from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.tensor_parallel import ColumnParallelLinear, RowParallelLinear
from megatron.core.transformer import ModuleSpec, TransformerLayer, TransformerLayerSubmodules
from megatron.core.transformer.attention import SelfAttentionSubmodules
from megatron.core.extensions.transformer_engine import TENorm
from megatron.core.transformer.dot_product_attention import DotProductAttention
from megatron.core.transformer.enums import AttnMaskType
from megatron.core.transformer.identity_op import IdentityOp
from megatron.training import get_args
from mindspeed_mm.models.audio.omni_audio_encoder import QwenOmniAudioSelfAttention
from mindspeed_mm.models.common.module_spec.llava_layer_spec import get_mlp_module_spec
def get_qwen_omni_audio_layer_spec(config=None, is_vit=True, *args, **kwargs) -> ModuleSpec:
attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.causal
if get_args().hetero_parallel:
from mindspeed_mm.utils.hetero_utils.hetero_CP_utils import get_hetero_dotproductattention
DOTPRODUCTATTENTION = get_hetero_dotproductattention(config)
else:
DOTPRODUCTATTENTION = DotProductAttention
mlp = get_mlp_module_spec(use_te=False)
return ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
input_layernorm=TENorm,
self_attention=ModuleSpec(
module=QwenOmniAudioSelfAttention,
params={
"attn_mask_type": attn_mask_type
},
submodules=SelfAttentionSubmodules(
linear_qkv=ColumnParallelLinear,
core_attention=DOTPRODUCTATTENTION,
linear_proj=RowParallelLinear,
q_layernorm=IdentityOp,
k_layernorm=IdentityOp,
),
),
self_attn_bda=get_bias_dropout_add,
pre_mlp_layernorm=TENorm,
mlp=mlp,
mlp_bda=get_bias_dropout_add,
),
)
