mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
Moved model backends to separate folders
added some model backend settings save/load
This commit is contained in:
ebolam
276
modeling/inference_models/generic_hf_torch/class.py
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276
modeling/inference_models/generic_hf_torch/class.py
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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 shutil
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from typing import Union
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from transformers import AutoModelForCausalLM, GPTNeoForCausalLM, GPT2LMHeadModel
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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
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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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model_backend_name = "Huggingface"
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class model_backend(HFTorchInferenceModel):
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def _initialize_model(self):
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return
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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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# If we specify a model and it's in the root directory, we need to move
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# it to the models directory (legacy folder structure to new)
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if self.get_local_model_path(legacy=True):
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shutil.move(
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self.get_local_model_path(legacy=True, ignore_existance=True),
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self.get_local_model_path(ignore_existance=True),
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)
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self.init_model_config()
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tf_kwargs = {
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"low_cpu_mem_usage": True,
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}
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if utils.koboldai_vars.model_type == "gpt2":
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# We must disable low_cpu_mem_usage and if using a GPT-2 model
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# because GPT-2 is not compatible with this feature yet.
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tf_kwargs.pop("low_cpu_mem_usage", None)
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# Also, lazy loader doesn't support GPT-2 models
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self.lazy_load = False
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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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logger.debug("lazy_load: {} hascuda: {} breakmodel: {} nobreakmode: {}".format(self.lazy_load, utils.koboldai_vars.hascuda, self.breakmodel, self.nobreakmodel))
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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 self.breakmodel
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and not self.nobreakmodel
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):
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logger.debug("loading breakmodel")
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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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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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except Exception as e:
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logger.warning(f"Gave up on lazy loading due to {e}")
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self.lazy_load = False
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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,
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filename,
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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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filename,
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),
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)
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shutil.rmtree("cache/")
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self.patch_embedding()
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if utils.koboldai_vars.hascuda:
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if utils.koboldai_vars.usegpu:
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# Use just VRAM
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self.model = self.model.half().to(utils.koboldai_vars.gpu_device)
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elif utils.koboldai_vars.breakmodel:
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# Use both RAM and VRAM (breakmodel)
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if not self.lazy_load:
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self.breakmodel_device_config(self.model.config)
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self._move_to_devices()
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elif breakmodel.disk_blocks > 0:
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# Use disk
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self._move_to_devices()
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else:
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# Use CPU
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self.model = self.model.to("cpu").float()
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elif breakmodel.disk_blocks > 0:
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self._move_to_devices()
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else:
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self.model = self.model.to("cpu").float()
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self.model.kai_model = self
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utils.koboldai_vars.modeldim = self.get_hidden_size()
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def _save_settings(self):
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with open("settings/{}.generic_hf_torch.model_backend.settings".format(self.model_name.replace("/", "_")), "w") as f:
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json.dump({"layers": self.layers if 'layers' in vars(self) else [], "disk_layers": self.disk_layers if 'disk_layers' in vars(self) else 0}, f, indent="")
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