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[release] Bye 2025 (#9702)

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This commit is contained in:
Yaowei Zheng
2025-12-31 22:22:40 +08:00
committed by GitHub
parent 000526908a
commit 95ac3f2373
59 changed files with 154 additions and 401 deletions
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19
scripts/vllm_infer.py
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@@ -18,9 +18,10 @@ import time
import av
import fire
from datasets import load_dataset
from eval_bleu_rouge import compute_metrics
from tqdm import tqdm
from transformers import Seq2SeqTrainingArguments
from datasets import load_dataset
from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
from llamafactory.extras.constants import IGNORE_INDEX
@@ -29,8 +30,6 @@ from llamafactory.extras.packages import is_vllm_available
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
from eval_bleu_rouge import compute_metrics
if is_vllm_available():
from vllm import LLM, SamplingParams
@@ -235,10 +234,10 @@ def vllm_infer(
print(f"{len(all_prompts)} total generated results have been saved at {save_name}.")
print("*" * 70)
# Write all matrix results when matrix_save_name is not None,
# Write all matrix results when matrix_save_name is not None,
# The result matrix is referencing src.llamafactory.train.sft.workflow.run_sft # 127~132
# trainer.save_metrics("predict", predict_results.metrics)
#
#
# {
# "predict_bleu-4": 4.349975,
# "predict_model_preparation_time": 0.0128,
@@ -265,11 +264,11 @@ def vllm_infer(
print(f"predict_{task}: {score:.4f}")
average_score["predict_" + task] = score
average_score['predict_model_preparation_time'] = preparation_time
average_score['predict_runtime'] = predict_time
average_score["predict_model_preparation_time"] = preparation_time
average_score["predict_runtime"] = predict_time
num_steps = len(range(0, len(train_dataset), batch_size))
average_score['predict_samples_per_second'] = len(dataset) / predict_time if predict_time > 0 else 0.0
average_score['predict_steps_per_second'] = num_steps / predict_time if predict_time > 0 else 0.0
average_score["predict_samples_per_second"] = len(dataset) / predict_time if predict_time > 0 else 0.0
average_score["predict_steps_per_second"] = num_steps / predict_time if predict_time > 0 else 0.0
with open(matrix_save_name, "w", encoding="utf-8") as f:
json.dump(average_score, f, indent=4)
@@ -280,4 +279,4 @@ def vllm_infer(
if __name__ == "__main__":
fire.Fire(vllm_infer)
fire.Fire(vllm_infer)