File size: 2,925 Bytes
da8c244 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | from __future__ import annotations
from pathlib import Path
import gradio as gr
import plotly.graph_objects as go
import torch
from data import VALUES, generate_bindings
from model import FastWeightProgrammer
from safetensors.torch import load_file
ARTIFACT_DIR = (
Path(__file__).resolve().parent / "artifacts" / "fast-weight-time-machine"
)
MODEL = FastWeightProgrammer()
MODEL.load_state_dict(load_file(ARTIFACT_DIR / "fast_weight.safetensors"))
MODEL.eval()
def inspect_binding(
seed: int, pairs: int, distractors: int
) -> tuple[go.Figure, dict]:
keys, values, writes, targets = generate_bindings(
1, int(pairs), int(distractors), int(seed)
)
with torch.inference_mode():
logits, strengths, contributions = MODEL(
torch.from_numpy(keys),
torch.from_numpy(values),
torch.from_numpy(writes),
return_trace=True,
)
probabilities = torch.softmax(logits, dim=1).numpy()[0]
contribution = contributions.numpy()[0]
labels = [
f"K{key}:V{value}" if value < VALUES else f"QUERY K{key}"
for key, value in zip(keys[0], values[0], strict=True)
]
colors = ["#f59e0b" if write else "#334155" for write in writes[0]]
figure = go.Figure(
go.Bar(
x=list(range(len(labels))),
y=contribution,
marker_color=colors,
customdata=labels,
hovertemplate="%{customdata}<br>read contribution=%{y:.3f}",
)
)
figure.update_layout(
title="Query-key contribution to the fast weight matrix",
xaxis_title="Sequence event",
yaxis_title="Contribution",
template="plotly_dark",
)
prediction = int(probabilities.argmax())
return figure, {
"query": labels[-1],
"target_value": int(targets[0]),
"predicted_value": prediction,
"correct": prediction == int(targets[0]),
"confidence": round(float(probabilities[prediction]), 4),
"mean_active_write_strength": round(
float(strengths.numpy()[0][writes[0] == 1].mean()), 4
),
}
with gr.Blocks(title="Fast-Weight Time Machine") as demo:
gr.Markdown(
"# Fast-Weight Time Machine\n"
"Watch a learned controller write temporary key/value bindings into a "
"sequence-local weight matrix, then retrieve one binding."
)
with gr.Row():
seed = gr.Number(2043, precision=0, label="Sequence seed")
pairs = gr.Slider(2, 12, 4, step=1, label="Stored bindings")
distractors = gr.Slider(0, 64, 12, step=4, label="Distractors")
run = gr.Button("Program fast memory", variant="primary")
trace = gr.Plot()
result = gr.JSON()
run.click(inspect_binding, [seed, pairs, distractors], [trace, result])
demo.load(inspect_binding, [seed, pairs, distractors], [trace, result])
if __name__ == "__main__":
demo.launch()
|