Distributed layers #1270
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Distributed layers #1270
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awni
reviewed
Jul 17, 2024
| sl = cls(input_dims, output_dims, False, group) | ||
| # The multiplication with 1.0 forces a copy, perhaps change to | ||
| # something better when available. | ||
| sl.weight = linear_layer.weight[r * step : (r + 1) * step] * 1 |
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Is it possible the input buffer could be donated so we'd still hold on to the memory?
If so, maybe another option is to do sl.weight[ ... ] = ... that will force the copy since it's a slice update?
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Nice! That does sound better actually!
|
I kind of like this design. I like that it's all quite simple and easy to follow and we have a lot of control over how to shard the model (as in ml-explore/mlx-examples#890). We could possibly find a way to reduce the code needed for adding a new custom linear-like layer.. but the simplicity is nice, I wouldn't want to give that up. |
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Adds linear layers that allow training and inference of a model sharded across several devices. The main things added are
simply changing linear layers to the above results in a model that works out of the box with distributed inference and training.
I am starting it as a draft so that we can iterate a bit on the design. The negative aspects of the above design are that we have yet another linear layer to think about when implementing LoRA and friends or weird new quantizations for instance. Perhaps it would be better to make the above layers with an internal linear layer so model surgery that swaps linear layers would still work out of the box.