[Kernel] reloading fused_moe config on the last chunk (vllm-project#6210

)

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -332,6 +332,31 @@ def get_default_config(
     return config
 
 
+def try_get_optimal_moe_config(
+    w1_shape: Tuple[int, ...],
+    w2_shape: Tuple[int, ...],
+    top_k: int,
+    dtype: Optional[str],
+    M: int,
+    override_config: Optional[Dict[str, Any]] = None,
+):
+    if override_config:
+        config = override_config
+    else:
+        # First try to load optimal config from the file
+        E, _, N = w2_shape
+        configs = get_moe_configs(E, N, dtype)
+
+        if configs:
+            # If an optimal configuration map has been found, look up the
+            # optimal config
+            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
+        else:
+            # Else use the default config
+            config = get_default_config(M, E, N, w1_shape[2], top_k, dtype)
+    return config
+
+
 def fused_topk(
     hidden_states: torch.Tensor,
     gating_output: torch.Tensor,
@@ -428,22 +453,16 @@ def fused_experts(hidden_states: torch.Tensor,
     CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
     M = min(num_tokens, CHUNK_SIZE)
 
-    if override_config:
-        config = override_config
-    else:
-        # First try to load optimal config from the file
-        configs = get_moe_configs(E, w2.shape[2],
-                                  "float8" if use_fp8 else None)
+    get_config_func = functools.partial(
+        try_get_optimal_moe_config,
+        w1.shape,
+        w2.shape,
+        topk_ids.shape[1],
+        "float8" if use_fp8 else None,
+        override_config=override_config,
+    )
 
-        if configs:
-            # If an optimal configuration map has been found, look up the
-            # optimal config
-            config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
-        else:
-            # Else use the default config
-            config = get_default_config(M, E, N, w1.shape[2],
-                                        topk_ids.shape[1],
-                                        "float8" if use_fp8 else None)
+    config = get_config_func(M)
 
     intermediate_cache1 = torch.empty((M, topk_ids.shape[1], N),
                                       device=hidden_states.device,
@@ -478,6 +497,8 @@ def fused_experts(hidden_states: torch.Tensor,
             intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
             intermediate_cache2 = intermediate_cache2[:tokens_in_chunk]
             intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
+            # reload config to get better performance on the last chunk
+            config = get_config_func(tokens_in_chunk)
 
         curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
         curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]