extend get_gpt_layer_modelopt_spec to support MoE

Signed-off-by: Alexandros Koumparoulis <akoumparouli@nvidia.com>
NVIDIA · Jun 25, 2024 · b9b309f · b9b309f
1 parent 26aef8e
commit b9b309f
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Showing 2 changed files with 31 additions and 10 deletions.
diff --git a/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py b/nemo/collections/nlp/models/language_modeling/megatron/gpt_layer_modelopt_spec.py
@@ -21,6 +21,7 @@
     from megatron.core.transformer.enums import AttnMaskType
     from megatron.core.transformer.identity_op import IdentityOp
     from megatron.core.transformer.mlp import MLP, MLPSubmodules
+    from megatron.core.transformer.moe.moe_layer import MoELayer
     from megatron.core.transformer.spec_utils import ModuleSpec
     from megatron.core.transformer.transformer_layer import TransformerLayer, TransformerLayerSubmodules
 
@@ -38,7 +39,7 @@
 
 
 # Use this spec for Model Optimizer PTQ and TensorRT-LLM export
-def get_gpt_layer_modelopt_spec() -> ModuleSpec:
+def get_gpt_layer_modelopt_spec(num_experts: int = None) -> ModuleSpec:
     """Mix the native spec with TENorm.
 
     This is essentially the native local spec except for the layernorm implementation
@@ -65,18 +66,38 @@ def get_gpt_layer_modelopt_spec() -> ModuleSpec:
             ),
             self_attn_bda=get_bias_dropout_add,
             pre_mlp_layernorm=TENorm,
-            mlp=ModuleSpec(
-                module=MLP,
-                submodules=MLPSubmodules(
-                    linear_fc1=ColumnParallelLinear,
-                    linear_fc2=RowParallelLinear,
-                ),
-            ),
+            mlp=_get_mlp_module_spec(num_experts=num_experts),
             mlp_bda=get_bias_dropout_add,
             # Map TE-layernorm-fusion keys back
             sharded_state_dict_keys_map={
                 'input_layernorm.': 'self_attention.linear_qkv.layer_norm_',
-                'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_',
+                **({'pre_mlp_layernorm.': 'mlp.linear_fc1.layer_norm_'} if num_experts is None else {}),
             },
         ),
     )
+
+
+# Helper function to get module spec for MLP/MoE
+def _get_mlp_module_spec(num_experts: int = None, moe_grouped_gemm: bool = False) -> ModuleSpec:
+    if num_experts is None:
+        # Dense MLP w/ or w/o TE modules.
+        return ModuleSpec(
+            module=MLP,
+            submodules=MLPSubmodules(
+                linear_fc1=ColumnParallelLinear,
+                linear_fc2=RowParallelLinear,
+            ),
+        )
+    else:
+        # Mixture of experts with modules in megatron core.
+        return ModuleSpec(
+            module=MoELayer,
+            submodules=(
+                MLPSubmodules(
+                    linear_fc1=ColumnParallelLinear,
+                    linear_fc2=RowParallelLinear,
+                )
+                if not moe_grouped_gemm
+                else None
+            ),
+        )
diff --git a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
@@ -155,7 +155,7 @@ def get_specs(spec_name, num_experts=None, moe_grouped_gemm=False, use_te=True,
         "te_gpt": get_gpt_layer_with_transformer_engine_spec(num_experts, moe_grouped_gemm),
         "megatron_falcon_gpt": get_falcon_layer_spec(),
         "megatron_gpt_full_te_layer_autocast": get_gpt_full_te_layer_autocast_spec(),
-        "modelopt": get_gpt_layer_modelopt_spec(),
+        "modelopt": get_gpt_layer_modelopt_spec(num_experts),
         "te_gpt_hyena": get_gpt_layer_with_te_and_hyena_spec(hyena_cfg),
     }
     if spec_name not in name_spec_dict: