mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-27 01:59:14 +01:00
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
from ctransformers import AutoConfig, AutoModelForCausalLM
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from modules import shared
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from modules.callbacks import Iteratorize
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from modules.logging_colors import logger
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class CtransformersModel:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(cls, path):
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result = cls()
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config = AutoConfig.from_pretrained(
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str(path),
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threads=shared.args.threads if shared.args.threads != 0 else -1,
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gpu_layers=shared.args.n_gpu_layers,
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batch_size=shared.args.n_batch,
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context_length=shared.args.n_ctx,
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stream=True,
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mmap=not shared.args.no_mmap,
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mlock=shared.args.mlock
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)
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result.model = AutoModelForCausalLM.from_pretrained(
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str(result.model_dir(path) if result.model_type_is_auto() else path),
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model_type=(None if result.model_type_is_auto() else shared.args.model_type),
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config=config
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)
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logger.info(f'Using ctransformers model_type: {result.model.model_type} for {result.model.model_path}')
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return result, result
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def model_type_is_auto(self):
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return shared.args.model_type is None or shared.args.model_type == "Auto" or shared.args.model_type == "None"
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def model_dir(self, path):
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if path.is_file():
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return path.parent
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return path
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def encode(self, string, **kwargs):
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return self.model.tokenize(string)
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def decode(self, ids):
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return self.model.detokenize(ids)
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def generate(self, prompt, state, callback=None):
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prompt = prompt if type(prompt) is str else prompt.decode()
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# ctransformers uses -1 for random seed
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generator = self.model(
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prompt=prompt,
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max_new_tokens=state['max_new_tokens'],
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temperature=state['temperature'],
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top_p=state['top_p'],
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top_k=state['top_k'],
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repetition_penalty=state['repetition_penalty'],
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last_n_tokens=state['repetition_penalty_range'],
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seed=int(state['seed'])
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)
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output = ""
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for token in generator:
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if callback:
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callback(token)
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output += token
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return output
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def generate_with_streaming(self, *args, **kwargs):
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with Iteratorize(self.generate, args, kwargs, callback=None) as generator:
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reply = ''
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for token in generator:
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reply += token
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yield reply
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