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Skip batches on load
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@ -302,6 +302,9 @@ def load_checkpoint_if_available(
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if "cur_epoch" in saved_params:
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if "cur_epoch" in saved_params:
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params["start_epoch"] = saved_params["cur_epoch"]
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params["start_epoch"] = saved_params["cur_epoch"]
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if "cur_batch_idx" in saved_params:
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params["cur_batch_idx"] = saved_params["cur_batch_idx"]
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return saved_params
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return saved_params
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@ -457,7 +460,14 @@ def train_one_epoch(
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tot_loss = MetricsTracker()
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tot_loss = MetricsTracker()
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cur_batch_idx = params.get("cur_batch_idx", 0)
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for batch_idx, batch in enumerate(train_dl):
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for batch_idx, batch in enumerate(train_dl):
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if batch_idx < cur_batch_idx:
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continue
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cur_batch_idx = batch_idx
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params.batch_idx_train += 1
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params.batch_idx_train += 1
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x, y, sentence_lengths = batch
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x, y, sentence_lengths = batch
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batch_size = x.size(0)
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batch_size = x.size(0)
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@ -482,6 +492,7 @@ def train_one_epoch(
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params.batch_idx_train > 0
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params.batch_idx_train > 0
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and params.batch_idx_train % params.save_every_n == 0
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and params.batch_idx_train % params.save_every_n == 0
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):
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):
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params.cur_batch_idx = batch_idx
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save_checkpoint_with_global_batch_idx(
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save_checkpoint_with_global_batch_idx(
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out_dir=params.exp_dir,
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out_dir=params.exp_dir,
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global_batch_idx=params.batch_idx_train,
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global_batch_idx=params.batch_idx_train,
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@ -490,6 +501,7 @@ def train_one_epoch(
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optimizer=optimizer,
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optimizer=optimizer,
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rank=rank,
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rank=rank,
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)
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)
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del params.cur_batch_idx
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if batch_idx % params.log_interval == 0:
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if batch_idx % params.log_interval == 0:
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# Note: "frames" here means "num_tokens"
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# Note: "frames" here means "num_tokens"
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