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"""Module for validating config files""" |
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import logging |
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import torch |
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LOG = logging.getLogger("axolotl") |
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def validate_config(cfg): |
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if cfg.gradient_accumulation_steps and cfg.batch_size: |
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raise ValueError( |
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"please set only one of gradient_accumulation_steps or batch_size" |
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) |
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if cfg.batch_size: |
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LOG.warning( |
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"%s\n%s", |
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"batch_size is not recommended. Please use gradient_accumulation_steps instead.", |
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"To calculate the equivalent gradient_accumulation_steps, divide batch_size / micro_batch_size / number of gpus.", |
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) |
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if cfg.load_4bit: |
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raise ValueError( |
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"cfg.load_4bit parameter has been deprecated and replaced by cfg.gptq" |
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) |
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if cfg.adapter == "qlora": |
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if cfg.merge_lora: |
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if cfg.load_in_8bit: |
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raise ValueError("Can't merge qlora if loaded in 8bit") |
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if cfg.gptq: |
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raise ValueError("Can't merge qlora if gptq") |
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if cfg.load_in_4bit: |
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raise ValueError("Can't merge qlora if loaded in 4bit") |
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else: |
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if cfg.load_in_8bit: |
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raise ValueError("Can't load qlora in 8bit") |
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if cfg.gptq: |
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raise ValueError("Can't load qlora if gptq") |
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if not cfg.load_in_4bit: |
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raise ValueError("Require cfg.load_in_4bit to be True for qlora") |
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if not cfg.load_in_8bit and cfg.adapter == "lora": |
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LOG.warning("We recommend setting `load_in_8bit: true` for LORA finetuning") |
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if cfg.trust_remote_code: |
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LOG.warning( |
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"`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model." |
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) |
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if cfg.push_dataset_to_hub and cfg.hf_use_auth_token is not True: |
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raise ValueError( |
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"Require cfg.hf_use_auth_token to be True for push_dataset_to_hub" |
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) |
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if (cfg.base_model and "falcon" in cfg.base_model.lower()) and cfg.fsdp: |
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raise ValueError("FSDP is not supported for falcon models") |
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if ( |
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cfg.base_model and "mpt" in cfg.base_model.lower() |
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) and cfg.gradient_checkpointing: |
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raise ValueError("gradient_checkpointing is not supported for MPT models") |
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if cfg.flash_optimum is True: |
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if cfg.adapter: |
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LOG.warning("BetterTransformers probably doesn't work with PEFT adapters") |
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if cfg.fp16 or cfg.bf16: |
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raise ValueError("AMP is not supported with BetterTransformer") |
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if cfg.float16 is not True and cfg.bloat16 is not True: |
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LOG.warning( |
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"You should probably set bfloat16 or float16 to true to " |
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"load the model in float16 for BetterTransformers" |
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) |
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if int(torch.__version__.split(".")[0]) < 2: |
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LOG.warning("torch>=2.0.0 required") |
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raise ValueError( |
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f"flash_optimum for BetterTransformers may not be used with {torch.__version__}" |
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) |
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if cfg.pretraining_dataset and cfg.group_by_length: |
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LOG.warning( |
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"You probably want to disable group_by_length as it will force a streamed dataset to download completely." |
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) |
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if any([cfg.adam_beta1, cfg.adam_beta2, cfg.adam_epsilon]) and ( |
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not cfg.optimizer or "adamw" not in cfg.optimizer |
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): |
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LOG.warning("adamw hyperparameters found, but no adamw optimizer set") |
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if cfg.push_to_hub_model_id: |
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raise ValueError( |
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"push_to_hub_model_id is deprecated. Please use hub_model_id instead." |
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) |
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