update onnx and add removed experimental ops to contrib ops (#723)

cgmanifest.json

@@ -49,7 +49,7 @@
          "component":{
             "type":"git",
             "git":{
-               "commitHash":"3a9a87871021a02d6b970c1e26ffa4c0dd720677",
+               "commitHash":"fdb978a580bcb461ea077b9772c973928b8dbf5f",
                "repositoryUrl":"https://github.com/onnx/onnx.git"
             }
          }

cmake/external/onnx

@@ -1 +1 @@
-Subproject commit 3a9a87871021a02d6b970c1e26ffa4c0dd720677
+Subproject commit fdb978a580bcb461ea077b9772c973928b8dbf5f

onnxruntime/core/graph/contrib_ops/contrib_defs.cc

@@ -286,6 +286,65 @@ If scale is not provided, crop the borders as provided.)DOC";
       .Output(0, "output", "Result, has same type as input, with H and W dimensions reduced.", "T")
       .TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.");
 
+  static const char* ThresholdedRelu_ver1_doc = R"DOC( 
+ThresholdedRelu takes one input data (Tensor<T>) and produces one output data 
+(Tensor<T>) where the rectified linear function, y = x for x > alpha, y = 0 otherwise, 
+is applied to the tensor elementwise. )DOC";
+
+  ONNX_CONTRIB_OPERATOR_SCHEMA(ThresholdedRelu)
+      .SinceVersion(1)
+      .SetDoc(ThresholdedRelu_ver1_doc)
+      .Attr("alpha", "Threshold value", AttributeProto::FLOAT, 1.0f)
+      .Input(0, "X", "Input tensor", "T")
+      .Output(0, "Y", "Output tensor", "T")
+      .TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.")
+      .TypeAndShapeInferenceFunction(ONNX_NAMESPACE::propagateShapeAndTypeFromFirstInput); 
+
+  static const char* DynamicSlice_ver1_doc = R"DOC(
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+Slices uses `axes`, `starts` and `ends` inputs to specify the start and end
+dimension for each axis in the list of axes, it uses this information to
+slice the input `data` tensor. If a negative value is passed for any of the
+start or end indices, it represent number of elements before the end of that
+dimension. If the value passed to start or end is larger than the `n` (the
+number of elements in this dimension), it represents `n`. For slicing to the
+end of a dimension with unknown size, it is recommended to pass in `INT_MAX`.
+If `axes` are omitted, they are set to `[0, ..., ndim-1]`.
+Example 1:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  axes = [0, 1]
+  starts = [1, 0]
+  ends = [2, 3]
+  result = [
+      [5, 6, 7],
+  ]
+Example 2:
+  data = [
+      [1, 2, 3, 4],
+      [5, 6, 7, 8],
+  ]
+  starts = [0, 1]
+  ends = [-1, 1000]
+  result = [
+      [2, 3, 4],
+  ]
+)DOC";
+
+  ONNX_CONTRIB_OPERATOR_SCHEMA(DynamicSlice)
+      .SinceVersion(1)
+      .SetDoc(DynamicSlice_ver1_doc)
+      .Input(0, "data", "Tensor of data to extract slices from.", "T")
+      .Input(1, "starts", "1-D tensor of starting indices of corresponding axis in `axes`", "Tind")
+      .Input(2, "ends", "1-D tensor of ending indices (exclusive) of corresponding axis in axes", "Tind")
+      .Input(3, "axes", "1-D tensor of axes that `starts` and `ends` apply to.", "Tind", OpSchema::Optional)
+      .Output(0, "output", "Sliced data tensor.", "T")
+      .TypeConstraint("T", OpSchema::all_tensor_types(), "Constrain input and output types to all tensor types.")
+      .TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain indices to integer types");
+
   ONNX_CONTRIB_OPERATOR_SCHEMA(Affine)
       .SinceVersion(10)
       .Deprecate()
@@ -332,6 +391,18 @@ If scale is not provided, crop the borders as provided.)DOC";
       .Output(0, "output", "Result, has same type as input, with H and W dimensions reduced.", "T")
       .TypeConstraint("T", {"tensor(float16)", "tensor(float)", "tensor(double)"}, "Constrain input and output types to float tensors.");
 
+  ONNX_CONTRIB_OPERATOR_SCHEMA(DynamicSlice)
+      .SinceVersion(10)
+      .Deprecate()
+      .SetDoc(DynamicSlice_ver1_doc)
+      .Input(0, "data", "Tensor of data to extract slices from.", "T")
+      .Input(1, "starts", "1-D tensor of starting indices of corresponding axis in `axes`", "Tind")
+      .Input(2, "ends", "1-D tensor of ending indices (exclusive) of corresponding axis in axes", "Tind")
+      .Input(3, "axes", "1-D tensor of axes that `starts` and `ends` apply to.", "Tind", OpSchema::Optional)
+      .Output(0, "output", "Sliced data tensor.", "T")
+      .TypeConstraint("T", OpSchema::all_tensor_types(), "Constrain input and output types to all tensor types.")
+      .TypeConstraint("Tind", {"tensor(int32)", "tensor(int64)"}, "Constrain indices to integer types");
+
   // End of ONNX exp ops(Affine, Crop, ParametricSoftplus, ImageScaler) old version history maintainance
 
   ONNX_CONTRIB_OPERATOR_SCHEMA(SampleOp)

onnxruntime/core/providers/cpu/activation/activations.cc

@@ -29,6 +29,7 @@ REGISTER_UNARY_ELEMENTWISE_KERNEL_ALIAS(Softplus, ParametricSoftplus, 1);
 REGISTER_UNARY_ELEMENTWISE_KERNEL(Softsign, 1);
 REGISTER_UNARY_ELEMENTWISE_KERNEL(Tanh, 6);
 REGISTER_UNARY_ELEMENTWISE_KERNEL(ThresholdedRelu, 1);
+REGISTER_UNARY_ELEMENTWISE_KERNEL(ThresholdedRelu, 10);
 
 template <>
 Status Sigmoid<float>::Compute(OpKernelContext* context) const {

onnxruntime/core/providers/cpu/cpu_execution_provider.cc

@@ -262,6 +262,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
 // Opset 10
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, StringNormalizer);
 class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, TopK);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ThresholdedRelu);
 
 void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
   kernel_registry.Register(BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 6, Clip)>());
@@ -513,6 +514,7 @@ void RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
   // Opset 10
   kernel_registry.Register(BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, StringNormalizer)>());
   kernel_registry.Register(BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, TopK)>());
+  kernel_registry.Register(BuildKernelCreateInfo<ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 10, ThresholdedRelu)>());
 }
 
 // Forward declarations of ml op kernels

onnxruntime/test/onnx/main.cc

@@ -318,7 +318,12 @@ int real_main(int argc, char* argv[], OrtEnv** p_env) {
       {"tf_inception_v4", "Cast opset 9 not supported yet"},
       {"tf_nasnet_large", "disable temporarily"},
       {"tf_nasnet_mobile", "disable temporarily"},
-      {"tf_pnasnet_large", "disable temporarily"}};
+      {"tf_pnasnet_large", "disable temporarily"},
+      {"slice", "Slice opset 10 not supported yet"},
+      {"slice_neg", "Slice opset 10 not supported yet"},
+      {"slice_start_out_of_bounds", "Slice opset 10 not supported yet"},
+      {"slice_end_out_of_bounds", "Slice opset 10 not supported yet"},
+      {"slice_default_axes", "Slice opset 10 not supported yet"}};
 
 #ifdef USE_CUDA
   broken_tests["maxpool_2d_default"] = "cudnn pooling only support input dimension >= 3";

onnxruntime/test/python/onnx_backend_test_series.py

@@ -83,6 +83,13 @@ backend_test.exclude(r'('
 '|^test_vgg19_cpu.*'
 '|^test_zfnet512_cpu.*'
 '|^test_gru_seq_length_cpu.*'
+'|^test_slice_cpu.*'
+'|^test_slice_default_axes_cpu.*'
+'|^test_slice_default_steps_cpu.*'
+'|^test_slice_end_out_of_bounds_cpu.*'
+'|^test_slice_neg_cpu.*'
+'|^test_slice_neg_steps_cpu.*'
+'|^test_slice_start_out_of_bounds_cpu.*'
 ')')
 
 # import all test cases at global scope to make

tools/ci_build/github/linux/docker/scripts/install_deps.sh

@@ -38,8 +38,8 @@ else
   #5af210ca8a1c73aa6bae8754c9346ec54d0a756e is v1.2.3
   #bae6333e149a59a3faa9c4d9c44974373dcf5256 is v1.3.0
   #9e55ace55aad1ada27516038dfbdc66a8a0763db is v1.4.1
-  #3a9a87871021a02d6b970c1e26ffa4c0dd720677 is v1.4.1 latest
-  for onnx_version in "5af210ca8a1c73aa6bae8754c9346ec54d0a756e" "bae6333e149a59a3faa9c4d9c44974373dcf5256" "9e55ace55aad1ada27516038dfbdc66a8a0763db" "3a9a87871021a02d6b970c1e26ffa4c0dd720677"; do
+  #fdb978a580bcb461ea077b9772c973928b8dbf5f" is v1.4.1 latest
+  for onnx_version in "5af210ca8a1c73aa6bae8754c9346ec54d0a756e" "bae6333e149a59a3faa9c4d9c44974373dcf5256" "9e55ace55aad1ada27516038dfbdc66a8a0763db" "fdb978a580bcb461ea077b9772c973928b8dbf5f" ; do
     if [ -z ${lastest_onnx_version+x} ]; then
       echo "first pass";
     else