[pre-commit.ci] auto fixes from pre-commit.com hooks
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bdb8eb9185
2 changed files with 105 additions and 52 deletions
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@ -41,38 +41,45 @@ _LP = {"fn": None}
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def _lpips(ref, arr):
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try:
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import torch, lpips
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if _LP["fn"] is None:
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_LP["fn"] = lpips.LPIPS(net="alex", verbose=False).cuda().eval()
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_LP["fn"] = lpips.LPIPS(net = "alex", verbose = False).cuda().eval()
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def t(x):
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return (torch.from_numpy(x).float().permute(2, 0, 1).unsqueeze(0) / 127.5 - 1.0).cuda()
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with torch.no_grad():
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return float(_LP["fn"](t(ref), t(arr)).item())
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except Exception as exc: # noqa: BLE001
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print(f" (lpips: {type(exc).__name__})", flush=True)
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print(f" (lpips: {type(exc).__name__})", flush = True)
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return None
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def _load_dense():
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import torch, diffusers
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t = diffusers.ZImageTransformer2DModel.from_pretrained(
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BASE, subfolder="transformer", torch_dtype=torch.bfloat16)
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pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype=torch.bfloat16, transformer=t)
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BASE, subfolder = "transformer", torch_dtype = torch.bfloat16
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)
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pipe = diffusers.ZImagePipeline.from_pretrained(BASE, torch_dtype = torch.bfloat16, transformer = t)
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pipe.to("cuda")
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return pipe
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def _big_linears(transformer, min_feat=512):
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def _big_linears(transformer, min_feat = 512):
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import torch.nn as nn
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return [
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m for m in transformer.modules()
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m
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for m in transformer.modules()
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if isinstance(m, nn.Linear) and m.in_features >= min_feat and m.out_features >= min_feat
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]
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def _prune_24_(transformer, min_feat=512):
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def _prune_24_(transformer, min_feat = 512):
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"""In-place 2:4 magnitude prune (zero the 2 smallest of every 4 along in_features)
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of the FLOP-heavy linears. Dense format -> measures the QUALITY of 2:4 with no kernel."""
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import torch
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n = 0
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for lin in _big_linears(transformer, min_feat):
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w = lin.weight.data
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@ -80,7 +87,7 @@ def _prune_24_(transformer, min_feat=512):
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if i % 4:
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continue
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g = w.view(o, i // 4, 4)
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idx = g.abs().argsort(dim=-1)[..., :2]
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idx = g.abs().argsort(dim = -1)[..., :2]
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g.scatter_(-1, idx, 0.0)
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n += 1
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return n
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@ -88,10 +95,18 @@ def _prune_24_(transformer, min_feat=512):
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def _gen(pipe, steps, seed, res):
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import torch
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g = torch.Generator(device="cuda").manual_seed(seed)
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torch.cuda.synchronize(); t0 = time.time()
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img = pipe(prompt=PROMPT, width=res, height=res, num_inference_steps=steps,
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guidance_scale=0.0, generator=g).images[0]
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g = torch.Generator(device = "cuda").manual_seed(seed)
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torch.cuda.synchronize()
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t0 = time.time()
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img = pipe(
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prompt = PROMPT,
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width = res,
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height = res,
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num_inference_steps = steps,
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guidance_scale = 0.0,
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generator = g,
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).images[0]
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torch.cuda.synchronize()
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return img, time.time() - t0
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@ -100,25 +115,35 @@ def _median(xs):
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return sorted(xs)[len(xs) // 2]
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def main(argv=None) -> int:
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def main(argv = None) -> int:
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p = argparse.ArgumentParser()
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p.add_argument("--steps", type=int, default=8)
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p.add_argument("--res", type=int, default=1024)
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p.add_argument("--seed", type=int, default=42)
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p.add_argument("--iters", type=int, default=3)
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p.add_argument("--min-feat", type=int, default=512)
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p.add_argument("--steps", type = int, default = 8)
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p.add_argument("--res", type = int, default = 1024)
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p.add_argument("--seed", type = int, default = 42)
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p.add_argument("--iters", type = int, default = 3)
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p.add_argument("--min-feat", type = int, default = 512)
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args = p.parse_args(argv)
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steps, res, seed, mf = args.steps, args.res, args.seed, args.min_feat
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import torch
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OUT.mkdir(parents=True, exist_ok=True)
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def filt(mod, fqn=""):
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OUT.mkdir(parents = True, exist_ok = True)
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def filt(mod, fqn = ""):
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import torch.nn as nn
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return (isinstance(mod, nn.Linear)
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and mod.in_features >= mf and mod.out_features >= mf)
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return isinstance(mod, nn.Linear) and mod.in_features >= mf and mod.out_features >= mf
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def run(tag, *, quant=None, fast_accum=True, prune=False, real_sparse=False, compile=True):
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torch.compiler.reset(); torch.cuda.empty_cache(); torch.cuda.reset_peak_memory_stats()
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def run(
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tag,
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*,
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quant = None,
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fast_accum = True,
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prune = False,
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real_sparse = False,
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compile = True,
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):
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torch.compiler.reset()
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats()
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pipe = _load_dense()
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note = ""
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if prune or real_sparse:
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@ -126,69 +151,98 @@ def main(argv=None) -> int:
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note += f" pruned24={n}"
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if real_sparse:
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from torchao.sparsity import sparsify_, semi_sparse_weight
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sparsify_(pipe.transformer, semi_sparse_weight(), filter_fn=filt)
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sparsify_(pipe.transformer, semi_sparse_weight(), filter_fn = filt)
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note += " +semi_sparse"
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if quant == "fp8":
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from torchao.quantization import (
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quantize_, Float8DynamicActivationFloat8WeightConfig)
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from torchao.quantization import quantize_, Float8DynamicActivationFloat8WeightConfig
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from torchao.float8 import Float8MMConfig
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cfg = Float8DynamicActivationFloat8WeightConfig(
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mm_config=Float8MMConfig(use_fast_accum=fast_accum))
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quantize_(pipe.transformer, cfg, filter_fn=filt)
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mm_config = Float8MMConfig(use_fast_accum = fast_accum)
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)
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quantize_(pipe.transformer, cfg, filter_fn = filt)
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note += f" fp8(fast_accum={fast_accum})"
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if compile:
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try:
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pipe.transformer.compile_repeated_blocks(fullgraph=True, dynamic=True)
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pipe.transformer.compile_repeated_blocks(fullgraph = True, dynamic = True)
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except Exception as exc: # noqa: BLE001
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note += f" [compile FAILED {type(exc).__name__}]"
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print(f" [{tag}]{note}", flush=True)
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print(f" [{tag}]{note}", flush = True)
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_gen(pipe, steps, seed, res) # warmup / compile
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dts = []
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img = None
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for _ in range(args.iters):
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img, dt = _gen(pipe, steps, seed, res); dts.append(dt)
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img, dt = _gen(pipe, steps, seed, res)
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dts.append(dt)
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gp = torch.cuda.max_memory_allocated() / 1e9
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arr = np.array(img); img.save(OUT / f"{tag}.png")
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del pipe; torch.cuda.empty_cache()
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arr = np.array(img)
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img.save(OUT / f"{tag}.png")
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del pipe
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torch.cuda.empty_cache()
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return _median(dts), arr, gp
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print(f"== sparse/accum probe (Z-Image dense, {res}px, {steps} steps, min_feat={mf}) ==", flush=True)
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print(
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f"== sparse/accum probe (Z-Image dense, {res}px, {steps} steps, min_feat={mf}) ==",
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flush = True,
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)
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rows = []
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# quality reference: dense bf16 eager (no compile, no quant)
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bref, ref, _ = run("bf16_eager", compile=False)
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bref, ref, _ = run("bf16_eager", compile = False)
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rows.append(("bf16_eager", bref, float("inf"), 0.0, None))
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print(f" bf16 eager ref: {bref:.3f}s", flush=True)
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print(f" bf16 eager ref: {bref:.3f}s", flush = True)
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specs = [
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("bf16_compile", dict()),
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("fp8_fastT_c", dict(quant="fp8", fast_accum=True)),
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("fp8_fastF_c", dict(quant="fp8", fast_accum=False)),
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("fake24_fp8_c", dict(quant="fp8", fast_accum=True, prune=True)), # quality of 2:4+fp8 (fake=no kernel)
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("real24_nocompile", dict(real_sparse=True, compile=False)), # sparse SPEED (no quant, no compile)
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("real24_compile_try", dict(real_sparse=True, compile=True)), # does sparse survive compile?
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("fp8_fastT_c", dict(quant = "fp8", fast_accum = True)),
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("fp8_fastF_c", dict(quant = "fp8", fast_accum = False)),
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(
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"fake24_fp8_c",
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dict(quant = "fp8", fast_accum = True, prune = True),
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), # quality of 2:4+fp8 (fake=no kernel)
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(
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"real24_nocompile",
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dict(real_sparse = True, compile = False),
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), # sparse SPEED (no quant, no compile)
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(
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"real24_compile_try",
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dict(real_sparse = True, compile = True),
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), # does sparse survive compile?
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]
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for tag, kw in specs:
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try:
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med, arr, gp = run(tag, **kw)
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ps, lp = _psnr(ref, arr), _lpips(ref, arr)
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rows.append((tag, med, ps, lp, gp))
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print(f" {tag:18s} {med:.3f}s ({bref/med:.2f}x vs eager) PSNR={ps:.1f} LPIPS={lp} VRAM={gp:.1f}G", flush=True)
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print(
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f" {tag:18s} {med:.3f}s ({bref/med:.2f}x vs eager) PSNR={ps:.1f} LPIPS={lp} VRAM={gp:.1f}G",
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flush = True,
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)
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except Exception as exc: # noqa: BLE001
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import traceback; traceback.print_exc()
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print(f" {tag:18s} FAILED: {type(exc).__name__}: {str(exc)[:160]}", flush=True)
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import traceback
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traceback.print_exc()
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print(f" {tag:18s} FAILED: {type(exc).__name__}: {str(exc)[:160]}", flush = True)
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rows.append((tag, None, None, None, None))
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print("\n==== SUMMARY (ref = bf16 dense eager) ====", flush=True)
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print("\n==== SUMMARY (ref = bf16 dense eager) ====", flush = True)
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base = next((r[1] for r in rows if r[0] == "fp8_fastT_c" and r[1]), None)
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for tag, med, ps, lp, gp in rows:
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if med is None:
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print(f" {tag:18s} FAILED"); continue
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print(f" {tag:18s} FAILED")
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continue
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vs_eager = f"{bref/med:.2f}x"
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vs_fp8 = f"{base/med:.2f}x" if base else "-"
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psv = "inf" if ps == float("inf") else f"{ps:.1f}"
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lpv = "ref" if (lp == 0.0 and tag == 'bf16_eager') else (f"{lp:.3f}" if lp is not None else "n/a")
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print(f" {tag:18s} {med:.3f}s eager:{vs_eager:>6s} fp8:{vs_fp8:>6s} PSNR={psv:>5s} LPIPS={lpv:>6s}", flush=True)
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print("SPARSE-ACCUM-DONE", flush=True)
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lpv = (
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"ref"
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if (lp == 0.0 and tag == "bf16_eager")
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else (f"{lp:.3f}" if lp is not None else "n/a")
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)
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print(
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f" {tag:18s} {med:.3f}s eager:{vs_eager:>6s} fp8:{vs_fp8:>6s} PSNR={psv:>5s} LPIPS={lpv:>6s}",
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flush = True,
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)
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print("SPARSE-ACCUM-DONE", flush = True)
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return 0
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@ -180,7 +180,6 @@ def _make_quant_config(scheme: str) -> Any:
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# throughput. (Negligible numeric effect for diffusion's short reductions.)
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try:
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from torchao.float8 import Float8MMConfig
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return Float8DynamicActivationFloat8WeightConfig(
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mm_config = Float8MMConfig(use_fast_accum = True)
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)
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