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[minor] Refactor TE fused-norm handling in GPTModelExporter #1061

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[minor] Refactor TE fused-norm handling in GPTModelExporter #1061

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38 changes: 20 additions & 18 deletions modelopt/torch/export/unified_export_megatron.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,16 +419,25 @@ def _get_state_dict(self):
if hasattr(model, "output_layer") and not model.share_embeddings_and_output_weights:
self.rules["output_layer"](model.output_layer)

def _get_fused_norm_weight(self, module):
"""Return ``module.layer_norm_weight`` when TE fuses the norm into a linear layer.

Returns ``None`` when the ``"fused_norm"`` rule is absent or the module has no
``layer_norm_weight`` attribute (or its value is ``None``).
"""
if "fused_norm" not in self.rules:
return None
return getattr(module, "layer_norm_weight", None)

def _get_transformer_layer_state_dict(self, layer, layer_id):
if not isinstance(layer.input_layernorm, IdentityOp):
self.rules["input_layernorm"](layer.input_layernorm, layer_id)
elif (
hasattr(layer.self_attention, "linear_qkv")
and hasattr(layer.self_attention.linear_qkv, "layer_norm_weight")
and layer.self_attention.linear_qkv.layer_norm_weight is not None
and "fused_norm" in self.rules
):
self.rules["fused_norm"](layer.self_attention.linear_qkv.layer_norm_weight, layer_id)
norm_weight := self._get_fused_norm_weight(
getattr(layer.self_attention, "linear_qkv", None)
)
) is not None:
self.rules["fused_norm"](norm_weight, layer_id)

if not isinstance(layer.self_attention, IdentityOp):
if "MLASelfAttention" in str(type(layer.self_attention)):
Expand Down Expand Up @@ -470,12 +479,10 @@ def _get_transformer_layer_state_dict(self, layer, layer_id):
elif (
not isinstance(layer.mlp, IdentityOp)
and "MoE" not in str(type(layer.mlp))
and hasattr(layer.mlp, "linear_fc1")
and hasattr(layer.mlp.linear_fc1, "layer_norm_weight")
and layer.mlp.linear_fc1.layer_norm_weight is not None
and "fused_norm" in self.rules
and (norm_weight := self._get_fused_norm_weight(getattr(layer.mlp, "linear_fc1", None)))
is not None
):
self.rules["fused_norm"](layer.mlp.linear_fc1.layer_norm_weight, layer_id)
self.rules["fused_norm"](norm_weight, layer_id)

if not isinstance(layer.mlp, IdentityOp):
if "MoE" in str(type(layer.mlp)):
Expand Down Expand Up @@ -555,14 +562,9 @@ def _get_mtp_state_dict(self) -> dict[str, torch.Tensor]:
def _get_mamba_layer_state_dict(self, layer, layer_id):
if not isinstance(layer.norm, IdentityOp):
self.rules["norm"](layer.norm, layer_id)
elif (
isinstance(layer.norm, IdentityOp)
and hasattr(layer.mixer.in_proj, "layer_norm_weight")
and layer.mixer.in_proj.layer_norm_weight is not None
and "fused_norm" in self.rules
):
elif (norm_weight := self._get_fused_norm_weight(layer.mixer.in_proj)) is not None:
# TE spec: norm is fused into in_proj (QuantTELayerNormColumnParallelLinear).
self.rules["fused_norm"](layer.mixer.in_proj.layer_norm_weight, layer_id)
self.rules["fused_norm"](norm_weight, layer_id)

self.rules["mixer_norm"](layer.mixer.norm, layer_id)
self.rules["A_log"](layer.mixer.A_log, layer_id)
Expand Down
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