from megatron.core.fusions.fused_bias_dropout import get_bias_dropout_add
from megatron.core.tensor_parallel.layers import ColumnParallelLinear, RowParallelLinear
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.core.transformer.spec_utils import ModuleSpec
from megatron.core.transformer.transformer_layer import (
TransformerLayer,
TransformerLayerSubmodules,
)
from megatron.core.models.gpt.gpt_layer_specs import _get_mlp_module_spec
from mindspeed_mm.models.common.module_spec.llava_layer_spec import get_mlp_module_spec
from mindspeed_mm.models.vision.vision_encoders.qwen2vl_vit_model import Qwen2vlVitSelfAttention, Qwen2vlSelfAttention
from mindspeed_mm.patchs.canonical_layer_patch import (
PatchSplitQKVSelfAttention,
_patch_get_mlp_module_spec,
PatchViTSelfAttention,
SplitQKVSelfAttentionSubmodules
)
def get_videoalign_llm_layer_spec(config=None, *args, **kwargs) -> ModuleSpec:
qk_layernorm = False
mlp = _patch_get_mlp_module_spec(use_te=False)
return ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
input_layernorm=TENorm,
self_attention=ModuleSpec(
module=PatchSplitQKVSelfAttention,
params={"attn_mask_type": AttnMaskType.padding_causal},
submodules=SplitQKVSelfAttentionSubmodules(
linear_qkv=ColumnParallelLinear,
q_proj=ColumnParallelLinear,
k_proj=ColumnParallelLinear,
v_proj=ColumnParallelLinear,
core_attention=DotProductAttention,
linear_proj=RowParallelLinear,
q_layernorm=TENorm if qk_layernorm else IdentityOp,
k_layernorm=TENorm if qk_layernorm else IdentityOp,
),
),
self_attn_bda=get_bias_dropout_add,
pre_mlp_layernorm=TENorm,
mlp=mlp,
mlp_bda=get_bias_dropout_add,
sharded_state_dict_keys_map={
'input_layernorm.': 'self_attention.linear_qkv.layer_norm_',
'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_',
},
),
)
def get_videoalign_layer_spec(config=None, is_vit=True, *args, **kwargs) -> ModuleSpec:
attn_mask_type = AttnMaskType.no_mask if is_vit else AttnMaskType.causal
mlp = get_mlp_module_spec(use_te=False)
return ModuleSpec(
module=TransformerLayer,
submodules=TransformerLayerSubmodules(
input_layernorm=TENorm,
self_attention=ModuleSpec(
module=PatchViTSelfAttention,
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,
),
)
