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[Model Runner V2] Skip building deprecated fields in attn metadata (#32132)

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Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>
This commit is contained in:
Woosuk Kwon
2026-01-11 14:31:04 -08:00
committed by GitHub
parent 3df619ac94
commit 19504ac07f
5 changed files with 7 additions and 39 deletions
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8
vllm/v1/worker/gpu/attn_utils.py
View File

@@ -3,7 +3,6 @@
from collections.abc import Sequence
from typing import Any, cast
import numpy as np
import torch
from vllm.config import VllmConfig, get_layers_from_vllm_config
@@ -147,16 +146,13 @@ def build_attn_metadata(
query_start_loc_gpu: torch.Tensor,
query_start_loc_cpu: torch.Tensor,
seq_lens: torch.Tensor,
seq_lens_np: np.ndarray,
num_computed_tokens_cpu: torch.Tensor | None,
max_seq_len: int,
block_tables: Sequence[torch.Tensor],
slot_mappings: torch.Tensor,
kv_cache_config: KVCacheConfig,
) -> dict[str, Any]:
max_query_len = int(query_start_loc_cpu.max())
seq_lens = seq_lens[:num_reqs]
seq_lens_cpu = torch.from_numpy(seq_lens_np)
max_seq_len = int(seq_lens_np.max())
attn_metadata: dict[str, Any] = {}
kv_cache_groups = kv_cache_config.kv_cache_groups
@@ -168,9 +164,7 @@ def build_attn_metadata(
query_start_loc=query_start_loc_gpu,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens=seq_lens,
_seq_lens_cpu=seq_lens_cpu,
max_seq_len=max_seq_len,
_num_computed_tokens_cpu=num_computed_tokens_cpu,
num_reqs=num_reqs,
num_actual_tokens=num_tokens,
max_query_len=max_query_len,

9
vllm/v1/worker/gpu/cudagraph_utils.py
View File

@@ -232,11 +232,9 @@ def prepare_inputs_to_capture(
query_start_loc.np[: num_reqs + 1] = np.arange(num_reqs + 1) * num_tokens_per_req
query_start_loc.np[num_reqs:] = num_tokens
query_start_loc.copy_to_gpu()
seq_lens_np = np.full(num_reqs, max_model_len, dtype=np.int32)
# HACK(woosuk): For faster warmup, we set seq_lens (GPU) to num_tokens
# rather than max_model_len. This introduces a discrepancy between
# seq_lens (on GPU) and seq_lens_np (on CPU), which may cause issues for
# certain attention backends.
# rather than max_model_len.
input_buffers.seq_lens[:num_reqs] = num_tokens
input_buffers.seq_lens[num_reqs:] = 0
@@ -250,8 +248,7 @@ def prepare_inputs_to_capture(
query_start_loc_gpu=query_start_loc.gpu[: num_reqs + 1],
query_start_loc_cpu=query_start_loc.cpu[: num_reqs + 1],
seq_lens=input_buffers.seq_lens,
seq_lens_np=seq_lens_np,
num_computed_tokens_cpu=None, # FIXME
max_seq_len=max_model_len,
block_tables=input_block_tables,
slot_mappings=slot_mappings,
kv_cache_config=kv_cache_config,

4
vllm/v1/worker/gpu/input_batch.py
View File

@@ -70,7 +70,6 @@ class InputBatch:
query_start_loc_np: np.ndarray
# [num_reqs]
seq_lens: torch.Tensor
seq_lens_np: np.ndarray
# [num_tokens_after_padding]
input_ids: torch.Tensor
@@ -109,8 +108,6 @@ class InputBatch:
query_start_loc_np = input_buffers.query_start_loc.np[: num_reqs + 1]
query_start_loc = input_buffers.query_start_loc.copy_to_gpu()[: num_reqs + 1]
# seq_len equals to query_len
seq_lens_np = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
seq_lens_np[-1] += num_tokens % num_reqs
input_buffers.seq_lens[:num_reqs] = num_tokens // num_reqs
input_buffers.seq_lens[num_reqs - 1] += num_tokens % num_reqs
input_buffers.seq_lens[num_reqs:] = 0
@@ -133,7 +130,6 @@ class InputBatch:
query_start_loc=query_start_loc,
query_start_loc_np=query_start_loc_np,
seq_lens=seq_lens,
seq_lens_np=seq_lens_np,
input_ids=input_ids,
positions=positions,
attn_metadata=None, # type: ignore

20
vllm/v1/worker/gpu/model_runner.py
View File

@@ -228,9 +228,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
slot_mappings = self.block_tables.get_dummy_slot_mappings(
input_batch.num_tokens
)
num_computed_tokens = torch.zeros(
input_batch.num_reqs, dtype=torch.int32, device=self.device
)
query_start_loc = self.input_buffers.query_start_loc
query_start_loc_gpu = query_start_loc.gpu[: input_batch.num_reqs + 1]
query_start_loc_cpu = query_start_loc.cpu[: input_batch.num_reqs + 1]
@@ -241,8 +238,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
query_start_loc_gpu=query_start_loc_gpu,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens=self.input_buffers.seq_lens,
seq_lens_np=input_batch.seq_lens_np,
num_computed_tokens_cpu=num_computed_tokens,
max_seq_len=self.max_model_len,
block_tables=block_tables,
slot_mappings=slot_mappings,
kv_cache_config=self.kv_cache_config,
@@ -522,16 +518,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
query_start_loc_gpu, self.input_buffers.positions[:num_tokens]
)
# Get num_computed_tokens.
# HACK(woosuk): Here, we use num_computed_tokens on GPU instead of
# num_computed_tokens_cpu. This works for most cases.
num_computed_tokens = self.req_states.num_computed_tokens[idx_mapping]
# HACK(woosuk): Only GPU has the exact seq_lens because at this point
# CPU does not know how many draft tokens are accepted/rejected in the
# previous step. Therefore, we use max_model_len to be safe.
# NOTE(woosuk): This only works for FA3 backend.
seq_lens_np = np.full(num_reqs, self.max_model_len, dtype=np.int32)
# Layer name -> attention metadata.
attn_metadata = build_attn_metadata(
attn_metadata_builders=self.attn_metadata_builders,
@@ -540,8 +526,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
query_start_loc_gpu=query_start_loc_gpu,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens=self.input_buffers.seq_lens,
seq_lens_np=seq_lens_np,
num_computed_tokens_cpu=num_computed_tokens,
max_seq_len=self.max_model_len,
block_tables=block_tables,
slot_mappings=slot_mappings,
kv_cache_config=self.kv_cache_config,
@@ -561,7 +546,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
query_start_loc=query_start_loc_gpu,
query_start_loc_np=query_start_loc_np,
seq_lens=seq_lens,
seq_lens_np=seq_lens_np,
input_ids=input_ids,
positions=positions,
attn_metadata=attn_metadata,

5
vllm/v1/worker/gpu/spec_decode/eagle.py
View File

@@ -288,8 +288,6 @@ class EagleSpeculator:
# Run eager mode.
query_start_loc.np[: num_reqs + 1] = np.arange(num_reqs + 1)
query_start_loc_cpu = query_start_loc.cpu[: num_reqs + 1]
# HACK(woosuk)
seq_lens_np = np.full(num_reqs, self.max_model_len, dtype=np.int32)
block_tables = [x[:num_reqs] for x in self.block_tables.input_block_tables]
# FIXME(woosuk): This is UNSAFE!!
@@ -300,8 +298,7 @@ class EagleSpeculator:
query_start_loc_gpu=query_start_loc_gpu,
query_start_loc_cpu=query_start_loc_cpu,
seq_lens=self.input_buffers.seq_lens[:num_reqs],
seq_lens_np=seq_lens_np,
num_computed_tokens_cpu=None, # FIXME
max_seq_len=self.max_model_len,
block_tables=block_tables,
slot_mappings=slot_mappings,
kv_cache_config=self.kv_cache_config,