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import os
from typing import Optional, Tuple
import torch
import triton
import triton.testing
from sgl_kernel import sgl_per_token_quant_fp8
# Optional vLLM import
try:
from vllm import _custom_ops as ops
VLLM_AVAILABLE = True
except ImportError:
ops = None
VLLM_AVAILABLE = False
from sglang.srt.utils import is_hip
_is_hip = is_hip()
# CI environment detection
IS_CI = (
os.getenv("CI", "false").lower() == "true"
or os.getenv("GITHUB_ACTIONS", "false").lower() == "true"
)
fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
# Get correct FP8 E4M3 maximum value
if _is_hip:
FP8_E4M3_MAX = 224.0 # ROCM uses 224.0
else:
# For CUDA, get the actual max value from the type
FP8_E4M3_MAX = float(torch.finfo(fp8_type_).max)
def torch_per_token_quant_fp8(
input: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
"""Pure PyTorch reference implementation for per-token FP8 quantization."""
device = input.device
dtype = input.dtype
# Find max absolute value per token (row) - exactly like CUDA kernel
max_vals = torch.abs(input).max(dim=1)[0] # [num_tokens]
# Calculate scale per token - exactly like CUDA kernel: scale = max_value / FP8_E4M3_MAX
scales = max_vals / FP8_E4M3_MAX # [num_tokens]
# No special zero handling - directly compute 1.0 / scale like CUDA kernel
scale_inv = 1.0 / scales # [num_tokens]
# Quantize: input * scale_inv, then clamp to FP8 range
quantized_float = input * scale_inv.unsqueeze(1) # Broadcast scale_inv
quantized_float = torch.clamp(quantized_float, -FP8_E4M3_MAX, FP8_E4M3_MAX)
# Convert to FP8 - use more explicit conversion
quantized_fp8 = quantized_float.to(fp8_type_)
return quantized_fp8, scales
def vllm_per_token_quant_fp8(
input: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
if not VLLM_AVAILABLE:
# Fallback to SGLang implementation
return sglang_per_token_quant_fp8(input)
return ops.scaled_fp8_quant(input, use_per_token_if_dynamic=True)
def sglang_per_token_quant_fp8(
input: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:
scale = torch.zeros(input.size(0), device=input.device, dtype=torch.float32)
output = torch.empty_like(input, device=input.device, dtype=fp8_type_)
sgl_per_token_quant_fp8(input, output, scale)
return output, scale
def calculate_diff(batch_size: int, seq_len: int, hidden_dim: int):
"""Compare Torch reference, VLLM, and SGLang implementations."""
device = torch.device("cuda")
x = torch.rand(
(batch_size * seq_len, hidden_dim), dtype=torch.float16, device=device
)
# Get all three implementations
torch_out, torch_scale = torch_per_token_quant_fp8(x)
vllm_out, vllm_scale = vllm_per_token_quant_fp8(x)
sglang_out, sglang_scale = sglang_per_token_quant_fp8(x)
if not VLLM_AVAILABLE:
print("⚠️ vLLM not available, skipping vLLM comparison")
# Only compare Torch vs SGLang
torch_sglang_scale_diff = torch.abs(torch_scale - sglang_scale).mean().item()
torch_sglang_out_diff = (
torch.abs(torch_out.float() - sglang_out.float()).mean().item()
)
print(f"Scale difference (Torch vs SGLang): {torch_sglang_scale_diff:.8f}")
print(f"Output difference (Torch vs SGLang): {torch_sglang_out_diff:.8f}")
return
print(f"\n=== Comparison for hidden_dim={hidden_dim} ===")
# Compare scales
torch_vllm_scale_diff = torch.abs(torch_scale - vllm_scale).mean().item()
torch_sglang_scale_diff = torch.abs(torch_scale - sglang_scale).mean().item()
vllm_sglang_scale_diff = torch.abs(vllm_scale - sglang_scale).mean().item()
print(f"Scale differences:")
print(f" Torch vs VLLM: {torch_vllm_scale_diff:.8f}")
print(f" Torch vs SGLang: {torch_sglang_scale_diff:.8f}")
print(f" VLLM vs SGLang: {vllm_sglang_scale_diff:.8f}")
# Compare outputs
torch_vllm_out_diff = torch.abs(torch_out.float() - vllm_out.float()).mean().item()
torch_sglang_out_diff = (
torch.abs(torch_out.float() - sglang_out.float()).mean().item()
)
vllm_sglang_out_diff = (
torch.abs(vllm_out.float() - sglang_out.float()).mean().item()
)
print(f"Output differences:")
print(f" Torch vs VLLM: {torch_vllm_out_diff:.8f}")
print(f" Torch vs SGLang: {torch_sglang_out_diff:.8f}")
print(f" VLLM vs SGLang: {vllm_sglang_out_diff:.8f}")
# Check tolerances
rtol, atol = 1e-3, 1e-5
torch_vllm_match = torch.allclose(
torch_out.float(), vllm_out.float(), rtol=rtol, atol=atol
) and torch.allclose(torch_scale, vllm_scale, rtol=rtol, atol=atol)
torch_sglang_match = torch.allclose(
torch_out.float(), sglang_out.float(), rtol=rtol, atol=atol
) and torch.allclose(torch_scale, sglang_scale, rtol=rtol, atol=atol)
if hidden_dim == 1368:
rtol = 1e-2
# we found vllm sglang has diff when hidden dim is not dividable by 16
# and we believe SGLang is closer to Torch implementation
vllm_sglang_match = torch.allclose(
vllm_out.float(), sglang_out.float(), rtol=rtol, atol=atol
) and torch.allclose(vllm_scale, sglang_scale, rtol=rtol, atol=atol)
print(f"Matches (rtol={rtol}, atol={atol}):")
print(f" Torch vs VLLM: {'✅' if torch_vllm_match else '❌'}")
print(f" Torch vs SGLang: {'✅' if torch_sglang_match else '❌'}")
print(f" VLLM vs SGLang: {'✅' if vllm_sglang_match else '❌'}")
# CI environment uses simplified parameters
if IS_CI:
batch_size_range = [16] # Single batch size for CI
seq_len_range = [64] # Single sequence length for CI
hidden_dim_range = [2048] # Single hidden dimension for CI
else:
batch_size_range = [16, 32, 64, 128]
seq_len_range = [64, 128, 256, 512, 1024, 2048, 4096]
hidden_dim_range = [1368, 2048, 4096]
configs = list(itertools.product(batch_size_range, seq_len_range, hidden_dim_range))
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=["batch_size", "seq_len", "hidden_dim"],
x_vals=configs,
line_arg="provider",
line_vals=(
["torch", "vllm", "sglang"] if VLLM_AVAILABLE else ["torch", "sglang"]
),
line_names=(
["Torch Reference", "VLLM", "SGL Kernel"]
if VLLM_AVAILABLE
else ["Torch Reference", "SGL Kernel"]
),
styles=(
[("red", "-"), ("blue", "-"), ("green", "-")]
if VLLM_AVAILABLE
else [("red", "-"), ("green", "-")]
),
ylabel="us",
plot_name="per-token-dynamic-quant-fp8-performance",
args={},
)
)
def benchmark_quantization(batch_size, seq_len, hidden_dim, provider):
dtype = torch.float16
device = torch.device("cuda")
x = torch.randn(batch_size * seq_len, hidden_dim, device=device, dtype=dtype)
quantiles = [0.5, 0.2, 0.8]
if provider == "torch":
fn = lambda: torch_per_token_quant_fp8(x.clone())
elif provider == "vllm":
if not VLLM_AVAILABLE:
return (0, 0, 0)
fn = lambda: vllm_per_token_quant_fp8(x.clone())
elif provider == "sglang":
fn = lambda: sglang_per_token_quant_fp8(x.clone())
ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
return 1000 * ms, 1000 * max_ms, 1000 * min_ms
if __name__ == "__main__":
# Test various hidden dimensions for correctness - simplified for CI
if IS_CI:
test_dims = [2048] # Single dimension for CI
batch_size, seq_len = 4, 64 # Smaller values for CI
else:
test_dims = [1368, 2048, 4096]
batch_size, seq_len = 4, 4096
for dim in test_dims:
calculate_diff(batch_size=batch_size, seq_len=seq_len, hidden_dim=dim)
print("\n" + "=" * 60)
print("Starting performance benchmark...")
benchmark_quantization.run(print_data=True)
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