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import gradio as gr
import matplotlib.pyplot as plt
def plot_forecast(num_param, batch_size, precision, seq_len):
# Convert number (input as B)
num_param = float(num_param) * 1e9
# Convert precision to bytes
precision = {"float32": 4, "float16": 2, "bfloat16": 2}[precision]
# Model Parameters: N×precision
y1 = num_param * precision / (1024**3)
# Activations: B×Sequence Length×K×precision
K = 4.6894e-04 * num_param + 1.8494e06
y2 = batch_size * seq_len * K * precision / (1024**3)
# Optimizer States: 2×N×precision
y3 = 2 * num_param * precision / (1024**3)
# Gradients: N×precision
y4 = num_param * 4 / (1024**3)
fig = plt.figure(figsize=(4, 4))
ax = fig.add_subplot(111)
# Create stacked bars
ax.bar(0, y1, color="r")
ax.bar(0, y2, bottom=y1, color="b")
ax.bar(0, y3, bottom=y1 + y2, color="g")
ax.bar(0, y4, bottom=y1 + y2 + y3, color="y")
# Add text labels inside the bars
ax.text(0, y1 / 2, "Model Parameters", ha="center", va="center", color="white", fontweight="bold")
ax.text(0, y1 + y2 / 2,"Activations", ha="center", va="center", color="white", fontweight="bold")
ax.text(0, y1 + y2 + y3 / 2, "Optimizer States", ha="center", va="center", color="white", fontweight="bold")
ax.text(0, y1 + y2 + y3 + y4 / 2, "Gradients", ha="center", va="center", color="white", fontweight="bold")
# remove x axis
ax.xaxis.set_visible(False)
# Set GB as the unit for the y-axis
ax.set_ylabel("Memory (GB)")
fig.tight_layout()
return fig
demo = gr.Interface(
plot_forecast,
[
gr.Number(7, label="Number of parameters (B)"),
gr.Radio([1, 2, 4, 8, 16, 32, 64, 128], value=8, label="Batch size"),
gr.Radio(["float32", "float16", "bfloat16"], value="float32", label="Precision"),
gr.Slider(1, 1024, label="Sequence Length", step=1, value=128),
],
gr.Plot(label="forecast", format="png"),
)
if __name__ == "__main__":
demo.launch()
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