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