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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 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 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
# 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)
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