[Benchmark] add a benchmark for hf/vllm/sglang rmsnorm (#2486)

benchmark/kernels/rmsnorm/benchmark_rmsnorm.py

+import itertools
+from typing import Optional, Tuple, Union
+
+import torch
+import triton
+import triton.language as tl
+from flashinfer.norm import fused_add_rmsnorm, rmsnorm
+from torch import nn
+from vllm import _custom_ops as vllm_ops
+
+
+class HuggingFaceRMSNorm(nn.Module):
+    def __init__(self, hidden_size: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        residual: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
+        orig_dtype = x.dtype
+        x = x.to(torch.float32)
+        if residual is not None:
+            x = x + residual.to(torch.float32)
+            residual = x.to(orig_dtype)
+
+        variance = x.pow(2).mean(dim=-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        x = x.to(orig_dtype) * self.weight
+        if residual is None:
+            return x
+        else:
+            return x, residual
+
+
+def rmsnorm_naive(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    residual: Optional[torch.Tensor] = None,
+    eps: float = 1e-6,
+):
+    naive_norm = HuggingFaceRMSNorm(x.shape[-1], eps=eps)
+    naive_norm.weight = nn.Parameter(weight)
+    naive_norm = naive_norm.to(x.device)
+
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+    if residual is not None:
+        residual = residual.view(-1, residual.shape[-1])
+
+    output = naive_norm(x, residual)
+
+    if isinstance(output, tuple):
+        output = (output[0].view(orig_shape), output[1].view(orig_shape))
+    else:
+        output = output.view(orig_shape)
+    return output
+
+
+def rmsnorm_flashinfer(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    residual: Optional[torch.Tensor] = None,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+    if residual is not None:
+        residual = residual.view(-1, residual.shape[-1])
+
+    if residual is not None:
+        fused_add_rmsnorm(x, residual, weight, eps)
+        output = (x, residual)
+    else:
+        output = rmsnorm(x, weight, eps)
+
+    if isinstance(output, tuple):
+        output = (output[0].view(orig_shape), output[1].view(orig_shape))
+    else:
+        output = output.view(orig_shape)
+    return output
+
+
+def rmsnorm_vllm(
+    x: torch.Tensor,
+    weight: torch.Tensor,
+    residual: Optional[torch.Tensor] = None,
+    eps: float = 1e-6,
+):
+    orig_shape = x.shape
+    x = x.view(-1, x.shape[-1])
+    if residual is not None:
+        residual = residual.view(-1, residual.shape[-1])
+
+    if residual is not None:
+        vllm_ops.fused_add_rms_norm(x, residual, weight, eps)
+        output = (x, residual)
+    else:
+        out = torch.empty_like(x)
+        vllm_ops.rms_norm(out, x, weight, eps)
+        output = out
+
+    if isinstance(output, tuple):
+        output = (output[0].view(orig_shape), output[1].view(orig_shape))
+    else:
+        output = output.view(orig_shape)
+    return output
+
+
+def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
+    dtype = torch.bfloat16
+    x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
+    weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
+    residual = torch.randn_like(x) if use_residual else None
+
+    output_naive = rmsnorm_naive(
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )
+    output_flashinfer = rmsnorm_flashinfer(
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )
+    output_vllm = rmsnorm_vllm(
+        x.clone(), weight, residual.clone() if residual is not None else None
+    )
+
+    if use_residual:
+        output_naive = output_naive[0]
+        output_flashinfer = output_flashinfer[0]
+        output_vllm = output_vllm[0]
+
+    print(f"Naive output={output_naive}")
+    print(f"FlashInfer output={output_flashinfer}")
+    print(f"VLLM output={output_vllm}")
+
+    if torch.allclose(
+        output_naive, output_flashinfer, atol=1e-2, rtol=1e-2
+    ) and torch.allclose(output_naive, output_vllm, atol=1e-2, rtol=1e-2):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [2**i for i in range(0, 7, 2)]
+seq_length_range = [2**i for i in range(6, 11, 1)]
+head_num_range = [32, 48]
+configs = list(itertools.product(head_num_range, batch_size_range, seq_length_range))
+
+
+def get_benchmark(use_residual):
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["head_num", "batch_size", "seq_len"],
+            x_vals=[list(_) for _ in configs],
+            line_arg="provider",
+            line_vals=["huggingface", "flashinfer", "vllm"],
+            line_names=["HuggingFace", "FlashInfer", "vLLM"],
+            styles=[("blue", "-"), ("green", "-"), ("red", "-")],
+            ylabel="us",
+            plot_name=f"rmsnorm-performance-{'with' if use_residual else 'without'}-residual",
+            args={},
+        )
+    )
+    def benchmark(head_num, batch_size, seq_len, provider):
+        dtype = torch.bfloat16
+        hidden_size = head_num * 128  # assuming head_dim = 128
+
+        x = torch.randn(batch_size, seq_len, hidden_size, dtype=dtype, device="cuda")
+        weight = torch.ones(hidden_size, dtype=dtype, device="cuda")
+        residual = torch.randn_like(x) if use_residual else None
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "huggingface":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: rmsnorm_naive(
+                    x.clone(),
+                    weight,
+                    residual.clone() if residual is not None else None,
+                ),
+                quantiles=quantiles,
+            )
+        elif provider == "flashinfer":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: rmsnorm_flashinfer(
+                    x.clone(),
+                    weight,
+                    residual.clone() if residual is not None else None,
+                ),
+                quantiles=quantiles,
+            )
+        else:
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: rmsnorm_vllm(
+                    x.clone(),
+                    weight,
+                    residual.clone() if residual is not None else None,
+                ),
+                quantiles=quantiles,
+            )
+
+        return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--use_residual", action="store_true", help="Whether to use residual connection"
+    )
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/rmsnorm/",
+        help="Path to save rmsnorm benchmark results",
+    )
+    args = parser.parse_args()
+
+    # Run correctness test
+    calculate_diff(
+        batch_size=4, seq_len=128, hidden_size=4096, use_residual=args.use_residual
+    )
+
+    # Get the benchmark function with proper use_residual setting
+    benchmark = get_benchmark(args.use_residual)
+    # Run performance benchmark
+    benchmark.run(print_data=True, save_path=args.save_path)