chore: minor optim changes (add apollo, improve docs, remove lion-pytorch) (#2444)
* feat: add apollo-torch * chore: update optimizer list * fix: deleted accidental requirements file * fix: remove mention of deprecated lion_pytorch
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@@ -506,7 +506,7 @@ lr_div_factor: # Learning rate div factor
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# Specify optimizer
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# Valid values are driven by the Transformers OptimizerNames class, see:
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# https://github.com/huggingface/transformers/blob/95b374952dc27d8511541d6f5a4e22c9ec11fb24/src/transformers/training_args.py#L134
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# https://github.com/huggingface/transformers/blob/cbf924b76c03828101a34069a96d209314114fd5/src/transformers/training_args.py#L144-L189
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#
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# Note that not all optimizers may be available in your environment, ex: 'adamw_anyprecision' is part of
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# torchdistx, 'adamw_bnb_8bit' is part of bnb.optim.Adam8bit, etc. When in doubt, it is recommended to start with the optimizer used
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@@ -516,25 +516,48 @@ lr_div_factor: # Learning rate div factor
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# - adamw_torch
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# - adamw_torch_fused
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# - adamw_torch_xla
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# - adamw_torch_npu_fused
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# - adamw_apex_fused
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# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
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# - adopt_adamw (an EXPERIMENTAL optimizer, only for torch version >= 2.5.1)
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# - adafactor
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# - adamw_anyprecision
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# - adamw_torch_4bit
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# - ademamix
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# - sgd
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# - adagrad
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# - adamw_bnb_8bit
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# - adamw_8bit # alias for adamw_bnb_8bit
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# - ademamix_8bit
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# - lion_8bit
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# - lion_32bit
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# - paged_adamw_32bit
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# - paged_adamw_8bit
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# - paged_ademamix_32bit
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# - paged_ademamix_8bit
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# - paged_lion_32bit
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# - paged_lion_8bit
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# - rmsprop
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# - rmsprop_bnb
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# - rmsprop_bnb_8bit
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# - rmsprop_bnb_32bit
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# - galore_adamw
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# - galore_adamw_8bit
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# - galore_adafactor
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# - galore_adamw_layerwise
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# - galore_adamw_8bit_layerwise
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# - galore_adafactor_layerwise
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# - lomo
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# - adalomo
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# - grokadamw
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# - schedule_free_adamw
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# - schedule_free_sgd
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# - apollo_adamw
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# - apollo_adamw_layerwise
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#
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# Additional custom optimizers include:
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# - optimi_adamw
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# - ao_adamw_8bit
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# - ao_adamw_fp8
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optimizer:
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# Dictionary of arguments to pass to the optimizer
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optim_args:
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