import re from functools import partial import torch from modules import shared from modules.callbacks import Iteratorize from modules.logging_colors import logger import llama_cpp if torch.cuda.is_available() and not torch.version.hip: try: import llama_cpp_cuda except: llama_cpp_cuda = None else: llama_cpp_cuda = None def llama_cpp_lib(): if shared.args.cpu or llama_cpp_cuda is None: return llama_cpp else: return llama_cpp_cuda def ban_eos_logits_processor(eos_token, input_ids, logits): logits[eos_token] = -float('inf') return logits class LlamaCppModel: def __init__(self): self.initialized = False def __del__(self): self.model.__del__() @classmethod def from_pretrained(self, path): Llama = llama_cpp_lib().Llama LlamaCache = llama_cpp_lib().LlamaCache result = self() cache_capacity = 0 if shared.args.cache_capacity is not None: if 'GiB' in shared.args.cache_capacity: cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 * 1000 elif 'MiB' in shared.args.cache_capacity: cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 else: cache_capacity = int(shared.args.cache_capacity) logger.info("Cache capacity is " + str(cache_capacity) + " bytes") params = { 'model_path': str(path), 'n_ctx': shared.args.n_ctx, 'seed': int(shared.args.llama_cpp_seed), 'n_threads': shared.args.threads or None, 'n_batch': shared.args.n_batch, 'use_mmap': not shared.args.no_mmap, 'use_mlock': shared.args.mlock, 'low_vram': shared.args.low_vram, 'n_gpu_layers': shared.args.n_gpu_layers, 'rope_freq_base': 10000 * shared.args.alpha_value ** (64/63.), 'rope_freq_scale': 1.0 / shared.args.compress_pos_emb, 'n_gqa': shared.args.n_gqa or None, 'rms_norm_eps': shared.args.rms_norm_eps or None, } result.model = Llama(**params) if cache_capacity > 0: result.model.set_cache(LlamaCache(capacity_bytes=cache_capacity)) # This is ugly, but the model and the tokenizer are the same object in this library. return result, result def encode(self, string): if type(string) is str: string = string.encode() return self.model.tokenize(string) def decode(self, tokens): return self.model.detokenize(tokens) def generate(self, prompt, state, callback=None): LogitsProcessorList = llama_cpp_lib().LogitsProcessorList prompt = prompt if type(prompt) is str else prompt.decode() completion_chunks = self.model.create_completion( prompt=prompt, max_tokens=state['max_new_tokens'], temperature=state['temperature'], top_p=state['top_p'], top_k=state['top_k'], repeat_penalty=state['repetition_penalty'], tfs_z=state['tfs'], mirostat_mode=int(state['mirostat_mode']), mirostat_tau=state['mirostat_tau'], mirostat_eta=state['mirostat_eta'], stream=True, logits_processor=LogitsProcessorList([ partial(ban_eos_logits_processor, self.model.token_eos()), ]) if state['ban_eos_token'] else None, ) output = "" for completion_chunk in completion_chunks: text = completion_chunk['choices'][0]['text'] output += text if callback: callback(text) return output def generate_with_streaming(self, *args, **kwargs): with Iteratorize(self.generate, args, kwargs, callback=None) as generator: reply = '' for token in generator: reply += token yield reply