Added NNX/Linen API consistency test for Linear/Dense layer

flax/experimental/nnx/tests/nn/test_linear.py

@@ -12,8 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import jax
 import jax.numpy as jnp
+from absl.testing import parameterized
+from jax.lax import Precision
+from numpy.testing import assert_array_equal
 
+from flax import linen
 from flax.experimental import nnx
 
 
@@ -35,3 +40,30 @@ def test_basic_multi_features(self):
     assert module.kernel.shape == (2, 3, 4)
     assert module.bias is not None
     assert module.bias.shape == (3, 4)
+
+
+class TestLinenConsistency(parameterized.TestCase):
+
+  @parameterized.product(
+      use_bias = [True, False],
+      dtype = [jnp.float32, jnp.float16],
+      param_dtype = [jnp.float32, jnp.float16],
+      precision = [Precision.DEFAULT, Precision.HIGH, Precision.HIGHEST],
+  )
+  def test_nnx_linen_equivalence(self, **kwargs):
+    key = jax.random.key(42)
+    rngs = nnx.Rngs(42)
+    IN_FEATURES = 32
+    OUT_FEATURES = 64
+
+    x = jax.numpy.ones((1, IN_FEATURES))
+    model_nnx = nnx.Linear.create_abstract(IN_FEATURES, OUT_FEATURES, **kwargs, rngs=rngs)
+    model = linen.Dense(OUT_FEATURES, **kwargs)
+    variables = model.init(key, x)
+    model_nnx.kernel = variables['params']['kernel']
+    if kwargs["use_bias"]:
+      model_nnx.bias = variables['params']['bias']
+
+    out_nnx = model_nnx(x)
+    out = model.apply(variables, x)
+    assert_array_equal(out, out_nnx)