biondizzle/vllm
1
0
Fork 0
Code Issues Pull Requests Actions 2 Packages Projects Releases Wiki Activity

[Chore] Remove debug code in model implementation (#35883)

Browse Source 
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
This commit is contained in:
Isotr0py
2026-03-04 11:50:58 +08:00
committed by GitHub
parent c1d963403c
commit 6e9f21e8a2
2 changed files with 0 additions and 178 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 from __future__ import annotations
import os
from collections.abc import Iterable, Mapping, Sequence from collections.abc import Iterable, Mapping, Sequence
from functools import cached_property from functools import cached_property
from typing import Any from typing import Any
@@ -924,53 +923,6 @@ class FunAudioChatForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
f"sequence of Tensors (got {type(speech_attention_mask)})" 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) group_size = int(self.audio_tower.group_size)
speech_maxlen = int(speech_ids.shape[-1]) speech_maxlen = int(speech_ids.shape[-1])
@@ -1019,38 +971,6 @@ class FunAudioChatForConditionalGeneration(nn.Module, SupportsMultiModal, Suppor
embeds = tuple( embeds = tuple(
audio_features[i, : int(length)] for i, length in enumerate(lengths) 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 return embeds
def forward( def forward(

98
vllm/model_executor/models/nano_nemotron_vl.py
View File

@@ -2225,104 +2225,6 @@ class NemotronH_Nano_VL_V2(
assert len(sound_weights) > 0 assert len(sound_weights) > 0
self.sound_encoder.load_weights(sound_weights) self.sound_encoder.load_weights(sound_weights)
def print_architecture(self, detailed: bool = True, save_to_file: str = None):
"""
Print model architecture with parameter names, shapes, and sizes.
Args:
detailed: If True, show detailed parameter breakdown
save_to_file: If provided, save output to this file path
"""
import sys
from io import StringIO
# Capture output if saving to file
original_stdout = sys.stdout
if save_to_file:
sys.stdout = StringIO()
try:
print("=" * 100)
print("NemotronH_Nano_VL_V2 Model Architecture")
print("=" * 100)
total_params = 0
param_groups = {
"language_model": [],
"vision_model": [],
"mlp1": [],
"other": [],
}
for name, param in self.named_parameters():
param_size = param.numel()
total_params += param_size
# Group parameters by main component
if name.startswith("language_model"):
param_groups["language_model"].append(
(name, param.shape, param_size, param.dtype)
)
elif name.startswith("vision_model"):
param_groups["vision_model"].append(
(name, param.shape, param_size, param.dtype)
)
elif name.startswith("mlp1"):
param_groups["mlp1"].append(
(name, param.shape, param_size, param.dtype)
)
else:
param_groups["other"].append(
(name, param.shape, param_size, param.dtype)
)
if detailed:
print(
f"{name:<70} | Shape: {str(param.shape):<25} | "
f"Size: {param_size:>12,} | Dtype: {param.dtype}"
)
print("=" * 100)
print("Summary by Component:")
print("-" * 60)
for component, params in param_groups.items():
if params: # Only show components that have parameters
component_total = sum(size for _, _, size, _ in params)
percentage = (
(component_total / total_params) * 100
if total_params > 0
else 0
)
print(
f"{component:<20} | Parameters: {len(params):>4} | "
f"Total Size: {component_total:>15,} | "
f"{percentage:>6.2f}%"
)
print("-" * 60)
print(f"{'Total Parameters':<20} | {total_params:>15,}")
# Estimate memory usage (assuming bfloat16 = 2 bytes per parameter)
memory_mb = total_params * 2 / (1024**2)
memory_gb = memory_mb / 1024
print(f"{'Est. Memory (MB)':<20} | {memory_mb:>15.2f}")
print(f"{'Est. Memory (GB)':<20} | {memory_gb:>15.2f}")
print("=" * 100)
# Save to file if requested
if save_to_file:
output = sys.stdout.getvalue()
sys.stdout = original_stdout
with open(save_to_file, "w") as f:
f.write(output)
print(f"Architecture saved to: {save_to_file}")
print(output) # Also print to console
finally:
if save_to_file and sys.stdout != original_stdout:
sys.stdout = original_stdout
def get_vit_model_from_radio_config(self, hf_config): def get_vit_model_from_radio_config(self, hf_config):
hf_config_vision = hf_config.vision_config hf_config_vision = hf_config.vision_config
model_name = hf_config_vision.args.get("model") model_name = hf_config_vision.args.get("model")