sgl-kernel use cutlass latest version for fp8 blockwise gemm (#5207)

sgl-kernel/benchmark/bench_fp8_blockwise_gemm.py

@@ -2,18 +2,22 @@ import argparse
 import copy
 import itertools
 
+import deep_gemm
 import torch
 import triton
+from deep_gemm import get_col_major_tma_aligned_tensor
 from sgl_kernel import fp8_blockwise_scaled_mm
 from vllm._custom_ops import cutlass_scaled_mm as vllm_scaled_mm
 
+from sglang.srt.layers.quantization.fp8_kernel import w8a8_block_fp8_matmul
+
 
 def get_weight_shapes(args):
     models_tps = list(itertools.product(args.models, args.tp_sizes))
     # NOTE(HandH1998): The weight shapes only works for DeepSeek-V3. Modify them, if you tune for another different model.
     # cannot TP
     total = [
-        # (512 + 64, 7168), # this weight is not supported by current kernel
+        (512 + 64, 7168),
         ((128 + 64) * 128, 7168),
         (128 * (128 + 128), 512),
         (7168, 16384),
@@ -52,6 +56,23 @@ def cdiv(a: int, b: int) -> int:
     return -(a // -b)
 
 
+def fp8_gemm_deepgemm(
+    x_fp8: torch.Tensor,
+    x_scale: torch.Tensor,
+    y_fp8: torch.Tensor,
+    y_scale: torch.Tensor,
+    m: int,
+    n: int,
+    k: int,
+):
+    """DeepGEMM implementation of FP8 GEMM"""
+    out = torch.empty((m, n), device="cuda", dtype=torch.bfloat16)
+
+    # Run DeepGEMM kernel
+    deep_gemm.gemm_fp8_fp8_bf16_nt((x_fp8, x_scale), (y_fp8, y_scale), out)
+    return out
+
+
 def scale_shape(shape, group_shape):
     assert len(shape) == len(group_shape)
     return tuple(cdiv(shape[i], group_shape[i]) for i in range(len(group_shape)))
@@ -60,12 +81,12 @@ def scale_shape(shape, group_shape):
 @triton.testing.perf_report(
     triton.testing.Benchmark(
         x_names=["batch_size"],
-        x_vals=[1, 16, 32, 64, 128, 256, 512, 1024, 2048],
+        x_vals=[1, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096],
         x_log=False,
         line_arg="provider",
-        line_vals=["vllm", "sgl-kernel"],
-        line_names=["vllm fp8 blockwise gemm", "sgl-kernel fp8 blockwise gemm"],
-        styles=[("blue", "-"), ("orange", "-")],
+        line_vals=["vllm", "sgl-kernel", "triton", "deepgemm"],
+        line_names=["vllm", "sgl-kernel", "sglang triton", "deepgemm"],
+        styles=[("blue", "-"), ("orange", "-"), ("red", "-"), ("yellow", "-")],
         ylabel="GB/s",
         plot_name="fp8 blockwise scaled matmul",
         args={},
@@ -80,7 +101,7 @@ def benchmark(batch_size, provider, N, K):
     a_fp8 = a_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
 
     b_fp32 = (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 * fp8_max
-    b_fp8 = b_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn).t()
+    b_fp8 = b_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
 
     scale_a_group_shape = (1, 128)
     scale_b_group_shape = (128, 128)
@@ -89,11 +110,11 @@ def benchmark(batch_size, provider, N, K):
 
     scale_a = torch.randn(scale_a_shape, device="cuda", dtype=torch.float32)
     scale_b = torch.randn(scale_b_shape, device="cuda", dtype=torch.float32)
-    scale_a = scale_a.t().contiguous().t()
-    scale_b = scale_b.t().contiguous().t()
 
     quantiles = [0.5, 0.2, 0.8]
     if provider == "sgl-kernel":
+        scale_a = scale_a.t().contiguous().t()
+        b_fp8, scale_b = b_fp8.t(), scale_b.t()
         ms, min_ms, max_ms = triton.testing.do_bench(
             lambda: fp8_blockwise_scaled_mm(
                 a_fp8, b_fp8, scale_a, scale_b, torch.float16
@@ -101,19 +122,28 @@ def benchmark(batch_size, provider, N, K):
             quantiles=quantiles,
         )
     if provider == "vllm":
+        scale_a = scale_a.t().contiguous().t()
+        b_fp8, scale_b = b_fp8.t(), scale_b.t()
         ms, min_ms, max_ms = triton.testing.do_bench(
             lambda: vllm_scaled_mm(a_fp8, b_fp8, scale_a, scale_b, torch.float16),
             quantiles=quantiles,
         )
-    gbps = (
-        lambda ms: (
-            (2 * M * N * K - M * N) * a_fp8.element_size()
-            + (3 * M * N) * scale_a.element_size()
+    if provider == "triton":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: w8a8_block_fp8_matmul(
+                a_fp8, b_fp8, scale_a, scale_b, [128, 128], torch.float16
+            ),
+            quantiles=quantiles,
         )
-        * 1e-9
-        / (ms * 1e-3)
-    )
-    return gbps(ms), gbps(max_ms), gbps(min_ms)
+    if provider == "deepgemm":
+        scale_a_col_major = get_col_major_tma_aligned_tensor(scale_a.clone())
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: fp8_gemm_deepgemm(
+                a_fp8, scale_a_col_major, b_fp8, scale_b, M, N, K
+            ),
+            quantiles=quantiles,
+        )
+    return ms * 1000, max_ms * 1000, min_ms * 1000  # convert to ms
 
 
 if __name__ == "__main__":
@@ -136,6 +166,9 @@ if __name__ == "__main__":
 
     NK_model_names = get_weight_shapes(args)
     for N, K, model_name in NK_model_names:
+        if N % 128 != 0 or K % 128 != 0:
+            print(f"Skip {N=}, {K=} now")
+            continue
         print(f"{model_name} N={N} K={K}: ")
         benchmark.run(
             print_data=True,