ZIBEE/bi_150-vllm
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

Upgrade to vllm 0.17.0 corex v4.1 overlay

Browse Source
This commit is contained in:
LZiBee
2026-04-29 19:38:22 +08:00
parent 8fac6062e4
commit 938d0854a5
430 changed files with 35969 additions and 14511 deletions
Download Patch File Download Diff File Expand all files Collapse all files

80
vllm/model_executor/models/funaudiochat.py
View File

@@ -13,7 +13,6 @@ positions via `inputs_embeds`, while `position_ids` (RoPE) remains standard 1D.
from __future__ import annotations
import os
from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property
from typing import Any
@@ -924,53 +923,6 @@ class FunAudioChatForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
f"sequence of Tensors (got {type(speech_attention_mask)})"
)
debug = os.getenv("VLLM_FUN_AUDIOCHAT_DEBUG", "") == "1"
if debug:
print(
f"[FunAudioChat] embed_multimodal speech_ids={tuple(speech_ids.shape)} "
f"speech_attention_mask={tuple(speech_attention_mask.shape)}",
flush=True,
)
attn_impl = getattr(
self.continuous_audio_tower.config, "_attn_implementation", None
)
print(
f"[FunAudioChat] audio_attn_impl={attn_impl}",
flush=True,
)
if hasattr(self.continuous_audio_tower, "conv1"):
conv1_w = self.continuous_audio_tower.conv1.weight
print(
f"[FunAudioChat] conv1_w_norm={float(conv1_w.norm().item()):.6g}",
flush=True,
)
try:
attn0 = self.continuous_audio_tower.layers[0].self_attn
q_norm = float(attn0.q_proj.weight.norm().item())
k_norm = float(attn0.k_proj.weight.norm().item())
v_norm = float(attn0.v_proj.weight.norm().item())
o_norm = float(attn0.out_proj.weight.norm().item())
print(
f"[FunAudioChat] attn0_q_norm={q_norm:.6g} "
f"k_norm={k_norm:.6g} "
f"v_norm={v_norm:.6g} "
f"o_norm={o_norm:.6g}",
flush=True,
)
except Exception:
pass
if isinstance(input_features, torch.Tensor):
print(
f"[FunAudioChat] input_features={tuple(input_features.shape)}",
flush=True,
)
if isinstance(feature_attention_mask, torch.Tensor):
print(
"[FunAudioChat] feature_attention_mask="
f"{tuple(feature_attention_mask.shape)}",
flush=True,
)
group_size = int(self.audio_tower.group_size)
speech_maxlen = int(speech_ids.shape[-1])
@@ -1019,38 +971,6 @@ class FunAudioChatForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
embeds = tuple(
audio_features[i, : int(length)] for i, length in enumerate(lengths)
)
if debug:
embed_lens = [int(t.shape[0]) for t in embeds]
print(f"[FunAudioChat] embed_multimodal out_lens={embed_lens}", flush=True)
if embeds:
t0 = embeds[0]
print(
f"[FunAudioChat] embed0 dtype={t0.dtype} device={t0.device} "
f"nan={bool(torch.isnan(t0).any())} "
f"norm={float(t0.norm().item()):.6g}",
flush=True,
)
dump_path = os.getenv("VLLM_FUN_AUDIOCHAT_DUMP_PATH", "")
if (
dump_path
and speech_ids.shape[0] == 1
and len(embeds) == 1
and embed_lens[0] > 10
):
if not os.path.exists(dump_path):
np.save(dump_path, embeds[0].detach().float().cpu().numpy())
print(f"[FunAudioChat] dumped embeds to {dump_path}", flush=True)
cont_path = dump_path.replace(".npy", "_cont.npy")
if continuous_audio_features is not None and not os.path.exists(
cont_path
):
np.save(
cont_path,
continuous_audio_features.detach().float().cpu().numpy(),
)
print(
f"[FunAudioChat] dumped continuous to {cont_path}", flush=True
)
return embeds
def forward(