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update steps calc with max_train_epochs
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@@ -194,7 +194,7 @@ def train(args):
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# 学習ステップ数を計算する
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if args.max_train_epochs is not None:
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args.max_train_steps = args.max_train_epochs * len(train_dataloader)
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args.max_train_steps = args.max_train_epochs * math.ceil(len(train_dataloader) / accelerator.num_processes / args.gradient_accumulation_steps)
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print(f"override steps. steps for {args.max_train_epochs} epochs is / 指定エポックまでのステップ数: {args.max_train_steps}")
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# lr schedulerを用意する
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@@ -240,7 +240,7 @@ def train(args):
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print(f" num epochs / epoch数: {num_train_epochs}")
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print(f" batch size per device / バッチサイズ: {args.train_batch_size}")
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print(f" total train batch size (with parallel & distributed & accumulation) / 総バッチサイズ(並列学習、勾配合計含む): {total_batch_size}")
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print(f" gradient ccumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
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print(f" gradient accumulation steps / 勾配を合計するステップ数 = {args.gradient_accumulation_steps}")
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print(f" total optimization steps / 学習ステップ数: {args.max_train_steps}")
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progress_bar = tqdm(range(args.max_train_steps), smoothing=0, disable=not accelerator.is_local_main_process, desc="steps")
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