mirror of
https://github.com/ostris/ai-toolkit.git
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361 lines
13 KiB
Python
361 lines
13 KiB
Python
"""
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Reproducible speed / VRAM / accuracy benchmarks for the toolkit quantization
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backends.
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Compares bf16 against the custom OstrisLinear backends (convrot8, convrot4 for
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now; add more qtypes to QTYPES as they land).
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Measures, per qtype:
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- layer inference latency across DiT-representative shapes (vs bf16)
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- layer training latency (forward + backward through the frozen layer)
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- VRAM on a transformer-ish block stack: resident weights, peak during a
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no-grad forward, peak during a train step
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- accuracy drift vs bf16: output relative error per layer shape and
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accumulated through the block stack
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- weight reconstruction error and one-time quantize (conversion) time
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Usage:
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python scripts/test_quantizations.py --gpu 1
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python scripts/test_quantizations.py --gpu 1 --qtypes bf16 convrot8
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"""
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import argparse
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import math
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import os
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import sys
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import time
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# set cuda bus ordering to be pcie
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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import torch # noqa: E402
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# (tokens, in_features, out_features) — FLUX/Wan-class projections
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SPEED_SHAPES = [
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(4096, 3072, 3072),
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(4096, 3072, 12288),
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(4096, 12288, 3072),
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(1024, 3072, 12288),
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]
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# block stack used for the vram/drift tests (mimics a DiT block's linears)
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VRAM_BLOCKS = 8
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VRAM_BLOCK_SHAPES = [(3072, 12288), (12288, 3072), (3072, 3072), (3072, 3072)]
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VRAM_TOKENS = 4096
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QTYPES = [
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"bf16", "qfloat8", "float8", "orbit4", "orbitvq4", "convrot8", "convrot4",
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"convrotint7", "convrotint6", "convrotint5", "convrotint4", "convrotint3",
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"convrotint2", "convrotbitnet", "convrotcomfyw4a4",
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]
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STACK_KEY = f"{VRAM_BLOCKS}-block stack"
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def convert(module: torch.nn.Linear, qtype: str) -> torch.nn.Linear:
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"""Quantize a linear with the given qtype. Returns the (possibly replaced)
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module — quanto swaps the module object, the ostris backends convert in place."""
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if qtype == "bf16":
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return module
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from toolkit.util.ostris_quant import convert_linear_to_ostris, get_ostris_quantizer
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q = get_ostris_quantizer(qtype)
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if q is not None:
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assert convert_linear_to_ostris(module, q), f"conversion refused for {qtype}"
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return module
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# quanto / torchao qtypes go through the shared toolkit quantize flow; use a
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# holder so quanto's module replacement has a parent to swap into
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from optimum.quanto import freeze
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from toolkit.util.quantize import get_qtype, quantize
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holder = torch.nn.Sequential(module)
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quantize(holder, weights=get_qtype(qtype))
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freeze(holder)
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return holder[0]
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def fp_weight(module: torch.nn.Linear) -> torch.Tensor:
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"""Dequantized weight in float32, whatever the backend."""
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if hasattr(module, "dequantize_weight"):
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return module.dequantize_weight().float()
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w = module.weight
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if hasattr(w, "dequantize"):
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return w.dequantize().float()
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return w.detach().float()
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def bench(fn, iters: int, device) -> float:
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for _ in range(max(3, iters // 5)):
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fn()
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torch.cuda.synchronize(device)
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t0 = time.perf_counter()
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for _ in range(iters):
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fn()
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torch.cuda.synchronize(device)
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return (time.perf_counter() - t0) / iters * 1000 # ms
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def gb(nbytes: int) -> str:
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return f"{nbytes / 1e9:6.2f} GB"
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def make_layer(k: int, n: int, device) -> torch.nn.Linear:
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lin = torch.nn.Linear(k, n, bias=True, dtype=torch.bfloat16, device=device)
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with torch.no_grad():
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lin.weight.mul_(0.02)
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return lin
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def make_stack(device) -> torch.nn.ModuleList:
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# default nn.Linear init (~1/sqrt(in) std) so block branches contribute at a
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# realistic O(1) scale to the residual stream — scaling weights down further
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# makes accumulated quantization drift look artificially tiny
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torch.manual_seed(0)
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blocks = torch.nn.ModuleList()
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for _ in range(VRAM_BLOCKS):
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blocks.append(torch.nn.ModuleList([
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torch.nn.Linear(k, n, bias=True, dtype=torch.bfloat16, device=device)
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for k, n in VRAM_BLOCK_SHAPES
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]))
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return blocks
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def block_forward(b, h):
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# pre-norm residual block like a real transformer, so activations stay at a
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# sane scale and quantization drift accumulates realistically across depth
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r = torch.nn.functional.layer_norm(h, h.shape[-1:])
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r = b[0](r) # 3072 -> 12288
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r = torch.nn.functional.gelu(r)
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h = h + b[1](r) # 12288 -> 3072
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r = torch.nn.functional.layer_norm(h, h.shape[-1:])
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return h + b[3](b[2](r)) # 3072 -> 3072 -> 3072
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def stack_forward(blocks, x, checkpoint=False):
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h = x
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for b in blocks:
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if checkpoint:
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h = torch.utils.checkpoint.checkpoint(
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block_forward, b, h, use_reentrant=False
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)
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else:
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h = block_forward(b, h)
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return h
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def run_speed(qtype: str, device, iters: int, results: dict):
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for m, k, n in SPEED_SHAPES:
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torch.manual_seed(0)
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lin = make_layer(k, n, device)
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lin = convert(lin, qtype)
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x = torch.randn(m, k, device=device, dtype=torch.bfloat16)
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with torch.no_grad():
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t_inf = bench(lambda: lin(x), iters, device)
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def train_step():
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xi = x.detach().requires_grad_(True)
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lin(xi).sum().backward()
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t_train = bench(train_step, max(10, iters // 3), device)
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results[(qtype, "inf", (m, k, n))] = t_inf
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results[(qtype, "train", (m, k, n))] = t_train
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torch.cuda.empty_cache()
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def run_vram(qtype: str, device, results: dict):
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torch.cuda.empty_cache()
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base = torch.cuda.memory_allocated(device)
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blocks = make_stack(device)
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for b in blocks:
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for i in range(len(b)):
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b[i] = convert(b[i], qtype)
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torch.cuda.empty_cache()
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results[(qtype, "vram_weights")] = torch.cuda.memory_allocated(device) - base
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x = torch.randn(VRAM_TOKENS, 3072, device=device, dtype=torch.bfloat16)
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# no-grad forward peak (sampling); warm up first so lazy-init allocations are
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# not counted as steady-state peak
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with torch.no_grad():
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stack_forward(blocks, x)
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torch.cuda.synchronize(device)
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torch.cuda.reset_peak_memory_stats(device)
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with torch.no_grad():
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stack_forward(blocks, x)
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torch.cuda.synchronize(device)
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results[(qtype, "vram_fwd_peak")] = torch.cuda.max_memory_allocated(device) - base
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# train step peak (frozen base; grads flow to the input like lora training),
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# with and without per-block gradient checkpointing (real training uses it)
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def train_step(checkpoint):
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xi = x.detach().requires_grad_(True)
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stack_forward(blocks, xi, checkpoint).float().pow(2).mean().backward()
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for key, ckpt in (("vram_train_peak", False), ("vram_train_ckpt_peak", True)):
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train_step(ckpt)
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torch.cuda.synchronize(device)
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torch.cuda.reset_peak_memory_stats(device)
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train_step(ckpt)
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torch.cuda.synchronize(device)
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results[(qtype, key)] = torch.cuda.max_memory_allocated(device) - base
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blocks = x = None # release before the allocator accounting of the next run
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torch.cuda.empty_cache()
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def run_drift(qtype: str, device, results: dict):
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"""Output error vs the bf16 reference, per layer shape and through the stack."""
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for m, k, n in SPEED_SHAPES:
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torch.manual_seed(0)
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lin = make_layer(k, n, device)
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x = torch.randn(m, k, device=device, dtype=torch.bfloat16)
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with torch.no_grad():
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y_ref = lin(x).float()
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lin = convert(lin, qtype)
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y_q = lin(x).float()
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results[(qtype, "drift", (m, k, n))] = ((y_q - y_ref).norm() / y_ref.norm()).item()
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torch.cuda.empty_cache()
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blocks = make_stack(device)
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x = torch.randn(VRAM_TOKENS, 3072, device=device, dtype=torch.bfloat16)
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with torch.no_grad():
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y_ref = stack_forward(blocks, x).float()
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for b in blocks:
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for i in range(len(b)):
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b[i] = convert(b[i], qtype)
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y_q = stack_forward(blocks, x).float()
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results[(qtype, "drift", STACK_KEY)] = ((y_q - y_ref).norm() / y_ref.norm()).item()
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blocks = x = None
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torch.cuda.empty_cache()
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def run_quality_and_quantize_time(qtype: str, device, results: dict):
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torch.manual_seed(0)
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lin = make_layer(3072, 3072, device)
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w0 = lin.weight.detach().float().clone()
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torch.cuda.synchronize(device)
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t0 = time.perf_counter()
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lin = convert(lin, qtype)
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torch.cuda.synchronize(device)
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results[(qtype, "quantize_ms")] = (time.perf_counter() - t0) * 1000
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if qtype == "bf16":
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results[(qtype, "weight_err")] = 0.0
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else:
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wq = fp_weight(lin)
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results[(qtype, "weight_err")] = ((wq - w0).norm() / w0.norm()).item()
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torch.cuda.empty_cache()
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def print_speed_table(title: str, kind: str, qts, results):
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print(f"\n=== {title} (ms; speedup vs bf16) ===")
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print(f"{'M x K -> N':<22}" + "".join(f"{qt:>18}" for qt in qts))
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for shape in SPEED_SHAPES:
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m, k, n = shape
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row = f"{f'{m} x {k} -> {n}':<22}"
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ref = results.get(("bf16", kind, shape))
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for qt in qts:
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t = results.get((qt, kind, shape))
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if t is None:
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row += f"{'-':>18}"
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continue
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sp = f" ({ref / t:4.2f}x)" if ref and qt != "bf16" else " " * 8
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row += f"{t:8.3f}ms{sp}"
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print(row)
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def main():
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ap = argparse.ArgumentParser(description=__doc__)
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ap.add_argument("--gpu", type=int, default=0, help="cuda device id to run on")
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ap.add_argument("--qtypes", nargs="+", default=QTYPES, help=f"subset of {QTYPES}")
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ap.add_argument("--iters", type=int, default=50, help="timing iterations per case")
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args = ap.parse_args()
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device = torch.device(f"cuda:{args.gpu}")
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torch.cuda.set_device(device)
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props = torch.cuda.get_device_properties(device)
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print(f"device: cuda:{args.gpu} ({props.name}, sm_{props.major}{props.minor}, "
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f"{props.total_memory / 1e9:.0f} GB)")
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print(f"torch {torch.__version__}\n")
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# warm the toolkit import chain (module imports + custom-op registration) so it
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# isn't charged to the first qtype's quantize timing
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if any(qt != "bf16" for qt in args.qtypes):
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from toolkit.util.ostris_quant import get_ostris_quantizer
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for qt in args.qtypes:
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if qt != "bf16":
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get_ostris_quantizer(qt)
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results = {}
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for qt in args.qtypes:
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print(f"benchmarking {qt} ...")
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run_quality_and_quantize_time(qt, device, results)
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run_drift(qt, device, results)
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run_speed(qt, device, args.iters, results)
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run_vram(qt, device, results)
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qts = args.qtypes
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print_speed_table("layer latency, inference", "inf", qts, results)
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print_speed_table("layer latency, train fwd+bwd", "train", qts, results)
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print(f"\n=== vram on the block stack ({VRAM_BLOCKS} blocks, {VRAM_TOKENS} tokens) ===")
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print(f"{'':<28}" + "".join(f"{qt:>18}" for qt in qts))
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for key, label in (("vram_weights", "weights resident"),
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("vram_fwd_peak", "peak, no-grad fwd"),
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("vram_train_peak", "peak, train step"),
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("vram_train_ckpt_peak", "peak, train step (ckpt)")):
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row = f"{label:<28}"
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for qt in qts:
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row += f"{gb(results[(qt, key)]):>18}"
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print(row)
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print("\n=== accuracy drift vs bf16 (output rel err, no-grad) ===")
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print(f"{'':<28}" + "".join(f"{qt:>18}" for qt in qts))
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for shape in SPEED_SHAPES + [STACK_KEY]:
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label = f"{shape[0]} x {shape[1]} -> {shape[2]}" if isinstance(shape, tuple) else shape
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row = f"{label:<28}"
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for qt in qts:
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row += f"{results[(qt, 'drift', shape)]:>18.5f}"
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print(row)
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print("\n=== quantization ===")
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print(f"{'':<28}" + "".join(f"{qt:>18}" for qt in qts))
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row = f"{'weight rel err':<28}"
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for qt in qts:
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row += f"{results[(qt, 'weight_err')]:>18.5f}"
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print(row)
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row = f"{'quantize time (ms)':<28}"
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for qt in qts:
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row += f"{results[(qt, 'quantize_ms')]:>18.1f}"
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print(row)
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# ---- clean per-qtype breakdown: speed (geomean over shapes) + accuracy ----
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def geomean_speedup(qt, kind):
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logs = []
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for shape in SPEED_SHAPES:
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ref = results.get(("bf16", kind, shape))
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t = results.get((qt, kind, shape))
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if ref and t:
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logs.append(math.log(ref / t))
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return math.exp(sum(logs) / len(logs)) if logs else float("nan")
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print("\n=== summary (speed = geomean speedup vs bf16; drift lower is better) ===")
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print(f"{'':<12}{'inference':>18}{'train':>18}{'accuracy drift':>20}{'max vram':>16}")
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for qt in qts:
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# real training checkpoints, so the ckpt peak is the meaningful train
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# number; the no-grad fwd peak still matters for sampling
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max_vram = max(results[(qt, "vram_fwd_peak")], results[(qt, "vram_train_ckpt_peak")])
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print(f"{qt:<12}"
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f"{geomean_speedup(qt, 'inf'):>17.2f}x"
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f"{geomean_speedup(qt, 'train'):>17.2f}x"
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f"{results[(qt, 'drift', STACK_KEY)]:>20.5f}"
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f"{gb(max_vram):>16}")
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if __name__ == "__main__":
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main()
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