import gradio as gr 
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator

INTRO = """# Harm's law 

The Chinchilla scaling laws focus on optimally scaling training compute but often we also care about inference cost. 
This tool follows [Harm de Vries' blog post](https://www.harmdevries.com/post/model-size-vs-compute-overhead/) and visualizes the tradeoff between training comput and inference cost (i.e. model size). 
"""

### CHINCHILLA PARAMS:
E = 1.62
A = 406.4
B = 410.7
alpha = 0.336
beta = 0.283

Bn = 10**9

G = ((alpha*A)/(beta*B))**(1/(alpha+beta)) 

### FUNCTIONS
def to_flops(N, D):
    return 6 * N * D

def n_opt(C):
    return G * ((C/6) ** (beta / (alpha+beta)))

def d_opt(C):
    return (1/G) * ((C/6) ** (alpha / (alpha+beta)))

def compute_kd(kn):
    frac = (A/B)*(G**(-alpha-beta))    
    kd = (1-((kn**-alpha -1)*frac))**(1/(-beta))
    return kd

def compute_overhead(kn, kd):
    return kn*kd - 1

### PRECOMPUTE CURVE:
kn_min = 0.2
kn_max = 2

kns = np.linspace(0.2, 2, 100)
overheads = []
for kn in kns:
    kd = compute_kd(kn)
    overheads.append(compute_overhead(kn, kd)*100)

def plot_curve(kn, kd):
    fig, ax = plt.subplots(dpi=200, figsize=(5, 3))
    plt.plot(kns, overheads, color="black", zorder=1)
    plt.scatter([kn], [compute_overhead(kn, kd)*100], s=100, marker="o", c="red", label="You are here!", zorder=2)
    plt.scatter([1.0], [0.0], marker="o", s=100, c="blue", label="Chinchilla optimal", zorder=2)
    plt.xlabel("Fraction of Chinchilla optimal model size")
    plt.ylabel("Compute overhead (%)")
    plt.legend(loc="best")
    plt.grid(True, which="both")
    plt.grid(True, which="minor", alpha=0.5)
    ax.yaxis.set_minor_locator(MultipleLocator(10))
    plt.tight_layout()

    return fig


def compute(N, D):
    
    C = to_flops(N * Bn, D * Bn)
    N_opt = n_opt(C)
    D_opt = d_opt(C)

    kn = Bn*N/N_opt
    kd = compute_kd(kn)
    
    fig = plot_curve(kn, kd)

    text = f"""\
## Compute:
Your specificied setting corresponds to the following training compute budget.

**Compute budget (TFLOPs): {C:.2E}**

## Chinchilla optimal:
If you are optimizeing for model performance and ignore inference cost this is the optimal setting for training:

**Optimal model size: {N_opt/Bn:.2f}B parametes**

**Optimal datset size: {D_opt/Bn:.2f}B tokens**

## Your setting trade-off:
Compared to the compute optimal model.

**Training compute overhead: {100*compute_overhead(kn, kd):.2f}%**

**Inference cost savings: {100 - kn*100:.2f}%** """
    return text, fig

with gr.Blocks() as demo:
    gr.Markdown(INTRO)
    with gr.Row():
        N = gr.Number(value=7, label="Model size (in B parameters):")
        D = gr.Number(value=2000, label="Dataset size (in B tokens):")
    
    button = gr.Button("Compute!")
    
    plot = gr.Plot(value=plt)
    md = gr.Markdown("")

    button.click(fn=compute, inputs=[N, D], outputs=[md, plot])
    demo.load(fn=compute, inputs=[N, D], outputs=[md, plot])
demo.launch()