277 lines
11 KiB
Diff
277 lines
11 KiB
Diff
diff --git a/tools/validate_cute_nvfp4_ring.py b/tools/validate_cute_nvfp4_ring.py
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new file mode 100644
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index 0000000..51217ec
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--- /dev/null
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+++ b/tools/validate_cute_nvfp4_ring.py
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@@ -0,0 +1,271 @@
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+"""Validate and time a bounded 128-row NVFP4 packed-tile ring prototype."""
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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 math
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+from pathlib import Path
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+
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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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+
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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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+
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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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+ "--linear",
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+ choices=("attn_qkv_proj", "attn_out_proj", "mlp_fc1"),
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+ required=True,
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+ )
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+ parser.add_argument("--warmup", type=int, default=5)
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+ parser.add_argument("--iterations", type=int, default=20)
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+ parser.add_argument("--rows", type=int, default=128)
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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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+
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+
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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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+
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+
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+def main() -> None:
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+ from comfy_kitchen.tensor import TensorCoreNVFP4Layout
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+ import comfy_kitchen as ck
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+
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+ args = parse_args()
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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 must be positive")
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+ if args.rows <= 0 or args.rows % 128:
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+ raise ValueError("--rows must be a positive multiple of 128")
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+
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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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+ linear = module_for_name(block, args.linear)
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+ full_activation = inputs[args.linear].reshape(-1, linear.in_features).contiguous()
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+ if args.rows > full_activation.shape[0]:
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+ raise ValueError(f"--rows exceeds the available {full_activation.shape[0]} rows")
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+ activation = full_activation[:args.rows].contiguous()
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+ if activation.shape[1] % 128:
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+ raise ValueError(f"Ring prototype requires K divisible by 128, got {activation.shape}")
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+ global_scale = nvfp4_activation_scale(full_activation).float()
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+
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+ with torch.inference_mode():
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+ expected_packed = vortex_quantize_nvfp4(activation, scale=global_scale)
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+ ring_packed = vortex_native_quantize_nvfp4(activation, scale=global_scale)
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+ packed_weight = linear._packed_weight()
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+ expected_qdata, expected_tensor_scale, expected_block_scales = (
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+ TensorCoreNVFP4Layout.get_plain_tensors(expected_packed)
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+ )
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+ ring_qdata, ring_tensor_scale, ring_block_scales = (
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+ TensorCoreNVFP4Layout.get_plain_tensors(ring_packed)
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+ )
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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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+ reference = functional.linear(expected_packed, packed_weight, None)
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+
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+ a, _ = fp4_tensor(ring_qdata, swap_nibbles=False, reencode=True)
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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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+ output_bf16 = torch.zeros(
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+ args.rows, b_qdata.shape[0], device="cuda", dtype=torch.bfloat16,
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+ )
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+ c = output_tensor(output_bf16)
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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, alpha_argument, max_active_clusters, stream,
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+ )
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+
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+ def run_consumer():
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+ return compiled_gemm(a, b, sfa, sfb, c, alpha_argument, stream)
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+
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+ def run_pack_into():
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+ return vortex_native_quantize_nvfp4_into(
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+ activation, global_scale, ring_qdata, ring_block_scales,
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+ )
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+
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+ def run_cute_ring():
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+ run_pack_into()
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+ return run_consumer()
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+
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+ def run_comfy_consumer():
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+ return ck.scaled_mm_nvfp4(
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+ ring_qdata,
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+ b_qdata,
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+ tensor_scale_a=ring_tensor_scale,
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+ tensor_scale_b=tensor_scale_b,
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+ block_scale_a=ring_block_scales,
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+ block_scale_b=b_block_scales,
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+ out_dtype=torch.bfloat16,
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+ alpha=ring_tensor_scale.float() * tensor_scale_b.float(),
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+ )
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+
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+ def run_comfy_ring():
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+ run_pack_into()
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+ return run_comfy_consumer()
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+
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+ run_consumer()
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+ torch.cuda.synchronize()
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+ candidate = output_bf16[:, : linear.out_features]
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+ delta = candidate.float() - reference.float()
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+
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+ _, scale_ms = measure_cuda(
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+ lambda: nvfp4_activation_scale(full_activation),
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ _, producer_ms = measure_cuda(
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+ run_pack_into,
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ _, consumer_ms = measure_cuda(
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+ run_consumer,
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ _, actual_ring_ms = measure_cuda(
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+ run_cute_ring,
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ comfy_candidate, comfy_consumer_ms = measure_cuda(
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+ run_comfy_consumer,
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ _, comfy_ring_ms = measure_cuda(
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+ run_comfy_ring,
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+ warmup=args.warmup,
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+ iterations=args.iterations,
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+ )
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+ _, reference_ms = measure_cuda(
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+ lambda: functional.linear(
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+ vortex_quantize_nvfp4(activation, scale=global_scale),
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+ packed_weight,
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+ 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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+
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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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+ chunk_count = math.ceil(full_activation.shape[0] / args.rows)
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+ modeled_cute_chunk_ms = producer_ms + consumer_ms
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+ modeled_comfy_chunk_ms = producer_ms + comfy_consumer_ms
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+ modeled_reference_canonical_ms = scale_ms + chunk_count * reference_ms
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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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+ "linear": args.linear,
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+ "mnk": [args.rows, linear.out_features, activation.shape[1]],
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+ "full_activation_rows": full_activation.shape[0],
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+ "modeled_chunk_count": chunk_count,
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+ "ring": {
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+ "row_capacity": args.rows,
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+ "producer": "vortex_native_quantize_nvfp4",
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+ "qdata_bytes": qdata_bytes,
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+ "sfa_bytes": sfa_bytes,
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+ "logical_bytes": qdata_bytes + sfa_bytes,
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+ },
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+ "parity": {
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+ "tensor_scale_equal": torch.equal(
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+ ring_tensor_scale, expected_tensor_scale,
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+ ),
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+ "fp4_difference_count": int(
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+ (ring_qdata != expected_qdata).sum().item()
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+ ),
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+ "block_scale_difference_count": int(
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+ (ring_block_scales.view(torch.uint8)
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+ != expected_block_scales.view(torch.uint8)).sum().item()
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+ ),
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+ "output_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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+ "comfy_output_equal": torch.equal(
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+ comfy_candidate[: args.rows, : linear.out_features], reference,
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+ ),
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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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+ "producer_ms": producer_ms,
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+ "global_scale_ms": scale_ms,
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+ "cute_consumer_ms": consumer_ms,
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+ "modeled_cute_chunk_ms": modeled_cute_chunk_ms,
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+ "modeled_comfy_chunk_ms": modeled_comfy_chunk_ms,
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+ "actual_into_ring_cute_gemm_ms": actual_ring_ms,
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+ "comfy_consumer_ms": comfy_consumer_ms,
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+ "actual_into_ring_comfy_gemm_ms": comfy_ring_ms,
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+ "reference_vortex_scale_comfy_pack_gemm_ms": reference_ms,
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+ "modeled_canonical_reference_ms": modeled_reference_canonical_ms,
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+ "modeled_canonical_cute_ring_ms": scale_ms + chunk_count * actual_ring_ms,
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+ "modeled_canonical_comfy_ring_ms": scale_ms + chunk_count * comfy_ring_ms,
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+ "actual_ring_vs_reference": actual_ring_ms / reference_ms,
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+ "comfy_ring_vs_reference": comfy_ring_ms / reference_ms,
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+ "modeled_canonical_cute_ring_vs_reference": (
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+ scale_ms + chunk_count * actual_ring_ms
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+ ) / modeled_reference_canonical_ms,
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+ "modeled_canonical_comfy_ring_vs_reference": (
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+ scale_ms + chunk_count * comfy_ring_ms
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+ ) / modeled_reference_canonical_ms,
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+ },
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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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+
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+
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+if __name__ == "__main__":
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+ main()
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