90 lines
3.2 KiB
Python
90 lines
3.2 KiB
Python
"""Validate the opt-in Nvfp4Linear CuTe QKV runtime dispatch."""
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from __future__ import annotations
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import argparse
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import json
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import os
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from pathlib import Path
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import torch
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from profile_nvfp4_linear import representative_inputs
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("--output", type=Path, required=True)
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parser.add_argument("--rows", type=int, default=2048)
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parser.add_argument("--warmup", type=int, default=3)
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parser.add_argument("--iterations", type=int, default=10)
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parser.add_argument("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
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parser.add_argument("--block-index", type=int, default=24)
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parser.add_argument("--width", type=int, default=1344)
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parser.add_argument("--height", type=int, default=768)
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parser.add_argument("--frames", type=int, default=124)
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parser.add_argument("--steps", type=int, default=12)
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parser.add_argument("--sampler-step", type=int, default=1)
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parser.add_argument("--seed", type=int, default=440420)
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parser.add_argument("--text-tokens", type=int, default=100)
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parser.add_argument("--attention", default="sage2")
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parser.add_argument("--device", default="cuda")
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return parser.parse_args()
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def measure(fn, warmup: int, iterations: int) -> float:
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for _ in range(warmup):
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fn()
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torch.cuda.synchronize()
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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start.record()
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for _ in range(iterations):
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fn()
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end.record()
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end.synchronize()
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return start.elapsed_time(end) / iterations
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def main() -> None:
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args = parse_args()
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block, inputs, metadata = representative_inputs(args)
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linear = block.attention.qkv_proj
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x = inputs["attn_qkv_proj"][: args.rows].contiguous()
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if linear.role != "h3_attn_qkv":
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raise RuntimeError(f"Expected h3_attn_qkv role, got {linear.role!r}")
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os.environ.pop("H3_CUTE_QKV_RING", None)
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with torch.inference_mode():
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reference = linear(x)
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os.environ["H3_CUTE_QKV_RING"] = "1"
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with torch.inference_mode():
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candidate = linear(x)
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torch.cuda.synchronize()
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delta = candidate.float() - reference.float()
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with torch.inference_mode():
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ring_ms = measure(lambda: linear(x), args.warmup, args.iterations)
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os.environ.pop("H3_CUTE_QKV_RING", None)
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reference_ms = measure(lambda: linear(x), args.warmup, args.iterations)
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report = {
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"device": torch.cuda.get_device_name(),
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"metadata": metadata,
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"block_index": args.block_index,
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"rows": args.rows,
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"role": linear.role,
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"equal": torch.equal(candidate, reference),
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"max_abs": delta.abs().max().item(),
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"mean_abs": delta.abs().mean().item(),
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"ring_ms": ring_ms,
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"reference_ms": reference_ms,
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"improvement_percent": (1.0 - ring_ms / reference_ms) * 100.0,
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}
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args.output.parent.mkdir(parents=True, exist_ok=True)
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args.output.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
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print(json.dumps(report, indent=2), flush=True)
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if __name__ == "__main__":
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main()
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