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Prevent unwanted log messages from modules

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oobabooga 2023-05-21 22:42:34 -03:00
parent fb91406e93
commit e116d31180
20 changed files with 120 additions and 111 deletions
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6
extensions/llava/script.py
View File

@ -1,8 +1,8 @@
import logging
import gradio as gr
from modules.logging_colors import logger
def ui():
gr.Markdown("### This extension is deprecated, use \"multimodal\" extension instead")
logging.error("LLaVA extension is deprecated, use \"multimodal\" extension instead")
logger.error("LLaVA extension is deprecated, use \"multimodal\" extension instead")

6
extensions/multimodal/multimodal_embedder.py
View File

@ -1,5 +1,4 @@
import base64
import logging
import re
from dataclasses import dataclass
from io import BytesIO
@ -10,6 +9,7 @@ from PIL import Image
from extensions.multimodal.pipeline_loader import load_pipeline
from modules import shared
from modules.logging_colors import logger
from modules.text_generation import encode, get_max_prompt_length
@ -26,7 +26,7 @@ class MultimodalEmbedder:
def __init__(self, params: dict):
pipeline, source = load_pipeline(params)
self.pipeline = pipeline
logging.info(f'Multimodal: loaded pipeline {self.pipeline.name()} from pipelines/{source} ({self.pipeline.__class__.__name__})')
logger.info(f'Multimodal: loaded pipeline {self.pipeline.name()} from pipelines/{source} ({self.pipeline.__class__.__name__})')
def _split_prompt(self, prompt: str, load_images: bool = False) -> List[PromptPart]:
"""Splits a prompt into a list of `PromptParts` to separate image data from text.
@ -138,7 +138,7 @@ class MultimodalEmbedder:
# notify user if we truncated an image
if removed_images > 0:
logging.warning(f"Multimodal: removed {removed_images} image(s) from prompt. Try decreasing max_new_tokens if generation is broken")
logger.warning(f"Multimodal: removed {removed_images} image(s) from prompt. Try decreasing max_new_tokens if generation is broken")
return encoded

8
extensions/multimodal/pipeline_loader.py
View File

@ -1,4 +1,3 @@
import logging
import traceback
from importlib import import_module
from pathlib import Path
@ -6,6 +5,7 @@ from typing import Tuple
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
from modules import shared
from modules.logging_colors import logger
def _get_available_pipeline_modules():
@ -21,8 +21,8 @@ def load_pipeline(params: dict) -> Tuple[AbstractMultimodalPipeline, str]:
try:
pipeline_modules[name] = import_module(f'extensions.multimodal.pipelines.{name}.pipelines')
except:
logging.warning(f'Failed to get multimodal pipelines from {name}')
logging.warning(traceback.format_exc())
logger.warning(f'Failed to get multimodal pipelines from {name}')
logger.warning(traceback.format_exc())
if shared.args.multimodal_pipeline is not None:
for k in pipeline_modules:
@ -48,5 +48,5 @@ def load_pipeline(params: dict) -> Tuple[AbstractMultimodalPipeline, str]:
log = f'Multimodal - ERROR: Failed to load multimodal pipeline "{shared.args.multimodal_pipeline}", available pipelines are: {available}.'
else:
log = f'Multimodal - ERROR: Failed to determine multimodal pipeline for model {shared.args.model}, please select one manually using --multimodal-pipeline [PIPELINE]. Available pipelines are: {available}.'
logging.critical(f'{log} Please specify a correct pipeline, or disable the extension')
logger.critical(f'{log} Please specify a correct pipeline, or disable the extension')
raise RuntimeError(f'{log} Please specify a correct pipeline, or disable the extension')

15
extensions/multimodal/pipelines/llava/llava.py
View File

@ -1,16 +1,17 @@
import logging
import time
from abc import abstractmethod
from typing import List, Tuple
import torch
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
from huggingface_hub import hf_hub_download
from modules import shared
from modules.text_generation import encode
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionModel
from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline
from modules import shared
from modules.logging_colors import logger
from modules.text_generation import encode
class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
CLIP_REPO = "openai/clip-vit-large-patch14"
@ -26,11 +27,11 @@ class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
def _load_models(self):
start_ts = time.time()
logging.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
logger.info(f"LLaVA - Loading CLIP from {LLaVA_v0_Pipeline.CLIP_REPO} as {self.clip_dtype} on {self.clip_device}...")
image_processor = CLIPImageProcessor.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype)
vision_tower = CLIPVisionModel.from_pretrained(LLaVA_v0_Pipeline.CLIP_REPO, torch_dtype=self.clip_dtype).to(self.clip_device)
logging.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
logger.info(f"LLaVA - Loading projector from {self.llava_projector_repo()} as {self.projector_dtype} on {self.projector_device}...")
projector_path = hf_hub_download(self.llava_projector_repo(), self.llava_projector_filename())
mm_projector = torch.nn.Linear(*self.llava_projector_shape())
projector_data = torch.load(projector_path)
@ -38,7 +39,7 @@ class LLaVA_v0_Pipeline(AbstractMultimodalPipeline):
mm_projector.bias = torch.nn.Parameter(projector_data['model.mm_projector.bias'].to(dtype=self.projector_dtype), False)
mm_projector = mm_projector.to(self.projector_device)
logging.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
logger.info(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds")
return image_processor, vision_tower, mm_projector
@staticmethod

4
extensions/multimodal/script.py
View File

@ -1,5 +1,4 @@
import base64
import logging
import re
import time
from functools import partial
@ -10,6 +9,7 @@ import torch
from extensions.multimodal.multimodal_embedder import MultimodalEmbedder
from modules import shared
from modules.logging_colors import logger
params = {
"add_all_images_to_prompt": False,
@ -78,7 +78,7 @@ def tokenizer_modifier(state, prompt, input_ids, input_embeds):
return prompt, input_ids, input_embeds
prompt, input_ids, input_embeds, total_embedded = multimodal_embedder.forward(prompt, state, params)
logging.info(f'Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s')
logger.info(f'Embedded {total_embedded} image(s) in {time.time()-start_ts:.2f}s')
return (prompt,
input_ids.unsqueeze(0).to(shared.model.device, dtype=torch.int64),
input_embeds.unsqueeze(0).to(shared.model.device, dtype=shared.model.dtype))

9
extensions/superbooga/chromadb.py
View File

@ -1,13 +1,12 @@
import logging
import chromadb
import posthog
import torch
from chromadb.config import Settings
from sentence_transformers import SentenceTransformer
import chromadb
from chromadb.config import Settings
from modules.logging_colors import logger
logging.info('Intercepting all calls to posthog :)')
logger.info('Intercepting all calls to posthog :)')
posthog.capture = lambda *args, **kwargs: None

6
extensions/superbooga/script.py
View File

@ -1,4 +1,3 @@
import logging
import re
import textwrap
@ -6,6 +5,7 @@ import gradio as gr
from bs4 import BeautifulSoup
from modules import chat, shared
from modules.logging_colors import logger
from .chromadb import add_chunks_to_collector, make_collector
from .download_urls import download_urls
@ -123,14 +123,14 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
if shared.history['internal'][id_][0] != '<|BEGIN-VISIBLE-CHAT|>':
additional_context += make_single_exchange(id_)
logging.warning(f'Adding the following new context:\n{additional_context}')
logger.warning(f'Adding the following new context:\n{additional_context}')
state['context'] = state['context'].strip() + '\n' + additional_context
kwargs['history'] = {
'internal': [shared.history['internal'][i] for i in range(hist_size) if i not in best_ids],
'visible': ''
}
except RuntimeError:
logging.error("Couldn't query the database, moving on...")
logger.error("Couldn't query the database, moving on...")
return chat.generate_chat_prompt(user_input, state, **kwargs)

8
modules/AutoGPTQ_loader.py
View File

@ -1,9 +1,9 @@
import logging
from pathlib import Path
from auto_gptq import AutoGPTQForCausalLM
import modules.shared as shared
from modules.logging_colors import logger
from modules.models import get_max_memory_dict
@ -17,13 +17,13 @@ def load_quantized(model_name):
found = list(path_to_model.glob(f"*{ext}"))
if len(found) > 0:
if len(found) > 1:
logging.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
pt_path = found[-1]
break
if pt_path is None:
logging.error("The model could not be loaded because its checkpoint file in .bin/.pt/.safetensors format could not be located.")
logger.error("The model could not be loaded because its checkpoint file in .bin/.pt/.safetensors format could not be located.")
return
# Define the params for AutoGPTQForCausalLM.from_quantized
@ -35,6 +35,6 @@ def load_quantized(model_name):
'max_memory': get_max_memory_dict()
}
logging.warning(f"The AutoGPTQ params are: {params}")
logger.warning(f"The AutoGPTQ params are: {params}")
model = AutoGPTQForCausalLM.from_quantized(path_to_model, **params)
return model

22
modules/GPTQ_loader.py
View File

@ -1,5 +1,4 @@
import inspect
import logging
import re
import sys
from pathlib import Path
@ -10,14 +9,15 @@ import transformers
from transformers import AutoConfig, AutoModelForCausalLM
import modules.shared as shared
from modules.logging_colors import logger
sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
try:
import llama_inference_offload
except ImportError:
logging.error('Failed to load GPTQ-for-LLaMa')
logging.error('See https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md')
logger.error('Failed to load GPTQ-for-LLaMa')
logger.error('See https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md')
sys.exit(-1)
try:
@ -127,7 +127,7 @@ def find_quantized_model_file(model_name):
found = list(path_to_model.glob(f"*{ext}"))
if len(found) > 0:
if len(found) > 1:
logging.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
logger.warning(f'More than one {ext} model has been found. The last one will be selected. It could be wrong.')
pt_path = found[-1]
break
@ -138,8 +138,8 @@ def find_quantized_model_file(model_name):
# The function that loads the model in modules/models.py
def load_quantized(model_name):
if shared.args.model_type is None:
logging.error("The model could not be loaded because its type could not be inferred from its name.")
logging.error("Please specify the type manually using the --model_type argument.")
logger.error("The model could not be loaded because its type could not be inferred from its name.")
logger.error("Please specify the type manually using the --model_type argument.")
return None
# Select the appropriate load_quant function
@ -148,21 +148,21 @@ def load_quantized(model_name):
load_quant = llama_inference_offload.load_quant
elif model_type in ('llama', 'opt', 'gptj'):
if shared.args.pre_layer:
logging.warning("Ignoring --pre_layer because it only works for llama model type.")
logger.warning("Ignoring --pre_layer because it only works for llama model type.")
load_quant = _load_quant
else:
logging.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
logger.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported")
exit()
# Find the quantized model weights file (.pt/.safetensors)
path_to_model = Path(f'{shared.args.model_dir}/{model_name}')
pt_path = find_quantized_model_file(model_name)
if not pt_path:
logging.error("Could not find the quantized model in .pt or .safetensors format, exiting...")
logger.error("Could not find the quantized model in .pt or .safetensors format, exiting...")
exit()
else:
logging.info(f"Found the following quantized model: {pt_path}")
logger.info(f"Found the following quantized model: {pt_path}")
# qwopqwop200's offload
if model_type == 'llama' and shared.args.pre_layer:
@ -190,7 +190,7 @@ def load_quantized(model_name):
max_memory = accelerate.utils.get_balanced_memory(model)
device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LlamaDecoderLayer"])
logging.info("Using the following device map for the quantized model:", device_map)
logger.info("Using the following device map for the quantized model:", device_map)
# https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model
model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True)

6
modules/LoRA.py
View File

@ -1,10 +1,10 @@
import logging
from pathlib import Path
import torch
from peft import PeftModel
import modules.shared as shared
from modules.logging_colors import logger
def add_lora_to_model(lora_names):
@ -19,7 +19,7 @@ def add_lora_to_model(lora_names):
# Add a LoRA when another LoRA is already present
if len(removed_set) == 0 and len(prior_set) > 0:
logging.info(f"Adding the LoRA(s) named {added_set} to the model...")
logger.info(f"Adding the LoRA(s) named {added_set} to the model...")
for lora in added_set:
shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
@ -31,7 +31,7 @@ def add_lora_to_model(lora_names):
shared.model = shared.model.base_model.model
if len(lora_names) > 0:
logging.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
params = {}
if not shared.args.cpu:
params['dtype'] = shared.model.dtype

18
modules/chat.py
View File

@ -3,7 +3,6 @@ import base64
import copy
import io
import json
import logging
import re
from datetime import datetime
from pathlib import Path
@ -14,6 +13,7 @@ from PIL import Image
import modules.shared as shared
from modules.extensions import apply_extensions
from modules.html_generator import chat_html_wrapper, make_thumbnail
from modules.logging_colors import logger
from modules.text_generation import (generate_reply, get_encoded_length,
get_max_prompt_length)
from modules.utils import replace_all
@ -187,7 +187,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
output = copy.deepcopy(history)
output = apply_extensions('history', output)
if shared.model_name == 'None' or shared.model is None:
logging.error("No model is loaded! Select one in the Model tab.")
logger.error("No model is loaded! Select one in the Model tab.")
yield output
return
@ -278,7 +278,7 @@ def chatbot_wrapper(text, history, state, regenerate=False, _continue=False, loa
def impersonate_wrapper(text, state):
if shared.model_name == 'None' or shared.model is None:
logging.error("No model is loaded! Select one in the Model tab.")
logger.error("No model is loaded! Select one in the Model tab.")
yield ''
return
@ -584,7 +584,7 @@ def upload_character(json_file, img, tavern=False):
img = Image.open(io.BytesIO(img))
img.save(Path(f'characters/{outfile_name}.png'))
logging.info(f'New character saved to "characters/{outfile_name}.json".')
logger.info(f'New character saved to "characters/{outfile_name}.json".')
return outfile_name
@ -608,18 +608,18 @@ def upload_your_profile_picture(img):
else:
img = make_thumbnail(img)
img.save(Path('cache/pfp_me.png'))
logging.info('Profile picture saved to "cache/pfp_me.png"')
logger.info('Profile picture saved to "cache/pfp_me.png"')
def delete_file(path):
if path.exists():
logging.warning(f'Deleting {path}')
logger.warning(f'Deleting {path}')
path.unlink(missing_ok=True)
def save_character(name, greeting, context, picture, filename, instruct=False):
if filename == "":
logging.error("The filename is empty, so the character will not be saved.")
logger.error("The filename is empty, so the character will not be saved.")
return
folder = 'characters' if not instruct else 'characters/instruction-following'
@ -634,11 +634,11 @@ def save_character(name, greeting, context, picture, filename, instruct=False):
with filepath.open('w') as f:
yaml.dump(data, f)
logging.info(f'Wrote {filepath}')
logger.info(f'Wrote {filepath}')
path_to_img = Path(f'{folder}/{filename}.png')
if picture and not instruct:
picture.save(path_to_img)
logging.info(f'Wrote {path_to_img}')
logger.info(f'Wrote {path_to_img}')
elif path_to_img.exists():
delete_file(path_to_img)

6
modules/extensions.py
View File

@ -1,4 +1,3 @@
import logging
import traceback
from functools import partial
@ -6,6 +5,7 @@ import gradio as gr
import extensions
import modules.shared as shared
from modules.logging_colors import logger
state = {}
available_extensions = []
@ -29,7 +29,7 @@ def load_extensions():
for i, name in enumerate(shared.args.extensions):
if name in available_extensions:
if name != 'api':
logging.info(f'Loading the extension "{name}"...')
logger.info(f'Loading the extension "{name}"...')
try:
exec(f"import extensions.{name}.script")
extension = getattr(extensions, name).script
@ -40,7 +40,7 @@ def load_extensions():
state[name] = [True, i]
except:
logging.error(f'Failed to load the extension "{name}".')
logger.error(f'Failed to load the extension "{name}".')
traceback.print_exc()

8
modules/llama_attn_hijack.py
View File

@ -1,4 +1,3 @@
import logging
import math
import sys
from typing import Optional, Tuple
@ -8,21 +7,22 @@ import torch.nn as nn
import transformers.models.llama.modeling_llama
import modules.shared as shared
from modules.logging_colors import logger
if shared.args.xformers:
try:
import xformers.ops
except Exception:
logging.error("xformers not found! Please install it before trying to use it.", file=sys.stderr)
logger.error("xformers not found! Please install it before trying to use it.", file=sys.stderr)
def hijack_llama_attention():
if shared.args.xformers:
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
logging.info("Replaced attention with xformers_attention")
logger.info("Replaced attention with xformers_attention")
elif shared.args.sdp_attention:
transformers.models.llama.modeling_llama.LlamaAttention.forward = sdp_attention_forward
logging.info("Replaced attention with sdp_attention")
logger.info("Replaced attention with sdp_attention")
def xformers_forward(

6
modules/llamacpp_model.py
View File

@ -6,20 +6,20 @@ Documentation:
https://abetlen.github.io/llama-cpp-python/
'''
import logging
import re
from llama_cpp import Llama, LlamaCache
from modules import shared
from modules.callbacks import Iteratorize
from modules.logging_colors import logger
class LlamaCppModel:
def __init__(self):
self.initialized = False
def __del__(self):
def __del__(self):
self.model.__del__()
@classmethod
@ -35,7 +35,7 @@ class LlamaCppModel:
else:
cache_capacity = int(shared.args.cache_capacity)
logging.info("Cache capacity is " + str(cache_capacity) + " bytes")
logger.info("Cache capacity is " + str(cache_capacity) + " bytes")
params = {
'model_path': str(path),

5
modules/logging_colors.py
View File

@ -3,6 +3,8 @@
import logging
import platform
logging.basicConfig(format='%(levelname)s:%(message)s')
def add_coloring_to_emit_windows(fn):
# add methods we need to the class
@ -107,3 +109,6 @@ else:
# log.addFilter(log_filter())
# //hdlr = logging.StreamHandler()
# //hdlr.setFormatter(formatter())
logger = logging.getLogger('text-generation-webui')
logger.setLevel(logging.DEBUG)

26
modules/models.py
View File

@ -1,6 +1,5 @@
import gc
import json
import logging
import os
import re
import time
@ -17,6 +16,7 @@ from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM,
import modules.shared as shared
from modules import llama_attn_hijack
from modules.logging_colors import logger
transformers.logging.set_verbosity_error()
@ -71,12 +71,12 @@ def find_model_type(model_name):
def load_model(model_name):
logging.info(f"Loading {model_name}...")
logger.info(f"Loading {model_name}...")
t0 = time.time()
shared.model_type = find_model_type(model_name)
if shared.model_type == 'None':
logging.error('The path to the model does not exist. Exiting.')
logger.error('The path to the model does not exist. Exiting.')
return None, None
if shared.args.autogptq:
@ -106,7 +106,7 @@ def load_model(model_name):
if any((shared.args.xformers, shared.args.sdp_attention)):
llama_attn_hijack.hijack_llama_attention()
logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
logger.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.\n")
return model, tokenizer
@ -119,7 +119,7 @@ def load_tokenizer(model_name, model):
if shared.model_type not in ['llava', 'oasst']:
for p in [Path(f"{shared.args.model_dir}/llama-tokenizer/"), Path(f"{shared.args.model_dir}/oobabooga_llama-tokenizer/")]:
if p.exists():
logging.info(f"Loading the universal LLaMA tokenizer from {p}...")
logger.info(f"Loading the universal LLaMA tokenizer from {p}...")
tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True)
return tokenizer
@ -162,7 +162,7 @@ def huggingface_loader(model_name):
model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
model = deepspeed.initialize(model=model, config_params=ds_config, model_parameters=None, optimizer=None, lr_scheduler=None)[0]
model.module.eval() # Inference
logging.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
logger.info(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}")
# Custom
else:
@ -172,7 +172,7 @@ def huggingface_loader(model_name):
}
if not any((shared.args.cpu, torch.cuda.is_available(), torch.has_mps)):
logging.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.")
logger.warning("torch.cuda.is_available() returned False. This means that no GPU has been detected. Falling back to CPU mode.")
shared.args.cpu = True
if shared.args.cpu:
@ -254,7 +254,7 @@ def llamacpp_loader(model_name):
else:
model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0]
logging.info(f"llama.cpp weights detected: {model_file}\n")
logger.info(f"llama.cpp weights detected: {model_file}\n")
model, tokenizer = LlamaCppModel.from_pretrained(model_file)
return model, tokenizer
@ -263,7 +263,7 @@ def GPTQ_loader(model_name):
# Monkey patch
if shared.args.monkey_patch:
logging.warning("Applying the monkey patch for using LoRAs in 4-bit mode. It may cause undefined behavior outside its intended scope.")
logger.warning("Applying the monkey patch for using LoRAs in 4-bit mode. It may cause undefined behavior outside its intended scope.")
from modules.monkey_patch_gptq_lora import load_model_llama
model, _ = load_model_llama(model_name)
@ -302,7 +302,7 @@ def get_max_memory_dict():
suggestion -= 1000
suggestion = int(round(suggestion / 1000))
logging.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.")
logger.warning(f"Auto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors. You can manually set other values.")
max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'}
return max_memory if len(max_memory) > 0 else None
@ -333,13 +333,13 @@ def load_soft_prompt(name):
zf.extract('tensor.npy')
zf.extract('meta.json')
j = json.loads(open('meta.json', 'r').read())
logging.info(f"\nLoading the softprompt \"{name}\".")
logger.info(f"\nLoading the softprompt \"{name}\".")
for field in j:
if field != 'name':
if type(j[field]) is list:
logging.info(f"{field}: {', '.join(j[field])}")
logger.info(f"{field}: {', '.join(j[field])}")
else:
logging.info(f"{field}: {j[field]}")
logger.info(f"{field}: {j[field]}")
tensor = np.load('tensor.npy')
Path('tensor.npy').unlink()

9
modules/shared.py
View File

@ -1,10 +1,11 @@
import argparse
import logging
from collections import OrderedDict
from pathlib import Path
import yaml
from modules.logging_colors import logger
model = None
tokenizer = None
model_name = "None"
@ -180,14 +181,14 @@ args_defaults = parser.parse_args([])
deprecated_dict = {}
for k in deprecated_dict:
if getattr(args, k) != deprecated_dict[k][1]:
logging.warning(f"--{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
logger.warning(f"--{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
setattr(args, deprecated_dict[k][0], getattr(args, k))
# Security warnings
if args.trust_remote_code:
logging.warning("trust_remote_code is enabled. This is dangerous.")
logger.warning("trust_remote_code is enabled. This is dangerous.")
if args.share:
logging.warning("The gradio \"share link\" feature downloads a proprietary and unaudited blob to create a reverse tunnel. This is potentially dangerous.")
logger.warning("The gradio \"share link\" feature downloads a proprietary and unaudited blob to create a reverse tunnel. This is potentially dangerous.")
def add_extension(name):

4
modules/text_generation.py
View File

@ -1,5 +1,4 @@
import ast
import logging
import random
import re
import time
@ -14,6 +13,7 @@ from modules.callbacks import (Iteratorize, Stream,
_SentinelTokenStoppingCriteria)
from modules.extensions import apply_extensions
from modules.html_generator import generate_4chan_html, generate_basic_html
from modules.logging_colors import logger
from modules.models import clear_torch_cache, local_rank
@ -159,7 +159,7 @@ def generate_reply(question, state, eos_token=None, stopping_strings=None, is_ch
generate_func = apply_extensions('custom_generate_reply')
if generate_func is None:
if shared.model_name == 'None' or shared.model is None:
logging.error("No model is loaded! Select one in the Model tab.")
logger.error("No model is loaded! Select one in the Model tab.")
yield question
return

39
modules/training.py
View File

@ -1,5 +1,4 @@
import json
import logging
import math
import sys
import threading
@ -15,8 +14,9 @@ from peft import (LoraConfig, get_peft_model, prepare_model_for_int8_training,
set_peft_model_state_dict)
from modules import shared, ui, utils
from modules.evaluate import calculate_perplexity, generate_markdown_table, save_past_evaluations
from modules.evaluate import (calculate_perplexity, generate_markdown_table,
save_past_evaluations)
from modules.logging_colors import logger
# This mapping is from a very recent commit, not yet released.
# If not available, default to a backup map for some common model types.
@ -24,7 +24,8 @@ try:
from peft.utils.other import \
TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING as \
model_to_lora_modules
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from transformers.models.auto.modeling_auto import \
MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
MODEL_CLASSES = {v: k for k, v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES}
except:
standard_modules = ["q_proj", "v_proj"]
@ -217,13 +218,13 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
if model_type == "PeftModelForCausalLM":
if len(shared.args.lora_names) > 0:
yield "You are trying to train a LoRA while you already have another LoRA loaded. This will work, but may have unexpected effects. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
logging.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
logger.warning("Training LoRA over top of another LoRA. May have unexpected effects.")
else:
yield "Model ID not matched due to LoRA loading. Consider reloading base model. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
logging.warning("Model ID not matched due to LoRA loading. Consider reloading base model.")
logger.warning("Model ID not matched due to LoRA loading. Consider reloading base model.")
else:
yield "LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. Unexpected errors may follow. *(Will continue anyway in 5 seconds, press `Interrupt` to stop.)*"
logging.warning(f"LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. (Found model type: {model_type})")
logger.warning(f"LoRA training has only currently been validated for LLaMA, OPT, GPT-J, and GPT-NeoX models. (Found model type: {model_type})")
time.sleep(5)
@ -233,7 +234,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
elif not shared.args.load_in_8bit and shared.args.wbits <= 0:
yield "It is highly recommended you use `--load-in-8bit` for LoRA training. *(Will continue anyway in 2 seconds, press `Interrupt` to stop.)*"
logging.warning("It is highly recommended you use `--load-in-8bit` for LoRA training.")
logger.warning("It is highly recommended you use `--load-in-8bit` for LoRA training.")
time.sleep(2) # Give it a moment for the message to show in UI before continuing
if cutoff_len <= 0 or micro_batch_size <= 0 or batch_size <= 0 or actual_lr <= 0 or lora_rank <= 0 or lora_alpha <= 0:
@ -253,7 +254,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
# == Prep the dataset, format, etc ==
if raw_text_file not in ['None', '']:
logging.info("Loading raw text file dataset...")
logger.info("Loading raw text file dataset...")
with open(clean_path('training/datasets', f'{raw_text_file}.txt'), 'r', encoding='utf-8') as file:
raw_text = file.read().replace('\r', '')
@ -311,7 +312,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
prompt = generate_prompt(data_point)
return tokenize(prompt)
logging.info("Loading JSON datasets...")
logger.info("Loading JSON datasets...")
data = load_dataset("json", data_files=clean_path('training/datasets', f'{dataset}.json'))
train_data = data['train'].map(generate_and_tokenize_prompt)
@ -323,10 +324,10 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
# == Start prepping the model itself ==
if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'):
logging.info("Getting model ready...")
logger.info("Getting model ready...")
prepare_model_for_int8_training(shared.model)
logging.info("Prepping for training...")
logger.info("Prepping for training...")
config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
@ -337,10 +338,10 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
)
try:
logging.info("Creating LoRA model...")
logger.info("Creating LoRA model...")
lora_model = get_peft_model(shared.model, config)
if not always_override and Path(f"{lora_file_path}/adapter_model.bin").is_file():
logging.info("Loading existing LoRA data...")
logger.info("Loading existing LoRA data...")
state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin")
set_peft_model_state_dict(lora_model, state_dict_peft)
except:
@ -418,7 +419,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
json.dump({x: vars[x] for x in PARAMETERS}, file)
# == Main run and monitor loop ==
logging.info("Starting training...")
logger.info("Starting training...")
yield "Starting..."
if WANT_INTERRUPT:
yield "Interrupted before start."
@ -428,7 +429,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
trainer.train()
# Note: save in the thread in case the gradio thread breaks (eg browser closed)
lora_model.save_pretrained(lora_file_path)
logging.info("LoRA training run is completed and saved.")
logger.info("LoRA training run is completed and saved.")
tracked.did_save = True
thread = threading.Thread(target=threaded_run)
@ -460,14 +461,14 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
# Saving in the train thread might fail if an error occurs, so save here if so.
if not tracked.did_save:
logging.info("Training complete, saving...")
logger.info("Training complete, saving...")
lora_model.save_pretrained(lora_file_path)
if WANT_INTERRUPT:
logging.info("Training interrupted.")
logger.info("Training interrupted.")
yield f"Interrupted. Incomplete LoRA saved to `{lora_file_path}`"
else:
logging.info("Training complete!")
logger.info("Training complete!")
yield f"Done! LoRA saved to `{lora_file_path}`"

20
server.py
View File

@ -1,17 +1,18 @@
import logging
import os
import requests
import warnings
import modules.logging_colors
import requests
from modules.logging_colors import logger
os.environ['GRADIO_ANALYTICS_ENABLED'] = 'False'
os.environ['BITSANDBYTES_NOWELCOME'] = '1'
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.INFO)
# This is a hack to prevent Gradio from phoning home when it gets imported
def my_get(url, **kwargs):
logging.info('Gradio HTTP request redirected to localhost :)')
logger.info('Gradio HTTP request redirected to localhost :)')
kwargs.setdefault('allow_redirects', True)
return requests.api.request('get', 'http://127.0.0.1/', **kwargs)
@ -49,7 +50,8 @@ from modules.extensions import apply_extensions
from modules.html_generator import chat_html_wrapper
from modules.LoRA import add_lora_to_model
from modules.models import load_model, load_soft_prompt, unload_model
from modules.text_generation import generate_reply_wrapper, get_encoded_length, stop_everything_event
from modules.text_generation import (generate_reply_wrapper,
get_encoded_length, stop_everything_event)
def load_model_wrapper(selected_model, autoload=False):
@ -388,7 +390,7 @@ def create_model_menus():
shared.gradio['no_mmap'] = gr.Checkbox(label="no-mmap", value=shared.args.no_mmap)
shared.gradio['mlock'] = gr.Checkbox(label="mlock", value=shared.args.mlock)
with gr.Row():
with gr.Row():
shared.gradio['model_status'] = gr.Markdown('No model is loaded' if shared.model_name == 'None' else 'Ready')
# In this event handler, the interface state is read and updated
@ -971,7 +973,7 @@ if __name__ == "__main__":
settings_file = Path('settings.json')
if settings_file is not None:
logging.info(f"Loading settings from {settings_file}...")
logger.info(f"Loading settings from {settings_file}...")
new_settings = json.loads(open(settings_file, 'r').read())
for item in new_settings:
shared.settings[item] = new_settings[item]
@ -1015,7 +1017,7 @@ if __name__ == "__main__":
# Select the model from a command-line menu
elif shared.args.model_menu:
if len(available_models) == 0:
logging.error('No models are available! Please download at least one.')
logger.error('No models are available! Please download at least one.')
sys.exit(0)
else:
print('The following models are available:\n')