Add tfds tests (#8)

.github/workflows/test.yaml

@@ -35,8 +35,8 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install .[dev]
-        pip install -U jax flax optax orbax
+        pip install .[dev,tfds]
+        pip install -U jax flax optax orbax tensorflow tensorflow_datasets
     - name: Run tests
       run: |
         pytest -n auto jax_ml_stack
@@ -60,7 +60,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install .[dev]
+        pip install .[dev,tfds]
     - name: Run tests
       run: |
         pytest -n auto jax_ml_stack

jax_ml_stack/tests/test_nnx_with_tfds.py

@@ -0,0 +1,74 @@
+# Copyright 2024 The JAX 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
+#
+#     https://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.
+
+import contextlib
+import functools
+import unittest
+from flax import nnx
+import numpy as np
+import optax
+import tensorflow_datasets as tfds
+
+
+# Simple CNN from https://flax.readthedocs.io/en/latest/nnx/mnist_tutorial.html
+class CNN(nnx.Module):
+
+  def __init__(self, *, rngs: nnx.Rngs):
+    self.conv1 = nnx.Conv(1, 32, kernel_size=(3, 3), rngs=rngs)
+    self.conv2 = nnx.Conv(32, 64, kernel_size=(3, 3), rngs=rngs)
+    self.avg_pool = functools.partial(
+        nnx.avg_pool, window_shape=(2, 2), strides=(2, 2)
+    )
+    self.linear1 = nnx.Linear(448, 64, rngs=rngs)
+    self.linear2 = nnx.Linear(64, 10, rngs=rngs)
+
+  def __call__(self, x):
+    x = self.avg_pool(nnx.relu(self.conv1(x)))
+    x = self.avg_pool(nnx.relu(self.conv2(x)))
+    x = x.reshape(x.shape[0], -1)  # flatten
+    x = nnx.relu(self.linear1(x))
+    x = self.linear2(x)
+    return x
+
+
+class NNXTFDSTest(unittest.TestCase):
+
+  def setUp(self):
+    if hasattr(self, 'enterContext'):  # Python 3.11 or newer
+      self.enterContext(tfds.testing.mock_data(num_examples=5))
+    else:
+      with contextlib.ExitStack() as stack:
+        stack.enter_context(tfds.testing.mock_data(num_examples=5))
+        self.addCleanup(stack.pop_all().close)
+
+  def test_nnx_with_tfds(self):
+    train_ds = tfds.load('mnist', split='train')
+    model = CNN(rngs=nnx.Rngs(0))
+    optimizer = nnx.Optimizer(model, optax.adamw(learning_rate=0.005))
+
+    def loss_fn(model, batch):
+      logits = model(batch['image'])
+      loss = optax.softmax_cross_entropy_with_integer_labels(
+          logits=logits, labels=np.ravel(batch['label'])
+      ).mean()
+      return loss, logits
+
+    for batch in train_ds.as_numpy_iterator():
+      grad_fn = nnx.value_and_grad(loss_fn, has_aux=True)
+      _, grads = grad_fn(model, batch)
+      optimizer.update(grads)
+
+
+if __name__ == '__main__':
+  unittest.main()

pyproject.toml

@@ -33,6 +33,10 @@ dev = [
     "pytest",
     "pytest-xdist",
 ]
+tfds = [
+    "tensorflow==2.17.0",
+    "tensorflow_datasets==4.9.6",
+]
 
 [tool.pyink]
 # Formatting configuration to follow Google style-guide