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In prior releases, conversion required equally the enter & output checkpoints being stored in memory for the duration of conversion. This intended that large types required a prohibitively massive degree of memory as a way to accomplish conversion. In launch 2.0.0, we introduce streaming conversion, which significantly lessens the height memory utilization by accomplishing conversion incrementally.
This solution presents a far more transparent and manageable way to set the configurations with the checkpoint conversion course of action.
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The script supports tensor parallelism and pipeline parallelism to distribute the design across numerous GPUs.
Here's an illustration of the conversion process for the pre-experienced OpenAI GPT product, assuming that the NumPy checkpoint
You merely should run this conversion script after to get a PyTorch design. It is possible to then disregard the TensorFlow