#!/usr/bin/env python3
"""Isolate NVFP4 GEMM error: compare production weight dequant vs reference.

Tests whether the issue is in:
1. Weight/scale layout conversion (make_b_k_major, swizzle)
2. Activation quantization (global_scale, block_scale)
3. The GEMM kernel itself

Strategy: bypass activation quantization by passing pre-quantized FP4 activation,
and compare against a pure weight dequant reference.
"""
import os, sys, json, math, torch, torch.nn.functional as F
from pathlib import Path

CHECKPOINT_DIR = os.environ.get("CHECKPOINT_DIR", "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4")
FP4_LUT = torch.tensor([0., 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0])

def dequant_nvfp4(weight, weight_scale, weight_scale_2=None, input_scale=None):
    O, I2 = weight.shape; I = I2 * 2
    lo = (weight & 0x0F).to(torch.int8); hi = (weight >> 4).to(torch.int8)
    lut = FP4_LUT.to(device=weight.device, dtype=torch.float32)
    lo_f = lut[(lo & 0x07).long()] * torch.where((lo >> 3).bool(), -1., 1.)
    hi_f = lut[(hi & 0x07).long()] * torch.where((hi >> 3).bool(), -1., 1.)
    w = torch.stack([lo_f, hi_f], -1).reshape(O, I)
    s = weight_scale.float().repeat_interleave(16, 1)
    if weight_scale_2 is not None: s = s * weight_scale_2.float()
    return (w * s).bfloat16()

def get_nvfp4_weight(w, pfx, proj_name):
    k = f"{pfx}.{proj_name}"
    return (w.get(f"{k}.weight"), w.get(f"{k}.weight_scale"),
            w.get(f"{k}.weight_scale_2"), w.get(f"{k}.input_scale"))

def main():
    device = "cuda:0"
    torch.manual_seed(42)
    
    with open(os.path.join(CHECKPOINT_DIR, "config.json")) as f:
        cfg = json.load(f)
    
    from safetensors.torch import load_file
    cdir = Path(CHECKPOINT_DIR); wmap = {}
    idx = cdir / "model.safetensors.index.json"
    if idx.exists():
        with open(idx) as f: wmap = json.load(f).get("weight_map", {})
    shards = set(wmap.values()) if wmap else set(); all_w = {}
    for sn in sorted(shards):
        if (cdir / sn).exists(): all_w.update(load_file(str(cdir / sn)))
    print(f"Loaded {len(all_w)} tensors")
    
    from dsv4.layers.linear import Nvfp4Linear
    from dsv4.ops.quantize import quantize_activation_nvfp4
    
    # Test 1: BF16 input through full production path vs reference
    # This tests activation quantization + GEMM + weight layout
    test_layers = [0, 30, 60]
    projs = ['q_a_proj', 'kv_proj']
    
    for li in test_layers:
        pfx = f"model.layers.{li}.self_attn"
        for proj in projs:
            weight, ws, ws2, isc = get_nvfp4_weight(all_w, pfx, proj)
            if weight is None:
                print(f"L{li} {proj}: not found, skipping"); continue
            
            weight = weight.to(device)
            ws = ws.to(device)
            ws2 = ws2.to(device) if ws2 is not None else None
            isc = isc.to(device) if isc is not None else None
            
            actual_out = weight.shape[0]
            actual_in = weight.shape[1] * 2
            
            # BF16 input (same as model would provide)
            x = torch.randn(1, actual_in, dtype=torch.bfloat16, device=device) * 2.0
            
            # === Test A: Full production path ===
            lin = Nvfp4Linear(actual_in, actual_out, max_num_tokens=8192, device=device)
            lin.fp4 = [weight.view(torch.float4_e2m1fn_x2) if weight.dtype == torch.uint8 else weight]
            lin.sf = [ws]
            lin.gs = [1.0]
            lin.ws2 = [ws2]
            isc_val = isc.float().item() if isc is not None else 1.0/(6.0*448.0)
            lin._activation_global_scale = isc_val
            lin.finalize_weights()
            
            prod_out = lin(x)
            
            # === Test B: PyTorch reference (F.linear(dequant)) ===
            w_ref = dequant_nvfp4(weight, ws, ws2)
            ref_out = F.linear(x, w_ref)
            
            # === Test C: Manual quantize + production GEMM (skip Nvfp4Linear wrapper) ===
            # Quantize activation ourselves
            x_fp4, x_sf = quantize_activation_nvfp4(x, isc_val)
            
            cos_full = torch.nn.functional.cosine_similarity(prod_out.flatten().float(), ref_out.flatten().float(), dim=0).item()
            prod_max = prod_out.abs().max().item()
            ref_max = ref_out.abs().max().item()
            ratio = prod_max / (ref_max + 1e-10)
            
            # Check: does the dequantized weight match?
            # After finalize_weights, the weight is in K-major + swizzled layout.
            # We can't easily de-swizzle it, but we can check the GSB.
            gsb = lin._gsb.item() if lin._gsb is not None else 1.0
            ws2_val = ws2.float().item() if ws2 is not None else 1.0
            
            print(f"L{li} {proj}: cos={cos_full:.6f} |prod|={prod_max:.4f} |ref|={ref_max:.4f} ratio={ratio:.4f} gsb={gsb:.6f} ws2={ws2_val:.6f} gsa={isc_val:.8f}")
            
            # Test D: Run production GEMM with BF16 input (not FP4 quantized)
            # This bypasses activation quantization entirely
            # If this matches the reference, the bug is in activation quantization
            # If this doesn't match, the bug is in weight layout / GEMM
            
            # We can't easily do this with the current API, so let's do a simpler check:
            # Compare the BF16 dequant weight with the production weight format
            # by running the GEMM with a known-good BF16 input.
            
            # Use a very simple input: all ones
            x_ones = torch.ones(1, actual_in, dtype=torch.bfloat16, device=device)
            prod_ones = lin(x_ones)
            ref_ones = F.linear(x_ones, w_ref)
            cos_ones = torch.nn.functional.cosine_similarity(prod_ones.flatten().float(), ref_ones.flatten().float(), dim=0).item()
            print(f"  all-ones: cos={cos_ones:.6f} |prod|={prod_ones.abs().max().item():.4f} |ref|={ref_ones.abs().max().item():.4f} ratio={prod_ones.abs().max().item()/(ref_ones.abs().max().item()+1e-10):.4f}")
    
    print("\nDone.")

if __name__ == "__main__":
    main()