[OpenSora-hpcai] T5 refactor

[hadipash]

This PR replaces OpenSora's T5 with the one from mindone.transformers.

Differences between the refactored and the previous T5:

• In T5LayerNorm, hidden_states are now calculated as hidden_states = hidden_states * ops.rsqrt(variance + self.variance_epsilon) instead of hidden_states = hidden_states / ops.sqrt(variance + self.variance_epsilon) previously.
• In T5DenseActDense, a custom GELU activation is used (NewGELUActivation), instead of nn.GELU() used previously.
• [BFloat16 only] In T5Attention._relative_position_bucket, relative_position_if_large is calculated as follows to align with the PyTorch implementation:

relative_position_if_large = (
            max_exact
            + (
                ops.log(relative_position.float() / max_exact)
                / ops.log(Tensor(max_distance, ms.float32) / max_exact)
                * (num_buckets - max_exact)
            ).long()
        )

If the previous and current T5s are aligned (considering the points mentioned above), the outputs will be identical in both Pynative and Graph modes for all data types, i.e., FP32, FP16, and BF16.

Error compared to HF's transformers on 6 random long text samples:

Version	MS Precision	PT Precision	Mean Error	Max Error
Previous T5	FP32	FP32	9.1761e-5	4.5508e-2
Refactored T5	FP32	FP32	9.1782e-5	4.5497e-2
Previous T5	BF16	FP32	1.9345e-3	5.1995e-1
Refactored T5	BF16	FP32	1.9730e-3	4.4061e-1
Previous T5	BF16	BF16	2.2863e-3	5.3125e-1
Refactored T5	BF16	BF16	2.3284e-3	4.6875e-1

[SamitHuang]

lgtm