[ONNX] Update ONNX version to 1.5.0

Transformers don't need to export symbols.

Convert op/experimental to type_name descriptions

Switch core ops to type_name description.

* Convert CPU ops to new form

Remove obsolete sigmoid

* Fix export

* fix typo

* typo

[Py] Added clamp operator to Python API

* Convert PlaidML ops to new form

* style

* Add CF for Gather

* Style

* Move SlicePlan out of DynElimination, for reuse in ConstantFolding

* Add CF support for Slice

* Add CF for DynSlice

* Add <algorithm> to slice_plan.cpp, to make Windows happy

* Update Reshape CF to support all ETs

* Add CF for DynReshape

* Add CF for Transpose

* Add #include <numeric>, for std::iota

* style, oops

* Add Output support for a number of builders

* ../src/ngraph/builder/autobroadcast.cpp

* Remove some _values

* Simplify

* Simplify, add some more ops/builders

* ONNX failures

* Some GetOutputElement changes to help with Output<Node>

* Convert some ops to use Output<Node> inputs

* Review comments

* Some more changes of nodes to outputs

* Chane names for OutputVector helpers to avoid API change

* Cleanup

* Some more changes of nodes to outputs

* Chane names for OutputVector helpers to avoid API change

* [MLIR] Bump MLIR repo to commit 59167c2, 07/25/2019.

MLIR commit:
Author: River Riddle <riverriddle@google.com>
Date:   Wed Jul 24 16:41:11 2019 -0700

    NFC: Use ValueOfRange instead of T in Diagnostic::appendRange.

        For iterator_range, T is often the name of another iterator type
        and not the the value of the range.

LLVM commit:
Author: Marshall Clow <mclow.lists@gmail.com>
Date:   Thu Jul 25 03:26:05 2019 +0000

    Implement change #4 of P1466: Change weekday to accept both 0 and 7
    as Sunday. Add accessors 'c_encoding' and 'iso_encoding' to provide
    different interpretations of the weekday. Remove 'operator unsigned'

* style

* Move MLIR/LLVM repos a bit more forward

* CF for Sign, and extend element type capabilities for unary arithop CF

* CF for Ceiling and Floor

* Update CPU CF builders

* Update CPU CF builders

* CF for Not

* Add tests for new CPU CF folders

* Add tests for recently added CPU CF functors

* Add tests for non-CPU ceiling/floor CF

* Unit tests

* Add test for CPU folder

* CF for Sign, and extend element type capabilities for unary arithop CF

* CF for Ceiling and Floor

* Update CPU CF builders

* Update CPU CF builders

* Add tests for new CPU CF folders

* Add tests for recently added CPU CF functors

* Add tests for non-CPU ceiling/floor CF

* Unit tests

* Add tests for new CPU CF folders

* Add tests for recently added CPU CF functors

* CF for And and Or

* CF support for comparison ops

* Fix predicate for binary elementwise; add unit tests for non-arithmetic binops

* Update CPU CF builders

* Make validation a pass and add it after every pass by default

* cleanup

* update per review comments

* Switch plaid to new API for disabling  pass validation

* address review comment

* [MLIR] Enable affine dialect loop fusion

Enable affine dialect loop fusion in nGraph pipeline. It also adds an
opt flag to enable/disable it when ngraph-opt is in place. Fusion seems
to work for simple cases. It wasn't able to fuse dot + add, though, at
least in my test case. One example that worked:

Input:
  %6 = alloc() : memref<2500x2500xf32>
  affine.for %i3 = 0 to 2500 {
    affine.for %i4 = 0 to 2500 {
      %7 = load %arg0[%i3, %i4] : memref<2500x2500xf32>
      %8 = load %0[%i3, %i4] : memref<2500x2500xf32>
      %9 = addf %8, %7 : f32
      store %9, %6[%i3, %i4] : memref<2500x2500xf32>
    }
  }
  %10 = alloc() : memref<2500x2500xf32>
  affine.for %i5 = 0 to 2500 {
    affine.for %i6 = 0 to 2500 {
      %11 = load %arg2[%i5, %i6] : memref<2500x2500xf32>
      %12 = load %0[%i5, %i6] : memref<2500x2500xf32>
      %13 = addf %12, %11 : f32
      store %13, %10[%i5, %i6] : memref<2500x2500xf32>
    }
  }
  %14 = alloc() : memref<2500x2500xf32>
  affine.for %i7 = 0 to 2500 {
    affine.for %i8 = 0 to 2500 {
      %15 = load %10[%i7, %i8] : memref<2500x2500xf32>
      %16 = load %6[%i7, %i8] : memref<2500x2500xf32>
      %17 = addf %16, %15 : f32
      store %17, %14[%i7, %i8] : memref<2500x2500xf32>
    }
  }

Output:
  %8 = alloc() : memref<2500x2500xf32>
  affine.for %i3 = 0 to 2500 {
    affine.for %i4 = 0 to 2500 {
      %9 = load %arg2[%i3, %i4] : memref<2500x2500xf32>
      %10 = load %2[%i3, %i4] : memref<2500x2500xf32>
      %11 = addf %10, %9 : f32
      %12 = affine.apply #map2(%i3, %i4, %i3, %i4)
      %13 = affine.apply #map3(%i3, %i4, %i3, %i4)
      store %11, %0[%12, %13] : memref<1x1xf32>
      %14 = load %arg0[%i3, %i4] : memref<2500x2500xf32>
      %15 = load %2[%i3, %i4] : memref<2500x2500xf32>
      %16 = addf %15, %14 : f32
      %17 = affine.apply #map2(%i3, %i4, %i3, %i4)
      %18 = affine.apply #map3(%i3, %i4, %i3, %i4)
      store %16, %1[%17, %18] : memref<1x1xf32>
      %19 = affine.apply #map2(%i3, %i4, %i3, %i4)
      %20 = affine.apply #map3(%i3, %i4, %i3, %i4)
      %21 = load %0[%19, %20] : memref<1x1xf32>
      %22 = affine.apply #map2(%i3, %i4, %i3, %i4)
      %23 = affine.apply #map3(%i3, %i4, %i3, %i4)
      %24 = load %1[%22, %23] : memref<1x1xf32>
      %25 = addf %24, %21 : f32
      store %25, %8[%i3, %i4] : memref<2500x2500xf32>
    }
  }

* Rename MLIR_LLVM_OPTIONS to NGRAPH_MLIR_OPTIONS

Something like this works now:
NGRAPH_MLIR_OPTIONS="--enable-affine-loop-fusion=false"

* Disable loop fusion by default and fix typo