mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 01:09:22 +01:00
Merge branch 'oobabooga:main' into exllama-module
This commit is contained in:
jllllll
GitHub
commit
6254203f84
@ -212,7 +212,7 @@ Optionally, you can use the following command-line flags:
|
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|
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| Flag | Description |
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|--------------------------------------------|-------------|
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| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, llamacpp, rwkv, flexgen |
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| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, flexgen |
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#### Accelerate/transformers
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4
characters/instruction-following/Starchat-Beta.yaml
Normal file
4
characters/instruction-following/Starchat-Beta.yaml
Normal file
@ -0,0 +1,4 @@
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user: "<|user|>"
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bot: "<|assistant|>"
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context: "<|system|>\n<|end|>\n"
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turn_template: "<|user|>\n<|user-message|><|end|>\n<|bot|>\n<|bot-message|><|end|>\n"
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4
characters/instruction-following/Tulu.yaml
Normal file
4
characters/instruction-following/Tulu.yaml
Normal file
@ -0,0 +1,4 @@
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user: "<|user|>"
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bot: "<|assistant|>"
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context: ""
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turn_template: "<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n"
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19
css/chat.css
19
css/chat.css
@ -93,3 +93,22 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
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.message-body :not(pre) > code {
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white-space: normal !important;
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}
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@media print {
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body {
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visibility: hidden;
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}
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.chat {
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visibility: visible;
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position: absolute;
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left: 0;
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top: 0;
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max-width: none;
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max-height: none;
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width: 100%;
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height: fit-content;
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display: flex;
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flex-direction: column-reverse;
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}
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}
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@ -17,6 +17,10 @@
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margin-bottom: 1.25em !important;
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}
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.message-body ul, .message-body ol {
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margin-bottom: 1.25em !important;
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}
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.dark .message-body p em {
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color: rgb(198, 202, 214) !important;
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}
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@ -26,7 +26,7 @@ LABEL maintainer="Your Name <your.email@example.com>"
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LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI"
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RUN apt-get update && \
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apt-get install --no-install-recommends -y libportaudio2 libasound-dev git python3 python3-pip make g++ && \
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apt-get install --no-install-recommends -y python3-dev libportaudio2 libasound-dev git python3 python3-pip make g++ && \
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rm -rf /var/lib/apt/lists/*
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RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv
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@ -1,5 +1,5 @@
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'''
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Downloads models from Hugging Face to models/model-name.
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Downloads models from Hugging Face to models/username_modelname.
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Example:
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python download-model.py facebook/opt-1.3b
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@ -11,8 +11,8 @@ import base64
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import datetime
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import hashlib
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import json
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import re
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import os
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import re
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import sys
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from pathlib import Path
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@ -21,63 +21,12 @@ import tqdm
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from tqdm.contrib.concurrent import thread_map
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def select_model_from_default_options():
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models = {
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"OPT 6.7B": ("facebook", "opt-6.7b", "main"),
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"OPT 2.7B": ("facebook", "opt-2.7b", "main"),
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"OPT 1.3B": ("facebook", "opt-1.3b", "main"),
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"OPT 350M": ("facebook", "opt-350m", "main"),
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"GALACTICA 6.7B": ("facebook", "galactica-6.7b", "main"),
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"GALACTICA 1.3B": ("facebook", "galactica-1.3b", "main"),
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"GALACTICA 125M": ("facebook", "galactica-125m", "main"),
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"Pythia-6.9B-deduped": ("EleutherAI", "pythia-6.9b-deduped", "main"),
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"Pythia-2.8B-deduped": ("EleutherAI", "pythia-2.8b-deduped", "main"),
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"Pythia-1.4B-deduped": ("EleutherAI", "pythia-1.4b-deduped", "main"),
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"Pythia-410M-deduped": ("EleutherAI", "pythia-410m-deduped", "main"),
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}
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choices = {}
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print("Select the model that you want to download:\n")
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for i, name in enumerate(models):
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char = chr(ord('A') + i)
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choices[char] = name
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print(f"{char}) {name}")
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char_hugging = chr(ord('A') + len(models))
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print(f"{char_hugging}) Manually specify a Hugging Face model")
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char_exit = chr(ord('A') + len(models) + 1)
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print(f"{char_exit}) Do not download a model")
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print()
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print("Input> ", end='')
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choice = input()[0].strip().upper()
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if choice == char_exit:
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exit()
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elif choice == char_hugging:
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print("""\nType the name of your desired Hugging Face model in the format organization/name.
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Examples:
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facebook/opt-1.3b
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EleutherAI/pythia-1.4b-deduped
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""")
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print("Input> ", end='')
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model = input()
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branch = "main"
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else:
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arr = models[choices[choice]]
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model = f"{arr[0]}/{arr[1]}"
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branch = arr[2]
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return model, branch
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class ModelDownloader:
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def __init__(self):
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self.s = requests.Session()
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if os.getenv('HF_USER') is not None and os.getenv('HF_PASS') is not None:
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self.s.auth = (os.getenv('HF_USER'), os.getenv('HF_PASS'))
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def sanitize_model_and_branch_names(self, model, branch):
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if model[-1] == '/':
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model = model[:-1]
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@ -92,7 +41,6 @@ class ModelDownloader:
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return model, branch
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def get_download_links_from_huggingface(self, model, branch, text_only=False):
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base = "https://huggingface.co"
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page = f"/api/models/{model}/tree/{branch}"
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@ -163,7 +111,6 @@ class ModelDownloader:
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return links, sha256, is_lora
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def get_output_folder(self, model, branch, is_lora, base_folder=None):
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if base_folder is None:
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base_folder = 'models' if not is_lora else 'loras'
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@ -171,59 +118,64 @@ class ModelDownloader:
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output_folder = f"{'_'.join(model.split('/')[-2:])}"
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if branch != 'main':
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output_folder += f'_{branch}'
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output_folder = Path(base_folder) / output_folder
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return output_folder
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def get_single_file(self, url, output_folder, start_from_scratch=False):
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filename = Path(url.rsplit('/', 1)[1])
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output_path = output_folder / filename
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headers = {}
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mode = 'wb'
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if output_path.exists() and not start_from_scratch:
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# Check if the file has already been downloaded completely
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r = self.s.get(url, stream=True, timeout=20)
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total_size = int(r.headers.get('content-length', 0))
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if output_path.stat().st_size >= total_size:
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return
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# Otherwise, resume the download from where it left off
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headers = {'Range': f'bytes={output_path.stat().st_size}-'}
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mode = 'ab'
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else:
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headers = {}
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mode = 'wb'
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r = self.s.get(url, stream=True, headers=headers, timeout=20)
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with open(output_path, mode) as f:
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with self.s.get(url, stream=True, headers=headers, timeout=20) as r:
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r.raise_for_status() # Do not continue the download if the request was unsuccessful
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total_size = int(r.headers.get('content-length', 0))
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block_size = 1024
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with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
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for data in r.iter_content(block_size):
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t.update(len(data))
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f.write(data)
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block_size = 1024 * 1024 # 1MB
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with open(output_path, mode) as f:
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with tqdm.tqdm(total=total_size, unit='iB', unit_scale=True, bar_format='{l_bar}{bar}| {n_fmt:6}/{total_fmt:6} {rate_fmt:6}') as t:
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count = 0
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for data in r.iter_content(block_size):
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t.update(len(data))
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f.write(data)
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if total_size != 0 and self.progress_bar is not None:
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count += len(data)
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self.progress_bar(float(count) / float(total_size), f"Downloading {filename}")
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def start_download_threads(self, file_list, output_folder, start_from_scratch=False, threads=1):
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thread_map(lambda url: self.get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True)
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def download_model_files(self, model, branch, links, sha256, output_folder, progress_bar=None, start_from_scratch=False, threads=1):
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self.progress_bar = progress_bar
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def download_model_files(self, model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1):
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# Creating the folder and writing the metadata
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if not output_folder.exists():
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output_folder.mkdir(parents=True, exist_ok=True)
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with open(output_folder / 'huggingface-metadata.txt', 'w') as f:
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f.write(f'url: https://huggingface.co/{model}\n')
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f.write(f'branch: {branch}\n')
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f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n')
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sha256_str = ''
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for i in range(len(sha256)):
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sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n'
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if sha256_str != '':
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f.write(f'sha256sum:\n{sha256_str}')
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output_folder.mkdir(parents=True, exist_ok=True)
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metadata = f'url: https://huggingface.co/{model}\n' \
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f'branch: {branch}\n' \
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f'download date: {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}\n'
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sha256_str = '\n'.join([f' {item[1]} {item[0]}' for item in sha256])
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if sha256_str:
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metadata += f'sha256sum:\n{sha256_str}'
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metadata += '\n'
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(output_folder / 'huggingface-metadata.txt').write_text(metadata)
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# Downloading the files
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print(f"Downloading the model to {output_folder}")
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self.start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads)
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def check_model_files(self, model, branch, links, sha256, output_folder):
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# Validate the checksums
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validated = True
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@ -264,8 +216,6 @@ if __name__ == '__main__':
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branch = args.branch
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model = args.MODEL
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if model is None:
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model, branch = select_model_from_default_options()
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downloader = ModelDownloader()
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# Cleaning up the model/branch names
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@ -50,7 +50,7 @@ llama-65b-gptq-3bit:
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.*vicuna.*v0:
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mode: 'instruct'
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instruction_template: 'Vicuna-v0'
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.*vicuna.*(1.1|1_1):
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.*vicuna.*(1.1|1_1|1.3|1_3):
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mode: 'instruct'
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instruction_template: 'Vicuna-v1.1'
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.*wizard.*vicuna:
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@ -184,7 +184,7 @@ llama-65b-gptq-3bit:
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.*Nous-Hermes-13b:
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mode: 'instruct'
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instruction_template: 'Alpaca'
|
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.*airoboros-13b-gpt4:
|
||||
.*airoboros:
|
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mode: 'instruct'
|
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instruction_template: 'Vicuna-v1.1'
|
||||
.*WizardLM-30B-V1.0:
|
||||
@ -193,7 +193,7 @@ llama-65b-gptq-3bit:
|
||||
TheBloke_WizardLM-30B-GPTQ:
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||||
mode: 'instruct'
|
||||
instruction_template: 'Vicuna-v1.1'
|
||||
.*(A|a)lpa(cino|sta):
|
||||
.*alpa(cino|sta):
|
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mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*hippogriff:
|
||||
@ -202,9 +202,33 @@ TheBloke_WizardLM-30B-GPTQ:
|
||||
.*gpt4all-.*-snoozy:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'WizardLM'
|
||||
.*(L|l)azarus:
|
||||
.*lazarus:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*(G|g)uanaco-.*(7|13|33|65)(b|B):
|
||||
.*guanaco-.*(7|13|33|65)b:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Guanaco'
|
||||
.*hypermantis:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*open-llama-.*-open-instruct:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*starcoder-gpteacher-code-instruct:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*tulu:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Tulu'
|
||||
.*chronos:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*samantha:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Samantha'
|
||||
.*wizardcoder:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Alpaca'
|
||||
.*starchat-beta:
|
||||
mode: 'instruct'
|
||||
instruction_template: 'Starchat-Beta'
|
||||
82
modules/exllama_hf.py
Normal file
82
modules/exllama_hf.py
Normal file
@ -0,0 +1,82 @@
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from typing import *
|
||||
|
||||
import torch
|
||||
from transformers import (
|
||||
GenerationConfig,
|
||||
LlamaTokenizer,
|
||||
PretrainedConfig,
|
||||
PreTrainedModel
|
||||
)
|
||||
from transformers.modeling_outputs import CausalLMOutputWithPast
|
||||
|
||||
from modules import shared
|
||||
from modules.logging_colors import logger
|
||||
from modules.relative_imports import RelativeImport
|
||||
|
||||
with RelativeImport("repositories/exllama"):
|
||||
from model import ExLlama, ExLlamaCache, ExLlamaConfig
|
||||
|
||||
|
||||
class ExllamaHF(PreTrainedModel):
|
||||
def __init__(self, config: ExLlamaConfig):
|
||||
super().__init__(PretrainedConfig())
|
||||
self.ex_config = config
|
||||
self.ex_model = ExLlama(self.ex_config)
|
||||
self.generation_config = GenerationConfig()
|
||||
|
||||
def _validate_model_class(self):
|
||||
pass
|
||||
|
||||
def _validate_model_kwargs(self, model_kwargs: Dict[str, Any]):
|
||||
pass
|
||||
|
||||
def prepare_inputs_for_generation(self, input_ids, **kwargs):
|
||||
return {'input_ids': input_ids, **kwargs}
|
||||
|
||||
@property
|
||||
def device(self) -> torch.device:
|
||||
# TODO: May cause problem on multi-gpu inference?
|
||||
return torch.device(0)
|
||||
|
||||
def __call__(self, *args, **kwargs):
|
||||
# TODO: Some decoding methods (such as Contrastive Search) may not work at this time
|
||||
assert len(args) == 0, 'no *args should be passed to forward'
|
||||
use_cache = kwargs['use_cache']
|
||||
seq = kwargs['input_ids'][0].tolist()
|
||||
cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
|
||||
if cache is None:
|
||||
cache = ExLlamaCache(self.ex_model)
|
||||
self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True)
|
||||
logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache).to(self.device)
|
||||
return CausalLMOutputWithPast(logits=logits, past_key_values=cache if use_cache else None)
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.PathLike]], *model_args, **kwargs):
|
||||
assert len(model_args) == 0 and len(kwargs) == 0, "extra args is currently not supported"
|
||||
if isinstance(pretrained_model_name_or_path, str):
|
||||
pretrained_model_name_or_path = Path(pretrained_model_name_or_path)
|
||||
|
||||
pretrained_model_name_or_path = Path(f'{shared.args.model_dir}') / Path(pretrained_model_name_or_path)
|
||||
config = ExLlamaConfig(pretrained_model_name_or_path / 'config.json')
|
||||
|
||||
# from 'oobabooga/text-generation-webui/modules/exllama.py'
|
||||
weight_path = None
|
||||
for ext in ['.safetensors', '.pt', '.bin']:
|
||||
found = list(pretrained_model_name_or_path.glob(f"*{ext}"))
|
||||
if len(found) > 0:
|
||||
weight_path = found[-1]
|
||||
break
|
||||
assert weight_path is not None, f'could not find weight in "{pretrained_model_name_or_path}"'
|
||||
|
||||
config.model_path = str(weight_path)
|
||||
|
||||
# This slowes down a bit but align better with autogptq generation.
|
||||
# TODO: Should give user choice to tune the exllama config
|
||||
config.act_order = True
|
||||
config.fused_attn = False
|
||||
config.fused_mlp_thd = 0
|
||||
|
||||
return ExllamaHF(config)
|
||||
@ -52,9 +52,9 @@ class LlamaCppModel:
|
||||
'n_gpu_layers': shared.args.n_gpu_layers
|
||||
}
|
||||
|
||||
self.model = Llama(**params)
|
||||
result.model = Llama(**params)
|
||||
if cache_capacity > 0:
|
||||
self.model.set_cache(LlamaCache(capacity_bytes=cache_capacity))
|
||||
result.model.set_cache(LlamaCache(capacity_bytes=cache_capacity))
|
||||
|
||||
# This is ugly, but the model and the tokenizer are the same object in this library.
|
||||
return result, result
|
||||
|
||||
@ -55,6 +55,10 @@ loaders_and_params = {
|
||||
'ExLlama' : [
|
||||
'gpu_split',
|
||||
'exllama_info',
|
||||
],
|
||||
'ExLlama_HF' : [
|
||||
'gpu_split',
|
||||
'exllama_HF_info',
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
@ -49,7 +49,8 @@ def load_model(model_name, loader=None):
|
||||
'llama.cpp': llamacpp_loader,
|
||||
'FlexGen': flexgen_loader,
|
||||
'RWKV': RWKV_loader,
|
||||
'ExLlama': ExLlama_loader
|
||||
'ExLlama': ExLlama_loader,
|
||||
'ExLlama_HF': ExLlama_HF_loader
|
||||
}
|
||||
|
||||
if loader is None:
|
||||
@ -278,6 +279,12 @@ def ExLlama_loader(model_name):
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
def ExLlama_HF_loader(model_name):
|
||||
from modules.exllama_hf import ExllamaHF
|
||||
|
||||
return ExllamaHF.from_pretrained(model_name)
|
||||
|
||||
|
||||
def get_max_memory_dict():
|
||||
max_memory = {}
|
||||
if shared.args.gpu_memory:
|
||||
|
||||
@ -98,7 +98,7 @@ parser.add_argument('--extensions', type=str, nargs="+", help='The list of exten
|
||||
parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
|
||||
|
||||
# Model loader
|
||||
parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, llamacpp, rwkv, flexgen')
|
||||
parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, flexgen')
|
||||
|
||||
# Accelerate/transformers
|
||||
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
|
||||
@ -218,6 +218,8 @@ def fix_loader_name(name):
|
||||
return 'GPTQ-for-LLaMa'
|
||||
elif name in ['exllama', 'ex-llama', 'ex_llama', 'exlama']:
|
||||
return 'ExLlama'
|
||||
elif name in ['exllama-hf', 'exllama_hf', 'exllama hf', 'ex-llama-hf', 'ex_llama_hf']:
|
||||
return 'ExLlama_HF'
|
||||
|
||||
|
||||
if args.loader is not None:
|
||||
|
||||
@ -104,9 +104,8 @@ def get_reply_from_output_ids(output_ids, input_ids, original_question, state, i
|
||||
else:
|
||||
new_tokens = len(output_ids) - len(input_ids[0])
|
||||
reply = decode(output_ids[-new_tokens:], state['skip_special_tokens'])
|
||||
|
||||
# Prevent LlamaTokenizer from skipping a space
|
||||
if type(shared.tokenizer) is transformers.LlamaTokenizer and len(output_ids) > 0:
|
||||
if type(shared.tokenizer) in [transformers.LlamaTokenizer, transformers.LlamaTokenizerFast] and len(output_ids) > 0:
|
||||
if shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'):
|
||||
reply = ' ' + reply
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@ import gradio as gr
|
||||
import torch
|
||||
import transformers
|
||||
from datasets import Dataset, load_dataset
|
||||
from peft import (LoraConfig, get_peft_model, prepare_model_for_int8_training,
|
||||
from peft import (LoraConfig, get_peft_model, prepare_model_for_kbit_training,
|
||||
set_peft_model_state_dict)
|
||||
|
||||
from modules import shared, ui, utils
|
||||
@ -30,14 +30,17 @@ try:
|
||||
MODEL_CLASSES = {v: k for k, v in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES}
|
||||
except:
|
||||
standard_modules = ["q_proj", "v_proj"]
|
||||
model_to_lora_modules = {"llama": standard_modules, "opt": standard_modules, "gptj": standard_modules, "gpt_neox": ["query_key_value"]}
|
||||
model_to_lora_modules = {"llama": standard_modules, "opt": standard_modules, "gptj": standard_modules, "gpt_neox": ["query_key_value"], "rw":["query_key_value"]}
|
||||
MODEL_CLASSES = {
|
||||
"LlamaForCausalLM": "llama",
|
||||
"OPTForCausalLM": "opt",
|
||||
"GPTJForCausalLM": "gptj",
|
||||
"GPTNeoXForCausalLM": "gpt_neox"
|
||||
"GPTNeoXForCausalLM": "gpt_neox",
|
||||
"RWForCausalLM": "rw"
|
||||
|
||||
}
|
||||
|
||||
train_log = {}
|
||||
|
||||
WANT_INTERRUPT = False
|
||||
PARAMETERS = ["lora_name", "always_override", "save_steps", "micro_batch_size", "batch_size", "epochs", "learning_rate", "lr_scheduler_type", "lora_rank", "lora_alpha", "lora_dropout", "cutoff_len", "dataset", "eval_dataset", "format", "eval_steps", "raw_text_file", "overlap_len", "newline_favor_len", "higher_rank_limit", "warmup_steps", "optimizer", "hard_cut_string", "train_only_after"]
|
||||
@ -357,7 +360,7 @@ 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'):
|
||||
logger.info("Getting model ready...")
|
||||
prepare_model_for_int8_training(shared.model)
|
||||
prepare_model_for_kbit_training(shared.model)
|
||||
|
||||
logger.info("Prepping for training...")
|
||||
config = LoraConfig(
|
||||
@ -406,12 +409,19 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
||||
control.should_training_stop = True
|
||||
elif state.global_step > 0 and actual_save_steps > 0 and state.global_step % actual_save_steps == 0:
|
||||
lora_model.save_pretrained(f"{lora_file_path}/checkpoint-{tracked.current_steps}/")
|
||||
# Save log
|
||||
with open(f"{lora_file_path}/checkpoint-{tracked.current_steps}/training_log.json", 'w', encoding='utf-8') as file:
|
||||
json.dump(train_log, file, indent=2)
|
||||
|
||||
|
||||
def on_substep_end(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs):
|
||||
tracked.current_steps += 1
|
||||
if WANT_INTERRUPT:
|
||||
control.should_epoch_stop = True
|
||||
control.should_training_stop = True
|
||||
|
||||
def on_log(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, logs, **kwargs):
|
||||
train_log.update(logs)
|
||||
|
||||
trainer = transformers.Trainer(
|
||||
model=lora_model,
|
||||
@ -448,7 +458,7 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
||||
# == Save parameters for reuse ==
|
||||
with open(f"{lora_file_path}/training_parameters.json", 'w', encoding='utf-8') as file:
|
||||
vars = locals()
|
||||
json.dump({x: vars[x] for x in PARAMETERS}, file)
|
||||
json.dump({x: vars[x] for x in PARAMETERS}, file, indent=2)
|
||||
|
||||
# == Main run and monitor loop ==
|
||||
logger.info("Starting training...")
|
||||
@ -462,7 +472,9 @@ def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch
|
||||
# Note: save in the thread in case the gradio thread breaks (eg browser closed)
|
||||
lora_model.save_pretrained(lora_file_path)
|
||||
logger.info("LoRA training run is completed and saved.")
|
||||
tracked.did_save = True
|
||||
# Save log
|
||||
with open(f"{lora_file_path}/training_log.json", 'w', encoding='utf-8') as file:
|
||||
json.dump(train_log, file, indent=2)
|
||||
|
||||
thread = threading.Thread(target=threaded_run)
|
||||
thread.start()
|
||||
|
||||
@ -17,10 +17,10 @@ tqdm
|
||||
scipy
|
||||
transformers==4.30.2
|
||||
git+https://github.com/huggingface/peft@03eb378eb914fbee709ff7c86ba5b1d033b89524
|
||||
bitsandbytes==0.39.0; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.39.0-py3-none-any.whl; platform_system == "Windows"
|
||||
llama-cpp-python==0.1.62; platform_system != "Windows"
|
||||
https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.62/llama_cpp_python-0.1.62-cp310-cp310-win_amd64.whl; platform_system == "Windows"
|
||||
bitsandbytes==0.39.1; platform_system != "Windows"
|
||||
https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whl; platform_system == "Windows"
|
||||
llama-cpp-python==0.1.64; platform_system != "Windows"
|
||||
https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.64/llama_cpp_python-0.1.64-cp310-cp310-win_amd64.whl; platform_system == "Windows"
|
||||
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
|
||||
https://github.com/PanQiWei/AutoGPTQ/releases/download/v0.2.2/auto_gptq-0.2.2+cu117-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64"
|
||||
https://github.com/jllllll/exllama/releases/download/0.0.1/exllama-0.0.1+cu117-cp310-cp310-win_amd64.whl; platform_system == "Windows"
|
||||
|
||||
13
server.py
13
server.py
@ -122,7 +122,7 @@ def count_tokens(text):
|
||||
return 'Couldn\'t count the number of tokens. Is a tokenizer loaded?'
|
||||
|
||||
|
||||
def download_model_wrapper(repo_id):
|
||||
def download_model_wrapper(repo_id, progress=gr.Progress()):
|
||||
try:
|
||||
downloader_module = importlib.import_module("download-model")
|
||||
downloader = downloader_module.ModelDownloader()
|
||||
@ -131,6 +131,7 @@ def download_model_wrapper(repo_id):
|
||||
branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "main"
|
||||
check = False
|
||||
|
||||
progress(0.0)
|
||||
yield ("Cleaning up the model/branch names")
|
||||
model, branch = downloader.sanitize_model_and_branch_names(model, branch)
|
||||
|
||||
@ -141,13 +142,16 @@ def download_model_wrapper(repo_id):
|
||||
output_folder = downloader.get_output_folder(model, branch, is_lora)
|
||||
|
||||
if check:
|
||||
progress(0.5)
|
||||
yield ("Checking previously downloaded files")
|
||||
downloader.check_model_files(model, branch, links, sha256, output_folder)
|
||||
progress(1.0)
|
||||
else:
|
||||
yield (f"Downloading files to {output_folder}")
|
||||
downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1)
|
||||
downloader.download_model_files(model, branch, links, sha256, output_folder, progress_bar=progress, threads=1)
|
||||
yield ("Done!")
|
||||
except:
|
||||
progress(1.0)
|
||||
yield traceback.format_exc()
|
||||
|
||||
|
||||
@ -193,7 +197,7 @@ def create_model_menus():
|
||||
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "AutoGPTQ", "GPTQ-for-LLaMa", "ExLlama", "llama.cpp"], value=None)
|
||||
shared.gradio['loader'] = gr.Dropdown(label="Model loader", choices=["Transformers", "AutoGPTQ", "GPTQ-for-LLaMa", "ExLlama", "ExLlama_HF", "llama.cpp"], value=None)
|
||||
with gr.Box():
|
||||
with gr.Row():
|
||||
with gr.Column():
|
||||
@ -233,6 +237,7 @@ def create_model_menus():
|
||||
shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
|
||||
shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa is currently 2x faster than AutoGPTQ on some systems. It is installed by default with the one-click installers. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
|
||||
shared.gradio['exllama_info'] = gr.Markdown('ExLlama has to be installed manually. See the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
|
||||
shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s still a bit buggy, so feel free to help out by fixing issues.\n\nCheck out PR [#2777](https://github.com/oobabooga/text-generation-webui/pull/2777) for more details.')
|
||||
|
||||
with gr.Column():
|
||||
with gr.Row():
|
||||
@ -276,7 +281,7 @@ def create_model_menus():
|
||||
save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False)
|
||||
|
||||
shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
|
||||
shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
|
||||
shared.gradio['download_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=True)
|
||||
shared.gradio['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load)
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user