diff --git a/docs/OperatorKernels.md b/docs/OperatorKernels.md
index 10227e2d05..f4f395a2f8 100644
--- a/docs/OperatorKernels.md
+++ b/docs/OperatorKernels.md
@@ -38,7 +38,8 @@ Do not modify directly.*
|AveragePool|*in* X:**T**
*out* Y:**T**|11+|**T** = tensor(float)|
|||10|**T** = tensor(float)|
|||[7, 9]|**T** = tensor(float)|
-|BatchNormalization|*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* input_mean:**U**
*in* input_var:**U**
*out* Y:**T**
*out* running_mean:**U**
*out* running_var:**U**
or
*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* mean:**T**
*in* var:**T**
*out* Y:**T**
*out* mean:**T**
*out* var:**T**
*out* saved_mean:**T**
*out* saved_var:**T**
or
*in* X:**T**
*in* scale:**T1**
*in* B:**T1**
*in* input_mean:**T2**
*in* input_var:**T2**
*out* Y:**T**
*out* running_mean:**T2**
*out* running_var:**T2**|14+|**T** = tensor(double), tensor(float)|
+|BatchNormalization|*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* input_mean:**U**
*in* input_var:**U**
*out* Y:**T**
*out* running_mean:**U**
*out* running_var:**U**
or
*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* mean:**T**
*in* var:**T**
*out* Y:**T**
*out* mean:**T**
*out* var:**T**
*out* saved_mean:**T**
*out* saved_var:**T**
or
*in* X:**T**
*in* scale:**T1**
*in* B:**T1**
*in* input_mean:**T2**
*in* input_var:**T2**
*out* Y:**T**
*out* running_mean:**T2**
*out* running_var:**T2**|15+|**T** = tensor(double), tensor(float)
**T1** = tensor(double), tensor(float)
**T2** = tensor(double), tensor(float)|
+|||14|**T** = tensor(double), tensor(float)
**U** = tensor(double), tensor(float)|
|||[9, 13]|**T** = tensor(double), tensor(float)|
|||[7, 8]|**T** = tensor(double), tensor(float)|
|BitShift|*in* X:**T**
*in* Y:**T**
*out* Z:**T**|11+|**T** = tensor(uint32), tensor(uint64), tensor(uint8)|
@@ -202,7 +203,8 @@ Do not modify directly.*
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)|
|||[2, 10]|**T** = tensor(double), tensor(float)|
|ParametricSoftplus|*in* X:**T**
*out* Y:**T**|1+|**T** = tensor(float)|
-|Pow|*in* X:**T**
*in* Y:**T**
*out* Z:**T**
or
*in* X:**T**
*in* Y:**T1**
*out* Z:**T**|13+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
+|Pow|*in* X:**T**
*in* Y:**T**
*out* Z:**T**
or
*in* X:**T**
*in* Y:**T1**
*out* Z:**T**|15+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
+|||[13, 14]|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||12|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|||[7, 11]|**T** = tensor(double), tensor(float)|
|QLinearConv|*in* x:**T1**
*in* x_scale:**tensor(float)**
*in* x_zero_point:**T1**
*in* w:**T2**
*in* w_scale:**tensor(float)**
*in* w_zero_point:**T2**
*in* y_scale:**tensor(float)**
*in* y_zero_point:**T3**
*in* B:**T4**
*out* y:**T3**|10+|**T1** = tensor(uint8)
**T2** = tensor(int8), tensor(uint8)
**T3** = tensor(uint8)
**T4** = tensor(int32)|
@@ -280,7 +282,8 @@ Do not modify directly.*
|SequenceErase|*in* input_sequence:**S**
*in* position:**I**
*out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceInsert|*in* input_sequence:**S**
*in* tensor:**T**
*in* position:**I**
*out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceLength|*in* input_sequence:**S**
*out* length:**I**|11+|**I** = tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(string)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
-|Shape|*in* data:**T**
*out* shape:**T1**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
+|Shape|*in* data:**T**
*out* shape:**T1**|15+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
+|||[13, 14]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
|Shrink|*in* input:**T**
*out* output:**T**|9+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sigmoid|*in* X:**T**
*out* Y:**T**|13+|**T** = tensor(double), tensor(float)|
@@ -446,7 +449,8 @@ Do not modify directly.*
|AveragePool|*in* X:**T**
*out* Y:**T**|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|||10|**I** = tensor(int64)
**T** = tensor(double), tensor(float), tensor(float16)|
|||[7, 9]|**I** = tensor(int64)
**T** = tensor(double), tensor(float), tensor(float16)|
-|BatchNormalization|*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* input_mean:**U**
*in* input_var:**U**
*out* Y:**T**
*out* running_mean:**U**
*out* running_var:**U**
or
*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* mean:**T**
*in* var:**T**
*out* Y:**T**
*out* mean:**T**
*out* var:**T**
*out* saved_mean:**T**
*out* saved_var:**T**
or
*in* X:**T**
*in* scale:**T1**
*in* B:**T1**
*in* input_mean:**T2**
*in* input_var:**T2**
*out* Y:**T**
*out* running_mean:**T2**
*out* running_var:**T2**|14+|**T** = tensor(double), tensor(float), tensor(float16)|
+|BatchNormalization|*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* input_mean:**U**
*in* input_var:**U**
*out* Y:**T**
*out* running_mean:**U**
*out* running_var:**U**
or
*in* X:**T**
*in* scale:**T**
*in* B:**T**
*in* mean:**T**
*in* var:**T**
*out* Y:**T**
*out* mean:**T**
*out* var:**T**
*out* saved_mean:**T**
*out* saved_var:**T**
or
*in* X:**T**
*in* scale:**T1**
*in* B:**T1**
*in* input_mean:**T2**
*in* input_var:**T2**
*out* Y:**T**
*out* running_mean:**T2**
*out* running_var:**T2**|15+|**T** = tensor(double), tensor(float), tensor(float16)
**T1** = tensor(double), tensor(float), tensor(float16)
**T2** = tensor(double), tensor(float), tensor(float16)|
+|||14|**T** = tensor(double), tensor(float), tensor(float16)
**U** = tensor(double), tensor(float), tensor(float16)|
|||[9, 13]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|Cast|*in* input:**T1**
*out* output:**T2**|13+|**T1** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T2** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
@@ -582,7 +586,8 @@ Do not modify directly.*
|||[11, 12]|**T** = tensor(double), tensor(float), tensor(float16)|
|||[2, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|ParametricSoftplus|*in* X:**T**
*out* Y:**T**|1+|**T** = tensor(double), tensor(float), tensor(float16)|
-|Pow|*in* X:**T**
*in* Y:**T**
*out* Z:**T**
or
*in* X:**T**
*in* Y:**T1**
*out* Z:**T**|13+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
+|Pow|*in* X:**T**
*in* Y:**T**
*out* Z:**T**
or
*in* X:**T**
*in* Y:**T1**
*out* Z:**T**|15+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
+|||[13, 14]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||12|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)|
|||[7, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|QuantizeLinear|*in* x:**T1**
*in* y_scale:**tensor(float)**
*in* y_zero_point:**T2**
*out* y:**T2**|10+|**T1** = tensor(float)
**T2** = tensor(int8), tensor(uint8)|
@@ -653,7 +658,8 @@ Do not modify directly.*
|SequenceErase|*in* input_sequence:**S**
*in* position:**I**
*out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceInsert|*in* input_sequence:**S**
*in* tensor:**T**
*in* position:**I**
*out* output_sequence:**S**|11+|**I** = tensor(int32), tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
|SequenceLength|*in* input_sequence:**S**
*out* length:**I**|11+|**I** = tensor(int64)
**S** = seq(tensor(bfloat16)), seq(tensor(bool)), seq(tensor(double)), seq(tensor(float)), seq(tensor(float16)), seq(tensor(int16)), seq(tensor(int32)), seq(tensor(int64)), seq(tensor(int8)), seq(tensor(uint16)), seq(tensor(uint32)), seq(tensor(uint64)), seq(tensor(uint8))|
-|Shape|*in* data:**T**
*out* shape:**T1**|13+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
+|Shape|*in* data:**T**
*out* shape:**T1**|15+|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
+|||[13, 14]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
|||[1, 12]|**T** = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
**T1** = tensor(int64)|
|Shrink|*in* input:**T**
*out* output:**T**|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|Sigmoid|*in* X:**T**
*out* Y:**T**|13+|**T** = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)|
diff --git a/include/onnxruntime/core/framework/tensor_shape.h b/include/onnxruntime/core/framework/tensor_shape.h
index 9a2609bc1f..89d9b10594 100644
--- a/include/onnxruntime/core/framework/tensor_shape.h
+++ b/include/onnxruntime/core/framework/tensor_shape.h
@@ -72,6 +72,15 @@ class TensorShape : private std::vector {
memcpy(dims, data(), sizeof(value_type) * std::min(num_dims, NumDimensions()));
}
+ /**
+ Copy dims from a specific start dim into an array with given size
+ `start_dim` is expected to be in the inclusive range [0, NumDimensions() - 1]
+ and this function does no checks to ensure that
+ */
+ void CopyDims(int64_t* dims, size_t start_dim, size_t num_dims) const {
+ memcpy(dims, data() + start_dim, sizeof(value_type) * std::min(num_dims, NumDimensions() - start_dim));
+ }
+
/**
Return underlying vector representation.
*/
diff --git a/onnxruntime/core/optimizer/constant_folding.cc b/onnxruntime/core/optimizer/constant_folding.cc
index e78c957bca..af585db08d 100644
--- a/onnxruntime/core/optimizer/constant_folding.cc
+++ b/onnxruntime/core/optimizer/constant_folding.cc
@@ -1,6 +1,8 @@
// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
+#include
+
#include "core/optimizer/constant_folding.h"
#include "core/optimizer/utils.h"
#include "core/graph/graph_utils.h"
@@ -25,6 +27,20 @@ ConstantFolding::ConstantFolding(const IExecutionProvider& execution_provider,
// We need to handle a Shape node separately as the input doesn't need to be a constant initializer for
// Shape to be able to be constant folded.
static bool ConstantFoldShapeNode(Graph& graph, Node& node) {
+ // Opset-15 Shape supports slicing using a 'start' and 'end' attribute
+ const auto& shape_attributes = node.GetAttributes();
+
+ int64_t start = 0;
+ int64_t end = std::numeric_limits::max();
+
+ for (const auto& attr : shape_attributes) {
+ if (attr.first == "start") {
+ start = attr.second.i();
+ } else if (attr.first == "end") {
+ end = attr.second.i();
+ }
+ }
+
auto shape = node.MutableInputDefs()[0]->Shape();
bool is_concrete_shape = true;
std::vector dim_values;
@@ -42,14 +58,30 @@ static bool ConstantFoldShapeNode(Graph& graph, Node& node) {
}
if (is_concrete_shape) {
+ int64_t rank = static_cast(dim_values.size());
+
+ // We ascertain the "true" starts/ends (if they were provided)
+ // Opset-15 Shape op supports slicing shape values
+
+ // Deal with negatives and clamp
+ start = start < 0 ? start + rank : start;
+ start = start < 0 ? 0 : ((start > rank) ? rank : start);
+
+ end = end < 0 ? end + rank : end;
+ end = end < 0 ? 0 : ((end > rank) ? rank : end);
+
+ int64_t slice_length = end - start;
+ size_t clamped_slice_length = slice_length < 0 ? 0 : static_cast(slice_length);
+
ONNX_NAMESPACE::TensorProto shape_constant;
auto* constant_arg_out = node.MutableOutputDefs()[0];
shape_constant.set_name(constant_arg_out->Name());
shape_constant.set_data_type(ONNX_NAMESPACE::TensorProto_DataType_INT64);
- shape_constant.add_dims(dim_values.size());
- shape_constant.set_raw_data(dim_values.data(), dim_values.size() * sizeof(int64_t));
+ shape_constant.add_dims(clamped_slice_length);
+ shape_constant.set_raw_data(dim_values.data() + start,
+ clamped_slice_length * sizeof(int64_t));
ONNX_NAMESPACE::TensorShapeProto result_shape;
- result_shape.add_dim()->set_dim_value(dim_values.size());
+ result_shape.add_dim()->set_dim_value(clamped_slice_length);
constant_arg_out->SetShape(result_shape);
graph.AddInitializedTensor(shape_constant);
}
diff --git a/onnxruntime/core/optimizer/graph_transformer_utils.cc b/onnxruntime/core/optimizer/graph_transformer_utils.cc
index fead80b22c..6a0bc646bf 100644
--- a/onnxruntime/core/optimizer/graph_transformer_utils.cc
+++ b/onnxruntime/core/optimizer/graph_transformer_utils.cc
@@ -37,7 +37,6 @@
#include "core/optimizer/relu_clip_fusion.h"
#include "core/optimizer/reshape_fusion.h"
#include "core/optimizer/rule_based_graph_transformer.h"
-#include "core/optimizer/shape_to_initializer.h"
#include "core/optimizer/skip_layer_norm_fusion.h"
#include "core/optimizer/slice_elimination.h"
#include "core/optimizer/unsqueeze_elimination.h"
@@ -75,7 +74,6 @@ std::vector> GenerateRewriteRules(
rules.push_back(std::make_unique());
rules.push_back(std::make_unique());
rules.push_back(std::make_unique());
- rules.push_back(std::make_unique());
rules.push_back(std::make_unique());
rules.push_back(std::make_unique());
rules.push_back(std::make_unique());
diff --git a/onnxruntime/core/optimizer/shape_to_initializer.cc b/onnxruntime/core/optimizer/shape_to_initializer.cc
deleted file mode 100644
index 02aa17d772..0000000000
--- a/onnxruntime/core/optimizer/shape_to_initializer.cc
+++ /dev/null
@@ -1,80 +0,0 @@
-// Copyright (c) Microsoft Corporation. All rights reserved.
-// Licensed under the MIT License.
-
-#include "core/optimizer/shape_to_initializer.h"
-#include "core/graph/graph.h"
-#include "core/graph/graph_utils.h"
-#include "core/graph/op.h"
-#include "core/optimizer/initializer.h"
-#include "core/optimizer/optimizer_execution_frame.h"
-#include "core/framework/op_kernel.h"
-#include "core/framework/tensorprotoutils.h"
-
-namespace onnxruntime {
-
-Status ShapeToInitializer::Apply(Graph& graph, Node& node, RewriteRuleEffect& rule_effect, const logging::Logger&) const {
- // Store the statically inferred shape of the input to the Shape operator.
- const ONNX_NAMESPACE::TensorShapeProto* input_shape_proto = node.InputDefs()[0]->Shape();
- std::vector input_dims;
- int num_dimensions = input_shape_proto->dim_size();
- for (int i = 0; i < num_dimensions; i++) {
- input_dims.push_back(gsl::narrow_cast(input_shape_proto->dim(i).dim_value()));
- }
-
- // Create the TensorProto that will be used as initializer in place of the Shape operator.
- const auto* shape_out_def = node.OutputDefs()[0];
-
- ONNX_NAMESPACE::TensorProto shape_initializer_proto;
-
- shape_initializer_proto.set_name(shape_out_def->Name());
-
- TensorShape tensor_shape({gsl::narrow_cast(num_dimensions)});
- for (auto& dim : tensor_shape.GetDims()) {
- shape_initializer_proto.add_dims(dim);
- }
-
- auto tensor_proto_data_type = shape_out_def->TypeAsProto()->tensor_type().elem_type();
- shape_initializer_proto.set_data_type(tensor_proto_data_type);
-
- // Here we expect little-endian format to set raw data of the TensorProto.
- shape_initializer_proto.set_raw_data(input_dims.data(),
- input_dims.size() * sizeof(decltype(input_dims)::value_type));
-
- auto& new_node_arg = graph_utils::AddInitializer(graph, shape_initializer_proto);
-
- if (graph_utils::ReplaceNodeWithInitializer(graph, node, new_node_arg)) {
- rule_effect = RewriteRuleEffect::kRemovedCurrentNode;
- }
-
- return Status::OK();
-}
-
-bool ShapeToInitializer::SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger& logger) const {
- if (!graph_utils::IsSupportedOptypeVersionAndDomain(node, "Shape", {1, 13})) {
- return false;
- }
-
- // The shape of the input has to be statically known. Moreover, each dimension should have a meaningful value
- // (the rule cannot be applied if one of the dimensions has a negative value or if it is a symbolic variable).
- const auto* input_shape = node.InputDefs()[0]->Shape();
- if (!input_shape) {
- return false;
- }
-
- for (int i = 0, num_dims = input_shape->dim_size(); i < num_dims; i++) {
- const auto& input_dim = input_shape->dim(i);
- if (!utils::HasDimValue(input_dim) || input_dim.dim_value() < 0) {
- return false;
- }
- }
-
- // we're going to create an initializer with the same name as the node output
- const auto& new_initializer_name = node.OutputDefs()[0]->Name();
- if (!graph_utils::CanReplaceNodeWithInitializer(graph, node, new_initializer_name, logger)) {
- return false;
- }
-
- return true;
-}
-
-} // namespace onnxruntime
diff --git a/onnxruntime/core/optimizer/shape_to_initializer.h b/onnxruntime/core/optimizer/shape_to_initializer.h
deleted file mode 100644
index fd749821d5..0000000000
--- a/onnxruntime/core/optimizer/shape_to_initializer.h
+++ /dev/null
@@ -1,32 +0,0 @@
-// Copyright (c) Microsoft Corporation. All rights reserved.
-// Licensed under the MIT License.
-
-#pragma once
-
-#include "core/optimizer/rewrite_rule.h"
-
-namespace onnxruntime {
-
-/**
-@Class ShapeToInitializer
-
-When the input to a Shape operator is statically known (through shape inference), this rule replaces the Shape node
-with an initializer to the downstream nodes.
-
-It is attempted to be triggered only on nodes with op type "Shape".
-*/
-class ShapeToInitializer : public RewriteRule {
- public:
- ShapeToInitializer() noexcept : RewriteRule("ShapeToInitializer") {}
-
- std::vector TargetOpTypes() const noexcept override {
- return {"Shape"};
- }
-
- private:
- bool SatisfyCondition(const Graph& graph, const Node& node, const logging::Logger& logger) const override;
-
- Status Apply(Graph& graph, Node& node, RewriteRuleEffect& rule_effect, const logging::Logger& logger) const override;
-};
-
-} // namespace onnxruntime
diff --git a/onnxruntime/core/providers/cpu/cpu_execution_provider.cc b/onnxruntime/core/providers/cpu/cpu_execution_provider.cc
index 2186d7c2f2..620f1632bf 100644
--- a/onnxruntime/core/providers/cpu/cpu_execution_provider.cc
+++ b/onnxruntime/core/providers/cpu/cpu_execution_provider.cc
@@ -527,7 +527,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, ArgMin);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, int32_t, ArgMin);
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 13, Reshape);
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Shape);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 14, Shape);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Concat);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Less);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Less);
@@ -590,7 +590,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Exp);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, float, Log);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, double, Log);
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Pow);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, 14, Pow);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, DepthToSpace);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, SpaceToDepth);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 13, Slice);
@@ -686,12 +686,17 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, int64_t, Div);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, Reshape);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, Identity);
-class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, float, BatchNormalization);
-class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, double, BatchNormalization);
+class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 14, float, BatchNormalization);
+class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, 14, double, BatchNormalization);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, GRU);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, LSTM);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 14, RNN);
+// Opset 15
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, Pow);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, float, BatchNormalization);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, double, BatchNormalization);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCpuExecutionProvider, kOnnxDomain, 15, Shape);
// !!PLEASE READ BELOW!! Following that, add new entries above this comment
@@ -1151,9 +1156,9 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
+ BatchNormalization)>,
BuildKernelCreateInfo,
+ BatchNormalization)>,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1547,7 +1552,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
int32_t, ArgMin)>,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1650,7 +1655,7 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1810,13 +1815,22 @@ Status RegisterOnnxOperatorKernels(KernelRegistry& kernel_registry) {
Div)>,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
+
+ // Opset 15
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+
};
for (auto& function_table_entry : function_table) {
diff --git a/onnxruntime/core/providers/cpu/math/element_wise_ops.cc b/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
index 52b80a0923..b00de30dca 100644
--- a/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
+++ b/onnxruntime/core/providers/cpu/math/element_wise_ops.cc
@@ -259,13 +259,27 @@ REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Sqrt, 6, 12, double, Sqrt);
REG_ELEMENTWISE_TYPED_KERNEL(Sqrt, 13, float, Sqrt);
REG_ELEMENTWISE_TYPED_KERNEL(Sqrt, 13, double, Sqrt);
-REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Pow, 7, 11, Pow, BuildKernelDefConstraintsFromTypeList(), BuildKernelDefConstraintsFromTypeList());
+REG_ELEMENTWISE_VERSIONED_KERNEL_NONT(Pow, 7, 11, Pow,
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList());
+
REG_ELEMENTWISE_VERSIONED_KERNEL_NONT_2(Pow, 12, 12, Pow,
- BuildKernelDefConstraintsFromTypeList(), BuildKernelDefConstraintsFromTypeList(),
- BuildKernelDefConstraintsFromTypeList(), BuildKernelDefConstraintsFromTypeList());
-REG_ELEMENTWISE_KERNEL_NONT_2(Pow, 13, Pow,
- BuildKernelDefConstraintsFromTypeList(), BuildKernelDefConstraintsFromTypeList(),
- BuildKernelDefConstraintsFromTypeList(), BuildKernelDefConstraintsFromTypeList());
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList());
+
+REG_ELEMENTWISE_VERSIONED_KERNEL_NONT_2(Pow, 13, 14, Pow,
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList());
+
+REG_ELEMENTWISE_KERNEL_NONT_2(Pow, 15, Pow,
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList(),
+ BuildKernelDefConstraintsFromTypeList());
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Exp, 6, 12, float, Exp);
REG_ELEMENTWISE_VERSIONED_TYPED_KERNEL(Exp, 6, 12, double, Exp);
diff --git a/onnxruntime/core/providers/cpu/nn/batch_norm.cc b/onnxruntime/core/providers/cpu/nn/batch_norm.cc
index 92fff6974b..ebd1af6c0e 100644
--- a/onnxruntime/core/providers/cpu/nn/batch_norm.cc
+++ b/onnxruntime/core/providers/cpu/nn/batch_norm.cc
@@ -31,18 +31,52 @@ ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(BatchNormalization, 7, 8, double,
// We alias the running mean to the mean so it stays preserved across multiple batches
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(BatchNormalization, 9, 13, float,
- KernelDefBuilder().Alias(3,1).Alias(4,2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
- BatchNorm);
+ KernelDefBuilder().Alias(3, 1).Alias(4, 2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
+ BatchNorm);
ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(BatchNormalization, 9, 13, double,
- KernelDefBuilder().Alias(3,1).Alias(4,2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
- BatchNorm);
+ KernelDefBuilder().Alias(3, 1).Alias(4, 2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
+ BatchNorm);
-ONNX_CPU_OPERATOR_TYPED_KERNEL(BatchNormalization, 14, float,
- KernelDefBuilder().Alias(3,1).Alias(4,2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
+ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(BatchNormalization, 14, 14, float,
+ KernelDefBuilder()
+ .Alias(3, 1)
+ .Alias(4, 2)
+ // ORT 1.8 was shipped with just the "T" type constraint and
+ // we want to maintain backwards compatibility for
+ // the hash and hence just use "T" for the hash generation
+ .FixedTypeConstraintForHash("T", {DataTypeImpl::GetTensorType()})
+ .TypeConstraint("T", DataTypeImpl::GetTensorType())
+ .TypeConstraint("U", DataTypeImpl::GetTensorType()),
+ BatchNorm);
+
+ONNX_CPU_OPERATOR_VERSIONED_TYPED_KERNEL(BatchNormalization, 14, 14, double,
+ KernelDefBuilder()
+ .Alias(3, 1)
+ .Alias(4, 2)
+ // ORT 1.8 was shipped with just the "T" type constraint and
+ // we want to maintain backwards compatibility for
+ // the hash and hence just use "T" for the hash generation
+ .FixedTypeConstraintForHash("T", {DataTypeImpl::GetTensorType()})
+ .TypeConstraint("T", DataTypeImpl::GetTensorType())
+ .TypeConstraint("U", DataTypeImpl::GetTensorType()),
+ BatchNorm);
+
+ONNX_CPU_OPERATOR_TYPED_KERNEL(BatchNormalization, 15, float,
+ KernelDefBuilder()
+ .Alias(3, 1)
+ .Alias(4, 2)
+ .TypeConstraint("T", DataTypeImpl::GetTensorType())
+ .TypeConstraint("T1", DataTypeImpl::GetTensorType())
+ .TypeConstraint("T2", DataTypeImpl::GetTensorType()),
BatchNorm);
-ONNX_CPU_OPERATOR_TYPED_KERNEL(BatchNormalization, 14, double,
- KernelDefBuilder().Alias(3,1).Alias(4,2).TypeConstraint("T", DataTypeImpl::GetTensorType()),
+ONNX_CPU_OPERATOR_TYPED_KERNEL(BatchNormalization, 15, double,
+ KernelDefBuilder()
+ .Alias(3, 1)
+ .Alias(4, 2)
+ .TypeConstraint("T", DataTypeImpl::GetTensorType())
+ .TypeConstraint("T1", DataTypeImpl::GetTensorType())
+ .TypeConstraint("T2", DataTypeImpl::GetTensorType()),
BatchNorm);
} // namespace onnxruntime
diff --git a/onnxruntime/core/providers/cpu/tensor/shape_op.cc b/onnxruntime/core/providers/cpu/tensor/shape_op.cc
index 4020d53685..780cf4b733 100644
--- a/onnxruntime/core/providers/cpu/tensor/shape_op.cc
+++ b/onnxruntime/core/providers/cpu/tensor/shape_op.cc
@@ -12,9 +12,16 @@ ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::AllTensorTypes()).TypeConstraint("T1", DataTypeImpl::GetTensorType()),
Shape);
-ONNX_CPU_OPERATOR_KERNEL(
+ONNX_CPU_OPERATOR_VERSIONED_KERNEL(
Shape,
- 13,
+ 13, 14,
KernelDefBuilder().TypeConstraint("T", DataTypeImpl::AllTensorTypes()).TypeConstraint("T1", DataTypeImpl::GetTensorType()),
Shape);
+
+ONNX_CPU_OPERATOR_KERNEL(
+ Shape,
+ 15,
+ KernelDefBuilder().TypeConstraint("T", DataTypeImpl::AllTensorTypes()).TypeConstraint("T1", DataTypeImpl::GetTensorType()),
+ Shape);
+
} // namespace onnxruntime
diff --git a/onnxruntime/core/providers/cpu/tensor/shape_op.h b/onnxruntime/core/providers/cpu/tensor/shape_op.h
index 6588109261..1bdfb8e0c9 100644
--- a/onnxruntime/core/providers/cpu/tensor/shape_op.h
+++ b/onnxruntime/core/providers/cpu/tensor/shape_op.h
@@ -9,25 +9,62 @@
#endif
#include "gsl/gsl"
+#include
namespace onnxruntime {
class Shape final : public OpKernel {
public:
Shape(const OpKernelInfo& info) : OpKernel(info) {
+ info.GetAttrOrDefault("start", &start_index_, 0);
+
+ if (start_index_ != 0) {
+ // "start" is provided and is non-default (default is 0)
+ needs_slicing_ = true;
+ }
+
+ if (info.GetAttr("end", &end_index_).IsOK()) {
+ needs_slicing_ = true;
+ }
}
// Takes a tensor as input and outputs an 1D int64 tensor
// containing the shape of the input tensor.
Status Compute(OpKernelContext* context) const override {
const auto* input = context->Input(0);
- const TensorShape& inputShape = input->Shape();
+ const TensorShape& input_shape = input->Shape();
- size_t nDims = inputShape.NumDimensions();
- Tensor* output = context->Output(0, {gsl::narrow_cast(nDims)});
+ int64_t rank = gsl::narrow_cast(input_shape.NumDimensions());
+
+ if (!needs_slicing_) { // vanilla use of Shape (no slicing)
+ Tensor* output = context->Output(0, {rank});
+ input_shape.CopyDims(output->template MutableData(), static_cast(rank));
+ } else { // slicing is needed
+ int64_t true_start = start_index_;
+ int64_t true_end = end_index_;
+
+ // Deal with negative(s) and clamp
+ true_start = true_start < 0 ? true_start + rank : true_start;
+ true_start = true_start < 0 ? 0 : ((true_start > rank) ? rank : true_start);
+
+ true_end = true_end < 0 ? true_end + rank : true_end;
+ true_end = true_end < 0 ? 0 : ((true_end > rank) ? rank : true_end);
+
+ auto slice_length = true_end - true_start;
+ Tensor* output = context->Output(0, {slice_length < 0 ? 0 : slice_length});
+
+ if (slice_length > 0) {
+ input_shape.CopyDims(output->template MutableData(), true_start, slice_length);
+ }
+ }
- inputShape.CopyDims(output->template MutableData(), nDims);
return Status::OK();
}
+
+ private:
+ bool needs_slicing_ = false;
+ int64_t start_index_ = 0;
+ int64_t end_index_ = std::numeric_limits::max();
};
+
} //namespace onnxruntime
diff --git a/onnxruntime/core/providers/cuda/cuda_execution_provider.cc b/onnxruntime/core/providers/cuda/cuda_execution_provider.cc
index 83d9f6a3fd..726cf1d133 100755
--- a/onnxruntime/core/providers/cuda/cuda_execution_provider.cc
+++ b/onnxruntime/core/providers/cuda/cuda_execution_provider.cc
@@ -892,7 +892,7 @@ class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDom
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 12, Einsum);
//OpSet 13
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, Pow);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 14, Pow);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 13, int32_t, Add);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 13, int64_t, Add);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 13, uint32_t, Add);
@@ -1005,7 +1005,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, uint64_t, Cast);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, bool, Cast);
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 13, Reshape);
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, Shape);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, 14, Shape);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, Size);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, Transpose);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 13, ScatterElements);
@@ -1163,9 +1163,9 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, float, LSTM);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, double, LSTM);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, MLFloat16, LSTM);
-class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, float, BatchNormalization);
-class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, double, BatchNormalization);
-class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, MLFloat16, BatchNormalization);
+class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, 14, float, BatchNormalization);
+class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, 14, double, BatchNormalization);
+class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, 14, MLFloat16, BatchNormalization);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, float, ReduceMin);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, double, ReduceMin);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, MLFloat16, ReduceMin);
@@ -1182,6 +1182,13 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 14, BFloat16, Relu);
#endif
+//OpSet 15
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 15, Pow);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 15, float, BatchNormalization);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 15, double, BatchNormalization);
+class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 15, MLFloat16, BatchNormalization);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kCudaExecutionProvider, kOnnxDomain, 15, Shape);
+
template <>
KernelCreateInfo BuildKernelCreateInfo() {
return {};
@@ -1728,7 +1735,7 @@ static Status RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
// OpSet 13
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1841,7 +1848,7 @@ static Status RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1998,9 +2005,9 @@ static Status RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
- BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -2015,6 +2022,14 @@ static Status RegisterCudaKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
#endif
+
+ // OpSet 15
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
+
};
for (auto& function_table_entry : function_table) {
diff --git a/onnxruntime/core/providers/cuda/math/binary_elementwise_ops.cc b/onnxruntime/core/providers/cuda/math/binary_elementwise_ops.cc
index a20e8e15f3..f5b8c9aa6b 100644
--- a/onnxruntime/core/providers/cuda/math/binary_elementwise_ops.cc
+++ b/onnxruntime/core/providers/cuda/math/binary_elementwise_ops.cc
@@ -205,9 +205,9 @@ Status BinaryElementwise::Prepare(OpKernelContext* context, Bin
#define BINARY_OP_TYPED_VERSIONED_V_BF16(name, class_name, startver, endver)
#endif
-#define BINARY_OP_VERSIONED_HFD(name, startver, endver) \
- BINARY_OP_VERSIONED_TYPED(name, startver, endver, MLFloat16) \
- BINARY_OP_VERSIONED_TYPED(name, startver, endver, float) \
+#define BINARY_OP_VERSIONED_HFD(name, startver, endver) \
+ BINARY_OP_VERSIONED_TYPED(name, startver, endver, MLFloat16) \
+ BINARY_OP_VERSIONED_TYPED(name, startver, endver, float) \
BINARY_OP_VERSIONED_TYPED(name, startver, endver, double)
#define BINARY_OP_VERSIONED_UZILHFD(name, startver, endver) \
@@ -318,15 +318,29 @@ ONNX_OPERATOR_VERSIONED_KERNEL_EX(
kOnnxDomain,
12, 12,
kCudaExecutionProvider,
- (*KernelDefBuilder::Create()).TypeConstraint("T", BuildKernelDefConstraints()).TypeConstraint("T1", BuildKernelDefConstraints()),
+ (*KernelDefBuilder::Create())
+ .TypeConstraint("T", BuildKernelDefConstraints())
+ .TypeConstraint("T1", BuildKernelDefConstraints()),
+ Pow);
+
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+ Pow,
+ kOnnxDomain,
+ 13, 14,
+ kCudaExecutionProvider,
+ (*KernelDefBuilder::Create())
+ .TypeConstraint("T", BuildKernelDefConstraints())
+ .TypeConstraint("T1", BuildKernelDefConstraints()),
Pow);
ONNX_OPERATOR_KERNEL_EX(
Pow,
kOnnxDomain,
- 13,
+ 15,
kCudaExecutionProvider,
- (*KernelDefBuilder::Create()).TypeConstraint("T", BuildKernelDefConstraints()).TypeConstraint("T1", BuildKernelDefConstraints()),
+ (*KernelDefBuilder::Create())
+ .TypeConstraint("T", BuildKernelDefConstraints())
+ .TypeConstraint("T1", BuildKernelDefConstraints()),
Pow);
namespace pow12_internal {
@@ -524,6 +538,5 @@ BINARY_OP_REGISTER_VERSIONED_HFD(Less, 7, 8)
BINARY_LOGICALOP_REGISTER_UZILHFD(GreaterOrEqual, 12)
BINARY_LOGICALOP_REGISTER_UZILHFD(LessOrEqual, 12)
-
} // namespace cuda
} // namespace onnxruntime
diff --git a/onnxruntime/core/providers/cuda/nn/batch_norm.cc b/onnxruntime/core/providers/cuda/nn/batch_norm.cc
index 1a09ec8f45..675f002cb8 100644
--- a/onnxruntime/core/providers/cuda/nn/batch_norm.cc
+++ b/onnxruntime/core/providers/cuda/nn/batch_norm.cc
@@ -11,33 +11,45 @@ using namespace std;
namespace onnxruntime {
namespace cuda {
-#define REGISTER_KERNEL_TYPED(T) \
- ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
- BatchNormalization, \
- kOnnxDomain, \
- 7, 8, \
- T, \
- kCudaExecutionProvider, \
- (*KernelDefBuilder::Create()) \
- .TypeConstraint("T", DataTypeImpl::GetTensorType()), \
- BatchNorm); \
- ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
- BatchNormalization, \
- kOnnxDomain, \
- 9, 13, \
- T, \
- kCudaExecutionProvider, \
- (*KernelDefBuilder::Create()) \
- .TypeConstraint("T", DataTypeImpl::GetTensorType()), \
- BatchNorm); \
- ONNX_OPERATOR_TYPED_KERNEL_EX( \
- BatchNormalization, \
- kOnnxDomain, \
- 14, \
- T, \
- kCudaExecutionProvider, \
- (*KernelDefBuilder::Create()) \
- .TypeConstraint("T", DataTypeImpl::GetTensorType()), \
+#define REGISTER_KERNEL_TYPED(T) \
+ ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
+ BatchNormalization, \
+ kOnnxDomain, \
+ 7, 8, \
+ T, \
+ kCudaExecutionProvider, \
+ (*KernelDefBuilder::Create()) \
+ .TypeConstraint("T", DataTypeImpl::GetTensorType()), \
+ BatchNorm); \
+ ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
+ BatchNormalization, \
+ kOnnxDomain, \
+ 9, 13, \
+ T, \
+ kCudaExecutionProvider, \
+ (*KernelDefBuilder::Create()) \
+ .TypeConstraint("T", DataTypeImpl::GetTensorType()), \
+ BatchNorm); \
+ ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_EX( \
+ BatchNormalization, \
+ kOnnxDomain, \
+ 14, 14, \
+ T, \
+ kCudaExecutionProvider, \
+ (*KernelDefBuilder::Create()) \
+ .TypeConstraint("T", DataTypeImpl::GetTensorType()) \
+ .TypeConstraint("U", DataTypeImpl::GetTensorType()), \
+ BatchNorm); \
+ ONNX_OPERATOR_TYPED_KERNEL_EX( \
+ BatchNormalization, \
+ kOnnxDomain, \
+ 15, \
+ T, \
+ kCudaExecutionProvider, \
+ (*KernelDefBuilder::Create()) \
+ .TypeConstraint("T", DataTypeImpl::GetTensorType()) \
+ .TypeConstraint("T1", DataTypeImpl::GetTensorType()) \
+ .TypeConstraint("T2", DataTypeImpl::GetTensorType()), \
BatchNorm);
template
diff --git a/onnxruntime/core/providers/cuda/nn/batch_norm.h b/onnxruntime/core/providers/cuda/nn/batch_norm.h
index e8ded6a571..792e7da664 100644
--- a/onnxruntime/core/providers/cuda/nn/batch_norm.h
+++ b/onnxruntime/core/providers/cuda/nn/batch_norm.h
@@ -39,8 +39,8 @@ class BatchNorm final : public CudaKernel {
const auto& node = op_kernel_info.node();
auto opset = node.SinceVersion();
- // batch norm opset 14 is not implemented for training mode
- ORT_ENFORCE(!(is_training_mode_ && opset==14), "Training mode does not support BN opset 14 yet.");
+ // batch norm opset 14 (or higher) is not implemented for training mode
+ ORT_ENFORCE(!(is_training_mode_ && opset >= 14), "Training mode does not support BN opset 14 (or higher) yet.");
}
Status ComputeInternal(OpKernelContext* context) const override;
@@ -50,7 +50,7 @@ class BatchNorm final : public CudaKernel {
int64_t spatial_ = 1; // default as per spec
cudnnBatchNormMode_t cudnn_batch_norm_mode_;
double momentum_;
- bool is_training_mode_ = 0; //default as per spec
+ bool is_training_mode_ = 0; //default as per spec
};
} // namespace cuda
diff --git a/onnxruntime/core/providers/cuda/tensor/shape_op.cc b/onnxruntime/core/providers/cuda/tensor/shape_op.cc
index 5680831f5d..1007fcc50b 100644
--- a/onnxruntime/core/providers/cuda/tensor/shape_op.cc
+++ b/onnxruntime/core/providers/cuda/tensor/shape_op.cc
@@ -20,10 +20,22 @@ ONNX_OPERATOR_VERSIONED_KERNEL_EX(
.TypeConstraint("T1", DataTypeImpl::GetTensorType()),
Shape);
+ONNX_OPERATOR_VERSIONED_KERNEL_EX(
+ Shape,
+ kOnnxDomain,
+ 13, 14,
+ kCudaExecutionProvider,
+ (*KernelDefBuilder::Create())
+ // properly force CPU/GPU synch inside the kernel
+ .OutputMemoryType(OrtMemTypeCPUInput, 0)
+ .TypeConstraint("T", DataTypeImpl::AllFixedSizeTensorTypes())
+ .TypeConstraint("T1", DataTypeImpl::GetTensorType()),
+ Shape);
+
ONNX_OPERATOR_KERNEL_EX(
Shape,
kOnnxDomain,
- 13,
+ 15,
kCudaExecutionProvider,
(*KernelDefBuilder::Create())
// properly force CPU/GPU synch inside the kernel
diff --git a/onnxruntime/core/providers/rocm/rocm_execution_provider.cc b/onnxruntime/core/providers/rocm/rocm_execution_provider.cc
index ec5bf56356..1c2574cdc7 100644
--- a/onnxruntime/core/providers/rocm/rocm_execution_provider.cc
+++ b/onnxruntime/core/providers/rocm/rocm_execution_provider.cc
@@ -827,7 +827,7 @@ class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDom
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 12, Einsum);
//OpSet 13
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, Pow);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 14, Pow);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 13, int32_t, Add);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 13, int64_t, Add);
class ONNX_OPERATOR_VERSIONED_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 13, uint32_t, Add);
@@ -934,7 +934,7 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, uint64_t, Cast);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, bool, Cast);
class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 13, Reshape);
-class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, Shape);
+class ONNX_OPERATOR_VERSIONED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, 14, Shape);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, Size);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, Transpose);
class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, ScatterElements);
@@ -1029,6 +1029,10 @@ class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain,
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, double, Pad);
class ONNX_OPERATOR_TYPED_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 13, MLFloat16, Pad);
+// opset 15
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 15, Shape);
+class ONNX_OPERATOR_KERNEL_CLASS_NAME(kRocmExecutionProvider, kOnnxDomain, 15, Pow);
+
template <>
KernelCreateInfo BuildKernelCreateInfo() {
KernelCreateInfo info;
@@ -1555,7 +1559,7 @@ static Status RegisterRocmKernels(KernelRegistry& kernel_registry) {
// BuildKernelCreateInfo,
// OpSet 13
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1662,7 +1666,7 @@ static Status RegisterRocmKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
- BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
@@ -1756,6 +1760,10 @@ static Status RegisterRocmKernels(KernelRegistry& kernel_registry) {
BuildKernelCreateInfo,
BuildKernelCreateInfo,
BuildKernelCreateInfo,
+
+ // opset 15
+ BuildKernelCreateInfo,
+ BuildKernelCreateInfo,
};
for (auto& function_table_entry : function_table) {
diff --git a/onnxruntime/test/onnx/main.cc b/onnxruntime/test/onnx/main.cc
index f8dc8ee9e4..9085613600 100644
--- a/onnxruntime/test/onnx/main.cc
+++ b/onnxruntime/test/onnx/main.cc
@@ -291,7 +291,8 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
double per_sample_tolerance = 1e-3;
// when cuda is enabled, set it to a larger value for resolving random MNIST test failure
// when openvino is enabled, set it to a larger value for resolving MNIST accuracy mismatch
- double relative_per_sample_tolerance = enable_cuda ? 0.017 : enable_openvino ? 0.009 : 1e-3;
+ double relative_per_sample_tolerance = enable_cuda ? 0.017 : enable_openvino ? 0.009
+ : 1e-3;
Ort::SessionOptions sf;
@@ -480,8 +481,7 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
ORT_TSTR("operator_pow"),
ORT_TSTR("bernoulli"),
ORT_TSTR("bernoulli_double"),
- ORT_TSTR("bernoulli_seed")
- };
+ ORT_TSTR("bernoulli_seed")};
static const ORTCHAR_T* cuda_flaky_tests[] = {
ORT_TSTR("fp16_inception_v1"),
@@ -600,16 +600,6 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
{"bernoulli_seed", "By design. Test data is for informational purpose because the generator is non deterministic."},
{"bernoulli_seed_expanded", "By design. Test data is for informational purpose because the generator is non deterministic."},
{"bernoulli_expanded", "By design. Test data is for informational purpose because the generator is non deterministic."},
- {"shape", "opset15 updates not supported yet."},
- {"shape_clip_end", "opset15 updates not supported yet."},
- {"shape_clip_start", "opset15 updates not supported yet."},
- {"shape_end_1", "opset15 updates not supported yet."},
- {"shape_end_negative_1", "opset15 updates not supported yet."},
- {"shape_example", "opset15 updates not supported yet."},
- {"shape_start_1", "opset15 updates not supported yet."},
- {"shape_start_1_end_2", "opset15 updates not supported yet."},
- {"shape_start_1_end_negative_1", "opset15 updates not supported yet."},
- {"shape_start_negative_1", "opset15 updates not supported yet."},
{"test_optional_get_element", "opset15 updates not supported yet."},
{"test_optional_get_element_sequence", "opset15 updates not supported yet."},
{"test_optional_has_element", "opset15 updates not supported yet."},
diff --git a/onnxruntime/test/optimizer/graph_transform_test.cc b/onnxruntime/test/optimizer/graph_transform_test.cc
index d981343f65..ec36c2988e 100644
--- a/onnxruntime/test/optimizer/graph_transform_test.cc
+++ b/onnxruntime/test/optimizer/graph_transform_test.cc
@@ -53,7 +53,6 @@
#include "core/optimizer/relu_clip_fusion.h"
#include "core/optimizer/reshape_fusion.h"
#include "core/optimizer/rule_based_graph_transformer.h"
-#include "core/optimizer/shape_to_initializer.h"
#include "core/optimizer/skip_layer_norm_fusion.h"
#include "core/optimizer/slice_elimination.h"
#include "core/optimizer/unsqueeze_elimination.h"
@@ -480,7 +479,7 @@ TEST_F(GraphTransformationTests, ConstantFolding_RemoveDanglingInputNodesToConst
ASSERT_TRUE(op_to_count["RandomUniform"] == 0);
}
-TEST_F(GraphTransformationTests, ShapeToInitializer) {
+TEST_F(GraphTransformationTests, ConstantFoldingAShapeNodeDeepInTheGraph) {
auto model_uri = MODEL_FOLDER "shape-add.onnx";
std::shared_ptr model;
ASSERT_STATUS_OK(Model::Load(model_uri, model, nullptr, *logger_));
@@ -489,17 +488,21 @@ TEST_F(GraphTransformationTests, ShapeToInitializer) {
ASSERT_TRUE(op_to_count["Shape"] == 4);
onnxruntime::GraphTransformerManager graph_transformation_mgr{5};
- auto rule_transformer_L1 = std::make_unique("RuleTransformerL1");
- rule_transformer_L1->Register(std::make_unique());
- graph_transformation_mgr.Register(std::move(rule_transformer_L1), TransformerLevel::Level1);
-
+ std::unique_ptr e =
+ std::make_unique(CPUExecutionProviderInfo());
+ graph_transformation_mgr.Register(std::make_unique(*e.get(),
+ false /*skip_dequantize_linear*/),
+ TransformerLevel::Level1);
ASSERT_STATUS_OK(graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level1, *logger_));
op_to_count = CountOpsInGraph(graph);
- // Two of the Shapes are not eliminated because:
- // One includes a symbolic dimension.
- // Another one includes a negative dimension
- ASSERT_TRUE(op_to_count["Shape"] == 2);
+
+ // A Shape node very deep in the graph (feeding into an Identity
+ // node that produces the graph output) gets constant folded which
+ // removes all its ancestors and the Identity node consuming this Shape's
+ // output is subsequently constant folded to leave the graph with no
+ // nodes.
+ ASSERT_TRUE(op_to_count.size() == 0);
}
// Check transformations in the case of a subgraph with constant inputs.
@@ -674,8 +677,8 @@ TEST_F(GraphTransformationTests, FuseCudaConvAddRelu) {
graph_transformation_mgr.Register(std::make_unique(), TransformerLevel::Level2);
ASSERT_STATUS_OK(graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level2, *logger_));
op_to_count = CountOpsInGraph(graph);
- ASSERT_TRUE(op_to_count["Add"] == 0); //Add removed from graph
- ASSERT_TRUE(op_to_count["Relu"] == 0); //Relu removed from graph
+ ASSERT_TRUE(op_to_count["Add"] == 0); //Add removed from graph
+ ASSERT_TRUE(op_to_count["Relu"] == 0); //Relu removed from graph
}
//Conv->Add->Relu will be left intact since there is Identity depend on Add
@@ -695,9 +698,9 @@ TEST_F(GraphTransformationTests, FuseCudaConvAddReluIdentity) {
graph_transformation_mgr.Register(std::make_unique(), TransformerLevel::Level2);
ASSERT_STATUS_OK(graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level2, *logger_));
op_to_count = CountOpsInGraph(graph);
- ASSERT_TRUE(op_to_count["Add"] == 1); //Add remains
- ASSERT_TRUE(op_to_count["Relu"] == 1); //Relu remains
- ASSERT_TRUE(op_to_count["Identity"] == 1); //Identity remains
+ ASSERT_TRUE(op_to_count["Add"] == 1); //Add remains
+ ASSERT_TRUE(op_to_count["Relu"] == 1); //Relu remains
+ ASSERT_TRUE(op_to_count["Identity"] == 1); //Identity remains
}
//Conv->Add will be left intact since there is no Relu follows
@@ -715,7 +718,7 @@ TEST_F(GraphTransformationTests, FuseCudaConvAdd) {
graph_transformation_mgr.Register(std::make_unique(), TransformerLevel::Level2);
ASSERT_STATUS_OK(graph_transformation_mgr.ApplyTransformers(graph, TransformerLevel::Level2, *logger_));
op_to_count = CountOpsInGraph(graph);
- ASSERT_TRUE(op_to_count["Add"] == 1); //Add remains, no transform applied to the graph
+ ASSERT_TRUE(op_to_count["Add"] == 1); //Add remains, no transform applied to the graph
}
#endif
@@ -4131,13 +4134,13 @@ TEST_F(GraphTransformationTests, FilterEnabledOptimizers) {
const auto& graph = session_object.GetGraph();
- // check the ops that should go away if the constant folding transformer or ShapeToInitializer rewrite rule run
+ // check the ops that should go away if the constant folding transformer runs
std::map op_to_count = CountOpsInGraph(graph);
ASSERT_TRUE(op_to_count["Shape"] == 1);
ASSERT_TRUE(op_to_count["ConstantOfShape"] == 1);
ASSERT_TRUE(op_to_count["Add"] == 1);
- ASSERT_STATUS_OK(session_object.FilterEnabledOptimizers({"ConstantFolding", "ShapeToInitializer"}));
+ ASSERT_STATUS_OK(session_object.FilterEnabledOptimizers({"ConstantFolding"}));
ASSERT_STATUS_OK(session_object.Initialize()); // Initialize runs the transformers
op_to_count = CountOpsInGraph(graph);
diff --git a/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc b/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
index 8de061e42d..f4047b2c88 100644
--- a/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
+++ b/onnxruntime/test/providers/cpu/math/element_wise_ops_test.cc
@@ -688,6 +688,21 @@ TEST(MathOpTest, Pow_Float_12) {
test.Run();
}
+TEST(MathOpTest, Pow_Float_15) {
+ OpTester test("Pow", 15);
+ std::vector dims{2, 2};
+ test.AddInput("X", dims,
+ {2.0f, 2.0f,
+ std::sqrt(2.0f), 1.0f});
+ test.AddInput("Y", dims,
+ {0.0f, 8.0f,
+ 2.0f, 9.0f});
+ test.AddOutput("Z", dims,
+ {1.0f, 256.0f,
+ 2.0f, 1.0f});
+ test.Run();
+}
+
TEST(MathOpTest, Pow_Double_12) {
OpTester test("Pow", 12);
std::vector dims{2, 2};
@@ -1635,7 +1650,7 @@ TEST(MathOpTest, LessOrEqual) {
test.AddInput("B", dims, {1.0f, 1.0f, 2.0f, -1.0f});
test.AddOutput("C", dims, {true, true, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_Scalar0) {
@@ -1644,7 +1659,7 @@ TEST(MathOpTest, LessOrEqual_Scalar0) {
test.AddInput("B", {4}, {1.0f, 1.5f, 2.0f, -1.0f});
test.AddOutput("C", {4}, {true, true, true, false});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_Scalar1) {
@@ -1653,7 +1668,7 @@ TEST(MathOpTest, LessOrEqual_Scalar1) {
test.AddInput("B", {1}, {1.0f});
test.AddOutput("C", {4}, {true, true, false, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_int64_Scalar1) {
@@ -1662,7 +1677,7 @@ TEST(MathOpTest, LessOrEqual_int64_Scalar1) {
test.AddInput("B", {1}, {1});
test.AddOutput("C", {4}, {true, true, false, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_broadcastAB) {
OpTester test("LessOrEqual", 12);
@@ -1670,7 +1685,7 @@ TEST(MathOpTest, LessOrEqual_broadcastAB) {
test.AddInput("B", {2}, {15, 7});
test.AddOutput("C", {4, 2}, {true, false, true, false, true, false, false, false});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_broadcastBA) {
@@ -1679,7 +1694,7 @@ TEST(MathOpTest, LessOrEqual_broadcastBA) {
test.AddInput("B", {4, 2}, {10, 11, 12, 13, 14, 15, 16, 17});
test.AddOutput("C", {4, 2}, {false, true, false, true, false, true, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_multidiretional_broadcastAB) {
@@ -1688,7 +1703,7 @@ TEST(MathOpTest, LessOrEqual_multidiretional_broadcastAB) {
test.AddInput("B", {2}, {15, 7});
test.AddOutput("C", {4, 2}, {true, false, true, false, true, false, true, false});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, LessOrEqual_multidiretional_broadcastBA) {
@@ -1697,7 +1712,7 @@ TEST(MathOpTest, LessOrEqual_multidiretional_broadcastBA) {
test.AddInput("B", {4, 1}, {10, 11, 12, 13});
test.AddOutput("C", {4, 2}, {false, true, false, true, false, true, false, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, Greater_7) {
@@ -1784,7 +1799,7 @@ TEST(MathOpTest, GreaterOrEqual_12_float) {
test.AddInput("B", dims, {1.0f, 1.0f, 2.0f, -1.0f});
test.AddOutput("C", dims, {true, false, false, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_12_double) {
@@ -1794,7 +1809,7 @@ TEST(MathOpTest, GreaterOrEqual_12_double) {
test.AddInput("B", dims, {1.0, 1.0, 2.0, -1.0});
test.AddOutput("C", dims, {true, false, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_12_int32) {
@@ -1804,7 +1819,7 @@ TEST(MathOpTest, GreaterOrEqual_12_int32) {
test.AddInput("B", dims, {15, 7, 12, 9});
test.AddOutput("C", dims, {false, true, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_12_int64) {
@@ -1814,7 +1829,7 @@ TEST(MathOpTest, GreaterOrEqual_12_int64) {
test.AddInput("B", dims, {15, 7, 12, 9});
test.AddOutput("C", dims, {false, true, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_broadcastAB) {
@@ -1823,7 +1838,7 @@ TEST(MathOpTest, GreaterOrEqual_broadcastAB) {
test.AddInput("B", {2}, {15, 7});
test.AddOutput("C", {4, 2}, {false, true, false, true, false, true, true, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_broadcastBA) {
@@ -1832,7 +1847,7 @@ TEST(MathOpTest, GreaterOrEqual_broadcastBA) {
test.AddInput("B", {4, 2}, {10, 11, 12, 13, 14, 15, 16, 17});
test.AddOutput("C", {4, 2}, {true, false, true, false, true, false, false, false});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_multidiretional_broadcastAB) {
@@ -1841,7 +1856,7 @@ TEST(MathOpTest, GreaterOrEqual_multidiretional_broadcastAB) {
test.AddInput("B", {2}, {15, 7});
test.AddOutput("C", {4, 2}, {false, true, false, true, false, true, false, true});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, GreaterOrEqual_multidiretional_broadcastBA) {
@@ -1850,7 +1865,7 @@ TEST(MathOpTest, GreaterOrEqual_multidiretional_broadcastBA) {
test.AddInput("B", {4, 1}, {10, 11, 12, 13});
test.AddOutput("C", {4, 2}, {true, false, true, false, true, false, true, false});
test.Run(OpTester::ExpectResult::kExpectSuccess, "",
- {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
+ {kTensorrtExecutionProvider, kNnapiExecutionProvider, kOpenVINOExecutionProvider});
}
TEST(MathOpTest, Equal_bool) {
diff --git a/onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc b/onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc
index 1b08b25512..730631cbd6 100644
--- a/onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc
+++ b/onnxruntime/test/providers/cpu/nn/batch_norm_op_test.cc
@@ -790,5 +790,30 @@ TEST(BatchNormTest, ForwardTrainingTestOpset14) {
test.Run(OpTester::ExpectResult::kExpectSuccess, "", {kCudaExecutionProvider, kTensorrtExecutionProvider, kOpenVINOExecutionProvider, kDnnlExecutionProvider});
}
+TEST(BatchNormTest, ForwardTrainingTestOpset15) {
+ OpTester test("BatchNormalization", 15);
+ float epsilon = 1e-05f;
+ float momentum = 0.1f;
+ int64_t training_mode = 1;
+ test.AddAttribute("epsilon", epsilon);
+ test.AddAttribute("momentum", momentum);
+ test.AddAttribute("training_mode", training_mode);
+ std::vector input_output_dims{2, 2, 2, 2};
+ std::vector channel_dims{2};
+ test.AddInput("X", input_output_dims, {-0.2953f, 0.1180f, 1.0973f, -0.1931f, -0.1999f, -0.0237f, 1.5181f, 0.0076f, -1.0830f, -1.5433f, 0.4327f, -0.9813f, 0.7875f, -0.4080f, -2.3144f, 1.5493f});
+ test.AddInput("scale", channel_dims, {1.0f, 1.0f});
+ test.AddInput("B", channel_dims, {0.0f, 0.0f});
+ test.AddInput("mean", channel_dims, {1.0f, 2.0f});
+ test.AddInput("var", channel_dims, {1.0f, 2.0f});
+
+ test.AddOutput("Y", input_output_dims, {0.0131f, 0.5210f, 1.7244f, 0.1387f, -0.2708f, -0.1191f, 1.2089f, -0.0922f, -0.9548f, -1.5203f, 0.9077f, -0.8298f, 0.5796f, -0.4501f, -2.0921f, 1.2358f});
+
+ test.AddOutput("running_mean", channel_dims, {-0.1754f, 0.303106f});
+ test.AddOutput