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This is no longer needed since isort's version is 5.10

Also fix black version to 22.3.0 to fix issue with click
dependency.

Update files that now fail with new version of black {a = 2 ** 4} ->
{a = 2**4}

Add GradScaler to Fairscale, subclassing PyTorch's GradScaler. Use GradScaler in the pipe benchmark; though it is not needed in this case, it is a good example of how to use gradient scaling for larger models that do require gradient scaling in order to converge.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Implement scaling of optimizer state when using pure-fp16 training to avoid underflow. Update benchmark to use pure-fp16. Modify state_dict methods to store and load the optimizer state scale.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Set the torch seed for tests. xfail mixed precision and memory-efficient mixed-precision state_dict tests due to their states being cast to FP16 and back to FP32 during load_state_dict.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Refactor tests to remove duplicated code. Fix the state_dict test to instantiate the second optimizer with the correct precision. Fix Adam.load_state_dict to make optimizer state the right type.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Allow training with optimizer state in fp16. Use an enum to select from full-precision, mixed precision, memory efficient mixed precision and pure fp16. Improve clarity of testing code
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Add support for mixed-precision (half precision params, full precision gradients) and memory-efficient (half precision params and half precision gradients) training with Adam
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>

Add FusedAdam, update benchmark and add tests.
Co-authored-by: Jun Ru Anderson <andersonic@fb.com>