iHore/vLLM
8
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Mypy Fix] Mypy fix for "vllm/model_executor/models/[eE][fF]" (#53381)

Browse Source 
Signed-off-by: yewentao256 <zhyanwentao@126.com>
This commit is contained in:
Wentao Ye
2026-08-22 15:41:27 -04:00
committed by GitHub
parent 236f78cc5c
commit 811d12ee26
16 changed files with 71 additions and 84 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tools/pre_commit/mypy.py
-2
View File
@@ -112,8 +112,6 @@ SEPARATE_GROUPS = [
]
EXCLUDE = [
r"vllm/model_executor/models/[eE]",
r"vllm/model_executor/models/[fF]",
r"vllm/model_executor/models/[gG]",
r"vllm/model_executor/models/[hH]",
r"vllm/model_executor/models/[iI]",
vllm/compilation/decorators.py
+4 -2
View File
@@ -106,8 +106,10 @@ def support_torch_compile(
@overload
def support_torch_compile(
*,
dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]] | None,
mark_unbacked_dims: dict[str, int | list[int]] | None,
dynamic_arg_dims: dict[str, int | list[int] | dict[int, str]] | None = None,
mark_unbacked_dims: dict[str, int | list[int]] | None = None,
enable_if: Callable[[VllmConfig], bool] | None = None,
is_encoder: bool = False,
) -> Callable[[type[_T]], type[_T]]: ...
vllm/model_executor/models/eagle2_5_vl.py
+2 -1
View File
@@ -150,6 +150,7 @@ class Eagle2_5_VLForConditionalGeneration(
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
multimodal_config = vllm_config.model_config.multimodal_config
assert multimodal_config is not None
self.config = config
self.multimodal_config = multimodal_config
@@ -187,7 +188,7 @@ class Eagle2_5_VLForConditionalGeneration(
prefix=maybe_prefix(prefix, "language_model"),
)
self.img_context_token_id = None
self.img_context_token_id: int | None = None
self.make_empty_intermediate_tensors = (
self.language_model.make_empty_intermediate_tensors
vllm/model_executor/models/ernie45_moe.py
+2 -1
View File
@@ -127,7 +127,7 @@ class Ernie4_5_MoeMoE(nn.Module):
self.layer_idx = layer_idx
self.tp_size = get_tensor_model_parallel_world_size()
self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", None)
self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", 0)
self.ep_group = get_ep_group().device_group
self.ep_size = self.ep_group.size()
self.n_routed_experts: int = config.moe_num_experts
@@ -163,6 +163,7 @@ class Ernie4_5_MoeMoE(nn.Module):
torch.empty(config.moe_num_experts, dtype=torch.float32)
)
self.shared_experts: Ernie4_5_MoeMLP | None
if self.has_shared_experts:
intermediate_size = (
config.moe_intermediate_size * config.moe_num_shared_experts
vllm/model_executor/models/ernie45_vl.py
+27 -20
View File
@@ -1076,7 +1076,7 @@ class Ernie4_5VLMultiModalProcessor(BaseMultiModalProcessor[Ernie4_5_VLProcessin
def _pixel_values_norm(
self,
pixel_values: torch.Tensor,
mm_kwargs: object,
mm_kwargs: Mapping[str, object],
) -> torch.Tensor:
hf_config = self.info.get_hf_config()
vision_config = hf_config.vision_config
@@ -1125,6 +1125,7 @@ class Ernie4_5VLMultiModalProcessor(BaseMultiModalProcessor[Ernie4_5_VLProcessin
prompt_text = self.dummy_inputs.get_dummy_text(mm_items.get_all_counts())
mm_data = dict(mm_data)
if "images" not in mm_data:
mm_data["images"] = []
if "videos" not in mm_data:
@@ -1136,15 +1137,15 @@ class Ernie4_5VLMultiModalProcessor(BaseMultiModalProcessor[Ernie4_5_VLProcessin
hf_processor, "supports_video_metadata", False
)
if mm_data["videos"] and not supports_video_metadata:
videos = mm_data["videos"]
assert isinstance(videos, Sequence)
if videos and not supports_video_metadata:
# Old HF processor, unwrap tuple to pure frames
logger.warning_once(
"HF processor doesn't support video metadata. "
"Timestamps will NOT be rendered. Please upgrade the model."
)
mm_data["videos"] = [
v[0] if isinstance(v, tuple) else v for v in mm_data["videos"]
]
mm_data["videos"] = [v[0] if isinstance(v, tuple) else v for v in videos]
processor_output = self.info.ctx.call_hf_processor(
hf_processor,
@@ -1286,6 +1287,7 @@ class Ernie4_5_VLDummyInputsBuilder(BaseDummyInputsBuilder[Ernie4_5_VLProcessing
image_overrides = mm_options.get("image")
video_overrides = mm_options.get("video")
assert video_overrides is None or isinstance(video_overrides, VideoDummyOptions)
return {
"image": self._get_dummy_images(
@@ -1541,10 +1543,14 @@ class Ernie4_5_VLMoeForConditionalGeneration(
offset = mm_feature.mm_position.offset
if mm_feature.modality == "image":
t, h, w = mm_feature.data["image_grid_thw"].data.tolist()
grid_thw = mm_feature.data["image_grid_thw"].data
assert isinstance(grid_thw, torch.Tensor)
t, h, w = grid_thw.tolist()
yield offset, t, h // spatial_conv_size, w // spatial_conv_size
elif mm_feature.modality == "video":
t, h, w = mm_feature.data["video_grid_thw"].data.tolist()
grid_thw = mm_feature.data["video_grid_thw"].data
assert isinstance(grid_thw, torch.Tensor)
t, h, w = grid_thw.tolist()
yield (
offset,
t // temporal_conv_size,
@@ -1563,12 +1569,11 @@ class Ernie4_5_VLMoeForConditionalGeneration(
if pixel_values is None:
return None
if pixel_values is not None:
return Ernie4_5_VLImagePixelInputs(
type="pixel_values",
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
)
return Ernie4_5_VLImagePixelInputs(
type="pixel_values",
pixel_values=pixel_values,
image_grid_thw=image_grid_thw,
)
def _parse_and_validate_video_input(
self, **kwargs: object
@@ -1579,12 +1584,11 @@ class Ernie4_5_VLMoeForConditionalGeneration(
if pixel_values_videos is None:
return None
if pixel_values_videos is not None:
return Ernie4_5_VLVideoPixelInputs(
type="pixel_values_videos",
pixel_values_videos=pixel_values_videos,
video_grid_thw=video_grid_thw,
)
return Ernie4_5_VLVideoPixelInputs(
type="pixel_values_videos",
pixel_values_videos=pixel_values_videos,
video_grid_thw=video_grid_thw,
)
# -- SupportsEncoderCudaGraph protocol methods --
#
@@ -1748,6 +1752,7 @@ class Ernie4_5_VLMoeForConditionalGeneration(
# the actual batch grid_thw, then scatter the post-merge embeddings.
# Ernie only uses the single "default" encoder path.
output = outputs["default"]
assert batch_mm_kwargs is not None
grid_thw_cpu = batch_mm_kwargs["image_grid_thw"]
grid_thw = grid_thw_cpu.to(output.device, non_blocking=True)
# The valid token count slices the graph output for the eager
@@ -1799,7 +1804,9 @@ class Ernie4_5_VLMoeForConditionalGeneration(
return video_embeds.split(sizes.tolist())
def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict:
modalities = {}
modalities: dict[
str, Ernie4_5_VLImageInputs | Ernie4_5_VLVideoInputs | None
] = {}
# Preserve the order of modalities if there are multiple of them
# from the order of kwargs.
vllm/model_executor/models/ernie45_vl_moe.py
+4 -2
View File
@@ -232,6 +232,7 @@ class Ernie4_5_VLMoeMoE(nn.Module):
assert text_moe_layer_start_index <= text_moe_layer_end_index
self.shared_experts: Ernie4_5_VLMoeMLP | None
if self.has_shared_experts:
intermediate_size = (
config.moe_intermediate_size[0] * config.moe_num_shared_experts
@@ -771,9 +772,10 @@ class Ernie4_5_VLMoeForCausalLM(nn.Module, SupportsPP):
if is_pp_missing_parameter(name, self):
continue
# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
remapped_name = maybe_remap_kv_scale_name(name, params_dict)
if remapped_name is None:
continue
name = remapped_name
param = params_dict[name]
vllm/model_executor/models/exaone4_5.py
+1
View File
@@ -332,6 +332,7 @@ class Exaone4_5_ForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
config: Exaone4_5_Config = vllm_config.model_config.hf_config
self.vllm_config = vllm_config
multimodal_config = vllm_config.model_config.multimodal_config
assert multimodal_config is not None
self.use_data_parallel = multimodal_config.mm_encoder_tp_mode == "data"
self.config = config
vllm/model_executor/models/exaone4_5_mtp.py
+1 -8
View File
@@ -48,17 +48,11 @@ class Exaone4_5MultiTokenPredictor(ExaoneMoeMultiTokenPredictor):
model_config = vllm_config.model_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
config = model_config.hf_config
text_config = config.text_config
self.config = config
lora_vocab = (
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
if lora_config
else 0
)
self.vocab_size = config.vocab_size + lora_vocab
self.vocab_size = config.vocab_size
self.org_vocab_size = config.vocab_size
self.mtp_start_layer_idx = text_config.num_hidden_layers
@@ -67,7 +61,6 @@ class Exaone4_5MultiTokenPredictor(ExaoneMoeMultiTokenPredictor):
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
text_config.hidden_size,
org_num_embeddings=config.vocab_size,
)
self.fc = ColumnParallelLinear(
vllm/model_executor/models/exaone_moe.py
+3 -20
View File
@@ -41,7 +41,6 @@ from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
DEFAULT_VOCAB_PADDING_SIZE,
ParallelLMHead,
VocabParallelEmbedding,
)
@@ -112,6 +111,7 @@ class ExaoneMoe(nn.Module):
self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts
self.n_local_physical_experts = self.n_physical_experts // self.ep_size
self.shared_experts: ExaoneMoeGatedMLP | None
if getattr(config, "num_shared_experts", 0) > 0:
intermediate_size = config.moe_intermediate_size * config.num_shared_experts
self.shared_experts = ExaoneMoeGatedMLP(
@@ -292,7 +292,7 @@ class ExaoneMoeDecoderLayer(nn.Module):
config: PretrainedConfig,
cache_config: CacheConfig | None = None,
quant_config: QuantizationConfig | None = None,
is_mtp: bool = None,
is_mtp: bool = False,
prefix: str = "",
) -> None:
super().__init__()
@@ -381,26 +381,19 @@ class ExaoneMoeModel(nn.Module):
config = vllm_config.model_config.hf_config
cache_config = vllm_config.cache_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.num_redundant_experts = (
vllm_config.parallel_config.eplb_config.num_redundant_experts
)
self.config = config
self.quant_config = quant_config
lora_vocab = (
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
if lora_config
else 0
)
self.vocab_size = config.vocab_size + lora_vocab
self.vocab_size = config.vocab_size
if get_pp_group().is_first_rank or (
config.tie_word_embeddings and get_pp_group().is_last_rank
):
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
quant_config=quant_config,
)
else:
@@ -497,10 +490,8 @@ class ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
super().__init__()
config = vllm_config.model_config.hf_config.get_text_config()
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.lora_config = lora_config
self.quant_config = quant_config
self.model = ExaoneMoeModel(
@@ -509,17 +500,9 @@ class ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
)
if get_pp_group().is_last_rank:
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
self.lm_head = ParallelLMHead(
self.unpadded_vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
padding_size=DEFAULT_VOCAB_PADDING_SIZE
# We need bigger padding if using lora for kernel
# compatibility
if not lora_config
else lora_config.lora_vocab_padding_size,
quant_config=quant_config,
)
if config.tie_word_embeddings:
vllm/model_executor/models/exaone_moe_mtp.py
+1 -8
View File
@@ -46,16 +46,10 @@ class ExaoneMoeMultiTokenPredictor(nn.Module):
model_config = vllm_config.model_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
config = model_config.hf_config
self.config = config
lora_vocab = (
(lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1))
if lora_config
else 0
)
self.vocab_size = config.vocab_size + lora_vocab
self.vocab_size = config.vocab_size
self.org_vocab_size = config.vocab_size
self.mtp_start_layer_idx = config.num_hidden_layers
@@ -64,7 +58,6 @@ class ExaoneMoeMultiTokenPredictor(nn.Module):
self.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
)
self.fc = ColumnParallelLinear(
vllm/model_executor/models/extract_hidden_states.py
+4 -2
View File
@@ -231,7 +231,7 @@ class CacheOnlyAttentionLayer(nn.Module, AttentionLayerBase):
head_size: int,
cache_config: CacheConfig | None = None,
prefix: str = "",
attn_type: str = AttentionType.DECODER,
attn_type: AttentionType = AttentionType.DECODER,
):
super().__init__()
@@ -329,7 +329,9 @@ class ExtractHiddenStatesModel(nn.Module):
super().__init__()
self.vllm_config = vllm_config
self.hf_config = vllm_config.speculative_config.draft_model_config.hf_config
speculative_config = vllm_config.speculative_config
assert speculative_config is not None
self.hf_config = speculative_config.draft_model_config.hf_config
self.hidden_size = vllm_config.model_config.get_hidden_size()
self.target_num_hidden_layers = (
vllm_config.model_config.get_total_num_hidden_layers()
vllm/model_executor/models/falcon.py
+1
View File
@@ -413,6 +413,7 @@ class FalconModel(nn.Module):
else:
hidden_states = self.embed_input_ids(input_ids)
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
for layer in islice(self.h, self.start_layer, self.end_layer):
hidden_states = layer(positions, hidden_states)
vllm/model_executor/models/fireredasr2.py
+2 -1
View File
@@ -12,7 +12,7 @@ from transformers import (
)
from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.config.multimodal import AudioDummyOptions, BaseDummyOptions
from vllm.config.speech_to_text import SpeechToTextParams
from vllm.inputs import MultiModalDataDict, PromptType
from vllm.logger import init_logger
@@ -216,6 +216,7 @@ class FireRedASR2DummyInputsBuilder(BaseDummyInputsBuilder[FireRedASR2Processing
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio")
assert audio_overrides is None or isinstance(audio_overrides, AudioDummyOptions)
ret = {
"audio": self._get_dummy_audios(
vllm/model_executor/models/fireredlid.py
+8 -11
View File
@@ -18,14 +18,13 @@ from __future__ import annotations
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Literal
import numpy as np
import torch
from torch import nn
from transformers import BatchFeature
from vllm.config import ModelConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.config.speech_to_text import SpeechToTextConfig
from vllm.config.multimodal import AudioDummyOptions, BaseDummyOptions
from vllm.config.speech_to_text import SpeechToTextConfig, SpeechToTextParams
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import MultiModalDataDict, PromptType
from vllm.logger import init_logger
@@ -431,6 +430,7 @@ class FireRedLIDDummyInputsBuilder(BaseDummyInputsBuilder[FireRedLIDProcessingIn
audio_len = feature_extractor.chunk_length * sampling_rate
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio")
assert audio_overrides is None or isinstance(audio_overrides, AudioDummyOptions)
return {
"audio": self._get_dummy_audios(
length=audio_len,
@@ -747,13 +747,7 @@ class FireRedLIDForConditionalGeneration(
@classmethod
def get_generation_prompt(
cls,
audio: np.ndarray,
stt_config: SpeechToTextConfig,
model_config: ModelConfig,
language: str | None,
task_type: Literal["transcribe", "translate"],
request_prompt: str,
to_language: str | None,
stt_params: SpeechToTextParams,
) -> PromptType:
"""Build the prompt for the FireRedLID encoder-decoder model.
@@ -764,7 +758,10 @@ class FireRedLIDForConditionalGeneration(
"encoder_prompt": {
"prompt": "",
"multi_modal_data": {
"audio": (audio, int(stt_config.sample_rate)),
"audio": (
stt_params.audio,
int(stt_params.stt_config.sample_rate),
),
},
},
"decoder_prompt": {
vllm/model_executor/models/funasr.py
+8 -5
View File
@@ -14,7 +14,7 @@ from transformers import (
)
from vllm.config import ModelConfig, SpeechToTextConfig, VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.config.multimodal import AudioDummyOptions, BaseDummyOptions
from vllm.config.speech_to_text import SpeechToTextParams
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.inputs import MultiModalDataDict, PromptType
@@ -261,8 +261,8 @@ class SinusoidalPositionEncoder(torch.nn.Module):
def encode(
self,
positions: torch.Tensor = None,
depth: int = None,
positions: torch.Tensor,
depth: int,
dtype: torch.dtype = torch.float32,
):
batch_size = positions.size(0)
@@ -573,8 +573,8 @@ class Transformer(nn.Module):
]
)
def forward(self, hidden_states: torch.Tensor, ilens: int = 0):
max_len = max(ilens)
def forward(self, hidden_states: torch.Tensor, ilens: torch.Tensor):
max_len = ilens.max()
hidden_states = hidden_states[:, :max_len, :]
batch_size, seq_len, dim = hidden_states.size()
chunk_num = (seq_len - 1) // self.k + 1
@@ -682,6 +682,8 @@ class FunASRModel(nn.Module):
self.feat_permute = False
if self.feat_permute:
if not isinstance(speech, torch.Tensor):
raise NotImplementedError("List audio inputs cannot be permuted")
encoder_out, encoder_out_lens = self.encoder.audio_encoder(
speech.permute(0, 2, 1), speech_lengths
)
@@ -739,6 +741,7 @@ class FunASRDummyInputsBuilder(BaseDummyInputsBuilder[FunASRProcessingInfo]):
num_audios = mm_counts.get("audio", 0)
audio_overrides = mm_options.get("audio")
assert audio_overrides is None or isinstance(audio_overrides, AudioDummyOptions)
return {
"audio": self._get_dummy_audios(
vllm/model_executor/models/funaudiochat.py
+3 -1
View File
@@ -26,7 +26,7 @@ from transformers.feature_extraction_utils import BatchFeature
from transformers.modeling_outputs import BaseModelOutput
from vllm.config import VllmConfig
from vllm.config.multimodal import BaseDummyOptions
from vllm.config.multimodal import AudioDummyOptions, BaseDummyOptions
from vllm.inputs import MultiModalDataDict
from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention
from vllm.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
@@ -600,6 +600,7 @@ class FunAudioChatDummyInputsBuilder(
num_audios = int(mm_counts.get("audio", 0))
audio_overrides = mm_options.get("audio")
assert audio_overrides is None or isinstance(audio_overrides, AudioDummyOptions)
return {
"audio": self._get_dummy_audios(
length=audio_len,
@@ -635,6 +636,7 @@ class FunAudioChatMultiModalProcessor(
processed_data = BatchFeature({"input_ids": input_ids})
processed_data.update(passthrough_data)
return processed_data
assert isinstance(audios, Sequence)
feature_extractor = self.info.get_feature_extractor(**hf_processor_mm_kwargs)
sr = int(feature_extractor.sampling_rate)