Fuse MLA DOWN projection GEMMs

gpt_builders.py

@@ -115,43 +115,42 @@ def gpt_builder(args, pre_process, post_process, vp_stage=None, config=None, pg_
 
 def _get_transformer_layer_spec(use_te, config):
     """Get transformer layer specification based on configuration.
-    
+
     Args:
         use_te (bool): Whether to use Transformer Engine
-        args: Training arguments
         config: Model configuration
-        
+
     Returns:
         transformer_layer_spec: The transformer layer specification
     """
-    args = get_args()
     if use_te:
         return get_gpt_layer_with_transformer_engine_spec(
-            args.num_experts,
-            args.moe_grouped_gemm,
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            args.experimental_attention_variant,
-            qk_l2_norm=args.qk_l2_norm,
+            config.num_moe_experts,
+            config.moe_grouped_gemm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            config.experimental_attention_variant,
+            qk_l2_norm=config.qk_l2_norm,
             use_kitchen=config.use_kitchen,
             use_te_activation_func=config.use_te_activation_func,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
         )
     elif config.transformer_impl == "inference_optimized":
         return get_gpt_layer_with_inference_spec(
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            qk_l2_norm=args.qk_l2_norm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            qk_l2_norm=config.qk_l2_norm,
         )
     else:
         return get_gpt_layer_local_spec(
-            args.num_experts,
-            args.moe_grouped_gemm,
-            args.qk_layernorm,
-            args.multi_latent_attention,
-            args.experimental_attention_variant,
-            normalization=args.normalization,
+            config.num_moe_experts,
+            config.moe_grouped_gemm,
+            config.qk_layernorm,
+            config.multi_latent_attention,
+            config.experimental_attention_variant,
+            normalization=config.normalization,
             use_kitchen=config.use_kitchen,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,

megatron/core/models/gpt/experimental_attention_variant_module_specs.py

@@ -397,6 +397,7 @@ def _get_self_attention_module_spec(
         use_te_activation_func=config.use_te_activation_func,
         use_kitchen_attention=config.use_kitchen_attention,
         kitchen_attention_backend=config.kitchen_attention_backend,
+        mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
     )
     attn_spec = layer_spec.submodules.self_attention
     if config.multi_latent_attention:

megatron/core/models/gpt/gpt_layer_specs.py

@@ -14,6 +14,7 @@
 from megatron.core.transformer.identity_op import IdentityOp
 from megatron.core.transformer.mlp import MLP, MLPSubmodules
 from megatron.core.transformer.multi_latent_attention import (
+    FusedMLASelfAttention,
     MLASelfAttention,
     MLASelfAttentionSubmodules,
 )
@@ -184,6 +185,7 @@ def get_gpt_layer_with_transformer_engine_submodules(
     use_te_activation_func: bool = False,
     use_kitchen_attention: bool = False,
     kitchen_attention_backend: str = "sdpa",
+    mla_down_proj_fusion: bool = False,
 ) -> TransformerLayerSubmodules:
     """Use these submodules to use lower-level Transformer Engine modules (required for fp8
     training).
@@ -198,6 +200,9 @@ def get_gpt_layer_with_transformer_engine_submodules(
         qk_l2_norm (bool, optional): To use l2 norm for queries/keys. Defaults to False.
         use_te_op_fuser (bool, optional): Use Transformer Engine's operation-based API, which may
                                           enable certain operation fusions. Defaults to False.
+        mla_down_proj_fusion (bool, optional): Enable fused q/kv down-projection and fused input
+                                               layernorm when backend supports. Otherwise fall back
+                                               to the unfused MLA.
 
     Returns:
         TransformerLayerSubmodules: TE modules to construct a TransformerLayer
@@ -243,6 +248,45 @@ def get_gpt_layer_with_transformer_engine_submodules(
             if qk_layernorm
             else backend.column_parallel_linear()
         )
+
+        if mla_down_proj_fusion:
+            fuse_input_layernorm = backend.column_parallel_layer_norm_linear() is not None
+            input_layernorm = IdentityOp if fuse_input_layernorm else backend.layer_norm()
+            down_proj_linear = (
+                backend.column_parallel_layer_norm_linear()
+                if fuse_input_layernorm
+                else backend.linear()
+            )
+            return TransformerLayerSubmodules(
+                input_layernorm=input_layernorm,
+                self_attention=ModuleSpec(
+                    module=FusedMLASelfAttention,
+                    params={"attn_mask_type": AttnMaskType.causal},
+                    submodules=MLASelfAttentionSubmodules(
+                        linear_q_proj=backend.column_parallel_linear(),
+                        linear_qkv_down_proj=down_proj_linear,
+                        linear_q_up_proj=linear_q_up_proj,
+                        linear_kv_up_proj=linear_kv_up_proj,
+                        core_attention=backend.core_attention(),
+                        linear_proj=backend.row_parallel_linear(),
+                        q_layernorm=IdentityOp,
+                        kv_layernorm=IdentityOp,
+                    ),
+                ),
+                self_attn_bda=get_bias_dropout_add,
+                pre_mlp_layernorm=backend.layer_norm() if num_experts else IdentityOp,
+                mlp=mlp,
+                mlp_bda=get_bias_dropout_add,
+                sharded_state_dict_keys_map=(
+                    {
+                        "self_attention.linear_q_down_proj.layer_norm_": "input_layernorm.",
+                        "self_attention.linear_kv_down_proj.layer_norm_": "input_layernorm.",
+                        "self_attention.linear_qkv_down_proj.layer_norm_": "input_layernorm.",
+                    }
+                    if fuse_input_layernorm
+                    else {}
+                ),
+            )
         return TransformerLayerSubmodules(
             input_layernorm=backend.layer_norm(has_residual=True),
             self_attention=ModuleSpec(
@@ -526,6 +570,7 @@ def get_gpt_decoder_layer_specs(
             use_te_activation_func=config.use_te_activation_func,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
         )
         moe_layer_spec = get_gpt_layer_with_transformer_engine_spec(
             num_experts=config.num_moe_experts,
@@ -537,6 +582,7 @@ def get_gpt_decoder_layer_specs(
             use_te_activation_func=config.use_te_activation_func,
             use_kitchen_attention=config.use_kitchen_attention,
             kitchen_attention_backend=config.kitchen_attention_backend,
+            mla_down_proj_fusion=getattr(config, "mla_down_proj_fusion", False),
         )
     elif config.transformer_impl == "inference_optimized":
         layer_norm_impl = TENorm