Harden trainer edge cases, vectorize input mapping, and unify pytest configuration

odyssnet/training/trainer.py

@@ -3,6 +3,7 @@
 import torch.optim as optim
 import time
 import math
+import numbers
 from typing import Callable
 from ..utils.data import prepare_input, to_tensor
 
@@ -194,6 +195,12 @@ def train_batch(self, input_features, target_values, thinking_steps, gradient_ac
         """
         Runs a single training step on a batch.
         """
+        if isinstance(gradient_accumulation_steps, bool) or not isinstance(gradient_accumulation_steps, numbers.Integral):
+            raise ValueError("gradient_accumulation_steps must be an integer >= 1")
+        gradient_accumulation_steps = int(gradient_accumulation_steps)
+        if gradient_accumulation_steps < 1:
+            raise ValueError("gradient_accumulation_steps must be an integer >= 1")
+
         self.model.train()
 
         self._ensure_scaler()
@@ -386,6 +393,15 @@ def fit(self, input_features, target_values, epochs, batch_size=32, thinking_ste
         input_features = to_tensor(input_features, self.device)
         target_values = to_tensor(target_values, self.device)
 
+        if isinstance(batch_size, bool) or not isinstance(batch_size, int):
+            raise TypeError("batch_size must be an integer")
+        if batch_size < 1:
+            raise ValueError("batch_size must be >= 1")
+        if len(input_features) != len(target_values):
+            raise ValueError("input_features and target_values must have the same length")
+        if len(input_features) == 0:
+            raise ValueError("input_features and target_values must be non-empty")
+
         history = []
 
         # Prepare Data

odyssnet/utils/data.py

@@ -48,8 +48,9 @@ def prepare_input(input_features, model_input_ids, num_neurons, device):
             x_input = torch.zeros(batch_size, steps, num_neurons, device=device)
 
             num_assigned = min(num_features, len(model_input_ids))
-            for k in range(num_assigned):
-                x_input[:, :, model_input_ids[k]] = input_features[:, :, k]
+            if num_assigned > 0:
+                target_ids = model_input_ids[:num_assigned]
+                x_input[:, :, target_ids] = input_features[:, :, :num_assigned]
 
             return x_input, batch_size
 
@@ -61,8 +62,9 @@ def prepare_input(input_features, model_input_ids, num_neurons, device):
         num_assigned = min(num_features, len(model_input_ids))
 
         # Assign features to neurons
-        for k in range(num_assigned):
-            x_input[:, model_input_ids[k]] = input_features[:, k]
+        if num_assigned > 0:
+            target_ids = model_input_ids[:num_assigned]
+            x_input[:, target_ids] = input_features[:, :num_assigned]
 
     return x_input, batch_size
 

pyproject.toml

@@ -50,6 +50,3 @@ all = [
 
 [tool.setuptools.packages.find]
 include = ["odyssnet*"]
-
-[tool.pytest.ini_options]
-testpaths = ["tests"]

tests/training/test_trainer.py

@@ -242,6 +242,16 @@ def test_non_pulse_2d_input_full_sequence(self):
         loss = t.train_batch(x, y, thinking_steps=10, full_sequence=True)
         assert isinstance(loss, float)
 
+    def test_gradient_accumulation_steps_must_be_positive(self):
+        model = _model()
+        t = _trainer(model)
+        x = _batch()
+        y = _targets()
+        with pytest.raises(ValueError):
+            t.train_batch(x, y, thinking_steps=2, gradient_accumulation_steps=0)
+        with pytest.raises(ValueError):
+            t.train_batch(x, y, thinking_steps=2, gradient_accumulation_steps=-1)
+
 
 # ===========================================================================
 # predict
@@ -341,6 +351,30 @@ def test_fit_loss_trend_downward_on_simple_data(self):
         history = t.fit(x, y, epochs=20, batch_size=n, thinking_steps=5, verbose=False)
         assert history[-1] < history[0], "Loss should decrease over training"
 
+    def test_fit_empty_dataset_raises(self):
+        model = _model()
+        t = _trainer(model)
+        x = torch.empty(0, 5)
+        y = torch.empty(0, 2)
+        with pytest.raises(ValueError):
+            t.fit(x, y, epochs=1, batch_size=4, thinking_steps=2, verbose=False)
+
+    def test_fit_length_mismatch_raises(self):
+        model = _model()
+        t = _trainer(model)
+        x = torch.randn(3, 5)
+        y = torch.randn(2, 2)
+        with pytest.raises(ValueError):
+            t.fit(x, y, epochs=1, batch_size=2, thinking_steps=2, verbose=False)
+
+    def test_fit_invalid_batch_size_raises(self):
+        model = _model()
+        t = _trainer(model)
+        x = torch.randn(4, 5)
+        y = torch.randn(4, 2)
+        with pytest.raises(ValueError):
+            t.fit(x, y, epochs=1, batch_size=0, thinking_steps=2, verbose=False)
+
 
 # ===========================================================================
 # regenerate_synapses