fix tensor dimensions are not compatible for FP8 issue in sft

[hyxcl]

What does this PR do ?

when we use nemo for sft FP8 training, it will reports
File "/usr/local/lib/python3.10/dist-packages/torch/autograd/function.py", line 289, in apply return user_fn(self, *args) File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/module/linear.py", line 447, in backward wgrad, _ = fp8_gemm( File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/cpp_extensions/gemm.py", line 45, in fp8_gemm assert_dim_for_fp8_exec(A) File "/usr/local/lib/python3.10/dist-packages/transformer_engine/pytorch/utils.py", line 221, in assert_dim_for_fp8_exec assert check_dim_for_fp8_exec(tensor), ( AssertionError: Tensor dimensions are not compatible for FP8 execution: (3584 % 8 != 0, 1912 % 16 != 0)

fp8 GEMM requires tensor dimension be multiple of 16. This means the sequence dimension of input data should be padded to multiple of 16, which is not implemented in NeMo chat style data loader yet.​
so need to change
max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 8)
to
max_length = min(self.max_seq_length, self._ceil_to_nearest(max_length, 16)
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