lower torch.aten.isinf to linalg (#2638)

Co-authored-by: Rob Suderman <rob.suderman@gmail.com>

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -426,6 +426,12 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
   }
   if (isa<AtenAbsOp>(op))
     return b.create<math::AbsFOp>(loc, payloadArgs[0]);
+  if (isa<AtenIsinfOp>(op)){
+    Value abs = b.create<math::AbsFOp>(loc, payloadArgs[0]);
+    Value infinity = b.create<arith::ConstantOp>(
+        loc, b.getFloatAttr(abs.getType(), std::numeric_limits<double>::infinity()));
+    return createEqual(b, loc, abs.getType(), abs, infinity);
+  }
   if (isa<AtenSigmoidOp>(op)) {
     auto negate = createCalculationForMathOpWithDtypeConversion<arith::NegFOp>(
         b, converter, payloadArgs[0], op);
@@ -1343,7 +1349,7 @@ public:
              AtenThresholdBackwardOp, AtenHardtanhBackwardOp, AtenCloneOp,
              AtenSinOp, AtenCosOp, AtenNeScalarOp, AtenNegOp,
              AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp,
-             AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenTrilOp,
+             AtenLogicalXorOp, AtenLogicalNotOp, AtenIsinfOp, AtenTriuOp, AtenTrilOp,
              AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp, AtenFillTensorOp,
              AtenAtanOp, AtenAcosOp, AtenRealOp, AtenImagOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
@@ -1992,7 +1998,8 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenLtTensorOp, AtenLeTensorOp, AtenThresholdOp, AtenThresholdBackwardOp,
       AtenHardtanhBackwardOp, AtenCloneOp, AtenSinOp, AtenCosOp, AtenNeScalarOp,
       AtenMaskedFillTensorOp, AtenLogicalOrOp, AtenLogicalAndOp, AtenAtanOp,
-      AtenAcosOp, AtenLogicalXorOp, AtenLogicalNotOp, AtenTriuOp, AtenTrilOp,
+      AtenAcosOp, AtenLogicalXorOp, AtenLogicalNotOp, AtenIsinfOp, AtenTriuOp,
+      AtenTrilOp,
       AtenRemainderScalarOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
       AtenFillTensorOp, AtenRealOp, AtenImagOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);

projects/ltc/csrc/base_lazy_backend/shape_inference.cpp

@@ -39,6 +39,10 @@ std::vector<torch::lazy::Shape> compute_shape_div(const at::Tensor& self,
   return {Shape(self.scalar_type(), self.sizes().vec())};
 }
 
+std::vector<torch::lazy::Shape> compute_shape_isinf(const at::Tensor& self) {
+  return {Shape(at::kBool, self.sizes().vec())};
+}
+
 std::vector<torch::lazy::Shape> compute_shape_max_pool3d_with_indices(
     const at::Tensor& self, at::IntArrayRef kernel_size,
     at::IntArrayRef stride, at::IntArrayRef padding, at::IntArrayRef dilation,

projects/pt1/e2e_testing/xfail_sets.py

@@ -1033,6 +1033,7 @@ TOSA_PASS_SET = {
     "ElementwiseAddScalarIntModule_basic",
     "ElementwiseAddScalar_TensorLiteralInt32_Module_basic",
     "ElementwiseAtenDivIntScalarModule_basic",
+    "ElementwiseAtenIsinfOpModule_basic",
     "ElementwiseAtenWhereSelfModule_basic",
     "ElementwiseBinaryModule_basic",
     "ElementwiseBinaryStaticShapeModule_basic",
@@ -1328,6 +1329,8 @@ MAKE_FX_TOSA_PASS_SET = (TOSA_PASS_SET | {
     "SliceWholeTensorModule_basic",
     "TensorFloatModule_basic",
     "TensorIntModule_basic",
+    "AdaptiveAvgPool1dNonUnitOutputSizeStaticModule_basic",
+    "AdaptiveAvgPool1dUnitOutputSizeStaticModule_basic",
 }) - {
 ### Test failing in make_fx_tosa but not in tosa
 
@@ -1489,5 +1492,4 @@ LTC_XFAIL_SET = {
     "ElementwiseBitwiseAndScalarInt64Module_basic",
     "ElementwiseBitwiseAndScalarInt32Module_basic",
     "ElementwiseBitwiseAndScalarInt8Module_basic",
-    "ElementwiseIsinfModule_basic",
 }

projects/pt1/python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -3385,6 +3385,31 @@ def ElementwiseAtenLogicalNotOpModule_basic(module, tu: TestUtils):
     module.forward(tu.randint(4, 5, high=2).bool())
 
 
+# ==============================================================================
+
+class ElementwiseAtenIsinfOpModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([2, 5], torch.float32, True),
+    ])
+    def forward(self, x):
+        return torch.ops.aten.isinf(x)
+
+@register_test_case(module_factory=lambda: ElementwiseAtenIsinfOpModule())
+def ElementwiseAtenIsinfOpModule_basic(module, tu: TestUtils):
+    test_input = torch.tensor(
+        [
+            [1, float('inf'), 2, float('-inf'), float('nan')],
+            [1, float('inf'), float('-inf'), float('nan'), 3],
+        ]
+    )
+    module.forward(test_input)
+
+
 # ==============================================================================