optimize per token group quant fp8 (#3490)

sgl-kernel/benchmark/bench_per_token_group_quant_fp8.py

@@ -0,0 +1,209 @@
+import itertools
+import math
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+import triton
+import triton.language as tl
+from sgl_kernel import sgl_per_token_group_quant_fp8
+
+from sglang.srt.utils import get_device_core_count, get_device_name, is_hip
+
+is_hip_ = is_hip()
+fp8_type_ = torch.float8_e4m3fnuz if is_hip_ else torch.float8_e4m3fn
+
+
+@triton.jit
+def _per_token_group_quant_fp8(
+    # Pointers to inputs and output
+    y_ptr,
+    y_q_ptr,
+    y_s_ptr,
+    # Stride of input
+    y_stride,
+    # Collums of input
+    N,
+    # Avoid to divide zero
+    eps,
+    # Information for float8
+    fp8_min,
+    fp8_max,
+    # Meta-parameters
+    BLOCK: tl.constexpr,
+):
+    """A Triton-accelerated function to perform per-token-group quantization on a
+    tensor.
+
+    This function converts the tensor values into float8 values.
+    """
+    # Map the program id to the row of X and Y it should compute.
+    g_id = tl.program_id(0)
+    y_ptr += g_id * y_stride
+    y_q_ptr += g_id * y_stride
+    y_s_ptr += g_id
+
+    cols = tl.arange(0, BLOCK)  # N <= BLOCK
+    mask = cols < N
+
+    y = tl.load(y_ptr + cols, mask=mask, other=0.0).to(tl.float32)
+    # Quant
+    _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
+    y_s = _absmax / fp8_max
+    y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
+
+    tl.store(y_q_ptr + cols, y_q, mask=mask)
+    tl.store(y_s_ptr, y_s)
+
+
+def triton_per_token_group_quant_fp8(
+    x: torch.Tensor,
+    group_size: int,
+    eps: float = 1e-10,
+    dtype: torch.dtype = fp8_type_,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Function to perform per-token-group quantization on an input tensor `x`.
+
+    It converts the tensor values into signed float8 values and returns the
+    quantized tensor along with the scaling factor used for quantization.
+
+    Args:
+        x: The input tenosr with ndim >= 2.
+        group_size: The group size used for quantization.
+        eps: The minimum to avoid dividing zero.
+        dtype: The dype of output tensor. Note that only `torch.float8_e4m3fn` is supported for now.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization.
+    """
+    assert (
+        x.shape[-1] % group_size == 0
+    ), "the last dimension of `x` cannot be divisible by `group_size`"
+    assert x.is_contiguous(), "`x` is not contiguous"
+
+    finfo = torch.finfo(dtype)
+    fp8_max = finfo.max
+
+    fp8_min = -fp8_max
+
+    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
+    M = x.numel() // group_size
+    N = group_size
+    x_s = torch.empty(
+        x.shape[:-1] + (x.shape[-1] // group_size,),
+        device=x.device,
+        dtype=torch.float32,
+    )
+
+    BLOCK = triton.next_power_of_2(N)
+    # heuristics for number of warps
+    num_warps = min(max(BLOCK // 256, 1), 8)
+    num_stages = 1
+    _per_token_group_quant_fp8[(M,)](
+        x,
+        x_q,
+        x_s,
+        group_size,
+        N,
+        eps,
+        fp8_min=fp8_min,
+        fp8_max=fp8_max,
+        BLOCK=BLOCK,
+        num_warps=num_warps,
+        num_stages=num_stages,
+    )
+
+    return x_q, x_s
+
+
+def sglang_per_token_group_quant_fp8(
+    x: torch.Tensor,
+    group_size: int,
+    eps: float = 1e-10,
+    dtype: torch.dtype = fp8_type_,
+):
+    assert (
+        x.shape[-1] % group_size == 0
+    ), "the last dimension of `x` cannot be divisible by `group_size`"
+    assert x.is_contiguous(), "`x` is not contiguous"
+
+    finfo = torch.finfo(dtype)
+    fp8_max = finfo.max
+
+    fp8_min = -fp8_max
+
+    x_q = torch.empty_like(x, device=x.device, dtype=dtype)
+    M = x.numel() // group_size
+    N = group_size
+    x_s = torch.empty(
+        x.shape[:-1] + (x.shape[-1] // group_size,),
+        device=x.device,
+        dtype=torch.float32,
+    )
+
+    sgl_per_token_group_quant_fp8(x, x_q, x_s, group_size, eps, fp8_min, fp8_max)
+
+    return x_q, x_s
+
+
+def calculate_diff(batch_size, seq_len, group_size):
+    dtype = torch.float16
+    device = torch.device("cuda")
+    hidden_dim = group_size * 2
+
+    x = torch.randn(batch_size, seq_len, hidden_dim, device=device, dtype=dtype)
+
+    x_q_triton, x_s_triton = triton_per_token_group_quant_fp8(x.clone(), group_size)
+    x_q_sglang, x_s_sglang = sglang_per_token_group_quant_fp8(x.clone(), group_size)
+
+    if torch.allclose(
+        x_q_triton.to(torch.float32), x_q_sglang.to(torch.float32), rtol=1e-3, atol=1e-5
+    ) and torch.allclose(x_s_triton, x_s_sglang, rtol=1e-3, atol=1e-5):
+        print("✅ All implementations match")
+    else:
+        print("❌ Implementations differ")
+
+
+batch_size_range = [1, 2, 4, 8, 16, 32, 64]
+seq_len_range = [64, 128, 256, 512, 1024, 2048]
+group_size_range = [128]  # For DeepSeek V3/R1
+
+configs = list(itertools.product(batch_size_range, seq_len_range, group_size_range))
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=["batch_size", "seq_len", "group_size"],
+        x_vals=configs,
+        line_arg="provider",
+        line_vals=["triton", "sglang"],
+        line_names=["Triton", "SGL Kernel"],
+        styles=[("blue", "-"), ("green", "-")],
+        ylabel="us",
+        plot_name="per-token-group-quant-fp8-performance",
+        args={},
+    )
+)
+def benchmark(batch_size, seq_len, group_size, provider):
+    dtype = torch.bfloat16
+    device = torch.device("cuda")
+    hidden_dim = group_size * 2
+
+    x = torch.randn(batch_size, seq_len, hidden_dim, device=device, dtype=dtype)
+
+    quantiles = [0.5, 0.2, 0.8]
+
+    if provider == "triton":
+        fn = lambda: triton_per_token_group_quant_fp8(x.clone(), group_size)
+    elif provider == "sglang":
+        fn = lambda: sglang_per_token_group_quant_fp8(x.clone(), group_size)
+
+    ms, min_ms, max_ms = triton.testing.do_bench(fn, quantiles=quantiles)
+
+    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
+
+
+if __name__ == "__main__":
+
+    calculate_diff(batch_size=4, seq_len=128, group_size=64)
+
+    benchmark.run(print_data=True)