text-generation-webui/modules/exllama_hf.py

82 lines
3.1 KiB
Python

import os
import sys
from pathlib import Path
from typing import *
import torch
from transformers import (
GenerationConfig,
LlamaTokenizer,
PretrainedConfig,
PreTrainedModel
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from modules import shared
from modules.logging_colors import logger
from modules.relative_imports import RelativeImport
with RelativeImport("repositories/exllama"):
from model import ExLlama, ExLlamaCache, ExLlamaConfig
class ExllamaHF(PreTrainedModel):
def __init__(self, config: ExLlamaConfig):
super().__init__(PretrainedConfig())
self.ex_config = config
self.ex_model = ExLlama(self.ex_config)
self.generation_config = GenerationConfig()
def _validate_model_class(self):
pass
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
pass
def prepare_inputs_for_generation(self, input_ids, **kwargs):
return {'input_ids': input_ids, **kwargs}
@property
def device(self) -> torch.device:
# TODO: May cause problem on multi-gpu inference?
return torch.device(0)
def __call__(self, *args, **kwargs):
# TODO: Some decoding methods (such as Contrastive Search) may not work at this time
assert len(args) == 0, 'no *args should be passed to forward'
use_cache = kwargs['use_cache']
seq = kwargs['input_ids'][0].tolist()
cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
if cache is None:
cache = ExLlamaCache(self.ex_model)
self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True)
logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache).to(self.device)
return CausalLMOutputWithPast(logits=logits, past_key_values=cache if use_cache else None)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
if isinstance(pretrained_model_name_or_path, str):
pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
pretrained_model_name_or_path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path)
config = ExLlamaConfig(pretrained_model_name_or_path / 'config.json')
# from 'oobabooga/text-generation-webui/modules/exllama.py'
weight_path = None
for ext in ['.safetensors', '.pt', '.bin']:
found = list(pretrained_model_name_or_path.glob(f"*{ext}"))
if len(found) > 0:
weight_path = found[-1]
break
assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"'
config.model_path = str(weight_path)
# This slowes down a bit but align better with autogptq generation.
# TODO: Should give user choice to tune the exllama config
config.act_order = True
config.fused_attn = False
config.fused_mlp_thd = 0
return ExllamaHF(config)