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[ModelRunner V2] Misc code simplification and cleanup (#33266)

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Signed-off-by: Nick Hill <nickhill123@gmail.com>
This commit is contained in:
Nick Hill
2026-01-28 14:41:23 -08:00
committed by GitHub
parent 77c4f45c6c
commit 6bf3b46d78
3 changed files with 40 additions and 84 deletions
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53
vllm/v1/worker/gpu/async_utils.py
View File

@@ -1,6 +1,5 @@
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from contextlib import contextmanager
import numpy as np
import torch
@@ -28,33 +27,27 @@ class AsyncOutput(AsyncModelRunnerOutput):
self.model_runner_output = model_runner_output
self.sampler_output = sampler_output
self.num_sampled_tokens = num_sampled_tokens
self.copy_stream = copy_stream
self.copy_event = copy_event
default_stream = torch.cuda.current_stream()
with torch.cuda.stream(self.copy_stream):
self.copy_stream.wait_stream(default_stream)
with torch.cuda.stream(copy_stream):
copy_stream.wait_stream(default_stream)
self.sampled_token_ids = async_copy_to_np(sampler_output.sampled_token_ids)
self.logprobs_tensors: LogprobsTensors | None = None
if sampler_output.logprobs_tensors is not None:
self.logprobs_tensors: LogprobsTensors | None = (
self.logprobs_tensors = (
sampler_output.logprobs_tensors.to_cpu_nonblocking()
)
else:
self.logprobs_tensors = None
self.num_nans: np.ndarray | None = None
if sampler_output.num_nans is not None:
self.num_nans = async_copy_to_np(sampler_output.num_nans)
else:
self.num_nans = None
self.num_sampled_tokens_np = async_copy_to_np(num_sampled_tokens)
self.prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
if self.model_runner_output.prompt_logprobs_dict:
for k, v in self.model_runner_output.prompt_logprobs_dict.items():
if v is not None:
self.prompt_logprobs_dict[k] = v.to_cpu_nonblocking()
else:
self.prompt_logprobs_dict[k] = None
self.copy_event.record(self.copy_stream)
self.prompt_logprobs_dict = {
k: v.to_cpu_nonblocking() if v is not None else None
for k, v in self.model_runner_output.prompt_logprobs_dict.items()
}
self.copy_event.record(copy_stream)
def get_output(self) -> ModelRunnerOutput:
self.copy_event.synchronize()
@@ -64,18 +57,15 @@ class AsyncOutput(AsyncModelRunnerOutput):
# Going forward, we should keep the data structures as NumPy arrays
# rather than Python lists.
sampled_token_ids: list[list[int]] = self.sampled_token_ids.tolist()
num_reqs = len(sampled_token_ids)
num_sampled_tokens = self.num_sampled_tokens_np.tolist()
for i in range(num_reqs):
del sampled_token_ids[i][num_sampled_tokens[i] :]
num_sampled_tokens: list[int] = self.num_sampled_tokens_np.tolist()
for token_ids, num_tokens in zip(sampled_token_ids, num_sampled_tokens):
del token_ids[num_tokens:]
self.model_runner_output.sampled_token_ids = sampled_token_ids
if self.num_nans is not None:
num_nans = self.num_nans.tolist()
self.model_runner_output.num_nans_in_logits = {
req_id: num_nans[i]
for i, req_id in enumerate(self.model_runner_output.req_ids)
}
self.model_runner_output.num_nans_in_logits = dict(
zip(self.model_runner_output.req_ids, self.num_nans.tolist())
)
if self.logprobs_tensors is not None:
self.model_runner_output.logprobs = self.logprobs_tensors.tolists()
@@ -83,16 +73,5 @@ class AsyncOutput(AsyncModelRunnerOutput):
return self.model_runner_output
@contextmanager
def async_barrier(event: torch.cuda.Event | None):
if event is not None:
event.synchronize()
try:
yield
finally:
if event is not None:
event.record()
def async_copy_to_np(x: torch.Tensor) -> np.ndarray:
return x.to("cpu", non_blocking=True).numpy()

68
vllm/v1/worker/gpu/model_runner.py
View File

@@ -123,12 +123,6 @@ class GPUModelRunner(LoRAModelRunnerMixin):
self.use_async_scheduling = self.scheduler_config.async_scheduling
self.output_copy_stream = torch.cuda.Stream(self.device)
self.output_copy_event = torch.cuda.Event()
if self.use_async_scheduling:
self.input_prep_event = torch.cuda.Event()
self.structured_outputs_event = torch.cuda.Event()
else:
self.input_prep_event = None
self.structured_outputs_event = None
if self.speculative_config is not None:
self.do_spec_decode = True
@@ -179,7 +173,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
self.max_model_len = max_model_len
self.req_states.max_model_len = max_model_len
def get_supported_tasks(self) -> tuple[str]:
@staticmethod
def get_supported_tasks() -> tuple[str]:
return ("generate",)
def load_model(self, *args, **kwargs) -> None:
@@ -194,9 +189,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
)
if self.lora_config:
self.model = self.load_lora_model(
self.model,
self.vllm_config,
self.device,
self.model, self.vllm_config, self.device
)
if self.do_spec_decode:
self.speculator.load_model(self.model)
@@ -238,9 +231,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
)
self.attn_backends, self.attn_metadata_builders = init_attn_backend(
self.kv_cache_config,
self.vllm_config,
self.device,
self.kv_cache_config, self.vllm_config, self.device
)
if self.do_spec_decode:
# HACK(woosuk)
@@ -288,11 +279,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
@torch.inference_mode()
def _dummy_run(
self,
num_tokens: int,
*args,
skip_attn: bool = True,
**kwargs,
self, num_tokens: int, *args, skip_attn: bool = True, **kwargs
) -> tuple[torch.Tensor, torch.Tensor]:
# Create a dummy scheduler output.
num_reqs = min(num_tokens, self.max_num_reqs)
@@ -320,10 +307,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
return hidden_states, sample_hidden_states
@torch.inference_mode()
def _dummy_sampler_run(
self,
hidden_states: torch.Tensor,
) -> None:
def _dummy_sampler_run(self, hidden_states: torch.Tensor) -> None:
num_reqs = hidden_states.shape[0]
logits = self.model.compute_logits(hidden_states)
idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=self.device)
@@ -337,8 +321,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
@torch.inference_mode()
def profile_run(self) -> None:
hidden_states, sample_hidden_states = self._dummy_run(
self.max_num_tokens,
skip_attn=True,
self.max_num_tokens, skip_attn=True
)
self._dummy_sampler_run(sample_hidden_states)
if self.do_spec_decode:
@@ -482,11 +465,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
def update_requests(self, scheduler_output: SchedulerOutput) -> None:
# Add new blocks for the existing requests.
cached_reqs = scheduler_output.scheduled_cached_reqs
for i, req_id in enumerate(cached_reqs.req_ids):
req_index = self.req_states.req_id_to_index[req_id]
req_new_block_ids = cached_reqs.new_block_ids[i]
reqs = scheduler_output.scheduled_cached_reqs
for req_new_block_ids, req_id in zip(reqs.new_block_ids, reqs.req_ids):
if req_new_block_ids is not None:
req_index = self.req_states.req_id_to_index[req_id]
self.block_tables.append_block_ids(
req_index, req_new_block_ids, overwrite=False
)
@@ -517,7 +499,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
idx_mapping = async_copy_to_gpu(idx_mapping_np, device=self.device)
# Get the number of draft tokens for each request.
if not scheduler_output.scheduled_spec_decode_tokens:
draft_tokens = scheduler_output.scheduled_spec_decode_tokens
if not draft_tokens:
# No draft token scheduled (common case).
total_num_draft_tokens = 0
total_num_logits = num_reqs
@@ -527,12 +510,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
)
expanded_idx_mapping = idx_mapping
else:
draft_tokens = scheduler_output.scheduled_spec_decode_tokens
num_draft_tokens = np.array(
[
len(draft_tokens[req_id]) if req_id in draft_tokens else 0
for req_id in req_ids
],
[len(draft_tokens.get(req_id, ())) for req_id in req_ids],
dtype=np.int32,
)
total_num_draft_tokens = int(num_draft_tokens.sum())
@@ -544,11 +523,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
np.cumsum(num_logits, out=cu_num_logits_np[1:])
cu_num_logits = async_copy_to_gpu(cu_num_logits_np, device=self.device)
max_expand_len = self.num_speculative_steps + 1
expanded_idx_mapping = expand_idx_mapping(
idx_mapping,
total_num_logits,
cu_num_logits,
max_expand_len=self.num_speculative_steps + 1,
idx_mapping, total_num_logits, cu_num_logits, max_expand_len
)
# Block tables: num_kv_cache_groups x [num_reqs, max_num_blocks]
@@ -640,9 +617,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
positions = self.input_buffers.positions[:num_tokens_after_padding]
mrope_positions = None
if self.uses_mrope:
mrope_positions = self.mrope_states.mrope_positions[
:, :num_tokens_after_padding
]
mrope_positions = self.mrope_states.mrope_positions
mrope_positions = mrope_positions[:, :num_tokens_after_padding]
return InputBatch(
req_ids=req_ids,
num_reqs=num_reqs,
@@ -762,10 +738,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# Update the number of computed prefill tokens.
idx_mapping_np = input_batch.idx_mapping_np
computed_prefill = self.req_states.num_computed_prefill_tokens
# TODO(woosuk): Simplify this.
computed_prefill[idx_mapping_np] = np.minimum(
computed_prefill[idx_mapping_np] + input_batch.num_scheduled_tokens,
self.req_states.prefill_len.np[idx_mapping_np],
computed_prefill[idx_mapping_np] += input_batch.num_scheduled_tokens
np.minimum(
computed_prefill, self.req_states.prefill_len.np, out=computed_prefill
)
@torch.inference_mode()
@@ -834,8 +809,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
# Common case.
# Prepare all the inputs and copy to the input buffers.
input_batch = self.prepare_inputs(
scheduler_output,
num_tokens_after_padding,
scheduler_output, num_tokens_after_padding
)
if self.lora_config:
# Activate LoRA adapters.

3
vllm/v1/worker/gpu_worker.py
View File

@@ -107,6 +107,9 @@ class Worker(WorkerBase):
self.use_v2_model_runner = envs.VLLM_USE_V2_MODEL_RUNNER
if self.use_v2_model_runner:
logger.info_once("Using V2 Model Runner", scope="global")
def sleep(self, level: int = 1) -> None:
from vllm.device_allocator.cumem import CuMemAllocator