Decompose aten.fmod into aten.mul,sub,div etc. (#3689)

As titled, create a new decomposition for `aten.fmod.Tensor` to
`aten.div`, `aten.trunc`, `aten.mul` and `aten.sub`. Note that we only
use `aten.trunc` for floating point operations. This further gets
decomposed to `aten.where` etc. by other existing decompositions.

This decomposition now makes TOSA pass for a simple model with
`aten.fmod` while it makes `stablehlo` fail. For now, we disallow this
decomposition for `stablehlo`

---------

Co-authored-by: Srinath Avadhanula <srinath.avadhanula@getcruise.com>

lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp

@@ -7545,6 +7545,44 @@ class DecomposeAtenTruncOp : public OpRewritePattern<AtenTruncOp> {
 };
 } // namespace
 
+namespace {
+// decompose `fmod(x, y)` to `x - trunc(x/y) * y`
+class DecomposeAtenFmodTensorOp : public OpRewritePattern<AtenFmodTensorOp> {
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenFmodTensorOp op,
+                                PatternRewriter &rewriter) const override {
+    Location loc = op.getLoc();
+    Value self = op.getSelf();
+    Value other = op.getOther();
+
+    auto resultTy = dyn_cast<ValueTensorType>(op.getType());
+    if (!resultTy || !resultTy.hasDtype()) {
+      return rewriter.notifyMatchFailure(op, "result must have dtype");
+    }
+
+    if (isa<mlir::IntegerType>(resultTy.getDtype())) {
+      Value div = rewriter.create<AtenDivTensorOp>(loc, resultTy, self, other);
+      Value mul = rewriter.create<AtenMulTensorOp>(loc, resultTy, div, other);
+      Value alpha =
+          rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1));
+      rewriter.replaceOpWithNewOp<AtenSubTensorOp>(op, resultTy, self, mul,
+                                                   alpha);
+      return success();
+    } else if (isa<mlir::FloatType>(resultTy.getDtype())) {
+      Value div = rewriter.create<AtenDivTensorOp>(loc, resultTy, self, other);
+      Value trunc = rewriter.create<AtenTruncOp>(loc, resultTy, div);
+      Value mul = rewriter.create<AtenMulTensorOp>(loc, resultTy, trunc, other);
+      Value alpha =
+          rewriter.create<ConstantFloatOp>(loc, rewriter.getF64FloatAttr(1));
+      rewriter.replaceOpWithNewOp<AtenSubTensorOp>(op, resultTy, self, mul,
+                                                   alpha);
+      return success();
+    }
+    return failure();
+  }
+};
+} // namespace
+
 namespace {
 // Decompose `aten.baddbmm` op into `aten.bmm`, `aten.mul.Scalar`, and
 // `aten.add.Tensor` op.
@@ -9661,6 +9699,7 @@ public:
     addPatternIfTargetOpIsIllegal<DecomposeAtenRad2degOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenCosineSimilarityOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenTruncOp>(patterns);
+    addPatternIfTargetOpIsIllegal<DecomposeAtenFmodTensorOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenBaddbmmOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenFloorDivideOp>(patterns);
     addPatternIfTargetOpIsIllegal<DecomposeAtenFloorDivideScalarOp>(patterns);

projects/pt1/e2e_testing/xfail_sets.py

@@ -1778,6 +1778,9 @@ TOSA_PASS_SET = {
     "ElementwiseFloorModule_basic",
     "ElementwiseFmaxModule_basic",
     "ElementwiseFminModule_basic",
+    "ElementwiseFmodTensor_Float_basic",
+    "ElementwiseFmodTensor_Int_Float_basic",
+    "ElementwiseFmodTensor_Int_basic",
     "ElementwiseGeFloatIntScalarModule_basic",
     "ElementwiseGeFloatScalarModule_basic",
     "ElementwiseGeIntScalarModule_basic",
@@ -3253,9 +3256,6 @@ FX_IMPORTER_TOSA_XFAIL_SET = {
     "ElementwiseExpIntModule_basic",
     "ElementwiseExpm1IntModule_basic",
     "ElementwiseExpm1Module_basic",
-    "ElementwiseFmodTensor_Float_basic",
-    "ElementwiseFmodTensor_Int_Float_basic",
-    "ElementwiseFmodTensor_Int_basic",
     "ElementwiseGeFloatTensorModule_basic",
     "ElementwiseGeIntTensorModule_basic",
     "ElementwiseGeluApproximateTanhModule_basic",

projects/pt1/python/torch_mlir/torchscript.py

@@ -170,6 +170,7 @@ BACKEND_LEGAL_OPS = {
         "aten.amin",
         "aten.randn.generator",
         "aten.normal_functional",
+        "aten.fmod.Tensor",
     ],
 }
 

test/Dialect/Torch/decompose-complex-ops.mlir

@@ -128,3 +128,46 @@ func.func @test_einsum_inner_prod(%arg0: !torch.vtensor<[5],f64>, %arg1: !torch.
   %1 = torch.aten.einsum %str, %0, %none_0 : !torch.str, !torch.list<vtensor>, !torch.none -> !torch.vtensor<[],f64>
   return %1 : !torch.vtensor<[],f64>
 }
+
+// -----
+
+// CHECK:   func.func @torch.aten.fmod_int(%[[ARG0:.+]]: !torch.vtensor<[?],si32>, %[[ARG1:.+]]: !torch.vtensor<[1],si32>) -> !torch.vtensor<[?],si32> {
+// CHECK:     %[[FLOAT1:.+]] = torch.constant.float 1.000000e+00
+// CHECK:     %[[V0:.+]] = torch.aten.div.Tensor %[[ARG0]], %[[ARG1]] : !torch.vtensor<[?],si32>, !torch.vtensor<[1],si32> -> !torch.vtensor<[?],si32>
+// CHECK:     %[[V1:.+]] = torch.aten.mul.Tensor %[[V0]], %[[ARG1]] : !torch.vtensor<[?],si32>, !torch.vtensor<[1],si32> -> !torch.vtensor<[?],si32>
+// CHECK:     %[[V2:.+]] = torch.aten.sub.Tensor %[[ARG0]], %[[V1]], %[[FLOAT1]] : !torch.vtensor<[?],si32>, !torch.vtensor<[?],si32>, !torch.float -> !torch.vtensor<[?],si32>
+// CHECK:     return %[[V2]] : !torch.vtensor<[?],si32>
+func.func @torch.aten.fmod_int(%arg0: !torch.vtensor<[?],si32>, %arg1: !torch.vtensor<[1],si32>) -> !torch.vtensor<[?],si32> {
+    %0 = torch.aten.fmod.Tensor %arg0, %arg1 : !torch.vtensor<[?],si32>, !torch.vtensor<[1],si32> -> !torch.vtensor<[?],si32>
+    return %0 : !torch.vtensor<[?],si32>
+}
+
+// -----
+
+// CHECK:   func.func @torch.aten.fmod_float(%[[ARG0:.+]]: !torch.vtensor<[?],f16>, %[[ARG1:.+]]: !torch.vtensor<[1],f16>) -> !torch.vtensor<[?],f16> {
+// CHECK:     %[[FLOAT1:.+]] = torch.constant.float 1.000000e+00
+// CHECK:     %[[V0:.+]] = torch.vtensor.literal(dense<-1> : tensor<si64>) : !torch.vtensor<[],si64>
+// CHECK:     %[[V1:.+]] = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
+// CHECK:     %[[NONE:.+]] = torch.constant.none
+// CHECK:     %[[FALSE:.+]] = torch.constant.bool false
+// CHECK:     %[[INT5:.+]] = torch.constant.int 5
+// CHECK:     %[[V2:.+]] = torch.vtensor.literal(dense<1> : tensor<si64>) : !torch.vtensor<[],si64>
+// CHECK:     %[[INT0:.+]] = torch.constant.int 0
+// CHECK:     %[[V3:.+]] = torch.aten.div.Tensor %[[ARG0]], %[[ARG1]] : !torch.vtensor<[?],f16>, !torch.vtensor<[1],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V4:.+]] = torch.aten.gt.Scalar %[[V3]], %[[INT0]] : !torch.vtensor<[?],f16>, !torch.int -> !torch.vtensor<[?],i1>
+// CHECK:     %[[V5:.+]] = torch.aten.lt.Scalar %[[V3]], %[[INT0]] : !torch.vtensor<[?],f16>, !torch.int -> !torch.vtensor<[?],i1>
+// CHECK:     %[[V6:.+]] = torch.aten.to.dtype %[[V2]], %[[INT5]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[],si64>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[],f16>
+// CHECK:     %[[V7:.+]] = torch.aten.to.dtype %[[V1]], %[[INT5]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[],si64>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[],f16>
+// CHECK:     %[[V8:.+]] = torch.aten.where.self %[[V4]], %[[V6]], %[[V7]] : !torch.vtensor<[?],i1>, !torch.vtensor<[],f16>, !torch.vtensor<[],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V9:.+]] = torch.aten.to.dtype %[[V0]], %[[INT5]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.vtensor<[],si64>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[],f16>
+// CHECK:     %[[V10:.+]] = torch.aten.where.self %[[V5]], %[[V9]], %[[V8]] : !torch.vtensor<[?],i1>, !torch.vtensor<[],f16>, !torch.vtensor<[?],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V11:.+]] = torch.aten.abs %[[V3]] : !torch.vtensor<[?],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V12:.+]] = torch.aten.floor %[[V11]] : !torch.vtensor<[?],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V13:.+]] = torch.aten.mul.Tensor %[[V10]], %[[V12]] : !torch.vtensor<[?],f16>, !torch.vtensor<[?],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V14:.+]] = torch.aten.mul.Tensor %[[V13]], %[[ARG1]] : !torch.vtensor<[?],f16>, !torch.vtensor<[1],f16> -> !torch.vtensor<[?],f16>
+// CHECK:     %[[V15:.+]] = torch.aten.sub.Tensor %[[ARG0]], %[[V14]], %[[FLOAT1]] : !torch.vtensor<[?],f16>, !torch.vtensor<[?],f16>, !torch.float -> !torch.vtensor<[?],f16>
+// CHECK:     return %[[V15]] : !torch.vtensor<[?],f16>
+func.func @torch.aten.fmod_float(%arg0: !torch.vtensor<[?],f16>, %arg1: !torch.vtensor<[1],f16>) -> !torch.vtensor<[?],f16> {
+    %0 = torch.aten.fmod.Tensor %arg0, %arg1 : !torch.vtensor<[?],f16>, !torch.vtensor<[1],f16> -> !torch.vtensor<[?],f16>
+    return %0 : !torch.vtensor<[?],f16>
+}