fix kernelcofings.

lightllm/common/basemodel/triton_kernel/fused_moe/grouped_fused_moe.py

@@ -25,7 +25,6 @@
 from lightllm.utils.log_utils import init_logger
 from lightllm.utils.vllm_utils import vllm_ops
 from lightllm.utils.device_utils import triton_support_tensor_descriptor
-from .moe_kernel_configs import MoeGroupedGemmKernelConfig
 from .moe_silu_and_mul import silu_and_mul_fwd
 from .moe_sum_reduce import moe_sum_reduce
 from lightllm.common.basemodel.triton_kernel.quantization.fp8act_quant_kernel import per_token_group_quant_fp8
@@ -726,16 +725,26 @@ def grouped_matmul(
             block_size_k = expert_weights.shape[2] // expert_to_weights_scale.shape[2]
 
     if run_config is None:
-        run_config = MoeGroupedGemmKernelConfig.try_to_get_best_config(
-            M=token_inputs.shape[0],
-            N=n,
-            K=k,
-            topk_num=topk_num,
-            expert_num=expert_num,
-            mul_routed_weight=mul_routed_weight,
-            use_fp8_w8a8=use_fp8_w8a8,
-            out_dtype=str(out.dtype),
-        )
+        if token_inputs.shape[0] <= expert_num:
+            run_config = {
+                "BLOCK_SIZE_M": 16,
+                "BLOCK_SIZE_N": 32,
+                "BLOCK_SIZE_K": 64,
+                "GROUP_SIZE_M": 1,
+                "NEED_TRANS": False,
+                "num_warps": 4,
+                "num_stages": 1,
+            }
+        else:
+            run_config = {
+                "BLOCK_SIZE_M": 64,
+                "BLOCK_SIZE_N": 64,
+                "BLOCK_SIZE_K": 32,
+                "GROUP_SIZE_M": 8,
+                "NEED_TRANS": False,
+                "num_warps": 4,
+                "num_stages": 1,
+            }
 
     BLOCK_SIZE_M = run_config["BLOCK_SIZE_M"]
     BLOCK_SIZE_N = run_config["BLOCK_SIZE_N"]

lightllm/common/basemodel/triton_kernel/fused_moe/moe_silu_and_mul.py

@@ -2,7 +2,6 @@
 
 import triton
 import triton.language as tl
-from .moe_silu_and_mul_config import MoeSiluAndMulKernelConfig
 from lightllm.common.triton_utils.autotuner import autotune
 
 
@@ -121,7 +120,10 @@ def silu_and_mul_fwd(
     size_n = input.shape[-1] // 2
 
     if not run_config:
-        run_config = MoeSiluAndMulKernelConfig.try_to_get_best_config(M=size_m, N=size_n, out_dtype=str(output.dtype))
+        if size_m < 256:
+            run_config = {"BLOCK_M": 1, "BLOCK_N": 128, "num_warps": 1, "NUM_STAGES": 1}
+        else:
+            run_config = {"BLOCK_M": 16, "BLOCK_N": 128, "num_warps": 4, "NUM_STAGES": 5}
 
     BLOCK_M = run_config["BLOCK_M"]
     BLOCK_N = run_config["BLOCK_N"]

lightllm/common/basemodel/triton_kernel/fused_moe/moe_silu_and_mul_mix_quant_ep.py

@@ -2,7 +2,6 @@
 
 import triton
 import triton.language as tl
-from .moe_silu_and_mul_config import MoeSiluAndMulKernelConfig
 
 
 @triton.jit

lightllm/common/basemodel/triton_kernel/fused_moe/moe_sum_reduce.py

@@ -1,9 +1,7 @@
 import torch
-
 import triton
 import triton.language as tl
-from .moe_sum_recude_config import MoeSumReduceKernelConfig
-from typing import Any, Callable, Dict, Optional, Tuple
+from typing import Dict
 from lightllm.common.triton_utils.autotuner import autotune
 
 
@@ -77,9 +75,12 @@ def moe_sum_reduce(input: torch.Tensor, output: torch.Tensor, run_config: Dict =
     assert output.shape[0] == token_num and output.shape[1] == hidden_dim
 
     if not run_config:
-        run_config = MoeSumReduceKernelConfig.try_to_get_best_config(
-            M=token_num, topk_num=topk_num, hidden_dim=hidden_dim, out_dtype=str(output.dtype)
-        )
+        run_config = {
+            "BLOCK_M": 1,
+            "BLOCK_DIM": 128,
+            "NUM_STAGE": 1,
+            "num_warps": 2,
+        }
 
     BLOCK_M = run_config["BLOCK_M"]
     BLOCK_DIM = run_config["BLOCK_DIM"]