mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-23 00:18:20 +01:00
221 lines
8.7 KiB
Python
221 lines
8.7 KiB
Python
from pathlib import Path
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import torch
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import torch.nn.functional as F
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from torch import version as torch_version
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from modules import shared
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from modules.logging_colors import logger
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from modules.models import clear_torch_cache
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from modules.text_generation import get_max_prompt_length
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try:
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from exllama.generator import ExLlamaGenerator
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from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
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from exllama.tokenizer import ExLlamaTokenizer
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except:
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logger.warning('exllama module failed to import. Will attempt to import from repositories/.')
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try:
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from modules.relative_imports import RelativeImport
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with RelativeImport("repositories/exllama"):
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from generator import ExLlamaGenerator
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from model import ExLlama, ExLlamaCache, ExLlamaConfig
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from tokenizer import ExLlamaTokenizer
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except:
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logger.error(
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"Could not find repositories/exllama. Please ensure that exllama"
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" (https://github.com/turboderp/exllama) is cloned inside repositories/ and is up to date."
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)
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raise
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class ExllamaModel:
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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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tokenizer_model_path = path_to_model / "tokenizer.model"
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model_config_path = path_to_model / "config.json"
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# Find the model checkpoint
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model_path = None
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for ext in ['.safetensors', '.pt', '.bin']:
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found = list(path_to_model.glob(f"*{ext}"))
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if len(found) > 0:
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if len(found) > 1:
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logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
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model_path = found[-1]
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break
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config = ExLlamaConfig(str(model_config_path))
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config.model_path = str(model_path)
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config.max_seq_len = shared.args.max_seq_len
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config.compress_pos_emb = shared.args.compress_pos_emb
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if shared.args.gpu_split:
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config.set_auto_map(shared.args.gpu_split)
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config.gpu_peer_fix = True
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if shared.args.alpha_value > 1 and shared.args.rope_freq_base == 0:
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config.alpha_value = shared.args.alpha_value
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config.calculate_rotary_embedding_base()
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elif shared.args.rope_freq_base > 0:
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config.rotary_embedding_base = shared.args.rope_freq_base
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if torch_version.hip:
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config.rmsnorm_no_half2 = True
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config.rope_no_half2 = True
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config.matmul_no_half2 = True
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config.silu_no_half2 = True
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model = ExLlama(config)
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tokenizer = ExLlamaTokenizer(str(tokenizer_model_path))
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cache = ExLlamaCache(model)
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generator = ExLlamaGenerator(model, tokenizer, cache)
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result = self()
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result.config = config
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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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return result, result
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def encode(self, string, **kwargs):
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return self.tokenizer.encode(string, max_seq_len=self.model.config.max_seq_len, add_bos=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)[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)
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return self.model.forward(token_ids[:, -1:], self.cache, **kwargs).float().cpu()
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def generate_with_streaming(self, prompt, state):
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# The cache batch size must be 2 for CFG and 1 otherwise
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if state['guidance_scale'] == 1:
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if self.cache.batch_size == 2:
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del self.cache
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clear_torch_cache()
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self.cache = ExLlamaCache(self.model)
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self.generator = ExLlamaGenerator(self.model, self.tokenizer, self.cache)
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else:
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if self.cache.batch_size == 1:
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del self.cache
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clear_torch_cache()
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self.cache = ExLlamaCache(self.model, batch_size=2)
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self.generator = ExLlamaGenerator(self.model, self.tokenizer, self.cache)
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self.generator.settings.temperature = state['temperature']
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self.generator.settings.top_p = state['top_p']
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self.generator.settings.top_k = state['top_k']
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self.generator.settings.typical = state['typical_p']
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self.generator.settings.token_repetition_penalty_max = state['repetition_penalty']
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self.generator.settings.token_repetition_penalty_sustain = -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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self.generator.disallow_tokens([self.tokenizer.eos_token_id])
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else:
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self.generator.disallow_tokens(None)
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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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self.generator.disallow_tokens(to_ban)
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# Case 1: no CFG
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if state['guidance_scale'] == 1:
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self.generator.end_beam_search()
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# Tokenizing the input
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ids = self.generator.tokenizer.encode(prompt, max_seq_len=self.model.config.max_seq_len)
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if state['add_bos_token']:
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ids = torch.cat(
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[torch.tensor([[self.tokenizer.bos_token_id]]).to(ids.device),
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ids], dim=1
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).to(torch.int64)
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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.gen_begin_reuse(ids)
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initial_len = self.generator.sequence[0].shape[0]
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has_leading_space = False
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for i in range(max_new_tokens):
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token = self.generator.gen_single_token()
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if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
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has_leading_space = True
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decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
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if has_leading_space:
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decoded_text = ' ' + decoded_text
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yield decoded_text
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if token.item() == self.generator.tokenizer.eos_token_id or shared.stop_everything:
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break
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# Case 2: CFG
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# Copied from https://github.com/turboderp/exllama/blob/master/example_cfg.py
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else:
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alpha = state['guidance_scale']
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prompts = [prompt, state['negative_prompt'] or '']
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ids, mask = self.tokenizer.encode(
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prompts,
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return_mask=True,
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max_seq_len=self.model.config.max_seq_len,
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add_bos=state['add_bos_token']
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)
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if state['auto_max_new_tokens']:
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max_new_tokens = state['truncation_length'] - ids[0].shape[-1]
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else:
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max_new_tokens = state['max_new_tokens']
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self.generator.gen_begin(ids, mask=mask)
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initial_len = self.generator.sequence[0].shape[0]
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has_leading_space = False
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for i in range(max_new_tokens):
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logits = self.model.forward(self.generator.sequence[:, -1:], self.cache, input_mask=mask)
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self.generator.apply_rep_penalty(logits)
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logits = F.log_softmax(logits, dim=-1)
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logits_mixed = alpha * logits[0] + (1 - alpha) * logits[1]
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token, _ = self.generator.sample_current(logits_mixed)
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if i == 0 and self.generator.tokenizer.tokenizer.IdToPiece(int(token)).startswith('▁'):
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has_leading_space = True
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decoded_text = self.generator.tokenizer.decode(self.generator.sequence[0][initial_len:])
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if has_leading_space:
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decoded_text = ' ' + decoded_text
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yield decoded_text
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if token.item() == self.tokenizer.eos_token_id or shared.stop_everything:
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break
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batch_token = token.repeat(2, 1)
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self.generator.gen_accept_token(batch_token)
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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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