[JIT Kernel] Migrate per-token FP8 quantization from AOT to JIT (#34257)
This commit is contained in:
Mohammad Miadh Angkad
@@ -1,228 +0,0 @@
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import itertools
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import os
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from typing import Optional, Tuple
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import torch
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import triton
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import triton.testing
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from sgl_kernel import sgl_per_token_quant_fp8
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from sglang.utils import is_in_ci
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# Optional vLLM import
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try:
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from vllm import _custom_ops as ops
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VLLM_AVAILABLE = True
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except ImportError:
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ops = None
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VLLM_AVAILABLE = False
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from sglang.srt.utils import is_hip
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_is_hip = is_hip()
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IS_CI = is_in_ci()
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fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
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# Get correct FP8 E4M3 maximum value
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if _is_hip:
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FP8_E4M3_MAX = 224.0 # ROCM uses 224.0
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else:
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# For CUDA, get the actual max value from the type
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FP8_E4M3_MAX = float(torch.finfo(fp8_type_).max)
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def torch_per_token_quant_fp8(
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input: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Pure PyTorch reference implementation for per-token FP8 quantization."""
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device = input.device
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dtype = input.dtype
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# Find max absolute value per token (row) - exactly like CUDA kernel
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max_vals = torch.abs(input).max(dim=1)[0] # [num_tokens]
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# Calculate scale per token - exactly like CUDA kernel: scale = max_value / FP8_E4M3_MAX
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scales = max_vals / FP8_E4M3_MAX # [num_tokens]
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# No special zero handling - directly compute 1.0 / scale like CUDA kernel
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scale_inv = 1.0 / scales # [num_tokens]
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# Quantize: input * scale_inv, then clamp to FP8 range
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quantized_float = input * scale_inv.unsqueeze(1) # Broadcast scale_inv
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quantized_float = torch.clamp(quantized_float, -FP8_E4M3_MAX, FP8_E4M3_MAX)
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# Convert to FP8 - use more explicit conversion
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quantized_fp8 = quantized_float.to(fp8_type_)
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return quantized_fp8, scales
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def vllm_per_token_quant_fp8(
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input: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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if not VLLM_AVAILABLE:
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# Fallback to SGLang implementation
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return sglang_per_token_quant_fp8(input)
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return ops.scaled_fp8_quant(input, use_per_token_if_dynamic=True)
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def sglang_per_token_quant_fp8(
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input: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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scale = torch.zeros(input.size(0), device=input.device, dtype=torch.float32)
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output = torch.empty_like(input, device=input.device, dtype=fp8_type_)
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sgl_per_token_quant_fp8(input, output, scale)
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return output, scale
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def calculate_diff(batch_size: int, seq_len: int, hidden_dim: int):
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"""Compare Torch reference, VLLM, and SGLang implementations."""
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device = torch.device("cuda")
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x = torch.rand(
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(batch_size * seq_len, hidden_dim), dtype=torch.float16, device=device
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)
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# Get all three implementations
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torch_out, torch_scale = torch_per_token_quant_fp8(x)
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vllm_out, vllm_scale = vllm_per_token_quant_fp8(x)
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sglang_out, sglang_scale = sglang_per_token_quant_fp8(x)
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if not VLLM_AVAILABLE:
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print("⚠️ vLLM not available, skipping vLLM comparison")
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# Only compare Torch vs SGLang
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torch_sglang_scale_diff = torch.abs(torch_scale - sglang_scale).mean().item()
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torch_sglang_out_diff = (
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torch.abs(torch_out.float() - sglang_out.float()).mean().item()
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)
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print(f"Scale difference (Torch vs SGLang): {torch_sglang_scale_diff:.8f}")
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print(f"Output difference (Torch vs SGLang): {torch_sglang_out_diff:.8f}")
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return
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print(f"\n=== Comparison for hidden_dim={hidden_dim} ===")
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# Compare scales
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torch_vllm_scale_diff = torch.abs(torch_scale - vllm_scale).mean().item()
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torch_sglang_scale_diff = torch.abs(torch_scale - sglang_scale).mean().item()
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vllm_sglang_scale_diff = torch.abs(vllm_scale - sglang_scale).mean().item()
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print(f"Scale differences:")
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print(f" Torch vs VLLM: {torch_vllm_scale_diff:.8f}")
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print(f" Torch vs SGLang: {torch_sglang_scale_diff:.8f}")
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print(f" VLLM vs SGLang: {vllm_sglang_scale_diff:.8f}")
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# Compare outputs
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torch_vllm_out_diff = torch.abs(torch_out.float() - vllm_out.float()).mean().item()
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torch_sglang_out_diff = (
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torch.abs(torch_out.float() - sglang_out.float()).mean().item()
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)
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vllm_sglang_out_diff = (
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torch.abs(vllm_out.float() - sglang_out.float()).mean().item()
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)
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print(f"Output differences:")
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print(f" Torch vs VLLM: {torch_vllm_out_diff:.8f}")
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print(f" Torch vs SGLang: {torch_sglang_out_diff:.8f}")
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print(f" VLLM vs SGLang: {vllm_sglang_out_diff:.8f}")
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# Check tolerances
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rtol, atol = 1e-3, 1e-5
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torch_vllm_match = torch.allclose(
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torch_out.float(), vllm_out.float(), rtol=rtol, atol=atol
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) and torch.allclose(torch_scale, vllm_scale, rtol=rtol, atol=atol)
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torch_sglang_match = torch.allclose(
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torch_out.float(), sglang_out.float(), rtol=rtol, atol=atol
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) and torch.allclose(torch_scale, sglang_scale, rtol=rtol, atol=atol)
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if hidden_dim == 1368:
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rtol = 1e-2
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# we found vllm sglang has diff when hidden dim is not dividable by 16
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# and we believe SGLang is closer to Torch implementation
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vllm_sglang_match = torch.allclose(
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vllm_out.float(), sglang_out.float(), rtol=rtol, atol=atol
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) and torch.allclose(vllm_scale, sglang_scale, rtol=rtol, atol=atol)
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print(f"Matches (rtol={rtol}, atol={atol}):")
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print(f" Torch vs VLLM: {'✅' if torch_vllm_match else '❌'}")
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print(f" Torch vs SGLang: {'✅' if torch_sglang_match else '❌'}")
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print(f" VLLM vs SGLang: {'✅' if vllm_sglang_match else '❌'}")
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# CI environment uses simplified parameters
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if IS_CI:
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batch_size_range = [16] # Single batch size for CI
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seq_len_range = [64] # Single sequence length for CI
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hidden_dim_range = [2048] # Single hidden dimension for CI
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else:
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batch_size_range = [16, 32, 64, 128]
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seq_len_range = [64, 128, 256, 512, 1024, 2048, 4096]
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hidden_dim_range = [1368, 2048, 4096]
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configs = list(itertools.product(batch_size_range, seq_len_range, hidden_dim_range))
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=["batch_size", "seq_len", "hidden_dim"],
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x_vals=configs,
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line_arg="provider",
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line_vals=(
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["torch", "vllm", "sglang"] if VLLM_AVAILABLE else ["torch", "sglang"]
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),
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line_names=(
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["Torch Reference", "VLLM", "SGL Kernel"]
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if VLLM_AVAILABLE
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else ["Torch Reference", "SGL Kernel"]
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),
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styles=(
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[("red", "-"), ("blue", "-"), ("green", "-")]
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if VLLM_AVAILABLE
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else [("red", "-"), ("green", "-")]
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),
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ylabel="us",
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plot_name="per-token-dynamic-quant-fp8-performance",
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args={},
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)
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)
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def benchmark_quantization(batch_size, seq_len, hidden_dim, provider):
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dtype = torch.float16
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device = torch.device("cuda")
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x = torch.randn(batch_size * seq_len, hidden_dim, device=device, dtype=dtype)
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quantiles = [0.5, 0.2, 0.8]
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if provider == "torch":
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fn = lambda: torch_per_token_quant_fp8(x.clone())
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elif provider == "vllm":
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if not VLLM_AVAILABLE:
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return (0, 0, 0)
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fn = lambda: vllm_per_token_quant_fp8(x.clone())
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elif provider == "sglang":
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fn = lambda: sglang_per_token_quant_fp8(x.clone())
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ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
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return 1000 * ms, 1000 * max_ms, 1000 * min_ms
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if __name__ == "__main__":
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# Test various hidden dimensions for correctness - simplified for CI
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if IS_CI:
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test_dims = [2048] # Single dimension for CI
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batch_size, seq_len = 4, 64 # Smaller values for CI
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else:
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test_dims = [1368, 2048, 4096]
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batch_size, seq_len = 4, 4096
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for dim in test_dims:
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calculate_diff(batch_size=batch_size, seq_len=seq_len, hidden_dim=dim)
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print("\n" + "=" * 60)
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print("Starting performance benchmark...")
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benchmark_quantization.run(print_data=True)
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@@ -133,6 +133,8 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
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" float eps, float fp8_min, float fp8_max, bool scale_ue8m0, bool fuse_silu_and_mul, Tensor? masked_m) -> ()");
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m.impl("sgl_per_token_group_quant_8bit_v2", torch::kCUDA, &sgl_per_token_group_quant_8bit_v2);
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// Compatibility API: SGLang runtime dispatches to the JIT implementation,
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// but external sgl_kernel consumers still rely on this exported CUDA op.
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m.def("sgl_per_token_quant_fp8(Tensor input, Tensor! output_q, Tensor! output_s) -> ()");
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m.impl("sgl_per_token_quant_fp8", torch::kCUDA, &sgl_per_token_quant_fp8);
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@@ -1,64 +0,0 @@
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import itertools
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import sys
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from typing import Tuple
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import pytest
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import torch
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from sgl_kernel import sgl_per_token_quant_fp8
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from sglang.srt.utils import is_hip
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_is_hip = is_hip()
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fp8_type_ = torch.float8_e4m3fnuz if _is_hip else torch.float8_e4m3fn
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def torch_per_token_quant_fp8(tensor, inv_scale):
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# The reference implementation that fully aligns to
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# the kernel being tested.
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finfo = torch.finfo(torch.float8_e4m3fn)
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inv_scale = inv_scale.view(-1, 1)
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scale = inv_scale.reciprocal()
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qweight = (tensor.to(torch.float32) * scale).clamp(min=finfo.min, max=finfo.max)
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qweight = qweight.to(torch.float8_e4m3fn)
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return qweight
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def sglang_per_token_quant_fp8(
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input: torch.Tensor,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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scale = torch.zeros(input.size(0), device=input.device, dtype=torch.float32)
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output = torch.empty_like(input, device=input.device, dtype=fp8_type_)
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sgl_per_token_quant_fp8(input, output, scale)
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scale = scale.reshape(-1, 1)
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return output, scale
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PER_TOKEN_QUANT_CASES = list(
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itertools.product([128, 256, 512], [512, 1076, 1368, 2048, 4096])
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) + [
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(39, 1536),
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(1392, 1536),
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(7807, 1536),
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]
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@pytest.mark.parametrize("num_tokens,hidden_dim", PER_TOKEN_QUANT_CASES)
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def test_per_token_quant_compare_implementations(
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num_tokens: int,
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hidden_dim: int,
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):
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device = torch.device("cuda")
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x = torch.rand((num_tokens, hidden_dim), dtype=torch.float16, device=device)
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sglang_out, sglang_scale = sglang_per_token_quant_fp8(x)
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torch_out = torch_per_token_quant_fp8(x, sglang_scale)
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torch.testing.assert_close(
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sglang_out.float(), torch_out.float(), rtol=1e-3, atol=1e-3
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)
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if __name__ == "__main__":
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sys.exit(pytest.main([__file__]))
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@@ -0,0 +1,177 @@
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#include <sgl_kernel/tensor.h>
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#include <sgl_kernel/utils.h>
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#include <sgl_kernel/cta.cuh>
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#include <sgl_kernel/math.cuh>
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#include <sgl_kernel/runtime.cuh>
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#include <sgl_kernel/utils.cuh>
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#include <sgl_kernel/vec.cuh>
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#include <sgl_kernel/warp.cuh>
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#include <cstdint>
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namespace sglang {
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constexpr uint32_t kPerTokenQuantWarpSize = 32;
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constexpr uint32_t kPerTokenQuantTokensPerCTA = 8;
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/** \brief Quantize one token per warp for large token batches. */
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template <typename T, int kVecSize>
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__global__ void per_token_quant_fp8_warp_kernel(
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const T* __restrict__ input,
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fp8_e4m3_t* __restrict__ output_q,
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float* __restrict__ output_s,
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uint32_t hidden_dim,
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uint32_t num_tokens) {
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using namespace device;
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using input_vec_t = AlignedVector<T, kVecSize>;
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using output_vec_t = AlignedVector<fp8_e4m3_t, kVecSize>;
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const uint32_t warp_id = threadIdx.x / kPerTokenQuantWarpSize;
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const uint32_t lane_id = threadIdx.x % kPerTokenQuantWarpSize;
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const uint32_t token_id = blockIdx.x * kPerTokenQuantTokensPerCTA + warp_id;
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if (token_id >= num_tokens) {
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return;
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}
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const T* token_input = input + token_id * hidden_dim;
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fp8_e4m3_t* token_output = output_q + token_id * hidden_dim;
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const uint32_t num_vecs = hidden_dim / kVecSize;
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float max_value = 0.0f;
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for (uint32_t i = lane_id; i < num_vecs; i += kPerTokenQuantWarpSize) {
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input_vec_t input_vec;
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input_vec.load(token_input, i);
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#pragma unroll
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for (int j = 0; j < kVecSize; ++j) {
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max_value = math::max(max_value, math::abs(static_cast<float>(input_vec[j])));
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}
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}
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const float scale = warp::reduce_max(max_value) / math::FP8_E4M3_MAX;
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if (lane_id == 0) {
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output_s[token_id] = scale;
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}
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const float scale_inv = scale == 0.0f ? 0.0f : 1.0f / scale;
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for (uint32_t i = lane_id; i < num_vecs; i += kPerTokenQuantWarpSize) {
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input_vec_t input_vec;
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output_vec_t output_vec;
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input_vec.load(token_input, i);
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#pragma unroll
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for (int j = 0; j < kVecSize; ++j) {
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const float value = static_cast<float>(input_vec[j]) * scale_inv;
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output_vec[j] = static_cast<fp8_e4m3_t>(math::max(math::min(value, math::FP8_E4M3_MAX), -math::FP8_E4M3_MAX));
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}
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output_vec.store(token_output, i);
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}
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}
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/** \brief Quantize one token per CTA for small token batches. */
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template <typename T, int kVecSize>
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__global__ void per_token_quant_fp8_cta_kernel(
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const T* __restrict__ input, fp8_e4m3_t* __restrict__ output_q, float* __restrict__ output_s, uint32_t hidden_dim) {
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using namespace device;
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using input_vec_t = AlignedVector<T, kVecSize>;
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using output_vec_t = AlignedVector<fp8_e4m3_t, kVecSize>;
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const uint32_t token_id = blockIdx.x;
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const T* token_input = input + token_id * hidden_dim;
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fp8_e4m3_t* token_output = output_q + token_id * hidden_dim;
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const uint32_t num_vecs = hidden_dim / kVecSize;
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float max_value = 0.0f;
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for (uint32_t i = threadIdx.x; i < num_vecs; i += blockDim.x) {
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input_vec_t input_vec;
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input_vec.load(token_input, i);
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#pragma unroll
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for (int j = 0; j < kVecSize; ++j) {
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max_value = math::max(max_value, math::abs(static_cast<float>(input_vec[j])));
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}
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}
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__shared__ float reduction_smem[kPerTokenQuantWarpSize];
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__shared__ float scale_smem;
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cta::reduce_max(max_value, reduction_smem);
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__syncthreads();
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if (threadIdx.x == 0) {
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scale_smem = reduction_smem[0] / math::FP8_E4M3_MAX;
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output_s[token_id] = scale_smem;
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}
|
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__syncthreads();
|
||||
const float scale_inv = 1.0f / scale_smem;
|
||||
|
||||
for (uint32_t i = threadIdx.x; i < num_vecs; i += blockDim.x) {
|
||||
input_vec_t input_vec;
|
||||
output_vec_t output_vec;
|
||||
input_vec.load(token_input, i);
|
||||
#pragma unroll
|
||||
for (int j = 0; j < kVecSize; ++j) {
|
||||
const float value = static_cast<float>(input_vec[j]) * scale_inv;
|
||||
output_vec[j] = static_cast<fp8_e4m3_t>(math::max(math::min(value, math::FP8_E4M3_MAX), -math::FP8_E4M3_MAX));
|
||||
}
|
||||
output_vec.store(token_output, i);
|
||||
}
|
||||
}
|
||||
|
||||
template <typename T, int kVecSize>
|
||||
void launch_per_token_quant_fp8(
|
||||
DLDevice device, const T* input, fp8_e4m3_t* output_q, float* output_s, uint32_t hidden_dim, uint32_t num_tokens) {
|
||||
constexpr uint32_t kBlockSize = 256;
|
||||
const uint32_t sm_count = host::runtime::get_sm_count(device.device_id);
|
||||
const bool use_warp_kernel = num_tokens >= sm_count * 2 * kPerTokenQuantTokensPerCTA;
|
||||
if (use_warp_kernel) {
|
||||
const uint32_t grid = host::div_ceil(num_tokens, kPerTokenQuantTokensPerCTA);
|
||||
host::LaunchKernel(grid, kBlockSize, device)(
|
||||
per_token_quant_fp8_warp_kernel<T, kVecSize>, input, output_q, output_s, hidden_dim, num_tokens);
|
||||
} else {
|
||||
host::LaunchKernel(num_tokens, kBlockSize, device)(
|
||||
per_token_quant_fp8_cta_kernel<T, kVecSize>, input, output_q, output_s, hidden_dim);
|
||||
}
|
||||
}
|
||||
|
||||
/** \brief Validate and launch dynamic per-token FP8 E4M3 quantization. */
|
||||
template <typename T>
|
||||
void per_token_quant_fp8(tvm::ffi::TensorView input, tvm::ffi::TensorView output_q, tvm::ffi::TensorView output_s) {
|
||||
using namespace host;
|
||||
auto M = SymbolicSize{"num_tokens"};
|
||||
auto MOutput = SymbolicSize{"output_num_tokens"};
|
||||
auto K = SymbolicSize{"hidden_dim"};
|
||||
auto device = SymbolicDevice{};
|
||||
device.set_options<kDLCUDA>();
|
||||
|
||||
TensorMatcher({M, K}).with_dtype<T>().with_device(device).verify(input);
|
||||
TensorMatcher({MOutput, K}).with_dtype<fp8_e4m3_t>().with_device(device).verify(output_q);
|
||||
TensorMatcher({MOutput, 1}).with_dtype<float>().with_device(device).verify(output_s);
|
||||
|
||||
CHECK_HOST(M.unwrap() > 0) << "per_token_quant_fp8: num_tokens must be positive";
|
||||
CHECK_HOST(MOutput.unwrap() >= M.unwrap())
|
||||
<< "per_token_quant_fp8: output buffers must have at least " << M.unwrap() << " rows, got " << MOutput.unwrap();
|
||||
CHECK_HOST(K.unwrap() > 0 && K.unwrap() % 4 == 0)
|
||||
<< "per_token_quant_fp8: hidden_dim must be positive and divisible by 4, got " << K.unwrap();
|
||||
CHECK_HOST(M.unwrap() <= UINT32_MAX && K.unwrap() <= UINT32_MAX)
|
||||
<< "per_token_quant_fp8: dimensions exceed uint32 indexing";
|
||||
|
||||
const uint32_t num_tokens = static_cast<uint32_t>(M.unwrap());
|
||||
const uint32_t hidden_dim = static_cast<uint32_t>(K.unwrap());
|
||||
constexpr uint32_t kMaxVecSize = 16 / sizeof(T);
|
||||
if (hidden_dim % kMaxVecSize == 0) {
|
||||
launch_per_token_quant_fp8<T, kMaxVecSize>(
|
||||
device.unwrap(),
|
||||
static_cast<const T*>(input.data_ptr()),
|
||||
static_cast<fp8_e4m3_t*>(output_q.data_ptr()),
|
||||
static_cast<float*>(output_s.data_ptr()),
|
||||
hidden_dim,
|
||||
num_tokens);
|
||||
} else {
|
||||
launch_per_token_quant_fp8<T, 4>(
|
||||
device.unwrap(),
|
||||
static_cast<const T*>(input.data_ptr()),
|
||||
static_cast<fp8_e4m3_t*>(output_q.data_ptr()),
|
||||
static_cast<float*>(output_s.data_ptr()),
|
||||
hidden_dim,
|
||||
num_tokens);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace sglang
|
||||
@@ -21,14 +21,15 @@ _CUDA = frozenset({CapabilityRequirement.CUDA})
|
||||
register_kernel(
|
||||
KernelSpec(
|
||||
op="quantization.sgl_per_token_quant_fp8",
|
||||
backend=KernelBackend.AOT,
|
||||
target="sgl_kernel:sgl_per_token_quant_fp8",
|
||||
backend=KernelBackend.JIT,
|
||||
target="sglang.kernels.ops.quantization.per_token_quant_fp8:per_token_quant_fp8",
|
||||
capabilities=_CUDA,
|
||||
format_signature=FormatSignature(
|
||||
supported_dtypes=("float8_e4m3fn",),
|
||||
in_place=True,
|
||||
description="per-token FP8 quantization into output_q/output_s",
|
||||
),
|
||||
description="Per-token FP8 quantization (sgl_kernel wheel).",
|
||||
description="Per-token FP8 quantization (sglang.kernels.jit).",
|
||||
)
|
||||
)
|
||||
# fp8 / int8 are legacy aliases of the same 8bit kernel in the wheel; register
|
||||
@@ -78,7 +79,7 @@ def sgl_per_token_quant_fp8(
|
||||
output_s: torch.Tensor,
|
||||
) -> None:
|
||||
"""Per-token FP8 quantization, writing into ``output_q`` / ``output_s``."""
|
||||
return get_kernel("quantization.sgl_per_token_quant_fp8", KernelBackend.AOT)(
|
||||
return get_kernel("quantization.sgl_per_token_quant_fp8", KernelBackend.JIT)(
|
||||
input, output_q, output_s
|
||||
)
|
||||
|
||||
|
||||
@@ -46,7 +46,7 @@ _is_cpu = is_cpu()
|
||||
_is_musa = is_musa()
|
||||
_use_aiter = get_bool_env_var("SGLANG_USE_AITER") and _is_hip
|
||||
|
||||
if _is_cuda or _is_musa:
|
||||
if _is_cuda:
|
||||
from sglang.kernels.ops.quantization import (
|
||||
per_token_group_quant,
|
||||
sgl_per_token_quant_fp8,
|
||||
@@ -55,6 +55,13 @@ if _is_cuda or _is_musa:
|
||||
per_tensor_quant_fp8 as sgl_per_tensor_quant_fp8,
|
||||
)
|
||||
|
||||
if _is_musa:
|
||||
from sgl_kernel import sgl_per_token_quant_fp8
|
||||
|
||||
from sglang.kernels.ops.quantization.per_tensor_quant_fp8 import (
|
||||
per_tensor_quant_fp8 as sgl_per_tensor_quant_fp8,
|
||||
)
|
||||
|
||||
if _is_musa:
|
||||
# per_token_group_quant is CUDA-only JIT; MUSA keeps the AOT v2 group-quant op.
|
||||
from sglang.kernels.ops.quantization import sgl_per_token_group_quant_8bit
|
||||
@@ -2191,10 +2198,3 @@ def triton_scaled_mm(
|
||||
)
|
||||
|
||||
return result.to(out_dtype)
|
||||
|
||||
|
||||
if _is_cuda:
|
||||
|
||||
@register_fake_if_exists("sgl_kernel::sgl_per_token_quant_fp8")
|
||||
def _(input, output_q, output_s):
|
||||
return
|
||||
|
||||
@@ -0,0 +1,50 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import torch
|
||||
|
||||
from sglang.kernels.jit.utils import (
|
||||
cache_once,
|
||||
get_jit_cuda_arch,
|
||||
load_jit,
|
||||
make_cpp_args,
|
||||
)
|
||||
from sglang.srt.utils.custom_op import register_custom_op
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from tvm_ffi.module import Module
|
||||
|
||||
|
||||
@cache_once
|
||||
def _jit_per_token_quant_fp8_module(dtype: torch.dtype) -> Module:
|
||||
if dtype not in (torch.float16, torch.bfloat16, torch.float32):
|
||||
raise RuntimeError(
|
||||
f"Unsupported dtype {dtype}. Supported: float16, bfloat16, float32"
|
||||
)
|
||||
arch = get_jit_cuda_arch()
|
||||
use_fast_math = (arch.major, arch.minor) == (9, 0)
|
||||
math_mode = "fast_math" if use_fast_math else "precise_math"
|
||||
args = make_cpp_args(dtype)
|
||||
return load_jit(
|
||||
"per_token_quant_fp8",
|
||||
math_mode,
|
||||
*args,
|
||||
cuda_files=["gemm/per_token_quant_fp8.cuh"],
|
||||
cuda_wrappers=[("per_token_quant_fp8", f"per_token_quant_fp8<{args}>")],
|
||||
extra_cuda_cflags=["--use_fast_math"] if use_fast_math else [],
|
||||
)
|
||||
|
||||
|
||||
@register_custom_op(
|
||||
op_name="per_token_quant_fp8",
|
||||
mutates_args=["output_q", "output_s"],
|
||||
)
|
||||
def per_token_quant_fp8(
|
||||
input: torch.Tensor,
|
||||
output_q: torch.Tensor,
|
||||
output_s: torch.Tensor,
|
||||
) -> None:
|
||||
"""Dynamically quantize each row to FP8 E4M3."""
|
||||
module = _jit_per_token_quant_fp8_module(input.dtype)
|
||||
module.per_token_quant_fp8(input, output_q, output_s.view(output_s.shape[0], 1))
|
||||
Reference in New Issue
Block a user