278 lines
9.8 KiB
Python
278 lines
9.8 KiB
Python
"""Validate complete H3 projections through a reusable NVFP4 row ring."""
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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 math
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from pathlib import Path
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import cutlass
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import cutlass.cute as cute
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import cutlass.torch as cutlass_torch
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import torch
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import torch.nn.functional as functional
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from cutlass.cute.runtime import from_dlpack
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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_nvfp4,
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vortex_native_quantize_nvfp4_into,
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vortex_quantize_nvfp4,
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)
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from profile_nvfp4_linear import module_for_name, representative_inputs
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from validate_cute_nvfp4_h3 import (
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fp4_tensor,
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load_cutlass_example,
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output_tensor,
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scale_tensor,
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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("--cutlass-example", type=Path, required=True)
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parser.add_argument("--output", type=Path, required=True)
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parser.add_argument(
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"--linears",
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nargs="+",
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choices=("attn_qkv_proj", "attn_out_proj", "mlp_fc1"),
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default=("attn_qkv_proj", "attn_out_proj", "mlp_fc1"),
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)
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parser.add_argument("--capacity", type=int, default=2048)
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parser.add_argument("--warmup", type=int, default=1)
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parser.add_argument("--iterations", type=int, default=3)
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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_cuda(fn, *, warmup: int, iterations: int) -> tuple[object, float]:
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result = None
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for _ in range(warmup):
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result = fn()
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torch.cuda.synchronize()
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started = torch.cuda.Event(enable_timing=True)
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finished = torch.cuda.Event(enable_timing=True)
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started.record()
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for _ in range(iterations):
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result = fn()
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finished.record()
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finished.synchronize()
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return result, started.elapsed_time(finished) / iterations
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def validate_linear(args, example, block, inputs, name: str) -> dict:
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from comfy_kitchen.tensor import TensorCoreNVFP4Layout
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linear = module_for_name(block, name)
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activation = inputs[name].reshape(-1, linear.in_features).contiguous()
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rows, features = activation.shape
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global_scale = nvfp4_activation_scale(activation).float()
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packed_weight = linear._packed_weight()
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b_qdata, tensor_scale_b, b_block_scales = (
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TensorCoreNVFP4Layout.get_plain_tensors(packed_weight)
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)
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with torch.inference_mode():
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reference_packed = vortex_quantize_nvfp4(
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activation, scale=global_scale,
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)
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reference = functional.linear(reference_packed, packed_weight, None)
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ring_seed = vortex_native_quantize_nvfp4(
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activation[: args.capacity], scale=global_scale,
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)
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_, ring_tensor_scale, ring_block_scales = (
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TensorCoreNVFP4Layout.get_plain_tensors(ring_seed)
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)
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a, a_backing = cutlass_torch.cute_tensor_like(
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torch.zeros(
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args.capacity,
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features,
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1,
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device="cuda",
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dtype=torch.float32,
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),
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cutlass.Float4E2M1FN,
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is_dynamic_layout=True,
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assumed_align=16,
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)
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ring_qdata = a_backing.view(torch.uint8).flatten()[
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: args.capacity * features // 2
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].reshape(args.capacity, features // 2)
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b, _ = fp4_tensor(b_qdata, swap_nibbles=False, reencode=True)
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sfa = scale_tensor(ring_block_scales)
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sfb = scale_tensor(b_block_scales)
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chunks = [
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(start, min(start + args.capacity, rows))
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for start in range(0, rows, args.capacity)
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]
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padded_rows = len(chunks) * args.capacity
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candidate_padded = torch.zeros(
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padded_rows, b_qdata.shape[0], device="cuda", dtype=torch.bfloat16,
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)
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c_chunks = [
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output_tensor(
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candidate_padded[
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index * args.capacity : (index + 1) * args.capacity
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]
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)
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for index in range(len(chunks))
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]
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alpha = ring_tensor_scale.float() * tensor_scale_b.float()
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alpha_argument = from_dlpack(alpha.reshape(1).contiguous(), assumed_align=4)
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gemm = example.Sm120BlockScaledGemmKernel(
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cutlass.Float32, 16, (128, 128, 128), (128, 128),
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)
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max_active_clusters = cutlass.utils.HardwareInfo().get_max_active_clusters(1)
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stream = cutlass_torch.default_stream()
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compiled_gemm = cute.compile(
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gemm, a, b, sfa, sfb, c_chunks[0], alpha_argument,
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max_active_clusters, stream,
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)
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def run_chunks():
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for index, (start, end) in enumerate(chunks):
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vortex_native_quantize_nvfp4_into(
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activation[start:end],
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global_scale,
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ring_qdata,
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ring_block_scales,
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hi_first=False,
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)
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compiled_gemm(
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a, b, sfa, sfb, c_chunks[index], alpha_argument, stream,
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)
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return candidate_padded
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def run_complete_ring():
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nvfp4_activation_scale(activation)
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return run_chunks()
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chunk_reports = []
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for index, (start, end) in enumerate(chunks):
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vortex_native_quantize_nvfp4_into(
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activation[start:end],
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global_scale,
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ring_qdata,
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ring_block_scales,
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hi_first=False,
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)
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compiled_gemm(
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a, b, sfa, sfb, c_chunks[index], alpha_argument, stream,
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)
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torch.cuda.synchronize()
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candidate = candidate_padded[start:end, : linear.out_features]
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expected = reference[start:end]
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delta = candidate.float() - expected.float()
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chunk_reports.append({
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"start": start,
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"rows": end - start,
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"equal": torch.equal(candidate, expected),
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"max_abs": delta.abs().max().item(),
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"mean_abs": delta.abs().mean().item(),
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})
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complete_candidate = candidate_padded[:rows, : linear.out_features]
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reference_checksum = reference.float().sum().item()
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candidate_checksum = complete_candidate.float().sum().item()
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output_bytes = candidate_padded.numel() * candidate_padded.element_size()
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del reference
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del reference_packed
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torch.cuda.empty_cache()
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_, ring_ms = measure_cuda(
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run_complete_ring, warmup=args.warmup, iterations=args.iterations,
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)
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del candidate
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del complete_candidate
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del c_chunks
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del candidate_padded
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torch.cuda.empty_cache()
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_, reference_ms = measure_cuda(
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lambda: functional.linear(
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vortex_quantize_nvfp4(activation), packed_weight, None,
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),
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warmup=args.warmup,
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iterations=args.iterations,
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)
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qdata_bytes = ring_qdata.numel() * ring_qdata.element_size()
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sfa_bytes = ring_block_scales.numel() * ring_block_scales.element_size()
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return {
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"name": name,
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"mnk": [rows, linear.out_features, features],
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"capacity": args.capacity,
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"chunk_count": len(chunks),
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"final_chunk_rows": chunks[-1][1] - chunks[-1][0],
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"ring_bytes": qdata_bytes + sfa_bytes,
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"parity": {
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"all_chunks_equal": all(chunk["equal"] for chunk in chunk_reports),
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"max_abs": max(chunk["max_abs"] for chunk in chunk_reports),
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"mean_abs_max": max(chunk["mean_abs"] for chunk in chunk_reports),
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"reference_checksum": reference_checksum,
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"candidate_checksum": candidate_checksum,
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},
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"timing": {
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"warmup": args.warmup,
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"iterations": args.iterations,
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"ring_complete_ms": ring_ms,
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"reference_complete_ms": reference_ms,
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"ring_vs_reference": ring_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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"chunks": chunk_reports,
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"output_bytes": output_bytes,
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}
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def main() -> None:
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args = parse_args()
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if args.capacity <= 0 or args.capacity % 128:
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raise ValueError("--capacity must be a positive multiple of 128")
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if args.warmup < 0 or args.iterations <= 0:
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raise ValueError("--warmup must be non-negative and --iterations positive")
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example = load_cutlass_example(args.cutlass_example, fuse_alpha=True)
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block, inputs, metadata = representative_inputs(args)
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results = [
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validate_linear(args, example, block, inputs, name)
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for name in args.linears
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]
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report = {
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"device": torch.cuda.get_device_name(),
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"cutlass_dsl": "4.6.2",
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"metadata": metadata,
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"block_index": args.block_index,
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"capacity": args.capacity,
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"all_equal": all(result["parity"]["all_chunks_equal"] for result in results),
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"projection_reference_total_ms": sum(
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result["timing"]["reference_complete_ms"] for result in results
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),
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"projection_ring_total_ms": sum(
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result["timing"]["ring_complete_ms"] for result in results
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),
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"results": results,
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}
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report["projection_total_improvement_percent"] = (
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1.0
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- report["projection_ring_total_ms"]
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/ report["projection_reference_total_ms"]
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) * 100.0
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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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