refine sgl_moe_align_block_size_benchmark (#4327)

benchmark/kernels/fused_moe_triton/benchmark_deepseekv3_moe_align_blocks.py → sgl-kernel/benchmark/bench_moe_align_block_size.py

@@ -4,7 +4,8 @@ import itertools
 import torch
 import triton
 import triton.language as tl
-from sgl_kernel import moe_align_block_size
+from sgl_kernel import moe_align_block_size as sgl_moe_align_block_size
+from vllm import _custom_ops as ops
 
 USE_RANDOM_PERM = False
 
@@ -139,15 +140,11 @@ def moe_align_block_size_triton(
     )
 
 
-def calculate_diff(batch_size, seq_len):
-    num_experts = 256
-    block_size = 128
-    topk = 8
-
+def calculate_diff(num_tokens, num_experts=256, block_size=128, topk=8):
     topk_ids = torch.stack(
         [
             torch.randperm(num_experts, dtype=torch.int32, device="cuda")[:topk]
-            for _ in range(batch_size * seq_len)
+            for _ in range(num_tokens)
         ]
     )
 
@@ -175,8 +172,13 @@ def calculate_diff(batch_size, seq_len):
     expert_ids_triton = torch.zeros_like(expert_ids_cuda)
     num_tokens_post_pad_triton = torch.empty_like(num_tokens_post_pad_cuda)
 
-    # compare the performance of cuda and triton implementation
-    moe_align_block_size(
+    sorted_ids_vllm = torch.empty_like(sorted_ids_cuda)
+    sorted_ids_vllm.fill_(topk_ids.numel())
+    expert_ids_vllm = torch.zeros_like(expert_ids_cuda)
+    num_tokens_post_pad_vllm = torch.empty_like(num_tokens_post_pad_cuda)
+
+    # compare the performance of cuda, triton and vllm implementation
+    sgl_moe_align_block_size(
         topk_ids,
         num_experts,
         block_size,
@@ -194,22 +196,43 @@ def calculate_diff(batch_size, seq_len):
         expert_ids_triton,
         num_tokens_post_pad_triton,
     )
+    ops.moe_align_block_size(
+        topk_ids,
+        num_experts,
+        block_size,
+        sorted_ids_vllm,
+        expert_ids_vllm,
+        num_tokens_post_pad_vllm,
+    )
 
     if torch.allclose(expert_ids_cuda, expert_ids_triton) and torch.allclose(
         num_tokens_post_pad_cuda, num_tokens_post_pad_triton
     ):
-        print("✅ CUDA and Triton implementations match")
+        print("✅ SGL and Triton implementations match")
     else:
-        print("❌ CUDA and Triton implementations do not match")
-        print("CUDA expert_ids:", expert_ids_cuda)
+        print("❌ SGL and Triton implementations do not match")
+        print("SGL expert_ids:", expert_ids_cuda)
         print("Triton expert_ids:", expert_ids_triton)
-        print("CUDA num_tokens_post_pad:", num_tokens_post_pad_cuda)
+        print("SGL num_tokens_post_pad:", num_tokens_post_pad_cuda)
         print("Triton num_tokens_post_pad:", num_tokens_post_pad_triton)
 
+    if torch.allclose(expert_ids_cuda, expert_ids_vllm) and torch.allclose(
+        num_tokens_post_pad_cuda, num_tokens_post_pad_vllm
+    ):
+        print("✅ SGL and VLLM implementations match")
+    else:
+        print("❌ SGL and VLLM implementations do not match")
+        print("SGL expert_ids:", expert_ids_cuda)
+        print("VLLM expert_ids:", expert_ids_vllm)
+        print("SGL num_tokens_post_pad:", num_tokens_post_pad_cuda)
+        print("VLLM num_tokens_post_pad:", num_tokens_post_pad_vllm)
+
 
-batch_size_range = [2**i for i in range(0, 8)]
-seq_length_range = [2**i for i in range(0, 16)]
-configs = list(itertools.product(batch_size_range, seq_length_range))
+num_tokens_range = [16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]
+num_experts_range = [32, 64, 128, 256]
+topk_range = [2, 4, 8]
+
+configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range))
 
 
 def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
@@ -223,29 +246,27 @@ def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
 
 @triton.testing.perf_report(
     triton.testing.Benchmark(
-        x_names=["batch_size", "seq_len"],
-        x_vals=[list(_) for _ in configs],
+        x_names=["num_tokens", "num_experts", "topk"],
+        x_vals=configs,
         line_arg="provider",
-        line_vals=["cuda", "triton"],
-        line_names=["CUDA", "Triton"],
-        styles=[("blue", "-"), ("red", "-")],
+        line_vals=["sgl", "triton", "vllm"],
+        line_names=["SGL", "Triton", "VLLM"],
+        styles=[("blue", "-"), ("red", "-"), ("green", "-")],
         ylabel="us",
         plot_name="moe-align-block-size-performance",
         args={},
     )
 )
-def benchmark(batch_size, seq_len, provider):
-    num_experts = 256
+def benchmark(num_tokens, num_experts, topk, provider):
     block_size = 128
-    topk = 8
 
     if USE_RANDOM_PERM:
-        topk_ids = get_topk_ids(batch_size * seq_len, num_experts, topk)
+        topk_ids = get_topk_ids(num_tokens, num_experts, topk)
     else:
         topk_ids = torch.randint(
             0,
             num_experts,
-            (batch_size * seq_len, topk),
+            (num_tokens, topk),
             dtype=torch.int32,
             device="cuda",
         )
@@ -268,9 +289,9 @@ def benchmark(batch_size, seq_len, provider):
     )
 
     quantiles = [0.5, 0.2, 0.8]
-    if provider == "cuda":
+    if provider == "sgl":
         ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: moe_align_block_size(
+            lambda: sgl_moe_align_block_size(
                 topk_ids,
                 num_experts,
                 block_size,
@@ -282,7 +303,7 @@ def benchmark(batch_size, seq_len, provider):
             ),
             quantiles=quantiles,
         )
-    else:
+    elif provider == "triton":
         ms, min_ms, max_ms = triton.testing.do_bench(
             lambda: moe_align_block_size_triton(
                 topk_ids,
@@ -294,6 +315,18 @@ def benchmark(batch_size, seq_len, provider):
             ),
             quantiles=quantiles,
         )
+    else:  # vllm
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: ops.moe_align_block_size(
+                topk_ids,
+                num_experts,
+                block_size,
+                sorted_ids.clone(),
+                expert_ids.clone(),
+                num_tokens_post_pad.clone(),
+            ),
+            quantiles=quantiles,
+        )
 
     return 1000 * ms, 1000 * max_ms, 1000 * min_ms
 
@@ -306,8 +339,22 @@ if __name__ == "__main__":
         default="./configs/benchmark_ops/moe_align_blocks/",
         help="Path to save moe align benchmark results",
     )
+    parser.add_argument(
+        "--num_experts",
+        type=int,
+        default=256,
+        choices=[8, 64, 128, 256],
+        help="Number of experts for benchmark",
+    )
+    parser.add_argument(
+        "--topk",
+        type=int,
+        default=8,
+        choices=[2, 4, 8],
+        help="Top-k value for benchmark",
+    )
     args = parser.parse_args()
 
-    calculate_diff(batch_size=4, seq_len=1024)
+    calculate_diff(num_tokens=1024, num_experts=args.num_experts, topk=args.topk)
 
     benchmark.run(print_data=True)