File size: 4,668 Bytes
b222eb5 | 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 | """Local smoke test: run baseline/CCD/CCD-DS on a tiny randomly-initialised Dream model.
Checks the mechanics of the decoder (Claim 1's machinery) without needing a 7B GPU:
* every method fully unmasks the response
* baseline and CCD use exactly `steps` forward passes with budget 1/step
* CCD-DS uses fewer steps and budgets in [1, V]
* the documented degeneracy (d=1, V=b_t) makes CCD identical to the baseline
"""
import sys, os
sys.path.insert(0, os.path.dirname(__file__))
import torch
from transformers import AutoConfig, AutoModel
import ccd_decode
MODEL = "Dream-org/Dream-v0-Instruct-7B"
MASK = 151666
def tiny_model():
cfg = AutoConfig.from_pretrained(MODEL, trust_remote_code=True)
cfg.num_hidden_layers = 2
cfg.hidden_size = 128
cfg.intermediate_size = 256
cfg.num_attention_heads = 4
cfg.num_key_value_heads = 2
cfg.vocab_size = 2000
cfg.pad_token_id = 0
cfg.mask_token_id = 1999
cfg.tie_word_embeddings = True
torch.manual_seed(0)
m = AutoModel.from_config(cfg, trust_remote_code=True).eval()
return NoMaskLogits(m, cfg.mask_token_id)
class NoMaskLogits(torch.nn.Module):
"""Forbid predicting the mask token.
A trained Dream never emits <|mask|> as a clean-data prediction; a randomly
initialised tiny model does, which would re-mask positions and corrupt the
step/budget bookkeeping. This makes the toy model behave like a trained one.
"""
def __init__(self, inner, mask_id):
super().__init__()
self.inner, self.mask_id = inner, mask_id
def forward(self, *a, **kw):
out = self.inner(*a, **kw)
out.logits[..., self.mask_id] = -1e4
return out
def main():
m = tiny_model()
global_mask = 1999 # in-range mask id for the tiny vocab
prompt = torch.randint(0, 1000, (1, 8))
N = 32
results = {}
for method in ["baseline", "ccd", "ccd_ds"]:
torch.manual_seed(0)
x, st = ccd_decode.generate(
m, prompt, max_new_tokens=N, steps=N, temperature=0.0,
mask_token_id=global_mask, method=method, buffer_V=4, history_d=3,
)
n_left = int((x[0, 8:] == global_mask).sum())
results[method] = (st, n_left)
print(f"{method:9s} steps={st['steps']:3d} masks_left={n_left} "
f"fallbacks={st['fallbacks']:3d} budgets(min/max/mean)="
f"{min(st['budgets'])}/{max(st['budgets'])}/{sum(st['budgets'])/len(st['budgets']):.2f} "
f"total_decoded={sum(st['budgets'])}")
print()
ok = True
for method, (st, n_left) in results.items():
if n_left != 0:
print(f"FAIL {method}: {n_left} positions left masked"); ok = False
if results["baseline"][0]["steps"] != N:
print("FAIL baseline did not use exactly N steps"); ok = False
if results["ccd"][0]["steps"] != N:
print("FAIL ccd did not use exactly N steps (fixed budget b_t=1)"); ok = False
if max(results["ccd"][0]["budgets"]) != 1:
print("FAIL ccd budget exceeded 1"); ok = False
if results["ccd_ds"][0]["steps"] >= N:
print("FAIL ccd_ds did not reduce steps"); ok = False
if max(results["ccd_ds"][0]["budgets"]) > 4:
print("FAIL ccd_ds budget exceeded V=4"); ok = False
print(f"CCD-DS speedup on tiny model: {N / results['ccd_ds'][0]['steps']:.2f}x")
# Degeneracy to the baseline. The paper (Sec. 4.2) says this happens at
# "d=1 and V=b_t". Under Eq. (16)/(17) as literally written, the buffer at
# history length d averages d+1 distributions, so d=1 still mixes the current
# step with one history step and does NOT reduce to Eq. (1). The degeneracy
# holds at d=0 (no history). We test both and report the discrepancy.
torch.manual_seed(0)
xb, _ = ccd_decode.generate(m, prompt, max_new_tokens=N, steps=N, temperature=0.0,
mask_token_id=global_mask, method="baseline")
for d in (0, 1):
torch.manual_seed(0)
xd, _ = ccd_decode.generate(m, prompt, max_new_tokens=N, steps=N, temperature=0.0,
mask_token_id=global_mask, method="ccd",
buffer_V=1, history_d=d)
same = bool((xb == xd).all())
print(f"degeneracy check (d={d}, V=b_t=1): CCD == baseline -> {same}")
if d == 0 and not same:
print("FAIL degeneracy at d=0"); ok = False
if d == 1 and same:
print("NOTE: d=1 also degenerates on this toy model (paper's Sec. 4.2 reading)")
print("\nSMOKE TEST:", "PASS" if ok else "FAIL")
return 0 if ok else 1
if __name__ == "__main__":
sys.exit(main())
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