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onnxruntime/docs/OperatorKernels.md
RandySheriffH 62226d030f
Cherry-pick tagged commits to 1.12.0 release candidate (#12097) 
* Update ONNX to 1.12 (#11924)

Follow-ups that need to happen after this and before the next ORT release:
* Support SequenceMap with https://github.com/microsoft/onnxruntime/pull/11731
* Support signal ops with https://github.com/microsoft/onnxruntime/pull/11778

Follow-ups that need to happen after this but don't necessarily need to happen before the release:
* Implement LayerNormalization kernel for opset version 17: https://github.com/microsoft/onnxruntime/issues/11916

Fixes #11640

* Dll version fix ovep4.1 (#11953)

* Setting default version values for ovep dlls as well

* Update backend_manager.cc

Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: mohsin <mohsinx.mohammad@intel.com>

* Optimize t5 encoder in beam search (#11926)

* ooptimize t5 encoder

* update

* update

* update

* refactor expand impl

* cuda tests passed

* update

* alignment

* more alignments

* review comments

* Allow saving on CPU usage for infrequent inference requests by reducing thread spinning (#11841)

Introduce Start/Stop threadpool spinning switch
Add a session config option to force spinning stop at the end of the Run()

* Restructure function inliner (#11731)

* Add nested function call tests

* Add overload for Specialize

* Pass symboltable to onnx shape inference

* Avoid renaming empty names

* Enable sequence_map tests which failed before this change

* Deprecate APIs returning raw ptrs and provide replacements (#11922)

Provider better documentation

* register signal ops for opset 17 (#11778)

* Register signal ops for op set 17

Note code is mostly being moved, not added. These ops were previously
only registered as Microsoft contrib ops and only built if
`BUILD_MS_EXPERIMENTAL_OPS=1`. They've been added to the ai.onnx
standard op set in version 17.

Main components of this change:

* Move the kernels from the conrib_ops directory to the
  core directory.
* Add function bodies for ms experimental ops. This will allow
  old models that use the contrib ops to continue to function.
  All the function bodies consist of a single op (the
  new standard op), so performance overhead should be minimal.

Minor clean-up also in this change:

* De-duplicate get_scalar_value_from_tensor: put it in a new utils.h.
* Fix some bugs that caused compilation errors with the experimental
  ops. Tested with `build.sh --ms_experimental`
* Fix some spelling errors and lint violations.
* Replace a couple of switch statements with `MLTypeCallDispatcher`.
* Use `InlineVector` instead of `std::vector`.

Unblocks https://github.com/microsoft/onnxruntime/issues/11640

* Include opset 15 in Conv+BatchNormalization fusion (#11960)

* Fix WinML Tests are still targetting deprecated (deleted) experimental signal op definitions (#12006)

* fix winml tests

* remove legacy test

* switch idft -> dft+inverse attr

* upgrade opset 13->17 for signal ops tests

* [C# Tests] Add support for double tensor output in TestPreTrainedModels. (#12008)

Add support for double tensor output in TestPreTrainedModels.

* DML EP ResNet50 opset 15 fails in ONNX checker for FusedBatchNormalization lacking training_mode attribute (#12010)

FusedBatchNormalization include training_mode attribute

* Generalize native op creation (#11539)

* create op from ep

* read input count from context

* create holder to host nodes

* fix typo

* cast type before comparison

* throw error on API fail

* silence warning from minimal build

* switch to unique_ptr with deleter to host nodes

* fix typo

* fix build err for minimal

* fix build err for minimal

* add UT for conv

* enable test on CUDA

* add comment

* fix typo

* use gsl::span and string view for Node constructor

* Added two APIs - CopyKernelInfo and ReleaseKernelInfo

* pass gsl::span by value

* switch to span<NodeArg* const> to allow for reference to const containers

* fix typo

* fix reduced build err

* fix reduced build err

* refactoring node construction logic

* rename exceptions

* add input and output count as arguments for op creation

* refactor static member

* use ORT_CATCH instead of catch

* cancel try catch

* add static value name map

* format input definition and set err code

* fix comments

* fix typo

* [DML EP] Pad operator: Handle negative pad counts (#11974)

* Pad fallback to CPU

* Added queryPad in operatorRegistration.cpp

* Acknowledged PR comments

* Used any_of

* used none_of instead of any_of

Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>

* Add warning about future computation change for ConvTranspose with auto_pad (#11984)

* Add warning about future computation change for Convtranspose with auto_pad

* improve msg

* update TODO to make lint happy

* update more contents for warning and add if

* valid was not infected

* move it into kernel registration

* parse auto_pad myself

* try to use conv_transpose_attrs_.auto_pad directly

* update roialign cuda impl to onnx opset16 (#12036)

* roialign opset16

* fix

* fix

* Fix windows eager build break by pinning to torch version 1.11.0 (#12033)

Fix windows and linux eager build to torch 1.11.0.

* Skip Constant Folding for ops producing an optional type output (#11839)

* Disable sequence-type tests since C# infra doesn't support well (#12037)

* Extend lifetime of KernelDef when creating a standalone op (#12057)

place tmp kernel def as local variable to cover the lifetime of kernel creation

* Add targets files for new .net6 frameworks (#12016)

* Add net6 targets.
Remove maccatalyst as we don't have a native build targetting that.

* Set platform in macos targets

* Add targetFramework entries

* Move NativeLib.DllName definition and set using preprocessor values for simplicity. Couldn't get it to build with the preprocessor based setup when it was in a separate file.

Update the nuspec generation to set platform version for .net6 targets. TODO: Validate versions. I copied them from the managed nuget package the packaging pipeline generated prior to adding targets. Possibly w could/should lower some of the versions.

Hopefully the need to specify a version goes away when the release version of VS2022 supports .net6.

* Try android 31.1 as https://github.com/actions/virtual-environments/blob/main/images/win/Windows2022-Readme.md suggests that should be available on the CI machines

* Fix patch version mismatch
Add some extra debug info in case it helps

* Debug nuget location in CI

* Add workspace entry back in

* Add steps

* One more attempt with hardcoded nuget.exe path and original android31.0 version

* Better fix - found explicit nuget download and updated version there.

* flake8 fixes

* Fix black complaints.

* Exit Microsoft_ML_OnnxRuntime_CheckPrerequisites for net6 iOS.

* Removed outdated comment

* Fix DML custom operators which set descriptor heap to command list (#12059)

* Make C# runtest.sh automatically set latest opset (#12039)

* Update C# runtest.sh for opset 17

Should have been part of https://github.com/microsoft/onnxruntime/pull/11924

* get appropriate opset version from onnx doc

* use absolute rather than relative path

* fix typo in var name

* Disable DML command list reuse for Xbox (#12063)

disable cl reuse for xbox

* Add data type check in ConvAddRelu fusion (#12058)

* Add undocumented attribute to disable generation of Java bindings from the Android AAR. (#12075)

The generated bindings causes C# build errors that require workaround code. Disabling generation should avoid the need for any workarounds.

As the user has the C# ORT package with the C# to C bindings there's no need for binding generation that calls the ORT Java API (which is C# -> Java ->C).

* enable the extensions custom build for java and android (#11823)

* generate quantization parameter for outputs (#12089)

* DML EP Update to DML 1.9 (#12090)

* Update to DML 1.9

* Appease obnoxious Python formatting tool

* Fix orttraining-linux-ci-pipeline - Symbolic shape infer (#11965)

fix symbolic shape error due to upgraded numpy + legacy sympy

* check consumers of dq node before swap dq and transpose (#12099)

* check consumers of dq node before swap dq and transpose

* add unit test

Co-authored-by: Gary Miguel <garymiguel@microsoft.com>
Co-authored-by: Preetha Veeramalai <preetha.veeramalai@intel.com>
Co-authored-by: mayavijx <mayax.vijayan@intel.com>
Co-authored-by: mohsin <mohsinx.mohammad@intel.com>
Co-authored-by: Ye Wang <52801275+wangyems@users.noreply.github.com>
Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com>
Co-authored-by: G. Ramalingam <grama@microsoft.com>
Co-authored-by: Dwayne Robinson <dwayner@microsoft.com>
Co-authored-by: Sheil Kumar <smk2007@gmail.com>
Co-authored-by: Edward Chen <18449977+edgchen1@users.noreply.github.com>
Co-authored-by: sumitsays <sumitagarwal330@gmail.com>
Co-authored-by: Sumit Agarwal <sumitagarwal@microsoft.com>
Co-authored-by: Chun-Wei Chen <jacky82226@gmail.com>
Co-authored-by: George Wu <jywu@microsoft.com>
Co-authored-by: Wil Brady <25513670+WilBrady@users.noreply.github.com>
Co-authored-by: Hariharan Seshadri <shariharan91@gmail.com>
Co-authored-by: Wei-Sheng Chin <wschin@outlook.com>
Co-authored-by: Scott McKay <skottmckay@gmail.com>
Co-authored-by: Jeff Bloomfield <38966965+jeffbloo@users.noreply.github.com>
Co-authored-by: Justin Stoecker <justoeck@microsoft.com>
Co-authored-by: Wenbing Li <10278425+wenbingl@users.noreply.github.com>
Co-authored-by: Yufeng Li <liyufeng1987@gmail.com>
Co-authored-by: pengwa <pengwa@microsoft.com>
2022-07-06 21:35:19 -07:00

131 KiB
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Supported Operators and Data Types

This file is automatically generated from the registered kernels by this script. Do not modify directly.

Execution Providers

Operators implemented by CPUExecutionProvider

Op Name Parameters OpSet Version Types Supported
Operator Domain: ai.onnx
Abs in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
[6, 12] T = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
Acos in input:T
out output:T		 7+ T = tensor(float)
Acosh in input:T
out output:T		 9+ T = tensor(float)
Add in A:T
in B:T
out C:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
13 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[7, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
Affine in X:T
out Y:T		 1+ T = tensor(float)
And in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
ArgMax in data:T
out reduced:tensor(int64)		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int8), tensor(uint8)
[11, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int8), tensor(uint8)
[1, 10] T = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
ArgMin in data:T
out reduced:tensor(int64)		 13+ T = tensor(double), tensor(float), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(int32)
[1, 10] T = tensor(float), tensor(int32)
Asin in input:T
out output:T		 7+ T = tensor(float)
Asinh in input:T
out output:T		 9+ T = tensor(float)
Atan in input:T
out output:T		 7+ T = tensor(float)
Atanh in input:T
out output:T		 9+ T = tensor(float)
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		 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)
BlackmanWindow in size:T1
out output:T2		 17+ T1 = tensor(int32), tensor(int64)
T2 = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
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(string), 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(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
[6, 12] T1 = 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)
T2 = 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)
Ceil in X:T
out Y:T		 13+ T = tensor(float)
[6, 12] T = tensor(float)
Celu in X:T
out Y:T		 12+ T = tensor(float)
Clip in input:T
in min:T
in max:T
out output:T

or

in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)
11 T = tensor(float)
[6, 10] T = tensor(float)
Compress in input:T
in condition:T1
out output:T		 11+ 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(bool)
[9, 10] 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(bool)
Concat in inputs:T
out concat_result:T		 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)
[11, 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)
[4, 10] 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)
ConcatFromSequence in input_sequence:S
out concat_result:T		 11+ 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))
ConstantOfShape in input:T1
out output:T2		 9+ T1 = tensor(int64)
T2 = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
Conv in X:T
in W:T
in B:T
out Y:T		 11+ T = tensor(float)
[1, 10] T = tensor(float)
ConvInteger in x:T1
in w:T2
in x_zero_point:T1
in w_zero_point:T2
out y:T3		 10+ T1 = tensor(uint8)
T2 = tensor(uint8)
T3 = tensor(int32)
ConvTranspose in X:T
in W:T
in B:T
out Y:T		 11+ T = tensor(float)
[1, 10] T = tensor(float)
Cos in input:T
out output:T		 7+ T = tensor(float)
Cosh in input:T
out output:T		 9+ T = tensor(float)
Crop in input:T
out output:T		 1+ T = tensor(float)
CumSum in x:T
in axis:T2
out y:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int32), tensor(int64)
[11, 13] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int32), tensor(int64)
DFT in input:T1
in dft_length:T2
out output:T1		 17+ T1 = tensor(double), tensor(float)
T2 = tensor(int32), tensor(int64)
DepthToSpace in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[11, 12] T = tensor(double), tensor(float)
[1, 10] T = tensor(double), tensor(float)
DequantizeLinear in x:T
in x_scale:tensor(float)
in x_zero_point:T
out y:tensor(float)		 13+ T = tensor(int32), tensor(int8), tensor(uint8)
[10, 12] T = tensor(int32), tensor(int8), tensor(uint8)
Det in X:T
out Y:T		 11+ T = tensor(float)
Div in A:T
in B:T
out C:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
13 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[7, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
Dropout in data:T
in ratio:T1
in training_mode:T2
out output:T
out mask:T2

or

in data:T
out output:T
out mask:T

or

in data:T
out output:T
out mask:T1		 13+ T = tensor(double), tensor(float)
T1 = tensor(double), tensor(float)
T2 = tensor(bool)
12 T = tensor(double), tensor(float)
T1 = tensor(double), tensor(float)
T2 = tensor(bool)
[10, 11] T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(bool)
[7, 9] T = tensor(double), tensor(float), tensor(float16)
DynamicQuantizeLinear in x:T1
out y:T2
out y_scale:tensor(float)
out y_zero_point:T2		 11+ T2 = tensor(uint8)
DynamicSlice in data:T
in starts:Tind
in ends:Tind
in axes:Tind
out output:T		 1+ 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)
Tind = tensor(int32), tensor(int64)
Einsum in Inputs:T
out Output:T		 12+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
Elu in X:T
out Y:T		 6+ T = tensor(float)
Equal in A:T
in B:T
out C:T1		 13+ T = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[11, 12] T = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[7, 10] T = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
Erf in input:T
out output:T		 13+ T = tensor(float)
[9, 12] T = tensor(float)
Exp in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
Expand in input:T
in shape:tensor(int64)
out output:T		 13+ T = 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)
[8, 12] T = 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)
EyeLike in input:T1
out output:T2		 9+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)
T2 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)
Flatten in input:T
out output:T		 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)
[11, 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)
[9, 10] 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)
[1, 8] 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)
Floor in X:T
out Y:T		 13+ T = tensor(float)
[6, 12] T = tensor(float)
GRU in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
out Y:T
out Y_h:T		 14+ T = tensor(double), tensor(float)
T1 = tensor(int32)
[7, 13] T = tensor(double), tensor(float)
T1 = tensor(int32)
Gather in data:T
in indices:Tind
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
[1, 10] 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)
Tind = tensor(int32), tensor(int64)
GatherElements in data:T
in indices:Tind
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
GatherND in data:T
in indices:tensor(int64)
out output:T		 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)
indices = tensor(int64)
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)
indices = tensor(int64)
11 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)
indices = tensor(int64)
Gemm in A:T
in B:T
in C:T
out Y:T		 13+ T = tensor(double), tensor(float)
[11, 12] T = tensor(double), tensor(float)
[9, 10] T = tensor(double), tensor(float)
[7, 8] T = tensor(double), tensor(float)
GlobalAveragePool in X:T
out Y:T		 1+ T = tensor(float)
GlobalLpPool in X:T
out Y:T		 2+ T = tensor(float)
GlobalMaxPool in X:T
out Y:T		 1+ T = tensor(float)
Greater in A:T
in B:T
out C:T1		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[9, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[7, 8] T = tensor(double), tensor(float)
T1 = tensor(bool)
GreaterOrEqual in A:T
in B:T
out C:T1		 16+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[12, 15] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
GridSample in X:T1
in grid:T1
out Y:T2		 16+ T1 = tensor(float)
T2 = tensor(float)
HammingWindow in size:T1
out output:T2		 17+ T1 = tensor(int32), tensor(int64)
T2 = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
HannWindow in size:T1
out output:T2		 17+ T1 = tensor(int32), tensor(int64)
T2 = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
HardSigmoid in X:T
out Y:T		 6+ T = tensor(float)
Hardmax in input:T
out output:T		 13+ T = tensor(float)
[11, 12] T = tensor(float)
[1, 10] T = tensor(float)
Identity in input:T
out output:T

or

in input:V
out output:V		 16+ V = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), 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)), 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)
[14, 15] V = 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)), 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)
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)
[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)
If in cond:B
out outputs:V		 16+ B = tensor(bool)
V = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), 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)), 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)
[13, 15] B = tensor(bool)
V = 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)), 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)
[11, 12] B = tensor(bool)
V = 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)
[1, 10] B = tensor(bool)
V = 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)
ImageScaler in input:T
out output:T		 1+ T = tensor(float)
InstanceNormalization in input:T
in scale:T
in B:T
out output:T		 6+ T = tensor(float)
IsInf in X:T1
out Y:T2		 10+ T1 = tensor(double), tensor(float)
T2 = tensor(bool)
IsNaN in X:T1
out Y:T2		 13+ T1 = tensor(float), tensor(float16)
T2 = tensor(bool)
[9, 12] T1 = tensor(float), tensor(float16)
T2 = tensor(bool)
LRN in X:T
out Y:T		 13+ T = tensor(float)
[1, 12] T = tensor(float)
LSTM in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
in initial_c:T
in P:T
out Y:T
out Y_h:T
out Y_c:T		 14+ T = tensor(double), tensor(float)
T1 = tensor(int32)
[7, 13] T = tensor(double), tensor(float)
T1 = tensor(int32)
LayerNormalization in X:T
in Scale:T
in B:T
out Y:T
out Mean:U
out InvStdDev:U

or

in X:T
in Scale:V
in B:V
out Y:V
out Mean:U
out InvStdDev:U		 1+ T = tensor(double), tensor(float)
U = tensor(double), tensor(float)
V = tensor(double), tensor(float)
LeakyRelu in X:T
out Y:T		 16+ T = tensor(float)
[6, 15] T = tensor(float)
Less in A:T
in B:T
out C:T1		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[9, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[7, 8] T = tensor(double), tensor(float)
T1 = tensor(bool)
LessOrEqual in A:T
in B:T
out C:T1		 16+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
[12, 15] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
T1 = tensor(bool)
Log in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
LogSoftmax in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[11, 12] T = tensor(double), tensor(float)
[1, 10] T = tensor(double), tensor(float)
Loop in M:I
in cond:B
in v_initial:V
out v_final_and_scan_outputs:V		 16+ B = tensor(bool)
I = tensor(int64)
V = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8)), 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)), 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)
[13, 15] B = tensor(bool)
I = tensor(int64)
V = 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)), 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)
[11, 12] B = tensor(bool)
I = tensor(int64)
V = 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)
[1, 10] B = tensor(bool)
I = tensor(int64)
V = 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)
LpNormalization in input:T
out output:T		 1+ T = tensor(double), tensor(float)
LpPool in X:T
out Y:T		 11+ T = tensor(float)
[2, 10] T = tensor(float)
MatMul in A:T
in B:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[9, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[1, 8] T = tensor(double), tensor(float)
MatMulInteger in A:T1
in B:T2
in a_zero_point:T1
in b_zero_point:T2
out Y:T3		 10+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(int32)
Max in data_0:T
out max:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
12 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[8, 11] T = tensor(double), tensor(float)
[6, 7] T = tensor(float)
MaxPool in X:T
out Y:T

or

in X:T
out Y:T
out Indices:I		 12+ I = tensor(int64)
T = tensor(double), tensor(float), tensor(int8), tensor(uint8)
[8, 11] I = tensor(int64)
T = tensor(double), tensor(float)
[1, 7] T = tensor(float)
MaxRoiPool in X:T
in rois:T
out Y:T		 1+ T = tensor(float)
MaxUnpool in X:T1
in I:T2
in output_shape:T2
out output:T1		 11+ T1 = tensor(float)
T2 = tensor(int64)
[9, 10] T1 = tensor(float)
T2 = tensor(int64)
Mean in data_0:T
out mean:T		 13+ T = tensor(float)
[8, 12] T = tensor(float)
[6, 7] T = tensor(float)
MeanVarianceNormalization in X:T
out Y:T

or

in input:T
out output:T		 13+ T = tensor(float)
[9, 12] T = tensor(float)
[1, 8] T = tensor(float)
MelWeightMatrix in num_mel_bins:T1
in dft_length:T1
in sample_rate:T1
in lower_edge_hertz:T2
in upper_edge_hertz:T2
out output:T3		 17+ T1 = tensor(int32), tensor(int64)
T2 = tensor(float)
T3 = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
Min in data_0:T
out min:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
12 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[8, 11] T = tensor(double), tensor(float)
[6, 7] T = tensor(float)
Mod in A:T
in B:T
out C:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
[10, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
Mul in A:T
in B:T
out C:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
13 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[7, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
Multinomial in input:T1
out output:T2		 7+ T1 = tensor(float)
T2 = tensor(int32), tensor(int64)
Neg in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8)
[6, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8)
NonZero in X:T
out Y:tensor(int64)		 13+ T = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)
[9, 12] T = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)
Not in X:T
out Y:T		 1+ T = tensor(bool)
OneHot in indices:T1
in depth:T2
in values:T3
out output:T3		 11+ T1 = tensor(float), tensor(int32), tensor(int64)
T2 = tensor(float), tensor(int32), tensor(int64)
T3 = tensor(float), tensor(int32), tensor(int64), tensor(string)
[9, 10] T1 = tensor(float), tensor(int32), tensor(int64)
T2 = tensor(float), tensor(int32), tensor(int64)
T3 = tensor(float), tensor(int32), tensor(int64), tensor(string)
Optional in input:V
out output:O		 15+ O = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))
V = 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)), 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)
OptionalGetElement in input:O
out output:V		 15+ O = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))
V = 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)), 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)
OptionalHasElement in input:O
out output:B		 15+ B = tensor(bool)
O = optional(seq(tensor(bfloat16))), optional(seq(tensor(bool))), optional(seq(tensor(double))), optional(seq(tensor(float))), optional(seq(tensor(float16))), optional(seq(tensor(int16))), optional(seq(tensor(int32))), optional(seq(tensor(int64))), optional(seq(tensor(int8))), optional(seq(tensor(string))), optional(seq(tensor(uint16))), optional(seq(tensor(uint32))), optional(seq(tensor(uint64))), optional(seq(tensor(uint8))), optional(tensor(bfloat16)), optional(tensor(bool)), optional(tensor(double)), optional(tensor(float)), optional(tensor(float16)), optional(tensor(int16)), optional(tensor(int32)), optional(tensor(int64)), optional(tensor(int8)), optional(tensor(string)), optional(tensor(uint16)), optional(tensor(uint32)), optional(tensor(uint64)), optional(tensor(uint8))
Or in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
PRelu in X:T
in slope:T
out Y:T		 16+ T = tensor(float)
[9, 15] T = tensor(float)
[7, 8] T = tensor(float)
Pad in data:T
in pads:tensor(int64)
in constant_value:T
out output:T

or

in data:T
out output:T		 13+ T = tensor(bool), tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint32), tensor(uint64), tensor(uint8)
[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		 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(int8), tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(int8), tensor(uint8)
T4 = tensor(int32)
QLinearMatMul in a:T1
in a_scale:tensor(float)
in a_zero_point:T1
in b:T2
in b_scale:tensor(float)
in b_zero_point:T2
in y_scale:tensor(float)
in y_zero_point:T3
out y:T3		 10+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(int8), tensor(uint8)
QuantizeLinear in x:T1
in y_scale:tensor(float)
in y_zero_point:T2
out y:T2		 13+ T1 = tensor(float)
T2 = tensor(int8), tensor(uint8)
[10, 12] T1 = tensor(float)
T2 = tensor(int8), tensor(uint8)
RNN in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
out Y:T
out Y_h:T		 14+ T = tensor(float)
T1 = tensor(int32)
[7, 13] T = tensor(float)
T1 = tensor(int32)
RandomNormal out output:T 1+ T = tensor(double), tensor(float)
RandomNormalLike in input:T1
out output:T2		 1+ T1 = 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)
T2 = tensor(double), tensor(float)
RandomUniform out output:T 1+ T = tensor(double), tensor(float)
RandomUniformLike in input:T1
out output:T2		 1+ T1 = 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)
T2 = tensor(double), tensor(float)
Range in start:T
in limit:T
in delta:T
out output:T		 11+ T = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)
Reciprocal in X:T
out Y:T		 13+ T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
ReduceL1 in data:T
out reduced:T		 13+ T = tensor(float), tensor(int32)
[11, 12] T = tensor(float), tensor(int32)
[1, 10] T = tensor(float), tensor(int32)
ReduceL2 in data:T
out reduced:T		 13+ T = tensor(float), tensor(int32)
[11, 12] T = tensor(float), tensor(int32)
[1, 10] T = tensor(float), tensor(int32)
ReduceLogSum in data:T
out reduced:T		 13+ T = tensor(float), tensor(int32)
[11, 12] T = tensor(float), tensor(int32)
[1, 10] T = tensor(float), tensor(int32)
ReduceLogSumExp in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(int32)
ReduceMax in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
11 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[1, 10] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
ReduceMean in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(int32)
ReduceMin in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
11 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[1, 10] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
ReduceProd in data:T
out reduced:T		 13+ T = tensor(float), tensor(int32), tensor(int64)
[11, 12] T = tensor(float), tensor(int32), tensor(int64)
[1, 10] T = tensor(float), tensor(int32), tensor(int64)
ReduceSum in data:T
in axes:tensor(int64)
out reduced:T

or

in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[11, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[1, 10] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
ReduceSumSquare in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(int32)
Relu in X:T
out Y:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int8)
13 T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
Reshape in data:T
in shape:tensor(int64)
out reshaped:T

or

in data:T
out reshaped:T		 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)
shape = tensor(int64)
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)
shape = tensor(int64)
[5, 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)
shape = tensor(int64)
[1, 4] 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)
Resize in X:T
in scales:tensor(float)
out Y:T

or

in X:T1
in roi:T2
in scales:tensor(float)
in sizes:tensor(int64)
out Y:T1		 13+ T1 = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
[11, 12] T1 = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
10 T = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
ReverseSequence in input:T
in sequence_lens:tensor(int64)
out Y:T		 10+ 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)
RoiAlign in X:T1
in rois:T1
in batch_indices:T2
out Y:T1		 16+ T1 = tensor(double), tensor(float)
T2 = tensor(int64)
[10, 15] T1 = tensor(double), tensor(float)
T2 = tensor(int64)
Round in X:T
out Y:T		 11+ T = tensor(double), tensor(float), tensor(float16)
STFT in signal:T1
in frame_step:T2
in window:T1
in frame_length:T2
out output:T1		 17+ T1 = tensor(double), tensor(float)
T2 = tensor(int32), tensor(int64)
Scale in input:T
out output:T		 1+ T = tensor(float)
ScaledTanh in input:T
out output:T		 1+ T = tensor(float)
Scan in initial_state_and_scan_inputs:V
out final_state_and_scan_outputs:V

or

in sequence_lens:I
in initial_state_and_scan_inputs:V
out final_state_and_scan_outputs:V		 16+ V = 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)
[11, 15] V = 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)
[9, 10] V = 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)
8 I = tensor(int64)
V = 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)
Scatter in data:T
in indices:Tind
in updates:T
out output:T		 [9, 10] 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)
Tind = tensor(int32), tensor(int64)
ScatterElements in data:T
in indices:Tind
in updates:T
out output:T		 16+ 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)
Tind = tensor(int32), tensor(int64)
[13, 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
ScatterND in data:T
in indices:tensor(int64)
in updates:T
out output:T		 16+ 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)
[13, 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)
[11, 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)
Selu in X:T
out Y:T		 6+ T = tensor(float)
SequenceAt in input_sequence:S
in position:I
out tensor:T		 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))
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)
SequenceConstruct in inputs:T
out output_sequence:S		 11+ 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))
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)
SequenceEmpty out output:S 11+ 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))
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		 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)
[6, 12] T = tensor(double), tensor(float)
Sign in input:T
out output:T		 13+ 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)
[9, 12] 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)
SimplifiedLayerNormalization in X:T
in scale:V
out Y:V
out inv_std_var:U		 1+ T = tensor(double), tensor(float)
U = tensor(double), tensor(float)
V = tensor(double), tensor(float)
Sin in input:T
out output:T		 7+ T = tensor(double), tensor(float)
Sinh in input:T
out output:T		 9+ T = tensor(float)
Size in data:T
out size:T1		 13+ T = tensor(bool), tensor(double), tensor(float), 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(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
T1 = tensor(int64)
Slice in data:T
in starts:Tind
in ends:Tind
in axes:Tind
in steps:Tind
out output:T

or

in data:T
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
10 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)
Tind = tensor(int32), tensor(int64)
[1, 9] 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)
Softmax in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[11, 12] T = tensor(double), tensor(float)
[1, 10] T = tensor(double), tensor(float)
Softplus in X:T
out Y:T		 1+ T = tensor(float)
Softsign in input:T
out output:T		 1+ T = tensor(float)
SpaceToDepth in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[1, 12] T = tensor(double), tensor(float)
Split in input:T
in split:T
out outputs...:T

or

in input:T
in split:tensor(int64)
out outputs:T

or

in input:T
out outputs:T		 13+ T = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint8)
[11, 12] T = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint8)
[2, 10] T = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint8)
SplitToSequence in input:T
in split: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))
T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)
Sqrt in X:T
out Y:T		 13+ T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
Squeeze in data:T
in axes:tensor(int64)
out squeezed:T

or

in data:T
out squeezed:T		 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)
[11, 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)
[1, 10] 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)
StringNormalizer in X:tensor(string)
out Y:tensor(string)		 10+ X = tensor(string)
Sub in A:T
in B:T
out C:T		 14+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
13 T = tensor(double), tensor(float), tensor(int32), tensor(int64)
[7, 12] T = tensor(double), tensor(float), tensor(int32), tensor(int64)
Sum in data_0:T
out sum:T		 13+ T = tensor(double), tensor(float)
[8, 12] T = tensor(double), tensor(float)
[6, 7] T = tensor(double), tensor(float)
Tan in input:T
out output:T		 7+ T = tensor(float)
Tanh in input:T
out output:T		 13+ T = tensor(double), tensor(float)
[6, 12] T = tensor(double), tensor(float)
TfIdfVectorizer in X:T
out Y:T1		 9+ T = tensor(int32), tensor(int64), tensor(string)
T1 = tensor(float)
ThresholdedRelu in X:T
out Y:T		 10+ T = tensor(float)
[1, 9] T = tensor(float)
Tile in input:T
in repeats:T1
out output:T

or

in input:T
in tiles:T
in axis:T
out output:T		 13+ T = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
T1 = tensor(int64)
[6, 12] T = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
T1 = tensor(int64)
TopK in X:T
in K:tensor(int64)
out Values:T
out Indices:I

or

in X:T
out Values:T
out Indices:I		 11+ I = tensor(int64)
T = tensor(double), tensor(float), tensor(int32), tensor(int64)
10 I = tensor(int64)
T = tensor(double), tensor(float)
[1, 9] I = tensor(int64)
T = tensor(double), tensor(float)
Transpose in data:T
out transposed:T		 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)
[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)
Trilu in input:T
in k:tensor(int64)
out output:T		 14+ T = tensor(double), tensor(float), tensor(int64)
Unique in X:T
out Y:T
out indices:tensor(int64)
out inverse_indices:tensor(int64)
out counts:tensor(int64)		 11+ T = tensor(float), tensor(int64), tensor(int8), tensor(string)
Unsqueeze in data:T
in axes:tensor(int64)
out expanded:T

or

in data:T
out expanded:T		 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)
[11, 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)
[1, 10] 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)
Upsample in X:T
in scales:tensor(float)
out Y:T

or

in X:T
out Y:T		 9 T = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
[7, 8] T = tensor(float), tensor(int32), tensor(int8), tensor(uint8)
Where in condition:B
in X:T
in Y:T
out output:T		 16+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint8)
[9, 15] T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint8)
Xor in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
Operator Domain: ai.onnx.ml
ArrayFeatureExtractor in X:T
in Y:tensor(int64)
out Z:T		 1+ T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)
Binarizer in X:T
out Y:T		 1+ T = tensor(float)
CastMap in X:T1
out Y:T2		 1+ T1 = map(int64,tensor(float)), map(int64,tensor(string))
T2 = tensor(float), tensor(int64), tensor(string)
CategoryMapper in X:T1
out Y:T2		 1+ T1 = tensor(int64), tensor(string)
T2 = tensor(int64), tensor(string)
DictVectorizer in X:T1
out Y:T2		 1+ T1 = map(int64,tensor(double)), map(int64,tensor(float)), map(int64,tensor(string)), map(string,tensor(double)), map(string,tensor(float)), map(string,tensor(int64))
T2 = tensor(double), tensor(float), tensor(int64), tensor(string)
FeatureVectorizer in X:T1
out Y:tensor(float)		 1+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64)
Imputer in X:T
out Y:T		 1+ T = tensor(float), tensor(int64)
LabelEncoder in X:T1
out Y:T2		 2+ T1 = tensor(float), tensor(int64), tensor(string)
T2 = tensor(float), tensor(int64), tensor(string)
1 T1 = tensor(int64), tensor(string)
T2 = tensor(int64), tensor(string)
LinearClassifier in X:T1
out Y:T2
out Z:tensor(float)		 1+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int64), tensor(string)
LinearRegressor in X:T
out Y:tensor(float)		 1+ T = tensor(float)
Normalizer in X:T
out Y:tensor(float)		 1+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
OneHotEncoder in X:T
out Y:tensor(float)		 1+ T = tensor(double), tensor(float), tensor(int64), tensor(string)
SVMClassifier in X:T1
out Y:T2
out Z:tensor(float)		 1+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int64), tensor(string)
SVMRegressor in X:T
out Y:tensor(float)		 1+ T = tensor(float)
Scaler in X:T
out Y:tensor(float)		 1+ T = tensor(double), tensor(float), tensor(int32), tensor(int64)
TreeEnsembleClassifier in X:T1
out Y:T2
out Z:tensor(float)		 3+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int64), tensor(string)
[1, 2] T1 = tensor(double), tensor(float), tensor(int32), tensor(int64)
T2 = tensor(int64), tensor(string)
TreeEnsembleRegressor in X:T
out Y:tensor(float)		 3+ T = tensor(double), tensor(float)
[1, 2] T = tensor(double), tensor(float)
ZipMap in X:tensor(float)
out Z:T		 1+ T = seq(map(int64,tensor(float))), seq(map(string,tensor(float)))
Operator Domain: com.microsoft
Attention in input:T
in weight:T
in bias:T
in mask_index:M
in past:T
in extra_add:T
out output:T
out present:T		 1+ T = tensor(float)
AttnLSTM in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
in initial_c:T
in P:T
in QW:T
in MW:T
in V:T
in M:T
in memory_seq_lens:T1
in AW:T
out Y:T
out Y_h:T
out Y_c:T		 1+ T = tensor(double), tensor(float)
T1 = tensor(int32)
BeamSearch in input_ids:I
in max_length:I
in min_length:I
in num_beams:I
in num_return_sequences:I
in length_penalty:T
in repetition_penalty:T
in vocab_mask:M
in prefix_vocab_mask:M
in attention_mask:I
out sequences:I
out sequences_scores:T
out scores:T		 1+ T = tensor(float)
BiasGelu in A:T
in B:T
out C:T		 1+ T = tensor(float)
BifurcationDetector in src_tokens:T
in cur_tokens:T
in prev_suffix_match_idx:T
in pred_tokens:T
out tokens:T
out suffix_match_idx:T		 1+ T = tensor(int64)
CDist in A:T
in B:T
out C:T		 1+ T = tensor(double), tensor(float)
ConvTransposeWithDynamicPads in X:T
in W:T
in Pads:tensor(int64)
in B:T
out Y:T		 1+ T = tensor(float)
CropAndResize in X:T1
in rois:T1
in batch_indices:T2
in crop_size:T2
out Y:T1		 1+ T1 = tensor(float)
T2 = tensor(int32)
DequantizeLinear in x:T1
in x_scale:T2
in x_zero_point:T1
out y:T2		 1+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(float)
DynamicQuantizeLSTM in X:T
in W:T2
in R:T2
in B:T
in sequence_lens:T1
in initial_h:T
in initial_c:T
in P:T
in W_scale:T
in W_zero_point:T2
in R_scale:T
in R_zero_point:T2
out Y:T
out Y_h:T
out Y_c:T		 1+ T = tensor(float)
T1 = tensor(int32)
T2 = tensor(int8), tensor(uint8)
DynamicQuantizeMatMul in A:T1
in B:T2
in b_scale:T1
in b_zero_point:T2
in bias:T1
out Y:T1		 1+ T1 = tensor(float)
T2 = tensor(int8), tensor(uint8)
EmbedLayerNormalization in input_ids:T1
in segment_ids:T1
in word_embedding:T
in position_embedding:T
in segment_embedding:T
in gamma:T
in beta:T
in mask:T1
in position_ids:T1
out output:T
out mask_index:T1
out embedding_sum:T		 1+ T = tensor(float)
ExpandDims in X:T
in axis:tensor(int32)
out Y:T		 1+ 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)
axis = tensor(int32)
FastGelu in X:T
in bias:T
out Y:T		 1+ T = tensor(float)
FusedConv in X:T
in W:T
in B:T
in Z:T
out Y:T		 1+ T = tensor(float)
FusedGemm in A:T
in B:T
in C:T
out Y:T		 1+ T = tensor(float)
FusedMatMul in A:T
in B:T
out Y:T		 1+ T = tensor(float)
GatherND in data:T
in indices:Tind
out output:T		 1+ 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)
Tind = tensor(int32), tensor(int64)
Gelu in X:T
out Y:T		 1+ T = tensor(float)
GridSample in X:T1
in Grid:T1
out Y:T2		 1+ T1 = tensor(float)
T2 = tensor(float)
Inverse in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
MatMulInteger16 in A:T1
in B:T2
out Y:T3		 1+ T1 = tensor(int16)
T2 = tensor(int16)
T3 = tensor(int32)
MatMulIntegerToFloat in A:T1
in B:T2
in a_scale:T3
in b_scale:T3
in a_zero_point:T1
in b_zero_point:T2
in bias:T3
out Y:T3		 1+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(float)
MaxpoolWithMask in X:T
in M:tensor(int32)
out Y:T		 1+ X = tensor(float)
MurmurHash3 in X:T1
out Y:T2		 1+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint32), tensor(uint64)
T2 = tensor(int32), tensor(uint32)
NGramRepeatBlock in input_ids:Tid
in scores:T
out scores_out:T		 1+ T = tensor(float)
Tid = tensor(int64)
NhwcMaxPool in x:T
out y:T		 1+ T = tensor(int8), tensor(uint8)
Pad in data:T
in pads:tensor(int64)
in value:T
out output:T		 1+ T = tensor(float)
QAttention in input:T1
in weight:T2
in bias:T3
in input_scale:T3
in weight_scale:T3
in mask_index:T4
in input_zero_point:T1
in weight_zero_point:T2
in past:T3
out output:T3
out present:T3		 1+ T1 = tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(float)
T4 = tensor(int32)
QEmbedLayerNormalization in input_ids:T1
in segment_ids:T1
in word_embedding_quant:T2
in position_embedding_quant:T2
in segment_embedding:T2
in gamma_quant:T2
in beta_quant:T2
in mask:T1
in word_embedding_scale:T
in position_embedding_scale:T
in segment_embedding_scale:T
in gamma_scale:T
in beta_scale:T
in word_embedding_zero_point:T2
in position_embedding_zero_point:T2
in segment_embedding_zero_point:T2
in gamma_zero_point:T2
in beta_zero_point:T2
out layernorm_out:T
out mask_index_out:T1		 1+ T = tensor(float)
QGemm in A:TA
in a_scale:T
in a_zero_point:TA
in B:TB
in b_scale:T
in b_zero_point:TB
in C:TC
in y_scale:T
in y_zero_point:TYZ
out Y:TY		 1+ T = tensor(float)
TA = tensor(int8), tensor(uint8)
TB = tensor(int8), tensor(uint8)
TC = tensor(int32)
TY = tensor(float), tensor(int8), tensor(uint8)
TYZ = tensor(int8), tensor(uint8)
QLinearAdd in A:T
in A_scale:tensor(float)
in A_zero_point:T
in B:T
in B_scale:tensor(float)
in B_zero_point:T
in C_scale:tensor(float)
in C_zero_point:T
out C:T		 1+ T = tensor(int8), tensor(uint8)
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		 1+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(int8), tensor(uint8)
T3 = tensor(int8), tensor(uint8)
T4 = tensor(int32)
QLinearLeakyRelu in X:T
in X_scale:tensor(float)
in X_zero_point:T
in Y_scale:tensor(float)
in Y_zero_point:T
out Y:T		 1+ T = tensor(int8), tensor(uint8)
QLinearMul in A:T
in A_scale:tensor(float)
in A_zero_point:T
in B:T
in B_scale:tensor(float)
in B_zero_point:T
in C_scale:tensor(float)
in C_zero_point:T
out C:T		 1+ T = tensor(int8), tensor(uint8)
QLinearSigmoid in X:T
in X_scale:tensor(float)
in X_zero_point:T
in Y_scale:tensor(float)
in Y_zero_point:T
out Y:T		 1+ T = tensor(int8), tensor(uint8)
QuantizeLinear in x:T1
in y_scale:T1
in y_zero_point:T2
out y:T2		 1+ T1 = tensor(float)
T2 = tensor(int8), tensor(uint8)
Range in start:T
in limit:T
in delta:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)
SampleOp in X:T
out Y:T		 1+ T = tensor(float)
SkipLayerNormalization in input:T
in skip:T
in gamma:T
in beta:T
in bias:T
out output:T
out mean:U
out inv_std_var:U		 1+ T = tensor(double), tensor(float)
SparseToDenseMatMul in A:T
in B:T1
out Y:T1		 1+ T = sparse_tensor(double), sparse_tensor(float), sparse_tensor(int32), sparse_tensor(int64), sparse_tensor(uint32), sparse_tensor(uint64)
T1 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
Tokenizer in X:T
out Y:T		 1+ T = tensor(string)
TransposeMatMul in A:T
in B:T
out Y:T		 1+ T = tensor(float)
Trilu in X:T
in k:tensor(int64)
out Y:T		 1+ T = tensor(double), tensor(float), tensor(int64)
Unique in x:T
out y:T
out idx:tensor(int64)
out counts:tensor(int64)		 1+ T = tensor(float)
WordConvEmbedding in Sequence:T
in W:T1
in B:T1
in C:T1
out Y:T1		 1+ T = tensor(int32)
T1 = tensor(float)
Operator Domain: com.microsoft.nchwc
AveragePool in X:T
out Y:T		 1+ T = tensor(float)
Conv in X:T
in W:T
in B:T
in Sum:T
out Y:T		 1+ T = tensor(float)
GlobalAveragePool in X:T
out Y:T		 1+ T = tensor(float)
GlobalMaxPool in X:T
out Y:T		 1+ T = tensor(float)
MaxPool in X:T
out Y:T		 1+ T = tensor(float)
ReorderInput in X:T
out Y:T		 1+ T = tensor(float)
ReorderOutput in X:T
out Y:T		 1+ T = tensor(float)
Upsample in X:T
out Y:T		 1+ T = tensor(float)

Operators implemented by CUDAExecutionProvider

Op Name Parameters OpSet Version Types Supported
Operator Domain: ai.onnx
Abs in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
[6, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)
Add in A:T
in B:T
out C:T		 14+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
13 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
Affine in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
And in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
ArgMax in data:T
out reduced:tensor(int64)		 11+ T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
ArgMin in data:T
out reduced:tensor(int64)		 11+ T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
AveragePool in X:T
out Y:T		 11+ T = tensor(double), tensor(float), tensor(float16)
10 T = tensor(double), tensor(float), tensor(float16)
[7, 9] 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)
[9, 12] T1 = 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)
[6, 8] T1 = 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)
Ceil in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
Clip in input:T
in min:T
in max:T
out output:T

or

in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(float16), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)
11 T = tensor(float)
[6, 10] T = tensor(float)
Compress in input:T
in condition:T1
out output:T		 11+ 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(bool)
[9, 10] 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(bool)
Concat in inputs:T
out concat_result:T		 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)
[11, 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)
[4, 10] 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)
ConcatFromSequence in input_sequence:S
out concat_result:T		 11+ 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))
ConstantOfShape in input:T1
out output:T2		 9+ T1 = tensor(int64)
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)
Conv in X:T
in W:T
in B:T
out Y:T		 11+ T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
ConvTranspose in X:T
in W:T
in B:T
out Y:T		 11+ T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
Cos in input:T
out output:T		 7+ T = tensor(double), tensor(float), tensor(float16)
Crop in input:T
out output:T		 1+ T = tensor(double), tensor(float), tensor(float16)
CumSum in x:T
in axis:T2
out y:T		 14+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T2 = tensor(int32), tensor(int64)
[11, 13] T = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T2 = tensor(int32), tensor(int64)
DepthToSpace in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
DequantizeLinear in x:T
in x_scale:tensor(float)
in x_zero_point:T
out y:tensor(float)		 10+ T = tensor(int8), tensor(uint8)
Div in A:T
in B:T
out C:T		 14+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
13 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
Dropout in data:T
in ratio:T1
in training_mode:T2
out output:T
out mask:T2

or

in data:T
out output:T
out mask:T

or

in data:T
out output:T
out mask:T1		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T1 = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T2 = tensor(bool)
12 T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(double), tensor(float), tensor(float16)
T2 = tensor(bool)
[10, 11] T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(bool)
[7, 9] T = tensor(double), tensor(float), tensor(float16)
DynamicSlice in data:T
in starts:Tind
in ends:Tind
in axes:Tind
out output:T		 1+ 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)
Tind = tensor(int32), tensor(int64)
Einsum in Inputs:T
out Output:T		 12+ T = tensor(double), tensor(float), tensor(float16)
Elu in X:T
out Y:T		 6+ T = tensor(double), tensor(float), tensor(float16)
Equal in A:T
in B:T
out C:T1		 13+ T = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
[11, 12] T = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 10] T = tensor(bool), tensor(int32), tensor(int64)
Erf in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[9, 12] T = tensor(double), tensor(float), tensor(float16)
Exp in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
Expand in input:T
in shape:tensor(int64)
out output:T		 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)
[8, 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)
EyeLike in input:T1
out output:T2		 9+ T1 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)
T2 = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)
Flatten in input:T
out output:T		 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)
[11, 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)
[9, 10] 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)
[1, 8] 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)
Floor in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
GRU in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
out Y:T
out Y_h:T		 14+ T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
[7, 13] T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
Gather in data:T
in indices:Tind
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
[1, 10] 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)
Tind = tensor(int32), tensor(int64)
GatherElements in data:T
in indices:Tind
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
GatherND in data:T
in indices:tensor(int64)
out output:T		 13+ T = tensor(bfloat16), tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)
indices = tensor(int64)
12 T = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)
indices = tensor(int64)
11 T = tensor(bool), tensor(double), tensor(float), tensor(float16), tensor(int64)
indices = tensor(int64)
Gemm in A:T
in B:T
in C:T
out Y:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[9, 10] T = tensor(double), tensor(float), tensor(float16)
[7, 8] T = tensor(double), tensor(float), tensor(float16)
GlobalAveragePool in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
GlobalMaxPool in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
Greater in A:T
in B:T
out C:T1		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
[9, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 8] T = tensor(double), tensor(float), tensor(float16)
GreaterOrEqual in A:T
in B:T
out C:T1		 16+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
[12, 15] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
HardSigmoid in X:T
out Y:T		 6+ T = tensor(double), tensor(float), tensor(float16)
Identity in input:T
out output:T

or

in input:V
out output:V		 14+ V = 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)), 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)
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)
[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)
If in cond:B
out outputs:V		 13+ B = tensor(bool)
V = 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)), 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)
[11, 12] B = tensor(bool)
V = 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)
[1, 10] B = tensor(bool)
V = 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)
ImageScaler in input:T
out output:T		 1+ T = tensor(double), tensor(float), tensor(float16)
InstanceNormalization in input:T
in scale:T
in B:T
out output:T		 6+ T = tensor(double), tensor(float), tensor(float16)
LRN in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[1, 12] T = tensor(double), tensor(float), tensor(float16)
LSTM in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
in initial_c:T
in P:T
out Y:T
out Y_h:T
out Y_c:T		 14+ T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
[7, 13] T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
LayerNormalization in X:T
in Scale:T
in B:T
out Y:T
out Mean:U
out InvStdDev:U

or

in X:T
in Scale:V
in B:V
out Y:V
out Mean:U
out InvStdDev:U		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
U = tensor(double), tensor(float)
V = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
LeakyRelu in X:T
out Y:T		 16+ T = tensor(double), tensor(float), tensor(float16)
[6, 15] T = tensor(double), tensor(float), tensor(float16)
Less in A:T
in B:T
out C:T1		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
[9, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 8] T = tensor(double), tensor(float), tensor(float16)
LessOrEqual in A:T
in B:T
out C:T1		 16+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
[12, 15] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
T1 = tensor(bool)
Log in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
LogSoftmax in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
Loop in M:I
in cond:B
in v_initial:V
out v_final_and_scan_outputs:V		 13+ B = tensor(bool)
I = tensor(int64)
V = 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)), 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)
[11, 12] B = tensor(bool)
I = tensor(int64)
V = 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)
[1, 10] B = tensor(bool)
I = tensor(int64)
V = 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)
MatMul in A:T
in B:T
out Y:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[9, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 8] T = tensor(double), tensor(float), tensor(float16)
MatMulInteger in A:T1
in B:T2
in a_zero_point:T1
in b_zero_point:T2
out Y:T3		 10+ T1 = tensor(int8)
T2 = tensor(int8)
T3 = tensor(int32)
Max in data_0:T
out max:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
12 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[6, 11] T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
MaxPool in X:T
out Y:T

or

in X:T
out Y:T
out Indices:I		 12+ I = tensor(int64)
T = tensor(double), tensor(float), tensor(float16), tensor(int8), tensor(uint8)
11 I = tensor(int64)
T = tensor(double), tensor(float), tensor(float16)
10 I = tensor(int64)
T = tensor(double), tensor(float), tensor(float16)
[8, 9] I = tensor(int64)
T = tensor(double), tensor(float), tensor(float16)
[1, 7] T = tensor(double), tensor(float), tensor(float16)
MemcpyFromHost in X:T
out Y:T		 1+ T = 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)), 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)
MemcpyToHost in X:T
out Y:T		 1+ T = 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)), 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)
Min in data_0:T
out min:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
12 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[6, 11] T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
Mul in A:T
in B:T
out C:T		 14+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
13 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
Neg in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)
[6, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)
NonZero in X:T
out Y:tensor(int64)		 13+ T = tensor(bool), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)
[9, 12] T = tensor(bool), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)
Not in X:T
out Y:T		 1+ T = tensor(bool)
OneHot in indices:T1
in depth:T2
in values:T3
out output:T3		 11+ T1 = tensor(int32), tensor(int64)
T2 = tensor(int32), tensor(int64)
T3 = tensor(float), tensor(float16), tensor(int64)
Or in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
PRelu in X:T
in slope:T
out Y:T		 16+ T = tensor(double), tensor(float), tensor(float16)
[9, 15] T = tensor(double), tensor(float), tensor(float16)
[7, 8] T = tensor(double), tensor(float), tensor(float16)
Pad in data:T
in pads:tensor(int64)
in constant_value:T
out output:T

or

in data:T
out output:T		 13+ T = tensor(bool), tensor(double), tensor(float), tensor(float16)
[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		 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)
RNN in X:T
in W:T
in R:T
in B:T
in sequence_lens:T1
in initial_h:T
out Y:T
out Y_h:T		 14+ T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
[7, 13] T = tensor(double), tensor(float), tensor(float16)
T1 = tensor(int32)
RandomNormal out output:T 1+ T = tensor(double), tensor(float), tensor(float16)
RandomNormalLike in input:T1
out output:T2		 1+ T1 = 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)
T2 = tensor(double), tensor(float), tensor(float16)
RandomUniform out output:T 1+ T = tensor(double), tensor(float), tensor(float16)
RandomUniformLike in input:T1
out output:T2		 1+ T1 = 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)
T2 = tensor(double), tensor(float), tensor(float16)
Range in start:T
in limit:T
in delta:T
out output:T		 11+ T = tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64)
Reciprocal in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
ReduceL1 in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
ReduceL2 in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
ReduceLogSum in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
ReduceLogSumExp in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
ReduceMax in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
11 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
ReduceMean in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
ReduceMin in data:T
out reduced:T		 14+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
13 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
12 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)
11 T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
ReduceProd in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[11, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32)
ReduceSum in data:T
in axes:tensor(int64)
out reduced:T

or

in data:T
out reduced:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
[11, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
[1, 10] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
ReduceSumSquare in data:T
out reduced:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
Relu in X:T
out Y:T		 14+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
13 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
Reshape in data:T
in shape:tensor(int64)
out reshaped:T

or

in data:T
out reshaped:T		 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)
shape = tensor(int64)
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)
shape = tensor(int64)
[5, 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)
shape = tensor(int64)
[1, 4] 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)
Resize in X:T
in scales:tensor(float)
out Y:T

or

in X:T1
in roi:T2
in scales:tensor(float)
in sizes:tensor(int64)
out Y:T1		 13+ T1 = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)
[11, 12] T1 = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)
10 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)
ReverseSequence in input:T
in sequence_lens:tensor(int64)
out Y:T		 10+ 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)
RoiAlign in X:T1
in rois:T1
in batch_indices:T2
out Y:T1		 10+ T1 = tensor(double), tensor(float)
T2 = tensor(int64)
Round in X:T
out Y:T		 11+ T = tensor(double), tensor(float), tensor(float16)
ScaledTanh in input:T
out output:T		 1+ T = tensor(double), tensor(float), tensor(float16)
Scan in initial_state_and_scan_inputs:V
out final_state_and_scan_outputs:V

or

in sequence_lens:I
in initial_state_and_scan_inputs:V
out final_state_and_scan_outputs:V		 16+ V = 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)
[11, 15] V = 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)
[9, 10] V = 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)
8 I = tensor(int64)
V = 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)
Scatter in data:T
in indices:Tind
in updates:T
out output:T		 [9, 10] 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)
Tind = tensor(int32), tensor(int64)
ScatterElements in data:T
in indices:Tind
in updates:T
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
ScatterND in data:T
in indices:tensor(int64)
in updates:T
out output:T		 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)
[11, 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)
Selu in X:T
out Y:T		 6+ T = tensor(double), tensor(float), tensor(float16)
SequenceAt in input_sequence:S
in position:I
out tensor:T		 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))
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)
SequenceConstruct in inputs:T
out output_sequence:S		 11+ 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))
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)
SequenceEmpty out output:S 11+ 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))
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		 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)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
SimplifiedLayerNormalization in X:T
in scale:V
out Y:V
out inv_std_var:U		 1+ T = tensor(double), tensor(float), tensor(float16)
U = tensor(double), tensor(float)
V = tensor(double), tensor(float), tensor(float16)
Sin in input:T
out output:T		 7+ T = tensor(double), tensor(float), tensor(float16)
Size in data:T
out size: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)
[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)
Slice in data:T
in starts:Tind
in ends:Tind
in axes:Tind
in steps:Tind
out output:T

or

in data:T
out output:T		 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)
Tind = tensor(int32), tensor(int64)
[11, 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)
Tind = tensor(int32), tensor(int64)
10 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)
Tind = tensor(int32), tensor(int64)
[1, 9] 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)
Softmax in input:T
out output:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[11, 12] T = tensor(double), tensor(float), tensor(float16)
[1, 10] T = tensor(double), tensor(float), tensor(float16)
Softplus in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
Softsign in input:T
out output:T		 1+ T = tensor(double), tensor(float), tensor(float16)
SpaceToDepth in input:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[1, 12] T = tensor(double), tensor(float), tensor(float16)
Split in input:T
in split:T
out outputs...:T

or

in input:T
in split:tensor(int64)
out outputs:T

or

in input:T
out outputs:T		 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)
[11, 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)
[2, 10] 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)
Sqrt in X:T
out Y:T		 13+ T = tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
Squeeze in data:T
in axes:tensor(int64)
out squeezed:T

or

in data:T
out squeezed:T		 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)
[11, 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)
[1, 10] 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)
Sub in A:T
in B:T
out C:T		 14+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
13 T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
[7, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)
Sum in data_0:T
out sum:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[8, 12] T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[6, 7] T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
Tanh in input:T
out output:T		 13+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
[6, 12] T = tensor(double), tensor(float), tensor(float16)
ThresholdedRelu in X:T
out Y:T		 10+ T = tensor(double), tensor(float), tensor(float16)
1+ T = tensor(double), tensor(float), tensor(float16)
Tile in input:T
in repeats:T1
out output:T

or

in input:T
in tiles:T
in axis:T
out output:T		 13+ T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
T1 = tensor(int64)
[6, 12] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64)
T1 = tensor(int64)
TopK in X:T
in K:tensor(int64)
out Values:T
out Indices:I

or

in X:T
out Values:T
out Indices:I		 11+ I = tensor(int64)
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)
10 I = tensor(int64)
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)
[1, 9] 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)
Transpose in data:T
out transposed:T		 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)
[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)
Trilu in input:T
in k:tensor(int64)
out output:T		 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)
Unsqueeze in data:T
in axes:tensor(int64)
out expanded:T

or

in data:T
out expanded:T		 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)
[11, 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)
[1, 10] 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)
Upsample in X:T
in scales:tensor(float)
out Y:T

or

in X:T
out Y:T		 9 T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)
[7, 8] T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)
Where in condition:B
in X:T
in Y:T
out output:T		 16+ B = tensor(bool)
T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)
[9, 15] B = tensor(bool)
T = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)
Xor in A:T
in B:T
out C:T1		 7+ T = tensor(bool)
T1 = tensor(bool)
Operator Domain: com.microsoft
Attention in input:T
in weight:T
in bias:T
in mask_index:M
in past:T
in extra_add:T
out output:T
out present:T		 1+ T = tensor(float), tensor(float16)
BeamSearch in input_ids:I
in max_length:I
in min_length:I
in num_beams:I
in num_return_sequences:I
in length_penalty:T
in repetition_penalty:T
in vocab_mask:M
in prefix_vocab_mask:M
in attention_mask:I
out sequences:I
out sequences_scores:T
out scores:T		 1+ T = tensor(float), tensor(float16)
BiasDropout in data:T
in bias:T
in residual:T
in ratio:T1
in training_mode:T2
out output:T
out mask:T2		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T1 = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T2 = tensor(bool)
BiasGelu in A:T
in B:T
out C:T		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
BiasSoftmax in data:T
in bias:T
out output:T		 1+ T = tensor(double), tensor(float), tensor(float16)
BitmaskBiasDropout in data:T
in bias:T
in residual:T
in ratio:T1
in training_mode:T2
out output:T
out mask:T3		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T1 = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T2 = tensor(bool)
T3 = tensor(uint32)
BitmaskDropout in data:T
in ratio:T1
in training_mode:T2
out output:T
out mask:T3		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T1 = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
T2 = tensor(bool)
T3 = tensor(uint32)
ComplexMul in A:T
in B:T
out C:T		 1+ T = tensor(float), tensor(float16)
ComplexMulConj in A:T
in B:T
out C:T		 1+ T = tensor(float), tensor(float16)
ConvTransposeWithDynamicPads in X:T
in W:T
in Pads:tensor(int64)
in B:T
out Y:T		 1+ T = tensor(float)
DecoderAttention in query:T
in key:T
in q_weight:T
in kv_weight:T
in bias:T
in key_padding_mask:B
in key_cache:T
in value_cache:T
in static_kv:B
in use_past:B
in has_layer_state:B
in has_key_padding_mask:B
out output:T
out new_key_cache:T
out new_value_cache:T		 1+ T = tensor(float), tensor(float16)
DequantizeLinear in x:T1
in x_scale:T2
in x_zero_point:T1
out y:T2		 1+ T1 = tensor(int8), tensor(uint8)
T2 = tensor(float16)
EmbedLayerNormalization in input_ids:T1
in segment_ids:T1
in word_embedding:T
in position_embedding:T
in segment_embedding:T
in gamma:T
in beta:T
in mask:T1
in position_ids:T1
out output:T
out mask_index:T1
out embedding_sum:T		 1+ T = tensor(float), tensor(float16)
FastGelu in X:T
in bias:T
out Y:T		 1+ T = tensor(bfloat16), tensor(float), tensor(float16)
FusedConv in X:T
in W:T
in B:T
in Z:T
out Y:T		 1+ T = tensor(float)
FusedMatMul in A:T
in B:T
out Y:T		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
Gelu in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
GridSample in X:T1
in Grid:T1
out Y:T2		 1+ T1 = tensor(float)
T2 = tensor(float)
Inverse in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
Irfft in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
LongformerAttention in input:T
in weight:T
in bias:T
in mask:T
in global_weight:T
in global_bias:T
in global:G
out output:T		 1+ T = tensor(float), tensor(float16)
NGramRepeatBlock in input_ids:Tid
in scores:T
out scores_out:T		 1+ T = tensor(float)
Tid = tensor(int64)
QAttention in input:T1
in weight:T2
in bias:T3
in input_scale:T3
in weight_scale:T3
in mask_index:T4
in input_zero_point:T1
in weight_zero_point:T2
in past:T3
out output:T3
out present:T3		 1+ T1 = tensor(int8)
T2 = tensor(int8)
T3 = tensor(float), tensor(float16)
T4 = tensor(int32)
QuantizeLinear in x:T1
in y_scale:T1
in y_zero_point:T2
out y:T2		 1+ T1 = tensor(float16)
T2 = tensor(int8), tensor(uint8)
Rfft in X:T
out Y:T		 1+ T = tensor(double), tensor(float), tensor(float16)
SkipLayerNormalization in input:T
in skip:T
in gamma:T
in beta:T
in bias:T
out output:T
out mean:U
out inv_std_var:U		 1+ T = tensor(float), tensor(float16)
TransposeMatMul in A:T
in B:T
out Y:T		 1+ T = tensor(bfloat16), tensor(double), tensor(float), tensor(float16)
Trilu in X:T
in k:tensor(int64)
out Y:T		 1+ 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)