mirror of https://github.com/llvm/torch-mlir
393 lines
12 KiB
TableGen
393 lines
12 KiB
TableGen
//===-------------------------------------------------------*- tablegen -*-===//
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//
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// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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// Operation summaries and descriptions were systematically derived from public
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// API docstrings and are licensed accordingly:
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// https://github.com/pytorch/pytorch/blob/master/LICENSE
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//===----------------------------------------------------------------------===//
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//
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// This file is automatically generated. Please do not edit.
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// Generated via:
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// python -m torch_mlir_utils.codegen.torch_ods_gen
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//
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//===----------------------------------------------------------------------===//
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def Torch_AtenTanhOp : Torch_Op<"aten.tanh", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::tanh : (Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenTanh_Op : Torch_Op<"aten.tanh_", [
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IsTrailingUnderscoreInplaceVariant,
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AllowsTypeRefinement
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]> {
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let summary = "Generated op for `aten::tanh_ : (Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenReluOp : Torch_Op<"aten.relu", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::relu : (Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenRelu_Op : Torch_Op<"aten.relu_", [
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IsTrailingUnderscoreInplaceVariant,
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AllowsTypeRefinement
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]> {
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let summary = "Generated op for `aten::relu_ : (Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenAddTensorOp : Torch_Op<"aten.add.Tensor", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::add.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchTensorType:$other,
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AnyTorchScalarType:$alpha
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $other `,` $alpha attr-dict `:` type($self) `,` type($other) `,` type($alpha) `->` type($result)";
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}
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def Torch_AtenAdd_TensorOp : Torch_Op<"aten.add_.Tensor", [
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IsTrailingUnderscoreInplaceVariant,
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AllowsTypeRefinement
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]> {
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let summary = "Generated op for `aten::add_.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchTensorType:$other,
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AnyTorchScalarType:$alpha
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $other `,` $alpha attr-dict `:` type($self) `,` type($other) `,` type($alpha) `->` type($result)";
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}
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def Torch_AtenLinearOp : Torch_Op<"aten.linear", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::linear : (Tensor, Tensor, Tensor?) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$input,
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AnyTorchTensorType:$weight,
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AnyTorchOptionalTensor:$bias
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$input `,` $weight `,` $bias attr-dict `:` type($input) `,` type($weight) `,` type($bias) `->` type($result)";
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}
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def Torch_AtenMmOp : Torch_Op<"aten.mm", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::mm : (Tensor, Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchTensorType:$mat2
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $mat2 attr-dict `:` type($self) `,` type($mat2) `->` type($result)";
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}
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def Torch_AtenConv2dOp : Torch_Op<"aten.conv2d", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::conv2d : (Tensor, Tensor, Tensor?, int[], int[], int[], int) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$input,
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AnyTorchTensorType:$weight,
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AnyTorchOptionalTensor:$bias,
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AnyTorchIntListType:$stride,
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AnyTorchIntListType:$padding,
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AnyTorchIntListType:$dilation,
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AnyTorchIntType:$groups
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$input `,` $weight `,` $bias `,` $stride `,` $padding `,` $dilation `,` $groups attr-dict `:` type($input) `,` type($weight) `,` type($bias) `,` type($stride) `,` type($padding) `,` type($dilation) `,` type($groups) `->` type($result)";
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}
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def Torch_AtenBatchNormOp : Torch_Op<"aten.batch_norm", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::batch_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float, bool) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$input,
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AnyTorchOptionalTensor:$weight,
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AnyTorchOptionalTensor:$bias,
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AnyTorchOptionalTensor:$running_mean,
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AnyTorchOptionalTensor:$running_var,
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AnyTorchBoolType:$training,
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AnyFloat:$momentum,
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AnyFloat:$eps,
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AnyTorchBoolType:$cudnn_enabled
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$input `,` $weight `,` $bias `,` $running_mean `,` $running_var `,` $training `,` $momentum `,` $eps `,` $cudnn_enabled attr-dict `:` type($input) `,` type($weight) `,` type($bias) `,` type($running_mean) `,` type($running_var) `,` type($training) `,` type($momentum) `,` type($eps) `,` type($cudnn_enabled) `->` type($result)";
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}
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def Torch_AtenMaxPool2dOp : Torch_Op<"aten.max_pool2d", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::max_pool2d : (Tensor, int[], int[], int[], int[], bool) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchIntListType:$kernel_size,
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AnyTorchIntListType:$stride,
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AnyTorchIntListType:$padding,
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AnyTorchIntListType:$dilation,
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AnyTorchBoolType:$ceil_mode
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $kernel_size `,` $stride `,` $padding `,` $dilation `,` $ceil_mode attr-dict `:` type($self) `,` type($kernel_size) `,` type($stride) `,` type($padding) `,` type($dilation) `,` type($ceil_mode) `->` type($result)";
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}
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def Torch_AtenAdaptiveAvgPool2dOp : Torch_Op<"aten.adaptive_avg_pool2d", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::adaptive_avg_pool2d : (Tensor, int[]) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchIntListType:$output_size
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $output_size attr-dict `:` type($self) `,` type($output_size) `->` type($result)";
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}
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def Torch_AtenFlattenUsingIntsOp : Torch_Op<"aten.flatten.using_ints", [
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AllowsTypeRefinement
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]> {
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let summary = "Generated op for `aten::flatten.using_ints : (Tensor, int, int) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$self,
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AnyTorchIntType:$start_dim,
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AnyTorchIntType:$end_dim
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);
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let results = (outs
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AnyTorchTensorType:$result
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);
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let assemblyFormat = "$self `,` $start_dim `,` $end_dim attr-dict `:` type($self) `,` type($start_dim) `,` type($end_dim) `->` type($result)";
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}
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def Torch_AtenDimOp : Torch_Op<"aten.dim", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::dim : (Tensor) -> (int)`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchIntType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenSizeOp : Torch_Op<"aten.size", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::size : (Tensor) -> (int[])`";
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let arguments = (ins
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AnyTorchTensorType:$self
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);
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let results = (outs
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AnyTorchIntListType:$result
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);
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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let hasCanonicalizer = 1;
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}
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def Torch_AtenGtIntOp : Torch_Op<"aten.gt.int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::gt.int : (int, int) -> (bool)`";
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let arguments = (ins
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AnyTorchIntType:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyTorchBoolType:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenNeIntOp : Torch_Op<"aten.ne.int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::ne.int : (int, int) -> (bool)`";
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let arguments = (ins
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AnyTorchIntType:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyTorchBoolType:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenAddIntOp : Torch_Op<"aten.add.int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::add.int : (int, int) -> (int)`";
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let arguments = (ins
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AnyTorchIntType:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyTorchIntType:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenMulIntOp : Torch_Op<"aten.mul.int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::mul.int : (int, int) -> (int)`";
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let arguments = (ins
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AnyTorchIntType:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyTorchIntType:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenAddFloatIntOp : Torch_Op<"aten.add.float_int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::add.float_int : (float, int) -> (float)`";
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let arguments = (ins
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AnyFloat:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyFloat:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenMulFloatOp : Torch_Op<"aten.mul.float", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::mul.float : (float, float) -> (float)`";
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let arguments = (ins
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AnyFloat:$a,
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AnyFloat:$b
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);
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let results = (outs
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AnyFloat:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_AtenLtFloatIntOp : Torch_Op<"aten.lt.float_int", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::lt.float_int : (float, int) -> (bool)`";
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let arguments = (ins
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AnyFloat:$a,
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AnyTorchIntType:$b
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);
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let results = (outs
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AnyTorchBoolType:$result
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);
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let assemblyFormat = "$a `,` $b attr-dict `:` type($a) `,` type($b) `->` type($result)";
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}
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def Torch_Aten__Is__Op : Torch_Op<"aten.__is__", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::__is__ : (t1, t2) -> (bool)`";
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let arguments = (ins
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AnyTorchType:$self,
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AnyTorchType:$obj
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);
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let results = (outs
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AnyTorchBoolType:$result
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);
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let assemblyFormat = "$self `,` $obj attr-dict `:` type($self) `,` type($obj) `->` type($result)";
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let hasFolder = 1;
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}
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def Torch_AtenLenTOp : Torch_Op<"aten.len.t", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::len.t : (t[]) -> (int)`";
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let arguments = (ins
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Basicpy_ListType:$a
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);
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let results = (outs
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AnyTorchIntType:$result
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);
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let assemblyFormat = "$a attr-dict `:` type($a) `->` type($result)";
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let hasCanonicalizer = 1;
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}
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