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@ -3,17 +3,15 @@
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[def files](/onnxruntime/core/graph/contrib_ops/contrib_defs.cc) via [this script](/tools/python/gen_doc.py).
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Do not modify directly and instead edit operator definitions.*
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* ai.onnx.training
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* <a href="#ai.onnx.training.Adagrad">ai.onnx.training.Adagrad</a>
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* <a href="#ai.onnx.training.Gradient">ai.onnx.training.Gradient</a>
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* <a href="#ai.onnx.training.GraphCall">ai.onnx.training.GraphCall</a>
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* <a href="#ai.onnx.training.Momentum">ai.onnx.training.Momentum</a>
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* com.microsoft
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* <a href="#com.microsoft.AttnLSTM">com.microsoft.AttnLSTM</a>
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* <a href="#com.microsoft.CDist">com.microsoft.CDist</a>
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* <a href="#com.microsoft.ComplexMul">com.microsoft.ComplexMul</a>
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* <a href="#com.microsoft.ComplexMulConj">com.microsoft.ComplexMulConj</a>
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* <a href="#com.microsoft.ConvTransposeWithDynamicPads">com.microsoft.ConvTransposeWithDynamicPads</a>
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* <a href="#com.microsoft.CropAndResize">com.microsoft.CropAndResize</a>
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* <a href="#com.microsoft.DequantizeLinear">com.microsoft.DequantizeLinear</a>
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* <a href="#com.microsoft.DynamicQuantizeMatMul">com.microsoft.DynamicQuantizeMatMul</a>
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* <a href="#com.microsoft.ExpandDims">com.microsoft.ExpandDims</a>
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* <a href="#com.microsoft.FusedConv">com.microsoft.FusedConv</a>
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* <a href="#com.microsoft.FusedGemm">com.microsoft.FusedGemm</a>
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@ -26,6 +24,7 @@
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* <a href="#com.microsoft.Pad">com.microsoft.Pad</a>
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* <a href="#com.microsoft.QLinearAdd">com.microsoft.QLinearAdd</a>
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* <a href="#com.microsoft.QLinearAveragePool">com.microsoft.QLinearAveragePool</a>
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* <a href="#com.microsoft.QLinearLeakyRelu">com.microsoft.QLinearLeakyRelu</a>
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* <a href="#com.microsoft.QLinearMul">com.microsoft.QLinearMul</a>
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* <a href="#com.microsoft.QLinearReduceMean">com.microsoft.QLinearReduceMean</a>
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* <a href="#com.microsoft.QuantizeLinear">com.microsoft.QuantizeLinear</a>
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@ -41,7 +40,10 @@
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* <sub>experimental</sub> <a href="#com.microsoft.EmbedLayerNormalization">com.microsoft.EmbedLayerNormalization</a>
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* <sub>experimental</sub> <a href="#com.microsoft.FastGelu">com.microsoft.FastGelu</a>
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* <sub>experimental</sub> <a href="#com.microsoft.Gelu">com.microsoft.Gelu</a>
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* <sub>experimental</sub> <a href="#com.microsoft.Inverse">com.microsoft.Inverse</a>
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* <sub>experimental</sub> <a href="#com.microsoft.QAttention">com.microsoft.QAttention</a>
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* <sub>experimental</sub> <a href="#com.microsoft.SkipLayerNormalization">com.microsoft.SkipLayerNormalization</a>
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* <sub>experimental</sub> <a href="#com.microsoft.TransposeMatMul">com.microsoft.TransposeMatMul</a>
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* com.microsoft.nchwc
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* <a href="#com.microsoft.nchwc.AveragePool">com.microsoft.nchwc.AveragePool</a>
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* <a href="#com.microsoft.nchwc.Conv">com.microsoft.nchwc.Conv</a>
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@ -52,504 +54,6 @@
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* <a href="#com.microsoft.nchwc.ReorderOutput">com.microsoft.nchwc.ReorderOutput</a>
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* <a href="#com.microsoft.nchwc.Upsample">com.microsoft.nchwc.Upsample</a>
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## ai.onnx.training
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### <a name="ai.onnx.training.Adagrad"></a><a name="ai.onnx.training.adagrad">**ai.onnx.training.Adagrad**</a>
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Compute one iteration of ADAGRAD, a stochastic gradient based optimization
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algorithm. This operator can conduct the optimization of multiple tensor variables.
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Let's define the behavior of this operator. As you can imagine, ADAGRAD requires
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some parameters:
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- The initial learning-rate "R".
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- The update count "T". That is, the number of training iterations conducted.
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- A L2-norm regularization coefficient "norm_coefficient".
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- A learning-rate decay factor "decay_factor".
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- A small constant "epsilon" to avoid dividing-by-zero.
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At each ADAGRAD iteration, the optimized tensors are moved along a direction
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computed based on their estimated gradient and accumulated squared gradient. Assume
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that only a single tensor "X" is updated by this operator. We need the value of "X",
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its gradient "G", and its accumulated squared gradient "H". Therefore, variables in
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this operator's input list are sequentially "R", "T", "X", "G", and "H". Other
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parameters are given as attributes because they are usually constants. Also, the
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corresponding output tensors are the new value of "X" (called "X_new"), and then
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the new accumulated squared gradient (called "H_new"). Those outputs are computed
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from the given inputs following the pseudo code below.
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Let "+", "-", "*", and "/" are all element-wise arithmetic operations with
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numpy-style broadcasting support. The pseudo code to compute those outputs is:
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// Compute a scalar learning-rate factor. At the first update of X, T is generally
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// 0 (0-based update index) or 1 (1-based update index).
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r = R / (1 + T * decay_factor);
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// Add gradient of 0.5 * norm_coefficient * ||X||_2^2, where ||X||_2 is the 2-norm.
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G_regularized = norm_coefficient * X + G;
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// Compute new accumulated squared gradient.
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H_new = H + G_regularized * G_regularized;
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// Compute the adaptive part of per-coordinate learning rate. Note that Sqrt(...)
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// computes element-wise square-root.
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H_adaptive = Sqrt(H_new) + epsilon
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// Compute the new value of "X".
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X_new = X - r * G_regularized / H_adaptive;
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If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2", the same
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pseudo code may be extended to handle all tensors jointly. More specifically, we can view "X" as a
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concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
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be concatenated too) and then just reuse the entire pseudo code.
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Note that ADAGRAD was first proposed in http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf.
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In that reference paper, this operator is a special case of the Figure 1's composite mirror
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descent update.
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#### Version
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This version of the operator has been available since version 1 of the 'ai.onnx.training' operator set.
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#### Attributes
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<dl>
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<dt><tt>decay_factor</tt> : float</dt>
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<dd>The decay factor of learning rate after one update.The effective learning rate is computed by r = R / (1 + T * decay_factor). Default to 0 so that increasing update counts doesn't reduce the learning rate.</dd>
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<dt><tt>epsilon</tt> : float</dt>
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<dd>Small scalar to avoid dividing by zero.</dd>
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<dt><tt>norm_coefficient</tt> : float</dt>
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<dd>Regularization coefficient in 0.5 * norm_coefficient * ||X||_2^2. Default to 0, which means no regularization.</dd>
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</dl>
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#### Inputs (3 - ∞)
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<dl>
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<dt><tt>R</tt> : T1</dt>
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<dd>The initial learning rate.</dd>
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<dt><tt>T</tt> : T2</dt>
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<dd>The update count of "X". It should be a scalar.</dd>
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<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
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<dd>The current values of optimized tensors, followed by their respective gradients, followed by their respective accumulated squared gradients.For example, if two tensor "X_1" and "X_2" are optimized, The input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", accumulated squared gradient of "X_1", accumulated squared gradient of "X_2"].</dd>
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</dl>
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#### Outputs (1 - ∞)
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<dl>
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<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
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<dd>Updated values of optimized tensors, followed by their updated values of accumulated squared gradients. For example, if two tensor "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new accumulated squared gradient of "X_1", new accumulated squared gradient of "X_2"].</dd>
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</dl>
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#### Type Constraints
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<dl>
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<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
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<dd>Constrain input types to float scalars.</dd>
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<dt><tt>T2</tt> : tensor(int64)</dt>
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<dd>Constrain input types to 64-bit integer scalars.</dd>
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<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
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<dd>Constrain input and output types to float tensors.</dd>
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</dl>
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### <a name="ai.onnx.training.Gradient"></a><a name="ai.onnx.training.gradient">**ai.onnx.training.Gradient**</a>
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Gradient operator computes the partial derivatives of a specific tensor w.r.t.
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some other tensors. This operator is widely used in gradient-based training
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algorithms. To illustrate its use, let's consider a computation graph,
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```
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X -----.
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|
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v
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W --> Conv --> H --> Gemm --> Y
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^
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|
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Z
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```
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, where W and Z are trainable tensors. Note that operators' attributes are
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omitted for the sake of simplicity. Let dY/dW (dY/dZ) be the gradient of
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Y with respect to W (Z). The user can compute gradient by inserting Gradient
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operator to form another graph shown below.
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```
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W --> Conv --> H --> Gemm --> Y
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| ^ ^
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| | |
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| X Z
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| | |
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| | .----------'
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| | | (W/Z/X is the 1st/2nd/3rd input of Gradient as shown in
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| | | "xs" followed by "zs")
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| v v
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'---> Gradient(xs=["W", "Z"], zs=["X"], y="Y")
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| |
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| '-----------------------------------> dY/dW (1st output of Gradient)
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|
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'---------------------------------------> dY/dZ (2nd output of Gradient)
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```
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By definition, the tensor "y" is a function of independent variables in "xs"
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and "zs". Since we only compute the gradient of "y" w.r.t. the differentiable
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variables in "xs", this Gradient only outputs dY/dW and dY/dZ. Note that "H"
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cannot appear in "xs" and "zs". The reason is that "H" can be determined by
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tensors "W" and "X" and therefore "H" is not an independent variable.
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All outputs are optional. If needed, for example, user can assign an empty
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string to the 1st output name of that Gradient to skip the generation of dY/dW.
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Note that the concept of optional outputs can also be found in ONNX's RNN, GRU,
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and LSTM.
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Gradient operator can compute derivative against intermediate tensors. For
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example, the gradient of Y with respect to H can be done via
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```
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W --> Conv --> H --> Gemm --> Y
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^ | ^
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| | |
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X | Z
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.-------' |
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| .----------'
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| | (H/Z is the 1st/2nd input of Gradient as shown in "xs")
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v v
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Gradient(xs=["H", "Z"], y="Y")
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| |
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| '-----------------------------------> dY/dH (1st output of Gradient)
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|
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'---------------------------------------> dY/dZ (2nd output of Gradient)
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```
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It is possible to represent high-order differentiation using Gradient operators.
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For example, given the following linear model:
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```
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W --> Gemm --> Y --> Loss --> O
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^ ^
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| |
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X L
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```
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To compute the 2nd order derivative of O with respect to W (denoted by
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d^2O/dW^2), one can do
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```
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W --> Gemm --> Y --> Loss --> O
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| ^ ^
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| | |
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| X .------------L
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| | | |
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| | | v
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+------+-+> Gradient(xs=["X", "W"], zs=["L"], y="O") ---> dO/dX (1st output of Gradient)
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| | | |
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| | | '---> dO/dW (2nd output of Gradient)
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| v v
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'---> Gradient(xs=["X", "W"], zs=["L"], y="dO/dW") ---> d(dO/dW)dX (1st output of
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| Gradient)
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|
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|
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'---> d^2O/dW^2 (2nd output of Gradient)
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```
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The tensors named in attributes "xs", "zs", and "y" define the differentiated
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computation graph, and the inputs to Gradient node define the values at
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which the gradient is computed. We can feed different tensors to the identified
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graph. For example, one can compute the gradient of Y with respect to H at
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a specific value of H, H_1, by providing that value as an input to the Gradient
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node.
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```
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W --> Conv --> H --> Gemm --> Y
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^ ^
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| |
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X Z
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Z_1 (2nd input of Gradient)
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|
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v
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H_1 --> Gradient(xs=["H", "Z"], y="Y") ---> dY/dH when H = H_1 and Y = Y_1.
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|
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'------------------------------> dY/dZ (2nd output of Gradient)
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```
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When the inputs of Gradient are the tensors named in "xs" and "zs", the
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computation can be optimized. More specifically, intermediate variables in
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forward pass can be reused if the gradient is computed via reverse-mode
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auto-differentiation.
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#### Version
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This version of the operator has been available since version 1 of the 'ai.onnx.training' operator set.
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#### Attributes
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<dl>
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<dt><tt>xs</tt> : list of strings (required)</dt>
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<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
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<dt><tt>y</tt> : string (required)</dt>
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<dd>The targeted tensor. It can be viewed as the output of the differentiated function. The attribute "xs" and attribute "zs" are the minimal independent variable set that determines the value of "y".</dd>
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<dt><tt>zs</tt> : list of strings</dt>
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<dd>Input tensor names of the differentiated sub-graph. It contains only the necessary non-differentiated inputs of a (sub-)graph. Variables (usually called intermediate variables) that can be generated from inputs cannot be included in this attribute.</dd>
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</dl>
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#### Inputs (1 - ∞)
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<dl>
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<dt><tt>Inputs</tt> (variadic, heterogeneous) : T1</dt>
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<dd>The values fed into graph identified by the attributes. The i-th input is the value of the i-th tensor specified in the concatenated list of the attribute "xs" and the attribute "zs". For example, if xs=["A", "B"] and zs=["C"], the first input is used as the value of symbol "A" and the 3rd input is substituted for all the occurrences of "C".</dd>
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</dl>
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#### Outputs (1 - ∞)
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<dl>
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<dt><tt>Outputs</tt> (variadic, heterogeneous) : T2</dt>
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<dd>The gradient of the tensor specified by the attribute "y" with respect to each of tensors specified in the attribute "xs". The i-th output is the gradient of "y" with respect to the i-th tensor specified in the attribute "xs".</dd>
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</dl>
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#### Type Constraints
|
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|
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<dl>
|
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<dt><tt>T1</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
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<dd>Allow outputs to be any kind of tensor.</dd>
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<dt><tt>T2</tt> : tensor(float16), tensor(float), tensor(double)</dt>
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<dd>Allow inputs to be any kind of floating-point tensor.</dd>
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</dl>
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### <a name="ai.onnx.training.GraphCall"></a><a name="ai.onnx.training.graphcall">**ai.onnx.training.GraphCall**</a>
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The GraphCall operator invokes a graph inside TrainingInfoProto's
|
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algorithm field. The GraphCall inputs and outputs are bound to those of
|
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invoked graph by position. If a graph input has an initializer, that input
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is considered optional. All graph outputs are optional.
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Below Python syntax is used for describing dictionary and list.
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Assume that ModelProto's graph field has
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- name: "MyInferenceGraph"
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- input: ["X", "W", "Z"]
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- initializer: [W]
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- output: ["Y"]
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|
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as visualized below for inference.
|
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|
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```
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X -----.
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|
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v
|
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W --> Conv --> H --> Gemm --> Y
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^
|
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|
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Z
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```
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|
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Assume that the training algorithm contains
|
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|
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- inputs: ["X_1", "Z_1", "C"]
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- initializer: [T]
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- outputs: ["W_new"]
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with a dictionary
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- update_binding: {"W": "W_new", "T": "T_new"}
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Inside the training algorithm graph, one can invoke the inference
|
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graph via adding a GraphCall node with
|
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|
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- inputs: ["X_1", "W", Z_1"]
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- outputs: ["Y_1"]
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- an attribute graph_name="MyInferenceGraph",
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The initializers, "W" and "T" in this case, in update_binding
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are considered globally-visible and mutable variables, which
|
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can be used as inputs of operators in the training graph.
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An example training algorithm graph may look like
|
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|
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```
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.-------- W (a global and mutable variable from
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| | the inference graph)
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| |
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| .-----'-----------.
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| | |
|
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| | v
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| | .-- X_1 --> GraphCall(graph_name="MyInferenceGraph")
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| | | | |
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| | | | |
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| | | Z_1 -----' |
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| | | | V
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| | | | Y_1 ---> Loss ---> O
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| | | | ^
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| | | | |
|
||||
| | `--. | C
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| | | | |
|
||||
| | | | .----------------'
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| | | | |
|
||||
| | v v v
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||||
| `--> Gradient(xs=["W"], zs=["X_1", "Z_1", "C"], y="O")
|
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| |
|
||||
| v
|
||||
| dO_dW (gradient of W) 1 (a scalar one)
|
||||
| | |
|
||||
| V v
|
||||
| Div <--- T ------------> Add ---> T_new
|
||||
| | (T is the number of training iterations.
|
||||
| | T is also globally visible and mutable.)
|
||||
| v
|
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`-----> Sub ----> W_new
|
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```
|
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|
||||
where Loss is a dummy node which computes the minimized objective function.
|
||||
|
||||
The variable "W" is an optional input in the called graph.
|
||||
If the user omits it, the input list of GraphCall becomes ["X_1", "", "Z_1"].
|
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In this case, from the view of computation graph, the Conv operator invoked by
|
||||
GraphCall's may be still connected the global "W" variable and therefore the
|
||||
structure of the computation graph is unchanged.
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'ai.onnx.training' operator set.
|
||||
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>graph_name</tt> : string (required)</dt>
|
||||
<dd>The invoked graph's name. The only allowed value is the name of the inference graph, which is stored in "ModelProto.graph.name" in the ONNX model format.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (1 - ∞)
|
||||
|
||||
<dl>
|
||||
<dt><tt>Inputs</tt> (variadic, heterogeneous) : T</dt>
|
||||
<dd>Inputs fed to the invoked graph. The i-th input here goes to the i-th input of the invoked graph. To omit an optional input in this field, the user can drop it or use an empty string.</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs (1 - ∞)
|
||||
|
||||
<dl>
|
||||
<dt><tt>Outputs</tt> (variadic, heterogeneous) : T</dt>
|
||||
<dd>The outputs generated by the called graph. Its i-th value is bound to the i-th output of the called graph. Similar to the inputs, all outputs are optional.</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(uint8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(int8), tensor(int16), tensor(int32), tensor(int64), tensor(float16), tensor(float), tensor(double), tensor(string), tensor(bool), tensor(complex64), tensor(complex128)</dt>
|
||||
<dd>Allow inputs and outputs to be any kind of tensor.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="ai.onnx.training.Momentum"></a><a name="ai.onnx.training.momentum">**ai.onnx.training.Momentum**</a>
|
||||
|
||||
Compute one iteration of stochastic gradient update with momentum.
|
||||
This operator can conduct the optimization of multiple tensor variables.
|
||||
|
||||
Let's define the behavior of this operator. As you can imagine, SG with momentum requires
|
||||
several parameters:
|
||||
|
||||
- The learning-rate "R".
|
||||
- The update count "T". That is, the number of conducted training iterations. It should
|
||||
be zero in the first training iteration.
|
||||
- A L2-norm regularization coefficient "norm_coefficient".
|
||||
- A decay coefficient of previous accumulated gradient (i.e., momentum) "alpha".
|
||||
- The scaling coefficient of current gradient "beta".
|
||||
- An attribute to choose either standard momentum or Nesterov's momentum "mode" should
|
||||
be used.
|
||||
|
||||
For the sake of simplicity, assume that there is only one tensor (called "X") to be optimized.
|
||||
Other necessary inputs are "X"'s gradient (called "G") and "X"'s momentum (called "V"). This
|
||||
Momentum operator maps all these inputs to the new value of "X" (called "X_new") and its new
|
||||
momentum (called "V_new").
|
||||
|
||||
This operator supports two different momentum algorithms. Set the attribute "mode" to
|
||||
"nesterov" if Nesterov's momentum is desired. Otherwise, set the attribute "model" to
|
||||
"standard" to use standard momentum. Computation details are described subsequently.
|
||||
|
||||
Let "+", "-", "*", and "/" are all element-wise operations with numpy-style broadcasting.
|
||||
|
||||
Pseudo code for SG with standard momentum:
|
||||
|
||||
// Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
|
||||
// values of all elements in X.
|
||||
G_regularized = norm_coefficient * X + G
|
||||
|
||||
// In the first training iteration, beta should always be 1.
|
||||
beta_adjusted = T > 0 ? beta : 1
|
||||
|
||||
// Compute the current momentum based on previous momentum and the current gradient.
|
||||
V_new = alpha * V + beta_adjusted * G_regularized
|
||||
|
||||
// Update X.
|
||||
X_new = X - R * V_new
|
||||
|
||||
Pseudo code for SG with Nesterov's momentum:
|
||||
|
||||
// Add gradient of 0.5 * norm_coefficient * ||X||^2, where ||X|| is the sum of squared
|
||||
// values of all elements in X.
|
||||
G_regularized = norm_coefficient * X + G;
|
||||
|
||||
// In the first training iteration, beta should always be 1.
|
||||
beta_adjusted = T > 0 ? beta : 1
|
||||
|
||||
// Compute the current momentum based on previous momentum and the current gradient.
|
||||
V_new = alpha * V + beta_adjusted * G_regularized;
|
||||
|
||||
// Compute final update direction and then update X.
|
||||
X_new = X - R * (G_regularized + alpha * V_new)
|
||||
|
||||
If one assign this operators to optimize multiple inputs, for example, "X_1" and "X_2". The same
|
||||
pseudo code would be extended to handle all tensors jointly. More specifically, we can view "X" as a
|
||||
concatenation of "X_1" and "X_2" (of course, their gradient and accumulate gradient should
|
||||
be concatenated too) and then our pseudo code becomes applicable.
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'ai.onnx.training' operator set.
|
||||
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>alpha</tt> : float (required)</dt>
|
||||
<dd>The decay factor of momentum. It should be a scalar.</dd>
|
||||
<dt><tt>beta</tt> : float (required)</dt>
|
||||
<dd>The coefficient of gradient in computing new momentum. It should be a scalar.</dd>
|
||||
<dt><tt>mode</tt> : string (required)</dt>
|
||||
<dd>Its value should be either "nesterov" or "standard". The value "nesterov" leads to the use of Nesterov's momentum while "standard" invokes stochastic gradient method using standard momentum</dd>
|
||||
<dt><tt>norm_coefficient</tt> : float (required)</dt>
|
||||
<dd>Coefficient of 0.5 * norm_coefficient * ||X||^2.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (3 - ∞)
|
||||
|
||||
<dl>
|
||||
<dt><tt>R</tt> : T1</dt>
|
||||
<dd>The learning rate.</dd>
|
||||
<dt><tt>T</tt> : T2</dt>
|
||||
<dd>Update count of "X". It should be a scalar.</dd>
|
||||
<dt><tt>inputs</tt> (variadic, heterogeneous) : T3</dt>
|
||||
<dd>It sequentially contains the current values of optimized tensors, then their gradient tensors, and finally their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, The expected input list would be ["X_1", "X_2", gradient of "X_1", gradient of "X_2", momentum of "X_1", momentum of "X_2"].</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs (1 - ∞)
|
||||
|
||||
<dl>
|
||||
<dt><tt>outputs</tt> (variadic, heterogeneous) : T3</dt>
|
||||
<dd>It sequentially contains the new values of optimized tensors and then the new values of their momentum tensors. For example, if two tensors "X_1" and "X_2" are optimized, the output list would be [new value of "X_1," new value of "X_2" new momentum of "X_1", new momentum of "X_2"].</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T1</tt> : tensor(float), tensor(double)</dt>
|
||||
<dd>Constrain input types to float scalars.</dd>
|
||||
<dt><tt>T2</tt> : tensor(int64)</dt>
|
||||
<dd>Constrain input types to 64-bit integer scalars.</dd>
|
||||
<dt><tt>T3</tt> : tensor(float), tensor(double)</dt>
|
||||
<dd>Constrain input types to float tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
## com.microsoft
|
||||
### <a name="com.microsoft.AttnLSTM"></a><a name="com.microsoft.attnlstm">**com.microsoft.AttnLSTM**</a>
|
||||
|
||||
|
|
@ -797,6 +301,66 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.ComplexMul"></a><a name="com.microsoft.complexmul">**com.microsoft.ComplexMul**</a>
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
|
||||
|
||||
#### Inputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>A</tt> : T</dt>
|
||||
<dd>input_0</dd>
|
||||
<dt><tt>B</tt> : T</dt>
|
||||
<dd>input_1</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>C</tt> : T</dt>
|
||||
<dd>output tensor</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(float), tensor(double), tensor(float16)</dt>
|
||||
<dd>Constrain input and output types to float or half tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.ComplexMulConj"></a><a name="com.microsoft.complexmulconj">**com.microsoft.ComplexMulConj**</a>
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
|
||||
|
||||
#### Inputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>A</tt> : T</dt>
|
||||
<dd>input_0</dd>
|
||||
<dt><tt>B</tt> : T</dt>
|
||||
<dd>input_1</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>C</tt> : T</dt>
|
||||
<dd>output tensor</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(float), tensor(double), tensor(float16)</dt>
|
||||
<dd>Constrain input and output types to float or half tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.ConvTransposeWithDynamicPads"></a><a name="com.microsoft.convtransposewithdynamicpads">**com.microsoft.ConvTransposeWithDynamicPads**</a>
|
||||
|
||||
#### Version
|
||||
|
|
@ -945,6 +509,42 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.DynamicQuantizeMatMul"></a><a name="com.microsoft.dynamicquantizematmul">**com.microsoft.DynamicQuantizeMatMul**</a>
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
|
||||
|
||||
#### Inputs (3 - 4)
|
||||
|
||||
<dl>
|
||||
<dt><tt>A</tt> : T1</dt>
|
||||
<dd>N-dimensional matrix A</dd>
|
||||
<dt><tt>B</tt> : T2</dt>
|
||||
<dd>N-dimensional matrix B</dd>
|
||||
<dt><tt>b_scale</tt> : T1</dt>
|
||||
<dd>Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.</dd>
|
||||
<dt><tt>b_zero_point</tt> (optional) : T2</dt>
|
||||
<dd>Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'.</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>Y</tt> : T1</dt>
|
||||
<dd>Matrix multiply results from A * B</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T1</tt> : tensor(float)</dt>
|
||||
<dd>Constrain input A, b_scale and output Y data type as float tensor.</dd>
|
||||
<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
|
||||
<dd>Constrain input B data type to 8-bit integer tensor.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.ExpandDims"></a><a name="com.microsoft.expanddims">**com.microsoft.ExpandDims**</a>
|
||||
|
||||
ExpandDims echo operator.
|
||||
|
|
@ -1047,12 +647,16 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
<dl>
|
||||
<dt><tt>activation</tt> : string</dt>
|
||||
<dd></dd>
|
||||
<dt><tt>activation_alpha</tt> : float</dt>
|
||||
<dd></dd>
|
||||
<dt><tt>activation_beta</tt> : float</dt>
|
||||
<dd></dd>
|
||||
<dt><tt>activation_gamma</tt> : float</dt>
|
||||
<dd></dd>
|
||||
<dt><tt>alpha</tt> : float</dt>
|
||||
<dd>Scalar multiplier for the product of input tensors A * B.</dd>
|
||||
<dt><tt>beta</tt> : float</dt>
|
||||
<dd>Scalar multiplier for input tensor C.</dd>
|
||||
<dt><tt>leaky_relu_alpha</tt> : float</dt>
|
||||
<dd></dd>
|
||||
<dt><tt>transA</tt> : int</dt>
|
||||
<dd>Whether A should be transposed</dd>
|
||||
<dt><tt>transB</tt> : int</dt>
|
||||
|
|
@ -1338,7 +942,7 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T1</tt> : tensor(uint32), tensor(int32), tensor(string)</dt>
|
||||
<dt><tt>T1</tt> : tensor(uint32), tensor(int32), tensor(uint64), tensor(int64), tensor(float), tensor(double), tensor(string)</dt>
|
||||
<dd>Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode.</dd>
|
||||
<dt><tt>T2</tt> : tensor(uint32), tensor(int32)</dt>
|
||||
<dd>Constrain output type to unsigned and signed 32-bit integer tensor.</dd>
|
||||
|
|
@ -1534,6 +1138,53 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.QLinearLeakyRelu"></a><a name="com.microsoft.qlinearleakyrelu">**com.microsoft.QLinearLeakyRelu**</a>
|
||||
|
||||
QLinearLeakyRelu takes quantized input data (Tensor), an argument alpha, and quantize parameter for output,
|
||||
and produces one output data (Tensor<T>) where the function `f(x) = quantize(alpha * dequantize(x)) for dequantize(x) < 0`,
|
||||
`f(x) = quantize(dequantize(x)) for dequantize(x) >= 0`, is applied to the data tensor elementwise.
|
||||
|
||||
#### Version
|
||||
|
||||
This version of the operator has been available since version 1 of the 'com.microsoft' operator set.
|
||||
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>alpha</tt> : float</dt>
|
||||
<dd>Coefficient of leakage.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (4 - 5)
|
||||
|
||||
<dl>
|
||||
<dt><tt>X</tt> : T</dt>
|
||||
<dd>Input tensor</dd>
|
||||
<dt><tt>X_scale</tt> : tensor(float)</dt>
|
||||
<dd>Input X's scale. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>X_zero_point</tt> (optional) : T</dt>
|
||||
<dd>Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>Y_scale</tt> : tensor(float)</dt>
|
||||
<dd>Output Y's scale. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>Y_zero_point</tt> (optional) : T</dt>
|
||||
<dd>Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>Y</tt> : T</dt>
|
||||
<dd>Output tensor</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(uint8), tensor(int8)</dt>
|
||||
<dd>Constrain input and output types to 8 bit tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <a name="com.microsoft.QLinearMul"></a><a name="com.microsoft.qlinearmul">**com.microsoft.QLinearMul**</a>
|
||||
|
||||
Performs element-wise binary multiplication on 8 bit data types (with Numpy-style broadcasting support).
|
||||
|
|
@ -2002,9 +1653,13 @@ This version of the operator has been available since version 1 of the 'com.micr
|
|||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.Attention"></a><a name="com.microsoft.attention">**com.microsoft.Attention**</a>
|
||||
|
||||
Multi-Head Self Attention that can be either unidirectional (like GPT2) or bidirectional (like BERT).
|
||||
The mask_index input is optional. Unidirectional and mask_index input are mutually exclusive. When unidirectional is 1, the
|
||||
mask_index shall not be provided.
|
||||
Multi-Head Self Attention that can be either unidirectional (like GPT-2) or bidirectional (like BERT).
|
||||
The mask_index input is optional. Besides raw attention mask with shape (batch_size, past_sequence_length + sequence_length),
|
||||
we also support other two formats: When input has right-side padding, mask_index is one dimension with shape (batch_size),
|
||||
where value of each element is the end position, or valid length of actual sequence excluding padding. When input has
|
||||
left-side padding, mask_index has shape (2 * batch_size), where the values are the exclusive end positions followed by
|
||||
the inclusive start positions. When unidirectional is 1, and each token only attend to previous tokens. For GPT-2, both past
|
||||
and present state are optional. Present state could appear in output even when past state is not in input.
|
||||
|
||||
#### Version
|
||||
|
||||
|
|
@ -2018,7 +1673,7 @@ No versioning maintained for experimental ops.
|
|||
<dd>Whether every token can only attend to previous tokens. Default value is 0.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (3 - 4)
|
||||
#### Inputs (3 - 5)
|
||||
|
||||
<dl>
|
||||
<dt><tt>input</tt> : T</dt>
|
||||
|
|
@ -2028,14 +1683,18 @@ No versioning maintained for experimental ops.
|
|||
<dt><tt>bias</tt> : T</dt>
|
||||
<dd>1D input tensor with shape (3 * hidden_size)</dd>
|
||||
<dt><tt>mask_index</tt> (optional) : M</dt>
|
||||
<dd>Attention mask index with shape (batch_size).</dd>
|
||||
<dd>Attention mask with shape (batch_size, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size).</dd>
|
||||
<dt><tt>past</tt> (optional) : T</dt>
|
||||
<dd>past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
#### Outputs (1 - 2)
|
||||
|
||||
<dl>
|
||||
<dt><tt>output</tt> : T</dt>
|
||||
<dd>3D output tensor with shape (batch_size, sequence_length, hidden_size)</dd>
|
||||
<dd>3D output tensor with shape (batch_size, append_length, hidden_size)</dd>
|
||||
<dt><tt>present</tt> (optional) : T</dt>
|
||||
<dd>present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
|
@ -2091,6 +1750,13 @@ No versioning maintained for experimental ops.
|
|||
#### Version
|
||||
|
||||
No versioning maintained for experimental ops.
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>epsilon</tt> : float</dt>
|
||||
<dd>The epsilon value to use to avoid division by zero.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (7 - 8)
|
||||
|
||||
<dl>
|
||||
|
|
@ -2133,7 +1799,7 @@ No versioning maintained for experimental ops.
|
|||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.FastGelu"></a><a name="com.microsoft.fastgelu">**com.microsoft.FastGelu**</a>
|
||||
|
||||
GELU (Gaussian Error Linear Unit) approximation: Y=0.5*X*(1+tanh(0.797885*X+0.035677*X*X*X))) with an optional input of bias that will be added to X before GELU.
|
||||
GELU (Gaussian Error Linear Unit) approximation: Y=0.5*X*(1+tanh(0.797885*X+0.035677*X*X*X)) with an optional input of bias that will be added to X before GELU.
|
||||
|
||||
#### Version
|
||||
|
||||
|
|
@ -2195,6 +1861,95 @@ No versioning maintained for experimental ops.
|
|||
</dl>
|
||||
|
||||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.Inverse"></a><a name="com.microsoft.inverse">**com.microsoft.Inverse**</a>
|
||||
|
||||
#### Version
|
||||
|
||||
No versioning maintained for experimental ops.
|
||||
#### Inputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>X</tt> : T</dt>
|
||||
<dd>Input tensor. Every matrix in the batch must be invertible.</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>Y</tt> : T</dt>
|
||||
<dd>Output tensor of the same type and shape as the input tensor.</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
|
||||
<dd>Constrain input and output types to float tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.QAttention"></a><a name="com.microsoft.qattention">**com.microsoft.QAttention**</a>
|
||||
|
||||
Quantization of Multi-Head Self Attention.
|
||||
|
||||
#### Version
|
||||
|
||||
No versioning maintained for experimental ops.
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>num_heads</tt> : int (required)</dt>
|
||||
<dd>Number of attention heads</dd>
|
||||
<dt><tt>unidirectional</tt> : int</dt>
|
||||
<dd>Whether every token can only attend to previous tokens. Default value is 0.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (5 - 9)
|
||||
|
||||
<dl>
|
||||
<dt><tt>input</tt> : T1</dt>
|
||||
<dd>3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size</dd>
|
||||
<dt><tt>weight</tt> : T2</dt>
|
||||
<dd>2D input tensor with shape (hidden_size, 3 * hidden_size)</dd>
|
||||
<dt><tt>bias</tt> : T3</dt>
|
||||
<dd>1D input tensor with shape (3 * hidden_size)</dd>
|
||||
<dt><tt>input_scale</tt> : T3</dt>
|
||||
<dd>scale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>weight_scale</tt> : T3</dt>
|
||||
<dd>scale of weight scale. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>mask_index</tt> (optional) : T4</dt>
|
||||
<dd>Attention mask index with shape (batch_size)</dd>
|
||||
<dt><tt>input_zero_point</tt> (optional) : T1</dt>
|
||||
<dd>zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>weight_zero_point</tt> (optional) : T2</dt>
|
||||
<dd>zero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization.</dd>
|
||||
<dt><tt>past</tt> (optional) : T3</dt>
|
||||
<dd>past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size).</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs (1 - 2)
|
||||
|
||||
<dl>
|
||||
<dt><tt>output</tt> : T3</dt>
|
||||
<dd>3D output tensor with shape (batch_size, sequence_length, hidden_size)</dd>
|
||||
<dt><tt>present</tt> (optional) : T3</dt>
|
||||
<dd>present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size)</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T1</tt> : tensor(int8), tensor(uint8)</dt>
|
||||
<dd>Constrain input and output types to int8 tensors.</dd>
|
||||
<dt><tt>T2</tt> : tensor(int8), tensor(uint8)</dt>
|
||||
<dd>Constrain input and output types to int8 tensors.</dd>
|
||||
<dt><tt>T3</tt> : tensor(float), tensor(float16)</dt>
|
||||
<dd>Constrain input and output types to float tensors.</dd>
|
||||
<dt><tt>T4</tt> : tensor(int32)</dt>
|
||||
<dd>Constrain mask index to integer types</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.SkipLayerNormalization"></a><a name="com.microsoft.skiplayernormalization">**com.microsoft.SkipLayerNormalization**</a>
|
||||
|
||||
Skip and Layer Normalization Fusion
|
||||
|
|
@ -2202,6 +1957,13 @@ No versioning maintained for experimental ops.
|
|||
#### Version
|
||||
|
||||
No versioning maintained for experimental ops.
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>epsilon</tt> : float</dt>
|
||||
<dd>The epsilon value to use to avoid division by zero.</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs (4 - 5)
|
||||
|
||||
<dl>
|
||||
|
|
@ -2238,6 +2000,46 @@ No versioning maintained for experimental ops.
|
|||
</dl>
|
||||
|
||||
|
||||
### <sub>experimental</sub> <a name="com.microsoft.TransposeMatMul"></a><a name="com.microsoft.transposematmul">**com.microsoft.TransposeMatMul**</a>
|
||||
|
||||
Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
|
||||
|
||||
#### Version
|
||||
|
||||
No versioning maintained for experimental ops.
|
||||
#### Attributes
|
||||
|
||||
<dl>
|
||||
<dt><tt>transA</tt> : int</dt>
|
||||
<dd>Whether A should be transposed on the last two dimensions before doing multiplication</dd>
|
||||
<dt><tt>transB</tt> : int</dt>
|
||||
<dd>Whether B should be transposed on the last two dimensions before doing multiplication</dd>
|
||||
</dl>
|
||||
|
||||
#### Inputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>A</tt> : T</dt>
|
||||
<dd>N-dimensional matrix A</dd>
|
||||
<dt><tt>B</tt> : T</dt>
|
||||
<dd>N-dimensional matrix B</dd>
|
||||
</dl>
|
||||
|
||||
#### Outputs
|
||||
|
||||
<dl>
|
||||
<dt><tt>Y</tt> : T</dt>
|
||||
<dd>Matrix multiply results</dd>
|
||||
</dl>
|
||||
|
||||
#### Type Constraints
|
||||
|
||||
<dl>
|
||||
<dt><tt>T</tt> : tensor(float16), tensor(float), tensor(double)</dt>
|
||||
<dd>Constrain input and output types to float tensors.</dd>
|
||||
</dl>
|
||||
|
||||
|
||||
## com.microsoft.nchwc
|
||||
### <a name="com.microsoft.nchwc.AveragePool"></a><a name="com.microsoft.nchwc.averagepool">**com.microsoft.nchwc.AveragePool**</a>
|
||||
|
||||
|
|
|
|||
|
|
@ -9,388 +9,295 @@
|
|||
|
||||
| Op Name | Parameters | OpSet Version | Types Supported |
|
||||
|---------|------------|---------------|-----------------|
|
||||
**Operator Domain:** *ai.onnx.ml*
|
||||
|**Operator Domain:** *ai.onnx.ml*||||
|
||||
|Abs|(*in* X:**T**, *out* Y:**T**)|6+|**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**)|7+|**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)|
|
||||
|And|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|ArgMax|(*in* data:**T**, *out* reduced:**tensor(int64)**)|12+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
| | |[11, 11]|**T** = tensor(float), tensor(int32)|
|
||||
|||11|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ArgMin|(*in* data:**T**, *out* reduced:**tensor(int64)**)|12+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
| | |[11, 11]|**T** = tensor(float), tensor(int32)|
|
||||
|||11|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ArrayFeatureExtractor|(*in* X:**T**, *in* Y:**tensor(int64)**, *out* Z:**T**)|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
|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, 10]|**T** = tensor(float)|
|
||||
| | |[7, 9]|**T** = tensor(float)|
|
||||
|BatchNormalization|(*in* X:**T**, *in* scale:**T**, *in* B:**T**, *in* mean:**T**, *in* var:**T**, *in* training_mode:**T1**, *out* Y:**T**, *out* output_mean:**T**, *out* output_var:**T**, *out* saved_mean:**T**, *out* saved_var:**T**) 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**)|[7, 9]|**T** = tensor(double), tensor(float)|
|
||||
|||10|**T** = tensor(float)|
|
||||
|||[7, 9]|**T** = tensor(float)|
|
||||
|BatchNormalization|(*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**)|[7, 9]|**T** = tensor(double), tensor(float)|
|
||||
|Binarizer|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(float)|
|
||||
|BitShift|(*in* X:**T**, *in* Y:**T**, *out* Z:**T**)|11+|**T** = tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|Cast|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(string)|
|
||||
| | ||**T2** = 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, 9]|**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(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)|
|
||||
|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)|
|
||||
|Cast|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(string)<br/> **T2** = 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, 9]|**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)<br/> **T2** = 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)|
|
||||
|CastMap|(*in* X:**T1**, *out* Y:**T2**)|1+|**T1** = map(int64,tensor(float)), map(int64,tensor(string))<br/> **T2** = tensor(float), tensor(int64), tensor(string)|
|
||||
|CategoryMapper|(*in* X:**T1**, *out* Y:**T2**)|1+|**T1** = tensor(int64), tensor(string)<br/> **T2** = tensor(int64), tensor(string)|
|
||||
|Ceil|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(float)|
|
||||
|Clip|(*in* input:**T**, *in* min:**T**, *in* max:**T**, *out* output:**T**) or (*in* input:**T**, *out* output:**T**)|12+|**T** = tensor(double), tensor(float), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)|
|
||||
| | |[11, 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)|
|
||||
|||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)<br/> **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)<br/> **T1** = tensor(bool)|
|
||||
|Concat|(*in* inputs:**T**, *out* concat_result:**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)|
|
||||
| | |[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)|
|
||||
|||[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)|
|
||||
|ConstantOfShape|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(int64)<br/> **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)|
|
||||
|||[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)<br/> **T2** = tensor(uint8)<br/> **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)|
|
||||
|||[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**)|11+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
| | ||**T2** = tensor(int32), tensor(int64)|
|
||||
|CumSum|(*in* x:**T**, *in* axis:**T2**, *out* y:**T**)|11+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)|
|
||||
|DepthToSpace|(*in* input:**T**, *out* output:**T**)|11+|**T** = tensor(float)|
|
||||
| | |[1, 10]|**T** = tensor(float)|
|
||||
|||[1, 10]|**T** = tensor(float)|
|
||||
|DequantizeLinear|(*in* x:**T**, *in* x_scale:**tensor(float)**, *in* x_zero_point:**T**, *out* y:**tensor(float)**)|10+|**T** = tensor(int8), tensor(uint8)|
|
||||
|Det|(*in* X:**T**, *out* Y:**T**)|11+|**T** = tensor(float)|
|
||||
|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)|
|
||||
|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))<br/> **T2** = tensor(double), tensor(float), tensor(int64), tensor(string)|
|
||||
|Div|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|7+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|Dropout|(*in* data:**T**, *in* ratio:**T1**, *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**)|10+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|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**)|12+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
|
||||
|||10+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 9]|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(bool)|
|
||||
|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)|
|
||||
|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)<br/> **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**)|11+|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 10]|**T** = tensor(bool), tensor(int32), tensor(int64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|Equal|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|11+|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 10]|**T** = tensor(bool), tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|
||||
|Erf|(*in* input:**T**, *out* output:**T**)|9+|**T** = tensor(float)|
|
||||
|Exp|(*in* input:**T**, *out* output:**T**)|6+|**T** = tensor(double), tensor(float)|
|
||||
|Expand|(*in* input:**T**, *in* shape:**tensor(int64)**, *out* output:**T**)|8+|**T** = 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)|
|
||||
|EyeLike|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)<br/> **T2** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)|
|
||||
|FeatureVectorizer|(*in* X:**T1**, *out* Y:**tensor(float)**)|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|Flatten|(*in* input:**T**, *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)|
|
||||
| | |[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)|
|
||||
| | |[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)|
|
||||
|||[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**)|6+|**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**)|7+|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T1** = tensor(int32)|
|
||||
|Gather|(*in* data:**T**, *in* indices:**Tind**, *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)|
|
||||
| | ||**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**)|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)|
|
||||
| | ||**Tind** = tensor(int32), tensor(int64)|
|
||||
|GatherND|(*in* data:**T**, *in* indices:**tensor(int64)**, *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)|
|
||||
| | ||**Tind** = tensor(int64)|
|
||||
|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**)|7+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|Gather|(*in* data:**T**, *in* indices:**Tind**, *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)<br/> **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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|GatherElements|(*in* data:**T**, *in* indices:**Tind**, *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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|GatherND|(*in* data:**T**, *in* indices:**tensor(int64)**, *out* output:**T**)|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)<br/> **Tind** = 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)<br/> **Tind** = tensor(int64)|
|
||||
|Gemm|(*in* A:**T**, *in* B:**T**, *in* C:**T**, *out* Y:**T**)|11+|**T** = tensor(float)|
|
||||
| | |[7, 8]|**T** = tensor(float)|
|
||||
| | |[9, 10]|**T** = tensor(float)|
|
||||
|||[9, 10]|**T** = tensor(float)|
|
||||
|||[7, 8]|**T** = 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**)|9+|**T** = tensor(int32), tensor(int64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 9]|**T** = tensor(float)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|Greater|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 9]|**T** = tensor(float)<br/> **T1** = tensor(bool)|
|
||||
|HardSigmoid|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(float)|
|
||||
|Hardmax|(*in* input:**T**, *out* output:**T**)|11+|**T** = tensor(float)|
|
||||
| | |[1, 10]|**T** = tensor(float)|
|
||||
|||[1, 10]|**T** = tensor(float)|
|
||||
|Identity|(*in* input:**T**, *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)|
|
||||
|If|(*in* cond:**B**, *out* outputs:**V**)|11+|**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)|
|
||||
|If|(*in* cond:**B**, *out* outputs:**V**)|11+|**B** = tensor(bool)<br/> **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)<br/> **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)|
|
||||
|Imputer|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(float), tensor(int64)|
|
||||
|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**)|9+|**T1** = tensor(float), tensor(float16)|
|
||||
| | ||**T2** = tensor(bool)|
|
||||
|IsInf|(*in* X:**T1**, *out* Y:**T2**)|10+|**T1** = tensor(double), tensor(float)<br/> **T2** = tensor(bool)|
|
||||
|IsNaN|(*in* X:**T1**, *out* Y:**T2**)|9+|**T1** = tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|
||||
|LRN|(*in* X:**T**, *out* Y:**T**)|1+|**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**)|7+|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T1** = tensor(int32)|
|
||||
|LabelEncoder|(*in* X:**T1**, *out* Y:**T2**)|2+|**T1** = tensor(float), tensor(int64), tensor(string)|
|
||||
| | ||**T2** = tensor(float), tensor(int64), tensor(string)|
|
||||
| | |[1, 1]|**T1** = tensor(int64), tensor(string)|
|
||||
| | ||**T2** = tensor(int64), tensor(string)|
|
||||
|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**)|7+|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(int32)|
|
||||
|LabelEncoder|(*in* X:**T1**, *out* Y:**T2**)|2+|**T1** = tensor(float), tensor(int64), tensor(string)<br/> **T2** = tensor(float), tensor(int64), tensor(string)|
|
||||
|||1|**T1** = tensor(int64), tensor(string)<br/> **T2** = tensor(int64), tensor(string)|
|
||||
|LayerNormalization|(*in* X:**T**, *in* scale:**T**, *in* B:**T**, *out* Y:**T**, *out* mean:**U**, *out* inv_std_var:**U**)|1+|**T** = tensor(double), tensor(float)|
|
||||
|LeakyRelu|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(float)|
|
||||
|Less|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(int32), tensor(int64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 9]|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|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)|
|
||||
|Less|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(int32), tensor(int64)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 9]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(bool)|
|
||||
|LinearClassifier|(*in* X:**T1**, *out* Y:**T2**, *out* Z:**tensor(float)**)|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|
||||
|LinearRegressor|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(float)|
|
||||
|Log|(*in* input:**T**, *out* output:**T**)|6+|**T** = tensor(float)|
|
||||
|LogSoftmax|(*in* input:**T**, *out* output:**T**)|11+|**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**)|11+|**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)|
|
||||
|||[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**)|11+|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **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)<br/> **I** = tensor(int64)<br/> **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(float)|
|
||||
|LpPool|(*in* X:**T**, *out* Y:**T**)|11+|**T** = tensor(float)|
|
||||
| | |[2, 10]|**T** = tensor(float)|
|
||||
|||[2, 10]|**T** = tensor(float)|
|
||||
|MatMul|(*in* A:**T**, *in* B:**T**, *out* Y:**T**)|9+|**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(uint8)|
|
||||
| | ||**T2** = tensor(int8), tensor(uint8)|
|
||||
| | ||**T3** = tensor(int32)|
|
||||
|Max|(*in* data_0:**T**, *out* max:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | ||**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | |[6, 7]|**T** = tensor(float)|
|
||||
| | |[8, 11]|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T1** = tensor(double), 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)|
|
||||
| | |[1, 7]|**T** = tensor(float)|
|
||||
| | |[8, 11]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float)|
|
||||
|||[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(uint8)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(int32)|
|
||||
|Max|(*in* data_0:**T**, *out* max:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|||[8, 11]|**T** = tensor(double), tensor(float)<br/> **T1** = 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)<br/> **T** = tensor(double), tensor(float), tensor(int8), tensor(uint8)|
|
||||
|||[8, 11]|**I** = tensor(int64)<br/> **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)|
|
||||
|MaxUnpool|(*in* X:**T1**, *in* I:**T2**, *in* output_shape:**T2**, *out* output:**T1**)|11+|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|||[9, 10]|**T1** = tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|Mean|(*in* data_0:**T**, *out* mean:**T**)|8+|**T** = tensor(float)|
|
||||
| | |[6, 7]|**T** = tensor(float)|
|
||||
|||[6, 7]|**T** = tensor(float)|
|
||||
|MeanVarianceNormalization|(*in* X:**T**, *out* Y:**T**) or (*in* input:**T**, *out* output:**T**)|9+|**T** = tensor(float)|
|
||||
| | |[1, 8]|**T** = tensor(float)|
|
||||
|Min|(*in* data_0:**T**, *out* min:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | ||**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | |[6, 7]|**T** = tensor(float)|
|
||||
| | |[8, 11]|**T** = tensor(float)|
|
||||
| | ||**T1** = tensor(float)|
|
||||
|||[1, 8]|**T** = tensor(float)|
|
||||
|Min|(*in* data_0:**T**, *out* min:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|||[8, 11]|**T** = tensor(float)<br/> **T1** = tensor(float)|
|
||||
|||[6, 7]|**T** = tensor(float)|
|
||||
|Mod|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|10+|**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**)|7+|**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)|
|
||||
|Multinomial|(*in* input:**T1**, *out* output:**T2**)|7+|**T1** = tensor(float)<br/> **T2** = tensor(int32), tensor(int64)|
|
||||
|Neg|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(int8)|
|
||||
|NonZero|(*in* X:**T**, *out* Y:**tensor(int64)**)|9+|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)|
|
||||
|Normalizer|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|Not|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(bool)|
|
||||
| | ||**T1** = 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)|
|
||||
|Not|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|OneHot|(*in* indices:**T1**, *in* depth:**T2**, *in* values:**T3**, *out* output:**T3**)|11+|**T1** = tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(float), tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
|||[9, 10]|**T1** = tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(float), tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
|OneHotEncoder|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(double), tensor(float), tensor(int64), tensor(string)|
|
||||
|Or|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|Or|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|PRelu|(*in* X:**T**, *in* slope:**T**, *out* Y:**T**)|[7, 9]|**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**)|11+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |[2, 10]|**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**)|11+|**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**)|7+|**T** = tensor(double), tensor(float)|
|
||||
|QLinearConv|(*in* x:**T1**, *in* x_scale:**tensor(float)**, *in* x_zero_point:**T1**, *in* w:**T2**, *in* w_scale:**tensor(float)**, *in* w_zero_point:**T2**, *in* y_scale:**tensor(float)**, *in* y_zero_point:**T3**, *in* B:**T4**, *out* y:**T3**)|10+|**T1** = tensor(uint8)|
|
||||
| | ||**T2** = tensor(uint8)|
|
||||
| | ||**T3** = 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(uint8)|
|
||||
| | ||**T2** = tensor(uint8)|
|
||||
| | ||**T3** = tensor(uint8)|
|
||||
|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**)|7+|**T** = tensor(float)|
|
||||
| | ||**T1** = tensor(int32)|
|
||||
|Pow|(*in* X:**T**, *in* Y:**T**, *out* Z:**T**) or (*in* X:**T**, *in* Y:**T1**, *out* Z:**T**)|12+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|||[7, 11]|**T** = tensor(double), tensor(float)<br/> **T1** = tensor(double), tensor(float)|
|
||||
|QLinearConv|(*in* x:**T1**, *in* x_scale:**tensor(float)**, *in* x_zero_point:**T1**, *in* w:**T2**, *in* w_scale:**tensor(float)**, *in* w_zero_point:**T2**, *in* y_scale:**tensor(float)**, *in* y_zero_point:**T3**, *in* B:**T4**, *out* y:**T3**)|10+|**T1** = tensor(uint8)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(uint8)<br/> **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(uint8)<br/> **T2** = tensor(uint8)<br/> **T3** = tensor(uint8)|
|
||||
|QuantizeLinear|(*in* x:**T1**, *in* y_scale:**tensor(float)**, *in* y_zero_point:**T2**, *out* y:**T2**)|10+|**T1** = tensor(float)<br/> **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**)|7+|**T** = tensor(float)<br/> **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)|
|
||||
|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)<br/> **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)|
|
||||
|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)<br/> **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**)|6+|**T** = tensor(float)|
|
||||
|ReduceL1|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceL2|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceLogSum|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceLogSumExp|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceMax|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |12+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceMax|(*in* data:**T**, *out* reduced:**T**)|12+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|
||||
|||11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|ReduceMean|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceMin|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |12+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|ReduceMin|(*in* data:**T**, *out* reduced:**T**)|12+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(int8), tensor(uint8)|
|
||||
|||11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|ReduceProd|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|ReduceSum|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|ReduceSumSquare|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(float), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|ReduceSum|(*in* data:**T**, *out* reduced:**T**)|11+|**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**)|11+|**T** = tensor(double), tensor(float), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(int32)|
|
||||
|Relu|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(float)|
|
||||
|Reshape|(*in* data:**T**, *in* shape:**tensor(int64)**, *out* reshaped:**T**) or (*in* data:**T**, *out* reshaped:**T**)|5+|**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)|
|
||||
|Reshape|(*in* data:**T**, *in* shape:**tensor(int64)**, *out* reshaped:**T**) or (*in* data:**T**, *out* reshaped:**T**)|5+|**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)<br/> **shape** = tensor(int64)|
|
||||
|Reshape_1||[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**)|11+|**T1** = tensor(float), tensor(int32), tensor(uint8)|
|
||||
| | |[10, 10]|**T** = tensor(float), tensor(int32), tensor(uint8)|
|
||||
|||10|**T** = tensor(float), 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(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)|
|
||||
|RoiAlign|(*in* X:**T1**, *in* rois:**T1**, *in* batch_indices:**T2**, *out* Y:**T1**)|10+|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T2** = tensor(int64)|
|
||||
|RoiAlign|(*in* X:**T1**, *in* rois:**T1**, *in* batch_indices:**T2**, *out* Y:**T1**)|10+|**T** = tensor(double), tensor(float)<br/> **T2** = tensor(int64)|
|
||||
|Round|(*in* X:**T**, *out* Y:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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)|
|
||||
|SVMClassifier|(*in* X:**T1**, *out* Y:**T2**, *out* Z:**tensor(float)**)|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|
||||
|SVMRegressor|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(float)|
|
||||
|Scale|(*in* input:**T**, *out* output:**T**)|1+|**T** = tensor(float)|
|
||||
|ScaledTanh|(*in* input:**T**, *out* output:**T**)|1+|**T** = tensor(float)|
|
||||
|Scaler|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|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**)|11+|**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)|
|
||||
| | |[8, 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)|
|
||||
| | |[9, 10]|**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**)|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)|
|
||||
| | ||**Tind** = tensor(int32), tensor(int64)|
|
||||
|ScatterND|(*in* data:**T**, *in* indices:**tensor(int64)**, *in* updates:**T**, *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)|
|
||||
| | ||**Tind** = tensor(int64)|
|
||||
|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**)|11+|**I** = tensor(int64)<br/> **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]|**I** = tensor(int64)<br/> **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)<br/> **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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|ScatterElements|(*in* data:**T**, *in* indices:**Tind**, *in* updates:**T**, *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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|ScatterND|(*in* data:**T**, *in* indices:**tensor(int64)**, *in* updates:**T**, *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)<br/> **Tind** = tensor(int64)|
|
||||
|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)|
|
||||
|SequenceAt|(*in* input_sequence:**S**, *in* position:**I**, *out* tensor:**T**)|11+|**I** = tensor(int32), tensor(int64)<br/> **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))<br/> **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))<br/> **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**)|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)|
|
||||
| | ||**T1** = tensor(int64)|
|
||||
|SequenceErase|(*in* input_sequence:**S**, *in* position:**I**, *out* output_sequence:**S**)|11+|**I** = tensor(int32), tensor(int64)<br/> **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)<br/> **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)<br/> **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**)|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)<br/> **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**)|6+|**T** = tensor(float)|
|
||||
|Sign|(*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)|
|
||||
|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**)|1+|**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**)|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)|
|
||||
| | ||**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)|
|
||||
| | |[10, 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)|
|
||||
|Size|(*in* data:**T**, *out* size:**T1**)|1+|**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)<br/> **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**)|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)<br/> **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)<br/> **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**)|11+|**T** = tensor(double), tensor(float)|
|
||||
| | |[1, 10]|**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**)|1+|**T** = tensor(float)|
|
||||
|Split|(*in* input:**T**, *in* split:**T**, *out* outputs...:**T**) or (*in* input:**T**, *out* outputs:**T**)|11+|**T** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
| | |[2, 10]|**T** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
|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(string)|
|
||||
|||[2, 10]|**T** = tensor(float), tensor(int32), tensor(int64), tensor(string)|
|
||||
|SplitToSequence|(*in* input:**T**, *in* split:**I**, *out* output_sequence:**S**)|11+|**I** = tensor(int32), tensor(int64)<br/> **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))<br/> **T** = tensor(double), tensor(float), tensor(int32), tensor(string)|
|
||||
|Sqrt|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float)|
|
||||
|Squeeze|(*in* data:**T**, *out* squeezed:**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)|
|
||||
| | |[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)|
|
||||
|||[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+|**T** = tensor(string)|
|
||||
|Sub|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|7+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)|
|
||||
|Sum|(*in* data_0:**T**, *out* sum:**T**)|8+|**T** = tensor(float)|
|
||||
| | |[6, 7]|**T** = tensor(float)|
|
||||
|||[6, 7]|**T** = tensor(float)|
|
||||
|Tan|(*in* input:**T**, *out* output:**T**)|7+|**T** = tensor(float)|
|
||||
|Tanh|(*in* input:**T**, *out* output:**T**)|6+|**T** = tensor(float)|
|
||||
|TfIdfVectorizer|(*in* X:**T**, *out* Y:**T1**)|9+|**T** = tensor(int32), tensor(int64), tensor(string)|
|
||||
| | ||**T1** = tensor(float)|
|
||||
|TfIdfVectorizer|(*in* X:**T**, *out* Y:**T1**)|9+|**T** = tensor(int32), tensor(int64), tensor(string)<br/> **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**)|6+|**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(float), tensor(int64)|
|
||||
| | |[1, 9]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(float)|
|
||||
| | |[10, 10]|**I** = tensor(int64)|
|
||||
| | ||**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**)|6+|**T** = tensor(bool), tensor(double), tensor(float), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8)<br/> **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)<br/> **T** = tensor(float), tensor(int64)|
|
||||
|||10|**I** = tensor(int64)<br/> **T** = tensor(float)|
|
||||
|||[1, 9]|**I** = tensor(int64)<br/> **T** = tensor(float)|
|
||||
|Transpose|(*in* data:**T**, *out* transposed:**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)|
|
||||
|TreeEnsembleClassifier|(*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)|
|
||||
|TreeEnsembleClassifier|(*in* X:**T1**, *out* Y:**T2**, *out* Z:**tensor(float)**)|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T2** = tensor(int64), tensor(string)|
|
||||
|TreeEnsembleRegressor|(*in* X:**T**, *out* Y:**tensor(float)**)|1+|**T** = tensor(double), tensor(float)|
|
||||
|Unique|(*in* X:**T**, *out* Y:**T**, *out* indices:**tensor(int64)**, *out* inverse_indices:**tensor(int64)**, *out* counts:**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)|
|
||||
|Unsqueeze|(*in* data:**T**, *out* expanded:**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)|
|
||||
| | |[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)|
|
||||
|||[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**)|[7, 9]|**T** = tensor(float), tensor(int32), tensor(uint8)|
|
||||
|Where|(*in* condition:**B**, *in* X:**T**, *in* Y:**T**, *out* output:**T**)|9+|**T** = 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)|
|
||||
|Xor|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|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**, *out* output:**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)|
|
||||
|**Operator Domain:** *com.microsoft*||||
|
||||
|Attention|(*in* input:**T**, *in* weight:**T**, *in* bias:**T**, *in* mask_index:**M**, *in* past:**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)<br/> **T1** = tensor(int32)|
|
||||
|BiasGelu|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|1+|**T** = tensor(float)|
|
||||
|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+|**T** = 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)|
|
||||
|CropAndResize|(*in* X:**T1**, *in* rois:**T1**, *in* batch_indices:**T2**, *in* crop_size:**T2**, *out* Y:**T1**)|1+|**T** = tensor(float)<br/> **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)<br/> **T2** = tensor(float)|
|
||||
|DynamicQuantizeMatMul|(*in* A:**T1**, *in* B:**T2**, *in* b_scale:**T1**, *in* b_zero_point:**T2**, *out* Y:**T1**)|1+|**T1** = tensor(float)<br/> **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**, *out* output:**T**, *out* mask_index:**T1**)|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)|
|
||||
|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)<br/> **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**, *out* Y:**T**)|1+|**T** = tensor(float)|
|
||||
|FusedGemm|(*in* A:**T**, *in* B:**T**, *in* C:**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)|
|
||||
|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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|Gelu|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(float)|
|
||||
|MatMulInteger16|(*in* A:**T1**, *in* B:**T2**, *out* Y:**T3**)|1+|**T1** = tensor(int16)|
|
||||
| | ||**T2** = tensor(int16)|
|
||||
| | ||**T3** = tensor(int32)|
|
||||
|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)<br/> **T2** = tensor(int16)<br/> **T3** = tensor(int32)|
|
||||
|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(int32), tensor(string), tensor(uint32)|
|
||||
| | ||**T2** = tensor(int32), tensor(uint32)|
|
||||
|MurmurHash3|(*in* X:**T1**, *out* Y:**T2**)|1+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(string), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(int32), tensor(uint32)|
|
||||
|Pad|(*in* data:**T**, *in* pads:**tensor(int64)**, *in* value:**T**, *out* output:**T**)|1+|**T** = tensor(float)|
|
||||
|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)|
|
||||
|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)<br/> **T2** = tensor(int8), tensor(uint8)<br/> **T3** = tensor(float)<br/> **T4** = tensor(int32)|
|
||||
|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)|
|
||||
|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)|
|
||||
|QuantizeLinear|(*in* x:**T1**, *in* y_scale:**T1**, *in* y_zero_point:**T2**, *out* y:**T2**)|1+|**T1** = tensor(float)<br/> **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)|
|
||||
|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)|
|
||||
|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)|
|
||||
|WordConvEmbedding|(*in* Sequence:**T**, *in* W:**T1**, *in* B:**T1**, *in* C:**T1**, *out* Y:**T1**)|1+|**T** = tensor(int32)<br/> **T1** = tensor(float)|
|
||||
| |
|
||||
| |
|
||||
**Operator Domain:** *com.microsoft.nchwc*
|
||||
|**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)|
|
||||
|
|
@ -407,243 +314,201 @@
|
|||
|
||||
| Op Name | Parameters | OpSet Version | Types Supported |
|
||||
|---------|------------|---------------|-----------------|
|
||||
**Operator Domain:** *ai.onnx.ml*
|
||||
|**Operator Domain:** *ai.onnx.ml*||||
|
||||
|Abs|(*in* X:**T**, *out* Y:**T**)|6+|**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**)|7+|**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)|
|
||||
|And|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **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)|
|
||||
|||[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)|
|
||||
|||[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, 10]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[7, 9]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|BatchNormalization|(*in* X:**T**, *in* scale:**T**, *in* B:**T**, *in* mean:**T**, *in* var:**T**, *in* training_mode:**T1**, *out* Y:**T**, *out* output_mean:**T**, *out* output_var:**T**, *out* saved_mean:**T**, *out* saved_var:**T**) 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**)|9+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Cast|(*in* input:**T1**, *out* output:**T2**)|9+|**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(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(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)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[7, 9]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|BatchNormalization|(*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**)|9+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Cast|(*in* input:**T1**, *out* output:**T2**)|9+|**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)<br/> **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)|
|
||||
|||[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)<br/> **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)|
|
||||
|Ceil|(*in* X:**T**, *out* Y:**T**)|6+|**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**)|12+|**T** = tensor(double), tensor(float), tensor(int64), tensor(int8), tensor(uint64), tensor(uint8)|
|
||||
| | |[11, 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)|
|
||||
|||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)<br/> **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)<br/> **T1** = tensor(bool)|
|
||||
|Concat|(*in* inputs:**T**, *out* concat_result:**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)|
|
||||
| | |[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)|
|
||||
|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)|
|
||||
|||[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)|
|
||||
|ConstantOfShape|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(int64)<br/> **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)|
|
||||
|||[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)|
|
||||
|||[1, 10]|**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**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | ||**T2** = tensor(int32), tensor(int64)|
|
||||
|CumSum|(*in* x:**T**, *in* axis:**T2**, *out* y:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T2** = tensor(int32), tensor(int64)|
|
||||
|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**)|7+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|Dropout|(*in* data:**T**, *in* ratio:**T1**, *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**)|10+|**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)|
|
||||
|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**)|12+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(double), tensor(float), tensor(float16)<br/> **T2** = tensor(bool)|
|
||||
|||10+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|Einsum|(*in* Inputs:**T**, *out* Output:**T**)|12+|**T** = tensor(double), tensor(float)|
|
||||
|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**)|11+|**T** = tensor(bool), tensor(int32), tensor(int64)|
|
||||
| | |[7, 10]|**T** = tensor(bool), tensor(int32), tensor(int64)|
|
||||
|||[7, 10]|**T** = tensor(bool), tensor(int32), tensor(int64)|
|
||||
|Erf|(*in* input:**T**, *out* output:**T**)|9+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Exp|(*in* input:**T**, *out* output:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Expand|(*in* input:**T**, *in* shape:**tensor(int64)**, *out* output:**T**)|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)|
|
||||
|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)|
|
||||
|EyeLike|(*in* input:**T1**, *out* output:**T2**)|9+|**T1** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)<br/> **T2** = tensor(double), tensor(float), tensor(int32), tensor(int64), tensor(uint64)|
|
||||
|Flatten|(*in* input:**T**, *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)|
|
||||
| | |[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)|
|
||||
| | |[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)|
|
||||
|||[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**)|6+|**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**)|7+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**T1** = tensor(int32)|
|
||||
|Gather|(*in* data:**T**, *in* indices:**Tind**, *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)|
|
||||
| | ||**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**)|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)|
|
||||
| | ||**Tind** = tensor(int32), tensor(int64)|
|
||||
|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**)|7+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|
||||
|Gather|(*in* data:**T**, *in* indices:**Tind**, *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)<br/> **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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|GatherElements|(*in* data:**T**, *in* indices:**Tind**, *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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|GatherND|(*in* data:**T**, *in* indices:**tensor(int64)**, *out* output:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **Tind** = tensor(int64)|
|
||||
|Gemm|(*in* A:**T**, *in* B:**T**, *in* C:**T**, *out* Y:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[9, 10]|**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**)|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Greater|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|HardSigmoid|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Identity|(*in* input:**T**, *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)|
|
||||
|If|(*in* cond:**B**, *out* outputs:**V**)|11+|**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)|
|
||||
|If|(*in* cond:**B**, *out* outputs:**V**)|11+|**B** = tensor(bool)<br/> **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)<br/> **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**)|1+|**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**)|7+|**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* inv_std_var:**U**)|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**U** = 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**)|7+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|
||||
|LayerNormalization|(*in* X:**T**, *in* scale:**T**, *in* B:**T**, *out* Y:**T**, *out* mean:**U**, *out* inv_std_var:**U**)|1+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **U** = tensor(float)|
|
||||
|LeakyRelu|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Less|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
| | |[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Less|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|9+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)<br/> **T1** = tensor(bool)|
|
||||
|||[7, 8]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Log|(*in* input:**T**, *out* output:**T**)|6+|**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**)|11+|**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)|
|
||||
|Loop|(*in* M:**I**, *in* cond:**B**, *in* v_initial:**V**, *out* v_final_and_scan_outputs:**V**)|11+|**B** = tensor(bool)<br/> **I** = tensor(int64)<br/> **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)<br/> **I** = tensor(int64)<br/> **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**)|9+|**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)|
|
||||
|||[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)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(int32)|
|
||||
|Max|(*in* data_0:**T**, *out* max:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | |[6, 7]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[8, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[8, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[6, 7]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|MaxPool|(*in* X:**T**, *out* Y:**T**) or (*in* X:**T**, *out* Y:**T**, *out* Indices:**I**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int8), tensor(uint8)|
|
||||
| | |[1, 7]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[10, 10]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[11, 11]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[8, 9]|**I** = tensor(int64)|
|
||||
| | ||**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||11|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||10|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[8, 9]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[1, 7]|**I** = tensor(int64)<br/> **T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|MemcpyFromHost|(*in* X:**T**, *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)|
|
||||
|MemcpyToHost|(*in* X:**T**, *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)|
|
||||
|Min|(*in* data_0:**T**, *out* min:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | |[6, 7]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[8, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[8, 11]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[6, 7]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Mul|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|7+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|Neg|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8)|
|
||||
|NonZero|(*in* X:**T**, *out* Y:**tensor(int64)**)|9+|**T** = tensor(bool), tensor(float), tensor(int32), tensor(int64), tensor(uint8)|
|
||||
|Not|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(bool)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|Or|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)|
|
||||
| | ||**T1** = tensor(bool)|
|
||||
|Not|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|OneHot|(*in* indices:**T1**, *in* depth:**T2**, *in* values:**T3**, *out* output:**T3**)|11+|**T1** = tensor(int32), tensor(int64)<br/> **T2** = tensor(int32), tensor(int64)<br/> **T3** = tensor(float), tensor(float16), tensor(int64)|
|
||||
|Or|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
|PRelu|(*in* X:**T**, *in* slope:**T**, *out* Y:**T**)|7+|**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**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[2, 10]|**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**)|7+|**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**)|7+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**T1** = tensor(int32)|
|
||||
|Pow|(*in* X:**T**, *in* Y:**T**, *out* Z:**T**) or (*in* X:**T**, *in* Y:**T1**, *out* Z:**T**)|12+|**T** = tensor(double), tensor(float), tensor(int32), tensor(int64)<br/> **T1** = tensor(double), tensor(float), 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)<br/> **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**)|7+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **T1** = tensor(int32)|
|
||||
|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**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|ReduceL1|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[1, 10]|**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**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[1, 10]|**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**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|ReduceLogSumExp|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|ReduceMax|(*in* data:**T**, *out* reduced:**T**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int8), tensor(uint8)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[11, 11]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|||11|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|ReduceMean|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[1, 10]|**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**)|12+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int8), tensor(uint8)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[11, 11]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|||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**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|ReduceSum|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32)|
|
||||
|ReduceSumSquare|(*in* data:**T**, *out* reduced:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|||[1, 10]|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Relu|(*in* X:**T**, *out* Y:**T**)|6+|**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**)|5+|**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)|
|
||||
|Reshape|(*in* data:**T**, *in* shape:**tensor(int64)**, *out* reshaped:**T**) or (*in* data:**T**, *out* reshaped:**T**)|5+|**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)<br/> **shape** = tensor(int64)|
|
||||
|Reshape_1||[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**)|11+|**T1** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
|
||||
| | |[10, 10]|**T** = 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+|**T** = tensor(double), tensor(float)|
|
||||
| | ||**T2** = tensor(int64)|
|
||||
|RoiAlign|(*in* X:**T1**, *in* rois:**T1**, *in* batch_indices:**T2**, *out* Y:**T1**)|10+|**T** = tensor(double), tensor(float)<br/> **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**)|11+|**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)|
|
||||
| | |[8, 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)|
|
||||
| | |[9, 10]|**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)|
|
||||
|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**)|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)|
|
||||
| | ||**Tind** = tensor(int32), tensor(int64)|
|
||||
|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**)|11+|**I** = tensor(int64)<br/> **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]|**I** = tensor(int64)<br/> **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)<br/> **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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|ScatterElements|(*in* data:**T**, *in* indices:**Tind**, *in* updates:**T**, *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)<br/> **Tind** = tensor(int32), tensor(int64)|
|
||||
|Selu|(*in* X:**T**, *out* Y:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Shape|(*in* data:**T**, *out* shape:**T1**)|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)|
|
||||
| | ||**T1** = tensor(int64)|
|
||||
|Shape|(*in* data:**T**, *out* shape:**T1**)|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)<br/> **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**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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**)|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)|
|
||||
| | ||**Tind** = tensor(float), 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)|
|
||||
| | ||**Tind** = tensor(float), tensor(int32), tensor(int64)|
|
||||
| | |[10, 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(float), tensor(int32), 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**)|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)<br/> **Tind** = tensor(float), 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)<br/> **Tind** = tensor(float), 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)<br/> **Tind** = tensor(float), tensor(int32), tensor(int64)|
|
||||
|Softmax|(*in* input:**T**, *out* output:**T**)|11+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |[1, 10]|**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)|
|
||||
|Split|(*in* input:**T**, *in* split:**T**, *out* outputs...:**T**) or (*in* input:**T**, *out* outputs:**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)|
|
||||
| | |[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)|
|
||||
|||[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**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|Squeeze|(*in* data:**T**, *out* squeezed:**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)|
|
||||
| | |[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)|
|
||||
|||[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**)|7+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|Sum|(*in* data_0:**T**, *out* sum:**T**)|8+|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
| | |[6, 7]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|||[6, 7]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint32), tensor(uint64)|
|
||||
|Tanh|(*in* input:**T**, *out* output:**T**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|ThresholdedRelu|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | |10+|**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**)|6+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
| | ||**T1** = tensor(int64)|
|
||||
|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**)|6+|**T** = tensor(double), tensor(float), tensor(float16)<br/> **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+|**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)|
|
||||
| | |[10, 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)|
|
||||
|||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, 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**)|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)|
|
||||
|Unsqueeze|(*in* data:**T**, *out* expanded:**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)|
|
||||
| | |[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)|
|
||||
|||[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**)|[7, 9]|**T** = tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(uint8)|
|
||||
|Where|(*in* condition:**B**, *in* X:**T**, *in* Y:**T**, *out* output:**T**)|9+|**B** = tensor(bool)|
|
||||
| | ||**T** = 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)|
|
||||
|Where|(*in* condition:**B**, *in* X:**T**, *in* Y:**T**, *out* output:**T**)|9+|**B** = tensor(bool)<br/> **T** = tensor(float), tensor(float16), tensor(int32), tensor(int64), tensor(uint8)|
|
||||
|Xor|(*in* A:**T**, *in* B:**T**, *out* C:**T1**)|7+|**T** = tensor(bool)<br/> **T1** = tensor(bool)|
|
||||
| |
|
||||
| |
|
||||
**Operator Domain:** *com.microsoft*
|
||||
|Attention|(*in* input:**T**, *in* weight:**T**, *in* bias:**T**, *in* mask_index:**M**, *out* output:**T**)|1+|**T** = tensor(float), tensor(float16)|
|
||||
|**Operator Domain:** *com.microsoft*||||
|
||||
|Attention|(*in* input:**T**, *in* weight:**T**, *in* bias:**T**, *in* mask_index:**M**, *in* past:**T**, *out* output:**T**, *out* present:**T**)|1+|**T** = tensor(float), tensor(float16)|
|
||||
|BiasGelu|(*in* A:**T**, *in* B:**T**, *out* C:**T**)|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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)|
|
||||
|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)|
|
||||
|DequantizeLinear|(*in* x:**T1**, *in* x_scale:**T2**, *in* x_zero_point:**T1**, *out* y:**T2**)|1+|**T1** = tensor(int8), tensor(uint8)<br/> **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**, *out* output:**T**, *out* mask_index:**T1**)|1+|**T** = tensor(float), tensor(float16)|
|
||||
|FastGelu|(*in* X:**T**, *in* bias:**T**, *out* Y:**T**)|1+|**T** = tensor(float), tensor(float16)|
|
||||
|Gelu|(*in* X:**T**, *out* Y:**T**)|1+|**T** = tensor(double), tensor(float), tensor(float16)|
|
||||
|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)|
|
||||
|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)|
|
||||
|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)<br/> **T2** = tensor(int8)<br/> **T3** = tensor(float), tensor(float16)<br/> **T4** = tensor(int32)|
|
||||
|QuantizeLinear|(*in* x:**T1**, *in* y_scale:**T1**, *in* y_zero_point:**T2**, *out* y:**T2**)|1+|**T1** = tensor(float16)<br/> **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(double), tensor(float), tensor(float16)|
|
||||
| |
|
||||
| |
|
||||
|
||||
|
|
@ -652,7 +517,7 @@
|
|||
|
||||
| Op Name | Parameters | OpSet Version | Types Supported |
|
||||
|---------|------------|---------------|-----------------|
|
||||
**Operator Domain:** *ai.onnx.ml*
|
||||
|**Operator Domain:** *ai.onnx.ml*||||
|
||||
|Gemm|(*in* A:**T**, *in* B:**T**, *in* C:**T**, *out* Y:**T**)|7+|**T** = tensor(float)|
|
||||
| |
|
||||
| |
|
||||
|
|
|
|||
|
|
@ -1550,20 +1550,20 @@ def generate_documentation(source_dir, build_dir, configs):
|
|||
# Copy the gen_contrib_doc.py.
|
||||
shutil.copy(
|
||||
os.path.join(source_dir, 'tools', 'python', 'gen_contrib_doc.py'),
|
||||
os.path.join(build_dir, config, config))
|
||||
os.path.join(build_dir, config))
|
||||
shutil.copy(
|
||||
os.path.join(source_dir, 'tools', 'python', 'gen_opkernel_doc.py'),
|
||||
os.path.join(build_dir, config, config))
|
||||
os.path.join(build_dir, config))
|
||||
run_subprocess(
|
||||
[sys.executable,
|
||||
'gen_contrib_doc.py',
|
||||
'--output_path', operator_doc_path],
|
||||
cwd=os.path.join(build_dir, config, config))
|
||||
cwd=os.path.join(build_dir, config))
|
||||
run_subprocess(
|
||||
[sys.executable,
|
||||
'gen_opkernel_doc.py',
|
||||
'--output_path', opkernel_doc_path],
|
||||
cwd=os.path.join(build_dir, config, config))
|
||||
cwd=os.path.join(build_dir, config))
|
||||
docdiff = ''
|
||||
try:
|
||||
docdiff = subprocess.check_output(['git', 'diff', opkernel_doc_path])
|
||||
|
|
|
|||
|
|
@ -16,7 +16,10 @@ def format_version_range(v):
|
|||
if (v[1] >= 2147483647):
|
||||
return str(v[0])+'+'
|
||||
else:
|
||||
return '['+str(v[0])+', '+str(v[1])+']'
|
||||
if (v[0] == v[1]):
|
||||
return str(v[0])
|
||||
else:
|
||||
return '['+str(v[0])+', '+str(v[1])+']'
|
||||
|
||||
|
||||
def format_type_constraints(tc):
|
||||
|
|
@ -102,35 +105,33 @@ def main(args): # type: (Type[Args]) -> None
|
|||
fout.write('| Op Name | Parameters | OpSet Version | Types Supported |\n')
|
||||
fout.write('|---------|------------|---------------|-----------------|\n')
|
||||
for domain, namemap in sorted(domainmap.items()):
|
||||
fout.write('**Operator Domain:** *'+domain+'*\n')
|
||||
fout.write('|**Operator Domain:** *'+domain+'*||||\n')
|
||||
for name, ops in sorted(namemap.items()):
|
||||
version_type_index = defaultdict(lambda: defaultdict(set))
|
||||
for op in ops:
|
||||
formatted_version_range = format_version_range(op.version_range)
|
||||
for tname, tclist in op.type_constraints.items():
|
||||
for c in tclist:
|
||||
version_type_index[formatted_version_range][tname].add(c)
|
||||
version_type_index[op.version_range][tname].add(c)
|
||||
|
||||
namefirsttime = True
|
||||
for version, typemap in sorted(version_type_index.items()):
|
||||
versionfirsttime = True
|
||||
for version_range, typemap in sorted(version_type_index.items(), key=lambda x: x[0], reverse=True):
|
||||
if (namefirsttime):
|
||||
params = paramdict.get(domain+'.'+name, None)
|
||||
fout.write('|' + name + '|' + format_param_strings(params) + '|')
|
||||
namefirsttime = False
|
||||
else:
|
||||
fout.write('|||')
|
||||
fout.write(format_version_range(version_range) + '|')
|
||||
tnameindex = 0
|
||||
for tname, tcset in sorted(typemap.items()):
|
||||
if (namefirsttime):
|
||||
params = paramdict.get(domain+'.'+name, None)
|
||||
fout.write('|'+name+'|'+format_param_strings(params) + '|')
|
||||
namefirsttime = False
|
||||
else:
|
||||
fout.write('| | |')
|
||||
if (versionfirsttime):
|
||||
versionfirsttime = False
|
||||
fout.write(version+'|')
|
||||
else:
|
||||
fout.write('|')
|
||||
|
||||
tnameindex += 1
|
||||
tclist = []
|
||||
for tc in sorted(tcset):
|
||||
tclist.append(tc)
|
||||
fout.write('**'+tname+'** = '+format_type_constraints(tclist)+'|\n')
|
||||
fout.write('**'+tname+'** = '+format_type_constraints(tclist))
|
||||
if (tnameindex < len(typemap)):
|
||||
fout.write('<br/> ')
|
||||
fout.write('|\n')
|
||||
|
||||
fout.write('| |\n| |\n')
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue