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f0975ca | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | """Correctness checks for the Dory PyTorch port (dory/modeling.py).
norms weight row/column norms (is the checkpoint unit-normalized, nGPT style?)
nll assistant-token NLL on held Beam SFT examples at 1-4 loops
generate greedy chat generation at 2 loops
ckpt gradient checkpointing gives identical loss and gradients
train a few full-parameter AdamW steps on 2 examples: loss must fall, no NaNs
"""
import argparse
import json
import sys
import time
from pathlib import Path
import torch
ROOT = Path(__file__).resolve().parent.parent
sys.path.insert(0, str(ROOT))
from dory.data import encode_example # noqa: E402
from dory.modeling import DoryForCausalLM # noqa: E402
from transformers import PreTrainedTokenizerFast # noqa: E402
def sync(dev):
if dev == "mps":
torch.mps.synchronize()
elif dev == "cuda":
torch.cuda.synchronize()
def check_norms(model):
print("== weight norms (mean ± std); ~1.0 means unit-normalized along that dim")
layer = model.backbone.layers
named = {
"embeddings [V,H]": model.backbone.embeddings.weight,
"lm_head [V,H]": model.lm_head.weight,
"L0 q_proj [HD,H]": layer[0].mixer.q_proj.weight,
"L0 o_proj [H,HD]": layer[0].mixer.o_proj.weight,
"L1 gate_proj [F,H]": layer[1].mixer.gate_proj.weight,
"L1 down_proj [H,F]": layer[1].mixer.down_proj.weight,
"L20 q_proj [HD,H]": layer[20].mixer.q_proj.weight,
}
for name, w in named.items():
w = w.float()
r, c = w.norm(dim=1), w.norm(dim=0)
print(f" {name:22s} rows {r.mean():.4f}±{r.std():.4f} cols {c.mean():.4f}±{c.std():.4f}")
def load_examples(tok, n, max_len):
out = []
for line in open(ROOT / "data" / "final" / "sft_verified.jsonl"):
ex = json.loads(line)
enc = encode_example(tok, ex["messages"], max_len=max_len)
if enc is not None:
out.append((ex, *enc))
if len(out) >= n:
break
return out
@torch.no_grad()
def check_nll(model, examples, dev):
print("== assistant-token NLL on Beam traces (lower is better; garbage model ~>5)")
model.eval()
for loops in (1, 2, 3, 4):
tot, cnt, t0 = 0.0, 0, time.time()
for _, ids, labels in examples:
loss, n = model(ids[None].to(dev), labels=labels[None].to(dev), n_loops=loops)
tot += float(loss) * n
cnt += n
sync(dev)
print(f" loops={loops}: nll={tot / cnt:.4f} ppl={torch.tensor(tot / cnt).exp():.2f} "
f"over {cnt} tokens ({time.time() - t0:.1f}s)")
@torch.no_grad()
def check_generate(model, tok, dev, prompt, max_new):
print(f"== greedy generation, 2 loops: {prompt!r}")
model.eval()
text = tok.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False, add_generation_prompt=True)
ids = tok(text, add_special_tokens=False, return_tensors="pt")["input_ids"].to(dev)
eos = {tok.convert_tokens_to_ids("<|im_end|>")}
t0 = time.time()
out = model.generate(ids, max_new_tokens=max_new, n_loops=2, eos_ids=eos)
sync(dev)
print(f" ({out.shape[1]} tokens, {time.time() - t0:.1f}s)\n" + tok.decode(out[0]))
def check_ckpt(model, examples, dev):
print("== gradient checkpointing equivalence")
_, ids, labels = examples[0]
ids, labels = ids[None, :256].to(dev), labels[None, :256].to(dev)
labels[:, :8] = -100
labels[:, 8:] = ids[:, 8:]
layers = model.backbone.layers
probe = [layers[2].mixer.q_proj.weight, layers[30].attn_alpha, layers[31].mixer.suv_up,
layers[60].mixer.o_proj.weight, model.lm_head.logit_scale]
results = []
for ckpt in (False, True):
model.train()
model.backbone.gradient_checkpointing = ckpt
model.zero_grad(set_to_none=True)
loss, _ = model(ids, labels=labels, n_loops=2)
loss.backward()
results.append((float(loss), [p.grad.float().clone() for p in probe]))
model.backbone.gradient_checkpointing = False
model.zero_grad(set_to_none=True)
(l0, g0), (l1, g1) = results
diffs = [float((a - b).abs().max() / (a.abs().max() + 1e-12)) for a, b in zip(g0, g1)]
print(f" loss {l0:.6f} vs {l1:.6f}; max relative grad diff {max(diffs):.2e}; "
f"grads finite: {all(torch.isfinite(g).all() for g in g0)}")
@torch.no_grad()
def check_pack(model, examples, dev):
print("== packing equivalence: 2 docs packed with doc_ids/position_ids vs separately")
model.eval()
docs = [(ids[:400], labels[:400]) for _, ids, labels in examples[:2]]
sep_sum, sep_n = 0.0, 0
for ids, labels in docs:
loss, n = model(ids[None].to(dev), labels=labels[None].to(dev), n_loops=2)
sep_sum += float(loss) * n
sep_n += n
ids = torch.cat([d[0] for d in docs])
labels = torch.cat([d[1] for d in docs]).clone()
labels[len(docs[0][0])] = -100 # second doc's first token has no in-doc predecessor
doc_ids = torch.cat([torch.full((len(d[0]),), i) for i, d in enumerate(docs)])
pos = torch.cat([torch.arange(len(d[0])) for d in docs])
loss, n = model(ids[None].to(dev), labels=labels[None].to(dev), n_loops=2,
doc_ids=doc_ids[None].to(dev), position_ids=pos[None].to(dev))
print(f" separate: {sep_sum / sep_n:.6f} over {sep_n} tokens; packed: {float(loss):.6f} over {n} tokens")
def check_train(model, examples, dev, steps, lr):
print(f"== full-parameter AdamW smoke test: {steps} steps, lr={lr}, 2 loops, checkpointing on")
model.train()
model.gradient_checkpointing_enable()
opt = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=0.0, foreach=False)
batch = examples[:2]
for step in range(steps):
t0 = time.time()
opt.zero_grad(set_to_none=True)
tot = 0.0
for _, ids, labels in batch:
loss, _ = model(ids[None].to(dev), labels=labels[None].to(dev), n_loops=2)
(loss / len(batch)).backward()
tot += float(loss.detach()) / len(batch)
gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
model.normalize_matrices_()
sync(dev)
print(f" step {step}: loss={tot:.4f} grad_norm={float(gnorm):.3f} ({time.time() - t0:.1f}s)", flush=True)
check_norms(model)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--model", default=str(ROOT / "models" / "dory-4b"))
ap.add_argument("--device", default="mps" if torch.backends.mps.is_available() else "cpu")
ap.add_argument("--checks", default="norms,nll,generate,ckpt")
ap.add_argument("--n-examples", type=int, default=6)
ap.add_argument("--max-len", type=int, default=1536)
ap.add_argument("--max-new", type=int, default=200)
ap.add_argument("--prompt", default="What is 17 * 23? Reply with just the number.")
ap.add_argument("--train-steps", type=int, default=8)
ap.add_argument("--lr", type=float, default=1e-5)
args = ap.parse_args()
checks = set(args.checks.split(","))
torch.manual_seed(0)
t0 = time.time()
tok = PreTrainedTokenizerFast.from_pretrained(args.model)
model = DoryForCausalLM.from_pretrained(args.model, device=args.device)
print(f"loaded on {args.device} in {time.time() - t0:.1f}s; "
f"{sum(p.numel() for p in model.parameters()) / 1e9:.3f}B params")
examples = load_examples(tok, args.n_examples, args.max_len)
print(f"{len(examples)} SFT examples, lengths {[len(e[1]) for e in examples]}")
if "norms" in checks:
check_norms(model)
if "nll" in checks:
check_nll(model, examples, args.device)
if "generate" in checks:
check_generate(model, tok, args.device, args.prompt, args.max_new)
if "pack" in checks:
check_pack(model, examples, args.device)
if "ckpt" in checks:
check_ckpt(model, examples, args.device)
if "train" in checks:
check_train(model, examples, args.device, args.train_steps, args.lr)
if __name__ == "__main__":
main()
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