mirror of https://github.com/llvm/torch-mlir
[TORCH][MLIR] Add E2E support for `aten.ceil` op
This commit adds lowering of `aten.ceil` op as a part of element-wise ops lowering. Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>pull/472/head snapshot-20211211.138
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03b6edce68
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@ -521,6 +521,22 @@ class ElementwiseFloorModule(torch.nn.Module):
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def ElementwiseFloorModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class ElementwiseCeilModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@export
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@annotate_args([
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None,
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([-1, -1], torch.float32, True),
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])
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def forward(self, a):
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return torch.ceil(a)
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@register_test_case(module_factory=lambda: ElementwiseCeilModule())
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def ElementwiseCeilModule_basic(module, tu: TestUtils):
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module.forward(tu.rand(3, 4))
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class ElementwisePowModule(torch.nn.Module):
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def __init__(self):
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super().__init__()
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@ -298,6 +298,34 @@ def Torch_AtenFloor_Op : Torch_Op<"aten.floor_", [
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let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
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}
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def Torch_AtenCeilOp : Torch_Op<"aten.ceil", [
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AllowsTypeRefinement,
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HasValueSemantics
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]> {
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let summary = "Generated op for `aten::ceil : (Tensor) -> (Tensor)`";
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let arguments = (ins
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AnyTorchTensorType:$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_AtenCeil_Op : Torch_Op<"aten.ceil_", [
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IsTrailingUnderscoreInplaceVariant,
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AllowsTypeRefinement
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]> {
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let summary = "Generated op for `aten::ceil_ : (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_AtenBitwiseNotOp : Torch_Op<"aten.bitwise_not", [
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AllowsTypeRefinement,
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HasValueSemantics
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@ -1512,6 +1512,8 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
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return b.create<math::ExpOp>(loc, payloadArgs[0]);
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if (isa<AtenFloorOp>(op))
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return b.create<math::FloorOp>(loc, payloadArgs[0]);
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if (isa<AtenCeilOp>(op))
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return b.create<math::CeilOp>(loc, payloadArgs[0]);
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if (isa<AtenLogOp>(op))
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return b.create<math::LogOp>(loc, payloadArgs[0]);
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if (isa<AtenSqrtOp>(op))
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@ -2067,7 +2069,8 @@ struct ConvertElementwiseOp : ConversionPattern {
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AtenClampOp, AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp,
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AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp, AtenLog2Op,
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AtenRsqrtOp, AtenDivScalarOp, AtenAbsOp, AtenReciprocalOp,
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AtenBitwiseAndTensorOp, AtenGtScalarOp, AtenWhereSelfOp>(op))
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AtenBitwiseAndTensorOp, AtenGtScalarOp, AtenWhereSelfOp,
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AtenCeilOp>(op))
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return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
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if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
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@ -3635,8 +3638,8 @@ public:
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AtenAddTensorOp, AtenMulTensorOp, AtenDivTensorOp, AtenSubTensorOp,
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AtenLerpTensorOp, AtenSigmoidOp, AtenMinimumOp, AtenMaximumOp,
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AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp, AtenLogOp, AtenSqrtOp,
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AtenFloorOp, AtenPowTensorScalarOp, AtenLog2Op, AtenRsqrtOp, AtenAbsOp,
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AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenGtScalarOp,
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AtenFloorOp, AtenCeilOp, AtenPowTensorScalarOp, AtenLog2Op, AtenRsqrtOp,
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AtenAbsOp, AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenGtScalarOp,
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AtenWhereSelfOp>();
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patterns.add<ConvertElementwiseOp>(typeConverter, context);
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target.addIllegalOp<AtenSqueezeOp>();
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@ -242,7 +242,7 @@ public:
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AtenLayerNormOp, AtenClampOp, AtenLogOp, AtenNegOp, AtenSqrtOp,
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AtenFloorOp, AtenLog2Op, Aten_SoftmaxBackwardDataOp, AtenRsqrtOp,
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AtenDropoutOp, AtenTanhBackwardOp, Aten_LogSoftmaxBackwardDataOp,
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AtenAddIntOp, AtenAbsOp, AtenReciprocalOp>(op)) {
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AtenAddIntOp, AtenAbsOp, AtenReciprocalOp, AtenCeilOp>(op)) {
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return getLatticeElement(op->getResult(0)).join(*operands[0]);
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}
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@ -447,6 +447,7 @@ def emit_aten_ops(torch_ir_dir: str, registry: Registry):
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"aten::cos : (Tensor) -> (Tensor)",
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"aten::neg : (Tensor) -> (Tensor)",
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"aten::floor : (Tensor) -> (Tensor)",
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"aten::ceil : (Tensor) -> (Tensor)",
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"aten::bitwise_not : (Tensor) -> (Tensor)",
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"aten::add.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)",
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"aten::sub.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)",
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