diff --git a/.gitignore b/.gitignore index 1b7f0fb8eea3e894694d9ca1541a896102d40376..dac4ce876e1214da64d8d213b6547a297c6c95c6 100644 --- a/.gitignore +++ b/.gitignore @@ -1,21 +1,31 @@ -cache/* -characters/* -extensions/silero_tts/outputs/* -extensions/elevenlabs_tts/outputs/* -logs/* -models/* -softprompts/* -torch-dumps/* +cache +characters +training/datasets +extensions/silero_tts/outputs +extensions/elevenlabs_tts/outputs +extensions/sd_api_pictures/outputs +extensions/multimodal/pipelines +logs +loras +models +repositories +softprompts +torch-dumps *pycache* */*pycache* */*/pycache* +venv/ +.venv/ +.vscode +*.bak +*.ipynb +*.log settings.json img_bot* img_me* +prompts/[0-9]* +models/config-user.yaml -!characters/Example.json -!characters/Example.png -!models/place-your-models-here.txt -!softprompts/place-your-softprompts-here.txt -!torch-dumps/place-your-pt-models-here.txt +.DS_Store +Thumbs.db diff --git a/api-example-stream.py b/api-example-stream.py index a5ed420252fdceab73cc26d83a7b87f60981ec95..ad8f7bf8105bf0bfa3a8a39e0af8e88b0d4b57d1 100644 --- a/api-example-stream.py +++ b/api-example-stream.py @@ -1,90 +1,67 @@ -''' - -Contributed by SagsMug. Thank you SagsMug. -https://github.com/oobabooga/text-generation-webui/pull/175 - -''' - import asyncio import json -import random -import string +import sys + +try: + import websockets +except ImportError: + print("Websockets package not found. Make sure it's installed.") -import websockets +# For local streaming, the websockets are hosted without ssl - ws:// +HOST = 'localhost:5005' +URI = f'ws://{HOST}/api/v1/stream' +# For reverse-proxied streaming, the remote will likely host with ssl - wss:// +# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream' -def random_hash(): - letters = string.ascii_lowercase + string.digits - return ''.join(random.choice(letters) for i in range(9)) async def run(context): - server = "127.0.0.1" - params = { - 'max_new_tokens': 200, + # Note: the selected defaults change from time to time. + request = { + 'prompt': context, + 'max_new_tokens': 250, 'do_sample': True, - 'temperature': 0.5, - 'top_p': 0.9, + 'temperature': 1.3, + 'top_p': 0.1, 'typical_p': 1, - 'repetition_penalty': 1.05, - 'top_k': 0, + 'repetition_penalty': 1.18, + 'top_k': 40, 'min_length': 0, 'no_repeat_ngram_size': 0, 'num_beams': 1, 'penalty_alpha': 0, 'length_penalty': 1, 'early_stopping': False, + 'seed': -1, + 'add_bos_token': True, + 'truncation_length': 2048, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'stopping_strings': [] } - session = random_hash() - async with websockets.connect(f"ws://{server}:7860/queue/join") as websocket: - while content := json.loads(await websocket.recv()): - #Python3.10 syntax, replace with if elif on older - match content["msg"]: - case "send_hash": - await websocket.send(json.dumps({ - "session_hash": session, - "fn_index": 7 - })) - case "estimation": - pass - case "send_data": - await websocket.send(json.dumps({ - "session_hash": session, - "fn_index": 7, - "data": [ - context, - params['max_new_tokens'], - params['do_sample'], - params['temperature'], - params['top_p'], - params['typical_p'], - params['repetition_penalty'], - params['top_k'], - params['min_length'], - params['no_repeat_ngram_size'], - params['num_beams'], - params['penalty_alpha'], - params['length_penalty'], - params['early_stopping'], - ] - })) - case "process_starts": - pass - case "process_generating" | "process_completed": - yield content["output"]["data"][0] - # You can search for your desired end indicator and - # stop generation by closing the websocket here - if (content["msg"] == "process_completed"): - break + async with websockets.connect(URI, ping_interval=None) as websocket: + await websocket.send(json.dumps(request)) + + yield context # Remove this if you just want to see the reply + + while True: + incoming_data = await websocket.recv() + incoming_data = json.loads(incoming_data) + + match incoming_data['event']: + case 'text_stream': + yield incoming_data['text'] + case 'stream_end': + return -prompt = "What I would like to say is the following: " -async def get_result(): +async def print_response_stream(prompt): async for response in run(prompt): - # Print intermediate steps - print(response) + print(response, end='') + sys.stdout.flush() # If we don't flush, we won't see tokens in realtime. - # Print final result - print(response) -asyncio.run(get_result()) +if __name__ == '__main__': + prompt = "In order to make homemade bread, follow these steps:\n1)" + asyncio.run(print_response_stream(prompt)) diff --git a/api-example.py b/api-example.py index 0306b7ab8a3fa3d6f57d8474ad74d67f13557b6d..f35ea1db76f291bf1cae90a1a7801d2d19be3acc 100644 --- a/api-example.py +++ b/api-example.py @@ -1,59 +1,44 @@ -''' - -This is an example on how to use the API for oobabooga/text-generation-webui. - -Make sure to start the web UI with the following flags: - -python server.py --model MODEL --listen --no-stream - -Optionally, you can also add the --share flag to generate a public gradio URL, -allowing you to use the API remotely. - -''' import requests -# Server address -server = "127.0.0.1" - -# Generation parameters -# Reference: https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig -params = { - 'max_new_tokens': 200, - 'do_sample': True, - 'temperature': 0.5, - 'top_p': 0.9, - 'typical_p': 1, - 'repetition_penalty': 1.05, - 'top_k': 0, - 'min_length': 0, - 'no_repeat_ngram_size': 0, - 'num_beams': 1, - 'penalty_alpha': 0, - 'length_penalty': 1, - 'early_stopping': False, -} - -# Input prompt -prompt = "What I would like to say is the following: " - -response = requests.post(f"http://{server}:7860/run/textgen", json={ - "data": [ - prompt, - params['max_new_tokens'], - params['do_sample'], - params['temperature'], - params['top_p'], - params['typical_p'], - params['repetition_penalty'], - params['top_k'], - params['min_length'], - params['no_repeat_ngram_size'], - params['num_beams'], - params['penalty_alpha'], - params['length_penalty'], - params['early_stopping'], - ] -}).json() - -reply = response["data"][0] -print(reply) +# For local streaming, the websockets are hosted without ssl - http:// +HOST = 'localhost:5000' +URI = f'http://{HOST}/api/v1/generate' + +# For reverse-proxied streaming, the remote will likely host with ssl - https:// +# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate' + + +def run(prompt): + request = { + 'prompt': prompt, + 'max_new_tokens': 250, + 'do_sample': True, + 'temperature': 1.3, + 'top_p': 0.1, + 'typical_p': 1, + 'repetition_penalty': 1.18, + 'top_k': 40, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0, + 'length_penalty': 1, + 'early_stopping': False, + 'seed': -1, + 'add_bos_token': True, + 'truncation_length': 2048, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'stopping_strings': [] + } + + response = requests.post(URI, json=request) + + if response.status_code == 200: + result = response.json()['results'][0]['text'] + print(prompt + result) + + +if __name__ == '__main__': + prompt = "In order to make homemade bread, follow these steps:\n1)" + run(prompt) diff --git a/characters/Example.json b/characters/Example.json deleted file mode 100644 index 496869c4e6cd643c910fbdf86d748c1c70987020..0000000000000000000000000000000000000000 --- a/characters/Example.json +++ /dev/null @@ -1,7 +0,0 @@ -{ - "char_name": "Chiharu Yamada", - "char_persona": "Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.", - "char_greeting": "*Chiharu strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air*\nHey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!", - "world_scenario": "", - "example_dialogue": "{{user}}: So how did you get into computer engineering?\n{{char}}: I've always loved tinkering with technology since I was a kid.\n{{user}}: That's really impressive!\n{{char}}: *She chuckles bashfully* Thanks!\n{{user}}: So what do you do when you're not working on computers?\n{{char}}: I love exploring, going out with friends, watching movies, and playing video games.\n{{user}}: What's your favorite type of computer hardware to work with?\n{{char}}: Motherboards, they're like puzzles and the backbone of any system.\n{{user}}: That sounds great!\n{{char}}: Yeah, it's really fun. I'm lucky to be able to do this as a job." -} diff --git a/convert-to-flexgen.py b/convert-to-flexgen.py index 917f023c3fe395c2e3cbcad11c9cdc6b85ef1e7e..7654593b539541deebfe904403ce73daa4a8651c 100644 --- a/convert-to-flexgen.py +++ b/convert-to-flexgen.py @@ -13,10 +13,11 @@ import torch from tqdm import tqdm from transformers import AutoModelForCausalLM, AutoTokenizer -parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54)) +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.") args = parser.parse_args() + def disable_torch_init(): """ Disable the redundant torch default initialization to accelerate model creation. @@ -31,20 +32,22 @@ def disable_torch_init(): torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) + def restore_torch_init(): """Rollback the change made by disable_torch_init.""" import torch setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup) setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup) + if __name__ == '__main__': path = Path(args.MODEL) model_name = path.name print(f"Loading {model_name}...") - #disable_torch_init() + # disable_torch_init() model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True) - #restore_torch_init() + # restore_torch_init() tokenizer = AutoTokenizer.from_pretrained(path) diff --git a/convert-to-safetensors.py b/convert-to-safetensors.py index 63baaa9726ab48025d2ba473d029bb3f1153aa3a..3b721e7cd4d15cf7e5e03caaee57ef83a41553bc 100644 --- a/convert-to-safetensors.py +++ b/convert-to-safetensors.py @@ -17,7 +17,7 @@ from pathlib import Path import torch from transformers import AutoModelForCausalLM, AutoTokenizer -parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54)) +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.") parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).') parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).") diff --git a/css/chat.css b/css/chat.css new file mode 100644 index 0000000000000000000000000000000000000000..b5102e9a72ca0b066b12d52ab371d8a24774ac19 --- /dev/null +++ b/css/chat.css @@ -0,0 +1,43 @@ +.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx { + height: 66.67vh +} + +.gradio-container { + margin-left: auto !important; + margin-right: auto !important; +} + +.w-screen { + width: unset +} + +div.svelte-362y77>*, div.svelte-362y77>.form>* { + flex-wrap: nowrap +} + +/* fixes the API documentation in chat mode */ +.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h { + display: grid; +} + +.pending.svelte-1ed2p3z { + opacity: 1; +} + +#extensions { + padding: 0; + padding: 0; +} + +#gradio-chatbot { + height: 66.67vh; +} + +.wrap.svelte-6roggh.svelte-6roggh { + max-height: 92.5%; +} + +/* This is for the microphone button in the whisper extension */ +.sm.svelte-1ipelgc { + width: 100%; +} diff --git a/css/chat.js b/css/chat.js new file mode 100644 index 0000000000000000000000000000000000000000..e304f1254732e475bf177ee849ac51d4f3e30f46 --- /dev/null +++ b/css/chat.js @@ -0,0 +1,4 @@ +document.getElementById("main").childNodes[0].style = "max-width: 800px; margin-left: auto; margin-right: auto"; +document.getElementById("extensions").style.setProperty("max-width", "800px"); +document.getElementById("extensions").style.setProperty("margin-left", "auto"); +document.getElementById("extensions").style.setProperty("margin-right", "auto"); diff --git a/css/chat_style-TheEncrypted777.css b/css/chat_style-TheEncrypted777.css new file mode 100644 index 0000000000000000000000000000000000000000..cac8015f505413b041df36552283c294caa94392 --- /dev/null +++ b/css/chat_style-TheEncrypted777.css @@ -0,0 +1,137 @@ +/* All credits to TheEncrypted777: https://www.reddit.com/r/Oobabooga/comments/12xe6vq/updated_css_styling_with_color_customization_for/ */ + +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 300px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + display: grid; + grid-template-columns: 60px minmax(0, 1fr); + padding-bottom: 28px; + font-size: 18px; + /*Change 'Quicksand' to a font you like or leave it*/ + font-family: Quicksand, Arial, sans-serif; + line-height: 1.428571429; +} + +.circle-you { + background-color: gray; + border-radius: 1rem; + /*Change color to any you like to be the border of your image*/ + border: 2px solid white; +} + +.circle-bot { + background-color: gray; + border-radius: 1rem; + /*Change color to any you like to be the border of the bot's image*/ + border: 2px solid white; +} + +.circle-bot img, +.circle-you img { + border-radius: 10%; + width: 100%; + height: 100%; + object-fit: cover; +} + +.circle-you, .circle-bot { + /*You can set the size of the profile images here, but if you do, you have to also adjust the .text{padding-left: 90px} to a different number according to the width of the image which is right below here*/ + width: 135px; + height: 175px; +} + +.text { + /*Change this to move the message box further left or right depending on the size of your profile pic*/ + padding-left: 90px; + text-shadow: 2px 2px 2px rgb(0, 0, 0); +} + +.text p { + margin-top: 2px; +} + +.username { + padding-left: 10px; + font-size: 22px; + font-weight: bold; + border-top: 1px solid rgb(51, 64, 90); + padding: 3px; +} + +.message-body { + position: relative; + border-radius: 1rem; + border: 1px solid rgba(255, 255, 255, 0.459); + border-radius: 10px; + padding: 10px; + padding-top: 5px; + /*Message gradient background color - remove the line bellow if you don't want a background color or gradient*/ + background: linear-gradient(to bottom, #171730, #1b263f); + } + + /*Adds 2 extra lines at the top and bottom of the message*/ + .message-body:before, + .message-body:after { + content: ""; + position: absolute; + left: 10px; + right: 10px; + height: 1px; + background-color: rgba(255, 255, 255, 0.13); + } + + .message-body:before { + top: 6px; + } + + .message-body:after { + bottom: 6px; + } + + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 18px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} diff --git a/css/chat_style-cai-chat.css b/css/chat_style-cai-chat.css new file mode 100644 index 0000000000000000000000000000000000000000..f601de3248b7ee94d6da58026354f8b9afeb9297 --- /dev/null +++ b/css/chat_style-cai-chat.css @@ -0,0 +1,91 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + display: grid; + grid-template-columns: 60px minmax(0, 1fr); + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.circle-you { + width: 50px; + height: 50px; + background-color: rgb(238, 78, 59); + border-radius: 50%; +} + +.circle-bot { + width: 50px; + height: 50px; + background-color: rgb(59, 78, 244); + border-radius: 50%; +} + +.circle-bot img, +.circle-you img { + border-radius: 50%; + width: 100%; + height: 100%; + object-fit: cover; +} + +.text {} + +.text p { + margin-top: 5px; +} + +.username { + font-weight: bold; +} + +.message-body {} + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 15px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} \ No newline at end of file diff --git a/css/chat_style-messenger.css b/css/chat_style-messenger.css new file mode 100644 index 0000000000000000000000000000000000000000..4d4bba0d902fe0d544a0203a8bf68d9c243ccbf6 --- /dev/null +++ b/css/chat_style-messenger.css @@ -0,0 +1,124 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.circle-you { + width: 50px; + height: 50px; + background-color: rgb(238, 78, 59); + border-radius: 50%; +} + +.circle-bot { + width: 50px; + height: 50px; + background-color: rgb(59, 78, 244); + border-radius: 50%; + float: left; + margin-right: 10px; + margin-top: 5px; +} + +.circle-bot img, +.circle-you img { + border-radius: 50%; + width: 100%; + height: 100%; + object-fit: cover; +} +.circle-you { + margin-top: 5px; + float: right; +} +.circle-bot + .text, .circle-you + .text { + border-radius: 18px; + padding: 8px 12px; +} + +.circle-bot + .text { + background-color: #E4E6EB; + float: left; +} + +.circle-you + .text { + float: right; + background-color: rgb(0, 132, 255); + margin-right: 10px; +} + +.circle-you + .text div, .circle-you + .text *, .dark .circle-you + .text div, .dark .circle-you + .text * { + color: #FFF !important; +} +.circle-you + .text .username { + text-align: right; +} + +.dark .circle-bot + .text div, .dark .circle-bot + .text * { + color: #000; +} + +.text { + max-width: 80%; +} + +.text p { + margin-top: 5px; +} + +.username { + font-weight: bold; +} + +.message-body {} + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 15px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} diff --git a/css/chat_style-wpp.css b/css/chat_style-wpp.css new file mode 100644 index 0000000000000000000000000000000000000000..a54a10734c0c14a1abe3ecd7fdb89602bc362dec --- /dev/null +++ b/css/chat_style-wpp.css @@ -0,0 +1,86 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.text-you { + background-color: #d9fdd3; + border-radius: 15px; + padding: 10px; + padding-top: 5px; + float: right; +} + +.text-bot { + background-color: #f2f2f2; + border-radius: 15px; + padding: 10px; + padding-top: 5px; +} + +.dark .text-you { + background-color: #005c4b; + color: #111b21; +} + +.dark .text-bot { + background-color: #1f2937; + color: #111b21; +} + +.text-bot p, .text-you p { + margin-top: 5px; +} + +.message-body {} + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 15px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} \ No newline at end of file diff --git a/css/html_4chan_style.css b/css/html_4chan_style.css new file mode 100644 index 0000000000000000000000000000000000000000..843e8a97fea80b010004f90f02ce63e8d13fe758 --- /dev/null +++ b/css/html_4chan_style.css @@ -0,0 +1,103 @@ +#parent #container { + background-color: #eef2ff; + padding: 17px; +} +#parent #container .reply { + background-color: rgb(214, 218, 240); + border-bottom-color: rgb(183, 197, 217); + border-bottom-style: solid; + border-bottom-width: 1px; + border-image-outset: 0; + border-image-repeat: stretch; + border-image-slice: 100%; + border-image-source: none; + border-image-width: 1; + border-left-color: rgb(0, 0, 0); + border-left-style: none; + border-left-width: 0px; + border-right-color: rgb(183, 197, 217); + border-right-style: solid; + border-right-width: 1px; + border-top-color: rgb(0, 0, 0); + border-top-style: none; + border-top-width: 0px; + color: rgb(0, 0, 0); + display: table; + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + margin-bottom: 4px; + margin-left: 0px; + margin-right: 0px; + margin-top: 4px; + overflow-x: hidden; + overflow-y: hidden; + padding-bottom: 4px; + padding-left: 2px; + padding-right: 2px; + padding-top: 4px; +} + +#parent #container .number { + color: rgb(0, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + width: 342.65px; + margin-right: 7px; +} + +#parent #container .op { + color: rgb(0, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + margin-bottom: 8px; + margin-left: 0px; + margin-right: 0px; + margin-top: 4px; + overflow-x: hidden; + overflow-y: hidden; +} + +#parent #container .op blockquote { + margin-left: 0px !important; +} + +#parent #container .name { + color: rgb(17, 119, 67); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + font-weight: 700; + margin-left: 7px; +} + +#parent #container .quote { + color: rgb(221, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + text-decoration-color: rgb(221, 0, 0); + text-decoration-line: underline; + text-decoration-style: solid; + text-decoration-thickness: auto; +} + +#parent #container .greentext { + color: rgb(120, 153, 34); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; +} + +#parent #container blockquote { + margin: 0px !important; + margin-block-start: 1em; + margin-block-end: 1em; + margin-inline-start: 40px; + margin-inline-end: 40px; + margin-top: 13.33px !important; + margin-bottom: 13.33px !important; + margin-left: 40px !important; + margin-right: 40px !important; +} + +#parent #container .message { + color: black; + border: none; +} \ No newline at end of file diff --git a/css/html_instruct_style.css b/css/html_instruct_style.css new file mode 100644 index 0000000000000000000000000000000000000000..2fd751d5672e6bc58d9f0e37d4ed79a501530d3d --- /dev/null +++ b/css/html_instruct_style.css @@ -0,0 +1,83 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + display: grid; + grid-template-columns: 60px 1fr; + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.username { + display: none; +} + +.message-body {} + +.message-body p { + font-size: 15px !important; + line-height: 1.75 !important; + margin-bottom: 1.25em !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(198, 202, 214) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} + +.gradio-container .chat .assistant-message { + padding: 15px; + border-radius: 20px; + background-color: #0000000f; + margin-top: 9px !important; + margin-bottom: 18px !important; +} + +.gradio-container .chat .user-message { + padding: 15px; + border-radius: 20px; + margin-bottom: 9px !important; +} + +.dark .chat .assistant-message { + background-color: #374151; +} + +code { + background-color: white !important; +} + +.dark code { + background-color: #1a212f !important; +} \ No newline at end of file diff --git a/css/html_readable_style.css b/css/html_readable_style.css new file mode 100644 index 0000000000000000000000000000000000000000..83fa46b58f04c5c467e2203e1ed950d6daf17d7e --- /dev/null +++ b/css/html_readable_style.css @@ -0,0 +1,29 @@ +.container { + max-width: 600px; + margin-left: auto; + margin-right: auto; + background-color: rgb(31, 41, 55); + padding:3em; + word-break: break-word; + overflow-wrap: anywhere; + color: #efefef !important; +} + +.container p, .container li { + font-size: 16px !important; + color: #efefef !important; + margin-bottom: 22px; + line-height: 1.4 !important; +} + +.container li > p { + display: inline !important; +} + +.container code { + overflow-x: auto; +} + +.container :not(pre) > code { + white-space: normal !important; +} \ No newline at end of file diff --git a/css/main.css b/css/main.css new file mode 100644 index 0000000000000000000000000000000000000000..b87053bef0688b990a45864739a01a4f3d79aafd --- /dev/null +++ b/css/main.css @@ -0,0 +1,117 @@ +.tabs.svelte-710i53 { + margin-top: 0 +} + +.py-6 { + padding-top: 2.5rem +} + +.dark #refresh-button { + background-color: #ffffff1f; +} + +#refresh-button { + flex: none; + margin: 0; + padding: 0; + min-width: 50px; + border: none; + box-shadow: none; + border-radius: 10px; + background-color: #0000000d; +} + +#download-label, #upload-label { + min-height: 0 +} + +#accordion { +} + +.dark svg { + fill: white; +} + +.dark a { + color: white !important; + text-decoration: none !important; +} + +ol li p, ul li p { + display: inline-block; +} + +#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab, #model-tab { + border: 0; +} + +.gradio-container-3-18-0 .prose * h1, h2, h3, h4 { + color: white; +} + +.gradio-container { + max-width: 100% !important; + padding-top: 0 !important; +} + +#extensions { + padding: 15px; + margin-bottom: 35px; +} + +.extension-tab { + border: 0 !important; +} + +span.math.inline { + font-size: 27px; + vertical-align: baseline !important; +} + +div.svelte-15lo0d8 > *, div.svelte-15lo0d8 > .form > * { + flex-wrap: nowrap; +} + +.header_bar { + background-color: #f7f7f7; + margin-bottom: 40px; +} + +.dark .header_bar { + border: none !important; + background-color: #8080802b; +} + +.textbox_default textarea { + height: calc(100vh - 391px); +} + +.textbox_default_output textarea { + height: calc(100vh - 210px); +} + +.textbox textarea { + height: calc(100vh - 261px); +} + +.textbox_default textarea, .textbox_default_output textarea, .textbox textarea { + font-size: 16px !important; + color: #46464A !important; +} + +.dark textarea { + color: #efefef !important; +} + +/* Hide the gradio footer*/ +footer { + display: none !important; +} + +button { + font-size: 14px !important; +} + +.small-button { + max-width: 171px; +} \ No newline at end of file diff --git a/css/main.js b/css/main.js new file mode 100644 index 0000000000000000000000000000000000000000..32820ebe15ddb80ca5fbcd2c4f88cc7c244cf3c5 --- /dev/null +++ b/css/main.js @@ -0,0 +1,18 @@ +document.getElementById("main").parentNode.childNodes[0].classList.add("header_bar"); +document.getElementById("main").parentNode.style = "padding: 0; margin: 0"; +document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0"; + +// Get references to the elements +let main = document.getElementById('main'); +let main_parent = main.parentNode; +let extensions = document.getElementById('extensions'); + +// Add an event listener to the main element +main_parent.addEventListener('click', function(e) { + // Check if the main element is visible + if (main.offsetHeight > 0 && main.offsetWidth > 0) { + extensions.style.display = 'flex'; + } else { + extensions.style.display = 'none'; + } +}); diff --git a/docker/.dockerignore b/docker/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..6073533e0929aac9e917a5980198334a2a01f8ef --- /dev/null +++ b/docker/.dockerignore @@ -0,0 +1,9 @@ +.env +Dockerfile +/characters +/loras +/models +/presets +/prompts +/softprompts +/training diff --git a/docker/.env.example b/docker/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..3119a9f07f47b1ad964ce337c33f1ce63d79f377 --- /dev/null +++ b/docker/.env.example @@ -0,0 +1,30 @@ +# by default the Dockerfile specifies these versions: 3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX +# however for me to work i had to specify the exact version for my card ( 2060 ) it was 7.5 +# https://developer.nvidia.com/cuda-gpus you can find the version for your card here +TORCH_CUDA_ARCH_LIST=7.5 + +# these commands worked for me with roughly 4.5GB of vram +CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices + +# the following examples have been tested with the files linked in docs/README_docker.md: +# example running 13b with 4bit/128 groupsize : CLI_ARGS=--model llama-13b-4bit-128g --wbits 4 --listen --groupsize 128 --pre_layer 25 +# example with loading api extension and public share: CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices --no-stream --extensions api --share +# example running 7b with 8bit groupsize : CLI_ARGS=--model llama-7b --load-in-8bit --listen --auto-devices + +# the port the webui binds to on the host +HOST_PORT=7860 +# the port the webui binds to inside the container +CONTAINER_PORT=7860 + +# the port the api binds to on the host +HOST_API_PORT=5000 +# the port the api binds to inside the container +CONTAINER_API_PORT=5000 + +# the port the api stream endpoint binds to on the host +HOST_API_STREAM_PORT=5005 +# the port the api stream endpoint binds to inside the container +CONTAINER_API_STREAM_PORT=5005 + +# the version used to install text-generation-webui from +WEBUI_VERSION=HEAD diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b4fc91216606d74fc4505c7d85330b557341a4f1 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,68 @@ +FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder + +RUN apt-get update && \ + apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \ + rm -rf /var/lib/apt/lists/* + +RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa /build + +WORKDIR /build + +RUN python3 -m venv /build/venv +RUN . /build/venv/bin/activate && \ + pip3 install --upgrade pip setuptools && \ + pip3 install torch torchvision torchaudio && \ + pip3 install -r requirements.txt + +# https://developer.nvidia.com/cuda-gpus +# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5" +ARG TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX" +RUN . /build/venv/bin/activate && \ + python3 setup_cuda.py bdist_wheel -d . + +FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04 + +LABEL maintainer="Your Name " +LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI" + +RUN apt-get update && \ + apt-get install --no-install-recommends -y libportaudio2 libasound-dev git python3 python3-pip make g++ && \ + rm -rf /var/lib/apt/lists/* + +RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv +RUN mkdir /app + +WORKDIR /app + +ARG WEBUI_VERSION +RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source" + +RUN virtualenv /app/venv +RUN . /app/venv/bin/activate && \ + pip3 install --upgrade pip setuptools && \ + pip3 install torch torchvision torchaudio + +COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa +RUN . /app/venv/bin/activate && \ + pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl + +COPY extensions/api/requirements.txt /app/extensions/api/requirements.txt +COPY extensions/elevenlabs_tts/requirements.txt /app/extensions/elevenlabs_tts/requirements.txt +COPY extensions/google_translate/requirements.txt /app/extensions/google_translate/requirements.txt +COPY extensions/silero_tts/requirements.txt /app/extensions/silero_tts/requirements.txt +COPY extensions/whisper_stt/requirements.txt /app/extensions/whisper_stt/requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt + +COPY requirements.txt /app/requirements.txt +RUN . /app/venv/bin/activate && \ + pip3 install -r requirements.txt + +RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so + +COPY . /app/ +ENV CLI_ARGS="" +CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS} diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..bc59dc3bb11d6ffcb692ef8e7a84f3054b126d4d --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,32 @@ +version: "3.3" +services: + text-generation-webui: + build: + context: . + args: + # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus + TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST} + WEBUI_VERSION: ${WEBUI_VERSION} + env_file: .env + ports: + - "${HOST_PORT}:${CONTAINER_PORT}" + - "${HOST_API_PORT}:${CONTAINER_API_PORT}" + - "${HOST_API_STREAM_PORT}:${CONTAINER_API_STREAM_PORT}" + stdin_open: true + tty: true + volumes: + - ./characters:/app/characters + - ./extensions:/app/extensions + - ./loras:/app/loras + - ./models:/app/models + - ./presets:/app/presets + - ./prompts:/app/prompts + - ./softprompts:/app/softprompts + - ./training:/app/training + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + capabilities: [gpu] diff --git a/docs/Chat-mode.md b/docs/Chat-mode.md new file mode 100644 index 0000000000000000000000000000000000000000..08dd290dadbd8a590ace65d557b8916a2707fc26 --- /dev/null +++ b/docs/Chat-mode.md @@ -0,0 +1,45 @@ +## Chat characters + +Custom chat mode characters are defined by `.yaml` files inside the `characters` folder. An example is included: [Example.yaml](https://github.com/oobabooga/text-generation-webui/blob/main/characters/Example.yaml) + +The following fields may be defined: + +| Field | Description | +|-------|-------------| +| `name` or `bot` | The character's name. | +| `your_name` or `user` (optional) | Your name. This overwrites what you had previously written in the `Your name` field in the interface. | +| `context` | A string that appears at the top of the prompt. It usually contains a description of the character's personality. | +| `greeting` (optional) | The character's opening message when a new conversation is started. | +| `example_dialogue` (optional) | A few example messages to guide the model. | +| `turn_template` (optional) | Used to define where the spaces and new line characters should be in Instruct mode. See the characters in `characters/instruction-following` for examples. | + +#### Special tokens + +* `{{char}}` or ``: are replaced with the character's name +* `{{user}}` or ``: are replaced with your name + +These replacements happen when the character is loaded, and they apply to the `context`, `greeting`, and `example_dialogue` fields. + +#### How do I add a profile picture for my character? + +Put an image with the same name as your character's yaml file into the `characters` folder. For example, if your bot is `Character.yaml`, add `Character.jpg` or `Character.png` to the folder. + +#### Is the chat history truncated in the prompt? + +Once your prompt reaches the 2048 token limit, old messages will be removed one at a time. The context string will always stay at the top of the prompt and will never get truncated. + +#### Pygmalion format characters + +These are also supported out of the box. Simply put the JSON file in the `characters` folder, or upload it directly from the web UI by clicking on the "Upload character" tab at the bottom. + +## Chat styles + +Custom chat styles can be defined in the `text-generation-webui/css` folder. Simply create a new file with name starting in `chat_style-` and ending in `.css` and it will automatically appear in the "Chat style" dropdown menu in the interface. Examples: + +``` +chat_style-cai-chat.css +chat_style-TheEncrypted777.css +chat_style-wpp.css +``` + +You should use the same class names as in `chat_style-cai-chat.css` in your custom style. \ No newline at end of file diff --git a/docs/DeepSpeed.md b/docs/DeepSpeed.md new file mode 100644 index 0000000000000000000000000000000000000000..6170f6819ca072ff50fd1146b64d73f74ab00473 --- /dev/null +++ b/docs/DeepSpeed.md @@ -0,0 +1,24 @@ +An alternative way of reducing the GPU memory usage of models is to use the `DeepSpeed ZeRO-3` optimization. + +With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`. + +As far as I know, DeepSpeed is only available for Linux at the moment. + +### How to use it + +1. Install DeepSpeed: + +``` +conda install -c conda-forge mpi4py mpich +pip install -U deepspeed +``` + +2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example: + +``` +deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B +``` + +### Learn more + +For more information, check out [this comment](https://github.com/oobabooga/text-generation-webui/issues/40#issuecomment-1412038622) by 81300, who came up with the DeepSpeed support in this web UI. \ No newline at end of file diff --git a/docs/Docker.md b/docs/Docker.md new file mode 100644 index 0000000000000000000000000000000000000000..b1e92253cd72423a86d72f6bb057da9bed19a4bc --- /dev/null +++ b/docs/Docker.md @@ -0,0 +1,181 @@ +Docker Compose is a way of installing and launching the web UI in an isolated Ubuntu image using only a few commands. + +In order to create the image as described in the main README, you must have docker compose 2.17 or higher: + +``` +~$ docker compose version +Docker Compose version v2.17.2 +``` + +# Intructions by [@loeken](https://github.com/loeken) + +- [Ubuntu 22.04](#ubuntu-2204) + - [0. youtube video](#0-youtube-video) + - [1. update the drivers](#1-update-the-drivers) + - [2. reboot](#2-reboot) + - [3. install docker](#3-install-docker) + - [4. docker \& container toolkit](#4-docker--container-toolkit) + - [5. clone the repo](#5-clone-the-repo) + - [6. prepare models](#6-prepare-models) + - [7. prepare .env file](#7-prepare-env-file) + - [8. startup docker container](#8-startup-docker-container) +- [Manjaro](#manjaro) + - [update the drivers](#update-the-drivers) + - [reboot](#reboot) + - [docker \& container toolkit](#docker--container-toolkit) + - [continue with ubuntu task](#continue-with-ubuntu-task) +- [Windows](#windows) + - [0. youtube video](#0-youtube-video-1) + - [1. choco package manager](#1-choco-package-manager) + - [2. install drivers/dependencies](#2-install-driversdependencies) + - [3. install wsl](#3-install-wsl) + - [4. reboot](#4-reboot) + - [5. git clone \&\& startup](#5-git-clone--startup) + - [6. prepare models](#6-prepare-models-1) + - [7. startup](#7-startup) +- [notes](#notes) + +# Ubuntu 22.04 + +## 0. youtube video +A video walking you through the setup can be found here: + +[![oobabooga text-generation-webui setup in docker on ubuntu 22.04](https://img.youtube.com/vi/ELkKWYh8qOk/0.jpg)](https://www.youtube.com/watch?v=ELkKWYh8qOk) + + +## 1. update the drivers +in the the “software updater” update drivers to the last version of the prop driver. + +## 2. reboot +to switch using to new driver + +## 3. install docker +```bash +sudo apt update +sudo apt-get install curl +sudo mkdir -m 0755 -p /etc/apt/keyrings +curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg +echo \ + "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \ + "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \ + sudo tee /etc/apt/sources.list.d/docker.list > /dev/null +sudo apt update +sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-compose -y +sudo usermod -aG docker $USER +newgrp docker +``` + +## 4. docker & container toolkit +```bash +curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg +echo "deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/amd64 /" | \ +sudo tee /etc/apt/sources.list.d/nvidia.list > /dev/null +sudo apt update +sudo apt install nvidia-docker2 nvidia-container-runtime -y +sudo systemctl restart docker +``` + +## 5. clone the repo +``` +git clone https://github.com/oobabooga/text-generation-webui +cd text-generation-webui +``` + +## 6. prepare models +download and place the models inside the models folder. tested with: + +4bit +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +8bit: +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 + +## 7. prepare .env file +edit .env values to your needs. +```bash +cp .env.example .env +nano .env +``` + +## 8. startup docker container +```bash +docker compose up --build +``` + +# Manjaro +manjaro/arch is similar to ubuntu just the dependency installation is more convenient + +## update the drivers +```bash +sudo mhwd -a pci nonfree 0300 +``` +## reboot +```bash +reboot +``` +## docker & container toolkit +```bash +yay -S docker docker-compose buildkit gcc nvidia-docker +sudo usermod -aG docker $USER +newgrp docker +sudo systemctl restart docker # required by nvidia-container-runtime +``` + +## continue with ubuntu task +continue at [5. clone the repo](#5-clone-the-repo) + +# Windows +## 0. youtube video +A video walking you through the setup can be found here: +[![oobabooga text-generation-webui setup in docker on windows 11](https://img.youtube.com/vi/ejH4w5b5kFQ/0.jpg)](https://www.youtube.com/watch?v=ejH4w5b5kFQ) + +## 1. choco package manager +install package manager (https://chocolatey.org/ ) +``` +Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1')) +``` + +## 2. install drivers/dependencies +``` +choco install nvidia-display-driver cuda git docker-desktop +``` + +## 3. install wsl +wsl --install + +## 4. reboot +after reboot enter username/password in wsl + +## 5. git clone && startup +clone the repo and edit .env values to your needs. +``` +cd Desktop +git clone https://github.com/oobabooga/text-generation-webui +cd text-generation-webui +COPY .env.example .env +notepad .env +``` + +## 6. prepare models +download and place the models inside the models folder. tested with: + +4bit https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +8bit: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 + +## 7. startup +``` +docker compose up +``` + +# notes + +on older ubuntus you can manually install the docker compose plugin like this: +``` +DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker} +mkdir -p $DOCKER_CONFIG/cli-plugins +curl -SL https://github.com/docker/compose/releases/download/v2.17.2/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose +chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose +export PATH="$HOME/.docker/cli-plugins:$PATH" +``` diff --git a/docs/Extensions.md b/docs/Extensions.md new file mode 100644 index 0000000000000000000000000000000000000000..0e396ce2c350242abf45056f869e27c3e3381de7 --- /dev/null +++ b/docs/Extensions.md @@ -0,0 +1,220 @@ +Extensions are defined by files named `script.py` inside subfolders of `text-generation-webui/extensions`. They are loaded at startup if specified with the `--extensions` flag. + +For instance, `extensions/silero_tts/script.py` gets loaded with `python server.py --extensions silero_tts`. + +## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions) + +The link above contains a directory of user extensions for text-generation-webui. + +If you create an extension, you are welcome to host it in a GitHub repository and submit it to the list above. + +## Built-in extensions + +Most of these have been created by the extremely talented contributors that you can find here: [contributors](https://github.com/oobabooga/text-generation-webui/graphs/contributors?from=2022-12-18&to=&type=a). + +|Extension|Description| +|---------|-----------| +|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` port 5000. This is the main API for this web UI. | +|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.| +|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that biases the bot's responses in chat mode.| +|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. | +|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, it replaces the responses with an audio widget. | +|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. | +|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. | +|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. | +|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). | +|[multimodal](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal) | Adds multimodality support (text+images). For a detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal/README.md) in the extension directory. | +|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. | +|[superbooga](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/superbooga)| An extension that uses ChromaDB to create an arbitrarily large pseudocontext, taking as input text files, URLs, or pasted text. Based on https://github.com/kaiokendev/superbig. | + +## How to write an extension + +script.py may define the special functions and variables below. + +#### Predefined functions + +| Function | Description | +|-------------|-------------| +| `def ui()` | Creates custom gradio elements when the UI is launched. | +| `def custom_css()` | Returns custom CSS as a string. It is applied whenever the web UI is loaded. | +| `def custom_js()` | Same as above but for javascript. | +| `def input_modifier(string)` | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. | +| `def output_modifier(string)` | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. | +| `def state_modifier(state)` | Modifies the dictionary containing the UI input parameters before it is used by the text generation functions. | +| `def bot_prefix_modifier(string)` | Applied in chat mode to the prefix for the bot's reply. | +| `def custom_generate_reply(...)` | Overrides the main text generation function. | +| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. | +| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See the `multimodal` extension for an example. | +| `def custom_tokenized_length(prompt)` | Used in conjunction with `tokenizer_modifier`, returns the length in tokens of `prompt`. See the `multimodal` extension for an example. | + +#### `params` dictionary + +In this dictionary, `display_name` is used to define the displayed name of the extension in the UI, and `is_tab` is used to define whether the extension should appear in a new tab. By default, extensions appear at the bottom of the "Text generation" tab. + +Example: + +```python +params = { + "display_name": "Google Translate", + "is_tab": True, +} +``` + +Additionally, `params` may contain variables that you want to be customizable through a `settings.json` file. For instance, assuming the extension is in `extensions/google_translate`, the variable `language string` in + +```python +params = { + "display_name": "Google Translate", + "is_tab": True, + "language string": "jp" +} +``` + +can be customized by adding a key called `google_translate-language string` to `settings.json`: + +```python +"google_translate-language string": "fr", +``` + +That is, the syntax is `extension_name-variable_name`. + +#### `input_hijack` dictionary + +```python +input_hijack = { + 'state': False, + 'value': ["", ""] +} +``` +This is only used in chat mode. If your extension sets `input_hijack['state'] = True` at any moment, the next call to `modules.chat.chatbot_wrapper` will use the values inside `input_hijack['value']` as the user input for text generation. See the `send_pictures` extension above for an example. + +Additionally, your extension can set the value to be a callback in the form of `def cb(text: str, visible_text: str) -> [str, str]`. See the `multimodal` extension above for an example. + +## Using multiple extensions at the same time + +In order to use your extension, you must start the web UI with the `--extensions` flag followed by the name of your extension (the folder under `text-generation-webui/extension` where `script.py` resides). + +You can activate more than one extension at a time by providing their names separated by spaces. The input, output, and bot prefix modifiers will be applied in the specified order. + + +``` +python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate +python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm +``` + +Do note, that for: +- `custom_generate_chat_prompt` +- `custom_generate_reply` +- `tokenizer_modifier` +- `custom_tokenized_length` + +only the first declaration encountered will be used and the rest will be ignored. + +## The `bot_prefix_modifier` + +In chat mode, this function modifies the prefix for a new bot message. For instance, if your bot is named `Marie Antoinette`, the default prefix for a new message will be + +``` +Marie Antoinette: +``` + +Using `bot_prefix_modifier`, you can change it to: + +``` +Marie Antoinette: *I am very enthusiastic* +``` + +Marie Antoinette will become very enthusiastic in all her messages. + +## `custom_generate_reply` example + +Once defined in a `script.py`, this function is executed in place of the main generation functions. You can use it to connect the web UI to an external API, or to load a custom model that is not supported yet. + +Note that in chat mode, this function must only return the new text, whereas in other modes it must return the original prompt + the new text. + +```python +import datetime + +def custom_generate_reply(question, original_question, seed, state, eos_token, stopping_strings): + cumulative = '' + for i in range(10): + cumulative += f"Counting: {i}...\n" + yield cumulative + + cumulative += f"Done! {str(datetime.datetime.now())}" + yield cumulative +``` + +## `custom_generate_chat_prompt` example + +Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode. + +```python +def custom_generate_chat_prompt(user_input, state, **kwargs): + impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False + _continue = kwargs['_continue'] if '_continue' in kwargs else False + also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False + is_instruct = state['mode'] == 'instruct' + rows = [state['context'] if is_instruct else f"{state['context'].strip()}\n"] + min_rows = 3 + + # Finding the maximum prompt size + chat_prompt_size = state['chat_prompt_size'] + if shared.soft_prompt: + chat_prompt_size -= shared.soft_prompt_tensor.shape[1] + + max_length = min(get_max_prompt_length(state), chat_prompt_size) + + # Building the turn templates + if 'turn_template' not in state or state['turn_template'] == '': + if is_instruct: + template = '<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n' + else: + template = '<|user|>: <|user-message|>\n<|bot|>: <|bot-message|>\n' + else: + template = state['turn_template'].replace(r'\n', '\n') + + replacements = { + '<|user|>': state['name1'].strip(), + '<|bot|>': state['name2'].strip(), + } + + user_turn = replace_all(template.split('<|bot|>')[0], replacements) + bot_turn = replace_all('<|bot|>' + template.split('<|bot|>')[1], replacements) + user_turn_stripped = replace_all(user_turn.split('<|user-message|>')[0], replacements) + bot_turn_stripped = replace_all(bot_turn.split('<|bot-message|>')[0], replacements) + + # Building the prompt + i = len(shared.history['internal']) - 1 + while i >= 0 and get_encoded_length(''.join(rows)) < max_length: + if _continue and i == len(shared.history['internal']) - 1: + rows.insert(1, bot_turn_stripped + shared.history['internal'][i][1].strip()) + else: + rows.insert(1, bot_turn.replace('<|bot-message|>', shared.history['internal'][i][1].strip())) + + string = shared.history['internal'][i][0] + if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']: + rows.insert(1, replace_all(user_turn, {'<|user-message|>': string.strip(), '<|round|>': str(i)})) + + i -= 1 + + if impersonate: + min_rows = 2 + rows.append(user_turn_stripped.rstrip(' ')) + elif not _continue: + # Adding the user message + if len(user_input) > 0: + rows.append(replace_all(user_turn, {'<|user-message|>': user_input.strip(), '<|round|>': str(len(shared.history["internal"]))})) + + # Adding the Character prefix + rows.append(apply_extensions("bot_prefix", bot_turn_stripped.rstrip(' '))) + + while len(rows) > min_rows and get_encoded_length(''.join(rows)) >= max_length: + rows.pop(1) + + prompt = ''.join(rows) + if also_return_rows: + return prompt, rows + else: + return prompt +``` diff --git a/docs/FlexGen.md b/docs/FlexGen.md new file mode 100644 index 0000000000000000000000000000000000000000..dce71f9e6e35ab1f55d8379852316f55b013962a --- /dev/null +++ b/docs/FlexGen.md @@ -0,0 +1,64 @@ +>FlexGen is a high-throughput generation engine for running large language models with limited GPU memory (e.g., a 16GB T4 GPU or a 24GB RTX3090 gaming card!). + +https://github.com/FMInference/FlexGen + +## Installation + +No additional installation steps are necessary. FlexGen is in the `requirements.txt` file for this project. + +## Converting a model + +FlexGen only works with the OPT model, and it needs to be converted to numpy format before starting the web UI: + +``` +python convert-to-flexgen.py models/opt-1.3b/ +``` + +The output will be saved to `models/opt-1.3b-np/`. + +## Usage + +The basic command is the following: + +``` +python server.py --model opt-1.3b --flexgen +``` + +For large models, the RAM usage may be too high and your computer may freeze. If that happens, you can try this: + +``` +python server.py --model opt-1.3b --flexgen --compress-weight +``` + +With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB VRAM**, and the speed was good in both cases. + +You can also manually set the offload strategy with + +``` +python server.py --model opt-1.3b --flexgen --percent 0 100 100 0 100 0 +``` + +where the six numbers after `--percent` are: + +``` +the percentage of weight on GPU +the percentage of weight on CPU +the percentage of attention cache on GPU +the percentage of attention cache on CPU +the percentage of activations on GPU +the percentage of activations on CPU +``` + +You should typically only change the first two numbers. If their sum is less than 100, the remaining layers will be offloaded to the disk, by default into the `text-generation-webui/cache` folder. + +## Performance + +In my experiments with OPT-30B using a RTX 3090 on Linux, I have obtained these results: + +* `--flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token. +* `--flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token. + +## Limitations + +* Only works with the OPT models. +* Only two generation parameters are available: `temperature` and `do_sample`. \ No newline at end of file diff --git a/docs/GPTQ-models-(4-bit-mode).md b/docs/GPTQ-models-(4-bit-mode).md new file mode 100644 index 0000000000000000000000000000000000000000..0ec28fa6c6be7a3d8d22c76cde53a1bcde06f6f2 --- /dev/null +++ b/docs/GPTQ-models-(4-bit-mode).md @@ -0,0 +1,144 @@ +In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU. + +This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa + +GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323 + +## GPTQ-for-LLaMa branches + +Different branches of GPTQ-for-LLaMa are available: + +| Branch | Comment | +|----|----| +| [Old CUDA branch (recommended)](https://github.com/oobabooga/GPTQ-for-LLaMa/) | The fastest branch, works on Windows and Linux. | +| [Up-to-date triton branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa) | Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux. | +| [Up-to-date CUDA branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda) | As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size. | + +Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI. + +## Installation + +### Step 0: install nvcc + +``` +conda activate textgen +conda install -c conda-forge cudatoolkit-dev +``` + +The command above takes some 10 minutes to run and shows no progress bar or updates along the way. + +See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571 + +### Step 1: install GPTQ-for-LLaMa + +Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it: + +``` +mkdir repositories +cd repositories +git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda +cd GPTQ-for-LLaMa +python setup_cuda.py install +``` + +You are going to need to have a C++ compiler installed into your system for the last command. On Linux, `sudo apt install build-essential` or equivalent is enough. + +If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands: + +``` +cd repositories +rm -r GPTQ-for-LLaMa +pip uninstall -y quant-cuda +git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda +... +``` + +``` +cd repositories +rm -r GPTQ-for-LLaMa +pip uninstall -y quant-cuda +git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton +... +``` + + +https://github.com/qwopqwop200/GPTQ-for-LLaMa + +### Step 2: get the pre-converted weights + +* Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 +* Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights). + +### Step 3: Start the web UI: + +For the models converted without `group-size`: + +``` +python server.py --model llama-7b-4bit +``` + +For the models converted with `group-size`: + +``` +python server.py --model llama-13b-4bit-128g +``` + +The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like + +``` +python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128 +``` + +## CPU offloading + +It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU. + +With this command, I can run llama-7b with 4GB VRAM: + +``` +python server.py --model llama-7b-4bit --pre_layer 20 +``` + +This is the performance: + +``` +Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens) +``` + +You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of GPTQ, eg `--pre_layer 30 60` will load a LLaMA-30B model half onto your first GPU and half onto your second, or `--pre_layer 20 40` will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU. + +## Using LoRAs in 4-bit mode + +At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit + +In order to use it: + +1. Make sure that your requirements are up to date: + +``` +cd text-generation-webui +pip install -r requirements.txt --upgrade +``` + +2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder: + +``` +cd text-generation-webui/repositories +git clone https://github.com/johnsmith0031/alpaca_lora_4bit +``` + +⚠️ I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0` + +3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command: + +``` +pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit +``` + +4. Start the UI with the `--monkey-patch` flag: + +``` +python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch +``` diff --git a/docs/LLaMA-model.md b/docs/LLaMA-model.md new file mode 100644 index 0000000000000000000000000000000000000000..338d458b13b56b3d0f02dd3f4b7d5156a82b88e9 --- /dev/null +++ b/docs/LLaMA-model.md @@ -0,0 +1,45 @@ +LLaMA is a Large Language Model developed by Meta AI. + +It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters. + +This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode) +](GPTQ-models-(4-bit-mode).md). + +## Getting the weights + +### Option 1: pre-converted weights + +* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 +* Direct download: https://huggingface.co/Neko-Institute-of-Science + +⚠️ The tokenizers for the Torrent source above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer: + +``` +python download-model.py oobabooga/llama-tokenizer +``` + +Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load. + +### Option 2: convert the weights yourself + +1. Install the `protobuf` library: + +``` +pip install protobuf==3.20.1 +``` + +2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link: + +### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) + +``` +python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b +``` + +3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder. + +## Starting the web UI + +```python +python server.py --model llama-7b +``` diff --git a/docs/Low-VRAM-guide.md b/docs/Low-VRAM-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..1dc86f9c7f764a886c454f7f76a2a89a77140655 --- /dev/null +++ b/docs/Low-VRAM-guide.md @@ -0,0 +1,51 @@ +If you GPU is not large enough to fit a model, try these in the following order: + +### Load the model in 8-bit mode + +``` +python server.py --load-in-8bit +``` + +This reduces the memory usage by half with no noticeable loss in quality. Only newer GPUs support 8-bit mode. + +### Split the model across your GPU and CPU + +``` +python server.py --auto-devices +``` + +If you can load the model with this command but it runs out of memory when you try to generate text, try increasingly limiting the amount of memory allocated to the GPU until the error stops happening: + +``` +python server.py --auto-devices --gpu-memory 10 +python server.py --auto-devices --gpu-memory 9 +python server.py --auto-devices --gpu-memory 8 +... +``` + +where the number is in GiB. + +For finer control, you can also specify the unit in MiB explicitly: + +``` +python server.py --auto-devices --gpu-memory 8722MiB +python server.py --auto-devices --gpu-memory 4725MiB +python server.py --auto-devices --gpu-memory 3500MiB +... +``` + +Additionally, you can also set the `--no-cache` value to reduce the GPU usage while generating text at a performance cost. This may allow you to set a higher value for `--gpu-memory`, resulting in a net performance gain. + +### Send layers to a disk cache + +As a desperate last measure, you can split the model across your GPU, CPU, and disk: + +``` +python server.py --auto-devices --disk +``` + +With this, I am able to load a 30b model into my RTX 3090, but it takes 10 seconds to generate 1 word. + +### DeepSpeed (experimental) + +An experimental alternative to all of the above is to use DeepSpeed: [guide](DeepSpeed.md). diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..65dadd7cfc906247d9c6995896ba6a144a0d31c1 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,19 @@ +# text-generation-webui documentation + +## Table of contents + +* [GPTQ models (4 bit mode)](GPTQ-models-(4-bit-mode).md) +* [LLaMA model](LLaMA-model.md) +* [Using LoRAs](Using-LoRAs.md) +* [llama.cpp models](llama.cpp-models.md) +* [RWKV model](RWKV-model.md) +* [Extensions](Extensions.md) +* [Chat mode](Chat-mode.md) +* [DeepSpeed](DeepSpeed.md) +* [FlexGen](FlexGen.md) +* [Spell book](Spell-book.md) +* [Low-VRAM-guide](Low-VRAM-guide.md) +* [System requirements](System-requirements.md) +* [Windows installation guide](Windows-installation-guide.md) +* [WSL installation guide](WSL-installation-guide.md) +* [Docker Compose](Docker.md) diff --git a/docs/RWKV-model.md b/docs/RWKV-model.md new file mode 100644 index 0000000000000000000000000000000000000000..27db3d10ca98faffee7c254a3244405f8cce2793 --- /dev/null +++ b/docs/RWKV-model.md @@ -0,0 +1,54 @@ +> RWKV: RNN with Transformer-level LLM Performance +> +> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state). + +https://github.com/BlinkDL/RWKV-LM + +https://github.com/BlinkDL/ChatRWKV + +## Using RWKV in the web UI + +#### 1. Download the model + +It is available in different sizes: + +* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/ +* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/ +* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/ + +There are also older releases with smaller sizes like: + +* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth + +Download the chosen `.pth` and put it directly in the `models` folder. + +#### 2. Download the tokenizer + +[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json) + +Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`. + +#### 3. Launch the web UI + +No additional steps are required. Just launch it as you would with any other model. + +``` +python server.py --listen --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth +``` + +## Setting a custom strategy + +It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include: + +``` +"cpu fp32" # CPU mode +"cuda fp16" # GPU mode with float16 precision +"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation. +"cuda fp16i8" # GPU mode with 8-bit precision +``` + +See the README for the PyPl package for more details: https://pypi.org/project/rwkv/ + +## Compiling the CUDA kernel + +You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet. \ No newline at end of file diff --git a/docs/Spell-book.md b/docs/Spell-book.md new file mode 100644 index 0000000000000000000000000000000000000000..9b7c76c953f76f8a486bbe5156de4e9ebb3f0ec0 --- /dev/null +++ b/docs/Spell-book.md @@ -0,0 +1,107 @@ +You have now entered a hidden corner of the internet. + +A confusing yet intriguing realm of paradoxes and contradictions. + +A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along. + +![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg) + +*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.* + +#### You can train LoRAs in CPU mode + +Load the web UI with + +``` +python server.py --cpu +``` + +and start training the LoRA from the training tab as usual. + +#### 8-bit mode works with CPU offloading + +``` +python server.py --load-in-8bit --gpu-memory 4000MiB +``` + +#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models + +``` +python server.py --wbits 4 --groupsize 128 --pre_layer 20 +``` + +#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes + +``` +python server.py --cpu +python server.py +python server.py --load-in-8bit +python server.py --wbits 4 +``` + +#### The web UI works with any version of GPTQ-for-LLaMa + +Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time: + +``` +cd text-generation-webui/repositories +rm -r GPTQ-for-LLaMa +pip uninstall quant-cuda +git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch +cd GPTQ-for-LLaMa +python setup_cuda.py install +``` + +#### Instruction-following templates are represented as chat characters + +https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following + +#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode + +Otherwise the prompt will not be formatted correctly. + +1. Start the web UI with + +``` +python server.py --chat +``` + +2. Click on the "instruct" option under "Chat modes" + +3. Select the correct template in the hidden dropdown menu that will become visible. + +#### Notebook mode is best mode + +Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating. + +#### RWKV is a RNN + +Most models are transformers, but not RWKV, which is a RNN. It's a great model. + +#### `--gpu-memory` is not a hard limit on the GPU memory + +It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory. + +#### Contrastive search perhaps the best preset + +But it uses a ton of VRAM. + +#### You can check the sha256sum of downloaded models with the download script + +``` +python download-model.py facebook/galactica-125m --check +``` + +#### The download script continues interrupted downloads by default + +It doesn't start over. + +#### You can download models with multiple threads + +``` +python download-model.py facebook/galactica-125m --threads 8 +``` + +#### LoRAs work in 4-bit mode + +You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag. diff --git a/docs/System-requirements.md b/docs/System-requirements.md new file mode 100644 index 0000000000000000000000000000000000000000..3a88416d34ad7c8babd90a81db902e95288a8197 --- /dev/null +++ b/docs/System-requirements.md @@ -0,0 +1,42 @@ +These are the VRAM and RAM requirements (in MiB) to run some examples of models **in 16-bit (default) precision**: + +| model | VRAM (GPU) | RAM | +|:-----------------------|-------------:|--------:| +| arxiv_ai_gpt2 | 1512.37 | 5824.2 | +| blenderbot-1B-distill | 2441.75 | 4425.91 | +| opt-1.3b | 2509.61 | 4427.79 | +| gpt-neo-1.3b | 2605.27 | 5851.58 | +| opt-2.7b | 5058.05 | 4863.95 | +| gpt4chan_model_float16 | 11653.7 | 4437.71 | +| gpt-j-6B | 11653.7 | 5633.79 | +| galactica-6.7b | 12697.9 | 4429.89 | +| opt-6.7b | 12700 | 4368.66 | +| bloomz-7b1-p3 | 13483.1 | 4470.34 | + +#### GPU mode with 8-bit precision + +Allows you to load models that would not normally fit into your GPU. Enabled by default for 13b and 20b models in this web UI. + +| model | VRAM (GPU) | RAM | +|:---------------|-------------:|--------:| +| opt-13b | 12528.1 | 1152.39 | +| gpt-neox-20b | 20384 | 2291.7 | + +#### CPU mode (32-bit precision) + +A lot slower, but does not require a GPU. + +On my i5-12400F, 6B models take around 10-20 seconds to respond in chat mode, and around 5 minutes to generate a 200 tokens completion. + +| model | RAM | +|:-----------------------|---------:| +| arxiv_ai_gpt2 | 4430.82 | +| gpt-neo-1.3b | 6089.31 | +| opt-1.3b | 8411.12 | +| blenderbot-1B-distill | 8508.16 | +| opt-2.7b | 14969.3 | +| bloomz-7b1-p3 | 21371.2 | +| gpt-j-6B | 24200.3 | +| gpt4chan_model | 24246.3 | +| galactica-6.7b | 26561.4 | +| opt-6.7b | 29596.6 | diff --git a/docs/Training-LoRAs.md b/docs/Training-LoRAs.md new file mode 100644 index 0000000000000000000000000000000000000000..406ec1e4a135288867dc5c876594426aa827d568 --- /dev/null +++ b/docs/Training-LoRAs.md @@ -0,0 +1,167 @@ +## Training Your Own LoRAs + +The WebUI seeks to make training your own LoRAs as easy as possible. It comes down to just a few simple steps: + +### **Step 1**: Make a plan. +- What base model do you want to use? The LoRA you make has to be matched up to a single architecture (eg LLaMA-13B) and cannot be transferred to others (eg LLaMA-7B, StableLM, etc. would all be different). Derivatives of the same model (eg Alpaca finetune of LLaMA-13B) might be transferrable, but even then it's best to train exactly on what you plan to use. +- What model format do you want? At time of writing, 8-bit models are most stable, and 4-bit are supported but experimental. In the near future it is likely that 4-bit will be the best option for most users. +- What are you training it on? Do you want it to learn real information, a simple format, ...? + +### **Step 2**: Gather a dataset. +- If you use a dataset similar to the [Alpaca](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json) format, that is natively supported by the `Formatted Dataset` input in the WebUI, with premade formatter options. +- If you use a dataset that isn't matched to Alpaca's format, but uses the same basic JSON structure, you can make your own format file by copying `training/formats/alpaca-format.json` to a new file and [editing its content](#format-files). +- If you can get the dataset into a simple text file, that works too! You can train using the `Raw text file` input option. + - This means you can for example just copy/paste a chatlog/documentation page/whatever you want, shove it in a plain text file, and train on it. +- If you use a structured dataset not in this format, you may have to find an external way to convert it - or open an issue to request native support. + +### **Step 3**: Do the training. +- **3.1**: Load the WebUI, and your model. + - Make sure you don't have any LoRAs already loaded (unless you want to train for multi-LoRA usage). +- **3.2**: Open the `Training` tab at the top, `Train LoRA` sub-tab. +- **3.3**: Fill in the name of the LoRA, select your dataset in the dataset options. +- **3.4**: Select other parameters to your preference. See [parameters below](#parameters). +- **3.5**: click `Start LoRA Training`, and wait. + - It can take a few hours for a large dataset, or just a few minute if doing a small run. + - You may want to monitor your [loss value](#loss) while it goes. + +### **Step 4**: Evaluate your results. +- Load the LoRA under the Models Tab. +- You can go test-drive it on the `Text generation` tab, or you can use the `Perplexity evaluation` sub-tab of the `Training` tab. +- If you used the `Save every n steps` option, you can grab prior copies of the model from sub-folders within the LoRA model's folder and try them instead. + +### **Step 5**: Re-run if you're unhappy. +- Make sure to unload the LoRA before training it. +- You can simply resume a prior run - use `Copy parameters from` to select your LoRA, and edit parameters. Note that you cannot change the `Rank` of an already created LoRA. + - If you want to resume from a checkpoint saved along the way, simply copy the contents of the checkpoint folder into the LoRA's folder. + - (Note: `adapter_model.bin` is the important file that holds the actual LoRA content). + - This will start Learning Rate and Steps back to the start. If you want to resume as if you were midway through, you can adjust your Learning Rate to the last reported LR in logs and reduce your epochs. +- Or, you can start over entirely if you prefer. +- If your model is producing corrupted outputs, you probably need to start over and use a lower Learning Rate. +- If your model isn't learning detailed information but you want it to, you might need to just run more epochs, or you might need a higher Rank. +- If your model is enforcing a format you didn't want, you may need to tweak your dataset, or start over and not train as far. + +## Format Files + +If using JSON formatted datasets, they are presumed to be in the following approximate format: + +```json +[ + { + "somekey": "somevalue", + "key2": "value2" + }, + { + // etc + } +] +``` + +Where the keys (eg `somekey`, `key2` above) are standardized, and relatively consistent across the dataset, and the values (eg `somevalue`, `value2`) contain the content actually intended to be trained. + +For Alpaca, the keys are `instruction`, `input`, and `output`, wherein `input` is sometimes blank. + +A simple format file for Alpaca to be used as a chat bot is: + +```json +{ + "instruction,output": "User: %instruction%\nAssistant: %output%", + "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%" +} +``` + +Note that the keys (eg `instruction,output`) are a comma-separated list of dataset keys, and the values are a simple string that use those keys with `%%`. + +So for example if a dataset has `"instruction": "answer my question"`, then the format file's `User: %instruction%\n` will be automatically filled in as `User: answer my question\n`. + +If you have different sets of key inputs, you can make your own format file to match it. This format-file is designed to be as simple as possible to enable easy editing to match your needs. + +## Parameters + +The basic purpose and function of each parameter is documented on-page in the WebUI, so read through them in the UI to understand your options. + +That said, here's a guide to the most important parameter choices you should consider: + +### VRAM + +- First, you must consider your VRAM availability. + - Generally, under default settings, VRAM usage for training with default parameters is very close to when generating text (with 1000+ tokens of context) (ie, if you can generate text, you can train LoRAs). + - Note: worse by default in the 4-bit monkeypatch currently. Reduce `Micro Batch Size` to `1` to restore this to expectations. + - If you have VRAM to spare, setting higher batch sizes will use more VRAM and get you better quality training in exchange. + - If you have large data, setting a higher cutoff length may be beneficial, but will cost significant VRAM. If you can spare some, set your batch size to `1` and see how high you can push your cutoff length. + - If you're low on VRAM, reducing batch size or cutoff length will of course improve that. + - Don't be afraid to just try it and see what happens. If it's too much, it will just error out, and you can lower settings and try again. + +### Rank + +- Second, you want to consider the amount of learning you want. + - For example, you may wish to just learn a dialogue format (as in the case of Alpaca) in which case setting a low `Rank` value (32 or lower) works great. + - Or, you might be training on project documentation you want the bot to understand and be able to understand questions about, in which case the higher the rank, the better. + - Generally, higher Rank = more precise learning = more total content learned = more VRAM usage while training. + +### Learning Rate and Epochs + +- Third, how carefully you want it to be learned. + - In other words, how okay or not you are with the model losing unrelated understandings. + - You can control this with 3 key settings: the Learning Rate, its scheduler, and your total epochs. + - The learning rate controls how much change is made to the model by each token it sees. + - It's in scientific notation normally, so for example `3e-4` means `3 * 10^-4` which is `0.0003`. The number after `e-` controls how many `0`s are in the number. + - Higher values let training run faster, but also are more likely to corrupt prior data in the model. + - You essentially have two variables to balance: the LR, and Epochs. + - If you make LR higher, you can set Epochs equally lower to match. High LR + low epochs = very fast, low quality training. + - If you make LR low, set epochs high. Low LR + high epochs = slow but high-quality training. + - The scheduler controls change-over-time as you train - it starts high, and then goes low. This helps balance getting data in, and having decent quality, at the same time. + - You can see graphs of the different scheduler options [in the HuggingFace docs here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_1/en/main_classes/optimizer_schedules#transformers.SchedulerType) + +## Loss + +When you're running training, the WebUI's console window will log reports that include, among other things, a numeric value named `Loss`. It will start as a high number, and gradually get lower and lower as it goes. + +"Loss" in the world of AI training theoretically means "how close is the model to perfect", with `0` meaning "absolutely perfect". This is calculated by measuring the difference between the model outputting exactly the text you're training it to output, and what it actually outputs. + +In practice, a good LLM should have a very complex variable range of ideas running in its artificial head, so a loss of `0` would indicate that the model has broken and forgotten to how think about anything other than what you trained it. + +So, in effect, Loss is a balancing game: you want to get it low enough that it understands your data, but high enough that it isn't forgetting everything else. Generally, if it goes below `1.0`, it's going to start forgetting its prior memories, and you should stop training. In some cases you may prefer to take it as low as `0.5` (if you want it to be very very predictable). Different goals have different needs, so don't be afraid to experiment and see what works best for you. + +Note: if you see Loss start at or suddenly jump to exactly `0`, it is likely something has gone wrong in your training process (eg model corruption). + +## Note: 4-Bit Monkeypatch + +The [4-bit LoRA monkeypatch](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) works for training, but has side effects: +- VRAM usage is higher currently. You can reduce the `Micro Batch Size` to `1` to compensate. +- Models do funky things. LoRAs apply themselves, or refuse to apply, or spontaneously error out, or etc. It can be helpful to reload base model or restart the WebUI between training/usage to minimize chances of anything going haywire. +- Loading or working with multiple LoRAs at the same time doesn't currently work. +- Generally, recognize and treat the monkeypatch as the dirty temporary hack it is - it works, but isn't very stable. It will get better in time when everything is merged upstream for full official support. + +## Legacy notes + +LoRA training was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570). + +### Using the original alpaca-lora code + +Kept here for reference. The Training tab has much more features than this method. + +``` +conda activate textgen +git clone https://github.com/tloen/alpaca-lora +``` + +Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda: + +``` +model = LlamaForCausalLM.from_pretrained( + "models/llama-7b", + load_in_8bit=True, + device_map="auto", +) +tokenizer = LlamaTokenizer.from_pretrained( + "models/llama-7b", add_eos_token=True +) +``` + +Run the script with: + +``` +python finetune.py +``` + +It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode). diff --git a/docs/Using-LoRAs.md b/docs/Using-LoRAs.md new file mode 100644 index 0000000000000000000000000000000000000000..fafd6cde2d87bfdf46d942ab841a74bf50facdb5 --- /dev/null +++ b/docs/Using-LoRAs.md @@ -0,0 +1,55 @@ +Based on https://github.com/tloen/alpaca-lora + +## Instructions + +1. Download a LoRA, for instance: + +``` +python download-model.py tloen/alpaca-lora-7b +``` + +2. Load the LoRA. 16-bit, 8-bit, and CPU modes work: + +``` +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu +``` + +* For using LoRAs in 4-bit mode, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode). + +* Instead of using the `--lora` command-line flag, you can also select the LoRA in the "Parameters" tab of the interface. + +## Prompt +For the Alpaca LoRA in particular, the prompt must be formatted like this: + +``` +Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Write a Python script that generates text using the transformers library. +### Response: +``` + +Sample output: + +``` +Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Write a Python script that generates text using the transformers library. +### Response: + +import transformers +from transformers import AutoTokenizer, AutoModelForCausalLM +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") +model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") +texts = ["Hello world", "How are you"] +for sentence in texts: +sentence = tokenizer(sentence) +print(f"Generated {len(sentence)} tokens from '{sentence}'") +output = model(sentences=sentence).predict() +print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}") +``` + +## Training a LoRA + +You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details. diff --git a/docs/WSL-installation-guide.md b/docs/WSL-installation-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..7de38b114f7b0fb6e522c20520b3aadbb8161970 --- /dev/null +++ b/docs/WSL-installation-guide.md @@ -0,0 +1,79 @@ +Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton. + +----- + +Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11: + +## Step 1: Enable WSL + +1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges. +2. In the PowerShell window, type the following command and press Enter: + +``` +wsl --install +``` + +If this command doesn't work, you can enable WSL with the following command for Windows 10: + +``` +wsl --set-default-version 1 +``` + +For Windows 11, you can use: + +``` +wsl --set-default-version 2 +``` + +You may be prompted to restart your computer. If so, save your work and restart. + +## Step 2: Install Ubuntu + +1. Open the Microsoft Store. +2. Search for "Ubuntu" in the search bar. +3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app. +4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app. + +## Step 3: Set up Ubuntu + +1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment. +2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment. + +## Step 4: Update and upgrade packages + +1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal: + +``` +sudo apt update +sudo apt upgrade +``` + +2. Enter your password when prompted. This will update the package list and upgrade any outdated packages. + +Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files. + +You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal. + +## Step 5: Proceed with Linux instructions + +1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt: + +``` +sudo apt install [missing package] +``` + +You will probably need to install build-essential + +``` +sudo apt install build-essential +``` + +If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/ + +## Bonus: Port Forwarding + +By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). + +``` +netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860 +``` diff --git a/docs/Windows-installation-guide.md b/docs/Windows-installation-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..83b22efa38b1839d07a5a58494dbc26ba86397ee --- /dev/null +++ b/docs/Windows-installation-guide.md @@ -0,0 +1,9 @@ +If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering: + +* Windows installation +* 8-bit mode on Windows +* LLaMA +* LLaMA 4-bit + +The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/ + diff --git a/docs/llama.cpp-models.md b/docs/llama.cpp-models.md new file mode 100644 index 0000000000000000000000000000000000000000..153f70affedf55df3b58af5c46eb0396b5ecf010 --- /dev/null +++ b/docs/llama.cpp-models.md @@ -0,0 +1,43 @@ +# Using llama.cpp in the web UI + +## Setting up the models + +#### Pre-converted + +Place the model in the `models` folder, making sure that its name contains `ggml` somewhere and ends in `.bin`. + +#### Convert LLaMA yourself + +Follow the instructions in the llama.cpp README to generate the `ggml-model.bin` file: https://github.com/ggerganov/llama.cpp#usage + +## GPU offloading + +Enabled with the `--n-gpu-layers` parameter. If you have enough VRAM, use a high number like `--n-gpu-layers 200000` to offload all layers to the GPU. + +Note that you need to manually install `llama-cpp-python` with GPU support. To do that: + +#### Linux + +``` +pip uninstall -y llama-cpp-python +CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir +``` + +#### Windows + +``` +pip uninstall -y llama-cpp-python +set CMAKE_ARGS="-DLLAMA_CUBLAS=on" +set FORCE_CMAKE=1 +pip install llama-cpp-python --no-cache-dir +``` + +Here you can find the different compilation options for OpenBLAS / cuBLAS / CLBlast: https://pypi.org/project/llama-cpp-python/ + +## Performance + +This was the performance of llama-7b int4 on my i5-12400F (cpu only): + +> Output generated in 33.07 seconds (6.05 tokens/s, 200 tokens, context 17) + +You can change the number of threads with `--threads N`. diff --git a/download-model.py b/download-model.py index 8be398c4e0d3ca0c0a915efb442f432fc2056834..44b87a8c71386bc6e1ca3ae3a3788c7283857eea 100644 --- a/download-model.py +++ b/download-model.py @@ -8,78 +8,55 @@ python download-model.py facebook/opt-1.3b import argparse import base64 +import datetime +import hashlib import json -import multiprocessing import re import sys from pathlib import Path import requests import tqdm +from tqdm.contrib.concurrent import thread_map -parser = argparse.ArgumentParser() -parser.add_argument('MODEL', type=str, default=None, nargs='?') -parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.') -parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.') -parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).') -args = parser.parse_args() - -def get_file(args): - url = args[0] - output_folder = args[1] - idx = args[2] - tot = args[3] - - print(f"Downloading file {idx} of {tot}...") - r = requests.get(url, stream=True) - with open(output_folder / Path(url.split('/')[-1]), 'wb') as f: - total_size = int(r.headers.get('content-length', 0)) - block_size = 1024 - t = tqdm.tqdm(total=total_size, unit='iB', unit_scale=True) - for data in r.iter_content(block_size): - t.update(len(data)) - f.write(data) - t.close() - -def sanitize_branch_name(branch_name): - pattern = re.compile(r"^[a-zA-Z0-9._-]+$") - if pattern.match(branch_name): - return branch_name - else: - raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.") def select_model_from_default_options(): models = { - "Pygmalion 6B original": ("PygmalionAI", "pygmalion-6b", "b8344bb4eb76a437797ad3b19420a13922aaabe1"), - "Pygmalion 6B main": ("PygmalionAI", "pygmalion-6b", "main"), - "Pygmalion 6B dev": ("PygmalionAI", "pygmalion-6b", "dev"), - "Pygmalion 2.7B": ("PygmalionAI", "pygmalion-2.7b", "main"), - "Pygmalion 1.3B": ("PygmalionAI", "pygmalion-1.3b", "main"), - "Pygmalion 350m": ("PygmalionAI", "pygmalion-350m", "main"), - "OPT 6.7b": ("facebook", "opt-6.7b", "main"), - "OPT 2.7b": ("facebook", "opt-2.7b", "main"), - "OPT 1.3b": ("facebook", "opt-1.3b", "main"), - "OPT 350m": ("facebook", "opt-350m", "main"), + "OPT 6.7B": ("facebook", "opt-6.7b", "main"), + "OPT 2.7B": ("facebook", "opt-2.7b", "main"), + "OPT 1.3B": ("facebook", "opt-1.3b", "main"), + "OPT 350M": ("facebook", "opt-350m", "main"), + "GALACTICA 6.7B": ("facebook", "galactica-6.7b", "main"), + "GALACTICA 1.3B": ("facebook", "galactica-1.3b", "main"), + "GALACTICA 125M": ("facebook", "galactica-125m", "main"), + "Pythia-6.9B-deduped": ("EleutherAI", "pythia-6.9b-deduped", "main"), + "Pythia-2.8B-deduped": ("EleutherAI", "pythia-2.8b-deduped", "main"), + "Pythia-1.4B-deduped": ("EleutherAI", "pythia-1.4b-deduped", "main"), + "Pythia-410M-deduped": ("EleutherAI", "pythia-410m-deduped", "main"), } - choices = {} + choices = {} print("Select the model that you want to download:\n") - for i,name in enumerate(models): - char = chr(ord('A')+i) + for i, name in enumerate(models): + char = chr(ord('A') + i) choices[char] = name print(f"{char}) {name}") - char = chr(ord('A')+len(models)) - print(f"{char}) None of the above") + char_hugging = chr(ord('A') + len(models)) + print(f"{char_hugging}) Manually specify a Hugging Face model") + char_exit = chr(ord('A') + len(models) + 1) + print(f"{char_exit}) Do not download a model") print() print("Input> ", end='') choice = input()[0].strip().upper() - if choice == char: - print("""\nThen type the name of your desired Hugging Face model in the format organization/name. + if choice == char_exit: + exit() + elif choice == char_hugging: + print("""\nType the name of your desired Hugging Face model in the format organization/name. Examples: -PygmalionAI/pygmalion-6b facebook/opt-1.3b +EleutherAI/pythia-1.4b-deduped """) print("Input> ", end='') @@ -92,17 +69,38 @@ facebook/opt-1.3b return model, branch -def get_download_links_from_huggingface(model, branch): + +def sanitize_model_and_branch_names(model, branch): + if model[-1] == '/': + model = model[:-1] + if branch is None: + branch = "main" + else: + pattern = re.compile(r"^[a-zA-Z0-9._-]+$") + if not pattern.match(branch): + raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.") + + return model, branch + + +def get_download_links_from_huggingface(model, branch, text_only=False): base = "https://huggingface.co" - page = f"/api/models/{model}/tree/{branch}?cursor=" + page = f"/api/models/{model}/tree/{branch}" cursor = b"" links = [] + sha256 = [] classifications = [] has_pytorch = False + has_pt = False + has_ggml = False has_safetensors = False + is_lora = False while True: - content = requests.get(f"{base}{page}{cursor.decode()}").content + url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "") + r = requests.get(url, timeout=10) + r.raise_for_status() + content = r.content dict = json.loads(content) if len(dict) == 0: @@ -110,18 +108,24 @@ def get_download_links_from_huggingface(model, branch): for i in range(len(dict)): fname = dict[i]['path'] + if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')): + is_lora = True - is_pytorch = re.match("pytorch_model.*\.bin", fname) - is_safetensors = re.match("model.*\.safetensors", fname) - is_tokenizer = re.match("tokenizer.*\.model", fname) - is_text = re.match(".*\.(txt|json)", fname) or is_tokenizer + is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname) + is_safetensors = re.match(".*\.safetensors", fname) + is_pt = re.match(".*\.pt", fname) + is_ggml = re.match(".*ggml.*\.bin", fname) + is_tokenizer = re.match("(tokenizer|ice).*\.model", fname) + is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer - if any((is_pytorch, is_safetensors, is_text, is_tokenizer)): + if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)): + if 'lfs' in dict[i]: + sha256.append([fname, dict[i]['lfs']['oid']]) if is_text: links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") classifications.append('text') continue - if not args.text_only: + if not text_only: links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") if is_safetensors: has_safetensors = True @@ -129,48 +133,145 @@ def get_download_links_from_huggingface(model, branch): elif is_pytorch: has_pytorch = True classifications.append('pytorch') + elif is_pt: + has_pt = True + classifications.append('pt') + elif is_ggml: + has_ggml = True + classifications.append('ggml') cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50' cursor = base64.b64encode(cursor) cursor = cursor.replace(b'=', b'%3D') # If both pytorch and safetensors are available, download safetensors only - if has_pytorch and has_safetensors: - for i in range(len(classifications)-1, -1, -1): - if classifications[i] == 'pytorch': + if (has_pytorch or has_pt) and has_safetensors: + for i in range(len(classifications) - 1, -1, -1): + if classifications[i] in ['pytorch', 'pt']: links.pop(i) - return links + return links, sha256, is_lora -if __name__ == '__main__': - model = args.MODEL - branch = args.branch - if model is None: - model, branch = select_model_from_default_options() - else: - if model[-1] == '/': - model = model[:-1] - branch = args.branch - if branch is None: - branch = "main" - else: - try: - branch = sanitize_branch_name(branch) - except ValueError as err_branch: - print(f"Error: {err_branch}") - sys.exit() + +def get_output_folder(model, branch, is_lora, base_folder=None): + if base_folder is None: + base_folder = 'models' if not is_lora else 'loras' + + output_folder = f"{'_'.join(model.split('/')[-2:])}" if branch != 'main': - output_folder = Path("models") / (model.split('/')[-1] + f'_{branch}') + output_folder += f'_{branch}' + output_folder = Path(base_folder) / output_folder + return output_folder + + +def get_single_file(url, output_folder, start_from_scratch=False): + filename = Path(url.rsplit('/', 1)[1]) + output_path = output_folder / filename + if output_path.exists() and not start_from_scratch: + # Check if the file has already been downloaded completely + r = requests.get(url, stream=True, timeout=10) + total_size = int(r.headers.get('content-length', 0)) + if output_path.stat().st_size >= total_size: + return + # Otherwise, resume the download from where it left off + headers = {'Range': f'bytes={output_path.stat().st_size}-'} + mode = 'ab' else: - output_folder = Path("models") / model.split('/')[-1] + headers = {} + mode = 'wb' + + r = requests.get(url, stream=True, headers=headers, timeout=10) + with open(output_path, mode) as f: + total_size = int(r.headers.get('content-length', 0)) + block_size = 1024 + 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: + for data in r.iter_content(block_size): + t.update(len(data)) + f.write(data) + + +def start_download_threads(file_list, output_folder, start_from_scratch=False, threads=1): + thread_map(lambda url: get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True) + + +def download_model_files(model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1): + # Creating the folder and writing the metadata if not output_folder.exists(): output_folder.mkdir() - - links = get_download_links_from_huggingface(model, branch) + with open(output_folder / 'huggingface-metadata.txt', 'w') as f: + f.write(f'url: https://huggingface.co/{model}\n') + f.write(f'branch: {branch}\n') + f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n') + sha256_str = '' + for i in range(len(sha256)): + sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n' + if sha256_str != '': + f.write(f'sha256sum:\n{sha256_str}') # Downloading the files print(f"Downloading the model to {output_folder}") - pool = multiprocessing.Pool(processes=args.threads) - results = pool.map(get_file, [[links[i], output_folder, i+1, len(links)] for i in range(len(links))]) - pool.close() - pool.join() + start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads) + + +def check_model_files(model, branch, links, sha256, output_folder): + # Validate the checksums + validated = True + for i in range(len(sha256)): + fpath = (output_folder / sha256[i][0]) + + if not fpath.exists(): + print(f"The following file is missing: {fpath}") + validated = False + continue + + with open(output_folder / sha256[i][0], "rb") as f: + bytes = f.read() + file_hash = hashlib.sha256(bytes).hexdigest() + if file_hash != sha256[i][1]: + print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}') + validated = False + else: + print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}') + + if validated: + print('[+] Validated checksums of all model files!') + else: + print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.') + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('MODEL', type=str, default=None, nargs='?') + parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.') + parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.') + parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).') + parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.') + parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.') + parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.') + args = parser.parse_args() + + branch = args.branch + model = args.MODEL + if model is None: + model, branch = select_model_from_default_options() + + # Cleaning up the model/branch names + try: + model, branch = sanitize_model_and_branch_names(model, branch) + except ValueError as err_branch: + print(f"Error: {err_branch}") + sys.exit() + + # Getting the download links from Hugging Face + links, sha256, is_lora = get_download_links_from_huggingface(model, branch, text_only=args.text_only) + + # Getting the output folder + output_folder = get_output_folder(model, branch, is_lora, base_folder=args.output) + + if args.check: + # Check previously downloaded files + check_model_files(model, branch, links, sha256, output_folder) + else: + # Download files + download_model_files(model, branch, links, sha256, output_folder, threads=args.threads) diff --git a/extensions/api/blocking_api.py b/extensions/api/blocking_api.py new file mode 100644 index 0000000000000000000000000000000000000000..134e99d4ccdf00b3ea8cd229e471db59d6d1b73a --- /dev/null +++ b/extensions/api/blocking_api.py @@ -0,0 +1,87 @@ +import json +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from threading import Thread + +from extensions.api.util import build_parameters, try_start_cloudflared +from modules import shared +from modules.text_generation import encode, generate_reply + + +class Handler(BaseHTTPRequestHandler): + def do_GET(self): + if self.path == '/api/v1/model': + self.send_response(200) + self.end_headers() + response = json.dumps({ + 'result': shared.model_name + }) + + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + def do_POST(self): + content_length = int(self.headers['Content-Length']) + body = json.loads(self.rfile.read(content_length).decode('utf-8')) + + if self.path == '/api/v1/generate': + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + prompt = body['prompt'] + generate_params = build_parameters(body) + stopping_strings = generate_params.pop('stopping_strings') + generate_params['stream'] = False + + generator = generate_reply( + prompt, generate_params, stopping_strings=stopping_strings, is_chat=False) + + answer = '' + for a in generator: + answer = a + + response = json.dumps({ + 'results': [{ + 'text': answer + }] + }) + self.wfile.write(response.encode('utf-8')) + elif self.path == '/api/v1/token-count': + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + tokens = encode(body['prompt'])[0] + response = json.dumps({ + 'results': [{ + 'tokens': len(tokens) + }] + }) + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + +def _run_server(port: int, share: bool = False): + address = '0.0.0.0' if shared.args.listen else '127.0.0.1' + + server = ThreadingHTTPServer((address, port), Handler) + + def on_start(public_url: str): + print(f'Starting non-streaming server at public url {public_url}/api') + + if share: + try: + try_start_cloudflared(port, max_attempts=3, on_start=on_start) + except Exception: + pass + else: + print( + f'Starting API at http://{address}:{port}/api') + + server.serve_forever() + + +def start_server(port: int, share: bool = False): + Thread(target=_run_server, args=[port, share], daemon=True).start() diff --git a/extensions/api/requirements.txt b/extensions/api/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..14e29d3521915c9fd4fb62385dc971e7a8c4bc83 --- /dev/null +++ b/extensions/api/requirements.txt @@ -0,0 +1,2 @@ +flask_cloudflared==0.0.12 +websockets==11.0.2 \ No newline at end of file diff --git a/extensions/api/script.py b/extensions/api/script.py new file mode 100644 index 0000000000000000000000000000000000000000..5544de725a406c2619e8d465a14f5969f093387e --- /dev/null +++ b/extensions/api/script.py @@ -0,0 +1,7 @@ +import extensions.api.blocking_api as blocking_api +import extensions.api.streaming_api as streaming_api +from modules import shared + +def setup(): + blocking_api.start_server(shared.args.api_blocking_port, share=shared.args.public_api) + streaming_api.start_server(shared.args.api_streaming_port, share=shared.args.public_api) diff --git a/extensions/api/streaming_api.py b/extensions/api/streaming_api.py new file mode 100644 index 0000000000000000000000000000000000000000..e50dfa2266594f9edc7fb2b6f8659f275236279f --- /dev/null +++ b/extensions/api/streaming_api.py @@ -0,0 +1,78 @@ +import asyncio +import json +from threading import Thread + +from websockets.server import serve + +from extensions.api.util import build_parameters, try_start_cloudflared +from modules import shared +from modules.text_generation import generate_reply + +PATH = '/api/v1/stream' + + +async def _handle_connection(websocket, path): + + if path != PATH: + print(f'Streaming api: unknown path: {path}') + return + + async for message in websocket: + message = json.loads(message) + + prompt = message['prompt'] + generate_params = build_parameters(message) + stopping_strings = generate_params.pop('stopping_strings') + generate_params['stream'] = True + + generator = generate_reply( + prompt, generate_params, stopping_strings=stopping_strings, is_chat=False) + + # As we stream, only send the new bytes. + skip_index = 0 + message_num = 0 + + for a in generator: + to_send = a[skip_index:] + await websocket.send(json.dumps({ + 'event': 'text_stream', + 'message_num': message_num, + 'text': to_send + })) + + await asyncio.sleep(0) + + skip_index += len(to_send) + message_num += 1 + + await websocket.send(json.dumps({ + 'event': 'stream_end', + 'message_num': message_num + })) + + +async def _run(host: str, port: int): + async with serve(_handle_connection, host, port, ping_interval=None): + await asyncio.Future() # run forever + + +def _run_server(port: int, share: bool = False): + address = '0.0.0.0' if shared.args.listen else '127.0.0.1' + + def on_start(public_url: str): + public_url = public_url.replace('https://', 'wss://') + print(f'Starting streaming server at public url {public_url}{PATH}') + + if share: + try: + try_start_cloudflared(port, max_attempts=3, on_start=on_start) + except Exception as e: + print(e) + else: + print(f'Starting streaming server at ws://{address}:{port}{PATH}') + + asyncio.run(_run(host=address, port=port)) + + +def start_server(port: int, share: bool = False): + Thread(target=_run_server, args=[port, share], daemon=True).start() diff --git a/extensions/api/util.py b/extensions/api/util.py new file mode 100644 index 0000000000000000000000000000000000000000..e637ac0ec29d8c251952da470b507edf0962180a --- /dev/null +++ b/extensions/api/util.py @@ -0,0 +1,71 @@ +import time +import traceback +from threading import Thread +from typing import Callable, Optional + +from modules.text_generation import get_encoded_length + + +def build_parameters(body): + prompt = body['prompt'] + + prompt_lines = [k.strip() for k in prompt.split('\n')] + max_context = body.get('max_context_length', 2048) + while len(prompt_lines) >= 0 and get_encoded_length('\n'.join(prompt_lines)) > max_context: + prompt_lines.pop(0) + + prompt = '\n'.join(prompt_lines) + + generate_params = { + 'max_new_tokens': int(body.get('max_new_tokens', body.get('max_length', 200))), + 'do_sample': bool(body.get('do_sample', True)), + 'temperature': float(body.get('temperature', 0.5)), + 'top_p': float(body.get('top_p', 1)), + 'typical_p': float(body.get('typical_p', body.get('typical', 1))), + 'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))), + 'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)), + 'top_k': int(body.get('top_k', 0)), + 'min_length': int(body.get('min_length', 0)), + 'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size', 0)), + 'num_beams': int(body.get('num_beams', 1)), + 'penalty_alpha': float(body.get('penalty_alpha', 0)), + 'length_penalty': float(body.get('length_penalty', 1)), + 'early_stopping': bool(body.get('early_stopping', False)), + 'seed': int(body.get('seed', -1)), + 'add_bos_token': bool(body.get('add_bos_token', True)), + 'truncation_length': int(body.get('truncation_length', 2048)), + 'ban_eos_token': bool(body.get('ban_eos_token', False)), + 'skip_special_tokens': bool(body.get('skip_special_tokens', True)), + 'custom_stopping_strings': '', # leave this blank + 'stopping_strings': body.get('stopping_strings', []), + } + + return generate_params + + +def try_start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None): + Thread(target=_start_cloudflared, args=[ + port, max_attempts, on_start], daemon=True).start() + + +def _start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None): + try: + from flask_cloudflared import _run_cloudflared + except ImportError: + print('You should install flask_cloudflared manually') + raise Exception( + 'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.') + + for _ in range(max_attempts): + try: + public_url = _run_cloudflared(port, port + 1) + + if on_start: + on_start(public_url) + + return + except Exception: + traceback.print_exc() + time.sleep(3) + + raise Exception('Could not start cloudflared.') diff --git a/extensions/character_bias/script.py b/extensions/character_bias/script.py index 35b38c0edcb38512f2472937578a363343a4468c..ff12f3afdc28be4ead12ffab90bd9fbd783514a2 100644 --- a/extensions/character_bias/script.py +++ b/extensions/character_bias/script.py @@ -1,42 +1,83 @@ +import os + import gradio as gr +# get the current directory of the script +current_dir = os.path.dirname(os.path.abspath(__file__)) + +# check if the bias_options.txt file exists, if not, create it +bias_file = os.path.join(current_dir, "bias_options.txt") +if not os.path.isfile(bias_file): + with open(bias_file, "w") as f: + f.write("*I am so happy*\n*I am so sad*\n*I am so excited*\n*I am so bored*\n*I am so angry*") + +# read bias options from the text file +with open(bias_file, "r") as f: + bias_options = [line.strip() for line in f.readlines()] + params = { "activate": True, "bias string": " *I am so happy*", + "use custom string": False, } + def input_modifier(string): """ This function is applied to your text inputs before they are fed into the model. - """ - + """ return string + def output_modifier(string): """ This function is applied to the model outputs. """ - return string + def bot_prefix_modifier(string): """ This function is only applied in chat mode. It modifies the prefix text for the Bot and can be used to bias its behavior. """ - - if params['activate'] == True: - return f'{string} {params["bias string"].strip()} ' + if params['activate']: + if params['use custom string']: + return f'{string} {params["custom string"].strip()} ' + else: + return f'{string} {params["bias string"].strip()} ' else: return string + def ui(): # Gradio elements activate = gr.Checkbox(value=params['activate'], label='Activate character bias') - string = gr.Textbox(value=params["bias string"], label='Character bias') + dropdown_string = gr.Dropdown(choices=bias_options, value=params["bias string"], label='Character bias', info='To edit the options in this dropdown edit the "bias_options.txt" file') + use_custom_string = gr.Checkbox(value=False, label='Use custom bias textbox instead of dropdown') + custom_string = gr.Textbox(value="", placeholder="Enter custom bias string", label="Custom Character Bias", info='To use this textbox activate the checkbox above') # Event functions to update the parameters in the backend - string.change(lambda x: params.update({"bias string": x}), string, None) + def update_bias_string(x): + if x: + params.update({"bias string": x}) + else: + params.update({"bias string": dropdown_string.get()}) + return x + + def update_custom_string(x): + params.update({"custom string": x}) + + dropdown_string.change(update_bias_string, dropdown_string, None) + custom_string.change(update_custom_string, custom_string, None) activate.change(lambda x: params.update({"activate": x}), activate, None) + use_custom_string.change(lambda x: params.update({"use custom string": x}), use_custom_string, None) + + # Group elements together depending on the selected option + def bias_string_group(): + if use_custom_string.value: + return gr.Group([use_custom_string, custom_string]) + else: + return dropdown_string diff --git a/extensions/elevenlabs_tts/requirements.txt b/extensions/elevenlabs_tts/requirements.txt index 8ec07a8a7fcf02ca48cc00520e66fcb58c447393..2cfc1960ce2cef55d0f85fb3c88bbb4d28bfcd80 100644 --- a/extensions/elevenlabs_tts/requirements.txt +++ b/extensions/elevenlabs_tts/requirements.txt @@ -1,3 +1 @@ -elevenlabslib -soundfile -sounddevice +elevenlabs==0.2.* diff --git a/extensions/elevenlabs_tts/script.py b/extensions/elevenlabs_tts/script.py index 90d61efc6aa77bc2377c435eefe4cf623b588168..327a9a1192d23847f8b214ca43b447251f928bec 100644 --- a/extensions/elevenlabs_tts/script.py +++ b/extensions/elevenlabs_tts/script.py @@ -1,113 +1,180 @@ +import re from pathlib import Path +import elevenlabs import gradio as gr -from elevenlabslib import * -from elevenlabslib.helpers import * +from modules import chat, shared params = { 'activate': True, - 'api_key': '12345', + 'api_key': None, 'selected_voice': 'None', + 'autoplay': False, + 'show_text': True, } -initial_voice = ['None'] +voices = None wav_idx = 0 -user = ElevenLabsUser(params['api_key']) -user_info = None - - -# Check if the API is valid and refresh the UI accordingly. -def check_valid_api(): - - global user, user_info, params - - user = ElevenLabsUser(params['api_key']) - user_info = user._get_subscription_data() - print('checking api') - if params['activate'] == False: - return gr.update(value='Disconnected') - elif user_info is None: - print('Incorrect API Key') - return gr.update(value='Disconnected') - else: - print('Got an API Key!') - return gr.update(value='Connected') - -# Once the API is verified, get the available voices and update the dropdown list + + +def update_api_key(key): + params['api_key'] = key + if key is not None: + elevenlabs.set_api_key(key) + + def refresh_voices(): - - global user, user_info - - your_voices = [None] - if user_info is not None: - for voice in user.get_available_voices(): - your_voices.append(voice.initialName) - return gr.Dropdown.update(choices=your_voices) - else: - return + global params + your_voices = elevenlabs.voices() + voice_names = [voice.name for voice in your_voices] + return voice_names + + +def refresh_voices_dd(): + all_voices = refresh_voices() + return gr.Dropdown.update(value=all_voices[0], choices=all_voices) + + +def remove_tts_from_history(): + for i, entry in enumerate(shared.history['internal']): + shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]] + + +def toggle_text_in_history(): + for i, entry in enumerate(shared.history['visible']): + visible_reply = entry[1] + if visible_reply.startswith('')[0]}\n\n{reply}" + ] + else: + shared.history['visible'][i] = [ + shared.history['visible'][i][0], f"{visible_reply.split('')[0]}" + ] + def remove_surrounded_chars(string): - new_string = "" - in_star = False - for char in string: - if char == '*': - in_star = not in_star - elif not in_star: - new_string += char - return new_string + # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR + # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string' + return re.sub('\*[^\*]*?(\*|$)', '', string) + + +def state_modifier(state): + state['stream'] = False + return state + def input_modifier(string): """ This function is applied to your text inputs before they are fed into the model. """ + # Remove autoplay from the last reply + if shared.is_chat() and len(shared.history['internal']) > 0: + shared.history['visible'][-1] = [ + shared.history['visible'][-1][0], + shared.history['visible'][-1][1].replace('controls autoplay>', 'controls>') + ] + + if params['activate']: + shared.processing_message = "*Is recording a voice message...*" return string + def output_modifier(string): """ This function is applied to the model outputs. """ - global params, wav_idx, user, user_info - - if params['activate'] == False: - return string - elif user_info == None: + global params, wav_idx + + if not params['activate']: return string + original_string = string string = remove_surrounded_chars(string) string = string.replace('"', '') string = string.replace('“', '') string = string.replace('\n', ' ') string = string.strip() - if string == '': string = 'empty reply, try regenerating' - - output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'.format(wav_idx)) - voice = user.get_voices_by_name(params['selected_voice'])[0] - audio_data = voice.generate_audio_bytes(string) - save_bytes_to_path(Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'), audio_data) - - string = f'' - wav_idx += 1 + + output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.mp3'.format(wav_idx)) + print(f'Outputing audio to {str(output_file)}') + try: + audio = elevenlabs.generate(text=string, voice=params['selected_voice'], model="eleven_monolingual_v1") + elevenlabs.save(audio, str(output_file)) + + autoplay = 'autoplay' if params['autoplay'] else '' + string = f'' + wav_idx += 1 + except elevenlabs.api.error.UnauthenticatedRateLimitError: + string = "🤖 ElevenLabs Unauthenticated Rate Limit Reached - Please create an API key to continue\n\n" + except elevenlabs.api.error.RateLimitError: + string = "🤖 ElevenLabs API Tier Limit Reached\n\n" + except elevenlabs.api.error.APIError as err: + string = f"🤖 ElevenLabs Error: {err}\n\n" + + if params['show_text']: + string += f'\n\n{original_string}' + + shared.processing_message = "*Is typing...*" return string + def ui(): + global voices + if not voices: + voices = refresh_voices() + params['selected_voice'] = voices[0] # Gradio elements with gr.Row(): activate = gr.Checkbox(value=params['activate'], label='Activate TTS') - connection_status = gr.Textbox(value='Disconnected', label='Connection Status') - voice = gr.Dropdown(value=params['selected_voice'], choices=initial_voice, label='TTS Voice') + autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically') + show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player') + + with gr.Row(): + voice = gr.Dropdown(value=params['selected_voice'], choices=voices, label='TTS Voice') + refresh = gr.Button(value='Refresh') + with gr.Row(): api_key = gr.Textbox(placeholder="Enter your API key.", label='API Key') - connect = gr.Button(value='Connect') + + with gr.Row(): + convert = gr.Button('Permanently replace audios with the message texts') + convert_cancel = gr.Button('Cancel', visible=False) + convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False) + + # Convert history with confirmation + convert_arr = [convert_confirm, convert, convert_cancel] + convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr) + convert_confirm.click( + lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then( + remove_tts_from_history, None, None).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) + + # Toggle message text in history + show_text.change( + lambda x: params.update({"show_text": x}), show_text, None).then( + toggle_text_in_history, None, None).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) # Event functions to update the parameters in the backend activate.change(lambda x: params.update({'activate': x}), activate, None) voice.change(lambda x: params.update({'selected_voice': x}), voice, None) - api_key.change(lambda x: params.update({'api_key': x}), api_key, None) - connect.click(check_valid_api, [], connection_status) - connect.click(refresh_voices, [], voice) + api_key.change(update_api_key, api_key, None) + # connect.click(check_valid_api, [], connection_status) + refresh.click(refresh_voices_dd, [], voice) + # Event functions to update the parameters in the backend + autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None) diff --git a/extensions/gallery/script.py b/extensions/gallery/script.py index 8a2d7cf988734a7ab0966d047ff3d31ba58324b7..993ef273839e7cfbf9e80f2d7f9d4a71d208b446 100644 --- a/extensions/gallery/script.py +++ b/extensions/gallery/script.py @@ -3,18 +3,27 @@ from pathlib import Path import gradio as gr from modules.html_generator import get_image_cache +from modules.shared import gradio -def generate_html(): +def generate_css(): css = """ - .character-gallery { + .character-gallery > .gallery { margin: 1rem 0; - display: grid; + display: grid !important; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); grid-column-gap: 0.4rem; grid-row-gap: 1.2rem; } + .character-gallery > .label { + display: none !important; + } + + .character-gallery button.gallery-item { + display: contents; + } + .character-container { cursor: pointer; text-align: center; @@ -45,38 +54,43 @@ def generate_html(): overflow-wrap: anywhere; } """ + return css - container_html = f'
' +def generate_html(): + cards = [] # Iterate through files in image folder for file in sorted(Path("characters").glob("*")): - if file.name.endswith(".json"): - character = file.name.replace(".json", "") - container_html += f'
' + if file.suffix in [".json", ".yml", ".yaml"]: + character = file.stem + container_html = '
' image_html = "
" - for i in [ - f"characters/{character}.png", - f"characters/{character}.jpg", - f"characters/{character}.jpeg", - ]: - - path = Path(i) + for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]: if path.exists(): - try: - image_html = f'' - break - except: - continue + image_html = f'' + break container_html += f'{image_html} {character}' container_html += "
" + cards.append([container_html, character]) + + return cards + + +def select_character(evt: gr.SelectData): + return (evt.value[1]) - container_html += "
" - return container_html def ui(): - with gr.Accordion("Character gallery"): + with gr.Accordion("Character gallery", open=False): update = gr.Button("Refresh") - gallery = gr.HTML(value=generate_html()) + gr.HTML(value="") + gallery = gr.Dataset(components=[gr.HTML(visible=False)], + label="", + samples=generate_html(), + elem_classes=["character-gallery"], + samples_per_page=50 + ) update.click(generate_html, [], gallery) + gallery.select(select_character, None, gradio['character_menu']) diff --git a/extensions/google_translate/script.py b/extensions/google_translate/script.py index 68bc54b293086bed1a070a310d276060ee939d44..63226107b2c2afe086fc343c7b7f7df78bef3f8a 100644 --- a/extensions/google_translate/script.py +++ b/extensions/google_translate/script.py @@ -7,14 +7,16 @@ params = { language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'} + def input_modifier(string): """ This function is applied to your text inputs before they are fed into the model. - """ + """ return GoogleTranslator(source=params['language string'], target='en').translate(string) + def output_modifier(string): """ This function is applied to the model outputs. @@ -22,6 +24,7 @@ def output_modifier(string): return GoogleTranslator(source='en', target=params['language string']).translate(string) + def bot_prefix_modifier(string): """ This function is only applied in chat mode. It modifies @@ -31,6 +34,7 @@ def bot_prefix_modifier(string): return string + def ui(): # Finding the language name from the language code to use as the default value language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])] diff --git a/extensions/llama_prompts/script.py b/extensions/llama_prompts/script.py deleted file mode 100644 index 22c96f7c2d6763213a728d77ee6666496d9c4aa3..0000000000000000000000000000000000000000 --- a/extensions/llama_prompts/script.py +++ /dev/null @@ -1,18 +0,0 @@ -import gradio as gr -import modules.shared as shared -import pandas as pd - -df = pd.read_csv("https://raw.githubusercontent.com/devbrones/llama-prompts/main/prompts/prompts.csv") - -def get_prompt_by_name(name): - if name == 'None': - return '' - else: - return df[df['Prompt name'] == name].iloc[0]['Prompt'].replace('\\n', '\n') - -def ui(): - if not shared.args.chat or shared.args.cai_chat: - choices = ['None'] + list(df['Prompt name']) - - prompts_menu = gr.Dropdown(value=choices[0], choices=choices, label='Prompt') - prompts_menu.change(get_prompt_by_name, prompts_menu, shared.gradio['textbox']) diff --git a/extensions/llava/script.py b/extensions/llava/script.py new file mode 100644 index 0000000000000000000000000000000000000000..3f6c73a239166182419061339b49e24a1c223d83 --- /dev/null +++ b/extensions/llava/script.py @@ -0,0 +1,8 @@ +import logging + +import gradio as gr + + +def ui(): + gr.Markdown("### This extension is deprecated, use \"multimodal\" extension instead") + logging.error("LLaVA extension is deprecated, use \"multimodal\" extension instead") diff --git a/extensions/multimodal/DOCS.md b/extensions/multimodal/DOCS.md new file mode 100644 index 0000000000000000000000000000000000000000..eaa4365e9a304a14ebbdb1d4d435f3a2a1f7a7d2 --- /dev/null +++ b/extensions/multimodal/DOCS.md @@ -0,0 +1,85 @@ +# Technical description of multimodal extension + +## Working principle +Multimodality extension does most of the stuff which is required for any image input: + +- adds the UI +- saves the images as base64 JPEGs to history +- provides the hooks to the UI +- if there are images in the prompt, it: + - splits the prompt to text and image parts + - adds image start/end markers to text parts, then encodes and embeds the text parts + - calls the vision pipeline to embed the images + - stitches the embeddings together, and returns them to text generation +- loads the appropriate vision pipeline, selected either from model name, or by specifying --multimodal-pipeline parameter + +Now, for the pipelines, they: + +- load the required vision models +- return some consts, for example the number of tokens taken up by image +- and most importantly: return the embeddings for LLM, given a list of images + +## Prompts/history + +To save images in prompt/history, this extension is using a base64 JPEG, wrapped in a HTML tag, like so: +``` + +``` +where `{img_str}` is the actual image data. This format makes displaying them in the UI for free. Do note, that this format is required to be exactly the same, the regex used to find the images is: ``. + +## LLM input +To describe the input, let's see it on an example prompt: +``` +text1text2text3 +``` +where `textN` is N-th text, `` is N-th image, in HTML format specified above. + +**The first step is to split the prompt into image/text parts**, so we get: +``` +['text1', '', 'text2', '', 'text3'] +``` +this is done in `MultimodalEmbedder._split_prompt(...)` function, which returns a list of `PromptPart`s - dataclasses wrapping the separate parts. + +This function also appends the image start/end markers to text, which are provided by `AbstractMultimodalPipeline.image_start()` / `AbstractMultimodalPipeline.image_end()` functions. If image start is ``, and end is ``, this function will return: +``` +['text1', '', 'text2', '', 'text3'] +``` + +**The returned prompt parts are then turned into token embeddings.** + +First, they are modified to token IDs, for the text it is done using standard `modules.text_generation.encode()` function, and for the images the returned token IDs are changed to placeholders. The placeholder is a list of `N` times `placeholder token id`, where `N` is specified using `AbstractMultimodalPipeline.num_image_embeds()`, and placeholder token IDs using `AbstractMultimodalPipeline.placeholder_token_id()`. + +Now, based on the token IDs, the prompt might get truncated, especially if `max_new_tokens` are unreasonably high. Unfortunately, it can't be done simply, just by trimming the prompt to be short enough. This way will lead to sometimes splitting the prompt in the middle of an image embedding, which usually breaks the generation. Therefore, in this case, the entire image needs to be removed from input. This is done inside `MultimodalEmbedder._encode_text(...)` function. + +**After the tokenization, the tokens need to get embedded**, the text and images are once again treated separately. + +The text parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_tokens(...)` function. It uses standard embedding function from the model, but to support many LLMs, the actual function is returned by the pipeline (as it might be different for different LLMs), for LLaMA it is `shared.model.model.embed_tokens(...)`. + +The image parts are turned to embeddings, using `AbstractMultimodalPipeline.embed_images(...)` function. This function is specific for a given pipeline, it takes the images as input, forwards them through vision model/projector, and returns the embeddings. + +**Now, the returned embeddings are stitched together**, using `torch.cat()`, this is creating the final input to the LLM. + +## Pipelines + +All of the pipelines should subclass `AbstractMultimodalPipeline` class. The idea is to allow for new pipelines to be added in the same way as user extensions - git clone into `extensions/multimodal/pipelines`. + +The pipelines are the description of the vision part, containing vision model/multimodal projector. All of the pipelines should have an unique `name()`, which is then selected by user, in `--multimodal-pipeline` CLI argument. For an example, see `pipelines/llava/llava.py`. + +## Pipeline modules + +Pipelines are organized into "pipeline modules" - subdirectories in `pipelines` directory. The pipeline modules should contain a file called `pipelines.py`, that should contain the following fields: +- `available_pipelines: List[str]` - list of pipelines provided by this module, shown as the list of available pipelines to the user +- `def get_pipeline(name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a concrete pipeline by `name`, if `name` doesn't match any, should return `None`. `params` is the user settings for multimodal extension +- `def get_pipeline_from_model_name(model_name: str, params: dict) -> Optional[AbstractMultimodalPipeline]`: - a function to get a pipeline from `model_name`, should be eager to return `None`, unless the determination can be done clearly (for example: minigpt-4 bases on vicuna - it should never return the pipeline, but llava can, as it has its own specific LLM finetune) + +**NOTE**: A pipeline module should lazy-import the pipelines only when necessary, and it should keep its imports to minimum + +## Pipeline params + +The pipelines will get the extension `params` in the constructor. They should honor the following fields: +- `vision_device` - string, specifying `torch.device` to run the vision model (CLIP/ViT) on +- `vision_bits` - int, number of fp bits to load the vision model(s) in +- `projector_device` - string, specifying `torch.device` to run the projector models (Linear layers, QFormer, etc.) on +- `projector_bits` - int, number of fp bits to load the projector models in + +As a helper, `AbstractMultimodalPipeline` has `_get_device(self, setting_name: str, params: dict)` and `_get_dtype(self, setting_name: str, params: dict)` helper functions, which parse string/int and return `torch.device` / `torch.dtype`. diff --git a/extensions/multimodal/README.md b/extensions/multimodal/README.md new file mode 100644 index 0000000000000000000000000000000000000000..0f515ae69f6b8680522df9854c515ea698710629 --- /dev/null +++ b/extensions/multimodal/README.md @@ -0,0 +1,81 @@ +# Multimodal + +## Description + +Adds support for multimodality (text+images) to text-generation-webui. + +https://user-images.githubusercontent.com/3718215/233817203-69b57e77-0c55-4fd6-b742-3204bb13b8fc.mp4 + +## Usage + +To run this extension, download a LLM that supports multimodality, and then start server.py with the appropriate `--multimodal-pipeline` argument. Examples: + +``` +python server.py --model wojtab_llava-7b-v0-4bit-128g --multimodal-pipeline llava-7b --chat +python3 server.py --model wojtab_llava-13b-v0-4bit-128g --multimodal-pipeline llava-13b --chat +python server.py --model anon8231489123_vicuna-13b-GPTQ-4bit-128g --multimodal-pipeline minigpt4-13b --chat +python server.py --model llama-7b-4bit --multimodal-pipeline minigpt4-7b --chat +``` + +There is built-in support for LLaVA-v0-13B and LLaVA-v0-7b. To install `minigpt4`: + +- clone https://github.com/Wojtab/minigpt-4-pipeline into `extensions/multimodal/pipelines` +- install the requirements.txt + +The same procedure should be used to install other pipelines, which can then be used with `--multimodal-pipeline [pipeline name]`. For additional multimodal pipelines refer to the compatibility section below. + +Do note, that each image takes up a considerable amount of tokens, so adjust `max_new_tokens` to be at most 1700 (recommended value is between 200 to 500), so the images don't get truncated. + +To send an image, just upload it to the extension field below chat, and send a prompt as always. The image will be added to the end of your message. If you wish to modify the placement, include a string `` in your prompt. + +Additionally, there is *Embed all images, not only the last one* checkbox. It modifies the image embeddings, by default (if it's unchecked), all but the most recent images have their embeddings empty, so they are not fed to the network. It seems as if some multimodal networks consider the features in all images at the same time as if they were a single image. Due to this behavior, by default, the extension skips previous images. However, it can lead to sub-par generation on other pipelines. If you want to include all images, just tick this checkbox. + +## Compatibility +As of now, the following multimodal pipelines are supported: +|Pipeline|`--multimodal-pipeline`|Default LLM|LLM info(for the linked model)|Pipeline repository| +|-|-|-|-|-| +|[LLaVA 13B](https://github.com/haotian-liu/LLaVA)|`llava-13b`|[LLaVA 13B](https://huggingface.co/wojtab/llava-13b-v0-4bit-128g)|GPTQ 4-bit quant, old CUDA|built-in| +|[LLaVA 7B](https://github.com/haotian-liu/LLaVA)|`llava-7b`|[LLaVA 7B](https://huggingface.co/wojtab/llava-7b-v0-4bit-128g)|GPTQ 4-bit quant, old CUDA|built-in| +|[MiniGPT-4 7B](https://github.com/Vision-CAIR/MiniGPT-4)|`minigpt4-7b`|[Vicuna v0 7B](https://huggingface.co/TheBloke/vicuna-7B-GPTQ-4bit-128g)|GPTQ 4-bit quant, new format|[Wojtab/minigpt-4-pipeline](https://github.com/Wojtab/minigpt-4-pipeline)| +|[MiniGPT-4 13B](https://github.com/Vision-CAIR/MiniGPT-4)|`minigpt4-13b`|[Vicuna v0 13B](https://huggingface.co/anon8231489123/vicuna-13b-GPTQ-4bit-128g)|GPTQ 4-bit quant, old CUDA|[Wojtab/minigpt-4-pipeline](https://github.com/Wojtab/minigpt-4-pipeline)| + +Some pipelines could support different LLMs but do note that while it might work, it isn't a supported configuration. + +DO NOT report bugs if you are using a different LLM. + +DO NOT report bugs with pipelines in this repository (unless they are built-in) + +## Extension config +This extension uses the following parameters (from `settings.json`): +|Parameter|Description| +|---------|-----------| +|`multimodal-vision_bits`|Number of bits to load vision models (CLIP/ViT) feature extractor in (most pipelines should support either 32 or 16, default=32)| +|`multimodal-vision_device`|Torch device to run the feature extractor on, for example, `cpu` or `cuda:0`, by default `cuda:0` if available| +|`multimodal-projector_bits`|Number of bits to load feature projector model(s) in (most pipelines should support either 32 or 16, default=32)| +|`multimodal-projector_device`|Torch device to run the feature projector model(s) on, for example `cpu` or `cuda:0`, by default `cuda:0` if available| +|`multimodal-add_all_images_to_prompt`|Default value of "Embed all images, not only the last one" checkbox| + +## Usage through API + +You can run the multimodal inference through API, by inputting the images to prompt. Images are embedded like so: `f''`, where `img_str` is base-64 jpeg data. Note that you will need to launch `server.py` with the arguments `--api --extensions multimodal`. + +Python example: + +```Python +import base64 +import requests + +CONTEXT = "You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Follow the instructions carefully and explain your answers in detail.### Human: Hi!### Assistant: Hi there! How can I help you today?\n" + +with open('extreme_ironing.jpg', 'rb') as f: + img_str = base64.b64encode(f.read()).decode('utf-8') + prompt = CONTEXT + f'### Human: What is unusual about this image: \n### Assistant: ' + print(requests.post('http://127.0.0.1:5000/api/v1/generate', json={'prompt': prompt, 'stopping_strings': ['\n###']}).json()) +``` +script output: +```Python +{'results': [{'text': "The unusual aspect of this image is that a man is standing on top of a yellow minivan while doing his laundry. He has set up a makeshift clothes line using the car's rooftop as an outdoor drying area. This scene is uncommon because people typically do their laundry indoors, in a dedicated space like a laundromat or a room in their home, rather than on top of a moving vehicle. Additionally, hanging clothes on the car could be potentially hazardous or illegal in some jurisdictions due to the risk of damaging the vehicle or causing accidents on the road.\n##"}]} +``` + +## For pipeline developers/technical description +see [DOCS.md](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/multimodal/DOCS.md) diff --git a/extensions/multimodal/abstract_pipeline.py b/extensions/multimodal/abstract_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..584219419d256e7743fd4d5120c56bcfa8f2a9f9 --- /dev/null +++ b/extensions/multimodal/abstract_pipeline.py @@ -0,0 +1,62 @@ +from abc import ABC, abstractmethod +from typing import List, Optional + +import torch +from PIL import Image + + +class AbstractMultimodalPipeline(ABC): + @staticmethod + @abstractmethod + def name() -> str: + 'name of the pipeline, should be same as in --multimodal-pipeline' + pass + + @staticmethod + @abstractmethod + def image_start() -> Optional[str]: + 'return image start string, string representation of image start token, or None if not applicable' + pass + + @staticmethod + @abstractmethod + def image_end() -> Optional[str]: + 'return image end string, string representation of image end token, or None if not applicable' + pass + + @staticmethod + @abstractmethod + def placeholder_token_id() -> int: + 'return placeholder token id' + pass + + @staticmethod + @abstractmethod + def num_image_embeds() -> int: + 'return the number of embeds used by a single image (for example: 256 for LLaVA)' + pass + + @abstractmethod + def embed_images(self, images: List[Image.Image]) -> torch.Tensor: + 'forward the images through vision pipeline, and return their embeddings' + pass + + @staticmethod + @abstractmethod + def embed_tokens(input_ids: torch.Tensor) -> torch.Tensor: + 'embed tokens, the exact function varies by LLM, for LLaMA it is `shared.model.model.embed_tokens`' + pass + + @staticmethod + @abstractmethod + def placeholder_embeddings() -> torch.Tensor: + 'get placeholder embeddings if there are multiple images, and `add_all_images_to_prompt` is False' + pass + + def _get_device(self, setting_name: str, params: dict): + if params[setting_name] is None: + return torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + return torch.device(params[setting_name]) + + def _get_dtype(self, setting_name: str, params: dict): + return torch.float32 if int(params[setting_name]) == 32 else torch.float16 diff --git a/extensions/multimodal/multimodal_embedder.py b/extensions/multimodal/multimodal_embedder.py new file mode 100644 index 0000000000000000000000000000000000000000..62e99ca7c950bcdae65049d0cb426a2fa53ba2b7 --- /dev/null +++ b/extensions/multimodal/multimodal_embedder.py @@ -0,0 +1,178 @@ +import base64 +import logging +import re +from dataclasses import dataclass +from io import BytesIO +from typing import Any, List, Optional + +import torch +from PIL import Image + +from extensions.multimodal.pipeline_loader import load_pipeline +from modules import shared +from modules.text_generation import encode, get_max_prompt_length + + +@dataclass +class PromptPart: + text: str + image: Optional[Image.Image] = None + is_image: bool = False + input_ids: Optional[torch.Tensor] = None + embedding: Optional[torch.Tensor] = None + + +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__})') + + 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. + It will also append `image_start` and `image_end` before and after the image, and optionally parse and load the images, + if `load_images` is `True`. + """ + parts: List[PromptPart] = [] + curr = 0 + while True: + match = re.search(r'', prompt[curr:]) + if match is None: + # no more image tokens, append the rest of the prompt + if curr > 0: + # add image end token after last image + parts.append(PromptPart(text=self.pipeline.image_end() + prompt[curr:])) + else: + parts.append(PromptPart(text=prompt)) + break + # found an image, append image start token to the text + if match.start() > 0: + parts.append(PromptPart(text=prompt[curr:curr + match.start()] + self.pipeline.image_start())) + else: + parts.append(PromptPart(text=self.pipeline.image_start())) + # append the image + parts.append(PromptPart( + text=match.group(0), + image=Image.open(BytesIO(base64.b64decode(match.group(1)))) if load_images else None, + is_image=True + )) + curr += match.end() + return parts + + def _len_in_tokens_prompt_parts(self, parts: List[PromptPart]) -> int: + """Total length in tokens of all `parts`""" + tokens = 0 + for part in parts: + if part.is_image: + tokens += self.pipeline.num_image_embeds() + elif part.input_ids is not None: + tokens += len(part.input_ids) + else: + tokens += len(encode(part.text)[0]) + return tokens + + def len_in_tokens(self, prompt: str) -> int: + """Total length in tokens for a given text `prompt`""" + parts = self._split_prompt(prompt, False) + return self._len_in_tokens_prompt_parts(parts) + + def _encode_single_text(self, part: PromptPart, add_bos_token: bool) -> PromptPart: + """Encode a single prompt `part` to `input_ids`. Returns a `PromptPart`""" + if part.is_image: + placeholders = torch.ones((self.pipeline.num_image_embeds())) * self.pipeline.placeholder_token_id() + part.input_ids = placeholders.to(shared.model.device, dtype=torch.int64) + else: + part.input_ids = encode(part.text, add_bos_token=add_bos_token)[0].to(shared.model.device, dtype=torch.int64) + return part + + @staticmethod + def _num_images(parts: List[PromptPart]) -> int: + count = 0 + for part in parts: + if part.is_image: + count += 1 + return count + + def _encode_text(self, state, parts: List[PromptPart]) -> List[PromptPart]: + """Encode text to token_ids, also truncate the prompt, if necessary. + + The chat/instruct mode should make prompts that fit in get_max_prompt_length, but if max_new_tokens are set + such that the context + min_rows don't fit, we can get a prompt which is too long. + We can't truncate image embeddings, as it leads to broken generation, so remove the images instead and warn the user + """ + encoded: List[PromptPart] = [] + for i, part in enumerate(parts): + encoded.append(self._encode_single_text(part, i == 0 and state['add_bos_token'])) + + # truncation: + max_len = get_max_prompt_length(state) + removed_images = 0 + + # 1. remove entire text/image blocks + while self._len_in_tokens_prompt_parts(encoded[1:]) > max_len: + if encoded[0].is_image: + removed_images += 1 + encoded = encoded[1:] + + # 2. check if the last prompt part doesn't need to get truncated + if self._len_in_tokens_prompt_parts(encoded) > max_len: + if encoded[0].is_image: + # don't truncate image embeddings, just remove the image, otherwise generation will be broken + removed_images += 1 + encoded = encoded[1:] + elif len(encoded) > 1 and encoded[0].text.endswith(self.pipeline.image_start()): + # see if we can keep image_start token + len_image_start = len(encode(self.pipeline.image_start(), add_bos_token=state['add_bos_token'])[0]) + if self._len_in_tokens_prompt_parts(encoded[1:]) + len_image_start > max_len: + # we can't -> remove this text, and the image + encoded = encoded[2:] + removed_images += 1 + else: + # we can -> just truncate the text + trunc_len = self._len_in_tokens_prompt_parts(encoded) - max_len + encoded[0].input_ids = encoded[0].input_ids[trunc_len:] + elif len(encoded) > 0: + # only one text left, truncate it normally + trunc_len = self._len_in_tokens_prompt_parts(encoded) - max_len + encoded[0].input_ids = encoded[0].input_ids[trunc_len:] + + # 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") + + return encoded + + def _embed(self, parts: List[PromptPart]) -> List[PromptPart]: + # batch images + image_indicies = [i for i, part in enumerate(parts) if part.is_image] + embedded = self.pipeline.embed_images([parts[i].image for i in image_indicies]) + for i, embeds in zip(image_indicies, embedded): + parts[i].embedding = embeds + # embed text + for (i, part) in enumerate(parts): + if not part.is_image: + parts[i].embedding = self.pipeline.embed_tokens(part.input_ids) + return parts + + def _remove_old_images(self, parts: List[PromptPart], params: dict) -> List[PromptPart]: + if params['add_all_images_to_prompt']: + return parts + already_added = False + for i, part in reversed(list(enumerate(parts))): + if part.is_image: + if already_added: + parts[i].embedding = self.pipeline.placeholder_embeddings() + else: + already_added = True + return parts + + def forward(self, prompt: str, state: Any, params: dict): + prompt_parts = self._split_prompt(prompt, True) + prompt_parts = self._encode_text(state, prompt_parts) + prompt_parts = self._embed(prompt_parts) + prompt_parts = self._remove_old_images(prompt_parts, params) + embeds = tuple(part.embedding for part in prompt_parts) + ids = tuple(part.input_ids for part in prompt_parts) + input_embeds = torch.cat(embeds, dim=0) + input_ids = torch.cat(ids, dim=0) + return prompt, input_ids, input_embeds, self._num_images(prompt_parts) diff --git a/extensions/multimodal/pipeline_loader.py b/extensions/multimodal/pipeline_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..3ebdb1044b7e924ce3633120967be517474dd4d4 --- /dev/null +++ b/extensions/multimodal/pipeline_loader.py @@ -0,0 +1,52 @@ +import logging +import traceback +from importlib import import_module +from pathlib import Path +from typing import Tuple + +from extensions.multimodal.abstract_pipeline import AbstractMultimodalPipeline +from modules import shared + + +def _get_available_pipeline_modules(): + pipeline_path = Path(__file__).parent / 'pipelines' + modules = [p for p in pipeline_path.iterdir() if p.is_dir()] + return [m.name for m in modules if (m / 'pipelines.py').exists()] + + +def load_pipeline(params: dict) -> Tuple[AbstractMultimodalPipeline, str]: + pipeline_modules = {} + available_pipeline_modules = _get_available_pipeline_modules() + for name in available_pipeline_modules: + 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()) + + if shared.args.multimodal_pipeline is not None: + for k in pipeline_modules: + if hasattr(pipeline_modules[k], 'get_pipeline'): + pipeline = getattr(pipeline_modules[k], 'get_pipeline')(shared.args.multimodal_pipeline, params) + if pipeline is not None: + return (pipeline, k) + else: + model_name = shared.args.model.lower() + for k in pipeline_modules: + if hasattr(pipeline_modules[k], 'get_pipeline_from_model_name'): + pipeline = getattr(pipeline_modules[k], 'get_pipeline_from_model_name')(model_name, params) + if pipeline is not None: + return (pipeline, k) + + available = [] + for k in pipeline_modules: + if hasattr(pipeline_modules[k], 'available_pipelines'): + pipelines = getattr(pipeline_modules[k], 'available_pipelines') + available += pipelines + + if shared.args.multimodal_pipeline is not None: + 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') + raise RuntimeError(f'{log} Please specify a correct pipeline, or disable the extension') diff --git a/extensions/multimodal/script.py b/extensions/multimodal/script.py new file mode 100644 index 0000000000000000000000000000000000000000..2ca11bf56fd413d173428599b927528d3d100534 --- /dev/null +++ b/extensions/multimodal/script.py @@ -0,0 +1,103 @@ +import base64 +import logging +import re +import time +from functools import partial +from io import BytesIO + +import gradio as gr +import torch + +from extensions.multimodal.multimodal_embedder import MultimodalEmbedder +from modules import shared + +params = { + "add_all_images_to_prompt": False, + # device to run vision encoder on + "vision_device": None, + # bits to load vision encoder in, either 16 or 32 + "vision_bits": 32, + # device to run multimodal projector on + "projector_device": None, + # multimodal projector bits, either 32 or 16 + "projector_bits": 32 +} + + +# If 'state' is True, will hijack the next chat generation +input_hijack = { + 'state': False, + 'value': ["", ""] +} + + +# initialized in ui, so that params are loaded from settings +multimodal_embedder: MultimodalEmbedder = None + + +def add_chat_picture(picture, text, visible_text): + # resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable) + max_hw, min_hw = max(picture.size), min(picture.size) + aspect_ratio = max_hw / min_hw + shortest_edge = int(max(300 / aspect_ratio, 224)) + longest_edge = int(shortest_edge * aspect_ratio) + w = shortest_edge if picture.width < picture.height else longest_edge + h = shortest_edge if picture.width >= picture.height else longest_edge + picture = picture.resize((w, h)) + + buffer = BytesIO() + picture.save(buffer, format="JPEG") + img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') + image = f'' + + if '' in text: + text = text.replace('', image) + else: + text = text + '\n' + image + + if visible_text == '' or visible_text is None: + visible_text = text + elif '' in visible_text: + visible_text = visible_text.replace('', image) + else: + visible_text = visible_text + '\n' + image + + return text, visible_text + + +def custom_tokenized_length(prompt): + return multimodal_embedder.len_in_tokens(prompt) + + +def tokenizer_modifier(state, prompt, input_ids, input_embeds): + global params + start_ts = time.time() + image_match = re.search(r'', prompt) + + if image_match is None: + 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') + 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)) + + +def ui(): + global multimodal_embedder + multimodal_embedder = MultimodalEmbedder(params) + with gr.Column(): + picture_select = gr.Image(label='Send a picture', type='pil') + # The models don't seem to deal well with multiple images + single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one') + # Prepare the input hijack + picture_select.upload( + lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}), + [picture_select], + None + ) + picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["", ""]}), None, None) + single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None) + shared.gradio['Generate'].click(lambda: None, None, picture_select) + shared.gradio['textbox'].submit(lambda: None, None, picture_select) diff --git a/extensions/openai/README.md b/extensions/openai/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b20eba3326b297630e64a3bedf96ef82c0d359b8 --- /dev/null +++ b/extensions/openai/README.md @@ -0,0 +1,144 @@ +# An OpenedAI API (openai like) + +This extension creates an API that works kind of like openai (ie. api.openai.com). +It's incomplete so far but perhaps is functional enough for you. + +## Setup & installation + +Optional (for flask_cloudflared, embeddings): + +``` +pip3 install -r requirements.txt +``` + +It listens on tcp port 5001 by default. You can use the OPENEDAI_PORT environment variable to change this. + +To enable the bare bones image generation (txt2img) set: SD_WEBUI_URL to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)). + +Example: +``` +SD_WEBUI_URL=http://127.0.0.1:7861 +``` + +### Embeddings (alpha) + +Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future. + +| model name | dimensions | input max tokens | speed | size | Avg. performance | +| --- | --- | --- | --- | --- | --- | +| text-embedding-ada-002 | 1536 | 8192| - | - | - | +| text-davinci-002 | 768 | 2046 | - | - | - | +| all-mpnet-base-v2 | 768 | 384 | 2800 | 420M | 63.3 | +| all-MiniLM-L6-v2 | 384 | 256 | 14200 | 80M | 58.8 | + +In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable OPENEDAI_EMBEDDING_MODEL, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2". + +Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable. + +### Client Application Setup + +Almost everything you use it with will require you to set a dummy OpenAI API key environment variable. + +With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so: + +``` +OPENAI_API_KEY=dummy +OPENAI_API_BASE=http://127.0.0.1:5001/v1 +``` + +If needed, replace 127.0.0.1 with the IP/port of your server. + +If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, you can ensure compatibility by loading the .env file before loading the openai module, like so in python: + +``` +from dotenv import load_dotenv +load_dotenv() +import openai +``` + +With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so: + +``` +const openai = OpenAI(Configuration({ + apiKey: process.env.OPENAI_API_KEY, + basePath: process.env.OPENAI_API_BASE, +})); +``` + +For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api): + +``` +const api = new ChatGPTAPI({ + apiKey: process.env.OPENAI_API_KEY, + apiBaseUrl: process.env.OPENAI_API_BASE, +}) +``` + +## Compatibility & not so compatibility + +| API endpoint | tested with | notes | +| --- | --- | --- | +| /v1/models | openai.Model.list() | returns the currently loaded model_name and some mock compatibility options | +| /v1/models/{id} | openai.Model.get() | returns whatever you ask for, model does nothing yet anyways | +| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far | +| /v1/chat/completions | openai.ChatCompletion.create() | depending on the model, this may add leading linefeeds | +| /v1/edits | openai.Edit.create() | Assumes an instruction following model, but may work with others | +| /v1/images/generations | openai.Image.create() | Bare bones, no model configuration, response_format='b64_json' only. | +| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. | +| /v1/moderations | openai.Moderation.create() | does nothing. successfully. | +| /v1/engines/\*/... completions, embeddings, generate | python-openai v0.25 and earlier | Legacy engines endpoints | +| /v1/images/edits | openai.Image.create_edit() | not supported | +| /v1/images/variations | openai.Image.create_variation() | not supported | +| /v1/audio/\* | openai.Audio.\* | not supported | +| /v1/files\* | openai.Files.\* | not supported | +| /v1/fine-tunes\* | openai.FineTune.\* | not supported | + +The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048 tokens instead of 4096, 8k, etc). To mitigate some of this, the max_tokens value is halved until it is less than truncation_length for the model (typically 2k). + +Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly. + +Some hacky mappings: + +| OpenAI | text-generation-webui | note | +| --- | --- | --- | +| frequency_penalty | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way | +| presence_penalty | repetition_penalty | same issues as frequency_penalty, hardcoded to 1.0 | +| best_of | top_k | | +| stop | custom_stopping_strings | this is also stuffed with ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] for good measure. | +| n | 1 | hardcoded, it may be worth implementing this but I'm not sure how yet | +| 1.0 | typical_p | hardcoded | +| 1 | num_beams | hardcoded | +| max_tokens | max_new_tokens | max_tokens is scaled down by powers of 2 until it's smaller than truncation length. | +| logprobs | - | ignored | + +defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent. + +### Models + +This has been successfully tested with Koala, Alpaca, gpt4-x-alpaca, GPT4all-snoozy, wizard-vicuna, stable-vicuna and Vicuna 1.1 - ie. Instruction Following models. If you test with other models please let me know how it goes. Less than satisfying results (so far): RWKV-4-Raven, llama, mpt-7b-instruct/chat + +### Applications + +Everything needs OPENAI_API_KEY=dummy set. + +| Compatibility | Application/Library | url | notes / setting | +| --- | --- | --- | --- | +| ✅❌ | openai-python | https://github.com/openai/openai-python | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅❌ | openai-node | https://github.com/openai/openai-node | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) | +| ✅❌ | chatgpt-api | https://github.com/transitive-bullshit/chatgpt-api | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) | +| ✅ | shell_gpt | https://github.com/TheR1D/shell_gpt | OPENAI_API_HOST=http://127.0.0.1:5001 | +| ✅ | gpt-shell | https://github.com/jla/gpt-shell | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅ | gpt-discord-bot | https://github.com/openai/gpt-discord-bot | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅❌ | langchain | https://github.com/hwchase17/langchain | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. | +| ✅❌ | Auto-GPT | https://github.com/Significant-Gravitas/Auto-GPT | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context | +| ✅❌ | babyagi | https://github.com/yoheinakajima/babyagi | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | + +## Future plans +* better error handling +* model changing, esp. something for swapping loras or embedding models +* consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard) +* do something about rate limiting or locking requests for completions, most systems will only be able handle a single request at a time before OOM + +## Bugs? Feedback? Comments? Pull requests? + +Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible. diff --git a/extensions/openai/cache_embedding_model.py b/extensions/openai/cache_embedding_model.py new file mode 100644 index 0000000000000000000000000000000000000000..44ac1dcd663d09a9f36bf9793ee2fa653339cbb3 --- /dev/null +++ b/extensions/openai/cache_embedding_model.py @@ -0,0 +1,8 @@ +#!/usr/bin/env python3 +# preload the embedding model, useful for Docker images to prevent re-download on config change +# Dockerfile: +# ENV OPENEDAI_EMBEDDING_MODEL=all-mpnet-base-v2 # Optional +# RUN python3 cache_embedded_model.py +import os, sentence_transformers +st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2" +model = sentence_transformers.SentenceTransformer(st_model) diff --git a/extensions/openai/requirements.txt b/extensions/openai/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5193a0acef91177f49cea0ba1c0fd0329b8703de --- /dev/null +++ b/extensions/openai/requirements.txt @@ -0,0 +1,2 @@ +flask_cloudflared==0.0.12 +sentence-transformers \ No newline at end of file diff --git a/extensions/openai/script.py b/extensions/openai/script.py new file mode 100644 index 0000000000000000000000000000000000000000..712cfe3887338bbb6c3d301817bd98c159bf63d5 --- /dev/null +++ b/extensions/openai/script.py @@ -0,0 +1,701 @@ +import base64 +import json +import os +import time +import requests +import yaml +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from threading import Thread + +import numpy as np + +from modules import shared +from modules.text_generation import encode, generate_reply + +params = { + 'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001, +} + +debug = True if 'OPENEDAI_DEBUG' in os.environ else False + +# Optional, install the module and download the model to enable +# v1/embeddings +try: + from sentence_transformers import SentenceTransformer +except ImportError: + pass + +st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2" +embedding_model = None + +standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] + +# little helper to get defaults if arg is present but None and should be the same type as default. +def default(dic, key, default): + val = dic.get(key, default) + if type(val) != type(default): + # maybe it's just something like 1 instead of 1.0 + try: + v = type(default)(val) + if type(val)(v) == val: # if it's the same value passed in, it's ok. + return v + except: + pass + + val = default + return val + + +def clamp(value, minvalue, maxvalue): + return max(minvalue, min(value, maxvalue)) + + +def deduce_template(): + # Alpaca is verbose so a good default prompt + default_template = ( + "Below is an instruction that describes a task, paired with an input that provides further context. " + "Write a response that appropriately completes the request.\n\n" + "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n" + ) + + # Use the special instruction/input/response template for anything trained like Alpaca + if shared.settings['instruction_template'] in ['Alpaca', 'Alpaca-Input']: + return default_template + + try: + instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r')) + + template = instruct['turn_template'] + template = template\ + .replace('<|user|>', instruct.get('user', ''))\ + .replace('<|bot|>', instruct.get('bot', ''))\ + .replace('<|user-message|>', '{instruction}\n{input}') + return instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ') + except: + return default_template + + +def float_list_to_base64(float_list): + # Convert the list to a float32 array that the OpenAPI client expects + float_array = np.array(float_list, dtype="float32") + + # Get raw bytes + bytes_array = float_array.tobytes() + + # Encode bytes into base64 + encoded_bytes = base64.b64encode(bytes_array) + + # Turn raw base64 encoded bytes into ASCII + ascii_string = encoded_bytes.decode('ascii') + return ascii_string + + +class Handler(BaseHTTPRequestHandler): + def do_GET(self): + if self.path.startswith('/v1/models'): + + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + # TODO: list all models and allow model changes via API? Lora's? + # This API should list capabilities, limits and pricing... + models = [{ + "id": shared.model_name, # The real chat/completions model + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": st_model, # The real sentence transformer embeddings model + "object": "model", + "owned_by": "user", + "permission": [] + }, { # these are expected by so much, so include some here as a dummy + "id": "gpt-3.5-turbo", # /v1/chat/completions + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": "text-curie-001", # /v1/completions, 2k context + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768 + "object": "model", + "owned_by": "user", + "permission": [] + }] + + response = '' + if self.path == '/v1/models': + response = json.dumps({ + "object": "list", + "data": models, + }) + else: + the_model_name = self.path[len('/v1/models/'):] + response = json.dumps({ + "id": the_model_name, + "object": "model", + "owned_by": "user", + "permission": [] + }) + + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + def do_POST(self): + if debug: + print(self.headers) # did you know... python-openai sends your linux kernel & python version? + content_length = int(self.headers['Content-Length']) + body = json.loads(self.rfile.read(content_length).decode('utf-8')) + + if debug: + print(body) + + if '/completions' in self.path or '/generate' in self.path: + is_legacy = '/generate' in self.path + is_chat = 'chat' in self.path + resp_list = 'data' if is_legacy else 'choices' + + # XXX model is ignored for now + # model = body.get('model', shared.model_name) # ignored, use existing for now + model = shared.model_name + created_time = int(time.time()) + cmpl_id = "conv-%d" % (created_time) + + # Try to use openai defaults or map them to something with the same intent + stopping_strings = default(shared.settings, 'custom_stopping_strings', []) + if 'stop' in body: + if isinstance(body['stop'], str): + stopping_strings = [body['stop']] + elif isinstance(body['stop'], list): + stopping_strings = body['stop'] + + truncation_length = default(shared.settings, 'truncation_length', 2048) + truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length) + + default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it. + + max_tokens_str = 'length' if is_legacy else 'max_tokens' + max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens)) + + # hard scale this, assuming the given max is for GPT3/4, perhaps inspect the requested model and lookup the context max + while truncation_length <= max_tokens: + max_tokens = max_tokens // 2 + + req_params = { + 'max_new_tokens': max_tokens, + 'temperature': default(body, 'temperature', 1.0), + 'top_p': default(body, 'top_p', 1.0), + 'top_k': default(body, 'best_of', 1), + # XXX not sure about this one, seems to be the right mapping, but the range is different (-2..2.0) vs 0..2 + # 0 is default in openai, but 1.0 is default in other places. Maybe it's scaled? scale it. + 'repetition_penalty': 1.18, # (default(body, 'presence_penalty', 0) + 2.0 ) / 2.0, # 0 the real default, 1.2 is the model default, but 1.18 works better. + # XXX not sure about this one either, same questions. (-2..2.0), 0 is default not 1.0, scale it. + 'encoder_repetition_penalty': 1.0, # (default(body, 'frequency_penalty', 0) + 2.0) / 2.0, + 'suffix': body.get('suffix', None), + 'stream': default(body, 'stream', False), + 'echo': default(body, 'echo', False), + ##################################################### + 'seed': shared.settings.get('seed', -1), + # int(body.get('n', 1)) # perhaps this should be num_beams or chat_generation_attempts? 'n' doesn't have a direct map + # unofficial, but it needs to get set anyways. + 'truncation_length': truncation_length, + # no more args. + 'add_bos_token': shared.settings.get('add_bos_token', True), + 'do_sample': True, + 'typical_p': 1.0, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0.0, + 'length_penalty': 1, + 'early_stopping': False, + 'ban_eos_token': False, + 'skip_special_tokens': True, + } + + # fixup absolute 0.0's + for par in ['temperature', 'repetition_penalty', 'encoder_repetition_penalty']: + req_params[par] = clamp(req_params[par], 0.001, 1.999) + + self.send_response(200) + if req_params['stream']: + self.send_header('Content-Type', 'text/event-stream') + self.send_header('Cache-Control', 'no-cache') + # self.send_header('Connection', 'keep-alive') + else: + self.send_header('Content-Type', 'application/json') + self.end_headers() + + token_count = 0 + completion_token_count = 0 + prompt = '' + stream_object_type = '' + object_type = '' + + if is_chat: + stream_object_type = 'chat.completions.chunk' + object_type = 'chat.completions' + + messages = body['messages'] + + system_msg = '' # You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date} + if 'prompt' in body: # Maybe they sent both? This is not documented in the API, but some clients seem to do this. + system_msg = body['prompt'] + + chat_msgs = [] + + for m in messages: + role = m['role'] + content = m['content'] + # name = m.get('name', 'user') + if role == 'system': + system_msg += content + else: + chat_msgs.extend([f"\n{role}: {content.strip()}"]) # Strip content? linefeed? + + system_token_count = len(encode(system_msg)[0]) + remaining_tokens = req_params['truncation_length'] - req_params['max_new_tokens'] - system_token_count + chat_msg = '' + + while chat_msgs: + new_msg = chat_msgs.pop() + new_size = len(encode(new_msg)[0]) + if new_size <= remaining_tokens: + chat_msg = new_msg + chat_msg + remaining_tokens -= new_size + else: + # TODO: clip a message to fit? + # ie. user: ... + break + + if len(chat_msgs) > 0: + print(f"truncating chat messages, dropping {len(chat_msgs)} messages.") + + if system_msg: + prompt = 'system: ' + system_msg + '\n' + chat_msg + '\nassistant: ' + else: + prompt = chat_msg + '\nassistant: ' + + token_count = len(encode(prompt)[0]) + + # pass with some expected stop strings. + # some strange cases of "##| Instruction: " sneaking through. + stopping_strings += standard_stopping_strings + req_params['custom_stopping_strings'] = stopping_strings + else: + stream_object_type = 'text_completion.chunk' + object_type = 'text_completion' + + # ... encoded as a string, array of strings, array of tokens, or array of token arrays. + if is_legacy: + prompt = body['context'] # Older engines.generate API + else: + prompt = body['prompt'] # XXX this can be different types + + if isinstance(prompt, list): + prompt = ''.join(prompt) # XXX this is wrong... need to split out to multiple calls? + + token_count = len(encode(prompt)[0]) + if token_count >= req_params['truncation_length']: + new_len = int(len(prompt) * (float(shared.settings['truncation_length']) - req_params['max_new_tokens']) / token_count) + prompt = prompt[-new_len:] + print(f"truncating prompt to {new_len} characters, was {token_count} tokens. Now: {len(encode(prompt)[0])} tokens.") + + # pass with some expected stop strings. + # some strange cases of "##| Instruction: " sneaking through. + stopping_strings += standard_stopping_strings + req_params['custom_stopping_strings'] = stopping_strings + + if req_params['stream']: + shared.args.chat = True + # begin streaming + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": shared.model_name, + resp_list: [{ + "index": 0, + "finish_reason": None, + }], + } + + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]["text"] = "" + else: + # This is coming back as "system" to the openapi cli, not sure why. + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''} + chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''} + # { "role": "assistant" } + + response = 'data: ' + json.dumps(chunk) + '\n' + self.wfile.write(response.encode('utf-8')) + + # generate reply ####################################### + if debug: + print({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings}) + generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False) + + answer = '' + seen_content = '' + longest_stop_len = max([len(x) for x in stopping_strings]) + + for a in generator: + answer = a + + stop_string_found = False + len_seen = len(seen_content) + search_start = max(len_seen - longest_stop_len, 0) + + for string in stopping_strings: + idx = answer.find(string, search_start) + if idx != -1: + answer = answer[:idx] # clip it. + stop_string_found = True + + if stop_string_found: + break + + # If something like "\nYo" is generated just before "\nYou:" + # is completed, buffer and generate more, don't send it + buffer_and_continue = False + + for string in stopping_strings: + for j in range(len(string) - 1, 0, -1): + if answer[-j:] == string[:j]: + buffer_and_continue = True + break + else: + continue + break + + if buffer_and_continue: + continue + + if req_params['stream']: + # Streaming + new_content = answer[len_seen:] + + if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet. + continue + + seen_content = answer + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": shared.model_name, + resp_list: [{ + "index": 0, + "finish_reason": None, + }], + } + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]['text'] = new_content + else: + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]['message'] = {'content': new_content} + chunk[resp_list][0]['delta'] = {'content': new_content} + response = 'data: ' + json.dumps(chunk) + '\n' + self.wfile.write(response.encode('utf-8')) + completion_token_count += len(encode(new_content)[0]) + + if req_params['stream']: + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": model, # TODO: add Lora info? + resp_list: [{ + "index": 0, + "finish_reason": "stop", + }], + "usage": { + "prompt_tokens": token_count, + "completion_tokens": completion_token_count, + "total_tokens": token_count + completion_token_count + } + } + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]['text'] = '' + else: + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]['message'] = {'content': ''} + chunk[resp_list][0]['delta'] = {} + response = 'data: ' + json.dumps(chunk) + '\ndata: [DONE]\n' + self.wfile.write(response.encode('utf-8')) + # Finished if streaming. + if debug: + print({'response': answer}) + return + + if debug: + print({'response': answer}) + + completion_token_count = len(encode(answer)[0]) + stop_reason = "stop" + if token_count + completion_token_count >= req_params['truncation_length']: + stop_reason = "length" + + resp = { + "id": cmpl_id, + "object": object_type, + "created": created_time, + "model": model, # TODO: add Lora info? + resp_list: [{ + "index": 0, + "finish_reason": stop_reason, + }], + "usage": { + "prompt_tokens": token_count, + "completion_tokens": completion_token_count, + "total_tokens": token_count + completion_token_count + } + } + + if is_chat: + resp[resp_list][0]["message"] = {"role": "assistant", "content": answer} + else: + resp[resp_list][0]["text"] = answer + + response = json.dumps(resp) + self.wfile.write(response.encode('utf-8')) + elif '/edits' in self.path: + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + created_time = int(time.time()) + + # Using Alpaca format, this may work with other models too. + instruction = body['instruction'] + input = body.get('input', '') + + instruction_template = deduce_template() + edit_task = instruction_template.format(instruction=instruction, input=input) + + truncation_length = default(shared.settings, 'truncation_length', 2048) + token_count = len(encode(edit_task)[0]) + max_tokens = truncation_length - token_count + + req_params = { + 'max_new_tokens': max_tokens, + 'temperature': clamp(default(body, 'temperature', 1.0), 0.001, 1.999), + 'top_p': clamp(default(body, 'top_p', 1.0), 0.001, 1.0), + 'top_k': 1, + 'repetition_penalty': 1.18, + 'encoder_repetition_penalty': 1.0, + 'suffix': None, + 'stream': False, + 'echo': False, + 'seed': shared.settings.get('seed', -1), + # 'n' : default(body, 'n', 1), # 'n' doesn't have a direct map + 'truncation_length': truncation_length, + 'add_bos_token': shared.settings.get('add_bos_token', True), + 'do_sample': True, + 'typical_p': 1.0, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0.0, + 'length_penalty': 1, + 'early_stopping': False, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'custom_stopping_strings': [], + } + + if debug: + print({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count}) + + generator = generate_reply(edit_task, req_params, stopping_strings=standard_stopping_strings, is_chat=False) + + answer = '' + for a in generator: + answer = a + + # some reply's have an extra leading space to fit the instruction template, just clip it off from the reply. + if edit_task[-1] != '\n' and answer and answer[0] == ' ': + answer = answer[1:] + + completion_token_count = len(encode(answer)[0]) + + resp = { + "object": "edit", + "created": created_time, + "choices": [{ + "text": answer, + "index": 0, + }], + "usage": { + "prompt_tokens": token_count, + "completion_tokens": completion_token_count, + "total_tokens": token_count + completion_token_count + } + } + + if debug: + print({'answer': answer, 'completion_token_count': completion_token_count}) + + response = json.dumps(resp) + self.wfile.write(response.encode('utf-8')) + elif '/images/generations' in self.path and 'SD_WEBUI_URL' in os.environ: + # Stable Diffusion callout wrapper for txt2img + # Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E + # the results will be limited and likely poor. SD has hundreds of models and dozens of settings. + # If you want high quality tailored results you should just use the Stable Diffusion API directly. + # it's too general an API to try and shape the result with specific tags like "masterpiece", etc, + # Will probably work best with the stock SD models. + # SD configuration is beyond the scope of this API. + # At this point I will not add the edits and variations endpoints (ie. img2img) because they + # require changing the form data handling to accept multipart form data, also to properly support + # url return types will require file management and a web serving files... Perhaps later! + + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + width, height = [ int(x) for x in default(body, 'size', '1024x1024').split('x') ] # ignore the restrictions on size + response_format = default(body, 'response_format', 'url') # or b64_json + + payload = { + 'prompt': body['prompt'], # ignore prompt limit of 1000 characters + 'width': width, + 'height': height, + 'batch_size': default(body, 'n', 1) # ignore the batch limits of max 10 + } + + resp = { + 'created': int(time.time()), + 'data': [] + } + + # TODO: support SD_WEBUI_AUTH username:password pair. + sd_url = f"{os.environ['SD_WEBUI_URL']}/sdapi/v1/txt2img" + + response = requests.post(url=sd_url, json=payload) + r = response.json() + # r['parameters']... + for b64_json in r['images']: + if response_format == 'b64_json': + resp['data'].extend([{'b64_json': b64_json}]) + else: + resp['data'].extend([{'url': f'data:image/png;base64,{b64_json}'}]) # yeah it's lazy. requests.get() will not work with this + + response = json.dumps(resp) + self.wfile.write(response.encode('utf-8')) + elif '/embeddings' in self.path and embedding_model is not None: + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + input = body['input'] if 'input' in body else body['text'] + if type(input) is str: + input = [input] + + embeddings = embedding_model.encode(input).tolist() + + def enc_emb(emb): + # If base64 is specified, encode. Otherwise, do nothing. + if body.get("encoding_format", "") == "base64": + return float_list_to_base64(emb) + else: + return emb + data = [{"object": "embedding", "embedding": enc_emb(emb), "index": n} for n, emb in enumerate(embeddings)] + + response = json.dumps({ + "object": "list", + "data": data, + "model": st_model, # return the real model + "usage": { + "prompt_tokens": 0, + "total_tokens": 0, + } + }) + + if debug: + print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}") + self.wfile.write(response.encode('utf-8')) + elif '/moderations' in self.path: + # for now do nothing, just don't error. + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + response = json.dumps({ + "id": "modr-5MWoLO", + "model": "text-moderation-001", + "results": [{ + "categories": { + "hate": False, + "hate/threatening": False, + "self-harm": False, + "sexual": False, + "sexual/minors": False, + "violence": False, + "violence/graphic": False + }, + "category_scores": { + "hate": 0.0, + "hate/threatening": 0.0, + "self-harm": 0.0, + "sexual": 0.0, + "sexual/minors": 0.0, + "violence": 0.0, + "violence/graphic": 0.0 + }, + "flagged": False + }] + }) + self.wfile.write(response.encode('utf-8')) + + elif self.path == '/api/v1/token-count': + # NOT STANDARD. lifted from the api extension, but it's still very useful to calculate tokenized length client side. + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + tokens = encode(body['prompt'])[0] + response = json.dumps({ + 'results': [{ + 'tokens': len(tokens) + }] + }) + self.wfile.write(response.encode('utf-8')) + else: + print(self.path, self.headers) + self.send_error(404) + + +def run_server(): + global embedding_model + try: + embedding_model = SentenceTransformer(st_model) + print(f"\nLoaded embedding model: {st_model}, max sequence length: {embedding_model.max_seq_length}") + except: + print(f"\nFailed to load embedding model: {st_model}") + pass + + server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port']) + server = ThreadingHTTPServer(server_addr, Handler) + if shared.args.share: + try: + from flask_cloudflared import _run_cloudflared + public_url = _run_cloudflared(params['port'], params['port'] + 1) + print(f'Starting OpenAI compatible api at\nOPENAI_API_BASE={public_url}/v1') + except ImportError: + print('You should install flask_cloudflared manually') + else: + print(f'Starting OpenAI compatible api:\nOPENAI_API_BASE=http://{server_addr[0]}:{server_addr[1]}/v1') + + server.serve_forever() + + +def setup(): + Thread(target=run_server, daemon=True).start() diff --git a/extensions/sd_api_pictures/README.MD b/extensions/sd_api_pictures/README.MD new file mode 100644 index 0000000000000000000000000000000000000000..67c75e145ccc8301505d96d858da04713ad4337d --- /dev/null +++ b/extensions/sd_api_pictures/README.MD @@ -0,0 +1,90 @@ +## Description: +TL;DR: Lets the bot answer you with a picture! + +Stable Diffusion API pictures for TextGen, v.1.2.0 +An extension to [oobabooga's textgen-webui](https://github.com/oobabooga/text-generation-webui) allowing you to receive pics generated by [Automatic1111's SD-WebUI API](https://github.com/AUTOMATIC1111/stable-diffusion-webui) + +
+Interface overview + +![Interface](https://raw.githubusercontent.com/Brawlence/SD_api_pics/main/illust/Interface.jpg) + +
+ +Load it in the `--chat` mode with `--extension sd_api_pictures` alongside `send_pictures` +(it's not really required, but completes the picture, *pun intended*). + + +## History + +Consider the version included with [oobabooga's repository](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures) to be STABLE, experimental developments and untested features are pushed in [Brawlence/SD_api_pics](https://github.com/Brawlence/SD_api_pics) + +Lastest change: +1.1.0 → 1.1.1 Fixed not having Auto1111's metadata in received images + +## Details + +The image generation is triggered: +- manually through the 'Force the picture response' button while in `Manual` or `Immersive/Interactive` modes OR +- automatically in `Immersive/Interactive` mode if the words `'send|main|message|me'` are followed by `'image|pic|picture|photo|snap|snapshot|selfie|meme'` in the user's prompt +- always on in `Picturebook/Adventure` mode (if not currently suppressed by 'Suppress the picture response') + +## Prerequisites + +One needs an available instance of Automatic1111's webui running with an `--api` flag. Ain't tested with a notebook / cloud hosted one but should be possible. +To run it locally in parallel on the same machine, specify custom `--listen-port` for either Auto1111's or ooba's webUIs. + +## Features overview +- Connection to API check (press enter in the address box) +- [VRAM management (model shuffling)](https://github.com/Brawlence/SD_api_pics/wiki/VRAM-management-feature) +- [Three different operation modes](https://github.com/Brawlence/SD_api_pics/wiki/Modes-of-operation) (manual, interactive, always-on) +- User-defined persistent settings via settings.json + +### Connection check + +Insert the Automatic1111's WebUI address and press Enter: +![API-check](https://raw.githubusercontent.com/Brawlence/SD_api_pics/main/illust/API-check.gif) +Green mark confirms the ability to communicate with Auto1111's API on this address. Red cross means something's not right (the ext won't work). + +### Persistents settings + +Create or modify the `settings.json` in the `text-generation-webui` root directory to override the defaults +present in script.py, ex: + +```json +{ + "sd_api_pictures-manage_VRAM": 1, + "sd_api_pictures-save_img": 1, + "sd_api_pictures-prompt_prefix": "(Masterpiece:1.1), detailed, intricate, colorful, (solo:1.1)", + "sd_api_pictures-sampler_name": "DPM++ 2M Karras" +} +``` + +will automatically set the `Manage VRAM` & `Keep original images` checkboxes and change the texts in `Prompt Prefix` and `Sampler name` on load. + +--- + +## Demonstrations: + +Those are examples of the version 1.0.0, but the core functionality is still the same + +
+Conversation 1 + +![EXA1](https://user-images.githubusercontent.com/42910943/224866564-939a3bcb-e7cf-4ac0-a33f-b3047b55054d.jpg) +![EXA2](https://user-images.githubusercontent.com/42910943/224866566-38394054-1320-45cf-9515-afa76d9d7745.jpg) +![EXA3](https://user-images.githubusercontent.com/42910943/224866568-10ea47b7-0bac-4269-9ec9-22c387a13b59.jpg) +![EXA4](https://user-images.githubusercontent.com/42910943/224866569-326121ad-1ea1-4874-9f6b-4bca7930a263.jpg) + + +
+ +
+Conversation 2 + +![Hist1](https://user-images.githubusercontent.com/42910943/224865517-c6966b58-bc4d-4353-aab9-6eb97778d7bf.jpg) +![Hist2](https://user-images.githubusercontent.com/42910943/224865527-b2fe7c2e-0da5-4c2e-b705-42e233b07084.jpg) +![Hist3](https://user-images.githubusercontent.com/42910943/224865535-a38d94e7-8975-4a46-a655-1ae1de41f85d.jpg) + +
+ diff --git a/extensions/sd_api_pictures/script.py b/extensions/sd_api_pictures/script.py new file mode 100644 index 0000000000000000000000000000000000000000..949531c9ea3578c67bad7be7fe694836e04ef03c --- /dev/null +++ b/extensions/sd_api_pictures/script.py @@ -0,0 +1,332 @@ +import base64 +import io +import re +import time +from datetime import date +from pathlib import Path + +import gradio as gr +import requests +import torch +from PIL import Image + +import modules.shared as shared +from modules.models import reload_model, unload_model + +torch._C._jit_set_profiling_mode(False) + +# parameters which can be customized in settings.json of webui +params = { + 'address': 'http://127.0.0.1:7860', + 'mode': 0, # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on) + 'manage_VRAM': False, + 'save_img': False, + 'SD_model': 'NeverEndingDream', # not used right now + 'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful', + 'negative_prompt': '(worst quality, low quality:1.3)', + 'width': 512, + 'height': 512, + 'denoising_strength': 0.61, + 'restore_faces': False, + 'enable_hr': False, + 'hr_upscaler': 'ESRGAN_4x', + 'hr_scale': '1.0', + 'seed': -1, + 'sampler_name': 'DDIM', + 'steps': 32, + 'cfg_scale': 7 +} + + +def give_VRAM_priority(actor): + global shared, params + + if actor == 'SD': + unload_model() + print("Requesting Auto1111 to re-load last checkpoint used...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') + response.raise_for_status() + + elif actor == 'LLM': + print("Requesting Auto1111 to vacate VRAM...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') + response.raise_for_status() + reload_model() + + elif actor == 'set': + print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') + response.raise_for_status() + + elif actor == 'reset': + print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint") + response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') + response.raise_for_status() + + else: + raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!') + + response.raise_for_status() + del response + + +if params['manage_VRAM']: + give_VRAM_priority('set') + +samplers = ['DDIM', 'DPM++ 2M Karras'] # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers +SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select + +picture_response = False # specifies if the next model response should appear as a picture + + +def remove_surrounded_chars(string): + # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR + # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string' + return re.sub('\*[^\*]*?(\*|$)', '', string) + + +def triggers_are_in(string): + string = remove_surrounded_chars(string) + # regex searches for send|main|message|me (at the end of the word) followed by + # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s), + # (?aims) are regex parser flags + return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string)) + + +def state_modifier(state): + if picture_response: + state['stream'] = False + + return state + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + + global params + + if not params['mode'] == 1: # if not in immersive/interactive mode, do nothing + return string + + if triggers_are_in(string): # if we're in it, check for trigger words + toggle_generation(True) + string = string.lower() + if "of" in string: + subject = string.split('of', 1)[1] # subdivide the string once by the first 'of' instance and get what's coming after it + string = "Please provide a detailed and vivid description of " + subject + else: + string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now" + + return string + +# Get and save the Stable Diffusion-generated picture +def get_SD_pictures(description): + global params + + if params['manage_VRAM']: + give_VRAM_priority('SD') + + payload = { + "prompt": params['prompt_prefix'] + description, + "seed": params['seed'], + "sampler_name": params['sampler_name'], + "enable_hr": params['enable_hr'], + "hr_scale": params['hr_scale'], + "hr_upscaler": params['hr_upscaler'], + "denoising_strength": params['denoising_strength'], + "steps": params['steps'], + "cfg_scale": params['cfg_scale'], + "width": params['width'], + "height": params['height'], + "restore_faces": params['restore_faces'], + "override_settings_restore_afterwards": True, + "negative_prompt": params['negative_prompt'] + } + + print(f'Prompting the image generator via the API on {params["address"]}...') + response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload) + response.raise_for_status() + r = response.json() + + visible_result = "" + for img_str in r['images']: + if params['save_img']: + img_data = base64.b64decode(img_str) + + variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}' + output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png') + output_file.parent.mkdir(parents=True, exist_ok=True) + + with open(output_file.as_posix(), 'wb') as f: + f.write(img_data) + + visible_result = visible_result + f'{description}\n' + else: + image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0]))) + # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history + image.thumbnail((300, 300)) + buffered = io.BytesIO() + image.save(buffered, format="JPEG") + buffered.seek(0) + image_bytes = buffered.getvalue() + img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode() + visible_result = visible_result + f'{description}\n' + + if params['manage_VRAM']: + give_VRAM_priority('LLM') + + return visible_result + +# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history) +# and replace it with 'text' for the purposes of logging? +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global picture_response, params + + if not picture_response: + return string + + string = remove_surrounded_chars(string) + string = string.replace('"', '') + string = string.replace('“', '') + string = string.replace('\n', ' ') + string = string.strip() + + if string == '': + string = 'no viable description in reply, try regenerating' + return string + + text = "" + if (params['mode'] < 2): + toggle_generation(False) + text = f'*Sends a picture which portrays: “{string}”*' + else: + text = string + + string = get_SD_pictures(string) + "\n" + text + + return string + + +def bot_prefix_modifier(string): + """ + This function is only applied in chat mode. It modifies + the prefix text for the Bot and can be used to bias its + behavior. + """ + + return string + + +def toggle_generation(*args): + global picture_response, shared + + if not args: + picture_response = not picture_response + else: + picture_response = args[0] + + shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*" + + +def filter_address(address): + address = address.strip() + # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash + address = re.sub('\/$', '', address) # remove trailing /s + if not address.startswith('http'): + address = 'http://' + address + return address + + +def SD_api_address_update(address): + + global params + + msg = "✔️ SD API is found on:" + address = filter_address(address) + params.update({"address": address}) + try: + response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models') + response.raise_for_status() + # r = response.json() + except: + msg = "❌ No SD API endpoint on:" + + return gr.Textbox.update(label=msg) + + +def custom_css(): + path_to_css = Path(__file__).parent.resolve() / 'style.css' + return open(path_to_css, 'r').read() + + +def ui(): + + # Gradio elements + # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title + with gr.Accordion("Parameters", open=True, elem_classes="SDAP"): + with gr.Row(): + address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address') + modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"] + mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index") + with gr.Column(scale=1, min_width=300): + manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM') + save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat') + + force_pic = gr.Button("Force the picture response") + suppr_pic = gr.Button("Suppress the picture response") + + with gr.Accordion("Generation parameters", open=False): + prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)') + negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt') + with gr.Row(): + with gr.Column(): + width = gr.Slider(256, 768, value=params['width'], step=64, label='Width') + height = gr.Slider(256, 768, value=params['height'], step=64, label='Height') + with gr.Column(): + sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampling method', elem_id="sampler_box") + steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps") + with gr.Row(): + seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box") + cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box") + with gr.Column() as hr_options: + restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces') + enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix') + with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options: + hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by') + denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength') + hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler') + + # Event functions to update the parameters in the backend + address.change(lambda x: params.update({"address": filter_address(x)}), address, None) + mode.select(lambda x: params.update({"mode": x}), mode, None) + mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None) + manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None) + manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None) + save_img.change(lambda x: params.update({"save_img": x}), save_img, None) + + address.submit(fn=SD_api_address_update, inputs=address, outputs=address) + prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None) + negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None) + width.change(lambda x: params.update({"width": x}), width, None) + height.change(lambda x: params.update({"height": x}), height, None) + hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None) + denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None) + restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None) + hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None) + enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None) + enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options) + + sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None) + steps.change(lambda x: params.update({"steps": x}), steps, None) + seed.change(lambda x: params.update({"seed": x}), seed, None) + cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None) + + force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None) + suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None) diff --git a/extensions/sd_api_pictures/style.css b/extensions/sd_api_pictures/style.css new file mode 100644 index 0000000000000000000000000000000000000000..a10e6397a1a0f5e0fbd98b98d65a572a2290807b --- /dev/null +++ b/extensions/sd_api_pictures/style.css @@ -0,0 +1,25 @@ +/* Align the elements for SD_api_picture extension */ +.SDAP #sampler_box { + padding-top: var(--spacing-sm); + padding-bottom: var(--spacing-sm); +} + +.SDAP #seed_box, +.SDAP #cfg_box { + padding-top: var(--spacing-md); +} + +.SDAP #sampler_box span, +.SDAP #seed_box span, +.SDAP #cfg_box span{ + margin-bottom: var(--spacing-sm); +} + +.SDAP svg.dropdown-arrow { + flex-shrink: 0 !important; + margin: 0px !important; +} + +.SDAP .hires_opts input[type="number"] { + width: 6em !important; +} diff --git a/extensions/send_pictures/script.py b/extensions/send_pictures/script.py index b0c356329a51edf026f7223a0ee7e5427d8751ce..dbbeb0fd7c13b169d60999b236a3237ac3a5f0c5 100644 --- a/extensions/send_pictures/script.py +++ b/extensions/send_pictures/script.py @@ -5,8 +5,8 @@ import gradio as gr import torch from transformers import BlipForConditionalGeneration, BlipProcessor -import modules.chat as chat -import modules.shared as shared +from modules import chat, shared +from modules.ui import gather_interface_values # If 'state' is True, will hijack the next chat generation with # custom input text given by 'value' in the format [text, visible_text] @@ -18,29 +18,30 @@ input_hijack = { processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu") + def caption_image(raw_image): inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32) out = model.generate(**inputs, max_new_tokens=100) return processor.decode(out[0], skip_special_tokens=True) + def generate_chat_picture(picture, name1, name2): - text = f'*{name1} sends {name2} a picture that contains the following: "{caption_image(picture)}"*' + text = f'*{name1} sends {name2} a picture that contains the following: “{caption_image(picture)}”*' + # lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history + picture.thumbnail((300, 300)) buffer = BytesIO() picture.save(buffer, format="JPEG") img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') - visible_text = f'' + visible_text = f'{text}' return text, visible_text + def ui(): picture_select = gr.Image(label='Send a picture', type='pil') - function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' - - # Prepare the hijack with custom inputs - picture_select.upload(lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None) - - # Call the generation function - picture_select.upload(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream) - - # Clear the picture from the upload field - picture_select.upload(lambda : None, [], [picture_select], show_progress=False) + # Prepare the input hijack, update the interface values, call the generation function, and clear the picture + picture_select.upload( + lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None).then( + gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( + lambda: None, None, picture_select, show_progress=False) diff --git a/extensions/silero_tts/requirements.txt b/extensions/silero_tts/requirements.txt index f2f0bff55a862de8e643496d90c01713785801a2..1017bf0d7accb9930872ededd8a4bc077d393958 100644 --- a/extensions/silero_tts/requirements.txt +++ b/extensions/silero_tts/requirements.txt @@ -1,6 +1,5 @@ ipython +num2words omegaconf pydub PyYAML -torch -torchaudio diff --git a/extensions/silero_tts/script.py b/extensions/silero_tts/script.py index f611dc27b7480cd357b77c0c407fcc2bd6df2679..2129eb291526ac441b494a4b290fee78e6244fb5 100644 --- a/extensions/silero_tts/script.py +++ b/extensions/silero_tts/script.py @@ -3,12 +3,13 @@ from pathlib import Path import gradio as gr import torch +from modules import chat, shared -import modules.chat as chat -import modules.shared as shared +from extensions.silero_tts import tts_preprocessor torch._C._jit_set_profiling_mode(False) + params = { 'activate': True, 'speaker': 'en_56', @@ -20,6 +21,7 @@ params = { 'autoplay': True, 'voice_pitch': 'medium', 'voice_speed': 'medium', + 'local_cache_path': '' # User can override the default cache path to something other via settings.json } current_params = params.copy() @@ -36,31 +38,30 @@ table = str.maketrans({ '"': """, }) + def xmlesc(txt): return txt.translate(table) + def load_model(): - model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id']) + torch_cache_path = torch.hub.get_dir() if params['local_cache_path'] == '' else params['local_cache_path'] + model_path = torch_cache_path + "/snakers4_silero-models_master/src/silero/model/" + params['model_id'] + ".pt" + if Path(model_path).is_file(): + print(f'\nUsing Silero TTS cached checkpoint found at {torch_cache_path}') + model, example_text = torch.hub.load(repo_or_dir=torch_cache_path + '/snakers4_silero-models_master/', model='silero_tts', language=params['language'], speaker=params['model_id'], source='local', path=model_path, force_reload=True) + else: + print(f'\nSilero TTS cache not found at {torch_cache_path}. Attempting to download...') + model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id']) model.to(params['device']) return model -model = load_model() - -def remove_surrounded_chars(string): - new_string = "" - in_star = False - for char in string: - if char == '*': - in_star = not in_star - elif not in_star: - new_string += char - return new_string - -def remove_tts_from_history(name1, name2): + + +def remove_tts_from_history(): for i, entry in enumerate(shared.history['internal']): shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]] - return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character) -def toggle_text_in_history(name1, name2): + +def toggle_text_in_history(): for i, entry in enumerate(shared.history['visible']): visible_reply = entry[1] if visible_reply.startswith('')[0]}\n\n{reply}"] else: shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('')[0]}"] - return chat.generate_chat_output(shared.history['visible'], name1, name2, shared.character) + + +def state_modifier(state): + state['stream'] = False + return state + def input_modifier(string): """ @@ -78,18 +84,19 @@ def input_modifier(string): """ # Remove autoplay from the last reply - if (shared.args.chat or shared.args.cai_chat) and len(shared.history['internal']) > 0: - shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>','controls>')] + if shared.is_chat() and len(shared.history['internal']) > 0: + shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>', 'controls>')] shared.processing_message = "*Is recording a voice message...*" return string + def output_modifier(string): """ This function is applied to the model outputs. """ - global model, current_params + global model, current_params, streaming_state for i in params: if params[i] != current_params[i]: @@ -97,15 +104,11 @@ def output_modifier(string): current_params = params.copy() break - if params['activate'] == False: + if not params['activate']: return string original_string = string - string = remove_surrounded_chars(string) - string = string.replace('"', '') - string = string.replace('“', '') - string = string.replace('\n', ' ') - string = string.strip() + string = tts_preprocessor.preprocess(string) if string == '': string = '*Empty reply, try regenerating*' @@ -123,6 +126,7 @@ def output_modifier(string): shared.processing_message = "*Is typing...*" return string + def bot_prefix_modifier(string): """ This function is only applied in chat mode. It modifies @@ -132,34 +136,49 @@ def bot_prefix_modifier(string): return string + +def setup(): + global model + model = load_model() + + def ui(): # Gradio elements with gr.Accordion("Silero TTS"): with gr.Row(): activate = gr.Checkbox(value=params['activate'], label='Activate TTS') autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically') + show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player') voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice') with gr.Row(): v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch') v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed') + with gr.Row(): convert = gr.Button('Permanently replace audios with the message texts') convert_cancel = gr.Button('Cancel', visible=False) convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False) + gr.Markdown('[Click here for Silero audio samples](https://oobabooga.github.io/silero-samples/index.html)') + # Convert history with confirmation convert_arr = [convert_confirm, convert, convert_cancel] - convert.click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr) - convert_confirm.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) - convert_confirm.click(remove_tts_from_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display']) - convert_confirm.click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) - convert_cancel.click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) + convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr) + convert_confirm.click( + lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr).then( + remove_tts_from_history, None, None).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) # Toggle message text in history - show_text.change(lambda x: params.update({"show_text": x}), show_text, None) - show_text.change(toggle_text_in_history, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display']) - show_text.change(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) + show_text.change( + lambda x: params.update({"show_text": x}), show_text, None).then( + toggle_text_in_history, None, None).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) # Event functions to update the parameters in the backend activate.change(lambda x: params.update({"activate": x}), activate, None) diff --git a/extensions/silero_tts/test_tts.py b/extensions/silero_tts/test_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc2c102a9ef29f21141429232f957421989cdd4 --- /dev/null +++ b/extensions/silero_tts/test_tts.py @@ -0,0 +1,81 @@ +import time +from pathlib import Path + +import torch +import tts_preprocessor + +torch._C._jit_set_profiling_mode(False) + + +params = { + 'activate': True, + 'speaker': 'en_49', + 'language': 'en', + 'model_id': 'v3_en', + 'sample_rate': 48000, + 'device': 'cpu', + 'show_text': True, + 'autoplay': True, + 'voice_pitch': 'medium', + 'voice_speed': 'medium', +} + +current_params = params.copy() +voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115'] +voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high'] +voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast'] + +# Used for making text xml compatible, needed for voice pitch and speed control +table = str.maketrans({ + "<": "<", + ">": ">", + "&": "&", + "'": "'", + '"': """, +}) + + +def xmlesc(txt): + return txt.translate(table) + + +def load_model(): + model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id']) + model.to(params['device']) + return model + + +model = load_model() + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global model, current_params + + original_string = string + string = tts_preprocessor.preprocess(string) + processed_string = string + + if string == '': + string = '*Empty reply, try regenerating*' + else: + output_file = Path(f'extensions/silero_tts/outputs/test_{int(time.time())}.wav') + prosody = ''.format(params['voice_speed'], params['voice_pitch']) + silero_input = f'{prosody}{xmlesc(string)}' + model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file)) + + autoplay = 'autoplay' if params['autoplay'] else '' + string = f'' + + if params['show_text']: + string += f'\n\n{original_string}\n\nProcessed:\n{processed_string}' + + print(string) + + +if __name__ == '__main__': + import sys + output_modifier(sys.argv[1]) diff --git a/extensions/silero_tts/tts_preprocessor.py b/extensions/silero_tts/tts_preprocessor.py new file mode 100644 index 0000000000000000000000000000000000000000..daefdcbda6c9b20a87c6f3d84d2a759c2c51289c --- /dev/null +++ b/extensions/silero_tts/tts_preprocessor.py @@ -0,0 +1,200 @@ +import re + +from num2words import num2words + +punctuation = r'[\s,.?!/)\'\]>]' +alphabet_map = { + "A": " Ei ", + "B": " Bee ", + "C": " See ", + "D": " Dee ", + "E": " Eee ", + "F": " Eff ", + "G": " Jee ", + "H": " Eich ", + "I": " Eye ", + "J": " Jay ", + "K": " Kay ", + "L": " El ", + "M": " Emm ", + "N": " Enn ", + "O": " Ohh ", + "P": " Pee ", + "Q": " Queue ", + "R": " Are ", + "S": " Ess ", + "T": " Tee ", + "U": " You ", + "V": " Vee ", + "W": " Double You ", + "X": " Ex ", + "Y": " Why ", + "Z": " Zed " # Zed is weird, as I (da3dsoul) am American, but most of the voice models sound British, so it matches +} + + +def preprocess(string): + # the order for some of these matter + # For example, you need to remove the commas in numbers before expanding them + string = remove_surrounded_chars(string) + string = string.replace('"', '') + string = string.replace('\u201D', '').replace('\u201C', '') # right and left quote + string = string.replace('\u201F', '') # italic looking quote + string = string.replace('\n', ' ') + string = convert_num_locale(string) + string = replace_negative(string) + string = replace_roman(string) + string = hyphen_range_to(string) + string = num_to_words(string) + + # TODO Try to use a ML predictor to expand abbreviations. It's hard, dependent on context, and whether to actually + # try to say the abbreviation or spell it out as I've done below is not agreed upon + + # For now, expand abbreviations to pronunciations + # replace_abbreviations adds a lot of unnecessary whitespace to ensure separation + string = replace_abbreviations(string) + string = replace_lowercase_abbreviations(string) + + # cleanup whitespaces + # remove whitespace before punctuation + string = re.sub(rf'\s+({punctuation})', r'\1', string) + string = string.strip() + # compact whitespace + string = ' '.join(string.split()) + + return string + + +def remove_surrounded_chars(string): + # first this expression will check if there is a string nested exclusively between a alt= + # and a style= string. This would correspond to only a the alt text of an embedded image + # If it matches it will only keep that part as the string, and rend it for further processing + # Afterwards this expression matches to 'as few symbols as possible (0 upwards) between any + # asterisks' OR' as few symbols as possible (0 upwards) between an asterisk and the end of the string' + if re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL): + m = re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL) + string = m.group(0) + return re.sub(r'\*[^*]*?(\*|$)', '', string) + + +def convert_num_locale(text): + # This detects locale and converts it to American without comma separators + pattern = re.compile(r'(?:\s|^)\d{1,3}(?:\.\d{3})+(,\d+)(?:\s|$)') + result = text + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + result[start:end].replace('.', '').replace(',', '.') + result[end:len(result)] + + # removes comma separators from existing American numbers + pattern = re.compile(r'(\d),(\d)') + result = pattern.sub(r'\1\2', result) + + return result + + +def replace_negative(string): + # handles situations like -5. -5 would become negative 5, which would then be expanded to negative five + return re.sub(rf'(\s)(-)(\d+)({punctuation})', r'\1negative \3\4', string) + + +def replace_roman(string): + # find a string of roman numerals. + # Only 2 or more, to avoid capturing I and single character abbreviations, like names + pattern = re.compile(rf'\s[IVXLCDM]{{2,}}{punctuation}') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start + 1] + str(roman_to_int(result[start + 1:end - 1])) + result[end - 1:len(result)] + + return result + + +def roman_to_int(s): + rom_val = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000} + int_val = 0 + for i in range(len(s)): + if i > 0 and rom_val[s[i]] > rom_val[s[i - 1]]: + int_val += rom_val[s[i]] - 2 * rom_val[s[i - 1]] + else: + int_val += rom_val[s[i]] + return int_val + + +def hyphen_range_to(text): + pattern = re.compile(r'(\d+)[-–](\d+)') + result = pattern.sub(lambda x: x.group(1) + ' to ' + x.group(2), text) + return result + + +def num_to_words(text): + # 1000 or 10.23 + pattern = re.compile(r'\d+\.\d+|\d+') + result = pattern.sub(lambda x: num2words(float(x.group())), text) + return result + + +def replace_abbreviations(string): + # abbreviations 1 to 4 characters long. It will get things like A and I, but those are pronounced with their letter + pattern = re.compile(rf'(^|[\s(.\'\[<])([A-Z]{{1,4}})({punctuation}|$)') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + replace_abbreviation(result[start:end]) + result[end:len(result)] + + return result + + +def replace_lowercase_abbreviations(string): + # abbreviations 1 to 4 characters long, separated by dots i.e. e.g. + pattern = re.compile(rf'(^|[\s(.\'\[<])(([a-z]\.){{1,4}})({punctuation}|$)') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + replace_abbreviation(result[start:end].upper()) + result[end:len(result)] + + return result + + +def replace_abbreviation(string): + result = "" + for char in string: + result += match_mapping(char) + + return result + + +def match_mapping(char): + for mapping in alphabet_map.keys(): + if char == mapping: + return alphabet_map[char] + + return char + + +def __main__(args): + print(preprocess(args[1])) + + +if __name__ == "__main__": + import sys + __main__(sys.argv) diff --git a/extensions/superbooga/chromadb.py b/extensions/superbooga/chromadb.py new file mode 100644 index 0000000000000000000000000000000000000000..52f4854bdbaaa9d0fcde18ea395c50e216b06b8e --- /dev/null +++ b/extensions/superbooga/chromadb.py @@ -0,0 +1,95 @@ +import logging + +import posthog +import torch +from sentence_transformers import SentenceTransformer + +import chromadb +from chromadb.config import Settings + +logging.info('Intercepting all calls to posthog :)') +posthog.capture = lambda *args, **kwargs: None + + +class Collecter(): + def __init__(self): + pass + + def add(self, texts: list[str]): + pass + + def get(self, search_strings: list[str], n_results: int) -> list[str]: + pass + + def clear(self): + pass + + +class Embedder(): + def __init__(self): + pass + + def embed(self, text: str) -> list[torch.Tensor]: + pass + + +class ChromaCollector(Collecter): + def __init__(self, embedder: Embedder): + super().__init__() + self.chroma_client = chromadb.Client(Settings(anonymized_telemetry=False)) + self.embedder = embedder + self.collection = self.chroma_client.create_collection(name="context", embedding_function=embedder.embed) + self.ids = [] + + def add(self, texts: list[str]): + self.ids = [f"id{i}" for i in range(len(texts))] + self.collection.add(documents=texts, ids=self.ids) + + def get_documents_and_ids(self, search_strings: list[str], n_results: int): + n_results = min(len(self.ids), n_results) + result = self.collection.query(query_texts=search_strings, n_results=n_results, include=['documents']) + documents = result['documents'][0] + ids = list(map(lambda x: int(x[2:]), result['ids'][0])) + return documents, ids + + # Get chunks by similarity + def get(self, search_strings: list[str], n_results: int) -> list[str]: + documents, _ = self.get_documents_and_ids(search_strings, n_results) + return documents + + # Get ids by similarity + def get_ids(self, search_strings: list[str], n_results: int) -> list[str]: + _ , ids = self.get_documents_and_ids(search_strings, n_results) + return ids + + # Get chunks by similarity and then sort by insertion order + def get_sorted(self, search_strings: list[str], n_results: int) -> list[str]: + documents, ids = self.get_documents_and_ids(search_strings, n_results) + return [x for _, x in sorted(zip(ids, documents))] + + # Get ids by similarity and then sort by insertion order + def get_ids_sorted(self, search_strings: list[str], n_results: int) -> list[str]: + _ , ids = self.get_documents_and_ids(search_strings, n_results) + return sorted(ids) + + def clear(self): + self.collection.delete(ids=self.ids) + + +class SentenceTransformerEmbedder(Embedder): + def __init__(self) -> None: + self.model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2") + self.embed = self.model.encode + + +def make_collector(): + global embedder + return ChromaCollector(embedder) + + +def add_chunks_to_collector(chunks, collector): + collector.clear() + collector.add(chunks) + + +embedder = SentenceTransformerEmbedder() diff --git a/extensions/superbooga/download_urls.py b/extensions/superbooga/download_urls.py new file mode 100644 index 0000000000000000000000000000000000000000..efe300d28393e4550f241808073f04c98fb33ace --- /dev/null +++ b/extensions/superbooga/download_urls.py @@ -0,0 +1,32 @@ +import concurrent.futures + +import requests + + +def download_single(url): + response = requests.get(url, timeout=5) + if response.status_code == 200: + return response.content + else: + raise Exception("Failed to download URL") + + +def download_urls(urls, threads=1): + with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor: + futures = [] + for url in urls: + future = executor.submit(download_single, url) + futures.append(future) + + results = [] + i = 0 + for future in concurrent.futures.as_completed(futures): + try: + result = future.result() + results.append(result) + i += 1 + yield f"{i}/{len(urls)}", results + except Exception: + pass + + yield "Done", results diff --git a/extensions/superbooga/requirements.txt b/extensions/superbooga/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..dd2cbde6fbe65f966a5a418132dd65c2c9350027 --- /dev/null +++ b/extensions/superbooga/requirements.txt @@ -0,0 +1,4 @@ +beautifulsoup4==4.12.2 +chromadb==0.3.18 +posthog==2.4.2 +sentence_transformers==2.2.2 diff --git a/extensions/superbooga/script.py b/extensions/superbooga/script.py new file mode 100644 index 0000000000000000000000000000000000000000..a1d66add9945a9cc300345c0e3cb3f0360c04362 --- /dev/null +++ b/extensions/superbooga/script.py @@ -0,0 +1,249 @@ +import logging +import re +import textwrap + +import gradio as gr +from bs4 import BeautifulSoup +from modules import chat, shared + +from .chromadb import add_chunks_to_collector, make_collector +from .download_urls import download_urls + + +params = { + 'chunk_count': 5, + 'chunk_length': 700, + 'chunk_separator': '', + 'strong_cleanup': False, + 'threads': 4, +} + +collector = make_collector() +chat_collector = make_collector() +chunk_count = 5 + + +def feed_data_into_collector(corpus, chunk_len, chunk_sep): + global collector + + # Defining variables + chunk_len = int(chunk_len) + chunk_sep = chunk_sep.replace(r'\n', '\n') + cumulative = '' + + # Breaking the data into chunks and adding those to the db + cumulative += "Breaking the input dataset...\n\n" + yield cumulative + if chunk_sep: + data_chunks = corpus.split(chunk_sep) + data_chunks = [[data_chunk[i:i + chunk_len] for i in range(0, len(data_chunk), chunk_len)] for data_chunk in data_chunks] + data_chunks = [x for y in data_chunks for x in y] + else: + data_chunks = [corpus[i:i + chunk_len] for i in range(0, len(corpus), chunk_len)] + cumulative += f"{len(data_chunks)} chunks have been found.\n\nAdding the chunks to the database...\n\n" + yield cumulative + add_chunks_to_collector(data_chunks, collector) + cumulative += "Done." + yield cumulative + + +def feed_file_into_collector(file, chunk_len, chunk_sep): + yield 'Reading the input dataset...\n\n' + text = file.decode('utf-8') + for i in feed_data_into_collector(text, chunk_len, chunk_sep): + yield i + + +def feed_url_into_collector(urls, chunk_len, chunk_sep, strong_cleanup, threads): + all_text = '' + cumulative = '' + + urls = urls.strip().split('\n') + cumulative += f'Loading {len(urls)} URLs with {threads} threads...\n\n' + yield cumulative + for update, contents in download_urls(urls, threads=threads): + yield cumulative + update + + cumulative += 'Processing the HTML sources...' + yield cumulative + for content in contents: + soup = BeautifulSoup(content, features="html.parser") + for script in soup(["script", "style"]): + script.extract() + + strings = soup.stripped_strings + if strong_cleanup: + strings = [s for s in strings if re.search("[A-Za-z] ", s)] + + text = '\n'.join([s.strip() for s in strings]) + all_text += text + + for i in feed_data_into_collector(all_text, chunk_len, chunk_sep): + yield i + + +def apply_settings(_chunk_count): + global chunk_count + chunk_count = int(_chunk_count) + settings_to_display = { + 'chunk_count': chunk_count, + } + + yield f"The following settings are now active: {str(settings_to_display)}" + + +def custom_generate_chat_prompt(user_input, state, **kwargs): + global chat_collector + + if state['mode'] == 'instruct': + results = collector.get_sorted(user_input, n_results=chunk_count) + additional_context = '\nYour reply should be based on the context below:\n\n' + '\n'.join(results) + user_input += additional_context + else: + + def make_single_exchange(id_): + output = '' + output += f"{state['name1']}: {shared.history['internal'][id_][0]}\n" + output += f"{state['name2']}: {shared.history['internal'][id_][1]}\n" + return output + + if len(shared.history['internal']) > chunk_count and user_input != '': + chunks = [] + hist_size = len(shared.history['internal']) + for i in range(hist_size-1): + chunks.append(make_single_exchange(i)) + + add_chunks_to_collector(chunks, chat_collector) + query = '\n'.join(shared.history['internal'][-1] + [user_input]) + try: + best_ids = chat_collector.get_ids_sorted(query, n_results=chunk_count) + additional_context = '\n' + for id_ in best_ids: + 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}') + state['context'] = state['context'].strip() + '\n' + additional_context + state['history'] = [shared.history['internal'][i] for i in range(hist_size) if i not in best_ids] + except RuntimeError: + logging.error("Couldn't query the database, moving on...") + + return chat.generate_chat_prompt(user_input, state, **kwargs) + + +def remove_special_tokens(string): + pattern = r'(<\|begin-user-input\|>|<\|end-user-input\|>|<\|injection-point\|>)' + return re.sub(pattern, '', string) + + +def input_modifier(string): + if shared.is_chat(): + return string + + # Find the user input + pattern = re.compile(r"<\|begin-user-input\|>(.*?)<\|end-user-input\|>", re.DOTALL) + match = re.search(pattern, string) + if match: + user_input = match.group(1).strip() + + # Get the most similar chunks + results = collector.get_sorted(user_input, n_results=chunk_count) + + # Make the injection + string = string.replace('<|injection-point|>', '\n'.join(results)) + + return remove_special_tokens(string) + + +def ui(): + with gr.Accordion("Click for more information...", open=False): + gr.Markdown(textwrap.dedent(""" + + ## About + + This extension takes a dataset as input, breaks it into chunks, and adds the result to a local/offline Chroma database. + + The database is then queried during inference time to get the excerpts that are closest to your input. The idea is to create an arbitrarily large pseudo context. + + The core methodology was developed and contributed by kaiokendev, who is working on improvements to the method in this repository: https://github.com/kaiokendev/superbig + + ## Data input + + Start by entering some data in the interface below and then clicking on "Load data". + + Each time you load some new data, the old chunks are discarded. + + ## Chat mode + + #### Instruct + + On each turn, the chunks will be compared to your current input and the most relevant matches will be appended to the input in the following format: + + ``` + Consider the excerpts below as additional context: + ... + ``` + + The injection doesn't make it into the chat history. It is only used in the current generation. + + #### Regular chat + + The chunks from the external data sources are ignored, and the chroma database is built based on the chat history instead. The most relevant past exchanges relative to the present input are added to the context string. This way, the extension acts as a long term memory. + + ## Notebook/default modes + + Your question must be manually specified between `<|begin-user-input|>` and `<|end-user-input|>` tags, and the injection point must be specified with `<|injection-point|>`. + + The special tokens mentioned above (`<|begin-user-input|>`, `<|end-user-input|>`, and `<|injection-point|>`) are removed in the background before the text generation begins. + + Here is an example in Vicuna 1.1 format: + + ``` + A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. + + USER: + + <|begin-user-input|> + What datasets are mentioned in the text below? + <|end-user-input|> + + <|injection-point|> + + ASSISTANT: + ``` + + ⚠️ For best results, make sure to remove the spaces and new line characters after `ASSISTANT:`. + + *This extension is currently experimental and under development.* + + """)) + + with gr.Row(): + with gr.Column(min_width=600): + with gr.Tab("Text input"): + data_input = gr.Textbox(lines=20, label='Input data') + update_data = gr.Button('Load data') + + with gr.Tab("URL input"): + url_input = gr.Textbox(lines=10, label='Input URLs', info='Enter one or more URLs separated by newline characters.') + strong_cleanup = gr.Checkbox(value=params['strong_cleanup'], label='Strong cleanup', info='Only keeps html elements that look like long-form text.') + threads = gr.Number(value=params['threads'], label='Threads', info='The number of threads to use while downloading the URLs.', precision=0) + update_url = gr.Button('Load data') + + with gr.Tab("File input"): + file_input = gr.File(label='Input file', type='binary') + update_file = gr.Button('Load data') + + with gr.Tab("Generation settings"): + chunk_count = gr.Number(value=params['chunk_count'], label='Chunk count', info='The number of closest-matching chunks to include in the prompt.') + update_settings = gr.Button('Apply changes') + + chunk_len = gr.Number(value=params['chunk_length'], label='Chunk length', info='In characters, not tokens. This value is used when you click on "Load data".') + chunk_sep = gr.Textbox(value=params['chunk_separator'], label='Chunk separator', info='Used to manually split chunks. Manually split chunks longer than chunk length are split again. This value is used when you click on "Load data".') + with gr.Column(): + last_updated = gr.Markdown() + + update_data.click(feed_data_into_collector, [data_input, chunk_len, chunk_sep], last_updated, show_progress=False) + update_url.click(feed_url_into_collector, [url_input, chunk_len, chunk_sep, strong_cleanup, threads], last_updated, show_progress=False) + update_file.click(feed_file_into_collector, [file_input, chunk_len, chunk_sep], last_updated, show_progress=False) + update_settings.click(apply_settings, [chunk_count], last_updated, show_progress=False) diff --git a/extensions/whisper_stt/requirements.txt b/extensions/whisper_stt/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a37a5e9b0b5986f7e57d822a85afcdcdd5ae445 --- /dev/null +++ b/extensions/whisper_stt/requirements.txt @@ -0,0 +1,4 @@ +git+https://github.com/Uberi/speech_recognition.git@010382b80267f0f7794169fccc8e875ee7da7c19 +openai-whisper +soundfile +ffmpeg diff --git a/extensions/whisper_stt/script.py b/extensions/whisper_stt/script.py new file mode 100644 index 0000000000000000000000000000000000000000..32226404b63c443ac588bbc5e66741a332cbe05d --- /dev/null +++ b/extensions/whisper_stt/script.py @@ -0,0 +1,47 @@ +import gradio as gr +import speech_recognition as sr + +from modules import shared + +input_hijack = { + 'state': False, + 'value': ["", ""] +} + + +def do_stt(audio): + transcription = "" + r = sr.Recognizer() + + # Convert to AudioData + audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4) + + try: + transcription = r.recognize_whisper(audio_data, language="english", model="base.en") + except sr.UnknownValueError: + print("Whisper could not understand audio") + except sr.RequestError as e: + print("Could not request results from Whisper", e) + + return transcription + + +def auto_transcribe(audio, auto_submit): + if audio is None: + return "", "" + + transcription = do_stt(audio) + if auto_submit: + input_hijack.update({"state": True, "value": [transcription, transcription]}) + + return transcription, None + + +def ui(): + with gr.Row(): + audio = gr.Audio(source="microphone") + auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=True) + + audio.change( + auto_transcribe, [audio, auto_submit], [shared.gradio['textbox'], audio]).then( + None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}") diff --git a/modules/AutoGPTQ_loader.py b/modules/AutoGPTQ_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..adbee7eb45c598d1ce06455e27781f2c9dd318f6 --- /dev/null +++ b/modules/AutoGPTQ_loader.py @@ -0,0 +1,40 @@ +import logging +from pathlib import Path + +from auto_gptq import AutoGPTQForCausalLM + +import modules.shared as shared +from modules.models import get_max_memory_dict + + +def load_quantized(model_name): + path_to_model = Path(f'{shared.args.model_dir}/{model_name}') + pt_path = None + use_safetensors = False + + # Find the model checkpoint + for ext in ['.safetensors', '.pt', '.bin']: + 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.') + + 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.") + return + + # Define the params for AutoGPTQForCausalLM.from_quantized + params = { + 'model_basename': pt_path.stem, + 'device': "cuda:0" if not shared.args.cpu else "cpu", + 'use_triton': shared.args.triton, + 'use_safetensors': use_safetensors, + 'max_memory': get_max_memory_dict() + } + + logging.warning(f"The AutoGPTQ params are: {params}") + model = AutoGPTQForCausalLM.from_quantized(path_to_model, **params) + return model diff --git a/modules/GPTQ_loader.py b/modules/GPTQ_loader.py index c2723490bbe214e351634ca4054f74a0b5334b28..8fef5a8716d5c1a9f0e34aac84ac9a9909d33557 100644 --- a/modules/GPTQ_loader.py +++ b/modules/GPTQ_loader.py @@ -1,71 +1,201 @@ +import inspect +import logging +import re import sys from pathlib import Path import accelerate import torch +import transformers +from transformers import AutoConfig, AutoModelForCausalLM import modules.shared as shared sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) -import llama -import opt +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') + sys.exit(-1) -def load_quantized(model_name): - if not shared.args.gptq_model_type: - # Try to determine model type from model name - model_type = model_name.split('-')[0].lower() - if model_type not in ('llama', 'opt'): - print("Can't determine model type from model name. Please specify it manually using --gptq-model-type " - "argument") - exit() - else: - model_type = shared.args.gptq_model_type.lower() +try: + from modelutils import find_layers +except ImportError: + from utils import find_layers + +try: + from quant import make_quant + is_triton = False +except ImportError: + import quant + is_triton = True + + +# This function is a replacement for the load_quant function in the +# GPTQ-for_LLaMa repository. It supports more models and branches. +def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=None, kernel_switch_threshold=128, eval=True): + exclude_layers = exclude_layers or ['lm_head'] + + def noop(*args, **kwargs): + pass + + config = AutoConfig.from_pretrained(model, trust_remote_code=shared.args.trust_remote_code) + torch.nn.init.kaiming_uniform_ = noop + torch.nn.init.uniform_ = noop + torch.nn.init.normal_ = noop + + torch.set_default_dtype(torch.half) + transformers.modeling_utils._init_weights = False + torch.set_default_dtype(torch.half) + model = AutoModelForCausalLM.from_config(config, trust_remote_code=shared.args.trust_remote_code) + torch.set_default_dtype(torch.float) + if eval: + model = model.eval() + + layers = find_layers(model) + for name in exclude_layers: + if name in layers: + del layers[name] - if model_type == 'llama': - load_quant = llama.load_quant - elif model_type == 'opt': - load_quant = opt.load_quant + if not is_triton: + gptq_args = inspect.getfullargspec(make_quant).args + + make_quant_kwargs = { + 'module': model, + 'names': layers, + 'bits': wbits, + } + if 'groupsize' in gptq_args: + make_quant_kwargs['groupsize'] = groupsize + if 'faster' in gptq_args: + make_quant_kwargs['faster'] = faster_kernel + if 'kernel_switch_threshold' in gptq_args: + make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold + + make_quant(**make_quant_kwargs) else: - print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported") - exit() + quant.make_quant_linear(model, layers, wbits, groupsize) - path_to_model = Path(f'models/{model_name}') - if path_to_model.name.lower().startswith('llama-7b'): - pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-13b'): - pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-30b'): - pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt' - elif path_to_model.name.lower().startswith('llama-65b'): - pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt' + del layers + if checkpoint.endswith('.safetensors'): + from safetensors.torch import load_file as safe_load + model.load_state_dict(safe_load(checkpoint), strict=False) else: - pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt' + model.load_state_dict(torch.load(checkpoint), strict=False) + + if is_triton: + if shared.args.quant_attn: + quant.make_quant_attn(model) + + if eval and shared.args.fused_mlp: + quant.make_fused_mlp(model) + + if shared.args.warmup_autotune: + quant.autotune_warmup_linear(model, transpose=not eval) + if eval and shared.args.fused_mlp: + quant.autotune_warmup_fused(model) + + model.seqlen = 2048 + return model + - # Try to find the .pt both in models/ and in the subfolder +# Used to locate the .pt/.safetensors quantized file +def find_quantized_model_file(model_name): + if shared.args.checkpoint: + return Path(shared.args.checkpoint) + + path_to_model = Path(f'{shared.args.model_dir}/{model_name}') pt_path = None - for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]: + priority_name_list = [ + Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}') + for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else ['']) + for ext in ['.safetensors', '.pt'] + for hyphen in ['-', f'/{model_name}-', '/'] + ] + + for path in priority_name_list: if path.exists(): pt_path = path + break + # If the model hasn't been found with a well-behaved name, pick the last .pt + # or the last .safetensors found in its folder as a last resort if not pt_path: - print(f"Could not find {pt_model}, exiting...") - exit() + for ext in ['.pt', '.safetensors']: + 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.') + + pt_path = found[-1] + break + + return pt_path + + +# 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.") + return None + + # Select the appropriate load_quant function + model_type = shared.args.model_type.lower() + if shared.args.pre_layer and model_type == 'llama': + 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.") - model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits) + load_quant = _load_quant + else: + logging.error("Unknown pre-quantized model type specified. Only 'llama', 'opt' and 'gptj' are supported") + exit() - # Multiple GPUs or GPU+CPU - if shared.args.gpu_memory: - max_memory = {} - for i in range(len(shared.args.gpu_memory)): - max_memory[i] = f"{shared.args.gpu_memory[i]}GiB" - max_memory['cpu'] = f"{shared.args.cpu_memory or '99'}GiB" + # 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...") + exit() + else: + logging.info(f"Found the following quantized model: {pt_path}") - device_map = accelerate.infer_auto_device_map(model, max_memory=max_memory, no_split_module_classes=["LLaMADecoderLayer"]) - model = accelerate.dispatch_model(model, device_map=device_map) + # qwopqwop200's offload + if model_type == 'llama' and shared.args.pre_layer: + if len(shared.args.pre_layer) == 1: + pre_layer = shared.args.pre_layer[0] + else: + pre_layer = shared.args.pre_layer - # Single GPU + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, pre_layer) else: - model = model.to(torch.device('cuda:0')) + threshold = False if model_type == 'gptj' else 128 + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold) + + # accelerate offload (doesn't work properly) + if shared.args.gpu_memory or torch.cuda.device_count() > 1: + if shared.args.gpu_memory: + memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) + max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' + max_memory = {} + for i in range(len(memory_map)): + max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] + + max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory + else: + 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) + # https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model + model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True) + + # No offload + elif not shared.args.cpu: + model = model.to(torch.device('cuda:0')) return model diff --git a/modules/LoRA.py b/modules/LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..08bf5b88c6d80e6b4c597942e34373c9b4c99bb4 --- /dev/null +++ b/modules/LoRA.py @@ -0,0 +1,55 @@ +import logging +from pathlib import Path + +import torch +from peft import PeftModel + +import modules.shared as shared + + +def add_lora_to_model(lora_names): + prior_set = set(shared.lora_names) + added_set = set(lora_names) - prior_set + removed_set = prior_set - set(lora_names) + shared.lora_names = list(lora_names) + + # If no LoRA needs to be added or removed, exit + if len(added_set) == 0 and len(removed_set) == 0: + return + + # 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...") + for lora in added_set: + shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) + + return + + # If any LoRA needs to be removed, start over + if len(removed_set) > 0: + shared.model.disable_adapter() + 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))) + params = {} + if not shared.args.cpu: + params['dtype'] = shared.model.dtype + if hasattr(shared.model, "hf_device_map"): + params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()} + elif shared.args.load_in_8bit: + params['device_map'] = {'': 0} + + shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params) + + for lora in lora_names[1:]: + shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) + + if not shared.args.load_in_8bit and not shared.args.cpu: + shared.model.half() + if not hasattr(shared.model, "hf_device_map"): + if torch.has_mps: + device = torch.device('mps') + shared.model = shared.model.to(device) + else: + shared.model = shared.model.cuda() diff --git a/modules/RWKV.py b/modules/RWKV.py index 5cf8937ad37944c0cebeeb8e0891bec1474724ea..bb6bab50c7d644f499e1ada84ec8b09d6adadf7e 100644 --- a/modules/RWKV.py +++ b/modules/RWKV.py @@ -1,3 +1,4 @@ +import copy import os from pathlib import Path @@ -10,7 +11,7 @@ from modules.callbacks import Iteratorize np.set_printoptions(precision=4, suppress=True, linewidth=200) os.environ['RWKV_JIT_ON'] = '1' -os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster) +os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster) from rwkv.model import RWKV from rwkv.utils import PIPELINE, PIPELINE_ARGS @@ -23,37 +24,108 @@ class RWKVModel: @classmethod def from_pretrained(self, path, dtype="fp16", device="cuda"): tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json") - if shared.args.rwkv_strategy is None: model = RWKV(model=str(path), strategy=f'{device} {dtype}') else: model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy) - pipeline = PIPELINE(model, str(tokenizer_path)) + pipeline = PIPELINE(model, str(tokenizer_path)) result = self() result.pipeline = pipeline + result.model = model + result.cached_context = "" + result.cached_model_state = None + result.cached_output_logits = None return result - def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None): + def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=None, token_stop=None, callback=None): args = PIPELINE_ARGS( - temperature = temperature, - top_p = top_p, - top_k = top_k, - alpha_frequency = alpha_frequency, # Frequency Penalty (as in GPT-3) - alpha_presence = alpha_presence, # Presence Penalty (as in GPT-3) - token_ban = token_ban, # ban the generation of some tokens - token_stop = token_stop + temperature=temperature, + top_p=top_p, + top_k=top_k, + alpha_frequency=alpha_frequency, # Frequency Penalty (as in GPT-3) + alpha_presence=alpha_presence, # Presence Penalty (as in GPT-3) + token_ban=token_ban or [0], # ban the generation of some tokens + token_stop=token_stop or [] ) - return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback) + if self.cached_context != "": + if context.startswith(self.cached_context): + context = context[len(self.cached_context):] + else: + self.cached_context = "" + self.cached_model_state = None + self.cached_output_logits = None + + # out = self.pipeline.generate(context, token_count=token_count, args=args, callback=callback) + out = self.generate_from_cached_state(context, token_count=token_count, args=args, callback=callback) + return out def generate_with_streaming(self, **kwargs): with Iteratorize(self.generate, kwargs, callback=None) as generator: - reply = kwargs['context'] + reply = '' for token in generator: reply += token yield reply + # Similar to the PIPELINE.generate, but lets us maintain the cached_model_state + def generate_from_cached_state(self, ctx="", token_count=20, args=None, callback=None): + all_tokens = [] + out_str = '' + occurrence = {} + state = copy.deepcopy(self.cached_model_state) if self.cached_model_state is not None else None + + # if we ended up with an empty context, just reuse the cached logits + # this can happen if a user undoes a message and then sends the exact message again + # in that case the full context ends up being the same as the cached_context, so the remaining context is empty. + if ctx == "": + out = self.cached_output_logits + + for i in range(token_count): + # forward + tokens = self.pipeline.encode(ctx) if i == 0 else [token] + while len(tokens) > 0: + out, state = self.model.forward(tokens[:args.chunk_len], state) + tokens = tokens[args.chunk_len:] + + # cache the model state after scanning the context + # we don't cache the state after processing our own generated tokens because + # the output string might be post-processed arbitrarily. Therefore, what's fed into the model + # on the next round of chat might be slightly different what what it output on the previous round + if i == 0: + self.cached_context += ctx + self.cached_model_state = copy.deepcopy(state) + self.cached_output_logits = copy.deepcopy(out) + + # adjust probabilities + for n in args.token_ban: + out[n] = -float('inf') + + for n in occurrence: + out[n] -= (args.alpha_presence + occurrence[n] * args.alpha_frequency) + + # sampler + token = self.pipeline.sample_logits(out, temperature=args.temperature, top_p=args.top_p, top_k=args.top_k) + if token in args.token_stop: + break + + all_tokens += [token] + if token not in occurrence: + occurrence[token] = 1 + else: + occurrence[token] += 1 + + # output + tmp = self.pipeline.decode([token]) + if '\ufffd' not in tmp: # is valid utf-8 string? + if callback: + callback(tmp) + + out_str += tmp + + return out_str + + class RWKVTokenizer: def __init__(self): pass @@ -62,7 +134,6 @@ class RWKVTokenizer: def from_pretrained(self, path): tokenizer_path = path / "20B_tokenizer.json" tokenizer = Tokenizer.from_file(str(tokenizer_path)) - result = self() result.tokenizer = tokenizer return result diff --git a/modules/callbacks.py b/modules/callbacks.py index faa4a5e9991e1ae711589fed61e7d1f48e28fed3..5996ba4e5a98f4b500781ef858012ac8eb445f24 100644 --- a/modules/callbacks.py +++ b/modules/callbacks.py @@ -1,4 +1,5 @@ import gc +import traceback from queue import Queue from threading import Thread @@ -7,29 +8,34 @@ import transformers import modules.shared as shared -# Copied from https://github.com/PygmalionAI/gradio-ui/ + class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria): - def __init__(self, sentinel_token_ids: torch.LongTensor, - starting_idx: int): + def __init__(self, sentinel_token_ids: list, starting_idx: int): transformers.StoppingCriteria.__init__(self) self.sentinel_token_ids = sentinel_token_ids self.starting_idx = starting_idx + self.shortest = min([x.shape[-1] for x in sentinel_token_ids]) - def __call__(self, input_ids: torch.LongTensor, - _scores: torch.FloatTensor) -> bool: + def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool: for sample in input_ids: trimmed_sample = sample[self.starting_idx:] - # Can't unfold, output is still too tiny. Skip. - if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]: + trimmed_len = trimmed_sample.shape[-1] + if trimmed_len < self.shortest: continue - for window in trimmed_sample.unfold( - 0, self.sentinel_token_ids.shape[-1], 1): - if torch.all(torch.eq(self.sentinel_token_ids, window)): + for sentinel in self.sentinel_token_ids: + sentinel_len = sentinel.shape[-1] + if trimmed_len < sentinel_len: + continue + + window = trimmed_sample[-sentinel_len:] + if torch.all(torch.eq(sentinel, window)): return True + return False + class Stream(transformers.StoppingCriteria): def __init__(self, callback_func=None): self.callback_func = callback_func @@ -39,23 +45,26 @@ class Stream(transformers.StoppingCriteria): self.callback_func(input_ids[0]) return False + class Iteratorize: """ Transforms a function that takes a callback into a lazy iterator (generator). + + Adapted from: https://stackoverflow.com/a/9969000 """ - def __init__(self, func, kwargs={}, callback=None): - self.mfunc=func - self.c_callback=callback + def __init__(self, func, kwargs=None, callback=None): + self.mfunc = func + self.c_callback = callback self.q = Queue() self.sentinel = object() - self.kwargs = kwargs + self.kwargs = kwargs or {} self.stop_now = False def _callback(val): - if self.stop_now: + if self.stop_now or shared.stop_everything: raise ValueError self.q.put(val) @@ -64,6 +73,10 @@ class Iteratorize: ret = self.mfunc(callback=_callback, **self.kwargs) except ValueError: pass + except: + traceback.print_exc() + pass + clear_torch_cache() self.q.put(self.sentinel) if self.c_callback: @@ -76,7 +89,7 @@ class Iteratorize: return self def __next__(self): - obj = self.q.get(True,None) + obj = self.q.get(True, None) if obj is self.sentinel: raise StopIteration else: @@ -92,6 +105,7 @@ class Iteratorize: self.stop_now = True clear_torch_cache() + def clear_torch_cache(): gc.collect() if not shared.args.cpu: diff --git a/modules/chat.py b/modules/chat.py index bd45b879f92f366255c6f2308ccf135dd61bda1d..3055a97a65bd4bd2137f6ce1ec182e8d63637a13 100644 --- a/modules/chat.py +++ b/modules/chat.py @@ -1,214 +1,322 @@ +import ast import base64 import copy import io import json +import logging import re from datetime import datetime from pathlib import Path +import yaml from PIL import Image -import modules.extensions as extensions_module import modules.shared as shared from modules.extensions import apply_extensions -from modules.html_generator import generate_chat_html -from modules.text_generation import encode, generate_reply, get_max_prompt_length +from modules.html_generator import chat_html_wrapper, make_thumbnail +from modules.text_generation import (generate_reply, get_encoded_length, + get_max_prompt_length) +from modules.utils import replace_all -# This gets the new line characters right. -def clean_chat_message(text): - text = text.replace('\n', '\n\n') - text = re.sub(r"\n{3,}", "\n\n", text) - text = text.strip() - return text - -def generate_chat_output(history, name1, name2, character): - if shared.args.cai_chat: - return generate_chat_html(history, name1, name2, character) +def get_turn_substrings(state, instruct=False): + if instruct: + if 'turn_template' not in state or state['turn_template'] == '': + template = '<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n' + else: + template = state['turn_template'].replace(r'\n', '\n') else: - return history + template = '<|user|>: <|user-message|>\n<|bot|>: <|bot-message|>\n' + + replacements = { + '<|user|>': state['name1_instruct' if instruct else 'name1'].strip(), + '<|bot|>': state['name2_instruct' if instruct else 'name2'].strip(), + } + + output = { + 'user_turn': template.split('<|bot|>')[0], + 'bot_turn': '<|bot|>' + template.split('<|bot|>')[1], + 'user_turn_stripped': template.split('<|bot|>')[0].split('<|user-message|>')[0], + 'bot_turn_stripped': '<|bot|>' + template.split('<|bot|>')[1].split('<|bot-message|>')[0], + } + + for k in output: + output[k] = replace_all(output[k], replacements) + + return output + -def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=False): - user_input = clean_chat_message(user_input) - rows = [f"{context.strip()}\n"] +def generate_chat_prompt(user_input, state, **kwargs): + impersonate = kwargs.get('impersonate', False) + _continue = kwargs.get('_continue', False) + also_return_rows = kwargs.get('also_return_rows', False) + history = state.get('history', shared.history['internal']) + is_instruct = state['mode'] == 'instruct' + # Finding the maximum prompt size + chat_prompt_size = state['chat_prompt_size'] if shared.soft_prompt: - chat_prompt_size -= shared.soft_prompt_tensor.shape[1] - max_length = min(get_max_prompt_length(max_new_tokens), chat_prompt_size) - - i = len(shared.history['internal'])-1 - while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length: - rows.insert(1, f"{name2}: {shared.history['internal'][i][1].strip()}\n") - if not (shared.history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'): - rows.insert(1, f"{name1}: {shared.history['internal'][i][0].strip()}\n") + chat_prompt_size -= shared.soft_prompt_tensor.shape[1] + + max_length = min(get_max_prompt_length(state), chat_prompt_size) + + all_substrings = { + 'chat': get_turn_substrings(state, instruct=False), + 'instruct': get_turn_substrings(state, instruct=True) + } + substrings = all_substrings['instruct' if is_instruct else 'chat'] + + # Creating the template for "chat-instruct" mode + if state['mode'] == 'chat-instruct': + wrapper = '' + command = state['chat-instruct_command'].replace('<|character|>', state['name2'] if not impersonate else state['name1']) + wrapper += state['context_instruct'] + wrapper += all_substrings['instruct']['user_turn'].replace('<|user-message|>', command) + wrapper += all_substrings['instruct']['bot_turn_stripped'] + if impersonate: + wrapper += substrings['user_turn_stripped'].rstrip(' ') + else: + wrapper += apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' ')) + else: + wrapper = '<|prompt|>' + + # Building the prompt + min_rows = 3 + i = len(history) - 1 + rows = [state['context_instruct'] if is_instruct else f"{state['context'].strip()}\n"] + while i >= 0 and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) < max_length: + if _continue and i == len(history) - 1: + rows.insert(1, substrings['bot_turn_stripped'] + history[i][1].strip()) + else: + rows.insert(1, substrings['bot_turn'].replace('<|bot-message|>', history[i][1].strip())) + + string = history[i][0] + if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']: + rows.insert(1, replace_all(substrings['user_turn'], {'<|user-message|>': string.strip(), '<|round|>': str(i)})) + i -= 1 - if not impersonate: - rows.append(f"{name1}: {user_input}\n") - rows.append(apply_extensions(f"{name2}:", "bot_prefix")) - limit = 3 + if impersonate: + if state['mode'] == 'chat-instruct': + min_rows = 1 + else: + min_rows = 2 + rows.append(substrings['user_turn_stripped'].rstrip(' ')) + elif not _continue: + # Adding the user message + if len(user_input) > 0: + rows.append(replace_all(substrings['user_turn'], {'<|user-message|>': user_input.strip(), '<|round|>': str(len(history))})) + + # Adding the Character prefix + if state['mode'] != 'chat-instruct': + rows.append(apply_extensions("bot_prefix", substrings['bot_turn_stripped'].rstrip(' '))) + + while len(rows) > min_rows and get_encoded_length(wrapper.replace('<|prompt|>', ''.join(rows))) >= max_length: + rows.pop(1) + + prompt = wrapper.replace('<|prompt|>', ''.join(rows)) + if also_return_rows: + return prompt, rows else: - rows.append(f"{name1}:") - limit = 2 + return prompt - while len(rows) > limit and len(encode(''.join(rows), max_new_tokens)[0]) >= max_length: - rows.pop(1) - prompt = ''.join(rows) - return prompt +def get_stopping_strings(state): + stopping_strings = [] + if state['mode'] in ['instruct', 'chat-instruct']: + stopping_strings += [ + state['turn_template'].split('<|user-message|>')[1].split('<|bot|>')[0] + '<|bot|>', + state['turn_template'].split('<|bot-message|>')[1] + '<|user|>' + ] -def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False): - next_character_found = False + replacements = { + '<|user|>': state['name1_instruct'], + '<|bot|>': state['name2_instruct'] + } - asker = name1 if not impersonate else name2 - replier = name2 if not impersonate else name1 + for i in range(len(stopping_strings)): + stopping_strings[i] = replace_all(stopping_strings[i], replacements).rstrip(' ').replace(r'\n', '\n') - previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)] - idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)] - idx = idx[max(len(previous_idx)-1, 0)] + if state['mode'] in ['chat', 'chat-instruct']: + stopping_strings += [ + f"\n{state['name1']}:", + f"\n{state['name2']}:" + ] + + stopping_strings += ast.literal_eval(f"[{state['custom_stopping_strings']}]") + return stopping_strings - if not impersonate: - reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):] - else: - reply = reply[idx + 1 + len(f"{replier}:"):] - if check: +def extract_message_from_reply(reply, state): + next_character_found = False + stopping_strings = get_stopping_strings(state) + + if state['stop_at_newline']: lines = reply.split('\n') reply = lines[0].strip() if len(lines) > 1: next_character_found = True else: - idx = reply.find(f"\n{asker}:") - if idx != -1: - reply = reply[:idx] - next_character_found = True - reply = clean_chat_message(reply) + for string in stopping_strings: + idx = reply.find(string) + if idx != -1: + reply = reply[:idx] + next_character_found = True # If something like "\nYo" is generated just before "\nYou:" # is completed, trim it - next_turn = f"\n{asker}:" - for j in range(len(next_turn)-1, 0, -1): - if reply[-j:] == next_turn[:j]: - reply = reply[:-j] + if not next_character_found: + for string in stopping_strings: + for j in range(len(string) - 1, 0, -1): + if reply[-j:] == string[:j]: + reply = reply[:-j] + break + else: + continue + break return reply, next_character_found -def stop_everything_event(): - shared.stop_everything = True -def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1, regenerate=False): - shared.stop_everything = False - just_started = True - eos_token = '\n' if check else None - name1_original = name1 - if 'pygmalion' in shared.model_name.lower(): - name1 = "You" +def chatbot_wrapper(text, state, regenerate=False, _continue=False): + if shared.model_name == 'None' or shared.model is None: + logging.error("No model is loaded! Select one in the Model tab.") + yield shared.history['visible'] + return - # Check if any extension wants to hijack this function call + # Defining some variables + cumulative_reply = '' + just_started = True visible_text = None - custom_generate_chat_prompt = None - for extension, _ in extensions_module.iterator(): - if hasattr(extension, 'input_hijack') and extension.input_hijack['state'] == True: - extension.input_hijack['state'] = False - text, visible_text = extension.input_hijack['value'] - if custom_generate_chat_prompt is None and hasattr(extension, 'custom_generate_chat_prompt'): - custom_generate_chat_prompt = extension.custom_generate_chat_prompt - - if visible_text is None: - visible_text = text - if shared.args.chat: - visible_text = visible_text.replace('\n', '
') - text = apply_extensions(text, "input") - - if custom_generate_chat_prompt is None: - prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size) + eos_token = '\n' if state['stop_at_newline'] else None + stopping_strings = get_stopping_strings(state) + + # Preparing the input + if not any((regenerate, _continue)): + text, visible_text = apply_extensions('input_hijack', text, visible_text) + if visible_text is None: + visible_text = text + + text = apply_extensions('input', text) + # *Is typing...* + yield shared.history['visible'] + [[visible_text, shared.processing_message]] else: - prompt = custom_generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size) - - # Yield *Is typing...* - if not regenerate: - yield shared.history['visible']+[[visible_text, shared.processing_message]] + text, visible_text = shared.history['internal'][-1][0], shared.history['visible'][-1][0] + if regenerate: + shared.history['visible'].pop() + shared.history['internal'].pop() + # *Is typing...* + yield shared.history['visible'] + [[visible_text, shared.processing_message]] + elif _continue: + last_reply = [shared.history['internal'][-1][1], shared.history['visible'][-1][1]] + yield shared.history['visible'][:-1] + [[visible_text, last_reply[1] + '...']] + + # Generating the prompt + kwargs = {'_continue': _continue} + prompt = apply_extensions('custom_generate_chat_prompt', text, state, **kwargs) + if prompt is None: + prompt = generate_chat_prompt(text, state, **kwargs) # Generate - reply = '' - for i in range(chat_generation_attempts): - for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name1}:"): + for i in range(state['chat_generation_attempts']): + reply = None + for j, reply in enumerate(generate_reply(prompt + cumulative_reply, state, eos_token=eos_token, stopping_strings=stopping_strings, is_chat=True)): + reply = cumulative_reply + reply # Extracting the reply - reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check) - visible_reply = re.sub("(||{{user}})", name1_original, reply) - visible_reply = apply_extensions(visible_reply, "output") - if shared.args.chat: - visible_reply = visible_reply.replace('\n', '
') + reply, next_character_found = extract_message_from_reply(reply, state) + visible_reply = re.sub("(||{{user}})", state['name1'], reply) + visible_reply = apply_extensions("output", visible_reply) # We need this global variable to handle the Stop event, # otherwise gradio gets confused if shared.stop_everything: return shared.history['visible'] + if just_started: just_started = False - shared.history['internal'].append(['', '']) - shared.history['visible'].append(['', '']) + if not _continue: + shared.history['internal'].append(['', '']) + shared.history['visible'].append(['', '']) - shared.history['internal'][-1] = [text, reply] - shared.history['visible'][-1] = [visible_text, visible_reply] - if not shared.args.no_stream: + if _continue: + shared.history['internal'][-1] = [text, last_reply[0] + reply] + shared.history['visible'][-1] = [visible_text, last_reply[1] + visible_reply] yield shared.history['visible'] + elif not (j == 0 and visible_reply.strip() == ''): + shared.history['internal'][-1] = [text, reply] + shared.history['visible'][-1] = [visible_text, visible_reply] + yield shared.history['visible'] + if next_character_found: break - yield shared.history['visible'] - -def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): - eos_token = '\n' if check else None + if reply in [None, '']: + break + else: + cumulative_reply = reply - if 'pygmalion' in shared.model_name.lower(): - name1 = "You" + yield shared.history['visible'] - prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True) - reply = '' - # Yield *Is typing...* - yield shared.processing_message - for i in range(chat_generation_attempts): - for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=eos_token, stopping_string=f"\n{name2}:"): - reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True) +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.") + yield '' + return + + # Defining some variables + cumulative_reply = '' + eos_token = '\n' if state['stop_at_newline'] else None + prompt = generate_chat_prompt('', state, impersonate=True) + stopping_strings = get_stopping_strings(state) + + yield text + '...' + cumulative_reply = text + for i in range(state['chat_generation_attempts']): + reply = None + for reply in generate_reply(prompt + cumulative_reply, state, eos_token=eos_token, stopping_strings=stopping_strings, is_chat=True): + reply = cumulative_reply + reply + reply, next_character_found = extract_message_from_reply(reply, state) yield reply if next_character_found: break - yield reply -def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): - for _history in chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts): - yield generate_chat_html(_history, name1, name2, shared.character) + if reply in [None, '']: + break + else: + cumulative_reply = reply -def regenerate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1): - if (shared.character != 'None' and len(shared.history['visible']) == 1) or len(shared.history['internal']) == 0: - yield generate_chat_output(shared.history['visible'], name1, name2, shared.character) - else: - last_visible = shared.history['visible'].pop() - last_internal = shared.history['internal'].pop() - # Yield '*Is typing...*' - yield generate_chat_output(shared.history['visible']+[[last_visible[0], shared.processing_message]], name1, name2, shared.character) - for _history in chatbot_wrapper(last_internal[0], max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, name1, name2, context, check, chat_prompt_size, chat_generation_attempts, regenerate=True): - if shared.args.cai_chat: - shared.history['visible'][-1] = [last_visible[0], _history[-1][1]] - else: - shared.history['visible'][-1] = (last_visible[0], _history[-1][1]) - yield generate_chat_output(shared.history['visible'], name1, name2, shared.character) + yield cumulative_reply + + +def generate_chat_reply(text, state, regenerate=False, _continue=False): + if regenerate or _continue: + text = '' + if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0: + yield shared.history['visible'] + return + + for history in chatbot_wrapper(text, state, regenerate=regenerate, _continue=_continue): + yield history -def remove_last_message(name1, name2): - if len(shared.history['visible']) > 0 and not shared.history['internal'][-1][0] == '<|BEGIN-VISIBLE-CHAT|>': + +# Same as above but returns HTML +def generate_chat_reply_wrapper(text, state, regenerate=False, _continue=False): + for history in generate_chat_reply(text, state, regenerate, _continue): + yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode'], state['chat_style']) + + +def remove_last_message(): + if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>': last = shared.history['visible'].pop() shared.history['internal'].pop() else: last = ['', ''] - if shared.args.cai_chat: - return generate_chat_html(shared.history['visible'], name1, name2, shared.character), last[0] - else: - return shared.history['visible'], last[0] + return last[0] + def send_last_reply_to_input(): if len(shared.history['internal']) > 0: @@ -216,56 +324,58 @@ def send_last_reply_to_input(): else: return '' -def replace_last_reply(text, name1, name2): + +def replace_last_reply(text): if len(shared.history['visible']) > 0: - if shared.args.cai_chat: - shared.history['visible'][-1][1] = text - else: - shared.history['visible'][-1] = (shared.history['visible'][-1][0], text) - shared.history['internal'][-1][1] = apply_extensions(text, "input") - - return generate_chat_output(shared.history['visible'], name1, name2, shared.character) - -def clear_html(): - return generate_chat_html([], "", "", shared.character) - -def clear_chat_log(name1, name2): - if shared.character != 'None': - found = False - for i in range(len(shared.history['internal'])): - if '<|BEGIN-VISIBLE-CHAT|>' in shared.history['internal'][i][0]: - shared.history['visible'] = [['', apply_extensions(shared.history['internal'][i][1], "output")]] - shared.history['internal'] = [shared.history['internal'][i]] - found = True - break - if not found: - shared.history['visible'] = [] - shared.history['internal'] = [] - else: - shared.history['internal'] = [] - shared.history['visible'] = [] + shared.history['visible'][-1][1] = text + shared.history['internal'][-1][1] = apply_extensions("input", text) - return generate_chat_output(shared.history['visible'], name1, name2, shared.character) -def redraw_html(name1, name2): - return generate_chat_html(shared.history['visible'], name1, name2, shared.character) +def send_dummy_message(text): + shared.history['visible'].append([text, '']) + shared.history['internal'].append([apply_extensions("input", text), '']) + + +def send_dummy_reply(text): + if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '': + shared.history['visible'].append(['', '']) + shared.history['internal'].append(['', '']) + + shared.history['visible'][-1][1] = text + shared.history['internal'][-1][1] = apply_extensions("input", text) + + +def clear_chat_log(greeting, mode): + shared.history['visible'] = [] + shared.history['internal'] = [] + + if mode != 'instruct': + if greeting != '': + shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]] + shared.history['visible'] += [['', apply_extensions("output", greeting)]] + + save_history(mode) + + +def redraw_html(name1, name2, mode, style, reset_cache=False): + return chat_html_wrapper(shared.history['visible'], name1, name2, mode, style, reset_cache=reset_cache) -def tokenize_dialogue(dialogue, name1, name2): - _history = [] +def tokenize_dialogue(dialogue, name1, name2): + history = [] + messages = [] dialogue = re.sub('', '', dialogue) dialogue = re.sub('', '', dialogue) dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue) dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue) idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)] if len(idx) == 0: - return _history + return history - messages = [] - for i in range(len(idx)-1): - messages.append(dialogue[idx[i]:idx[i+1]].strip()) - messages.append(dialogue[idx[-1]:].strip()) + for i in range(len(idx) - 1): + messages.append(dialogue[idx[i]:idx[i + 1]].strip()) + messages.append(dialogue[idx[-1]:].strip()) entry = ['', ''] for i in messages: if i.startswith(f'{name1}:'): @@ -273,32 +383,46 @@ def tokenize_dialogue(dialogue, name1, name2): elif i.startswith(f'{name2}:'): entry[1] = i[len(f'{name2}:'):].strip() if not (len(entry[0]) == 0 and len(entry[1]) == 0): - _history.append(entry) + history.append(entry) + entry = ['', ''] print("\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='') - for row in _history: + for row in history: for column in row: print("\n") for line in column.strip().split('\n'): - print("| "+line+"\n") + print("| " + line + "\n") + print("|\n") print("------------------------------") - return _history + return history -def save_history(timestamp=True): - prefix = '' if shared.character == 'None' else f"{shared.character}_" - if timestamp: - fname = f"{prefix}{datetime.now().strftime('%Y%m%d-%H%M%S')}.json" + +def save_history(mode, timestamp=False): + # Instruct mode histories should not be saved as if + # Alpaca or Vicuna were characters + if mode == 'instruct': + if not timestamp: + return + + fname = f"Instruct_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json" else: - fname = f"{prefix}persistent.json" + if timestamp: + fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json" + else: + fname = f"{shared.character}_persistent.json" + if not Path('logs').exists(): Path('logs').mkdir() + with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f: f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2)) + return Path(f'logs/{fname}') + def load_history(file, name1, name2): file = file.decode('utf-8') try: @@ -309,62 +433,118 @@ def load_history(file, name1, name2): shared.history['visible'] = j['data_visible'] else: shared.history['visible'] = copy.deepcopy(shared.history['internal']) - # Compatibility with Pygmalion AI's official web UI - elif 'chat' in j: - shared.history['internal'] = [':'.join(x.split(':')[1:]).strip() for x in j['chat']] - if len(j['chat']) > 0 and j['chat'][0].startswith(f'{name2}:'): - shared.history['internal'] = [['<|BEGIN-VISIBLE-CHAT|>', shared.history['internal'][0]]] + [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(1, len(shared.history['internal'])-1, 2)] - shared.history['visible'] = copy.deepcopy(shared.history['internal']) - shared.history['visible'][0][0] = '' - else: - shared.history['internal'] = [[shared.history['internal'][i], shared.history['internal'][i+1]] for i in range(0, len(shared.history['internal'])-1, 2)] - shared.history['visible'] = copy.deepcopy(shared.history['internal']) except: shared.history['internal'] = tokenize_dialogue(file, name1, name2) shared.history['visible'] = copy.deepcopy(shared.history['internal']) -def load_default_history(name1, name2): - if Path('logs/persistent.json').exists(): - load_history(open(Path('logs/persistent.json'), 'rb').read(), name1, name2) - else: - shared.history['internal'] = [] - shared.history['visible'] = [] -def load_character(_character, name1, name2): +def replace_character_names(text, name1, name2): + text = text.replace('{{user}}', name1).replace('{{char}}', name2) + return text.replace('', name1).replace('', name2) + + +def build_pygmalion_style_context(data): context = "" - shared.history['internal'] = [] - shared.history['visible'] = [] - if _character != 'None': - shared.character = _character - data = json.loads(open(Path(f'characters/{_character}.json'), 'r', encoding='utf-8').read()) - name2 = data['char_name'] - if 'char_persona' in data and data['char_persona'] != '': - context += f"{data['char_name']}'s Persona: {data['char_persona']}\n" - if 'world_scenario' in data and data['world_scenario'] != '': - context += f"Scenario: {data['world_scenario']}\n" - context = f"{context.strip()}\n\n" - if 'example_dialogue' in data and data['example_dialogue'] != '': - data['example_dialogue'] = data['example_dialogue'].replace('{{user}}', name1).replace('{{char}}', name2) - data['example_dialogue'] = data['example_dialogue'].replace('', name1).replace('', name2) + if 'char_persona' in data and data['char_persona'] != '': + context += f"{data['char_name']}'s Persona: {data['char_persona']}\n" + + if 'world_scenario' in data and data['world_scenario'] != '': + context += f"Scenario: {data['world_scenario']}\n" + + context = f"{context.strip()}\n\n" + return context + + +def generate_pfp_cache(character): + cache_folder = Path("cache") + if not cache_folder.exists(): + cache_folder.mkdir() + + for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]: + if path.exists(): + img = make_thumbnail(Image.open(path)) + img.save(Path('cache/pfp_character.png'), format='PNG') + return img + + return None + + +def load_character(character, name1, name2, instruct=False): + shared.character = character + context = greeting = turn_template = "" + greeting_field = 'greeting' + picture = None + + # Deleting the profile picture cache, if any + if Path("cache/pfp_character.png").exists(): + Path("cache/pfp_character.png").unlink() + + if character != 'None': + folder = 'characters' if not instruct else 'characters/instruction-following' + picture = generate_pfp_cache(character) + for extension in ["yml", "yaml", "json"]: + filepath = Path(f'{folder}/{character}.{extension}') + if filepath.exists(): + break + + file_contents = open(filepath, 'r', encoding='utf-8').read() + data = json.loads(file_contents) if extension == "json" else yaml.safe_load(file_contents) + + # Finding the bot's name + for k in ['name', 'bot', '<|bot|>', 'char_name']: + if k in data and data[k] != '': + name2 = data[k] + break + + # Find the user name (if any) + for k in ['your_name', 'user', '<|user|>']: + if k in data and data[k] != '': + name1 = data[k] + break + + for field in ['context', 'greeting', 'example_dialogue', 'char_persona', 'char_greeting', 'world_scenario']: + if field in data: + data[field] = replace_character_names(data[field], name1, name2) + + if 'context' in data: + context = data['context'] + if not instruct: + context = context.strip() + '\n' + elif "char_persona" in data: + context = build_pygmalion_style_context(data) + greeting_field = 'char_greeting' + + if 'example_dialogue' in data: context += f"{data['example_dialogue'].strip()}\n" - if 'char_greeting' in data and len(data['char_greeting'].strip()) > 0: - shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', data['char_greeting']]] - shared.history['visible'] += [['', apply_extensions(data['char_greeting'], "output")]] - else: - shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', "Hello there!"]] - shared.history['visible'] += [['', "Hello there!"]] - else: - shared.character = None - context = shared.settings['context_pygmalion'] - name2 = shared.settings['name2_pygmalion'] - if Path(f'logs/{shared.character}_persistent.json').exists(): - load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2) + if greeting_field in data: + greeting = data[greeting_field] + + if 'turn_template' in data: + turn_template = data['turn_template'] - if shared.args.cai_chat: - return name2, context, generate_chat_html(shared.history['visible'], name1, name2, shared.character) else: - return name2, context, shared.history['visible'] + context = shared.settings['context'] + name2 = shared.settings['name2'] + greeting = shared.settings['greeting'] + turn_template = shared.settings['turn_template'] + + if not instruct: + shared.history['internal'] = [] + shared.history['visible'] = [] + if Path(f'logs/{shared.character}_persistent.json').exists(): + load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2) + else: + # Insert greeting if it exists + if greeting != "": + shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]] + shared.history['visible'] += [['', apply_extensions("output", greeting)]] + + # Create .json log files since they don't already exist + save_history('instruct' if instruct else 'chat') + + return name1, name2, picture, greeting, context, repr(turn_template)[1:-1] + def upload_character(json_file, img, tavern=False): json_file = json_file if type(json_file) == str else json_file.decode('utf-8') @@ -374,16 +554,21 @@ def upload_character(json_file, img, tavern=False): while Path(f'characters/{outfile_name}.json').exists(): outfile_name = f'{data["char_name"]}_{i:03d}' i += 1 + if tavern: outfile_name = f'TavernAI-{outfile_name}' + with open(Path(f'characters/{outfile_name}.json'), 'w', encoding='utf-8') as f: f.write(json_file) + if img is not None: img = Image.open(io.BytesIO(img)) img.save(Path(f'characters/{outfile_name}.png')) - print(f'New character saved to "characters/{outfile_name}.json".') + + logging.info(f'New character saved to "characters/{outfile_name}.json".') return outfile_name + def upload_tavern_character(img, name1, name2): _img = Image.open(io.BytesIO(img)) _img.getexif() @@ -392,7 +577,16 @@ def upload_tavern_character(img, name1, name2): _json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']} return upload_character(json.dumps(_json), img, tavern=True) + def upload_your_profile_picture(img): - img = Image.open(io.BytesIO(img)) - img.save(Path('img_me.png')) - print('Profile picture saved to "img_me.png"') + cache_folder = Path("cache") + if not cache_folder.exists(): + cache_folder.mkdir() + + if img is None: + if Path("cache/pfp_me.png").exists(): + Path("cache/pfp_me.png").unlink() + else: + img = make_thumbnail(img) + img.save(Path('cache/pfp_me.png')) + logging.info('Profile picture saved to "cache/pfp_me.png"') diff --git a/modules/deepspeed_parameters.py b/modules/deepspeed_parameters.py index 3dbed437f5b5196d0b1fcbc582085319fb8d40d1..9116f5792fea4edf4b536b6605ee40e254109a98 100644 --- a/modules/deepspeed_parameters.py +++ b/modules/deepspeed_parameters.py @@ -1,5 +1,4 @@ def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir): - ''' DeepSpeed configration https://huggingface.co/docs/transformers/main_classes/deepspeed diff --git a/modules/evaluate.py b/modules/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..adafa7137f8a676fee0595aa987dc37179561340 --- /dev/null +++ b/modules/evaluate.py @@ -0,0 +1,144 @@ +import datetime +import traceback +from pathlib import Path + +import pandas as pd +import torch +from datasets import load_dataset +from tqdm import tqdm + +from modules import shared +from modules.models import load_model, unload_model +from modules.text_generation import encode +from server import get_model_specific_settings, update_model_parameters + + +def load_past_evaluations(): + if Path('logs/evaluations.csv').exists(): + df = pd.read_csv(Path('logs/evaluations.csv'), dtype=str) + df['Perplexity'] = pd.to_numeric(df['Perplexity']) + return df + else: + return pd.DataFrame(columns=['Model', 'LoRAs', 'Dataset', 'Perplexity', 'stride', 'max_length', 'Date', 'Comment']) + + +past_evaluations = load_past_evaluations() + + +def save_past_evaluations(df): + global past_evaluations + past_evaluations = df + df.to_csv(Path('logs/evaluations.csv'), index=False) + + +def calculate_perplexity(models, input_dataset, stride, _max_length): + ''' + Based on: + https://huggingface.co/docs/transformers/perplexity#calculating-ppl-with-fixedlength-models + ''' + + global past_evaluations + cumulative_log = '' + cumulative_log += "Loading the input dataset...\n" + yield cumulative_log + + # Copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/triton/utils/datautils.py + if input_dataset == 'wikitext': + data = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test') + text = "\n\n".join(data['text']) + elif input_dataset == 'ptb': + data = load_dataset('ptb_text_only', 'penn_treebank', split='validation') + text = "\n\n".join(data['sentence']) + elif input_dataset == 'ptb_new': + data = load_dataset('ptb_text_only', 'penn_treebank', split='test') + text = " ".join(data['sentence']) + else: + with open(Path(f'training/datasets/{input_dataset}.txt'), 'r', encoding='utf-8') as f: + text = f.read() + + for model in models: + if is_in_past_evaluations(model, input_dataset, stride, _max_length): + cumulative_log += f"{model} has already been tested. Ignoring.\n" + yield cumulative_log + continue + + if model != 'current model': + try: + yield cumulative_log + f"Loading {model}...\n" + model_settings = get_model_specific_settings(model) + shared.settings.update(model_settings) # hijacking the interface defaults + update_model_parameters(model_settings) # hijacking the command-line arguments + shared.model_name = model + unload_model() + shared.model, shared.tokenizer = load_model(shared.model_name) + except: + cumulative_log += f"Failed to load {model}. Moving on.\n" + yield cumulative_log + continue + + cumulative_log += f"Processing {model}...\n" + yield cumulative_log + "Tokenizing the input dataset...\n" + encodings = encode(text, add_special_tokens=False) + seq_len = encodings.shape[1] + max_length = _max_length or shared.model.config.max_position_embeddings + nlls = [] + prev_end_loc = 0 + for begin_loc in tqdm(range(0, seq_len, stride)): + yield cumulative_log + f"Evaluating... {100*begin_loc/seq_len:.2f}%" + end_loc = min(begin_loc + max_length, seq_len) + trg_len = end_loc - prev_end_loc # may be different from stride on last loop + input_ids = encodings[:, begin_loc:end_loc] + target_ids = input_ids.clone() + target_ids[:, :-trg_len] = -100 + + with torch.no_grad(): + outputs = shared.model(input_ids, labels=target_ids) + + # loss is calculated using CrossEntropyLoss which averages over valid labels + # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels + # to the left by 1. + neg_log_likelihood = outputs.loss + + nlls.append(neg_log_likelihood) + + prev_end_loc = end_loc + if end_loc == seq_len: + break + + ppl = torch.exp(torch.stack(nlls).mean()) + add_entry_to_past_evaluations(float(ppl), shared.model_name, input_dataset, stride, _max_length) + save_past_evaluations(past_evaluations) + cumulative_log += f"Done. The perplexity is: {float(ppl)}\n\n" + yield cumulative_log + + +def add_entry_to_past_evaluations(perplexity, model, dataset, stride, max_length): + global past_evaluations + entry = { + 'Model': model, + 'LoRAs': ', '.join(shared.lora_names) or '-', + 'Dataset': dataset, + 'Perplexity': perplexity, + 'stride': str(stride), + 'max_length': str(max_length), + 'Date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'Comment': '' + } + past_evaluations = pd.concat([past_evaluations, pd.DataFrame([entry])], ignore_index=True) + + +def is_in_past_evaluations(model, dataset, stride, max_length): + entries = past_evaluations[(past_evaluations['Model'] == model) & + (past_evaluations['Dataset'] == dataset) & + (past_evaluations['max_length'] == str(max_length)) & + (past_evaluations['stride'] == str(stride))] + + if entries.shape[0] > 0: + return True + else: + return False + + +def generate_markdown_table(): + sorted_df = past_evaluations.sort_values(by=['Dataset', 'stride', 'Perplexity', 'Date']) + return sorted_df diff --git a/modules/extensions.py b/modules/extensions.py index c8de8a7bc9ebd331d65704996a764e7cc279a6e5..2f68caf558a6f02115d678b2333126b07c6fd9e8 100644 --- a/modules/extensions.py +++ b/modules/extensions.py @@ -1,45 +1,183 @@ +import logging +import traceback +from functools import partial + +import gradio as gr + import extensions import modules.shared as shared state = {} available_extensions = [] +setup_called = set() + + +def apply_settings(extension, name): + if not hasattr(extension, 'params'): + return + + for param in extension.params: + _id = f"{name}-{param}" + if _id not in shared.settings: + continue + + extension.params[param] = shared.settings[_id] + def load_extensions(): - global state + global state, setup_called for i, name in enumerate(shared.args.extensions): if name in available_extensions: - print(f'Loading the extension "{name}"... ', end='') - exec(f"import extensions.{name}.script") - state[name] = [True, i] - print('Ok.') + if name != 'api': + logging.info(f'Loading the extension "{name}"...') + try: + exec(f"import extensions.{name}.script") + extension = getattr(extensions, name).script + apply_settings(extension, name) + if extension not in setup_called and hasattr(extension, "setup"): + setup_called.add(extension) + extension.setup() + + state[name] = [True, i] + except: + logging.error(f'Failed to load the extension "{name}".') + traceback.print_exc() + # This iterator returns the extensions in the order specified in the command-line def iterator(): - for name in sorted(state, key=lambda x : state[x][1]): - if state[name][0] == True: - yield eval(f"extensions.{name}.script"), name + for name in sorted(state, key=lambda x: state[x][1]): + if state[name][0]: + yield getattr(extensions, name).script, name + # Extension functions that map string -> string -def apply_extensions(text, typ): - for extension, _ in iterator(): - if typ == "input" and hasattr(extension, "input_modifier"): - text = extension.input_modifier(text) - elif typ == "output" and hasattr(extension, "output_modifier"): - text = extension.output_modifier(text) - elif typ == "bot_prefix" and hasattr(extension, "bot_prefix_modifier"): - text = extension.bot_prefix_modifier(text) +def _apply_string_extensions(function_name, text): + for extension, _ in iterator(): + if hasattr(extension, function_name): + text = getattr(extension, function_name)(text) + return text + +# Input hijack of extensions +def _apply_input_hijack(text, visible_text): + for extension, _ in iterator(): + if hasattr(extension, 'input_hijack') and extension.input_hijack['state']: + extension.input_hijack['state'] = False + if callable(extension.input_hijack['value']): + text, visible_text = extension.input_hijack['value'](text, visible_text) + else: + text, visible_text = extension.input_hijack['value'] + + return text, visible_text + + +# custom_generate_chat_prompt handling - currently only the first one will work +def _apply_custom_generate_chat_prompt(text, state, **kwargs): + for extension, _ in iterator(): + if hasattr(extension, 'custom_generate_chat_prompt'): + return extension.custom_generate_chat_prompt(text, state, **kwargs) + + return None + + +# Extension that modifies the input parameters before they are used +def _apply_state_modifier_extensions(state): + for extension, _ in iterator(): + if hasattr(extension, "state_modifier"): + state = getattr(extension, "state_modifier")(state) + + return state + + +# Extension functions that override the default tokenizer output - currently only the first one will work +def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_embeds): + for extension, _ in iterator(): + if hasattr(extension, function_name): + return getattr(extension, function_name)(state, prompt, input_ids, input_embeds) + + return prompt, input_ids, input_embeds + + +# Get prompt length in tokens after applying extension functions which override the default tokenizer output +# currently only the first one will work +def _apply_custom_tokenized_length(prompt): + for extension, _ in iterator(): + if hasattr(extension, 'custom_tokenized_length'): + return getattr(extension, 'custom_tokenized_length')(prompt) + + return None + + +# Custom generate reply handling - currently only the first one will work +def _apply_custom_generate_reply(): + for extension, _ in iterator(): + if hasattr(extension, 'custom_generate_reply'): + return getattr(extension, 'custom_generate_reply') + + return None + + +def _apply_custom_css(): + all_css = '' + for extension, _ in iterator(): + if hasattr(extension, 'custom_css'): + all_css += getattr(extension, 'custom_css')() + + return all_css + + +def _apply_custom_js(): + all_js = '' + for extension, _ in iterator(): + if hasattr(extension, 'custom_js'): + all_js += getattr(extension, 'custom_js')() + + return all_js + + def create_extensions_block(): - # Updating the default values + to_display = [] for extension, name in iterator(): - if hasattr(extension, 'params'): - for param in extension.params: - _id = f"{name}-{param}" - if _id in shared.settings: - extension.params[param] = shared.settings[_id] + if hasattr(extension, "ui") and not (hasattr(extension, 'params') and extension.params.get('is_tab', False)): + to_display.append((extension, name)) # Creating the extension ui elements + if len(to_display) > 0: + with gr.Column(elem_id="extensions"): + for row in to_display: + extension, name = row + display_name = getattr(extension, 'params', {}).get('display_name', name) + gr.Markdown(f"\n### {display_name}") + extension.ui() + + +def create_extensions_tabs(): for extension, name in iterator(): - if hasattr(extension, "ui"): - extension.ui() + if hasattr(extension, "ui") and (hasattr(extension, 'params') and extension.params.get('is_tab', False)): + display_name = getattr(extension, 'params', {}).get('display_name', name) + with gr.Tab(display_name, elem_classes="extension-tab"): + extension.ui() + + +EXTENSION_MAP = { + "input": partial(_apply_string_extensions, "input_modifier"), + "output": partial(_apply_string_extensions, "output_modifier"), + "state": _apply_state_modifier_extensions, + "bot_prefix": partial(_apply_string_extensions, "bot_prefix_modifier"), + "tokenizer": partial(_apply_tokenizer_extensions, "tokenizer_modifier"), + "input_hijack": _apply_input_hijack, + "custom_generate_chat_prompt": _apply_custom_generate_chat_prompt, + "custom_generate_reply": _apply_custom_generate_reply, + "tokenized_length": _apply_custom_tokenized_length, + "css": _apply_custom_css, + "js": _apply_custom_js +} + + +def apply_extensions(typ, *args, **kwargs): + if typ not in EXTENSION_MAP: + raise ValueError(f"Invalid extension type {typ}") + + return EXTENSION_MAP[typ](*args, **kwargs) diff --git a/modules/html_generator.py b/modules/html_generator.py index 162040bac68c2e987b33a02ccb12e90b51a63b2d..ceb167e010d6ccdd1adb9dcc270900120b0caa03 100644 --- a/modules/html_generator.py +++ b/modules/html_generator.py @@ -1,37 +1,81 @@ ''' -This is a library for formatting GPT-4chan and chat outputs as nice HTML. +This is a library for formatting text outputs as nice HTML. ''' import os import re +import time from pathlib import Path -from PIL import Image +import markdown +from PIL import Image, ImageOps + +from modules.utils import get_available_chat_styles # This is to store the paths to the thumbnails of the profile pictures image_cache = {} -def generate_basic_html(s): - css = """ - .container { - max-width: 600px; - margin-left: auto; - margin-right: auto; - background-color: rgb(31, 41, 55); - padding:3em; - } - .container p { - font-size: 16px !important; - color: white !important; - margin-bottom: 22px; - line-height: 1.4 !important; - } - """ - s = '\n'.join([f'

{line}

' for line in s.split('\n')]) - s = f'
{s}
' - return s +with open(Path(__file__).resolve().parent / '../css/html_readable_style.css', 'r') as f: + readable_css = f.read() +with open(Path(__file__).resolve().parent / '../css/html_4chan_style.css', 'r') as css_f: + _4chan_css = css_f.read() +with open(Path(__file__).resolve().parent / '../css/html_instruct_style.css', 'r') as f: + instruct_css = f.read() + +# Custom chat styles +chat_styles = {} +for k in get_available_chat_styles(): + chat_styles[k] = open(Path(f'css/chat_style-{k}.css'), 'r').read() + + +def fix_newlines(string): + string = string.replace('\n', '\n\n') + string = re.sub(r"\n{3,}", "\n\n", string) + string = string.strip() + return string + + +def replace_blockquote(m): + return m.group().replace('\n', '\n> ').replace('\\begin{blockquote}', '').replace('\\end{blockquote}', '') + + +def convert_to_markdown(string): + + # Blockquote + pattern = re.compile(r'\\begin{blockquote}(.*?)\\end{blockquote}', re.DOTALL) + string = pattern.sub(replace_blockquote, string) + + # Code + string = string.replace('\\begin{code}', '```') + string = string.replace('\\end{code}', '```') + string = re.sub(r"(.)```", r"\1\n```", string) + + result = '' + is_code = False + for line in string.split('\n'): + if line.lstrip(' ').startswith('```'): + is_code = not is_code + + result += line + if is_code or line.startswith('|'): # Don't add an extra \n for tables or code + result += '\n' + else: + result += '\n\n' + + if is_code: + result = result + '```' # Unfinished code block + + string = result.strip() + return markdown.markdown(string, extensions=['fenced_code', 'tables']) + + +def generate_basic_html(string): + string = convert_to_markdown(string) + string = f'
{string}
' + return string + def process_post(post, c): t = post.split('\n') @@ -47,114 +91,8 @@ def process_post(post, c): src = f'Anonymous No.{number}\n{src}' return src -def generate_4chan_html(f): - css = """ - - #parent #container { - background-color: #eef2ff; - padding: 17px; - } - #parent #container .reply { - background-color: rgb(214, 218, 240); - border-bottom-color: rgb(183, 197, 217); - border-bottom-style: solid; - border-bottom-width: 1px; - border-image-outset: 0; - border-image-repeat: stretch; - border-image-slice: 100%; - border-image-source: none; - border-image-width: 1; - border-left-color: rgb(0, 0, 0); - border-left-style: none; - border-left-width: 0px; - border-right-color: rgb(183, 197, 217); - border-right-style: solid; - border-right-width: 1px; - border-top-color: rgb(0, 0, 0); - border-top-style: none; - border-top-width: 0px; - color: rgb(0, 0, 0); - display: table; - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 4px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - padding-bottom: 4px; - padding-left: 2px; - padding-right: 2px; - padding-top: 4px; - } - - #parent #container .number { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - width: 342.65px; - margin-right: 7px; - } - - #parent #container .op { - color: rgb(0, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - margin-bottom: 8px; - margin-left: 0px; - margin-right: 0px; - margin-top: 4px; - overflow-x: hidden; - overflow-y: hidden; - } - - #parent #container .op blockquote { - margin-left: 0px !important; - } - - #parent #container .name { - color: rgb(17, 119, 67); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - font-weight: 700; - margin-left: 7px; - } - - #parent #container .quote { - color: rgb(221, 0, 0); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - text-decoration-color: rgb(221, 0, 0); - text-decoration-line: underline; - text-decoration-style: solid; - text-decoration-thickness: auto; - } - - #parent #container .greentext { - color: rgb(120, 153, 34); - font-family: arial, helvetica, sans-serif; - font-size: 13.3333px; - } - - #parent #container blockquote { - margin: 0px !important; - margin-block-start: 1em; - margin-block-end: 1em; - margin-inline-start: 40px; - margin-inline-end: 40px; - margin-top: 13.33px !important; - margin-bottom: 13.33px !important; - margin-left: 40px !important; - margin-right: 40px !important; - } - - #parent #container .message { - color: black; - border: none; - } - """ +def generate_4chan_html(f): posts = [] post = '' c = -2 @@ -179,9 +117,9 @@ def generate_4chan_html(f): posts[i] = f'
{posts[i]}
\n' else: posts[i] = f'
{posts[i]}
\n' - + output = '' - output += f'
' + output += f'
' for post in posts: output += post output += '
' @@ -193,6 +131,15 @@ def generate_4chan_html(f): return output + +def make_thumbnail(image): + image = image.resize((350, round(image.size[1] / image.size[0] * 350)), Image.Resampling.LANCZOS) + if image.size[1] > 470: + image = ImageOps.fit(image, (350, 470), Image.ANTIALIAS) + + return image + + def get_image_cache(path): cache_folder = Path("cache") if not cache_folder.exists(): @@ -200,158 +147,133 @@ def get_image_cache(path): mtime = os.stat(path).st_mtime if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache): - img = Image.open(path) - img.thumbnail((200, 200)) + img = make_thumbnail(Image.open(path)) output_file = Path(f'cache/{path.name}_cache.png') img.convert('RGB').save(output_file, format='PNG') image_cache[path] = [mtime, output_file.as_posix()] return image_cache[path][1] -def generate_chat_html(history, name1, name2, character): - css = """ - .chat { - margin-left: auto; - margin-right: auto; - max-width: 800px; - height: 66.67vh; - overflow-y: auto; - padding-right: 20px; - display: flex; - flex-direction: column-reverse; - } - - .message { - display: grid; - grid-template-columns: 60px 1fr; - padding-bottom: 25px; - font-size: 15px; - font-family: Helvetica, Arial, sans-serif; - line-height: 1.428571429; - } - - .circle-you { - width: 50px; - height: 50px; - background-color: rgb(238, 78, 59); - border-radius: 50%; - } - - .circle-bot { - width: 50px; - height: 50px; - background-color: rgb(59, 78, 244); - border-radius: 50%; - } - - .circle-bot img, .circle-you img { - border-radius: 50%; - width: 100%; - height: 100%; - object-fit: cover; - } - - .text { - } - - .text p { - margin-top: 5px; - } - - .username { - font-weight: bold; - } - - .message-body { - } - - .message-body img { - max-width: 300px; - max-height: 300px; - border-radius: 20px; - } - - .message-body p { - margin-bottom: 0 !important; - font-size: 15px !important; - line-height: 1.428571429 !important; - } - - .dark .message-body p em { - color: rgb(138, 138, 138) !important; - } - - .message-body p em { - color: rgb(110, 110, 110) !important; - } - - """ - output = '' - output += f'
' - img = '' - - for i in [ - f"characters/{character}.png", - f"characters/{character}.jpg", - f"characters/{character}.jpeg", - "img_bot.png", - "img_bot.jpg", - "img_bot.jpeg" - ]: - - path = Path(i) - if path.exists(): - img = f'' - break - - img_me = '' - for i in ["img_me.png", "img_me.jpg", "img_me.jpeg"]: - path = Path(i) - if path.exists(): - img_me = f'' - break - - for i,_row in enumerate(history[::-1]): - row = _row.copy() - row[0] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[0]) - row[1] = re.sub(r"(\*\*)([^\*\n]*)(\*\*)", r"\2", row[1]) - row[0] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[0]) - row[1] = re.sub(r"(\*)([^\*\n]*)(\*)", r"\2", row[1]) - p = '\n'.join([f"

{x}

" for x in row[1].split('\n')]) +def generate_instruct_html(history): + output = f'
' + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + + output += f""" +
+
+
+ {row[1]} +
+
+
+ """ + + if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+
+ {row[0]} +
+
+
+ """ + + output += "
" + + return output + + +def generate_cai_chat_html(history, name1, name2, style, reset_cache=False): + output = f'
' + + # We use ?name2 and ?time.time() to force the browser to reset caches + img_bot = f'' if Path("cache/pfp_character.png").exists() else '' + img_me = f'' if Path("cache/pfp_me.png").exists() else '' + + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + output += f"""
- {img} + {img_bot}
{name2}
- {p} + {row[1]} +
+
+
+ """ + + if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+ {img_me} +
+
+
+ {name1} +
+
+ {row[0]} +
+
+
+ """ + + output += "
" + return output + + +def generate_chat_html(history, name1, name2, reset_cache=False): + output = f'
' + + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + + output += f""" +
+
+
+ {row[1]}
""" - if not (i == len(history)-1 and len(row[0]) == 0): - p = '\n'.join([f"

{x}

" for x in row[0].split('\n')]) - output += f""" -
-
- {img_me} -
-
-
- {name1} -
-
- {p} -
-
+ if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+
+ {row[0]}
- """ +
+
+ """ output += "
" return output + + +def chat_html_wrapper(history, name1, name2, mode, style, reset_cache=False): + if mode == 'instruct': + return generate_instruct_html(history) + elif style == 'wpp': + return generate_chat_html(history, name1, name2) + else: + return generate_cai_chat_html(history, name1, name2, style, reset_cache) diff --git a/modules/llama_attn_hijack.py b/modules/llama_attn_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..e953f523d6c54581af1a30deb8b922f85b3e557a --- /dev/null +++ b/modules/llama_attn_hijack.py @@ -0,0 +1,171 @@ +import logging +import math +import sys +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import transformers.models.llama.modeling_llama + +import modules.shared as shared + +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) + + +def hijack_llama_attention(): + if shared.args.xformers: + transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward + logging.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") + + +def xformers_forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # We only apply xformers optimizations if we don't need to output the whole attention matrix + if not output_attentions: + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros. + # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros. + if attention_mask is None or attention_mask[0, 0, 0, 1] == 0: + # input and output should be of form (bsz, q_len, num_heads, head_dim) + attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None) + else: + # input and output should be of form (bsz, q_len, num_heads, head_dim) + attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask()) + attn_weights = None + else: + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights, past_key_value + + +def sdp_attention_forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # We only apply sdp attention if we don't need to output the whole attention matrix + if not output_attentions: + attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False) + attn_weights = None + else: + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights, past_key_value diff --git a/modules/llamacpp_model.py b/modules/llamacpp_model.py new file mode 100644 index 0000000000000000000000000000000000000000..0ed33543dcf5ca61f0dddc6b3c35add9d535df59 --- /dev/null +++ b/modules/llamacpp_model.py @@ -0,0 +1,86 @@ +''' +Based on +https://github.com/abetlen/llama-cpp-python + +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 + + +class LlamaCppModel: + def __init__(self): + self.initialized = False + + def __del__(self): + self.model.__del__() + + @classmethod + def from_pretrained(self, path): + result = self() + + cache_capacity = 0 + if shared.args.cache_capacity is not None: + if 'GiB' in shared.args.cache_capacity: + cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 * 1000 + elif 'MiB' in shared.args.cache_capacity: + cache_capacity = int(re.sub('[a-zA-Z]', '', shared.args.cache_capacity)) * 1000 * 1000 + else: + cache_capacity = int(shared.args.cache_capacity) + + logging.info("Cache capacity is " + str(cache_capacity) + " bytes") + + params = { + 'model_path': str(path), + 'n_ctx': 2048, + 'seed': 0, + 'n_threads': shared.args.threads or None, + 'n_batch': shared.args.n_batch, + 'use_mmap': not shared.args.no_mmap, + 'use_mlock': shared.args.mlock, + 'n_gpu_layers': shared.args.n_gpu_layers + } + self.model = Llama(**params) + if cache_capacity > 0: + self.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 + + def encode(self, string): + if type(string) is str: + string = string.encode() + return self.model.tokenize(string) + + def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None): + context = context if type(context) is str else context.decode() + completion_chunks = self.model.create_completion( + prompt=context, + max_tokens=token_count, + temperature=temperature, + top_p=top_p, + top_k=top_k, + repeat_penalty=repetition_penalty, + stream=True + ) + output = "" + for completion_chunk in completion_chunks: + text = completion_chunk['choices'][0]['text'] + output += text + if callback: + callback(text) + return output + + def generate_with_streaming(self, **kwargs): + with Iteratorize(self.generate, kwargs, callback=None) as generator: + reply = '' + for token in generator: + reply += token + yield reply diff --git a/modules/logging_colors.py b/modules/logging_colors.py new file mode 100644 index 0000000000000000000000000000000000000000..5c9714f7cd08f88f30335dfc0b7a694879414a68 --- /dev/null +++ b/modules/logging_colors.py @@ -0,0 +1,109 @@ +# Copied from https://stackoverflow.com/a/1336640 + +import logging +import platform + + +def add_coloring_to_emit_windows(fn): + # add methods we need to the class + def _out_handle(self): + import ctypes + return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) + out_handle = property(_out_handle) + + def _set_color(self, code): + import ctypes + + # Constants from the Windows API + self.STD_OUTPUT_HANDLE = -11 + hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) + ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code) + + setattr(logging.StreamHandler, '_set_color', _set_color) + + def new(*args): + FOREGROUND_BLUE = 0x0001 # text color contains blue. + FOREGROUND_GREEN = 0x0002 # text color contains green. + FOREGROUND_RED = 0x0004 # text color contains red. + FOREGROUND_INTENSITY = 0x0008 # text color is intensified. + FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED + # winbase.h + # STD_INPUT_HANDLE = -10 + # STD_OUTPUT_HANDLE = -11 + # STD_ERROR_HANDLE = -12 + + # wincon.h + # FOREGROUND_BLACK = 0x0000 + FOREGROUND_BLUE = 0x0001 + FOREGROUND_GREEN = 0x0002 + # FOREGROUND_CYAN = 0x0003 + FOREGROUND_RED = 0x0004 + FOREGROUND_MAGENTA = 0x0005 + FOREGROUND_YELLOW = 0x0006 + # FOREGROUND_GREY = 0x0007 + FOREGROUND_INTENSITY = 0x0008 # foreground color is intensified. + + # BACKGROUND_BLACK = 0x0000 + # BACKGROUND_BLUE = 0x0010 + # BACKGROUND_GREEN = 0x0020 + # BACKGROUND_CYAN = 0x0030 + # BACKGROUND_RED = 0x0040 + # BACKGROUND_MAGENTA = 0x0050 + BACKGROUND_YELLOW = 0x0060 + # BACKGROUND_GREY = 0x0070 + BACKGROUND_INTENSITY = 0x0080 # background color is intensified. + + levelno = args[1].levelno + if (levelno >= 50): + color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY + elif (levelno >= 40): + color = FOREGROUND_RED | FOREGROUND_INTENSITY + elif (levelno >= 30): + color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY + elif (levelno >= 20): + color = FOREGROUND_GREEN + elif (levelno >= 10): + color = FOREGROUND_MAGENTA + else: + color = FOREGROUND_WHITE + args[0]._set_color(color) + + ret = fn(*args) + args[0]._set_color(FOREGROUND_WHITE) + # print "after" + return ret + return new + + +def add_coloring_to_emit_ansi(fn): + # add methods we need to the class + def new(*args): + levelno = args[1].levelno + if (levelno >= 50): + color = '\x1b[31m' # red + elif (levelno >= 40): + color = '\x1b[31m' # red + elif (levelno >= 30): + color = '\x1b[33m' # yellow + elif (levelno >= 20): + color = '\x1b[32m' # green + elif (levelno >= 10): + color = '\x1b[35m' # pink + else: + color = '\x1b[0m' # normal + args[1].msg = color + args[1].msg + '\x1b[0m' # normal + # print "after" + return fn(*args) + return new + + +if platform.system() == 'Windows': + # Windows does not support ANSI escapes and we are using API calls to set the console color + logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit) +else: + # all non-Windows platforms are supporting ANSI escapes so we use them + logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit) + # log = logging.getLogger() + # log.addFilter(log_filter()) + # //hdlr = logging.StreamHandler() + # //hdlr.setFormatter(formatter()) diff --git a/modules/models.py b/modules/models.py index f4bb11fd3f7292657b008ab644b5be121d9980e5..867f463c4a4be3240663c518377d66b8be1f9dc7 100644 --- a/modules/models.py +++ b/modules/models.py @@ -1,5 +1,8 @@ +import gc import json +import logging import os +import re import time import zipfile from pathlib import Path @@ -7,18 +10,17 @@ from pathlib import Path import numpy as np import torch import transformers -from transformers import AutoModelForCausalLM, AutoTokenizer +from accelerate import infer_auto_device_map, init_empty_weights +from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM, + AutoModelForSeq2SeqLM, AutoTokenizer, + BitsAndBytesConfig, LlamaTokenizer) import modules.shared as shared +from modules import llama_attn_hijack transformers.logging.set_verbosity_error() local_rank = None - -if shared.args.flexgen: - from flexgen.flex_opt import (CompressionConfig, ExecutionEnv, OptLM, - Policy, str2bool) - if shared.args.deepspeed: import deepspeed from transformers.deepspeed import (HfDeepSpeedConfig, @@ -32,113 +34,296 @@ if shared.args.deepspeed: torch.cuda.set_device(local_rank) deepspeed.init_distributed() ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir) - dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration + dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration + + +# Some models require special treatment in various parts of the code. +# This function detects those models +def find_model_type(model_name): + path_to_model = Path(f'{shared.args.model_dir}/{model_name}') + if not path_to_model.exists(): + return 'None' + + model_name_lower = model_name.lower() + if 'rwkv-' in model_name_lower: + return 'rwkv' + elif len(list(path_to_model.glob('*ggml*.bin'))) > 0: + return 'llamacpp' + elif re.match('.*ggml.*\.bin', model_name_lower): + return 'llamacpp' + elif 'chatglm' in model_name_lower: + return 'chatglm' + elif 'galactica' in model_name_lower: + return 'galactica' + elif 'llava' in model_name_lower: + return 'llava' + elif 'oasst' in model_name_lower: + return 'oasst' + elif any((k in model_name_lower for k in ['gpt4chan', 'gpt-4chan'])): + return 'gpt4chan' + else: + config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) + # Not a "catch all", but fairly accurate + if config.to_dict().get("is_encoder_decoder", False): + return 'HF_seq2seq' + else: + return 'HF_generic' def load_model(model_name): - print(f"Loading {model_name}...") + logging.info(f"Loading {model_name}...") t0 = time.time() - shared.is_RWKV = model_name.lower().startswith('rwkv-') + shared.model_type = find_model_type(model_name) + if shared.model_type == 'None': + logging.error('The path to the model does not exist. Exiting.') + return None, None - # Default settings - if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]): - if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')): - model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True) + if shared.args.autogptq: + load_func = AutoGPTQ_loader + elif shared.args.wbits > 0: + load_func = GPTQ_loader + elif shared.model_type == 'llamacpp': + load_func = llamacpp_loader + elif shared.model_type == 'rwkv': + load_func = RWKV_loader + elif shared.args.flexgen: + load_func = flexgen_loader + else: + load_func = huggingface_loader + + output = load_func(model_name) + if type(output) is tuple: + model, tokenizer = output + else: + model = output + if model is None: + return None, None else: - model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16).cuda() + tokenizer = load_tokenizer(model_name, model) - # FlexGen - elif shared.args.flexgen: - # Initialize environment - env = ExecutionEnv.create(shared.args.disk_cache_dir) - - # Offloading policy - policy = Policy(1, 1, - shared.args.percent[0], shared.args.percent[1], - shared.args.percent[2], shared.args.percent[3], - shared.args.percent[4], shared.args.percent[5], - overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight, - cpu_cache_compute=False, attn_sparsity=1.0, - compress_weight=shared.args.compress_weight, - comp_weight_config=CompressionConfig( - num_bits=4, group_size=64, - group_dim=0, symmetric=False), - compress_cache=False, - comp_cache_config=CompressionConfig( - num_bits=4, group_size=64, - group_dim=2, symmetric=False)) - - model = OptLM(f"facebook/{shared.model_name}", env, "models", policy) + # Hijack attention with xformers + 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") + return model, tokenizer - # DeepSpeed ZeRO-3 - elif shared.args.deepspeed: - model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.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 - print(f"DeepSpeed ZeRO-3 is enabled: {is_deepspeed_zero3_enabled()}") - # RMKV model (not on HuggingFace) - elif shared.is_RWKV: - from modules.RWKV import RWKVModel, RWKVTokenizer +def load_tokenizer(model_name, model): + tokenizer = None + if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists(): + tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/")) + elif type(model) is transformers.LlamaForCausalLM: + # Try to load an universal LLaMA tokenizer + 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}...") + tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True) + return tokenizer - model = RWKVModel.from_pretrained(Path(f'models/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda") - tokenizer = RWKVTokenizer.from_pretrained(Path('models')) + # Otherwise, load it from the model folder and hope that these + # are not outdated tokenizer files. + tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True) + try: + tokenizer.eos_token_id = 2 + tokenizer.bos_token_id = 1 + tokenizer.pad_token_id = 0 + except: + pass + else: + path_to_model = Path(f"{shared.args.model_dir}/{model_name}/") + if path_to_model.exists(): + tokenizer = AutoTokenizer.from_pretrained(path_to_model, trust_remote_code=shared.args.trust_remote_code) - return model, tokenizer + return tokenizer - # Quantized model - elif shared.args.gptq_bits > 0: - from modules.GPTQ_loader import load_quantized - model = load_quantized(model_name) +def huggingface_loader(model_name): + if shared.model_type == 'chatglm': + LoaderClass = AutoModel + elif shared.model_type == 'HF_seq2seq': + LoaderClass = AutoModelForSeq2SeqLM + else: + LoaderClass = AutoModelForCausalLM + + # Load the model in simple 16-bit mode by default + if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.auto_devices, shared.args.disk, shared.args.deepspeed, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None]): + model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=shared.args.trust_remote_code) + if torch.has_mps: + device = torch.device('mps') + model = model.to(device) + else: + model = model.cuda() + + # DeepSpeed ZeRO-3 + elif shared.args.deepspeed: + 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()}") # Custom else: - command = "AutoModelForCausalLM.from_pretrained" - params = ["low_cpu_mem_usage=True"] - if not shared.args.cpu and not torch.cuda.is_available(): - print("Warning: no GPU has been detected.\nFalling back to CPU mode.\n") + params = { + "low_cpu_mem_usage": True, + "trust_remote_code": shared.args.trust_remote_code + } + + 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.") shared.args.cpu = True if shared.args.cpu: - params.append("low_cpu_mem_usage=True") - params.append("torch_dtype=torch.float32") + params["torch_dtype"] = torch.float32 else: - params.append("device_map='auto'") - params.append("load_in_8bit=True" if shared.args.load_in_8bit else "torch_dtype=torch.bfloat16" if shared.args.bf16 else "torch_dtype=torch.float16") - - if shared.args.gpu_memory: - memory_map = shared.args.gpu_memory - max_memory = f"max_memory={{0: '{memory_map[0]}GiB'" - for i in range(1, len(memory_map)): - max_memory += (f", {i}: '{memory_map[i]}GiB'") - max_memory += (f", 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}") - params.append(max_memory) - elif not shared.args.load_in_8bit: - total_mem = (torch.cuda.get_device_properties(0).total_memory/(1024*1024)) - suggestion = round((total_mem-1000)/1000)*1000 - if total_mem-suggestion < 800: - suggestion -= 1000 - suggestion = int(round(suggestion/1000)) - print(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m") - params.append(f"max_memory={{0: '{suggestion}GiB', 'cpu': '{shared.args.cpu_memory or '99'}GiB'}}") + params["device_map"] = 'auto' + if shared.args.load_in_8bit and any((shared.args.auto_devices, shared.args.gpu_memory)): + params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) + elif shared.args.load_in_8bit: + params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True) + elif shared.args.bf16: + params["torch_dtype"] = torch.bfloat16 + else: + params["torch_dtype"] = torch.float16 + + params['max_memory'] = get_max_memory_dict() if shared.args.disk: - params.append(f"offload_folder='{shared.args.disk_cache_dir}'") + params["offload_folder"] = shared.args.disk_cache_dir + + checkpoint = Path(f'{shared.args.model_dir}/{model_name}') + if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto': + config = AutoConfig.from_pretrained(checkpoint, trust_remote_code=shared.args.trust_remote_code) + with init_empty_weights(): + model = LoaderClass.from_config(config, trust_remote_code=shared.args.trust_remote_code) + + model.tie_weights() + params['device_map'] = infer_auto_device_map( + model, + dtype=torch.int8, + max_memory=params['max_memory'], + no_split_module_classes=model._no_split_modules + ) + + model = LoaderClass.from_pretrained(checkpoint, **params) + + return model + + +def flexgen_loader(model_name): + from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy + + # Initialize environment + env = ExecutionEnv.create(shared.args.disk_cache_dir) - command = f"{command}(Path(f'models/{shared.model_name}'), {', '.join(set(params))})" - model = eval(command) + # Offloading policy + policy = Policy(1, 1, + shared.args.percent[0], shared.args.percent[1], + shared.args.percent[2], shared.args.percent[3], + shared.args.percent[4], shared.args.percent[5], + overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight, + cpu_cache_compute=False, attn_sparsity=1.0, + compress_weight=shared.args.compress_weight, + comp_weight_config=CompressionConfig( + num_bits=4, group_size=64, + group_dim=0, symmetric=False), + compress_cache=False, + comp_cache_config=CompressionConfig( + num_bits=4, group_size=64, + group_dim=2, symmetric=False)) - # Loading the tokenizer - if shared.model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')) and Path("models/gpt-j-6B/").exists(): - tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/")) + model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy) + return model + + +def RWKV_loader(model_name): + from modules.RWKV import RWKVModel, RWKVTokenizer + + model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda") + tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir)) + return model, tokenizer + + +def llamacpp_loader(model_name): + from modules.llamacpp_model import LlamaCppModel + + path = Path(f'{shared.args.model_dir}/{model_name}') + if path.is_file(): + model_file = path else: - tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{shared.model_name}/")) - tokenizer.truncation_side = 'left' + model_file = list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))[0] - print(f"Loaded the model in {(time.time()-t0):.2f} seconds.") + logging.info(f"llama.cpp weights detected: {model_file}\n") + model, tokenizer = LlamaCppModel.from_pretrained(model_file) return model, tokenizer + +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.") + from modules.monkey_patch_gptq_lora import load_model_llama + + model, _ = load_model_llama(model_name) + + # No monkey patch + else: + import modules.GPTQ_loader + + model = modules.GPTQ_loader.load_quantized(model_name) + + return model + + +def AutoGPTQ_loader(model_name): + import modules.AutoGPTQ_loader + + return modules.AutoGPTQ_loader.load_quantized(model_name) + + +def get_max_memory_dict(): + max_memory = {} + if shared.args.gpu_memory: + memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) + for i in range(len(memory_map)): + max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] + + max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' + max_memory['cpu'] = f'{max_cpu_memory}GiB' if not re.match('.*ib$', max_cpu_memory.lower()) else max_cpu_memory + + # If --auto-devices is provided standalone, try to get a reasonable value + # for the maximum memory of device :0 + elif shared.args.auto_devices: + total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024)) + suggestion = round((total_mem - 1000) / 1000) * 1000 + if total_mem - suggestion < 800: + 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.") + 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 + + +def clear_torch_cache(): + gc.collect() + if not shared.args.cpu: + torch.cuda.empty_cache() + + +def unload_model(): + shared.model = shared.tokenizer = None + clear_torch_cache() + + +def reload_model(): + unload_model() + shared.model, shared.tokenizer = load_model(shared.model_name) + + def load_soft_prompt(name): if name == 'None': shared.soft_prompt = False @@ -148,20 +333,21 @@ def load_soft_prompt(name): zf.extract('tensor.npy') zf.extract('meta.json') j = json.loads(open('meta.json', 'r').read()) - print(f"\nLoading the softprompt \"{name}\".") + logging.info(f"\nLoading the softprompt \"{name}\".") for field in j: if field != 'name': if type(j[field]) is list: - print(f"{field}: {', '.join(j[field])}") + logging.info(f"{field}: {', '.join(j[field])}") else: - print(f"{field}: {j[field]}") - print() + logging.info(f"{field}: {j[field]}") + + logging.info() tensor = np.load('tensor.npy') Path('tensor.npy').unlink() Path('meta.json').unlink() + tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype) tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1])) - shared.soft_prompt = True shared.soft_prompt_tensor = tensor diff --git a/modules/monkey_patch_gptq_lora.py b/modules/monkey_patch_gptq_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..a37e790671f513b6a5744cc469424a967a75d43b --- /dev/null +++ b/modules/monkey_patch_gptq_lora.py @@ -0,0 +1,39 @@ +# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit"))) + +import autograd_4bit +from amp_wrapper import AMPWrapper +from autograd_4bit import (Autograd4bitQuantLinear, + load_llama_model_4bit_low_ram) +from monkeypatch.peft_tuners_lora_monkey_patch import ( + Linear4bitLt, replace_peft_model_with_gptq_lora_model) + +from modules import shared +from modules.GPTQ_loader import find_quantized_model_file + +replace_peft_model_with_gptq_lora_model() + + +def load_model_llama(model_name): + config_path = str(Path(f'{shared.args.model_dir}/{model_name}')) + model_path = str(find_quantized_model_file(model_name)) + model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False) + for n, m in model.named_modules(): + if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt): + if m.is_v1_model: + m.zeros = m.zeros.half() + m.scales = m.scales.half() + m.bias = m.bias.half() + + autograd_4bit.use_new = True + autograd_4bit.auto_switch = True + + model.half() + wrapper = AMPWrapper(model) + wrapper.apply_generate() + + return model, tokenizer diff --git a/modules/shared.py b/modules/shared.py index ea2eb50b7f586e5c562bf2e7c75429c91f21ec6c..7f945366cf7aca78b8a2a87b749964d038107f21 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -1,11 +1,17 @@ import argparse +import logging +from collections import OrderedDict +from pathlib import Path + +import yaml model = None tokenizer = None -model_name = "" +model_name = "None" +model_type = None +lora_names = [] soft_prompt_tensor = None soft_prompt = False -is_RWKV = False # Chat variables history = {'internal': [], 'visible': []} @@ -16,42 +22,61 @@ processing_message = '*Is typing...*' # UI elements (buttons, sliders, HTML, etc) gradio = {} -# Generation input parameters -input_params = [] +# For keeping the values of UI elements on page reload +persistent_interface_state = {} + +input_params = [] # Generation input parameters +reload_inputs = [] # Parameters for reloading the chat interface + +# For restarting the interface +need_restart = False settings = { + 'autoload_model': True, 'max_new_tokens': 200, 'max_new_tokens_min': 1, 'max_new_tokens_max': 2000, - 'name1': 'Person 1', - 'name2': 'Person 2', - 'context': 'This is a conversation between two people.', - 'stop_at_newline': True, + 'seed': -1, + 'character': 'None', + 'name1': 'You', + 'name2': 'Assistant', + 'context': 'This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.', + 'greeting': '', + 'turn_template': '', + 'custom_stopping_strings': '', + 'stop_at_newline': False, + 'add_bos_token': True, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'truncation_length': 2048, + 'truncation_length_min': 0, + 'truncation_length_max': 8192, + 'mode': 'chat', + 'chat_style': 'cai-chat', + 'instruction_template': 'None', + 'chat-instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>', 'chat_prompt_size': 2048, 'chat_prompt_size_min': 0, 'chat_prompt_size_max': 2048, 'chat_generation_attempts': 1, 'chat_generation_attempts_min': 1, - 'chat_generation_attempts_max': 5, - 'name1_pygmalion': 'You', - 'name2_pygmalion': 'Kawaii', - 'context_pygmalion': "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n", - 'stop_at_newline_pygmalion': False, + 'chat_generation_attempts_max': 10, 'default_extensions': [], 'chat_default_extensions': ["gallery"], 'presets': { - 'default': 'NovelAI-Sphinx Moth', - 'pygmalion-*': 'Pygmalion', - 'RWKV-*': 'Naive', + 'default': 'Default', + '.*(alpaca|llama|llava)': "LLaMA-Precise", + '.*pygmalion': 'NovelAI-Storywriter', + '.*RWKV': 'Naive', + '.*moss': 'MOSS', }, 'prompts': { - 'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:', - '^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n', - '(rosey|chip|joi)_.*_instruct.*': 'User: \n', - 'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>' + 'default': 'QA', + '.*(gpt4chan|gpt-4chan|4chan)': 'GPT-4chan', } } + def str2bool(v): if isinstance(v, bool): return v @@ -62,42 +87,144 @@ def str2bool(v): else: raise argparse.ArgumentTypeError('Boolean value expected.') -parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog,max_help_position=54)) -parser.add_argument('--model', type=str, help='Name of the model to load by default.') + +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) + +# Basic settings parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.') -parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode.') -parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.') -parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.') -parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') -parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.') -parser.add_argument('--gptq-bits', type=int, default=0, help='Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.') -parser.add_argument('--gptq-model-type', type=str, help='Model type of pre-quantized model. Currently only LLaMa and OPT are supported.') -parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') +parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.') +parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.') +parser.add_argument('--model', type=str, help='Name of the model to load by default.') +parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.') +parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models") +parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras") +parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.') +parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.') +parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.') +parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.') +parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') + +# Accelerate/transformers +parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.') parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') +parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.') +parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.') parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.') parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".') -parser.add_argument('--gpu-memory', type=int, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs.') -parser.add_argument('--cpu-memory', type=int, help='Maximum CPU memory in GiB to allocate for offloaded weights. Must be an integer number. Defaults to 99.') +parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') +parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') +parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.') +parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.") +parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.") +parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM.") + +# llama.cpp +parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.') +parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.') +parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.') +parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.') +parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.') +parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.') + +# GPTQ +parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.') +parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.') +parser.add_argument('--groupsize', type=int, default=-1, help='Group size.') +parser.add_argument('--pre_layer', type=int, nargs="+", help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.') +parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.') +parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') +parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable quant attention.') +parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.') +parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.') + +# AutoGPTQ +parser.add_argument('--autogptq', action='store_true', help='Use AutoGPTQ for loading quantized models instead of the internal GPTQ loader.') +parser.add_argument('--triton', action='store_true', help='Use triton.') + +# FlexGen parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.') parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).') parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.") parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).") + +# DeepSpeed parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.') parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.') parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.') + +# RWKV parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".') parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.') -parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.') -parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.') -parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.') + +# Gradio parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.') +parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.') parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.') parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.') parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') -parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') +parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None) + +# API +parser.add_argument('--api', action='store_true', help='Enable the API extension.') +parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.') +parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.') +parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') + +# Multimodal +parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.') + args = parser.parse_args() +args_defaults = parser.parse_args([]) + +# Deprecation warnings for parameters that have been renamed +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.") + 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.") +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.") + + +def add_extension(name): + if args.extensions is None: + args.extensions = [name] + elif 'api' not in args.extensions: + args.extensions.append(name) + + +# Activating the API extension +if args.api or args.public_api: + add_extension('api') + +# Activating the multimodal extension +if args.multimodal_pipeline is not None: + add_extension('multimodal') + + +def is_chat(): + return args.chat + + +# Loading model-specific settings +with Path(f'{args.model_dir}/config.yaml') as p: + if p.exists(): + model_config = yaml.safe_load(open(p, 'r').read()) + else: + model_config = {} + +# Applying user-defined model settings +with Path(f'{args.model_dir}/config-user.yaml') as p: + if p.exists(): + user_config = yaml.safe_load(open(p, 'r').read()) + for k in user_config: + if k in model_config: + model_config[k].update(user_config[k]) + else: + model_config[k] = user_config[k] -# Provisional, this will be deleted later -if args.load_in_4bit: - print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n") - args.gptq_bits = 4 +model_config = OrderedDict(model_config) diff --git a/modules/text_generation.py b/modules/text_generation.py index d64481b24ec4542e55de1605a6181f97d9a50de9..7bc5d25dc49c96c02a2f5462c8f8f07adb4c4185 100644 --- a/modules/text_generation.py +++ b/modules/text_generation.py @@ -1,6 +1,9 @@ -import gc +import ast +import logging +import random import re import time +import traceback import numpy as np import torch @@ -11,55 +14,81 @@ 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.models import local_rank +from modules.models import clear_torch_cache, local_rank -def get_max_prompt_length(tokens): - max_length = 2048-tokens +def get_max_prompt_length(state): + max_length = state['truncation_length'] - state['max_new_tokens'] if shared.soft_prompt: max_length -= shared.soft_prompt_tensor.shape[1] + return max_length -def encode(prompt, tokens_to_generate=0, add_special_tokens=True): - if shared.is_RWKV: + +def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None): + if shared.model_type in ['rwkv', 'llamacpp']: input_ids = shared.tokenizer.encode(str(prompt)) input_ids = np.array(input_ids).reshape(1, len(input_ids)) return input_ids else: - input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', truncation=True, max_length=get_max_prompt_length(tokens_to_generate), add_special_tokens=add_special_tokens) - if shared.args.cpu: - return input_ids - elif shared.args.flexgen: - return input_ids.numpy() - elif shared.args.deepspeed: - return input_ids.to(device=local_rank) - else: - return input_ids.cuda() + input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens) + + # This is a hack for making replies more creative. + if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id: + input_ids = input_ids[:, 1:] + + # Llama adds this extra token when the first character is '\n', and this + # compromises the stopping criteria, so we just remove it + if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871: + input_ids = input_ids[:, 1:] + + # Handling truncation + if truncation_length is not None: + input_ids = input_ids[:, -truncation_length:] -def decode(output_ids): - # Open Assistant relies on special tokens like <|endoftext|> - if re.match('oasst-*', shared.model_name.lower()): - return shared.tokenizer.decode(output_ids, skip_special_tokens=False) + if shared.model_type in ['rwkv', 'llamacpp'] or shared.args.cpu: + return input_ids + elif shared.args.flexgen: + return input_ids.numpy() + elif shared.args.deepspeed: + return input_ids.to(device=local_rank) + elif torch.has_mps: + device = torch.device('mps') + return input_ids.to(device) else: - reply = shared.tokenizer.decode(output_ids, skip_special_tokens=True) - reply = reply.replace(r'<|endoftext|>', '') - return reply + return input_ids.cuda() + + +def get_encoded_length(prompt): + length_after_extensions = apply_extensions('tokenized_length', prompt) + if length_after_extensions is not None: + return length_after_extensions + + return len(encode(prompt)[0]) + + +def decode(output_ids, skip_special_tokens=True): + return shared.tokenizer.decode(output_ids, skip_special_tokens) + def generate_softprompt_input_tensors(input_ids): inputs_embeds = shared.model.transformer.wte(input_ids) inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1) filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device) - #filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens + # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens return inputs_embeds, filler_input_ids + # Removes empty replies from gpt4chan outputs def fix_gpt4chan(s): for i in range(10): s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s) s = re.sub("--- [0-9]*\n *\n---", "---", s) s = re.sub("--- [0-9]*\n\n\n---", "---", s) + return s + # Fix the LaTeX equations in galactica def fix_galactica(s): s = s.replace(r'\[', r'$') @@ -71,118 +100,161 @@ def fix_galactica(s): s = re.sub(r"\n{3,}", "\n\n", s) return s + +def get_reply_from_output_ids(output_ids, input_ids, original_question, state, is_chat=False): + if shared.model_type == 'HF_seq2seq': + reply = decode(output_ids, state['skip_special_tokens']) + 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 shared.tokenizer.convert_ids_to_tokens(int(output_ids[-new_tokens])).startswith('▁'): + reply = ' ' + reply + + if not is_chat: + reply = apply_extensions('output', reply) + + return reply + + def formatted_outputs(reply, model_name): - if not (shared.args.chat or shared.args.cai_chat): - if model_name.lower().startswith('galactica'): - reply = fix_galactica(reply) - return reply, reply, generate_basic_html(reply) - elif model_name.lower().startswith(('gpt4chan', 'gpt-4chan', '4chan')): - reply = fix_gpt4chan(reply) - return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply) - else: - return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply) + if shared.model_type == 'galactica': + reply = fix_galactica(reply) + return reply, reply, generate_basic_html(reply) + elif shared.model_type == 'gpt4chan': + reply = fix_gpt4chan(reply) + return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply) else: - return reply + return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply) -def clear_torch_cache(): - gc.collect() - if not shared.args.cpu: - torch.cuda.empty_cache() -def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, eos_token=None, stopping_string=None): - clear_torch_cache() - t0 = time.time() +def set_manual_seed(seed): + seed = int(seed) + if seed == -1: + seed = random.randint(1, 2**31) + + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + return seed + + +def stop_everything_event(): + shared.stop_everything = True + + +def generate_reply_wrapper(question, state, eos_token=None, stopping_strings=None): + for reply in generate_reply(question, state, eos_token, stopping_strings, is_chat=False): + if shared.model_type not in ['HF_seq2seq']: + reply = question + reply - # These models are not part of Hugging Face, so we handle them - # separately and terminate the function call earlier - if shared.is_RWKV: - try: - if shared.args.no_stream: - reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k) - yield formatted_outputs(reply, shared.model_name) - else: - yield formatted_outputs(question, shared.model_name) - # RWKV has proper streaming, which is very nice. - # No need to generate 8 tokens at a time. - for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k): - yield formatted_outputs(reply, shared.model_name) - finally: - t1 = time.time() - output = encode(reply)[0] - input_ids = encode(question) - print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)") + yield formatted_outputs(reply, shared.model_name) + + +def generate_reply(question, state, eos_token=None, stopping_strings=None, is_chat=False): + state = apply_extensions('state', state) + 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.") + yield question return + if shared.model_type in ['rwkv', 'llamacpp']: + generate_func = generate_reply_custom + elif shared.args.flexgen: + generate_func = generate_reply_flexgen + else: + generate_func = generate_reply_HF + + # Preparing the input original_question = question - if not (shared.args.chat or shared.args.cai_chat): - question = apply_extensions(question, "input") + if not is_chat: + question = apply_extensions('input', question) + if shared.args.verbose: - print(f"\n\n{question}\n--------------------\n") + print(f'\n\n{question}\n--------------------\n') - input_ids = encode(question, max_new_tokens) - original_input_ids = input_ids + shared.stop_everything = False + clear_torch_cache() + seed = set_manual_seed(state['seed']) + for reply in generate_func(question, original_question, seed, state, eos_token, stopping_strings, is_chat=is_chat): + yield reply + + +def generate_reply_HF(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False): + generate_params = {} + for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']: + generate_params[k] = state[k] + + if state['ban_eos_token']: + generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id] + + if shared.args.no_cache: + generate_params.update({'use_cache': False}) + + if shared.args.deepspeed: + generate_params.update({'synced_gpus': True}) + + # Encode the input + input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) output = input_ids[0] - cuda = "" if (shared.args.cpu or shared.args.deepspeed or shared.args.flexgen) else ".cuda()" + cuda = not any((shared.args.cpu, shared.args.deepspeed)) + + # Find the eos tokens eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] if eos_token is not None: eos_token_ids.append(int(encode(eos_token)[0][-1])) - stopping_criteria_list = transformers.StoppingCriteriaList() - if stopping_string is not None: - # Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py - t = encode(stopping_string, 0, add_special_tokens=False) - stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0]))) - - if not shared.args.flexgen: - generate_params = [ - f"max_new_tokens=max_new_tokens", - f"eos_token_id={eos_token_ids}", - f"stopping_criteria=stopping_criteria_list", - f"do_sample={do_sample}", - f"temperature={temperature}", - f"top_p={top_p}", - f"typical_p={typical_p}", - f"repetition_penalty={repetition_penalty}", - f"top_k={top_k}", - f"min_length={min_length if shared.args.no_stream else 0}", - f"no_repeat_ngram_size={no_repeat_ngram_size}", - f"num_beams={num_beams}", - f"penalty_alpha={penalty_alpha}", - f"length_penalty={length_penalty}", - f"early_stopping={early_stopping}", - ] - else: - generate_params = [ - f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}", - f"do_sample={do_sample}", - f"temperature={temperature}", - f"stop={eos_token_ids[-1]}", - ] - if shared.args.deepspeed: - generate_params.append("synced_gpus=True") + + # Add the encoded tokens to generate_params if shared.soft_prompt: inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) - generate_params.insert(0, "inputs_embeds=inputs_embeds") - generate_params.insert(0, "inputs=filler_input_ids") + question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds) + original_input_ids = input_ids + generate_params.update({'inputs_embeds': inputs_embeds}) + generate_params.update({'inputs': filler_input_ids}) else: - generate_params.insert(0, "inputs=input_ids") + question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None) + original_input_ids = input_ids + generate_params.update({'inputs': input_ids}) + if inputs_embeds is not None: + generate_params.update({'inputs_embeds': inputs_embeds}) + + # Create the StoppingCriteriaList with the stopping strings (needs to be done after tokenizer extensions) + stopping_criteria_list = transformers.StoppingCriteriaList() + for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")): + if type(st) is list and len(st) > 0: + sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st] + stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0]))) + break + # Update generate_params with the eos token and the stopping strings + generate_params['eos_token_id'] = eos_token_ids + generate_params['stopping_criteria'] = stopping_criteria_list + + t0 = time.time() try: + if not is_chat and shared.model_type != 'HF_seq2seq': + yield '' + # Generate the entire reply at once. - if shared.args.no_stream: + if not state['stream']: with torch.no_grad(): - output = eval(f"shared.model.generate({', '.join(generate_params)}){cuda}")[0] + output = shared.model.generate(**generate_params)[0] + if cuda: + output = output.cuda() + if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) - reply = decode(output) - if not (shared.args.chat or shared.args.cai_chat): - reply = original_question + apply_extensions(reply[len(question):], "output") - - yield formatted_outputs(reply, shared.model_name) + yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat) # Stream the reply 1 token at a time. # This is based on the trick of using 'stopping_criteria' to create an iterator. - elif not shared.args.flexgen: + else: def generate_with_callback(callback=None, **kwargs): kwargs['stopping_criteria'].append(Stream(callback_func=callback)) @@ -193,46 +265,120 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi def generate_with_streaming(**kwargs): return Iteratorize(generate_with_callback, kwargs, callback=None) - yield formatted_outputs(original_question, shared.model_name) - with eval(f"generate_with_streaming({', '.join(generate_params)})") as generator: + with generate_with_streaming(**generate_params) as generator: for output in generator: if shared.soft_prompt: output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) - reply = decode(output) - - if not (shared.args.chat or shared.args.cai_chat): - reply = original_question + apply_extensions(reply[len(question):], "output") + yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat) if output[-1] in eos_token_ids: break - yield formatted_outputs(reply, shared.model_name) - yield formatted_outputs(reply, shared.model_name) + except Exception: + traceback.print_exc() + finally: + t1 = time.time() + original_tokens = len(original_input_ids[0]) + new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0) + print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') + return + + +def generate_reply_custom(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False): + seed = set_manual_seed(state['seed']) + generate_params = {'token_count': state['max_new_tokens']} + for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']: + generate_params[k] = state[k] + + t0 = time.time() + try: + if not is_chat: + yield '' + + if not state['stream']: + reply = shared.model.generate(context=question, **generate_params) + if not is_chat: + reply = apply_extensions('output', reply) + + yield reply + else: + for reply in shared.model.generate_with_streaming(context=question, **generate_params): + if not is_chat: + reply = apply_extensions('output', reply) + + yield reply + + except Exception: + traceback.print_exc() + finally: + t1 = time.time() + original_tokens = len(encode(original_question)[0]) + new_tokens = len(encode(original_question + reply)[0]) - original_tokens + print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') + return + + +def generate_reply_flexgen(question, original_question, seed, state, eos_token=None, stopping_strings=None, is_chat=False): + generate_params = {} + for k in ['max_new_tokens', 'do_sample', 'temperature']: + generate_params[k] = state[k] + + if state['stream']: + generate_params['max_new_tokens'] = 8 + + # Encode the input + input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) + output = input_ids[0] + + # Find the eos tokens + eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] + if eos_token is not None: + eos_token_ids.append(int(encode(eos_token)[0][-1])) + + # Add the encoded tokens to generate_params + question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None) + original_input_ids = input_ids + generate_params.update({'inputs': input_ids}) + if inputs_embeds is not None: + generate_params.update({'inputs_embeds': inputs_embeds}) + + # Update generate_params with the eos token and the stopping strings + generate_params['stop'] = eos_token_ids[-1] + + t0 = time.time() + try: + if not is_chat: + yield '' + + # Generate the entire reply at once. + if not state['stream']: + with torch.no_grad(): + output = shared.model.generate(**generate_params)[0] + + yield get_reply_from_output_ids(output, input_ids, original_question, state, is_chat=is_chat) # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria' else: - for i in range(max_new_tokens//8+1): + for i in range(state['max_new_tokens'] // 8 + 1): + if shared.stop_everything: + break + clear_torch_cache() with torch.no_grad(): - output = eval(f"shared.model.generate({', '.join(generate_params)})")[0] - if shared.soft_prompt: - output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) - reply = decode(output) - - if not (shared.args.chat or shared.args.cai_chat): - reply = original_question + apply_extensions(reply[len(question):], "output") + output = shared.model.generate(**generate_params)[0] if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): break - yield formatted_outputs(reply, shared.model_name) + yield get_reply_from_output_ids(output, original_input_ids, original_question, state) input_ids = np.reshape(output, (1, output.shape[0])) - if shared.soft_prompt: - inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) - - yield formatted_outputs(reply, shared.model_name) + generate_params.update({'inputs': input_ids}) + except Exception: + traceback.print_exc() finally: t1 = time.time() - print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)") + original_tokens = len(original_input_ids[0]) + new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0) + print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') return diff --git a/modules/training.py b/modules/training.py new file mode 100644 index 0000000000000000000000000000000000000000..e2410edcccc0d66f87508534d9df5bf7bdb18f3f --- /dev/null +++ b/modules/training.py @@ -0,0 +1,491 @@ +import json +import logging +import math +import sys +import threading +import time +import traceback +from pathlib import Path + +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, + set_peft_model_state_dict) + +from modules import shared, ui, utils +from modules.evaluate import calculate_perplexity, generate_markdown_table, save_past_evaluations + + +# This mapping is from a very recent commit, not yet released. +# If not available, default to a backup map for some common model types. +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 + 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_CLASSES = { + "LlamaForCausalLM": "llama", + "OPTForCausalLM": "opt", + "GPTJForCausalLM": "gptj", + "GPTNeoXForCausalLM": "gpt_neox" + } + +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"] + + +def create_train_interface(): + with gr.Tab('Train LoRA', elem_id='lora-train-tab'): + gr.Markdown("Confused? [[Click here for a guide]](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Training-LoRAs.md)") + + with gr.Row(): + lora_name = gr.Textbox(label='Name', info='The name of your new LoRA file') + always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name given is the same as an existing file, checking this will replace that file. Leaving unchecked will load that file and continue from it (must use the same rank value as the original had).') + save_steps = gr.Number(label='Save every n steps', value=0, info='If above 0, a checkpoint of the LoRA will be saved every time this many steps pass.') + + with gr.Row(): + copy_from = gr.Dropdown(label='Copy parameters from', value='None', choices=utils.get_available_loras()) + ui.create_refresh_button(copy_from, lambda: None, lambda: {'choices': utils.get_available_loras()}, 'refresh-button') + + with gr.Row(): + # TODO: Implement multi-device support. + micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.') + batch_size = gr.Slider(label='Batch Size', value=128, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.') + + with gr.Row(): + epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.') + learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='Learning rate, in scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.') + lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.') + + # TODO: What is the actual maximum rank? Likely distinct per model. This might be better to somehow be on a log scale. + lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='LoRA Rank, or dimension count. Higher values produce a larger file with better control over the model\'s content. Smaller values produce a smaller file with less overall control. Small values like 4 or 8 are great for stylistic guidance, higher values like 128 or 256 are good for teaching content upgrades, extremely high values (1024+) are difficult to train but may improve fine-detail learning for large datasets. Higher ranks also require higher VRAM.') + lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='LoRA Alpha. This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.') + + cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.') + + with gr.Tab(label='Formatted Dataset'): + with gr.Row(): + dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.') + ui.create_refresh_button(dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button') + eval_dataset = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.') + ui.create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'json')}, 'refresh-button') + format = gr.Dropdown(choices=utils.get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.') + ui.create_refresh_button(format, lambda: None, lambda: {'choices': utils.get_datasets('training/formats', 'json')}, 'refresh-button') + + eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.') + + with gr.Tab(label="Raw text file"): + with gr.Row(): + raw_text_file = gr.Dropdown(choices=utils.get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The raw text file to use for training.') + ui.create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': utils.get_datasets('training/datasets', 'txt')}, 'refresh-button') + + with gr.Row(): + overlap_len = gr.Slider(label='Overlap Length', minimum=0, maximum=512, value=128, step=16, info='Overlap length - ie how many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length below). Setting overlap to exactly half the cutoff length may be ideal.') + newline_favor_len = gr.Slider(label='Prefer Newline Cut Length', minimum=0, maximum=512, value=128, step=16, info='Length (in characters, not tokens) of the maximum distance to shift an overlap cut by to ensure chunks cut at newlines. If too low, cuts may occur in the middle of lines.') + + with gr.Accordion(label='Advanced Options', open=False): + lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.') + warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate will be lower than normal. This helps the trainer prepare the model and precompute statistics to improve the quality of training after the start.') + optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.') + + with gr.Row(): + higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.') + + with gr.Row(): + start_button = gr.Button("Start LoRA Training") + stop_button = gr.Button("Interrupt") + + output = gr.Markdown(value="Ready") + + with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'): + with gr.Row(): + with gr.Column(): + models = gr.Dropdown(utils.get_available_models(), label='Models', multiselect=True) + evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + utils.get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.') + with gr.Row(): + stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.') + max_length = gr.Slider(label='max_length', minimum=0, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.') + + with gr.Row(): + start_current_evaluation = gr.Button("Evaluate loaded model") + start_evaluation = gr.Button("Evaluate selected models") + stop_evaluation = gr.Button("Interrupt") + + with gr.Column(): + evaluation_log = gr.Markdown(value='') + + evaluation_table = gr.Dataframe(value=generate_markdown_table(), interactive=True) + save_comments = gr.Button('Save comments') + + # Training events + all_params = [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] + copy_from.change(do_copy_params, [copy_from] + all_params, all_params) + start_button.click(do_train, all_params, output) + stop_button.click(do_interrupt, None, None, queue=False) + higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha]) + + # Evaluation events. For some reason, the interrupt event + # doesn't work with the .then() syntax, so I write them one + # by one in this ugly but functional way. + ev = start_evaluation.click(calculate_perplexity, [models, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False) + start_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False) + + tmp = gr.State('') + start_current_evaluation.click(lambda: ['current model'], None, tmp) + ev_cur = start_current_evaluation.click(calculate_perplexity, [tmp, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False) + start_current_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False) + + stop_evaluation.click(None, None, None, cancels=[ev, ev_cur], queue=False) + save_comments.click( + save_past_evaluations, evaluation_table, None).then( + lambda: "Comments saved.", None, evaluation_log, show_progress=False) + + +def do_interrupt(): + global WANT_INTERRUPT + WANT_INTERRUPT = True + + +def do_copy_params(lora_name: str, *args): + f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json" + if Path(f_name).is_file(): + with open(f_name, 'r', encoding='utf-8') as format_file: + params: dict[str, str] = json.load(format_file) + else: + params = {} + + result = list() + for i in range(0, len(PARAMETERS)): + key = PARAMETERS[i] + if key in params: + result.append(params[key]) + else: + result.append(args[i]) + + return result + + +def change_rank_limit(use_higher_ranks: bool): + mult = 2 if use_higher_ranks else 1 + return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"} + + +def clean_path(base_path: str, path: str): + """"Strips unusual symbols and forcibly builds a path as relative to the intended directory.""" + # TODO: Probably could do with a security audit to guarantee there's no ways this can be bypassed to target an unwanted path. + # Or swap it to a strict whitelist of [a-zA-Z_0-9] + path = path.replace('\\', '/').replace('..', '_') + if base_path is None: + return path + + return f'{Path(base_path).absolute()}/{path}' + + +def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str): + + if shared.args.monkey_patch: + from monkeypatch.peft_tuners_lora_monkey_patch import \ + replace_peft_model_with_gptq_lora_model + replace_peft_model_with_gptq_lora_model() + + global WANT_INTERRUPT + WANT_INTERRUPT = False + + # == Input validation / processing == + yield "Prepping..." + lora_file_path = clean_path(None, lora_name) + if lora_file_path.strip() == '': + yield "Missing or invalid LoRA file name input." + return + + lora_file_path = f"{shared.args.lora_dir}/{lora_file_path}" + actual_lr = float(learning_rate) + model_type = type(shared.model).__name__ + + if model_type in MODEL_CLASSES: + model_id = MODEL_CLASSES[model_type] + else: + model_id = "llama" + 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.") + 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.") + 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})") + + time.sleep(5) + + if shared.args.wbits > 0 and not shared.args.monkey_patch: + yield "LoRA training in 4-bit requires loading with `--monkey-patch`" + return + + 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.") + 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: + yield "Cannot input zeroes." + return + + gradient_accumulation_steps = batch_size // micro_batch_size + shared.tokenizer.pad_token_id = 0 + shared.tokenizer.padding_side = "left" + + def tokenize(prompt): + result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length") + return { + "input_ids": result["input_ids"][:-1], + "attention_mask": result["attention_mask"][:-1], + } + + # == Prep the dataset, format, etc == + if raw_text_file not in ['None', '']: + logging.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() + + tokens = shared.tokenizer.encode(raw_text) + del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM + tokens = list(split_chunks(tokens, cutoff_len - overlap_len)) + for i in range(1, len(tokens)): + tokens[i] = tokens[i - 1][-overlap_len:] + tokens[i] + + text_chunks = [shared.tokenizer.decode(x) for x in tokens] + del tokens + if newline_favor_len > 0: + text_chunks = [cut_chunk_for_newline(x, newline_favor_len) for x in text_chunks] + + train_data = Dataset.from_list([tokenize(x) for x in text_chunks]) + del text_chunks + eval_data = None + + else: + if dataset in ['None', '']: + yield "**Missing dataset choice input, cannot continue.**" + return + + if format in ['None', '']: + yield "**Missing format choice input, cannot continue.**" + return + + with open(clean_path('training/formats', f'{format}.json'), 'r', encoding='utf-8') as formatFile: + format_data: dict[str, str] = json.load(formatFile) + + def generate_prompt(data_point: dict[str, str]): + for options, data in format_data.items(): + if set(options.split(',')) == set(x[0] for x in data_point.items() if (x[1] is not None and len(x[1].strip()) > 0)): + for key, val in data_point.items(): + if val is not None: + data = data.replace(f'%{key}%', val) + return data + raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"') + + def generate_and_tokenize_prompt(data_point): + prompt = generate_prompt(data_point) + return tokenize(prompt) + + logging.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) + + if eval_dataset == 'None': + eval_data = None + else: + eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json')) + eval_data = eval_data['train'].map(generate_and_tokenize_prompt) + + # == Start prepping the model itself == + if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'): + logging.info("Getting model ready...") + prepare_model_for_int8_training(shared.model) + + logging.info("Prepping for training...") + config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + target_modules=model_to_lora_modules[model_id], + lora_dropout=lora_dropout, + bias="none", + task_type="CAUSAL_LM" + ) + + try: + logging.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...") + state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin") + set_peft_model_state_dict(lora_model, state_dict_peft) + except: + yield traceback.format_exc() + return + + if shared.args.monkey_patch: + for n, m in lora_model.named_modules(): + if '4bit' in str(type(m)): + if m.is_v1_model: + m.zeros = m.zeros.half() + + m.scales = m.scales.half() + + class Tracked(): + def __init__(self): + self.current_steps = 0 + self.max_steps = 0 + self.did_save = False + + tracked = Tracked() + actual_save_steps = math.ceil(save_steps / gradient_accumulation_steps) + + class Callbacks(transformers.TrainerCallback): + def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs): + tracked.current_steps = state.global_step * gradient_accumulation_steps + tracked.max_steps = state.max_steps * gradient_accumulation_steps + if WANT_INTERRUPT: + control.should_epoch_stop = True + 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}/") + + 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 + + trainer = transformers.Trainer( + model=lora_model, + train_dataset=train_data, + eval_dataset=eval_data, + args=transformers.TrainingArguments( + per_device_train_batch_size=micro_batch_size, + gradient_accumulation_steps=gradient_accumulation_steps, + warmup_steps=math.ceil(warmup_steps / gradient_accumulation_steps), + num_train_epochs=epochs, + learning_rate=actual_lr, + fp16=False if shared.args.cpu else True, + optim=optimizer, + logging_steps=5, + evaluation_strategy="steps" if eval_data is not None else "no", + eval_steps=math.ceil(eval_steps / gradient_accumulation_steps) if eval_data is not None else None, + save_strategy="steps" if eval_data is not None else "no", + output_dir=lora_file_path, + lr_scheduler_type=lr_scheduler_type, + load_best_model_at_end=eval_data is not None, + # TODO: Enable multi-device support + ddp_find_unused_parameters=None, + no_cuda=shared.args.cpu + ), + data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False), + callbacks=list([Callbacks()]) + ) + + lora_model.config.use_cache = False + + if torch.__version__ >= "2" and sys.platform != "win32": + lora_model = torch.compile(lora_model) + + # == 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) + + # == Main run and monitor loop == + logging.info("Starting training...") + yield "Starting..." + if WANT_INTERRUPT: + yield "Interrupted before start." + return + + def threaded_run(): + 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.") + tracked.did_save = True + + thread = threading.Thread(target=threaded_run) + thread.start() + last_step = 0 + start_time = time.perf_counter() + + while thread.is_alive(): + time.sleep(0.5) + if WANT_INTERRUPT: + yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*" + + elif tracked.current_steps != last_step: + last_step = tracked.current_steps + time_elapsed = time.perf_counter() - start_time + if time_elapsed <= 0: + timer_info = "" + total_time_estimate = 999 + else: + its = tracked.current_steps / time_elapsed + if its > 1: + timer_info = f"`{its:.2f}` it/s" + else: + timer_info = f"`{1.0/its:.2f}` s/it" + + total_time_estimate = (1.0 / its) * (tracked.max_steps) + + yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining" + + # 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...") + lora_model.save_pretrained(lora_file_path) + + if WANT_INTERRUPT: + logging.info("Training interrupted.") + yield f"Interrupted. Incomplete LoRA saved to `{lora_file_path}`" + else: + logging.info("Training complete!") + yield f"Done! LoRA saved to `{lora_file_path}`" + + +def split_chunks(arr, step): + for i in range(0, len(arr), step): + yield arr[i:i + step] + + +def cut_chunk_for_newline(chunk: str, max_length: int): + if '\n' not in chunk: + return chunk + + first_newline = chunk.index('\n') + if first_newline < max_length: + chunk = chunk[first_newline + 1:] + + if '\n' not in chunk: + return chunk + + last_newline = chunk.rindex('\n') + if len(chunk) - last_newline < max_length: + chunk = chunk[:last_newline] + + return chunk + + +def format_time(seconds: float): + if seconds < 120: + return f"`{seconds:.0f}` seconds" + + minutes = seconds / 60 + if minutes < 120: + return f"`{minutes:.0f}` minutes" + + hours = minutes / 60 + return f"`{hours:.0f}` hours" diff --git a/modules/ui.py b/modules/ui.py index bb193e35c11b2a3d474ea89e7567206a3343395a..1e9c4ab0cb4933f59318eab1d823144146d1ccc7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -1,68 +1,66 @@ +from pathlib import Path + import gradio as gr +import torch + +from modules import shared + +with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f: + css = f.read() +with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f: + chat_css = f.read() +with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f: + main_js = f.read() +with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f: + chat_js = f.read() refresh_symbol = '\U0001f504' # 🔄 +theme = gr.themes.Default( + font=['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'], + font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'], +).set( + border_color_primary='#c5c5d2', + button_large_padding='6px 12px', + body_text_color_subdued='#484848', + background_fill_secondary='#eaeaea' +) + + +def list_model_elements(): + elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'wbits', 'groupsize', 'model_type', 'pre_layer', 'threads', 'n_batch', 'no_mmap', 'mlock', 'n_gpu_layers'] + for i in range(torch.cuda.device_count()): + elements.append(f'gpu_memory_{i}') + return elements + + +def list_interface_input_elements(chat=False): + elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu', 'stream'] + if chat: + elements += ['name1', 'name2', 'greeting', 'context', 'chat_prompt_size', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu', 'name1_instruct', 'name2_instruct', 'context_instruct', 'turn_template', 'chat_style', 'chat-instruct_command'] + + elements += list_model_elements() + return elements + + +def gather_interface_values(*args): + output = {} + for i, element in enumerate(shared.input_elements): + output[element] = args[i] + + shared.persistent_interface_state = output + return output + + +def apply_interface_values(state, use_persistent=False): + if use_persistent: + state = shared.persistent_interface_state + + elements = list_interface_input_elements(chat=shared.is_chat()) + if len(state) == 0: + return [gr.update() for k in elements] # Dummy, do nothing + else: + return [state[k] if k in state else gr.update() for k in elements] -css = """ -.tabs.svelte-710i53 { - margin-top: 0 -} -.py-6 { - padding-top: 2.5rem -} -.dark #refresh-button { - background-color: #ffffff1f; -} -#refresh-button { - flex: none; - margin: 0; - padding: 0; - min-width: 50px; - border: none; - box-shadow: none; - border-radius: 10px; - background-color: #0000000d; -} -#download-label, #upload-label { - min-height: 0 -} -#accordion { -} -.dark svg { - fill: white; -} -svg { - display: unset !important; - vertical-align: middle !important; - margin: 5px; -} -ol li p, ul li p { - display: inline-block; -} -""" - -chat_css = """ -.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx { - height: 66.67vh -} -.gradio-container { - max-width: 800px !important; - margin-left: auto !important; - margin-right: auto !important; -} -.w-screen { - width: unset -} -div.svelte-362y77>*, div.svelte-362y77>.form>* { - flex-wrap: nowrap -} -/* fixes the API documentation in chat mode */ -.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h { - display: grid; -} -.pending.svelte-1ed2p3z { - opacity: 1; -} -""" class ToolButton(gr.Button, gr.components.FormComponent): """Small button with single emoji as text, fits inside gradio forms""" @@ -73,6 +71,7 @@ class ToolButton(gr.Button, gr.components.FormComponent): def get_block_name(self): return "button" + def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): def refresh(): refresh_method() diff --git a/modules/utils.py b/modules/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6722022d89003221980ed89cc9e9a0d5e1d7a429 --- /dev/null +++ b/modules/utils.py @@ -0,0 +1,76 @@ +import os +import re +from pathlib import Path + +from modules import shared + + +def atoi(text): + return int(text) if text.isdigit() else text.lower() + + +# Replace multiple string pairs in a string +def replace_all(text, dic): + for i, j in dic.items(): + text = text.replace(i, j) + + return text + + +def natural_keys(text): + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_available_models(): + if shared.args.flexgen: + return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=natural_keys) + else: + return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json', '.yaml'))], key=natural_keys) + + +def get_available_presets(): + return sorted(set((k.stem for k in Path('presets').glob('*.txt'))), key=natural_keys) + + +def get_available_prompts(): + prompts = [] + files = set((k.stem for k in Path('prompts').glob('*.txt'))) + prompts += sorted([k for k in files if re.match('^[0-9]', k)], key=natural_keys, reverse=True) + prompts += sorted([k for k in files if re.match('^[^0-9]', k)], key=natural_keys) + prompts += ['Instruct-' + k for k in get_available_instruction_templates() if k != 'None'] + prompts += ['None'] + return prompts + + +def get_available_characters(): + paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml')) + return ['None'] + sorted(set((k.stem for k in paths if k.stem != "instruction-following")), key=natural_keys) + + +def get_available_instruction_templates(): + path = "characters/instruction-following" + paths = [] + if os.path.exists(path): + paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml')) + + return ['None'] + sorted(set((k.stem for k in paths)), key=natural_keys) + + +def get_available_extensions(): + return sorted(set(map(lambda x: x.parts[1], Path('extensions').glob('*/script.py'))), key=natural_keys) + + +def get_available_softprompts(): + return ['None'] + sorted(set((k.stem for k in Path('softprompts').glob('*.zip'))), key=natural_keys) + + +def get_available_loras(): + return sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=natural_keys) + + +def get_datasets(path: str, ext: str): + return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys) + + +def get_available_chat_styles(): + return sorted(set(('-'.join(k.stem.split('-')[1:]) for k in Path('css').glob('chat_style*.css'))), key=natural_keys) diff --git a/presets/Default.txt b/presets/Default.txt index 9f0983ec7f67e44ac6a383bc13636eec8ad01c78..d28ce62f0e36d1f7824fe40d6e40018c9d78ea21 100644 --- a/presets/Default.txt +++ b/presets/Default.txt @@ -1,12 +1,6 @@ do_sample=True -temperature=1 -top_p=1 -typical_p=1 -repetition_penalty=1 -top_k=50 -num_beams=1 -penalty_alpha=0 -min_length=0 -length_penalty=1 -no_repeat_ngram_size=0 -early_stopping=False +top_p=0.5 +top_k=40 +temperature=0.7 +repetition_penalty=1.2 +typical_p=1.0 diff --git a/presets/Individual Today.txt b/presets/Individual Today.txt deleted file mode 100644 index f40b879cefc3d3e7914fc03f0f2322758c51cc05..0000000000000000000000000000000000000000 --- a/presets/Individual Today.txt +++ /dev/null @@ -1,6 +0,0 @@ -do_sample=True -top_p=0.9 -top_k=50 -temperature=1.39 -repetition_penalty=1.08 -typical_p=0.2 diff --git a/presets/LLaMA-Precise.txt b/presets/LLaMA-Precise.txt new file mode 100644 index 0000000000000000000000000000000000000000..8098b390a097fc9438a2a82ec2bdd58adb2a771b --- /dev/null +++ b/presets/LLaMA-Precise.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.1 +top_k=40 +temperature=0.7 +repetition_penalty=1.18 +typical_p=1.0 diff --git a/presets/MOSS.txt b/presets/MOSS.txt new file mode 100644 index 0000000000000000000000000000000000000000..e895e88623ef393abbcfd99ae5e53bc43f468763 --- /dev/null +++ b/presets/MOSS.txt @@ -0,0 +1,3 @@ +temperature=0.7 +top_p=0.8 +repetition_penalty=1.02 diff --git a/presets/Verbose (Beam Search).txt b/presets/Verbose (Beam Search).txt index a3be1b94f27e31e1d0e762a15fd0300abb32e460..464a4a5f0dda62348fda2cbbba4a98036c744d5c 100644 --- a/presets/Verbose (Beam Search).txt +++ b/presets/Verbose (Beam Search).txt @@ -1,6 +1,6 @@ num_beams=10 min_length=200 -length_penalty =1.4 +length_penalty=1.4 no_repeat_ngram_size=2 early_stopping=True temperature=0.7 diff --git a/prompts/Alpaca-with-Input.txt b/prompts/Alpaca-with-Input.txt new file mode 100644 index 0000000000000000000000000000000000000000..56df0e285be9689ab1f8ea698ce748e6d1b02af2 --- /dev/null +++ b/prompts/Alpaca-with-Input.txt @@ -0,0 +1,10 @@ +Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. + +### Instruction: +Instruction + +### Input: +Input + +### Response: + diff --git a/prompts/GPT-4chan.txt b/prompts/GPT-4chan.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bc8c7f4613f982e3dfa367562a764cf5bd4c73b --- /dev/null +++ b/prompts/GPT-4chan.txt @@ -0,0 +1,6 @@ +----- +--- 865467536 +Hello, AI frens! +How are you doing on this fine day? +--- 865467537 + diff --git a/prompts/QA.txt b/prompts/QA.txt new file mode 100644 index 0000000000000000000000000000000000000000..32b0e2350f3c0a7f447dcd1aba11d6ae2247e5a8 --- /dev/null +++ b/prompts/QA.txt @@ -0,0 +1,4 @@ +Common sense questions and answers + +Question: +Factual answer: diff --git a/requirements.txt b/requirements.txt index 30d93ffdf83ee2dcd2ae673e45dc18f84f7bf142..23f154085afd2f8439443b2d33cbd60499a3379b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,21 @@ -torch -torchvision -torchaudio -transformers -accelerate==0.17.1 -bitsandbytes==0.37.1 +accelerate==0.19.0 +colorama +datasets flexgen==0.1.7 -gradio==3.18.0 +gradio_client==0.2.5 +gradio==3.31.0 +markdown numpy +pandas +Pillow>=9.5.0 +pyyaml requests -rwkv==0.4.2 -safetensors==0.3.0 +rwkv==0.7.3 +safetensors==0.3.1 sentencepiece tqdm -git+https://github.com/zphang/transformers.git@68d640f7c368bcaaaecfc678f11908ebbd3d6176 \ No newline at end of file +git+https://github.com/huggingface/peft +transformers==4.29.1 +bitsandbytes==0.38.1; platform_system != "Windows" +llama-cpp-python==0.1.50; platform_system != "Windows" +https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.50/llama_cpp_python-0.1.50-cp310-cp310-win_amd64.whl; platform_system == "Windows" diff --git a/run.py b/run.py index e4f0f246cc56b9727f5f1eebb5486f7125dd2a84..5fa7ad38bf6c6c533d170074690124231000532d 100644 --- a/run.py +++ b/run.py @@ -1,4 +1,4 @@ import os os.system('python download-model.py PygmalionAI/pygmalion-350m --branch main') # os.system('python download-model.py waifu-workshop/pygmalion-6b --branch original-sharded') -os.system('python server.py --cpu --cai-chat --model PygmalionAI/pygmalion-350m') \ No newline at end of file +os.system('python server.py --cpu --cai-chat --model pygmalion-350m') \ No newline at end of file diff --git a/server.py b/server.py index 6a17f26287d94e9187a4f315fe9fb7d2dc6ec171..c8cb0d386a6172f4668170c54efc38ce2a6ee286 100644 --- a/server.py +++ b/server.py @@ -1,71 +1,91 @@ -import gc +import logging +import os +import requests +import warnings +import modules.logging_colors + +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 :)') + kwargs.setdefault('allow_redirects', True) + return requests.api.request('get', 'http://127.0.0.1/', **kwargs) + + +original_get = requests.get +requests.get = my_get +import gradio as gr +requests.get = original_get + +import matplotlib +matplotlib.use('Agg') # This fixes LaTeX rendering on some systems + +import importlib import io import json +import math +import os import re import sys import time +import traceback import zipfile +from datetime import datetime +from functools import partial from pathlib import Path -import gradio as gr +import psutil import torch +import yaml +from PIL import Image -import modules.chat as chat import modules.extensions as extensions_module -import modules.shared as shared -import modules.ui as ui -from modules.html_generator import generate_chat_html -from modules.models import load_model, load_soft_prompt -from modules.text_generation import generate_reply - -# Loading custom settings -settings_file = None -if shared.args.settings is not None and Path(shared.args.settings).exists(): - settings_file = Path(shared.args.settings) -elif Path('settings.json').exists(): - settings_file = Path('settings.json') -if settings_file is not None: - print(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] - -def get_available_models(): - if shared.args.flexgen: - return sorted([re.sub('-np$', '', item.name) for item in list(Path('models/').glob('*')) if item.name.endswith('-np')], key=str.lower) - else: - return sorted([item.name for item in list(Path('models/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt'))], key=str.lower) +from modules import chat, shared, training, ui, utils +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 -def get_available_presets(): - return sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('presets').glob('*.txt'))), key=str.lower) -def get_available_characters(): - return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('characters').glob('*.json'))), key=str.lower) +def load_model_wrapper(selected_model, autoload=False): + if not autoload: + yield f"The settings for {selected_model} have been updated.\nClick on \"Load the model\" to load it." + return -def get_available_extensions(): - return sorted(set(map(lambda x : x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower) + if selected_model == 'None': + yield "No model selected" + else: + try: + yield f"Loading {selected_model}..." + shared.model_name = selected_model + unload_model() + if selected_model != '': + shared.model, shared.tokenizer = load_model(shared.model_name) -def get_available_softprompts(): - return ['None'] + sorted(set(map(lambda x : '.'.join(str(x.name).split('.')[:-1]), Path('softprompts').glob('*.zip'))), key=str.lower) + yield f"Successfully loaded {selected_model}" + except: + yield traceback.format_exc() -def load_model_wrapper(selected_model): - if selected_model != shared.model_name: - shared.model_name = selected_model - shared.model = shared.tokenizer = None - if not shared.args.cpu: - gc.collect() - torch.cuda.empty_cache() - shared.model, shared.tokenizer = load_model(shared.model_name) - return selected_model +def load_lora_wrapper(selected_loras): + yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras))) + add_lora_to_model(selected_loras) + yield ("Successfuly applied the LoRAs") + -def load_preset_values(preset_menu, return_dict=False): +def load_preset_values(preset_menu, state, return_dict=False): generate_params = { 'do_sample': True, 'temperature': 1, 'top_p': 1, 'typical_p': 1, 'repetition_penalty': 1, + 'encoder_repetition_penalty': 1, 'top_k': 50, 'num_beams': 1, 'penalty_alpha': 0, @@ -80,13 +100,14 @@ def load_preset_values(preset_menu, return_dict=False): i = i.rstrip(',').strip().split('=') if len(i) == 2 and i[0].strip() != 'tokens': generate_params[i[0].strip()] = eval(i[1].strip()) - generate_params['temperature'] = min(1.99, generate_params['temperature']) if return_dict: return generate_params else: - return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['top_k'], generate_params['min_length'], generate_params['no_repeat_ngram_size'], generate_params['num_beams'], generate_params['penalty_alpha'], generate_params['length_penalty'], generate_params['early_stopping'] + state.update(generate_params) + return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] + def upload_soft_prompt(file): with zipfile.ZipFile(io.BytesIO(file)) as zf: @@ -100,283 +121,884 @@ def upload_soft_prompt(file): return name -def create_settings_menus(default_preset): - generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', return_dict=True) + +def open_save_prompt(): + fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}" + return gr.update(value=fname, visible=True), gr.update(visible=False), gr.update(visible=True) + + +def save_prompt(text, fname): + if fname != "": + with open(Path(f'prompts/{fname}.txt'), 'w', encoding='utf-8') as f: + f.write(text) + + message = f"Saved to prompts/{fname}.txt" + else: + message = "Error: No prompt name given." + + return message, gr.update(visible=False), gr.update(visible=True), gr.update(visible=False) + + +def load_prompt(fname): + if fname in ['None', '']: + return '' + elif fname.startswith('Instruct-'): + fname = re.sub('^Instruct-', '', fname) + with open(Path(f'characters/instruction-following/{fname}.yaml'), 'r', encoding='utf-8') as f: + data = yaml.safe_load(f) + output = '' + if 'context' in data: + output += data['context'] + + replacements = { + '<|user|>': data['user'], + '<|bot|>': data['bot'], + '<|user-message|>': 'Input', + } + + output += utils.replace_all(data['turn_template'].split('<|bot-message|>')[0], replacements) + return output.rstrip(' ') + else: + with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f: + text = f.read() + if text[-1] == '\n': + text = text[:-1] + + return text + + +def count_tokens(text): + tokens = get_encoded_length(text) + return f'{tokens} tokens in the input.' + + +def download_model_wrapper(repo_id): + try: + downloader = importlib.import_module("download-model") + repo_id_parts = repo_id.split(":") + model = repo_id_parts[0] if len(repo_id_parts) > 0 else repo_id + branch = repo_id_parts[1] if len(repo_id_parts) > 1 else "main" + check = False + + yield ("Cleaning up the model/branch names") + model, branch = downloader.sanitize_model_and_branch_names(model, branch) + + yield ("Getting the download links from Hugging Face") + links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False) + + yield ("Getting the output folder") + output_folder = downloader.get_output_folder(model, branch, is_lora) + + if check: + yield ("Checking previously downloaded files") + downloader.check_model_files(model, branch, links, sha256, output_folder) + else: + yield (f"Downloading files to {output_folder}") + downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1) + yield ("Done!") + except: + yield traceback.format_exc() + + +# Update the command-line arguments based on the interface values +def update_model_parameters(state, initial=False): + elements = ui.list_model_elements() # the names of the parameters + gpu_memories = [] + + for i, element in enumerate(elements): + if element not in state: + continue + + value = state[element] + if element.startswith('gpu_memory'): + gpu_memories.append(value) + continue + + if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]: + continue + + # Setting null defaults + if element in ['wbits', 'groupsize', 'model_type'] and value == 'None': + value = vars(shared.args_defaults)[element] + elif element in ['cpu_memory'] and value == 0: + value = vars(shared.args_defaults)[element] + + # Making some simple conversions + if element in ['wbits', 'groupsize', 'pre_layer']: + value = int(value) + elif element == 'cpu_memory' and value is not None: + value = f"{value}MiB" + + if element in ['pre_layer']: + value = [value] if value > 0 else None + + setattr(shared.args, element, value) + + found_positive = False + for i in gpu_memories: + if i > 0: + found_positive = True + break + + if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']): + if found_positive: + shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories] + else: + shared.args.gpu_memory = None + + +def get_model_specific_settings(model): + settings = shared.model_config + model_settings = {} + + for pat in settings: + if re.match(pat.lower(), model.lower()): + for k in settings[pat]: + model_settings[k] = settings[pat][k] + + return model_settings + + +def load_model_specific_settings(model, state, return_dict=False): + model_settings = get_model_specific_settings(model) + for k in model_settings: + if k in state: + state[k] = model_settings[k] + + return state + + +def save_model_settings(model, state): + if model == 'None': + yield ("Not saving the settings because no model is loaded.") + return + + with Path(f'{shared.args.model_dir}/config-user.yaml') as p: + if p.exists(): + user_config = yaml.safe_load(open(p, 'r').read()) + else: + user_config = {} + + model_regex = model + '$' # For exact matches + if model_regex not in user_config: + user_config[model_regex] = {} + + for k in ui.list_model_elements(): + user_config[model_regex][k] = state[k] + + with open(p, 'w') as f: + f.write(yaml.dump(user_config)) + + yield (f"Settings for {model} saved to {p}") + + +def create_model_menus(): + # Finding the default values for the GPU and CPU memories + total_mem = [] + for i in range(torch.cuda.device_count()): + total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024 * 1024))) + + default_gpu_mem = [] + if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0: + for i in shared.args.gpu_memory: + if 'mib' in i.lower(): + default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i))) + else: + default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)) * 1000) + while len(default_gpu_mem) < len(total_mem): + default_gpu_mem.append(0) + + total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024)) + if shared.args.cpu_memory is not None: + default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory) + else: + default_cpu_mem = 0 with gr.Row(): with gr.Column(): with gr.Row(): - shared.gradio['model_menu'] = gr.Dropdown(choices=available_models, value=shared.model_name, label='Model') - ui.create_refresh_button(shared.gradio['model_menu'], lambda : None, lambda : {'choices': get_available_models()}, 'refresh-button') + with gr.Column(): + with gr.Row(): + shared.gradio['model_menu'] = gr.Dropdown(choices=utils.get_available_models(), value=shared.model_name, label='Model') + ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': utils.get_available_models()}, 'refresh-button') + + with gr.Column(): + with gr.Row(): + shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=utils.get_available_loras(), value=shared.lora_names, label='LoRA(s)') + ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': utils.get_available_loras(), 'value': shared.lora_names}, 'refresh-button') + with gr.Column(): with gr.Row(): - shared.gradio['preset_menu'] = gr.Dropdown(choices=available_presets, value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset') - ui.create_refresh_button(shared.gradio['preset_menu'], lambda : None, lambda : {'choices': get_available_presets()}, 'refresh-button') + shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs') + with gr.Row(): + load = gr.Button("Load the model", visible=not shared.settings['autoload_model']) + unload = gr.Button("Unload the model") + reload = gr.Button("Reload the model") + save_settings = gr.Button("Save settings for this model") - with gr.Accordion('Custom generation parameters', open=False, elem_id='accordion'): - with gr.Row(): - with gr.Column(): - shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature') - shared.gradio['repetition_penalty'] = gr.Slider(1.0, 2.99, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty') - shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k') - shared.gradio['top_p'] = gr.Slider(0.0,1.0,value=generate_params['top_p'],step=0.01,label='top_p') - with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Box(): + gr.Markdown('Transformers parameters') + with gr.Row(): + with gr.Column(): + for i in range(len(total_mem)): + shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i]) + shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem) + + with gr.Column(): + shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices) + shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk) + shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu) + shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16) + shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit) + + with gr.Column(): + with gr.Box(): + gr.Markdown('GPTQ parameters') + with gr.Row(): + with gr.Column(): + shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None") + shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None") + + with gr.Column(): + shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None") + shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0) + + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['autoload_model'] = gr.Checkbox(value=shared.settings['autoload_model'], label='Autoload the model', info='Whether to load the model as soon as it is selected in the Model dropdown.') + + shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter the Hugging Face username/model path, for instance: facebook/galactica-125m. To specify a branch, add it at the end after a \":\" character like this: facebook/galactica-125m:main") + shared.gradio['download_model_button'] = gr.Button("Download") + + with gr.Column(): + with gr.Box(): + gr.Markdown('llama.cpp parameters') + with gr.Row(): + with gr.Column(): + shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads) + shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch) + shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers) + + with gr.Column(): + 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(): + 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 + # with the model defaults (if any), and then the model is loaded + # unless "autoload_model" is unchecked + shared.gradio['model_menu'].change( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + load_model_specific_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then( + ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then( + update_model_parameters, shared.gradio['interface_state'], None).then( + load_model_wrapper, [shared.gradio[k] for k in ['model_menu', 'autoload_model']], shared.gradio['model_status'], show_progress=False) + + load.click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + update_model_parameters, shared.gradio['interface_state'], None).then( + partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) + + unload.click( + unload_model, None, None).then( + lambda: "Model unloaded", None, shared.gradio['model_status']) + + reload.click( + unload_model, None, None).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + update_model_parameters, shared.gradio['interface_state'], None).then( + partial(load_model_wrapper, autoload=True), shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) + + save_settings.click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + 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['autoload_model'].change(lambda x: gr.update(visible=not x), shared.gradio['autoload_model'], load) + + +def create_settings_menus(default_preset): + + generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True) + + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['preset_menu'] = gr.Dropdown(choices=utils.get_available_presets(), value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset') + ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': utils.get_available_presets()}, 'refresh-button') + with gr.Column(): + shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)') + + with gr.Row(): + with gr.Column(): + with gr.Box(): + gr.Markdown('Custom generation parameters ([click here to view technical documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))') + with gr.Row(): + with gr.Column(): + shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.') + shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.') + shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.') + shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.') + with gr.Column(): + shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.') + shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.') + shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.') + shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.') shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample') - shared.gradio['typical_p'] = gr.Slider(0.0,1.0,value=generate_params['typical_p'],step=0.01,label='typical_p') - shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size') - shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'] if shared.args.no_stream else 0, label='min_length', interactive=shared.args.no_stream) + with gr.Column(): + with gr.Box(): + gr.Markdown('Contrastive search') + shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha') - gr.Markdown('Contrastive search:') - shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha') + gr.Markdown('Beam search (uses a lot of VRAM)') + with gr.Row(): + with gr.Column(): + shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams') + shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty') + with gr.Column(): + shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping') - gr.Markdown('Beam search (uses a lot of VRAM):') - with gr.Row(): - with gr.Column(): - shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams') - with gr.Column(): - shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty') - shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping') + with gr.Box(): + with gr.Row(): + with gr.Column(): + shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.') + shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"') + with gr.Column(): + shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.') + shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.') + + shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.') + shared.gradio['stream'] = gr.Checkbox(value=not shared.args.no_stream, label='Activate text streaming') - with gr.Accordion('Soft prompt', open=False, elem_id='accordion'): + with gr.Accordion('Soft prompt', open=False): with gr.Row(): - shared.gradio['softprompts_menu'] = gr.Dropdown(choices=available_softprompts, value='None', label='Soft prompt') - ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda : None, lambda : {'choices': get_available_softprompts()}, 'refresh-button') + shared.gradio['softprompts_menu'] = gr.Dropdown(choices=utils.get_available_softprompts(), value='None', label='Soft prompt') + ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': utils.get_available_softprompts()}, 'refresh-button') gr.Markdown('Upload a soft prompt (.zip format):') with gr.Row(): shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip']) - shared.gradio['model_menu'].change(load_model_wrapper, [shared.gradio['model_menu']], [shared.gradio['model_menu']], show_progress=True) - shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio['preset_menu']], [shared.gradio['do_sample'], shared.gradio['temperature'], shared.gradio['top_p'], shared.gradio['typical_p'], shared.gradio['repetition_penalty'], shared.gradio['top_k'], shared.gradio['min_length'], shared.gradio['no_repeat_ngram_size'], shared.gradio['num_beams'], shared.gradio['penalty_alpha'], shared.gradio['length_penalty'], shared.gradio['early_stopping']]) - shared.gradio['softprompts_menu'].change(load_soft_prompt, [shared.gradio['softprompts_menu']], [shared.gradio['softprompts_menu']], show_progress=True) - shared.gradio['upload_softprompt'].upload(upload_soft_prompt, [shared.gradio['upload_softprompt']], [shared.gradio['softprompts_menu']]) - -available_models = get_available_models() -available_presets = get_available_presets() -available_characters = get_available_characters() -available_softprompts = get_available_softprompts() - -# Default extensions -extensions_module.available_extensions = get_available_extensions() -if shared.args.chat or shared.args.cai_chat: - for extension in shared.settings['chat_default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) -else: - for extension in shared.settings['default_extensions']: - shared.args.extensions = shared.args.extensions or [] - if extension not in shared.args.extensions: - shared.args.extensions.append(extension) -if shared.args.extensions is not None and len(shared.args.extensions) > 0: - extensions_module.load_extensions() - -# Default model -if shared.args.model is not None: - shared.model_name = shared.args.model -else: - if len(available_models) == 0: - print('No models are available! Please download at least one.') - sys.exit(0) - elif len(available_models) == 1: - i = 0 + shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]) + shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True) + shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu']) + + +def set_interface_arguments(interface_mode, extensions, bool_active): + modes = ["default", "notebook", "chat", "cai_chat"] + cmd_list = vars(shared.args) + bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes] + + shared.args.extensions = extensions + for k in modes[1:]: + setattr(shared.args, k, False) + if interface_mode != "default": + setattr(shared.args, interface_mode, True) + + for k in bool_list: + setattr(shared.args, k, False) + for k in bool_active: + setattr(shared.args, k, True) + + shared.need_restart = True + + +def create_interface(): + + # Defining some variables + gen_events = [] + default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] + if len(shared.lora_names) == 1: + default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.lora_names[0].lower())), 'default')]) else: - print('The following models are available:\n') - for i, model in enumerate(available_models): - print(f'{i+1}. {model}') - print(f'\nWhich one do you want to load? 1-{len(available_models)}\n') - i = int(input())-1 - print() - shared.model_name = available_models[i] -shared.model, shared.tokenizer = load_model(shared.model_name) - -# Default UI settings -gen_events = [] -default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] -default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')] -title ='Text generation web UI' -description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n' -suffix = '_pygmalion' if 'pygmalion' in shared.model_name.lower() else '' - -if shared.args.chat or shared.args.cai_chat: - with gr.Blocks(css=ui.css+ui.chat_css, analytics_enabled=False, title=title) as shared.gradio['interface']: - gr.HTML('''Original github repo
-

For faster inference without waiting in queue, you may duplicate the space. Duplicate Space

-(👇 Scroll down to see the interface 👀)''') - if shared.args.cai_chat: - shared.gradio['display'] = gr.HTML(value=generate_chat_html(shared.history['visible'], shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}'], shared.character)) - else: - shared.gradio['display'] = gr.Chatbot(value=shared.history['visible']).style(color_map=("#326efd", "#212528")) - shared.gradio['textbox'] = gr.Textbox(label='Input') - with gr.Row(): - shared.gradio['Stop'] = gr.Button('Stop') - shared.gradio['Generate'] = gr.Button('Generate') - with gr.Row(): - shared.gradio['Impersonate'] = gr.Button('Impersonate') - shared.gradio['Regenerate'] = gr.Button('Regenerate') - with gr.Row(): - shared.gradio['Copy last reply'] = gr.Button('Copy last reply') - shared.gradio['Replace last reply'] = gr.Button('Replace last reply') - shared.gradio['Remove last'] = gr.Button('Remove last') - - shared.gradio['Clear history'] = gr.Button('Clear history') - shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant="stop", visible=False) - shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False) - with gr.Tab('Chat settings'): - shared.gradio['name1'] = gr.Textbox(value=shared.settings[f'name1{suffix}'], lines=1, label='Your name') - shared.gradio['name2'] = gr.Textbox(value=shared.settings[f'name2{suffix}'], lines=1, label='Bot\'s name') - shared.gradio['context'] = gr.Textbox(value=shared.settings[f'context{suffix}'], lines=5, label='Context') - with gr.Row(): - shared.gradio['character_menu'] = gr.Dropdown(choices=available_characters, value='None', label='Character', elem_id='character-menu') - ui.create_refresh_button(shared.gradio['character_menu'], lambda : None, lambda : {'choices': get_available_characters()}, 'refresh-button') + default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]) + title = 'Text generation web UI' + + # Authentication variables + auth = None + if shared.args.gradio_auth_path is not None: + gradio_auth_creds = [] + with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file: + for line in file.readlines(): + gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()] + auth = [tuple(cred.split(':')) for cred in gradio_auth_creds] + + # Importing the extension files and executing their setup() functions + if shared.args.extensions is not None and len(shared.args.extensions) > 0: + extensions_module.load_extensions() + + # css/js strings + css = ui.css if not shared.is_chat() else ui.css + ui.chat_css + js = ui.main_js if not shared.is_chat() else ui.main_js + ui.chat_js + css += apply_extensions('css') + js += apply_extensions('js') + + with gr.Blocks(css=css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']: + + # Create chat mode interface + if shared.is_chat(): + shared.input_elements = ui.list_interface_input_elements(chat=True) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['Chat input'] = gr.State() + shared.gradio['dummy'] = gr.State() + + with gr.Tab('Text generation', elem_id='main'): + shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'chat', 'cai-chat')) + shared.gradio['textbox'] = gr.Textbox(label='Input') + with gr.Row(): + shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop') + shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate', variant='primary') + shared.gradio['Continue'] = gr.Button('Continue') - with gr.Row(): - shared.gradio['check'] = gr.Checkbox(value=shared.settings[f'stop_at_newline{suffix}'], label='Stop generating at new line character?') - with gr.Row(): - with gr.Tab('Chat history'): - with gr.Row(): - with gr.Column(): - gr.Markdown('Upload') - shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt']) - with gr.Column(): - gr.Markdown('Download') - shared.gradio['download'] = gr.File() - shared.gradio['download_button'] = gr.Button(value='Click me') - with gr.Tab('Upload character'): + with gr.Row(): + shared.gradio['Copy last reply'] = gr.Button('Copy last reply') + shared.gradio['Regenerate'] = gr.Button('Regenerate') + shared.gradio['Replace last reply'] = gr.Button('Replace last reply') + + with gr.Row(): + shared.gradio['Impersonate'] = gr.Button('Impersonate') + shared.gradio['Send dummy message'] = gr.Button('Send dummy message') + shared.gradio['Send dummy reply'] = gr.Button('Send dummy reply') + + with gr.Row(): + shared.gradio['Remove last'] = gr.Button('Remove last') + shared.gradio['Clear history'] = gr.Button('Clear history') + shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False) + shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False) + + shared.gradio['mode'] = gr.Radio(choices=['chat', 'chat-instruct', 'instruct'], value=shared.settings['mode'] if shared.settings['mode'] in ['chat', 'instruct', 'chat-instruct'] else 'chat', label='Mode', info='Defines how the chat prompt is generated. In instruct and chat-instruct modes, the instruction template selected under "Chat settings" must match the current model.') + shared.gradio['chat_style'] = gr.Dropdown(choices=utils.get_available_chat_styles(), label='Chat style', value=shared.settings['chat_style'], visible=shared.settings['mode'] != 'instruct') + + with gr.Tab('Chat settings', elem_id='chat-settings'): + with gr.Row(): + shared.gradio['character_menu'] = gr.Dropdown(choices=utils.get_available_characters(), label='Character', elem_id='character-menu', info='Used in chat and chat-instruct modes.') + ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': utils.get_available_characters()}, 'refresh-button') + + with gr.Row(): + with gr.Column(scale=8): + shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name') + shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name') + shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context') + shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting') + + with gr.Column(scale=1): + shared.gradio['character_picture'] = gr.Image(label='Character picture', type='pil') + shared.gradio['your_picture'] = gr.Image(label='Your picture', type='pil', value=Image.open(Path('cache/pfp_me.png')) if Path('cache/pfp_me.png').exists() else None) + + shared.gradio['instruction_template'] = gr.Dropdown(choices=utils.get_available_instruction_templates(), label='Instruction template', value='None', info='Change this according to the model/LoRA that you are using. Used in instruct and chat-instruct modes.') + shared.gradio['name1_instruct'] = gr.Textbox(value='', lines=2, label='User string') + shared.gradio['name2_instruct'] = gr.Textbox(value='', lines=1, label='Bot string') + shared.gradio['context_instruct'] = gr.Textbox(value='', lines=4, label='Context') + shared.gradio['turn_template'] = gr.Textbox(value=shared.settings['turn_template'], lines=1, label='Turn template', info='Used to precisely define the placement of spaces and new line characters in instruction prompts.') + with gr.Row(): + shared.gradio['chat-instruct_command'] = gr.Textbox(value=shared.settings['chat-instruct_command'], lines=4, label='Command for chat-instruct mode', info='<|character|> gets replaced by the bot name, and <|prompt|> gets replaced by the regular chat prompt.') + + with gr.Row(): + with gr.Tab('Chat history'): + with gr.Row(): + with gr.Column(): + gr.Markdown('## Upload') + shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt']) + + with gr.Column(): + gr.Markdown('## Download') + shared.gradio['download'] = gr.File() + shared.gradio['download_button'] = gr.Button(value='Click me') + + with gr.Tab('Upload character'): + gr.Markdown('## JSON format') + with gr.Row(): + with gr.Column(): + gr.Markdown('1. Select the JSON file') + shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json']) + + with gr.Column(): + gr.Markdown('2. Select your character\'s profile picture (optional)') + shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image']) + + shared.gradio['Upload character'] = gr.Button(value='Submit') + gr.Markdown('## TavernAI PNG format') + shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image']) + + with gr.Tab("Parameters", elem_id="parameters"): + with gr.Box(): + gr.Markdown("Chat parameters") with gr.Row(): with gr.Column(): - gr.Markdown('1. Select the JSON file') - shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json']) + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + shared.gradio['chat_prompt_size'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size']) + with gr.Column(): - gr.Markdown('2. Select your character\'s profile picture (optional)') - shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image']) - shared.gradio['Upload character'] = gr.Button(value='Submit') - with gr.Tab('Upload your profile picture'): - shared.gradio['upload_img_me'] = gr.File(type='binary', file_types=['image']) - with gr.Tab('Upload TavernAI Character Card'): - shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image']) - - with gr.Tab('Generation settings'): - with gr.Row(): - with gr.Column(): - shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) - with gr.Column(): - shared.gradio['chat_prompt_size_slider'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size']) - shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)') - create_settings_menus(default_preset) - - shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']] - if shared.args.extensions is not None: - with gr.Tab('Extensions'): - extensions_module.create_extensions_block() - - function_call = 'chat.cai_chatbot_wrapper' if shared.args.cai_chat else 'chat.chatbot_wrapper' - - gen_events.append(shared.gradio['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream, api_name='textgen')) - gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) - gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream)) - gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream)) - shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events) - - shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream) - shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream) - - # Clear history with confirmation - clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] - shared.gradio['Clear history'].click(lambda :[gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr) - shared.gradio['Clear history-confirm'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) - shared.gradio['Clear history-confirm'].click(chat.clear_chat_log, [shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display']) - shared.gradio['Clear history-cancel'].click(lambda :[gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) - - shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False) - shared.gradio['download_button'].click(chat.save_history, inputs=[], outputs=[shared.gradio['download']]) - shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) - - # Clearing stuff and saving the history - for i in ['Generate', 'Regenerate', 'Replace last reply']: - shared.gradio[i].click(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False) - shared.gradio[i].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) - shared.gradio['Clear history-confirm'].click(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) - shared.gradio['textbox'].submit(lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False) - shared.gradio['textbox'].submit(lambda : chat.save_history(timestamp=False), [], [], show_progress=False) - - shared.gradio['character_menu'].change(chat.load_character, [shared.gradio['character_menu'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['name2'], shared.gradio['context'], shared.gradio['display']]) - shared.gradio['upload_chat_history'].upload(chat.load_history, [shared.gradio['upload_chat_history'], shared.gradio['name1'], shared.gradio['name2']], []) - shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']]) - shared.gradio['upload_img_me'].upload(chat.upload_your_profile_picture, [shared.gradio['upload_img_me']], []) - - reload_func = chat.redraw_html if shared.args.cai_chat else lambda : shared.history['visible'] - reload_inputs = [shared.gradio['name1'], shared.gradio['name2']] if shared.args.cai_chat else [] - shared.gradio['upload_chat_history'].upload(reload_func, reload_inputs, [shared.gradio['display']]) - shared.gradio['upload_img_me'].upload(reload_func, reload_inputs, [shared.gradio['display']]) - shared.gradio['Stop'].click(reload_func, reload_inputs, [shared.gradio['display']]) - - shared.gradio['interface'].load(lambda : chat.load_default_history(shared.settings[f'name1{suffix}'], shared.settings[f'name2{suffix}']), None, None) - shared.gradio['interface'].load(reload_func, reload_inputs, [shared.gradio['display']], show_progress=True) - -elif shared.args.notebook: - with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']: - gr.Markdown(description) - with gr.Tab('Raw'): - shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=23) - with gr.Tab('Markdown'): - shared.gradio['markdown'] = gr.Markdown() - with gr.Tab('HTML'): - shared.gradio['html'] = gr.HTML() - - shared.gradio['Generate'] = gr.Button('Generate') - shared.gradio['Stop'] = gr.Button('Stop') - shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) - - create_settings_menus(default_preset) - if shared.args.extensions is not None: - extensions_module.create_extensions_block() - - shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] - output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] - gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) - gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) - shared.gradio['Stop'].click(None, None, None, cancels=gen_events) - -else: - with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']: - gr.Markdown(description) - with gr.Row(): - with gr.Column(): - shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input') - shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) - shared.gradio['Generate'] = gr.Button('Generate') + shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)', info='New generations will be called until either this number is reached or no new content is generated between two iterations') + shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character') + + create_settings_menus(default_preset) + + # Create notebook mode interface + elif shared.args.notebook: + shared.input_elements = ui.list_interface_input_elements(chat=False) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['last_input'] = gr.State('') + with gr.Tab("Text generation", elem_id="main"): + with gr.Row(): + with gr.Column(scale=4): + with gr.Tab('Raw'): + shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox", lines=27) + + with gr.Tab('Markdown'): + shared.gradio['markdown'] = gr.Markdown() + + with gr.Tab('HTML'): + shared.gradio['html'] = gr.HTML() + + with gr.Row(): + shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") + shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") + shared.gradio['Undo'] = gr.Button('Undo', elem_classes="small-button") + shared.gradio['Regenerate'] = gr.Button('Regenerate', elem_classes="small-button") + + with gr.Column(scale=1): + gr.HTML('
') + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + with gr.Row(): + shared.gradio['prompt_menu'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt') + ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button') + + shared.gradio['open_save_prompt'] = gr.Button('Save prompt') + shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False) + shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False) + shared.gradio['count_tokens'] = gr.Button('Count tokens') + shared.gradio['status'] = gr.Markdown('') + + with gr.Tab("Parameters", elem_id="parameters"): + create_settings_menus(default_preset) + + # Create default mode interface + else: + shared.input_elements = ui.list_interface_input_elements(chat=False) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['last_input'] = gr.State('') + with gr.Tab("Text generation", elem_id="main"): with gr.Row(): with gr.Column(): - shared.gradio['Continue'] = gr.Button('Continue') + shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox_default", lines=27, label='Input') + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + with gr.Row(): + shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") + shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") + shared.gradio['Continue'] = gr.Button('Continue', elem_classes="small-button") + shared.gradio['open_save_prompt'] = gr.Button('Save prompt', elem_classes="small-button") + shared.gradio['save_prompt'] = gr.Button('Confirm save prompt', visible=False, elem_classes="small-button") + shared.gradio['count_tokens'] = gr.Button('Count tokens', elem_classes="small-button") + + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['prompt_menu'] = gr.Dropdown(choices=utils.get_available_prompts(), value='None', label='Prompt') + ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': utils.get_available_prompts()}, 'refresh-button') + + with gr.Column(): + shared.gradio['prompt_to_save'] = gr.Textbox(elem_classes="textbox_default", lines=1, label='Prompt name:', interactive=True, visible=False) + shared.gradio['status'] = gr.Markdown('') + with gr.Column(): - shared.gradio['Stop'] = gr.Button('Stop') + with gr.Tab('Raw'): + shared.gradio['output_textbox'] = gr.Textbox(elem_classes="textbox_default_output", lines=27, label='Output') + + with gr.Tab('Markdown'): + shared.gradio['markdown'] = gr.Markdown() + + with gr.Tab('HTML'): + shared.gradio['html'] = gr.HTML() + with gr.Tab("Parameters", elem_id="parameters"): create_settings_menus(default_preset) - if shared.args.extensions is not None: - extensions_module.create_extensions_block() - - with gr.Column(): - with gr.Tab('Raw'): - shared.gradio['output_textbox'] = gr.Textbox(lines=15, label='Output') - with gr.Tab('Markdown'): - shared.gradio['markdown'] = gr.Markdown() - with gr.Tab('HTML'): - shared.gradio['html'] = gr.HTML() - - shared.input_params = [shared.gradio[k] for k in ['textbox', 'max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] - output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] - gen_events.append(shared.gradio['Generate'].click(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream, api_name='textgen')) - gen_events.append(shared.gradio['textbox'].submit(generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream)) - gen_events.append(shared.gradio['Continue'].click(generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream)) - shared.gradio['Stop'].click(None, None, None, cancels=gen_events) - -shared.gradio['interface'].queue() -if shared.args.listen: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name='0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch) -else: - shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch) - -# I think that I will need this later -while True: - time.sleep(0.5) + + # Model tab + with gr.Tab("Model", elem_id="model-tab"): + create_model_menus() + + # Training tab + with gr.Tab("Training", elem_id="training-tab"): + training.create_train_interface() + + # Interface mode tab + with gr.Tab("Interface mode", elem_id="interface-mode"): + modes = ["default", "notebook", "chat"] + current_mode = "default" + for mode in modes[1:]: + if getattr(shared.args, mode): + current_mode = mode + break + + cmd_list = vars(shared.args) + bool_list = sorted([k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes + ui.list_model_elements()]) + bool_active = [k for k in bool_list if vars(shared.args)[k]] + + shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode") + shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=utils.get_available_extensions(), value=shared.args.extensions, label="Available extensions") + shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags") + shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface") + + # Reset interface event + shared.gradio['reset_interface'].click( + set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then( + lambda: None, None, None, _js='() => {document.body.innerHTML=\'

Reloading...

\'; setTimeout(function(){location.reload()},2500); return []}') + + # chat mode event handlers + if shared.is_chat(): + shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']] + clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] + shared.reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode', 'chat_style']] + + gen_events.append(shared.gradio['Generate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( + chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['textbox'].submit( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( + chat.generate_chat_reply_wrapper, shared.input_params, shared.gradio['display'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Regenerate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + partial(chat.generate_chat_reply_wrapper, regenerate=True), shared.input_params, shared.gradio['display'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Continue'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + partial(chat.generate_chat_reply_wrapper, _continue=True), shared.input_params, shared.gradio['display'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Impersonate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + lambda x: x, shared.gradio['textbox'], shared.gradio['Chat input'], show_progress=False).then( + chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=False) + ) + + shared.gradio['Replace last reply'].click( + chat.replace_last_reply, shared.gradio['textbox'], None).then( + lambda: '', None, shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['Send dummy message'].click( + chat.send_dummy_message, shared.gradio['textbox'], None).then( + lambda: '', None, shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['Send dummy reply'].click( + chat.send_dummy_reply, shared.gradio['textbox'], None).then( + lambda: '', None, shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['Clear history-confirm'].click( + lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then( + chat.clear_chat_log, [shared.gradio[k] for k in ['greeting', 'mode']], None).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['Stop'].click( + stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['mode'].change( + lambda x: gr.update(visible=x != 'instruct'), shared.gradio['mode'], shared.gradio['chat_style'], show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + + shared.gradio['chat_style'].change(chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + shared.gradio['instruction_template'].change( + partial(chat.load_character, instruct=True), [shared.gradio[k] for k in ['instruction_template', 'name1_instruct', 'name2_instruct']], [shared.gradio[k] for k in ['name1_instruct', 'name2_instruct', 'dummy', 'dummy', 'context_instruct', 'turn_template']]) + + shared.gradio['upload_chat_history'].upload( + chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=False) + shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr) + shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) + shared.gradio['Remove last'].click( + chat.remove_last_message, None, shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download']) + shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) + shared.gradio['character_menu'].change( + partial(chat.load_character, instruct=False), [shared.gradio[k] for k in ['character_menu', 'name1', 'name2']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'dummy']]).then( + chat.redraw_html, shared.reload_inputs, shared.gradio['display']) + + shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']]) + shared.gradio['your_picture'].change( + chat.upload_your_profile_picture, shared.gradio['your_picture'], None).then( + partial(chat.redraw_html, reset_cache=True), shared.reload_inputs, shared.gradio['display']) + + # notebook/default modes event handlers + else: + shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']] + if shared.args.notebook: + output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] + else: + output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] + + gen_events.append(shared.gradio['Generate'].click( + lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + + gen_events.append(shared.gradio['textbox'].submit( + lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + + if shared.args.notebook: + shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False) + gen_events.append(shared.gradio['Regenerate'].click( + lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply_wrapper, shared.input_params, output_params, show_progress=False) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + else: + gen_events.append(shared.gradio['Continue'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply_wrapper, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=False) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}") + ) + + shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None) + shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False) + shared.gradio['open_save_prompt'].click(open_save_prompt, None, [shared.gradio[k] for k in ['prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) + shared.gradio['save_prompt'].click(save_prompt, [shared.gradio[k] for k in ['textbox', 'prompt_to_save']], [shared.gradio[k] for k in ['status', 'prompt_to_save', 'open_save_prompt', 'save_prompt']], show_progress=False) + shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) + + shared.gradio['interface'].load(None, None, None, _js=f"() => {{{js}}}") + shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False) + # Extensions tabs + extensions_module.create_extensions_tabs() + + # Extensions block + extensions_module.create_extensions_block() + + # Launch the interface + shared.gradio['interface'].queue() + if shared.args.listen: + shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name=shared.args.listen_host or '0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) + else: + shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) + + +if __name__ == "__main__": + # Loading custom settings + settings_file = None + if shared.args.settings is not None and Path(shared.args.settings).exists(): + settings_file = Path(shared.args.settings) + elif Path('settings.json').exists(): + settings_file = Path('settings.json') + + if settings_file is not None: + logging.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] + + # Set default model settings based on settings.json + shared.model_config['.*'] = { + 'wbits': 'None', + 'model_type': 'None', + 'groupsize': 'None', + 'pre_layer': 0, + 'mode': shared.settings['mode'], + 'skip_special_tokens': shared.settings['skip_special_tokens'], + 'custom_stopping_strings': shared.settings['custom_stopping_strings'], + } + + shared.model_config.move_to_end('.*', last=False) # Move to the beginning + + # Default extensions + extensions_module.available_extensions = utils.get_available_extensions() + if shared.is_chat(): + for extension in shared.settings['chat_default_extensions']: + shared.args.extensions = shared.args.extensions or [] + if extension not in shared.args.extensions: + shared.args.extensions.append(extension) + else: + for extension in shared.settings['default_extensions']: + shared.args.extensions = shared.args.extensions or [] + if extension not in shared.args.extensions: + shared.args.extensions.append(extension) + + available_models = utils.get_available_models() + + # Model defined through --model + if shared.args.model is not None: + shared.model_name = shared.args.model + + # Only one model is available + elif len(available_models) == 1: + shared.model_name = available_models[0] + + # 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.') + sys.exit(0) + else: + print('The following models are available:\n') + for i, model in enumerate(available_models): + print(f'{i+1}. {model}') + + print(f'\nWhich one do you want to load? 1-{len(available_models)}\n') + i = int(input()) - 1 + print() + + shared.model_name = available_models[i] + + # If any model has been selected, load it + if shared.model_name != 'None': + model_settings = get_model_specific_settings(shared.model_name) + shared.settings.update(model_settings) # hijacking the interface defaults + update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments + + # Load the model + shared.model, shared.tokenizer = load_model(shared.model_name) + if shared.args.lora: + add_lora_to_model(shared.args.lora) + + # Force a character to be loaded + if shared.is_chat(): + shared.persistent_interface_state.update({ + 'mode': shared.settings['mode'], + 'character_menu': shared.args.character or shared.settings['character'], + 'instruction_template': shared.settings['instruction_template'] + }) + + # Launch the web UI + create_interface() + while True: + time.sleep(0.5) + if shared.need_restart: + shared.need_restart = False + shared.gradio['interface'].close() + time.sleep(0.5) + create_interface() diff --git a/settings-template.json b/settings-template.json index 9da4397012ccf6821dffb048d741071cf97fff6f..fffef0d92a0bab31d3aa6a73baf292922bd32193 100644 --- a/settings-template.json +++ b/settings-template.json @@ -1,35 +1,46 @@ { + "autoload_model": true, "max_new_tokens": 200, "max_new_tokens_min": 1, "max_new_tokens_max": 2000, - "name1": "Person 1", - "name2": "Person 2", - "context": "This is a conversation between two people.", - "stop_at_newline": true, + "seed": -1, + "character": "None", + "name1": "You", + "name2": "Assistant", + "context": "This is a conversation with your Assistant. It is a computer program designed to help you with various tasks such as answering questions, providing recommendations, and helping with decision making. You can ask it anything you want and it will do its best to give you accurate and relevant information.", + "greeting": "", + "turn_template": "", + "custom_stopping_strings": "", + "stop_at_newline": false, + "add_bos_token": true, + "ban_eos_token": false, + "skip_special_tokens": true, + "truncation_length": 2048, + "truncation_length_min": 0, + "truncation_length_max": 8192, + "mode": "chat", + "chat_style": "cai-chat", + "instruction_template": "None", + "chat-instruct_command": "Continue the chat dialogue below. Write a single reply for the character \"<|character|>\".\n\n<|prompt|>", "chat_prompt_size": 2048, "chat_prompt_size_min": 0, "chat_prompt_size_max": 2048, "chat_generation_attempts": 1, "chat_generation_attempts_min": 1, - "chat_generation_attempts_max": 5, - "name1_pygmalion": "You", - "name2_pygmalion": "Kawaii", - "context_pygmalion": "Kawaii's persona: Kawaii is a cheerful person who loves to make others smile. She is an optimist who loves to spread happiness and positivity wherever she goes.\n", - "stop_at_newline_pygmalion": false, + "chat_generation_attempts_max": 10, "default_extensions": [], "chat_default_extensions": [ "gallery" ], "presets": { - "default": "NovelAI-Sphinx Moth", - "pygmalion-*": "Pygmalion", - "RWKV-*": "Naive", - "(rosey|chip|joi)_.*_instruct.*": "Instruct Joi (Contrastive Search)" + "default": "Default", + ".*(alpaca|llama|llava)": "LLaMA-Precise", + ".*pygmalion": "NovelAI-Storywriter", + ".*RWKV": "Naive", + ".*moss": "MOSS" }, "prompts": { - "default": "Common sense questions and answers\n\nQuestion: \nFactual answer:", - "^(gpt4chan|gpt-4chan|4chan)": "-----\n--- 865467536\nInput text\n--- 865467537\n", - "(rosey|chip|joi)_.*_instruct.*": "User: \n", - "oasst-*": "<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>" + "default": "QA", + ".*(gpt4chan|gpt-4chan|4chan)": "GPT-4chan" } } diff --git a/text-generation-webui-main/.github/FUNDING.yml b/text-generation-webui-main/.github/FUNDING.yml new file mode 100644 index 0000000000000000000000000000000000000000..57b7f6982f7d7b7b9677c795488b11864d69d19e --- /dev/null +++ b/text-generation-webui-main/.github/FUNDING.yml @@ -0,0 +1 @@ +ko_fi: oobabooga diff --git a/text-generation-webui-main/.github/ISSUE_TEMPLATE/bug_report_template.yml b/text-generation-webui-main/.github/ISSUE_TEMPLATE/bug_report_template.yml new file mode 100644 index 0000000000000000000000000000000000000000..bd30a0c9c17dd514bf364846fe7914b6d10a4584 --- /dev/null +++ b/text-generation-webui-main/.github/ISSUE_TEMPLATE/bug_report_template.yml @@ -0,0 +1,53 @@ +name: "Bug report" +description: Report a bug +labels: [ "bug" ] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + - type: textarea + id: bug-description + attributes: + label: Describe the bug + description: A clear and concise description of what the bug is. + placeholder: Bug description + validations: + required: true + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the issue you encountered. + options: + - label: I have searched the existing issues + required: true + - type: textarea + id: reproduction + attributes: + label: Reproduction + description: Please provide the steps necessary to reproduce your issue. + placeholder: Reproduction + validations: + required: true + - type: textarea + id: screenshot + attributes: + label: Screenshot + description: "If possible, please include screenshot(s) so that we can understand what the issue is." + - type: textarea + id: logs + attributes: + label: Logs + description: "Please include the full stacktrace of the errors you get in the command-line (if any)." + render: shell + validations: + required: true + - type: textarea + id: system-info + attributes: + label: System Info + description: "Please share your system info with us: operating system, GPU brand, and GPU model. If you are using a Google Colab notebook, mention that instead." + render: shell + placeholder: + validations: + required: true diff --git a/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md b/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000000000000000000000000000000000000..b94974f865491731a1251e3e9736e01cbe81b06f --- /dev/null +++ b/text-generation-webui-main/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,16 @@ +--- +name: Feature request +about: Suggest an improvement or new feature for the web UI +title: '' +labels: 'enhancement' +assignees: '' + +--- + +**Description** + +A clear and concise description of what you want to be implemented. + +**Additional Context** + +If applicable, please provide any extra information, external links, or screenshots that could be useful. diff --git a/text-generation-webui-main/.github/dependabot.yml b/text-generation-webui-main/.github/dependabot.yml new file mode 100644 index 0000000000000000000000000000000000000000..91abb11fdf507883caeeb2d2958e1c65fb6cbdc1 --- /dev/null +++ b/text-generation-webui-main/.github/dependabot.yml @@ -0,0 +1,11 @@ +# To get started with Dependabot version updates, you'll need to specify which +# package ecosystems to update and where the package manifests are located. +# Please see the documentation for all configuration options: +# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates + +version: 2 +updates: + - package-ecosystem: "pip" # See documentation for possible values + directory: "/" # Location of package manifests + schedule: + interval: "weekly" diff --git a/text-generation-webui-main/.github/workflows/stale.yml b/text-generation-webui-main/.github/workflows/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..ce603a4f0a90845b7107da863b6ff1d9fb5d4bf2 --- /dev/null +++ b/text-generation-webui-main/.github/workflows/stale.yml @@ -0,0 +1,22 @@ +name: Close inactive issues +on: + schedule: + - cron: "10 23 * * *" + +jobs: + close-issues: + runs-on: ubuntu-latest + permissions: + issues: write + pull-requests: write + steps: + - uses: actions/stale@v5 + with: + stale-issue-message: "" + close-issue-message: "This issue has been closed due to inactivity for 30 days. If you believe it is still relevant, please leave a comment below." + days-before-issue-stale: 30 + days-before-issue-close: 0 + stale-issue-label: "stale" + days-before-pr-stale: -1 + days-before-pr-close: -1 + repo-token: ${{ secrets.GITHUB_TOKEN }} diff --git a/text-generation-webui-main/.gitignore b/text-generation-webui-main/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..12f94ba09604be62e6079b7e34ec7e73d6ea1687 --- /dev/null +++ b/text-generation-webui-main/.gitignore @@ -0,0 +1,27 @@ +cache +characters +training/datasets +extensions/silero_tts/outputs +extensions/elevenlabs_tts/outputs +extensions/sd_api_pictures/outputs +logs +loras +models +repositories +softprompts +torch-dumps +*pycache* +*/*pycache* +*/*/pycache* +venv/ +.venv/ +.vscode +*.bak +*.ipynb +*.log + +settings.json +img_bot* +img_me* +prompts/[0-9]* +models/config-user.yaml diff --git a/text-generation-webui-main/LICENSE b/text-generation-webui-main/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0ad25db4bd1d86c452db3f9602ccdbe172438f52 --- /dev/null +++ b/text-generation-webui-main/LICENSE @@ -0,0 +1,661 @@ + GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/text-generation-webui-main/README.md b/text-generation-webui-main/README.md new file mode 100644 index 0000000000000000000000000000000000000000..868e822b568a3e4e991aca968d9f895eda695fa1 --- /dev/null +++ b/text-generation-webui-main/README.md @@ -0,0 +1,313 @@ +# Text generation web UI + +A gradio web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. + +Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation. + +|![Image1](https://github.com/oobabooga/screenshots/raw/main/qa.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/cai3.png) | +|:---:|:---:| +|![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png) | ![Image4](https://github.com/oobabooga/screenshots/raw/main/galactica.png) | + +## Features + +* Dropdown menu for switching between models +* Notebook mode that resembles OpenAI's playground +* Chat mode for conversation and role playing +* Instruct mode compatible with Alpaca, Vicuna, Open Assistant, Dolly, Koala, and ChatGLM formats +* Nice HTML output for GPT-4chan +* Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX rendering +* [Custom chat characters](docs/Custom-chat-characters.md) +* Advanced chat features (send images, get audio responses with TTS) +* Very efficient text streaming +* Parameter presets +* 8-bit mode +* Layers splitting across GPU(s), CPU, and disk +* CPU mode +* [FlexGen](docs/FlexGen.md) +* [DeepSpeed ZeRO-3](docs/DeepSpeed.md) +* API [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-stream.py) streaming and [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming +* [LLaMA model](docs/LLaMA-model.md) +* [4-bit GPTQ mode](docs/GPTQ-models-(4-bit-mode).md) +* [llama.cpp](docs/llama.cpp-models.md) +* [RWKV model](docs/RWKV-model.md) +* [LoRA (loading and training)](docs/Using-LoRAs.md) +* Softprompts +* [Extensions](docs/Extensions.md) - see the [user extensions list](https://github.com/oobabooga/text-generation-webui-extensions) + +## Installation + +### One-click installers + +| Windows | Linux | macOS | +|-------|--------|--------| +| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) | + +Just download the zip above, extract it, and double click on "start". The web UI and all its dependencies will be installed in the same folder. + +* The source codes are here: https://github.com/oobabooga/one-click-installers +* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers. +* There is no need to run the installers as admin. + +### Manual installation using Conda + +Recommended if you have some experience with the command-line. + +On Windows, I additionally recommend carrying out the installation on WSL instead of the base system: [WSL installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/WSL-installation-guide.md). + +#### 0. Install Conda + +https://docs.conda.io/en/latest/miniconda.html + +On Linux or WSL, it can be automatically installed with these two commands: + +``` +curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh" +bash Miniconda3.sh +``` +Source: https://educe-ubc.github.io/conda.html + +#### 0.1 (Ubuntu/WSL) Install build tools + +``` +sudo apt install build-essential +``` + + +#### 1. Create a new conda environment + +``` +conda create -n textgen python=3.10.9 +conda activate textgen +``` + +#### 2. Install Pytorch + +| System | GPU | Command | +|--------|---------|---------| +| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio` | +| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2` | +| MacOS + MPS (untested) | Any | `pip3 install torch torchvision torchaudio` | + +The up to date commands can be found here: https://pytorch.org/get-started/locally/. + +#### 2.1 Special instructions + +* MacOS users: https://github.com/oobabooga/text-generation-webui/pull/393 +* AMD users: https://rentry.org/eq3hg + +#### 3. Install the web UI + +``` +git clone https://github.com/oobabooga/text-generation-webui +cd text-generation-webui +pip install -r requirements.txt +``` + +### Alternative: manual Windows installation + +As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Windows installation guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Windows-installation-guide.md). + +### Alternative: Docker + +``` +ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} . +cp docker/.env.example .env +# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model +docker compose up --build +``` + +You need to have docker compose v2.17 or higher installed in your system. To see how to install docker compose itself, see the guide in [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md). + +Contributed by [@loeken](https://github.com/loeken) in [#633](https://github.com/oobabooga/text-generation-webui/pull/633) + +### Updating the requirements + +From time to time, the `requirements.txt` changes. To update, use this command: + +``` +conda activate textgen +cd text-generation-webui +pip install -r requirements.txt --upgrade +``` +## Downloading models + +Models should be placed inside the `models` folder. + +[Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads) is the main place to download models. These are some examples: + +* [Pythia](https://huggingface.co/models?sort=downloads&search=eleutherai%2Fpythia+deduped) +* [OPT](https://huggingface.co/models?search=facebook/opt) +* [GALACTICA](https://huggingface.co/models?search=facebook/galactica) +* [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main) + +You can automatically download a model from HF using the script `download-model.py`: + + python download-model.py organization/model + +For example: + + python download-model.py facebook/opt-1.3b + +If you want to download a model manually, note that all you need are the json, txt, and pytorch\*.bin (or model*.safetensors) files. The remaining files are not necessary. + +#### GPT-4chan + +[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options: + +* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model) +* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/) + +The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version. + +After downloading the model, follow these steps: + +1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`. +2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json). +3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan): + +``` +python download-model.py EleutherAI/gpt-j-6B --text-only +``` + +## Starting the web UI + + conda activate textgen + cd text-generation-webui + python server.py + +Then browse to + +`http://localhost:7860/?__theme=dark` + +Optionally, you can use the following command-line flags: + +#### Basic settings + +| Flag | Description | +|--------------------------------------------|-------------| +| `-h`, `--help` | Show this help message and exit. | +| `--notebook` | Launch the web UI in notebook mode, where the output is written to the same text box as the input. | +| `--chat` | Launch the web UI in chat mode. | +| `--character CHARACTER` | The name of the character to load in chat mode by default. | +| `--model MODEL` | Name of the model to load by default. | +| `--lora LORA [LORA ...]` | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. | +| `--model-dir MODEL_DIR` | Path to directory with all the models. | +| `--lora-dir LORA_DIR` | Path to directory with all the loras. | +| `--model-menu` | Show a model menu in the terminal when the web UI is first launched. | +| `--no-stream` | Don't stream the text output in real time. | +| `--settings SETTINGS_FILE` | Load the default interface settings from this json file. See `settings-template.json` for an example. If you create a file called `settings.json`, this file will be loaded by default without the need to use the `--settings` flag. | +| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. | +| `--verbose` | Print the prompts to the terminal. | + +#### Accelerate/transformers + +| Flag | Description | +|---------------------------------------------|-------------| +| `--cpu` | Use the CPU to generate text. Warning: Training on CPU is extremely slow.| +| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU. | +| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maxmimum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. | +| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.| +| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. | +| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to `cache/`. | +| `--load-in-8bit` | Load the model with 8-bit precision.| +| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. | +| `--no-cache` | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. | +| `--xformers` | Use xformer's memory efficient attention. This should increase your tokens/s. | +| `--sdp-attention` | Use torch 2.0's sdp attention. | +| `--trust-remote-code` | Set trust_remote_code=True while loading a model. Necessary for ChatGLM. | + +#### llama.cpp + +| Flag | Description | +|-------------|-------------| +| `--threads` | Number of threads to use. | +| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. | +| `--no-mmap` | Prevent mmap from being used. | +| `--mlock` | Force the system to keep the model in RAM. | + +#### GPTQ + +| Flag | Description | +|---------------------------|-------------| +| `--wbits WBITS` | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. | +| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. | +| `--groupsize GROUPSIZE` | Group size. | +| `--pre_layer PRE_LAYER` | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. | +| `--monkey-patch` | Apply the monkey patch for using LoRAs with quantized models. +| `--quant_attn` | (triton) Enable quant attention. +| `--warmup_autotune` | (triton) Enable warmup autotune. +| `--fused_mlp` | (triton) Enable fused mlp. + +#### FlexGen + +| Flag | Description | +|------------------|-------------| +| `--flexgen` | Enable the use of FlexGen offloading. | +| `--percent PERCENT [PERCENT ...]` | FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0). | +| `--compress-weight` | FlexGen: Whether to compress weight (default: False).| +| `--pin-weight [PIN_WEIGHT]` | FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%). | + +#### DeepSpeed + +| Flag | Description | +|---------------------------------------|-------------| +| `--deepspeed` | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. | +| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. | +| `--local_rank LOCAL_RANK` | DeepSpeed: Optional argument for distributed setups. | + +#### RWKV + +| Flag | Description | +|---------------------------------|-------------| +| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". | +| `--rwkv-cuda-on` | RWKV: Compile the CUDA kernel for better performance. | + +#### Gradio + +| Flag | Description | +|---------------------------------------|-------------| +| `--listen` | Make the web UI reachable from your local network. | +| `--listen-host LISTEN_HOST` | The hostname that the server will use. | +| `--listen-port LISTEN_PORT` | The listening port that the server will use. | +| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. | +| `--auto-launch` | Open the web UI in the default browser upon launch. | +| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" | + +#### API + +| Flag | Description | +|---------------------------------------|-------------| +| `--api` | Enable the API extension. | +| `--public-api` | Create a public URL for the API using Cloudfare. | + +Out of memory errors? [Check the low VRAM guide](docs/Low-VRAM-guide.md). + +## Presets + +Inference settings presets can be created under `presets/` as text files. These files are detected automatically at startup. + +By default, 10 presets by NovelAI and KoboldAI are included. These were selected out of a sample of 43 presets after applying a K-Means clustering algorithm and selecting the elements closest to the average of each cluster. + +[Visualization](https://user-images.githubusercontent.com/112222186/228956352-1addbdb9-2456-465a-b51d-089f462cd385.png) + +## System requirements + +Check the [wiki](docs/System-requirements.md) for some examples of VRAM and RAM usage in both GPU and CPU mode. + +## Contributing + +Pull requests, suggestions, and issue reports are welcome. + +You are also welcome to review open pull requests. + +Before reporting a bug, make sure that you have: + +1. Created a conda environment and installed the dependencies exactly as in the *Installation* section above. +2. [Searched](https://github.com/oobabooga/text-generation-webui/issues) to see if an issue already exists for the issue you encountered. + +## Credits + +- Gradio dropdown menu refresh button, code for reloading the interface: https://github.com/AUTOMATIC1111/stable-diffusion-webui +- Verbose preset: Anonymous 4chan user. +- NovelAI and KoboldAI presets: https://github.com/KoboldAI/KoboldAI-Client/wiki/Settings-Presets +- Code for early stopping in chat mode, code for some of the sliders: https://github.com/PygmalionAI/gradio-ui/ diff --git a/text-generation-webui-main/api-example-stream.py b/text-generation-webui-main/api-example-stream.py new file mode 100644 index 0000000000000000000000000000000000000000..49058776927c7d85e49f5f717d8a77135fb2f8a1 --- /dev/null +++ b/text-generation-webui-main/api-example-stream.py @@ -0,0 +1,66 @@ +import asyncio +import json +import sys + +try: + import websockets +except ImportError: + print("Websockets package not found. Make sure it's installed.") + +# For local streaming, the websockets are hosted without ssl - ws:// +HOST = 'localhost:5005' +URI = f'ws://{HOST}/api/v1/stream' + +# For reverse-proxied streaming, the remote will likely host with ssl - wss:// +# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream' + +async def run(context): + # Note: the selected defaults change from time to time. + request = { + 'prompt': context, + 'max_new_tokens': 250, + 'do_sample': True, + 'temperature': 1.3, + 'top_p': 0.1, + 'typical_p': 1, + 'repetition_penalty': 1.18, + 'top_k': 40, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0, + 'length_penalty': 1, + 'early_stopping': False, + 'seed': -1, + 'add_bos_token': True, + 'truncation_length': 2048, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'stopping_strings': [] + } + + async with websockets.connect(URI, ping_interval=None) as websocket: + await websocket.send(json.dumps(request)) + + yield context # Remove this if you just want to see the reply + + while True: + incoming_data = await websocket.recv() + incoming_data = json.loads(incoming_data) + + match incoming_data['event']: + case 'text_stream': + yield incoming_data['text'] + case 'stream_end': + return + + +async def print_response_stream(prompt): + async for response in run(prompt): + print(response, end='') + sys.stdout.flush() # If we don't flush, we won't see tokens in realtime. + + +if __name__ == '__main__': + prompt = "In order to make homemade bread, follow these steps:\n1)" + asyncio.run(print_response_stream(prompt)) diff --git a/text-generation-webui-main/api-example.py b/text-generation-webui-main/api-example.py new file mode 100644 index 0000000000000000000000000000000000000000..d6053fdad1aa2c76129553e221f4e5360bb6b6b1 --- /dev/null +++ b/text-generation-webui-main/api-example.py @@ -0,0 +1,42 @@ +import requests + +# For local streaming, the websockets are hosted without ssl - http:// +HOST = 'localhost:5000' +URI = f'http://{HOST}/api/v1/generate' + +# For reverse-proxied streaming, the remote will likely host with ssl - https:// +# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate' + +def run(prompt): + request = { + 'prompt': prompt, + 'max_new_tokens': 250, + 'do_sample': True, + 'temperature': 1.3, + 'top_p': 0.1, + 'typical_p': 1, + 'repetition_penalty': 1.18, + 'top_k': 40, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0, + 'length_penalty': 1, + 'early_stopping': False, + 'seed': -1, + 'add_bos_token': True, + 'truncation_length': 2048, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'stopping_strings': [] + } + + response = requests.post(URI, json=request) + + if response.status_code == 200: + result = response.json()['results'][0]['text'] + print(prompt + result) + +if __name__ == '__main__': + prompt = "In order to make homemade bread, follow these steps:\n1)" + run(prompt) diff --git a/text-generation-webui-main/convert-to-flexgen.py b/text-generation-webui-main/convert-to-flexgen.py new file mode 100644 index 0000000000000000000000000000000000000000..7654593b539541deebfe904403ce73daa4a8651c --- /dev/null +++ b/text-generation-webui-main/convert-to-flexgen.py @@ -0,0 +1,63 @@ +''' + +Converts a transformers model to a format compatible with flexgen. + +''' + +import argparse +import os +from pathlib import Path + +import numpy as np +import torch +from tqdm import tqdm +from transformers import AutoModelForCausalLM, AutoTokenizer + +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) +parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.") +args = parser.parse_args() + + +def disable_torch_init(): + """ + Disable the redundant torch default initialization to accelerate model creation. + """ + import torch + global torch_linear_init_backup + global torch_layer_norm_init_backup + + torch_linear_init_backup = torch.nn.Linear.reset_parameters + setattr(torch.nn.Linear, "reset_parameters", lambda self: None) + + torch_layer_norm_init_backup = torch.nn.LayerNorm.reset_parameters + setattr(torch.nn.LayerNorm, "reset_parameters", lambda self: None) + + +def restore_torch_init(): + """Rollback the change made by disable_torch_init.""" + import torch + setattr(torch.nn.Linear, "reset_parameters", torch_linear_init_backup) + setattr(torch.nn.LayerNorm, "reset_parameters", torch_layer_norm_init_backup) + + +if __name__ == '__main__': + path = Path(args.MODEL) + model_name = path.name + + print(f"Loading {model_name}...") + # disable_torch_init() + model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, low_cpu_mem_usage=True) + # restore_torch_init() + + tokenizer = AutoTokenizer.from_pretrained(path) + + out_folder = Path(f"models/{model_name}-np") + if not Path(out_folder).exists(): + os.mkdir(out_folder) + + print(f"Saving the converted model to {out_folder}...") + for name, param in tqdm(list(model.model.named_parameters())): + name = name.replace("decoder.final_layer_norm", "decoder.layer_norm") + param_path = os.path.join(out_folder, name) + with open(param_path, "wb") as f: + np.save(f, param.cpu().detach().numpy()) diff --git a/text-generation-webui-main/convert-to-safetensors.py b/text-generation-webui-main/convert-to-safetensors.py new file mode 100644 index 0000000000000000000000000000000000000000..3b721e7cd4d15cf7e5e03caaee57ef83a41553bc --- /dev/null +++ b/text-generation-webui-main/convert-to-safetensors.py @@ -0,0 +1,38 @@ +''' + +Converts a transformers model to safetensors format and shards it. + +This makes it faster to load (because of safetensors) and lowers its RAM usage +while loading (because of sharding). + +Based on the original script by 81300: + +https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303 + +''' + +import argparse +from pathlib import Path + +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) +parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.") +parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).') +parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).") +parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') +args = parser.parse_args() + +if __name__ == '__main__': + path = Path(args.MODEL) + model_name = path.name + + print(f"Loading {model_name}...") + model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16) + tokenizer = AutoTokenizer.from_pretrained(path) + + out_folder = args.output or Path(f"models/{model_name}_safetensors") + print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...") + model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True) + tokenizer.save_pretrained(out_folder) diff --git a/text-generation-webui-main/css/chat.css b/text-generation-webui-main/css/chat.css new file mode 100644 index 0000000000000000000000000000000000000000..b5102e9a72ca0b066b12d52ab371d8a24774ac19 --- /dev/null +++ b/text-generation-webui-main/css/chat.css @@ -0,0 +1,43 @@ +.h-\[40vh\], .wrap.svelte-byatnx.svelte-byatnx.svelte-byatnx { + height: 66.67vh +} + +.gradio-container { + margin-left: auto !important; + margin-right: auto !important; +} + +.w-screen { + width: unset +} + +div.svelte-362y77>*, div.svelte-362y77>.form>* { + flex-wrap: nowrap +} + +/* fixes the API documentation in chat mode */ +.api-docs.svelte-1iguv9h.svelte-1iguv9h.svelte-1iguv9h { + display: grid; +} + +.pending.svelte-1ed2p3z { + opacity: 1; +} + +#extensions { + padding: 0; + padding: 0; +} + +#gradio-chatbot { + height: 66.67vh; +} + +.wrap.svelte-6roggh.svelte-6roggh { + max-height: 92.5%; +} + +/* This is for the microphone button in the whisper extension */ +.sm.svelte-1ipelgc { + width: 100%; +} diff --git a/text-generation-webui-main/css/chat.js b/text-generation-webui-main/css/chat.js new file mode 100644 index 0000000000000000000000000000000000000000..e304f1254732e475bf177ee849ac51d4f3e30f46 --- /dev/null +++ b/text-generation-webui-main/css/chat.js @@ -0,0 +1,4 @@ +document.getElementById("main").childNodes[0].style = "max-width: 800px; margin-left: auto; margin-right: auto"; +document.getElementById("extensions").style.setProperty("max-width", "800px"); +document.getElementById("extensions").style.setProperty("margin-left", "auto"); +document.getElementById("extensions").style.setProperty("margin-right", "auto"); diff --git a/text-generation-webui-main/css/html_4chan_style.css b/text-generation-webui-main/css/html_4chan_style.css new file mode 100644 index 0000000000000000000000000000000000000000..843e8a97fea80b010004f90f02ce63e8d13fe758 --- /dev/null +++ b/text-generation-webui-main/css/html_4chan_style.css @@ -0,0 +1,103 @@ +#parent #container { + background-color: #eef2ff; + padding: 17px; +} +#parent #container .reply { + background-color: rgb(214, 218, 240); + border-bottom-color: rgb(183, 197, 217); + border-bottom-style: solid; + border-bottom-width: 1px; + border-image-outset: 0; + border-image-repeat: stretch; + border-image-slice: 100%; + border-image-source: none; + border-image-width: 1; + border-left-color: rgb(0, 0, 0); + border-left-style: none; + border-left-width: 0px; + border-right-color: rgb(183, 197, 217); + border-right-style: solid; + border-right-width: 1px; + border-top-color: rgb(0, 0, 0); + border-top-style: none; + border-top-width: 0px; + color: rgb(0, 0, 0); + display: table; + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + margin-bottom: 4px; + margin-left: 0px; + margin-right: 0px; + margin-top: 4px; + overflow-x: hidden; + overflow-y: hidden; + padding-bottom: 4px; + padding-left: 2px; + padding-right: 2px; + padding-top: 4px; +} + +#parent #container .number { + color: rgb(0, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + width: 342.65px; + margin-right: 7px; +} + +#parent #container .op { + color: rgb(0, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + margin-bottom: 8px; + margin-left: 0px; + margin-right: 0px; + margin-top: 4px; + overflow-x: hidden; + overflow-y: hidden; +} + +#parent #container .op blockquote { + margin-left: 0px !important; +} + +#parent #container .name { + color: rgb(17, 119, 67); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + font-weight: 700; + margin-left: 7px; +} + +#parent #container .quote { + color: rgb(221, 0, 0); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; + text-decoration-color: rgb(221, 0, 0); + text-decoration-line: underline; + text-decoration-style: solid; + text-decoration-thickness: auto; +} + +#parent #container .greentext { + color: rgb(120, 153, 34); + font-family: arial, helvetica, sans-serif; + font-size: 13.3333px; +} + +#parent #container blockquote { + margin: 0px !important; + margin-block-start: 1em; + margin-block-end: 1em; + margin-inline-start: 40px; + margin-inline-end: 40px; + margin-top: 13.33px !important; + margin-bottom: 13.33px !important; + margin-left: 40px !important; + margin-right: 40px !important; +} + +#parent #container .message { + color: black; + border: none; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/html_bubble_chat_style.css b/text-generation-webui-main/css/html_bubble_chat_style.css new file mode 100644 index 0000000000000000000000000000000000000000..a54a10734c0c14a1abe3ecd7fdb89602bc362dec --- /dev/null +++ b/text-generation-webui-main/css/html_bubble_chat_style.css @@ -0,0 +1,86 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.text-you { + background-color: #d9fdd3; + border-radius: 15px; + padding: 10px; + padding-top: 5px; + float: right; +} + +.text-bot { + background-color: #f2f2f2; + border-radius: 15px; + padding: 10px; + padding-top: 5px; +} + +.dark .text-you { + background-color: #005c4b; + color: #111b21; +} + +.dark .text-bot { + background-color: #1f2937; + color: #111b21; +} + +.text-bot p, .text-you p { + margin-top: 5px; +} + +.message-body {} + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 15px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/html_cai_style.css b/text-generation-webui-main/css/html_cai_style.css new file mode 100644 index 0000000000000000000000000000000000000000..f601de3248b7ee94d6da58026354f8b9afeb9297 --- /dev/null +++ b/text-generation-webui-main/css/html_cai_style.css @@ -0,0 +1,91 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + display: grid; + grid-template-columns: 60px minmax(0, 1fr); + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.circle-you { + width: 50px; + height: 50px; + background-color: rgb(238, 78, 59); + border-radius: 50%; +} + +.circle-bot { + width: 50px; + height: 50px; + background-color: rgb(59, 78, 244); + border-radius: 50%; +} + +.circle-bot img, +.circle-you img { + border-radius: 50%; + width: 100%; + height: 100%; + object-fit: cover; +} + +.text {} + +.text p { + margin-top: 5px; +} + +.username { + font-weight: bold; +} + +.message-body {} + +.message-body img { + max-width: 300px; + max-height: 300px; + border-radius: 20px; +} + +.message-body p { + margin-bottom: 0 !important; + font-size: 15px !important; + line-height: 1.428571429 !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(138, 138, 138) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/html_instruct_style.css b/text-generation-webui-main/css/html_instruct_style.css new file mode 100644 index 0000000000000000000000000000000000000000..55c66fd295c21d0149e5b7639ad8b81e449770de --- /dev/null +++ b/text-generation-webui-main/css/html_instruct_style.css @@ -0,0 +1,73 @@ +.chat { + margin-left: auto; + margin-right: auto; + max-width: 800px; + height: calc(100vh - 306px); + overflow-y: auto; + padding-right: 20px; + display: flex; + flex-direction: column-reverse; + word-break: break-word; + overflow-wrap: anywhere; +} + +.message { + display: grid; + grid-template-columns: 60px 1fr; + padding-bottom: 25px; + font-size: 15px; + font-family: Helvetica, Arial, sans-serif; + line-height: 1.428571429; +} + +.username { + display: none; +} + +.message-body {} + +.message-body p { + font-size: 15px !important; +} + +.message-body li { + margin-top: 0.5em !important; + margin-bottom: 0.5em !important; +} + +.message-body li > p { + display: inline !important; +} + +.message-body code { + overflow-x: auto; +} +.message-body :not(pre) > code { + white-space: normal !important; +} + +.dark .message-body p em { + color: rgb(198, 202, 214) !important; +} + +.message-body p em { + color: rgb(110, 110, 110) !important; +} + +.gradio-container .chat .assistant-message { + padding: 15px; + border-radius: 20px; + background-color: #0000000f; + margin-top: 9px !important; + margin-bottom: 18px !important; +} + +.gradio-container .chat .user-message { + padding: 15px; + border-radius: 20px; + margin-bottom: 9px !important; +} + +.dark .chat .assistant-message { + background-color: #374151; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/html_readable_style.css b/text-generation-webui-main/css/html_readable_style.css new file mode 100644 index 0000000000000000000000000000000000000000..83fa46b58f04c5c467e2203e1ed950d6daf17d7e --- /dev/null +++ b/text-generation-webui-main/css/html_readable_style.css @@ -0,0 +1,29 @@ +.container { + max-width: 600px; + margin-left: auto; + margin-right: auto; + background-color: rgb(31, 41, 55); + padding:3em; + word-break: break-word; + overflow-wrap: anywhere; + color: #efefef !important; +} + +.container p, .container li { + font-size: 16px !important; + color: #efefef !important; + margin-bottom: 22px; + line-height: 1.4 !important; +} + +.container li > p { + display: inline !important; +} + +.container code { + overflow-x: auto; +} + +.container :not(pre) > code { + white-space: normal !important; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/main.css b/text-generation-webui-main/css/main.css new file mode 100644 index 0000000000000000000000000000000000000000..cdde27058683844ca3878451946907ef2a81c046 --- /dev/null +++ b/text-generation-webui-main/css/main.css @@ -0,0 +1,139 @@ +.tabs.svelte-710i53 { + margin-top: 0 +} + +.py-6 { + padding-top: 2.5rem +} + +.dark #refresh-button { + background-color: #ffffff1f; +} + +#refresh-button { + flex: none; + margin: 0; + padding: 0; + min-width: 50px; + border: none; + box-shadow: none; + border-radius: 10px; + background-color: #0000000d; +} + +#download-label, #upload-label { + min-height: 0 +} + +#accordion { +} + +.dark svg { + fill: white; +} + +.dark a { + color: white !important; + text-decoration: none !important; +} + +ol li p, ul li p { + display: inline-block; +} + +#main, #parameters, #chat-settings, #interface-mode, #lora, #training-tab, #model-tab { + border: 0; +} + +.gradio-container-3-18-0 .prose * h1, h2, h3, h4 { + color: white; +} + +.gradio-container { + max-width: 100% !important; + padding-top: 0 !important; +} + +#extensions { + padding: 15px; + margin-bottom: 35px; +} + +span.math.inline { + font-size: 27px; + vertical-align: baseline !important; +} + +div.svelte-15lo0d8 > *, div.svelte-15lo0d8 > .form > * { + flex-wrap: nowrap; +} + +.header_bar { + background-color: #f7f7f7; + margin-bottom: 40px; +} + +.dark .header_bar { + border: none !important; + background-color: #8080802b; +} + +.textbox_default textarea { + height: calc(100vh - 391px); +} + +.textbox_default_output textarea { + height: calc(100vh - 210px); +} + +.textbox textarea { + height: calc(100vh - 261px); +} + +.textbox_default textarea, .textbox_default_output textarea, .textbox textarea { + font-size: 16px !important; + color: #46464A !important; +} + +.dark textarea { + color: #efefef !important; +} + +/* Hide the gradio footer*/ +footer { + display: none !important; +} + +button { + font-size: 14px !important; +} + +.small-button { + max-width: 171px; +} + +/* Align the elements for SD_api_picture extension */ +.SDAP #sampler_box { + padding-top: var(--spacing-sm); + padding-bottom: var(--spacing-sm); +} + +.SDAP #seed_box, +.SDAP #cfg_box { + padding-top: var(--spacing-md); +} + +.SDAP #sampler_box span, +.SDAP #seed_box span, +.SDAP #cfg_box span{ + margin-bottom: var(--spacing-sm); +} + +.SDAP svg.dropdown-arrow { + flex-shrink: 0 !important; + margin: 0px !important; +} + +.SDAP .hires_opts input[type="number"] { + width: 6em !important; +} \ No newline at end of file diff --git a/text-generation-webui-main/css/main.js b/text-generation-webui-main/css/main.js new file mode 100644 index 0000000000000000000000000000000000000000..32820ebe15ddb80ca5fbcd2c4f88cc7c244cf3c5 --- /dev/null +++ b/text-generation-webui-main/css/main.js @@ -0,0 +1,18 @@ +document.getElementById("main").parentNode.childNodes[0].classList.add("header_bar"); +document.getElementById("main").parentNode.style = "padding: 0; margin: 0"; +document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0"; + +// Get references to the elements +let main = document.getElementById('main'); +let main_parent = main.parentNode; +let extensions = document.getElementById('extensions'); + +// Add an event listener to the main element +main_parent.addEventListener('click', function(e) { + // Check if the main element is visible + if (main.offsetHeight > 0 && main.offsetWidth > 0) { + extensions.style.display = 'flex'; + } else { + extensions.style.display = 'none'; + } +}); diff --git a/text-generation-webui-main/docker/.dockerignore b/text-generation-webui-main/docker/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..6073533e0929aac9e917a5980198334a2a01f8ef --- /dev/null +++ b/text-generation-webui-main/docker/.dockerignore @@ -0,0 +1,9 @@ +.env +Dockerfile +/characters +/loras +/models +/presets +/prompts +/softprompts +/training diff --git a/text-generation-webui-main/docker/.env.example b/text-generation-webui-main/docker/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..d20300b776745fa8c767c6880fed1d21c6754d09 --- /dev/null +++ b/text-generation-webui-main/docker/.env.example @@ -0,0 +1,25 @@ +# by default the Dockerfile specifies these versions: 3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX +# however for me to work i had to specify the exact version for my card ( 2060 ) it was 7.5 +# https://developer.nvidia.com/cuda-gpus you can find the version for your card here +TORCH_CUDA_ARCH_LIST=7.5 + +# these commands worked for me with roughly 4.5GB of vram +CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices + +# the following examples have been tested with the files linked in docs/README_docker.md: +# example running 13b with 4bit/128 groupsize : CLI_ARGS=--model llama-13b-4bit-128g --wbits 4 --listen --groupsize 128 --pre_layer 25 +# example with loading api extension and public share: CLI_ARGS=--model llama-7b-4bit --wbits 4 --listen --auto-devices --no-stream --extensions api --share +# example running 7b with 8bit groupsize : CLI_ARGS=--model llama-7b --load-in-8bit --listen --auto-devices + +# the port the webui binds to on the host +HOST_PORT=7860 +# the port the webui binds to inside the container +CONTAINER_PORT=7860 + +# the port the api binds to on the host +HOST_API_PORT=5000 +# the port the api binds to inside the container +CONTAINER_API_PORT=5000 + +# the version used to install text-generation-webui from +WEBUI_VERSION=HEAD diff --git a/text-generation-webui-main/docker/Dockerfile b/text-generation-webui-main/docker/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..b4fc91216606d74fc4505c7d85330b557341a4f1 --- /dev/null +++ b/text-generation-webui-main/docker/Dockerfile @@ -0,0 +1,68 @@ +FROM nvidia/cuda:11.8.0-devel-ubuntu22.04 as builder + +RUN apt-get update && \ + apt-get install --no-install-recommends -y git vim build-essential python3-dev python3-venv && \ + rm -rf /var/lib/apt/lists/* + +RUN git clone https://github.com/oobabooga/GPTQ-for-LLaMa /build + +WORKDIR /build + +RUN python3 -m venv /build/venv +RUN . /build/venv/bin/activate && \ + pip3 install --upgrade pip setuptools && \ + pip3 install torch torchvision torchaudio && \ + pip3 install -r requirements.txt + +# https://developer.nvidia.com/cuda-gpus +# for a rtx 2060: ARG TORCH_CUDA_ARCH_LIST="7.5" +ARG TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5;8.0;8.6+PTX" +RUN . /build/venv/bin/activate && \ + python3 setup_cuda.py bdist_wheel -d . + +FROM nvidia/cuda:11.8.0-runtime-ubuntu22.04 + +LABEL maintainer="Your Name " +LABEL description="Docker image for GPTQ-for-LLaMa and Text Generation WebUI" + +RUN apt-get update && \ + apt-get install --no-install-recommends -y libportaudio2 libasound-dev git python3 python3-pip make g++ && \ + rm -rf /var/lib/apt/lists/* + +RUN --mount=type=cache,target=/root/.cache/pip pip3 install virtualenv +RUN mkdir /app + +WORKDIR /app + +ARG WEBUI_VERSION +RUN test -n "${WEBUI_VERSION}" && git reset --hard ${WEBUI_VERSION} || echo "Using provided webui source" + +RUN virtualenv /app/venv +RUN . /app/venv/bin/activate && \ + pip3 install --upgrade pip setuptools && \ + pip3 install torch torchvision torchaudio + +COPY --from=builder /build /app/repositories/GPTQ-for-LLaMa +RUN . /app/venv/bin/activate && \ + pip3 install /app/repositories/GPTQ-for-LLaMa/*.whl + +COPY extensions/api/requirements.txt /app/extensions/api/requirements.txt +COPY extensions/elevenlabs_tts/requirements.txt /app/extensions/elevenlabs_tts/requirements.txt +COPY extensions/google_translate/requirements.txt /app/extensions/google_translate/requirements.txt +COPY extensions/silero_tts/requirements.txt /app/extensions/silero_tts/requirements.txt +COPY extensions/whisper_stt/requirements.txt /app/extensions/whisper_stt/requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/api && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/elevenlabs_tts && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/google_translate && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/silero_tts && pip3 install -r requirements.txt +RUN --mount=type=cache,target=/root/.cache/pip . /app/venv/bin/activate && cd extensions/whisper_stt && pip3 install -r requirements.txt + +COPY requirements.txt /app/requirements.txt +RUN . /app/venv/bin/activate && \ + pip3 install -r requirements.txt + +RUN cp /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda118.so /app/venv/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so + +COPY . /app/ +ENV CLI_ARGS="" +CMD . /app/venv/bin/activate && python3 server.py ${CLI_ARGS} diff --git a/text-generation-webui-main/docker/docker-compose.yml b/text-generation-webui-main/docker/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..5d9b795719d020b9ab0e32ab6cb7fef5e29ed065 --- /dev/null +++ b/text-generation-webui-main/docker/docker-compose.yml @@ -0,0 +1,31 @@ +version: "3.3" +services: + text-generation-webui: + build: + context: . + args: + # specify which cuda version your card supports: https://developer.nvidia.com/cuda-gpus + TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST} + WEBUI_VERSION: ${WEBUI_VERSION} + env_file: .env + ports: + - "${HOST_PORT}:${CONTAINER_PORT}" + - "${HOST_API_PORT}:${CONTAINER_API_PORT}" + stdin_open: true + tty: true + volumes: + - ./characters:/app/characters + - ./extensions:/app/extensions + - ./loras:/app/loras + - ./models:/app/models + - ./presets:/app/presets + - ./prompts:/app/prompts + - ./softprompts:/app/softprompts + - ./training:/app/training + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + capabilities: [gpu] diff --git a/text-generation-webui-main/docs/Custom-chat-characters.md b/text-generation-webui-main/docs/Custom-chat-characters.md new file mode 100644 index 0000000000000000000000000000000000000000..eeb22d1c2b64222626faa166828b2cb06e9f66e7 --- /dev/null +++ b/text-generation-webui-main/docs/Custom-chat-characters.md @@ -0,0 +1,31 @@ +Custom chat mode characters are defined by `.yaml` files inside the `characters` folder. An example is included: [Example.yaml](https://github.com/oobabooga/text-generation-webui/blob/main/characters/Example.yaml) + +The following fields may be defined: + +| Field | Description | +|-------|-------------| +| `name` or `bot` | The character's name. | +| `your_name` or `user` (optional) | Your name. This overwrites what you had previously written in the `Your name` field in the interface. | +| `context` | A string that appears at the top of the prompt. It usually contains a description of the character's personality. | +| `greeting` (optional) | The character's opening message when a new conversation is started. | +| `example_dialogue` (optional) | A few example messages to guide the model. | +| `turn_template` (optional) | Used to define where the spaces and new line characters should be in Instruct mode. See the characters in `characters/instruction-following` for examples. | + +#### Special tokens + +* `{{char}}` or ``: are replaced with the character's name +* `{{user}}` or ``: are replaced with your name + +These replacements happen when the character is loaded, and they apply to the `context`, `greeting`, and `example_dialogue` fields. + +#### How do I add a profile picture for my character? + +Put an image with the same name as your character's yaml file into the `characters` folder. For example, if your bot is `Character.yaml`, add `Character.jpg` or `Character.png` to the folder. + +#### Is the chat history truncated in the prompt? + +Once your prompt reaches the 2048 token limit, old messages will be removed one at a time. The context string will always stay at the top of the prompt and will never get truncated. + +#### Pygmalion format characters + +These are also supported out of the box. Simply put the JSON file in the `characters` folder, or upload it directly from the web UI by clicking on the "Upload character" tab at the bottom. \ No newline at end of file diff --git a/text-generation-webui-main/docs/DeepSpeed.md b/text-generation-webui-main/docs/DeepSpeed.md new file mode 100644 index 0000000000000000000000000000000000000000..70cd81519a6954ebc7cdaf82e03a169bed878106 --- /dev/null +++ b/text-generation-webui-main/docs/DeepSpeed.md @@ -0,0 +1,23 @@ +An alternative way of reducing the GPU memory usage of models is to use the `DeepSpeed ZeRO-3` optimization. + +With this, I have been able to load a 6b model (GPT-J 6B) with less than 6GB of VRAM. The speed of text generation is very decent and much better than what would be accomplished with `--auto-devices --gpu-memory 6`. + +As far as I know, DeepSpeed is only available for Linux at the moment. + +### How to use it + +1. Install DeepSpeed: + +``` +pip install deepspeed +``` + +2. Start the web UI replacing `python` with `deepspeed --num_gpus=1` and adding the `--deepspeed` flag. Example: + +``` +deepspeed --num_gpus=1 server.py --deepspeed --chat --model gpt-j-6B +``` + +### Learn more + +For more information, check out [this comment](https://github.com/oobabooga/text-generation-webui/issues/40#issuecomment-1412038622) by 81300, who came up with the DeepSpeed support in this web UI. \ No newline at end of file diff --git a/text-generation-webui-main/docs/Docker.md b/text-generation-webui-main/docs/Docker.md new file mode 100644 index 0000000000000000000000000000000000000000..b1e92253cd72423a86d72f6bb057da9bed19a4bc --- /dev/null +++ b/text-generation-webui-main/docs/Docker.md @@ -0,0 +1,181 @@ +Docker Compose is a way of installing and launching the web UI in an isolated Ubuntu image using only a few commands. + +In order to create the image as described in the main README, you must have docker compose 2.17 or higher: + +``` +~$ docker compose version +Docker Compose version v2.17.2 +``` + +# Intructions by [@loeken](https://github.com/loeken) + +- [Ubuntu 22.04](#ubuntu-2204) + - [0. youtube video](#0-youtube-video) + - [1. update the drivers](#1-update-the-drivers) + - [2. reboot](#2-reboot) + - [3. install docker](#3-install-docker) + - [4. docker \& container toolkit](#4-docker--container-toolkit) + - [5. clone the repo](#5-clone-the-repo) + - [6. prepare models](#6-prepare-models) + - [7. prepare .env file](#7-prepare-env-file) + - [8. startup docker container](#8-startup-docker-container) +- [Manjaro](#manjaro) + - [update the drivers](#update-the-drivers) + - [reboot](#reboot) + - [docker \& container toolkit](#docker--container-toolkit) + - [continue with ubuntu task](#continue-with-ubuntu-task) +- [Windows](#windows) + - [0. youtube video](#0-youtube-video-1) + - [1. choco package manager](#1-choco-package-manager) + - [2. install drivers/dependencies](#2-install-driversdependencies) + - [3. install wsl](#3-install-wsl) + - [4. reboot](#4-reboot) + - [5. git clone \&\& startup](#5-git-clone--startup) + - [6. prepare models](#6-prepare-models-1) + - [7. startup](#7-startup) +- [notes](#notes) + +# Ubuntu 22.04 + +## 0. youtube video +A video walking you through the setup can be found here: + +[![oobabooga text-generation-webui setup in docker on ubuntu 22.04](https://img.youtube.com/vi/ELkKWYh8qOk/0.jpg)](https://www.youtube.com/watch?v=ELkKWYh8qOk) + + +## 1. update the drivers +in the the “software updater” update drivers to the last version of the prop driver. + +## 2. reboot +to switch using to new driver + +## 3. install docker +```bash +sudo apt update +sudo apt-get install curl +sudo mkdir -m 0755 -p /etc/apt/keyrings +curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg +echo \ + "deb [arch="$(dpkg --print-architecture)" signed-by=/etc/apt/keyrings/docker.gpg] https://download.docker.com/linux/ubuntu \ + "$(. /etc/os-release && echo "$VERSION_CODENAME")" stable" | \ + sudo tee /etc/apt/sources.list.d/docker.list > /dev/null +sudo apt update +sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin docker-compose -y +sudo usermod -aG docker $USER +newgrp docker +``` + +## 4. docker & container toolkit +```bash +curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg +echo "deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://nvidia.github.io/libnvidia-container/stable/ubuntu22.04/amd64 /" | \ +sudo tee /etc/apt/sources.list.d/nvidia.list > /dev/null +sudo apt update +sudo apt install nvidia-docker2 nvidia-container-runtime -y +sudo systemctl restart docker +``` + +## 5. clone the repo +``` +git clone https://github.com/oobabooga/text-generation-webui +cd text-generation-webui +``` + +## 6. prepare models +download and place the models inside the models folder. tested with: + +4bit +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +8bit: +https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 + +## 7. prepare .env file +edit .env values to your needs. +```bash +cp .env.example .env +nano .env +``` + +## 8. startup docker container +```bash +docker compose up --build +``` + +# Manjaro +manjaro/arch is similar to ubuntu just the dependency installation is more convenient + +## update the drivers +```bash +sudo mhwd -a pci nonfree 0300 +``` +## reboot +```bash +reboot +``` +## docker & container toolkit +```bash +yay -S docker docker-compose buildkit gcc nvidia-docker +sudo usermod -aG docker $USER +newgrp docker +sudo systemctl restart docker # required by nvidia-container-runtime +``` + +## continue with ubuntu task +continue at [5. clone the repo](#5-clone-the-repo) + +# Windows +## 0. youtube video +A video walking you through the setup can be found here: +[![oobabooga text-generation-webui setup in docker on windows 11](https://img.youtube.com/vi/ejH4w5b5kFQ/0.jpg)](https://www.youtube.com/watch?v=ejH4w5b5kFQ) + +## 1. choco package manager +install package manager (https://chocolatey.org/ ) +``` +Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1')) +``` + +## 2. install drivers/dependencies +``` +choco install nvidia-display-driver cuda git docker-desktop +``` + +## 3. install wsl +wsl --install + +## 4. reboot +after reboot enter username/password in wsl + +## 5. git clone && startup +clone the repo and edit .env values to your needs. +``` +cd Desktop +git clone https://github.com/oobabooga/text-generation-webui +cd text-generation-webui +COPY .env.example .env +notepad .env +``` + +## 6. prepare models +download and place the models inside the models folder. tested with: + +4bit https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +8bit: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 + +## 7. startup +``` +docker compose up +``` + +# notes + +on older ubuntus you can manually install the docker compose plugin like this: +``` +DOCKER_CONFIG=${DOCKER_CONFIG:-$HOME/.docker} +mkdir -p $DOCKER_CONFIG/cli-plugins +curl -SL https://github.com/docker/compose/releases/download/v2.17.2/docker-compose-linux-x86_64 -o $DOCKER_CONFIG/cli-plugins/docker-compose +chmod +x $DOCKER_CONFIG/cli-plugins/docker-compose +export PATH="$HOME/.docker/cli-plugins:$PATH" +``` diff --git a/text-generation-webui-main/docs/Extensions.md b/text-generation-webui-main/docs/Extensions.md new file mode 100644 index 0000000000000000000000000000000000000000..dd4af96d7506be68fbe7668451f74fa5f50d85f3 --- /dev/null +++ b/text-generation-webui-main/docs/Extensions.md @@ -0,0 +1,166 @@ +This web UI supports extensions. They are simply files under + +``` +extensions/your_extension_name/script.py +``` + +which can be invoked with the + +``` +--extension your_extension_name +``` + +command-line flag. + +## [text-generation-webui-extensions](https://github.com/oobabooga/text-generation-webui-extensions) + +The link above contains a directory of user extensions for text-generation-webui. + +If you create an extension, you are welcome to host it in a GitHub repository and submit it to the list above. + +## Built-in extensions + +Most of these have been created by the extremely talented contributors that you can find here: [contributors](https://github.com/oobabooga/text-generation-webui/graphs/contributors?from=2022-12-18&to=&type=a). + +|Extension|Description| +|---------|-----------| +|[api](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/api)| Creates an API with two endpoints, one for streaming at `/api/v1/stream` port 5005 and another for blocking at `/api/v1/generate` por 5000. This is the main API for this web UI. | +|[google_translate](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/google_translate)| Automatically translates inputs and outputs using Google Translate.| +|[character_bias](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/character_bias)| Just a very simple example that biases the bot's responses in chat mode.| +|[gallery](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/gallery/)| Creates a gallery with the chat characters and their pictures. | +|[silero_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/silero_tts)| Text-to-speech extension using [Silero](https://github.com/snakers4/silero-models). When used in chat mode, it replaces the responses with an audio widget. | +|[elevenlabs_tts](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/elevenlabs_tts)| Text-to-speech extension using the [ElevenLabs](https://beta.elevenlabs.io/) API. You need an API key to use it. | +|[send_pictures](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/send_pictures/)| Creates an image upload field that can be used to send images to the bot in chat mode. Captions are automatically generated using BLIP. | +|[whisper_stt](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/whisper_stt)| Allows you to enter your inputs in chat mode using your microphone. | +|[sd_api_pictures](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures)| Allows you to request pictures from the bot in chat mode, which will be generated using the AUTOMATIC1111 Stable Diffusion API. See examples [here](https://github.com/oobabooga/text-generation-webui/pull/309). | +|[llava](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava) | Adds LLaVA multimodal model support. For detailed description see [README.md](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava/README.md) in the extension directory. | +|[openai](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/openai)| Creates an API that mimics the OpenAI API and can be used as a drop-in replacement. | + +## How to write an extension + +`script.py` has access to all variables in the UI through the `modules.shared` module, and it may define the following functions: + +| Function | Description | +|-------------|-------------| +| `def ui()` | Creates custom gradio elements when the UI is launched. | +| `def input_modifier(string)` | Modifies the input string before it enters the model. In chat mode, it is applied to the user message. Otherwise, it is applied to the entire prompt. | +| `def output_modifier(string)` | Modifies the output string before it is presented in the UI. In chat mode, it is applied to the bot's reply. Otherwise, it is applied to the entire output. | +| `def bot_prefix_modifier(string)` | Applied in chat mode to the prefix for the bot's reply (more on that below). | +| `def custom_generate_chat_prompt(...)` | Overrides the prompt generator in chat mode. | +| `def tokenizer_modifier(state, prompt, input_ids, input_embeds)` | Modifies the `input_ids`/`input_embeds` fed to the model. Should return `prompt`, `input_ids`, `input_embeds`. See `llava` extension for an example | + +Additionally, the script may define two special global variables: + +#### `params` dictionary + +```python +params = { + "language string": "ja", +} +``` + +This dicionary can be used to make the extension parameters customizable by adding entries to a `settings.json` file like this: + +```python +"google_translate-language string": "fr", +``` + +#### `input_hijack` dictionary + +```python +input_hijack = { + 'state': False, + 'value': ["", ""] +} +``` +This is only relevant in chat mode. If your extension sets `input_hijack['state']` to `True` at any moment, the next call to `modules.chat.chatbot_wrapper` will use the values inside `input_hijack['value']` as the user input for text generation. See the `send_pictures` extension above for an example. + +Additionally, your extension can set the value to be a callback, in the form of `def cb(text: str, visible_text: str) -> [str, str]`. See the `llava` extension above for an example. + +## The `bot_prefix_modifier` + +In chat mode, this function modifies the prefix for a new bot message. For instance, if your bot is named `Marie Antoinette`, the default prefix for a new message will be + +``` +Marie Antoinette: +``` + +Using `bot_prefix_modifier`, you can change it to: + +``` +Marie Antoinette: *I am very enthusiastic* +``` + +Marie Antoinette will become very enthusiastic in all her messages. + +## Using multiple extensions at the same time + +In order to use your extension, you must start the web UI with the `--extensions` flag followed by the name of your extension (the folder under `text-generation-webui/extension` where `script.py` resides). + +You can activate more than one extension at a time by providing their names separated by spaces. The input, output and bot prefix modifiers will be applied in the specified order. For `custom_generate_chat_prompt`, only the first declaration encountered will be used and the rest will be ignored. + +``` +python server.py --extensions enthusiasm translate # First apply enthusiasm, then translate +python server.py --extensions translate enthusiasm # First apply translate, then enthusiasm +``` + +## `custom_generate_chat_prompt` example + +Below is an extension that just reproduces the default prompt generator in `modules/chat.py`. You can modify it freely to come up with your own prompts in chat mode. + +```python +def custom_generate_chat_prompt(user_input, state, **kwargs): + impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False + _continue = kwargs['_continue'] if '_continue' in kwargs else False + also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False + is_instruct = state['mode'] == 'instruct' + rows = [f"{state['context'].strip()}\n"] + + # Finding the maximum prompt size + chat_prompt_size = state['chat_prompt_size'] + if shared.soft_prompt: + chat_prompt_size -= shared.soft_prompt_tensor.shape[1] + max_length = min(get_max_prompt_length(state), chat_prompt_size) + + if is_instruct: + prefix1 = f"{state['name1']}\n" + prefix2 = f"{state['name2']}\n" + else: + prefix1 = f"{state['name1']}: " + prefix2 = f"{state['name2']}: " + + i = len(shared.history['internal']) - 1 + while i >= 0 and len(encode(''.join(rows))[0]) < max_length: + if _continue and i == len(shared.history['internal']) - 1: + rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}") + else: + rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}{state['end_of_turn']}\n") + string = shared.history['internal'][i][0] + if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']: + rows.insert(1, f"{prefix1}{string.strip()}{state['end_of_turn']}\n") + i -= 1 + + if impersonate: + rows.append(f"{prefix1.strip() if not is_instruct else prefix1}") + limit = 2 + elif _continue: + limit = 3 + else: + # Adding the user message + user_input = fix_newlines(user_input) + if len(user_input) > 0: + rows.append(f"{prefix1}{user_input}{state['end_of_turn']}\n") + + # Adding the Character prefix + rows.append(apply_extensions(f"{prefix2.strip() if not is_instruct else prefix2}", "bot_prefix")) + limit = 3 + + while len(rows) > limit and len(encode(''.join(rows))[0]) >= max_length: + rows.pop(1) + prompt = ''.join(rows) + + if also_return_rows: + return prompt, rows + else: + return prompt +``` diff --git a/text-generation-webui-main/docs/FlexGen.md b/text-generation-webui-main/docs/FlexGen.md new file mode 100644 index 0000000000000000000000000000000000000000..dce71f9e6e35ab1f55d8379852316f55b013962a --- /dev/null +++ b/text-generation-webui-main/docs/FlexGen.md @@ -0,0 +1,64 @@ +>FlexGen is a high-throughput generation engine for running large language models with limited GPU memory (e.g., a 16GB T4 GPU or a 24GB RTX3090 gaming card!). + +https://github.com/FMInference/FlexGen + +## Installation + +No additional installation steps are necessary. FlexGen is in the `requirements.txt` file for this project. + +## Converting a model + +FlexGen only works with the OPT model, and it needs to be converted to numpy format before starting the web UI: + +``` +python convert-to-flexgen.py models/opt-1.3b/ +``` + +The output will be saved to `models/opt-1.3b-np/`. + +## Usage + +The basic command is the following: + +``` +python server.py --model opt-1.3b --flexgen +``` + +For large models, the RAM usage may be too high and your computer may freeze. If that happens, you can try this: + +``` +python server.py --model opt-1.3b --flexgen --compress-weight +``` + +With this second command, I was able to run both OPT-6.7b and OPT-13B with **2GB VRAM**, and the speed was good in both cases. + +You can also manually set the offload strategy with + +``` +python server.py --model opt-1.3b --flexgen --percent 0 100 100 0 100 0 +``` + +where the six numbers after `--percent` are: + +``` +the percentage of weight on GPU +the percentage of weight on CPU +the percentage of attention cache on GPU +the percentage of attention cache on CPU +the percentage of activations on GPU +the percentage of activations on CPU +``` + +You should typically only change the first two numbers. If their sum is less than 100, the remaining layers will be offloaded to the disk, by default into the `text-generation-webui/cache` folder. + +## Performance + +In my experiments with OPT-30B using a RTX 3090 on Linux, I have obtained these results: + +* `--flexgen --compress-weight --percent 0 100 100 0 100 0`: 0.99 seconds per token. +* `--flexgen --compress-weight --percent 100 0 100 0 100 0`: 0.765 seconds per token. + +## Limitations + +* Only works with the OPT models. +* Only two generation parameters are available: `temperature` and `do_sample`. \ No newline at end of file diff --git a/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md b/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md new file mode 100644 index 0000000000000000000000000000000000000000..37f6496cbfbca70ba504bbd955de6802c5da3398 --- /dev/null +++ b/text-generation-webui-main/docs/GPTQ-models-(4-bit-mode).md @@ -0,0 +1,142 @@ +In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU. + +This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa + +GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323 + +## GPTQ-for-LLaMa branches + +Different branches of GPTQ-for-LLaMa are available: + +| Branch | Comment | +|----|----| +| [Old CUDA branch (recommended)](https://github.com/oobabooga/GPTQ-for-LLaMa/) | The fastest branch, works on Windows and Linux. | +| [Up-to-date triton branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa) | Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux. | +| [Up-to-date CUDA branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda) | As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size. | + +Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI. + +## Installation + +### Step 0: install nvcc + +``` +conda activate textgen +conda install -c conda-forge cudatoolkit-dev +``` + +The command above takes some 10 minutes to run and shows no progress bar or updates along the way. + +See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571 + +### Step 1: install GPTQ-for-LLaMa + +Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it: + +``` +mkdir repositories +cd repositories +git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda +cd GPTQ-for-LLaMa +python setup_cuda.py install +``` + +You are going to need to have a C++ compiler installed into your system for the last command. On Linux, `sudo apt install build-essential` or equivalent is enough. + +If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands: + +``` +cd repositories +rm -r GPTQ-for-LLaMa +pip uninstall -y quant-cuda +git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda +... +``` + +``` +cd repositories +rm -r GPTQ-for-LLaMa +pip uninstall -y quant-cuda +git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton +... +``` + + +https://github.com/qwopqwop200/GPTQ-for-LLaMa + +### Step 2: get the pre-converted weights + +* Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617 +* Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105 + +⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights). + +### Step 3: Start the web UI: + +For the models converted without `group-size`: + +``` +python server.py --model llama-7b-4bit +``` + +For the models converted with `group-size`: + +``` +python server.py --model llama-13b-4bit-128g +``` + +The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like + +``` +python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128 +``` + +## CPU offloading + +It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU. + +With this command, I can run llama-7b with 4GB VRAM: + +``` +python server.py --model llama-7b-4bit --pre_layer 20 +``` + +This is the performance: + +``` +Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens) +``` + +## Using LoRAs in 4-bit mode + +At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit + +In order to use it: + +1. Make sure that your requirements are up to date: + +``` +cd text-generation-webui +pip install -r requirements.txt --upgrade +``` + +2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder: + +``` +cd text-generation-webui/repositories +git clone https://github.com/johnsmith0031/alpaca_lora_4bit +``` + +⚠️ I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0` + +3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command: + +``` +pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit +``` + +4. Start the UI with the `--monkey-patch` flag: + +``` +python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch +``` diff --git a/text-generation-webui-main/docs/LLaMA-model.md b/text-generation-webui-main/docs/LLaMA-model.md new file mode 100644 index 0000000000000000000000000000000000000000..338d458b13b56b3d0f02dd3f4b7d5156a82b88e9 --- /dev/null +++ b/text-generation-webui-main/docs/LLaMA-model.md @@ -0,0 +1,45 @@ +LLaMA is a Large Language Model developed by Meta AI. + +It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters. + +This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode) +](GPTQ-models-(4-bit-mode).md). + +## Getting the weights + +### Option 1: pre-converted weights + +* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789 +* Direct download: https://huggingface.co/Neko-Institute-of-Science + +⚠️ The tokenizers for the Torrent source above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer: + +``` +python download-model.py oobabooga/llama-tokenizer +``` + +Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load. + +### Option 2: convert the weights yourself + +1. Install the `protobuf` library: + +``` +pip install protobuf==3.20.1 +``` + +2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link: + +### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) + +``` +python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b +``` + +3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder. + +## Starting the web UI + +```python +python server.py --model llama-7b +``` diff --git a/text-generation-webui-main/docs/Low-VRAM-guide.md b/text-generation-webui-main/docs/Low-VRAM-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..1dc86f9c7f764a886c454f7f76a2a89a77140655 --- /dev/null +++ b/text-generation-webui-main/docs/Low-VRAM-guide.md @@ -0,0 +1,51 @@ +If you GPU is not large enough to fit a model, try these in the following order: + +### Load the model in 8-bit mode + +``` +python server.py --load-in-8bit +``` + +This reduces the memory usage by half with no noticeable loss in quality. Only newer GPUs support 8-bit mode. + +### Split the model across your GPU and CPU + +``` +python server.py --auto-devices +``` + +If you can load the model with this command but it runs out of memory when you try to generate text, try increasingly limiting the amount of memory allocated to the GPU until the error stops happening: + +``` +python server.py --auto-devices --gpu-memory 10 +python server.py --auto-devices --gpu-memory 9 +python server.py --auto-devices --gpu-memory 8 +... +``` + +where the number is in GiB. + +For finer control, you can also specify the unit in MiB explicitly: + +``` +python server.py --auto-devices --gpu-memory 8722MiB +python server.py --auto-devices --gpu-memory 4725MiB +python server.py --auto-devices --gpu-memory 3500MiB +... +``` + +Additionally, you can also set the `--no-cache` value to reduce the GPU usage while generating text at a performance cost. This may allow you to set a higher value for `--gpu-memory`, resulting in a net performance gain. + +### Send layers to a disk cache + +As a desperate last measure, you can split the model across your GPU, CPU, and disk: + +``` +python server.py --auto-devices --disk +``` + +With this, I am able to load a 30b model into my RTX 3090, but it takes 10 seconds to generate 1 word. + +### DeepSpeed (experimental) + +An experimental alternative to all of the above is to use DeepSpeed: [guide](DeepSpeed.md). diff --git a/text-generation-webui-main/docs/README.md b/text-generation-webui-main/docs/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f43efa6801d5a921c0e8c9d2fbe3278d72b48dc8 --- /dev/null +++ b/text-generation-webui-main/docs/README.md @@ -0,0 +1,19 @@ +# text-generation-webui manual + +## Table of contents + +* [Custom-chat-characters](Custom-chat-characters.md) +* [Docker Compose](Docker.md) +* [DeepSpeed](DeepSpeed.md) +* [Extensions](Extensions.md) +* [FlexGen](FlexGen.md) +* [GPTQ-models-(4-bit-mode)](GPTQ-models-(4-bit-mode).md) +* [llama.cpp-models](llama.cpp-models.md) +* [LLaMA-model](LLaMA-model.md) +* [Low-VRAM-guide](Low-VRAM-guide.md) +* [RWKV-model](RWKV-model.md) +* [Spell-book](Spell-book.md) +* [System-requirements](System-requirements.md) +* [Using-LoRAs](Using-LoRAs.md) +* [Windows-installation-guide](Windows-installation-guide.md) +* [WSL-installation-guide](WSL-installation-guide.md) diff --git a/text-generation-webui-main/docs/RWKV-model.md b/text-generation-webui-main/docs/RWKV-model.md new file mode 100644 index 0000000000000000000000000000000000000000..27db3d10ca98faffee7c254a3244405f8cce2793 --- /dev/null +++ b/text-generation-webui-main/docs/RWKV-model.md @@ -0,0 +1,54 @@ +> RWKV: RNN with Transformer-level LLM Performance +> +> It combines the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state). + +https://github.com/BlinkDL/RWKV-LM + +https://github.com/BlinkDL/ChatRWKV + +## Using RWKV in the web UI + +#### 1. Download the model + +It is available in different sizes: + +* https://huggingface.co/BlinkDL/rwkv-4-pile-3b/ +* https://huggingface.co/BlinkDL/rwkv-4-pile-7b/ +* https://huggingface.co/BlinkDL/rwkv-4-pile-14b/ + +There are also older releases with smaller sizes like: + +* https://huggingface.co/BlinkDL/rwkv-4-pile-169m/resolve/main/RWKV-4-Pile-169M-20220807-8023.pth + +Download the chosen `.pth` and put it directly in the `models` folder. + +#### 2. Download the tokenizer + +[20B_tokenizer.json](https://raw.githubusercontent.com/BlinkDL/ChatRWKV/main/v2/20B_tokenizer.json) + +Also put it directly in the `models` folder. Make sure to not rename it. It should be called `20B_tokenizer.json`. + +#### 3. Launch the web UI + +No additional steps are required. Just launch it as you would with any other model. + +``` +python server.py --listen --no-stream --model RWKV-4-Pile-169M-20220807-8023.pth +``` + +## Setting a custom strategy + +It is possible to have very fine control over the offloading and precision for the model with the `--rwkv-strategy` flag. Possible values include: + +``` +"cpu fp32" # CPU mode +"cuda fp16" # GPU mode with float16 precision +"cuda fp16 *30 -> cpu fp32" # GPU+CPU offloading. The higher the number after *, the higher the GPU allocation. +"cuda fp16i8" # GPU mode with 8-bit precision +``` + +See the README for the PyPl package for more details: https://pypi.org/project/rwkv/ + +## Compiling the CUDA kernel + +You can compile the CUDA kernel for the model with `--rwkv-cuda-on`. This should improve the performance a lot but I haven't been able to get it to work yet. \ No newline at end of file diff --git a/text-generation-webui-main/docs/Spell-book.md b/text-generation-webui-main/docs/Spell-book.md new file mode 100644 index 0000000000000000000000000000000000000000..9b7c76c953f76f8a486bbe5156de4e9ebb3f0ec0 --- /dev/null +++ b/text-generation-webui-main/docs/Spell-book.md @@ -0,0 +1,107 @@ +You have now entered a hidden corner of the internet. + +A confusing yet intriguing realm of paradoxes and contradictions. + +A place where you will find out that what you thought you knew, you in fact didn't know, and what you didn't know was in front of you all along. + +![](https://i.pinimg.com/originals/6e/e2/7b/6ee27bad351d3aca470d80f1033ba9c6.jpg) + +*In other words, here I will document little-known facts about this web UI that I could not find another place for in the wiki.* + +#### You can train LoRAs in CPU mode + +Load the web UI with + +``` +python server.py --cpu +``` + +and start training the LoRA from the training tab as usual. + +#### 8-bit mode works with CPU offloading + +``` +python server.py --load-in-8bit --gpu-memory 4000MiB +``` + +#### `--pre_layer`, and not `--gpu-memory`, is the right way to do CPU offloading with 4-bit models + +``` +python server.py --wbits 4 --groupsize 128 --pre_layer 20 +``` + +#### Models can be loaded in 32-bit, 16-bit, 8-bit, and 4-bit modes + +``` +python server.py --cpu +python server.py +python server.py --load-in-8bit +python server.py --wbits 4 +``` + +#### The web UI works with any version of GPTQ-for-LLaMa + +Including the up to date triton and cuda branches. But you have to delete the `repositories/GPTQ-for-LLaMa` folder and reinstall the new one every time: + +``` +cd text-generation-webui/repositories +rm -r GPTQ-for-LLaMa +pip uninstall quant-cuda +git clone https://github.com/oobabooga/GPTQ-for-LLaMa -b cuda # or any other repository and branch +cd GPTQ-for-LLaMa +python setup_cuda.py install +``` + +#### Instruction-following templates are represented as chat characters + +https://github.com/oobabooga/text-generation-webui/tree/main/characters/instruction-following + +#### The right way to run Alpaca, Open Assistant, Vicuna, etc is Instruct mode, not normal chat mode + +Otherwise the prompt will not be formatted correctly. + +1. Start the web UI with + +``` +python server.py --chat +``` + +2. Click on the "instruct" option under "Chat modes" + +3. Select the correct template in the hidden dropdown menu that will become visible. + +#### Notebook mode is best mode + +Ascended individuals have realized that notebook mode is the superset of chat mode and can do chats with ultimate flexibility, including group chats, editing replies, starting a new bot reply in a given way, and impersonating. + +#### RWKV is a RNN + +Most models are transformers, but not RWKV, which is a RNN. It's a great model. + +#### `--gpu-memory` is not a hard limit on the GPU memory + +It is simply a parameter that is passed to the `accelerate` library while loading the model. More memory will be allocated during generation. That's why this parameter has to be set to less than your total GPU memory. + +#### Contrastive search perhaps the best preset + +But it uses a ton of VRAM. + +#### You can check the sha256sum of downloaded models with the download script + +``` +python download-model.py facebook/galactica-125m --check +``` + +#### The download script continues interrupted downloads by default + +It doesn't start over. + +#### You can download models with multiple threads + +``` +python download-model.py facebook/galactica-125m --threads 8 +``` + +#### LoRAs work in 4-bit mode + +You need to follow [these instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) and then start the web UI with the `--monkey-patch` flag. diff --git a/text-generation-webui-main/docs/System-requirements.md b/text-generation-webui-main/docs/System-requirements.md new file mode 100644 index 0000000000000000000000000000000000000000..3a88416d34ad7c8babd90a81db902e95288a8197 --- /dev/null +++ b/text-generation-webui-main/docs/System-requirements.md @@ -0,0 +1,42 @@ +These are the VRAM and RAM requirements (in MiB) to run some examples of models **in 16-bit (default) precision**: + +| model | VRAM (GPU) | RAM | +|:-----------------------|-------------:|--------:| +| arxiv_ai_gpt2 | 1512.37 | 5824.2 | +| blenderbot-1B-distill | 2441.75 | 4425.91 | +| opt-1.3b | 2509.61 | 4427.79 | +| gpt-neo-1.3b | 2605.27 | 5851.58 | +| opt-2.7b | 5058.05 | 4863.95 | +| gpt4chan_model_float16 | 11653.7 | 4437.71 | +| gpt-j-6B | 11653.7 | 5633.79 | +| galactica-6.7b | 12697.9 | 4429.89 | +| opt-6.7b | 12700 | 4368.66 | +| bloomz-7b1-p3 | 13483.1 | 4470.34 | + +#### GPU mode with 8-bit precision + +Allows you to load models that would not normally fit into your GPU. Enabled by default for 13b and 20b models in this web UI. + +| model | VRAM (GPU) | RAM | +|:---------------|-------------:|--------:| +| opt-13b | 12528.1 | 1152.39 | +| gpt-neox-20b | 20384 | 2291.7 | + +#### CPU mode (32-bit precision) + +A lot slower, but does not require a GPU. + +On my i5-12400F, 6B models take around 10-20 seconds to respond in chat mode, and around 5 minutes to generate a 200 tokens completion. + +| model | RAM | +|:-----------------------|---------:| +| arxiv_ai_gpt2 | 4430.82 | +| gpt-neo-1.3b | 6089.31 | +| opt-1.3b | 8411.12 | +| blenderbot-1B-distill | 8508.16 | +| opt-2.7b | 14969.3 | +| bloomz-7b1-p3 | 21371.2 | +| gpt-j-6B | 24200.3 | +| gpt4chan_model | 24246.3 | +| galactica-6.7b | 26561.4 | +| opt-6.7b | 29596.6 | diff --git a/text-generation-webui-main/docs/Training-LoRAs.md b/text-generation-webui-main/docs/Training-LoRAs.md new file mode 100644 index 0000000000000000000000000000000000000000..3d75ec5aa2bc12e8c13d6a583bd9aefd118f04d7 --- /dev/null +++ b/text-generation-webui-main/docs/Training-LoRAs.md @@ -0,0 +1,167 @@ +## Training Your Own LoRAs + +The WebUI seeks to make training your own LoRAs as easy as possible. It comes down to just a few simple steps: + +### **Step 1**: Make a plan. +- What base model do you want to use? The LoRA you make has to be matched up to a single architecture (eg LLaMA-13B) and cannot be transferred to others (eg LLaMA-7B, StableLM, etc. would all be different). Derivatives of the same model (eg Alpaca finetune of LLaMA-13B) might be transferrable, but even then it's best to train exactly on what you plan to use. +- What model format do you want? At time of writing, 8-bit models are most stable, and 4-bit are supported but experimental. In the near future it is likely that 4-bit will be the best option for most users. +- What are you training it on? Do you want it to learn real information, a simple format, ...? + +### **Step 2**: Gather a dataset. +- If you use a dataset similar to the [Alpaca](https://github.com/gururise/AlpacaDataCleaned/blob/main/alpaca_data_cleaned.json) format, that is natively supported by the `Formatted Dataset` input in the WebUI, with premade formatter options. +- If you use a dataset that isn't matched to Alpaca's format, but uses the same basic JSON structure, you can make your own format file by copying `training/formats/alpaca-format.json` to a new file and [editing its content](#format-files). +- If you can get the dataset into a simple text file, that works too! You can train using the `Raw text file` input option. + - This means you can for example just copy/paste a chatlog/documentation page/whatever you want, shove it in a plain text file, and train on it. +- If you use a structured dataset not in this format, you may have to find an external way to convert it - or open an issue to request native support. + +### **Step 3**: Do the training. +- **3.1**: Load the WebUI, and your model. + - Make sure you don't have any LoRAs already loaded (unless you want to train for multi-LoRA usage). +- **3.2**: Open the `Training` tab at the top, `Train LoRA` sub-tab. +- **3.3**: Fill in the name lof the LoRA, select your dataset in the dataset options. +- **3.4**: Select other parameters to your preference. See [parameters below](#parameters). +- **3.5**: click `Start LoRA Training`, and wait. + - It can take a few hours for a large dataset, or just a few minute if doing a small run. + - You may want to monitor your [loss value](#loss) while it goes. + +### **Step 4**: Evaluate your results. +- Load the LoRA under the Models Tab. +- You can go test-drive it on the `Text generation` tab, or you can use the `Perplexity evaluation` sub-tab of the `Training` tab. +- If you used the `Save every n steps` option, you can grab prior copies of the model from sub-folders within the LoRA model's folder and try them instead. + +### **Step 5**: Re-run if you're unhappy. +- Make sure to unload the LoRA before training it. +- You can simply resume a prior run - use `Copy parameters from` to select your LoRA, and edit parameters. Note that you cannot change the `Rank` of an already created LoRA. + - If you want to resume from a checkpoint saved along the way, simply copy the contents of the checkpoint folder into the LoRA's folder. + - (Note: `adapter_model.bin` is the important file that holds the actual LoRA content). + - This will start Learning Rate and Steps back to the start. If you want to resume as if you were midway through, you can adjust your Learning Rate to the last reported LR in logs and reduce your epochs. +- Or, you can start over entirely if you prefer. +- If your model is producing corrupted outputs, you probably need to start over and use a lower Learning Rate. +- If your model isn't learning detailed information but you want it to, you might need to just run more epochs, or you might need a higher Rank. +- If your model is enforcing a format you didn't want, you may need to tweak your dataset, or start over and not train as far. + +## Format Files + +If using JSON formatted datasets, they are presumed to be in the following approximate format: + +```json +[ + { + "somekey": "somevalue", + "key2": "value2" + }, + { + // etc + } +] +``` + +Where the keys (eg `somekey`, `key2` above) are standardized, and relatively consistent across the dataset, and the values (eg `somevalue`, `value2`) contain the content actually intended to be trained. + +For Alpaca, the keys are `instruction`, `input`, and `output`, wherein `input` is sometimes blank. + +A simple format file for Alpaca to be used as a chat bot is: + +```json +{ + "instruction,output": "User: %instruction%\nAssistant: %output%", + "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%" +} +``` + +Note that the keys (eg `instruction,output`) are a comma-separated list of dataset keys, and the values are a simple string that use those keys with `%%`. + +So for example if a dataset has `"instruction": "answer my question"`, then the format file's `User: %instruction%\n` will be automatically filled in as `User: answer my question\n`. + +If you have different sets of key inputs, you can make your own format file to match it. This format-file is designed to be as simple as possible to enable easy editing to match your needs. + +## Parameters + +The basic purpose and function of each parameter is documented on-page in the WebUI, so read through them in the UI to understand your options. + +That said, here's a guide to the most important parameter choices you should consider: + +### VRAM + +- First, you must consider your VRAM availability. + - Generally, under default settings, VRAM usage for training with default parameters is very close to when generating text (with 1000+ tokens of context) (ie, if you can generate text, you can train LoRAs). + - Note: worse by default in the 4-bit monkeypatch currently. Reduce `Micro Batch Size` to `1` to restore this to expectations. + - If you have VRAM to spare, setting higher batch sizes will use more VRAM and get you better quality training in exchange. + - If you have large data, setting a higher cutoff length may be beneficial, but will cost significant VRAM. If you can spare some, set your batch size to `1` and see how high you can push your cutoff length. + - If you're low on VRAM, reducing batch size or cutoff length will of course improve that. + - Don't be afraid to just try it and see what happens. If it's too much, it will just error out, and you can lower settings and try again. + +### Rank + +- Second, you want to consider the amount of learning you want. + - For example, you may wish to just learn a dialogue format (as in the case of Alpaca) in which case setting a low `Rank` value (32 or lower) works great. + - Or, you might be training on project documentation you want the bot to understand and be able to understand questions about, in which case the higher the rank, the better. + - Generally, higher Rank = more precise learning = more total content learned = more VRAM usage while training. + +### Learning Rate and Epochs + +- Third, how carefully you want it to be learned. + - In other words, how okay or not you are with the model losing unrelated understandings. + - You can control this with 3 key settings: the Learning Rate, its scheduler, and your total epochs. + - The learning rate controls how much change is made to the model by each token it sees. + - It's in scientific notation normally, so for example `3e-4` means `3 * 10^-4` which is `0.0003`. The number after `e-` controls how many `0`s are in the number. + - Higher values let training run faster, but also are more likely to corrupt prior data in the model. + - You essentially have two variables to balance: the LR, and Epochs. + - If you make LR higher, you can set Epochs equally lower to match. High LR + low epochs = very fast, low quality training. + - If you make LR low, set epochs high. Low LR + high epochs = slow but high-quality training. + - The scheduler controls change-over-time as you train - it starts high, and then goes low. This helps balance getting data in, and having decent quality, at the same time. + - You can see graphs of the different scheduler options [in the HuggingFace docs here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_1/en/main_classes/optimizer_schedules#transformers.SchedulerType) + +## Loss + +When you're running training, the WebUI's console window will log reports that include, among other things, a numeric value named `Loss`. It will start as a high number, and gradually get lower and lower as it goes. + +"Loss" in the world of AI training theoretically means "how close is the model to perfect", with `0` meaning "absolutely perfect". This is calculated by measuring the difference between the model outputting exactly the text you're training it to output, and what it actually outputs. + +In practice, a good LLM should have a very complex variable range of ideas running in its artificial head, so a loss of `0` would indicate that the model has broken and forgotten to how think about anything other than what you trained it. + +So, in effect, Loss is a balancing game: you want to get it low enough that it understands your data, but high enough that it isn't forgetting everything else. Generally, if it goes below `1.0`, it's going to start forgetting its prior memories, and you should stop training. In some cases you may prefer to take it as low as `0.5` (if you want it to be very very predictable). Different goals have different needs, so don't be afraid to experiment and see what works best for you. + +Note: if you see Loss start at or suddenly jump to exactly `0`, it is likely something has gone wrong in your training process (eg model corruption). + +## Note: 4-Bit Monkeypatch + +The [4-bit LoRA monkeypatch](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode) works for training, but has side effects: +- VRAM usage is higher currently. You can reduce the `Micro Batch Size` to `1` to compensate. +- Models do funky things. LoRAs apply themselves, or refuse to apply, or spontaneously error out, or etc. It can be helpful to reload base model or restart the WebUI between training/usage to minimize chances of anything going haywire. +- Loading or working with multiple LoRAs at the same time doesn't currently work. +- Generally, recognize and treat the monkeypatch as the dirty temporary hack it is - it works, but isn't very stable. It will get better in time when everything is merged upstream for full official support. + +## Legacy notes + +LoRA training was contributed by [mcmonkey4eva](https://github.com/mcmonkey4eva) in PR [#570](https://github.com/oobabooga/text-generation-webui/pull/570). + +### Using the original alpaca-lora code + +Kept here for reference. The Training tab has much more features than this method. + +``` +conda activate textgen +git clone https://github.com/tloen/alpaca-lora +``` + +Edit those two lines in `alpaca-lora/finetune.py` to use your existing model folder instead of downloading everything from decapoda: + +``` +model = LlamaForCausalLM.from_pretrained( + "models/llama-7b", + load_in_8bit=True, + device_map="auto", +) +tokenizer = LlamaTokenizer.from_pretrained( + "models/llama-7b", add_eos_token=True +) +``` + +Run the script with: + +``` +python finetune.py +``` + +It just works. It runs at 22.32s/it, with 1170 iterations in total, so about 7 hours and a half for training a LoRA. RTX 3090, 18153MiB VRAM used, drawing maximum power (350W, room heater mode). diff --git a/text-generation-webui-main/docs/Using-LoRAs.md b/text-generation-webui-main/docs/Using-LoRAs.md new file mode 100644 index 0000000000000000000000000000000000000000..fafd6cde2d87bfdf46d942ab841a74bf50facdb5 --- /dev/null +++ b/text-generation-webui-main/docs/Using-LoRAs.md @@ -0,0 +1,55 @@ +Based on https://github.com/tloen/alpaca-lora + +## Instructions + +1. Download a LoRA, for instance: + +``` +python download-model.py tloen/alpaca-lora-7b +``` + +2. Load the LoRA. 16-bit, 8-bit, and CPU modes work: + +``` +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --load-in-8bit +python server.py --model llama-7b-hf --lora tloen_alpaca-lora-7b --cpu +``` + +* For using LoRAs in 4-bit mode, follow [these special instructions](GPTQ-models-(4-bit-mode).md#using-loras-in-4-bit-mode). + +* Instead of using the `--lora` command-line flag, you can also select the LoRA in the "Parameters" tab of the interface. + +## Prompt +For the Alpaca LoRA in particular, the prompt must be formatted like this: + +``` +Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Write a Python script that generates text using the transformers library. +### Response: +``` + +Sample output: + +``` +Below is an instruction that describes a task. Write a response that appropriately completes the request. +### Instruction: +Write a Python script that generates text using the transformers library. +### Response: + +import transformers +from transformers import AutoTokenizer, AutoModelForCausalLM +tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") +model = AutoModelForCausalLM.from_pretrained("bert-base-uncased") +texts = ["Hello world", "How are you"] +for sentence in texts: +sentence = tokenizer(sentence) +print(f"Generated {len(sentence)} tokens from '{sentence}'") +output = model(sentences=sentence).predict() +print(f"Predicted {len(output)} tokens for '{sentence}':\n{output}") +``` + +## Training a LoRA + +You can train your own LoRAs from the `Training` tab. See [Training LoRAs](Training-LoRAs.md) for details. diff --git a/text-generation-webui-main/docs/WSL-installation-guide.md b/text-generation-webui-main/docs/WSL-installation-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..eb06123c2deca98705e4941a4998e1b07b485600 --- /dev/null +++ b/text-generation-webui-main/docs/WSL-installation-guide.md @@ -0,0 +1,73 @@ +Guide created by [@jfryton](https://github.com/jfryton). Thank you jfryton. + +----- + +Here's an easy-to-follow, step-by-step guide for installing Windows Subsystem for Linux (WSL) with Ubuntu on Windows 10/11: + +## Step 1: Enable WSL + +1. Press the Windows key + X and click on "Windows PowerShell (Admin)" or "Windows Terminal (Admin)" to open PowerShell or Terminal with administrator privileges. +2. In the PowerShell window, type the following command and press Enter: + +``` +wsl --install +``` + +If this command doesn't work, you can enable WSL with the following command for Windows 10: + +``` +wsl --set-default-version 1 +``` + +For Windows 11, you can use: + +``` +wsl --set-default-version 2 +``` + +You may be prompted to restart your computer. If so, save your work and restart. + +## Step 2: Install Ubuntu + +1. Open the Microsoft Store. +2. Search for "Ubuntu" in the search bar. +3. Choose the desired Ubuntu version (e.g., Ubuntu 20.04 LTS) and click "Get" or "Install" to download and install the Ubuntu app. +4. Once the installation is complete, click "Launch" or search for "Ubuntu" in the Start menu and open the app. + +## Step 3: Set up Ubuntu + +1. When you first launch the Ubuntu app, it will take a few minutes to set up. Be patient as it installs the necessary files and sets up your environment. +2. Once the setup is complete, you will be prompted to create a new UNIX username and password. Choose a username and password, and make sure to remember them, as you will need them for future administrative tasks within the Ubuntu environment. + +## Step 4: Update and upgrade packages + +1. After setting up your username and password, it's a good idea to update and upgrade your Ubuntu system. Run the following commands in the Ubuntu terminal: + +``` +sudo apt update +sudo apt upgrade +``` + +2. Enter your password when prompted. This will update the package list and upgrade any outdated packages. + +Congratulations! You have now installed WSL with Ubuntu on your Windows 10/11 system. You can use the Ubuntu terminal for various tasks, like running Linux commands, installing packages, or managing files. + +You can launch your WSL Ubuntu installation by selecting the Ubuntu app (like any other program installed on your computer) or typing 'ubuntu' into Powershell or Terminal. + +## Step 5: Proceed with Linux instructions + +1. You can now follow the Linux setup instructions. If you receive any error messages about a missing tool or package, just install them using apt: + +``` +sudo apt install [missing package] +``` + +If you face any issues or need to troubleshoot, you can always refer to the official Microsoft documentation for WSL: https://docs.microsoft.com/en-us/windows/wsl/ + +## Bonus: Port Forwarding + +By default, you won't be able to access the webui from another device on your local network. You will need to setup the appropriate port forwarding using the following command (using PowerShell or Terminal with administrator privileges). + +``` +netsh interface portproxy add v4tov4 listenaddress=0.0.0.0 listenport=7860 connectaddress=localhost connectport=7860 +``` \ No newline at end of file diff --git a/text-generation-webui-main/docs/Windows-installation-guide.md b/text-generation-webui-main/docs/Windows-installation-guide.md new file mode 100644 index 0000000000000000000000000000000000000000..83b22efa38b1839d07a5a58494dbc26ba86397ee --- /dev/null +++ b/text-generation-webui-main/docs/Windows-installation-guide.md @@ -0,0 +1,9 @@ +If you are having trouble following the installation instructions in the README, Reddit user [Technical_Leather949](https://www.reddit.com/user/Technical_Leather949/) has created a more detailed, step-by-step guide covering: + +* Windows installation +* 8-bit mode on Windows +* LLaMA +* LLaMA 4-bit + +The guide can be found here: https://www.reddit.com/r/LocalLLaMA/comments/11o6o3f/how_to_install_llama_8bit_and_4bit/ + diff --git a/text-generation-webui-main/docs/llama.cpp-models.md b/text-generation-webui-main/docs/llama.cpp-models.md new file mode 100644 index 0000000000000000000000000000000000000000..57fbf61375c9b999ef862ed2b8618317d9ef34ad --- /dev/null +++ b/text-generation-webui-main/docs/llama.cpp-models.md @@ -0,0 +1,17 @@ +## Using llama.cpp in the web UI + +#### Pre-converted models + +Simply place the model in the `models` folder, making sure that its name contains `ggml` somewhere and ends in `.bin`. + +#### Convert LLaMA yourself + +Follow the instructions in the llama.cpp README to generate the `ggml-model-q4_0.bin` file: https://github.com/ggerganov/llama.cpp#usage + +## Performance + +This was the performance of llama-7b int4 on my i5-12400F: + +> Output generated in 33.07 seconds (6.05 tokens/s, 200 tokens, context 17) + +You can change the number of threads with `--threads N`. diff --git a/text-generation-webui-main/download-model.py b/text-generation-webui-main/download-model.py new file mode 100644 index 0000000000000000000000000000000000000000..a095f398789dd01b21674db41f821625c512a462 --- /dev/null +++ b/text-generation-webui-main/download-model.py @@ -0,0 +1,277 @@ +''' +Downloads models from Hugging Face to models/model-name. + +Example: +python download-model.py facebook/opt-1.3b + +''' + +import argparse +import base64 +import datetime +import hashlib +import json +import re +import sys +from pathlib import Path + +import requests +import tqdm +from tqdm.contrib.concurrent import thread_map + + +def select_model_from_default_options(): + models = { + "OPT 6.7B": ("facebook", "opt-6.7b", "main"), + "OPT 2.7B": ("facebook", "opt-2.7b", "main"), + "OPT 1.3B": ("facebook", "opt-1.3b", "main"), + "OPT 350M": ("facebook", "opt-350m", "main"), + "GALACTICA 6.7B": ("facebook", "galactica-6.7b", "main"), + "GALACTICA 1.3B": ("facebook", "galactica-1.3b", "main"), + "GALACTICA 125M": ("facebook", "galactica-125m", "main"), + "Pythia-6.9B-deduped": ("EleutherAI", "pythia-6.9b-deduped", "main"), + "Pythia-2.8B-deduped": ("EleutherAI", "pythia-2.8b-deduped", "main"), + "Pythia-1.4B-deduped": ("EleutherAI", "pythia-1.4b-deduped", "main"), + "Pythia-410M-deduped": ("EleutherAI", "pythia-410m-deduped", "main"), + } + choices = {} + + print("Select the model that you want to download:\n") + for i, name in enumerate(models): + char = chr(ord('A') + i) + choices[char] = name + print(f"{char}) {name}") + char_hugging = chr(ord('A') + len(models)) + print(f"{char_hugging}) Manually specify a Hugging Face model") + char_exit = chr(ord('A') + len(models) + 1) + print(f"{char_exit}) Do not download a model") + + print() + print("Input> ", end='') + choice = input()[0].strip().upper() + if choice == char_exit: + exit() + elif choice == char_hugging: + print("""\nThen type the name of your desired Hugging Face model in the format organization/name. + +Examples: +facebook/opt-1.3b +EleutherAI/pythia-1.4b-deduped +""") + + print("Input> ", end='') + model = input() + branch = "main" + else: + arr = models[choices[choice]] + model = f"{arr[0]}/{arr[1]}" + branch = arr[2] + + return model, branch + + +def sanitize_model_and_branch_names(model, branch): + if model[-1] == '/': + model = model[:-1] + if branch is None: + branch = "main" + else: + pattern = re.compile(r"^[a-zA-Z0-9._-]+$") + if not pattern.match(branch): + raise ValueError("Invalid branch name. Only alphanumeric characters, period, underscore and dash are allowed.") + + return model, branch + + +def get_download_links_from_huggingface(model, branch, text_only=False): + base = "https://huggingface.co" + page = f"/api/models/{model}/tree/{branch}" + cursor = b"" + + links = [] + sha256 = [] + classifications = [] + has_pytorch = False + has_pt = False + has_ggml = False + has_safetensors = False + is_lora = False + while True: + url = f"{base}{page}" + (f"?cursor={cursor.decode()}" if cursor else "") + r = requests.get(url) + r.raise_for_status() + content = r.content + + dict = json.loads(content) + if len(dict) == 0: + break + + for i in range(len(dict)): + fname = dict[i]['path'] + if not is_lora and fname.endswith(('adapter_config.json', 'adapter_model.bin')): + is_lora = True + + is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname) + is_safetensors = re.match(".*\.safetensors", fname) + is_pt = re.match(".*\.pt", fname) + is_ggml = re.match("ggml.*\.bin", fname) + is_tokenizer = re.match("(tokenizer|ice).*\.model", fname) + is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer + + if any((is_pytorch, is_safetensors, is_pt, is_ggml, is_tokenizer, is_text)): + if 'lfs' in dict[i]: + sha256.append([fname, dict[i]['lfs']['oid']]) + if is_text: + links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") + classifications.append('text') + continue + if not text_only: + links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}") + if is_safetensors: + has_safetensors = True + classifications.append('safetensors') + elif is_pytorch: + has_pytorch = True + classifications.append('pytorch') + elif is_pt: + has_pt = True + classifications.append('pt') + elif is_ggml: + has_ggml = True + classifications.append('ggml') + + cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50' + cursor = base64.b64encode(cursor) + cursor = cursor.replace(b'=', b'%3D') + + # If both pytorch and safetensors are available, download safetensors only + if (has_pytorch or has_pt) and has_safetensors: + for i in range(len(classifications) - 1, -1, -1): + if classifications[i] in ['pytorch', 'pt']: + links.pop(i) + + return links, sha256, is_lora + + +def get_output_folder(model, branch, is_lora, base_folder=None): + if base_folder is None: + base_folder = 'models' if not is_lora else 'loras' + + output_folder = f"{'_'.join(model.split('/')[-2:])}" + if branch != 'main': + output_folder += f'_{branch}' + output_folder = Path(base_folder) / output_folder + return output_folder + + +def get_single_file(url, output_folder, start_from_scratch=False): + filename = Path(url.rsplit('/', 1)[1]) + output_path = output_folder / filename + if output_path.exists() and not start_from_scratch: + # Check if the file has already been downloaded completely + r = requests.get(url, stream=True) + total_size = int(r.headers.get('content-length', 0)) + if output_path.stat().st_size >= total_size: + return + # Otherwise, resume the download from where it left off + headers = {'Range': f'bytes={output_path.stat().st_size}-'} + mode = 'ab' + else: + headers = {} + mode = 'wb' + + r = requests.get(url, stream=True, headers=headers) + with open(output_path, mode) as f: + total_size = int(r.headers.get('content-length', 0)) + block_size = 1024 + 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: + for data in r.iter_content(block_size): + t.update(len(data)) + f.write(data) + + +def start_download_threads(file_list, output_folder, start_from_scratch=False, threads=1): + thread_map(lambda url: get_single_file(url, output_folder, start_from_scratch=start_from_scratch), file_list, max_workers=threads, disable=True) + + +def download_model_files(model, branch, links, sha256, output_folder, start_from_scratch=False, threads=1): + # Creating the folder and writing the metadata + if not output_folder.exists(): + output_folder.mkdir() + with open(output_folder / 'huggingface-metadata.txt', 'w') as f: + f.write(f'url: https://huggingface.co/{model}\n') + f.write(f'branch: {branch}\n') + f.write(f'download date: {str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))}\n') + sha256_str = '' + for i in range(len(sha256)): + sha256_str += f' {sha256[i][1]} {sha256[i][0]}\n' + if sha256_str != '': + f.write(f'sha256sum:\n{sha256_str}') + + # Downloading the files + print(f"Downloading the model to {output_folder}") + start_download_threads(links, output_folder, start_from_scratch=start_from_scratch, threads=threads) + + +def check_model_files(model, branch, links, sha256, output_folder): + # Validate the checksums + validated = True + for i in range(len(sha256)): + fpath = (output_folder / sha256[i][0]) + + if not fpath.exists(): + print(f"The following file is missing: {fpath}") + validated = False + continue + + with open(output_folder / sha256[i][0], "rb") as f: + bytes = f.read() + file_hash = hashlib.sha256(bytes).hexdigest() + if file_hash != sha256[i][1]: + print(f'Checksum failed: {sha256[i][0]} {sha256[i][1]}') + validated = False + else: + print(f'Checksum validated: {sha256[i][0]} {sha256[i][1]}') + + if validated: + print('[+] Validated checksums of all model files!') + else: + print('[-] Invalid checksums. Rerun download-model.py with the --clean flag.') + + +if __name__ == '__main__': + + parser = argparse.ArgumentParser() + parser.add_argument('MODEL', type=str, default=None, nargs='?') + parser.add_argument('--branch', type=str, default='main', help='Name of the Git branch to download from.') + parser.add_argument('--threads', type=int, default=1, help='Number of files to download simultaneously.') + parser.add_argument('--text-only', action='store_true', help='Only download text files (txt/json).') + parser.add_argument('--output', type=str, default=None, help='The folder where the model should be saved.') + parser.add_argument('--clean', action='store_true', help='Does not resume the previous download.') + parser.add_argument('--check', action='store_true', help='Validates the checksums of model files.') + args = parser.parse_args() + + branch = args.branch + model = args.MODEL + if model is None: + model, branch = select_model_from_default_options() + + # Cleaning up the model/branch names + try: + model, branch = sanitize_model_and_branch_names(model, branch) + except ValueError as err_branch: + print(f"Error: {err_branch}") + sys.exit() + + # Getting the download links from Hugging Face + links, sha256, is_lora = get_download_links_from_huggingface(model, branch, text_only=args.text_only) + + # Getting the output folder + output_folder = get_output_folder(model, branch, is_lora, base_folder=args.output) + + if args.check: + # Check previously downloaded files + check_model_files(model, branch, links, sha256, output_folder) + else: + # Download files + download_model_files(model, branch, links, sha256, output_folder, threads=args.threads) diff --git a/text-generation-webui-main/extensions/api/blocking_api.py b/text-generation-webui-main/extensions/api/blocking_api.py new file mode 100644 index 0000000000000000000000000000000000000000..e66a6a50cbbdb6cce1151cb14cfa6fc0d273f0a6 --- /dev/null +++ b/text-generation-webui-main/extensions/api/blocking_api.py @@ -0,0 +1,90 @@ +import json +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from threading import Thread + +from modules import shared +from modules.text_generation import encode, generate_reply + +from extensions.api.util import build_parameters, try_start_cloudflared + + +class Handler(BaseHTTPRequestHandler): + def do_GET(self): + if self.path == '/api/v1/model': + self.send_response(200) + self.end_headers() + response = json.dumps({ + 'result': shared.model_name + }) + + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + def do_POST(self): + content_length = int(self.headers['Content-Length']) + body = json.loads(self.rfile.read(content_length).decode('utf-8')) + + if self.path == '/api/v1/generate': + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + prompt = body['prompt'] + generate_params = build_parameters(body) + stopping_strings = generate_params.pop('stopping_strings') + + generator = generate_reply( + prompt, generate_params, stopping_strings=stopping_strings) + + answer = '' + for a in generator: + if isinstance(a, str): + answer = a + else: + answer = a[0] + + response = json.dumps({ + 'results': [{ + 'text': answer if shared.is_chat() else answer[len(prompt):] + }] + }) + self.wfile.write(response.encode('utf-8')) + elif self.path == '/api/v1/token-count': + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + tokens = encode(body['prompt'])[0] + response = json.dumps({ + 'results': [{ + 'tokens': len(tokens) + }] + }) + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + +def _run_server(port: int, share: bool=False): + address = '0.0.0.0' if shared.args.listen else '127.0.0.1' + + server = ThreadingHTTPServer((address, port), Handler) + + def on_start(public_url: str): + print(f'Starting non-streaming server at public url {public_url}/api') + + if share: + try: + try_start_cloudflared(port, max_attempts=3, on_start=on_start) + except Exception: + pass + else: + print( + f'Starting API at http://{address}:{port}/api') + + server.serve_forever() + + +def start_server(port: int, share: bool = False): + Thread(target=_run_server, args=[port, share], daemon=True).start() diff --git a/text-generation-webui-main/extensions/api/requirements.txt b/text-generation-webui-main/extensions/api/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..14e29d3521915c9fd4fb62385dc971e7a8c4bc83 --- /dev/null +++ b/text-generation-webui-main/extensions/api/requirements.txt @@ -0,0 +1,2 @@ +flask_cloudflared==0.0.12 +websockets==11.0.2 \ No newline at end of file diff --git a/text-generation-webui-main/extensions/api/script.py b/text-generation-webui-main/extensions/api/script.py new file mode 100644 index 0000000000000000000000000000000000000000..efeed71f0362840ed8417caa1489b32c70117f17 --- /dev/null +++ b/text-generation-webui-main/extensions/api/script.py @@ -0,0 +1,10 @@ +import extensions.api.blocking_api as blocking_api +import extensions.api.streaming_api as streaming_api +from modules import shared + +BLOCKING_PORT = 5000 +STREAMING_PORT = 5005 + +def setup(): + blocking_api.start_server(BLOCKING_PORT, share=shared.args.public_api) + streaming_api.start_server(STREAMING_PORT, share=shared.args.public_api) diff --git a/text-generation-webui-main/extensions/api/streaming_api.py b/text-generation-webui-main/extensions/api/streaming_api.py new file mode 100644 index 0000000000000000000000000000000000000000..3b9ac658d07bba2b1886886d43aaaa4b36badc5d --- /dev/null +++ b/text-generation-webui-main/extensions/api/streaming_api.py @@ -0,0 +1,82 @@ +import json +import asyncio +from websockets.server import serve +from threading import Thread + +from modules import shared +from modules.text_generation import generate_reply + +from extensions.api.util import build_parameters, try_start_cloudflared + +PATH = '/api/v1/stream' + + +async def _handle_connection(websocket, path): + + if path != PATH: + print(f'Streaming api: unknown path: {path}') + return + + async for message in websocket: + message = json.loads(message) + + prompt = message['prompt'] + generate_params = build_parameters(message) + stopping_strings = generate_params.pop('stopping_strings') + + generator = generate_reply( + prompt, generate_params, stopping_strings=stopping_strings) + + # As we stream, only send the new bytes. + skip_index = len(prompt) if not shared.is_chat() else 0 + message_num = 0 + + for a in generator: + to_send = '' + if isinstance(a, str): + to_send = a[skip_index:] + else: + to_send = a[0][skip_index:] + + await websocket.send(json.dumps({ + 'event': 'text_stream', + 'message_num': message_num, + 'text': to_send + })) + + await asyncio.sleep(0) + + skip_index += len(to_send) + message_num += 1 + + await websocket.send(json.dumps({ + 'event': 'stream_end', + 'message_num': message_num + })) + + +async def _run(host: str, port: int): + async with serve(_handle_connection, host, port, ping_interval=None): + await asyncio.Future() # run forever + + +def _run_server(port: int, share: bool = False): + address = '0.0.0.0' if shared.args.listen else '127.0.0.1' + + def on_start(public_url: str): + public_url = public_url.replace('https://', 'wss://') + print(f'Starting streaming server at public url {public_url}{PATH}') + + if share: + try: + try_start_cloudflared(port, max_attempts=3, on_start=on_start) + except Exception as e: + print(e) + else: + print(f'Starting streaming server at ws://{address}:{port}{PATH}') + + asyncio.run(_run(host=address, port=port)) + + +def start_server(port: int, share: bool = False): + Thread(target=_run_server, args=[port, share], daemon=True).start() diff --git a/text-generation-webui-main/extensions/api/util.py b/text-generation-webui-main/extensions/api/util.py new file mode 100644 index 0000000000000000000000000000000000000000..63117aafc1c4ef8f7dabba4734d13353a1b34afc --- /dev/null +++ b/text-generation-webui-main/extensions/api/util.py @@ -0,0 +1,71 @@ +import time +import traceback +from threading import Thread +from typing import Callable, Optional + +from modules.text_generation import encode + + +def build_parameters(body): + prompt = body['prompt'] + + prompt_lines = [k.strip() for k in prompt.split('\n')] + max_context = body.get('max_context_length', 2048) + while len(prompt_lines) >= 0 and len(encode('\n'.join(prompt_lines))) > max_context: + prompt_lines.pop(0) + + prompt = '\n'.join(prompt_lines) + + generate_params = { + 'max_new_tokens': int(body.get('max_new_tokens', body.get('max_length', 200))), + 'do_sample': bool(body.get('do_sample', True)), + 'temperature': float(body.get('temperature', 0.5)), + 'top_p': float(body.get('top_p', 1)), + 'typical_p': float(body.get('typical_p', body.get('typical', 1))), + 'repetition_penalty': float(body.get('repetition_penalty', body.get('rep_pen', 1.1))), + 'encoder_repetition_penalty': float(body.get('encoder_repetition_penalty', 1.0)), + 'top_k': int(body.get('top_k', 0)), + 'min_length': int(body.get('min_length', 0)), + 'no_repeat_ngram_size': int(body.get('no_repeat_ngram_size', 0)), + 'num_beams': int(body.get('num_beams', 1)), + 'penalty_alpha': float(body.get('penalty_alpha', 0)), + 'length_penalty': float(body.get('length_penalty', 1)), + 'early_stopping': bool(body.get('early_stopping', False)), + 'seed': int(body.get('seed', -1)), + 'add_bos_token': bool(body.get('add_bos_token', True)), + 'truncation_length': int(body.get('truncation_length', 2048)), + 'ban_eos_token': bool(body.get('ban_eos_token', False)), + 'skip_special_tokens': bool(body.get('skip_special_tokens', True)), + 'custom_stopping_strings': '', # leave this blank + 'stopping_strings': body.get('stopping_strings', []), + } + + return generate_params + + +def try_start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None): + Thread(target=_start_cloudflared, args=[ + port, max_attempts, on_start], daemon=True).start() + + +def _start_cloudflared(port: int, max_attempts: int = 3, on_start: Optional[Callable[[str], None]] = None): + try: + from flask_cloudflared import _run_cloudflared + except ImportError: + print('You should install flask_cloudflared manually') + raise Exception( + 'flask_cloudflared not installed. Make sure you installed the requirements.txt for this extension.') + + for _ in range(max_attempts): + try: + public_url = _run_cloudflared(port, port + 1) + + if on_start: + on_start(public_url) + + return + except Exception: + traceback.print_exc() + time.sleep(3) + + raise Exception('Could not start cloudflared.') diff --git a/text-generation-webui-main/extensions/character_bias/script.py b/text-generation-webui-main/extensions/character_bias/script.py new file mode 100644 index 0000000000000000000000000000000000000000..614d9ce3c5283fdaf2d52eb2cc3e12c07d000457 --- /dev/null +++ b/text-generation-webui-main/extensions/character_bias/script.py @@ -0,0 +1,82 @@ +import gradio as gr +import os + +# get the current directory of the script +current_dir = os.path.dirname(os.path.abspath(__file__)) + +# check if the bias_options.txt file exists, if not, create it +bias_file = os.path.join(current_dir, "bias_options.txt") +if not os.path.isfile(bias_file): + with open(bias_file, "w") as f: + f.write("*I am so happy*\n*I am so sad*\n*I am so excited*\n*I am so bored*\n*I am so angry*") + +# read bias options from the text file +with open(bias_file, "r") as f: + bias_options = [line.strip() for line in f.readlines()] + +params = { + "activate": True, + "bias string": " *I am so happy*", + "use custom string": False, +} + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + return string + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + return string + + +def bot_prefix_modifier(string): + """ + This function is only applied in chat mode. It modifies + the prefix text for the Bot and can be used to bias its + behavior. + """ + if params['activate']: + if params['use custom string']: + return f'{string} {params["custom string"].strip()} ' + else: + return f'{string} {params["bias string"].strip()} ' + else: + return string + + +def ui(): + # Gradio elements + activate = gr.Checkbox(value=params['activate'], label='Activate character bias') + dropdown_string = gr.Dropdown(choices=bias_options, value=params["bias string"], label='Character bias', info='To edit the options in this dropdown edit the "bias_options.txt" file') + use_custom_string = gr.Checkbox(value=False, label='Use custom bias textbox instead of dropdown') + custom_string = gr.Textbox(value="", placeholder="Enter custom bias string", label="Custom Character Bias", info='To use this textbox activate the checkbox above') + + # Event functions to update the parameters in the backend + def update_bias_string(x): + if x: + params.update({"bias string": x}) + else: + params.update({"bias string": dropdown_string.get()}) + return x + + def update_custom_string(x): + params.update({"custom string": x}) + + dropdown_string.change(update_bias_string, dropdown_string, None) + custom_string.change(update_custom_string, custom_string, None) + activate.change(lambda x: params.update({"activate": x}), activate, None) + use_custom_string.change(lambda x: params.update({"use custom string": x}), use_custom_string, None) + + # Group elements together depending on the selected option + def bias_string_group(): + if use_custom_string.value: + return gr.Group([use_custom_string, custom_string]) + else: + return dropdown_string diff --git a/text-generation-webui-main/extensions/elevenlabs_tts/requirements.txt b/text-generation-webui-main/extensions/elevenlabs_tts/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8ec07a8a7fcf02ca48cc00520e66fcb58c447393 --- /dev/null +++ b/text-generation-webui-main/extensions/elevenlabs_tts/requirements.txt @@ -0,0 +1,3 @@ +elevenlabslib +soundfile +sounddevice diff --git a/text-generation-webui-main/extensions/elevenlabs_tts/script.py b/text-generation-webui-main/extensions/elevenlabs_tts/script.py new file mode 100644 index 0000000000000000000000000000000000000000..5c727a30792d427639e8b7e5783996c9e5bf8692 --- /dev/null +++ b/text-generation-webui-main/extensions/elevenlabs_tts/script.py @@ -0,0 +1,122 @@ +import re +from pathlib import Path + +import gradio as gr +from elevenlabslib import ElevenLabsUser +from elevenlabslib.helpers import save_bytes_to_path + +import modules.shared as shared + +params = { + 'activate': True, + 'api_key': '12345', + 'selected_voice': 'None', +} + +initial_voice = ['None'] +wav_idx = 0 +user = ElevenLabsUser(params['api_key']) +user_info = None + +if not shared.args.no_stream: + print("Please add --no-stream. This extension is not meant to be used with streaming.") + raise ValueError + +# Check if the API is valid and refresh the UI accordingly. + + +def check_valid_api(): + + global user, user_info, params + + user = ElevenLabsUser(params['api_key']) + user_info = user._get_subscription_data() + print('checking api') + if not params['activate']: + return gr.update(value='Disconnected') + elif user_info is None: + print('Incorrect API Key') + return gr.update(value='Disconnected') + else: + print('Got an API Key!') + return gr.update(value='Connected') + +# Once the API is verified, get the available voices and update the dropdown list + + +def refresh_voices(): + + global user, user_info + + your_voices = [None] + if user_info is not None: + for voice in user.get_available_voices(): + your_voices.append(voice.initialName) + return gr.Dropdown.update(choices=your_voices) + else: + return + + +def remove_surrounded_chars(string): + # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR + # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string' + return re.sub('\*[^\*]*?(\*|$)', '', string) + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + + return string + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global params, wav_idx, user, user_info + + if not params['activate']: + return string + elif user_info is None: + return string + + string = remove_surrounded_chars(string) + string = string.replace('"', '') + string = string.replace('“', '') + string = string.replace('\n', ' ') + string = string.strip() + + if string == '': + string = 'empty reply, try regenerating' + + output_file = Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'.format(wav_idx)) + voice = user.get_voices_by_name(params['selected_voice'])[0] + audio_data = voice.generate_audio_bytes(string) + save_bytes_to_path(Path(f'extensions/elevenlabs_tts/outputs/{wav_idx:06d}.wav'), audio_data) + + string = f'' + wav_idx += 1 + return string + + +def ui(): + + # Gradio elements + with gr.Row(): + activate = gr.Checkbox(value=params['activate'], label='Activate TTS') + connection_status = gr.Textbox(value='Disconnected', label='Connection Status') + voice = gr.Dropdown(value=params['selected_voice'], choices=initial_voice, label='TTS Voice') + with gr.Row(): + api_key = gr.Textbox(placeholder="Enter your API key.", label='API Key') + connect = gr.Button(value='Connect') + + # Event functions to update the parameters in the backend + activate.change(lambda x: params.update({'activate': x}), activate, None) + voice.change(lambda x: params.update({'selected_voice': x}), voice, None) + api_key.change(lambda x: params.update({'api_key': x}), api_key, None) + connect.click(check_valid_api, [], connection_status) + connect.click(refresh_voices, [], voice) diff --git a/text-generation-webui-main/extensions/gallery/script.py b/text-generation-webui-main/extensions/gallery/script.py new file mode 100644 index 0000000000000000000000000000000000000000..993ef273839e7cfbf9e80f2d7f9d4a71d208b446 --- /dev/null +++ b/text-generation-webui-main/extensions/gallery/script.py @@ -0,0 +1,96 @@ +from pathlib import Path + +import gradio as gr + +from modules.html_generator import get_image_cache +from modules.shared import gradio + + +def generate_css(): + css = """ + .character-gallery > .gallery { + margin: 1rem 0; + display: grid !important; + grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); + grid-column-gap: 0.4rem; + grid-row-gap: 1.2rem; + } + + .character-gallery > .label { + display: none !important; + } + + .character-gallery button.gallery-item { + display: contents; + } + + .character-container { + cursor: pointer; + text-align: center; + position: relative; + opacity: 0.85; + } + + .character-container:hover { + opacity: 1; + } + + .character-container .placeholder, .character-container img { + width: 150px; + height: 200px; + background-color: gray; + object-fit: cover; + margin: 0 auto; + border-radius: 1rem; + border: 3px solid white; + box-shadow: 3px 3px 6px 0px rgb(0 0 0 / 50%); + } + + .character-name { + margin-top: 0.3rem; + display: block; + font-size: 1.2rem; + font-weight: 600; + overflow-wrap: anywhere; + } + """ + return css + + +def generate_html(): + cards = [] + # Iterate through files in image folder + for file in sorted(Path("characters").glob("*")): + if file.suffix in [".json", ".yml", ".yaml"]: + character = file.stem + container_html = '
' + image_html = "
" + + for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]: + if path.exists(): + image_html = f'' + break + + container_html += f'{image_html} {character}' + container_html += "
" + cards.append([container_html, character]) + + return cards + + +def select_character(evt: gr.SelectData): + return (evt.value[1]) + + +def ui(): + with gr.Accordion("Character gallery", open=False): + update = gr.Button("Refresh") + gr.HTML(value="") + gallery = gr.Dataset(components=[gr.HTML(visible=False)], + label="", + samples=generate_html(), + elem_classes=["character-gallery"], + samples_per_page=50 + ) + update.click(generate_html, [], gallery) + gallery.select(select_character, None, gradio['character_menu']) diff --git a/text-generation-webui-main/extensions/google_translate/requirements.txt b/text-generation-webui-main/extensions/google_translate/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..554a00df62818f96ba7d396ae39d8e58efbe9bfe --- /dev/null +++ b/text-generation-webui-main/extensions/google_translate/requirements.txt @@ -0,0 +1 @@ +deep-translator==1.9.2 diff --git a/text-generation-webui-main/extensions/google_translate/script.py b/text-generation-webui-main/extensions/google_translate/script.py new file mode 100644 index 0000000000000000000000000000000000000000..63226107b2c2afe086fc343c7b7f7df78bef3f8a --- /dev/null +++ b/text-generation-webui-main/extensions/google_translate/script.py @@ -0,0 +1,46 @@ +import gradio as gr +from deep_translator import GoogleTranslator + +params = { + "language string": "ja", +} + +language_codes = {'Afrikaans': 'af', 'Albanian': 'sq', 'Amharic': 'am', 'Arabic': 'ar', 'Armenian': 'hy', 'Azerbaijani': 'az', 'Basque': 'eu', 'Belarusian': 'be', 'Bengali': 'bn', 'Bosnian': 'bs', 'Bulgarian': 'bg', 'Catalan': 'ca', 'Cebuano': 'ceb', 'Chinese (Simplified)': 'zh-CN', 'Chinese (Traditional)': 'zh-TW', 'Corsican': 'co', 'Croatian': 'hr', 'Czech': 'cs', 'Danish': 'da', 'Dutch': 'nl', 'English': 'en', 'Esperanto': 'eo', 'Estonian': 'et', 'Finnish': 'fi', 'French': 'fr', 'Frisian': 'fy', 'Galician': 'gl', 'Georgian': 'ka', 'German': 'de', 'Greek': 'el', 'Gujarati': 'gu', 'Haitian Creole': 'ht', 'Hausa': 'ha', 'Hawaiian': 'haw', 'Hebrew': 'iw', 'Hindi': 'hi', 'Hmong': 'hmn', 'Hungarian': 'hu', 'Icelandic': 'is', 'Igbo': 'ig', 'Indonesian': 'id', 'Irish': 'ga', 'Italian': 'it', 'Japanese': 'ja', 'Javanese': 'jw', 'Kannada': 'kn', 'Kazakh': 'kk', 'Khmer': 'km', 'Korean': 'ko', 'Kurdish': 'ku', 'Kyrgyz': 'ky', 'Lao': 'lo', 'Latin': 'la', 'Latvian': 'lv', 'Lithuanian': 'lt', 'Luxembourgish': 'lb', 'Macedonian': 'mk', 'Malagasy': 'mg', 'Malay': 'ms', 'Malayalam': 'ml', 'Maltese': 'mt', 'Maori': 'mi', 'Marathi': 'mr', 'Mongolian': 'mn', 'Myanmar (Burmese)': 'my', 'Nepali': 'ne', 'Norwegian': 'no', 'Nyanja (Chichewa)': 'ny', 'Pashto': 'ps', 'Persian': 'fa', 'Polish': 'pl', 'Portuguese (Portugal, Brazil)': 'pt', 'Punjabi': 'pa', 'Romanian': 'ro', 'Russian': 'ru', 'Samoan': 'sm', 'Scots Gaelic': 'gd', 'Serbian': 'sr', 'Sesotho': 'st', 'Shona': 'sn', 'Sindhi': 'sd', 'Sinhala (Sinhalese)': 'si', 'Slovak': 'sk', 'Slovenian': 'sl', 'Somali': 'so', 'Spanish': 'es', 'Sundanese': 'su', 'Swahili': 'sw', 'Swedish': 'sv', 'Tagalog (Filipino)': 'tl', 'Tajik': 'tg', 'Tamil': 'ta', 'Telugu': 'te', 'Thai': 'th', 'Turkish': 'tr', 'Ukrainian': 'uk', 'Urdu': 'ur', 'Uzbek': 'uz', 'Vietnamese': 'vi', 'Welsh': 'cy', 'Xhosa': 'xh', 'Yiddish': 'yi', 'Yoruba': 'yo', 'Zulu': 'zu'} + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + + return GoogleTranslator(source=params['language string'], target='en').translate(string) + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + return GoogleTranslator(source='en', target=params['language string']).translate(string) + + +def bot_prefix_modifier(string): + """ + This function is only applied in chat mode. It modifies + the prefix text for the Bot and can be used to bias its + behavior. + """ + + return string + + +def ui(): + # Finding the language name from the language code to use as the default value + language_name = list(language_codes.keys())[list(language_codes.values()).index(params['language string'])] + + # Gradio elements + language = gr.Dropdown(value=language_name, choices=[k for k in language_codes], label='Language') + + # Event functions to update the parameters in the backend + language.change(lambda x: params.update({"language string": language_codes[x]}), language, None) diff --git a/text-generation-webui-main/extensions/llava/README.md b/text-generation-webui-main/extensions/llava/README.md new file mode 100644 index 0000000000000000000000000000000000000000..287162efef3ab7a047bef9d5cb37c16871703fd4 --- /dev/null +++ b/text-generation-webui-main/extensions/llava/README.md @@ -0,0 +1,71 @@ +# LLaVA + +## Description +Adds [LLaVA 13B](https://github.com/haotian-liu/LLaVA) multimodality support to text-generation-webui. + +https://user-images.githubusercontent.com/3718215/233817203-69b57e77-0c55-4fd6-b742-3204bb13b8fc.mp4 + +## LLaVA-7B +7B version currently isn't supported. It will be supported if/when [more generic multimodality support](https://github.com/oobabooga/text-generation-webui/discussions/1687) gets implemented. + +## Usage +To run this extension, download LLaVA weights, for example from [here](https://huggingface.co/wojtab/llava-13b-v0-4bit-128g) (note: it's a 4-bit [GPTQ quantization](https://github.com/oobabooga/text-generation-webui/tree/main/docs/GPTQ-models-(4-bit-mode).md), done on "old CUDA" branch), and then start server.py with `--extensions llava` argument. + +Do note, that each image takes up 258 tokens, so adjust max_new_tokens to be at most 1700 (recommended value is between 200 to 500), so the images don't get truncated. + +To send an image, just upload it to the extension field below chat, and send a prompt as always. The image will be added to the end of your message. If you wish to modify the placement, include a string `` in your prompt. + +Additionally, there is *Embed all images, not only the last one* checkbox. It modifies the image embeddings, by default (if it's unchecked), all but the most recent images have their embeddings empty, so they are not fed to the network. From initial testing, it seems as LLaVA considers the features in all images at the same time, so by default the extension skips previous images. If you want to include them anyway, just tick this checkbox. + +## Extension config +This extension uses following parameters (from settings.json): +|Parameter|Description| +|---------|-----------| +|`llava-clip_bits`|Number of bits to load CLIP feature extractor in (either 32 or 16, default=32)| +|`llava-clip_device`|Torch device to run the extractor on, for example `cpu` or `cuda:0`, by default `cuda:0` if available| +|`llava-clip_repo`|Huggingface repository of CLIP model, `openai/clip-vit-large-patch14` by default. There should be no need to change it| +|`llava-projector_bits`|Number of bits to load CLIP->LLaMA feature projector in (either 32 or 16, default=32)| +|`llava-projector_device`|Torch device to run the CLIP->LLaMA feature projector on, for example `cpu` or `cuda:0`, by default `cuda:0` if available| +|`llava-projector_repo`|Huggingface repository of multimodal projector, `liuhaotian/LLaVA-13b-delta-v0` by default. There should be no need to change it| +|`llava-projector_filename`|The filename of multimodal projector weights, `mm_projector.bin` by default. There should be no need to change it| +|`llava-add_all_images_to_prompt`|Default value of "Embed all images, not only the last one" checkbox| +## Technical description + +### Original LLaVA +The default LLaVA implementation uses modified `transformers` library, however this extension forgoes this requirement. The transformers are modified in LLaVA in such a way, that the entire LLaVA model gets loaded, and the inference now looks as follows: +``` +images --> CLIP --> projector --> input embeddings for images --> | + | --> LLaMA +prompt -------------------------> input embeddings for text ----> | +``` +The images are represented in the prompt by the following token IDs: +- 32000 - `` - placeholder token for embeddings from projector +- 32001 - `` - token marking start of an image +- 32002 - `` - token marking end of an image + +By default, image will be represented as `*256`. The input embeddings for an image are converted with a single linear layer of the projector, then they are placed instead of `` tokens. +The concatenated prompt then gets fed to fine-tuned LLaMA. + +### In this extension + +Using default transformers, they only load the LLaMA part of LLaVA, ignoring the added projector weights, and not loading CLIP. We then reconstruct the `images -> CLIP -> projector` pipeline ourselves, then concatenate the input embeddings, and feed it to LLaMA loaded by transformers. This allows us to use normal flow from webui to load this model, and just hijack the model input with additional features. +Splitting it to 3 separate models, allows us to configure each of them, and to move them to different devices(for example we can run CLIP+projector on CPU and LLaMA on GPU). Also, it enables us to use 4-bit GPTQ quantization for LLaVA, massively cutting down the VRAM requirement (it should be possible to fit on 12GB of VRAM with full context size by moving CLIP and projector to CPU). + +### Usage through API + +You can run the multimodal inference through API, by inputting the images to prompt. Images are embedded like so: `f''`, where `img_str` is base-64 jpeg data. Python example: +```Python +import base64 +import requests + +CONTEXT = "You are LLaVA, a large language and vision assistant trained by UW Madison WAIV Lab. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language. Follow the instructions carefully and explain your answers in detail.\n### Human: \nHi!\n### Assistant: \nHi there! How can I help you today?\n" + +with open('extreme_ironing.jpg', 'rb') as f: + img_str = base64.b64encode(f.read()).decode('utf-8') + prompt = CONTEXT + f'### Human: \nWhat is unusual about this image: \n\n### Assistant: \n' + print(requests.post('http://127.0.0.1:5000/api/v1/generate', json={'prompt': prompt, 'stopping_strings': ['\n###']}).json()) +``` +script output: +```Python +{'results': [{'text': "The unusual aspect of this image is that a man is standing on top of a yellow minivan while doing his laundry. He has set up a makeshift clothes line using the car's rooftop as an outdoor drying area. This scene is uncommon because people typically do their laundry indoors, in a dedicated space like a laundromat or a room in their home, rather than on top of a moving vehicle. Additionally, hanging clothes on the car could be potentially hazardous or illegal in some jurisdictions due to the risk of damaging the vehicle or causing accidents on the road.\n##"}]} +``` \ No newline at end of file diff --git a/text-generation-webui-main/extensions/llava/script.py b/text-generation-webui-main/extensions/llava/script.py new file mode 100644 index 0000000000000000000000000000000000000000..0089e01e9f1c56377c0ed5f5878faba37aa5dae1 --- /dev/null +++ b/text-generation-webui-main/extensions/llava/script.py @@ -0,0 +1,272 @@ +import base64 +import re +import time +from dataclasses import dataclass +from functools import partial +from io import BytesIO + +import gradio as gr +import torch +from huggingface_hub import hf_hub_download +from PIL import Image +from transformers import CLIPImageProcessor, CLIPVisionModel + +from modules import shared +from modules.extensions import apply_extensions +from modules.text_generation import encode, get_max_prompt_length + +params = { + "add_all_images_to_prompt": False, + # device to run CLIP on + "clip_device": None, + # bits to load clip in either 32 or 16 (it doesn't support 8-bit) + "clip_bits": 32, + # clip repository + "clip_repo": "openai/clip-vit-large-patch14", + # device to run projector on + "projector_device": None, + # projector bits, either 32 or 16 + "projector_bits": 32, + # projector repository + "projector_repo": "liuhaotian/LLaVA-13b-delta-v0", + # file with the projector weights + "projector_file": "mm_projector.bin" +} + + +# If 'state' is True, will hijack the next chat generation +input_hijack = { + 'state': False, + 'value': ["", ""] +} + + +# initialized in ui, so that params are loaded from settings +llava_embedder = None + + +@dataclass +class Token: + token: str + id: int + + +class LLaVAEmbedder: + IM_PATCH = Token("", 32000) + IM_START = Token("", 32001) + IM_END = Token("", 32002) + + def __init__(self): + self.clip_device = self._get_device("clip_device") + self.clip_dtype = self._get_dtype("clip_bits") + self.projector_device = self._get_device("projector_device") + self.projector_dtype = self._get_dtype("projector_bits") + self.image_processor, self.vision_tower, self.mm_projector = self._load_models() + + def _get_device(self, setting_name): + if params[setting_name] is None: + return torch.device("cuda:0" if torch.cuda.is_available() else "cpu") + return torch.device(params[setting_name]) + + def _get_dtype(self, setting_name): + return torch.float32 if int(params[setting_name]) == 32 else torch.float16 + + def _load_models(self): + start_ts = time.time() + + print(f"LLaVA - Loading CLIP from {params['clip_repo']} as {self.clip_dtype} on {self.clip_device}...") + image_processor = CLIPImageProcessor.from_pretrained(params["clip_repo"], torch_dtype=self.clip_dtype) + vision_tower = CLIPVisionModel.from_pretrained(params["clip_repo"], torch_dtype=self.clip_dtype).to(self.clip_device) + + print(f"LLaVA - Loading projector from {params['projector_repo']} as {self.projector_dtype} on {self.projector_device}...") + projector_path = hf_hub_download(params["projector_repo"], params["projector_file"]) + mm_projector = torch.nn.Linear(1024, 5120) + projector_data = torch.load(projector_path) + mm_projector.weight = torch.nn.Parameter(projector_data['model.mm_projector.weight'].to(dtype=self.projector_dtype), False) + 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) + + print(f"LLaVA supporting models loaded, took {time.time() - start_ts:.2f} seconds") + return image_processor, vision_tower, mm_projector + + def _update_prompt(self, prompt, images): + for _ in images: + # replace the image token with the image patch token in the prompt (each occurrence) + replace_token = LLaVAEmbedder.IM_PATCH.token * 256 + replace_token = LLaVAEmbedder.IM_START.token + replace_token + LLaVAEmbedder.IM_END.token + prompt = re.sub(r'', replace_token, prompt, 1) + return prompt + + def _extract_image_features(self, images): + images = self.image_processor(images, return_tensors='pt')['pixel_values'] + images = images.to(self.clip_device, dtype=self.clip_dtype) + + with torch.no_grad(): + image_forward_outs = self.vision_tower(images, output_hidden_states=True) + select_hidden_state_layer = -2 + select_hidden_state = image_forward_outs.hidden_states[select_hidden_state_layer] + image_features = select_hidden_state[:, 1:].to(self.projector_device, dtype=self.projector_dtype) + image_features = self.mm_projector(image_features) + return image_features + + def forward(self, prompt, images, state): + prompt = self._update_prompt(prompt, images) + input_ids = encode(prompt, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state))[0] + input_embeds = shared.model.model.embed_tokens(input_ids).to(self.projector_device) + + if input_ids[0] == LLaVAEmbedder.IM_PATCH.id: + # prompt got truncated in the middle of an image, remove the image data + im_end = torch.where(input_ids == LLaVAEmbedder.IM_END.id)[0][0] + input_ids = input_ids[im_end+1:] + input_embeds = input_embeds[im_end+1:] + leftover_images = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0].shape[0] + print(f"LLaVA - WARNING: removed {len(images) - leftover_images} image(s) from prompt. The generation might be broken, try decreasing max_new_tokens") + images = images[-leftover_images:] + if len(images) == 0: + return prompt, input_ids, input_embeds, 0 + + total_embedded = 0 + image_features = self._extract_image_features(images).to(self.projector_device) + image_start_tokens = torch.where(input_ids == LLaVAEmbedder.IM_START.id)[0] + + if not torch.any(input_ids == LLaVAEmbedder.IM_PATCH.id) or len(image_start_tokens) == 0: + # multimodal LLM, but the current prompt is not multimodal/truncated + return prompt, input_ids, input_embeds, total_embedded + + cur_image_idx = 0 + if not params['add_all_images_to_prompt']: + image_start_tokens = [image_start_tokens[-1]] + cur_image_idx = -1 + + for image_start_token_pos in image_start_tokens: + cur_image_features = image_features[cur_image_idx] + num_patches = cur_image_features.shape[0] + input_embeds = torch.cat((input_embeds[:image_start_token_pos+1], cur_image_features, input_embeds[image_start_token_pos + num_patches + 1:]), dim=0) + cur_image_idx += 1 + total_embedded += 1 + + return prompt, input_ids, input_embeds, total_embedded + + @staticmethod + def len_in_tokens(text): + images = re.findall(r'', text) + image_tokens = 0 + for _ in images: + image_tokens += 258 + return len(encode(re.sub(r'', '', text))[0]) + image_tokens + + +def add_chat_picture(picture, text, visible_text): + # resize the image, so that shortest edge is at least 224 (size for CLIP), and at most 300 (to keep history manageable) + max_hw, min_hw = max(picture.size), min(picture.size) + aspect_ratio = max_hw / min_hw + shortest_edge = int(max(300 / aspect_ratio, 224)) + longest_edge = int(shortest_edge * aspect_ratio) + w = shortest_edge if picture.width < picture.height else longest_edge + h = shortest_edge if picture.width >= picture.height else longest_edge + picture = picture.resize((w,h)) + + buffer = BytesIO() + picture.save(buffer, format="JPEG") + img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') + image = f'' + + + if '' in text: + text = text.replace('', image) + else: + text = text + '\n' + image + + if visible_text == '' or visible_text is None: + visible_text = text + elif '' in visible_text: + visible_text = visible_text.replace('', image) + else: + visible_text = visible_text + '\n' + image + + return text, visible_text + + +def custom_generate_chat_prompt(user_input, state, **kwargs): + impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False + _continue = kwargs['_continue'] if '_continue' in kwargs else False + also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False + rows = [f"{state['context'].strip()}\n"] + min_rows = 3 + + # Finding the maximum prompt size + chat_prompt_size = state['chat_prompt_size'] + if shared.soft_prompt: + chat_prompt_size -= shared.soft_prompt_tensor.shape[1] + max_length = min(get_max_prompt_length(state), chat_prompt_size) + + prefix1 = f"{state['name1']}: " + prefix2 = f"{state['name2']}: " + + i = len(shared.history['internal']) - 1 + while i >= 0 and LLaVAEmbedder.len_in_tokens(''.join(rows)) < max_length: + if _continue and i == len(shared.history['internal']) - 1: + rows.insert(1, f"{prefix2}{shared.history['internal'][i][1]}") + else: + rows.insert(1, f"{prefix2}{shared.history['internal'][i][1].strip()}\n") + + string = shared.history['internal'][i][0] + if string != '': + rows.insert(1, f"{prefix1}{string.strip()}\n") + + i -= 1 + + if impersonate: + min_rows = 2 + rows.append(f"{prefix1}") + elif not _continue: + # Adding the user message + if len(user_input) > 0: + rows.append(f"{prefix1}{user_input}\n") + + # Adding the Character prefix + rows.append(apply_extensions("bot_prefix", f"{prefix2}")) + + while len(rows) > min_rows and LLaVAEmbedder.len_in_tokens(''.join(rows)) >= max_length: + rows.pop(1) + prompt = ''.join(rows) + + if also_return_rows: + return prompt, rows + else: + return prompt + + +def tokenizer_modifier(state, prompt, input_ids, input_embeds): + global params + start_ts = time.time() + image_matches = re.finditer(r'', prompt) + images = [Image.open(BytesIO(base64.b64decode(match.group(1)))) for match in image_matches] + + if len(images) == 0: + return prompt, input_ids, input_embeds + + prompt, input_ids, input_embeds, total_embedded = llava_embedder.forward(prompt, images, state) + print(f'LLaVA - 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)) + + +def ui(): + global llava_embedder + llava_embedder = LLaVAEmbedder() + with gr.Column(): + picture_select = gr.Image(label='Send a picture', type='pil') + # I found that it doesn't deal super well with multiple images, and demo ui had a bug where it included only the last image anyway + single_image_checkbox = gr.Checkbox(False, label='Embed all images, not only the last one') + # Prepare the input hijack + picture_select.upload( + lambda picture: input_hijack.update({"state": True, "value": partial(add_chat_picture, picture)}), + [picture_select], + None + ) + picture_select.clear(lambda: input_hijack.update({"state": False, "value": ["",""]}), None, None) + single_image_checkbox.change(lambda x: params.update({"add_all_images_to_prompt": x}), single_image_checkbox, None) + shared.gradio['Generate'].click(lambda: None, None, picture_select) + shared.gradio['textbox'].submit(lambda: None, None, picture_select) diff --git a/text-generation-webui-main/extensions/openai/README.md b/text-generation-webui-main/extensions/openai/README.md new file mode 100644 index 0000000000000000000000000000000000000000..b4d4ff3a7555e5a9fab2e588bbedda8d7ab02b59 --- /dev/null +++ b/text-generation-webui-main/extensions/openai/README.md @@ -0,0 +1,123 @@ +# An OpenedAI API (openai like) + +This extension creates an API that works kind of like openai (ie. api.openai.com). +It's incomplete so far but perhaps is functional enough for you. + +## Setup & installation + +Optional (for flask_cloudflared, embeddings): + +``` +pip3 install -r requirements.txt +``` + +### Embeddings (alpha) + +Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future. + +| model name | dimensions | input max tokens | speed | size | Avg. performance | +| --- | --- | --- | --- | --- | --- | +| text-embedding-ada-002 | 1536 | 8192| - | - | - | +| text-davinci-002 | 768 | 2046 | - | - | - | +| all-mpnet-base-v2 | 768 | 384 | 2800 | 420M | 63.3 | +| all-MiniLM-L6-v2 | 384 | 256 | 14200 | 80M | 58.8 | + +In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable OPENEDAI_EMBEDDING_MODEL, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2". + +Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable. + +### Client Application Setup + +Almost everything you use it with will require you to set a dummy OpenAI API key environment variable. + +With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so: + +``` +OPENAI_API_KEY=dummy +OPENAI_API_BASE=http://127.0.0.1:5001/v1 +``` + +If needed, replace 127.0.0.1 with the IP/port of your server. + +If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, you can ensure compatibility by loading the .env file before loading the openai module, like so in python: + +``` +from dotenv import load_dotenv +load_dotenv() +import openai +``` + +With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so: + +``` +const openai = OpenAI(Configuration({ + apiKey: process.env.OPENAI_API_KEY, + basePath: process.env.OPENAI_API_BASE, +})); +``` + +For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api): + +``` +const api = new ChatGPTAPI({ + apiKey: process.env.OPENAI_API_KEY, + apiBaseUrl: process.env.OPENAI_API_BASE, +}) +``` + +## Compatibility & not so compatibility + +What's working: + +| API endpoint | tested with | notes | +| --- | --- | --- | +| /v1/models | openai.Model.list() | returns the currently loaded model_name and some mock compatibility options | +| /v1/models/{id} | openai.Model.get() | returns whatever you ask for, model does nothing yet anyways | +| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far | +| /v1/chat/completions | openai.ChatCompletion.create() | depending on the model, this may add leading linefeeds | +| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. | +| /v1/moderations | openai.Moderation.create() | does nothing. successfully. | +| /v1/engines/\*/... completions, embeddings, generate | python-openai v0.25 and earlier | Legacy engines endpoints | + +The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048 tokens instead of 4096, 8k, etc). To mitigate some of this, the max_tokens value is halved until it is less than truncation_length for the model (typically 2k). + +Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly. + +Some hacky mappings: + +| OpenAI | text-generation-webui | note | +| --- | --- | --- | +| frequency_penalty | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way | +| presence_penalty | repetition_penalty | same issues as frequency_penalty, hardcoded to 1.0 | +| best_of | top_k | | +| stop | custom_stopping_strings | this is also stuffed with ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] for good measure. | +| n | 1 | hardcoded, it may be worth implementing this but I'm not sure how yet | +| 1.0 | typical_p | hardcoded | +| 1 | num_beams | hardcoded | +| max_tokens | max_new_tokens | max_tokens is scaled down by powers of 2 until it's smaller than truncation length. | +| logprobs | - | ignored | + +defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent. + +### Applications + +Everything needs OPENAI_API_KEY=dummy set. + +| Compatibility | Application/Library | url | notes / setting | +| --- | --- | --- | --- | +| ✅❌ | openai-python | https://github.com/openai/openai-python | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅❌ | openai-node | https://github.com/openai/openai-node | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) | +| ✅❌ | chatgpt-api | https://github.com/transitive-bullshit/chatgpt-api | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) | +| ✅ | shell_gpt | https://github.com/TheR1D/shell_gpt | OPENAI_API_HOST=http://127.0.0.1:5001 | +| ✅ | gpt-shell | https://github.com/jla/gpt-shell | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅ | gpt-discord-bot | https://github.com/openai/gpt-discord-bot | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | +| ✅❌ | langchain | https://github.com/hwchase17/langchain | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. | +| ✅❌ | Auto-GPT | https://github.com/Significant-Gravitas/Auto-GPT | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context | +| ✅❌ | babyagi | https://github.com/yoheinakajima/babyagi | OPENAI_API_BASE=http://127.0.0.1:5001/v1 | + +## Future plans +* better error handling +* model changing, esp. something for swapping loras or embedding models +* consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard) +* do something about rate limiting or locking requests for completions, most systems will only be able handle a single request at a time before OOM +* the whole api, images (stable diffusion), audio (whisper), fine-tunes (training), edits, files, etc. \ No newline at end of file diff --git a/text-generation-webui-main/extensions/openai/requirements.txt b/text-generation-webui-main/extensions/openai/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5193a0acef91177f49cea0ba1c0fd0329b8703de --- /dev/null +++ b/text-generation-webui-main/extensions/openai/requirements.txt @@ -0,0 +1,2 @@ +flask_cloudflared==0.0.12 +sentence-transformers \ No newline at end of file diff --git a/text-generation-webui-main/extensions/openai/script.py b/text-generation-webui-main/extensions/openai/script.py new file mode 100644 index 0000000000000000000000000000000000000000..f9373385224970ec78edbf0ddb12263df6d7aba2 --- /dev/null +++ b/text-generation-webui-main/extensions/openai/script.py @@ -0,0 +1,529 @@ +import json +import os +import time +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +from threading import Thread + +from modules import shared +from modules.text_generation import encode, generate_reply + +params = { + 'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001, +} + +debug = True if 'OPENEDAI_DEBUG' in os.environ else False + +# Optional, install the module and download the model to enable +# v1/embeddings +try: + from sentence_transformers import SentenceTransformer +except ImportError: + pass + +st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2" +embedding_model = None + +standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] + +# little helper to get defaults if arg is present but None and should be the same type as default. +def default(dic, key, default): + val = dic.get(key, default) + if type(val) != type(default): + # maybe it's just something like 1 instead of 1.0 + try: + v = type(default)(val) + if type(val)(v) == val: # if it's the same value passed in, it's ok. + return v + except: + pass + + val = default + return val + + +def clamp(value, minvalue, maxvalue): + return max(minvalue, min(value, maxvalue)) + + +class Handler(BaseHTTPRequestHandler): + def do_GET(self): + if self.path.startswith('/v1/models'): + + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + # TODO: list all models and allow model changes via API? Lora's? + # This API should list capabilities, limits and pricing... + models = [{ + "id": shared.model_name, # The real chat/completions model + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": st_model, # The real sentence transformer embeddings model + "object": "model", + "owned_by": "user", + "permission": [] + }, { # these are expected by so much, so include some here as a dummy + "id": "gpt-3.5-turbo", # /v1/chat/completions + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": "text-curie-001", # /v1/completions, 2k context + "object": "model", + "owned_by": "user", + "permission": [] + }, { + "id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768 + "object": "model", + "owned_by": "user", + "permission": [] + }] + + response = '' + if self.path == '/v1/models': + response = json.dumps({ + "object": "list", + "data": models, + }) + else: + the_model_name = self.path[len('/v1/models/'):] + response = json.dumps({ + "id": the_model_name, + "object": "model", + "owned_by": "user", + "permission": [] + }) + + self.wfile.write(response.encode('utf-8')) + else: + self.send_error(404) + + def do_POST(self): + content_length = int(self.headers['Content-Length']) + body = json.loads(self.rfile.read(content_length).decode('utf-8')) + + if debug: + print(self.headers) # did you know... python-openai sends your linux kernel & python version? + if debug: + print(body) + + if '/completions' in self.path or '/generate' in self.path: + is_legacy = '/generate' in self.path + is_chat = 'chat' in self.path + resp_list = 'data' if is_legacy else 'choices' + + # XXX model is ignored for now + # model = body.get('model', shared.model_name) # ignored, use existing for now + model = shared.model_name + created_time = int(time.time()) + cmpl_id = "conv-%d" % (created_time) + + # Try to use openai defaults or map them to something with the same intent + stopping_strings = default(shared.settings, 'custom_stopping_strings', []) + if 'stop' in body: + if isinstance(body['stop'], str): + stopping_strings = [body['stop']] + elif isinstance(body['stop'], list): + stopping_strings = body['stop'] + + truncation_length = default(shared.settings, 'truncation_length', 2048) + truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length) + + default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it., the default for chat is "inf" + + max_tokens_str = 'length' if is_legacy else 'max_tokens' + max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens)) + + # hard scale this, assuming the given max is for GPT3/4, perhaps inspect the requested model and lookup the context max + while truncation_length <= max_tokens: + max_tokens = max_tokens // 2 + + req_params = { + 'max_new_tokens': max_tokens, + 'temperature': default(body, 'temperature', 1.0), + 'top_p': default(body, 'top_p', 1.0), + 'top_k': default(body, 'best_of', 1), + # XXX not sure about this one, seems to be the right mapping, but the range is different (-2..2.0) vs 0..2 + # 0 is default in openai, but 1.0 is default in other places. Maybe it's scaled? scale it. + 'repetition_penalty': 1.18, # (default(body, 'presence_penalty', 0) + 2.0 ) / 2.0, # 0 the real default, 1.2 is the model default, but 1.18 works better. + # XXX not sure about this one either, same questions. (-2..2.0), 0 is default not 1.0, scale it. + 'encoder_repetition_penalty': 1.0, # (default(body, 'frequency_penalty', 0) + 2.0) / 2.0, + 'suffix': body.get('suffix', None), + 'stream': default(body, 'stream', False), + 'echo': default(body, 'echo', False), + ##################################################### + 'seed': shared.settings.get('seed', -1), + # int(body.get('n', 1)) # perhaps this should be num_beams or chat_generation_attempts? 'n' doesn't have a direct map + # unofficial, but it needs to get set anyways. + 'truncation_length': truncation_length, + # no more args. + 'add_bos_token': shared.settings.get('add_bos_token', True), + 'do_sample': True, + 'typical_p': 1.0, + 'min_length': 0, + 'no_repeat_ngram_size': 0, + 'num_beams': 1, + 'penalty_alpha': 0.0, + 'length_penalty': 1, + 'early_stopping': False, + 'ban_eos_token': False, + 'skip_special_tokens': True, + } + + # fixup absolute 0.0's + for par in ['temperature', 'repetition_penalty', 'encoder_repetition_penalty']: + req_params[par] = clamp(req_params[par], 0.001, 1.999) + + self.send_response(200) + if req_params['stream']: + self.send_header('Content-Type', 'text/event-stream') + self.send_header('Cache-Control', 'no-cache') + # self.send_header('Connection', 'keep-alive') + else: + self.send_header('Content-Type', 'application/json') + self.end_headers() + + token_count = 0 + completion_token_count = 0 + prompt = '' + stream_object_type = '' + object_type = '' + + if is_chat: + stream_object_type = 'chat.completions.chunk' + object_type = 'chat.completions' + + messages = body['messages'] + + system_msg = '' # You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date} + if 'prompt' in body: # Maybe they sent both? This is not documented in the API, but some clients seem to do this. + system_msg = body['prompt'] + + chat_msgs = [] + + for m in messages: + role = m['role'] + content = m['content'] + # name = m.get('name', 'user') + if role == 'system': + system_msg += content + else: + chat_msgs.extend([f"\n{role}: {content.strip()}"]) # Strip content? linefeed? + + system_token_count = len(encode(system_msg)[0]) + remaining_tokens = req_params['truncation_length'] - req_params['max_new_tokens'] - system_token_count + chat_msg = '' + + while chat_msgs: + new_msg = chat_msgs.pop() + new_size = len(encode(new_msg)[0]) + if new_size <= remaining_tokens: + chat_msg = new_msg + chat_msg + remaining_tokens -= new_size + else: + # TODO: clip a message to fit? + # ie. user: ... + break + + if len(chat_msgs) > 0: + print(f"truncating chat messages, dropping {len(chat_msgs)} messages.") + + if system_msg: + prompt = 'system: ' + system_msg + '\n' + chat_msg + '\nassistant: ' + else: + prompt = chat_msg + '\nassistant: ' + + token_count = len(encode(prompt)[0]) + + # pass with some expected stop strings. + # some strange cases of "##| Instruction: " sneaking through. + stopping_strings += standard_stopping_strings + req_params['custom_stopping_strings'] = stopping_strings + else: + stream_object_type = 'text_completion.chunk' + object_type = 'text_completion' + + # ... encoded as a string, array of strings, array of tokens, or array of token arrays. + if is_legacy: + prompt = body['context'] # Older engines.generate API + else: + prompt = body['prompt'] # XXX this can be different types + + if isinstance(prompt, list): + prompt = ''.join(prompt) # XXX this is wrong... need to split out to multiple calls? + + token_count = len(encode(prompt)[0]) + if token_count >= req_params['truncation_length']: + new_len = int(len(prompt) * (float(shared.settings['truncation_length']) - req_params['max_new_tokens']) / token_count) + prompt = prompt[-new_len:] + print(f"truncating prompt to {new_len} characters, was {token_count} tokens. Now: {len(encode(prompt)[0])} tokens.") + + # pass with some expected stop strings. + # some strange cases of "##| Instruction: " sneaking through. + stopping_strings += standard_stopping_strings + req_params['custom_stopping_strings'] = stopping_strings + + shared.args.no_stream = not req_params['stream'] + if not shared.args.no_stream: + shared.args.chat = True + # begin streaming + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": shared.model_name, + resp_list: [{ + "index": 0, + "finish_reason": None, + }], + } + + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]["text"] = "" + else: + # This is coming back as "system" to the openapi cli, not sure why. + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''} + chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''} + # { "role": "assistant" } + + response = 'data: ' + json.dumps(chunk) + '\n' + self.wfile.write(response.encode('utf-8')) + + # generate reply ####################################### + if debug: + print({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings}) + generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings) + + answer = '' + seen_content = '' + longest_stop_len = max([len(x) for x in stopping_strings]) + + for a in generator: + if isinstance(a, str): + answer = a + else: + answer = a[0] + + stop_string_found = False + len_seen = len(seen_content) + search_start = max(len_seen - longest_stop_len, 0) + + for string in stopping_strings: + idx = answer.find(string, search_start) + if idx != -1: + answer = answer[:idx] # clip it. + stop_string_found = True + + if stop_string_found: + break + + # If something like "\nYo" is generated just before "\nYou:" + # is completed, buffer and generate more, don't send it + buffer_and_continue = False + + for string in stopping_strings: + for j in range(len(string) - 1, 0, -1): + if answer[-j:] == string[:j]: + buffer_and_continue = True + break + else: + continue + break + + if buffer_and_continue: + continue + + if not shared.args.no_stream: + # Streaming + new_content = answer[len_seen:] + + if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet. + continue + + seen_content = answer + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": shared.model_name, + resp_list: [{ + "index": 0, + "finish_reason": None, + }], + } + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]['text'] = new_content + else: + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]['message'] = {'content': new_content} + chunk[resp_list][0]['delta'] = {'content': new_content} + response = 'data: ' + json.dumps(chunk) + '\n' + self.wfile.write(response.encode('utf-8')) + completion_token_count += len(encode(new_content)[0]) + + if not shared.args.no_stream: + chunk = { + "id": cmpl_id, + "object": stream_object_type, + "created": created_time, + "model": model, # TODO: add Lora info? + resp_list: [{ + "index": 0, + "finish_reason": "stop", + }], + "usage": { + "prompt_tokens": token_count, + "completion_tokens": completion_token_count, + "total_tokens": token_count + completion_token_count + } + } + if stream_object_type == 'text_completion.chunk': + chunk[resp_list][0]['text'] = '' + else: + # So yeah... do both methods? delta and messages. + chunk[resp_list][0]['message'] = {'content': ''} + chunk[resp_list][0]['delta'] = {} + response = 'data: ' + json.dumps(chunk) + '\ndata: [DONE]\n' + self.wfile.write(response.encode('utf-8')) + # Finished if streaming. + if debug: + print({'response': answer}) + return + + if debug: + print({'response': answer}) + + completion_token_count = len(encode(answer)[0]) + stop_reason = "stop" + if token_count + completion_token_count >= req_params['truncation_length']: + stop_reason = "length" + + resp = { + "id": cmpl_id, + "object": object_type, + "created": created_time, + "model": model, # TODO: add Lora info? + resp_list: [{ + "index": 0, + "finish_reason": stop_reason, + }], + "usage": { + "prompt_tokens": token_count, + "completion_tokens": completion_token_count, + "total_tokens": token_count + completion_token_count + } + } + + if is_chat: + resp[resp_list][0]["message"] = {"role": "assistant", "content": answer} + else: + resp[resp_list][0]["text"] = answer + + response = json.dumps(resp) + self.wfile.write(response.encode('utf-8')) + elif '/embeddings' in self.path and embedding_model is not None: + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + input = body['input'] if 'input' in body else body['text'] + if type(input) is str: + input = [input] + + embeddings = embedding_model.encode(input).tolist() + + data = [{"object": "embedding", "embedding": emb, "index": n} for n, emb in enumerate(embeddings)] + + response = json.dumps({ + "object": "list", + "data": data, + "model": st_model, # return the real model + "usage": { + "prompt_tokens": 0, + "total_tokens": 0, + } + }) + + if debug: + print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}") + self.wfile.write(response.encode('utf-8')) + elif '/moderations' in self.path: + # for now do nothing, just don't error. + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + response = json.dumps({ + "id": "modr-5MWoLO", + "model": "text-moderation-001", + "results": [{ + "categories": { + "hate": False, + "hate/threatening": False, + "self-harm": False, + "sexual": False, + "sexual/minors": False, + "violence": False, + "violence/graphic": False + }, + "category_scores": { + "hate": 0.0, + "hate/threatening": 0.0, + "self-harm": 0.0, + "sexual": 0.0, + "sexual/minors": 0.0, + "violence": 0.0, + "violence/graphic": 0.0 + }, + "flagged": False + }] + }) + self.wfile.write(response.encode('utf-8')) + + elif self.path == '/api/v1/token-count': + # NOT STANDARD. lifted from the api extension, but it's still very useful to calculate tokenized length client side. + self.send_response(200) + self.send_header('Content-Type', 'application/json') + self.end_headers() + + tokens = encode(body['prompt'])[0] + response = json.dumps({ + 'results': [{ + 'tokens': len(tokens) + }] + }) + self.wfile.write(response.encode('utf-8')) + else: + print(self.path, self.headers) + self.send_error(404) + + +def run_server(): + global embedding_model + try: + embedding_model = SentenceTransformer(st_model) + print(f"\nLoaded embedding model: {st_model}, max sequence length: {embedding_model.max_seq_length}") + except: + print(f"\nFailed to load embedding model: {st_model}") + pass + + server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port']) + server = ThreadingHTTPServer(server_addr, Handler) + if shared.args.share: + try: + from flask_cloudflared import _run_cloudflared + public_url = _run_cloudflared(params['port'], params['port'] + 1) + print(f'Starting OpenAI compatible api at {public_url}/') + except ImportError: + print('You should install flask_cloudflared manually') + else: + print(f'Starting OpenAI compatible api at http://{server_addr[0]}:{server_addr[1]}/') + server.serve_forever() + + +def setup(): + Thread(target=run_server, daemon=True).start() diff --git a/text-generation-webui-main/extensions/sd_api_pictures/README.MD b/text-generation-webui-main/extensions/sd_api_pictures/README.MD new file mode 100644 index 0000000000000000000000000000000000000000..67c75e145ccc8301505d96d858da04713ad4337d --- /dev/null +++ b/text-generation-webui-main/extensions/sd_api_pictures/README.MD @@ -0,0 +1,90 @@ +## Description: +TL;DR: Lets the bot answer you with a picture! + +Stable Diffusion API pictures for TextGen, v.1.2.0 +An extension to [oobabooga's textgen-webui](https://github.com/oobabooga/text-generation-webui) allowing you to receive pics generated by [Automatic1111's SD-WebUI API](https://github.com/AUTOMATIC1111/stable-diffusion-webui) + +
+Interface overview + +![Interface](https://raw.githubusercontent.com/Brawlence/SD_api_pics/main/illust/Interface.jpg) + +
+ +Load it in the `--chat` mode with `--extension sd_api_pictures` alongside `send_pictures` +(it's not really required, but completes the picture, *pun intended*). + + +## History + +Consider the version included with [oobabooga's repository](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/sd_api_pictures) to be STABLE, experimental developments and untested features are pushed in [Brawlence/SD_api_pics](https://github.com/Brawlence/SD_api_pics) + +Lastest change: +1.1.0 → 1.1.1 Fixed not having Auto1111's metadata in received images + +## Details + +The image generation is triggered: +- manually through the 'Force the picture response' button while in `Manual` or `Immersive/Interactive` modes OR +- automatically in `Immersive/Interactive` mode if the words `'send|main|message|me'` are followed by `'image|pic|picture|photo|snap|snapshot|selfie|meme'` in the user's prompt +- always on in `Picturebook/Adventure` mode (if not currently suppressed by 'Suppress the picture response') + +## Prerequisites + +One needs an available instance of Automatic1111's webui running with an `--api` flag. Ain't tested with a notebook / cloud hosted one but should be possible. +To run it locally in parallel on the same machine, specify custom `--listen-port` for either Auto1111's or ooba's webUIs. + +## Features overview +- Connection to API check (press enter in the address box) +- [VRAM management (model shuffling)](https://github.com/Brawlence/SD_api_pics/wiki/VRAM-management-feature) +- [Three different operation modes](https://github.com/Brawlence/SD_api_pics/wiki/Modes-of-operation) (manual, interactive, always-on) +- User-defined persistent settings via settings.json + +### Connection check + +Insert the Automatic1111's WebUI address and press Enter: +![API-check](https://raw.githubusercontent.com/Brawlence/SD_api_pics/main/illust/API-check.gif) +Green mark confirms the ability to communicate with Auto1111's API on this address. Red cross means something's not right (the ext won't work). + +### Persistents settings + +Create or modify the `settings.json` in the `text-generation-webui` root directory to override the defaults +present in script.py, ex: + +```json +{ + "sd_api_pictures-manage_VRAM": 1, + "sd_api_pictures-save_img": 1, + "sd_api_pictures-prompt_prefix": "(Masterpiece:1.1), detailed, intricate, colorful, (solo:1.1)", + "sd_api_pictures-sampler_name": "DPM++ 2M Karras" +} +``` + +will automatically set the `Manage VRAM` & `Keep original images` checkboxes and change the texts in `Prompt Prefix` and `Sampler name` on load. + +--- + +## Demonstrations: + +Those are examples of the version 1.0.0, but the core functionality is still the same + +
+Conversation 1 + +![EXA1](https://user-images.githubusercontent.com/42910943/224866564-939a3bcb-e7cf-4ac0-a33f-b3047b55054d.jpg) +![EXA2](https://user-images.githubusercontent.com/42910943/224866566-38394054-1320-45cf-9515-afa76d9d7745.jpg) +![EXA3](https://user-images.githubusercontent.com/42910943/224866568-10ea47b7-0bac-4269-9ec9-22c387a13b59.jpg) +![EXA4](https://user-images.githubusercontent.com/42910943/224866569-326121ad-1ea1-4874-9f6b-4bca7930a263.jpg) + + +
+ +
+Conversation 2 + +![Hist1](https://user-images.githubusercontent.com/42910943/224865517-c6966b58-bc4d-4353-aab9-6eb97778d7bf.jpg) +![Hist2](https://user-images.githubusercontent.com/42910943/224865527-b2fe7c2e-0da5-4c2e-b705-42e233b07084.jpg) +![Hist3](https://user-images.githubusercontent.com/42910943/224865535-a38d94e7-8975-4a46-a655-1ae1de41f85d.jpg) + +
+ diff --git a/text-generation-webui-main/extensions/sd_api_pictures/script.py b/text-generation-webui-main/extensions/sd_api_pictures/script.py new file mode 100644 index 0000000000000000000000000000000000000000..1189a593f775f814731c04afaa3b73bbb0cb1ec4 --- /dev/null +++ b/text-generation-webui-main/extensions/sd_api_pictures/script.py @@ -0,0 +1,321 @@ +import base64 +import io +import re +import time +from datetime import date +from pathlib import Path + +import gradio as gr +import modules.shared as shared +import requests +import torch +from modules.models import reload_model, unload_model +from PIL import Image + +torch._C._jit_set_profiling_mode(False) + +# parameters which can be customized in settings.json of webui +params = { + 'address': 'http://127.0.0.1:7860', + 'mode': 0, # modes of operation: 0 (Manual only), 1 (Immersive/Interactive - looks for words to trigger), 2 (Picturebook Adventure - Always on) + 'manage_VRAM': False, + 'save_img': False, + 'SD_model': 'NeverEndingDream', # not used right now + 'prompt_prefix': '(Masterpiece:1.1), detailed, intricate, colorful', + 'negative_prompt': '(worst quality, low quality:1.3)', + 'width': 512, + 'height': 512, + 'denoising_strength': 0.61, + 'restore_faces': False, + 'enable_hr': False, + 'hr_upscaler': 'ESRGAN_4x', + 'hr_scale': '1.0', + 'seed': -1, + 'sampler_name': 'DDIM', + 'steps': 32, + 'cfg_scale': 7 +} + + +def give_VRAM_priority(actor): + global shared, params + + if actor == 'SD': + unload_model() + print("Requesting Auto1111 to re-load last checkpoint used...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') + response.raise_for_status() + + elif actor == 'LLM': + print("Requesting Auto1111 to vacate VRAM...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') + response.raise_for_status() + reload_model() + + elif actor == 'set': + print("VRAM mangement activated -- requesting Auto1111 to vacate VRAM...") + response = requests.post(url=f'{params["address"]}/sdapi/v1/unload-checkpoint', json='') + response.raise_for_status() + + elif actor == 'reset': + print("VRAM mangement deactivated -- requesting Auto1111 to reload checkpoint") + response = requests.post(url=f'{params["address"]}/sdapi/v1/reload-checkpoint', json='') + response.raise_for_status() + + else: + raise RuntimeError(f'Managing VRAM: "{actor}" is not a known state!') + + response.raise_for_status() + del response + + +if params['manage_VRAM']: + give_VRAM_priority('set') + +samplers = ['DDIM', 'DPM++ 2M Karras'] # TODO: get the availible samplers with http://{address}}/sdapi/v1/samplers +SD_models = ['NeverEndingDream'] # TODO: get with http://{address}}/sdapi/v1/sd-models and allow user to select + +streaming_state = shared.args.no_stream # remember if chat streaming was enabled +picture_response = False # specifies if the next model response should appear as a picture + +def remove_surrounded_chars(string): + # this expression matches to 'as few symbols as possible (0 upwards) between any asterisks' OR + # 'as few symbols as possible (0 upwards) between an asterisk and the end of the string' + return re.sub('\*[^\*]*?(\*|$)', '', string) + + +def triggers_are_in(string): + string = remove_surrounded_chars(string) + # regex searches for send|main|message|me (at the end of the word) followed by + # a whole word of image|pic|picture|photo|snap|snapshot|selfie|meme(s), + # (?aims) are regex parser flags + return bool(re.search('(?aims)(send|mail|message|me)\\b.+?\\b(image|pic(ture)?|photo|snap(shot)?|selfie|meme)s?\\b', string)) + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + + global params + + if not params['mode'] == 1: # if not in immersive/interactive mode, do nothing + return string + + if triggers_are_in(string): # if we're in it, check for trigger words + toggle_generation(True) + string = string.lower() + if "of" in string: + subject = string.split('of', 1)[1] # subdivide the string once by the first 'of' instance and get what's coming after it + string = "Please provide a detailed and vivid description of " + subject + else: + string = "Please provide a detailed description of your appearance, your surroundings and what you are doing right now" + + return string + +# Get and save the Stable Diffusion-generated picture +def get_SD_pictures(description): + + global params + + if params['manage_VRAM']: + give_VRAM_priority('SD') + + payload = { + "prompt": params['prompt_prefix'] + description, + "seed": params['seed'], + "sampler_name": params['sampler_name'], + "enable_hr": params['enable_hr'], + "hr_scale": params['hr_scale'], + "hr_upscaler": params['hr_upscaler'], + "denoising_strength": params['denoising_strength'], + "steps": params['steps'], + "cfg_scale": params['cfg_scale'], + "width": params['width'], + "height": params['height'], + "restore_faces": params['restore_faces'], + "override_settings_restore_afterwards": True, + "negative_prompt": params['negative_prompt'] + } + + print(f'Prompting the image generator via the API on {params["address"]}...') + response = requests.post(url=f'{params["address"]}/sdapi/v1/txt2img', json=payload) + response.raise_for_status() + r = response.json() + + visible_result = "" + for img_str in r['images']: + if params['save_img']: + img_data = base64.b64decode(img_str) + + variadic = f'{date.today().strftime("%Y_%m_%d")}/{shared.character}_{int(time.time())}' + output_file = Path(f'extensions/sd_api_pictures/outputs/{variadic}.png') + output_file.parent.mkdir(parents=True, exist_ok=True) + + with open(output_file.as_posix(), 'wb') as f: + f.write(img_data) + + visible_result = visible_result + f'{description}\n' + else: + image = Image.open(io.BytesIO(base64.b64decode(img_str.split(",", 1)[0]))) + # lower the resolution of received images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history + image.thumbnail((300, 300)) + buffered = io.BytesIO() + image.save(buffered, format="JPEG") + buffered.seek(0) + image_bytes = buffered.getvalue() + img_str = "data:image/jpeg;base64," + base64.b64encode(image_bytes).decode() + visible_result = visible_result + f'{description}\n' + + if params['manage_VRAM']: + give_VRAM_priority('LLM') + + return visible_result + +# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history) +# and replace it with 'text' for the purposes of logging? +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global picture_response, params + + if not picture_response: + return string + + string = remove_surrounded_chars(string) + string = string.replace('"', '') + string = string.replace('“', '') + string = string.replace('\n', ' ') + string = string.strip() + + if string == '': + string = 'no viable description in reply, try regenerating' + return string + + text = "" + if (params['mode'] < 2): + toggle_generation(False) + text = f'*Sends a picture which portrays: “{string}”*' + else: + text = string + + string = get_SD_pictures(string) + "\n" + text + + return string + + +def bot_prefix_modifier(string): + """ + This function is only applied in chat mode. It modifies + the prefix text for the Bot and can be used to bias its + behavior. + """ + + return string + + +def toggle_generation(*args): + global picture_response, shared, streaming_state + + if not args: + picture_response = not picture_response + else: + picture_response = args[0] + + shared.args.no_stream = True if picture_response else streaming_state # Disable streaming cause otherwise the SD-generated picture would return as a dud + shared.processing_message = "*Is sending a picture...*" if picture_response else "*Is typing...*" + + +def filter_address(address): + address = address.strip() + # address = re.sub('http(s)?:\/\/|\/$','',address) # remove starting http:// OR https:// OR trailing slash + address = re.sub('\/$', '', address) # remove trailing /s + if not address.startswith('http'): + address = 'http://' + address + return address + + +def SD_api_address_update(address): + + global params + + msg = "✔️ SD API is found on:" + address = filter_address(address) + params.update({"address": address}) + try: + response = requests.get(url=f'{params["address"]}/sdapi/v1/sd-models') + response.raise_for_status() + # r = response.json() + except: + msg = "❌ No SD API endpoint on:" + + return gr.Textbox.update(label=msg) + +def ui(): + + # Gradio elements + # gr.Markdown('### Stable Diffusion API Pictures') # Currently the name of extension is shown as the title + with gr.Accordion("Parameters", open=True, elem_classes="SDAP"): + with gr.Row(): + address = gr.Textbox(placeholder=params['address'], value=params['address'], label='Auto1111\'s WebUI address') + modes_list = ["Manual", "Immersive/Interactive", "Picturebook/Adventure"] + mode = gr.Dropdown(modes_list, value=modes_list[params['mode']], label="Mode of operation", type="index") + with gr.Column(scale=1, min_width=300): + manage_VRAM = gr.Checkbox(value=params['manage_VRAM'], label='Manage VRAM') + save_img = gr.Checkbox(value=params['save_img'], label='Keep original images and use them in chat') + + force_pic = gr.Button("Force the picture response") + suppr_pic = gr.Button("Suppress the picture response") + + with gr.Accordion("Generation parameters", open=False): + prompt_prefix = gr.Textbox(placeholder=params['prompt_prefix'], value=params['prompt_prefix'], label='Prompt Prefix (best used to describe the look of the character)') + negative_prompt = gr.Textbox(placeholder=params['negative_prompt'], value=params['negative_prompt'], label='Negative Prompt') + with gr.Row(): + with gr.Column(): + width = gr.Slider(256, 768, value=params['width'], step=64, label='Width') + height = gr.Slider(256, 768, value=params['height'], step=64, label='Height') + with gr.Column(): + sampler_name = gr.Textbox(placeholder=params['sampler_name'], value=params['sampler_name'], label='Sampling method', elem_id="sampler_box") + steps = gr.Slider(1, 150, value=params['steps'], step=1, label="Sampling steps") + with gr.Row(): + seed = gr.Number(label="Seed", value=params['seed'], elem_id="seed_box") + cfg_scale = gr.Number(label="CFG Scale", value=params['cfg_scale'], elem_id="cfg_box") + with gr.Column() as hr_options: + restore_faces = gr.Checkbox(value=params['restore_faces'], label='Restore faces') + enable_hr = gr.Checkbox(value=params['enable_hr'], label='Hires. fix') + with gr.Row(visible=params['enable_hr'], elem_classes="hires_opts") as hr_options: + hr_scale = gr.Slider(1, 4, value=params['hr_scale'], step=0.1, label='Upscale by') + denoising_strength = gr.Slider(0, 1, value=params['denoising_strength'], step=0.01, label='Denoising strength') + hr_upscaler = gr.Textbox(placeholder=params['hr_upscaler'], value=params['hr_upscaler'], label='Upscaler') + + + # Event functions to update the parameters in the backend + address.change(lambda x: params.update({"address": filter_address(x)}), address, None) + mode.select(lambda x: params.update({"mode": x}), mode, None) + mode.select(lambda x: toggle_generation(x > 1), inputs=mode, outputs=None) + manage_VRAM.change(lambda x: params.update({"manage_VRAM": x}), manage_VRAM, None) + manage_VRAM.change(lambda x: give_VRAM_priority('set' if x else 'reset'), inputs=manage_VRAM, outputs=None) + save_img.change(lambda x: params.update({"save_img": x}), save_img, None) + + address.submit(fn=SD_api_address_update, inputs=address, outputs=address) + prompt_prefix.change(lambda x: params.update({"prompt_prefix": x}), prompt_prefix, None) + negative_prompt.change(lambda x: params.update({"negative_prompt": x}), negative_prompt, None) + width.change(lambda x: params.update({"width": x}), width, None) + height.change(lambda x: params.update({"height": x}), height, None) + hr_scale.change(lambda x: params.update({"hr_scale": x}), hr_scale, None) + denoising_strength.change(lambda x: params.update({"denoising_strength": x}), denoising_strength, None) + restore_faces.change(lambda x: params.update({"restore_faces": x}), restore_faces, None) + hr_upscaler.change(lambda x: params.update({"hr_upscaler": x}), hr_upscaler, None) + enable_hr.change(lambda x: params.update({"enable_hr": x}), enable_hr, None) + enable_hr.change(lambda x: hr_options.update(visible=params["enable_hr"]), enable_hr, hr_options) + + sampler_name.change(lambda x: params.update({"sampler_name": x}), sampler_name, None) + steps.change(lambda x: params.update({"steps": x}), steps, None) + seed.change(lambda x: params.update({"seed": x}), seed, None) + cfg_scale.change(lambda x: params.update({"cfg_scale": x}), cfg_scale, None) + + force_pic.click(lambda x: toggle_generation(True), inputs=force_pic, outputs=None) + suppr_pic.click(lambda x: toggle_generation(False), inputs=suppr_pic, outputs=None) diff --git a/text-generation-webui-main/extensions/send_pictures/script.py b/text-generation-webui-main/extensions/send_pictures/script.py new file mode 100644 index 0000000000000000000000000000000000000000..b21423e443f524fee120581ff95ed388ecf0de08 --- /dev/null +++ b/text-generation-webui-main/extensions/send_pictures/script.py @@ -0,0 +1,47 @@ +import base64 +from io import BytesIO + +import gradio as gr +import torch +from transformers import BlipForConditionalGeneration, BlipProcessor + +from modules import chat, shared +from modules.ui import gather_interface_values + +# If 'state' is True, will hijack the next chat generation with +# custom input text given by 'value' in the format [text, visible_text] +input_hijack = { + 'state': False, + 'value': ["", ""] +} + +processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base") +model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float32).to("cpu") + + +def caption_image(raw_image): + inputs = processor(raw_image.convert('RGB'), return_tensors="pt").to("cpu", torch.float32) + out = model.generate(**inputs, max_new_tokens=100) + return processor.decode(out[0], skip_special_tokens=True) + + +def generate_chat_picture(picture, name1, name2): + text = f'*{name1} sends {name2} a picture that contains the following: “{caption_image(picture)}”*' + # lower the resolution of sent images for the chat, otherwise the log size gets out of control quickly with all the base64 values in visible history + picture.thumbnail((300, 300)) + buffer = BytesIO() + picture.save(buffer, format="JPEG") + img_str = base64.b64encode(buffer.getvalue()).decode('utf-8') + visible_text = f'{text}' + return text, visible_text + + +def ui(): + picture_select = gr.Image(label='Send a picture', type='pil') + + # Prepare the input hijack, update the interface values, call the generation function, and clear the picture + picture_select.upload( + lambda picture, name1, name2: input_hijack.update({"state": True, "value": generate_chat_picture(picture, name1, name2)}), [picture_select, shared.gradio['name1'], shared.gradio['name2']], None).then( + gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( + lambda: None, None, picture_select, show_progress=False) diff --git a/text-generation-webui-main/extensions/silero_tts/requirements.txt b/text-generation-webui-main/extensions/silero_tts/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..1017bf0d7accb9930872ededd8a4bc077d393958 --- /dev/null +++ b/text-generation-webui-main/extensions/silero_tts/requirements.txt @@ -0,0 +1,5 @@ +ipython +num2words +omegaconf +pydub +PyYAML diff --git a/text-generation-webui-main/extensions/silero_tts/script.py b/text-generation-webui-main/extensions/silero_tts/script.py new file mode 100644 index 0000000000000000000000000000000000000000..460e76a888ae6ff74b74c34ee7437eae85a8c691 --- /dev/null +++ b/text-generation-webui-main/extensions/silero_tts/script.py @@ -0,0 +1,182 @@ +import time +from pathlib import Path + +import gradio as gr +import torch + +from extensions.silero_tts import tts_preprocessor +from modules import chat, shared +from modules.html_generator import chat_html_wrapper + +torch._C._jit_set_profiling_mode(False) + + +params = { + 'activate': True, + 'speaker': 'en_56', + 'language': 'en', + 'model_id': 'v3_en', + 'sample_rate': 48000, + 'device': 'cpu', + 'show_text': False, + 'autoplay': True, + 'voice_pitch': 'medium', + 'voice_speed': 'medium', + 'local_cache_path': '' # User can override the default cache path to something other via settings.json +} + +current_params = params.copy() +voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115'] +voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high'] +voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast'] +streaming_state = shared.args.no_stream # remember if chat streaming was enabled + +# Used for making text xml compatible, needed for voice pitch and speed control +table = str.maketrans({ + "<": "<", + ">": ">", + "&": "&", + "'": "'", + '"': """, +}) + + +def xmlesc(txt): + return txt.translate(table) + + +def load_model(): + torch_cache_path = torch.hub.get_dir() if params['local_cache_path'] == '' else params['local_cache_path'] + model_path = torch_cache_path + "/snakers4_silero-models_master/src/silero/model/" + params['model_id'] + ".pt" + if Path(model_path).is_file(): + print(f'\nUsing Silero TTS cached checkpoint found at {torch_cache_path}') + model, example_text = torch.hub.load(repo_or_dir=torch_cache_path + '/snakers4_silero-models_master/', model='silero_tts', language=params['language'], speaker=params['model_id'], source='local', path=model_path, force_reload=True) + else: + print(f'\nSilero TTS cache not found at {torch_cache_path}. Attempting to download...') + model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id']) + model.to(params['device']) + return model + + +def remove_tts_from_history(name1, name2, mode): + for i, entry in enumerate(shared.history['internal']): + shared.history['visible'][i] = [shared.history['visible'][i][0], entry[1]] + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def toggle_text_in_history(name1, name2, mode): + for i, entry in enumerate(shared.history['visible']): + visible_reply = entry[1] + if visible_reply.startswith('')[0]}\n\n{reply}"] + else: + shared.history['visible'][i] = [shared.history['visible'][i][0], f"{visible_reply.split('')[0]}"] + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def input_modifier(string): + """ + This function is applied to your text inputs before + they are fed into the model. + """ + + # Remove autoplay from the last reply + if shared.is_chat() and len(shared.history['internal']) > 0: + shared.history['visible'][-1] = [shared.history['visible'][-1][0], shared.history['visible'][-1][1].replace('controls autoplay>', 'controls>')] + + shared.processing_message = "*Is recording a voice message...*" + shared.args.no_stream = True # Disable streaming cause otherwise the audio output will stutter and begin anew every time the message is being updated + return string + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global model, current_params, streaming_state + + for i in params: + if params[i] != current_params[i]: + model = load_model() + current_params = params.copy() + break + + if not params['activate']: + return string + + original_string = string + string = tts_preprocessor.preprocess(string) + + if string == '': + string = '*Empty reply, try regenerating*' + else: + output_file = Path(f'extensions/silero_tts/outputs/{shared.character}_{int(time.time())}.wav') + prosody = ''.format(params['voice_speed'], params['voice_pitch']) + silero_input = f'{prosody}{xmlesc(string)}' + model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file)) + + autoplay = 'autoplay' if params['autoplay'] else '' + string = f'' + if params['show_text']: + string += f'\n\n{original_string}' + + shared.processing_message = "*Is typing...*" + shared.args.no_stream = streaming_state # restore the streaming option to the previous value + return string + + +def bot_prefix_modifier(string): + """ + This function is only applied in chat mode. It modifies + the prefix text for the Bot and can be used to bias its + behavior. + """ + + return string + + +def setup(): + global model + model = load_model() + + +def ui(): + # Gradio elements + with gr.Accordion("Silero TTS"): + with gr.Row(): + activate = gr.Checkbox(value=params['activate'], label='Activate TTS') + autoplay = gr.Checkbox(value=params['autoplay'], label='Play TTS automatically') + + show_text = gr.Checkbox(value=params['show_text'], label='Show message text under audio player') + voice = gr.Dropdown(value=params['speaker'], choices=voices_by_gender, label='TTS voice') + with gr.Row(): + v_pitch = gr.Dropdown(value=params['voice_pitch'], choices=voice_pitches, label='Voice pitch') + v_speed = gr.Dropdown(value=params['voice_speed'], choices=voice_speeds, label='Voice speed') + + with gr.Row(): + convert = gr.Button('Permanently replace audios with the message texts') + convert_cancel = gr.Button('Cancel', visible=False) + convert_confirm = gr.Button('Confirm (cannot be undone)', variant="stop", visible=False) + + # Convert history with confirmation + convert_arr = [convert_confirm, convert, convert_cancel] + convert.click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, convert_arr) + convert_confirm.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) + convert_confirm.click(remove_tts_from_history, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], shared.gradio['display']) + convert_confirm.click(lambda: chat.save_history(timestamp=False), [], [], show_progress=False) + convert_cancel.click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, convert_arr) + + # Toggle message text in history + show_text.change(lambda x: params.update({"show_text": x}), show_text, None) + show_text.change(toggle_text_in_history, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], shared.gradio['display']) + show_text.change(lambda: chat.save_history(timestamp=False), [], [], show_progress=False) + + # Event functions to update the parameters in the backend + activate.change(lambda x: params.update({"activate": x}), activate, None) + autoplay.change(lambda x: params.update({"autoplay": x}), autoplay, None) + voice.change(lambda x: params.update({"speaker": x}), voice, None) + v_pitch.change(lambda x: params.update({"voice_pitch": x}), v_pitch, None) + v_speed.change(lambda x: params.update({"voice_speed": x}), v_speed, None) diff --git a/text-generation-webui-main/extensions/silero_tts/test_tts.py b/text-generation-webui-main/extensions/silero_tts/test_tts.py new file mode 100644 index 0000000000000000000000000000000000000000..ebc2c102a9ef29f21141429232f957421989cdd4 --- /dev/null +++ b/text-generation-webui-main/extensions/silero_tts/test_tts.py @@ -0,0 +1,81 @@ +import time +from pathlib import Path + +import torch +import tts_preprocessor + +torch._C._jit_set_profiling_mode(False) + + +params = { + 'activate': True, + 'speaker': 'en_49', + 'language': 'en', + 'model_id': 'v3_en', + 'sample_rate': 48000, + 'device': 'cpu', + 'show_text': True, + 'autoplay': True, + 'voice_pitch': 'medium', + 'voice_speed': 'medium', +} + +current_params = params.copy() +voices_by_gender = ['en_99', 'en_45', 'en_18', 'en_117', 'en_49', 'en_51', 'en_68', 'en_0', 'en_26', 'en_56', 'en_74', 'en_5', 'en_38', 'en_53', 'en_21', 'en_37', 'en_107', 'en_10', 'en_82', 'en_16', 'en_41', 'en_12', 'en_67', 'en_61', 'en_14', 'en_11', 'en_39', 'en_52', 'en_24', 'en_97', 'en_28', 'en_72', 'en_94', 'en_36', 'en_4', 'en_43', 'en_88', 'en_25', 'en_65', 'en_6', 'en_44', 'en_75', 'en_91', 'en_60', 'en_109', 'en_85', 'en_101', 'en_108', 'en_50', 'en_96', 'en_64', 'en_92', 'en_76', 'en_33', 'en_116', 'en_48', 'en_98', 'en_86', 'en_62', 'en_54', 'en_95', 'en_55', 'en_111', 'en_3', 'en_83', 'en_8', 'en_47', 'en_59', 'en_1', 'en_2', 'en_7', 'en_9', 'en_13', 'en_15', 'en_17', 'en_19', 'en_20', 'en_22', 'en_23', 'en_27', 'en_29', 'en_30', 'en_31', 'en_32', 'en_34', 'en_35', 'en_40', 'en_42', 'en_46', 'en_57', 'en_58', 'en_63', 'en_66', 'en_69', 'en_70', 'en_71', 'en_73', 'en_77', 'en_78', 'en_79', 'en_80', 'en_81', 'en_84', 'en_87', 'en_89', 'en_90', 'en_93', 'en_100', 'en_102', 'en_103', 'en_104', 'en_105', 'en_106', 'en_110', 'en_112', 'en_113', 'en_114', 'en_115'] +voice_pitches = ['x-low', 'low', 'medium', 'high', 'x-high'] +voice_speeds = ['x-slow', 'slow', 'medium', 'fast', 'x-fast'] + +# Used for making text xml compatible, needed for voice pitch and speed control +table = str.maketrans({ + "<": "<", + ">": ">", + "&": "&", + "'": "'", + '"': """, +}) + + +def xmlesc(txt): + return txt.translate(table) + + +def load_model(): + model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=params['language'], speaker=params['model_id']) + model.to(params['device']) + return model + + +model = load_model() + + +def output_modifier(string): + """ + This function is applied to the model outputs. + """ + + global model, current_params + + original_string = string + string = tts_preprocessor.preprocess(string) + processed_string = string + + if string == '': + string = '*Empty reply, try regenerating*' + else: + output_file = Path(f'extensions/silero_tts/outputs/test_{int(time.time())}.wav') + prosody = ''.format(params['voice_speed'], params['voice_pitch']) + silero_input = f'{prosody}{xmlesc(string)}' + model.save_wav(ssml_text=silero_input, speaker=params['speaker'], sample_rate=int(params['sample_rate']), audio_path=str(output_file)) + + autoplay = 'autoplay' if params['autoplay'] else '' + string = f'' + + if params['show_text']: + string += f'\n\n{original_string}\n\nProcessed:\n{processed_string}' + + print(string) + + +if __name__ == '__main__': + import sys + output_modifier(sys.argv[1]) diff --git a/text-generation-webui-main/extensions/silero_tts/tts_preprocessor.py b/text-generation-webui-main/extensions/silero_tts/tts_preprocessor.py new file mode 100644 index 0000000000000000000000000000000000000000..eb2ca41b5d1fe175c0cf881c8924def4b3e2dd17 --- /dev/null +++ b/text-generation-webui-main/extensions/silero_tts/tts_preprocessor.py @@ -0,0 +1,200 @@ +import re + +from num2words import num2words + +punctuation = r'[\s,.?!/)\'\]>]' +alphabet_map = { + "A": " Ei ", + "B": " Bee ", + "C": " See ", + "D": " Dee ", + "E": " Eee ", + "F": " Eff ", + "G": " Jee ", + "H": " Eich ", + "I": " Eye ", + "J": " Jay ", + "K": " Kay ", + "L": " El ", + "M": " Emm ", + "N": " Enn ", + "O": " Ohh ", + "P": " Pee ", + "Q": " Queue ", + "R": " Are ", + "S": " Ess ", + "T": " Tee ", + "U": " You ", + "V": " Vee ", + "W": " Double You ", + "X": " Ex ", + "Y": " Why ", + "Z": " Zed " # Zed is weird, as I (da3dsoul) am American, but most of the voice models sound British, so it matches +} + + +def preprocess(string): + # the order for some of these matter + # For example, you need to remove the commas in numbers before expanding them + string = remove_surrounded_chars(string) + string = string.replace('"', '') + string = string.replace('\u201D', '').replace('\u201C', '') # right and left quote + string = string.replace('\u201F', '') # italic looking quote + string = string.replace('\n', ' ') + string = convert_num_locale(string) + string = replace_negative(string) + string = replace_roman(string) + string = hyphen_range_to(string) + string = num_to_words(string) + + # TODO Try to use a ML predictor to expand abbreviations. It's hard, dependent on context, and whether to actually + # try to say the abbreviation or spell it out as I've done below is not agreed upon + + # For now, expand abbreviations to pronunciations + # replace_abbreviations adds a lot of unnecessary whitespace to ensure separation + string = replace_abbreviations(string) + string = replace_lowercase_abbreviations(string) + + # cleanup whitespaces + # remove whitespace before punctuation + string = re.sub(rf'\s+({punctuation})', r'\1', string) + string = string.strip() + # compact whitespace + string = ' '.join(string.split()) + + return string + + +def remove_surrounded_chars(string): + # first this expression will check if there is a string nested exclusively between a alt= + # and a style= string. This would correspond to only a the alt text of an embedded image + # If it matches it will only keep that part as the string, and rend it for further processing + # Afterwards this expression matches to 'as few symbols as possible (0 upwards) between any + # asterisks' OR' as few symbols as possible (0 upwards) between an asterisk and the end of the string' + if re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL): + m = re.search(r'(?<=alt=)(.*)(?=style=)', string, re.DOTALL) + string = m.group(0) + return re.sub(r'\*[^*]*?(\*|$)', '', string) + + +def convert_num_locale(text): + # This detects locale and converts it to American without comma separators + pattern = re.compile(r'(?:\s|^)\d{1,3}(?:\.\d{3})+(,\d+)(?:\s|$)') + result = text + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + result[start:end].replace('.', '').replace(',', '.') + result[end:len(result)] + + # removes comma separators from existing American numbers + pattern = re.compile(r'(\d),(\d)') + result = pattern.sub(r'\1\2', result) + + return result + + +def replace_negative(string): + # handles situations like -5. -5 would become negative 5, which would then be expanded to negative five + return re.sub(rf'(\s)(-)(\d+)({punctuation})', r'\1negative \3\4', string) + + +def replace_roman(string): + # find a string of roman numerals. + # Only 2 or more, to avoid capturing I and single character abbreviations, like names + pattern = re.compile(rf'\s[IVXLCDM]{{2,}}{punctuation}') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start + 1] + str(roman_to_int(result[start + 1:end - 1])) + result[end - 1:len(result)] + + return result + + +def roman_to_int(s): + rom_val = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000} + int_val = 0 + for i in range(len(s)): + if i > 0 and rom_val[s[i]] > rom_val[s[i - 1]]: + int_val += rom_val[s[i]] - 2 * rom_val[s[i - 1]] + else: + int_val += rom_val[s[i]] + return int_val + + +def hyphen_range_to(text): + pattern = re.compile(r'(\d+)[-–](\d+)') + result = pattern.sub(lambda x: x.group(1) + ' to ' + x.group(2), text) + return result + + +def num_to_words(text): + # 1000 or 10.23 + pattern = re.compile(r'\d+\.\d+|\d+') + result = pattern.sub(lambda x: num2words(float(x.group())), text) + return result + + +def replace_abbreviations(string): + # abbreviations 1 to 4 characters long. It will get things like A and I, but those are pronounced with their letter + pattern = re.compile(rf'(^|[\s(.\'\[<])([A-Z]{{1,4}})({punctuation}|$)') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + replace_abbreviation(result[start:end]) + result[end:len(result)] + + return result + + +def replace_lowercase_abbreviations(string): + # abbreviations 1 to 4 characters long, separated by dots i.e. e.g. + pattern = re.compile(rf'(^|[\s(.\'\[<])(([a-z]\.){{1,4}})({punctuation}|$)') + result = string + while True: + match = pattern.search(result) + if match is None: + break + + start = match.start() + end = match.end() + result = result[0:start] + replace_abbreviation(result[start:end].upper()) + result[end:len(result)] + + return result + + +def replace_abbreviation(string): + result = "" + for char in string: + result += match_mapping(char) + + return result + + +def match_mapping(char): + for mapping in alphabet_map.keys(): + if char == mapping: + return alphabet_map[char] + + return char + + +def __main__(args): + print(preprocess(args[1])) + + +if __name__ == "__main__": + import sys + __main__(sys.argv) diff --git a/text-generation-webui-main/extensions/whisper_stt/requirements.txt b/text-generation-webui-main/extensions/whisper_stt/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..5a37a5e9b0b5986f7e57d822a85afcdcdd5ae445 --- /dev/null +++ b/text-generation-webui-main/extensions/whisper_stt/requirements.txt @@ -0,0 +1,4 @@ +git+https://github.com/Uberi/speech_recognition.git@010382b80267f0f7794169fccc8e875ee7da7c19 +openai-whisper +soundfile +ffmpeg diff --git a/text-generation-webui-main/extensions/whisper_stt/script.py b/text-generation-webui-main/extensions/whisper_stt/script.py new file mode 100644 index 0000000000000000000000000000000000000000..9daee7be147c55848fb0b620bc34de133869672b --- /dev/null +++ b/text-generation-webui-main/extensions/whisper_stt/script.py @@ -0,0 +1,46 @@ +import gradio as gr +import speech_recognition as sr +from modules import shared + +input_hijack = { + 'state': False, + 'value': ["", ""] +} + + +def do_stt(audio): + transcription = "" + r = sr.Recognizer() + + # Convert to AudioData + audio_data = sr.AudioData(sample_rate=audio[0], frame_data=audio[1], sample_width=4) + + try: + transcription = r.recognize_whisper(audio_data, language="english", model="base.en") + except sr.UnknownValueError: + print("Whisper could not understand audio") + except sr.RequestError as e: + print("Could not request results from Whisper", e) + + return transcription + + +def auto_transcribe(audio, auto_submit): + if audio is None: + return "", "" + + transcription = do_stt(audio) + if auto_submit: + input_hijack.update({"state": True, "value": [transcription, transcription]}) + + return transcription, None + + +def ui(): + with gr.Row(): + audio = gr.Audio(source="microphone") + auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=True) + + audio.change( + auto_transcribe, [audio, auto_submit], [shared.gradio['textbox'], audio]).then( + None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}") diff --git a/text-generation-webui-main/modules/GPTQ_loader.py b/text-generation-webui-main/modules/GPTQ_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..df18df569981cb308217f3b6a6f8dede5cc8b3db --- /dev/null +++ b/text-generation-webui-main/modules/GPTQ_loader.py @@ -0,0 +1,199 @@ +import inspect +import logging +import re +import sys +from pathlib import Path + +import accelerate +import torch +import transformers +from transformers import AutoConfig, AutoModelForCausalLM + +import modules.shared as shared + +sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa"))) +import llama_inference_offload + +try: + from modelutils import find_layers +except ImportError: + from utils import find_layers + +try: + from quant import make_quant + is_triton = False +except ImportError: + import quant + is_triton = True + + +# This function is a replacement for the load_quant function in the +# GPTQ-for_LLaMa repository. It supports more models and branches. +def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exclude_layers=['lm_head'], kernel_switch_threshold=128, eval=True): + + def noop(*args, **kwargs): + pass + + config = AutoConfig.from_pretrained(model) + torch.nn.init.kaiming_uniform_ = noop + torch.nn.init.uniform_ = noop + torch.nn.init.normal_ = noop + + torch.set_default_dtype(torch.half) + transformers.modeling_utils._init_weights = False + torch.set_default_dtype(torch.half) + model = AutoModelForCausalLM.from_config(config) + torch.set_default_dtype(torch.float) + if eval: + model = model.eval() + layers = find_layers(model) + for name in exclude_layers: + if name in layers: + del layers[name] + + if not is_triton: + gptq_args = inspect.getfullargspec(make_quant).args + + make_quant_kwargs = { + 'module': model, + 'names': layers, + 'bits': wbits, + } + if 'groupsize' in gptq_args: + make_quant_kwargs['groupsize'] = groupsize + if 'faster' in gptq_args: + make_quant_kwargs['faster'] = faster_kernel + if 'kernel_switch_threshold' in gptq_args: + make_quant_kwargs['kernel_switch_threshold'] = kernel_switch_threshold + + make_quant(**make_quant_kwargs) + else: + quant.make_quant_linear(model, layers, wbits, groupsize) + + del layers + + if checkpoint.endswith('.safetensors'): + from safetensors.torch import load_file as safe_load + model.load_state_dict(safe_load(checkpoint), strict=False) + else: + model.load_state_dict(torch.load(checkpoint), strict=False) + + if is_triton: + if shared.args.quant_attn: + quant.make_quant_attn(model) + if eval and shared.args.fused_mlp: + quant.make_fused_mlp(model) + + if shared.args.warmup_autotune: + quant.autotune_warmup_linear(model, transpose=not eval) + if eval and shared.args.fused_mlp: + quant.autotune_warmup_fused(model) + + model.seqlen = 2048 + return model + + +# Used to locate the .pt/.safetensors quantized file +def find_quantized_model_file(model_name): + path_to_model = Path(f'{shared.args.model_dir}/{model_name}') + pt_path = None + priority_name_list = [ + Path(f'{shared.args.model_dir}/{model_name}{hyphen}{shared.args.wbits}bit{group}{ext}') + for group in ([f'-{shared.args.groupsize}g', ''] if shared.args.groupsize > 0 else ['']) + for ext in ['.safetensors', '.pt'] + for hyphen in ['-', f'/{model_name}-', '/'] + ] + for path in priority_name_list: + if path.exists(): + pt_path = path + break + + # If the model hasn't been found with a well-behaved name, pick the last .pt + # or the last .safetensors found in its folder as a last resort + if not pt_path: + found_pts = list(path_to_model.glob("*.pt")) + found_safetensors = list(path_to_model.glob("*.safetensors")) + pt_path = None + + if len(found_pts) > 0: + if len(found_pts) > 1: + logging.warning('More than one .pt model has been found. The last one will be selected. It could be wrong.') + + pt_path = found_pts[-1] + elif len(found_safetensors) > 0: + if len(found_pts) > 1: + logging.warning('More than one .safetensors model has been found. The last one will be selected. It could be wrong.') + + pt_path = found_safetensors[-1] + + return pt_path + + +# The function that loads the model in modules/models.py +def load_quantized(model_name): + + # Find the model type + if not shared.args.model_type: + name = model_name.lower() + if any((k in name for k in ['llama', 'alpaca', 'vicuna', 'llava'])): + model_type = 'llama' + elif any((k in name for k in ['opt-', 'galactica'])): + model_type = 'opt' + elif any((k in name for k in ['gpt-j', 'pygmalion-6b'])): + model_type = 'gptj' + else: + logging.error("Can't determine model type from model name. Please specify it manually using --model_type argument") + exit() + else: + model_type = shared.args.model_type.lower() + + # Select the appropriate load_quant function + if shared.args.pre_layer and model_type == 'llama': + 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.") + + load_quant = _load_quant + else: + logging.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...") + exit() + else: + logging.info(f"Found the following quantized model: {pt_path}") + + # qwopqwop200's offload + if model_type == 'llama' and shared.args.pre_layer: + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer) + else: + threshold = False if model_type == 'gptj' else 128 + model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, kernel_switch_threshold=threshold) + + # accelerate offload (doesn't work properly) + if shared.args.gpu_memory or torch.cuda.device_count() > 1: + if shared.args.gpu_memory: + memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) + max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' + max_memory = {} + for i in range(len(memory_map)): + max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] + max_memory['cpu'] = max_cpu_memory + else: + 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) + # https://huggingface.co/docs/accelerate/package_reference/big_modeling#accelerate.dispatch_model + model = accelerate.dispatch_model(model, device_map=device_map, offload_buffers=True) + + # No offload + elif not shared.args.cpu: + model = model.to(torch.device('cuda:0')) + + return model diff --git a/text-generation-webui-main/modules/LoRA.py b/text-generation-webui-main/modules/LoRA.py new file mode 100644 index 0000000000000000000000000000000000000000..f996289a5646f659ac83dd4689d3f036ca23b57f --- /dev/null +++ b/text-generation-webui-main/modules/LoRA.py @@ -0,0 +1,54 @@ +import logging +from pathlib import Path + +import torch +from peft import PeftModel + +import modules.shared as shared + + +def add_lora_to_model(lora_names): + prior_set = set(shared.lora_names) + added_set = set(lora_names) - prior_set + removed_set = prior_set - set(lora_names) + shared.lora_names = list(lora_names) + + # If no LoRA needs to be added or removed, exit + if len(added_set) == 0 and len(removed_set) == 0: + return + + # 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...") + for lora in added_set: + shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) + + return + + # If any LoRA needs to be removed, start over + if len(removed_set) > 0: + shared.model.disable_adapter() + + if len(lora_names) > 0: + logging.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names))) + params = {} + if not shared.args.cpu: + params['dtype'] = shared.model.dtype + if hasattr(shared.model, "hf_device_map"): + params['device_map'] = {"base_model.model." + k: v for k, v in shared.model.hf_device_map.items()} + elif shared.args.load_in_8bit: + params['device_map'] = {'': 0} + + shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params) + + for lora in lora_names[1:]: + shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora) + + if not shared.args.load_in_8bit and not shared.args.cpu: + shared.model.half() + if not hasattr(shared.model, "hf_device_map"): + if torch.has_mps: + device = torch.device('mps') + shared.model = shared.model.to(device) + else: + shared.model = shared.model.cuda() diff --git a/text-generation-webui-main/modules/RWKV.py b/text-generation-webui-main/modules/RWKV.py new file mode 100644 index 0000000000000000000000000000000000000000..0405230eee3cae31c1b33491dff38e10c02b623b --- /dev/null +++ b/text-generation-webui-main/modules/RWKV.py @@ -0,0 +1,75 @@ +import os +from pathlib import Path + +import numpy as np +from tokenizers import Tokenizer + +import modules.shared as shared +from modules.callbacks import Iteratorize + +np.set_printoptions(precision=4, suppress=True, linewidth=200) + +os.environ['RWKV_JIT_ON'] = '1' +os.environ["RWKV_CUDA_ON"] = '1' if shared.args.rwkv_cuda_on else '0' # use CUDA kernel for seq mode (much faster) + +from rwkv.model import RWKV +from rwkv.utils import PIPELINE, PIPELINE_ARGS + + +class RWKVModel: + def __init__(self): + pass + + @classmethod + def from_pretrained(self, path, dtype="fp16", device="cuda"): + tokenizer_path = Path(f"{path.parent}/20B_tokenizer.json") + + if shared.args.rwkv_strategy is None: + model = RWKV(model=str(path), strategy=f'{device} {dtype}') + else: + model = RWKV(model=str(path), strategy=shared.args.rwkv_strategy) + pipeline = PIPELINE(model, str(tokenizer_path)) + + result = self() + result.pipeline = pipeline + return result + + def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=None, alpha_frequency=0.1, alpha_presence=0.1, token_ban=[0], token_stop=[], callback=None): + args = PIPELINE_ARGS( + temperature=temperature, + top_p=top_p, + top_k=top_k, + alpha_frequency=alpha_frequency, # Frequency Penalty (as in GPT-3) + alpha_presence=alpha_presence, # Presence Penalty (as in GPT-3) + token_ban=token_ban, # ban the generation of some tokens + token_stop=token_stop + ) + + return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback) + + def generate_with_streaming(self, **kwargs): + with Iteratorize(self.generate, kwargs, callback=None) as generator: + reply = '' + for token in generator: + reply += token + yield reply + + +class RWKVTokenizer: + def __init__(self): + pass + + @classmethod + def from_pretrained(self, path): + tokenizer_path = path / "20B_tokenizer.json" + tokenizer = Tokenizer.from_file(str(tokenizer_path)) + + result = self() + result.tokenizer = tokenizer + return result + + def encode(self, prompt): + return self.tokenizer.encode(prompt).ids + + def decode(self, ids): + return self.tokenizer.decode(ids) diff --git a/text-generation-webui-main/modules/callbacks.py b/text-generation-webui-main/modules/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..fb87ad56470e3222a0ea7c6609c2000e5f23ca69 --- /dev/null +++ b/text-generation-webui-main/modules/callbacks.py @@ -0,0 +1,112 @@ +import gc +import traceback +from queue import Queue +from threading import Thread + +import torch +import transformers + +import modules.shared as shared + + +class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria): + + def __init__(self, sentinel_token_ids: list, starting_idx: int): + transformers.StoppingCriteria.__init__(self) + self.sentinel_token_ids = sentinel_token_ids + self.starting_idx = starting_idx + self.shortest = min([x.shape[-1] for x in sentinel_token_ids]) + + def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool: + for sample in input_ids: + trimmed_sample = sample[self.starting_idx:] + trimmed_len = trimmed_sample.shape[-1] + if trimmed_len < self.shortest: + continue + + for sentinel in self.sentinel_token_ids: + sentinel_len = sentinel.shape[-1] + if trimmed_len < sentinel_len: + continue + + window = trimmed_sample[-sentinel_len:] + if torch.all(torch.eq(sentinel, window)): + return True + + return False + + +class Stream(transformers.StoppingCriteria): + def __init__(self, callback_func=None): + self.callback_func = callback_func + + def __call__(self, input_ids, scores) -> bool: + if self.callback_func is not None: + self.callback_func(input_ids[0]) + return False + + +class Iteratorize: + + """ + Transforms a function that takes a callback + into a lazy iterator (generator). + + Adapted from: https://stackoverflow.com/a/9969000 + """ + + def __init__(self, func, kwargs={}, callback=None): + self.mfunc = func + self.c_callback = callback + self.q = Queue() + self.sentinel = object() + self.kwargs = kwargs + self.stop_now = False + + def _callback(val): + if self.stop_now or shared.stop_everything: + raise ValueError + self.q.put(val) + + def gentask(): + try: + ret = self.mfunc(callback=_callback, **self.kwargs) + except ValueError: + pass + except: + traceback.print_exc() + pass + + clear_torch_cache() + self.q.put(self.sentinel) + if self.c_callback: + self.c_callback(ret) + + self.thread = Thread(target=gentask) + self.thread.start() + + def __iter__(self): + return self + + def __next__(self): + obj = self.q.get(True, None) + if obj is self.sentinel: + raise StopIteration + else: + return obj + + def __del__(self): + clear_torch_cache() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.stop_now = True + clear_torch_cache() + + +def clear_torch_cache(): + gc.collect() + if not shared.args.cpu: + torch.cuda.empty_cache() diff --git a/text-generation-webui-main/modules/chat.py b/text-generation-webui-main/modules/chat.py new file mode 100644 index 0000000000000000000000000000000000000000..98e171b0f35041ec12d01657297a1fc8b9fa91dd --- /dev/null +++ b/text-generation-webui-main/modules/chat.py @@ -0,0 +1,562 @@ +import ast +import base64 +import copy +import io +import json +import logging +import re +from datetime import datetime +from pathlib import Path + +import yaml +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.text_generation import (encode, generate_reply, + get_max_prompt_length) + + +# Replace multiple string pairs in a string +def replace_all(text, dic): + for i, j in dic.items(): + text = text.replace(i, j) + + return text + + +def generate_chat_prompt(user_input, state, **kwargs): + impersonate = kwargs['impersonate'] if 'impersonate' in kwargs else False + _continue = kwargs['_continue'] if '_continue' in kwargs else False + also_return_rows = kwargs['also_return_rows'] if 'also_return_rows' in kwargs else False + is_instruct = state['mode'] == 'instruct' + rows = [state['context'] if is_instruct else f"{state['context'].strip()}\n"] + min_rows = 3 + + # Finding the maximum prompt size + chat_prompt_size = state['chat_prompt_size'] + if shared.soft_prompt: + chat_prompt_size -= shared.soft_prompt_tensor.shape[1] + + max_length = min(get_max_prompt_length(state), chat_prompt_size) + + # Building the turn templates + if 'turn_template' not in state or state['turn_template'] == '': + if is_instruct: + template = '<|user|>\n<|user-message|>\n<|bot|>\n<|bot-message|>\n' + else: + template = '<|user|>: <|user-message|>\n<|bot|>: <|bot-message|>\n' + else: + template = state['turn_template'].replace(r'\n', '\n') + + replacements = { + '<|user|>': state['name1'].strip(), + '<|bot|>': state['name2'].strip(), + } + + user_turn = replace_all(template.split('<|bot|>')[0], replacements) + bot_turn = replace_all('<|bot|>' + template.split('<|bot|>')[1], replacements) + user_turn_stripped = replace_all(user_turn.split('<|user-message|>')[0], replacements) + bot_turn_stripped = replace_all(bot_turn.split('<|bot-message|>')[0], replacements) + + # Building the prompt + i = len(shared.history['internal']) - 1 + while i >= 0 and len(encode(''.join(rows))[0]) < max_length: + if _continue and i == len(shared.history['internal']) - 1: + rows.insert(1, bot_turn_stripped + shared.history['internal'][i][1].strip()) + else: + rows.insert(1, bot_turn.replace('<|bot-message|>', shared.history['internal'][i][1].strip())) + + string = shared.history['internal'][i][0] + if string not in ['', '<|BEGIN-VISIBLE-CHAT|>']: + rows.insert(1, replace_all(user_turn, {'<|user-message|>': string.strip(), '<|round|>': str(i)})) + + i -= 1 + + if impersonate: + min_rows = 2 + rows.append(user_turn_stripped.rstrip(' ')) + elif not _continue: + # Adding the user message + if len(user_input) > 0: + rows.append(replace_all(user_turn, {'<|user-message|>': user_input.strip(), '<|round|>': str(len(shared.history["internal"]))})) + + # Adding the Character prefix + rows.append(apply_extensions("bot_prefix", bot_turn_stripped.rstrip(' '))) + + while len(rows) > min_rows and len(encode(''.join(rows))[0]) >= max_length: + rows.pop(1) + + prompt = ''.join(rows) + if also_return_rows: + return prompt, rows + else: + return prompt + + +def get_stopping_strings(state): + if state['mode'] == 'instruct': + stopping_strings = [f"\n{state['name1']}", f"\n{state['name2']}"] + else: + stopping_strings = [f"\n{state['name1']}:", f"\n{state['name2']}:"] + + stopping_strings += ast.literal_eval(f"[{state['custom_stopping_strings']}]") + return stopping_strings + + +def extract_message_from_reply(reply, state): + next_character_found = False + stopping_strings = get_stopping_strings(state) + + if state['stop_at_newline']: + lines = reply.split('\n') + reply = lines[0].strip() + if len(lines) > 1: + next_character_found = True + else: + for string in stopping_strings: + idx = reply.find(string) + if idx != -1: + reply = reply[:idx] + next_character_found = True + + # If something like "\nYo" is generated just before "\nYou:" + # is completed, trim it + if not next_character_found: + for string in stopping_strings: + for j in range(len(string) - 1, 0, -1): + if reply[-j:] == string[:j]: + reply = reply[:-j] + break + else: + continue + + break + + return reply, next_character_found + + +def chatbot_wrapper(text, state, regenerate=False, _continue=False): + if shared.model_name == 'None' or shared.model is None: + logging.error("No model is loaded! Select one in the Model tab.") + yield shared.history['visible'] + return + + # Defining some variables + cumulative_reply = '' + just_started = True + visible_text = None + eos_token = '\n' if state['stop_at_newline'] else None + stopping_strings = get_stopping_strings(state) + + # Preparing the input + if not any((regenerate, _continue)): + text, visible_text = apply_extensions('input_hijack', text, visible_text) + if visible_text is None: + visible_text = text + + text = apply_extensions('input', text) + # *Is typing...* + yield shared.history['visible'] + [[visible_text, shared.processing_message]] + else: + text, visible_text = shared.history['internal'][-1][0], shared.history['visible'][-1][0] + if regenerate: + shared.history['visible'].pop() + shared.history['internal'].pop() + # *Is typing...* + yield shared.history['visible'] + [[visible_text, shared.processing_message]] + elif _continue: + last_reply = [shared.history['internal'][-1][1], shared.history['visible'][-1][1]] + yield shared.history['visible'][:-1] + [[visible_text, last_reply[1] + '...']] + + # Generating the prompt + kwargs = {'_continue': _continue} + prompt = apply_extensions('custom_generate_chat_prompt', text, state, **kwargs) + if prompt is None: + prompt = generate_chat_prompt(text, state, **kwargs) + + # Generate + for i in range(state['chat_generation_attempts']): + reply = None + for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", state, eos_token=eos_token, stopping_strings=stopping_strings): + reply = cumulative_reply + reply + + # Extracting the reply + reply, next_character_found = extract_message_from_reply(reply, state) + visible_reply = re.sub("(||{{user}})", state['name1'], reply) + visible_reply = apply_extensions("output", visible_reply) + if _continue: + sep = ' ' if last_reply[0][-1] not in [' ', '\n'] else '' + reply = last_reply[0] + sep + reply + sep = ' ' if last_reply[1][-1] not in [' ', '\n'] else '' + visible_reply = last_reply[1] + sep + visible_reply + + # We need this global variable to handle the Stop event, + # otherwise gradio gets confused + if shared.stop_everything: + return shared.history['visible'] + + if just_started: + just_started = False + if not _continue: + shared.history['internal'].append(['', '']) + shared.history['visible'].append(['', '']) + + shared.history['internal'][-1] = [text, reply] + shared.history['visible'][-1] = [visible_text, visible_reply] + yield shared.history['visible'] + if next_character_found: + break + + if reply is not None: + cumulative_reply = reply + + yield shared.history['visible'] + + +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.") + yield '' + return + + # Defining some variables + cumulative_reply = '' + eos_token = '\n' if state['stop_at_newline'] else None + prompt = generate_chat_prompt(text, state, impersonate=True) + stopping_strings = get_stopping_strings(state) + + # Yield *Is typing...* + yield shared.processing_message + for i in range(state['chat_generation_attempts']): + reply = None + for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", state, eos_token=eos_token, stopping_strings=stopping_strings): + reply = cumulative_reply + reply + reply, next_character_found = extract_message_from_reply(reply, state) + yield reply + if next_character_found: + break + + if reply is not None: + cumulative_reply = reply + + yield reply + + +def cai_chatbot_wrapper(text, state): + for history in chatbot_wrapper(text, state): + yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode']) + + +def regenerate_wrapper(text, state): + if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0: + yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode']) + else: + for history in chatbot_wrapper('', state, regenerate=True): + yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode']) + + +def continue_wrapper(text, state): + if (len(shared.history['visible']) == 1 and not shared.history['visible'][0][0]) or len(shared.history['internal']) == 0: + yield chat_html_wrapper(shared.history['visible'], state['name1'], state['name2'], state['mode']) + else: + for history in chatbot_wrapper('', state, _continue=True): + yield chat_html_wrapper(history, state['name1'], state['name2'], state['mode']) + + +def remove_last_message(name1, name2, mode): + if len(shared.history['visible']) > 0 and shared.history['internal'][-1][0] != '<|BEGIN-VISIBLE-CHAT|>': + last = shared.history['visible'].pop() + shared.history['internal'].pop() + else: + last = ['', ''] + + return chat_html_wrapper(shared.history['visible'], name1, name2, mode), last[0] + + +def send_last_reply_to_input(): + if len(shared.history['internal']) > 0: + return shared.history['internal'][-1][1] + else: + return '' + + +def replace_last_reply(text, name1, name2, mode): + if len(shared.history['visible']) > 0: + shared.history['visible'][-1][1] = text + shared.history['internal'][-1][1] = apply_extensions("input", text) + + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def send_dummy_message(text, name1, name2, mode): + shared.history['visible'].append([text, '']) + shared.history['internal'].append([apply_extensions("input", text), '']) + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def send_dummy_reply(text, name1, name2, mode): + if len(shared.history['visible']) > 0 and not shared.history['visible'][-1][1] == '': + shared.history['visible'].append(['', '']) + shared.history['internal'].append(['', '']) + + shared.history['visible'][-1][1] = text + shared.history['internal'][-1][1] = apply_extensions("input", text) + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def clear_html(): + return chat_html_wrapper([], "", "") + + +def clear_chat_log(name1, name2, greeting, mode): + shared.history['visible'] = [] + shared.history['internal'] = [] + + if greeting != '': + shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]] + shared.history['visible'] += [['', apply_extensions("output", greeting)]] + + # Save cleared logs + save_history(mode) + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def redraw_html(name1, name2, mode): + return chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def tokenize_dialogue(dialogue, name1, name2, mode): + history = [] + messages = [] + dialogue = re.sub('', '', dialogue) + dialogue = re.sub('', '', dialogue) + dialogue = re.sub('(\n|^)[Aa]non:', '\\1You:', dialogue) + dialogue = re.sub('(\n|^)\[CHARACTER\]:', f'\\g<1>{name2}:', dialogue) + idx = [m.start() for m in re.finditer(f"(^|\n)({re.escape(name1)}|{re.escape(name2)}):", dialogue)] + if len(idx) == 0: + return history + + for i in range(len(idx) - 1): + messages.append(dialogue[idx[i]:idx[i + 1]].strip()) + + messages.append(dialogue[idx[-1]:].strip()) + entry = ['', ''] + for i in messages: + if i.startswith(f'{name1}:'): + entry[0] = i[len(f'{name1}:'):].strip() + elif i.startswith(f'{name2}:'): + entry[1] = i[len(f'{name2}:'):].strip() + if not (len(entry[0]) == 0 and len(entry[1]) == 0): + history.append(entry) + + entry = ['', ''] + + print("\033[1;32;1m\nDialogue tokenized to:\033[0;37;0m\n", end='') + for row in history: + for column in row: + print("\n") + for line in column.strip().split('\n'): + print("| " + line + "\n") + + print("|\n") + print("------------------------------") + + return history + + +def save_history(mode, timestamp=False): + # Instruct mode histories should not be saved as if + # Alpaca or Vicuna were characters + if mode == 'instruct': + if not timestamp: + return + + fname = f"Instruct_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json" + else: + if timestamp: + fname = f"{shared.character}_{datetime.now().strftime('%Y%m%d-%H%M%S')}.json" + else: + fname = f"{shared.character}_persistent.json" + + if not Path('logs').exists(): + Path('logs').mkdir() + + with open(Path(f'logs/{fname}'), 'w', encoding='utf-8') as f: + f.write(json.dumps({'data': shared.history['internal'], 'data_visible': shared.history['visible']}, indent=2)) + + return Path(f'logs/{fname}') + + +def load_history(file, name1, name2): + file = file.decode('utf-8') + try: + j = json.loads(file) + if 'data' in j: + shared.history['internal'] = j['data'] + if 'data_visible' in j: + shared.history['visible'] = j['data_visible'] + else: + shared.history['visible'] = copy.deepcopy(shared.history['internal']) + except: + shared.history['internal'] = tokenize_dialogue(file, name1, name2) + shared.history['visible'] = copy.deepcopy(shared.history['internal']) + + +def replace_character_names(text, name1, name2): + text = text.replace('{{user}}', name1).replace('{{char}}', name2) + return text.replace('', name1).replace('', name2) + + +def build_pygmalion_style_context(data): + context = "" + if 'char_persona' in data and data['char_persona'] != '': + context += f"{data['char_name']}'s Persona: {data['char_persona']}\n" + + if 'world_scenario' in data and data['world_scenario'] != '': + context += f"Scenario: {data['world_scenario']}\n" + + context = f"{context.strip()}\n\n" + return context + + +def generate_pfp_cache(character): + cache_folder = Path("cache") + if not cache_folder.exists(): + cache_folder.mkdir() + + for path in [Path(f"characters/{character}.{extension}") for extension in ['png', 'jpg', 'jpeg']]: + if path.exists(): + img = make_thumbnail(Image.open(path)) + img.save(Path('cache/pfp_character.png'), format='PNG') + return img + + return None + + +def load_character(character, name1, name2, mode): + shared.character = character + context = greeting = turn_template = "" + greeting_field = 'greeting' + picture = None + + # Deleting the profile picture cache, if any + if Path("cache/pfp_character.png").exists(): + Path("cache/pfp_character.png").unlink() + + if character != 'None': + folder = 'characters' if not mode == 'instruct' else 'characters/instruction-following' + picture = generate_pfp_cache(character) + for extension in ["yml", "yaml", "json"]: + filepath = Path(f'{folder}/{character}.{extension}') + if filepath.exists(): + break + + file_contents = open(filepath, 'r', encoding='utf-8').read() + data = json.loads(file_contents) if extension == "json" else yaml.safe_load(file_contents) + + # Finding the bot's name + for k in ['name', 'bot', '<|bot|>', 'char_name']: + if k in data and data[k] != '': + name2 = data[k] + break + + # Find the user name (if any) + for k in ['your_name', 'user', '<|user|>']: + if k in data and data[k] != '': + name1 = data[k] + break + else: + name1 = shared.settings['name1'] + + for field in ['context', 'greeting', 'example_dialogue', 'char_persona', 'char_greeting', 'world_scenario']: + if field in data: + data[field] = replace_character_names(data[field], name1, name2) + + if 'context' in data: + context = data['context'] + if mode != 'instruct': + context = context.strip() + '\n\n' + elif "char_persona" in data: + context = build_pygmalion_style_context(data) + greeting_field = 'char_greeting' + + if 'example_dialogue' in data: + context += f"{data['example_dialogue'].strip()}\n" + + if greeting_field in data: + greeting = data[greeting_field] + + if 'turn_template' in data: + turn_template = data['turn_template'] + + else: + context = shared.settings['context'] + name2 = shared.settings['name2'] + greeting = shared.settings['greeting'] + turn_template = shared.settings['turn_template'] + + if mode != 'instruct': + shared.history['internal'] = [] + shared.history['visible'] = [] + if Path(f'logs/{shared.character}_persistent.json').exists(): + load_history(open(Path(f'logs/{shared.character}_persistent.json'), 'rb').read(), name1, name2) + else: + # Insert greeting if it exists + if greeting != "": + shared.history['internal'] += [['<|BEGIN-VISIBLE-CHAT|>', greeting]] + shared.history['visible'] += [['', apply_extensions("output", greeting)]] + + # Create .json log files since they don't already exist + save_history(mode) + + return name1, name2, picture, greeting, context, repr(turn_template)[1:-1], chat_html_wrapper(shared.history['visible'], name1, name2, mode) + + +def upload_character(json_file, img, tavern=False): + json_file = json_file if type(json_file) == str else json_file.decode('utf-8') + data = json.loads(json_file) + outfile_name = data["char_name"] + i = 1 + while Path(f'characters/{outfile_name}.json').exists(): + outfile_name = f'{data["char_name"]}_{i:03d}' + i += 1 + + if tavern: + outfile_name = f'TavernAI-{outfile_name}' + + with open(Path(f'characters/{outfile_name}.json'), 'w', encoding='utf-8') as f: + f.write(json_file) + + if img is not None: + 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".') + return outfile_name + + +def upload_tavern_character(img, name1, name2): + _img = Image.open(io.BytesIO(img)) + _img.getexif() + decoded_string = base64.b64decode(_img.info['chara']) + _json = json.loads(decoded_string) + _json = {"char_name": _json['name'], "char_persona": _json['description'], "char_greeting": _json["first_mes"], "example_dialogue": _json['mes_example'], "world_scenario": _json['scenario']} + return upload_character(json.dumps(_json), img, tavern=True) + + +def upload_your_profile_picture(img, name1, name2, mode): + cache_folder = Path("cache") + if not cache_folder.exists(): + cache_folder.mkdir() + + if img is None: + if Path("cache/pfp_me.png").exists(): + Path("cache/pfp_me.png").unlink() + else: + img = make_thumbnail(img) + img.save(Path('cache/pfp_me.png')) + logging.info('Profile picture saved to "cache/pfp_me.png"') + + return chat_html_wrapper(shared.history['visible'], name1, name2, mode, reset_cache=True) diff --git a/text-generation-webui-main/modules/deepspeed_parameters.py b/text-generation-webui-main/modules/deepspeed_parameters.py new file mode 100644 index 0000000000000000000000000000000000000000..3dbed437f5b5196d0b1fcbc582085319fb8d40d1 --- /dev/null +++ b/text-generation-webui-main/modules/deepspeed_parameters.py @@ -0,0 +1,75 @@ +def generate_ds_config(ds_bf16, train_batch_size, nvme_offload_dir): + + ''' + DeepSpeed configration + https://huggingface.co/docs/transformers/main_classes/deepspeed + ''' + + if nvme_offload_dir: + ds_config = { + "fp16": { + "enabled": not ds_bf16, + }, + "bf16": { + "enabled": ds_bf16, + }, + "zero_optimization": { + "stage": 3, + "offload_param": { + "device": "nvme", + "nvme_path": nvme_offload_dir, + "pin_memory": True, + "buffer_count": 5, + "buffer_size": 1e9, + "max_in_cpu": 1e9 + }, + "overlap_comm": True, + "reduce_bucket_size": "auto", + "contiguous_gradients": True, + "sub_group_size": 1e8, + "stage3_prefetch_bucket_size": "auto", + "stage3_param_persistence_threshold": "auto", + "stage3_max_live_parameters": "auto", + "stage3_max_reuse_distance": "auto", + }, + "aio": { + "block_size": 262144, + "queue_depth": 32, + "thread_count": 1, + "single_submit": False, + "overlap_events": True + }, + "steps_per_print": 2000, + "train_batch_size": train_batch_size, + "train_micro_batch_size_per_gpu": 1, + "wall_clock_breakdown": False + } + else: + ds_config = { + "fp16": { + "enabled": not ds_bf16, + }, + "bf16": { + "enabled": ds_bf16, + }, + "zero_optimization": { + "stage": 3, + "offload_param": { + "device": "cpu", + "pin_memory": True + }, + "overlap_comm": True, + "contiguous_gradients": True, + "reduce_bucket_size": "auto", + "stage3_prefetch_bucket_size": "auto", + "stage3_param_persistence_threshold": "auto", + "stage3_max_live_parameters": "auto", + "stage3_max_reuse_distance": "auto", + }, + "steps_per_print": 2000, + "train_batch_size": train_batch_size, + "train_micro_batch_size_per_gpu": 1, + "wall_clock_breakdown": False + } + + return ds_config diff --git a/text-generation-webui-main/modules/evaluate.py b/text-generation-webui-main/modules/evaluate.py new file mode 100644 index 0000000000000000000000000000000000000000..3134280c899500543e5d5e3d6960af4c627a40ef --- /dev/null +++ b/text-generation-webui-main/modules/evaluate.py @@ -0,0 +1,142 @@ +import datetime +import traceback +from pathlib import Path + +import pandas as pd +import torch +from datasets import load_dataset +from tqdm import tqdm + +from modules import shared +from modules.models import load_model, unload_model +from modules.text_generation import encode +from server import get_model_specific_settings, update_model_parameters + + +def load_past_evaluations(): + if Path('logs/evaluations.csv').exists(): + df = pd.read_csv(Path('logs/evaluations.csv'), dtype=str) + df['Perplexity'] = pd.to_numeric(df['Perplexity']) + return df + else: + return pd.DataFrame(columns=['Model', 'LoRAs', 'Dataset', 'Perplexity', 'stride', 'max_length', 'Date', 'Comment']) +past_evaluations = load_past_evaluations() + + +def save_past_evaluations(df): + global past_evaluations + past_evaluations = df + df.to_csv(Path('logs/evaluations.csv'), index=False) + + +def calculate_perplexity(models, input_dataset, stride, _max_length): + ''' + Based on: + https://huggingface.co/docs/transformers/perplexity#calculating-ppl-with-fixedlength-models + ''' + + global past_evaluations + cumulative_log = '' + cumulative_log += "Loading the input dataset...\n" + yield cumulative_log + + # Copied from https://github.com/qwopqwop200/GPTQ-for-LLaMa/blob/triton/utils/datautils.py + if input_dataset == 'wikitext': + data = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test') + text = "\n\n".join(data['text']) + elif input_dataset == 'ptb': + data = load_dataset('ptb_text_only', 'penn_treebank', split='validation') + text = "\n\n".join(data['sentence']) + elif input_dataset == 'ptb_new': + data = load_dataset('ptb_text_only', 'penn_treebank', split='test') + text = " ".join(data['sentence']) + else: + with open(Path(f'training/datasets/{input_dataset}.txt'), 'r', encoding='utf-8') as f: + text = f.read() + + for model in models: + if is_in_past_evaluations(model, input_dataset, stride, _max_length): + cumulative_log += f"{model} has already been tested. Ignoring.\n" + yield cumulative_log + continue + + if model != 'current model': + try: + yield cumulative_log + f"Loading {model}...\n" + model_settings = get_model_specific_settings(model) + shared.settings.update(model_settings) # hijacking the interface defaults + update_model_parameters(model_settings) # hijacking the command-line arguments + shared.model_name = model + unload_model() + shared.model, shared.tokenizer = load_model(shared.model_name) + except: + cumulative_log += f"Failed to load {model}. Moving on.\n" + yield cumulative_log + continue + + cumulative_log += f"Processing {model}...\n" + yield cumulative_log + "Tokenizing the input dataset...\n" + encodings = encode(text, add_special_tokens=False) + seq_len = encodings.shape[1] + max_length = _max_length or shared.model.config.max_position_embeddings + nlls = [] + prev_end_loc = 0 + for begin_loc in tqdm(range(0, seq_len, stride)): + yield cumulative_log + f"Evaluating... {100*begin_loc/seq_len:.2f}%" + end_loc = min(begin_loc + max_length, seq_len) + trg_len = end_loc - prev_end_loc # may be different from stride on last loop + input_ids = encodings[:, begin_loc:end_loc] + target_ids = input_ids.clone() + target_ids[:, :-trg_len] = -100 + + with torch.no_grad(): + outputs = shared.model(input_ids, labels=target_ids) + + # loss is calculated using CrossEntropyLoss which averages over valid labels + # N.B. the model only calculates loss over trg_len - 1 labels, because it internally shifts the labels + # to the left by 1. + neg_log_likelihood = outputs.loss + + nlls.append(neg_log_likelihood) + + prev_end_loc = end_loc + if end_loc == seq_len: + break + + ppl = torch.exp(torch.stack(nlls).mean()) + add_entry_to_past_evaluations(float(ppl), shared.model_name, input_dataset, stride, _max_length) + save_past_evaluations(past_evaluations) + cumulative_log += f"Done. The perplexity is: {float(ppl)}\n\n" + yield cumulative_log + + +def add_entry_to_past_evaluations(perplexity, model, dataset, stride, max_length): + global past_evaluations + entry = { + 'Model': model, + 'LoRAs': ', '.join(shared.lora_names) or '-', + 'Dataset': dataset, + 'Perplexity': perplexity, + 'stride': str(stride), + 'max_length': str(max_length), + 'Date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), + 'Comment': '' + } + past_evaluations = pd.concat([past_evaluations, pd.DataFrame([entry])], ignore_index=True) + + +def is_in_past_evaluations(model, dataset, stride, max_length): + entries = past_evaluations[(past_evaluations['Model'] == model) & + (past_evaluations['Dataset'] == dataset) & + (past_evaluations['max_length'] == str(max_length)) & + (past_evaluations['stride'] == str(stride))] + + if entries.shape[0] > 0: + return True + else: + return False + + +def generate_markdown_table(): + sorted_df = past_evaluations.sort_values(by=['Dataset', 'stride', 'Perplexity', 'Date']) + return sorted_df diff --git a/text-generation-webui-main/modules/extensions.py b/text-generation-webui-main/modules/extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..1bb36d52670a393a59a19616a1d55f9c9750e639 --- /dev/null +++ b/text-generation-webui-main/modules/extensions.py @@ -0,0 +1,130 @@ +import logging +import traceback +from functools import partial + +import gradio as gr + +import extensions +import modules.shared as shared + +state = {} +available_extensions = [] +setup_called = set() + + +def apply_settings(extension, name): + if not hasattr(extension, 'params'): + return + + for param in extension.params: + _id = f"{name}-{param}" + if _id not in shared.settings: + continue + + extension.params[param] = shared.settings[_id] + + +def load_extensions(): + global state, setup_called + for i, name in enumerate(shared.args.extensions): + if name in available_extensions: + if name != 'api': + logging.info(f'Loading the extension "{name}"...') + try: + exec(f"import extensions.{name}.script") + extension = getattr(extensions, name).script + apply_settings(extension, name) + if extension not in setup_called and hasattr(extension, "setup"): + setup_called.add(extension) + extension.setup() + + state[name] = [True, i] + except: + logging.error('Failed to load the extension "{name}".') + traceback.print_exc() + + +# This iterator returns the extensions in the order specified in the command-line +def iterator(): + for name in sorted(state, key=lambda x: state[x][1]): + if state[name][0]: + yield getattr(extensions, name).script, name + + +# Extension functions that map string -> string +def _apply_string_extensions(function_name, text): + for extension, _ in iterator(): + if hasattr(extension, function_name): + text = getattr(extension, function_name)(text) + + return text + + +# Input hijack of extensions +def _apply_input_hijack(text, visible_text): + for extension, _ in iterator(): + if hasattr(extension, 'input_hijack') and extension.input_hijack['state']: + extension.input_hijack['state'] = False + if callable(extension.input_hijack['value']): + text, visible_text = extension.input_hijack['value'](text, visible_text) + else: + text, visible_text = extension.input_hijack['value'] + + return text, visible_text + + +# custom_generate_chat_prompt handling +def _apply_custom_generate_chat_prompt(text, state, **kwargs): + custom_generate_chat_prompt = None + for extension, _ in iterator(): + if custom_generate_chat_prompt is None and hasattr(extension, 'custom_generate_chat_prompt'): + custom_generate_chat_prompt = extension.custom_generate_chat_prompt + + if custom_generate_chat_prompt is not None: + return custom_generate_chat_prompt(text, state, **kwargs) + + return None + + +# Extension functions that override the default tokenizer output +def _apply_tokenizer_extensions(function_name, state, prompt, input_ids, input_embeds): + for extension, _ in iterator(): + if hasattr(extension, function_name): + prompt, input_ids, input_embeds = getattr(extension, function_name)(state, prompt, input_ids, input_embeds) + + return prompt, input_ids, input_embeds + + +EXTENSION_MAP = { + "input": partial(_apply_string_extensions, "input_modifier"), + "output": partial(_apply_string_extensions, "output_modifier"), + "bot_prefix": partial(_apply_string_extensions, "bot_prefix_modifier"), + "tokenizer": partial(_apply_tokenizer_extensions, "tokenizer_modifier"), + "input_hijack": _apply_input_hijack, + "custom_generate_chat_prompt": _apply_custom_generate_chat_prompt +} + + +def apply_extensions(typ, *args, **kwargs): + if typ not in EXTENSION_MAP: + raise ValueError(f"Invalid extension type {typ}") + + return EXTENSION_MAP[typ](*args, **kwargs) + + +def create_extensions_block(): + global setup_called + + should_display_ui = False + for extension, name in iterator(): + if hasattr(extension, "ui"): + should_display_ui = True + break + + # Creating the extension ui elements + if should_display_ui: + with gr.Column(elem_id="extensions"): + for extension, name in iterator(): + if hasattr(extension, "ui"): + gr.Markdown(f"\n### {name}") + extension.ui() diff --git a/text-generation-webui-main/modules/html_generator.py b/text-generation-webui-main/modules/html_generator.py new file mode 100644 index 0000000000000000000000000000000000000000..5f0fd43b03b9232af615eb08d2b3264bb29053a4 --- /dev/null +++ b/text-generation-webui-main/modules/html_generator.py @@ -0,0 +1,263 @@ +''' + +This is a library for formatting text outputs as nice HTML. + +''' + +import os +import re +import time +from pathlib import Path + +import markdown +from PIL import Image, ImageOps + +# This is to store the paths to the thumbnails of the profile pictures +image_cache = {} + +with open(Path(__file__).resolve().parent / '../css/html_readable_style.css', 'r') as f: + readable_css = f.read() +with open(Path(__file__).resolve().parent / '../css/html_4chan_style.css', 'r') as css_f: + _4chan_css = css_f.read() +with open(Path(__file__).resolve().parent / '../css/html_cai_style.css', 'r') as f: + cai_css = f.read() +with open(Path(__file__).resolve().parent / '../css/html_bubble_chat_style.css', 'r') as f: + bubble_chat_css = f.read() +with open(Path(__file__).resolve().parent / '../css/html_instruct_style.css', 'r') as f: + instruct_css = f.read() + + +def fix_newlines(string): + string = string.replace('\n', '\n\n') + string = re.sub(r"\n{3,}", "\n\n", string) + string = string.strip() + return string + + +def replace_blockquote(m): + return m.group().replace('\n', '\n> ').replace('\\begin{blockquote}', '').replace('\\end{blockquote}', '') + + +def convert_to_markdown(string): + + # Blockquote + pattern = re.compile(r'\\begin{blockquote}(.*?)\\end{blockquote}', re.DOTALL) + string = pattern.sub(replace_blockquote, string) + + # Code + string = string.replace('\\begin{code}', '```') + string = string.replace('\\end{code}', '```') + string = re.sub(r"(.)```", r"\1\n```", string) + + string = fix_newlines(string) + return markdown.markdown(string, extensions=['fenced_code']) + + +def generate_basic_html(string): + string = convert_to_markdown(string) + string = f'
{string}
' + return string + + +def process_post(post, c): + t = post.split('\n') + number = t[0].split(' ')[1] + if len(t) > 1: + src = '\n'.join(t[1:]) + else: + src = '' + src = re.sub('>', '>', src) + src = re.sub('(>>[0-9]*)', '\\1', src) + src = re.sub('\n', '
\n', src) + src = f'
{src}\n' + src = f'Anonymous No.{number}\n{src}' + return src + + +def generate_4chan_html(f): + posts = [] + post = '' + c = -2 + for line in f.splitlines(): + line += "\n" + if line == '-----\n': + continue + elif line.startswith('--- '): + c += 1 + if post != '': + src = process_post(post, c) + posts.append(src) + post = line + else: + post += line + if post != '': + src = process_post(post, c) + posts.append(src) + + for i in range(len(posts)): + if i == 0: + posts[i] = f'
{posts[i]}
\n' + else: + posts[i] = f'
{posts[i]}
\n' + + output = '' + output += f'
' + for post in posts: + output += post + output += '
' + output = output.split('\n') + for i in range(len(output)): + output[i] = re.sub(r'^(>(.*?)(
|
))', r'\1', output[i]) + output[i] = re.sub(r'^
(>(.*?)(
|
))', r'
\1', output[i]) + output = '\n'.join(output) + + return output + + +def make_thumbnail(image): + image = image.resize((350, round(image.size[1] / image.size[0] * 350)), Image.Resampling.LANCZOS) + if image.size[1] > 470: + image = ImageOps.fit(image, (350, 470), Image.ANTIALIAS) + + return image + + +def get_image_cache(path): + cache_folder = Path("cache") + if not cache_folder.exists(): + cache_folder.mkdir() + + mtime = os.stat(path).st_mtime + if (path in image_cache and mtime != image_cache[path][0]) or (path not in image_cache): + img = make_thumbnail(Image.open(path)) + output_file = Path(f'cache/{path.name}_cache.png') + img.convert('RGB').save(output_file, format='PNG') + image_cache[path] = [mtime, output_file.as_posix()] + + return image_cache[path][1] + + +def generate_instruct_html(history): + output = f'
' + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + + output += f""" +
+
+
+ {row[1]} +
+
+
+ """ + + if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+
+ {row[0]} +
+
+
+ """ + + output += "
" + + return output + + +def generate_cai_chat_html(history, name1, name2, reset_cache=False): + output = f'
' + + # We use ?name2 and ?time.time() to force the browser to reset caches + img_bot = f'' if Path("cache/pfp_character.png").exists() else '' + img_me = f'' if Path("cache/pfp_me.png").exists() else '' + + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + + output += f""" +
+
+ {img_bot} +
+
+
+ {name2} +
+
+ {row[1]} +
+
+
+ """ + + if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+ {img_me} +
+
+
+ {name1} +
+
+ {row[0]} +
+
+
+ """ + + output += "
" + return output + + +def generate_chat_html(history, name1, name2, reset_cache=False): + output = f'
' + + for i, _row in enumerate(history[::-1]): + row = [convert_to_markdown(entry) for entry in _row] + + output += f""" +
+
+
+ {row[1]} +
+
+
+ """ + + if len(row[0]) == 0: # don't display empty user messages + continue + + output += f""" +
+
+
+ {row[0]} +
+
+
+ """ + + output += "
" + return output + + +def chat_html_wrapper(history, name1, name2, mode, reset_cache=False): + if mode == "cai-chat": + return generate_cai_chat_html(history, name1, name2, reset_cache) + elif mode == "chat": + return generate_chat_html(history, name1, name2) + elif mode == "instruct": + return generate_instruct_html(history) + else: + return '' diff --git a/text-generation-webui-main/modules/llama_attn_hijack.py b/text-generation-webui-main/modules/llama_attn_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..e953f523d6c54581af1a30deb8b922f85b3e557a --- /dev/null +++ b/text-generation-webui-main/modules/llama_attn_hijack.py @@ -0,0 +1,171 @@ +import logging +import math +import sys +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import transformers.models.llama.modeling_llama + +import modules.shared as shared + +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) + + +def hijack_llama_attention(): + if shared.args.xformers: + transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward + logging.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") + + +def xformers_forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # We only apply xformers optimizations if we don't need to output the whole attention matrix + if not output_attentions: + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + # This is a nasty hack. We know attention_mask in transformers is either LowerTriangular or all Zeros. + # We therefore check if one element in the upper triangular portion is zero. If it is, then the mask is all zeros. + if attention_mask is None or attention_mask[0, 0, 0, 1] == 0: + # input and output should be of form (bsz, q_len, num_heads, head_dim) + attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=None) + else: + # input and output should be of form (bsz, q_len, num_heads, head_dim) + attn_output = xformers.ops.memory_efficient_attention(query_states, key_states, value_states, attn_bias=xformers.ops.LowerTriangularMask()) + attn_weights = None + else: + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + attn_output = self.o_proj(attn_output) + return attn_output, attn_weights, past_key_value + + +def sdp_attention_forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Tuple[torch.Tensor]] = None, + output_attentions: bool = False, + use_cache: bool = False, +) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + + kv_seq_len = key_states.shape[-2] + if past_key_value is not None: + kv_seq_len += past_key_value[0].shape[-2] + cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + query_states, key_states = transformers.models.llama.modeling_llama.apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) + # [bsz, nh, t, hd] + + if past_key_value is not None: + # reuse k, v, self_attention + key_states = torch.cat([past_key_value[0], key_states], dim=2) + value_states = torch.cat([past_key_value[1], value_states], dim=2) + + past_key_value = (key_states, value_states) if use_cache else None + + # We only apply sdp attention if we don't need to output the whole attention matrix + if not output_attentions: + attn_output = torch.nn.functional.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask=attention_mask, is_causal=False) + attn_weights = None + else: + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len): + raise ValueError( + f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (bsz, 1, q_len, kv_seq_len): + raise ValueError( + f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}" + ) + attn_weights = attn_weights + attention_mask + attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)) + + # upcast attention to fp32 + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(bsz, q_len, self.hidden_size) + + attn_output = self.o_proj(attn_output) + + return attn_output, attn_weights, past_key_value diff --git a/text-generation-webui-main/modules/llamacpp_model.py b/text-generation-webui-main/modules/llamacpp_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d19eea27c19c37a0cbf3e85bdd63358c2bfbf640 --- /dev/null +++ b/text-generation-webui-main/modules/llamacpp_model.py @@ -0,0 +1,67 @@ +''' +Based on +https://github.com/abetlen/llama-cpp-python + +Documentation: +https://abetlen.github.io/llama-cpp-python/ +''' + +from llama_cpp import Llama, LlamaCache + +from modules import shared +from modules.callbacks import Iteratorize + + +class LlamaCppModel: + def __init__(self): + self.initialized = False + + @classmethod + def from_pretrained(self, path): + result = self() + + params = { + 'model_path': str(path), + 'n_ctx': 2048, + 'seed': 0, + 'n_threads': shared.args.threads or None, + 'n_batch': shared.args.n_batch, + 'use_mmap': not shared.args.no_mmap, + 'use_mlock': shared.args.mlock + } + self.model = Llama(**params) + self.model.set_cache(LlamaCache) + + # This is ugly, but the model and the tokenizer are the same object in this library. + return result, result + + def encode(self, string): + if type(string) is str: + string = string.encode() + return self.model.tokenize(string) + + def generate(self, context="", token_count=20, temperature=1, top_p=1, top_k=50, repetition_penalty=1, callback=None): + if type(context) is str: + context = context.encode() + tokens = self.model.tokenize(context) + + output = b"" + count = 0 + for token in self.model.generate(tokens, top_k=top_k, top_p=top_p, temp=temperature, repeat_penalty=repetition_penalty): + text = self.model.detokenize([token]) + output += text + if callback: + callback(text.decode()) + + count += 1 + if count >= token_count or (token == self.model.token_eos()): + break + + return output.decode() + + def generate_with_streaming(self, **kwargs): + with Iteratorize(self.generate, kwargs, callback=None) as generator: + reply = '' + for token in generator: + reply += token + yield reply diff --git a/text-generation-webui-main/modules/logging_colors.py b/text-generation-webui-main/modules/logging_colors.py new file mode 100644 index 0000000000000000000000000000000000000000..5485b0901677fbab117015097f3af78401ae3419 --- /dev/null +++ b/text-generation-webui-main/modules/logging_colors.py @@ -0,0 +1,107 @@ +# Copied from https://stackoverflow.com/a/1336640 + +import logging + +def add_coloring_to_emit_windows(fn): + # add methods we need to the class + def _out_handle(self): + import ctypes + return ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) + out_handle = property(_out_handle) + + def _set_color(self, code): + import ctypes + # Constants from the Windows API + self.STD_OUTPUT_HANDLE = -11 + hdl = ctypes.windll.kernel32.GetStdHandle(self.STD_OUTPUT_HANDLE) + ctypes.windll.kernel32.SetConsoleTextAttribute(hdl, code) + + setattr(logging.StreamHandler, '_set_color', _set_color) + + def new(*args): + FOREGROUND_BLUE = 0x0001 # text color contains blue. + FOREGROUND_GREEN = 0x0002 # text color contains green. + FOREGROUND_RED = 0x0004 # text color contains red. + FOREGROUND_INTENSITY = 0x0008 # text color is intensified. + FOREGROUND_WHITE = FOREGROUND_BLUE | FOREGROUND_GREEN | FOREGROUND_RED + # winbase.h + # STD_INPUT_HANDLE = -10 + # STD_OUTPUT_HANDLE = -11 + # STD_ERROR_HANDLE = -12 + + # wincon.h + # FOREGROUND_BLACK = 0x0000 + FOREGROUND_BLUE = 0x0001 + FOREGROUND_GREEN = 0x0002 + # FOREGROUND_CYAN = 0x0003 + FOREGROUND_RED = 0x0004 + FOREGROUND_MAGENTA = 0x0005 + FOREGROUND_YELLOW = 0x0006 + # FOREGROUND_GREY = 0x0007 + FOREGROUND_INTENSITY = 0x0008 # foreground color is intensified. + + # BACKGROUND_BLACK = 0x0000 + # BACKGROUND_BLUE = 0x0010 + # BACKGROUND_GREEN = 0x0020 + # BACKGROUND_CYAN = 0x0030 + # BACKGROUND_RED = 0x0040 + # BACKGROUND_MAGENTA = 0x0050 + BACKGROUND_YELLOW = 0x0060 + # BACKGROUND_GREY = 0x0070 + BACKGROUND_INTENSITY = 0x0080 # background color is intensified. + + levelno = args[1].levelno + if (levelno >= 50): + color = BACKGROUND_YELLOW | FOREGROUND_RED | FOREGROUND_INTENSITY | BACKGROUND_INTENSITY + elif (levelno >= 40): + color = FOREGROUND_RED | FOREGROUND_INTENSITY + elif (levelno >= 30): + color = FOREGROUND_YELLOW | FOREGROUND_INTENSITY + elif (levelno >= 20): + color = FOREGROUND_GREEN + elif (levelno >= 10): + color = FOREGROUND_MAGENTA + else: + color = FOREGROUND_WHITE + args[0]._set_color(color) + + ret = fn(*args) + args[0]._set_color(FOREGROUND_WHITE) + # print "after" + return ret + return new + + +def add_coloring_to_emit_ansi(fn): + # add methods we need to the class + def new(*args): + levelno = args[1].levelno + if (levelno >= 50): + color = '\x1b[31m' # red + elif (levelno >= 40): + color = '\x1b[31m' # red + elif (levelno >= 30): + color = '\x1b[33m' # yellow + elif (levelno >= 20): + color = '\x1b[32m' # green + elif (levelno >= 10): + color = '\x1b[35m' # pink + else: + color = '\x1b[0m' # normal + args[1].msg = color + args[1].msg + '\x1b[0m' # normal + # print "after" + return fn(*args) + return new + + +import platform +if platform.system() == 'Windows': + # Windows does not support ANSI escapes and we are using API calls to set the console color + logging.StreamHandler.emit = add_coloring_to_emit_windows(logging.StreamHandler.emit) +else: + # all non-Windows platforms are supporting ANSI escapes so we use them + logging.StreamHandler.emit = add_coloring_to_emit_ansi(logging.StreamHandler.emit) + # log = logging.getLogger() + # log.addFilter(log_filter()) + # //hdlr = logging.StreamHandler() + # //hdlr.setFormatter(formatter()) diff --git a/text-generation-webui-main/modules/models.py b/text-generation-webui-main/modules/models.py new file mode 100644 index 0000000000000000000000000000000000000000..8151c5e2d6fa3e6eeeb44ca0ebea93ada0922a0a --- /dev/null +++ b/text-generation-webui-main/modules/models.py @@ -0,0 +1,296 @@ +import gc +import json +import logging +import os +import re +import time +import zipfile +from pathlib import Path + +import numpy as np +import torch +import transformers +from accelerate import infer_auto_device_map, init_empty_weights +from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM, + AutoModelForSeq2SeqLM, AutoTokenizer, + BitsAndBytesConfig, LlamaTokenizer) + +import modules.shared as shared +from modules import llama_attn_hijack + +transformers.logging.set_verbosity_error() + +if shared.args.flexgen: + from flexgen.flex_opt import CompressionConfig, ExecutionEnv, OptLM, Policy + +local_rank = None +if shared.args.deepspeed: + import deepspeed + from transformers.deepspeed import (HfDeepSpeedConfig, + is_deepspeed_zero3_enabled) + + from modules.deepspeed_parameters import generate_ds_config + + # Distributed setup + local_rank = shared.args.local_rank if shared.args.local_rank is not None else int(os.getenv("LOCAL_RANK", "0")) + world_size = int(os.getenv("WORLD_SIZE", "1")) + torch.cuda.set_device(local_rank) + deepspeed.init_distributed() + ds_config = generate_ds_config(shared.args.bf16, 1 * world_size, shared.args.nvme_offload_dir) + dschf = HfDeepSpeedConfig(ds_config) # Keep this object alive for the Transformers integration + + +def find_model_type(model_name): + model_name_lower = model_name.lower() + if 'rwkv-' in model_name_lower: + return 'rwkv' + elif len(list(Path(f'{shared.args.model_dir}/{model_name}').glob('*ggml*.bin'))) > 0: + return 'llamacpp' + elif re.match('.*ggml.*\.bin', model_name_lower): + return 'llamacpp' + elif 'chatglm' in model_name_lower: + return 'chatglm' + elif 'galactica' in model_name_lower: + return 'galactica' + elif 'llava' in model_name_lower: + return 'llava' + elif any((k in model_name_lower for k in ['gpt4chan', 'gpt-4chan'])): + return 'gpt4chan' + else: + config = AutoConfig.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), trust_remote_code=shared.args.trust_remote_code) + # Not a "catch all", but fairly accurate + if config.to_dict().get("is_encoder_decoder", False): + return 'HF_seq2seq' + else: + return 'HF_generic' + + +def load_model(model_name): + logging.info(f"Loading {model_name}...") + t0 = time.time() + + shared.model_type = find_model_type(model_name) + trust_remote_code = shared.args.trust_remote_code + if shared.model_type == 'chatglm': + LoaderClass = AutoModel + elif shared.model_type == 'HF_seq2seq': + LoaderClass = AutoModelForSeq2SeqLM + else: + LoaderClass = AutoModelForCausalLM + + # Load the model in simple 16-bit mode by default + if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.model_type in ['rwkv', 'llamacpp']]): + model = LoaderClass.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16, trust_remote_code=trust_remote_code) + if torch.has_mps: + device = torch.device('mps') + model = model.to(device) + else: + model = model.cuda() + + # FlexGen + elif shared.args.flexgen: + # Initialize environment + env = ExecutionEnv.create(shared.args.disk_cache_dir) + + # Offloading policy + policy = Policy(1, 1, + shared.args.percent[0], shared.args.percent[1], + shared.args.percent[2], shared.args.percent[3], + shared.args.percent[4], shared.args.percent[5], + overlap=True, sep_layer=True, pin_weight=shared.args.pin_weight, + cpu_cache_compute=False, attn_sparsity=1.0, + compress_weight=shared.args.compress_weight, + comp_weight_config=CompressionConfig( + num_bits=4, group_size=64, + group_dim=0, symmetric=False), + compress_cache=False, + comp_cache_config=CompressionConfig( + num_bits=4, group_size=64, + group_dim=2, symmetric=False)) + + model = OptLM(f"facebook/{model_name}", env, shared.args.model_dir, policy) + + # DeepSpeed ZeRO-3 + elif shared.args.deepspeed: + 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()}") + + # RMKV model (not on HuggingFace) + elif shared.model_type == 'rwkv': + from modules.RWKV import RWKVModel, RWKVTokenizer + + model = RWKVModel.from_pretrained(Path(f'{shared.args.model_dir}/{model_name}'), dtype="fp32" if shared.args.cpu else "bf16" if shared.args.bf16 else "fp16", device="cpu" if shared.args.cpu else "cuda") + tokenizer = RWKVTokenizer.from_pretrained(Path(shared.args.model_dir)) + + return model, tokenizer + + # llamacpp model + elif shared.model_type == 'llamacpp': + from modules.llamacpp_model import LlamaCppModel + + path = Path(f'{shared.args.model_dir}/{model_name}') + if path.is_file(): + model_file = path + 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") + model, tokenizer = LlamaCppModel.from_pretrained(model_file) + return model, tokenizer + + # Quantized model + elif shared.args.wbits > 0: + + # 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.") + from modules.monkey_patch_gptq_lora import load_model_llama + + model, _ = load_model_llama(model_name) + + # No monkey patch + else: + from modules.GPTQ_loader import load_quantized + + model = load_quantized(model_name) + + # Custom + else: + params = {"low_cpu_mem_usage": True} + 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.") + shared.args.cpu = True + + if shared.args.cpu: + params["torch_dtype"] = torch.float32 + else: + params["device_map"] = 'auto' + params["trust_remote_code"] = trust_remote_code + if shared.args.load_in_8bit and any((shared.args.auto_devices, shared.args.gpu_memory)): + params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True) + elif shared.args.load_in_8bit: + params['quantization_config'] = BitsAndBytesConfig(load_in_8bit=True) + elif shared.args.bf16: + params["torch_dtype"] = torch.bfloat16 + else: + params["torch_dtype"] = torch.float16 + + if shared.args.gpu_memory: + memory_map = list(map(lambda x: x.strip(), shared.args.gpu_memory)) + max_cpu_memory = shared.args.cpu_memory.strip() if shared.args.cpu_memory is not None else '99GiB' + max_memory = {} + for i in range(len(memory_map)): + max_memory[i] = f'{memory_map[i]}GiB' if not re.match('.*ib$', memory_map[i].lower()) else memory_map[i] + + max_memory['cpu'] = max_cpu_memory + params['max_memory'] = max_memory + elif shared.args.auto_devices: + total_mem = (torch.cuda.get_device_properties(0).total_memory / (1024 * 1024)) + suggestion = round((total_mem - 1000) / 1000) * 1000 + if total_mem - suggestion < 800: + suggestion -= 1000 + + suggestion = int(round(suggestion / 1000)) + logging.warning(f"\033[1;32;1mAuto-assiging --gpu-memory {suggestion} for your GPU to try to prevent out-of-memory errors.\nYou can manually set other values.\033[0;37;0m") + max_memory = {0: f'{suggestion}GiB', 'cpu': f'{shared.args.cpu_memory or 99}GiB'} + params['max_memory'] = max_memory + + if shared.args.disk: + params["offload_folder"] = shared.args.disk_cache_dir + + checkpoint = Path(f'{shared.args.model_dir}/{model_name}') + if shared.args.load_in_8bit and params.get('max_memory', None) is not None and params['device_map'] == 'auto': + config = AutoConfig.from_pretrained(checkpoint) + with init_empty_weights(): + model = LoaderClass.from_config(config) + + model.tie_weights() + params['device_map'] = infer_auto_device_map( + model, + dtype=torch.int8, + max_memory=params['max_memory'], + no_split_module_classes=model._no_split_modules + ) + + model = LoaderClass.from_pretrained(checkpoint, **params) + + # Hijack attention with xformers + if any((shared.args.xformers, shared.args.sdp_attention)): + llama_attn_hijack.hijack_llama_attention() + + # Loading the tokenizer + if shared.model_type == 'gpt4chan' and Path(f"{shared.args.model_dir}/gpt-j-6B/").exists(): + tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/gpt-j-6B/")) + elif type(model) is transformers.LlamaForCausalLM: + tokenizer = None + + # Try to load an universal LLaMA tokenizer + if shared.model_type != 'llava': + 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}...") + tokenizer = LlamaTokenizer.from_pretrained(p, clean_up_tokenization_spaces=True) + break + + # Otherwise, load it from the model folder and hope that these + # are not outdated tokenizer files. + if tokenizer is None: + tokenizer = LlamaTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), clean_up_tokenization_spaces=True) + try: + tokenizer.eos_token_id = 2 + tokenizer.bos_token_id = 1 + tokenizer.pad_token_id = 0 + except: + pass + else: + tokenizer = AutoTokenizer.from_pretrained(Path(f"{shared.args.model_dir}/{model_name}/"), trust_remote_code=trust_remote_code) + + logging.info(f"Loaded the model in {(time.time()-t0):.2f} seconds.") + return model, tokenizer + + +def clear_torch_cache(): + gc.collect() + if not shared.args.cpu: + torch.cuda.empty_cache() + + +def unload_model(): + shared.model = shared.tokenizer = None + clear_torch_cache() + + +def reload_model(): + unload_model() + shared.model, shared.tokenizer = load_model(shared.model_name) + + +def load_soft_prompt(name): + if name == 'None': + shared.soft_prompt = False + shared.soft_prompt_tensor = None + else: + with zipfile.ZipFile(Path(f'softprompts/{name}.zip')) as zf: + zf.extract('tensor.npy') + zf.extract('meta.json') + j = json.loads(open('meta.json', 'r').read()) + logging.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])}") + else: + logging.info(f"{field}: {j[field]}") + logging.info() + tensor = np.load('tensor.npy') + Path('tensor.npy').unlink() + Path('meta.json').unlink() + + tensor = torch.Tensor(tensor).to(device=shared.model.device, dtype=shared.model.dtype) + tensor = torch.reshape(tensor, (1, tensor.shape[0], tensor.shape[1])) + shared.soft_prompt = True + shared.soft_prompt_tensor = tensor + + return name diff --git a/text-generation-webui-main/modules/monkey_patch_gptq_lora.py b/text-generation-webui-main/modules/monkey_patch_gptq_lora.py new file mode 100644 index 0000000000000000000000000000000000000000..a37e790671f513b6a5744cc469424a967a75d43b --- /dev/null +++ b/text-generation-webui-main/modules/monkey_patch_gptq_lora.py @@ -0,0 +1,39 @@ +# Copied from https://github.com/johnsmith0031/alpaca_lora_4bit + +import sys +from pathlib import Path + +sys.path.insert(0, str(Path("repositories/alpaca_lora_4bit"))) + +import autograd_4bit +from amp_wrapper import AMPWrapper +from autograd_4bit import (Autograd4bitQuantLinear, + load_llama_model_4bit_low_ram) +from monkeypatch.peft_tuners_lora_monkey_patch import ( + Linear4bitLt, replace_peft_model_with_gptq_lora_model) + +from modules import shared +from modules.GPTQ_loader import find_quantized_model_file + +replace_peft_model_with_gptq_lora_model() + + +def load_model_llama(model_name): + config_path = str(Path(f'{shared.args.model_dir}/{model_name}')) + model_path = str(find_quantized_model_file(model_name)) + model, tokenizer = load_llama_model_4bit_low_ram(config_path, model_path, groupsize=shared.args.groupsize, is_v1_model=False) + for n, m in model.named_modules(): + if isinstance(m, Autograd4bitQuantLinear) or isinstance(m, Linear4bitLt): + if m.is_v1_model: + m.zeros = m.zeros.half() + m.scales = m.scales.half() + m.bias = m.bias.half() + + autograd_4bit.use_new = True + autograd_4bit.auto_switch = True + + model.half() + wrapper = AMPWrapper(model) + wrapper.apply_generate() + + return model, tokenizer diff --git a/text-generation-webui-main/modules/shared.py b/text-generation-webui-main/modules/shared.py new file mode 100644 index 0000000000000000000000000000000000000000..8ce1ded24dfb9018df5e023633810491684f44d4 --- /dev/null +++ b/text-generation-webui-main/modules/shared.py @@ -0,0 +1,215 @@ +import argparse +import logging +from pathlib import Path + +import yaml + +model = None +tokenizer = None +model_name = "None" +model_type = None +lora_names = [] +soft_prompt_tensor = None +soft_prompt = False + +# Chat variables +history = {'internal': [], 'visible': []} +character = 'None' +stop_everything = False +processing_message = '*Is typing...*' + +# UI elements (buttons, sliders, HTML, etc) +gradio = {} + +# For keeping the values of UI elements on page reload +persistent_interface_state = {} + +# Generation input parameters +input_params = [] + +# For restarting the interface +need_restart = False + +settings = { + 'max_new_tokens': 200, + 'max_new_tokens_min': 1, + 'max_new_tokens_max': 2000, + 'seed': -1, + 'character': 'None', + 'name1': 'You', + 'name2': 'Assistant', + 'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.', + 'greeting': '', + 'turn_template': '', + 'custom_stopping_strings': '', + 'stop_at_newline': False, + 'add_bos_token': True, + 'ban_eos_token': False, + 'skip_special_tokens': True, + 'truncation_length': 2048, + 'truncation_length_min': 0, + 'truncation_length_max': 8192, + 'mode': 'cai-chat', + 'instruction_template': 'None', + 'chat_prompt_size': 2048, + 'chat_prompt_size_min': 0, + 'chat_prompt_size_max': 2048, + 'chat_generation_attempts': 1, + 'chat_generation_attempts_min': 1, + 'chat_generation_attempts_max': 5, + 'default_extensions': [], + 'chat_default_extensions': ["gallery"], + 'presets': { + 'default': 'Default', + '.*(alpaca|llama|llava)': "LLaMA-Precise", + '.*pygmalion': 'NovelAI-Storywriter', + '.*RWKV': 'Naive', + }, + 'prompts': { + 'default': 'QA', + '.*(gpt4chan|gpt-4chan|4chan)': 'GPT-4chan', + '.*oasst': 'Open Assistant', + '.*alpaca': "Alpaca", + }, + 'lora_prompts': { + 'default': 'QA', + '.*alpaca': "Alpaca", + } +} + + +def str2bool(v): + if isinstance(v, bool): + return v + if v.lower() in ('yes', 'true', 't', 'y', '1'): + return True + elif v.lower() in ('no', 'false', 'f', 'n', '0'): + return False + else: + raise argparse.ArgumentTypeError('Boolean value expected.') + + +parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54)) + +# Basic settings +parser.add_argument('--notebook', action='store_true', help='Launch the web UI in notebook mode, where the output is written to the same text box as the input.') +parser.add_argument('--chat', action='store_true', help='Launch the web UI in chat mode with a style similar to the Character.AI website.') +parser.add_argument('--cai-chat', action='store_true', help='DEPRECATED: use --chat instead.') +parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.') +parser.add_argument('--model', type=str, help='Name of the model to load by default.') +parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.') +parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models") +parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras") +parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.') +parser.add_argument('--no-stream', action='store_true', help='Don\'t stream the text output in real time.') +parser.add_argument('--settings', type=str, help='Load the default interface settings from this json file. See settings-template.json for an example. If you create a file called settings.json, this file will be loaded by default without the need to use the --settings flag.') +parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.') +parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.') + +# Accelerate/transformers +parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.') +parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.') +parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maxmimum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.') +parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.') +parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.') +parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".') +parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.') +parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.') +parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.') +parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.") +parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.") +parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM.") + +# llama.cpp +parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.') +parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.') +parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.') +parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.') + +# GPTQ +parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.') +parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.') +parser.add_argument('--groupsize', type=int, default=-1, help='Group size.') +parser.add_argument('--pre_layer', type=int, default=0, help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models.') +parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.') +parser.add_argument('--quant_attn', action='store_true', help='(triton) Enable quant attention.') +parser.add_argument('--warmup_autotune', action='store_true', help='(triton) Enable warmup autotune.') +parser.add_argument('--fused_mlp', action='store_true', help='(triton) Enable fused mlp.') + +# FlexGen +parser.add_argument('--flexgen', action='store_true', help='Enable the use of FlexGen offloading.') +parser.add_argument('--percent', type=int, nargs="+", default=[0, 100, 100, 0, 100, 0], help='FlexGen: allocation percentages. Must be 6 numbers separated by spaces (default: 0, 100, 100, 0, 100, 0).') +parser.add_argument("--compress-weight", action="store_true", help="FlexGen: activate weight compression.") +parser.add_argument("--pin-weight", type=str2bool, nargs="?", const=True, default=True, help="FlexGen: whether to pin weights (setting this to False reduces CPU memory by 20%%).") + +# DeepSpeed +parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.') +parser.add_argument('--nvme-offload-dir', type=str, help='DeepSpeed: Directory to use for ZeRO-3 NVME offloading.') +parser.add_argument('--local_rank', type=int, default=0, help='DeepSpeed: Optional argument for distributed setups.') + +# RWKV +parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8".') +parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.') + +# Gradio +parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.') +parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.') +parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.') +parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.') +parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.') +parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None) + +# API +parser.add_argument('--api', action='store_true', help='Enable the API extension.') +parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.') + + +args = parser.parse_args() +args_defaults = parser.parse_args([]) + +# Deprecation warnings for parameters that have been renamed +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.") + setattr(args, deprecated_dict[k][0], getattr(args, k)) + +# Deprecation warnings for parameters that have been removed +if args.cai_chat: + logging.warning("--cai-chat is deprecated. Use --chat instead.") + args.chat = True + +# Security warnings +if args.trust_remote_code: + logging.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.") + +# Activating the API extension +if args.api or args.public_api: + if args.extensions is None: + args.extensions = ['api'] + elif 'api' not in args.extensions: + args.extensions.append('api') + + +def is_chat(): + return args.chat + + +# Loading model-specific settings (default) +with Path(f'{args.model_dir}/config.yaml') as p: + if p.exists(): + model_config = yaml.safe_load(open(p, 'r').read()) + else: + model_config = {} + +# Applying user-defined model settings +with Path(f'{args.model_dir}/config-user.yaml') as p: + if p.exists(): + user_config = yaml.safe_load(open(p, 'r').read()) + for k in user_config: + if k in model_config: + model_config[k].update(user_config[k]) + else: + model_config[k] = user_config[k] diff --git a/text-generation-webui-main/modules/text_generation.py b/text-generation-webui-main/modules/text_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..2a994a6a596cc757f82253bf9899708b4a50a503 --- /dev/null +++ b/text-generation-webui-main/modules/text_generation.py @@ -0,0 +1,342 @@ +import ast +import logging +import random +import re +import time +import traceback + +import numpy as np +import torch +import transformers + +import modules.shared as shared +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.models import clear_torch_cache, local_rank + + +def get_max_prompt_length(state): + max_length = state['truncation_length'] - state['max_new_tokens'] + if shared.soft_prompt: + max_length -= shared.soft_prompt_tensor.shape[1] + return max_length + + +def encode(prompt, add_special_tokens=True, add_bos_token=True, truncation_length=None): + if shared.model_type in ['rwkv', 'llamacpp']: + input_ids = shared.tokenizer.encode(str(prompt)) + input_ids = np.array(input_ids).reshape(1, len(input_ids)) + return input_ids + else: + input_ids = shared.tokenizer.encode(str(prompt), return_tensors='pt', add_special_tokens=add_special_tokens) + + # This is a hack for making replies more creative. + if not add_bos_token and input_ids[0][0] == shared.tokenizer.bos_token_id: + input_ids = input_ids[:, 1:] + + # Llama adds this extra token when the first character is '\n', and this + # compromises the stopping criteria, so we just remove it + if type(shared.tokenizer) is transformers.LlamaTokenizer and input_ids[0][0] == 29871: + input_ids = input_ids[:, 1:] + + # Handling truncation + if truncation_length is not None: + input_ids = input_ids[:, -truncation_length:] + + if shared.model_type in ['rwkv', 'llamacpp'] or shared.args.cpu: + return input_ids + elif shared.args.flexgen: + return input_ids.numpy() + elif shared.args.deepspeed: + return input_ids.to(device=local_rank) + elif torch.has_mps: + device = torch.device('mps') + return input_ids.to(device) + else: + return input_ids.cuda() + + +def decode(output_ids, skip_special_tokens=True): + return shared.tokenizer.decode(output_ids, skip_special_tokens) + + +def get_reply_from_output_ids(output_ids, input_ids, original_question, state): + if shared.model_type == 'HF_seq2seq': + reply = decode(output_ids, state['skip_special_tokens']) + if not shared.is_chat(): + reply = apply_extensions('output', reply) + else: + new_tokens = len(output_ids) - len(input_ids[0]) + reply = decode(output_ids[-new_tokens:], state['skip_special_tokens']) + + if type(shared.tokenizer) is transformers.LlamaTokenizer: + if len(original_question) > 0 and original_question[-1] not in [' ', '\n']: + reply = ' ' + reply + + if not shared.is_chat(): + reply = original_question + apply_extensions('output', reply) + + return reply + + +def generate_softprompt_input_tensors(input_ids): + inputs_embeds = shared.model.transformer.wte(input_ids) + inputs_embeds = torch.cat((shared.soft_prompt_tensor, inputs_embeds), dim=1) + filler_input_ids = torch.zeros((1, inputs_embeds.shape[1]), dtype=input_ids.dtype).to(shared.model.device) + # filler_input_ids += shared.model.config.bos_token_id # setting dummy input_ids to bos tokens + return inputs_embeds, filler_input_ids + + +# Removes empty replies from gpt4chan outputs +def fix_gpt4chan(s): + for i in range(10): + s = re.sub("--- [0-9]*\n>>[0-9]*\n---", "---", s) + s = re.sub("--- [0-9]*\n *\n---", "---", s) + s = re.sub("--- [0-9]*\n\n\n---", "---", s) + return s + + +# Fix the LaTeX equations in galactica +def fix_galactica(s): + s = s.replace(r'\[', r'$') + s = s.replace(r'\]', r'$') + s = s.replace(r'\(', r'$') + s = s.replace(r'\)', r'$') + s = s.replace(r'$$', r'$') + s = re.sub(r'\n', r'\n\n', s) + s = re.sub(r"\n{3,}", "\n\n", s) + return s + + +def formatted_outputs(reply, model_name): + if not shared.is_chat(): + if shared.model_type == 'galactica': + reply = fix_galactica(reply) + return reply, reply, generate_basic_html(reply) + elif shared.model_type == 'gpt4chan': + reply = fix_gpt4chan(reply) + return reply, 'Only applicable for GALACTICA models.', generate_4chan_html(reply) + else: + return reply, 'Only applicable for GALACTICA models.', generate_basic_html(reply) + else: + return reply + + +def set_manual_seed(seed): + seed = int(seed) + if seed == -1: + seed = random.randint(1, 2**31) + + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) + + return seed + + +def stop_everything_event(): + shared.stop_everything = True + + +def get_generate_params(state): + generate_params = {} + + # Models that are not on transformers + if shared.model_type in ['rwkv', 'llamacpp']: + generate_params['token_count'] = state['max_new_tokens'] + for k in ['temperature', 'top_p', 'top_k', 'repetition_penalty']: + generate_params[k] = state[k] + else: + # FlexGen + if shared.args.flexgen: + for k in ['max_new_tokens', 'do_sample', 'temperature']: + generate_params[k] = state[k] + + if not shared.args.no_stream: + generate_params['max_new_tokens'] = 8 + + # transformers + else: + for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']: + generate_params[k] = state[k] + + if state['ban_eos_token']: + generate_params['suppress_tokens'] = [shared.tokenizer.eos_token_id] + + if shared.args.no_cache: + generate_params.update({'use_cache': False}) + + if shared.args.deepspeed: + generate_params.update({'synced_gpus': True}) + + return generate_params + + +def generate_reply(question, state, eos_token=None, stopping_strings=[]): + if shared.model_name == 'None' or shared.model is None: + logging.error("No model is loaded! Select one in the Model tab.") + yield formatted_outputs(question, shared.model_name) + return + + clear_torch_cache() + seed = set_manual_seed(state['seed']) + shared.stop_everything = False + generate_params = get_generate_params(state) + t0 = time.time() + + # Preparing the input + original_question = question + if not shared.is_chat(): + question = apply_extensions('input', question) + + # If the model is not on transformers, handle it separately and end this + # function call earlier. + if shared.model_type in ['rwkv', 'llamacpp']: + if shared.args.verbose: + print(f'\n\n{question}\n--------------------\n') + + try: + if shared.args.no_stream: + reply = shared.model.generate(context=question, **generate_params) + output = original_question + reply + if not shared.is_chat(): + reply = original_question + apply_extensions('output', reply) + + yield formatted_outputs(reply, shared.model_name) + else: + if not shared.is_chat(): + yield formatted_outputs(question, shared.model_name) + + for reply in shared.model.generate_with_streaming(context=question, **generate_params): + output = original_question + reply + if not shared.is_chat(): + reply = original_question + apply_extensions('output', reply) + + yield formatted_outputs(reply, shared.model_name) + + except Exception: + traceback.print_exc() + finally: + t1 = time.time() + original_tokens = len(encode(original_question)[0]) + new_tokens = len(encode(output)[0]) - original_tokens + print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') + return + + # Encode the input + input_ids = encode(question, add_bos_token=state['add_bos_token'], truncation_length=get_max_prompt_length(state)) + output = input_ids[0] + cuda = not any((shared.args.cpu, shared.args.deepspeed, shared.args.flexgen)) + if shared.args.verbose: + print(f'\n\n{decode(input_ids[0], False)}\n--------------------\n') + + # Find the eos tokens + eos_token_ids = [shared.tokenizer.eos_token_id] if shared.tokenizer.eos_token_id is not None else [] + if eos_token is not None: + eos_token_ids.append(int(encode(eos_token)[0][-1])) + + # Add the encoded tokens to generate_params + if shared.soft_prompt: + inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) + question, filler_input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, filler_input_ids, inputs_embeds) + original_input_ids = input_ids + generate_params.update({'inputs_embeds': inputs_embeds}) + generate_params.update({'inputs': filler_input_ids}) + else: + question, input_ids, inputs_embeds = apply_extensions('tokenizer', state, question, input_ids, None) + original_input_ids = input_ids + generate_params.update({'inputs': input_ids}) + if inputs_embeds is not None: + generate_params.update({'inputs_embeds': inputs_embeds}) + + # Create the StoppingCriteriaList with the stopping strings (needs to be done after tokenizer extensions) + stopping_criteria_list = transformers.StoppingCriteriaList() + for st in (stopping_strings, ast.literal_eval(f"[{state['custom_stopping_strings']}]")): + if type(st) is list and len(st) > 0: + sentinel_token_ids = [encode(string, add_special_tokens=False) for string in st] + stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=sentinel_token_ids, starting_idx=len(input_ids[0]))) + break + + # Update generate_params with the eos token and the stopping strings + if shared.args.flexgen: + generate_params['stop'] = eos_token_ids[-1] + else: + generate_params['eos_token_id'] = eos_token_ids + generate_params['stopping_criteria'] = stopping_criteria_list + + try: + # Generate the entire reply at once. + if shared.args.no_stream: + with torch.no_grad(): + output = shared.model.generate(**generate_params)[0] + if cuda: + output = output.cuda() + + if shared.soft_prompt: + output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + + reply = get_reply_from_output_ids(output, input_ids, original_question, state) + yield formatted_outputs(reply, shared.model_name) + + # Stream the reply 1 token at a time. + # This is based on the trick of using 'stopping_criteria' to create an iterator. + elif not shared.args.flexgen: + + def generate_with_callback(callback=None, **kwargs): + kwargs['stopping_criteria'].append(Stream(callback_func=callback)) + clear_torch_cache() + with torch.no_grad(): + shared.model.generate(**kwargs) + + def generate_with_streaming(**kwargs): + return Iteratorize(generate_with_callback, kwargs, callback=None) + + if not shared.is_chat() and shared.model_type != 'HF_seq2seq': + yield formatted_outputs(original_question, shared.model_name) + + with generate_with_streaming(**generate_params) as generator: + for output in generator: + if shared.soft_prompt: + output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + + reply = get_reply_from_output_ids(output, input_ids, original_question, state) + if output[-1] in eos_token_ids: + break + + yield formatted_outputs(reply, shared.model_name) + + # Stream the output naively for FlexGen since it doesn't support 'stopping_criteria' + else: + for i in range(state['max_new_tokens'] // 8 + 1): + clear_torch_cache() + with torch.no_grad(): + output = shared.model.generate(**generate_params)[0] + + if shared.soft_prompt: + output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:])) + + reply = get_reply_from_output_ids(output, input_ids, original_question, state) + if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)): + break + + yield formatted_outputs(reply, shared.model_name) + input_ids = np.reshape(output, (1, output.shape[0])) + if shared.soft_prompt: + inputs_embeds, filler_input_ids = generate_softprompt_input_tensors(input_ids) + generate_params.update({'inputs_embeds': inputs_embeds}) + generate_params.update({'inputs': filler_input_ids}) + else: + generate_params.update({'inputs': input_ids}) + + yield formatted_outputs(reply, shared.model_name) + + except Exception: + traceback.print_exc() + finally: + t1 = time.time() + original_tokens = len(original_input_ids[0]) + new_tokens = len(output) - (original_tokens if shared.model_type != 'HF_seq2seq' else 0) + print(f'Output generated in {(t1-t0):.2f} seconds ({new_tokens/(t1-t0):.2f} tokens/s, {new_tokens} tokens, context {original_tokens}, seed {seed})') + return diff --git a/text-generation-webui-main/modules/training.py b/text-generation-webui-main/modules/training.py new file mode 100644 index 0000000000000000000000000000000000000000..82e42f4d2928197564c0efd371ca4c3aaaae4e15 --- /dev/null +++ b/text-generation-webui-main/modules/training.py @@ -0,0 +1,495 @@ +import json +import logging +import math +import sys +import threading +import time +import traceback +from pathlib import Path + +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, + set_peft_model_state_dict) + +from modules import shared, ui +from modules.evaluate import calculate_perplexity, generate_markdown_table, save_past_evaluations +from server import get_available_loras, get_available_models + +# This mapping is from a very recent commit, not yet released. +# If not available, default to a backup map for some common model types. +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 + 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_CLASSES = { + "LlamaForCausalLM": "llama", + "OPTForCausalLM": "opt", + "GPTJForCausalLM": "gptj", + "GPTNeoXForCausalLM": "gpt_neox" + } + +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"] + + +def get_datasets(path: str, ext: str): + return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=str.lower) + + +def create_train_interface(): + with gr.Tab('Train LoRA', elem_id='lora-train-tab'): + gr.Markdown("Confused? [[Click here for a guide]](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Training-LoRAs.md)") + + with gr.Row(): + lora_name = gr.Textbox(label='Name', info='The name of your new LoRA file') + always_override = gr.Checkbox(label='Override Existing Files', value=False, info='If the name given is the same as an existing file, checking this will replace that file. Leaving unchecked will load that file and continue from it (must use the same rank value as the original had).') + save_steps = gr.Number(label='Save every n steps', value=0, info='If above 0, a checkpoint of the LoRA will be saved every time this many steps pass.') + + with gr.Row(): + copy_from = gr.Dropdown(label='Copy parameters from', value='None', choices=get_available_loras()) + ui.create_refresh_button(copy_from, lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button') + + with gr.Row(): + # TODO: Implement multi-device support. + micro_batch_size = gr.Slider(label='Micro Batch Size', value=4, minimum=1, maximum=128, step=1, info='Per-device batch size (NOTE: multiple devices not yet implemented). Increasing this will increase VRAM usage.') + batch_size = gr.Slider(label='Batch Size', value=128, minimum=0, maximum=1024, step=4, info='Global batch size. The two batch sizes together determine gradient accumulation (gradientAccum = batch / microBatch). Higher gradient accum values lead to better quality training.') + + with gr.Row(): + epochs = gr.Number(label='Epochs', value=3, info='Number of times every entry in the dataset should be fed into training. So 1 means feed each item in once, 5 means feed it in five times, etc.') + learning_rate = gr.Textbox(label='Learning Rate', value='3e-4', info='Learning rate, in scientific notation. 3e-4 is a good starting base point. 1e-2 is extremely high, 1e-6 is extremely low.') + lr_scheduler_type = gr.Dropdown(label='LR Scheduler', value='linear', choices=['linear', 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'polynomial', 'inverse_sqrt'], info='Learning rate scheduler - defines how the learning rate changes over time. "Constant" means never change, "linear" means to go in a straight line from the learning rate down to 0, cosine follows a curve, etc.') + + # TODO: What is the actual maximum rank? Likely distinct per model. This might be better to somehow be on a log scale. + lora_rank = gr.Slider(label='LoRA Rank', value=32, minimum=0, maximum=1024, step=4, info='LoRA Rank, or dimension count. Higher values produce a larger file with better control over the model\'s content. Smaller values produce a smaller file with less overall control. Small values like 4 or 8 are great for stylistic guidance, higher values like 128 or 256 are good for teaching content upgrades, extremely high values (1024+) are difficult to train but may improve fine-detail learning for large datasets. Higher ranks also require higher VRAM.') + lora_alpha = gr.Slider(label='LoRA Alpha', value=64, minimum=0, maximum=2048, step=4, info='LoRA Alpha. This divided by the rank becomes the scaling of the LoRA. Higher means stronger. A good standard value is twice your Rank.') + + cutoff_len = gr.Slider(label='Cutoff Length', minimum=0, maximum=2048, value=256, step=32, info='Cutoff length for text input. Essentially, how long of a line of text to feed in at a time. Higher values require drastically more VRAM.') + + with gr.Tab(label='Formatted Dataset'): + with gr.Row(): + dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Dataset', info='The dataset file to use for training.') + ui.create_refresh_button(dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button') + eval_dataset = gr.Dropdown(choices=get_datasets('training/datasets', 'json'), value='None', label='Evaluation Dataset', info='The (optional) dataset file used to evaluate the model after training.') + ui.create_refresh_button(eval_dataset, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'json')}, 'refresh-button') + format = gr.Dropdown(choices=get_datasets('training/formats', 'json'), value='None', label='Data Format', info='The format file used to decide how to format the dataset input.') + ui.create_refresh_button(format, lambda: None, lambda: {'choices': get_datasets('training/formats', 'json')}, 'refresh-button') + + eval_steps = gr.Number(label='Evaluate every n steps', value=100, info='If an evaluation dataset is given, test it every time this many steps pass.') + + with gr.Tab(label="Raw text file"): + with gr.Row(): + raw_text_file = gr.Dropdown(choices=get_datasets('training/datasets', 'txt'), value='None', label='Text file', info='The raw text file to use for training.') + ui.create_refresh_button(raw_text_file, lambda: None, lambda: {'choices': get_datasets('training/datasets', 'txt')}, 'refresh-button') + + with gr.Row(): + overlap_len = gr.Slider(label='Overlap Length', minimum=0, maximum=512, value=128, step=16, info='Overlap length - ie how many tokens from the prior chunk of text to include into the next chunk. (The chunks themselves will be of a size determined by Cutoff Length below). Setting overlap to exactly half the cutoff length may be ideal.') + newline_favor_len = gr.Slider(label='Prefer Newline Cut Length', minimum=0, maximum=512, value=128, step=16, info='Length (in characters, not tokens) of the maximum distance to shift an overlap cut by to ensure chunks cut at newlines. If too low, cuts may occur in the middle of lines.') + + with gr.Accordion(label='Advanced Options', open=False): + lora_dropout = gr.Slider(label='LoRA Dropout', minimum=0.0, maximum=1.0, step=0.025, value=0.05, info='Percentage probability for dropout of LoRA layers. This can help reduce overfitting. Most users should leave at default.') + warmup_steps = gr.Number(label='Warmup Steps', value=100, info='For this many steps at the start, the learning rate will be lower than normal. This helps the trainer prepare the model and precompute statistics to improve the quality of training after the start.') + optimizer = gr.Dropdown(label='Optimizer', value='adamw_torch', choices=['adamw_hf', 'adamw_torch', 'adamw_torch_fused', 'adamw_torch_xla', 'adamw_apex_fused', 'adafactor', 'adamw_bnb_8bit', 'adamw_anyprecision', 'sgd', 'adagrad'], info='Different optimizer implementation options, for advanced users. Effects of different options are not well documented yet.') + + with gr.Row(): + higher_rank_limit = gr.Checkbox(label='Enable higher ranks', value=False, info='If checked, changes Rank/Alpha slider above to go much higher. This will not work without a datacenter-class GPU.') + + with gr.Row(): + start_button = gr.Button("Start LoRA Training") + stop_button = gr.Button("Interrupt") + + output = gr.Markdown(value="Ready") + + with gr.Tab('Perplexity evaluation', elem_id='evaluate-tab'): + with gr.Row(): + with gr.Column(): + models = gr.Dropdown(get_available_models(), label='Models', multiselect=True) + evaluate_text_file = gr.Dropdown(choices=['wikitext', 'ptb', 'ptb_new'] + get_datasets('training/datasets', 'txt')[1:], value='wikitext', label='Input dataset', info='The raw text file on which the model will be evaluated. The first options are automatically downloaded: wikitext, ptb, and ptb_new. The next options are your local text files under training/datasets.') + with gr.Row(): + stride_length = gr.Slider(label='Stride', minimum=1, maximum=2048, value=512, step=1, info='Used to make the evaluation faster at the cost of accuracy. 1 = slowest but most accurate. 512 is a common value.') + max_length = gr.Slider(label='max_length', minimum=0, maximum=8096, value=0, step=1, info='The context for each evaluation. If set to 0, the maximum context length for the model will be used.') + + with gr.Row(): + start_current_evaluation = gr.Button("Evaluate loaded model") + start_evaluation = gr.Button("Evaluate selected models") + stop_evaluation = gr.Button("Interrupt") + + with gr.Column(): + evaluation_log = gr.Markdown(value='') + + evaluation_table = gr.Dataframe(value=generate_markdown_table(), interactive=True) + save_comments = gr.Button('Save comments') + + # Training events + all_params = [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] + copy_from.change(do_copy_params, [copy_from] + all_params, all_params) + start_button.click(do_train, all_params, output) + stop_button.click(do_interrupt, None, None, queue=False) + higher_rank_limit.change(change_rank_limit, [higher_rank_limit], [lora_rank, lora_alpha]) + + # Evaluation events. For some reason, the interrupt event + # doesn't work with the .then() syntax, so I write them one + # by one in this ugly but functional way. + ev = start_evaluation.click(calculate_perplexity, [models, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False) + start_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False) + + tmp = gr.State('') + start_current_evaluation.click(lambda: ['current model'], None, tmp) + ev_cur = start_current_evaluation.click(calculate_perplexity, [tmp, evaluate_text_file, stride_length, max_length], evaluation_log, show_progress=False) + start_current_evaluation.click(generate_markdown_table, None, evaluation_table, show_progress=False) + + stop_evaluation.click(None, None, None, cancels=[ev, ev_cur], queue=False) + save_comments.click( + save_past_evaluations, evaluation_table, None).then( + lambda: "Comments saved.", None, evaluation_log, show_progress=False) + + +def do_interrupt(): + global WANT_INTERRUPT + WANT_INTERRUPT = True + + +def do_copy_params(lora_name: str, *args): + f_name = f"{shared.args.lora_dir}/{clean_path(None, lora_name)}/training_parameters.json" + if Path(f_name).is_file(): + with open(f_name, 'r', encoding='utf-8') as format_file: + params: dict[str, str] = json.load(format_file) + else: + params = {} + + result = list() + for i in range(0, len(PARAMETERS)): + key = PARAMETERS[i] + if key in params: + result.append(params[key]) + else: + result.append(args[i]) + + return result + + +def change_rank_limit(use_higher_ranks: bool): + mult = 2 if use_higher_ranks else 1 + return {"maximum": 1024 * mult, "__type__": "update"}, {"maximum": 2048 * mult, "__type__": "update"} + + +def clean_path(base_path: str, path: str): + """"Strips unusual symbols and forcibly builds a path as relative to the intended directory.""" + # TODO: Probably could do with a security audit to guarantee there's no ways this can be bypassed to target an unwanted path. + # Or swap it to a strict whitelist of [a-zA-Z_0-9] + path = path.replace('\\', '/').replace('..', '_') + if base_path is None: + return path + + return f'{Path(base_path).absolute()}/{path}' + + +def do_train(lora_name: str, always_override: bool, save_steps: int, micro_batch_size: int, batch_size: int, epochs: int, learning_rate: str, lr_scheduler_type: str, lora_rank: int, lora_alpha: int, lora_dropout: float, cutoff_len: int, dataset: str, eval_dataset: str, format: str, eval_steps: int, raw_text_file: str, overlap_len: int, newline_favor_len: int, higher_rank_limit: bool, warmup_steps: int, optimizer: str): + + if shared.args.monkey_patch: + from monkeypatch.peft_tuners_lora_monkey_patch import \ + replace_peft_model_with_gptq_lora_model + replace_peft_model_with_gptq_lora_model() + + global WANT_INTERRUPT + WANT_INTERRUPT = False + + # == Input validation / processing == + yield "Prepping..." + lora_file_path = clean_path(None, lora_name) + if lora_file_path.strip() == '': + yield "Missing or invalid LoRA file name input." + return + + lora_file_path = f"{shared.args.lora_dir}/{lora_file_path}" + actual_lr = float(learning_rate) + model_type = type(shared.model).__name__ + + if model_type in MODEL_CLASSES: + model_id = MODEL_CLASSES[model_type] + else: + model_id = "llama" + 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.") + 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.") + 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})") + + time.sleep(5) + + if shared.args.wbits > 0 and not shared.args.monkey_patch: + yield "LoRA training in 4-bit requires loading with `--monkey-patch`" + return + + 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.") + 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: + yield "Cannot input zeroes." + return + + gradient_accumulation_steps = batch_size // micro_batch_size + shared.tokenizer.pad_token_id = 0 + shared.tokenizer.padding_side = "left" + + def tokenize(prompt): + result = shared.tokenizer(prompt, truncation=True, max_length=cutoff_len + 1, padding="max_length") + return { + "input_ids": result["input_ids"][:-1], + "attention_mask": result["attention_mask"][:-1], + } + + # == Prep the dataset, format, etc == + if raw_text_file not in ['None', '']: + logging.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() + + tokens = shared.tokenizer.encode(raw_text) + del raw_text # Note: could be a gig for a large dataset, so delete redundant data as we go to be safe on RAM + tokens = list(split_chunks(tokens, cutoff_len - overlap_len)) + for i in range(1, len(tokens)): + tokens[i] = tokens[i - 1][-overlap_len:] + tokens[i] + + text_chunks = [shared.tokenizer.decode(x) for x in tokens] + del tokens + if newline_favor_len > 0: + text_chunks = [cut_chunk_for_newline(x, newline_favor_len) for x in text_chunks] + + train_data = Dataset.from_list([tokenize(x) for x in text_chunks]) + del text_chunks + eval_data = None + + else: + if dataset in ['None', '']: + yield "**Missing dataset choice input, cannot continue.**" + return + + if format in ['None', '']: + yield "**Missing format choice input, cannot continue.**" + return + + with open(clean_path('training/formats', f'{format}.json'), 'r', encoding='utf-8') as formatFile: + format_data: dict[str, str] = json.load(formatFile) + + def generate_prompt(data_point: dict[str, str]): + for options, data in format_data.items(): + if set(options.split(',')) == set(x[0] for x in data_point.items() if (x[1] is not None and len(x[1].strip()) > 0)): + for key, val in data_point.items(): + if val is not None: + data = data.replace(f'%{key}%', val) + return data + raise RuntimeError(f'Data-point "{data_point}" has no keyset match within format "{list(format_data.keys())}"') + + def generate_and_tokenize_prompt(data_point): + prompt = generate_prompt(data_point) + return tokenize(prompt) + + logging.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) + + if eval_dataset == 'None': + eval_data = None + else: + eval_data = load_dataset("json", data_files=clean_path('training/datasets', f'{eval_dataset}.json')) + eval_data = eval_data['train'].map(generate_and_tokenize_prompt) + + # == Start prepping the model itself == + if not hasattr(shared.model, 'lm_head') or hasattr(shared.model.lm_head, 'weight'): + logging.info("Getting model ready...") + prepare_model_for_int8_training(shared.model) + + logging.info("Prepping for training...") + config = LoraConfig( + r=lora_rank, + lora_alpha=lora_alpha, + target_modules=model_to_lora_modules[model_id], + lora_dropout=lora_dropout, + bias="none", + task_type="CAUSAL_LM" + ) + + try: + logging.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...") + state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin") + set_peft_model_state_dict(lora_model, state_dict_peft) + except: + yield traceback.format_exc() + return + + if shared.args.monkey_patch: + for n, m in lora_model.named_modules(): + if '4bit' in str(type(m)): + if m.is_v1_model: + m.zeros = m.zeros.half() + + m.scales = m.scales.half() + + class Tracked(): + def __init__(self): + self.current_steps = 0 + self.max_steps = 0 + self.did_save = False + + tracked = Tracked() + actual_save_steps = math.ceil(save_steps / gradient_accumulation_steps) + + class Callbacks(transformers.TrainerCallback): + def on_step_begin(self, args: transformers.TrainingArguments, state: transformers.TrainerState, control: transformers.TrainerControl, **kwargs): + tracked.current_steps = state.global_step * gradient_accumulation_steps + tracked.max_steps = state.max_steps * gradient_accumulation_steps + if WANT_INTERRUPT: + control.should_epoch_stop = True + 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}/") + + 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 + + trainer = transformers.Trainer( + model=lora_model, + train_dataset=train_data, + eval_dataset=eval_data, + args=transformers.TrainingArguments( + per_device_train_batch_size=micro_batch_size, + gradient_accumulation_steps=gradient_accumulation_steps, + warmup_steps=math.ceil(warmup_steps / gradient_accumulation_steps), + num_train_epochs=epochs, + learning_rate=actual_lr, + fp16=False if shared.args.cpu else True, + optim=optimizer, + logging_steps=5, + evaluation_strategy="steps" if eval_data is not None else "no", + eval_steps=math.ceil(eval_steps / gradient_accumulation_steps) if eval_data is not None else None, + save_strategy="no", + output_dir=lora_file_path, + lr_scheduler_type=lr_scheduler_type, + load_best_model_at_end=True if eval_data is not None else False, + # TODO: Enable multi-device support + ddp_find_unused_parameters=None, + no_cuda=shared.args.cpu + ), + data_collator=transformers.DataCollatorForLanguageModeling(shared.tokenizer, mlm=False), + callbacks=list([Callbacks()]) + ) + + lora_model.config.use_cache = False + + if torch.__version__ >= "2" and sys.platform != "win32": + lora_model = torch.compile(lora_model) + + # == 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) + + # == Main run and monitor loop == + logging.info("Starting training...") + yield "Starting..." + if WANT_INTERRUPT: + yield "Interrupted before start." + return + + def threaded_run(): + 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.") + tracked.did_save = True + + thread = threading.Thread(target=threaded_run) + thread.start() + last_step = 0 + start_time = time.perf_counter() + + while thread.is_alive(): + time.sleep(0.5) + if WANT_INTERRUPT: + yield "Interrupting, please wait... *(Run will stop after the current training step completes.)*" + + elif tracked.current_steps != last_step: + last_step = tracked.current_steps + time_elapsed = time.perf_counter() - start_time + if time_elapsed <= 0: + timer_info = "" + total_time_estimate = 999 + else: + its = tracked.current_steps / time_elapsed + if its > 1: + timer_info = f"`{its:.2f}` it/s" + else: + timer_info = f"`{1.0/its:.2f}` s/it" + + total_time_estimate = (1.0 / its) * (tracked.max_steps) + + yield f"Running... **{tracked.current_steps}** / **{tracked.max_steps}** ... {timer_info}, {format_time(time_elapsed)} / {format_time(total_time_estimate)} ... {format_time(total_time_estimate - time_elapsed)} remaining" + + # 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...") + lora_model.save_pretrained(lora_file_path) + + if WANT_INTERRUPT: + logging.info("Training interrupted.") + yield f"Interrupted. Incomplete LoRA saved to `{lora_file_path}`" + else: + logging.info("Training complete!") + yield f"Done! LoRA saved to `{lora_file_path}`" + + +def split_chunks(arr, step): + for i in range(0, len(arr), step): + yield arr[i:i + step] + + +def cut_chunk_for_newline(chunk: str, max_length: int): + if '\n' not in chunk: + return chunk + + first_newline = chunk.index('\n') + if first_newline < max_length: + chunk = chunk[first_newline + 1:] + + if '\n' not in chunk: + return chunk + + last_newline = chunk.rindex('\n') + if len(chunk) - last_newline < max_length: + chunk = chunk[:last_newline] + + return chunk + + +def format_time(seconds: float): + if seconds < 120: + return f"`{seconds:.0f}` seconds" + + minutes = seconds / 60 + if minutes < 120: + return f"`{minutes:.0f}` minutes" + + hours = minutes / 60 + return f"`{hours:.0f}` hours" diff --git a/text-generation-webui-main/modules/ui.py b/text-generation-webui-main/modules/ui.py new file mode 100644 index 0000000000000000000000000000000000000000..53f6a24748641ded787cfce74f97e88d87b88f8c --- /dev/null +++ b/text-generation-webui-main/modules/ui.py @@ -0,0 +1,97 @@ +from pathlib import Path + +import gradio as gr +import torch + +from modules import shared + +with open(Path(__file__).resolve().parent / '../css/main.css', 'r') as f: + css = f.read() +with open(Path(__file__).resolve().parent / '../css/chat.css', 'r') as f: + chat_css = f.read() +with open(Path(__file__).resolve().parent / '../css/main.js', 'r') as f: + main_js = f.read() +with open(Path(__file__).resolve().parent / '../css/chat.js', 'r') as f: + chat_js = f.read() + +refresh_symbol = '\U0001f504' # 🔄 +theme = gr.themes.Default( + font=['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'], + font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'], +).set( + border_color_primary='#c5c5d2', + button_large_padding='6px 12px', + body_text_color_subdued='#484848', + background_fill_secondary='#eaeaea' +) + + +def list_model_elements(): + elements = ['cpu_memory', 'auto_devices', 'disk', 'cpu', 'bf16', 'load_in_8bit', 'wbits', 'groupsize', 'model_type', 'pre_layer'] + for i in range(torch.cuda.device_count()): + elements.append(f'gpu_memory_{i}') + return elements + + +def list_interface_input_elements(chat=False): + elements = ['max_new_tokens', 'seed', 'temperature', 'top_p', 'top_k', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'no_repeat_ngram_size', 'min_length', 'do_sample', 'penalty_alpha', 'num_beams', 'length_penalty', 'early_stopping', 'add_bos_token', 'ban_eos_token', 'truncation_length', 'custom_stopping_strings', 'skip_special_tokens', 'preset_menu'] + if chat: + elements += ['name1', 'name2', 'greeting', 'context', 'turn_template', 'chat_prompt_size', 'chat_generation_attempts', 'stop_at_newline', 'mode', 'instruction_template', 'character_menu'] + + elements += list_model_elements() + return elements + + +def gather_interface_values(*args): + output = {} + for i, element in enumerate(shared.input_elements): + output[element] = args[i] + + shared.persistent_interface_state = output + return output + + +def apply_interface_values(state, use_persistent=False): + if use_persistent: + state = shared.persistent_interface_state + + elements = list_interface_input_elements(chat=shared.is_chat()) + if len(state) == 0: + return [gr.update() for k in elements] # Dummy, do nothing + else: + if use_persistent and 'mode' in state: + if state['mode'] == 'instruct': + return [state[k] if (k not in ['character_menu'] and k in state) else gr.update() for k in elements] + else: + return [state[k] if (k not in ['instruction_template'] and k in state) else gr.update() for k in elements] + else: + return [state[k] if k in state else gr.update() for k in elements] + + +class ToolButton(gr.Button, gr.components.FormComponent): + """Small button with single emoji as text, fits inside gradio forms""" + + def __init__(self, **kwargs): + super().__init__(variant="tool", **kwargs) + + def get_block_name(self): + return "button" + + +def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id): + def refresh(): + refresh_method() + args = refreshed_args() if callable(refreshed_args) else refreshed_args + + for k, v in args.items(): + setattr(refresh_component, k, v) + + return gr.update(**(args or {})) + + refresh_button = ToolButton(value=refresh_symbol, elem_id=elem_id) + refresh_button.click( + fn=refresh, + inputs=[], + outputs=[refresh_component] + ) + return refresh_button diff --git a/text-generation-webui-main/presets/Contrastive Search.txt b/text-generation-webui-main/presets/Contrastive Search.txt new file mode 100644 index 0000000000000000000000000000000000000000..832bc9caf9b744d9d9c728f88d887f012a56ba3e --- /dev/null +++ b/text-generation-webui-main/presets/Contrastive Search.txt @@ -0,0 +1,3 @@ +do_sample=False +penalty_alpha=0.6 +top_k=4 diff --git a/text-generation-webui-main/presets/Debug-deterministic.txt b/text-generation-webui-main/presets/Debug-deterministic.txt new file mode 100644 index 0000000000000000000000000000000000000000..6673b71c8164effc401a486055b7f9a021b2acfb --- /dev/null +++ b/text-generation-webui-main/presets/Debug-deterministic.txt @@ -0,0 +1 @@ +do_sample=False diff --git a/text-generation-webui-main/presets/Default.txt b/text-generation-webui-main/presets/Default.txt new file mode 100644 index 0000000000000000000000000000000000000000..d28ce62f0e36d1f7824fe40d6e40018c9d78ea21 --- /dev/null +++ b/text-generation-webui-main/presets/Default.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.5 +top_k=40 +temperature=0.7 +repetition_penalty=1.2 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/Kobold-Godlike.txt b/text-generation-webui-main/presets/Kobold-Godlike.txt new file mode 100644 index 0000000000000000000000000000000000000000..0ba5b794b6d0130a1fa1d918bda9a276f7d23367 --- /dev/null +++ b/text-generation-webui-main/presets/Kobold-Godlike.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.5 +top_k=0 +temperature=0.7 +repetition_penalty=1.1 +typical_p=0.19 diff --git a/text-generation-webui-main/presets/Kobold-Liminal Drift.txt b/text-generation-webui-main/presets/Kobold-Liminal Drift.txt new file mode 100644 index 0000000000000000000000000000000000000000..be4dd3bd7a70af2d4eb6c847bed6bedee5379dce --- /dev/null +++ b/text-generation-webui-main/presets/Kobold-Liminal Drift.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=1.0 +top_k=0 +temperature=0.66 +repetition_penalty=1.1 +typical_p=0.6 diff --git a/text-generation-webui-main/presets/LLaMA-Precise.txt b/text-generation-webui-main/presets/LLaMA-Precise.txt new file mode 100644 index 0000000000000000000000000000000000000000..8098b390a097fc9438a2a82ec2bdd58adb2a771b --- /dev/null +++ b/text-generation-webui-main/presets/LLaMA-Precise.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.1 +top_k=40 +temperature=0.7 +repetition_penalty=1.18 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/Naive.txt b/text-generation-webui-main/presets/Naive.txt new file mode 100644 index 0000000000000000000000000000000000000000..aa8c058224c533f4084e230f6bbf77b63d5e81ea --- /dev/null +++ b/text-generation-webui-main/presets/Naive.txt @@ -0,0 +1,4 @@ +do_sample=True +temperature=0.7 +top_p=0.85 +top_k=50 diff --git a/text-generation-webui-main/presets/NovelAI-Best Guess.txt b/text-generation-webui-main/presets/NovelAI-Best Guess.txt new file mode 100644 index 0000000000000000000000000000000000000000..db3fa75b2a11d7e29b108177f9894e82d1e52126 --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Best Guess.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.9 +top_k=100 +temperature=0.8 +repetition_penalty=1.15 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/NovelAI-Decadence.txt b/text-generation-webui-main/presets/NovelAI-Decadence.txt new file mode 100644 index 0000000000000000000000000000000000000000..d3109f3e3f3a021810d171a0b98f615766b57e4b --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Decadence.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=1.0 +top_k=100 +temperature=2 +repetition_penalty=1 +typical_p=0.97 diff --git a/text-generation-webui-main/presets/NovelAI-Genesis.txt b/text-generation-webui-main/presets/NovelAI-Genesis.txt new file mode 100644 index 0000000000000000000000000000000000000000..cc7376b3b981a260448a65cd3c00c7b3904308e2 --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Genesis.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.98 +top_k=0 +temperature=0.63 +repetition_penalty=1.05 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/NovelAI-Lycaenidae.txt b/text-generation-webui-main/presets/NovelAI-Lycaenidae.txt new file mode 100644 index 0000000000000000000000000000000000000000..0134569cef76bc0de6b3dc7885d94d9d9afdfd62 --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Lycaenidae.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.85 +top_k=12 +temperature=2 +repetition_penalty=1.15 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/NovelAI-Ouroboros.txt b/text-generation-webui-main/presets/NovelAI-Ouroboros.txt new file mode 100644 index 0000000000000000000000000000000000000000..1e944b54e78e1f63bd4bb6f56a717e0fec751c6b --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Ouroboros.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=1.0 +top_k=100 +temperature=1.07 +repetition_penalty=1.05 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/NovelAI-Pleasing Results.txt b/text-generation-webui-main/presets/NovelAI-Pleasing Results.txt new file mode 100644 index 0000000000000000000000000000000000000000..330114a25db6d194dbc8689bf5476a81f649cf64 --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Pleasing Results.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=1.0 +top_k=0 +temperature=0.44 +repetition_penalty=1.15 +typical_p=1.0 diff --git a/text-generation-webui-main/presets/NovelAI-Sphinx Moth.txt b/text-generation-webui-main/presets/NovelAI-Sphinx Moth.txt new file mode 100644 index 0000000000000000000000000000000000000000..bace1e24b5dcc64fdde99097930f41a991e91b8e --- /dev/null +++ b/text-generation-webui-main/presets/NovelAI-Sphinx Moth.txt @@ -0,0 +1,6 @@ +do_sample=True +top_p=0.18 +top_k=30 +temperature=2.0 +repetition_penalty=1.15 +typical_p=1.0 diff --git a/presets/Pygmalion.txt b/text-generation-webui-main/presets/NovelAI-Storywriter.txt similarity index 68% rename from presets/Pygmalion.txt rename to text-generation-webui-main/presets/NovelAI-Storywriter.txt index f8b2ca55304ce8243e26bd28ebc757e40354a0e9..2df5f8181458c642ed4691925ade3d542de5391c 100644 --- a/presets/Pygmalion.txt +++ b/text-generation-webui-main/presets/NovelAI-Storywriter.txt @@ -1,6 +1,6 @@ do_sample=True -top_p=0.9 +top_p=0.73 top_k=0 -temperature=0.5 +temperature=0.72 repetition_penalty=1.1 typical_p=1.0 diff --git a/text-generation-webui-main/presets/Verbose (Beam Search).txt b/text-generation-webui-main/presets/Verbose (Beam Search).txt new file mode 100644 index 0000000000000000000000000000000000000000..464a4a5f0dda62348fda2cbbba4a98036c744d5c --- /dev/null +++ b/text-generation-webui-main/presets/Verbose (Beam Search).txt @@ -0,0 +1,9 @@ +num_beams=10 +min_length=200 +length_penalty=1.4 +no_repeat_ngram_size=2 +early_stopping=True +temperature=0.7 +top_k=150 +top_p=0.92 +repetition_penalty=4.5 diff --git a/text-generation-webui-main/prompts/Alpaca-with-Input.txt b/text-generation-webui-main/prompts/Alpaca-with-Input.txt new file mode 100644 index 0000000000000000000000000000000000000000..56df0e285be9689ab1f8ea698ce748e6d1b02af2 --- /dev/null +++ b/text-generation-webui-main/prompts/Alpaca-with-Input.txt @@ -0,0 +1,10 @@ +Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. + +### Instruction: +Instruction + +### Input: +Input + +### Response: + diff --git a/text-generation-webui-main/prompts/Alpaca.txt b/text-generation-webui-main/prompts/Alpaca.txt new file mode 100644 index 0000000000000000000000000000000000000000..b9064a4ee9ccbe55c879da3c7671d6004e80d194 --- /dev/null +++ b/text-generation-webui-main/prompts/Alpaca.txt @@ -0,0 +1,7 @@ +Below is an instruction that describes a task. Write a response that appropriately completes the request. + +### Instruction: +Write a poem about the transformers Python library. + +### Response: + diff --git a/text-generation-webui-main/prompts/GPT-4chan.txt b/text-generation-webui-main/prompts/GPT-4chan.txt new file mode 100644 index 0000000000000000000000000000000000000000..1bc8c7f4613f982e3dfa367562a764cf5bd4c73b --- /dev/null +++ b/text-generation-webui-main/prompts/GPT-4chan.txt @@ -0,0 +1,6 @@ +----- +--- 865467536 +Hello, AI frens! +How are you doing on this fine day? +--- 865467537 + diff --git a/text-generation-webui-main/prompts/Open Assistant.txt b/text-generation-webui-main/prompts/Open Assistant.txt new file mode 100644 index 0000000000000000000000000000000000000000..cf1ae4a2d0723afc8adee24fa496bafeaba0f492 --- /dev/null +++ b/text-generation-webui-main/prompts/Open Assistant.txt @@ -0,0 +1 @@ +<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|> diff --git a/text-generation-webui-main/prompts/QA.txt b/text-generation-webui-main/prompts/QA.txt new file mode 100644 index 0000000000000000000000000000000000000000..32b0e2350f3c0a7f447dcd1aba11d6ae2247e5a8 --- /dev/null +++ b/text-generation-webui-main/prompts/QA.txt @@ -0,0 +1,4 @@ +Common sense questions and answers + +Question: +Factual answer: diff --git a/text-generation-webui-main/requirements.txt b/text-generation-webui-main/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..e7ee1ab953344d933c8f05a7569fa2a913bdb185 --- /dev/null +++ b/text-generation-webui-main/requirements.txt @@ -0,0 +1,20 @@ +accelerate==0.18.0 +colorama +datasets +flexgen==0.1.7 +gradio==3.25.0 +markdown +numpy +pandas +Pillow>=9.5.0 +pyyaml +requests +rwkv==0.7.3 +safetensors==0.3.1 +sentencepiece +tqdm +git+https://github.com/huggingface/peft +transformers==4.28.1 +bitsandbytes==0.38.1; platform_system != "Windows" +llama-cpp-python==0.1.41; platform_system != "Windows" +https://github.com/abetlen/llama-cpp-python/releases/download/v0.1.41/llama_cpp_python-0.1.41-cp310-cp310-win_amd64.whl; platform_system == "Windows" diff --git a/text-generation-webui-main/server.py b/text-generation-webui-main/server.py new file mode 100644 index 0000000000000000000000000000000000000000..e940d3669b42bd82f52668cea3f1052f0f7eee23 --- /dev/null +++ b/text-generation-webui-main/server.py @@ -0,0 +1,934 @@ +import logging +import os +import requests +import warnings +import modules.logging_colors + +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 :)') + kwargs.setdefault('allow_redirects', True) + return requests.api.request('get', 'http://127.0.0.1/', **kwargs) + +original_get = requests.get +requests.get = my_get +import gradio as gr +requests.get = original_get + +import matplotlib +matplotlib.use('Agg') # This fixes LaTeX rendering on some systems + +import importlib +import io +import json +import math +import os +import re +import sys +import time +import traceback +import zipfile +from datetime import datetime +from functools import partial +from pathlib import Path + +import psutil +import torch +import yaml +from PIL import Image +import modules.extensions as extensions_module +from modules import chat, shared, training, ui +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 (encode, generate_reply, + stop_everything_event) + + +def get_available_models(): + if shared.args.flexgen: + return sorted([re.sub('-np$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if item.name.endswith('-np')], key=str.lower) + else: + return sorted([re.sub('.pth$', '', item.name) for item in list(Path(f'{shared.args.model_dir}/').glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json', '.yaml'))], key=str.lower) + + +def get_available_presets(): + return sorted(set((k.stem for k in Path('presets').glob('*.txt'))), key=str.lower) + + +def get_available_prompts(): + prompts = [] + prompts += sorted(set((k.stem for k in Path('prompts').glob('[0-9]*.txt'))), key=str.lower, reverse=True) + prompts += sorted(set((k.stem for k in Path('prompts').glob('*.txt'))), key=str.lower) + prompts += ['None'] + return prompts + + +def get_available_characters(): + paths = (x for x in Path('characters').iterdir() if x.suffix in ('.json', '.yaml', '.yml')) + return ['None'] + sorted(set((k.stem for k in paths if k.stem != "instruction-following")), key=str.lower) + + +def get_available_instruction_templates(): + path = "characters/instruction-following" + paths = [] + if os.path.exists(path): + paths = (x for x in Path(path).iterdir() if x.suffix in ('.json', '.yaml', '.yml')) + return ['None'] + sorted(set((k.stem for k in paths)), key=str.lower) + + +def get_available_extensions(): + return sorted(set(map(lambda x: x.parts[1], Path('extensions').glob('*/script.py'))), key=str.lower) + + +def get_available_softprompts(): + return ['None'] + sorted(set((k.stem for k in Path('softprompts').glob('*.zip'))), key=str.lower) + + +def get_available_loras(): + return sorted([item.name for item in list(Path(shared.args.lora_dir).glob('*')) if not item.name.endswith(('.txt', '-np', '.pt', '.json'))], key=str.lower) + + +def load_model_wrapper(selected_model): + try: + yield f"Loading {selected_model}..." + shared.model_name = selected_model + unload_model() + if selected_model != '': + shared.model, shared.tokenizer = load_model(shared.model_name) + + yield f"Successfully loaded {selected_model}" + except: + yield traceback.format_exc() + + +def load_lora_wrapper(selected_loras): + yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras))) + add_lora_to_model(selected_loras) + yield ("Successfuly applied the LoRAs") + + +def load_preset_values(preset_menu, state, return_dict=False): + generate_params = { + 'do_sample': True, + 'temperature': 1, + 'top_p': 1, + 'typical_p': 1, + 'repetition_penalty': 1, + 'encoder_repetition_penalty': 1, + 'top_k': 50, + 'num_beams': 1, + 'penalty_alpha': 0, + 'min_length': 0, + 'length_penalty': 1, + 'no_repeat_ngram_size': 0, + 'early_stopping': False, + } + with open(Path(f'presets/{preset_menu}.txt'), 'r') as infile: + preset = infile.read() + for i in preset.splitlines(): + i = i.rstrip(',').strip().split('=') + if len(i) == 2 and i[0].strip() != 'tokens': + generate_params[i[0].strip()] = eval(i[1].strip()) + generate_params['temperature'] = min(1.99, generate_params['temperature']) + + if return_dict: + return generate_params + else: + state.update(generate_params) + return state, *[generate_params[k] for k in ['do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']] + + +def upload_soft_prompt(file): + with zipfile.ZipFile(io.BytesIO(file)) as zf: + zf.extract('meta.json') + j = json.loads(open('meta.json', 'r').read()) + name = j['name'] + Path('meta.json').unlink() + + with open(Path(f'softprompts/{name}.zip'), 'wb') as f: + f.write(file) + + return name + + +def save_prompt(text): + fname = f"{datetime.now().strftime('%Y-%m-%d-%H%M%S')}.txt" + with open(Path(f'prompts/{fname}'), 'w', encoding='utf-8') as f: + f.write(text) + return f"Saved to prompts/{fname}" + + +def load_prompt(fname): + if fname in ['None', '']: + return '' + else: + with open(Path(f'prompts/{fname}.txt'), 'r', encoding='utf-8') as f: + text = f.read() + if text[-1] == '\n': + text = text[:-1] + return text + + +def count_tokens(text): + tokens = len(encode(text)[0]) + return f'{tokens} tokens in the input.' + + +def download_model_wrapper(repo_id): + try: + downloader = importlib.import_module("download-model") + + model = repo_id + branch = "main" + check = False + + yield ("Cleaning up the model/branch names") + model, branch = downloader.sanitize_model_and_branch_names(model, branch) + + yield ("Getting the download links from Hugging Face") + links, sha256, is_lora = downloader.get_download_links_from_huggingface(model, branch, text_only=False) + + yield ("Getting the output folder") + output_folder = downloader.get_output_folder(model, branch, is_lora) + + if check: + yield ("Checking previously downloaded files") + downloader.check_model_files(model, branch, links, sha256, output_folder) + else: + yield (f"Downloading files to {output_folder}") + downloader.download_model_files(model, branch, links, sha256, output_folder, threads=1) + yield ("Done!") + except: + yield traceback.format_exc() + + +# Update the command-line arguments based on the interface values +def update_model_parameters(state, initial=False): + elements = ui.list_model_elements() # the names of the parameters + gpu_memories = [] + + for i, element in enumerate(elements): + if element not in state: + continue + + value = state[element] + if element.startswith('gpu_memory'): + gpu_memories.append(value) + continue + + if initial and vars(shared.args)[element] != vars(shared.args_defaults)[element]: + continue + + # Setting null defaults + if element in ['wbits', 'groupsize', 'model_type'] and value == 'None': + value = vars(shared.args_defaults)[element] + elif element in ['cpu_memory'] and value == 0: + value = vars(shared.args_defaults)[element] + + # Making some simple conversions + if element in ['wbits', 'groupsize', 'pre_layer']: + value = int(value) + elif element == 'cpu_memory' and value is not None: + value = f"{value}MiB" + + setattr(shared.args, element, value) + + found_positive = False + for i in gpu_memories: + if i > 0: + found_positive = True + break + + if not (initial and vars(shared.args)['gpu_memory'] != vars(shared.args_defaults)['gpu_memory']): + if found_positive: + shared.args.gpu_memory = [f"{i}MiB" for i in gpu_memories] + else: + shared.args.gpu_memory = None + + +def get_model_specific_settings(model): + settings = shared.model_config + model_settings = {} + + for pat in settings: + if re.match(pat.lower(), model.lower()): + for k in settings[pat]: + model_settings[k] = settings[pat][k] + + return model_settings + + +def load_model_specific_settings(model, state, return_dict=False): + model_settings = get_model_specific_settings(model) + for k in model_settings: + if k in state: + state[k] = model_settings[k] + + return state + + +def save_model_settings(model, state): + if model == 'None': + yield ("Not saving the settings because no model is loaded.") + return + + with Path(f'{shared.args.model_dir}/config-user.yaml') as p: + if p.exists(): + user_config = yaml.safe_load(open(p, 'r').read()) + else: + user_config = {} + + if model not in user_config: + user_config[model] = {} + + for k in ui.list_model_elements(): + user_config[model][k] = state[k] + + with open(p, 'w') as f: + f.write(yaml.dump(user_config)) + + yield (f"Settings for {model} saved to {p}") + + +def create_model_menus(): + # Finding the default values for the GPU and CPU memories + total_mem = [] + for i in range(torch.cuda.device_count()): + total_mem.append(math.floor(torch.cuda.get_device_properties(i).total_memory / (1024 * 1024))) + + default_gpu_mem = [] + if shared.args.gpu_memory is not None and len(shared.args.gpu_memory) > 0: + for i in shared.args.gpu_memory: + if 'mib' in i.lower(): + default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i))) + else: + default_gpu_mem.append(int(re.sub('[a-zA-Z ]', '', i)) * 1000) + while len(default_gpu_mem) < len(total_mem): + default_gpu_mem.append(0) + + total_cpu_mem = math.floor(psutil.virtual_memory().total / (1024 * 1024)) + if shared.args.cpu_memory is not None: + default_cpu_mem = re.sub('[a-zA-Z ]', '', shared.args.cpu_memory) + else: + default_cpu_mem = 0 + + with gr.Row(): + with gr.Column(): + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['model_menu'] = gr.Dropdown(choices=get_available_models(), value=shared.model_name, label='Model') + ui.create_refresh_button(shared.gradio['model_menu'], lambda: None, lambda: {'choices': get_available_models()}, 'refresh-button') + + with gr.Column(): + with gr.Row(): + shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=get_available_loras(), value=shared.lora_names, label='LoRA(s)') + ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras(), 'value': shared.lora_names}, 'refresh-button') + + with gr.Column(): + with gr.Row(): + shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs') + with gr.Row(): + unload = gr.Button("Unload the model") + reload = gr.Button("Reload the model") + save_settings = gr.Button("Save settings for this model") + + with gr.Row(): + with gr.Column(): + with gr.Box(): + gr.Markdown('Transformers parameters') + with gr.Row(): + with gr.Column(): + for i in range(len(total_mem)): + shared.gradio[f'gpu_memory_{i}'] = gr.Slider(label=f"gpu-memory in MiB for device :{i}", maximum=total_mem[i], value=default_gpu_mem[i]) + shared.gradio['cpu_memory'] = gr.Slider(label="cpu-memory in MiB", maximum=total_cpu_mem, value=default_cpu_mem) + + with gr.Column(): + shared.gradio['auto_devices'] = gr.Checkbox(label="auto-devices", value=shared.args.auto_devices) + shared.gradio['disk'] = gr.Checkbox(label="disk", value=shared.args.disk) + shared.gradio['cpu'] = gr.Checkbox(label="cpu", value=shared.args.cpu) + shared.gradio['bf16'] = gr.Checkbox(label="bf16", value=shared.args.bf16) + shared.gradio['load_in_8bit'] = gr.Checkbox(label="load-in-8bit", value=shared.args.load_in_8bit) + + with gr.Column(): + with gr.Box(): + gr.Markdown('GPTQ parameters') + with gr.Row(): + with gr.Column(): + shared.gradio['wbits'] = gr.Dropdown(label="wbits", choices=["None", 1, 2, 3, 4, 8], value=shared.args.wbits if shared.args.wbits > 0 else "None") + shared.gradio['groupsize'] = gr.Dropdown(label="groupsize", choices=["None", 32, 64, 128, 1024], value=shared.args.groupsize if shared.args.groupsize > 0 else "None") + + with gr.Column(): + shared.gradio['model_type'] = gr.Dropdown(label="model_type", choices=["None", "llama", "opt", "gptj"], value=shared.args.model_type or "None") + shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer) + + with gr.Row(): + with gr.Column(): + shared.gradio['custom_model_menu'] = gr.Textbox(label="Download custom model or LoRA", info="Enter Hugging Face username/model path, e.g: facebook/galactica-125m") + shared.gradio['download_model_button'] = gr.Button("Download") + + with gr.Column(): + 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 + # with the model defaults (if any), and then the model is loaded + shared.gradio['model_menu'].change( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + load_model_specific_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['interface_state']).then( + ui.apply_interface_values, shared.gradio['interface_state'], [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False).then( + update_model_parameters, shared.gradio['interface_state'], None).then( + load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True) + + unload.click( + unload_model, None, None).then( + lambda: "Model unloaded", None, shared.gradio['model_status']) + + reload.click( + unload_model, None, None).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + update_model_parameters, shared.gradio['interface_state'], None).then( + load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=False) + + save_settings.click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + 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) + + +def create_settings_menus(default_preset): + + generate_params = load_preset_values(default_preset if not shared.args.flexgen else 'Naive', {}, return_dict=True) + + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['preset_menu'] = gr.Dropdown(choices=get_available_presets(), value=default_preset if not shared.args.flexgen else 'Naive', label='Generation parameters preset') + ui.create_refresh_button(shared.gradio['preset_menu'], lambda: None, lambda: {'choices': get_available_presets()}, 'refresh-button') + with gr.Column(): + shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)') + + with gr.Row(): + with gr.Column(): + with gr.Box(): + gr.Markdown('Custom generation parameters ([click here to view technical documentation](https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig))') + with gr.Row(): + with gr.Column(): + shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature', info='Primary factor to control randomness of outputs. 0 = deterministic (only the most likely token is used). Higher value = more randomness.') + shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p', info='If not set to 1, select tokens with probabilities adding up to less than this number. Higher value = higher range of possible random results.') + shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k', info='Similar to top_p, but select instead only the top_k most likely tokens. Higher value = higher range of possible random results.') + shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p', info='If not set to 1, select only tokens that are at least this much more likely to appear than random tokens, given the prior text.') + with gr.Column(): + shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty', info='Exponential penalty factor for repeating prior tokens. 1 means no penalty, higher value = less repetition, lower value = more repetition.') + shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'], step=0.01, label='encoder_repetition_penalty', info='Also known as the "Hallucinations filter". Used to penalize tokens that are *not* in the prior text. Higher value = more likely to stay in context, lower value = more likely to diverge.') + shared.gradio['no_repeat_ngram_size'] = gr.Slider(0, 20, step=1, value=generate_params['no_repeat_ngram_size'], label='no_repeat_ngram_size', info='If not set to 0, specifies the length of token sets that are completely blocked from repeating at all. Higher values = blocks larger phrases, lower values = blocks words or letters from repeating. Only 0 or high values are a good idea in most cases.') + shared.gradio['min_length'] = gr.Slider(0, 2000, step=1, value=generate_params['min_length'], label='min_length', info='Minimum generation length in tokens.') + shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample') + with gr.Column(): + with gr.Box(): + gr.Markdown('Contrastive search') + shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha') + + gr.Markdown('Beam search (uses a lot of VRAM)') + with gr.Row(): + with gr.Column(): + shared.gradio['num_beams'] = gr.Slider(1, 20, step=1, value=generate_params['num_beams'], label='num_beams') + shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty') + with gr.Column(): + shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping') + + with gr.Box(): + with gr.Row(): + with gr.Column(): + shared.gradio['truncation_length'] = gr.Slider(value=shared.settings['truncation_length'], minimum=shared.settings['truncation_length_min'], maximum=shared.settings['truncation_length_max'], step=1, label='Truncate the prompt up to this length', info='The leftmost tokens are removed if the prompt exceeds this length. Most models require this to be at most 2048.') + shared.gradio['custom_stopping_strings'] = gr.Textbox(lines=1, value=shared.settings["custom_stopping_strings"] or None, label='Custom stopping strings', info='In addition to the defaults. Written between "" and separated by commas. For instance: "\\nYour Assistant:", "\\nThe assistant:"') + with gr.Column(): + shared.gradio['add_bos_token'] = gr.Checkbox(value=shared.settings['add_bos_token'], label='Add the bos_token to the beginning of prompts', info='Disabling this can make the replies more creative.') + shared.gradio['ban_eos_token'] = gr.Checkbox(value=shared.settings['ban_eos_token'], label='Ban the eos_token', info='Forces the model to never end the generation prematurely.') + + shared.gradio['skip_special_tokens'] = gr.Checkbox(value=shared.settings['skip_special_tokens'], label='Skip special tokens', info='Some specific models need this unset.') + + with gr.Accordion('Soft prompt', open=False): + with gr.Row(): + shared.gradio['softprompts_menu'] = gr.Dropdown(choices=get_available_softprompts(), value='None', label='Soft prompt') + ui.create_refresh_button(shared.gradio['softprompts_menu'], lambda: None, lambda: {'choices': get_available_softprompts()}, 'refresh-button') + + gr.Markdown('Upload a soft prompt (.zip format):') + with gr.Row(): + shared.gradio['upload_softprompt'] = gr.File(type='binary', file_types=['.zip']) + + shared.gradio['preset_menu'].change(load_preset_values, [shared.gradio[k] for k in ['preset_menu', 'interface_state']], [shared.gradio[k] for k in ['interface_state', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping']]) + shared.gradio['softprompts_menu'].change(load_soft_prompt, shared.gradio['softprompts_menu'], shared.gradio['softprompts_menu'], show_progress=True) + shared.gradio['upload_softprompt'].upload(upload_soft_prompt, shared.gradio['upload_softprompt'], shared.gradio['softprompts_menu']) + + +def set_interface_arguments(interface_mode, extensions, bool_active): + modes = ["default", "notebook", "chat", "cai_chat"] + cmd_list = vars(shared.args) + bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes] + + shared.args.extensions = extensions + for k in modes[1:]: + setattr(shared.args, k, False) + if interface_mode != "default": + setattr(shared.args, interface_mode, True) + + for k in bool_list: + setattr(shared.args, k, False) + for k in bool_active: + setattr(shared.args, k, True) + + shared.need_restart = True + + +def create_interface(): + + # Defining some variables + gen_events = [] + default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')] + if len(shared.lora_names) == 1: + default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_names[0].lower())), 'default')]) + else: + default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]) + title = 'Text generation web UI' + + # Authentication variables + auth = None + if shared.args.gradio_auth_path is not None: + gradio_auth_creds = [] + with open(shared.args.gradio_auth_path, 'r', encoding="utf8") as file: + for line in file.readlines(): + gradio_auth_creds += [x.strip() for x in line.split(',') if x.strip()] + auth = [tuple(cred.split(':')) for cred in gradio_auth_creds] + + # Importing the extension files and executing their setup() functions + if shared.args.extensions is not None and len(shared.args.extensions) > 0: + extensions_module.load_extensions() + + with gr.Blocks(css=ui.css if not shared.is_chat() else ui.css + ui.chat_css, analytics_enabled=False, title=title, theme=ui.theme) as shared.gradio['interface']: + + # Create chat mode interface + if shared.is_chat(): + shared.input_elements = ui.list_interface_input_elements(chat=True) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['Chat input'] = gr.State() + + with gr.Tab('Text generation', elem_id='main'): + shared.gradio['display'] = gr.HTML(value=chat_html_wrapper(shared.history['visible'], shared.settings['name1'], shared.settings['name2'], 'cai-chat')) + shared.gradio['textbox'] = gr.Textbox(label='Input') + with gr.Row(): + shared.gradio['Stop'] = gr.Button('Stop', elem_id='stop') + shared.gradio['Generate'] = gr.Button('Generate', elem_id='Generate', variant='primary') + shared.gradio['Continue'] = gr.Button('Continue') + + with gr.Row(): + shared.gradio['Copy last reply'] = gr.Button('Copy last reply') + shared.gradio['Regenerate'] = gr.Button('Regenerate') + shared.gradio['Replace last reply'] = gr.Button('Replace last reply') + + with gr.Row(): + shared.gradio['Impersonate'] = gr.Button('Impersonate') + shared.gradio['Send dummy message'] = gr.Button('Send dummy message') + shared.gradio['Send dummy reply'] = gr.Button('Send dummy reply') + + with gr.Row(): + shared.gradio['Remove last'] = gr.Button('Remove last') + shared.gradio['Clear history'] = gr.Button('Clear history') + shared.gradio['Clear history-confirm'] = gr.Button('Confirm', variant='stop', visible=False) + shared.gradio['Clear history-cancel'] = gr.Button('Cancel', visible=False) + + shared.gradio['mode'] = gr.Radio(choices=['cai-chat', 'chat', 'instruct'], value=shared.settings['mode'], label='Mode') + shared.gradio['instruction_template'] = gr.Dropdown(choices=get_available_instruction_templates(), label='Instruction template', value='None', visible=shared.settings['mode'] == 'instruct', info='Change this according to the model/LoRA that you are using.') + + with gr.Tab('Character', elem_id='chat-settings'): + with gr.Row(): + with gr.Column(scale=8): + shared.gradio['name1'] = gr.Textbox(value=shared.settings['name1'], lines=1, label='Your name') + shared.gradio['name2'] = gr.Textbox(value=shared.settings['name2'], lines=1, label='Character\'s name') + shared.gradio['greeting'] = gr.Textbox(value=shared.settings['greeting'], lines=4, label='Greeting') + shared.gradio['context'] = gr.Textbox(value=shared.settings['context'], lines=4, label='Context') + shared.gradio['turn_template'] = gr.Textbox(value=shared.settings['turn_template'], lines=1, label='Turn template', info='Used to precisely define the placement of spaces and new line characters in instruction prompts.') + + with gr.Column(scale=1): + shared.gradio['character_picture'] = gr.Image(label='Character picture', type='pil') + shared.gradio['your_picture'] = gr.Image(label='Your picture', type='pil', value=Image.open(Path('cache/pfp_me.png')) if Path('cache/pfp_me.png').exists() else None) + + with gr.Row(): + shared.gradio['character_menu'] = gr.Dropdown(choices=get_available_characters(), label='Character', elem_id='character-menu') + ui.create_refresh_button(shared.gradio['character_menu'], lambda: None, lambda: {'choices': get_available_characters()}, 'refresh-button') + + with gr.Row(): + with gr.Tab('Chat history'): + with gr.Row(): + with gr.Column(): + gr.Markdown('Upload') + shared.gradio['upload_chat_history'] = gr.File(type='binary', file_types=['.json', '.txt']) + + with gr.Column(): + gr.Markdown('Download') + shared.gradio['download'] = gr.File() + shared.gradio['download_button'] = gr.Button(value='Click me') + + with gr.Tab('Upload character'): + gr.Markdown('# JSON format') + with gr.Row(): + with gr.Column(): + gr.Markdown('1. Select the JSON file') + shared.gradio['upload_json'] = gr.File(type='binary', file_types=['.json']) + + with gr.Column(): + gr.Markdown('2. Select your character\'s profile picture (optional)') + shared.gradio['upload_img_bot'] = gr.File(type='binary', file_types=['image']) + + shared.gradio['Upload character'] = gr.Button(value='Submit') + gr.Markdown('# TavernAI PNG format') + shared.gradio['upload_img_tavern'] = gr.File(type='binary', file_types=['image']) + + with gr.Tab("Parameters", elem_id="parameters"): + with gr.Box(): + gr.Markdown("Chat parameters") + with gr.Row(): + with gr.Column(): + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + shared.gradio['chat_prompt_size'] = gr.Slider(minimum=shared.settings['chat_prompt_size_min'], maximum=shared.settings['chat_prompt_size_max'], step=1, label='Maximum prompt size in tokens', value=shared.settings['chat_prompt_size']) + + with gr.Column(): + shared.gradio['chat_generation_attempts'] = gr.Slider(minimum=shared.settings['chat_generation_attempts_min'], maximum=shared.settings['chat_generation_attempts_max'], value=shared.settings['chat_generation_attempts'], step=1, label='Generation attempts (for longer replies)') + shared.gradio['stop_at_newline'] = gr.Checkbox(value=shared.settings['stop_at_newline'], label='Stop generating at new line character') + + create_settings_menus(default_preset) + + # Create notebook mode interface + elif shared.args.notebook: + shared.input_elements = ui.list_interface_input_elements(chat=False) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['last_input'] = gr.State('') + with gr.Tab("Text generation", elem_id="main"): + with gr.Row(): + with gr.Column(scale=4): + with gr.Tab('Raw'): + shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox", lines=27) + + with gr.Tab('Markdown'): + shared.gradio['markdown'] = gr.Markdown() + + with gr.Tab('HTML'): + shared.gradio['html'] = gr.HTML() + + with gr.Row(): + shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") + shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") + shared.gradio['Undo'] = gr.Button('Undo', elem_classes="small-button") + shared.gradio['Regenerate'] = gr.Button('Regenerate', elem_classes="small-button") + + with gr.Column(scale=1): + gr.HTML('
') + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + with gr.Row(): + shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt') + ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': get_available_prompts()}, 'refresh-button') + + shared.gradio['save_prompt'] = gr.Button('Save prompt') + shared.gradio['count_tokens'] = gr.Button('Count tokens') + shared.gradio['status'] = gr.Markdown('') + + with gr.Tab("Parameters", elem_id="parameters"): + create_settings_menus(default_preset) + + # Create default mode interface + else: + shared.input_elements = ui.list_interface_input_elements(chat=False) + shared.gradio['interface_state'] = gr.State({k: None for k in shared.input_elements}) + shared.gradio['last_input'] = gr.State('') + with gr.Tab("Text generation", elem_id="main"): + with gr.Row(): + with gr.Column(): + shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_classes="textbox_default", lines=27, label='Input') + shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens']) + with gr.Row(): + shared.gradio['Generate'] = gr.Button('Generate', variant='primary', elem_classes="small-button") + shared.gradio['Stop'] = gr.Button('Stop', elem_classes="small-button") + shared.gradio['Continue'] = gr.Button('Continue', elem_classes="small-button") + shared.gradio['save_prompt'] = gr.Button('Save prompt', elem_classes="small-button") + shared.gradio['count_tokens'] = gr.Button('Count tokens', elem_classes="small-button") + + with gr.Row(): + with gr.Column(): + with gr.Row(): + shared.gradio['prompt_menu'] = gr.Dropdown(choices=get_available_prompts(), value='None', label='Prompt') + ui.create_refresh_button(shared.gradio['prompt_menu'], lambda: None, lambda: {'choices': get_available_prompts()}, 'refresh-button') + + with gr.Column(): + shared.gradio['status'] = gr.Markdown('') + + with gr.Column(): + with gr.Tab('Raw'): + shared.gradio['output_textbox'] = gr.Textbox(elem_classes="textbox_default_output", lines=27, label='Output') + + with gr.Tab('Markdown'): + shared.gradio['markdown'] = gr.Markdown() + + with gr.Tab('HTML'): + shared.gradio['html'] = gr.HTML() + + with gr.Tab("Parameters", elem_id="parameters"): + create_settings_menus(default_preset) + + # Model tab + with gr.Tab("Model", elem_id="model-tab"): + create_model_menus() + + # Training tab + with gr.Tab("Training", elem_id="training-tab"): + training.create_train_interface() + + # Interface mode tab + with gr.Tab("Interface mode", elem_id="interface-mode"): + modes = ["default", "notebook", "chat", "cai_chat"] + current_mode = "default" + for mode in modes[1:]: + if getattr(shared.args, mode): + current_mode = mode + break + cmd_list = vars(shared.args) + bool_list = [k for k in cmd_list if type(cmd_list[k]) is bool and k not in modes + ui.list_model_elements()] + bool_active = [k for k in bool_list if vars(shared.args)[k]] + + gr.Markdown("*Experimental*") + shared.gradio['interface_modes_menu'] = gr.Dropdown(choices=modes, value=current_mode, label="Mode") + shared.gradio['extensions_menu'] = gr.CheckboxGroup(choices=get_available_extensions(), value=shared.args.extensions, label="Available extensions") + shared.gradio['bool_menu'] = gr.CheckboxGroup(choices=bool_list, value=bool_active, label="Boolean command-line flags") + shared.gradio['reset_interface'] = gr.Button("Apply and restart the interface") + + # Reset interface event + shared.gradio['reset_interface'].click( + set_interface_arguments, [shared.gradio[k] for k in ['interface_modes_menu', 'extensions_menu', 'bool_menu']], None).then( + lambda: None, None, None, _js='() => {document.body.innerHTML=\'

Reloading...

\'; setTimeout(function(){location.reload()},2500); return []}') + + # chat mode event handlers + if shared.is_chat(): + shared.input_params = [shared.gradio[k] for k in ['Chat input', 'interface_state']] + clear_arr = [shared.gradio[k] for k in ['Clear history-confirm', 'Clear history', 'Clear history-cancel']] + reload_inputs = [shared.gradio[k] for k in ['name1', 'name2', 'mode']] + + gen_events.append(shared.gradio['Generate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( + chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['textbox'].submit( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + lambda x: (x, ''), shared.gradio['textbox'], [shared.gradio['Chat input'], shared.gradio['textbox']], show_progress=False).then( + chat.cai_chatbot_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Regenerate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Continue'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + chat.continue_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + ) + + gen_events.append(shared.gradio['Impersonate'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream) + ) + + shared.gradio['Replace last reply'].click( + chat.replace_last_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( + lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + + shared.gradio['Send dummy message'].click( + chat.send_dummy_message, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( + lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + + shared.gradio['Send dummy reply'].click( + chat.send_dummy_reply, [shared.gradio[k] for k in ['textbox', 'name1', 'name2', 'mode']], shared.gradio['display'], show_progress=shared.args.no_stream).then( + lambda x: '', shared.gradio['textbox'], shared.gradio['textbox'], show_progress=False).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + + shared.gradio['Clear history-confirm'].click( + lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr).then( + chat.clear_chat_log, [shared.gradio[k] for k in ['name1', 'name2', 'greeting', 'mode']], shared.gradio['display']).then( + chat.save_history, shared.gradio['mode'], None, show_progress=False) + + shared.gradio['Stop'].click( + stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None).then( + chat.redraw_html, reload_inputs, shared.gradio['display']) + + shared.gradio['mode'].change( + lambda x: gr.update(visible=x == 'instruct'), shared.gradio['mode'], shared.gradio['instruction_template']).then( + lambda x: gr.update(interactive=x != 'instruct'), shared.gradio['mode'], shared.gradio['character_menu']).then( + chat.redraw_html, reload_inputs, shared.gradio['display']) + + shared.gradio['instruction_template'].change( + chat.load_character, [shared.gradio[k] for k in ['instruction_template', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'turn_template', 'display']]).then( + chat.redraw_html, reload_inputs, shared.gradio['display']) + + shared.gradio['upload_chat_history'].upload( + chat.load_history, [shared.gradio[k] for k in ['upload_chat_history', 'name1', 'name2']], None).then( + chat.redraw_html, reload_inputs, shared.gradio['display']) + + shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, None, shared.gradio['textbox'], show_progress=shared.args.no_stream) + shared.gradio['Clear history'].click(lambda: [gr.update(visible=True), gr.update(visible=False), gr.update(visible=True)], None, clear_arr) + shared.gradio['Clear history-cancel'].click(lambda: [gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)], None, clear_arr) + shared.gradio['Remove last'].click(chat.remove_last_message, [shared.gradio[k] for k in ['name1', 'name2', 'mode']], [shared.gradio['display'], shared.gradio['textbox']], show_progress=False) + shared.gradio['download_button'].click(lambda x: chat.save_history(x, timestamp=True), shared.gradio['mode'], shared.gradio['download']) + shared.gradio['Upload character'].click(chat.upload_character, [shared.gradio['upload_json'], shared.gradio['upload_img_bot']], [shared.gradio['character_menu']]) + shared.gradio['character_menu'].change(chat.load_character, [shared.gradio[k] for k in ['character_menu', 'name1', 'name2', 'mode']], [shared.gradio[k] for k in ['name1', 'name2', 'character_picture', 'greeting', 'context', 'turn_template', 'display']]) + shared.gradio['upload_img_tavern'].upload(chat.upload_tavern_character, [shared.gradio['upload_img_tavern'], shared.gradio['name1'], shared.gradio['name2']], [shared.gradio['character_menu']]) + shared.gradio['your_picture'].change(chat.upload_your_profile_picture, [shared.gradio[k] for k in ['your_picture', 'name1', 'name2', 'mode']], shared.gradio['display']) + shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js+ui.chat_js}}}") + + # notebook/default modes event handlers + else: + shared.input_params = [shared.gradio[k] for k in ['textbox', 'interface_state']] + if shared.args.notebook: + output_params = [shared.gradio[k] for k in ['textbox', 'markdown', 'html']] + else: + output_params = [shared.gradio[k] for k in ['output_textbox', 'markdown', 'html']] + + gen_events.append(shared.gradio['Generate'].click( + lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + + gen_events.append(shared.gradio['textbox'].submit( + lambda x: x, shared.gradio['textbox'], shared.gradio['last_input']).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + + if shared.args.notebook: + shared.gradio['Undo'].click(lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False) + gen_events.append(shared.gradio['Regenerate'].click( + lambda x: x, shared.gradio['last_input'], shared.gradio['textbox'], show_progress=False).then( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply, shared.input_params, output_params, show_progress=shared.args.no_stream) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[0]; element.scrollTop = element.scrollHeight}") + ) + else: + gen_events.append(shared.gradio['Continue'].click( + ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then( + generate_reply, [shared.gradio['output_textbox']] + shared.input_params[1:], output_params, show_progress=shared.args.no_stream) # .then( + # None, None, None, _js="() => {element = document.getElementsByTagName('textarea')[1]; element.scrollTop = element.scrollHeight}") + ) + + shared.gradio['Stop'].click(stop_everything_event, None, None, queue=False, cancels=gen_events if shared.args.no_stream else None) + shared.gradio['prompt_menu'].change(load_prompt, shared.gradio['prompt_menu'], shared.gradio['textbox'], show_progress=False) + shared.gradio['save_prompt'].click(save_prompt, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) + shared.gradio['count_tokens'].click(count_tokens, shared.gradio['textbox'], shared.gradio['status'], show_progress=False) + shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.main_js}}}") + + shared.gradio['interface'].load(partial(ui.apply_interface_values, {}, use_persistent=True), None, [shared.gradio[k] for k in ui.list_interface_input_elements(chat=shared.is_chat())], show_progress=False) + # Extensions block + if shared.args.extensions is not None: + extensions_module.create_extensions_block() + + # Launch the interface + shared.gradio['interface'].queue() + if shared.args.listen: + shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_name=shared.args.listen_host or '0.0.0.0', server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) + else: + shared.gradio['interface'].launch(prevent_thread_lock=True, share=shared.args.share, server_port=shared.args.listen_port, inbrowser=shared.args.auto_launch, auth=auth) + + +if __name__ == "__main__": + # Loading custom settings + settings_file = None + if shared.args.settings is not None and Path(shared.args.settings).exists(): + settings_file = Path(shared.args.settings) + elif Path('settings.json').exists(): + settings_file = Path('settings.json') + if settings_file is not None: + logging.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] + + # Default extensions + extensions_module.available_extensions = get_available_extensions() + if shared.is_chat(): + for extension in shared.settings['chat_default_extensions']: + shared.args.extensions = shared.args.extensions or [] + if extension not in shared.args.extensions: + shared.args.extensions.append(extension) + else: + for extension in shared.settings['default_extensions']: + shared.args.extensions = shared.args.extensions or [] + if extension not in shared.args.extensions: + shared.args.extensions.append(extension) + + available_models = get_available_models() + + # Model defined through --model + if shared.args.model is not None: + shared.model_name = shared.args.model + + # Only one model is available + elif len(available_models) == 1: + shared.model_name = available_models[0] + + # 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.') + sys.exit(0) + else: + print('The following models are available:\n') + for i, model in enumerate(available_models): + print(f'{i+1}. {model}') + + print(f'\nWhich one do you want to load? 1-{len(available_models)}\n') + i = int(input()) - 1 + print() + + shared.model_name = available_models[i] + + # If any model has been selected, load it + if shared.model_name != 'None': + model_settings = get_model_specific_settings(shared.model_name) + shared.settings.update(model_settings) # hijacking the interface defaults + update_model_parameters(model_settings, initial=True) # hijacking the command-line arguments + + # Load the model + shared.model, shared.tokenizer = load_model(shared.model_name) + if shared.args.lora: + add_lora_to_model(shared.args.lora) + + # Force a character to be loaded + if shared.is_chat(): + shared.persistent_interface_state.update({ + 'mode': shared.settings['mode'], + 'character_menu': shared.args.character or shared.settings['character'], + 'instruction_template': shared.settings['instruction_template'] + }) + + # Launch the web UI + create_interface() + while True: + time.sleep(0.5) + if shared.need_restart: + shared.need_restart = False + shared.gradio['interface'].close() + create_interface() diff --git a/text-generation-webui-main/settings-template.json b/text-generation-webui-main/settings-template.json new file mode 100644 index 0000000000000000000000000000000000000000..9465d79920205bd36d70425527c680797999b765 --- /dev/null +++ b/text-generation-webui-main/settings-template.json @@ -0,0 +1,48 @@ +{ + "max_new_tokens": 200, + "max_new_tokens_min": 1, + "max_new_tokens_max": 2000, + "seed": -1, + "character": "None", + "name1": "You", + "name2": "Assistant", + "context": "This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.", + "greeting": "", + "turn_template": "", + "custom_stopping_strings": "", + "stop_at_newline": false, + "add_bos_token": true, + "ban_eos_token": false, + "skip_special_tokens": true, + "truncation_length": 2048, + "truncation_length_min": 0, + "truncation_length_max": 8192, + "mode": "cai-chat", + "instruction_template": "None", + "chat_prompt_size": 2048, + "chat_prompt_size_min": 0, + "chat_prompt_size_max": 2048, + "chat_generation_attempts": 1, + "chat_generation_attempts_min": 1, + "chat_generation_attempts_max": 5, + "default_extensions": [], + "chat_default_extensions": [ + "gallery" + ], + "presets": { + "default": "Default", + ".*(alpaca|llama|llava)": "LLaMA-Precise", + ".*pygmalion": "NovelAI-Storywriter", + ".*RWKV": "Naive" + }, + "prompts": { + "default": "QA", + ".*(gpt4chan|gpt-4chan|4chan)": "GPT-4chan", + ".*oasst": "Open Assistant", + ".*alpaca": "Alpaca" + }, + "lora_prompts": { + "default": "QA", + ".*(alpaca-lora-7b|alpaca-lora-13b|alpaca-lora-30b)": "Alpaca" + } +} diff --git a/text-generation-webui-main/training/formats/alpaca-chatbot-format.json b/text-generation-webui-main/training/formats/alpaca-chatbot-format.json new file mode 100644 index 0000000000000000000000000000000000000000..4b38103f4c23de004666e0316855db62e57d2ad0 --- /dev/null +++ b/text-generation-webui-main/training/formats/alpaca-chatbot-format.json @@ -0,0 +1,4 @@ +{ + "instruction,output": "User: %instruction%\nAssistant: %output%", + "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%" +} diff --git a/text-generation-webui-main/training/formats/alpaca-format.json b/text-generation-webui-main/training/formats/alpaca-format.json new file mode 100644 index 0000000000000000000000000000000000000000..dd6df95640360297257b618715370093b715b21f --- /dev/null +++ b/text-generation-webui-main/training/formats/alpaca-format.json @@ -0,0 +1,4 @@ +{ + "instruction,output": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%", + "instruction,input,output": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%" +} diff --git a/training/formats/alpaca-chatbot-format.json b/training/formats/alpaca-chatbot-format.json new file mode 100644 index 0000000000000000000000000000000000000000..4b38103f4c23de004666e0316855db62e57d2ad0 --- /dev/null +++ b/training/formats/alpaca-chatbot-format.json @@ -0,0 +1,4 @@ +{ + "instruction,output": "User: %instruction%\nAssistant: %output%", + "instruction,input,output": "User: %instruction%: %input%\nAssistant: %output%" +} diff --git a/training/formats/alpaca-format.json b/training/formats/alpaca-format.json new file mode 100644 index 0000000000000000000000000000000000000000..dd6df95640360297257b618715370093b715b21f --- /dev/null +++ b/training/formats/alpaca-format.json @@ -0,0 +1,4 @@ +{ + "instruction,output": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Response:\n%output%", + "instruction,input,output": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n%instruction%\n\n### Input:\n%input%\n\n### Response:\n%output%" +}