Add SMP 1.2.0 release notes

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.md

@@ -1,3 +1,44 @@
+# Sagemaker Distributed Model Parallel 1.2.0 Release Notes
+
+- New Features
+- Bug Fixes
+- Known Issues
+
+## New Features
+
+### PyTorch
+
+#### Add support for PyTorch 1.7
+
+- Adds support for `gradient_as_bucket_view` (PyTorch 1.7 only), `find_unused_parameters` (PyTorch 1.7 only) and `broadcast_buffers` options to `smp.DistributedModel`. These options behave the same as the corresponding options (with the same names) in
+`torch.DistributedDataParallel` API. Please refer to the [SageMaker distributed model parallel API documentation](https://sagemaker.readthedocs.io/en/stable/api/training/smd_model_parallel_pytorch.html#smp.DistributedModel) for more information.
+
+- Adds support for `join` (PyTorch 1.7 only) context manager, which is to be used in conjunction with an instance of `smp.DistributedModel` to be able to train with uneven inputs across participating processes.
+
+- Adds support for `_register_comm_hook` (PyTorch 1.7 only) which will register the callable as a communication hook for DDP. NOTE: Like in DDP, this is an experimental API and subject to change.
+
+### Tensorflow
+
+- Adds support for Tensorflow 2.4
+
+## Bug Fixes
+
+### PyTorch
+
+- `Serialization`: Fix a bug with serialization/flattening where instances of subclasses of dict/OrderedDicts were serialized/deserialized or internally flattened/unflattened as
+regular dicts.
+
+### Tensorflow
+
+- Fix a bug that may cause a hang during evaluation when there is no model input for one partition.
+
+## Known Issues
+
+### PyTorch
+
+- A performance regression was observed when training on SMP with PyTorch 1.7.1 compared to 1.6. The rootcause was found to be the slowdown in performance of `.grad` method calls in PyTorch 1.7.1 compared to 1.6. Please see the related discussion: https://github.com/pytorch/pytorch/issues/50636.
+
+
 # Sagemaker Distributed Model Parallel 1.1.0 Release Notes
 
 - New Features