110 lines
4.5 KiB
Python
110 lines
4.5 KiB
Python
import configparser
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import torch
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from peft import PeftModel
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from sympy.printing import str
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class Context:
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model: PeftModel
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accel: bool
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device: str
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purgeTargetDirectories: bool
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showChatTemplate: bool
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locBaseModel: str
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locDataset: str
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locWorkdir: str
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locAdapter: str
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locFull: str
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locValidation: str
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locGraderModel: str
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locCustomPromptTemplate: str
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train: bool
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storeAdapter: bool
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trEpochs: int
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trMaxSeqLength: int
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trPerDeviceTrainBatchSize: int
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trFindAutoBatchSize: bool
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trGradientAccSteps: int
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trGradientCheckpointing: bool
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trGroupByLength: bool
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trPacking: bool
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trOptim : str
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trSchedulerType: str
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qLora : bool
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loraR: int
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loraAlpha: int
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loraDropout: float
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loraBias: str
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loraTaskType: str
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loraLayers: list
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validate: bool
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vInplace: bool
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vAbortOnFail: bool
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vPasses: int
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vGenMaxTokens: int
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vExpected: int
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vQuantModel: bool
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vQuantGrader: bool
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vGraderOnCpu: bool
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mergeFull: bool
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def load(self, cfgFile):
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cfg = configparser.ConfigParser()
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if not cfg.read(cfgFile):
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raise Exception("Cannot read configuration file: " + cfgFile)
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self.accel = torch.cuda.is_available() and cfg.get("Operation", "device") != "cpu"
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self.device = (cfg.get("Operation", "device") if self.accel else "cpu") or "cpu"
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self.purgeTargetDirectories = cfg.get("Operation", "purgeTargetDirectories").lower() == "true"
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self.showChatTemplate = cfg.get("Operation", "showChatTemplate").lower() == "true"
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self.locBaseModel = cfg.get("Trainer", "locBaseModel")
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self.locDataset = cfg.get("Trainer", "locDataset")
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self.locWorkdir = cfg.get("Trainer", "locWorkdir")
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self.locAdapter = cfg.get("Trainer", "locAdapter")
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self.locFull = cfg.get("Merger", "locFull")
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self.locValidation = cfg.get("Validation", "locValidation")
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self.locGraderModel = cfg.get("Validation", "locGraderModel")
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self.locCustomPromptTemplate = cfg.get("Trainer", "locCustomPromptTemplate") or None
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self.train = cfg.get("Trainer", "train").lower() == "true"
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self.storeAdapter = cfg.get("Trainer", "storeAdapter").lower() == "true"
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self.trEpochs = int(cfg.get("Trainer", "trEpochs") or "0") or None
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self.trMaxSeqLength = int(cfg.get("Trainer", "trMaxSeqLength") or "0") or None
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self.trPerDeviceTrainBatchSize = int(cfg.get("Trainer", "trPerDeviceTrainBatchSize") or "0") or None
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self.trFindAutoBatchSize = cfg.get("Trainer", "trFindAutoBatchSize").lower() == "true"
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self.trGradientAccSteps = int(cfg.get("Trainer", "trGradientAccSteps") or "0") or None
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self.trGradientCheckpointing = cfg.get("Trainer", "trGradientCheckpointing").lower() == "true"
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self.trGroupByLength = cfg.get("Trainer", "trGroupByLength").lower() == "true"
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self.trPacking = cfg.get("Trainer", "trPacking").lower() == "true"
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self.trOptim = cfg.get("Trainer", "trOptim") or None
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self.trSchedulerType = cfg.get("Trainer", "trSchedulerType") or None
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self.qLora = cfg.get("Lora", "qLora").lower() == "true"
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self.loraR = int(cfg.get("Lora", "loraR") or "0") or 64
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self.loraAlpha = int(cfg.get("Lora", "loraAlpha") or "0") or 16
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self.loraDropout = float(cfg.get("Lora", "loraDropout") or "0") or 0.1
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self.loraBias = cfg.get("Lora", "loraBias") or "none"
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self.loraTaskType = cfg.get("Lora", "loraTaskType") or "CAUSAL_LM"
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self.loraLayers = (cfg.get("Lora", "loraLayers") or "q_proj,k_proj").split(",")
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self.validate = cfg.get("Validation", "validate").lower() == "true"
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self.vInplace = cfg.get("Validation", "vInplace").lower() == "true"
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self.vAbortOnFail = cfg.get("Validation", "vAbortOnFail").lower() == "true"
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self.vPasses = int(cfg.get("Validation", "vPasses") or "0") or 10
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self.vGenMaxTokens = int(cfg.get("Validation", "vGenMaxTokens") or "0") or 100
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self.vExpected = int(cfg.get("Validation", "vExpected") or "0") or 70
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self.vQuantModel = cfg.get("Validation", "vQuantModel").lower() == "true"
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self.vQuantGrader = cfg.get("Validation", "vQuantGrader").lower() == "true"
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self.vGraderOnCpu = cfg.get("Validation", "vGraderOnCpu").lower() == "true"
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self.mergeFull = cfg.get("Merger", "mergeFull").lower() == "true"
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print(self.__dict__)
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