[Enhancement] Fix lint to improve grouped GEMM performance with TMA (#938)

* [Example]  Fix lint  to improve grouped GEMM performance with TMA

* fix lint

examples/grouped_gemm/example_grouped_gemm_fwd.py

@@ -4,8 +4,6 @@ import tilelang
 import tilelang.language as T
 import math
 
-tilelang.disable_cache()
-
 
 def torch_gmm(a, b, batch_sizes, batch_offsets_tensor, trans_b=False):
     """
@@ -57,6 +55,7 @@ def grouped_gemm(batch_sizes_list,
     batch_sum = sum(batch_sizes_list)
     batch_count = len(batch_sizes_list)
     accum_dtype = "float32"
+    total_m_blocks = sum((size + block_M - 1) // block_M for size in batch_sizes_list)
 
     @T.prim_func
     def kernel(
@@ -68,9 +67,7 @@ def grouped_gemm(batch_sizes_list,
             batch_padded_offsets: T.Tensor([batch_count], "int32"),  # type: ignore
     ):
 
-        with T.Kernel(
-                T.ceildiv(batch_sum, block_M) + batch_count, T.ceildiv(N, block_N),
-                threads=threads) as (bx, by):
+        with T.Kernel(total_m_blocks, T.ceildiv(N, block_N), threads=threads) as (bx, by):
             A_shared = T.alloc_shared([block_M, block_K], dtype)
             B_shared = T.alloc_shared([block_K, block_N], dtype)
             C_local = T.alloc_fragment([block_M, block_N], accum_dtype)
@@ -115,8 +112,7 @@ def construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype):
         batch_offsets_list.append(batch_offsets_list[-1] + batch_sizes_list[i])
     for i in range(batch_count - 1):
         batch_padded_offsets_list.append(batch_padded_offsets_list[-1] +
-                                         math.ceil((batch_sizes_list[i] + 1) / padding_M) *
-                                         padding_M)
+                                         math.ceil((batch_sizes_list[i]) / padding_M) * padding_M)
     A = torch.randn(batch_sum, K, device=device, dtype=dtype)
     B = torch.randn(batch_count, K, M, device=device, dtype=dtype)
     C = torch.empty(batch_sum, M, device=device, dtype=dtype)