File size: 3,432 Bytes
4968ea3 | 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 | """SMOKE A — verify the PEFT+FlexQwen3 architecture is sound (biggest risk point).
Loads FlexQwen3 (original tokenizer), injects LoRA via inject_adapter_in_model,
wraps in MetaMemModel, loads u0001's cartridge, runs a forward + flex_generate.
Pass = no TypeError from PEFT, generation produces tokens, pseudo-token strings can
be emitted under the original tokenizer.
Requires a GPU.
Usage:
python scripts/train/smoke_a_model.py --user-id u0001
"""
import argparse
import os
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
os.environ.setdefault("CARTRIDGES_DIR", os.path.join(PROJECT_ROOT, "cartridges-lib"))
os.environ.setdefault("CARTRIDGES_OUTPUT_DIR", os.path.join(PROJECT_ROOT, "checkpoints/cartridge"))
import torch
from src.model.metamem_model import load_metamem_base
from src.model.prompts import format_decision_prompt
from src.model.tokenizer_utils import extract_control_strings
from src.utils.cartridge_utils import find_cartridge_path
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--user-id", default="u0001")
ap.add_argument("--cartridge-dir", default="checkpoints/cartridge/Qwen3-8B")
args = ap.parse_args()
cartridge_dir = os.path.join(PROJECT_ROOT, args.cartridge_dir)
print("[A] Loading MetaMem base (FlexQwen3 + injected LoRA)...")
mm, tok = load_metamem_base(adapter_names=("policy",), set_trainable="policy")
print("[A] Loaded. Model class:", mm.model.__class__.__name__)
from cartridges.cache import TrainableCache
cpath = find_cartridge_path(cartridge_dir, args.user_id)
assert cpath is not None, f"No cartridge for {args.user_id} under {cartridge_dir}"
print(f"[A] Cartridge: {cpath}")
cache = TrainableCache.from_pretrained(cpath, device="cuda").to("cuda")
mm.set_cartridge(cache)
print(f"[A] cartridge tokens: {cache.num_cartridge_tokens()}")
# Forward pass on a short decision prompt
prompt = format_decision_prompt("What do you remember about me?", tok)
ids = tok.encode(prompt, add_special_tokens=False)
input_ids = torch.tensor(ids, device="cuda")
seq_ids = torch.zeros_like(input_ids)
position_ids = torch.arange(len(input_ids), device="cuda")
with torch.no_grad():
out = mm.forward(input_ids, seq_ids, position_ids, mode="train")
print("[A] forward OK. logits shape:", tuple(out.logits.shape))
mm.clear_cache()
# flex_generate
from cartridges.generation import flex_generate
gen = flex_generate(
model=mm.model,
tokenizer=tok,
input_ids=input_ids,
seq_ids=seq_ids,
position_ids=position_ids,
cache=cache,
max_new_tokens=64,
temperature=1.0,
)
gen_ids = list(gen.values())[0]
text = tok.decode(gen_ids)
print("[A] generated text:", repr(text[:300]))
# Confirm pseudo-token strings tokenize/detokenize cleanly under original vocab
sample = "[MS:PM] [ACT:REWRITE] some query [EOQ]"
rt = tok.decode(tok.encode(sample, add_special_tokens=False))
parsed = extract_control_strings(rt)
print("[A] roundtrip pseudo-token:", repr(rt))
print("[A] parsed:", parsed)
assert parsed["valid"], "pseudo-token parse failed"
print("\n[A] SMOKE A PASSED ✅")
if __name__ == "__main__":
main()
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