Merge branch 'main' into HideLord-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

.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.

.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"

.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, you can reopen it (if you are the author) or 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 }}

README.md

@@ -60,7 +60,9 @@ pip3 install torch torchvision torchaudio --extra-index-url https://download.pyt
 conda install pytorch torchvision torchaudio git -c pytorch
 ```
 
-See also: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
+> **Note**
+> 1. If you are on Windows, it may be easier to run the commands above in a WSL environment. The performance may also be better.
+> 2. For a more detailed, user-contributed guide, see: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
 
 ## Installation option 2: one-click installers
 
@@ -140,8 +142,9 @@ Optionally, you can use the following command-line flags:
 | `--cai-chat`  | 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. |
 | `--cpu`       | Use the CPU to generate text.|
 | `--load-in-8bit`  | Load the model with 8-bit precision.|
-| `--load-in-4bit`  | Load the model with 4-bit precision. Currently only works with LLaMA.|
+| `--load-in-4bit`  | DEPRECATED: use `--gptq-bits 4` instead. |
-| `--gptq-bits GPTQ_BITS`  |  Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA. |
+| `--gptq-bits GPTQ_BITS`  |  Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
+| `--gptq-model-type MODEL_TYPE`  |  Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
 | `--bf16`  | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
 | `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
 | `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |

api-example-stream.py

@@ -26,6 +26,7 @@ async def run(context):
         'top_p': 0.9,
         'typical_p': 1,
         'repetition_penalty': 1.05,
+        'encoder_repetition_penalty': 1.0,
         'top_k': 0,
         'min_length': 0,
         'no_repeat_ngram_size': 0,
@@ -59,6 +60,7 @@ async def run(context):
                             params['top_p'],
                             params['typical_p'],
                             params['repetition_penalty'],
+                            params['encoder_repetition_penalty'],
                             params['top_k'],
                             params['min_length'],
                             params['no_repeat_ngram_size'],

api-example.py

@@ -24,6 +24,7 @@ params = {
     'top_p': 0.9,
     'typical_p': 1,
     'repetition_penalty': 1.05,
+    'encoder_repetition_penalty': 1.0,
     'top_k': 0,
     'min_length': 0,
     'no_repeat_ngram_size': 0,
@@ -45,6 +46,7 @@ response = requests.post(f"http://{server}:7860/run/textgen", json={
         params['top_p'],
         params['typical_p'],
         params['repetition_penalty'],
+        params['encoder_repetition_penalty'],
         params['top_k'],
         params['min_length'],
         params['no_repeat_ngram_size'],

extensions/gallery/script.py

@@ -76,7 +76,7 @@ def generate_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())
     update.click(generate_html, [], gallery)

extensions/silero_tts/script.py

@@ -81,6 +81,7 @@ def input_modifier(string):
     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>')]
 
+    shared.processing_message = "*Is recording a voice message...*"
     return string
 
 def output_modifier(string):
@@ -119,6 +120,7 @@ def output_modifier(string):
         if params['show_text']:
             string += f'\n\n{original_string}'
 
+    shared.processing_message = "*Is typing...*"
     return string
 
 def bot_prefix_modifier(string):

modules/GPTQ_loader.py

@@ -7,28 +7,40 @@ import torch
 import modules.shared as shared
 
 sys.path.insert(0, str(Path("repositories/GPTQ-for-LLaMa")))
-from llama import load_quant
+import llama
+import opt
 
 
-# 4-bit LLaMA
+def load_quantized(model_name):
-def load_quantized_LLaMA(model_name):
+    if not shared.args.gptq_model_type:
-    if shared.args.load_in_4bit:
+        # Try to determine model type from model name
-        bits = 4
+        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:
-        bits = shared.args.gptq_bits
+        model_type = shared.args.gptq_model_type.lower()
+
+    if model_type == 'llama':
+        load_quant = llama.load_quant
+    elif model_type == 'opt':
+        load_quant = opt.load_quant
+    else:
+        print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
+        exit()
 
     path_to_model = Path(f'models/{model_name}')
-    pt_model = ''
     if path_to_model.name.lower().startswith('llama-7b'):
-        pt_model = f'llama-7b-{bits}bit.pt'
+        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-{bits}bit.pt'
+        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-{bits}bit.pt'
+        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-{bits}bit.pt'
+        pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
     else:
-        pt_model = f'{model_name}-{bits}bit.pt'
+        pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
 
     # Try to find the .pt both in models/ and in the subfolder
     pt_path = None
@@ -40,7 +52,7 @@ def load_quantized_LLaMA(model_name):
         print(f"Could not find {pt_model}, exiting...")
         exit()
 
-    model = load_quant(str(path_to_model), str(pt_path), bits)
+    model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
 
     # Multiple GPUs or GPU+CPU
     if shared.args.gpu_memory:

modules/chat.py

@@ -97,7 +97,7 @@ def extract_message_from_reply(question, reply, name1, name2, check, impersonate
 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):
+def chatbot_wrapper(text, 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, 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
@@ -126,13 +126,14 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
     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, '*Is typing...*']]
+        yield shared.history['visible']+[[visible_text, shared.processing_message]]
 
     # 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 reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", 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, eos_token=eos_token, stopping_string=f"\n{name1}:"):
 
             # Extracting the reply
             reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
@@ -159,7 +160,7 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
 
     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):
+def impersonate_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
     eos_token = '\n' if check else None
 
     if 'pygmalion' in shared.model_name.lower():
@@ -168,28 +169,29 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
     prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
 
     reply = ''
-    yield '*Is typing...*'
+    # 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}:"):
+        for reply in generate_reply(prompt+reply, 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, eos_token=eos_token, stopping_string=f"\n{name2}:"):
             reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
             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):
+def cai_chatbot_wrapper(text, 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, 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):
+    for _history in chatbot_wrapper(text, 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, name1, name2, context, check, chat_prompt_size, chat_generation_attempts):
         yield generate_chat_html(_history, name1, name2, shared.character)
 
-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):
+def regenerate_wrapper(text, 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, 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], '*Is typing...*']], name1, name2, shared.character)
+        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):
+        for _history in chatbot_wrapper(last_internal[0], 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, 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:

modules/models.py

@@ -1,6 +1,5 @@
 import json
 import os
-import sys
 import time
 import zipfile
 from pathlib import Path
@@ -35,6 +34,7 @@ if shared.args.deepspeed:
     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 load_model(model_name):
     print(f"Loading {model_name}...")
     t0 = time.time()
@@ -42,7 +42,7 @@ def load_model(model_name):
     shared.is_RWKV = model_name.lower().startswith('rwkv-')
 
     # Default settings
-    if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.load_in_4bit, shared.args.gptq_bits > 0, 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 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)
         else:
@@ -87,11 +87,11 @@ def load_model(model_name):
 
         return model, tokenizer
 
-    # 4-bit LLaMA
+    # Quantized model
-    elif shared.args.gptq_bits > 0 or shared.args.load_in_4bit:
+    elif shared.args.gptq_bits > 0:
-        from modules.quantized_LLaMA import load_quantized_LLaMA
+        from modules.GPTQ_loader import load_quantized
 
-        model = load_quantized_LLaMA(model_name)
+        model = load_quantized(model_name)
 
     # Custom
     else:

modules/shared.py

@@ -11,6 +11,7 @@ is_RWKV = False
 history = {'internal': [], 'visible': []}
 character = 'None'
 stop_everything = False
+processing_message = '*Is typing...*'
 
 # UI elements (buttons, sliders, HTML, etc)
 gradio = {}
@@ -68,8 +69,9 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
 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='Load the model with 4-bit precision. Currently only works with LLaMA.')
+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.')
+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('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
 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.')
@@ -94,3 +96,8 @@ parser.add_argument('--share', action='store_true', help='Create a public URL. T
 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.')
 args = parser.parse_args()
+
+# 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

modules/text_generation.py

@@ -89,7 +89,7 @@ def clear_torch_cache():
     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):
+def generate_reply(question, 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, eos_token=None, stopping_string=None):
     clear_torch_cache()
     t0 = time.time()
 
@@ -122,7 +122,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
     input_ids = encode(question, max_new_tokens)
     original_input_ids = input_ids
     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, shared.args.flexgen))
     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]))
@@ -132,45 +132,49 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
         t = encode(stopping_string, 0, add_special_tokens=False)
         stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
 
+    generate_params = {}
     if not shared.args.flexgen:
-        generate_params = [
+        generate_params.update({
-            f"max_new_tokens=max_new_tokens",
+            "max_new_tokens": max_new_tokens,
-            f"eos_token_id={eos_token_ids}",
+            "eos_token_id": eos_token_ids,
-            f"stopping_criteria=stopping_criteria_list",
+            "stopping_criteria": stopping_criteria_list,
-            f"do_sample={do_sample}",
+            "do_sample": do_sample,
-            f"temperature={temperature}",
+            "temperature": temperature,
-            f"top_p={top_p}",
+            "top_p": top_p,
-            f"typical_p={typical_p}",
+            "typical_p": typical_p,
-            f"repetition_penalty={repetition_penalty}",
+            "repetition_penalty": repetition_penalty,
-            f"top_k={top_k}",
+            "encoder_repetition_penalty": encoder_repetition_penalty,
-            f"min_length={min_length if shared.args.no_stream else 0}",
+            "top_k": top_k,
-            f"no_repeat_ngram_size={no_repeat_ngram_size}",
+            "min_length": min_length if shared.args.no_stream else 0,
-            f"num_beams={num_beams}",
+            "no_repeat_ngram_size": no_repeat_ngram_size,
-            f"penalty_alpha={penalty_alpha}",
+            "num_beams": num_beams,
-            f"length_penalty={length_penalty}",
+            "penalty_alpha": penalty_alpha,
-            f"early_stopping={early_stopping}",
+            "length_penalty": length_penalty,
-        ]
+            "early_stopping": early_stopping,
+        })
     else:
-        generate_params = [
+        generate_params.update({
-            f"max_new_tokens={max_new_tokens if shared.args.no_stream else 8}",
+            "max_new_tokens": max_new_tokens if shared.args.no_stream else 8,
-            f"do_sample={do_sample}",
+            "do_sample": do_sample,
-            f"temperature={temperature}",
+            "temperature": temperature,
-            f"stop={eos_token_ids[-1]}",
+            "stop": eos_token_ids[-1],
-        ]
+        })
     if shared.args.deepspeed:
-        generate_params.append("synced_gpus=True")
+        generate_params.update({"synced_gpus": True})
     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.update({"inputs_embeds": inputs_embeds})
-        generate_params.insert(0, "inputs=filler_input_ids")
+        generate_params.update({"inputs": filler_input_ids})
     else:
-        generate_params.insert(0, "inputs=input_ids")
+        generate_params.update({"inputs": input_ids})
 
     try:
         # Generate the entire reply at once.
         if shared.args.no_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]:]))
 
@@ -194,7 +198,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
                 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]:]))
@@ -214,7 +218,7 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
             for i in range(max_new_tokens//8+1):
                 clear_torch_cache()
                 with torch.no_grad():
-                    output = eval(f"shared.model.generate({', '.join(generate_params)})")[0]
+                    output = shared.model.generate(**generate_params)[0]
                 if shared.soft_prompt:
                     output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
                 reply = decode(output)

modules/ui.py

@@ -38,6 +38,9 @@ svg {
 ol li p, ul li p {
     display: inline-block;
 }
+#main, #settings, #extensions, #chat-settings {
+  border: 0;
+}
 """
 
 chat_css = """
@@ -64,6 +67,12 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
 }
 """
 
+page_js = """
+document.getElementById("main").parentNode.childNodes[0].style = "border: none; background-color: #8080802b; margin-bottom: 40px"
+document.getElementById("main").parentNode.style = "padding: 0; margin: 0"
+document.getElementById("main").parentNode.parentNode.parentNode.style = "padding: 0"
+"""
+
 class ToolButton(gr.Button, gr.components.FormComponent):
     """Small button with single emoji as text, fits inside gradio forms"""
 

requirements.txt

@@ -1,10 +1,10 @@
-accelerate==0.17.0
+accelerate==0.17.1
-bitsandbytes==0.37.0
+bitsandbytes==0.37.1
 flexgen==0.1.7
 gradio==3.18.0
 numpy
 requests
-rwkv==0.3.1
+rwkv==0.4.2
 safetensors==0.3.0
 sentencepiece
 tqdm

server.py

@@ -66,6 +66,7 @@ def load_preset_values(preset_menu, return_dict=False):
         'top_p': 1,
         'typical_p': 1,
         'repetition_penalty': 1,
+        'encoder_repetition_penalty': 1,
         'top_k': 50,
         'num_beams': 1,
         'penalty_alpha': 0,
@@ -86,7 +87,7 @@ def load_preset_values(preset_menu, return_dict=False):
     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']
+        return generate_params['do_sample'], generate_params['temperature'], generate_params['top_p'], generate_params['typical_p'], generate_params['repetition_penalty'], generate_params['encoder_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']
 
 def upload_soft_prompt(file):
     with zipfile.ZipFile(io.BytesIO(file)) as zf:
@@ -100,9 +101,7 @@ def upload_soft_prompt(file):
 
     return name
 
-def create_settings_menus(default_preset):
+def create_model_and_preset_menus():
-    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():
@@ -113,22 +112,29 @@ def create_settings_menus(default_preset):
                 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')
 
-    with gr.Accordion('Custom generation parameters', open=False, elem_id='accordion'):
+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.Box():
+        gr.Markdown('Custom generation parameters')
         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():
+                shared.gradio['top_k'] = gr.Slider(0,200,value=generate_params['top_k'],step=1,label='top_k')
-                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')
+            with gr.Column():
+                shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'],step=0.01,label='repetition_penalty')
+                shared.gradio['encoder_repetition_penalty'] = gr.Slider(0.8, 1.5, value=generate_params['encoder_repetition_penalty'],step=0.01,label='encoder_repetition_penalty')
                 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)
+        shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
 
+    with gr.Box():
         gr.Markdown('Contrastive search:')
         shared.gradio['penalty_alpha'] = gr.Slider(0, 5, value=generate_params['penalty_alpha'], label='penalty_alpha')
 
+    with gr.Box():
         gr.Markdown('Beam search (uses a lot of VRAM):')
         with gr.Row():
             with gr.Column():
@@ -137,7 +143,8 @@ def create_settings_menus(default_preset):
                 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.Accordion('Soft prompt', open=False, elem_id='accordion'):
+    with gr.Box():
+        gr.Markdown('Soft prompt')
         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')
@@ -147,7 +154,7 @@ def create_settings_menus(default_preset):
             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['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['encoder_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']])
 
@@ -200,6 +207,7 @@ 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']:
+        with gr.Tab("Text generation", elem_id="main"):
             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:
@@ -219,7 +227,21 @@ if shared.args.chat or shared.args.cai_chat:
                 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'):
+
+            create_model_and_preset_menus()
+
+            with gr.Box():
+                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_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'])
+                    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)')
+
+            if shared.args.extensions is not None:
+                extensions_module.create_extensions_block()
+
+        with gr.Tab("Chat settings", elem_id="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')
@@ -253,23 +275,13 @@ if shared.args.chat or shared.args.cai_chat:
                 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.Tab("Settings", elem_id="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'
+        shared.input_params = [shared.gradio[k] for k in ['textbox', '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', 'name1', 'name2', 'context', 'check', 'chat_prompt_size_slider', 'chat_generation_attempts']]
 
-        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['Generate'].click(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
         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))
@@ -308,36 +320,42 @@ if shared.args.chat or shared.args.cai_chat:
         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(None, None, None, _js=f"() => {{{ui.page_js}}}")
         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("Text generation", elem_id="main"):
             with gr.Tab('Raw'):
-            shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=23)
+                shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=25)
             with gr.Tab('Markdown'):
                 shared.gradio['markdown'] = gr.Markdown()
             with gr.Tab('HTML'):
                 shared.gradio['html'] = gr.HTML()
 
-        shared.gradio['Generate'] = gr.Button('Generate')
+            with gr.Row():
                 shared.gradio['Stop'] = gr.Button('Stop')
+                shared.gradio['Generate'] = gr.Button('Generate')
             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)
+            create_model_and_preset_menus()
             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']]
+        with gr.Tab("Settings", elem_id="settings"):
+            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', 'encoder_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)
+        shared.gradio['interface'].load(None, None, None, _js=f"() => {{{ui.page_js}}}")
 
 else:
     with gr.Blocks(css=ui.css, analytics_enabled=False, title=title) as shared.gradio['interface']:
-        gr.Markdown(description)
+        with gr.Tab("Text generation", elem_id="main"):
             with gr.Row():
                 with gr.Column():
                     shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=15, label='Input')
@@ -349,24 +367,27 @@ else:
                         with gr.Column():
                             shared.gradio['Stop'] = gr.Button('Stop')
 
-                create_settings_menus(default_preset)
+                    create_model_and_preset_menus()
                     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')
+                        shared.gradio['output_textbox'] = gr.Textbox(lines=25, label='Output')
                     with gr.Tab('Markdown'):
                         shared.gradio['markdown'] = gr.Markdown()
                     with gr.Tab('HTML'):
                         shared.gradio['html'] = gr.HTML()
+        with gr.Tab("Settings", elem_id="settings"):
+            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']]
+        shared.input_params = [shared.gradio[k] for k in ['textbox', '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']]
         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'].load(None, None, None, _js=f"() => {{{ui.page_js}}}")
 
 shared.gradio['interface'].queue()
 if shared.args.listen: