mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
import traceback
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from pathlib import Path
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import torch
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from exllamav2 import (
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ExLlamaV2,
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ExLlamaV2Cache,
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ExLlamaV2Cache_8bit,
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ExLlamaV2Config,
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ExLlamaV2Tokenizer
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)
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from exllamav2.generator import ExLlamaV2Sampler, ExLlamaV2StreamingGenerator
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from modules import shared
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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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try:
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import flash_attn
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except ModuleNotFoundError:
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logger.warning(
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'You are running ExLlamaV2 without flash-attention. This will cause the VRAM usage '
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'to be a lot higher than it could be.\n'
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'Try installing flash-attention following the instructions here: '
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'https://github.com/Dao-AILab/flash-attention#installation-and-features'
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)
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pass
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except Exception:
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logger.warning('Failed to load flash-attention due to the following error:\n')
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traceback.print_exc()
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class Exllamav2Model:
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def __init__(self):
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pass
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@classmethod
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def from_pretrained(self, path_to_model):
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path_to_model = Path(f'{shared.args.model_dir}') / Path(path_to_model)
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config = ExLlamaV2Config()
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config.model_dir = str(path_to_model)
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config.prepare()
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config.max_seq_len = shared.args.max_seq_len
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config.scale_pos_emb = shared.args.compress_pos_emb
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config.scale_alpha_value = shared.args.alpha_value
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config.no_flash_attn = shared.args.no_flash_attn
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config.num_experts_per_token = int(shared.args.num_experts_per_token)
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model = ExLlamaV2(config)
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split = None
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if shared.args.gpu_split:
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split = [float(alloc) for alloc in shared.args.gpu_split.split(",")]
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model.load(split)
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tokenizer = ExLlamaV2Tokenizer(config)
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if shared.args.cache_8bit:
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cache = ExLlamaV2Cache_8bit(model)
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else:
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cache = ExLlamaV2Cache(model)
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generator = ExLlamaV2StreamingGenerator(model, cache, tokenizer)
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result = self()
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result.model = model
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result.cache = cache
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result.tokenizer = tokenizer
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result.generator = generator
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result.loras = None
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return result, result
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def encode(self, string, **kwargs):
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return self.tokenizer.encode(string, add_bos=True, encode_special_tokens=True)
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def decode(self, ids, **kwargs):
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if isinstance(ids, list):
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ids = torch.tensor([ids])
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elif isinstance(ids, torch.Tensor) and ids.numel() == 1:
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ids = ids.view(1, -1)
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return self.tokenizer.decode(ids, decode_special_tokens=True)[0]
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def get_logits(self, token_ids, **kwargs):
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self.cache.current_seq_len = 0
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if token_ids.shape[-1] > 1:
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self.model.forward(token_ids[:, :-1], self.cache, input_mask=None, preprocess_only=True, loras=self.loras)
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return self.model.forward(token_ids[:, -1:], self.cache, input_mask=None, loras=self.loras, **kwargs).float().cpu()
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def generate_with_streaming(self, prompt, state):
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settings = ExLlamaV2Sampler.Settings()
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settings.temperature = state['temperature']
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settings.top_k = state['top_k']
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settings.top_p = state['top_p']
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settings.min_p = state['min_p']
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settings.tfs = state['tfs']
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settings.typical = state['typical_p']
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settings.mirostat = state['mirostat_mode'] == 2
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settings.mirostat_tau = state['mirostat_tau']
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settings.mirostat_eta = state['mirostat_eta']
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settings.token_repetition_penalty = state['repetition_penalty']
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settings.token_repetition_range = -1 if state['repetition_penalty_range'] <= 0 else state['repetition_penalty_range']
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if state['ban_eos_token']:
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settings.disallow_tokens(self.tokenizer, [self.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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settings.disallow_tokens(self.tokenizer, to_ban)
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ids = self.tokenizer.encode(prompt, add_bos=state['add_bos_token'], encode_special_tokens=True)
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ids = ids[:, -get_max_prompt_length(state):]
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if state['auto_max_new_tokens']:
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max_new_tokens = state['truncation_length'] - ids.shape[-1]
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else:
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max_new_tokens = state['max_new_tokens']
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self.generator.begin_stream(ids, settings, loras=self.loras)
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decoded_text = ''
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for i in range(max_new_tokens):
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chunk, eos, _ = self.generator.stream()
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if eos or shared.stop_everything:
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break
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decoded_text += chunk
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yield decoded_text
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def generate(self, prompt, state):
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output = ''
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for output in self.generate_with_streaming(prompt, state):
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pass
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return output
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