Do not cast x

keras_nlp/models/gemma/gemma_attention.py

@@ -92,9 +92,6 @@ def build(self, inputs_shape):
     def _apply_rope(self, x, positions):
         """Rope rotate q or k."""
         # TODO: refactor to use RotaryEmbedding layer?
-        x = ops.cast(
-            x, dtype="float32"
-        )  # Carry out rope in float32, then downcast
         max_wavelength = 10000
         x_shape = ops.shape(x)
         freq_exponents = (2.0 / x_shape[-1]) * ops.arange(
@@ -103,14 +100,14 @@ def _apply_rope(self, x, positions):
         timescale = max_wavelength**freq_exponents
         radians = positions[..., None] / timescale[None, None, :]
         radians = radians[..., None, :]
-        sin, cos = ops.sin(radians), ops.cos(radians)
+        sin = ops.cast(ops.sin(radians), self.compute_dtype)
+        cos = ops.cast(ops.cos(radians), self.compute_dtype)
         x1, x2 = ops.split(x, 2, axis=-1)
         # Avoid `ops.concatenate` for now, to avoid a obscure bug with XLA
         # compilation on jax. We should be able to remove this once the
         # following PR is in all jax releases we care about:
         # https://github.com/openxla/xla/pull/7875
         output = ops.stack([x1 * cos - x2 * sin, x2 * cos + x1 * sin], axis=-1)
-        output = ops.cast(output, dtype=self.compute_dtype)
         return ops.reshape(output, x_shape)
 
     def _compute_attention(