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Remove default values from InitVars so that they're not stored (#29859)

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Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
This commit is contained in:
Harry Mellor
2025-12-02 12:16:37 +00:00
committed by GitHub
parent d8c6210eea
commit 951445a52d
17 changed files with 139 additions and 77 deletions
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5
benchmarks/benchmark_ngram_proposer.py
View File

@@ -108,7 +108,10 @@ def benchmark_batched_propose(args):
device_config=DeviceConfig(device=current_platform.device_type),
parallel_config=ParallelConfig(),
load_config=LoadConfig(),
scheduler_config=SchedulerConfig(),
scheduler_config=SchedulerConfig(
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
),
)
# monkey patch vllm.v1.worker.gpu_model_runner.get_pp_group

15
tests/compile/test_fusion_attn.py
View File

@@ -318,13 +318,18 @@ def test_attention_quant_pattern(
torch.set_default_dtype(dtype)
torch.manual_seed(42)
model_config = ModelConfig(
model=model_name,
max_model_len=2048,
dtype=dtype,
)
vllm_config = VllmConfig(
model_config=ModelConfig(
model=model_name,
max_model_len=2048,
dtype=dtype,
model_config=model_config,
scheduler_config=SchedulerConfig(
max_num_seqs=1024,
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
),
scheduler_config=SchedulerConfig(max_num_seqs=1024),
compilation_config=CompilationConfig(
mode=CompilationMode.VLLM_COMPILE,
custom_ops=custom_ops_list,

25
tests/lora/test_worker.py
View File

@@ -33,14 +33,16 @@ def test_worker_apply_lora(qwen3_lora_files):
lora_requests, lora_mapping
)
model_config = ModelConfig(
MODEL_PATH,
seed=0,
dtype="float16",
max_model_len=127,
enforce_eager=True,
)
vllm_config = VllmConfig(
model_config=ModelConfig(
MODEL_PATH,
seed=0,
dtype="float16",
max_model_len=127,
enforce_eager=True,
),
model_config=model_config,
load_config=LoadConfig(
download_dir=None,
load_format="dummy",
@@ -50,7 +52,14 @@ def test_worker_apply_lora(qwen3_lora_files):
tensor_parallel_size=1,
data_parallel_size=1,
),
scheduler_config=SchedulerConfig("generate", 32, 32, 32),
scheduler_config=SchedulerConfig(
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
runner_type="generate",
max_num_batched_tokens=32,
max_num_seqs=32,
max_num_partial_prefills=32,
),
device_config=DeviceConfig("cuda"),
cache_config=CacheConfig(
block_size=16,

13
tests/test_config.py
View File

@@ -6,12 +6,14 @@ from dataclasses import MISSING, Field, asdict, dataclass, field
from unittest.mock import patch
import pytest
from pydantic import ValidationError
from vllm.compilation.backends import VllmBackend
from vllm.config import (
CompilationConfig,
ModelConfig,
PoolerConfig,
SchedulerConfig,
VllmConfig,
update_config,
)
@@ -1095,3 +1097,14 @@ def test_vllm_config_explicit_overrides():
# Other fields should still use defaults
assert config.compilation_config.mode == CompilationMode.VLLM_COMPILE
assert config.compilation_config.cudagraph_mode == CUDAGraphMode.FULL_AND_PIECEWISE
def test_scheduler_config_init():
with pytest.raises(ValidationError):
# Positional InitVars missing
# (InitVars cannot have defaults otherwise they will become attributes)
SchedulerConfig()
with pytest.raises(AttributeError):
# InitVar does not become an attribute
print(SchedulerConfig.default_factory().max_model_len)

2
tests/v1/attention/utils.py
View File

@@ -185,6 +185,8 @@ def create_vllm_config(
max_num_seqs=max_num_seqs,
max_num_batched_tokens=max_num_batched_tokens,
enable_chunked_prefill=enable_chunked_prefill,
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
)
device_config = DeviceConfig()

11
tests/v1/core/test_kv_cache_utils.py
View File

@@ -1128,7 +1128,11 @@ def test_estimate_max_model_len(model_id, max_model_len, want_estimated_max_len)
dtype="float16",
max_model_len=max_model_len,
)
scheduler_config = SchedulerConfig(max_num_batched_tokens=32768)
scheduler_config = SchedulerConfig(
max_num_batched_tokens=32768,
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
)
vllm_config = VllmConfig(
model_config=model_config,
@@ -1163,7 +1167,10 @@ def test_get_max_concurrency_for_kv_cache_config():
max_model_len=max_model_len,
)
scheduler_config = SchedulerConfig(
max_num_batched_tokens=1024, enable_chunked_prefill=True
max_num_batched_tokens=1024,
enable_chunked_prefill=True,
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
)
vllm_config = VllmConfig(

13
tests/v1/core/test_scheduler.py
View File

@@ -1508,6 +1508,12 @@ def create_scheduler_with_priority(
Returns:
{class}`Scheduler` instance with priority scheduling
"""
model_config = ModelConfig(
model=model,
trust_remote_code=True,
dtype="float16",
seed=42,
)
if max_model_len is None:
max_model_len = max_num_batched_tokens
scheduler_config = SchedulerConfig(
@@ -1517,14 +1523,9 @@ def create_scheduler_with_priority(
long_prefill_token_threshold=long_prefill_token_threshold,
disable_chunked_mm_input=disable_chunked_mm_input,
enable_chunked_prefill=True,
is_encoder_decoder=model_config.is_encoder_decoder,
policy="priority", # Enable priority scheduling
)
model_config = ModelConfig(
model=model,
trust_remote_code=True,
dtype="float16",
seed=42,
)
# Cache config, optionally force APC
cache_config = CacheConfig(
block_size=block_size,

15
tests/v1/core/utils.py
View File

@@ -69,6 +69,13 @@ def create_scheduler(
Returns:
{class}`Scheduler` instance
"""
model_config = ModelConfig(
model=model,
trust_remote_code=True,
dtype="float16",
seed=42,
skip_tokenizer_init=skip_tokenizer_init,
)
if max_model_len is None:
max_model_len = max_num_batched_tokens
scheduler_config = SchedulerConfig(
@@ -79,13 +86,7 @@ def create_scheduler(
disable_chunked_mm_input=disable_chunked_mm_input,
enable_chunked_prefill=enable_chunked_prefill,
async_scheduling=async_scheduling,
)
model_config = ModelConfig(
model=model,
trust_remote_code=True,
dtype="float16",
seed=42,
skip_tokenizer_init=skip_tokenizer_init,
is_encoder_decoder=model_config.is_encoder_decoder,
)
# Cache config, optionally force APC
cache_config = CacheConfig(

4
tests/v1/cudagraph/test_cudagraph_dispatch.py
View File

@@ -40,7 +40,9 @@ def _create_vllm_config(
) -> MagicMock:
mock_config = MagicMock(spec=VllmConfig)
mock_config.compilation_config = compilation_config
mock_config.scheduler_config = SchedulerConfig(max_num_seqs=max_num_seqs)
mock_config.scheduler_config = SchedulerConfig.default_factory(
max_num_seqs=max_num_seqs,
)
mock_config.parallel_config = ParallelConfig()
mock_config.speculative_config = None # No speculative decoding
if not lora_config:

13
tests/v1/engine/test_engine_core.py
View File

@@ -484,12 +484,6 @@ def test_encoder_instance_zero_kv_cache(
vision encoder, so they don't need KV cache for text generation.
"""
# Form vllm config
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
disable_hybrid_kv_cache_manager=True,
)
model_config = ModelConfig(
model="llava-hf/llava-1.5-7b-hf", # Multimodal model
enforce_eager=True,
@@ -497,6 +491,13 @@ def test_encoder_instance_zero_kv_cache(
dtype="float16",
seed=42,
)
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
disable_hybrid_kv_cache_manager=True,
is_encoder_decoder=model_config.is_encoder_decoder,
)
cache_config = CacheConfig(
block_size=16,
gpu_memory_utilization=gpu_memory_utilization,

13
tests/v1/kv_connector/unit/utils.py
View File

@@ -92,18 +92,19 @@ def create_vllm_config(
enable_permute_local_kv: bool = False,
) -> VllmConfig:
"""Initialize VllmConfig For Testing."""
scheduler_config = SchedulerConfig(
max_num_seqs=max_num_seqs,
max_num_batched_tokens=max_num_batched_tokens,
max_model_len=max_model_len,
enable_chunked_prefill=enable_chunked_prefill,
)
model_config = ModelConfig(
model=model,
trust_remote_code=True,
dtype="float16",
seed=42,
)
scheduler_config = SchedulerConfig(
max_num_seqs=max_num_seqs,
max_num_batched_tokens=max_num_batched_tokens,
max_model_len=max_model_len,
enable_chunked_prefill=enable_chunked_prefill,
is_encoder_decoder=model_config.is_encoder_decoder,
)
# Cache config, optionally force APC
cache_config = CacheConfig(
block_size=block_size,

5
tests/v1/spec_decode/test_eagle.py
View File

@@ -66,7 +66,10 @@ def _create_proposer(
device_config=DeviceConfig(device=current_platform.device_type),
parallel_config=ParallelConfig(),
load_config=LoadConfig(),
scheduler_config=SchedulerConfig(),
scheduler_config=SchedulerConfig(
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
),
)
return EagleProposer(vllm_config=vllm_config, device=current_platform.device_type)

5
tests/v1/spec_decode/test_mtp.py
View File

@@ -51,7 +51,10 @@ def _create_mtp_proposer(num_speculative_tokens: int) -> EagleProposer:
device_config=DeviceConfig(device=current_platform.device_type),
parallel_config=ParallelConfig(),
load_config=LoadConfig(),
scheduler_config=SchedulerConfig(),
scheduler_config=SchedulerConfig(
max_model_len=model_config.max_model_len,
is_encoder_decoder=model_config.is_encoder_decoder,
),
)
return EagleProposer(vllm_config=vllm_config, device=current_platform.device_type)

11
tests/v1/tpu/worker/test_tpu_model_runner.py
View File

@@ -26,16 +26,17 @@ from vllm.v1.worker.tpu_model_runner import (
def get_vllm_config():
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
)
model_config = ModelConfig(
model="facebook/opt-125m",
dtype="bfloat16", # TPUs typically use bfloat16
seed=42,
)
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
is_encoder_decoder=model_config.is_encoder_decoder,
)
cache_config = CacheConfig(
block_size=16,
gpu_memory_utilization=0.9,

20
tests/v1/worker/test_gpu_model_runner.py
View File

@@ -79,16 +79,17 @@ def initialize_kv_cache(runner: GPUModelRunner):
def get_vllm_config():
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
)
model_config = ModelConfig(
model="facebook/opt-125m",
dtype="float16",
seed=42,
)
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
is_encoder_decoder=model_config.is_encoder_decoder,
)
cache_config = CacheConfig(
block_size=BLOCK_SIZE,
gpu_memory_utilization=0.9,
@@ -784,14 +785,15 @@ def test_hybrid_attention_mamba_tensor_shapes(monkeypatch):
initialize_model_parallel(tensor_model_parallel_size=1)
torch.set_default_dtype(torch.float16)
model_config = ModelConfig(
model="ibm-granite/granite-4.0-tiny-preview",
dtype="float16",
)
scheduler_config = SchedulerConfig(
max_num_seqs=10,
max_num_batched_tokens=512,
max_model_len=512,
)
model_config = ModelConfig(
model="ibm-granite/granite-4.0-tiny-preview",
dtype="float16",
is_encoder_decoder=model_config.is_encoder_decoder,
)
cache_config = CacheConfig(
block_size=BLOCK_SIZE,

42
vllm/config/scheduler.py
View File

@@ -28,6 +28,19 @@ SchedulerPolicy = Literal["fcfs", "priority"]
class SchedulerConfig:
"""Scheduler configuration."""
max_model_len: InitVar[int]
"""Maximum length of a sequence (including prompt and generated text).
Note: This is stored in the ModelConfig, and is used only here to
provide fallbacks and validate other attributes."""
is_encoder_decoder: InitVar[bool]
"""True if the model is an encoder-decoder model.
Note: This is stored in the ModelConfig, and is used only here to
disable chunked prefill and prefix caching for encoder-decoder models.
"""
DEFAULT_MAX_NUM_BATCHED_TOKENS: ClassVar[int] = 2048
DEFAULT_MAX_NUM_SEQS: ClassVar[int] = 128
@@ -73,19 +86,6 @@ class SchedulerConfig:
is_multimodal_model: bool = False
"""True if the model is multimodal."""
max_model_len: InitVar[int] = 8192
"""Maximum length of a sequence (including prompt and generated text).
Note: This is stored in the ModelConfig, and is used only here to
provide fallbacks and validate other attributes."""
is_encoder_decoder: InitVar[bool] = False
"""True if the model is an encoder-decoder model.
Note: This is stored in the ModelConfig, and is used only here to
disable chunked prefill and prefix caching for encoder-decoder models.
"""
# TODO (ywang96): Make this configurable.
max_num_encoder_input_tokens: int = Field(init=False)
"""Multimodal encoder compute budget, only used in V1.
@@ -141,6 +141,17 @@ class SchedulerConfig:
while a larger value (e.g., 10) reduces host overhead and may increase throughput
by batching multiple tokens before sending."""
@staticmethod
def default_factory(**kwargs):
"""
Factory method to create `SchedulerConfig` with default values for `InitVar`s.
"""
if "max_model_len" not in kwargs:
kwargs["max_model_len"] = 8192
if "is_encoder_decoder" not in kwargs:
kwargs["is_encoder_decoder"] = False
return SchedulerConfig(**kwargs)
def get_scheduler_cls(self) -> type["SchedulerInterface"]:
if self.scheduler_cls is None:
if self.async_scheduling:
@@ -284,8 +295,3 @@ class SchedulerConfig:
)
return self
def __getattribute__(self, name: str) -> Any:
if name == "max_model_len" or name == "is_encoder_decoder":
raise AttributeError(f"{name} is an init-only parameter. ")
return object.__getattribute__(self, name)

4
vllm/config/vllm.py
View File

@@ -170,7 +170,9 @@ class VllmConfig:
"""Cache configuration."""
parallel_config: ParallelConfig = Field(default_factory=ParallelConfig)
"""Parallel configuration."""
scheduler_config: SchedulerConfig = Field(default_factory=SchedulerConfig)
scheduler_config: SchedulerConfig = Field(
default_factory=SchedulerConfig.default_factory,
)
"""Scheduler configuration."""
device_config: DeviceConfig = Field(default_factory=DeviceConfig)
"""Device configuration."""