SpaceMITExecutionProvider Accelerated Operators

• This page lists the operators accelerated by SpaceMITExecutionProvider and the constraints applied during the capability-check phase.
• ONNX operator reference
• ONNX contrib operator reference

• SpaceMITExecutionProvider Accelerated Operators
  • Dense
    • Conv
    • ConvTranspose
    • Gemm
    • MatMul
  • QDQ
    • DynamicQuantizeMatMul
    • MatMulInteger
    • DynamicQuantizeLinear
    • QuantizeLinear
    • DequantizeLinear
  • Pool
    • AveragePool
    • GlobalAveragePool
    • MaxPool
    • GlobalMaxPool
  • Reduce
    • ReduceMean
    • ReduceMax
  • Math
    • Add
    • Sub
    • Mul
    • Div
    • Pow
    • Sqrt
    • Abs
    • Log
    • Reciprocal
    • Sin
    • Cos
    • Tan
    • Sinh
    • Cosh
    • Floor
    • Ceil
  • Activation
    • Sigmoid
    • Swish
    • HardSigmoid
    • HardSwish
    • Tanh
    • LeakyRelu
    • Clip
    • Relu
    • Elu
    • Gelu
    • Celu
    • Softplus
    • Softsign
    • Erf
    • Softmax
  • Tensor
    • Cast
    • Concat
    • Split
    • Transpose
    • Unsqueeze
    • Squeeze
    • Reshape
    • Flatten
    • Gather
    • Slice
    • Resize
    • Where
  • Norm
    • LayerNormalization
    • InstanceNormalization
    • BatchNormalization
  • Compare
    • Equal
    • Greater
    • GreaterOrEqual
    • Less
    • LessOrEqual

Dense

Conv

• Domain: ai.onnx
• Opset: 11
• Attributes: kernel_shape must be present; W must be a constant initializer or provided by a DequantizeLinear node with no upstream input edges
• Type - T: tensor(float) | tensor(float16)
• Notes: kernel_shape rank must not exceed 3; supports 1D, 2D, and 3D convolutions

ConvTranspose

• Domain: ai.onnx
• Opset: 11
• Attributes: kernel_shape must be present; W must be a constant initializer or provided by a DequantizeLinear node with no upstream input edges
• Type - T: tensor(float)
• Notes: kernel_shape rank must not exceed 2; supports 1D and 2D

Gemm

• Domain: ai.onnx
• Opset: 13
• Attributes: transA == 0, alpha == 1.0, beta == 1.0
• Type - T: tensor(float) | tensor(float16)
• Notes: Supports QDQ quantization format. A is asymmetric per-tensor; B is symmetric per-channel. When B is non-constant, B must be asymmetric per-tensor.

MatMul

• Domain: ai.onnx
• Opset: 13
• Attributes: No additional attribute constraints
• Type - T: tensor(float) | tensor(float16)
• Notes: Supports QDQ quantization format. A is asymmetric per-tensor; B is symmetric per-channel. When B is non-constant, B must be asymmetric per-tensor.

QDQ

DynamicQuantizeMatMul

• Domain: com.microsoft
• Opset: 1 (see ONNX contrib operators)
• Attributes: None
• Type - T1: tensor(float)
• Type - T2: tensor(float)

MatMulInteger

• Domain: ai.onnx
• Opset: 10
• Attributes: None
• Type - T1: tensor(int8) | tensor(uint8)
• Type - T2: tensor(int32)

DynamicQuantizeLinear

• Domain: ai.onnx
• Opset: 11
• Attributes: None
• Type - T1: tensor(float)
• Type - T2: tensor(int8) | tensor(uint8)

QuantizeLinear

• Domain: ai.onnx
• Opset: 19
• Attributes: None
• Type - T1: tensor(float)
• Type - T2: tensor(int8) | tensor(uint8)

DequantizeLinear

• Domain: ai.onnx
• Opset: 19
• Attributes: None
• Type - T1: tensor(int8) | tensor(uint8) | tensor(int32)
• Type - T2: tensor(float)

Pool

AveragePool

• Domain: ai.onnx
• Opset: 22
• Attributes: If count_include_pad != 1, all pads values must be 0; if kernel_shape is present, its rank must not exceed 2
• Type - T: tensor(float) | tensor(float16)

GlobalAveragePool

• Domain: ai.onnx
• Opset: 1
• Attributes: If kernel_shape is present, its rank must not exceed 2
• Type - T: tensor(float) | tensor(float16)

MaxPool

• Domain: ai.onnx
• Opset: 12
• Attributes: If kernel_shape is present, its rank must not exceed 2
• Type - T: tensor(float) | tensor(float16)

GlobalMaxPool

• Domain: ai.onnx
• Opset: 1
• Attributes: If kernel_shape is present, its rank must not exceed 2
• Type - T: tensor(float) | tensor(float16)

Reduce

ReduceMean

• Domain: ai.onnx
• Opset: 18
• Attributes: All inputs beyond the first must be constant initializers
• Type - T: tensor(float) | tensor(float16)

ReduceMax

• Domain: ai.onnx
• Opset: 20
• Attributes: All inputs beyond the first must be constant initializers
• Type - T: tensor(float) | tensor(float16)

Math

Add

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Sub

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Mul

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Div

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Pow

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Sqrt

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Abs

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Log

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Reciprocal

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Sin

• Domain: ai.onnx
• Opset: 7
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Cos

• Domain: ai.onnx
• Opset: 7
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Tan

• Domain: ai.onnx
• Opset: 7
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Sinh

• Domain: ai.onnx
• Opset: 9
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Cosh

• Domain: ai.onnx
• Opset: 9
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Floor

• Domain: ai.onnx
• Opset: 6
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Ceil

• Domain: ai.onnx
• Opset: 6
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Activation

Sigmoid

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Swish

• Domain: ai.onnx
• Opset: 24
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

HardSigmoid

• Domain: ai.onnx
• Opset: 22
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

HardSwish

• Domain: ai.onnx
• Opset: 22
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Tanh

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

LeakyRelu

• Domain: ai.onnx
• Opset: 16
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Clip

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Relu

• Domain: ai.onnx
• Opset: 14
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Elu

• Domain: ai.onnx
• Opset: 22
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Gelu

• Domain: ai.onnx
• Opset: 20
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Celu

• Domain: ai.onnx
• Opset: 12
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Softplus

• Domain: ai.onnx
• Opset: 1
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Softsign

• Domain: ai.onnx
• Opset: 1
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Erf

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Softmax

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

Tensor

Cast

• Domain: ai.onnx
• Opset: 24
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Concat

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Split

• Domain: ai.onnx
• Opset: 18
• Attributes: All inputs beyond the first must be constant initializers
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Transpose

• Domain: ai.onnx
• Opset: 24
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int8)

Unsqueeze

• Domain: ai.onnx
• Opset: 24
• Attributes: The axes input must be a constant initializer
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Squeeze

• Domain: ai.onnx
• Opset: 24
• Attributes: The axes input must be a constant initializer
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Reshape

• Domain: ai.onnx
• Opset: 24
• Attributes: The shape input must be a constant initializer
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Flatten

• Domain: ai.onnx
• Opset: 24
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Gather

• Domain: ai.onnx
• Opset: 13
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Slice

• Domain: ai.onnx
• Opset: 13
• Attributes: All inputs beyond the first must be constant initializers; only opset >= 10 is supported
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Resize

• Domain: ai.onnx
• Opset: 19
• Attributes: coordinate_transformation_mode supports asymmetric and half_pixel only; mode supports nearest and linear only
• Type - T: tensor(float) | tensor(float16) | tensor(int8)

Where

• Domain: ai.onnx
• Opset: 9
• Attributes: None
• Type - T: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)

Norm

LayerNormalization

• Domain: ai.onnx
• Opset: 17
• Attributes: No additional constant constraints during the capability-check phase
• Type - T: tensor(float) | tensor(float16)

InstanceNormalization

• Domain: ai.onnx
• Opset: 6
• Attributes: None
• Type - T: tensor(float) | tensor(float16)

BatchNormalization

• Domain: ai.onnx
• Opset: 15
• Attributes: No additional constant constraints during the capability-check phase
• Type - T: tensor(float) | tensor(float16)

Compare

Equal

• Domain: ai.onnx
• Opset: 11
• Attributes: None
• Type - T1: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)
• Type - T2: tensor(uint8) | tensor(bool)

Greater

• Domain: ai.onnx
• Opset: 9
• Attributes: None
• Type - T1: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)
• Type - T2: tensor(uint8) | tensor(bool)

GreaterOrEqual

• Domain: ai.onnx
• Opset: 12
• Attributes: None
• Type - T1: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)
• Type - T2: tensor(uint8) | tensor(bool)

Less

• Domain: ai.onnx
• Opset: 9
• Attributes: None
• Type - T1: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)
• Type - T2: tensor(uint8) | tensor(bool)

LessOrEqual

• Domain: ai.onnx
• Opset: 12
• Attributes: None
• Type - T1: tensor(float) | tensor(float16) | tensor(int32) | tensor(uint32) | tensor(int8) | tensor(uint8) | tensor(bool)
• Type - T2: tensor(uint8) | tensor(bool)