Repository: gwkokab/gwkokab
Pull request #510: feat: change in neural VT architecture to `equinox.nn.MLP`
Author: login: Qazalbash
Labels: name: internal
description: An internal refactor or improvement
Created: 2025-04-01T12:20:36Z | Merged: 2025-04-01T13:16:31Z

## Summary

- We have changed neural VT architecture to `equinox.nn.MLP` from `equinox.nn.Sequential`.
- Introduced batching the loss evaluation and prediction.
- Changed the missed license headers in #499 to SPDX short forms.

## Related To

- #133
- #499

<!-- This is an auto-generated comment: release notes by coderabbit.ai -->

## Summary by CodeRabbit

- **Chores**
  - Simplified and standardized licensing information across the codebase by replacing extended notices with concise SPDX identifiers.

- **Refactor**
  - Updated the model architecture interfaces used in training, prediction, and model creation, switching from a sequential design to a multi-layer perceptron.
  - Renamed and restructured key training parameters (e.g., replacing a list of hidden layers with dedicated width and depth settings) and enhanced batch processing defaults for improved efficiency.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->


Diff:
diff --git a/src/gwkokab/vts/__init__.py b/src/gwkokab/vts/__init__.py
index b3f6393d..d2a1f6dc 100644
--- a/src/gwkokab/vts/__init__.py
+++ b/src/gwkokab/vts/__init__.py
@@ -1,16 +1,5 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
 
 
 #
diff --git a/src/gwkokab/vts/_abc.py b/src/gwkokab/vts/_abc.py
index fd72e081..410a5cfb 100644
--- a/src/gwkokab/vts/_abc.py
+++ b/src/gwkokab/vts/_abc.py
@@ -1,16 +1,6 @@
 # Copyright 2023 The GWKokab Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+
 
 from abc import abstractmethod
 from collections.abc import Callable, Sequence
diff --git a/src/gwkokab/vts/_injvt.py b/src/gwkokab/vts/_injvt.py
index e7d8e3fe..cf6d22d2 100644
--- a/src/gwkokab/vts/_injvt.py
+++ b/src/gwkokab/vts/_injvt.py
@@ -1,16 +1,5 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
 
 
 from collections.abc import Sequence
diff --git a/src/gwkokab/vts/_neuralvt.py b/src/gwkokab/vts/_neuralvt.py
index 8a73e98d..f4a5b67a 100644
--- a/src/gwkokab/vts/_neuralvt.py
+++ b/src/gwkokab/vts/_neuralvt.py
@@ -1,16 +1,5 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
 
 
 from collections.abc import Callable, Sequence
@@ -26,7 +15,7 @@
 
 
 class NeuralNetVolumeTimeSensitivity(VolumeTimeSensitivityInterface):
-    neural_vt_model: eqx.nn.Sequential = eqx.field(init=False)
+    neural_vt_model: eqx.nn.MLP = eqx.field(init=False)
     """The neural volume-time sensitivity model."""
 
     def __init__(
diff --git a/src/gwkokab/vts/_popmodelvt.py b/src/gwkokab/vts/_popmodelvt.py
index 82fe6341..e842a9a3 100644
--- a/src/gwkokab/vts/_popmodelvt.py
+++ b/src/gwkokab/vts/_popmodelvt.py
@@ -1,16 +1,6 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
+
 
 from collections.abc import Callable, Sequence
 from typing import Literal, Optional, overload, Tuple
diff --git a/src/gwkokab/vts/_train.py b/src/gwkokab/vts/_train.py
index c45c5283..a2b8c4e2 100644
--- a/src/gwkokab/vts/_train.py
+++ b/src/gwkokab/vts/_train.py
@@ -1,19 +1,8 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
-
-from typing import Optional
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
+
+
+from typing import Callable, Optional
 
 import equinox as eqx
 import jax
@@ -76,7 +65,8 @@ def train_regressor(
     *,
     input_keys: list[str],
     output_keys: list[str],
-    hidden_layers: list[int],
+    width_size: int,
+    depth: int,
     batch_size: int,
     data_path: str,
     checkpoint_path: Optional[str] = None,
@@ -90,8 +80,10 @@ def train_regressor(
         list of input keys
     output_keys : list[str]
         list of output keys
-    hidden_layers : list[int]
-        list of hidden layers
+    width_size : int
+        width size of the model
+    depth : int
+        depth of the model
     batch_size : int
         batch size for training
     data_path : str
@@ -119,16 +111,16 @@ def train_regressor(
 
     @eqx.filter_jit
     def make_step(
-        model: eqx.nn.Sequential,
+        model: eqx.nn.MLP,
         x: Array,
         y: Array,
         opt_state: optax.OptState,
-    ) -> tuple[eqx.nn.Sequential, optax.OptState, Array]:
+    ) -> tuple[eqx.nn.MLP, optax.OptState, Array]:
         """Make a step in the optimization process.
 
         Parameters
         ----------
-        model : eqx.nn.Sequential
+        model : eqx.nn.MLP
             Model to approximate the log of the VT function
         x : Array
             input data
@@ -139,7 +131,7 @@ def make_step(
 
         Returns
         -------
-        tuple[eqx.nn.Sequential, optax.OptState, Array]
+        tuple[eqx.nn.MLP, optax.OptState, Array]
             optimizer state
         """
         loss, grads = mse_loss_fn(model, x, y)
@@ -148,16 +140,16 @@ def make_step(
         return model, opt_state, loss
 
     def train_step(
-        model: eqx.nn.Sequential,
+        model: eqx.nn.MLP,
         x: Array,
         y: Array,
         opt_state: optax.OptState,
-    ) -> tuple[eqx.nn.Sequential, optax.OptState, Array]:
+    ) -> tuple[eqx.nn.MLP, optax.OptState, Array]:
         """Train the model for one epoch.
 
         Parameters
         ----------
-        model : eqx.nn.Sequential
+        model : eqx.nn.MLP
             Model to approximate the log of the VT function
         x : Array
             input data
@@ -168,7 +160,7 @@ def train_step(
 
         Returns
         -------
-        tuple[eqx.nn.Sequential, optax.OptState, Array]
+        tuple[eqx.nn.MLP, optax.OptState, Array]
             optimizer state
         """
         model, opt_state, loss = make_step(model, x, y, opt_state)
@@ -176,8 +168,8 @@ def train_step(
 
     df = read_data(data_path)
 
-    data_X = jax.device_put(df[input_keys].to_numpy())
-    data_Y = jax.device_put(df[output_keys].to_numpy())
+    data_X = jax.device_put(df[input_keys].to_numpy(), may_alias=True)
+    data_Y = jax.device_put(df[output_keys].to_numpy(), may_alias=True)
 
     log_data_Y = jnp.log(data_Y)
     log_data_Y = jnp.where(
@@ -196,7 +188,8 @@ def train_step(
     table.add_column("Value", justify="left")
     table.add_row("Input Keys", ", ".join(input_keys))
     table.add_row("Output Keys", ", ".join(output_keys))
-    table.add_row("Hidden Layers", ", ".join(map(str, hidden_layers)))
+    table.add_row("Width Size", str(width_size))
+    table.add_row("Depth", str(depth))
     table.add_row("Data Path", data_path)
     table.add_row("Checkpoint Path", checkpoint_path)
     table.add_row("Train Size", str(len(train_X)))
@@ -213,14 +206,19 @@ def train_step(
         key=jrd.PRNGKey(np.random.randint(0, 2**32 - 1)),
         input_layer=len(input_keys),
         output_layer=len(output_keys),
-        hidden_layers=hidden_layers,
+        width_size=width_size,
+        depth=depth,
     )
 
+    model: Callable = eqx.filter_checkpoint(model)  # type: ignore
+
     opt_state = optimizer.init(eqx.filter(model, eqx.is_inexact_array))
 
     loss_vals = []
     val_loss_vals = []
 
+    total = int(len(train_X) // batch_size)
+    epoch_loss = jnp.zeros(())
     with Progress(
         SpinnerColumn(),
         TextColumn("Epoch {task.fields[epoch]}"),
@@ -230,14 +228,12 @@ def train_step(
         TextColumn("[progress.description]{task.description}"),
         refresh_per_second=5,
     ) as progress:
-        total = int(len(train_X) // batch_size)
         for epoch in range(epochs):
             task_id = progress.add_task(
                 "",
                 total=total,
                 epoch=epoch + 1,
             )
-            epoch_loss = jnp.zeros(())
             for i in range(0, len(train_X), batch_size):
                 x = train_X[i : i + batch_size]
                 y = train_Y[i : i + batch_size]
@@ -258,11 +254,12 @@ def train_step(
             progress.update(
                 task_id,
                 completed=total,
+                refresh=True,
                 description=f"loss: {loss:.4f} - val loss: {val_loss:.4f}",
             )
 
     if checkpoint_path is not None:
-        save_model(filename=checkpoint_path, model=model, names=input_keys)
+        save_model(filename=checkpoint_path, model=model, names=input_keys)  # type: ignore
         plt.plot(loss_vals, label="loss")
         plt.plot(val_loss_vals, label="val loss")
         plt.yscale("log")
diff --git a/src/gwkokab/vts/_utils.py b/src/gwkokab/vts/_utils.py
index 21a7c130..84524698 100644
--- a/src/gwkokab/vts/_utils.py
+++ b/src/gwkokab/vts/_utils.py
@@ -1,21 +1,10 @@
-#  Copyright 2023 The GWKokab Authors
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# Copyright 2023 The GWKokab Authors
+# SPDX-License-Identifier: Apache-2.0
 
 
 import warnings
 from collections.abc import Sequence
-from typing import Any, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import equinox as eqx
 import h5py
@@ -38,7 +27,10 @@
 
 
 @eqx.filter_value_and_grad
-def mse_loss_fn(model: PyTree, x: Array, y: Array) -> Array:
+@eqx.filter_jit
+def mse_loss_fn(
+    model: PyTree, x: Array, y: Array, batch_size: Optional[int] = 256
+) -> Array:
     """Mean squared error loss function.
 
     Parameters
@@ -49,18 +41,22 @@ def mse_loss_fn(model: PyTree, x: Array, y: Array) -> Array:
         input data
     y : Array
         output data
+    batch_size : Optional[int], optional
+        batch size for training, by default 256. This is used to avoid OOM errors when
+        training large datasets. If None, the entire dataset will be trained
+        sequentially. This is not recommended for large datasets.
 
     Returns
     -------
     Array
         mean squared error
     """
-    y_pred = jax.vmap(model)(x)
+    y_pred = jax.lax.map(model, x, batch_size=batch_size)
     return jnp.mean(jnp.square(y - y_pred))  # mean squared error
 
 
 @eqx.filter_jit
-def predict(model: PyTree, x: Array) -> Array:
+def predict(model: PyTree, x: Array, batch_size: Optional[int] = 256) -> Array:
     """Predict the output of the model given the input data.
 
     Parameters
@@ -69,13 +65,17 @@ def predict(model: PyTree, x: Array) -> Array:
         Model to approximate the log of the VT function
     x : Array
         input data
+    batch_size : Optional[int], optional
+        batch size for prediction, by default 256. This is used to avoid OOM errors when
+        predicting large datasets. If None, the entire dataset will be predicted
+        sequentially. This is not recommended for large datasets.
 
     Returns
     -------
     Array
         predicted output
     """
-    return jax.vmap(model)(x)
+    return jax.lax.map(model, x, batch_size=batch_size)
 
 
 def read_data(data_path: str) -> pd.DataFrame:
@@ -102,8 +102,9 @@ def make_model(
     key: PRNGKeyArray,
     input_layer: int,
     output_layer: int,
-    hidden_layers: Optional[List[int]] = None,
-) -> eqx.nn.Sequential:
+    width_size: int,
+    depth: int,
+) -> eqx.nn.MLP:
     """Make a neural network model to approximate the log of the VT function.
 
     Parameters
@@ -114,61 +115,33 @@ def make_model(
         input layer of the model
     output_layer : int
         output layer of the model
-    hidden_layers : Optional[List[int]], optional
-        hidden layers of the model, by default None
+    width_size : int
+        width size of the model
+    depth : int
+        depth of the model
 
     Returns
     -------
-    eqx.nn.Sequential
+    eqx.nn.MLP
         neural network model
     """
     assert is_prng_key(key)
-    if hidden_layers is None:
-        keys = jrd.split(key, 2)
-        layers: List[eqx.nn.Linear | eqx.nn.Lambda] = [
-            eqx.nn.Linear(
-                in_features=input_layer,
-                out_features=output_layer,
-                key=keys[0],
-            )
-        ]
-        model = eqx.nn.Sequential(layers)
-        return model
-
-    keys = jrd.split(key, 2 + len(hidden_layers))
-
-    layers = [
-        eqx.nn.Linear(
-            in_features=input_layer,
-            out_features=hidden_layers[0],
-            key=keys[0],
-        ),
-        eqx.nn.Lambda(jnn.relu),
-    ]
-    for i in range(len(hidden_layers) - 1):
-        layers.append(
-            eqx.nn.Linear(
-                in_features=hidden_layers[i],
-                out_features=hidden_layers[i + 1],
-                key=keys[i + 1],
-            ),
-        )
-        layers.append(eqx.nn.Lambda(jnn.relu))
-    layers.append(
-        eqx.nn.Linear(
-            in_features=hidden_layers[-1], out_features=output_layer, key=keys[-1]
-        )
-    )
-
-    model = eqx.nn.Sequential(layers)
 
+    model = eqx.nn.MLP(
+        in_size=input_layer,
+        out_size=output_layer,
+        width_size=width_size,
+        depth=depth,
+        activation=jnn.relu,
+        key=key,
+    )
     return model
 
 
 def save_model(
     *,
     filename: str,
-    model: eqx.nn.Sequential,
+    model: eqx._ad._CheckpointWrapper,
     names: Optional[Sequence[str]] = None,
 ) -> None:
     """Save the model to the given file.
@@ -177,7 +150,7 @@ def save_model(
     ----------
     filename : str
         Name of the file to save the model
-    model : eqx.nn.Sequential
+    model : eqx.nn.MLP
         Model to approximate the log of the VT function
     names : Optional[Sequence[str]], optional
         names of the parameters, by default None
@@ -189,20 +162,22 @@ def save_model(
             warnings.warn(
                 f"Neural VT path does not end with .hdf5: {old_filename}. Saving to {filename} instead."
             )
-    num_layers = len(model.layers)
+    model: eqx.nn.MLP = model._fun  # type: ignore
     with h5py.File(filename, "w") as f:
         if names is not None:
             f.create_dataset("names", data=np.array(names, dtype="S"))
-        for i in range(0, num_layers, 2):
-            layer_number = i >> 1
-            layer_i = f.create_group(f"layer_{layer_number}")
-            layer_i.create_dataset(
-                f"weight_{layer_number}", data=model.layers[i].weight
-            )
-            layer_i.create_dataset(f"bias_{layer_number}", data=model.layers[i].bias)
-
-
-def load_model(filename) -> Tuple[List[str], eqx.nn.Sequential]:
+        f.create_dataset("in_size", data=model.in_size)  # type: ignore
+        f.create_dataset("out_size", data=model.out_size)  # type: ignore
+        f.create_dataset("width_size", data=model.width_size)  # type: ignore
+        f.create_dataset("depth", data=model.depth)  # type: ignore
+        num_layers = len(model.layers)  # type: ignore
+        for i in range(num_layers):
+            layer_i = f.create_group(f"layer_{i}")
+            layer_i.create_dataset(f"weight_{i}", data=model.layers[i].weight)  # type: ignore
+            layer_i.create_dataset(f"bias_{i}", data=model.layers[i].bias)  # type: ignore
+
+
+def load_model(filename) -> Tuple[List[str], eqx.nn.MLP]:
     """Load the model from the given file.
 
     Parameters
@@ -212,29 +187,31 @@ def load_model(filename) -> Tuple[List[str], eqx.nn.Sequential]:
 
     Returns
     -------
-    Tuple[List[str], eqx.nn.Sequential]
+    Tuple[List[str], eqx.nn.MLP]
         names of the parameters and the model
     """
-    layers: List[Any] = []
     with h5py.File(filename, "r") as f:
         names = f["names"][:]
         names = names.astype(str).tolist()
+        in_size = int(f["in_size"][()])
+        out_size = int(f["out_size"][()])
+        width_size = int(f["width_size"][()])
+        depth = int(f["depth"][()])
+        new_model = eqx.nn.MLP(
+            in_size=in_size,
+            out_size=out_size,
+            width_size=width_size,
+            depth=depth,
+            activation=jnn.relu,
+            key=jrd.PRNGKey(0),
+        )
         i = 0
         while f.get(f"layer_{i}"):
             layer_i = f[f"layer_{i}"]
             weight_i = jax.device_put(np.asarray(layer_i[f"weight_{i}"][:]))
             bias_i = jax.device_put(np.asarray(layer_i[f"bias_{i}"][:]))
-            nn = eqx.nn.Linear(
-                in_features=weight_i.shape[1],
-                out_features=weight_i.shape[0],
-                key=jrd.PRNGKey(0),
-            )
-            nn = eqx.tree_at(lambda l: l.weight, nn, weight_i)
-            nn = eqx.tree_at(lambda l: l.bias, nn, bias_i)
-            layers.append(nn)
-            layers.append(eqx.nn.Lambda(jnn.relu))
+            new_model = eqx.tree_at(lambda m: m.layers[i].weight, new_model, weight_i)
+            new_model = eqx.tree_at(lambda m: m.layers[i].bias, new_model, bias_i)
             i += 1
-    layers.pop(-1)  # remove the last relu layer
-    new_model = eqx.nn.Sequential(layers)
 
     return names, new_model
diff --git a/src/gwkokab/vts/_vt.py b/src/gwkokab/vts/_vt.py
index bbb6ccdb..a499d0b1 100644
--- a/src/gwkokab/vts/_vt.py
+++ b/src/gwkokab/vts/_vt.py
@@ -1,16 +1,6 @@
 # Copyright 2023 The GWKokab Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#    http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+
 
 import sys
 from typing import List


Reviews:
user: login: copilot-pull-request-reviewer[bot]
body: ## Pull Request Overview

This pull request changes the neural VT architecture from using eqx.nn.Sequential to eqx.nn.MLP, introduces batched loss evaluation and prediction to mitigate OOM issues, and updates license headers to use SPDX short forms.  
- Converted model construction and type hints from Sequential to MLP  
- Changed batching in mse_loss_fn and predict functions using jax.lax.map  
- Updated license headers across multiple files

### Reviewed Changes

Copilot reviewed 8 out of 8 changed files in this pull request and generated 1 comment.

| File                        | Description                                                              |
| --------------------------- | ------------------------------------------------------------------------ |
| src/gwkokab/vts/_vt.py      | Replaced Apache license header with SPDX identifier                      |
| src/gwkokab/vts/_utils.py   | Updated mse_loss_fn and predict to use jax.lax.map with batching; changed model creation to MLP |
| src/gwkokab/vts/_train.py   | Adjusted training functions and type hints to work with the new MLP model  |
| src/gwkokab/vts/_popmodelvt.py, _neuralvt.py, _injvt.py, _abc.py, __init__.py | Updated license headers and adjusted model types from Sequential to MLP where applicable |


<details>
<summary>Comments suppressed due to low confidence (1)</summary>

**src/gwkokab/vts/_utils.py:78**
* The use of jax.lax.map with a 'batch_size' parameter in the predict function may not be valid. Verify if the intended batching functionality is correctly implemented, or consider using an alternative approach that supports batching.
```
return jax.lax.map(model, x, batch_size=batch_size)
```
</details>


state: COMMENTED
author_association: NONE
submitted_at: 2025-04-01T12:21:14Z
user: login: coderabbitai[bot]
body: **Actionable comments posted: 6**

<details>
<summary>📜 Review details</summary>

**Configuration used: CodeRabbit UI**
**Review profile: ASSERTIVE**
**Plan: Pro**

<details>
<summary>📥 Commits</summary>

Reviewing files that changed from the base of the PR and between e85c2a4e01f19b76226c7ac9412495b2e94d26c0 and 455bd91b107d54aa678b92d387178a0a73d6e28c.

</details>

<details>
<summary>📒 Files selected for processing (8)</summary>

* `src/gwkokab/vts/__init__.py` (1 hunks)
* `src/gwkokab/vts/_abc.py` (1 hunks)
* `src/gwkokab/vts/_injvt.py` (1 hunks)
* `src/gwkokab/vts/_neuralvt.py` (2 hunks)
* `src/gwkokab/vts/_popmodelvt.py` (1 hunks)
* `src/gwkokab/vts/_train.py` (10 hunks)
* `src/gwkokab/vts/_utils.py` (9 hunks)
* `src/gwkokab/vts/_vt.py` (1 hunks)

</details>

<details>
<summary>🧰 Additional context used</summary>

<details>
<summary>🧬 Code Definitions (1)</summary>

<details>
<summary>src/gwkokab/vts/_train.py (1)</summary><blockquote>

<details>
<summary>src/gwkokab/vts/_utils.py (2)</summary>

* `read_data` (81-97)
* `save_model` (139-175)

</details>

</blockquote></details>

</details>

</details>

<details>
<summary>⏰ Context from checks skipped due to timeout of 90000ms (2)</summary>

* GitHub Check: build (py 3.11 on ubuntu-latest)
* GitHub Check: build (py 3.10 on ubuntu-latest)

</details>

<details>
<summary>🔇 Additional comments (36)</summary><blockquote>

<details>
<summary>src/gwkokab/vts/_abc.py (1)</summary>

`1-3`: **Licensing Header Standardization:** The licensing header has been updated to use the concise SPDX identifier (`# SPDX-License-Identifier: Apache-2.0`). This makes the file’s licensing information clearer and aligns with the project-wide update.

</details>
<details>
<summary>src/gwkokab/vts/_vt.py (2)</summary>

`1-3`: **Top Licensing Header Updated:** The top licensing header now uses the SPDX short form (`# SPDX-License-Identifier: Apache-2.0`), which improves consistency with the other modules.

---

`36-37`: **Multiple Licensing Notices Detected:** An additional licensing header appears at line 36 with `# SPDX-License-Identifier: MIT` under a different copyright note. Please verify that including both Apache-2.0 at the top and MIT here is intentional and complies with the project’s licensing policies.

</details>
<details>
<summary>src/gwkokab/vts/__init__.py (1)</summary>

`1-3`: **Licensing Header Consolidation:** The header has been simplified to adopt the SPDX identifier, ensuring that all files in the module now present a consistent, concise licensing statement.

</details>
<details>
<summary>src/gwkokab/vts/_injvt.py (1)</summary>

`1-3`: **Licensing Header Revision:** The licensing header has been streamlined using the SPDX format (`# SPDX-License-Identifier: Apache-2.0`), which is consistent with the updates in other parts of the repository.

</details>
<details>
<summary>src/gwkokab/vts/_popmodelvt.py (1)</summary>

`1-3`: **Licensing Header Update:** The extensive copyright comments have been replaced with a concise SPDX license identifier (`# SPDX-License-Identifier: Apache-2.0`). This improves readability and maintains proper attribution per the new standard.

</details>
<details>
<summary>src/gwkokab/vts/_neuralvt.py (2)</summary>

`1-2`: **License header looks good.**  
This update aligns with the simplified license format.

---

`18-18`: **Switching to `eqx.nn.MLP`.**  
Adopting `eqx.nn.MLP` consistently reflects the PR objective to replace `Sequential`. No problems identified.

</details>
<details>
<summary>src/gwkokab/vts/_train.py (11)</summary>

`114-118`: **Updated function signature to `eqx.nn.MLP`.**  
Switching from `eqx.nn.Sequential` to `eqx.nn.MLP` is consistent with the PR's architectural changes.

---

`123-123`: **Docstring alignment.**  
The docstring properly indicates `model : eqx.nn.MLP`. Matches the new signature.

---

`134-134`: **Return type.**  
Declaring a tuple containing `eqx.nn.MLP` is correct, matching the new model form.

---

`143-147`: **Second function signature update to `eqx.nn.MLP`.**  
Good consistency with the rest of the changes.

---

`152-152`: **Correct docstring type for `model`.**  
Matches the updated function signature.

---

`163-163`: **Return type for `train_step`.**  
Good to see the return signature includes the new MLP. No issues found.

---

`191-192`: **Adding rows for `Width Size` and `Depth`.**  
Great job updating the progress table to reflect the new parameters.

---

`209-210`: **Propagating parameters to `make_model`.**  
Setting `width_size` and `depth` aligns with the MLP constructor.

---

`220-221`: **Initializing counters for epoch tracking.**  
Storing total steps and initializing `epoch_loss` is clear and straightforward.

---

`257-257`: **Progress bar refresh.**  
Explicit `refresh=True` ensures the UI updates properly. Looks good.

---

`171-172`: <details>
<summary>✅ Verification successful</summary>

**Ensuring `may_alias=True` usage is valid.**  
This advanced feature can improve performance but can cause tricky corner cases. Please confirm it’s safe in your context.

---

🏁 Script executed:

```shell
#!/bin/bash
# Checking whether 'may_alias=True' usage appears elsewhere
rg 'may_alias=True'
```

Length of output: 1438

---



</details>

**Confirmed Consistent `may_alias=True` Usage**

The use of `may_alias=True` in `src/gwkokab/vts/_train.py` has been verified against similar usage throughout the repository (e.g., in `src/kokab/utils/common.py`, `src/gwkokab/vts/_injvt.py`, and others). Given its consistent application and assuming appropriate safeguards around immutability, the usage in this context appears safe. Please ensure that any corner cases related to aliasing are adequately covered by tests and documentation.

</details>
<details>
<summary>src/gwkokab/vts/_utils.py (17)</summary>

`1-2`: **License header updated.**  
This aligns with the project-wide switch to short SPDX notices.

---

`7-7`: **Importing `List` and `Tuple`.**  
No issues noticed. Provides clearer type hinting.

---

`30-33`: **`mse_loss_fn`: introduction of `batch_size`.**  
Note that a previous comment flagged `jax.lax.map` with a `batch_size` argument as potentially unsupported. This usage might still be non-standard.

---

`54-54`: **Mapping model with batch size.**  
Same usage as prior. The previous note about verifying correctness remains relevant.

---

`59-59`: **New `predict` function annotation.**  
Function is exposed via `@eqx.filter_jit`. No immediate issues noticed.

---

`78-78`: **Return statement within `predict`.**  
Identical usage of `jax.lax.map` with `batch_size`. Past concerns still apply.

---

`105-107`: **Function signature for `make_model`.**  
Using `width_size` and `depth` is a simpler approach than handling multiple hidden layers explicitly.

---

`123-123`: **Return annotation updated to `eqx.nn.MLP`.**  
Docstring is correct and matches the implemented function.

---

`128-135`: **Creating the MLP.**  
The new parameters and usage of `nn.relu` are standard. No concerns.

---

`142-142`: **Checkpoint wrapper in `save_model`.**  
Compatible with `eqx.filter_checkpoint`. No issues found.

---

`151-151`: **Docstring for `model`.**  
The docstring now references `eqx.nn.MLP` accurately.

---

`167-175`: **Storing model attributes in HDF5.**  
Capturing shape parameters and layer weights is correct for future restoration.

---

`178-178`: **Load model signature.**  
Good that it returns both parameter names and an MLP instance.

---

`188-188`: **Docstring return type.**  
Accurately matches the function signature.

---

`194-198`: **Reading MLP attributes from file.**  
Restructuring to support MLP-based fields is logical and consistent with `make_model`.

---

`199-205`: **Reconstructing `eqx.nn.MLP`.**  
Initialization with the stored sizes is correct. Keys are used for random state fallback.

---

`211-212`: **Layer weight and bias restoration.**  
Overwriting each layer’s `weight` and `bias` ensures correct state reload. Nicely done.

</details>

</blockquote></details>

</details>

<!-- This is an auto-generated comment by CodeRabbit for review status -->
state: COMMENTED
author_association: CONTRIBUTOR
submitted_at: 2025-04-01T12:35:30Z

Review comments:
diff_hunk: @@ -49,18 +41,22 @@ def mse_loss_fn(model: PyTree, x: Array, y: Array) -> Array:
         input data
     y : Array
         output data
+    batch_size : Optional[int], optional
+        batch size for training, by default 256. This is used to avoid OOM errors when
+        training large datasets. If None, the entire dataset will be trained
+        MLPly. This is not recommended for large datasets.
 
     Returns
     -------
     Array
         mean squared error
     """
-    y_pred = jax.vmap(model)(x)
+    y_pred = jax.lax.map(model, x, batch_size=batch_size)
path: src/gwkokab/vts/_utils.py
user: login: Copilot
body: The use of jax.lax.map with a 'batch_size' parameter may not be supported by the API. Consider verifying the correct batching function or mechanism (e.g., using jax.lax.scan or a custom batching loop) to avoid unexpected behavior.
```suggestion
    if batch_size is None:
        y_pred = model(x)
    else:
        y_pred = []
        for i in range(0, len(x), batch_size):
            batch_x = x[i:i + batch_size]
            batch_y_pred = model(batch_x)
            y_pred.append(batch_y_pred)
        y_pred = jnp.concatenate(y_pred, axis=0)
```
created_at: 2025-04-01T12:21:14Z
author_association: NONE
diff_hunk: @@ -90,8 +80,10 @@ def train_regressor(
         list of input keys
     output_keys : list[str]
         list of output keys
-    hidden_layers : list[int]
-        list of hidden layers
+    width_size : list[int]
+        width size of the model
+    depth : int
+        depth of the model
path: src/gwkokab/vts/_train.py
user: login: coderabbitai[bot]
body: _⚠️ Potential issue_

**Docstring mismatch for `width_size`.**  
The docstring states `width_size : list[int]`, but the function signature expects an `int`. This could cause confusion. Please fix the docstring to match the parameter type.  

Apply this diff to correct the docstring:

```diff
-    width_size : list[int]
+    width_size : int
```


<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

`````suggestion
    width_size : int
        width size of the model
    depth : int
        depth of the model
`````

</details>

<!-- suggestion_end -->

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR
diff_hunk: @@ -213,14 +206,19 @@ def train_step(
         key=jrd.PRNGKey(np.random.randint(0, 2**32 - 1)),
         input_layer=len(input_keys),
         output_layer=len(output_keys),
-        hidden_layers=hidden_layers,
+        width_size=width_size,
+        depth=depth,
     )
 
+    model: Callable = eqx.filter_checkpoint(model)  # type: ignore
+
path: src/gwkokab/vts/_train.py
user: login: coderabbitai[bot]
body: _🧹 Nitpick (assertive)_

**Use of `# type: ignore` on a wrapped model.**  
Consider removing the ignore comment or adding more precise type hints if possible.  

```diff
-    model: Callable = eqx.filter_checkpoint(model)  # type: ignore
+    model: eqx._ad._CheckpointWrapper = eqx.filter_checkpoint(model)
```


<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

`````suggestion
    model: eqx._ad._CheckpointWrapper = eqx.filter_checkpoint(model)
`````

</details>

<!-- suggestion_end -->

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR
diff_hunk: @@ -258,11 +254,12 @@ def train_step(
             progress.update(
                 task_id,
                 completed=total,
+                refresh=True,
                 description=f"loss: {loss:.4f} - val loss: {val_loss:.4f}",
             )
 
     if checkpoint_path is not None:
-        save_model(filename=checkpoint_path, model=model, names=input_keys)
+        save_model(filename=checkpoint_path, model=model, names=input_keys)  # type: ignore
path: src/gwkokab/vts/_train.py
user: login: coderabbitai[bot]
body: _🧹 Nitpick (assertive)_

**Ignore comment when saving the model.**  
Consider removing `# type: ignore` if no longer needed.  

```diff
-        save_model(filename=checkpoint_path, model=model, names=input_keys)  # type: ignore
+        save_model(filename=checkpoint_path, model=model, names=input_keys)
```


<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

`````suggestion
        save_model(filename=checkpoint_path, model=model, names=input_keys)
`````

</details>

<!-- suggestion_end -->

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR
diff_hunk: @@ -76,7 +65,8 @@ def train_regressor(
     *,
     input_keys: list[str],
     output_keys: list[str],
-    hidden_layers: list[int],
+    width_size: int,
+    depth: int,
path: src/gwkokab/vts/_train.py
user: login: coderabbitai[bot]
body: _💡 Verification agent_

<details>
<summary>🧩 Analysis chain</summary>

**New parameters `width_size` and `depth`.**  
These parameters are well-aligned with the new MLP-based architecture. Please confirm any unused references to `hidden_layers` are removed.  


---

🏁 Script executed:

```shell
#!/bin/bash
# Searching for any leftover references to 'hidden_layers' across the repo
rg -A 3 'hidden_layers'
```

Length of output: 526

---



</details>

**Action Required: Update Unused `hidden_layers` References**

The new parameters `width_size` and `depth` align with the new MLP-based architecture. However, the search shows lingering references to `hidden_layers` in the following locations:
- **src/gwkokab/vts/_utils.py** – appears in type annotations and documentation.
- **examples/nvt_training.ipynb** – still includes configuration for `hidden_layers`.

Please verify if these references are still necessary. If they aren’t used in the updated architecture, kindly remove or update them accordingly.

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR
diff_hunk: @@ -69,13 +65,17 @@ def predict(model: PyTree, x: Array) -> Array:
         Model to approximate the log of the VT function
     x : Array
         input data
+    batch_size : Optional[int], optional
+        batch size for prediction, by default 256. This is used to avoid OOM errors when
+        predicting large datasets. If None, the entire dataset will be predicted
+        MLPly. This is not recommended for large datasets.
path: src/gwkokab/vts/_utils.py
user: login: coderabbitai[bot]
body: _🧹 Nitpick (assertive)_

**Docstring grammar check.**  
Consider removing the “MLPly” phrasing and clarifying large dataset caution.  

Apply this diff to fix the docstring:

```diff
-        MLPly. This is not recommended for large datasets.
+        fully. This is not recommended for large datasets.
```


<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

`````suggestion
    batch_size : Optional[int], optional
        batch size for prediction, by default 256. This is used to avoid OOM errors when
        predicting large datasets. If None, the entire dataset will be predicted
        fully. This is not recommended for large datasets.
`````

</details>

<!-- suggestion_end -->

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR
diff_hunk: @@ -189,20 +160,22 @@ def save_model(
             warnings.warn(
                 f"Neural VT path does not end with .hdf5: {old_filename}. Saving to {filename} instead."
             )
-    num_layers = len(model.layers)
+    model: eqx.nn.MLP = model._fun  # type: ignore
path: src/gwkokab/vts/_utils.py
user: login: coderabbitai[bot]
body: _🧹 Nitpick (assertive)_

**Unwrapping `_fun` with `# type: ignore`.**  
Removing or refining the ignore comment may improve clarity.  

```diff
-    model: eqx.nn.MLP = model._fun  # type: ignore
+    model_unwrapped: eqx.nn.MLP = model._fun
```


<!-- suggestion_start -->

<details>
<summary>📝 Committable suggestion</summary>

> ‼️ **IMPORTANT**
> Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.

`````suggestion
    model_unwrapped: eqx.nn.MLP = model._fun
`````

</details>

<!-- suggestion_end -->

<!-- This is an auto-generated comment by CodeRabbit -->
created_at: 2025-04-01T12:35:29Z
author_association: CONTRIBUTOR

PR comments:
user: login: Qazalbash
created_at: 2025-04-01T12:24:45Z
body: @coderabbitai full review
author_association: MEMBER
user: login: coveralls
created_at: 2025-04-01T12:31:38Z
body: 
[![Coverage Status](https://coveralls.io/builds/73019253/badge)](https://coveralls.io/builds/73019253)

coverage: 42.755% (+0.07%) from 42.682%
when pulling **455bd91b107d54aa678b92d387178a0a73d6e28c on vt-training-optimize**
into **e85c2a4e01f19b76226c7ac9412495b2e94d26c0 on dev**.

author_association: NONE
user: login: coveralls
created_at: 2025-04-01T12:31:38Z
body: 
[![Coverage Status](https://coveralls.io/builds/73019918/badge)](https://coveralls.io/builds/73019918)

coverage: 42.755% (+0.07%) from 42.682%
when pulling **5fb40021fd86e71e22db1fe901f678034eb5ebe2 on vt-training-optimize**
into **e85c2a4e01f19b76226c7ac9412495b2e94d26c0 on dev**.

author_association: NONE
user: login: coderabbitai[bot]
created_at: 2025-04-01T12:35:26Z
body: <!-- This is an auto-generated comment: summarize by coderabbit.ai -->
<!-- walkthrough_start -->

## Walkthrough
This pull request standardizes licensing headers across several modules by replacing detailed multi‐line copyright statements with concise notices that include SPDX license identifiers. In addition, it updates the model architecture type in one module from a sequential network to a multi-layer perceptron and adjusts several function signatures. Parameter names have been renamed and new parameters have been introduced (such as `depth` and `batch_size`) in training and utility functions. These changes modify only declarations, documentation, and non-functional metadata while leaving core logic unchanged.

## Changes
| Files | Change Summary |
| --- | --- |
| `src/gwkokab/vts/__init__.py`, `src/gwkokab/vts/_abc.py`, `src/gwkokab/vts/_injvt.py`, `src/gwkokab/vts/_popmodelvt.py`, `src/gwkokab/vts/_vt.py` | Removed detailed licensing comments; replaced with concise copyright notices and SPDX license identifiers. |
| `src/gwkokab/vts/_neuralvt.py` | Changed `neural_vt_model` type from `eqx.nn.Sequential` to `eqx.nn.MLP` in `NeuralNetVolumeTimeSensitivity` class; updated licensing header. |
| `src/gwkokab/vts/_train.py` | Renamed parameter `hidden_layers` to `width_size` and added `depth` in `train_regressor`; updated `make_step` and `train_step` signatures from `eqx.nn.Sequential` to `eqx.nn.MLP`; modified docstrings, initialization, and progress update logic. |
| `src/gwkokab/vts/_utils.py` | Added optional `batch_size` parameter (defaulting to 256) to `mse_loss_fn` and `predict`; revised `make_model` to use `width_size` and `depth` and return an `eqx.nn.MLP`; updated `save_model` and `load_model` signatures and types to reflect the new model structure. |

## Suggested labels
`pull ready`

<!-- walkthrough_end -->
<!-- internal state start -->


<!-- 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-->

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<!-- finishing_touch_checkbox_start -->

<details>
<summary>✨ Finishing Touches</summary>

- [ ] <!-- {"checkboxId": "7962f53c-55bc-4827-bfbf-6a18da830691"} --> 📝 Generate Docstrings

</details>

<!-- finishing_touch_checkbox_end -->
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<summary>🪧 Tips</summary>

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</details>

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author_association: CONTRIBUTOR