[WIP] full finetune / qlora + ac/offload/optm in bwd #21
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Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
weifengpy
changed the title
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Should we call out that this table assumes that we are only applying QLoRA to the FFNs? |
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
drisspg
reviewed
Mar 7, 2024
| def nf4_detach(aten_op, args, kwargs=None): | ||
| # nn.Parameter need detach | ||
| quantized_data = aten_op(args[0].quantized_data, *args[1:], **kwargs) | ||
| tensor_meta = SubclassTensorArgs( |
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I hijacked the tensor unflatten/flatten to construct the new tensors,
Summary: Test Plan: Reviewers: Subscribers: Tasks: Tags:
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close is since QLoRA + FSDP2 and cpu offloading has been landed into torchtune: pytorch/torchtune#909 |
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Why composing FSDP with NF4Tensor
QLoRA : number of trainable parameters are reduced from xxx to xxx. parameter size are reduced by xx
Full finetuning original Llama with 4bit quantized params:
7B + QLora on FFNs: summarizing memory usage below with bf16, adamW, AC, cpu offloading