141 lines
5.6 KiB
Diff
141 lines
5.6 KiB
Diff
diff --git a/tools/validate_nvfp4_swiglu_producer.py b/tools/validate_nvfp4_swiglu_producer.py
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new file mode 100644
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index 0000000..7650115
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--- /dev/null
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+++ b/tools/validate_nvfp4_swiglu_producer.py
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@@ -0,0 +1,135 @@
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+"""Validate fused SwiGLU and native NVFP4 production on real H3 FC1 output."""
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+
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+from __future__ import annotations
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+
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+import argparse
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+import json
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+import time
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+from pathlib import Path
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+
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+import torch
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+
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+from h3_blackwell_runtime.adaln import H3CurveAdaLN
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+from h3_blackwell_runtime.block import H3DiTBlock
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+from h3_blackwell_runtime.checkpoint import H3Checkpoint
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+from h3_blackwell_runtime.nvfp4_quant import (
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+ nvfp4_activation_scale,
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+ vortex_native_quantize_swiglu_nvfp4,
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+)
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+from h3_blackwell_runtime.packing import H3PromptPacker
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+from h3_blackwell_runtime.rope import h3_rope_rotation
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+from h3_blackwell_runtime.sampler import _audio_sigma, _model_sigma, beta_sigmas
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+from h3_blackwell_runtime.t2v import random_av_latents
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+
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+
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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("--model-path", default="/models/minimax_h3_fl2va_pruned_nvfp4.safetensors")
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+ parser.add_argument("--output", type=Path, required=True)
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+ parser.add_argument("--block-index", type=int, required=True)
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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("--warmup", type=int, default=3)
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+ parser.add_argument("--iterations", type=int, default=10)
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+ parser.add_argument("--device", default="cuda")
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+ return parser.parse_args()
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+
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+
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+def main() -> None:
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+ args = parse_args()
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+ torch.manual_seed(args.seed)
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+ checkpoint = H3Checkpoint(args.model_path, device=args.device)
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+ block = H3DiTBlock.from_checkpoint(
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+ checkpoint, args.block_index, attention_backend="sage2",
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+ ).eval()
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+ adaln = H3CurveAdaLN.from_checkpoint(
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+ checkpoint, f"blocks.{args.block_index}.adaln_proj",
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+ ).eval()
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+ packer = H3PromptPacker(checkpoint)
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+ video, audio, _ = random_av_latents(
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+ args.width, args.height, args.frames, args.seed, device=args.device,
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+ )
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+ sigma = beta_sigmas(args.steps, device=args.device)[args.sampler_step - 1]
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+ native_audio = audio.to(torch.bfloat16) * (_audio_sigma(sigma) / sigma)
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+ text = torch.randn(
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+ 1, args.text_tokens, 5376, device=args.device, dtype=torch.bfloat16,
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+ )
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+ hidden, timesteps, segments, positions, _, _ = packer(
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+ text, video, native_audio, _model_sigma(sigma),
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+ )
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+ rotation = h3_rope_rotation(
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+ positions.to(args.device),
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+ checkpoint.tensor("rope.inv_freq", dtype=torch.float32),
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+ hidden.dtype,
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+ )
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+ modulation = tuple(value.detach() for value in adaln(timesteps))
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+ captured = []
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+ block.fused_nvfp4_modulation = False
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+ hook = block.mlp.fc1.register_forward_hook(
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+ lambda _module, _inputs, output: captured.append(output.detach()),
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+ )
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+ with torch.inference_mode():
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+ block(hidden, rotation, *modulation, segments)
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+ hook.remove()
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+ gate_up = captured[-1]
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+
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+ with torch.inference_mode():
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+ gate, up = gate_up.chunk(2, dim=-1)
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+ materialized = torch.nn.functional.silu(gate).mul_(up)
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+ reference_scale = nvfp4_activation_scale(materialized).float()
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+ import comfy_kitchen as ck
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+ from comfy_kitchen.tensor import TensorCoreNVFP4Layout
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+
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+ reference_qdata, reference_sfa = ck.quantize_nvfp4(
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+ materialized,
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+ reference_scale,
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+ pad_16x=TensorCoreNVFP4Layout.get_padded_shape(tuple(materialized.shape))
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+ != tuple(materialized.shape),
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+ )
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+ actual_scale, actual_qdata, actual_sfa = vortex_native_quantize_swiglu_nvfp4(
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+ gate_up,
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+ )
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+ torch.cuda.synchronize()
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+ for _ in range(args.warmup):
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+ vortex_native_quantize_swiglu_nvfp4(gate_up)
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+ times = []
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+ for _ in range(args.iterations):
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+ torch.cuda.synchronize()
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+ started = time.perf_counter()
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+ vortex_native_quantize_swiglu_nvfp4(gate_up)
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+ torch.cuda.synchronize()
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+ times.append(time.perf_counter() - started)
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+
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+ report = {
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+ "device": torch.cuda.get_device_name(),
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+ "block_index": args.block_index,
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+ "input_shape": list(gate_up.shape),
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+ "output_shape": list(materialized.shape),
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+ "scale_equal": torch.equal(actual_scale, reference_scale),
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+ "scale_reference": reference_scale.item(),
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+ "scale_actual": actual_scale.item(),
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+ "qdata_differences": torch.count_nonzero(actual_qdata != reference_qdata).item(),
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+ "sfa_differences": torch.count_nonzero(
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+ actual_sfa.view(torch.uint8) != reference_sfa.view(torch.uint8)
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+ ).item(),
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+ "producer_p50_ms": sorted(times)[len(times) // 2] * 1000.0,
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+ }
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+ report["equal"] = (
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+ report["scale_equal"]
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+ and report["qdata_differences"] == 0
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+ and report["sfa_differences"] == 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 not report["equal"]:
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+ raise RuntimeError("fused SwiGLU producer is not byte-exact")
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+
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+
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+if __name__ == "__main__":
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+ main()
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