Implement function for BERT quantization tutorial, resolves issue #1971 #2403
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quantization
carljparker
approved these changes
Jun 2, 2023
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I continue to wonder about the name=None parameter, but it does exist in the version of this function at the following URL, so I guess we can keep it.
Quantizing BERT Model
https://pytorch.org/tutorials/prototype/graph_mode_dynamic_bert_tutorial.html?highlight=transformer#setup
malfet
reviewed
Jun 3, 2023
| for _ in range(total_dims): | ||
| values.append(rng.randint(0, vocab_size - 1)) | ||
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| return torch.tensor(data=values, dtype=torch.long, device='cpu').view(shape).contiguous() |
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This would create int64 tensor, not int32 one, wouldn't it?
malfet
reviewed
Jun 3, 2023
Comment on lines
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| # Creates a random int32 tensor of the shape within the vocab size | ||
| if rng is None: | ||
| rng = global_rng | ||
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| total_dims = 1 | ||
| for dim in shape: | ||
| total_dims *= dim | ||
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| values = [] | ||
| for _ in range(total_dims): | ||
| values.append(rng.randint(0, vocab_size - 1)) | ||
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| return torch.tensor(data=values, dtype=torch.long, device='cpu').view(shape).contiguous() |
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Also, isn't it all just equivalent of
return torch.randint(0, vocab_size, shape=shape, dtype=torch.int, device='cpu')
svekars
added
docathon-h1-2023
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Fixes #1971
Fix missing implementation of
ids_tensor.