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from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from .common import dtype_for_device, write_json
from .evaluate import unigram_f1
from .io import load_feature_pair, select_rows
from .models import load_prefix
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument("--energy", default="artifacts/energy_free_test.pt")
parser.add_argument("--vision", default="artifacts/vision.pt")
parser.add_argument("--text", default="artifacts/text.pt")
parser.add_argument("--text-orbits")
parser.add_argument("--prefix", default="artifacts/prefix.pt")
parser.add_argument("--samples", type=int, default=100)
parser.add_argument("--max-new-tokens", type=int, default=32)
parser.add_argument("--device", default="cuda:3")
parser.add_argument(
"--output", default="artifacts/energy_prefix_evaluation.json"
)
return parser.parse_args()
def main() -> None:
args = parse_args()
energy = torch.load(args.energy, map_location="cpu", weights_only=False)
_, text, _, text_lookup = load_feature_pair(args.vision, args.text)
rows = energy["rows"][: args.samples]
initial = energy["initial_particles"][: args.samples]
final = energy["final_particles"][: args.samples]
oracle = select_rows(text["features"], text_lookup, rows)
if args.text_orbits:
orbit_state = torch.load(
args.text_orbits, map_location="cpu", weights_only=False
)
orbit_lookup = {
int(row): index
for index, row in enumerate(orbit_state["rows"])
}
orbit_mean = torch.nn.functional.normalize(
orbit_state["features"].float().mean(1), dim=-1
)
oracle = select_rows(orbit_mean, orbit_lookup, rows)
reference_lookup = {
int(row): captions
for row, captions in zip(text["rows"], text["all_captions"])
}
prefix, prefix_state = load_prefix(args.prefix, args.device)
if prefix.semantic_dim != initial.shape[-1]:
raise ValueError("Energy latent and text-only prefix dimensions differ")
tokenizer = AutoTokenizer.from_pretrained(text["model"])
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
dtype = dtype_for_device(args.device)
lm = AutoModelForCausalLM.from_pretrained(
text["model"], torch_dtype=dtype
).to(args.device)
lm.eval()
conditions = {"initial": initial, "final": final, "oracle": oracle}
decoded: dict[str, list[str]] = {key: [] for key in conditions}
with torch.inference_mode():
for name, semantic in conditions.items():
for chunk in semantic.split(16):
embeds = prefix(chunk.to(args.device)).to(dtype)
attention = torch.ones(
embeds.shape[:2], dtype=torch.long, device=args.device
)
output = lm.generate(
inputs_embeds=embeds,
attention_mask=attention,
max_new_tokens=args.max_new_tokens,
do_sample=False,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
decoded[name].extend(
tokenizer.batch_decode(
output, skip_special_tokens=True
)
)
scores = {}
per_condition: dict[str, list[float]] = {}
for name, generations in decoded.items():
values = [
unigram_f1(generation, reference_lookup[int(row)])
for row, generation in zip(rows, generations)
]
per_condition[name] = values
scores[name] = sum(values) / max(len(values), 1)
difference = np.asarray(per_condition["final"]) - np.asarray(
per_condition["initial"]
)
bootstrap_generator = np.random.default_rng(20260729)
bootstrap = difference[
bootstrap_generator.integers(
0, len(difference), size=(10_000, len(difference))
)
].mean(1)
result = {
"samples": len(rows),
"mean_best_reference_unigram_f1": scores,
"paired_final_minus_initial": {
"mean": float(difference.mean()),
"bootstrap_95_percentile_interval": [
float(np.quantile(bootstrap, 0.025)),
float(np.quantile(bootstrap, 0.975)),
],
"improved": int((difference > 0).sum()),
"tied": int((difference == 0).sum()),
"worsened": int((difference < 0).sum()),
},
"energy_protocol": energy["protocol"],
"prefix_training": prefix_state["training"],
"per_sample_unigram_f1": [
{
"row": int(row),
**{
name: per_condition[name][index]
for name in per_condition
},
}
for index, row in enumerate(rows)
],
"examples": [
{
"row": int(row),
"references": reference_lookup[int(row)],
**{name: decoded[name][i] for name in decoded},
}
for i, row in enumerate(rows[:25])
],
}
Path(args.output).parent.mkdir(parents=True, exist_ok=True)
write_json(args.output, result)
print(json.dumps(result, indent=2, ensure_ascii=False))
if __name__ == "__main__":
main()
|
