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very slight refactor, bit cleaner
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6
train.py
6
train.py
@ -112,12 +112,10 @@ def get_batch(split):
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ix = torch.randint(len(data) - block_size, (batch_size,))
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x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
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y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
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# pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
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if "cuda" in device:
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# GPU training
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if device_type == 'cuda':
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# pin arrays x,y, which allows us to move them to GPU asynchronously (non_blocking=True)
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x, y = x.pin_memory().to(device, non_blocking=True), y.pin_memory().to(device, non_blocking=True)
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else:
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# CPU or MPS training
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x, y = x.to(device), y.to(device)
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return x, y
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