onnxruntime

Add fusion support for Dnnl execution provider (#9897)

* Op fusion support added

In addition the following op fusions are detected
 - ConvRelu
 - MatMulAdd

This change includes
  - Change abstraction of Subgraph + node + tensor to support delete insert
    modify
  - add nodearg class to establish connection from tensor to node
  - add graphtransformer class to support fusion
  - add topological sort to ensure propoer node ordering after fusion
  - add convrelu + matmuladd primitive to support execution of fused nodes
  - Fix FusionResolution with missing tensors

    when fusing, if the target node contains fewer tensors then original
    patterns (Gelu and FastGelu ignores many initializers), potentially delete them
    also from inputs and initializers

    Also check tensor has no producer and consumer before deleting

Signed-off-by: Wang <zhaoyang.wang@intel.com>

* Gelu and FastGelu Fusion for DNNL EP

The basics of the Gelu/FastGelu code is modeled after:
   - core/optimizer/fast_gelu_fusion.cc and
   - core/optimizer/gelu_fusion.cc

OneDNN does not have support for 'Erf' unless it is part of 'Gelu'.

This results in detecting 'Gelu' fusion twice. Once when detecting
if the 'Erf' Operator is supported and again in the subgraph transformer
code. The capability code is finding the Gelu using onnxruntime:GraphViewer
and onnxruntime::Node.  While the transformer code is using DnnlSubgraph
and DnnlNode.  This results in two parts of code looking for the same
pattern but unfortanatly having little code reuse.

This also adds support for Biased versions of Gelu and FastGelu if they already
exist in a model.

Signed-off-by: George Nash <george.nash@intel.com>

* Code Clean Up

Signed-off-by: Wang <zhaoyang.wang@intel.com>

Co-authored-by: Wang <zhaoyang.wang@intel.com>