File size: 9,024 Bytes
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"""Step 7: evaluate an extracted FastH3 Omni LoRA's error relative to TARGET weights.
The extraction TSV reports ||Delta - LowRank(Delta)|| / ||Delta||. That number
alone cannot quantify how far the reconstructed model is from Omni itself.
This utility calculates ||Target|| and ||Base|| from the original safetensors,
then combines them with the exact per-matrix residual sums in the TSV.
No torch, GPU, or model checkpoint modifications. Reads in ~4 MiB chunks.
"""
import argparse
import csv
import json
import math
import struct
from collections import defaultdict
from pathlib import Path
import numpy as np
PREFIXES = ("model.diffusion_model.", "diffusion_model.")
TYPE_INFO = {"F32": ("<f4", 4), "F16": ("<f2", 2), "BF16": ("<u2", 2)}
def canonical(s):
for prefix in PREFIXES:
if s.startswith(prefix):
return s[len(prefix) :]
return s
class HeaderReader:
def __init__(self, path):
self.fp = open(path, "rb")
self.path = path
self.entries = {}
try:
header_size = struct.unpack("<Q", self.fp.read(8))[0]
if header_size > 256_000_000:
raise ValueError("Header is implausibly large")
header = json.loads(self.fp.read(header_size))
self.data_at = 8 + header_size
for raw, d in header.items():
if raw == "__metadata__":
continue
k = canonical(raw)
if k in self.entries:
raise ValueError(f"Duplicate canonical tensor: {k}")
self.entries[k] = d
except BaseException:
self.fp.close()
raise
def chunk(self, key, start, n):
e = self.entries[key]
dtype = e["dtype"]
if dtype not in TYPE_INFO:
raise ValueError(f"Unsupported dtype for {key}: {dtype}")
kind, width = TYPE_INFO[dtype]
count = math.prod(e["shape"])
if start < 0 or n < 0 or start + n > count:
raise ValueError(f"Invalid slice for {key}")
begin = e["data_offsets"][0]
self.fp.seek(self.data_at + begin + start * width)
raw = self.fp.read(n * width)
if len(raw) != n * width:
raise IOError(f"Short read of {key} in {self.path}")
a = np.frombuffer(raw, dtype=kind)
if dtype == "BF16":
return (a.astype(np.uint32) << np.uint32(16)).view(np.float32)
return a.astype(np.float32, copy=False)
def close(self):
self.fp.close()
def family(key):
if key.startswith("blocks."):
if ".attn.qkv_proj." in key:
return "backbone QKV"
if ".attn.out_proj." in key:
return "backbone attention out"
if ".mlp.fc1." in key:
return "backbone MLP FC1"
if ".mlp.fc2." in key:
return "backbone MLP FC2"
return "other"
def fmt(n, total, detail):
if total <= 0:
return "n/a"
return f"{100 * math.sqrt(max(n, 0.0) / total):.5f}%"
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("base", help="Original Ref2VA base .safetensors")
parser.add_argument("target", help="FastH3 Omni .safetensors")
parser.add_argument("report", help="FastH3_Omni_r64_report.tsv")
parser.add_argument("--out", default="h3_omni_r64_weight_error.tsv")
parser.add_argument("--chunk-elements", type=int, default=1_000_000)
args = parser.parse_args()
if args.chunk_elements <= 0:
parser.error("--chunk-elements must be positive")
with open(args.report, encoding="utf-8", newline="") as fp:
input_rows = list(csv.DictReader(fp, delimiter="\t"))
if not input_rows or not all(
x in input_rows[0] for x in ("key", "true_delta_norm_sq", "residual_norm_sq")
):
parser.error(
"Invalid extraction TSV; expected key,true_delta_norm_sq,residual_norm_sq"
)
if len({r["key"] for r in input_rows}) != len(input_rows):
parser.error("Duplicate key in extraction TSV")
b, t = HeaderReader(args.base), HeaderReader(args.target)
aggregate = defaultdict(
lambda: dict(num=0, base=0.0, target=0.0, delta=0.0, unreconstructed=0.0)
)
all_rows = []
mismatched_delta_norms = []
try:
for i, r in enumerate(input_rows, 1):
key = canonical(r["key"])
if key not in b.entries or key not in t.entries:
raise ValueError(f"Missing tensor in base or target: {key}")
shape = b.entries[key]["shape"]
if shape != t.entries[key]["shape"]:
raise ValueError(f"Shape mismatch: {key}")
n = math.prod(shape)
bn = tn = dn = 0.0
for off in range(0, n, args.chunk_elements):
length = min(args.chunk_elements, n - off)
bf = b.chunk(key, off, length)
tf = t.chunk(key, off, length)
# Use Float64 accumulation to keep global sums stable.
bv = bf.astype(np.float64)
tv = tf.astype(np.float64)
dv = tv - bv
bn += float(np.dot(bv, bv))
tn += float(np.dot(tv, tv))
dn += float(np.dot(dv, dv))
reported_dn = float(r["true_delta_norm_sq"])
error = float(r["residual_norm_sq"])
if abs(dn - reported_dn) > max(1e-5, 2e-4 * reported_dn):
mismatched_delta_norms.append((key, dn, reported_dn))
if error < -1e-6 or reported_dn < -1e-6:
raise ValueError(f"Invalid norm in report: {key}")
row = dict(
key=key,
family=family(key),
base_norm_sq=bn,
target_norm_sq=tn,
delta_norm_sq=dn,
residual_norm_sq=error,
delta_over_target_percent=(
100 * math.sqrt(dn / tn) if tn else float("nan")
),
residual_over_target_percent=(
100 * math.sqrt(error / tn) if tn else float("nan")
),
residual_over_delta_percent=(
100 * math.sqrt(error / dn) if dn else float("nan")
),
)
all_rows.append(row)
a = aggregate[row["family"]]
a["num"] += 1
a["base"] += bn
a["target"] += tn
a["delta"] += dn
a["unreconstructed"] += error
if i % 20 == 0 or i == len(input_rows):
print(f"[{i:3}/{len(input_rows)}] checked {key}", flush=True)
finally:
b.close()
t.close()
with open(args.out, "w", encoding="utf-8", newline="") as fp:
fields = list(all_rows[0].keys())
writer = csv.DictWriter(fp, fieldnames=fields, delimiter="\t")
writer.writeheader()
writer.writerows(all_rows)
whole = dict(num=0, base=0.0, target=0.0, delta=0.0, unreconstructed=0.0)
for a in aggregate.values():
for k in whole:
whole[k] += a[k]
print("\n" + "=" * 76)
print("STEP 7: R64 ERROR RELATIVE TO OMNI WEIGHTS")
print("=" * 76)
print(f"Matrices analyzed: {whole['num']}")
print(
f"Base -> Omni change: {fmt(whole['delta'], whole['target'], '')} of target norm"
)
print(
f"r64 reconstruction error: {fmt(whole['unreconstructed'], whole['target'], '')} of target norm"
)
print(
f"r64 delta residual: {fmt(whole['unreconstructed'], whole['delta'], '')} of delta norm"
)
print(
f"Squared delta energy kept: {100*(1-whole['unreconstructed']/whole['delta']):.2f}%"
)
print("\nPer matrix family:")
for name in sorted(aggregate):
a = aggregate[name]
print(
f" {name:23} {a['num']:3} "
+ f"error/target={fmt(a['unreconstructed'],a['target'],''):>10} "
+ f"error/delta={fmt(a['unreconstructed'],a['delta'],''):>10}"
)
print("\nTop 10 matrices by residual error relative to their TARGET weight:")
for r in sorted(
all_rows, key=lambda x: x["residual_over_target_percent"], reverse=True
)[:10]:
print(f" {r['residual_over_target_percent']:9.4f}% {r['key']}")
print(f"\nPer-layer TSV: {args.out}")
if mismatched_delta_norms:
print(
f"\nWARNING: {len(mismatched_delta_norms)} delta-norm mismatches against extraction TSV."
)
for k, observed, reported in mismatched_delta_norms[:8]:
print(f" {k}: recomputed={observed:.8e} report={reported:.8e}")
print("Check the report matches the exact base and target checkpoints!")
if __name__ == "__main__":
main()
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