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import gradio as gr
import requests
from PIL import Image
import io
from typing import Any, Tuple
import os
class Client:
def __init__(self, server_url: str):
self.server_url = server_url
def send_request(self, task_name: str, model_name: str, text: str, normalization_type: str) -> Tuple[Any, str]:
response = requests.post(
self.server_url,
json={
"task_name": task_name,
"model_name": model_name,
"text": text,
"normalization_type": normalization_type
},
timeout=60
)
if response.status_code == 200:
response_data = response.json()
img_data = bytes.fromhex(response_data["image"])
log_info = response_data["log"]
img = Image.open(io.BytesIO(img_data))
return img, log_info
else:
return "Error, please retry", "Error: Could not get response from server"
client = Client(f"http://{os.environ['SERVER']}/predict")
def get_layerwise_nonlinearity(task_name: str, model_name: str, text: str, normalization_type: str) -> Tuple[Any, str]:
return client.send_request(task_name, model_name, text, normalization_type)
with gr.Blocks() as demo:
with gr.Row():
model_selector = gr.Dropdown(
choices=[
"facebook/opt-1.3b",
"facebook/opt-2.7b",
# "microsoft/Phi-3-mini-128k-instruct"
],
value="facebook/opt-1.3b",
label="Select Model"
)
task_selector = gr.Dropdown(
choices=[
"Layer wise non-linearity (with first layer)",
"Next-token prediction from intermediate representations",
"Contextualization mesurment",
"Layerwise predictions and losses",
"Tokenwise loss without i-th layer"
],
value="Layer wise non-linearity (with first layer)",
label="Select Mode"
)
normalization_selector = gr.Dropdown(
choices=["global", "token-wise"], #, "sentence-wise"],
value="token-wise",
label="Select Normalization"
)
with gr.Column():
text_message = gr.Textbox(label="Enter your request:", value="I love to live my life")
submit = gr.Button("Submit")
box_for_plot = gr.Image(label="Visualization", type="pil")
log_output = gr.Textbox(label="Log Output", lines=10, interactive=False, value="")
def update_output(task_name: str, model_name: str, text: str, normalization_type: str, existing_log: str) -> Tuple[Any, str]:
img, new_log = get_layerwise_nonlinearity(task_name, model_name, text, normalization_type)
combined_log = existing_log + "---\n" + new_log + "\n"
return img, combined_log
def set_default(task_name: str) -> str:
if task_name == "Layer wise non-linearity (with first layer)":
return "token-wise"
if task_name == "Next-token prediction from intermediate representations":
return "token-wise"
if task_name == "Contextualization mesurment":
return "global"
if task_name == "Layerwise predictions and losses":
return "global"
if task_name == "Tokenwise loss without i-th layer":
return "token-wise"
def check_normalization(task_name: str, normalization_name) -> Tuple[str, str]:
if task_name == "Contextualization mesurment" and normalization_name == "token-wise":
return ("global", "\nALERT: Cannot apply token-wise normalization to one sentence, setting global normalization\n")
return (normalization_name, "")
task_selector.select(set_default, [task_selector], [normalization_selector])
normalization_selector.select(check_normalization, [task_selector, normalization_selector], [normalization_selector, log_output])
submit.click(
fn=update_output,
inputs=[task_selector, model_selector, text_message, normalization_selector, log_output],
outputs=[box_for_plot, log_output]
)
if __name__ == "__main__":
demo.launch(share=True, server_port=7860, server_name="0.0.0.0")
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