File size: 8,863 Bytes
2789831 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | """
Sequential evaluation using base models.
Loads one model at a time to avoid OOM.
Evaluates on 30 examples for speed.
"""
import json, time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
HUB_ORG = 'narcolepticchicken'
EVAL_DS = f'{HUB_ORG}/speculative-actions-eval'
ACTIONS = ['tool_call','retrieval','file_read','file_write','repair','verifier','ask_clarification','final_answer','blocked']
def load_model(name, device='cpu'):
print(f'Loading {name}...', flush=True)
tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True)
if tok.pad_token is None:
tok.pad_token = tok.eos_token
model = AutoModelForCausalLM.from_pretrained(
name,
torch_dtype=torch.float32,
trust_remote_code=True,
low_cpu_mem_usage=True,
)
model = model.to(device)
model.eval()
return model, tok
def predict_action(model, tokenizer, prompt, device='cpu', max_new_tokens=15):
with torch.no_grad():
inputs = tokenizer(prompt, return_tensors='pt', truncation=True, max_length=512)
if device != 'cpu':
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=False,
pad_token_id=tokenizer.pad_token_id,
)
text = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True).strip()
return text
def parse_action(text):
text_lower = text.lower()
for a in ACTIONS:
if a in text_lower:
return a
return 'tool_call'
def build_proposer_prompt(context, task_type):
return f"""Task: {task_type}
Context: {context}
Choose ONE action: tool_call, retrieval, file_read, file_write, repair, verifier, ask_clarification, final_answer, blocked
Action:"""
def build_verifier_prompt(context, task_type, proposed):
return f"""Task: {task_type}
Context: {context}
Proposed action: {proposed}
Is this correct? Answer YES or NO.
Answer:"""
def evaluate_config(data, proposer_name, verifier_name, strong_name, config, device='cpu'):
results = []
if config == 'A':
# Always strong
model, tok = load_model(strong_name, device)
for ex in data:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
pred = parse_action(predict_action(model, tok, prompt, device))
results.append({'pred': pred, 'true': ex['action'], 'cost': 1.0})
del model
elif config == 'B':
# Cheap only
model, tok = load_model(proposer_name, device)
for ex in data:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
pred = parse_action(predict_action(model, tok, prompt, device))
results.append({'pred': pred, 'true': ex['action'], 'cost': 0.2})
del model
elif config == 'C':
# Cheap + strong verifier
cheap, cheap_tok = load_model(proposer_name, device)
for ex in data:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
cheap_pred = parse_action(predict_action(cheap, cheap_tok, prompt, device))
results.append({'pred': cheap_pred, 'true': ex['action'], 'cost': 0.2, 'cheap_pred': cheap_pred})
del cheap
strong, strong_tok = load_model(strong_name, device)
for i, ex in enumerate(data):
verify_prompt = build_verifier_prompt(ex['context'], ex['task_type'], results[i]['cheap_pred'])
verify_text = predict_action(strong, strong_tok, verify_prompt, device, max_new_tokens=5)
accepted = 'yes' in verify_text.lower()
if accepted:
results[i]['cost'] = 0.2 + 0.3
else:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
pred = parse_action(predict_action(strong, strong_tok, prompt, device))
results[i]['pred'] = pred
results[i]['cost'] = 0.2 + 0.3 + 1.0
del strong
elif config == 'D':
# Cheap + trained verifier (base model as proxy)
cheap, cheap_tok = load_model(proposer_name, device)
for ex in data:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
cheap_pred = parse_action(predict_action(cheap, cheap_tok, prompt, device))
results.append({'pred': cheap_pred, 'true': ex['action'], 'cost': 0.2, 'cheap_pred': cheap_pred})
del cheap
verifier, verifier_tok = load_model(verifier_name, device)
for i, ex in enumerate(data):
verify_prompt = build_verifier_prompt(ex['context'], ex['task_type'], results[i]['cheap_pred'])
verify_text = predict_action(verifier, verifier_tok, verify_prompt, device, max_new_tokens=5)
accepted = 'yes' in verify_text.lower()
if accepted:
results[i]['cost'] = 0.2 + 0.15
else:
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
pred = parse_action(predict_action(verifier, verifier_tok, prompt, device))
results[i]['pred'] = pred
results[i]['cost'] = 0.2 + 0.15 + 0.6
del verifier
elif config == 'E':
# Multi-proposal reranking
cheap, cheap_tok = load_model(proposer_name, device)
proposals_list = []
for ex in data:
proposals = []
for _ in range(3):
prompt = build_proposer_prompt(ex['context'], ex['task_type'])
proposals.append(parse_action(predict_action(cheap, cheap_tok, prompt, device)))
proposals_list.append(proposals)
results.append({'pred': proposals[0], 'true': ex['action'], 'cost': 0.2 * 3})
del cheap
strong, strong_tok = load_model(strong_name, device)
for i, ex in enumerate(data):
scores = []
for prop in proposals_list[i]:
score_prompt = f"""Task: {ex['task_type']}
Context: {ex['context']}
Action: {prop}
Rate 1-10:
Score:"""
score_text = predict_action(strong, strong_tok, score_prompt, device, max_new_tokens=5)
score = 5
for word in score_text.split():
try:
score = int(word.strip('.,!?'))
break
except:
pass
scores.append(score)
best_idx = scores.index(max(scores))
results[i]['pred'] = proposals_list[i][best_idx]
results[i]['cost'] = 0.2 * 3 + 0.3 * 3
del strong
if device == 'cuda':
torch.cuda.empty_cache()
return results
def compute_metrics(results_list):
correct = sum(1 for r in results_list if r['pred'] == r['true'])
total = len(results_list)
accuracy = correct / total
avg_cost = sum(r['cost'] for r in results_list) / total
return {'accuracy': accuracy, 'avg_cost': avg_cost, 'n': total}
def main():
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Device: {device}', flush=True)
print('Loading eval dataset...', flush=True)
ds = load_dataset(EVAL_DS)['test']
data = [ds[i] for i in range(min(30, len(ds)))]
print(f'Evaluating on {len(data)} examples', flush=True)
proposer = 'Qwen/Qwen3-1.7B'
verifier = 'Qwen/Qwen3-4B'
strong = 'Qwen/Qwen2.5-7B'
all_results = {}
for cfg in ['A', 'B', 'C', 'D', 'E']:
print(f'\n=== Config {cfg} ===', flush=True)
start = time.time()
results = evaluate_config(data, proposer, verifier, strong, cfg, device)
elapsed = time.time() - start
metrics = compute_metrics(results)
all_results[cfg] = metrics
print(f"Config {cfg}: Accuracy={metrics['accuracy']:.3f}, Cost={metrics['avg_cost']:.2f}, Time={elapsed:.1f}s", flush=True)
print('\n=== Final Results ===', flush=True)
for cfg in ['A','B','C','D','E']:
r = all_results[cfg]
print(f"Config {cfg}: Accuracy={r['accuracy']:.3f}, Cost={r['avg_cost']:.2f}", flush=True)
with open('/tmp/eval_results_empirical.json', 'w') as f:
json.dump(all_results, f, indent=2)
print('\nSaved to /tmp/eval_results_empirical.json', flush=True)
# Upload to Hub
from huggingface_hub import HfApi
api = HfApi()
api.upload_file(
path_or_fileobj='/tmp/eval_results_empirical.json',
path_in_repo='eval_results_empirical.json',
repo_id=f'{HUB_ORG}/speculative-tool-actions',
repo_type='model'
)
print('Uploaded results to Hub', flush=True)
if __name__ == '__main__':
main()
|