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4dfafb0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | """Validate UniXcoderEncoder against the official microsoft/CodeBERT recipe.
Tests, in order of strength:
1. GOLDEN: element-wise match vs the official unixcoder.py (fetched from GitHub)
on single unpadded sequences — where our 1-D attention mask and upstream's
2-D mask are provably identical, so raw pooled vectors must match to ~1e-5.
2. ID framing: [<s>=0, <encoder-only>=6, </s>=2] + bpe + [</s>=2].
3. Padding-invariance: batched (padded) == each item alone, to ~1e-5 (mask test).
4. Unit-norm + dim==768 after normalize.
5. Toy retrieval: 5 code snippets vs their NL descriptions -> MRR == 1.0, and the
correct-pair cosine is discriminative (vs the degenerate ~0.20 of ST's default).
Exit non-zero on any failure.
"""
from __future__ import annotations
import sys
import types
import urllib.request
import numpy as np
import torch
sys.path.insert(0, "src")
from codesearch.embedding import UniXcoderEncoder # noqa: E402
MODEL = "microsoft/unixcoder-base"
_OFFICIAL_URL = (
"https://raw.githubusercontent.com/microsoft/CodeBERT/master/UniXcoder/unixcoder.py"
)
CODE = [
"def add(a, b):\n return a + b",
"def is_even(n):\n return n % 2 == 0",
"def reverse(s):\n return s[::-1]",
"def to_upper(s):\n return s.upper()",
"def read_file(path):\n with open(path) as f:\n return f.read()",
]
NL = [
"add two numbers",
"check whether a number is even",
"reverse a string",
"convert a string to uppercase",
"read the contents of a file",
]
_FAILS: list[str] = []
def check(cond: bool, msg: str) -> None:
print((" PASS " if cond else " FAIL ") + msg)
if not cond:
_FAILS.append(msg)
def load_official():
"""Fetch official unixcoder.py, import it as a module, return the UniXcoder class."""
src = urllib.request.urlopen(_OFFICIAL_URL, timeout=30).read().decode()
mod = types.ModuleType("official_unixcoder")
exec(compile(src, "official_unixcoder.py", "exec"), mod.__dict__)
return mod.UniXcoder
def main() -> int:
enc = UniXcoderEncoder(MODEL)
# --- 2. ID framing -------------------------------------------------------
ids = enc._build_ids(CODE[0])
check(ids[:3] == [0, 6, 2] and ids[-1] == 2,
f"ID framing [<s>,<enc-only>,</s>]...[</s>] got {ids[:3]}...{ids[-1]}")
# --- 1. GOLDEN cross-check vs the official recipe ------------------------
# The as-shipped official forward() cannot run under transformers 5.x: its 2-D
# (mask_i*mask_j) attention mask crashes on batched input and goes silently
# causal on single input. So we validate against the official recipe run the
# way tf5 requires: (a) its tokenizer framing must match ours id-for-id, and
# (b) the official model config (is_decoder=True) fed a full-bidirectional 4-D
# mask + mean-pool must match our vectors element-wise. is_decoder is a
# mask-only flag (zero weight effect), so this is a true faithfulness check.
try:
from transformers import AutoConfig, AutoModel
Official = load_official()
off = Official(MODEL) # only used for its .tokenize()
# (a) framing: official ids == ours
max_id_mismatch = 0
for text in CODE + NL:
off_ids = off.tokenize([text], max_length=512, mode="<encoder-only>")[0]
max_id_mismatch = max(max_id_mismatch, int(off_ids != enc._build_ids(text)))
check(max_id_mismatch == 0, "golden framing: official.tokenize ids == _build_ids")
# (b) element-wise: official config (is_decoder=True) + full bidirectional
cfg = AutoConfig.from_pretrained(MODEL)
cfg.is_decoder = True
ref_model = AutoModel.from_pretrained(MODEL, config=cfg)
ref_model.eval()
max_ediff = 0.0
for text in CODE + NL:
ids = torch.tensor([enc._build_ids(text)]) # single, unpadded
full4d = torch.zeros(1, 1, ids.shape[1], ids.shape[1]) # all-zeros => full bidir
with torch.no_grad():
ref = ref_model(ids, attention_mask=full4d)[0][0].mean(0)
ref_vec = ref.numpy().astype(np.float32)
mine_raw = enc.encode(text, normalize_embeddings=False)
max_ediff = max(max_ediff, float(np.abs(mine_raw - ref_vec).max()))
check(max_ediff < 1e-4,
f"golden element-wise match vs official bidirectional recipe: max|Δ|={max_ediff:.2e} (<1e-4)")
except Exception as e: # network / upstream unavailable
print(f" SKIP golden cross-check unavailable: {type(e).__name__}: {e}")
# --- 3. Padding-invariance (batched vs single) ---------------------------
batched = enc.encode(CODE, normalize_embeddings=True)
singles = np.vstack([enc.encode(c, normalize_embeddings=True) for c in CODE])
pad_diff = float(np.abs(batched - singles).max())
check(pad_diff < 1e-5, f"padding-invariance: max|Δ|={pad_diff:.2e} (<1e-5)")
# --- 4. Unit norm + dim --------------------------------------------------
norms = np.linalg.norm(batched, axis=1)
check(batched.shape[1] == 768, f"dim==768 got {batched.shape[1]}")
check(np.allclose(norms, 1.0, atol=1e-4), f"unit-norm rows norms in [{norms.min():.4f},{norms.max():.4f}]")
# --- 5. Toy retrieval sanity (MRR) --------------------------------------
code_emb = enc.encode(CODE, normalize_embeddings=True)
nl_emb = enc.encode(NL, normalize_embeddings=True)
sims = nl_emb @ code_emb.T # (query, doc) cosine
ranks = []
for i in range(len(NL)):
order = np.argsort(-sims[i])
ranks.append(int(np.where(order == i)[0][0]) + 1)
mrr = float(np.mean([1.0 / r for r in ranks]))
diag = float(np.mean(np.diag(sims)))
check(mrr == 1.0, f"toy retrieval MRR==1.0 ranks={ranks} mrr={mrr:.3f}")
check(diag > 0.30, f"correct-pair cosine discriminative: mean diag={diag:.3f} (>>0.20 degenerate)")
print("\n" + ("ALL PASS" if not _FAILS else f"{len(_FAILS)} FAILURE(S): " + "; ".join(_FAILS)))
return 1 if _FAILS else 0
if __name__ == "__main__":
sys.exit(main())
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