mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2024-12-12 08:36:28 +01:00
672 lines
27 KiB
Python
672 lines
27 KiB
Python
import abc
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import os
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import sys
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import math
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import numpy as np
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import termcolor
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import contextlib
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import traceback
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import random
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import zipfile
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import json
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import uuid
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import datetime
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import base64
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import pickle
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import hashlib
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import itertools
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import torch
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import torch.nn.functional as F
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from torch.nn import Embedding, CrossEntropyLoss
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import transformers
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from transformers import AutoTokenizer, GPT2TokenizerFast
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from mkultra.tuning import GPTPromptTuningMixin, GPTNeoPromptTuningLM
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from mkultra.soft_prompt import SoftPrompt
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from typing import List, Optional, TextIO, Union
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_PromptTuningPreTrainedModel = Union["UniversalPromptTuningMixin", GPTPromptTuningMixin, transformers.PreTrainedModel]
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class _WTEMixin:
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@property
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def wte(self: Union["_WTEMixin", transformers.PreTrainedModel]):
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return self.get_input_embeddings()
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@wte.setter
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def wte(self: Union["_WTEMixin", transformers.PreTrainedModel], v):
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self.set_input_embeddings(v)
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class UniversalPromptTuningMixin:
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs):
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model: _PromptTuningPreTrainedModel = super().from_pretrained(pretrained_model_name_or_path, **kwargs)
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if not hasattr(model, "transformer"):
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model.transformer = _WTEMixin()
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elif not hasattr(model.transformer, "wte"):
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assert isinstance(model.transformer, type)
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model.transformer.__class__ = type("_UniversalPromptTuning" + model.transformer.__class__.__name__, (_WTEMixin, model.transformer.__class__), {})
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model.__class__ = type("_UniversalPromptTuning" + model.__class__.__name__, (UniversalPromptTuningMixin, model.__class__), {})
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for param in model.parameters():
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param.requires_grad = False
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model.initialize_soft_prompt()
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return model
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def forward(
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self: _PromptTuningPreTrainedModel,
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input_ids: Optional[torch.Tensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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labels: Optional[torch.Tensor] = None,
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use_cache: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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**kwargs,
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):
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assert input_ids is not None
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assert input_ids.ndim == 2
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input_ids = F.pad(input_ids, (self.learned_embedding.size(0), 0, 0, 0), value=self.transformer.wte.weight.size(0) // 2)
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if labels is not None:
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labels = self._extend_labels(labels)
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if attention_mask is not None:
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attention_mask = self._extend_attention_mask(attention_mask)
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old_embedding_call = Embedding.__call__
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model = self
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def new_embedding_call(self, input_ids, *args, **kwargs):
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inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs)
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if model.transformer.wte is self:
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assert inputs_embeds.ndim == 3
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inputs_embeds[:, :model.learned_embedding.size(0), :] = model.learned_embedding[None]
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return inputs_embeds
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Embedding.__call__ = new_embedding_call
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try:
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return super().forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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labels=labels,
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use_cache=use_cache,
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return_dict=return_dict,
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)
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finally:
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Embedding.__call__ = old_embedding_call
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for k in dir(GPTPromptTuningMixin):
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v = getattr(GPTPromptTuningMixin, k)
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_v = getattr(UniversalPromptTuningMixin, k, None)
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if _v is None or (_v is getattr(object, k, None) and callable(_v) and not isinstance(_v, type)):
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setattr(UniversalPromptTuningMixin, k, v)
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class AutoPromptTuningLM(UniversalPromptTuningMixin, transformers.AutoModelForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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default_quiet = False
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def get_tokenizer(model_id, revision=None) -> transformers.PreTrainedTokenizerBase:
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if(os.path.isdir(model_id)):
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache")
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except Exception as e:
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pass
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try:
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tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision, cache_dir="cache", use_fast=False)
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained(model_id, revision=revision, cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
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elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
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try:
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tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache")
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except Exception as e:
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pass
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try:
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tokenizer = AutoTokenizer.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache", use_fast=False)
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=revision, cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
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else:
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try:
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tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=revision, cache_dir="cache")
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except Exception as e:
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pass
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try:
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tokenizer = AutoTokenizer.from_pretrained(vars.model, revision=revision, cache_dir="cache", use_fast=False)
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except Exception as e:
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try:
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tokenizer = GPT2TokenizerFast.from_pretrained(vars.model, revision=revision, cache_dir="cache")
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except Exception as e:
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tokenizer = GPT2TokenizerFast.from_pretrained("gpt2", revision=revision, cache_dir="cache")
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@contextlib.contextmanager
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def _kai_no_prefix():
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add_bos_token = getattr(tokenizer, "add_bos_token", False)
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add_prefix_space = getattr(tokenizer, "add_prefix_space", False)
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tokenizer.add_bos_token = False
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tokenizer.add_prefix_space = False
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try:
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yield
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finally:
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tokenizer.add_bos_token = add_bos_token
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tokenizer.add_prefix_space = add_prefix_space
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tokenizer._kai_no_prefix = _kai_no_prefix
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return tokenizer
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class ConfigurationError(Exception):
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def __init__(self, msg: str = "Unknown error", code: int = 1, quiet: Optional[bool] = None):
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if quiet is None:
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quiet = default_quiet
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super().__init__(msg)
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self.code = code
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self.quiet = quiet
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class TrainerBase(abc.ABC):
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@abc.abstractmethod
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def startup(self, step: int) -> None:
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...
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@abc.abstractmethod
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def get_batch(self, step: int, size: int) -> np.ndarray:
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...
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@abc.abstractmethod
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def get_num_sequences(self) -> int:
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...
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@abc.abstractmethod
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def get_initial_soft_embeddings(self, model: transformers.PreTrainedModel) -> SoftPrompt:
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...
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@abc.abstractmethod
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def tokenize_dataset_callback(self, tokenizer: transformers.PreTrainedTokenizerBase, text: str) -> List[int]:
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...
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class TrainerData:
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def __init__(self):
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self.__lazy_load_spec: Optional[dict] = None
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self.model_spec: Optional[dict] = None
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self.tokenizer_id: Optional[str] = None
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self.newlinemode: Optional[str] = None
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self.ckpt_path: Optional[str] = None
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self.save_file: Optional[str] = None
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self.params: Optional[dict] = None
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self.stparams: Optional[dict] = None
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self.gradient_accumulation_steps = -1
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self.soft_in_dim = -1
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self.prompt_method = "tokens"
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self.prompt_seed = 42
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@property
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def lazy_load_spec(self):
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print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr)
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return self.__lazy_load_spec
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@lazy_load_spec.setter
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def lazy_load_spec(self, value: Optional[dict]):
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print("WARNING: `TrainerData.lazy_load_spec` is currently unused", file=sys.stderr)
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self.__lazy_load_spec = value
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@property
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def kaiming_size(self): # backwards compatibility
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return self.soft_in_dim
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@kaiming_size.setter
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def kaiming_size(self, value: int): # backwards compatibility
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self.prompt_method = "kaiming"
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self.soft_in_dim = value
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data: TrainerData
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def __init__(self, universe: Optional[int] = None, quiet=False):
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self.quiet = quiet
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self.universe = universe
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self.data = self.TrainerData()
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self._spmodule: Optional[str] = None
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if universe is not None:
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print("WARNING: The `universe` argument of `TrainerBase.__init__` is currently unused", file=sys.stderr)
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def raise_configuration_error(self, msg, **kwargs):
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if "quiet" not in kwargs:
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kwargs["quiet"] = self.quiet
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raise ConfigurationError(msg, **kwargs)
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def get_hf_checkpoint_metadata(self) -> bool:
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return True
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def get_tokenizer(self) -> transformers.PreTrainedTokenizerBase:
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return get_tokenizer(self.data.ckpt_path)
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def save_data(self):
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pass
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def export_to_kobold(self, output_file: str, name: str, author: str, supported: str, description: str):
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try:
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z = torch.load(self.data.save_file)
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assert z["step"] > 0
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assert z["tensor"].ndim == 2 and "opt_state" in z
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assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
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self.data.soft_in_dim = z["tensor"].shape[0]
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except AssertionError:
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self.raise_configuration_error("MTJSP file is corrupted.", code=14)
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tensor = z["tensor"]
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meta = {
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"name": name,
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"author": author,
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"supported": supported,
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"description": description,
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}
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if len(meta["author"].strip()) == 0:
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meta.pop("author")
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meta["supported"] = list(map(lambda m: m.strip(), supported.split(",")))
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with zipfile.ZipFile(output_file, "w", compression=zipfile.ZIP_LZMA) as z:
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with z.open("tensor.npy", "w") as f:
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np.save(f, tensor, allow_pickle=False)
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with zipfile.ZipFile(output_file, "a", compression=zipfile.ZIP_STORED) as z:
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with z.open("meta.json", "w") as f:
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f.write(json.dumps(meta, indent=2).encode("utf-8"))
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def export_to_mkultra(self, output_file: str, soft_prompt_name: str, soft_prompt_description: str):
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try:
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z = torch.load(self.data.save_file)
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assert z["step"] > 0
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assert z["tensor"].ndim == 2 and "opt_state" in z
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assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
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self.data.soft_in_dim = z["tensor"].shape[0]
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_step = z["step"]
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except AssertionError:
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self.raise_configuration_error("MTJSP file is corrupted.", code=14)
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tensor = z["tensor"]
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with open(output_file, "w") as f:
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json.dump(
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{
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"metadata": {
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"step": _step,
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"loss": float(z["loss"].item()),
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"uuid": str(uuid.uuid4()),
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"name": soft_prompt_name,
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"description": soft_prompt_description,
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"epoch": datetime.datetime.now().timestamp(),
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},
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"tensor": base64.b64encode(
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pickle.dumps(
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tensor,
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protocol=4,
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),
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).decode("ascii"),
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},
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f,
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)
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def tokenize_dataset(
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self,
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dataset_path: Union[str, TextIO],
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output_file: Union[str, TextIO],
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batch_size=2048,
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epochs=1,
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use_ftfy=True,
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shuffle_seed: Optional[Union[int, float, str, bytes, bytearray]] = 1729,
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):
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dataset_path = dataset_path.replace("\\", "/")
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output_file = output_file.replace("\\", "/")
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if not isinstance(batch_size, int) or batch_size < 1:
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self.raise_configuration_error(
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"batch_size must be an integer greater than zero.", code=9
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)
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if (
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not isinstance(epochs, int) and not isinstance(epochs, float)
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) or epochs <= 0:
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self.raise_configuration_error(
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"epochs must be an int or float greater than zero.", code=10
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)
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if isinstance(output_file, str) and output_file.endswith("/"):
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self.raise_configuration_error(
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"output_file should be the path to a file, not a directory.", code=11
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)
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if isinstance(dataset_path, str) and not os.path.exists(dataset_path):
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self.raise_configuration_error(
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"dataset_path is not set to a valid file or directory.", code=12
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)
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if use_ftfy:
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import ftfy
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tokenizer = self.get_tokenizer()
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batch_size = min(
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batch_size,
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self.data.params["max_batch_size"] - self.data.soft_in_dim,
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)
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assert batch_size >= 0
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print(
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termcolor.colored(
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"\nIf you see a warning somewhere below about token indices, ignore it. That warning is normal.\n",
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"magenta",
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)
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)
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print("Batch size:", batch_size)
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print(termcolor.colored("Tokenizing your dataset...\n", "magenta"))
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if not isinstance(dataset_path, str):
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files = [dataset_path]
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elif os.path.isfile(dataset_path):
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files = [dataset_path]
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else:
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files = sorted(
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os.path.join(dataset_path, filename)
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for filename in os.listdir(dataset_path)
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)
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if shuffle_seed is not None:
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random.Random(shuffle_seed).shuffle(files)
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tokens = []
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eos = tokenizer.decode(self.data.params["eos_token"])
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for path in files:
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if isinstance(path, str):
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f = open(path)
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else:
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f = path
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try:
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text = f.read()
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if use_ftfy:
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text = ftfy.fix_text(text)
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text = text.replace("<|endoftext|>", eos)
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tokens.extend(self.tokenize_dataset_callback(tokenizer, text))
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finally:
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if isinstance(path, str):
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f.close()
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print("Dataset size (in tokens):", len(tokens))
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if len(tokens) < batch_size + 1:
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self.raise_configuration_error(
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"Your dataset is too small! The number of tokens has to be greater than the batch size. Try increasing the epochs.",
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code=13,
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)
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tail = len(tokens) % (batch_size + 1)
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if tail:
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print(
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f"We're removing the last {tail} tokens from your dataset to make the length a multiple of {batch_size+1}."
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)
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tokens = tokens[:-tail]
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tokens = np.array(tokens, dtype=np.uint16).reshape((-1, batch_size + 1))
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sequences_per_epoch = tokens.shape[0]
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_epochs = math.ceil(epochs)
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if _epochs > 1:
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rng = np.random.Generator(np.random.PCG64(1729))
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tokens = np.concatenate(
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(
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tokens,
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*(rng.permutation(tokens, axis=0) for i in range(_epochs - 1)),
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),
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axis=0,
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)
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tokens = tokens[: math.ceil(epochs * sequences_per_epoch)]
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print(f"Total sequences in your dataset: {tokens.shape[0]}")
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if isinstance(output_file, str):
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f = open(output_file, "w")
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else:
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f = output_file
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try:
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np.save(output_file, tokens)
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finally:
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if isinstance(output_file, str):
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f.close()
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def train(self):
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if self.data.params is not None and "max_batch_size" not in self.data.params:
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self.data.params["max_batch_size"] = 2048
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if not os.path.exists(self.data.save_file):
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print("We are starting a brand new soft-tuning session.\n")
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self.startup(step=-1)
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if self.data.soft_in_dim <= 0:
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self.raise_configuration_error(
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"You have not set a soft prompt size.", code=6
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)
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else:
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# If we're resuming a soft-tuning session, the soft prompt tensor is
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# already in the save file and we just have to decode it.
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try:
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z = torch.load(self.data.save_file)
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assert z["step"] > 0
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assert z["tensor"].ndim == 2 and "opt_state" in z
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assert z["tensor"].shape[0] < self.data.params["max_batch_size"]
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self.data.soft_in_dim = z["tensor"].shape[0]
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step = z["step"]
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opt_state = z["opt_state"]
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except AssertionError:
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self.raise_configuration_error("MTJSP file is corrupted.", code=14)
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print(f"We're resuming a previous soft-tuning session at step {step+1}.\n")
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self.startup(step=step + 1)
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soft_embeddings = z["tensor"]
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REVISION = None
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tokenizer = self.get_tokenizer()
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model: _PromptTuningPreTrainedModel
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if(os.path.isdir(self.data.ckpt_path)):
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try:
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model = AutoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
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except Exception as e:
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if("out of memory" in traceback.format_exc().lower()):
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raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
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model = GPTNeoPromptTuningLM.from_pretrained(self.data.ckpt_path, revision=REVISION, cache_dir="cache")
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elif(os.path.isdir("models/{}".format(vars.model.replace('/', '_')))):
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try:
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model = AutoPromptTuningLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=REVISION, cache_dir="cache")
|
|
except Exception as e:
|
|
if("out of memory" in traceback.format_exc().lower()):
|
|
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
|
|
model = GPTNeoPromptTuningLM.from_pretrained("models/{}".format(vars.model.replace('/', '_')), revision=REVISION, cache_dir="cache")
|
|
else:
|
|
try:
|
|
model = AutoPromptTuningLM.from_pretrained(vars.model, revision=REVISION, cache_dir="cache")
|
|
except Exception as e:
|
|
if("out of memory" in traceback.format_exc().lower()):
|
|
raise RuntimeError("One of your GPUs ran out of memory when KoboldAI tried to load your model.")
|
|
model = GPTNeoPromptTuningLM.from_pretrained(vars.model, revision=REVISION, cache_dir="cache")
|
|
|
|
if step == 0:
|
|
soft_embeddings = self.get_initial_soft_embeddings(model)
|
|
else:
|
|
soft_embeddings = SoftPrompt.from_inputs_embeds(soft_embeddings)
|
|
model.set_soft_prompt(soft_embeddings)
|
|
|
|
steps = self.get_num_sequences() // self.data.gradient_accumulation_steps
|
|
warmup_steps = max(1, round(steps * self.data.stparams["warmup"]))
|
|
|
|
beta1: Optional[float] = self.data.stparams.get("beta1", 0.0)
|
|
if beta1 == 0.0:
|
|
beta1 = None
|
|
optimizer = transformers.Adafactor(
|
|
params=model.get_soft_params(),
|
|
scale_parameter=False,
|
|
relative_step=False,
|
|
warmup_init=False,
|
|
lr=self.data.stparams["lr"],
|
|
beta1=beta1,
|
|
decay_rate=self.data.stparams.get("decay_rate", -0.8),
|
|
weight_decay=self.data.stparams.get("weight_decay", 0.1),
|
|
)
|
|
if step != 0:
|
|
optimizer.load_state_dict(opt_state)
|
|
scheduler = transformers.get_cosine_with_hard_restarts_schedule_with_warmup(
|
|
optimizer=optimizer,
|
|
num_warmup_steps=warmup_steps,
|
|
num_training_steps=steps - warmup_steps,
|
|
num_cycles=(steps - warmup_steps) // self.data.stparams.get("training_steps_per_cycle", 56),
|
|
)
|
|
|
|
torch.cuda.empty_cache()
|
|
optimizer.state['step'] = step
|
|
cross_entropy_loss = CrossEntropyLoss()
|
|
|
|
def save_mkusp(
|
|
loss,
|
|
grad_norm,
|
|
):
|
|
with open(self.data.save_file, "wb") as f:
|
|
torch.save(
|
|
{
|
|
"tensor": soft_embeddings.get_inputs_embeds(),
|
|
"opt_state": optimizer.state_dict(),
|
|
"step": step,
|
|
"loss": loss,
|
|
"grad_norm": grad_norm,
|
|
},
|
|
f,
|
|
)
|
|
self.save_data()
|
|
|
|
while step < steps:
|
|
model.train()
|
|
|
|
total_loss = total_grad = total_grad_norm = 0
|
|
|
|
for i in range(self.data.gradient_accumulation_steps):
|
|
# Get the next sequence from the dataset
|
|
block = self.get_batch(step, self.data.gradient_accumulation_steps).to(model.transformer.wte.weight.device)
|
|
|
|
# input_ids is the context to the model (without the soft prompt) and labels is what we expect the model to generate (the -100s represent soft prompt tokens for which loss is not calculated)
|
|
input_ids = block[:-1].unsqueeze(0).detach()
|
|
labels = torch.cat((torch.full((model.get_soft_params().size(0) - 1,), -100, device=block.device), block)).unsqueeze(0).cuda().detach()
|
|
|
|
# Give the context to the model and compare the model's output logits with the labels to compute the loss
|
|
logits = model(input_ids=input_ids, labels=input_ids).logits
|
|
loss: torch.Tensor = cross_entropy_loss(logits.view(-1, model.transformer.wte.weight.size(1)), labels.view(-1))
|
|
total_loss += loss.detach()
|
|
|
|
# Compute the gradient of the loss function and add it to model.get_soft_params().grad (model.get_soft_params().grad += gradient)
|
|
loss.backward()
|
|
|
|
total_grad_norm += torch.linalg.norm(model.get_soft_params().grad.detach() - total_grad)
|
|
total_grad = model.get_soft_params().grad.detach()
|
|
|
|
del input_ids
|
|
del labels
|
|
del logits
|
|
torch.cuda.empty_cache()
|
|
|
|
mean_loss = (total_loss / self.data.gradient_accumulation_steps).item()
|
|
mean_grad_norm = (total_grad_norm / self.data.gradient_accumulation_steps).item()
|
|
|
|
# Apply the optimization algorithm using the accumulated gradients, which changes the contents of the soft prompt matrix very slightly to reduce the loss
|
|
optimizer.step()
|
|
lr = optimizer.param_groups[0]["lr"]
|
|
scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
step += 1
|
|
|
|
# Save checkpoint every few steps
|
|
if step == 1 or step % self.data.stparams["save_every"] == 0:
|
|
save_mkusp(mean_loss, mean_grad_norm)
|
|
|
|
|
|
class BasicTrainer(TrainerBase):
|
|
class TrainerData(TrainerBase.TrainerData):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.dataset_file: Optional[str] = None
|
|
self.initial_softprompt: Optional[List[int]] = None
|
|
|
|
data: "BasicTrainer.TrainerData"
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
self.dataset: Optional[np.ndarray] = None
|
|
|
|
def startup(self, step: int) -> None:
|
|
if self.get_num_sequences() < self.data.gradient_accumulation_steps:
|
|
self.raise_configuration_error(
|
|
"Your dataset is too small! gradient_accumulation_steps must be less than or equal to the number of sequences.",
|
|
code=101,
|
|
)
|
|
if (
|
|
self.data.prompt_method == "tokens"
|
|
and step < 0
|
|
and self.data.initial_softprompt is None
|
|
):
|
|
self.raise_configuration_error(
|
|
"You have not set an initial soft prompt string.", code=103
|
|
)
|
|
if self.data.prompt_method == "tokens" and step < 0:
|
|
self.data.soft_in_dim = len(self.data.initial_softprompt)
|
|
|
|
def get_batch(self, step: int, size: int) -> np.ndarray:
|
|
return self.dataset[(step - 1) * size : step * size]
|
|
|
|
def get_num_sequences(self) -> int:
|
|
if self.dataset is None:
|
|
if self.data.dataset_file is None or not os.path.exists(
|
|
self.data.dataset_file
|
|
):
|
|
self.raise_configuration_error(
|
|
f"Dataset file not found at {repr(self.data.dataset_file)}",
|
|
code=102,
|
|
)
|
|
self.dataset = np.load(self.data.dataset_file, mmap_mode="r")
|
|
assert self.dataset.ndim >= 2
|
|
assert self.dataset.shape[0] >= 2
|
|
return self.dataset.shape[0]
|
|
|
|
def get_initial_soft_embeddings(self, model: transformers.PreTrainedModel) -> SoftPrompt:
|
|
if self.data.prompt_method == "vocab_sample":
|
|
rng = np.random.Generator(
|
|
np.random.PCG64(
|
|
[
|
|
self.data.prompt_seed,
|
|
int.from_bytes(hashlib.sha256(model.config.model_type.encode("utf8")).digest()[:4], "little"),
|
|
]
|
|
)
|
|
)
|
|
tokenizer = self.get_tokenizer()
|
|
with tokenizer._kai_no_prefix():
|
|
special_tokens = set(
|
|
itertools.chain.from_iterable(
|
|
tokenizer.encode(str(v))
|
|
for v in tokenizer.special_tokens_map_extended.values()
|
|
)
|
|
)
|
|
sample_space = [
|
|
k for k in range(self.data.params["n_vocab"]) if k not in special_tokens
|
|
]
|
|
sample = rng.choice(sample_space, self.data.soft_in_dim, False)
|
|
return model.get_input_embeddings()(torch.tensor(sample, dtype=torch.int32))
|
|
elif self.data.prompt_method == "tokens":
|
|
return model.get_input_embeddings()(torch.tensor(self.data.initial_softprompt, dtype=torch.int32))
|
|
self.raise_configuration_error(
|
|
f"Unknown prompt method {repr(self.data.prompt_method)}", code=104
|
|
)
|
|
|
|
def tokenize_dataset_callback(
|
|
self, tokenizer: transformers.PreTrainedTokenizerBase, text: str
|
|
) -> List[int]:
|
|
if self.data.newlinemode == "s":
|
|
text = text.replace("\n", "</s>")
|
|
with tokenizer._kai_no_prefix():
|
|
return tokenizer.encode(text) + self.data.params["eos_token"]
|