unsloth/scripts/perf_levers_probe.py at main · unslothai/unsloth

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# SPDX-License-Identifier: AGPL-3.0-only

"""Measure the next-phase diffusion levers on the real model, vs today's compiled baseline.

Variants (Z-Image dense bf16, regional compile = the shipped "default" speed profile):

baseline -- channels_last + compile_repeated_blocks (reference image)

inductor_flags -- + the lossless inductor autotune flags (conv_1x1_as_mm,

coordinate_descent_tuning(+all_dirs), epilogue_fusion=False)

attn_cudnn -- + set_attention_backend("_native_cudnn") (exact)

attn_flash4 -- + set_attention_backend("flash_4_hub") (exact, SM100)

attn_sage -- + set_attention_backend("sage") (INT8 QK, quantized)

fbcache -- + First-Block-Cache (threshold 0.12) (few-step headroom test)

Reports median latency, vs-baseline speedup, peak VRAM, and LPIPS vs baseline. One CUDA GPU."""

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" / "perf_levers_images"

_LP = {"fn": None}

def _lpips(ref, arr):

try:

import lpips

import torch

# Keep the metric model on CPU: cached on CUDA it stays resident and is charged to every later measurement.

if _LP["fn"] is None:

_LP["fn"] = lpips.LPIPS(net = "alex", verbose = False).eval()

def t(x):

return torch.from_numpy(x).float().permute(2, 0, 1).unsqueeze(0) / 127.5 - 1.0

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 _set_inductor_flags():

import torch._inductor.config as ic

ic.conv_1x1_as_mm = True

ic.coordinate_descent_tuning = True

ic.coordinate_descent_check_all_directions = True

ic.epilogue_fusion = False

try:

ic.force_fuse_int_mm_with_mul = True

except Exception: # noqa: BLE001

pass

def _reset_inductor_flags():

import torch._inductor.config as ic

ic.conv_1x1_as_mm = False

ic.coordinate_descent_tuning = False

ic.coordinate_descent_check_all_directions = False

ic.epilogue_fusion = True

# Reset the int-mm fusion flag too, else it leaks into every later compiled row.

try:

ic.force_fuse_int_mm_with_mul = False

except Exception: # noqa: BLE001

pass

def _load():

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")

try:

pipe.vae.to(memory_format = torch.channels_last)

except Exception: # noqa: BLE001

pass

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 run(

tag,

steps,

seed,

res,

iters,

attn = None,

fbcache = None,

inductor = False,

import torch

torch.compiler.reset()

torch.cuda.empty_cache()

torch.cuda.reset_peak_memory_stats()

_reset_inductor_flags()

if inductor:

_set_inductor_flags()

pipe = _load()

note = ""

if attn is not None:

try:

pipe.transformer.set_attention_backend(attn)

except Exception as exc: # noqa: BLE001

note = f"attn({attn})={type(exc).__name__}:{str(exc)[:60]}"

print(f" [{tag}] {note}", flush = True)

del pipe

torch.cuda.empty_cache()

return None

else:

# set_attention_backend is process-wide and fresh processors inherit it, so force native for no-attn variants.

try:

pipe.transformer.set_attention_backend("native")

except Exception as exc: # noqa: BLE001 - best-effort isolation

print(

f" [{tag}] attn(native-reset)={type(exc).__name__}:{str(exc)[:60]}", flush = True

)

if fbcache is not None:

try:

from diffusers.hooks import FirstBlockCacheConfig, apply_first_block_cache

apply_first_block_cache(pipe.transformer, FirstBlockCacheConfig(threshold = fbcache))

except Exception as exc: # noqa: BLE001

print(f" [{tag}] fbcache={type(exc).__name__}:{str(exc)[:60]}", flush = True)

del pipe

torch.cuda.empty_cache()

return None

try:

pipe.transformer.compile_repeated_blocks(fullgraph = True, dynamic = True)

except Exception as exc: # noqa: BLE001

print(f" [{tag}] compile={type(exc).__name__}:{str(exc)[:60]}", flush = True)

try:

_gen(pipe, steps, seed, res) # warmup / compile

except Exception as exc: # noqa: BLE001

import traceback

traceback.print_exc()

print(f" [{tag}] FAILED first gen: {type(exc).__name__}:{str(exc)[:80]}", flush = True)

del pipe

torch.cuda.empty_cache()

return None

dts, img = [], None

for _ in range(iters):

img, dt = _gen(pipe, steps, seed, res)

dts.append(dt)

peak = torch.cuda.max_memory_allocated() / 1e9

arr = np.array(img)

OUT.mkdir(parents = True, exist_ok = True)

img.save(OUT / f"{tag}.png")

del pipe

torch.cuda.empty_cache()

return _median(dts), arr, peak

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)

args = p.parse_args(argv)

s, r, seed, it = args.steps, args.res, args.seed, args.iters

print(f"== perf levers (Z-Image dense, {r}px, {s} steps) ==", flush = True)

base = run("baseline", s, seed, r, it)

if base is None:

print("baseline FAILED", flush = True)

return 1

bmed, ref, bpeak = base

print(f" baseline {bmed:.3f}s peak={bpeak:.1f}G", flush = True)

rows = [("baseline", bmed, bpeak, 0.0)]

variants = [

("inductor_flags", dict(inductor = True)),

("attn_cudnn", dict(attn = "_native_cudnn")),

("attn_flash4", dict(attn = "flash_4_hub")),

("attn_sage", dict(attn = "sage")),

("attn_sage_inductor", dict(attn = "sage", inductor = True)),

("fbcache_0p12", dict(fbcache = 0.12)),

]

for tag, kw in variants:

out = run(tag, s, seed, r, it, **kw)

if out is None:

rows.append((tag, None, None, None))

continue

med, arr, peak = out

lp = _lpips(ref, arr)

rows.append((tag, med, peak, lp))

spd = f"{bmed/med:.2f}x" if med else "-"

print(f" {tag:20s} {med:.3f}s ({spd} vs base) peak={peak:.1f}G LPIPS={lp}", flush = True)

print("\n==== SUMMARY (ref = baseline compile) ====", flush = True)

for tag, med, peak, lp in rows:

if med is None:

print(f" {tag:20s} FAILED")

continue

spd = f"{bmed/med:.2f}x" if med else "-"

lpv = "ref" if (tag == "baseline") else (f"{lp:.3f}" if lp is not None else "n/a")

print(f" {tag:20s} {med:.3f}s {spd:>6s} peak={peak:.1f}G LPIPS={lpv:>6s}", flush = True)

print("PERF-LEVERS-DONE", flush = True)

return 0

if __name__ == "__main__":

sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "studio" / "backend"))

sys.exit(main())

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