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"""
Gradio interface for plotting attention.
"""
import copy
import chess
import gradio as gr
from lczerolens.board import LczeroBoard
from demo import constants, utils, visualisation
def list_models():
"""
List the models in the model directory.
"""
models_info = utils.get_models_info(leela=False)
return sorted([[model_info[0]] for model_info in models_info])
def on_select_model_df(
evt: gr.SelectData,
):
"""
When a model is selected, update the statement.
"""
return evt.value
def compute_cache(
board_fen,
action_seq,
model_name,
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
):
if model_name == "":
gr.Warning("No model selected.")
return None, None, None, state_boards, state_cache
try:
board = LczeroBoard(board_fen)
except ValueError:
board = LczeroBoard()
gr.Warning("Invalid FEN, using starting position.")
state_boards = [board.copy()]
if action_seq:
try:
if action_seq.startswith("1."):
for action in action_seq.split():
if action.endswith("."):
continue
board.push_san(action)
state_boards.append(board.copy())
else:
for action in action_seq.split():
board.push_uci(action)
state_boards.append(board.copy())
except ValueError:
gr.Warning(f"Invalid action {action} stopping before it.")
try:
wrapper, lens = utils.get_wrapper_lens_from_state(
model_name,
"activation",
lens_name="attention",
module_exp=r"encoder\d+/mha/QK/softmax",
)
except ValueError:
gr.Warning("Could not load model.")
return None, None, None, state_boards, state_cache
state_cache = []
for board in state_boards:
attention_cache = copy.deepcopy(lens.analyse_board(board, wrapper))
state_cache.append(attention_cache)
return (
*make_plot(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
),
state_boards,
state_cache,
)
def make_plot(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
):
if state_cache == []:
gr.Warning("No cache available.")
return None, None, None
board = state_boards[state_board_index]
num_attention_layers = len(state_cache[state_board_index])
if attention_layer > num_attention_layers:
gr.Warning(
f"Attention layer {attention_layer} does not exist, " f"using layer {num_attention_layers} instead."
)
attention_layer = num_attention_layers
key = f"encoder{attention_layer-1}/mha/QK/softmax"
try:
attention_tensor = state_cache[state_board_index][key]
except KeyError:
gr.Warning(f"Combination {key} does not exist.")
return None, None, None
if attention_head > attention_tensor.shape[1]:
gr.Warning(
f"Attention head {attention_head} does not exist, " f"using head {attention_tensor.shape[1]+1} instead."
)
attention_head = attention_tensor.shape[1]
try:
square_index = chess.SQUARE_NAMES.index(square)
except ValueError:
gr.Warning(f"Invalid square {square}, using a1 instead.")
square_index = 0
square = "a1"
if board.turn == chess.BLACK:
square_index = chess.square_mirror(square_index)
heatmap = attention_tensor[0, attention_head - 1, square_index]
if board.turn == chess.BLACK:
heatmap = heatmap.view(8, 8).flip(0).view(64)
svg_board, fig = visualisation.render_heatmap(board, heatmap, square=square)
with open(f"{constants.FIGURE_DIRECTORY}/attention.svg", "w") as f:
f.write(svg_board)
return f"{constants.FIGURE_DIRECTORY}/attention.svg", board.fen(), fig
def previous_board(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
):
state_board_index -= 1
if state_board_index < 0:
gr.Warning("Already at first board.")
state_board_index = 0
return (
*make_plot(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
),
state_board_index,
)
def next_board(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
):
state_board_index += 1
if state_board_index >= len(state_boards):
gr.Warning("Already at last board.")
state_board_index = len(state_boards) - 1
return (
*make_plot(
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
),
state_board_index,
)
with gr.Blocks() as interface:
with gr.Row():
with gr.Column(scale=2):
model_df = gr.Dataframe(
headers=["Available models"],
datatype=["str"],
interactive=False,
type="array",
value=list_models,
)
with gr.Column(scale=1):
with gr.Row():
model_name = gr.Textbox(label="Selected model", lines=1, interactive=False, scale=7)
model_df.select(
on_select_model_df,
None,
model_name,
)
with gr.Row():
with gr.Column():
board_fen = gr.Textbox(
label="Board starting FEN",
lines=1,
max_lines=1,
value=chess.STARTING_FEN,
)
action_seq = gr.Textbox(
label="Action sequence",
lines=1,
max_lines=1,
value=("e2e3 b8c6 d2d4 e7e5 g1f3 d8e7 " "d4d5 e5e4 f3d4 c6e5 f2f4 e5g6"),
)
compute_cache_button = gr.Button("Compute cache")
with gr.Group():
with gr.Row():
attention_layer = gr.Slider(
label="Attention layer",
minimum=1,
maximum=24,
step=1,
value=1,
)
attention_head = gr.Slider(
label="Attention head",
minimum=1,
maximum=24,
step=1,
value=1,
)
with gr.Row():
square = gr.Textbox(
label="Square",
lines=1,
max_lines=1,
value="a1",
scale=1,
)
with gr.Row():
previous_board_button = gr.Button("Previous board")
next_board_button = gr.Button("Next board")
current_board_fen = gr.Textbox(
label="Board FEN",
lines=1,
max_lines=1,
)
colorbar = gr.Plot(label="Colorbar")
with gr.Column():
image = gr.Image(label="Board")
state_board_index = gr.State(0)
state_boards = gr.State([])
state_cache = gr.State([])
base_inputs = [
attention_layer,
attention_head,
square,
state_board_index,
state_boards,
state_cache,
]
outputs = [image, current_board_fen, colorbar]
compute_cache_button.click(
compute_cache,
inputs=[board_fen, action_seq, model_name] + base_inputs,
outputs=outputs + [state_boards, state_cache],
)
previous_board_button.click(
previous_board,
inputs=base_inputs,
outputs=outputs + [state_board_index],
)
next_board_button.click(next_board, inputs=base_inputs, outputs=outputs + [state_board_index])
attention_layer.change(make_plot, inputs=base_inputs, outputs=outputs)
attention_head.change(make_plot, inputs=base_inputs, outputs=outputs)
square.submit(make_plot, inputs=base_inputs, outputs=outputs)
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