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Prevent OOM during checkpoint save on colab for llama3-8b qlora recipe #1315
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@@ -104,3 +104,6 @@ profiler: | |
warmup_steps: 5 | ||
active_steps: 2 | ||
num_cycles: 1 | ||
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# For colab use True | ||
low_cpu_ram: False |
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@@ -22,7 +22,7 @@ | |
TransformerSelfAttentionLayer, | ||
) | ||
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from torchtune.modules.common_utils import reparametrize_as_dtype_state_dict_post_hook | ||
from torchtune.modules.common_utils import _register_reparametrize_state_dict_hooks | ||
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from torchtune.modules.peft import DoRALinear, LORA_ATTN_MODULES, LoRALinear | ||
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@@ -256,9 +256,7 @@ def lora_llama3( | |
if quantize_base: | ||
# For QLoRA, we reparametrize 4-bit tensors to bf16, and offload to CPU on the fly | ||
# so as to not increase peak memory | ||
model._register_state_dict_hook( | ||
partial(reparametrize_as_dtype_state_dict_post_hook, offload_to_cpu=True) | ||
) | ||
_register_reparametrize_state_dict_hooks(model) | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Doesn't have to be done in this PR, but we can think about adding this for other models that would have a similar memory situation when running QLoRA (Llama 3.1 8B is an obvious choice, but there are a handful of other similarly-sized models supported in our repo that could benefit from this) There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. will do in followup |
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return model | ||
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Ideally we want to only run state_dict post hooks once, so we should reuse the
state_dict
this does change the semantic slightly though -- before
adapter_*.pt
contained weights tagged with CUDA, but now it contains weights tagged with CPU.Not sure whether the old behavior was intended/whether this change is ok (but no CI seems to fail :D) Also when loading, we
map_location="cpu"
regardlessThere was a problem hiding this comment.
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Sorry I missed this comment before. But yeah I think this change makes sense, I don't think there's any reason we need to require CUDA weights. And as you point out since we load on CPU when resuming it was probably never really an issue. Plus not re-running the state dict post hooks is a nice bonus.