mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
155 lines
4.9 KiB
Python
155 lines
4.9 KiB
Python
import re
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from functools import partial
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import torch
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from modules import RoPE, shared
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from modules.callbacks import Iteratorize
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from modules.logging_colors import logger
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from modules.text_generation import get_max_prompt_length
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import llama_cpp
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if torch.cuda.is_available() and not torch.version.hip:
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try:
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import llama_cpp_cuda
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except:
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llama_cpp_cuda = None
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else:
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llama_cpp_cuda = None
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def llama_cpp_lib():
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if shared.args.cpu or llama_cpp_cuda is None:
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return llama_cpp
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else:
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return llama_cpp_cuda
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def ban_eos_logits_processor(eos_token, input_ids, logits):
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logits[eos_token] = -float('inf')
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return logits
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def custom_token_ban_logits_processor(token_ids, input_ids, logits):
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for token_id in token_ids:
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logits[token_id] = -float('inf')
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return logits
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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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Llama = llama_cpp_lib().Llama
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LlamaCache = llama_cpp_lib().LlamaCache
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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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if shared.args.tensor_split is None or shared.args.tensor_split.strip() == '':
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tensor_split_list = None
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else:
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tensor_split_list = [float(x) for x in shared.args.tensor_split.strip().split(",")]
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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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'mul_mat_q': shared.args.mul_mat_q,
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'low_vram': shared.args.low_vram,
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'n_gpu_layers': shared.args.n_gpu_layers,
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'rope_freq_base': RoPE.get_rope_freq_base(shared.args.alpha_value, shared.args.rope_freq_base),
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'tensor_split': tensor_split_list,
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'rope_freq_scale': 1.0 / shared.args.compress_pos_emb,
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}
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result.model = Llama(**params)
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if cache_capacity > 0:
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result.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 decode(self, tokens):
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return self.model.detokenize(tokens)
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def generate(self, prompt, state, callback=None):
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LogitsProcessorList = llama_cpp_lib().LogitsProcessorList
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prompt = prompt if type(prompt) is str else prompt.decode()
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# Handle truncation
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prompt = self.encode(prompt)
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prompt = prompt[-get_max_prompt_length(state):]
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prompt = self.decode(prompt).decode('utf-8')
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logit_processors = LogitsProcessorList()
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if state['ban_eos_token']:
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logit_processors.append(partial(ban_eos_logits_processor, self.model.tokenizer.eos_token_id))
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if state['custom_token_bans']:
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to_ban = [int(x) for x in state['custom_token_bans'].split(',')]
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if len(to_ban) > 0:
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logit_processors.append(partial(custom_token_ban_logits_processor, to_ban))
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completion_chunks = self.model.create_completion(
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prompt=prompt,
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max_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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repeat_penalty=state['repetition_penalty'],
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tfs_z=state['tfs'],
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mirostat_mode=int(state['mirostat_mode']),
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mirostat_tau=state['mirostat_tau'],
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mirostat_eta=state['mirostat_eta'],
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stream=True,
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logits_processor=logit_processors,
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)
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output = ""
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for completion_chunk in completion_chunks:
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if shared.stop_everything:
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break
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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, *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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