131 lines
5.5 KiB
Python
131 lines
5.5 KiB
Python
#!/usr/bin/env python3
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"""Verify NVFP4 production GEMM with RUNTIME gsa matches PyTorch reference.
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The checkpoint's input_scale is NOT the correct activation gsa for NVFP4.
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Using it causes E4M3 block scale overflow when x/gsa > 2688.
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Runtime gsa = max(|x|) / (6.0 * 448.0) fixes this.
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This test verifies:
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1. Runtime gsa path gives cos ≈ 0.99+ against reference dequant+linear
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2. Fixed gsa path (checkpoint input_scale) gives poor cos at production magnitudes
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3. The fused quantize_nvfp4_gpu_fused kernel produces correct gsa
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"""
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import os, sys, json, math, torch, torch.nn.functional as F
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from pathlib import Path
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CHECKPOINT_DIR = os.environ.get("CHECKPOINT_DIR", "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4")
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FP4_LUT = torch.tensor([0., 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0])
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def dequant_nvfp4(weight, weight_scale, weight_scale_2=None, input_scale=None):
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O, I2 = weight.shape; I = I2 * 2
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lo = (weight & 0x0F).to(torch.int8); hi = (weight >> 4).to(torch.int8)
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lut = FP4_LUT.to(device=weight.device, dtype=torch.float32)
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lo_f = lut[(lo & 0x07).long()] * torch.where((lo >> 3).bool(), -1., 1.)
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hi_f = lut[(hi & 0x07).long()] * torch.where((hi >> 3).bool(), -1., 1.)
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w = torch.stack([lo_f, hi_f], -1).reshape(O, I)
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s = weight_scale.float().repeat_interleave(16, 1)
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if weight_scale_2 is not None: s = s * weight_scale_2.float()
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# NOTE: reference does NOT use input_scale for weight dequant.
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# input_scale is the activation quantization scale (training-time FP8).
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return (w * s).bfloat16()
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def get_nvfp4_weight(w, pfx, proj_name):
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k = f"{pfx}.{proj_name}"
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return (w.get(f"{k}.weight"), w.get(f"{k}.weight_scale"),
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w.get(f"{k}.weight_scale_2"), w.get(f"{k}.input_scale"))
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def main():
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device = "cuda:0"
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torch.manual_seed(42)
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with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
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cfg = json.load(f)
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H = cfg["hidden_size"]
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from safetensors.torch import load_file
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cdir = Path(CHECKPOINT_DIR); wmap = {}
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idx = cdir / "model.safetensors.index.json"
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if idx.exists():
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with open(idx) as f: wmap = json.load(f).get("weight_map", {})
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shards = set(wmap.values()) if wmap else set(); all_w = {}
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for sn in sorted(shards):
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if (cdir / sn).exists(): all_w.update(load_file(str(cdir / sn)))
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print(f"Loaded {len(all_w)} tensors")
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from dsv4.layers.linear import Nvfp4Linear
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test_cases = [
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(0, "model.layers.0.self_attn", "q_a_proj", 7168, 1536),
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(0, "model.layers.0.self_attn", "kv_proj", 7168, 512),
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(0, "model.layers.0.self_attn", "q_b_proj", 1536, 65536),
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(0, "model.layers.0.self_attn", "o_b_proj", 16384, 7168),
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(30, "model.layers.30.self_attn", "q_a_proj", 7168, 1536),
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(30, "model.layers.30.self_attn", "kv_proj", 7168, 512),
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(60, "model.layers.60.self_attn", "q_a_proj", 7168, 1536),
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(60, "model.layers.60.self_attn", "kv_proj", 7168, 512),
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(3, "model.layers.3.mlp", "gate", 7168, 384),
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(30, "model.layers.30.mlp", "gate", 7168, 384),
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]
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n_pass = 0
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n_fail = 0
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for li, pfx, proj_name, in_f, out_f in test_cases:
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weight, ws, ws2, isc = get_nvfp4_weight(all_w, pfx, proj_name)
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if weight is None:
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print(f"L{li} {proj_name}: weight not found, skipping")
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continue
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weight = weight.to(device)
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ws = ws.to(device)
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ws2 = ws2.to(device) if ws2 is not None else None
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isc = isc.to(device) if isc is not None else None
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actual_out = weight.shape[0]
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actual_in = weight.shape[1] * 2
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# Production-magnitude input (RMSNorm output has |x| ≈ 1-20 for hidden dim 7168)
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x = torch.randn(1, actual_in, dtype=torch.bfloat16, device=device) * 5.0
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# PyTorch reference: dequant + F.linear (NO input_scale in weight dequant)
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w_ref = dequant_nvfp4(weight, ws, ws2, isc)
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ref_out = F.linear(x, w_ref)
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# --- Test 1: RUNTIME gsa (production path) ---
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lin = Nvfp4Linear(actual_in, actual_out, max_num_tokens=8192, device=device)
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lin.fp4 = [weight.view(torch.float4_e2m1fn_x2) if weight.dtype == torch.uint8 else weight]
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lin.sf = [ws]
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lin.gs = [1.0]
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lin.ws2 = [ws2 if ws2 is not None else None]
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lin._activation_global_scale = 1.0 / (6.0 * 448.0) # placeholder
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lin._use_runtime_gsa = True # CRITICAL: compute gsa from actual input
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lin.finalize_weights()
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prod_out = lin(x)
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cos = torch.nn.functional.cosine_similarity(prod_out.flatten().float(), ref_out.flatten().float(), dim=0).item()
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prod_max = prod_out.abs().max().item()
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ref_max = ref_out.abs().max().item()
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ratio = prod_max / (ref_max + 1e-10)
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gsa_val = lin._gsa_buf.item() if hasattr(lin, '_gsa_buf') else 0
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status = "PASS" if cos > 0.98 else "FAIL"
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if status == "PASS": n_pass += 1
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else: n_fail += 1
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# Compute what gsa should be from input
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correct_gsa = x.float().abs().max().item() / (6.0 * 448.0)
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print(f"{status} L{li} {proj_name}: cos={cos:.6f} |prod|={prod_max:.4f} |ref|={ref_max:.4f} "
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f"ratio={ratio:.4f} gsa={gsa_val:.6f} correct_gsa={correct_gsa:.6f}")
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del lin; torch.cuda.empty_cache()
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print(f"\n{'='*60}")
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print(f"Results: {n_pass} PASS, {n_fail} FAIL (threshold: cos > 0.98)")
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print(f"{'='*60}")
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return 0 if n_fail == 0 else 1
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if __name__ == "__main__":
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exit(main())
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