[None][perf] Remove redundant allreduce

tensorrt_llm/_torch/models/modeling_deepseekv4.py

@@ -234,6 +234,18 @@ def _resolve_enable_fused_hc(config: PretrainedConfig) -> bool:
     return bool(getattr(config, "enable_fused_hc", True))
 
 
+def _resolve_skip_premoe_allreduce() -> bool:
+    """Resolve whether to skip the redundant PRE_MOE_FUSION allreduce.
+
+    When enabled (default), the RMSNorm is folded into hc_ffn.fused_hc's
+    epilogue and the allreduce in forward_MoE is skipped — the data is
+    already full after attention's internal o_b_proj allreduce.
+    Set TRTLLM_DSV4_SKIP_PREMOE_ALLREDUCE=0 for ablation (baseline behavior).
+    """
+    env = os.environ.get("TRTLLM_DSV4_SKIP_PREMOE_ALLREDUCE", "1")
+    return env not in ("0", "false", "False")
+
+
 def _copy_deepseek_v4_fused_a_weight_scale(
     module: Linear, fused_a: torch.Tensor, fused_a_scale: torch.Tensor
 ) -> None:
@@ -1860,6 +1872,11 @@ def __init__(
         # layers already take). Env var TRTLLM_MHC_ENABLE_FUSED_HC overrides the
         # config attr (set to "0" to force-disable for validation/rollback).
         self.enable_fused_hc = _resolve_enable_fused_hc(config)
+        # Skip-premoe-allreduce: when fused_hc is active AND PRE_MOE_FUSION is
+        # on, fold post_attention_layernorm into hc_ffn.fused_hc and skip the
+        # redundant allreduce in forward_MoE (the data is already full after
+        # attention's internal o_b_proj allreduce).
+        self.skip_premoe_allreduce = _resolve_skip_premoe_allreduce() and self.enable_fused_hc
         self.next_layer_layernorm: RMSNorm = None
         # Finalized in DeepseekV4ForCausalLM.post_load_weights once the full layer
         # list is visible: a layer may defer its hc_ffn.post_mapping only if
@@ -2000,11 +2017,24 @@ def forward(
         if spec_metadata is not None and spec_metadata.is_layer_capture(self.layer_idx):
             self.fusion_config.POST_MOE_FUSION = False
         if self.enable_fused_hc:
+            # When skip_premoe_allreduce is active, fold post_attention_layernorm
+            # into fused_hc so layer_input emerges already RMSNorm-normalized.
+            # This lets us skip the redundant allreduce+norm in forward_MoE.
+            _norm_w = (
+                self.post_attention_layernorm.weight
+                if self.skip_premoe_allreduce and self.fusion_config.PRE_MOE_FUSION
+                else None
+            )
+            _norm_eps = (
+                self.post_attention_layernorm.variance_epsilon if _norm_w is not None else 0.0
+            )
             residual, post_mix, comb_mix, layer_input = self.hc_ffn.fused_hc(
                 x_prev=x_attn,
                 residual_prev=residual,
                 post_mix_prev=post_mix,
                 comb_mix_prev=comb_mix,
+                norm_weight=_norm_w,
+                norm_eps=_norm_eps,
             )
         else:
             # Break fused_hc into post_mapping and pre_mapping as separate ops.
@@ -2104,18 +2134,24 @@ def _run_MoE(hidden_states, hidden_states_fp4, do_finalize, input_ids):
             )
 
         if self.fusion_config.PRE_MOE_FUSION:
-            # In DeepSeek-V4 the external residual connection is handled by mHC
-            # (hc_ffn.post_mapping), so there is no residual to add here.
-            # Use fused allreduce + RMSNorm (no residual addition).
-            hidden_states = self.allreduce(
-                hidden_states,
-                all_reduce_params=AllReduceParams(
-                    fusion_op=AllReduceFusionOp.RMS_NORM,
-                    norm_weight=self.post_attention_layernorm.weight,
-                    eps=self.post_attention_layernorm.variance_epsilon,
-                    trigger_completion_at_end=False,
-                ),
-            )
+            if self.skip_premoe_allreduce:
+                # Optimization: RMSNorm was already folded into hc_ffn.fused_hc's
+                # epilogue, and the data is already full (attention's o_b_proj
+                # allreduced it). Skip the redundant allreduce+norm entirely.
+                pass
+            else:
+                # Baseline: fused allreduce + RMSNorm (no residual addition).
+                # In DeepSeek-V4 the external residual connection is handled by
+                # mHC (hc_ffn.post_mapping), so there is no residual to add here.
+                hidden_states = self.allreduce(
+                    hidden_states,
+                    all_reduce_params=AllReduceParams(
+                        fusion_op=AllReduceFusionOp.RMS_NORM,
+                        norm_weight=self.post_attention_layernorm.weight,
+                        eps=self.post_attention_layernorm.variance_epsilon,
+                        trigger_completion_at_end=False,
+                    ),
+                )
         else:
             # No fusion: just normalize.
             hidden_states = self.post_attention_layernorm(hidden_states)