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Delete basic 4bit

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And add code to handle dangling __pycache__s
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somebody
2023-07-17 18:16:03 -05:00
parent 5c3a8e295a
commit 1637760fa1
2 changed files with 38 additions and 367 deletions
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17
aiserver.py
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@@ -633,7 +633,22 @@ model_backend_type_crosswalk = {}
PRIORITIZED_BACKEND_MODULES = ["generic_hf_torch"]
for module in os.listdir("./modeling/inference_models"):
if not os.path.isfile(os.path.join("./modeling/inference_models",module)) and module != '__pycache__':
if module == '__pycache__':
continue
module_path = os.path.join("modeling/inference_models", module)
if not os.path.isdir(module_path):
# Drop-in modules must be folders
continue
if os.listdir(module_path) == ["__pycache__"]:
# Delete backends which have been deleted upstream. As __pycache__
# folders aren't tracked, they'll stick around until we zap em'
assert len(os.listdir(module_path)) == 1
logger.info(f"Deleting old backend {module}")
shutil.rmtree(module_path)
continue
try:
backend_code = importlib.import_module('modeling.inference_models.{}.class'.format(module))
backend_name = backend_code.model_backend_name

344
modeling/inference_models/basic_hf_4bit/class.py
View File

@@ -1,344 +0,0 @@
from __future__ import annotations
import gc
import os
import shutil
import time
import warnings
from typing import List, Optional, Union
import torch
import transformers
from transformers import AutoConfig, AutoModelForCausalLM, LogitsProcessorList
import utils
from logger import logger
import koboldai_settings
from modeling import warpers
from modeling.inference_model import (
GenerationResult,
GenerationSettings,
InferenceModel,
use_core_manipulations,
)
model_backend_name = "Basic Huggingface 4-bit"
model_backend_type = "Huggingface"
class model_backend(InferenceModel):
# Model backends must inherit from InferenceModel.
def __init__(self) -> None:
super().__init__()
import importlib
dependency_exists = importlib.util.find_spec("bitsandbytes")
if dependency_exists:
self.model_name = "Basic Huggingface"
self.path = None
else:
logger.warning("Bitsandbytes is not installed, you can not use Huggingface models in 4-bit")
self.disable = True
def is_valid(self, model_name, model_path, menu_path):
try:
if model_path is not None and os.path.exists(model_path):
self.model_config = AutoConfig.from_pretrained(model_path)
elif os.path.exists("models/{}".format(model_name.replace("/", "_"))):
self.model_config = AutoConfig.from_pretrained(
"models/{}".format(model_name.replace("/", "_")),
revision=utils.koboldai_vars.revision,
cache_dir="cache",
)
else:
self.model_config = AutoConfig.from_pretrained(
model_name, revision=utils.koboldai_vars.revision, cache_dir="cache"
)
return True
except:
return False
def get_requested_parameters(
self, model_name: str, model_path: str, menu_path: str, parameters: dict = {}
):
requested_parameters = []
if model_name == "customhuggingface":
requested_parameters.append(
{
"uitype": "text",
"unit": "text",
"label": "Huggingface Model Name",
"id": "custom_model_name",
"default": parameters.get("custom_model_name", ""),
"check": {"value": "", "check": "!="},
"tooltip": "Model name from https://huggingface.co/",
"menu_path": "",
"refresh_model_inputs": True,
"extra_classes": "",
}
)
if model_name != "customhuggingface" or "custom_model_name" in parameters:
model_name = parameters.get("custom_model_name", None) or model_name
alt_model_path = self.get_local_model_path()
if model_path and os.path.exists(model_path):
# Use passed model path
self.model_config = AutoConfig.from_pretrained(model_path)
elif alt_model_path:
# Use known model path
self.model_config = AutoConfig.from_pretrained(
alt_model_path,
revision=utils.koboldai_vars.revision,
cache_dir="cache",
)
else:
# No model path locally, we'll probably have to download
self.model_config = AutoConfig.from_pretrained(
model_name, revision=utils.koboldai_vars.revision, cache_dir="cache"
)
return requested_parameters
def set_input_parameters(self, parameters: dict):
self.model_name = parameters.get("custom_model_name", parameters["id"])
self.path = parameters.get("path", None)
logger.info(parameters)
def unload(self):
if hasattr(self, "model"):
self.model = None
if hasattr(self, "tokenizer"):
self.tokenizer = None
if hasattr(self, "model_config"):
self.model_config = None
with torch.no_grad():
with warnings.catch_warnings():
warnings.filterwarnings(
"ignore", message="torch.distributed.reduce_op is deprecated"
)
for tensor in gc.get_objects():
try:
if torch.is_tensor(tensor):
tensor.set_(
torch.tensor(
(), device=tensor.device, dtype=tensor.dtype
)
)
except:
pass
gc.collect()
try:
with torch.no_grad():
torch.cuda.empty_cache()
except:
pass
def _load(self, save_model: bool, initial_load: bool) -> None:
utils.koboldai_vars.allowsp = False
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()
self.model = AutoModelForCausalLM.from_pretrained(
self.get_local_model_path(), low_cpu_mem_usage=True, device_map="auto", load_in_4bit=True
)
self.tokenizer = self._get_tokenizer(self.get_local_model_path())
self.model.kai_model = self
self.badwordsids = koboldai_settings.badwordsids_default
utils.koboldai_vars.modeldim = self.model.get_input_embeddings().embedding_dim
# Patch Huggingface stuff to use our samplers
class KoboldLogitsWarperList(LogitsProcessorList):
def __call__(
_self, # Unused
input_ids: torch.LongTensor,
scores: torch.FloatTensor,
*args,
**kwargs,
):
# Kobold sampling is done here.
scores = self._apply_warpers(scores=scores, input_ids=input_ids)
# Things like Lua integration, phrase bias, and probability visualization are done here.
for processor in self.logits_processors:
scores = processor(self, scores=scores, input_ids=input_ids)
assert (
scores is not None
), f"Scores are None; processor '{processor}' is to blame"
return scores
def new_sample(self, *args, **kwargs):
assert kwargs.pop("logits_warper", None) is not None
kwargs["logits_warper"] = KoboldLogitsWarperList()
if utils.koboldai_vars.newlinemode in ["s", "ns"]:
kwargs["eos_token_id"] = -1
kwargs.setdefault("pad_token_id", 2)
return new_sample.old_sample(self, *args, **kwargs)
new_sample.old_sample = transformers.GenerationMixin.sample
use_core_manipulations.sample = new_sample
def _apply_warpers(
self, scores: torch.Tensor, input_ids: torch.Tensor
) -> torch.Tensor:
"""Applies samplers/warpers to the given scores, returning the altered scores.
Args:
scores (torch.Tensor): The original scores.
input_ids (torch.Tensor): The input token sequence.
Returns:
torch.Tensor: The altered scores.
"""
warpers.update_settings()
for sid in utils.koboldai_vars.sampler_order:
warper = warpers.Warper.from_id(sid)
if not warper.value_is_valid():
continue
if warper == warpers.RepetitionPenalty:
# Rep pen needs access to input tokens to decide what to penalize
scores = warper.torch(scores, input_ids=input_ids)
else:
scores = warper.torch(scores)
assert scores is not None, f"Scores are None; warper '{warper}' is to blame"
return scores
def _raw_generate(
self,
prompt_tokens: Union[List[int], torch.Tensor],
max_new: int,
gen_settings: GenerationSettings,
single_line: bool = False,
batch_count: int = 1,
seed: Optional[int] = None,
**kwargs,
) -> GenerationResult:
if not isinstance(prompt_tokens, torch.Tensor):
gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
else:
gen_in = prompt_tokens
device = self.get_auxilary_device()
gen_in = gen_in.to(device)
additional_bad_words_ids = [self.tokenizer.encode("\n")] if single_line else []
if seed is not None:
torch.manual_seed(seed)
with torch.no_grad():
start_time = time.time()
genout = self.model.generate(
gen_in,
do_sample=True,
max_length=min(
len(prompt_tokens) + max_new, utils.koboldai_vars.max_length
),
repetition_penalty=1.0,
bad_words_ids=self.badwordsids + additional_bad_words_ids,
use_cache=True,
num_return_sequences=batch_count,
)
logger.debug(
"torch_raw_generate: run generator {}s".format(time.time() - start_time)
)
return GenerationResult(
self,
out_batches=genout,
prompt=prompt_tokens,
is_whole_generation=False,
output_includes_prompt=True,
)
def get_local_model_path(
self, legacy: bool = False, ignore_existance: bool = False
) -> Optional[str]:
"""
Returns a string of the model's path locally, or None if it is not downloaded.
If ignore_existance is true, it will always return a path.
"""
if self.path is not None:
if os.path.exists(self.path):
return self.path
if self.model_name in [
"NeoCustom",
"GPT2Custom",
"TPUMeshTransformerGPTJ",
"TPUMeshTransformerGPTNeoX",
]:
model_path = self.path
assert model_path
# Path can be absolute or relative to models directory
if os.path.exists(model_path):
return model_path
model_path = os.path.join("models", model_path)
try:
assert os.path.exists(model_path)
except AssertionError:
logger.error(
f"Custom model does not exist at '{utils.koboldai_vars.custmodpth}' or '{model_path}'."
)
raise
return model_path
basename = self.model_name.replace("/", "_")
if legacy:
ret = basename
else:
ret = os.path.join("models", basename)
if os.path.isdir(ret) or ignore_existance:
return ret
return None
def init_model_config(self) -> None:
# Get the model_type from the config or assume a model type if it isn't present
try:
self.model_config = AutoConfig.from_pretrained(
self.get_local_model_path() or self.model_name,
revision=utils.koboldai_vars.revision,
cache_dir="cache",
)
self.model_type = self.model_config.model_type
except ValueError:
self.model_type = {
"NeoCustom": "gpt_neo",
"GPT2Custom": "gpt2",
}.get(self.model)
if not self.model_type:
logger.warning(
"No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)"
)
self.model_type = "gpt_neo"