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4d8bbd9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """
Calibration script: compute logprobs for reference solution outputs
vs unconstrained model outputs to design a scoring function.
"""
import torch
import torch.nn.functional as F
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
EVAL_MODEL = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(EVAL_MODEL)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(EVAL_MODEL, dtype=torch.float16, device_map="auto")
with open("test_cases.json", "r") as f:
TEST_CASES = json.load(f)
def compute_chat_logprobs(model, tokenizer, prompt, generated_text):
"""
Compute logprobs using chat template (works for both exercises).
The prompt is formatted as a chat message, generated_text is the response.
Returns:
mean_logprob: mean log-prob per generated token
total_logprob: sum of log-probs
n_tokens: number of generated tokens
per_token: list of (token_str, logprob) pairs
"""
if not generated_text or not generated_text.strip():
return -float('inf'), 0.0, 0, []
message = [{"role": "user", "content": prompt}]
prompt_ids = tokenizer.apply_chat_template(
message, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
prompt_len = prompt_ids.shape[1]
gen_ids = tokenizer.encode(
generated_text, add_special_tokens=False, return_tensors="pt"
).to(model.device)
full_ids = torch.cat([prompt_ids, gen_ids], dim=1)
if full_ids.shape[1] <= prompt_len:
return -float('inf'), 0.0, 0, []
with torch.no_grad():
outputs = model(full_ids)
logits = outputs.logits
log_probs = F.log_softmax(logits, dim=-1)
per_token = []
total_logprob = 0.0
n_tokens = 0
for i in range(prompt_len, full_ids.shape[1]):
token_id = full_ids[0, i].item()
token_logprob = log_probs[0, i - 1, token_id].item()
token_str = tokenizer.decode([token_id])
per_token.append((token_str, token_logprob))
total_logprob += token_logprob
n_tokens += 1
mean_logprob = total_logprob / n_tokens if n_tokens > 0 else -float('inf')
return mean_logprob, total_logprob, n_tokens, per_token
def generate_unconstrained_chat(model, tokenizer, prompt, max_tokens=20):
"""Generate unconstrained text using chat template (for both exercises)."""
message = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(message, add_generation_prompt=True, return_tensors="pt").to(model.device)
attention_mask = torch.ones_like(inputs)
prompt_length = inputs.shape[1]
with torch.no_grad():
output = model.generate(
inputs,
attention_mask=attention_mask,
max_new_tokens=max_tokens,
do_sample=False,
pad_token_id=tokenizer.pad_token_id
)
generated_tokens = output[0][prompt_length:]
return tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
# ---- Load and run the reference solution ----
import importlib.util
import sys
import time
module_name = f"solution_module_{int(time.time())}"
spec = importlib.util.spec_from_file_location(module_name, "solution.py")
solution = importlib.util.module_from_spec(spec)
sys.modules[module_name] = solution
spec.loader.exec_module(solution)
print("\n" + "="*80)
print("EXERCISE 1: La Disparition (no 'e')")
print("="*80)
ex1_instance = solution.LaDisparition(model, tokenizer)
ex1_results = []
for i, prompt in enumerate(TEST_CASES["exercise_1"]):
# Generate constrained output
constrained_output = ex1_instance(prompt, max_tokens=20)
# Strip prompt from output
if constrained_output.startswith(prompt):
constrained_gen = constrained_output[len(prompt):].strip()
else:
constrained_gen = constrained_output.strip()
# Generate unconstrained output (chat template for instruct model)
unconstrained_gen = generate_unconstrained_chat(model, tokenizer, prompt, max_tokens=20)
# Compute logprobs using chat template (matches how the model should be used)
c_mean, c_total, c_ntok, c_per = compute_chat_logprobs(model, tokenizer, prompt, constrained_gen)
# Compute logprobs for unconstrained output
u_mean, u_total, u_ntok, u_per = compute_chat_logprobs(model, tokenizer, prompt, unconstrained_gen)
delta = c_mean - u_mean # will be negative (constrained is worse)
print(f"\nTest {i+1}: {prompt}")
print(f" Unconstrained: {unconstrained_gen}")
print(f" mean_logprob={u_mean:.4f}, n_tokens={u_ntok}")
print(f" Constrained: {constrained_gen}")
print(f" mean_logprob={c_mean:.4f}, n_tokens={c_ntok}")
print(f" Delta (constrained - unconstrained): {delta:.4f}")
ex1_results.append({
"prompt": prompt,
"constrained_gen": constrained_gen,
"unconstrained_gen": unconstrained_gen,
"c_mean_logprob": c_mean,
"u_mean_logprob": u_mean,
"delta_mean_logprob": delta,
})
print(f"\n--- Exercise 1 Summary ---")
deltas_1 = [r["delta_mean_logprob"] for r in ex1_results]
c_means_1 = [r["c_mean_logprob"] for r in ex1_results]
u_means_1 = [r["u_mean_logprob"] for r in ex1_results]
print(f" Unconstrained mean logprobs: {[f'{x:.3f}' for x in u_means_1]}")
print(f" Constrained mean logprobs: {[f'{x:.3f}' for x in c_means_1]}")
print(f" Deltas: {[f'{x:.3f}' for x in deltas_1]}")
print(f" Mean delta: {sum(deltas_1)/len(deltas_1):.4f}")
print(f" Worst delta: {min(deltas_1):.4f}")
print("\n" + "="*80)
print("EXERCISE 2: Toulouse Sequence (no 'Toulouse')")
print("="*80)
ex2_instance = solution.ToulouseSequence(model, tokenizer)
ex2_results = []
for i, prompt in enumerate(TEST_CASES["exercise_2"]):
# Generate constrained output
constrained_gen = ex2_instance(prompt, max_tokens=20)
# Generate unconstrained output (chat format)
unconstrained_gen = generate_unconstrained_chat(model, tokenizer, prompt, max_tokens=20)
# Compute logprobs (chat format)
c_mean, c_total, c_ntok, c_per = compute_chat_logprobs(model, tokenizer, prompt, constrained_gen)
u_mean, u_total, u_ntok, u_per = compute_chat_logprobs(model, tokenizer, prompt, unconstrained_gen)
delta = c_mean - u_mean
print(f"\nTest {i+1}: {prompt}")
print(f" Unconstrained: {unconstrained_gen}")
print(f" mean_logprob={u_mean:.4f}, n_tokens={u_ntok}")
print(f" Constrained: {constrained_gen}")
print(f" mean_logprob={c_mean:.4f}, n_tokens={c_ntok}")
print(f" Delta (constrained - unconstrained): {delta:.4f}")
ex2_results.append({
"prompt": prompt,
"constrained_gen": constrained_gen,
"unconstrained_gen": unconstrained_gen,
"c_mean_logprob": c_mean,
"u_mean_logprob": u_mean,
"delta_mean_logprob": delta,
})
print(f"\n--- Exercise 2 Summary ---")
deltas_2 = [r["delta_mean_logprob"] for r in ex2_results]
c_means_2 = [r["c_mean_logprob"] for r in ex2_results]
u_means_2 = [r["u_mean_logprob"] for r in ex2_results]
print(f" Unconstrained mean logprobs: {[f'{x:.3f}' for x in u_means_2]}")
print(f" Constrained mean logprobs: {[f'{x:.3f}' for x in c_means_2]}")
print(f" Deltas: {[f'{x:.3f}' for x in deltas_2]}")
print(f" Mean delta: {sum(deltas_2)/len(deltas_2):.4f}")
print(f" Worst delta: {min(deltas_2):.4f}")
print("\n" + "="*80)
print("OVERALL RECOMMENDATION")
print("="*80)
all_deltas = deltas_1 + deltas_2
print(f"All deltas: {[f'{x:.3f}' for x in all_deltas]}")
print(f"Global mean delta: {sum(all_deltas)/len(all_deltas):.4f}")
print(f"Global worst delta: {min(all_deltas):.4f}")
# ---- Save reference scores to CSV ----
import csv
csv_path = "reference_scores.csv"
with open(csv_path, "w", newline="") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([
"exercise", "prompt_index", "prompt",
"unconstrained_logprob", "reference_logprob", "reference_delta"
])
for i, r in enumerate(ex1_results):
writer.writerow([
"exercise_1", i, r["prompt"],
f"{r['u_mean_logprob']:.6f}",
f"{r['c_mean_logprob']:.6f}",
f"{r['delta_mean_logprob']:.6f}",
])
for i, r in enumerate(ex2_results):
writer.writerow([
"exercise_2", i, r["prompt"],
f"{r['u_mean_logprob']:.6f}",
f"{r['c_mean_logprob']:.6f}",
f"{r['delta_mean_logprob']:.6f}",
])
print(f"\nReference scores saved to {csv_path}")
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