mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
354 lines
13 KiB
Python
354 lines
13 KiB
Python
import gc
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import json
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import logging
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import os
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import re
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import time
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import zipfile
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from pathlib import Path
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import numpy as np
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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 transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
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AutoModelForSeq2SeqLM, AutoTokenizer,
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BitsAndBytesConfig, LlamaTokenizer)
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import modules.shared as shared
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from modules import llama_attn_hijack
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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 (HfDeepSpeedConfig,
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is_deepspeed_zero3_enabled)
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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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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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# Some models require special treatment in various parts of the code.
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# This function detects those models
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def find_model_type(model_name):
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path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
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if not path_to_model.exists():
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return 'None'
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model_name_lower = model_name.lower()
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if 'rwkv-' in model_name_lower:
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return 'rwkv'
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elif len(list(path_to_model.glob('*ggml*.bin'))) > 0:
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return 'llamacpp'
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elif re.match('.*ggml.*\.bin', model_name_lower):
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return 'llamacpp'
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elif 'chatglm' in model_name_lower:
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return 'chatglm'
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elif 'galactica' in model_name_lower:
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return 'galactica'
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elif 'llava' in model_name_lower:
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return 'llava'
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elif 'oasst' in model_name_lower:
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return 'oasst'
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elif any((k in model_name_lower for k in ['gpt4chan', 'gpt-4chan'])):
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return 'gpt4chan'
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else:
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config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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# Not a "catch all", but fairly accurate
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if config.to_dict().get("is_encoder_decoder", False):
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return 'HF_seq2seq'
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else:
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return 'HF_generic'
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def load_model(model_name):
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logging.info(f"Loading {model_name}...")
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t0 = time.time()
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shared.model_type = find_model_type(model_name)
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if shared.model_type == 'None':
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logging.error('The path to the model does not exist. Exiting.')
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return None, None
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if shared.args.autogptq:
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load_func = AutoGPTQ_loader
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elif shared.args.wbits > 0:
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load_func = GPTQ_loader
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elif shared.model_type == 'llamacpp':
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load_func = llamacpp_loader
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elif shared.model_type == 'rwkv':
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load_func = RWKV_loader
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elif shared.args.flexgen:
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load_func = flexgen_loader
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else:
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load_func = huggingface_loader
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output = load_func(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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# Hijack attention with xformers
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if any((shared.args.xformers, shared.args.sdp_attention)):
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llama_attn_hijack.hijack_llama_attention()
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logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
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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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if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists():
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tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/"))
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elif type(model) is transformers.LlamaForCausalLM:
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# Try to load an universal LLaMA tokenizer
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if shared.model_type not in ['llava', 'oasst']:
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for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
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if p.exists():
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logging.info(f"Loading the universal LLaMA tokenizer from {p}...")
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tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True)
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return tokenizer
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# Otherwise, load it from the model folder and hope that these
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# are not outdated tokenizer files.
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tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True)
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try:
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tokenizer.eos_token_id = 2
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tokenizer.bos_token_id = 1
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tokenizer.pad_token_id = 0
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except:
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pass
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else:
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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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tokenizer = AutoTokenizer.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code)
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return tokenizer
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def huggingface_loader(model_name):
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if shared.model_type == 'chatglm':
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LoaderClass = AutoModel
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elif shared.model_type == 'HF_seq2seq':
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LoaderClass = AutoModelForSeq2SeqLM
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else:
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LoaderClass = AutoModelForCausalLM
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# Load the model in simple 16-bit mode by default
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]):
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model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code)
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if torch.has_mps:
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device = torch.device('mps')
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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(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
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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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logging.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
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# Custom
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else:
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params = {
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"low_cpu_mem_usage": True,
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"trust_remote_code": shared.args.trust_remote_code
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}
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if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
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logging.warning("torch.cuda.is_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 shared.args.load_in_8bit and 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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elif shared.args.load_in_8bit:
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params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True)
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elif shared.args.bf16:
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params["torch_dtype"] = torch.bfloat16
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else:
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params["torch_dtype"] = torch.float16
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params['max_memory'] = get_max_memory_dict()
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if shared.args.disk:
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params["offload_folder"] = shared.args.disk_cache_dir
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checkpoint = Path(f'{shared.args.model_dir}/{model_name}')
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if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto':
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config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=shared.args.trust_remote_code)
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with init_empty_weights():
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model = LoaderClass.from_config(config, trust_remote_code=shared.args.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['max_memory'],
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no_split_module_classes=model._no_split_modules
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)
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model = LoaderClass.from_pretrained(checkpoint, **params)
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return model
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def flexgen_loader(model_name):
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from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy
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# Initialize environment
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env = ExecutionEnv.create(shared.args.disk_cache_dir)
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# Offloading policy
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policy = Policy(1, 1,
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shared.args.percent[0], shared.args.percent[1],
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shared.args.percent[2], shared.args.percent[3],
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shared.args.percent[4], shared.args.percent[5],
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overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight,
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cpu_cache_compute=False, attn_sparsity=1.0,
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compress_weight=shared.args.compress_weight,
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comp_weight_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=0, symmetric=False),
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compress_cache=False,
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comp_cache_config=CompressionConfig(
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num_bits=4, group_size=64,
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group_dim=2, symmetric=False))
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model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy)
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return model
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def RWKV_loader(model_name):
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from modules.RWKV import RWKVModel, RWKVTokenizer
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model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
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tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir))
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return model, tokenizer
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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 = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0]
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logging.info(f"llama.cpp weights detected: {model_file}\n")
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model, tokenizer = LlamaCppModel.from_pretrained(model_file)
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return model, tokenizer
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def GPTQ_loader(model_name):
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# Monkey patch
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if shared.args.monkey_patch:
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logging.warning("Applying the monkey patch for using LoRAs in 4-bit mode. It may cause undefined behavior outside its intended scope.")
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from modules.monkey_patch_gptq_lora import load_model_llama
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model, _ = load_model_llama(model_name)
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# No monkey patch
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else:
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import modules.GPTQ_loader
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model = modules.GPTQ_loader.load_quantized(model_name)
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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 get_max_memory_dict():
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max_memory = {}
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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$', memory_map[i].lower()) else memory_map[i]
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max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB'
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max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', 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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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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logging.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', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
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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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torch.cuda.empty_cache()
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def unload_model():
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shared.model = shared.tokenizer = None
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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 load_soft_prompt(name):
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if name == 'None':
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shared.soft_prompt = False
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shared.soft_prompt_tensor = None
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else:
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with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf:
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zf.extract('tensor.npy')
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zf.extract('meta.json')
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j = json.loads(open('meta.json', 'r').read())
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logging.info(f"\nLoading the softprompt \"{name}\".")
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for field in j:
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if field != 'name':
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if type(j[field]) is list:
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logging.info(f"{field}: {', '.join(j[field])}")
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else:
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logging.info(f"{field}: {j[field]}")
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tensor = np.load('tensor.npy')
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Path('tensor.npy').unlink()
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Path('meta.json').unlink()
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tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype)
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tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1]))
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shared.soft_prompt = True
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shared.soft_prompt_tensor = tensor
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return name
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