fuse mlx training step

docs/compatibility.md

@@ -18,7 +18,7 @@ loss.backward()
 optimizer.step()
 ```
 
-MLX implements this sequence by recording the outer model call and replaying it inside a cached, compiled `value_and_grad` during `backward`. The optimizer update is compiled separately. Inputs and loss operands remain dynamic, so batches are not captured as constants. Random state is restored for the replay so dropout uses the same mask. Models with eager data-dependent operations fall back to an uncompiled replay. Unrecorded loss expressions, gradient hooks, parameter `.grad`, higher-order gradients, and multiple-forward losses remain unsupported.
+MLX implements this sequence by recording the outer model call and replaying the forward, backward, and AdamW update inside one cached compiled graph during `step`. Inputs and loss operands remain dynamic, so batches are not captured as constants. Random state is restored for the replay so dropout uses the same mask. Reading `loss.item()` before `step` materializes gradients through a separate compiled path. Models with eager data-dependent operations fall back to an uncompiled replay. Unrecorded loss expressions, gradient hooks, parameter `.grad`, higher-order gradients, and multiple-forward losses remain unsupported.
 
 Set `TORCHMLX_BACKEND=torch` before import to use native PyTorch for unsupported programs. TorchMLX never changes backend during an operation.