[wip] add token log for llava bench #1065
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Thanks for your contribution! |
jerrywgz
reviewed
Feb 19, 2025
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| class Statistical(object): |
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统计逻辑和callback可以单独提出来，是公共部分，并对model的input_shape和efficient_token_count做检查
| @@ -171,8 +171,15 @@ def get_paddle_memory_info(): | |||
| mem_gpu = ( | |||
| train_result.metrics["train_mem_gpu_peaked_delta"] + train_result.metrics["train_mem_gpu_alloc_delta"] | |||
| ) | |||
| logger.info(f'Memory_allocated:{memory_allocated}GB, max_memory_allocated: {max_memory_allocated}GB, memory_reserved:{memory_reserved}GB, max_memory_reserved: {max_memory_reserved}GB \n') | |||
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callback的on_log方法中已经提供了这些性能数据，是否还需要重复log？
| from paddlenlp.trainer.plugins.timer import get_timers | ||
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| PADDLE_EMA_WEIGHTS_NAME = "ema_state.pdparams" |
| record the infomation of saving model. | ||
| """ | ||
| pass | ||
| # end_save_time = time.time() |
| def __init__(self, **kwargs): | ||
| super().__init__(**kwargs) | ||
| # self.benchmark_callback = BenchmarkCallback(self, self.args.save_steps, skip_step=self.args.benchmark_skip_steps) | ||
| self.benchmark_callback = BenchmarkCallback(self, 12, 1) |
pkhk-1
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