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Add ExLlama+LoRA support (#2756)
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oobabooga
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commit
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170
modules/LoRA.py
170
modules/LoRA.py
@ -7,85 +7,117 @@ import modules.shared as shared
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from modules.logging_colors import logger
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from modules.models import reload_model
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try:
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from auto_gptq import get_gptq_peft_model
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from auto_gptq.utils.peft_utils import GPTQLoraConfig
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has_auto_gptq_peft = True
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except:
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has_auto_gptq_peft = False
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def add_lora_to_model(lora_names):
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if 'GPTQForCausalLM' in shared.model.__class__.__name__:
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add_lora_autogptq(lora_names)
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elif shared.model.__class__.__name__ == 'ExllamaModel':
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add_lora_exllama(lora_names)
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else:
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add_lora_transformers(lora_names)
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def add_lora_exllama(lora_names):
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try:
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from repositories.exllama.lora import ExLlamaLora
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except:
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logger.error("Could not find the file repositories/exllama/lora.py. Make sure that exllama is cloned inside repositories/ and is up to date.")
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return
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if len(lora_names) == 0:
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shared.model.generator.lora = None
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shared.lora_names = []
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return
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else:
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if len(lora_names) > 1:
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logger.warning('ExLlama can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
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lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}")
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lora_config_path = lora_path / "adapter_config.json"
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lora_adapter_path = lora_path / "adapter_model.bin"
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
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lora = ExLlamaLora(shared.model.model, str(lora_config_path), str(lora_adapter_path))
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shared.model.generator.lora = lora
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shared.lora_names = [lora_names[0]]
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return
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# Adapted from https://github.com/Ph0rk0z/text-generation-webui-testing
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def add_lora_autogptq(lora_names):
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try:
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from auto_gptq import get_gptq_peft_model
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from auto_gptq.utils.peft_utils import GPTQLoraConfig
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except:
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logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.")
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return
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if len(lora_names) == 0:
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if len(shared.lora_names) > 0:
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reload_model()
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shared.lora_names = []
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return
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else:
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if len(lora_names) > 1:
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logger.warning('AutoGPTQ can only work with 1 LoRA at the moment. Only the first one in the list will be loaded.')
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peft_config = GPTQLoraConfig(
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inference_mode=True,
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)
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lora_path = Path(f"{shared.args.lora_dir}/{lora_names[0]}")
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
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shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path)
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shared.lora_names = [lora_names[0]]
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return
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def add_lora_transformers(lora_names):
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prior_set = set(shared.lora_names)
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added_set = set(lora_names) - prior_set
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removed_set = prior_set - set(lora_names)
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shared.lora_names = list(lora_names)
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is_autogptq = 'GPTQForCausalLM' in shared.model.__class__.__name__
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# If no LoRA needs to be added or removed, exit
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if len(added_set) == 0 and len(removed_set) == 0:
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return
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# AutoGPTQ case. It doesn't use the peft functions.
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# Copied from https://github.com/Ph0rk0z/text-generation-webui-testing
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if is_autogptq:
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if not has_auto_gptq_peft:
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logger.error("This version of AutoGPTQ does not support LoRA. You need to install from source or wait for a new release.")
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return
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# Add a LoRA when another LoRA is already present
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if len(removed_set) == 0 and len(prior_set) > 0:
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logger.info(f"Adding the LoRA(s) named {added_set} to the model...")
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for lora in added_set:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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if len(prior_set) > 0:
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reload_model()
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return
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if len(shared.lora_names) == 0:
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return
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else:
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if len(shared.lora_names) > 1:
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logger.warning('AutoGPTQ can only work with 1 LoRA at the moment. Only the first one in the list will be loaded')
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# If any LoRA needs to be removed, start over
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if len(removed_set) > 0:
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shared.model.disable_adapter()
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shared.model = shared.model.base_model.model
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peft_config = GPTQLoraConfig(
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inference_mode=True,
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)
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if len(lora_names) > 0:
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params = {}
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if not shared.args.cpu:
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params['dtype'] = shared.model.dtype
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if hasattr(shared.model, "hf_device_map"):
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params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
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elif shared.args.load_in_8bit:
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params['device_map'] = {'': 0}
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lora_path = Path(f"{shared.args.lora_dir}/{shared.lora_names[0]}")
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
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shared.model = get_gptq_peft_model(shared.model, peft_config, lora_path)
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return
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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# Transformers case
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else:
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# If no LoRA needs to be added or removed, exit
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if len(added_set) == 0 and len(removed_set) == 0:
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return
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shared.lora_names = lora_names
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# Add a LoRA when another LoRA is already present
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if len(removed_set) == 0 and len(prior_set) > 0:
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logger.info(f"Adding the LoRA(s) named {added_set} to the model...")
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for lora in added_set:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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return
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# If any LoRA needs to be removed, start over
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if len(removed_set) > 0:
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shared.model.disable_adapter()
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shared.model = shared.model.base_model.model
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if len(lora_names) > 0:
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logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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params = {}
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if not shared.args.cpu:
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params['dtype'] = shared.model.dtype
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if hasattr(shared.model, "hf_device_map"):
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params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()}
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elif shared.args.load_in_8bit:
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params['device_map'] = {'': 0}
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), adapter_name=lora_names[0], **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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if not shared.args.load_in_8bit and not shared.args.cpu:
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shared.model.half()
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if not hasattr(shared.model, "hf_device_map"):
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if torch.has_mps:
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device = torch.device('mps')
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shared.model = shared.model.to(device)
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else:
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shared.model = shared.model.cuda()
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if not shared.args.load_in_8bit and not shared.args.cpu:
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shared.model.half()
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if not hasattr(shared.model, "hf_device_map"):
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if torch.has_mps:
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device = torch.device('mps')
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shared.model = shared.model.to(device)
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else:
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shared.model = shared.model.cuda()
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@ -3,11 +3,12 @@ from pathlib import Path
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from modules import shared
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from modules.logging_colors import logger
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from modules.relative_imports import RelativeImport
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sys.path.insert(0, str(Path("repositories/exllama")))
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from repositories.exllama.generator import ExLlamaGenerator
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from repositories.exllama.model import ExLlama, ExLlamaCache, ExLlamaConfig
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from repositories.exllama.tokenizer import ExLlamaTokenizer
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with RelativeImport("repositories/exllama"):
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from generator import ExLlamaGenerator
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from model import ExLlama, ExLlamaCache, ExLlamaConfig
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from tokenizer import ExLlamaTokenizer
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class ExllamaModel:
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13
modules/relative_imports.py
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13
modules/relative_imports.py
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@ -0,0 +1,13 @@
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import sys
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from pathlib import Path
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class RelativeImport:
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def __init__(self, path):
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self.import_path = Path(path)
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def __enter__(self):
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sys.path.insert(0, str(self.import_path))
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def __exit__(self, exc_type, exc_value, traceback):
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sys.path.remove(str(self.import_path))
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