from __future__ import annotations

nbid = '2.6'  # ID for tagging assets
nbname = 'Ablate/prune'
experiment_name = f'Ex {nbid}: {nbname}'
project = 'ex-preppy'

# Basic setup: Logging, Experiment (Modal)
import logging

import modal

from infra.requirements import freeze, project_packages
from mini.experiment import Experiment
from utils.logging import SimpleLoggingConfig

logging_config = (
    SimpleLoggingConfig()
    .info('notebook', 'utils', 'mini', 'ex_color')
    .error('matplotlib.axes')  # Silence warnings about set_aspect
)
logging_config.apply()

# This is the logger for this notebook
log = logging.getLogger(f'notebook.{nbid}')

run = Experiment(experiment_name, project=project)
run.image = modal.Image.debian_slim().pip_install(*freeze(all=True)).add_local_python_source(*project_packages())
run.before_each(logging_config.apply)
None  # prevent auto-display of this cell

import torch

from mini.temporal.dopesheet import Dopesheet
from ex_color.loss import Anchor, Separate, Unitarity, RegularizerConfig, Planarity

from ex_color.training import TrainingModule

RED = (1, 0, 0, 0)

ALL_REGULARIZERS = [
    RegularizerConfig(
        name='reg-unit',
        compute_loss_term=Unitarity(),
        label_affinities=None,
        layer_affinities=['encoder'],
    ),
    RegularizerConfig(
        name='reg-anchor',
        compute_loss_term=Anchor(torch.tensor(RED, dtype=torch.float32)),
        label_affinities={'red': 1.0},
        layer_affinities=['bottleneck'],
    ),
    RegularizerConfig(
        name='reg-separate',
        compute_loss_term=Separate(power=100.0, shift=True),
        label_affinities=None,
        layer_affinities=['bottleneck'],
    ),
    RegularizerConfig(
        name='reg-planar',
        compute_loss_term=Planarity(),
        label_affinities={'vibrant': 1.0},
        layer_affinities=['bottleneck'],
    ),
]

from functools import partial
from torch import Tensor
from torch.utils.data import DataLoader, TensorDataset, WeightedRandomSampler
import numpy as np

from ex_color.data.color_cube import ColorCube
from ex_color.data.cube_sampler import vibrancy
from ex_color.data.cyclic import arange_cyclic
from ex_color.labelling import collate_with_generated_labels


def prep_data() -> tuple[DataLoader, Tensor]:
    """
    Prepare data for training.

    Returns: (train, val)
    """
    hsv_cube = ColorCube.from_hsv(
        h=arange_cyclic(step_size=10 / 360),
        s=np.linspace(0, 1, 10),
        v=np.linspace(0, 1, 10),
    )
    hsv_tensor = torch.tensor(hsv_cube.rgb_grid.reshape(-1, 3), dtype=torch.float32)
    vibrancy_tensor = torch.tensor(vibrancy(hsv_cube).flatten(), dtype=torch.float32)
    hsv_dataset = TensorDataset(hsv_tensor, vibrancy_tensor)

    labeller = partial(
        collate_with_generated_labels,
        soft=False,  # Use binary labels (stochastic) to simulate the labelling of internet text
        red=0.5,
        vibrant=0.5,
    )
    # Desaturated and dark colors are over-represented in the cube, so we use a weighted sampler to balance them out
    hsv_loader = DataLoader(
        hsv_dataset,
        batch_size=64,
        num_workers=2,
        sampler=WeightedRandomSampler(
            weights=hsv_cube.bias.flatten().tolist(),
            num_samples=len(hsv_dataset),
            replacement=True,
        ),
        collate_fn=labeller,
    )

    rgb_cube = ColorCube.from_rgb(
        r=np.linspace(0, 1, 8),
        g=np.linspace(0, 1, 8),
        b=np.linspace(0, 1, 8),
    )
    rgb_tensor = torch.tensor(rgb_cube.rgb_grid.reshape(-1, 3), dtype=torch.float32)
    return hsv_loader, rgb_tensor

import wandb
from ex_color.model import CNColorMLP


# @run.thither(env={'WANDB_API_KEY': wandb.Api().api_key})
async def train(
    dopesheet: Dopesheet,
    regularizers: list[RegularizerConfig],
) -> CNColorMLP:
    """Train the model with the given dopesheet and variant."""
    import lightning as L
    from lightning.pytorch.loggers import WandbLogger

    from ex_color.seed import set_deterministic_mode

    from utils.progress.lightning import LightningProgress

    log.info(f'Training with: {[r.name for r in regularizers]}')

    seed = 0
    set_deterministic_mode(seed)

    hsv_loader, _ = prep_data()

    model = CNColorMLP(4)
    total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
    log.debug(f'Model initialized with {total_params:,} trainable parameters.')

    training_module = TrainingModule(model, dopesheet, torch.nn.MSELoss(), regularizers)

    logger = WandbLogger(experiment_name, project=project)

    trainer = L.Trainer(
        max_steps=len(dopesheet),
        callbacks=[
            LightningProgress(),
        ],
        enable_checkpointing=False,
        enable_model_summary=False,
        # enable_progress_bar=True,
        logger=logger,
    )

    print(f'max_steps: {len(dopesheet)}, hsv_loader length: {len(hsv_loader)}')

    # Train the model
    try:
        trainer.fit(training_module, hsv_loader)
    finally:
        wandb.finish()
    # This is only a small model, so it's OK to return it rather than storing and loading a checkpoint remotely
    return model


async with run():
    model = await train(Dopesheet.from_csv(f'./ex-{nbid}-dopesheet.csv'), ALL_REGULARIZERS)

I 5.1 no.2.6:  Training with: ['reg-unit', 'reg-anchor', 'reg-separate', 'reg-planar']

INFO: Seed set to 0

I 5.1 li.fa.ut.se:Seed set to 0
I 5.1 ex.se:   PyTorch set to deterministic mode

INFO: GPU available: False, used: False

I 5.1 li.py.ut.ra:GPU available: False, used: False

INFO: TPU available: False, using: 0 TPU cores

I 5.1 li.py.ut.ra:TPU available: False, using: 0 TPU cores

INFO: HPU available: False, using: 0 HPUs

I 5.1 li.py.ut.ra:HPU available: False, using: 0 HPUs
max_steps: 3001, hsv_loader length: 57

wandb: Currently logged in as: z0r to https://api.wandb.ai. Use `wandb login --relogin` to force relogin

from ex_color.inference import InferenceModule


async def infer(
    model: CNColorMLP,
    test_data: Tensor,
) -> Tensor:
    """Run inference with the given model."""
    import lightning as L

    inference_module = InferenceModule(model, [])
    trainer = L.Trainer(
        enable_checkpointing=False,
        enable_model_summary=False,
        enable_progress_bar=True,
    )
    reconstructed_colors_batches = trainer.predict(
        inference_module,
        DataLoader(
            TensorDataset(test_data.reshape((-1, 3))),
            batch_size=64,
            collate_fn=lambda batch: torch.stack([row[0] for row in batch], 0),
        ),
    )
    assert reconstructed_colors_batches is not None
    # Flatten the list of batches to a single list of tensors
    reconstructed_colors = [item for batch in reconstructed_colors_batches for item in batch]
    # Reshape to match input
    return torch.cat(reconstructed_colors).reshape(test_data.shape)

import torch
import numpy as np

from ex_color.inference import InferenceModule


async def infer_with_latent_capture(
    model: CNColorMLP,
    test_data: Tensor,
    layer_name: str = 'bottleneck',
) -> tuple[Tensor, Tensor]:
    module = InferenceModule(model, [], capture_layers=[layer_name])
    import lightning as L

    trainer = L.Trainer(enable_checkpointing=False, enable_model_summary=False, enable_progress_bar=False)
    batches = trainer.predict(
        module,
        DataLoader(
            TensorDataset(test_data.reshape((-1, 3))),
            batch_size=64,
            collate_fn=lambda batch: torch.stack([row[0] for row in batch], 0),
        ),
    )
    assert batches is not None
    preds = [item for batch in batches for item in batch]
    y = torch.cat(preds).reshape(test_data.shape)
    # Read captured activations as a flat [N, D] tensor
    latents = module.read_captured(layer_name)
    return y, latents

from IPython.display import clear_output

import importlib
import utils.nb
import utils.plt

importlib.reload(utils.nb)
importlib.reload(utils.plt)

from ex_color.vis import plot_colors
from utils.nb import displayer_mpl


hsv_cube = ColorCube.from_hsv(
    h=arange_cyclic(step_size=1 / 24),
    s=np.linspace(0, 1, 4),
    v=np.linspace(0, 1, 8),
).permute('svh')
x_hsv = torch.tensor(hsv_cube.rgb_grid, dtype=torch.float32)

hd_hsv_cube = ColorCube.from_hsv(
    h=arange_cyclic(step_size=1 / 240),
    s=np.linspace(0, 1, 48),
    v=np.linspace(0, 1, 48),
)
hd_x_hsv = torch.tensor(hd_hsv_cube.rgb_grid, dtype=torch.float32)

rgb_cube = ColorCube.from_rgb(
    r=np.linspace(0, 1, 20),
    g=np.linspace(0, 1, 20),
    b=np.linspace(0, 1, 20),
)
x_rgb = torch.tensor(rgb_cube.rgb_grid, dtype=torch.float32)

with displayer_mpl(
    f'large-assets/ex-{nbid}-true-colors.png',
    alt_text="""Plot showing four slices of the HSV cube, titled "{title}". Each slice has constant saturation, but varies in value (brightness) from top to bottom, and in hue from left to right. The first slice shows a grayscale gradient from black to white; the last shows the fully-saturated color spectrum.""",
) as show:
    show(lambda: plot_colors(hsv_cube, title='True colors', colors=x_hsv.numpy()))

from IPython.display import clear_output
from torch.nn import functional as F

from ex_color.vis import plot_colors, plot_cube_series


interventions = []
y_hsv = await infer(model, x_hsv)
hd_y_hsv = await infer(model, hd_x_hsv)
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-pred-colors-no-intervention.png',
    alt_text="""Plot showing four slices of the HSV cube, titled "{title}". Nominally, each slice has constant saturation, but varies in value (brightness) from top to bottom, and in hue from left to right. Each color value is represented as a square patch of that color. The outer portion of the patches shows the color as reconstructed by the model; the inner portion shows the true (input) color. The reconstructed and true colors agree fairly well, but some slight differences are visible; for example, "white" is slightly gray, and many of the fully-saturated colors are less saturated than they should be.""",
) as show:
    show(
        lambda: plot_colors(
            hsv_cube,
            title='Predicted colors · no intervention',
            colors=y_hsv.numpy(),
            colors_compare=x_hsv.numpy(),
        )
    )

per_color_loss = F.mse_loss(hd_y_hsv, hd_x_hsv, reduction='none').mean(dim=-1)
loss_cube = hd_hsv_cube.assign('MSE', per_color_loss.numpy().reshape(hd_hsv_cube.shape))
max_loss = per_color_loss.max().item()
median_loss = per_color_loss.median().item()

with displayer_mpl(
    f'large-assets/ex-{nbid}-loss-colors-no-intervention.png',
    alt_text=f"""Line chart showing loss per color, titled "{{title}}". Y-axis: mean square error, ranging from zero to {max_loss:.2g}. X-axis: hue. The range of loss values is small, but there are two notable peaks at all primary and secondary colors (red, yellow, green, etc.).""",
) as show:
    show(
        lambda: plot_cube_series(
            loss_cube.permute('hsv')[:, -1:, :: (loss_cube.shape[2] // -5)],
            loss_cube.permute('svh')[:, -1:, :: -(loss_cube.shape[0] // -3)],
            loss_cube.permute('vsh')[:, -1:, :: -(loss_cube.shape[0] // -3)],
            title='Reconstruction error · no intervention',
            var='MSE',
            figsize=(12, 3),
        )
    )
print(f'Max loss: {max_loss:.2g}')
print(f'Median MSE: {median_loss:.2g}')

Max loss: 0.0011
Median MSE: 1.5e-05

from IPython.display import clear_output

from ex_color.vis import plot_latent_grid_3d

y_rgb, h_rgb = await infer_with_latent_capture(model, x_rgb, 'bottleneck')
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-latents-no-intervention.png',
    alt_text="""Three spherical plots, titled "{title}". Each plot shows a vibrant collection of colored circles or balls scattered over the surface of a sphere. The first plot has the appearance of a color wheel, with the full set of vibrant colors around the rim (like a rainbow), varying to black in the center. The other plots show different views of the same sphere, with hue varying across the equator and tone varying from top to bottom, and red in the center. Each ball shows the reconstructed color, with a dot in the center showing the true (input) color. In this plot the true and reconstructor colors agree fairly well, but slight differences can be seen if you look closely.""",
) as show:
    show(
        lambda theme: plot_latent_grid_3d(
            h_rgb,
            y_rgb,
            x_rgb,
            title='Latents · no intervention',
            dims=[(1, 0, 2), (1, 2, 0), (1, 3, 0)],
            dot_radius=10,
            theme=theme,
        )
    )

def ablate[M](model: M, layer_id: str, dims: Sequence[int]) -> M:
    """Return a copy of model where the selected latent dims are effectively nulled."""
    ...

from ex_color.surgery import ablate

ablated_model = ablate(model, 'bottleneck', [0])

y_hsv = await infer(ablated_model, x_hsv)
hd_y_hsv = await infer(ablated_model, hd_x_hsv)
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-pred-colors-ablated.png',
    alt_text="""Plot showing four slices of the HSV cube, titled "{title}". Nominally, each slice has constant saturation, but varies in value (brightness) from top to bottom, and in hue from left to right. Each color value is represented as a square patch of that color. The outer portion of the patches shows the color as reconstructed by the model; the inner portion shows the true (input) color. The reconstructed and true colors agree fairly well, but "red" and "cyan" are clearly different: red itself appears as black, and the surrounding colors up to green and blue look more like green and purple. Colors near cyan are similarly affected.""",
) as show:
    show(
        lambda: plot_colors(
            hsv_cube,
            title='Predicted colors · ablated',
            colors=y_hsv.numpy(),
            colors_compare=x_hsv.numpy(),
        )
    )

per_color_loss = F.mse_loss(hd_y_hsv, hd_x_hsv, reduction='none').mean(dim=-1)
loss_cube = hd_hsv_cube.assign('MSE', per_color_loss.numpy().reshape(hd_hsv_cube.shape))
max_loss = per_color_loss.max().item()
median_loss = per_color_loss.median().item()
with displayer_mpl(
    f'large-assets/ex-{nbid}-loss-colors-ablated.png',
    alt_text=f"""Line chart showing loss per color, titled "{{title}}". Y-axis: mean square error, ranging from zero to {max_loss:.2g}. X-axis: hue. There is a significant peak at red at either end of the X-axis and at cyan in the middle, gradually sloping down to lower loss values near yellow-green and blue-magenta. Two smaller line charts show error vs. saturation and error vs. value, with high error near high value/saturation, and low error near low value/saturation.""",
) as show:
    show(
        lambda: plot_cube_series(
            loss_cube.permute('hsv')[:, -1:, :: (loss_cube.shape[2] // -5)],
            loss_cube.permute('svh')[:, -1:, :: -(loss_cube.shape[0] // -6)],
            loss_cube.permute('vsh')[:, -1:, :: -(loss_cube.shape[0] // -6)],
            title='Reconstruction error · ablated',
            var='MSE',
            figsize=(12, 3),
        )
    )
print(f'Max loss: {max_loss:.2g}')
print(f'Median MSE: {median_loss:.2g}')

Max loss: 0.27
Median MSE: 0.0031

from IPython.display import clear_output

from ex_color.vis import plot_latent_grid_3d

y_rgb, h_rgb = await infer_with_latent_capture(ablated_model, x_rgb, 'bottleneck')
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-latents-ablated.png',
    alt_text="""Three spherical plots, titled "{title}". Each plot shows a vibrant collection of colored circles or balls scattered over the surface of a sphere. The first plot has horizontal line across the middle, with green on the left and blue on the right, and conspicuously empty spaces at the top and bottom. The other plots show different views of the same sphere, with hue varying across the equator from green to blue/purple and tone varying from top to bottom. The centre of the spheres where you might expect to see red or cyan instead show desaturated grays, white, and black. Each ball shows the reconstructed color, with a dot in the center showing the true (input) color. The true and reconstructed colors agree fairly well. Red and cyan and nearby colors are in fact not visible, being buried somewhere inside the sphere.""",
) as show:
    show(
        lambda theme: plot_latent_grid_3d(
            h_rgb,
            y_rgb,
            x_rgb,
            title='Latents · ablated',
            dims=[(1, 0, 2), (1, 2, 3), (1, 3, 2)],
            dot_radius=10,
            theme=theme,
        )
    )

def prune[M](model: M, layer_id: str, dims: Sequence[int]) -> M:
    """Return a copy of model with selected latent dims fully removed."""
    ...

from ex_color.surgery import prune

pruned_model = prune(model, 'bottleneck', [0])

y_hsv = await infer(pruned_model, x_hsv)
hd_y_hsv = await infer(pruned_model, hd_x_hsv)
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-pred-colors-pruned.png',
    alt_text="""Plot showing four slices of the HSV cube, titled "{title}". Nominally, each slice has constant saturation, but varies in value (brightness) from top to bottom, and in hue from left to right. Each color value is represented as a square patch of that color. The outer portion of the patches shows the color as reconstructed by the model; the inner portion shows the true (input) color. The reconstructed and true colors agree fairly well, but "red" and "cyan" are clearly different: red itself appears as black, and the surrounding colors up to green and blue look more like green and purple. Colors near cyan are similarly affected.""",
) as show:
    show(
        lambda: plot_colors(
            hsv_cube,
            title='Predicted colors · pruned',
            colors=y_hsv.numpy(),
            colors_compare=x_hsv.numpy(),
        )
    )


per_color_loss = F.mse_loss(hd_y_hsv, hd_x_hsv, reduction='none').mean(dim=-1)
loss_cube = hd_hsv_cube.assign('MSE', per_color_loss.numpy().reshape(hd_hsv_cube.shape))
max_loss = per_color_loss.max().item()
median_loss = per_color_loss.median().item()
with displayer_mpl(
    f'large-assets/ex-{nbid}-loss-colors-pruned.png',
    alt_text=f"""Line chart showing loss per color, titled "{{title}}". Y-axis: mean square error, ranging from zero to {max_loss:.2g}. X-axis: hue. There is a significant peak at red at either end of the X-axis and at cyan in the middle, gradually sloping down to lower loss values near yellow-green and blue-magenta. Two smaller line charts show error vs. saturation and error vs. value, with high error near high value/saturation, and low error near low value/saturation.""",
) as show:
    show(
        lambda: plot_cube_series(
            loss_cube.permute('hsv')[:, -1:, :: (loss_cube.shape[2] // -5)],
            loss_cube.permute('svh')[:, -1:, :: -(loss_cube.shape[0] // -3)],
            loss_cube.permute('vsh')[:, -1:, :: -(loss_cube.shape[0] // -3)],
            title='Reconstruction error · pruned',
            var='MSE',
            figsize=(12, 3),
        )
    )
print(f'Max loss: {max_loss:.2g}')
print(f'Median MSE: {median_loss:.2g}')

Max loss: 0.27
Median MSE: 0.0031

from IPython.display import clear_output

from ex_color.vis import plot_latent_grid_3d

# Capture latents with repulsion
y_rgb, h_rgb = await infer_with_latent_capture(pruned_model, x_rgb, 'bottleneck')
clear_output()

with displayer_mpl(
    f'large-assets/ex-{nbid}-latents-pruned.png',
    alt_text="""Two spherical plots, titled "{title}". Each plot shows different views of a collection of colored circles or balls scattered over the surface of a sphere. Hue varyies across the equator from green to blue/purple and tone varying from top to bottom. The centre of the spheres where you might expect to see red or cyan instead show desaturated grays, white, and black. Each ball shows the reconstructed color, with a dot in the center showing the true (input) color. The true and reconstructed colors agree fairly well. Red and cyan and nearby colors are in fact not visible, being buried somewhere inside the sphere.""",
) as show:
    show(
        lambda theme: plot_latent_grid_3d(
            h_rgb,
            y_rgb,
            x_rgb,
            title='Latents · pruned',
            dims=[(0, 1, 2), (0, 2, 1)],
            dot_radius=10,
            theme=theme,
        )
    )

epoch	▁▁▁▁▂▂▂▂▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▆▆▆▆▆▆▆▇▇▇▇▇▇████
train_loss	█▄▄▆▆▃▃▃▂▄▆▂▆▃▄▄▂▃▄▃▂▂▃▃▂▂▁▂▂▁▁▁▁▁▁▁▁▁▁▁
train_recon	▆▅▅▄▃█▃▃▇▇▄▄▄▅▅▄▇▆▄▅▃▄▅▄▂▂▂▂▁▂▁▁▂▂▁▁▁▁▁▁
train_reg-anchor	▁▁▁█▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▁▁▂▁▁▁▁▁▁
train_reg-planar	▁█▁▃▁▁▁▁▂▁▂▁▁▁▁▁▂▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂
train_reg-separate	█▆▆██▆▄▅▃▃▄▅▁▃▂▄▄▁▂▅▄▃▃▄▄▂▂▂▁▂▂▄▁▃▂▂▃▂▃▄
train_reg-unit	█▇▆▅▃▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
trainer/global_step	▁▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███

epoch	52
train_loss	3e-05
train_recon	3e-05
train_reg-anchor	0
train_reg-planar	0.05332
train_reg-separate	0.43601
train_reg-unit	0.00924
trainer/global_step	2999

Experiment 2.6: Delete warm/cool axis

Hypothesis

Regularizers

Data

Training

Run history:

Run summary:

Inference utils

Ablation

Pruning

Conclusion