henk717
GitHub
commit
6347044b91
|
|
@ -29,6 +29,8 @@ dependencies:
|
|||
- --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
|
||||
- torch==2.1.0a0; sys_platform == 'linux'
|
||||
- intel-extension-for-pytorch==2.1.10+xpu; sys_platform == 'linux'
|
||||
- bigdl-llm
|
||||
- bigdl_core_xe
|
||||
- openvino
|
||||
- onnxruntime-openvino
|
||||
- flask-cloudflared==0.0.10
|
||||
|
|
|
|||
|
|
@ -0,0 +1,341 @@
|
|||
from __future__ import annotations
|
||||
try:
|
||||
import os
|
||||
import json
|
||||
import shutil
|
||||
import traceback
|
||||
from typing import Dict
|
||||
|
||||
import torch
|
||||
from torch.nn import Embedding
|
||||
from transformers.utils import WEIGHTS_NAME, WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF_WEIGHTS_NAME, FLAX_WEIGHTS_NAME, FLAX_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME
|
||||
|
||||
import utils
|
||||
from logger import logger
|
||||
from modeling.inference_models.hf_torch import HFTorchInferenceModel
|
||||
|
||||
from bigdl.llm.transformers import AutoModelForCausalLM
|
||||
|
||||
load_failed = False
|
||||
except Exception:
|
||||
load_failed = True
|
||||
|
||||
model_backend_name = "BigDL LLM"
|
||||
model_backend_type = "Huggingface" #This should be a generic name in case multiple model backends are compatible (think Hugging Face Custom and Basic Hugging Face)
|
||||
|
||||
class model_backend(HFTorchInferenceModel):
|
||||
def __init__(self) -> None:
|
||||
super().__init__()
|
||||
self.lazy_load = False
|
||||
self.nobreakmodel = True
|
||||
self.disable = load_failed
|
||||
self.has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
|
||||
|
||||
def _get_model(self, location: str, tf_kwargs: Dict):
|
||||
tf_kwargs["revision"] = utils.koboldai_vars.revision
|
||||
tf_kwargs["cache_dir"] = "cache"
|
||||
tf_kwargs["load_in_4bit"] = True
|
||||
|
||||
tf_kwargs.pop("low_cpu_mem_usage", None)
|
||||
|
||||
# Try to determine model type from either AutoModel or falling back to legacy
|
||||
try:
|
||||
model = AutoModelForCausalLM.from_pretrained(
|
||||
location,
|
||||
offload_folder="accelerate-disk-cache",
|
||||
torch_dtype=self._get_target_dtype(),
|
||||
**tf_kwargs,
|
||||
)
|
||||
|
||||
# We need to move the model to the desired device
|
||||
if (not self.usegpu) or (torch.cuda.device_count() <= 0 and not self.has_xpu):
|
||||
model = model.to("cpu")
|
||||
elif self.has_xpu:
|
||||
model = model.to("xpu")
|
||||
else:
|
||||
model = model.to("cuda")
|
||||
|
||||
return model
|
||||
except Exception as e:
|
||||
traceback_string = traceback.format_exc().lower()
|
||||
|
||||
if "out of memory" in traceback_string:
|
||||
raise RuntimeError(
|
||||
"One of your GPUs ran out of memory when KoboldAI tried to load your model."
|
||||
)
|
||||
|
||||
# Model corrupted or serious loading problem. Stop here.
|
||||
if "invalid load key" in traceback_string:
|
||||
logger.error("Invalid load key! Aborting.")
|
||||
raise
|
||||
|
||||
if utils.args.panic:
|
||||
raise
|
||||
|
||||
logger.warning(f"Failed to load model: {e}")
|
||||
logger.debug(traceback.format_exc())
|
||||
|
||||
# Function to patch transformers to use our soft prompt
|
||||
def patch_embedding(self) -> None:
|
||||
if getattr(Embedding, "_koboldai_patch_causallm_model", None):
|
||||
Embedding._koboldai_patch_causallm_model = self.model
|
||||
return
|
||||
|
||||
old_embedding_call = Embedding.__call__
|
||||
|
||||
kai_model = self
|
||||
|
||||
def new_embedding_call(self, input_ids, *args, **kwargs):
|
||||
# Don't touch embeddings for models other than the core inference model (that's us!)
|
||||
if (
|
||||
Embedding._koboldai_patch_causallm_model.get_input_embeddings()
|
||||
is not self
|
||||
):
|
||||
return old_embedding_call(self, input_ids, *args, **kwargs)
|
||||
|
||||
assert input_ids is not None
|
||||
|
||||
if utils.koboldai_vars.sp is not None:
|
||||
shifted_input_ids = input_ids - kai_model.model.vocab_size
|
||||
|
||||
input_ids.clamp_(max=kai_model.model.config.vocab_size - 1)
|
||||
inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs)
|
||||
|
||||
if utils.koboldai_vars.sp is not None:
|
||||
utils.koboldai_vars.sp = utils.koboldai_vars.sp.to(
|
||||
inputs_embeds.dtype
|
||||
).to(inputs_embeds.device)
|
||||
inputs_embeds = torch.where(
|
||||
(shifted_input_ids >= 0)[..., None],
|
||||
utils.koboldai_vars.sp[shifted_input_ids.clamp(min=0)],
|
||||
inputs_embeds,
|
||||
)
|
||||
|
||||
return inputs_embeds
|
||||
|
||||
Embedding.__call__ = new_embedding_call
|
||||
Embedding._koboldai_patch_causallm_model = self.model
|
||||
|
||||
def is_valid(self, model_name, model_path, menu_path):
|
||||
base_is_valid = super().is_valid(model_name, model_path, menu_path)
|
||||
path = False
|
||||
gen_path = "models/{}".format(model_name.replace('/', '_'))
|
||||
if model_path is not None and os.path.exists(model_path):
|
||||
path = model_path
|
||||
elif os.path.exists(gen_path):
|
||||
path = gen_path
|
||||
|
||||
fnames = [WEIGHTS_NAME, WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, TF2_WEIGHTS_INDEX_NAME, TF_WEIGHTS_NAME, FLAX_WEIGHTS_NAME, FLAX_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, SAFE_WEIGHTS_INDEX_NAME]
|
||||
|
||||
return base_is_valid and any(os.path.exists(os.path.join(path, fname)) for fname in fnames)
|
||||
|
||||
def _initialize_model(self):
|
||||
return
|
||||
|
||||
def get_requested_parameters(self, model_name, model_path, menu_path, parameters = {}):
|
||||
return super().get_requested_parameters(model_name, model_path, menu_path, parameters)
|
||||
|
||||
def set_input_parameters(self, parameters):
|
||||
super().set_input_parameters(parameters)
|
||||
self.usegpu = parameters['use_gpu'] if 'use_gpu' in parameters else False
|
||||
|
||||
def _load(self, save_model: bool, initial_load: bool) -> None:
|
||||
utils.koboldai_vars.allowsp = True
|
||||
|
||||
# Make model path the same as the model name to make this consistent
|
||||
# with the other loading method if it isn't a known model type. This
|
||||
# code is not just a workaround for below, it is also used to make the
|
||||
# behavior consistent with other loading methods - Henk717
|
||||
# if utils.koboldai_vars.model not in ["NeoCustom", "GPT2Custom"]:
|
||||
# utils.koboldai_vars.custmodpth = utils.koboldai_vars.model
|
||||
|
||||
if self.model_name == "NeoCustom":
|
||||
self.model_name = os.path.basename(os.path.normpath(self.path))
|
||||
utils.koboldai_vars.model = self.model_name
|
||||
|
||||
# If we specify a model and it's in the root directory, we need to move
|
||||
# it to the models directory (legacy folder structure to new)
|
||||
if self.get_local_model_path(legacy=True):
|
||||
shutil.move(
|
||||
self.get_local_model_path(legacy=True, ignore_existance=True),
|
||||
self.get_local_model_path(ignore_existance=True),
|
||||
)
|
||||
|
||||
self.init_model_config()
|
||||
|
||||
tf_kwargs = {
|
||||
"low_cpu_mem_usage": True,
|
||||
"use_cache": True # Workaround for models that accidentally turn cache to false
|
||||
}
|
||||
|
||||
|
||||
if self.model_type == "llama":
|
||||
tf_kwargs.update({
|
||||
"pretraining_tp": 1 # Workaround recommended by HF to fix their mistake on the config.json tuners adopted
|
||||
})
|
||||
|
||||
logger.debug(
|
||||
"hasgpu: {}".format(
|
||||
utils.koboldai_vars.hascuda,
|
||||
)
|
||||
)
|
||||
|
||||
# Download model from Huggingface if it does not exist, otherwise load locally
|
||||
if self.get_local_model_path():
|
||||
# Model is stored locally, load it.
|
||||
self.model = self._get_model(self.get_local_model_path(), tf_kwargs)
|
||||
self.tokenizer = self._get_tokenizer(self.get_local_model_path())
|
||||
else:
|
||||
# Model not stored locally, we need to download it.
|
||||
|
||||
# _rebuild_tensor patch for casting dtype and supporting LazyTensors
|
||||
old_rebuild_tensor = torch._utils._rebuild_tensor
|
||||
|
||||
def new_rebuild_tensor(
|
||||
storage: torch.Storage,
|
||||
storage_offset,
|
||||
shape,
|
||||
stride,
|
||||
):
|
||||
dtype = storage.dtype
|
||||
if dtype is torch.float32 and len(shape) >= 2:
|
||||
utils.koboldai_vars.fp32_model = True
|
||||
return old_rebuild_tensor(storage, storage_offset, shape, stride)
|
||||
|
||||
torch._utils._rebuild_tensor = new_rebuild_tensor
|
||||
self.model = self._get_model(self.model_name, tf_kwargs)
|
||||
self.tokenizer = self._get_tokenizer(self.model_name)
|
||||
torch._utils._rebuild_tensor = old_rebuild_tensor
|
||||
|
||||
if save_model:
|
||||
self.tokenizer.save_pretrained(
|
||||
self.get_local_model_path(ignore_existance=True)
|
||||
)
|
||||
|
||||
if utils.koboldai_vars.fp32_model:
|
||||
# Use save_pretrained to convert fp32 models to fp16,
|
||||
# unless we are using disk cache because save_pretrained
|
||||
# is not supported in that case
|
||||
self.model = self.model.half()
|
||||
self.model.save_pretrained(
|
||||
self.get_local_model_path(ignore_existance=True),
|
||||
max_shard_size="500MiB",
|
||||
)
|
||||
|
||||
else:
|
||||
# For fp16 models, we can just copy the model files directly
|
||||
import transformers.configuration_utils
|
||||
import transformers.modeling_utils
|
||||
import transformers.file_utils
|
||||
import huggingface_hub
|
||||
|
||||
# Save the config.json
|
||||
shutil.move(
|
||||
os.path.realpath(
|
||||
huggingface_hub.hf_hub_download(
|
||||
self.model_name,
|
||||
transformers.configuration_utils.CONFIG_NAME,
|
||||
revision=utils.koboldai_vars.revision,
|
||||
cache_dir="cache",
|
||||
local_files_only=True,
|
||||
legacy_cache_layout=False,
|
||||
)
|
||||
),
|
||||
os.path.join(
|
||||
self.get_local_model_path(ignore_existance=True),
|
||||
transformers.configuration_utils.CONFIG_NAME,
|
||||
),
|
||||
)
|
||||
|
||||
if utils.num_shards is None:
|
||||
# Save the pytorch_model.bin or model.safetensors of an unsharded model
|
||||
any_success = False
|
||||
possible_checkpoint_names = [
|
||||
transformers.modeling_utils.WEIGHTS_NAME,
|
||||
"model.safetensors",
|
||||
]
|
||||
|
||||
for possible_checkpoint_name in possible_checkpoint_names:
|
||||
try:
|
||||
shutil.move(
|
||||
os.path.realpath(
|
||||
huggingface_hub.hf_hub_download(
|
||||
self.model_name,
|
||||
possible_checkpoint_name,
|
||||
revision=utils.koboldai_vars.revision,
|
||||
cache_dir="cache",
|
||||
local_files_only=True,
|
||||
legacy_cache_layout=False,
|
||||
)
|
||||
),
|
||||
os.path.join(
|
||||
self.get_local_model_path(
|
||||
ignore_existance=True
|
||||
),
|
||||
possible_checkpoint_name,
|
||||
),
|
||||
)
|
||||
any_success = True
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
if not any_success:
|
||||
raise RuntimeError(
|
||||
f"Couldn't find any of {possible_checkpoint_names} in cache for {self.model_name} @ '{utils.koboldai_vars.revisison}'"
|
||||
)
|
||||
else:
|
||||
# Handle saving sharded models
|
||||
|
||||
with open(utils.from_pretrained_index_filename) as f:
|
||||
map_data = json.load(f)
|
||||
filenames = set(map_data["weight_map"].values())
|
||||
# Save the pytorch_model.bin.index.json of a sharded model
|
||||
shutil.move(
|
||||
os.path.realpath(utils.from_pretrained_index_filename),
|
||||
os.path.join(
|
||||
self.get_local_model_path(ignore_existance=True),
|
||||
transformers.modeling_utils.WEIGHTS_INDEX_NAME,
|
||||
),
|
||||
)
|
||||
# Then save the pytorch_model-#####-of-#####.bin files
|
||||
for filename in filenames:
|
||||
shutil.move(
|
||||
os.path.realpath(
|
||||
huggingface_hub.hf_hub_download(
|
||||
self.model_name,
|
||||
filename,
|
||||
revision=utils.koboldai_vars.revision,
|
||||
cache_dir="cache",
|
||||
local_files_only=True,
|
||||
legacy_cache_layout=False,
|
||||
)
|
||||
),
|
||||
os.path.join(
|
||||
self.get_local_model_path(ignore_existance=True),
|
||||
filename,
|
||||
),
|
||||
)
|
||||
shutil.rmtree("cache/")
|
||||
|
||||
self.patch_embedding()
|
||||
|
||||
self.model.kai_model = self
|
||||
utils.koboldai_vars.modeldim = self.get_hidden_size()
|
||||
|
||||
def _save_settings(self):
|
||||
with open(
|
||||
"settings/{}.hf_bigdl.model_backend.settings".format(
|
||||
self.model_name.replace("/", "_")
|
||||
),
|
||||
"w",
|
||||
) as f:
|
||||
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="",
|
||||
)
|
||||
|
|
@ -15,7 +15,6 @@ then
|
|||
source $ONEAPI_ROOT/setvars.sh
|
||||
fi
|
||||
|
||||
export LD_PRELOAD=/usr/lib/libstdc++.so
|
||||
export NEOReadDebugKeys=1
|
||||
export ClDeviceGlobalMemSizeAvailablePercent=100
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue