[TorchToLinalg] Support torch.isclose lower to linalg (#3631)

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -1506,6 +1506,48 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return value;
   }
 
+  if (auto isClose = dyn_cast<AtenIscloseOp>(op)) {
+    double rtol, atol;
+    bool equalNan;
+    if (!matchPattern(isClose.getRtol(), m_TorchConstantFloat(&rtol))) {
+      isClose.emitError("rtol must be a scalar constant");
+      return nullptr;
+    }
+    if (!matchPattern(isClose.getAtol(), m_TorchConstantFloat(&atol))) {
+      isClose.emitError("atol must be a scalar constant");
+      return nullptr;
+    }
+    if (!matchPattern(isClose.getEqualNan(), m_TorchConstantBool(&equalNan))) {
+      isClose.emitError("unimplemented: equal_nan is expected to be false");
+      return nullptr;
+    }
+    auto lhsType = mlir::dyn_cast<mlir::FloatType>(payloadArgs[0].getType());
+    auto rhsType = mlir::dyn_cast<mlir::FloatType>(payloadArgs[1].getType());
+    if (!lhsType || !rhsType) {
+      isClose.emitError("unimplemented: only FP element type is supported");
+      return nullptr;
+    }
+    // Choose the widest float type as compute type.
+    auto computeType =
+        lhsType.getWidth() > rhsType.getWidth() ? lhsType : rhsType;
+    computeType = computeType.getWidth() >= 32 ? computeType : b.getF32Type();
+    auto cvtArg0 = convertScalarToDtype(b, loc, payloadArgs[0], computeType);
+    auto cvtArg1 = convertScalarToDtype(b, loc, payloadArgs[1], computeType);
+    // Reference to the definition of torch.isclose:
+    //   ∣input − other∣ <= atol + rtol × ∣other∣
+    auto diff = b.create<arith::SubFOp>(loc, computeType, cvtArg0, cvtArg1);
+    auto absDiff = b.create<math::AbsFOp>(loc, computeType, diff);
+    auto cstRtol =
+        b.create<arith::ConstantOp>(loc, b.getFloatAttr(computeType, rtol));
+    auto absOther = b.create<math::AbsFOp>(loc, computeType, cvtArg1);
+    auto mul = b.create<arith::MulFOp>(loc, computeType, cstRtol, absOther);
+    auto cstAtol =
+        b.create<arith::ConstantOp>(loc, b.getFloatAttr(computeType, atol));
+    auto threshold = b.create<arith::AddFOp>(loc, computeType, cstAtol, mul);
+    return b.create<arith::CmpFOp>(loc, arith::CmpFPredicate::ULE, absDiff,
+                                   threshold);
+  }
+
   op->emitError("unimplemented lowering in "
                 "createLinalgPayloadCalculationForElementwiseOp");
   return nullptr;
@@ -1564,7 +1606,7 @@ public:
              AtenFillScalarOp, AtenFillTensorOp, AtenAtanOp, AtenAcosOp,
              AtenAtanhOp, AtenAcoshOp, AtenAsinOp, AtenAsinhOp, AtenRealOp,
              AtenImagOp, AtenDequantizeSelfOp, AtenDequantizeTensorOp,
-             AtenQuantizePerTensorOp>(op))
+             AtenQuantizePerTensorOp, AtenIscloseOp>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -3256,7 +3298,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenTrilOp, AtenRemainderScalarOp, AtenFmodTensorOp,
       AtenRemainderTensorOp, AtenBitwiseNotOp, AtenRoundOp, AtenFillScalarOp,
       AtenFillTensorOp, AtenRealOp, AtenImagOp, AtenDequantizeSelfOp,
-      AtenDequantizeTensorOp, AtenQuantizePerTensorOp>();
+      AtenDequantizeTensorOp, AtenQuantizePerTensorOp, AtenIscloseOp>();
   patterns.add<ConvertElementwiseOp>(typeConverter, context);
   target.addIllegalOp<AtenNllLossForwardOp>();
   patterns.add<ConvertAtenDetachOp>(typeConverter, context);

projects/pt1/e2e_testing/xfail_sets.py

@@ -16,8 +16,6 @@ from torch_mlir._version import torch_version_for_comparison, version
 print(f"TORCH_VERSION_FOR_COMPARISON =", torch_version_for_comparison())
 
 LINALG_XFAIL_SET = COMMON_TORCH_MLIR_LOWERING_XFAILS | {
-    "IscloseStaticModule_basic",
-    "IscloseStaticModuleTrue_basic",
     # lowering to torch backend IR fails due to unsupported op: aten.upsample_[mode/dims].vec
     # these interpolate tests are added specifically to test onnx.Resize.
     "InterpolateDynamicModule_sizes_bilinear",