onnxruntime

[T5 optimization] script fusions and fixes (#14967)

### Description
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1. added script for t5 encoder self attention and t5 decoder self/cross
attention fusions.
2. added simplified layernorm fusion for --external_data_format senario.
(otherwise relying on ORT optimizer)
3. added rel_pos_bias shape inference code, modified attention/mha shape
inference script.
4. reworked graph_topologic_sort() because the currently implementation
is not functioning correctly. also added an option to topo-sort the
graph in a deterministic way to let tests pass.

note:
1. the t5-beamsearch export code is slightly modified. specifically,
encoder_hidden_states(ehs) is no longer an input to the t5 decoder since
the ehs is not actually used in the graph execution.
2. recent PRs do not add optimizations to t5 on cpu. 
3. the fp32 model(encoder and decoder) for t5-small, t5-base and
t5-large can get a parity of e-5 and the corresponding beam search
models generate same results as pytorch.
4. fp16(mixed-precision) models, however, get a parity around 3e-2 and
some has maximum diff a bit over 3e-2. But the beam search models still
generate same results as pytorch (based on limited input data)
5. mt-5 model has a parity issue at the moment, even before any
optimization. will investigate later.

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

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Co-authored-by: Ubuntu <wy@v100-2.0cdb2e52twzevn1i4fi45bylyg.jx.internal.cloudapp.net>