mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
Move 4-bit loading code to separate inference_model file
This commit is contained in:
0cc4m
91
aiserver.py
91
aiserver.py
@@ -1778,56 +1778,6 @@ def unload_model():
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koboldai_vars.badwordsids = koboldai_settings.badwordsids_default
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def prepare_4bit_load(modelpath):
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paths_4bit = ["4bit*.safetensors", "4bit*.pt"]
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paths_4bit_old = ["4bit-old.pt", "4bit-old.safetensors"]
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result = False
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groupsize = -1
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for p in paths_4bit:
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p = os.path.join(modelpath, p)
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val = [v for v in glob.glob(p) if "4bit-old" not in v]
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if val:
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result = val[0]
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fname = Path(result).parts[-1]
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g = re.findall("^(?:4bit)(?:-)(\d+)(?:g-?)", fname)
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if g:
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groupsize = int(g[0])
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break
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global monkey_patched_4bit
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# Monkey-patch in old-format pt-file support
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if not result:
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print("4-bit file not found, falling back to old format.")
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for p in paths_4bit_old:
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p = os.path.join(modelpath, p)
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if os.path.isfile(p):
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result = p
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break
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if not result:
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print("4-bit old-format file not found, loading failed.")
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raise RuntimeError(f"4-bit load failed. PT-File not found.")
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import llama, opt, gptneox, gptj, old_quant
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llama.make_quant = old_quant.old_make_quant
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opt.make_quant = old_quant.old_make_quant
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gptneox.make_quant = old_quant.old_make_quant
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gptj.make_quant = old_quant.old_make_quant
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monkey_patched_4bit = True
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elif monkey_patched_4bit:
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# Undo monkey patch
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print("Undoing 4-bit old format monkey patch")
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import llama, opt, gptneox, gptj, quant
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llama.make_quant = quant.make_quant
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opt.make_quant = quant.make_quant
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gptneox.make_quant = quant.make_quant
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gptj.make_quant = quant.make_quant
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monkey_patched_4bit = False
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return result, groupsize
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def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=False, online_model="", use_breakmodel_args=False, breakmodel_args_default_to_cpu=False, url=None, use_8_bit=False, use_4_bit=False):
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global model
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global tokenizer
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@@ -2008,9 +1958,9 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
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except:
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pass
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try:
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from modeling.inference_models.generic_hf_torch import GenericHFTorchInferenceModel
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model = GenericHFTorchInferenceModel(
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if use_4_bit:
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from modeling.inference_models.hf_torch_4bit import HFTorch4BitInferenceModel
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model = HFTorch4BitInferenceModel(
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koboldai_vars.model,
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lazy_load=koboldai_vars.lazy_load,
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low_mem=args.lowmem
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@@ -2020,18 +1970,31 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
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save_model=not (args.colab or args.cacheonly) or args.savemodel,
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initial_load=initial_load,
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)
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except SuperLegacyModelError:
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from modeling.inference_models.legacy_gpt2_hf import CustomGPT2HFTorchInferenceModel
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model = CustomGPT2HFTorchInferenceModel(
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koboldai_vars.model,
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lazy_load=koboldai_vars.lazy_load,
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low_mem=args.lowmem
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)
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else:
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try:
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from modeling.inference_models.generic_hf_torch import GenericHFTorchInferenceModel
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model = GenericHFTorchInferenceModel(
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koboldai_vars.model,
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lazy_load=koboldai_vars.lazy_load,
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low_mem=args.lowmem
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)
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model.load(
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save_model=not (args.colab or args.cacheonly) or args.savemodel,
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initial_load=initial_load,
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)
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model.load(
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save_model=not (args.colab or args.cacheonly) or args.savemodel,
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initial_load=initial_load,
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)
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except SuperLegacyModelError:
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from modeling.inference_models.legacy_gpt2_hf import CustomGPT2HFTorchInferenceModel
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model = CustomGPT2HFTorchInferenceModel(
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koboldai_vars.model,
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lazy_load=koboldai_vars.lazy_load,
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low_mem=args.lowmem
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)
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model.load(
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save_model=not (args.colab or args.cacheonly) or args.savemodel,
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initial_load=initial_load,
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)
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logger.info(f"Pipeline created: {koboldai_vars.model}")
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else:
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385
modeling/inference_models/hf_torch_4bit.py
Normal file
385
modeling/inference_models/hf_torch_4bit.py
Normal file
@@ -0,0 +1,385 @@
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from __future__ import annotations
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import os
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import json
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import torch
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import re
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import shutil
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import sys
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from typing import Union
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from transformers import AutoModelForCausalLM, GPTNeoForCausalLM, AutoTokenizer, LlamaTokenizer
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from modeling.inference_model import SuperLegacyModelError
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import utils
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import modeling.lazy_loader as lazy_loader
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import koboldai_settings
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from logger import logger, set_logger_verbosity, quiesce_logger
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try:
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import breakmodel
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except ModuleNotFoundError as e:
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# Breakmodel is only expected to work on GPU
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if not utils.koboldai_vars.use_colab_tpu:
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raise e
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from modeling.inference_models.hf_torch import HFTorchInferenceModel
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# 4-bit dependencies
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from pathlib import Path
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import glob
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sys.path.insert(0, os.path.abspath(Path("repos/gptq")))
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from gptj import load_quant as gptj_load_quant
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from gptneox import load_quant as gptneox_load_quant
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from llama import load_quant as llama_load_quant
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from opt import load_quant as opt_load_quant
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from offload import load_quant_offload
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monkey_patched_4bit = False
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def prepare_4bit_load(modelpath):
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paths_4bit = ["4bit*.safetensors", "4bit*.pt"]
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paths_4bit_old = ["4bit-old.pt", "4bit-old.safetensors"]
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result = False
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groupsize = -1
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for p in paths_4bit:
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p = os.path.join(modelpath, p)
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val = [v for v in glob.glob(p) if "4bit-old" not in v]
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if val:
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result = val[0]
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fname = Path(result).parts[-1]
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g = re.findall("^(?:4bit)(?:-)(\d+)(?:g-?)", fname)
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if g:
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groupsize = int(g[0])
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break
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global monkey_patched_4bit
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# Monkey-patch in old-format pt-file support
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if not result:
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print("4-bit file not found, falling back to old format.")
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for p in paths_4bit_old:
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p = os.path.join(modelpath, p)
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if os.path.isfile(p):
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result = p
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break
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if not result:
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print("4-bit old-format file not found, loading failed.")
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raise RuntimeError("4-bit load failed. PT/Safetensors-File not found.")
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import llama, opt, gptneox, gptj, old_quant
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llama.make_quant = old_quant.old_make_quant
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opt.make_quant = old_quant.old_make_quant
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gptneox.make_quant = old_quant.old_make_quant
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gptj.make_quant = old_quant.old_make_quant
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monkey_patched_4bit = True
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elif monkey_patched_4bit:
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# Undo monkey patch
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print("Undoing 4-bit old format monkey patch")
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import llama, opt, gptneox, gptj, quant
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llama.make_quant = quant.make_quant
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opt.make_quant = quant.make_quant
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gptneox.make_quant = quant.make_quant
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gptj.make_quant = quant.make_quant
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monkey_patched_4bit = False
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return result, groupsize
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class HFTorch4BitInferenceModel(HFTorchInferenceModel):
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def _load(self, save_model: bool, initial_load: bool) -> None:
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utils.koboldai_vars.allowsp = True
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# Make model path the same as the model name to make this consistent
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# with the other loading method if it isn't a known model type. This
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# code is not just a workaround for below, it is also used to make the
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# behavior consistent with other loading methods - Henk717
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# if utils.koboldai_vars.model not in ["NeoCustom", "GPT2Custom"]:
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# utils.koboldai_vars.custmodpth = utils.koboldai_vars.model
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if self.model_name == "NeoCustom":
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self.model_name = os.path.basename(
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os.path.normpath(utils.koboldai_vars.custmodpth)
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)
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utils.koboldai_vars.model = self.model_name
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self.lazy_load = False
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self.init_model_config()
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gpulayers = utils.args.breakmodel_gpulayers
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try:
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gpu_layers_list = [int(l) for l in gpulayers.split(",")]
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except ValueError:
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gpu_layers_list = [utils.num_layers(self.model_config)]
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self.offload_4bit = sum(gpu_layers_list) < utils.num_layers(self.model_config)
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if self.offload_4bit:
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utils.koboldai_vars.lazy_load = False
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print("4-bit CPU offloader active")
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tf_kwargs = {
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"low_cpu_mem_usage": True,
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}
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# If we're using torch_lazy_loader, we need to get breakmodel config
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# early so that it knows where to load the individual model tensors
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if (
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self.lazy_load
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and utils.koboldai_vars.hascuda
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and utils.koboldai_vars.breakmodel
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and not utils.koboldai_vars.nobreakmodel
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):
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self.breakmodel_device_config(self.model_config)
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if self.lazy_load:
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# If we're using lazy loader, we need to figure out what the model's hidden layers are called
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with lazy_loader.use_lazy_load(
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dematerialized_modules=True, use_accelerate_init_empty_weights=True
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):
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try:
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metamodel = AutoModelForCausalLM.from_config(self.model_config)
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except Exception as e:
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logger.error(f"Fell back to neo for metamodel due to {e}")
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try:
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metamodel = GPTNeoForCausalLM.from_config(self.model_config)
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except Exception as e:
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logger.error(f"Falling back again due to {e}")
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raise SuperLegacyModelError
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utils.layers_module_names = utils.get_layers_module_names(metamodel)
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utils.module_names = list(metamodel.state_dict().keys())
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utils.named_buffers = list(metamodel.named_buffers(recurse=True))
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# Download model from Huggingface if it does not exist, otherwise load locally
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with self._maybe_use_float16(), lazy_loader.use_lazy_load(
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enable=self.lazy_load,
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callback=self._get_lazy_load_callback(utils.num_layers(self.model_config))
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if self.lazy_load
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else None,
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dematerialized_modules=True,
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):
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if self.lazy_load:
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# torch_lazy_loader.py and low_cpu_mem_usage can't be used at the same time
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tf_kwargs.pop("low_cpu_mem_usage", None)
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if self.get_local_model_path():
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# Model is stored locally, load it.
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self.model = self._get_model(self.get_local_model_path(), tf_kwargs)
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self.tokenizer = self._get_tokenizer(self.get_local_model_path())
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else:
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# Model not stored locally, we need to download it.
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# _rebuild_tensor patch for casting dtype and supporting LazyTensors
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old_rebuild_tensor = torch._utils._rebuild_tensor
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def new_rebuild_tensor(
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storage: Union[lazy_loader.LazyTensor, torch.Storage],
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storage_offset,
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shape,
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stride,
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):
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if not isinstance(storage, lazy_loader.LazyTensor):
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dtype = storage.dtype
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else:
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dtype = storage.storage_type.dtype
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if not isinstance(dtype, torch.dtype):
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dtype = storage.storage_type(0).dtype
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if dtype is torch.float32 and len(shape) >= 2:
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utils.koboldai_vars.fp32_model = True
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return old_rebuild_tensor(storage, storage_offset, shape, stride)
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torch._utils._rebuild_tensor = new_rebuild_tensor
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self.model = self._get_model(self.model_name, tf_kwargs)
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self.tokenizer = self._get_tokenizer(self.model_name)
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torch._utils._rebuild_tensor = old_rebuild_tensor
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if save_model:
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self.tokenizer.save_pretrained(
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self.get_local_model_path(ignore_existance=True)
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)
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if utils.koboldai_vars.fp32_model and not breakmodel.disk_blocks:
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# Use save_pretrained to convert fp32 models to fp16,
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# unless we are using disk cache because save_pretrained
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# is not supported in that case
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self.model = self.model.half()
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self.model.save_pretrained(
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self.get_local_model_path(ignore_existance=True),
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max_shard_size="500MiB",
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)
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else:
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# For fp16 models, we can just copy the model files directly
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import transformers.configuration_utils
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import transformers.modeling_utils
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import transformers.file_utils
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import huggingface_hub
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# Save the config.json
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shutil.move(
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os.path.realpath(
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huggingface_hub.hf_hub_download(
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self.model_name,
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transformers.configuration_utils.CONFIG_NAME,
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revision=utils.koboldai_vars.revision,
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cache_dir="cache",
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local_files_only=True,
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legacy_cache_layout=False,
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)
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),
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os.path.join(
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self.get_local_model_path(ignore_existance=True),
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transformers.configuration_utils.CONFIG_NAME,
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),
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)
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if utils.num_shards is None:
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# Save the pytorch_model.bin or model.safetensors of an unsharded model
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any_success = False
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possible_checkpoint_names = [
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transformers.modeling_utils.WEIGHTS_NAME,
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"model.safetensors",
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]
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for possible_checkpoint_name in possible_checkpoint_names:
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try:
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shutil.move(
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os.path.realpath(
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huggingface_hub.hf_hub_download(
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self.model_name,
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possible_checkpoint_name,
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revision=utils.koboldai_vars.revision,
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cache_dir="cache",
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local_files_only=True,
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legacy_cache_layout=False,
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)
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),
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os.path.join(
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self.get_local_model_path(
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ignore_existance=True
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),
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possible_checkpoint_name,
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),
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)
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any_success = True
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except Exception:
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pass
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if not any_success:
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raise RuntimeError(f"Couldn't find any of {possible_checkpoint_names} in cache for {self.model_name} @ '{utils.koboldai_vars.revisison}'")
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else:
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# Handle saving sharded models
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with open(utils.from_pretrained_index_filename) as f:
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map_data = json.load(f)
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filenames = set(map_data["weight_map"].values())
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# Save the pytorch_model.bin.index.json of a sharded model
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shutil.move(
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os.path.realpath(utils.from_pretrained_index_filename),
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os.path.join(
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self.get_local_model_path(ignore_existance=True),
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transformers.modeling_utils.WEIGHTS_INDEX_NAME,
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),
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)
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# Then save the pytorch_model-#####-of-#####.bin files
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for filename in filenames:
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shutil.move(
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os.path.realpath(
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huggingface_hub.hf_hub_download(
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self.model_name,
|
||||
filename,
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||||
revision=utils.koboldai_vars.revision,
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||||
cache_dir="cache",
|
||||
local_files_only=True,
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||||
legacy_cache_layout=False,
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||||
)
|
||||
),
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||||
os.path.join(
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self.get_local_model_path(
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ignore_existance=True
|
||||
),
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||||
filename,
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||||
),
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||||
)
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shutil.rmtree("cache/")
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if not self.lazy_load:
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utils.layers_module_names = utils.get_layers_module_names(self.model)
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||||
utils.module_names = list(self.model.state_dict().keys())
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utils.named_buffers = list(self.model.named_buffers(recurse=True))
|
||||
|
||||
if (
|
||||
utils.koboldai_vars.badwordsids is koboldai_settings.badwordsids_default
|
||||
and utils.koboldai_vars.model_type not in ("gpt2", "gpt_neo", "gptj")
|
||||
):
|
||||
utils.koboldai_vars.badwordsids = [
|
||||
[v]
|
||||
for k, v in self.tokenizer.get_vocab().items()
|
||||
if any(c in str(k) for c in "[]")
|
||||
]
|
||||
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||||
self.patch_embedding()
|
||||
|
||||
if utils.koboldai_vars.hascuda:
|
||||
if utils.koboldai_vars.usegpu:
|
||||
# Use just VRAM
|
||||
self.model = self.model.half().to(utils.koboldai_vars.gpu_device)
|
||||
elif utils.koboldai_vars.breakmodel:
|
||||
# Use both RAM and VRAM (breakmodel)
|
||||
if not self.lazy_load:
|
||||
self.breakmodel_device_config(self.model.config)
|
||||
self._move_to_devices()
|
||||
elif breakmodel.disk_blocks > 0:
|
||||
# Use disk
|
||||
self._move_to_devices()
|
||||
else:
|
||||
# Use CPU
|
||||
self.model = self.model.to("cpu").float()
|
||||
elif breakmodel.disk_blocks > 0:
|
||||
self._move_to_devices()
|
||||
else:
|
||||
self.model = self.model.to("cpu").float()
|
||||
|
||||
self.model.kai_model = self
|
||||
utils.koboldai_vars.modeldim = self.get_hidden_size()
|
||||
|
||||
def _get_model(self, location: str, tf_kwargs: Dict):
|
||||
path_4bit, groupsize = prepare_4bit_load(utils.koboldai_vars.custmodpth)
|
||||
print(f"Using 4-bit file: {path_4bit}, groupsize {groupsize}")
|
||||
|
||||
print(f"Trying to load {utils.koboldai_vars.model_type} model in 4-bit")
|
||||
if utils.koboldai_vars.model_type == "gptj":
|
||||
if self.offload_4bit:
|
||||
model = load_quant_offload(gptj_load_quant, utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize, gpu_layers_list)
|
||||
else:
|
||||
model = gptj_load_quant(utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize)
|
||||
elif utils.koboldai_vars.model_type == "gpt_neox":
|
||||
if self.offload_4bit:
|
||||
model = load_quant_offload(gptneox_load_quant, utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize, gpu_layers_list)
|
||||
else:
|
||||
model = gptneox_load_quant(utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize)
|
||||
elif utils.koboldai_vars.model_type == "llama":
|
||||
if self.offload_4bit:
|
||||
model = load_quant_offload(llama_load_quant, utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize, gpu_layers_list)
|
||||
else:
|
||||
model = llama_load_quant(utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize)
|
||||
elif utils.koboldai_vars.model_type == "opt":
|
||||
if self.offload_4bit:
|
||||
model = load_quant_offload(opt_load_quant, utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize, gpu_layers_list)
|
||||
else:
|
||||
model = opt_load_quant(utils.koboldai_vars.custmodpth, path_4bit, 4, groupsize)
|
||||
else:
|
||||
raise RuntimeError(f"4-bit load failed. Model type {utils.koboldai_vars.model_type} not supported in 4-bit")
|
||||
|
||||
return model.half()
|
||||
|
||||
def _get_tokenizer(self, location: str):
|
||||
if utils.koboldai_vars.model_type == "llama":
|
||||
tokenizer = LlamaTokenizer.from_pretrained(utils.koboldai_vars.custmodpth)
|
||||
else:
|
||||
tokenizer = AutoTokenizer.from_pretrained(utils.koboldai_vars.custmodpth)
|
||||
|
||||
return tokenizer
|
||||
Reference in New Issue
Block a user