File size: 5,980 Bytes
eec676d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
import os
from pathlib import Path
from typing import Any, Dict, Optional, Union
import torch
from torch.nn import CrossEntropyLoss
from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
from modules import RoPE, shared
from modules.logging_colors import logger
try:
from exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
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 model import ExLlama, ExLlamaCache, ExLlamaConfig
except:
logger.error("Could not find repositories/exllama/. Make sure that exllama is cloned inside repositories/ and is up to date.")
raise
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()
self.lora = None
self.ex_cache = ExLlamaCache(self.ex_model)
self.past_seq = None
if shared.args.cfg_cache:
self.ex_cache_negative = ExLlamaCache(self.ex_model)
self.past_seq_negative = None
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:
return torch.device(0)
def __call__(self, *args, **kwargs):
use_cache = kwargs.get('use_cache', True)
labels = kwargs.get('labels', None)
past_key_values = kwargs.get('past_key_values', None)
if len(args) > 0:
if not shared.args.cfg_cache:
logger.error("Please enable the cfg-cache option to use CFG with ExLlama_HF.")
return
input_ids = args[0]
is_negative = True
past_seq = self.past_seq_negative
ex_cache = self.ex_cache_negative
else:
input_ids = kwargs['input_ids']
is_negative = False
past_seq = self.past_seq
ex_cache = self.ex_cache
seq = input_ids[0].tolist()
if is_negative and past_key_values is not None:
seq = past_key_values + seq
seq_tensor = torch.tensor(seq)
# Make the forward call
if labels is None:
if past_seq is None or not torch.equal(past_seq, seq_tensor[:-1]):
ex_cache.current_seq_len = 0
self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), ex_cache, preprocess_only=True, lora=self.lora)
logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), ex_cache, lora=self.lora).to(input_ids.device)
else:
ex_cache.current_seq_len = 0
logits = self.ex_model.forward(torch.tensor([seq], dtype=torch.long), ex_cache, last_id_only=False, lora=self.lora)
if is_negative:
self.past_seq_negative = seq_tensor
else:
self.past_seq = seq_tensor
loss = None
if labels is not None:
# Shift so that tokens < n predict n
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss()
shift_logits = shift_logits.view(-1, logits.shape[-1])
shift_labels = shift_labels.view(-1)
# Enable model parallelism
shift_labels = shift_labels.to(shift_logits.device)
loss = loss_fct(shift_logits, shift_labels)
return CausalLMOutputWithPast(logits=logits, past_key_values=seq if use_cache else None, loss=loss)
@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)
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 or shared.args.rope_freq_base > 0:
config.alpha_value = RoPE.get_alpha_value(shared.args.alpha_value, shared.args.rope_freq_base)
config.calculate_rotary_embedding_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
# This slowes down a bit but align better with autogptq generation.
# TODO: Should give user choice to tune the exllama config
# config.fused_attn = False
# config.fused_mlp_thd = 0
return ExllamaHF(config)
|