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KoboldAI-Client
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Enable `low_cpu_mem_usage`

This commit is contained in:
Gnome Ann 2021-11-25 18:09:25 -05:00
parent 25c9be5d02
commit 32e1d4a7a8
1 changed files with 17 additions and 9 deletions
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26
aiserver.py
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@ -14,7 +14,8 @@ from tkinter import messagebox
import json import json
import collections import collections
import zipfile import zipfile
from typing import Union, Dict, Set, List import packaging
from typing import Any, Union, Dict, Set, List
import requests import requests
import html import html
@ -541,6 +542,7 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END)) print("{0}Initializing transformers, please wait...{1}".format(colors.PURPLE, colors.END))
from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM from transformers import StoppingCriteria, GPT2Tokenizer, GPT2LMHeadModel, GPTNeoForCausalLM, GPTNeoModel, AutoModelForCausalLM
import transformers.generation_utils import transformers.generation_utils
from transformers import __version__ as transformers_version
# Patch transformers to use our soft prompt # Patch transformers to use our soft prompt
def patch_causallm(cls): def patch_causallm(cls):
@ -702,14 +704,20 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
except: except:
return int(model.lm_head.in_features) return int(model.lm_head.in_features)
def maybe_low_cpu_mem_usage() -> Dict[str, Any]:
if(packaging.version.parse(transformers_version) < packaging.version.parse("4.11.0")):
print(f"\nWARNING: Please upgrade to transformers 4.11.0 for lower RAM usage. You have transformers {transformers_version}.", file=sys.stderr)
return {}
return {"low_cpu_mem_usage": True}
# If custom GPT Neo model was chosen # If custom GPT Neo model was chosen
if(vars.model == "NeoCustom"): if(vars.model == "NeoCustom"):
model_config = open(vars.custmodpth + "/config.json", "r") model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config) js = json.load(model_config)
if("model_type" in js): if("model_type" in js):
model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/") model = AutoModelForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
else: else:
model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/") model = GPTNeoForCausalLM.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/") tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/")
# Is CUDA available? If so, use GPU, otherwise fall back to CPU # Is CUDA available? If so, use GPU, otherwise fall back to CPU
@ -727,8 +735,8 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
elif(vars.model == "GPT2Custom"): elif(vars.model == "GPT2Custom"):
model_config = open(vars.custmodpth + "/config.json", "r") model_config = open(vars.custmodpth + "/config.json", "r")
js = json.load(model_config) js = json.load(model_config)
model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/") model = GPT2LMHeadModel.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/") tokenizer = GPT2Tokenizer.from_pretrained(vars.custmodpth, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
# Is CUDA available? If so, use GPU, otherwise fall back to CPU # Is CUDA available? If so, use GPU, otherwise fall back to CPU
if(vars.hascuda and vars.usegpu): if(vars.hascuda and vars.usegpu):
@ -742,20 +750,20 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
tokenizer = GPT2Tokenizer.from_pretrained(vars.model, cache_dir="cache/") tokenizer = GPT2Tokenizer.from_pretrained(vars.model, cache_dir="cache/")
if(vars.hascuda): if(vars.hascuda):
if(vars.usegpu): if(vars.usegpu):
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/") model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
model = model.half().to(0) model = model.half().to(0)
generator = model.generate generator = model.generate
elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel) elif(vars.breakmodel): # Use both RAM and VRAM (breakmodel)
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/") model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
device_config(model) device_config(model)
else: else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/") model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate generator = model.generate
else: else:
model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/") model = AutoModelForCausalLM.from_pretrained(vars.model, cache_dir="cache/", **maybe_low_cpu_mem_usage())
vars.modeldim = get_hidden_size_from_model(model) vars.modeldim = get_hidden_size_from_model(model)
generator = model.generate generator = model.generate