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from typing import Any, Optional
import torch
import torch.nn.functional as F
from torch import nn
from transformers import Qwen2ForCausalLM
from transformers.generation import GenerationMixin
from transformers.modeling_outputs import CausalLMOutputWithPast
from transformers.modeling_utils import PreTrainedModel
from .configuration_arkasr import ArkasrConfig
from .qwen3_asr_audio_config import Qwen3ASRAudioEncoderConfig
from .qwen3_asr_audio_model import Qwen3ASRAudioEncoder
class Qwen3AsrMlpTowerBlock(nn.Module):
def __init__(self, hidden_size: int, intermediate_size: Optional[int] = None, dropout: float = 0.0):
super().__init__()
hidden_size = int(hidden_size)
intermediate_size = int(intermediate_size or hidden_size * 4)
self.norm = nn.LayerNorm(hidden_size)
self.fc1 = nn.Linear(hidden_size, intermediate_size)
self.act = nn.GELU()
self.dropout = nn.Dropout(float(dropout))
self.fc2 = nn.Linear(intermediate_size, hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
residual = hidden_states
hidden_states = self.norm(hidden_states)
hidden_states = self.fc1(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.fc2(hidden_states)
hidden_states = self.dropout(hidden_states)
return residual + hidden_states
class Qwen3AsrMlpTower(nn.Module):
def __init__(
self,
hidden_size: int,
num_layers: int = 4,
intermediate_size: Optional[int] = None,
dropout: float = 0.0,
):
super().__init__()
hidden_size = int(hidden_size)
num_layers = int(num_layers)
intermediate_size = int(intermediate_size or hidden_size * 4)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_layers = num_layers
self.dropout = float(dropout)
self.layers = nn.ModuleList(
[
Qwen3AsrMlpTowerBlock(
hidden_size=hidden_size,
intermediate_size=intermediate_size,
dropout=dropout,
)
for _ in range(num_layers)
]
)
self.final_norm = nn.LayerNorm(hidden_size)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
for layer in self.layers:
hidden_states = layer(hidden_states)
return self.final_norm(hidden_states)
class ArkasrForConditionalGeneration(PreTrainedModel, GenerationMixin):
config_class = ArkasrConfig
base_model_prefix = "language_model"
_no_split_modules = ["Qwen3ASRAudioEncoder"]
_tied_weights_keys = ["language_model.lm_head.weight", "language_model.model.embed_tokens.weight"]
def __init__(self, config: ArkasrConfig):
super().__init__(config)
self.audio_token_id = getattr(config, "audio_token_id", None)
if self.audio_token_id is None:
raise ValueError("`audio_token_id` must be defined in config.")
audio_config = getattr(config, "qwen3_asr_audio_config", None)
if isinstance(audio_config, dict):
audio_config = Qwen3ASRAudioEncoderConfig(**audio_config)
if audio_config is None:
raise ValueError("`qwen3_asr_audio_config` must be defined in config.")
self.language_model = Qwen2ForCausalLM(config)
self.audio_encoder = Qwen3ASRAudioEncoder(audio_config)
audio_dim = int(getattr(audio_config, "output_dim", 0) or 0)
if audio_dim <= 0:
raise ValueError("qwen3_asr_audio_config.output_dim must be positive.")
layers = int(getattr(config, "qwen3_asr_mlp_tower_layers", 4) or 4)
intermediate_size = int(getattr(config, "qwen3_asr_mlp_tower_hidden_size", 0) or audio_dim * 4)
dropout = float(getattr(config, "qwen3_asr_mlp_tower_dropout", 0.0) or 0.0)
self.audio_mlp_tower = Qwen3AsrMlpTower(
hidden_size=audio_dim,
num_layers=layers,
intermediate_size=intermediate_size,
dropout=dropout,
)
self.audio_projector = nn.Sequential(
nn.LayerNorm(audio_dim),
nn.Linear(audio_dim, int(config.hidden_size)),
)
self.all_tied_weights_keys = {}
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
return self.language_model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
return self.language_model.set_output_embeddings(new_embeddings)
def resize_token_embeddings(self, *args, **kwargs):
return self.language_model.resize_token_embeddings(*args, **kwargs)
@staticmethod
def _cache_seq_len(past_key_values) -> int:
if past_key_values is None:
return 0
if hasattr(past_key_values, "get_seq_length"):
try:
return int(past_key_values.get_seq_length())
except Exception:
return 0
try:
return int(past_key_values[0][0].shape[-2])
except Exception:
return 0
def _project_audio_row(
self,
input_features: torch.Tensor,
token_count: int,
dtype: torch.dtype,
feature_length: Optional[int] = None,
) -> torch.Tensor:
token_count = int(token_count)
if token_count <= 0:
return input_features.new_zeros((0, self.get_input_embeddings().embedding_dim), dtype=dtype)
if input_features.dim() == 3 and input_features.size(0) == 1:
input_features = input_features.squeeze(0)
if input_features.dim() != 2:
raise ValueError(f"Expected audio features with shape [mel, frames], got {tuple(input_features.shape)}")
expected_mels = int(getattr(self.audio_encoder.config, "num_mel_bins", input_features.size(0)))
if input_features.size(0) != expected_mels and input_features.size(1) == expected_mels:
input_features = input_features.transpose(0, 1)
if input_features.size(0) != expected_mels:
raise ValueError(f"Audio feature bins mismatch: expected {expected_mels}, got {input_features.size(0)}")
feature_length = int(feature_length) if feature_length is not None else int(input_features.size(1))
feature_length = max(1, min(feature_length, int(input_features.size(1))))
input_features = input_features[:, :feature_length]
encoder_param = next(self.audio_encoder.parameters())
encoded = self.audio_encoder(
input_features.to(device=encoder_param.device, dtype=encoder_param.dtype),
feature_lens=torch.tensor([feature_length], dtype=torch.long, device=encoder_param.device),
)
hidden = getattr(encoded, "last_hidden_state", encoded)
if isinstance(hidden, (tuple, list)):
hidden = hidden[0]
if hidden.dim() == 3 and hidden.size(0) == 1:
hidden = hidden.squeeze(0)
if hidden.dim() != 2:
raise ValueError(f"Expected audio encoder output [time, dim], got {tuple(hidden.shape)}")
tower_param = next(self.audio_mlp_tower.parameters())
hidden = self.audio_mlp_tower(hidden.to(device=tower_param.device, dtype=tower_param.dtype))
projector_param = next(self.audio_projector.parameters())
hidden = hidden.to(device=projector_param.device, dtype=projector_param.dtype)
if int(hidden.size(0)) != token_count:
hidden = F.adaptive_avg_pool1d(
hidden.transpose(0, 1).float().unsqueeze(0),
output_size=token_count,
).squeeze(0).transpose(0, 1).to(dtype=projector_param.dtype)
return self.audio_projector(hidden).to(dtype=dtype)
def _inject_audio_embeddings(
self,
input_ids: torch.Tensor,
input_features: Optional[torch.Tensor] = None,
feature_lens: Optional[torch.Tensor] = None,
audios: Optional[torch.Tensor] = None,
audio_feature_lengths: Optional[torch.Tensor] = None,
) -> torch.Tensor:
input_embeddings = self.get_input_embeddings()(input_ids).clone()
if input_features is None:
input_features = audios
if feature_lens is None:
feature_lens = audio_feature_lengths
if input_features is None:
return input_embeddings
input_features = input_features.to(device=input_embeddings.device)
if input_features.dim() == 4 and input_features.size(1) == 1:
input_features = input_features.squeeze(1)
if feature_lens is not None and not torch.is_tensor(feature_lens):
feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=input_embeddings.device)
if torch.is_tensor(feature_lens):
feature_lens = feature_lens.to(device=input_embeddings.device)
audio_mask = input_ids.eq(self.audio_token_id)
for batch_i in range(int(input_ids.size(0))):
positions = torch.nonzero(audio_mask[batch_i], as_tuple=False).flatten()
if positions.numel() == 0:
continue
feature_length = None
if torch.is_tensor(feature_lens) and batch_i < int(feature_lens.numel()):
feature_length = int(feature_lens[batch_i].item())
projected = self._project_audio_row(
input_features[batch_i],
int(positions.numel()),
input_embeddings.dtype,
feature_length=feature_length,
)
input_embeddings[batch_i, positions, :] = projected.to(device=input_embeddings.device)
return input_embeddings
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
input_features: Optional[torch.Tensor] = None,
feature_lens: Optional[torch.Tensor] = None,
audios: Optional[torch.Tensor] = None,
audio_feature_lengths: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
past_key_values: Optional[Any] = None,
inputs_embeds: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
logits_to_keep: int | torch.Tensor = 0,
**kwargs,
) -> CausalLMOutputWithPast:
if inputs_embeds is None:
if input_ids is None:
raise ValueError("Either `input_ids` or `inputs_embeds` must be provided.")
inputs_embeds = self.language_model.model.embed_tokens(input_ids)
past_len = self._cache_seq_len(past_key_values)
audio_inputs = input_features if input_features is not None else audios
audio_lengths = feature_lens if feature_lens is not None else audio_feature_lengths
if audio_inputs is not None and input_ids is not None and past_len == 0:
inputs_embeds = self._inject_audio_embeddings(
input_ids=input_ids,
input_features=audio_inputs,
feature_lens=audio_lengths,
)
outputs = self.language_model.model(
input_ids=None,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
hidden_states = outputs[0]
if isinstance(logits_to_keep, int) and logits_to_keep > 0:
hidden_for_logits = hidden_states[:, -logits_to_keep:, :]
elif isinstance(logits_to_keep, torch.Tensor):
hidden_for_logits = hidden_states[:, logits_to_keep, :]
else:
hidden_for_logits = hidden_states
logits = self.language_model.lm_head(hidden_for_logits)
loss = None
if labels is not None:
loss = self.language_model.loss_function(
logits=logits,
labels=labels,
vocab_size=self.config.vocab_size,
**kwargs,
)
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
inputs_embeds=None,
**kwargs,
):
past_len = self._cache_seq_len(past_key_values)
if past_len > 0:
input_ids = input_ids[:, -1:]
model_inputs = {
"input_ids": input_ids,
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"input_features": kwargs.get("input_features", kwargs.get("audios", None)),
"feature_lens": kwargs.get("feature_lens", kwargs.get("audio_feature_lengths", None)),
}
if inputs_embeds is not None and past_key_values is None:
model_inputs["inputs_embeds"] = inputs_embeds
del model_inputs["input_ids"]
return model_inputs
__all__ = [
"ArkasrForConditionalGeneration",
"Qwen3AsrMlpTower",
"Qwen3AsrMlpTowerBlock",
]
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