mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 01:09:22 +01:00
92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
'''
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Based on
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https://github.com/abetlen/llama-cpp-python
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Documentation:
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https://abetlen.github.io/llama-cpp-python/
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'''
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import re
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from llama_cpp import Llama, LlamaCache
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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 LlamaCppModel:
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def __init__(self):
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self.initialized = False
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def __del__(self):
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self.model.__del__()
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@classmethod
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def from_pretrained(self, path):
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result = self()
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cache_capacity = 0
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if shared.args.cache_capacity is not None:
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if 'GiB' in shared.args.cache_capacity:
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cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 * 1000
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elif 'MiB' in shared.args.cache_capacity:
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cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000
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else:
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cache_capacity = int(shared.args.cache_capacity)
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logger.info("Cache capacity is " + str(cache_capacity) + " bytes")
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params = {
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'model_path': str(path),
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'n_ctx': shared.args.n_ctx,
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'seed': int(shared.args.llama_cpp_seed),
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'n_threads': shared.args.threads or None,
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'n_batch': shared.args.n_batch,
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'use_mmap': not shared.args.no_mmap,
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'use_mlock': shared.args.mlock,
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'n_gpu_layers': shared.args.n_gpu_layers
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}
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self.model = Llama(**params)
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if cache_capacity > 0:
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self.model.set_cache(LlamaCache(capacity_bytes=cache_capacity))
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# This is ugly, but the model and the tokenizer are the same object in this library.
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return result, result
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def encode(self, string):
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if type(string) is str:
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string = string.encode()
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return self.model.tokenize(string)
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def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, mirostat_mode=0, mirostat_tau=5, mirostat_eta=0.1, callback=None):
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context = context if type(context) is str else context.decode()
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completion_chunks = self.model.create_completion(
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prompt=context,
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max_tokens=token_count,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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repeat_penalty=repetition_penalty,
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mirostat_mode=int(mirostat_mode),
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mirostat_tau=mirostat_tau,
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mirostat_eta=mirostat_eta,
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stream=True
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)
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output = ""
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for completion_chunk in completion_chunks:
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text = completion_chunk['choices'][0]['text']
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output += text
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if callback:
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callback(text)
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return output
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def generate_with_streaming(self, **kwargs):
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with Iteratorize(self.generate, 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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