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import gradio as gr
from defaults import DEFAULTS
def greet(name, intensity) -> str:
return "Hello, " + name + "!" * int(intensity)
def create_parallelism_block():
with gr.Column():
gr.Markdown("# Parallelism Parameters")
tp = gr.Number(label="Tensor Parallelism", value=1, interactive=True)
pp = gr.Number(label="Pipeline Parallelism", value=1, interactive=True)
cp = gr.Number(label="Context Parallelism", value=1, interactive=True)
ep = gr.Number(label="Expert Parallelism", value=1, interactive=True)
return tp, pp, cp, ep
def create_model_block():
with gr.Column():
gr.Markdown("# Model Parameters")
layers = gr.Number(label="Number of Layers", value=32, interactive=True)
vocab = gr.Number(label="Vocab Size", value=32000, interactive=True)
hidden = gr.Number(label="Hidden Dim", value=4096, interactive=True)
intermediate = gr.Number(
label="Intermediate Dim", value=11008, interactive=True
)
presets = gr.Dropdown(list(DEFAULTS.keys()), label="Presets", interactive=True)
return layers, vocab, hidden, intermediate, presets
def create_training_block():
with gr.Column():
gr.Markdown("# Training Parameters")
seq_len = gr.Number(label="Sequence Length", value=8192, interactive=True)
batch_size = gr.Number(label="Batch Size", value=8, interactive=True)
return seq_len, batch_size
def calculate(*args) -> int:
out = 1
for arg in args:
out *= arg
return arg
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
tp, pp, cp, ep = create_parallelism_block()
layers, vocab, hidden, intermediate, presets = create_model_block()
seq_len, batch_size = create_training_block()
calculate_button = gr.Button("Calculate")
output = gr.Number(label="Output")
calculate_button.click(fn=calculate, inputs=[tp, pp, cp, ep], outputs=output)
demo.launch()
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