118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
"""Validate Vortex direct Sage2 V preparation against SageAttention 2.2.0."""
|
|
|
|
from __future__ import annotations
|
|
|
|
import argparse
|
|
import json
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
|
|
from h3_blackwell_runtime.sage2_entry import prepare_v
|
|
from profile_attention_path import summarize
|
|
|
|
|
|
def difference(actual: torch.Tensor, expected: torch.Tensor) -> dict:
|
|
delta = actual.float() - expected.float()
|
|
return {
|
|
"equal": torch.equal(actual, expected),
|
|
"different_elements": int(torch.count_nonzero(actual != expected).item()),
|
|
"max_abs": delta.abs().max().item() if delta.numel() else 0.0,
|
|
"mean_abs": delta.abs().mean().item() if delta.numel() else 0.0,
|
|
}
|
|
|
|
|
|
def measure(fn, *, warmup: int, iterations: int) -> dict:
|
|
for _ in range(warmup):
|
|
fn()
|
|
torch.cuda.synchronize()
|
|
samples = []
|
|
for _ in range(iterations):
|
|
started = torch.cuda.Event(enable_timing=True)
|
|
finished = torch.cuda.Event(enable_timing=True)
|
|
started.record()
|
|
fn()
|
|
finished.record()
|
|
finished.synchronize()
|
|
samples.append(started.elapsed_time(finished) / 1000.0)
|
|
return summarize(samples)
|
|
|
|
|
|
def baseline(v: torch.Tensor):
|
|
import sageattention.core as sage_core
|
|
|
|
return sage_core.per_channel_fp8(
|
|
v, tensor_layout="NHD", scale_max=2.25, smooth_v=False,
|
|
)
|
|
|
|
|
|
def run_case(sequence: int, heads: int, seed: int, warmup: int, iterations: int) -> dict:
|
|
generator = torch.Generator(device="cuda").manual_seed(seed)
|
|
storage = torch.randn(
|
|
(sequence, heads * 128 * 3),
|
|
generator=generator,
|
|
device="cuda",
|
|
dtype=torch.bfloat16,
|
|
)
|
|
v = storage[:, heads * 128 * 2 :].view(1, sequence, heads, 128)
|
|
reference_fp8, reference_scale, _ = baseline(v)
|
|
candidate_fp8, candidate_scale = prepare_v(v)
|
|
torch.cuda.synchronize()
|
|
result = {
|
|
"sequence": sequence,
|
|
"heads": heads,
|
|
"stride": list(v.stride()),
|
|
"fp8": difference(candidate_fp8, reference_fp8),
|
|
"scale": difference(candidate_scale, reference_scale),
|
|
}
|
|
if sequence >= 1024:
|
|
result["baseline_timing"] = measure(
|
|
lambda: baseline(v), warmup=warmup, iterations=iterations,
|
|
)
|
|
result["candidate_timing"] = measure(
|
|
lambda: prepare_v(v), warmup=warmup, iterations=iterations,
|
|
)
|
|
return result
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser(description=__doc__)
|
|
parser.add_argument("--output", type=Path, required=True)
|
|
parser.add_argument(
|
|
"--lengths",
|
|
nargs="+",
|
|
type=int,
|
|
default=(1, 31, 32, 33, 63, 64, 65, 127, 128, 129, 37760, 37761, 37810),
|
|
)
|
|
parser.add_argument("--heads", type=int, default=56)
|
|
parser.add_argument("--seed", type=int, default=440420)
|
|
parser.add_argument("--warmup", type=int, default=3)
|
|
parser.add_argument("--iterations", type=int, default=10)
|
|
return parser.parse_args()
|
|
|
|
|
|
def main() -> None:
|
|
args = parse_args()
|
|
cases = [
|
|
run_case(length, args.heads, args.seed + index, args.warmup, args.iterations)
|
|
for index, length in enumerate(args.lengths)
|
|
]
|
|
report = {
|
|
"status": "pass" if all(case["fp8"]["equal"] and case["scale"]["equal"] for case in cases) else "fail",
|
|
"contract": {
|
|
"tensor_layout": "NHD",
|
|
"head_dim": 128,
|
|
"scale_max": 2.25,
|
|
"output": "Sage2 padded/permuted E4M3 V and FP32 per-channel scale",
|
|
},
|
|
"cases": cases,
|
|
}
|
|
args.output.parent.mkdir(parents=True, exist_ok=True)
|
|
args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
|
|
print(json.dumps(report, indent=2), flush=True)
|
|
if report["status"] != "pass":
|
|
raise RuntimeError("Vortex Sage2 V preparation did not match SageAttention 2.2.0")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|