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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.ex_cache = ExLlamaCache(self.ex_model) | |
self.generation_config = GenerationConfig() | |
self.lora = 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} | |
def device(self) -> torch.device: | |
return torch.device(0) | |
def __call__(self, *args, **kwargs): | |
input_ids = args[0] if len(args) > 0 else kwargs['input_ids'] | |
use_cache = kwargs.get('use_cache', True) | |
labels = kwargs.get('labels', None) | |
cache = kwargs.get('past_key_values', None) | |
seq = input_ids[0].tolist() | |
if labels is None: | |
if cache is None: | |
self.ex_cache.current_seq_len = 0 | |
cache = self.ex_cache | |
self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True, lora=self.lora) | |
logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache, lora=self.lora).to(input_ids.device) | |
else: | |
if cache is None: | |
self.ex_cache.current_seq_len = 0 | |
cache = self.ex_cache | |
logits = self.ex_model.forward(torch.tensor([seq], dtype=torch.long), cache, last_id_only=False, lora=self.lora) | |
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=cache if use_cache else None, loss=loss) | |
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: | |
config.alpha_value = shared.args.alpha_value | |
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) | |