text-generation-webui/modules/models.py

import json
import os
import sys
import time
import zipfile
from pathlib import Path

import numpy as np
import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

import modules.shared as shared

transformers.logging.set_verbosity_error()

local_rank = None

if shared.args.flexgen:
    from flexgen.flex_opt import (CompressionConfig, ExecutionEnv, OptLM,
                                  Policy, str2bool)

if shared.args.deepspeed:
    import deepspeed
    from transformers.deepspeed import (HfDeepSpeedConfig,
                                        is_deepspeed_zero3_enabled)

    from modules.deepspeed_parameters import generate_ds_config

    # Distributed setup
    local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0"))
    world_size = int(os.getenv("WORLD_SIZE", "1"))
    torch.cuda.set_device(local_rank)
    deepspeed.init_distributed()
    ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir)
    dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration

def load_model(model_name):
    print(f"Loading {model_name}...")
    t0 = time.time()

    shared.is_RWKV = model_name.lower().startswith('rwkv-')

    # Default settings
    if not (shared.args.cpu or shared.args.load_in_8bit or shared.args.load_in_4bit or shared.args.auto_devices or shared.args.disk or shared.args.gpu_memory is not None or shared.args.cpu_memory is not None or shared.args.deepspeed or shared.args.flexgen or shared.is_RWKV):
        if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
        else:
            model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda()

    # FlexGen
    elif shared.args.flexgen:
        # Initialize environment
        env = ExecutionEnv.create(shared.args.disk_cache_dir)

        # Offloading policy
        policy = Policy(1, 1,
                        shared.args.percent[0], shared.args.percent[1],
                        shared.args.percent[2], shared.args.percent[3],
                        shared.args.percent[4], shared.args.percent[5],
                        overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight,
                        cpu_cache_compute=False, attn_sparsity=1.0,
                        compress_weight=shared.args.compress_weight,
                        comp_weight_config=CompressionConfig(
                            num_bits=4, group_size=64,
                            group_dim=0, symmetric=False),
                        compress_cache=False,
                        comp_cache_config=CompressionConfig(
                            num_bits=4, group_size=64,
                            group_dim=2, symmetric=False))

        model = OptLM(f"facebook/{shared.model_name}", env, "models", policy)

    # DeepSpeed ZeRO-3
    elif shared.args.deepspeed:
        model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
        model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
        model.module.eval() # Inference
        print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")

    # RMKV model (not on HuggingFace)
    elif shared.is_RWKV:
        from modules.RWKV import RWKVModel, RWKVTokenizer

        model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda")
        tokenizer = RWKVTokenizer.from_pretrained(Path('models'))

        return model, tokenizer

    # 4-bit LLaMA
    elif shared.args.load_in_4bit:
        sys.path.append(os.path.abspath(Path("repositories/GPTQ-for-LLaMa")))

        from llama import load_quant

        path_to_model = Path(f'models/{model_name}')
        pt_model = ''
        if path_to_model.name.lower().startswith('llama-7b'):
            pt_model = 'llama-7b-4bit.pt'
        if path_to_model.name.lower().startswith('llama-13b'):
            pt_model = 'llama-13b-4bit.pt'
        if path_to_model.name.lower().startswith('llama-30b'):
            pt_model = 'llama-30b-4bit.pt'

        if not Path(f"models/{pt_model}").exists():
            print(f"Could not find models/{pt_model}, exiting...")
            exit()

        model = load_quant(path_to_model, Path(f"models/{pt_model}"), 4)
        model = model.to(torch.device('cuda:0'))

    # Custom
    else:
        command = "AutoModelForCausalLM.from_pretrained"
        params = ["low_cpu_mem_usage=True"]
        if not shared.args.cpu and not torch.cuda.is_available():
            print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n")
            shared.args.cpu = True

        if shared.args.cpu:
            params.append("low_cpu_mem_usage=True")
            params.append("torch_dtype=torch.float32")
        else:
            params.append("device_map='auto'")
            params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16")

            if shared.args.gpu_memory:
                memory_map = shared.args.gpu_memory
                max_memory = f"max_memory={{0: '{memory_map[0]}GiB'"
                for i in range(1, len(memory_map)):
                    max_memory += (f", {i}: '{memory_map[i]}GiB'")
                max_memory += (f", 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
                params.append(max_memory)
            elif not shared.args.load_in_8bit:
                total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024))
                suggestion = round((total_mem-1000)/1000)*1000
                if total_mem-suggestion < 800:
                    suggestion -= 1000
                suggestion = int(round(suggestion/1000))
                print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m")
                params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}")
            if shared.args.disk:
                params.append(f"offload_folder='{shared.args.disk_cache_dir}'")

        command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})"
        model = eval(command)

    # Loading the tokenizer
    if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists():
        tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
    else:
        tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/"))
    tokenizer.truncation_side = 'left'

    print(f"Loaded the model in {(time.time()-t0):.2f} seconds.")
    return model, tokenizer

def load_soft_prompt(name):
    if name == 'None':
        shared.soft_prompt = False
        shared.soft_prompt_tensor = None
    else:
        with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf:
            zf.extract('tensor.npy')
            zf.extract('meta.json')
            j = json.loads(open('meta.json', 'r').read())
            print(f"\nLoading the softprompt \"{name}\".")
            for field in j:
                if field != 'name':
                    if type(j[field]) is list:
                        print(f"{field}: {', '.join(j[field])}")
                    else:
                        print(f"{field}: {j[field]}")
            print()
            tensor = np.load('tensor.npy')
            Path('tensor.npy').unlink()
            Path('meta.json').unlink()
        tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype)
        tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1]))

        shared.soft_prompt = True
        shared.soft_prompt_tensor = tensor

    return name