perf: log predictions for prompts

src/autora/doc/pipelines/main.py

@@ -111,19 +111,20 @@ def eval_prompt(
     pred_time = timer_end - timer_start
     mlflow.log_metric("prediction_time/doc", pred_time / (len(inputs)))
     for i in range(len(inputs)):
-        mlflow.log_text(labels[i], f"prompt_{prompt_index}_label_{i}.txt")
-        mlflow.log_text(inputs[i], f"prompt_{prompt_index}_input_{i}.py")
         mlflow.log_text(predictions[i], f"prompt_{prompt_index}_prediction_{i}.txt")
 
     # flatten predictions for counting tokens
     predictions_flat = list(itertools.chain.from_iterable(predictions))
     tokens = pred.tokenize(predictions_flat)["input_ids"]
     total_tokens = sum([len(token) for token in tokens])
-    mlflow.log_metric(f"prompt_{prompt_index}_total_tokens", total_tokens)
-    mlflow.log_metric(f"prompt_{prompt_index}_tokens/sec", total_tokens / pred_time)
-    mlflow.log_metric(f"prompt_{prompt_index}_bleu_score", round(bleu, 5))
-    mlflow.log_metric(f"prompt_{prompt_index}_meteor_score", round(meteor, 5))
-    mlflow.log_metric(f"prompt_{prompt_index}_semscore", round(semscore, 5))
+    metrics_dict = {
+        f"prompt_{prompt_index}_total_tokens": total_tokens,
+        f"prompt_{prompt_index}_tokens/sec": total_tokens / pred_time,
+        f"prompt_{prompt_index}_bleu_score": round(bleu, 5),
+        f"prompt_{prompt_index}_meteor_score": round(meteor, 5),
+        f"prompt_{prompt_index}_semscore": round(semscore, 5),
+    }
+    mlflow.log_metrics(metrics_dict)
     return EvalResult(predictions, prompt, bleu, meteor, semscore)