Create src/optimization.py

src/optimization.py

@@ -0,0 +1,66 @@
+from collections import Counter
+from itertools import chain
+import math
+import torch
+from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction
+
+
+def ngrams(sequence, n):
+    return [tuple(sequence[i:i+n]) for i in range(len(sequence)-n+1)]
+
+def count_ngrams(sequence, max_n):
+    counts = Counter()
+    for n in range(1, max_n + 1):
+        counts.update(ngrams(sequence, n))
+    return counts
+
+def self_bleu(outputs):
+    smoothing_function = SmoothingFunction().method1
+    scores = []
+    for i in range(len(outputs)):
+        references = outputs[:i] + outputs[i+1:]
+        # Avoid calculating BLEU score for empty references
+        if references:
+            scores.append(sentence_bleu(references, outputs[i], smoothing_function=smoothing_function))
+    # If all references are empty, return a default value
+    if not scores:
+        return 0
+    return sum(scores) / len(scores)
+
+def dist_n(outputs, n):
+    all_ngrams = list(chain(*[ngrams(output, n) for output in outputs]))
+    unique_ngrams = set(all_ngrams)
+    return len(unique_ngrams) / len(all_ngrams) if all_ngrams else 0
+
+def perplexity(model, tokenizer, texts):
+    encodings = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)
+    max_length = model.config.n_positions
+    stride = 512
+    lls = []
+    for i in range(0, encodings.input_ids.size(1), stride):
+        begin_loc = max(i + stride - max_length, 0)
+        end_loc = i + stride
+        trg_len = end_loc - i
+        input_ids = encodings.input_ids[:, begin_loc:end_loc].to(model.device)
+        target_ids = input_ids.clone()
+        target_ids[:, :-trg_len] = -100
+
+        with torch.no_grad():
+            outputs = model(input_ids, labels=target_ids)
+            log_likelihood = outputs.loss * trg_len
+        lls.append(log_likelihood)
+
+    ppl = torch.exp(torch.stack(lls).sum() / end_loc)
+    return ppl.item()
+
+def js_divergence(p, q):
+    def kl_divergence(p, q):
+        return sum(p[i] * math.log(p[i] / q[i]) for i in range(len(p)) if p[i] != 0 and q[i] != 0)
+    
+    p_norm = [float(i)/sum(p) for i in p]
+    q_norm = [float(i)/sum(q) for i in q]
+    
+    m = [(p_norm[i] + q_norm[i]) / 2 for i in range(len(p_norm))]
+    
+    return (kl_divergence(p_norm, m) + kl_divergence(q_norm, m)) / 2
+