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import torch
import torch.nn as nn
from transformers.modeling_outputs import CausalLMOutputWithPast, BaseModelOutputWithPast
from transformers.utils import logging
from transformers.models.qwen3.modeling_qwen3 import Qwen3Model, Qwen3PreTrainedModel, Qwen3DecoderLayer
from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import Qwen3OmniMoeAudioEncoder
from transformers.models.qwen3_omni_moe.configuration_qwen3_omni_moe import Qwen3OmniMoeAudioEncoderConfig
from transformers.models.qwen3.configuration_qwen3 import Qwen3Config
from transformers.utils.auto_docstring import auto_docstring
from transformers.modeling_utils import PreTrainedModel
from transformers.generation.utils import GenerationMixin
class MusciConfig(Qwen3Config):
model_type = "musci"
is_composition = True
# Make the architecture discoverable by Megatron-Bridge's AutoBridge
# when loading configs from disk.
architectures = ["MusciForCausalLM"]
def __init__(
self,
audio_config=None,
language_config=None,
adapter_hidden_size=8192,
ignore_index=-100,
**kwargs
):
num_hidden_layers = None
if language_config is not None:
if isinstance(language_config, dict):
num_hidden_layers = language_config.get("num_hidden_layers", None)
elif isinstance(language_config, Qwen3Config):
num_hidden_layers = language_config.num_hidden_layers
if num_hidden_layers is not None:
kwargs.update({"num_hidden_layers": num_hidden_layers})
# Initialize parent Qwen3Config with kwargs to handle standard config params
super().__init__(**kwargs)
if isinstance(audio_config, dict):
audio_config = Qwen3OmniMoeAudioEncoderConfig(**audio_config)
if isinstance(audio_config, Qwen3OmniMoeAudioEncoderConfig):
audio_config = audio_config
elif audio_config is None:
audio_config = Qwen3OmniMoeAudioEncoderConfig()
if isinstance(language_config, dict):
language_config = Qwen3Config(**language_config)
elif isinstance(language_config, Qwen3Config):
language_config = language_config
elif language_config is None:
language_config = Qwen3Config()
self.audio_config = audio_config
self.language_config = language_config
self.adapter_hidden_size = adapter_hidden_size
self.ignore_index = ignore_index
self.dtype = language_config.dtype
def to_dict(self):
output = super().to_dict()
if self.audio_config is not None:
if hasattr(self.audio_config, "to_dict"):
output["audio_config"] = self.audio_config.to_dict()
else:
output["audio_config"] = self.audio_config
if self.language_config is not None:
if hasattr(self.language_config, "to_dict"):
output["language_config"] = self.language_config.to_dict()
else:
output["language_config"] = self.language_config
return output
class MusciGatedMLP(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super().__init__()
self.gate_proj = nn.Linear(input_size, hidden_size, bias=False)
self.up_proj = nn.Linear(input_size, hidden_size, bias=False)
self.down_proj = nn.Linear(hidden_size, output_size, bias=False)
self.act_fn = nn.SiLU()
def forward(self, x):
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
@auto_docstring
class MusciPreTrainedModel(PreTrainedModel):
config: MusciConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["Qwen3DecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn = True
_supports_sdpa = True
_supports_flex_attn = True
_can_compile_fullgraph = False
_supports_attention_backend = True
_can_record_outputs = {
"hidden_states": Qwen3DecoderLayer,
}
class MusciModel(MusciPreTrainedModel):
config_class = MusciConfig
def __init__(self, config: MusciConfig):
super().__init__(config)
self.audio_model = Qwen3OmniMoeAudioEncoder(config.audio_config)
self.language_model = Qwen3Model(config.language_config)
self.audio_adapter = MusciGatedMLP(
input_size=config.audio_config.output_dim,
hidden_size=config.adapter_hidden_size,
output_size=config.language_config.hidden_size
)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
def set_input_embeddings(self, value):
self.language_model.set_input_embeddings(value)
def get_audio_features(self, input_features, feature_lens):
audio_outputs = self.audio_model(
input_features=input_features,
feature_lens=feature_lens,
)
return audio_outputs.last_hidden_state
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
audio_data: Optional[torch.FloatTensor] = None,
audio_data_seqlens: Optional[torch.Tensor] = None,
audio_input_mask: Optional[torch.Tensor] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# 1. Get text embeddings
if inputs_embeds is None:
inputs_embeds = self.get_input_embeddings()(input_ids)
# 2. Process audio and merge embeddings if audio is present
if audio_data is not None:
# [B, Audio_Len, D]
audio_embeds = self.get_audio_features(audio_data, audio_data_seqlens)
audio_embeds = self.audio_adapter(audio_embeds)
# audio_input_mask: [B, L] -> [B, L, 1] -> [B, L, D]
# D elements will be replaced by audio embeddings
mask_expanded = audio_input_mask.unsqueeze(-1).expand_as(inputs_embeds)
inputs_embeds.masked_scatter_(mask_expanded, audio_embeds)
# 3. Forward pass through language model
return self.language_model(
input_ids=None, # We pass inputs_embeds
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,
return_dict=return_dict,
cache_position=cache_position,
)
class MusciForCausalLM(MusciPreTrainedModel, GenerationMixin):
config_class = MusciConfig
_tied_weights_keys = ["lm_head.weight"]
_keys_to_ignore_on_save = ["lm_head.weight"]
def __init__(self, config: MusciConfig):
super().__init__(config)
self.model = MusciModel(config)
self.vocab_size = config.language_config.vocab_size
self.lm_head = nn.Linear(config.language_config.hidden_size, self.vocab_size, bias=False)
# Initialize weights and apply final processing
self.post_init()
def tie_weights(self):
super().tie_weights()
# tie lm_head to input embeddings
self.lm_head.weight = self.model.language_model.embed_tokens.weight
def get_input_embeddings(self):
return self.model.get_input_embeddings()
def set_input_embeddings(self, value):
self.model.set_input_embeddings(value)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
audio_data: Optional[torch.FloatTensor] = None,
audio_data_seqlens: Optional[torch.Tensor] = None,
audio_input_mask: Optional[torch.Tensor] = None,
cache_position: Optional[torch.LongTensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model(
input_ids=input_ids,
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,
return_dict=return_dict,
audio_data=audio_data,
audio_data_seqlens=audio_data_seqlens,
audio_input_mask=audio_input_mask,
cache_position=cache_position,
)
hidden_states = outputs[0]
logits = self.lm_head(hidden_states)
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.ignore_index)
shift_logits = shift_logits.view(-1, self.config.language_config.vocab_size)
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
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,
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,
cache_position=None,
**kwargs
):
# decoding step (KV cache present) keeps only the last token and drops audio inputs;
# prefill step pulls audio inputs from kwargs.
position_ids = kwargs.get("position_ids", None)
if cache_position is not None and cache_position[0] > 0:
input_ids = input_ids[:, -1:]
if position_ids is not None:
position_ids = position_ids[:, -1:]
audio_data = None
audio_input_mask = None
audio_data_seqlens = None
else:
audio_data = kwargs.get("audio_data", None)
audio_input_mask = kwargs.get("audio_input_mask", None)
audio_data_seqlens = kwargs.get("audio_data_seqlens", None)
# prefer inputs_embeds at the first step when present
if inputs_embeds is not None and past_key_values is None:
model_inputs = {"inputs_embeds": inputs_embeds}
else:
model_inputs = {"input_ids": input_ids}
model_inputs.update({
"past_key_values": past_key_values,
"use_cache": kwargs.get("use_cache"),
"attention_mask": attention_mask,
"position_ids": position_ids,
"audio_data": audio_data,
"audio_input_mask": audio_input_mask,
"audio_data_seqlens": audio_data_seqlens,
})
return model_inputs
__all__ = [
"MusciConfig",
"MusciModel",
"MusciForCausalLM",
]
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