mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 08:07:56 +01:00
420 lines
15 KiB
Python
420 lines
15 KiB
Python
import gc
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import os
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import pprint
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import re
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import time
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import traceback
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from pathlib import Path
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import torch
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import transformers
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from accelerate import infer_auto_device_map, init_empty_weights
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from accelerate.utils import (
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is_ccl_available,
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is_npu_available,
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is_xpu_available
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)
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from transformers import (
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AutoConfig,
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AutoModel,
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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BitsAndBytesConfig,
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GPTQConfig
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)
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import modules.shared as shared
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from modules import sampler_hijack
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from modules.logging_colors import logger
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from modules.models_settings import get_model_metadata
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transformers.logging.set_verbosity_error()
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local_rank = None
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if shared.args.deepspeed:
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import deepspeed
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from transformers.deepspeed import (
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HfDeepSpeedConfig,
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is_deepspeed_zero3_enabled
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)
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from modules.deepspeed_parameters import generate_ds_config
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# Distributed setup
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local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
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world_size = int(os.getenv("WORLD_SIZE", "1"))
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if is_xpu_available() and is_ccl_available():
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torch.xpu.set_device(local_rank)
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deepspeed.init_distributed(backend="ccl")
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elif is_npu_available():
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torch.npu.set_device(local_rank)
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deepspeed.init_distributed(dist_backend="hccl")
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else:
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torch.cuda.set_device(local_rank)
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deepspeed.init_distributed()
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ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
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dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration
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sampler_hijack.hijack_samplers()
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last_generation_time = time.time()
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def load_model(model_name, loader=None):
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logger.info(f"Loading \"{model_name}\"")
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t0 = time.time()
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shared.is_seq2seq = False
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shared.model_name = model_name
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load_func_map = {
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'Transformers': huggingface_loader,
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'AutoGPTQ': AutoGPTQ_loader,
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'llama.cpp': llamacpp_loader,
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'llamacpp_HF': llamacpp_HF_loader,
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'ExLlamav2': ExLlamav2_loader,
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'ExLlamav2_HF': ExLlamav2_HF_loader,
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'AutoAWQ': AutoAWQ_loader,
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'HQQ': HQQ_loader,
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'TensorRT-LLM': TensorRT_LLM_loader,
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}
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metadata = get_model_metadata(model_name)
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if loader is None:
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if shared.args.loader is not None:
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loader = shared.args.loader
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else:
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loader = metadata['loader']
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if loader is None:
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logger.error('The path to the model does not exist. Exiting.')
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raise ValueError
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shared.args.loader = loader
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output = load_func_map[loader](model_name)
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if type(output) is tuple:
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model, tokenizer = output
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else:
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model = output
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if model is None:
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return None, None
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else:
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tokenizer = load_tokenizer(model_name, model)
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shared.settings.update({k: v for k, v in metadata.items() if k in shared.settings})
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if loader.lower().startswith('exllama') or loader.lower().startswith('tensorrt'):
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shared.settings['truncation_length'] = shared.args.max_seq_len
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elif loader in ['llama.cpp', 'llamacpp_HF']:
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shared.settings['truncation_length'] = shared.args.n_ctx
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logger.info(f"Loaded \"{model_name}\" in {(time.time()-t0):.2f} seconds.")
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logger.info(f"LOADER: \"{loader}\"")
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logger.info(f"TRUNCATION LENGTH: {shared.settings['truncation_length']}")
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logger.info(f"INSTRUCTION TEMPLATE: \"{metadata['instruction_template']}\"")
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return model, tokenizer
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def load_tokenizer(model_name, model):
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tokenizer = None
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path_to_model = Path(f"{shared.args.model_dir}/{model_name}/")
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if path_to_model.exists():
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if shared.args.no_use_fast:
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logger.info('Loading the tokenizer with use_fast=False.')
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tokenizer = AutoTokenizer.from_pretrained(
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path_to_model,
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trust_remote_code=shared.args.trust_remote_code,
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use_fast=not shared.args.no_use_fast
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)
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return tokenizer
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def huggingface_loader(model_name):
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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params = {
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'low_cpu_mem_usage': True,
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'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16,
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}
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if shared.args.trust_remote_code:
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params['trust_remote_code'] = True
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if shared.args.use_flash_attention_2:
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params['use_flash_attention_2'] = True
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if shared.args.force_safetensors:
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params['force_safetensors'] = True
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if shared.args.use_eager_attention:
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params['attn_implementation'] = 'eager'
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config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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if 'chatglm' in model_name.lower():
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LoaderClass = AutoModel
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else:
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if config.to_dict().get('is_encoder_decoder', False):
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LoaderClass = AutoModelForSeq2SeqLM
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shared.is_seq2seq = True
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else:
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LoaderClass = AutoModelForCausalLM
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# Load the model without any special settings
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.compress_pos_emb > 1, shared.args.alpha_value > 1, shared.args.disable_exllama, shared.args.disable_exllamav2]):
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logger.info("TRANSFORMERS_PARAMS=")
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pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(params)
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print()
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model = LoaderClass.from_pretrained(path_to_model, **params)
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if not (hasattr(model, 'is_loaded_in_4bit') and model.is_loaded_in_4bit):
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if torch.backends.mps.is_available():
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device = torch.device('mps')
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model = model.to(device)
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elif is_xpu_available():
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device = torch.device("xpu")
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model = model.to(device)
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elif is_npu_available():
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device = torch.device("npu")
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model = model.to(device)
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else:
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model = model.cuda()
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# DeepSpeed ZeRO-3
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elif shared.args.deepspeed:
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model = LoaderClass.from_pretrained(path_to_model, torch_dtype=params['torch_dtype'], trust_remote_code=params.get('trust_remote_code'))
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model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
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model.module.eval() # Inference
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logger.info(f'DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}')
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# Load with quantization and/or offloading
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else:
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if not any((shared.args.cpu, torch.cuda.is_available(), is_xpu_available(), torch.backends.mps.is_available())):
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logger.warning('torch.cuda.is_available() and is_xpu_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.')
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shared.args.cpu = True
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if shared.args.cpu:
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params['torch_dtype'] = torch.float32
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else:
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params['device_map'] = 'auto'
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if x := get_max_memory_dict():
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params['max_memory'] = x
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if shared.args.load_in_4bit:
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# See https://github.com/huggingface/transformers/pull/23479/files
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# and https://huggingface.co/blog/4bit-transformers-bitsandbytes
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quantization_config_params = {
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'load_in_4bit': True,
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'bnb_4bit_compute_dtype': eval("torch.{}".format(shared.args.compute_dtype)) if shared.args.compute_dtype in ["bfloat16", "float16", "float32"] else None,
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'bnb_4bit_quant_type': shared.args.quant_type,
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'bnb_4bit_use_double_quant': shared.args.use_double_quant,
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'llm_int8_enable_fp32_cpu_offload': True
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}
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params['quantization_config'] = BitsAndBytesConfig(**quantization_config_params)
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elif shared.args.load_in_8bit:
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if any((shared.args.auto_devices, shared.args.gpu_memory)):
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params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)
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else:
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params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
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if params.get('max_memory') is not None:
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with init_empty_weights():
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model = LoaderClass.from_config(config, trust_remote_code=params.get('trust_remote_code'))
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model.tie_weights()
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params['device_map'] = infer_auto_device_map(
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model,
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dtype=torch.int8,
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max_memory=params.get('max_memory'),
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no_split_module_classes=model._no_split_modules
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)
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if shared.args.disk:
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params['offload_folder'] = shared.args.disk_cache_dir
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if shared.args.disable_exllama or shared.args.disable_exllamav2:
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try:
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gptq_config = GPTQConfig(
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bits=config.quantization_config.get('bits', 4),
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disable_exllama=shared.args.disable_exllama,
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disable_exllamav2=shared.args.disable_exllamav2,
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)
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params['quantization_config'] = gptq_config
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logger.info(f'Loading with disable_exllama={shared.args.disable_exllama} and disable_exllamav2={shared.args.disable_exllamav2}.')
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except:
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exc = traceback.format_exc()
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logger.error('Failed to disable exllama. Does the config.json for this model contain the necessary quantization info?')
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print(exc)
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if shared.args.compress_pos_emb > 1:
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params['rope_scaling'] = {'type': 'linear', 'factor': shared.args.compress_pos_emb}
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elif shared.args.alpha_value > 1:
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params['rope_scaling'] = {'type': 'dynamic', 'factor': shared.args.alpha_value}
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logger.info("TRANSFORMERS_PARAMS=")
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pprint.PrettyPrinter(indent=4, sort_dicts=False).pprint(params)
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print()
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model = LoaderClass.from_pretrained(path_to_model, **params)
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return model
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def llamacpp_loader(model_name):
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from modules.llamacpp_model import LlamaCppModel
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path = Path(f'{shared.args.model_dir}/{model_name}')
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if path.is_file():
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model_file = path
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else:
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model_file = sorted(Path(f'{shared.args.model_dir}/{model_name}').glob('*.gguf'))[0]
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logger.info(f"llama.cpp weights detected: \"{model_file}\"")
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model, tokenizer = LlamaCppModel.from_pretrained(model_file)
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return model, tokenizer
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def llamacpp_HF_loader(model_name):
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from modules.llamacpp_hf import LlamacppHF
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path = Path(f'{shared.args.model_dir}/{model_name}')
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# Check if a HF tokenizer is available for the model
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if all((path / file).exists() for file in ['tokenizer_config.json']):
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logger.info(f'Using tokenizer from: \"{path}\"')
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else:
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logger.error("Could not load the model because a tokenizer in Transformers format was not found.")
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return None, None
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model = LlamacppHF.from_pretrained(model_name)
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return model
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def AutoAWQ_loader(model_name):
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from awq import AutoAWQForCausalLM
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model_dir = Path(f'{shared.args.model_dir}/{model_name}')
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model = AutoAWQForCausalLM.from_quantized(
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quant_path=model_dir,
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max_new_tokens=shared.args.max_seq_len,
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trust_remote_code=shared.args.trust_remote_code,
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fuse_layers=not shared.args.no_inject_fused_attention,
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max_memory=get_max_memory_dict(),
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batch_size=1,
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safetensors=any(model_dir.glob('*.safetensors')),
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)
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return model
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def AutoGPTQ_loader(model_name):
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import modules.AutoGPTQ_loader
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return modules.AutoGPTQ_loader.load_quantized(model_name)
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def ExLlamav2_loader(model_name):
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from modules.exllamav2 import Exllamav2Model
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model, tokenizer = Exllamav2Model.from_pretrained(model_name)
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return model, tokenizer
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def ExLlamav2_HF_loader(model_name):
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from modules.exllamav2_hf import Exllamav2HF
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return Exllamav2HF.from_pretrained(model_name)
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def HQQ_loader(model_name):
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from hqq.core.quantize import HQQBackend, HQQLinear
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from hqq.models.hf.base import AutoHQQHFModel
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logger.info(f"Loading HQQ model with backend: \"{shared.args.hqq_backend}\"")
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model_dir = Path(f'{shared.args.model_dir}/{model_name}')
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model = AutoHQQHFModel.from_quantized(str(model_dir))
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HQQLinear.set_backend(getattr(HQQBackend, shared.args.hqq_backend))
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return model
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def TensorRT_LLM_loader(model_name):
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from modules.tensorrt_llm import TensorRTLLMModel
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model = TensorRTLLMModel.from_pretrained(model_name)
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return model
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def get_max_memory_dict():
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max_memory = {}
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max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
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if shared.args.gpu_memory:
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memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory))
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for i in range(len(memory_map)):
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max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i]
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max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
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# If --auto-devices is provided standalone, try to get a reasonable value
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# for the maximum memory of device :0
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elif shared.args.auto_devices:
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if is_xpu_available():
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total_mem = (torch.xpu.get_device_properties(0).total_memory / (1024 * 1024))
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else:
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total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024))
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suggestion = round((total_mem - 1000) / 1000) * 1000
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if total_mem - suggestion < 800:
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suggestion -= 1000
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suggestion = int(round(suggestion / 1000))
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logger.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.")
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max_memory[0] = f'{suggestion}GiB'
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max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory
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return max_memory if len(max_memory) > 0 else None
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def clear_torch_cache():
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gc.collect()
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if not shared.args.cpu:
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if is_xpu_available():
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torch.xpu.empty_cache()
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else:
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torch.cuda.empty_cache()
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def unload_model():
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shared.model = shared.tokenizer = None
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shared.previous_model_name = shared.model_name
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shared.model_name = 'None'
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shared.lora_names = []
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shared.model_dirty_from_training = False
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clear_torch_cache()
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def reload_model():
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unload_model()
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shared.model, shared.tokenizer = load_model(shared.model_name)
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def unload_model_if_idle():
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global last_generation_time
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logger.info(f"Setting a timeout of {shared.args.idle_timeout} minutes to unload the model in case of inactivity.")
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while True:
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shared.generation_lock.acquire()
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try:
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if time.time() - last_generation_time > shared.args.idle_timeout * 60:
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if shared.model is not None:
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logger.info("Unloading the model for inactivity.")
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unload_model()
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finally:
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shared.generation_lock.release()
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time.sleep(60)
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