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83537ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 | """Nemotron-Dense Audex audio-understanding model for HuggingFace inference."""
from __future__ import annotations
from typing import Optional
import torch
from torch import nn
import torch.nn.functional as F
from transformers.cache_utils import DynamicCache
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from .configuration_nemotron_h_audio import NemotronDenseAudexConfig
from .modeling_nemotron_dense import NemotronDenseForCausalLM
class NemotronDenseAudexRMSNorm(nn.Module):
def __init__(self, hidden_size: int, eps: float = 1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.eps = eps
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
input_dtype = hidden_states.dtype
hidden_states = hidden_states.float()
variance = hidden_states.pow(2).mean(dim=-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
return (self.weight.float() * hidden_states).to(input_dtype)
class NemotronDenseAudexProjector(nn.Module):
"""Megatron sound_projection equivalent for TP1 HF inference."""
def __init__(self, config: NemotronDenseAudexConfig):
super().__init__()
self.norm = NemotronDenseAudexRMSNorm(
config.audio_encoder_hidden_size,
eps=config.audio_projector_norm_eps,
)
self.fc1 = nn.Linear(
config.audio_encoder_hidden_size,
config.audio_projector_intermediate_size,
bias=False,
)
self.fc2 = nn.Linear(
config.audio_projector_intermediate_size,
config.hidden_size,
bias=False,
)
self.activation = config.audio_projector_activation
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.norm(hidden_states)
hidden_states = self.fc1(hidden_states)
if self.activation == "relu2":
hidden_states = F.relu(hidden_states).pow(2)
elif self.activation == "gelu":
hidden_states = F.gelu(hidden_states)
else:
raise ValueError(f"Unsupported audio projector activation: {self.activation}")
return self.fc2(hidden_states)
def _build_qwen2_audio_encoder(audio_config: dict) -> nn.Module:
try:
from transformers.models.qwen2_audio.configuration_qwen2_audio import Qwen2AudioEncoderConfig
from transformers.models.qwen2_audio.modeling_qwen2_audio import Qwen2AudioEncoder
except Exception as exc: # pragma: no cover - version/environment guard
raise ImportError(
"Qwen2-Audio support is required for NV-Whisper. "
"Install a transformers build that provides transformers.models.qwen2_audio."
) from exc
cfg = Qwen2AudioEncoderConfig(**dict(audio_config))
return Qwen2AudioEncoder(cfg)
class NemotronDenseAudexForConditionalGeneration(NemotronDenseForCausalLM):
"""Nemotron-Dense CausalLM plus NV-Whisper encoder and sound projection.
State dict layout intentionally keeps the baseline LLM key names:
`model.*` and `lm_head.*` load exactly as in the LLM-only checkpoint.
New audio tensors live under `audio_encoder.*` and `audio_projector.*`.
"""
config_class = NemotronDenseAudexConfig
_tp_plan = None
_base_model_tp_plan = None
base_model_tp_plan = None
def __init__(self, config: NemotronDenseAudexConfig):
super().__init__(config)
self.audio_encoder = _build_qwen2_audio_encoder(config.audio_config)
self.audio_projector = NemotronDenseAudexProjector(config)
def encode_audio(self, input_features: torch.Tensor) -> torch.Tensor:
"""Encode Whisper features into LLM hidden-space audio embeddings."""
encoder_param = next(self.audio_encoder.parameters())
input_features = input_features.to(device=encoder_param.device, dtype=encoder_param.dtype)
encoder_outputs = self.audio_encoder(input_features=input_features, return_dict=True)
audio_hidden = encoder_outputs.last_hidden_state
projector_param = next(self.audio_projector.parameters())
audio_hidden = audio_hidden.to(device=projector_param.device, dtype=projector_param.dtype)
return self.audio_projector(audio_hidden)
def _audio_embeddings_by_sample(
self,
input_features: Optional[torch.Tensor],
audio_embeddings: Optional[torch.Tensor],
batch_size: int,
) -> list[torch.Tensor]:
if audio_embeddings is None:
if input_features is None:
raise ValueError("input_features or audio_embeddings must be provided for audio injection")
if input_features.ndim == 3:
projected = self.encode_audio(input_features)
if batch_size != 1:
raise ValueError(
"3D input_features represent a single sample. "
"Use 4D (batch, clips, mel_bins, frames) features for batched audio."
)
return [projected.reshape(-1, projected.shape[-1])]
if input_features.ndim == 4:
bsz, clips, mel_bins, frames = input_features.shape
if bsz != batch_size:
raise ValueError(f"input_features batch {bsz} != input_ids batch {batch_size}")
flat_features = input_features.reshape(bsz * clips, mel_bins, frames)
projected = self.encode_audio(flat_features)
projected = projected.reshape(bsz, clips * projected.shape[1], projected.shape[-1])
return [projected[idx] for idx in range(bsz)]
raise ValueError(f"Expected 3D or 4D input_features, got {tuple(input_features.shape)}")
if audio_embeddings.ndim == 2:
if batch_size != 1:
raise ValueError("2D audio_embeddings only support batch_size=1")
return [audio_embeddings.to(device=self.device)]
if audio_embeddings.ndim == 3:
if audio_embeddings.shape[0] != batch_size:
raise ValueError(f"audio_embeddings batch {audio_embeddings.shape[0]} != input_ids batch {batch_size}")
return [audio_embeddings[idx].to(device=self.device) for idx in range(batch_size)]
raise ValueError(f"Expected 2D or 3D audio_embeddings, got {tuple(audio_embeddings.shape)}")
def prepare_inputs_embeds(
self,
input_ids: torch.LongTensor,
input_features: Optional[torch.Tensor] = None,
audio_embeddings: Optional[torch.Tensor] = None,
) -> torch.Tensor:
if input_ids is None:
raise ValueError("input_ids are required when injecting audio embeddings")
if self.config.sound_token_id is None:
raise ValueError("config.sound_token_id is required for audio embedding injection")
embed_device = self.model.embed_tokens.weight.device
input_ids = input_ids.to(embed_device)
inputs_embeds = self.model.embed_tokens(input_ids).clone()
audio_by_sample = self._audio_embeddings_by_sample(
input_features=input_features,
audio_embeddings=audio_embeddings,
batch_size=input_ids.shape[0],
)
for batch_idx, audio in enumerate(audio_by_sample):
mask = input_ids[batch_idx].to(self.device) == self.config.sound_token_id
mask = mask.to(embed_device)
expected = int(mask.sum().item())
if expected != audio.shape[0]:
raise ValueError(
"Mismatch between <so_embedding> token count and projected audio tokens: "
f"sample={batch_idx} placeholders={expected} audio_tokens={audio.shape[0]}"
)
inputs_embeds[batch_idx, mask] = audio.to(device=embed_device, dtype=inputs_embeds.dtype)
return inputs_embeds
@staticmethod
def _is_prefill(past_key_values) -> bool:
if past_key_values is None:
return True
get_len = getattr(past_key_values, "get_seq_length", None)
if callable(get_len):
return get_len() == 0
return len(past_key_values) == 0
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
input_features=None,
audio_embeddings=None,
cache_position=None,
position_ids=None,
use_cache=True,
**kwargs,
):
if self._is_prefill(past_key_values) and inputs_embeds is None and (
input_features is not None or audio_embeddings is not None
):
inputs_embeds = self.prepare_inputs_embeds(
input_ids=input_ids,
input_features=input_features,
audio_embeddings=audio_embeddings,
)
return super().prepare_inputs_for_generation(
input_ids=input_ids,
past_key_values=past_key_values,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
cache_position=cache_position,
position_ids=position_ids,
use_cache=use_cache,
**kwargs,
)
def _dense_forward_from_embeds(
self,
inputs_embeds: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values=None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if use_cache and past_key_values is None:
past_key_values = DynamicCache()
hidden_states = inputs_embeds
for decoder_layer in self.model.layers:
hidden_states = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
)
hidden_states = self.model.norm(hidden_states)
if not return_dict:
return tuple(v for v in [hidden_states, past_key_values] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
input_features: Optional[torch.Tensor] = None,
audio_embeddings: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values=None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None,
**kwargs,
):
if inputs_embeds is None and self._is_prefill(past_key_values) and (
input_features is not None or audio_embeddings is not None
):
inputs_embeds = self.prepare_inputs_embeds(
input_ids=input_ids,
input_features=input_features,
audio_embeddings=audio_embeddings,
)
input_ids = None
if inputs_embeds is None:
return super().forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
labels=labels,
use_cache=use_cache,
return_dict=return_dict,
**kwargs,
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self._dense_forward_from_embeds(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=use_cache,
return_dict=return_dict,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
labels = labels.to(logits.device)
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(shift_logits.view(-1, self.config.vocab_size), shift_labels.view(-1))
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
)
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