[Kernel] reloading fused_moe config on the last chunk (#6210)
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@ -332,6 +332,31 @@ def get_default_config(
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return config
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def try_get_optimal_moe_config(
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w1_shape: Tuple[int, ...],
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w2_shape: Tuple[int, ...],
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top_k: int,
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dtype: Optional[str],
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M: int,
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override_config: Optional[Dict[str, Any]] = None,
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):
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if override_config:
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config = override_config
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else:
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# First try to load optimal config from the file
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E, _, N = w2_shape
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configs = get_moe_configs(E, N, dtype)
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if configs:
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# If an optimal configuration map has been found, look up the
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# optimal config
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config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
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else:
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# Else use the default config
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config = get_default_config(M, E, N, w1_shape[2], top_k, dtype)
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return config
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def fused_topk(
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hidden_states: torch.Tensor,
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gating_output: torch.Tensor,
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@ -428,22 +453,16 @@ def fused_experts(hidden_states: torch.Tensor,
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CHUNK_SIZE = envs.VLLM_FUSED_MOE_CHUNK_SIZE
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M = min(num_tokens, CHUNK_SIZE)
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if override_config:
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config = override_config
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else:
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# First try to load optimal config from the file
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configs = get_moe_configs(E, w2.shape[2],
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"float8" if use_fp8 else None)
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get_config_func = functools.partial(
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try_get_optimal_moe_config,
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w1.shape,
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w2.shape,
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topk_ids.shape[1],
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"float8" if use_fp8 else None,
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override_config=override_config,
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)
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if configs:
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# If an optimal configuration map has been found, look up the
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# optimal config
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config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
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else:
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# Else use the default config
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config = get_default_config(M, E, N, w1.shape[2],
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topk_ids.shape[1],
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"float8" if use_fp8 else None)
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config = get_config_func(M)
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intermediate_cache1 = torch.empty((M, topk_ids.shape[1], N),
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device=hidden_states.device,
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@ -478,6 +497,8 @@ def fused_experts(hidden_states: torch.Tensor,
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intermediate_cache1 = intermediate_cache1[:tokens_in_chunk]
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intermediate_cache2 = intermediate_cache2[:tokens_in_chunk]
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intermediate_cache3 = intermediate_cache3[:tokens_in_chunk]
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# reload config to get better performance on the last chunk
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config = get_config_func(tokens_in_chunk)
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curr_topk_ids = topk_ids[begin_chunk_idx:end_chunk_idx]
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curr_topk_weights = topk_weights[begin_chunk_idx:end_chunk_idx]
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