mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-01 15:10:15 +01:00
144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
import torch
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from modules import chat, shared
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from modules.text_generation import (
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decode,
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encode,
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generate_reply,
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)
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from transformers import LogitsProcessor
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import gradio as gr
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params = {
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"display_name": "Long replies",
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"is_tab": False,
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"min_length": 120,
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}
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initial_size = 0
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class MyLogits(LogitsProcessor):
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"""
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Manipulates the probabilities for the next token before it gets sampled.
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Used in the logits_processor_modifier function below.
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"""
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def __init__(self):
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self.newline_id = shared.tokenizer.encode('\n')[-1]
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pass
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def __call__(self, input_ids, scores):
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if input_ids.shape[-1] - initial_size < params["min_length"]:
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scores[...,self.newline_id] = -1000
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# scores[...,shared.tokenizer.eos_token_id] = -1000
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# probs = torch.softmax(scores, dim=-1, dtype=torch.float)
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# probs[0] /= probs[0].sum()
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# scores = torch.log(probs / (1 - probs))
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return scores
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def history_modifier(history):
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"""
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Modifies the chat history.
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Only used in chat mode.
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"""
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return history
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def state_modifier(state):
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"""
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Modifies the state variable, which is a dictionary containing the input
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values in the UI like sliders and checkboxes.
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"""
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return state
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def chat_input_modifier(text, visible_text, state):
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"""
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Modifies the user input string in chat mode (visible_text).
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You can also modify the internal representation of the user
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input (text) to change how it will appear in the prompt.
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"""
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return text, visible_text
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def input_modifier(string, state):
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"""
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In default/notebook modes, modifies the whole prompt.
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In chat mode, it is the same as chat_input_modifier but only applied
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to "text", here called "string", and not to "visible_text".
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"""
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return string
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def bot_prefix_modifier(string, state):
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"""
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Modifies the prefix for the next bot reply in chat mode.
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By default, the prefix will be something like "Bot Name:".
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"""
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return string
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def tokenizer_modifier(state, prompt, input_ids, input_embeds):
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"""
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Modifies the input ids and embeds.
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Used by the multimodal extension to put image embeddings in the prompt.
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Only used by loaders that use the transformers library for sampling.
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"""
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global initial_size
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initial_size = input_ids.shape[-1]
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return prompt, input_ids, input_embeds
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def logits_processor_modifier(processor_list, input_ids):
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"""
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Adds logits processors to the list, allowing you to access and modify
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the next token probabilities.
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Only used by loaders that use the transformers library for sampling.
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"""
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processor_list.append(MyLogits())
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return processor_list
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def output_modifier(string, state):
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"""
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Modifies the LLM output before it gets presented.
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In chat mode, the modified version goes into history['visible'],
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and the original version goes into history['internal'].
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"""
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return string
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def custom_generate_chat_prompt(user_input, state, **kwargs):
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"""
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Replaces the function that generates the prompt from the chat history.
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Only used in chat mode.
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"""
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result = chat.generate_chat_prompt(user_input, state, **kwargs)
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return result
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def custom_css():
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"""
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Returns a CSS string that gets appended to the CSS for the webui.
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"""
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return ''
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def custom_js():
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"""
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Returns a javascript string that gets appended to the javascript
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for the webui.
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"""
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return ''
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def setup():
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"""
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Gets executed only once, when the extension is imported.
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"""
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pass
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def ui():
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"""
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Gets executed when the UI is drawn. Custom gradio elements and
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their corresponding event handlers should be defined here.
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To learn about gradio components, check out the docs:
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https://gradio.app/docs/
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"""
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min_length = gr.Slider(0, 800, step=10, value=params['min_length'], label='Minimum reply length')
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min_length.change(lambda x: params.update({'min_length': x}), min_length, None)
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