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perf: improve logging for MLflow metrics #42

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merged 4 commits into from
Feb 15, 2024
Merged

perf: improve logging for MLflow metrics #42

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fbbdd82
perf: improve logging for MLflow metrics
anujsinha3 Feb 14, 2024
9853ca2
git pull main - resolve merge conflicts
anujsinha3 Feb 14, 2024
5f99fb1
git pull main
anujsinha3 Feb 14, 2024
8db2bc0
perf: log predictions for prompts
anujsinha3 Feb 15, 2024
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23 changes: 12 additions & 11 deletions src/autora/doc/pipelines/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@ def eval_prompts(
predictor = Predictor(model_path)
for i in range(len(prompts_list)):
logger.info(f"Starting to run model on prompt {i}")
eval_result = eval_prompt(data_file, predictor, prompts_list[i], param_dict)
eval_result = eval_prompt(data_file, predictor, prompts_list[i], param_dict, i)
logger.info(f"Model run completed on prompt {i}: {prompts_list[i]}")
results_list.append(eval_result)
return results_list
Expand Down Expand Up @@ -96,7 +96,9 @@ def eval(
return eval_prompt(data_file, pred, prompt, param_dict)


def eval_prompt(data_file: str, pred: Predictor, prompt: str, param_dict: Dict[str, float]) -> EvalResult:
def eval_prompt(
data_file: str, pred: Predictor, prompt: str, param_dict: Dict[str, float], prompt_index: int = 0
) -> EvalResult:
import mlflow

inputs, labels = load_data(data_file)
Expand All @@ -107,22 +109,21 @@ def eval_prompt(data_file: str, pred: Predictor, prompt: str, param_dict: Dict[s
bleu, meteor = eval_bleu_meteor(predictions, labels)
semscore = eval_semscore(predictions, labels)
pred_time = timer_end - timer_start
prompt_hash = hash(prompt)
mlflow.log_metric("prediction_time/doc", pred_time / (len(inputs)))
for i in range(len(inputs)):
mlflow.log_text(labels[i], f"{prompt_hash}_label_{i}.txt")
mlflow.log_text(inputs[i], f"{prompt_hash}_input_{i}.py")
mlflow.log_text(predictions[i], f"{prompt_hash}_prediction_{i}.txt")
mlflow.log_text(labels[i], f"prompt_{prompt_index}_label_{i}.txt")
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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("total_tokens", total_tokens)
mlflow.log_metric("tokens/sec", total_tokens / pred_time)
mlflow.log_metric("bleu_score", round(bleu, 5))
mlflow.log_metric("meteor_score", round(meteor, 5))
mlflow.log_metric("semscore", round(semscore, 5))
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))
return EvalResult(predictions, prompt, bleu, meteor, semscore)


Expand Down
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