fix moe align blocks benchmark (#3003)

benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py

@@ -7,6 +7,8 @@ import triton
 import triton.language as tl
 from sgl_kernel import moe_align_block_size
 
+USE_RANDOM_PERM = False
+
 
 def ceil_div(a, b):
     return (a + b - 1) // b
@@ -141,8 +143,13 @@ def moe_align_block_size_triton(
 def calculate_diff(batch_size, seq_len):
     num_experts = 256
     block_size = 128
-    topk_ids = torch.randint(
-        0, num_experts, (batch_size, seq_len), dtype=torch.int32, device="cuda"
+    topk = 8
+
+    topk_ids = torch.stack(
+        [
+            torch.randperm(num_experts, dtype=torch.int32, device="cuda")[:topk]
+            for _ in range(batch_size * seq_len)
+        ]
     )
 
     max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
@@ -169,7 +176,7 @@ def calculate_diff(batch_size, seq_len):
     expert_ids_triton = torch.empty_like(expert_ids_cuda)
     num_tokens_post_pad_triton = torch.empty_like(num_tokens_post_pad_cuda)
 
-    # 运行两个实现
+    # compare the performance of cuda and triton implementation
     moe_align_block_size(
         topk_ids,
         num_experts,
@@ -206,6 +213,15 @@ seq_length_range = [2**i for i in range(0, 16)]
 configs = list(itertools.product(batch_size_range, seq_length_range))
 
 
+def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
+    topk_ids = torch.zeros((num_tokens, topk), dtype=torch.int32, device="cuda")
+    for i in range(num_tokens):
+        topk_ids[i, :] = torch.randperm(num_experts, dtype=torch.int32, device="cuda")[
+            :topk
+        ]
+    return topk_ids
+
+
 @triton.testing.perf_report(
     triton.testing.Benchmark(
         x_names=["batch_size", "seq_len"],
@@ -223,9 +239,17 @@ def benchmark(batch_size, seq_len, provider):
     num_experts = 256
     block_size = 128
     topk = 8
-    topk_ids = torch.randint(
-        0, num_experts, (batch_size * seq_len, topk), dtype=torch.int32, device="cuda"
-    )
+
+    if USE_RANDOM_PERM:
+        topk_ids = get_topk_ids(batch_size * seq_len, num_experts, topk)
+    else:
+        topk_ids = torch.randint(
+            0,
+            num_experts,
+            (batch_size * seq_len, topk),
+            dtype=torch.int32,
+            device="cuda",
+        )
 
     max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
     sorted_ids = torch.empty(