global request ID from request state."""
|
|
req_state = self.requests.get(req_id)
|
|
if not req_state:
|
|
return req_id
|
|
|
|
add_info = getattr(req_state, "additional_information_cpu", {}) or {}
|
|
global_id = add_info.get("global_request_id")
|
|
if global_id:
|
|
if isinstance(global_id, list) and global_id:
|
|
global_id = global_id[0]
|
|
if isinstance(global_id, bytes):
|
|
return global_id.decode("utf-8")
|
|
return str(global_id)
|
|
return req_id
|