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benchmark/README.md

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+## DeepGEMM kernels benchmark
+
+- `benchmark_deepgemm_fp8_gemm.py`
+    - You should install [DeepGemm](https://github.com/deepseek-ai/DeepGEMM) from source before run `benchmark_deepgemm_fp8_gemm.py`.
+    - You can use the `--run_correctness` parameter to verify all kernels results's correctness.
+    - You can use the `--tp_size` parameter to benchmark all FP8 w8a8 block-wise matrix multiplications involved in DeepSeek V3/R1 under the current tensor parallelism (TP) setting. This benchmark compares DeepSeek's open-source [DeepGemm](https://github.com/deepseek-ai/DeepGEMM) implementation with SGLang's and VLLM Triton implementation.

benchmark/benchmark_deepgemm_fp8_gemm.py

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+import itertools
+from typing import Tuple
+
+import deep_gemm
+import numpy as np
+import torch
+import triton
+import triton.language as tl
+from deep_gemm import ceil_div, get_col_major_tma_aligned_tensor
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    w8a8_block_fp8_matmul as vllm_w8a8_block_fp8_matmul,
+)
+
+from sglang.srt.layers.quantization.fp8_kernel import w8a8_block_fp8_matmul
+
+
+def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(
+        m, n
+    ), (x_amax / 448.0).view(m, -1)
+
+
+def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros(
+        (ceil_div(m, 128) * 128, ceil_div(n, 128) * 128), dtype=x.dtype, device=x.device
+    )
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(
+        x_view.size(0), x_view.size(2)
+    )
+
+
+def fp8_gemm_deepgemm(x: torch.Tensor, y: torch.Tensor, m: int, n: int, k: int):
+    """DeepGEMM implementation of FP8 GEMM"""
+    # Convert inputs to FP8 format
+    x_fp8, x_scale = per_token_cast_to_fp8(x)
+    y_fp8, y_scale = per_block_cast_to_fp8(y)
+
+    # Transpose earlier so that the testing will not trigger transposing kernels
+    x_scale = get_col_major_tma_aligned_tensor(x_scale)
+
+    # Prepare inputs for DeepGEMM
+    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 fp8_gemm_sglang(x: torch.Tensor, y: torch.Tensor, m: int, n: int, k: int):
+    """SGLang implementation of FP8 GEMM"""
+    # Convert inputs to FP8 format
+    x_fp8, x_scale = per_token_cast_to_fp8(x)
+    y_fp8, y_scale = per_block_cast_to_fp8(y)
+
+    block_size = [128, 128]  # Matches the block size in per_block_cast_to_fp8
+
+    # Run SGLang kernel
+    out = w8a8_block_fp8_matmul(
+        x_fp8, y_fp8, x_scale, y_scale, block_size, torch.bfloat16
+    )
+    return out
+
+
+def fp8_gemm_vllm(x: torch.Tensor, y: torch.Tensor, m: int, n: int, k: int):
+    """vLLM implementation of FP8 GEMM"""
+    # Convert inputs to FP8 format
+    x_fp8, x_scale = per_token_cast_to_fp8(x)
+    y_fp8, y_scale = per_block_cast_to_fp8(y)
+
+    block_size = [128, 128]  # Matches the block size in per_block_cast_to_fp8
+
+    # Run vLLM kernel
+    out = vllm_w8a8_block_fp8_matmul(
+        x_fp8, y_fp8, x_scale, y_scale, block_size, torch.bfloat16
+    )
+    return out
+
+
+def calculate_diff(m: int, n: int, k: int):
+    x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+    y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+
+    out_deepgemm = fp8_gemm_deepgemm(x.clone(), y.clone(), m, n, k)
+    out_sglang = fp8_gemm_sglang(x.clone(), y.clone(), m, n, k)
+    out_vllm = fp8_gemm_vllm(x.clone(), y.clone(), m, n, k)
+
+    diff_sglang_deepgemm = torch.abs(out_deepgemm - out_sglang).mean().item()
+    diff_vllm_deepgemm = torch.abs(out_deepgemm - out_vllm).mean().item()
+    diff_vllm_sglang = torch.abs(out_vllm - out_sglang).mean().item()
+
+    print(f"Shape m={m}, n={n}, k={k}:")
+    print(f"DeepGEMM output: {out_deepgemm[0, 0:5]}")
+    print(f"SGLang output: {out_sglang[0, 0:5]}")
+    print(f"vLLM output: {out_vllm[0, 0:5]}")
+    print(f"Mean absolute difference (SGLang-DeepGEMM): {diff_sglang_deepgemm}")
+    print(f"Mean absolute difference (vLLM-DeepGEMM): {diff_vllm_deepgemm}")
+    print(f"Mean absolute difference (vLLM-SGLang): {diff_vllm_sglang}")
+
+    sglang_deepgemm_match = torch.allclose(
+        out_deepgemm, out_sglang, atol=1e-2, rtol=1e-2
+    )
+    vllm_deepgemm_match = torch.allclose(out_deepgemm, out_vllm, atol=1e-2, rtol=1e-2)
+    vllm_sglang_match = torch.allclose(out_vllm, out_sglang, atol=1e-2, rtol=1e-2)
+
+    if sglang_deepgemm_match and vllm_deepgemm_match and vllm_sglang_match:
+        print("✅ All implementations match\n")
+    else:
+        print("❌ Some implementations differ:")
+        print(f"  - SGLang vs DeepGEMM: {'✅' if sglang_deepgemm_match else '❌'}")
+        print(f"  - vLLM vs DeepGEMM: {'✅' if vllm_deepgemm_match else '❌'}")
+        print(f"  - vLLM vs SGLang: {'✅' if vllm_sglang_match else '❌'}\n")
+
+
+def get_weight_shapes(tp_size):
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+
+    # Remove shapes with n=7168 when tp_size=16 to avoid "Misaligned barriers" error in DeepGEMM
+    if tp_size == 16:
+        weight_shapes = [shape for shape in weight_shapes if shape[0] != 7168]
+
+    return weight_shapes
+
+
+def create_benchmark_configs(tp_size):
+    configs = []
+    weight_shapes = get_weight_shapes(tp_size)
+    batch_sizes = [8, 16, 32, 64, 128, 256, 1024, 2048, 4096]
+
+    for n, k in weight_shapes:
+        for m in batch_sizes:
+            configs.append((m, n, k, tp_size))
+
+    return configs
+
+
+def get_benchmark(tp_size):
+    all_configs = create_benchmark_configs(tp_size)
+
+    @triton.testing.perf_report(
+        triton.testing.Benchmark(
+            x_names=["m", "n", "k", "tp_size"],
+            x_vals=[list(config) for config in all_configs],
+            line_arg="provider",
+            line_vals=["deepgemm", "sglang", "vllm"],
+            line_names=["DeepGEMM", "SGLang", "vLLM"],
+            styles=[("blue", "-"), ("red", "-"), ("green", "-")],
+            ylabel="ms",
+            plot_name=f"fp8-gemm-performance-comparison-tp{tp_size}",
+            args={},
+        )
+    )
+    def benchmark(m, n, k, tp_size, provider):
+        print(f"Shape (m={m}, n={n}, k={k}, tp={tp_size}), Provider: {provider}")
+        x = torch.randn((m, k), device="cuda", dtype=torch.bfloat16)
+        y = torch.randn((n, k), device="cuda", dtype=torch.bfloat16)
+
+        quantiles = [0.5, 0.2, 0.8]
+
+        if provider == "deepgemm":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_deepgemm(x.clone(), y.clone(), m, n, k),
+                quantiles=quantiles,
+            )
+        elif provider == "sglang":
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_sglang(x.clone(), y.clone(), m, n, k),
+                quantiles=quantiles,
+            )
+        else:  # vllm
+            ms, min_ms, max_ms = triton.testing.do_bench(
+                lambda: fp8_gemm_vllm(x.clone(), y.clone(), m, n, k),
+                quantiles=quantiles,
+            )
+
+        # Calculate TFLOPS
+        flops = 2 * m * n * k  # multiply-adds
+        tflops = flops / (ms * 1e-3) / 1e12
+
+        # Print shape-specific results with TFLOPS
+        print(f"Time: {ms*1000:.2f} ms, TFLOPS: {tflops:.2f}")
+        return ms * 1000, max_ms * 1000, min_ms * 1000  # convert to ms
+
+    return benchmark
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--save_path",
+        type=str,
+        default="./configs/benchmark_ops/fp8_gemm/",
+        help="Path to save fp8 gemm benchmark results",
+    )
+    parser.add_argument(
+        "--run_correctness",
+        action="store_true",
+        help="Whether to run correctness test",
+    )
+    parser.add_argument(
+        "--tp_size",
+        type=int,
+        default=1,
+        help="Tensor parallelism size to benchmark (default: 1)",
+    )
+    args = parser.parse_args()
+
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+    torch.cuda.manual_seed(0)
+
+    # Enable TF32, adapted from https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_core.py#L148
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    # Run correctness tests on a few examples
+    if args.run_correctness:
+        print("Running correctness tests...")
+        calculate_diff(64, 512, 7168)  # Small test
+        calculate_diff(64, 7168, 16384)  # Medium test
+        calculate_diff(64, 18432, 7168)  # Large test
+
+    # Get the benchmark function with the specified tp_size
+    benchmark = get_benchmark(args.tp_size)
+
+    print(f"Running performance benchmark for TP size = {args.tp_size}...")
+    benchmark.run(print_data=True, save_path=args.save_path)