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# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
"""torch>=2.11 NVFP4 probe. Three parts:
A. diagnostics -- torch/torchao versions, cpp-extension load state, device.
B. GEMM micro -- isolated per-linear forward latency (bf16 / fp8 / nvfp4-cutlass /
nvfp4-triton) at Z-Image-like shapes, to measure raw FP4
tensor-core throughput free of pipeline overhead.
C. end-to-end -- real dense Z-Image transformer, latency + LPIPS + PSNR + VRAM,
reference = dense bf16 eager.
Run on one CUDA (Blackwell) GPU. This is the experiment that decides whether NVFP4
becomes a genuine speedup once torch>=2.11 + torchao's CUTLASS FP4 GEMM is present."""
from __future__ import annotations
import argparse
import sys
import time
from pathlib import Path
import numpy as np
BASE = "Tongyi-MAI/Z-Image-Turbo"
PROMPT = "A cinematic photograph of a red fox in a snowy forest at dawn, highly detailed"
OUT = Path(__file__).resolve().parent.parent / "outputs" / "quant_research" / "nvfp4_t211_images"
# ----------------------------------------------------------------------------- diag
def diagnostics() -> None:
import torch
import torchao
print("== A. diagnostics ==", flush = True)
print(f" torch {torch.__version__}", flush = True)
print(f" torchao {torchao.__version__}", flush = True)
print(f" cuda {torch.version.cuda}", flush = True)
if torch.cuda.is_available():
print(
f" device {torch.cuda.get_device_name(0)} sm{torch.cuda.get_device_capability(0)}",
flush = True,
)
print(f" torch.ops.torchao present: {hasattr(torch.ops, 'torchao')}", flush = True)
print(
f" fp4 primitives: e2m1={hasattr(torch, 'float4_e2m1fn_x2')} "
f"e8m0={hasattr(torch, 'float8_e8m0fnu')} _scaled_mm={hasattr(torch, '_scaled_mm')}",
flush = True,
)
# torchao prints "Skipping import of cpp extensions" on torch<2.11;
# its absence means CUTLASS FP4 is live.
print(
" (no 'Skipping import of cpp extensions' line above => cpp/CUTLASS ext loaded)",
flush = True,
)
# ----------------------------------------------------------------------------- micro
def _configs():
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig as FP8
from torchao.prototype.mx_formats import NVFP4DynamicActivationNVFP4WeightConfig as NV
return {
"bf16": None,
"fp8": FP8(),
"nvfp4_cutlass": NV(use_triton_kernel = False),
"nvfp4_triton": NV(use_triton_kernel = True),
}
def _bench_linear(K, N, M, cfg, iters, compile_):
import torch
import torch.nn as nn
from torchao.quantization import quantize_
torch.compiler.reset()
torch.cuda.empty_cache()
m = nn.Sequential(nn.Linear(K, N, bias = False)).cuda().to(torch.bfloat16)
if cfg is not None:
quantize_(m, cfg)
fn = torch.compile(m, fullgraph = True, dynamic = False) if compile_ else m
x = torch.randn(M, K, device = "cuda", dtype = torch.bfloat16)
with torch.no_grad():
for _ in range(3): # warmup / compile
fn(x)
torch.cuda.synchronize()
dts = []
for _ in range(iters):
t0 = time.perf_counter()
fn(x)
torch.cuda.synchronize()
dts.append(time.perf_counter() - t0)
del m, fn, x
torch.cuda.empty_cache()
med = sorted(dts)[len(dts) // 2]
tflops = 2.0 * M * K * N / med / 1e12
return med, tflops
def micro(M, iters, compile_):
print(f"\n== B. GEMM micro (M={M}, compile={compile_}, iters={iters}) ==", flush = True)
# (K, N): qkv-ish, mlp-up, mlp-down for a ~3072-dim DiT
shapes = [(3072, 3072), (3072, 12288), (12288, 3072)]
cfgs = _configs()
for K, N in shapes:
print(f" shape K={K} N={N}:", flush = True)
base_ms = None
fp8_ms = None
for name, cfg in cfgs.items():
try:
med, tfl = _bench_linear(K, N, M, cfg, iters, compile_)
ms = med * 1e3
if name == "bf16":
base_ms = ms
if name == "fp8":
fp8_ms = ms
vs_bf16 = f"{base_ms/ms:.2f}x" if base_ms else "-"
vs_fp8 = f"{fp8_ms/ms:.2f}x" if fp8_ms else "-"
print(
f" {name:16s} {ms:7.3f} ms {tfl:7.1f} TFLOPS vs_bf16={vs_bf16:>6s} vs_fp8={vs_fp8:>6s}",
flush = True,
)
except Exception as exc: # noqa: BLE001
print(f" {name:16s} FAILED: {type(exc).__name__}: {str(exc)[:120]}", flush = True)
# ----------------------------------------------------------------------------- e2e
def _psnr(a, b):
mse = float(np.mean((a.astype(np.float64) - b.astype(np.float64)) ** 2))
return float("inf") if mse == 0 else float(10 * np.log10(255.0**2 / mse))
_LP = {"fn": None}
def _lpips(ref, arr):
try:
import lpips
import torch
if _LP["fn"] is None:
_LP["fn"] = lpips.LPIPS(net = "alex", verbose = False).cuda().eval()
def t(x):
return (torch.from_numpy(x).float().permute(2, 0, 1).unsqueeze(0) / 127.5 - 1.0).cuda()
with torch.no_grad():
return float(_LP["fn"](t(ref), t(arr)).item())
except Exception as exc: # noqa: BLE001
print(f" (lpips: {type(exc).__name__})", flush = True)
return None
def _load_dense():
import diffusers
import torch
t = diffusers.ZImageTransformer2DModel.from_pretrained(
BASE, subfolder = "transformer", torch_dtype = torch.bfloat16
)
pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype = torch.bfloat16, transformer = t)
pipe.to("cuda")
return pipe
def _gen(pipe, steps, seed, res):
import torch
g = torch.Generator(device = "cuda").manual_seed(seed)
torch.cuda.synchronize()
t0 = time.time()
img = pipe(
prompt = PROMPT,
width = res,
height = res,
num_inference_steps = steps,
guidance_scale = 0.0,
generator = g,
).images[0]
torch.cuda.synchronize()
return img, time.time() - t0
def _median(xs):
return sorted(xs)[len(xs) // 2]
def e2e(steps, res, seed, iters, mf):
import torch
import torch.nn as nn
OUT.mkdir(parents = True, exist_ok = True)
def filt(mod, fqn = ""):
return isinstance(mod, nn.Linear) and mod.in_features >= mf and mod.out_features >= mf
def run(
tag,
*,
cfg = None,
compile = True,
):
torch.compiler.reset()
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
pipe = _load_dense()
if cfg is not None:
from torchao.quantization import quantize_
quantize_(pipe.transformer, cfg, filter_fn = filt)
if compile:
try:
pipe.transformer.compile_repeated_blocks(fullgraph = True, dynamic = True)
except Exception as exc: # noqa: BLE001
print(
f" [{tag}] compile failed: {type(exc).__name__}: {str(exc)[:90]}", flush = True
)
_gen(pipe, steps, seed, res) # warmup / compile
dts, img = [], None
for _ in range(iters):
img, dt = _gen(pipe, steps, seed, res)
dts.append(dt)
gp = torch.cuda.max_memory_allocated() / 1e9
arr = np.array(img)
img.save(OUT / f"{tag}.png")
del pipe
torch.cuda.empty_cache()
return _median(dts), arr, gp
from torchao.prototype.mx_formats import NVFP4DynamicActivationNVFP4WeightConfig as NV
from torchao.quantization import Float8DynamicActivationFloat8WeightConfig as FP8
print(
f"\n== C. end-to-end (Z-Image dense, {res}px, {steps} steps, min_feat={mf}) ==", flush = True
)
bref, ref, _ = run("bf16_eager", cfg = None, compile = False)
print(f" bf16 eager ref: {bref:.3f}s", flush = True)
rows = [("bf16_eager", bref, float("inf"), 0.0, None)]
specs = [
("bf16_compile", None, True),
("fp8_compile", FP8(), True),
("nvfp4_cutlass_compile", NV(use_triton_kernel = False), True),
("nvfp4_triton_compile", NV(use_triton_kernel = True), True),
]
for tag, cfg, comp in specs:
try:
med, arr, gp = run(tag, cfg = cfg, compile = comp)
ps, lp = _psnr(ref, arr), _lpips(ref, arr)
rows.append((tag, med, ps, lp, gp))
print(
f" {tag:24s} {med:.3f}s ({bref/med:.2f}x vs eager) PSNR={ps:.1f} LPIPS={lp} VRAM={gp:.1f}G",
flush = True,
)
except Exception as exc: # noqa: BLE001
import traceback
traceback.print_exc()
print(f" {tag:24s} FAILED: {type(exc).__name__}: {str(exc)[:160]}", flush = True)
rows.append((tag, None, None, None, None))
fp8 = next((r[1] for r in rows if r[0] == "fp8_compile" and r[1]), None)
print("\n==== SUMMARY (ref = bf16 dense eager) ====", flush = True)
for tag, med, ps, lp, gp in rows:
if med is None:
print(f" {tag:24s} FAILED")
continue
vs_fp8 = f"{fp8/med:.2f}x" if fp8 else "-"
psv = "inf" if ps == float("inf") else f"{ps:.1f}"
lpv = (
"ref"
if (lp == 0.0 and tag == "bf16_eager")
else (f"{lp:.3f}" if lp is not None else "n/a")
)
print(
f" {tag:24s} {med:.3f}s vs_fp8:{vs_fp8:>6s} PSNR={psv:>5s} LPIPS={lpv:>6s}",
flush = True,
)
def main(argv = None) -> int:
p = argparse.ArgumentParser()
p.add_argument("--steps", type = int, default = 8)
p.add_argument("--res", type = int, default = 1024)
p.add_argument("--seed", type = int, default = 42)
p.add_argument("--iters", type = int, default = 3)
p.add_argument("--micro-M", type = int, default = 4096)
p.add_argument("--min-feat", type = int, default = 512)
p.add_argument("--only", choices = ["diag", "micro", "e2e", "all"], default = "all")
args = p.parse_args(argv)
diagnostics()
if args.only in ("micro", "all"):
micro(args.micro_M, args.iters, compile_ = True)
if args.only in ("e2e", "all"):
e2e(args.steps, args.res, args.seed, args.iters, args.min_feat)
print("NVFP4-T211-PROBE-DONE", flush = True)
return 0
if __name__ == "__main__":
sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "studio" / "backend"))
sys.exit(main())