[Torch] add fold logic for some ops (#3794)

include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td

@@ -3425,6 +3425,7 @@ def Torch_AtenDivTensorModeOp : Torch_Op<"aten.div.Tensor_mode", [
       printDefaultTorchOp(printer, *this, 3, 1);
     }
   }];
+  let hasFolder = 1;
   let hasCanonicalizer = 1;
 }
 
@@ -4902,6 +4903,7 @@ def Torch_AtenRsubScalarOp : Torch_Op<"aten.rsub.Scalar", [
       printDefaultTorchOp(printer, *this, 3, 1);
     }
   }];
+  let hasFolder = 1;
   let hasCanonicalizer = 1;
 }
 
@@ -12540,6 +12542,7 @@ def Torch_AtenIntTensorOp : Torch_Op<"aten.Int.Tensor", [
       printDefaultTorchOp(printer, *this, 1, 1);
     }
   }];
+  let hasFolder = 1;
   let hasCanonicalizer = 1;
 }
 
@@ -15236,6 +15239,7 @@ def Torch_AtenRemainderScalarOp : Torch_Op<"aten.remainder.Scalar", [
       printDefaultTorchOp(printer, *this, 2, 1);
     }
   }];
+  let hasFolder = 1;
 }
 
 def Torch_AtenRemainderTensorOp : Torch_Op<"aten.remainder.Tensor", [

lib/Dialect/Torch/IR/TorchOps.cpp

@@ -1531,6 +1531,24 @@ void AtenRsubScalarOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
   });
 }
 
+// ===----------------------------------------------------------------------===//
+// AtenRSubScalarOp
+// ===----------------------------------------------------------------------===//
+
+OpFoldResult AtenRsubScalarOp::fold(FoldAdaptor adaptor) {
+  auto fpFold = [](llvm::ArrayRef<double> inputs) {
+    assert(inputs.size() == 3);
+    return inputs[1] - inputs[0] * inputs[2];
+  };
+
+  auto intFold = [](llvm::ArrayRef<APInt> inputs) {
+    assert(inputs.size() == 3);
+    return inputs[1] - inputs[0] * inputs[2];
+  };
+
+  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
+}
+
 //===----------------------------------------------------------------------===//
 // AtenMulTensorOp
 //===----------------------------------------------------------------------===//
@@ -1975,6 +1993,58 @@ void AtenDivTensorModeOp::getCanonicalizationPatterns(
   });
 }
 
+// ===----------------------------------------------------------------------===//
+// AtenDivTensorModeOp
+// ===----------------------------------------------------------------------===//
+
+OpFoldResult AtenDivTensorModeOp::fold(FoldAdaptor adaptor) {
+  auto resultTy = dyn_cast_or_null<ValueTensorType>(getType());
+  if (!resultTy || !resultTy.hasDtype()) {
+    return nullptr;
+  }
+  std::function<double(ArrayRef<double>)> fpFold;
+  std::function<APInt(ArrayRef<APInt>)> intFold;
+
+  auto roundMode = dyn_cast_or_null<StringAttr>(adaptor.getRoundingMode());
+  auto unsign = false;
+  if (isa<mlir::IntegerType>(resultTy.getDtype())) {
+    unsign = cast<IntegerType>(resultTy.getDtype()).isUnsigned();
+  }
+
+  fpFold = [roundMode](llvm::ArrayRef<double> inputs) {
+    assert(inputs.size() == 2);
+    if (!roundMode) {
+      return (double)inputs[0] / inputs[1];
+    } else if (roundMode.getValue().str() == "floor") {
+      return std::floor((double)inputs[0] / inputs[1]);
+    } else {
+      return std::trunc((double)inputs[0] / inputs[1]);
+    }
+  };
+
+  intFold = [unsign, roundMode](llvm::ArrayRef<APInt> inputs) {
+    assert(inputs.size() == 2);
+    auto lhs = unsign ? inputs[0].getZExtValue() : inputs[0].getSExtValue();
+    auto rhs = unsign ? inputs[1].getZExtValue() : inputs[1].getSExtValue();
+    int64_t bits = std::max(inputs[0].getBitWidth(), inputs[1].getBitWidth());
+    int64_t res;
+    if (roundMode.getValue().str() == "floor") {
+      res = std::floor(lhs / rhs);
+    } else {
+      res = std::trunc(lhs / rhs);
+    }
+    return APInt(bits, res);
+  };
+
+  if (!roundMode) {
+    return naryFolderHelper({adaptor.getSelf(), adaptor.getOther()}, getType(),
+                            fpFold, std::nullopt);
+  }
+
+  return naryFolderHelper({adaptor.getSelf(), adaptor.getOther()}, getType(),
+                          fpFold, intFold);
+}
+
 //===----------------------------------------------------------------------===//
 // AtenDivScalarModeOp
 //===----------------------------------------------------------------------===//
@@ -3447,6 +3517,34 @@ OpFoldResult AtenRemainderIntOp::fold(FoldAdaptor adaptor) {
       adaptor.getOperands(), [](int64_t a, int64_t b) { return a % b; });
 }
 
+// ===----------------------------------------------------------------------===//
+// AtenRemainderScalarOp
+// ===----------------------------------------------------------------------===//
+
+OpFoldResult AtenRemainderScalarOp::fold(FoldAdaptor adaptor) {
+  auto resultTy = dyn_cast_or_null<ValueTensorType>(getType());
+  if (!resultTy || !resultTy.hasDtype()) {
+    return nullptr;
+  }
+
+  auto unsign = false;
+  if (isa<mlir::IntegerType>(resultTy.getDtype())) {
+    unsign = cast<IntegerType>(resultTy.getDtype()).isUnsigned();
+  }
+  auto fpFold = [](llvm::ArrayRef<double> inputs) {
+    assert(inputs.size() == 2);
+    return std::fmod(inputs[0], inputs[1]);
+  };
+
+  auto intFold = [unsign](llvm::ArrayRef<APInt> inputs) {
+    assert(inputs.size() == 2);
+    auto ret = unsign ? inputs[0].urem(inputs[1]) : inputs[0].srem(inputs[1]);
+    return ret;
+  };
+
+  return naryFolderHelper(adaptor.getOperands(), getType(), fpFold, intFold);
+}
+
 //===----------------------------------------------------------------------===//
 // AtenAddIntOp
 //===----------------------------------------------------------------------===//
@@ -4040,6 +4138,42 @@ void AtenIntTensorOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
   });
 }
 
+//===----------------------------------------------------------------------===//
+// AtenIntTensorOp
+//===----------------------------------------------------------------------===//
+
+OpFoldResult AtenIntTensorOp::fold(FoldAdaptor adaptor) {
+  auto value = adaptor.getA();
+  auto dense = dyn_cast_or_null<DenseElementsAttr>(value);
+  if (!dense || !dense.isSplat()) {
+    return nullptr;
+  }
+
+  auto splat = dense.getSplatValue<Attribute>();
+  if (auto intAttr = dyn_cast<IntegerAttr>(splat)) {
+    auto type = getType();
+    if (!isa<mlir::IntegerType>(type)) {
+      return nullptr;
+    }
+
+    if (type.isSignlessInteger()) {
+      return getI64IntegerAttr(getContext(), intAttr.getInt());
+    } else if (type.isSignedInteger()) {
+      return getI64IntegerAttr(getContext(), intAttr.getSInt());
+    } else {
+      return getI64IntegerAttr(getContext(), intAttr.getUInt());
+    }
+  }
+
+  if (auto floatAttr = dyn_cast<FloatAttr>(splat)) {
+    return getI64IntegerAttr(
+        getContext(),
+        static_cast<long>(floatAttr.getValue().convertToDouble()));
+  }
+
+  return nullptr;
+}
+
 //===----------------------------------------------------------------------===//
 // AtenFloatTensorOp
 //===----------------------------------------------------------------------===//

projects/pt1/python/torch_mlir/jit_ir_importer/build_tools/torch_ods_gen.py

@@ -379,6 +379,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     # variants.
     emit_with_mutating_variants(
         "aten::div.Tensor_mode : (Tensor, Tensor, str?) -> (Tensor)",
+        has_folder=True,
         has_canonicalizer=True,
     )
     emit_with_mutating_variants(
@@ -481,6 +482,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::xlogy.Tensor : (Tensor, Tensor) -> (Tensor)")
     emit(
         "aten::rsub.Scalar : (Tensor, Scalar, Scalar) -> (Tensor)",
+        has_folder=True,
         has_canonicalizer=True,
     )
     emit("aten::gelu : (Tensor, str) -> (Tensor)")
@@ -922,7 +924,9 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit(
         "aten::tensor.float : (float, int?, Device?, bool) -> (Tensor)", has_folder=True
     )
-    emit("aten::Int.Tensor : (Tensor) -> (int)", has_canonicalizer=True)
+    emit(
+        "aten::Int.Tensor : (Tensor) -> (int)", has_folder=True, has_canonicalizer=True
+    )
     emit("aten::Float.Tensor : (Tensor) -> (float)", has_folder=True)
     emit_with_mutating_variants("aten::dropout : (Tensor, float, bool) -> (Tensor)")
     emit("aten::native_dropout : (Tensor, float, bool?) -> (Tensor, Tensor)")
@@ -1070,7 +1074,7 @@ def emit_ops(emitter_td: TextEmitter, registry: Registry):
     emit("aten::eq.int : (int, int) -> (bool)", has_folder=True)
     emit("aten::floordiv.int : (int, int) -> (int)", has_folder=True)
     emit("aten::remainder.int : (int, int) -> (int)", has_folder=True)
-    emit("aten::remainder.Scalar : (Tensor, Scalar) -> (Tensor)")
+    emit("aten::remainder.Scalar : (Tensor, Scalar) -> (Tensor)", has_folder=True)
     emit("aten::remainder.Tensor : (Tensor, Tensor) -> (Tensor)")
     emit("aten::add.int : (int, int) -> (int)", has_folder=True)
     emit("aten::sub.int : (int, int) -> (int)", has_folder=True)

test/Dialect/Torch/torch-nary-canonicalize.mlir

@@ -141,3 +141,113 @@ func.func @fold_aten_mul_splat_float() -> !torch.vtensor<[4],f32> {
   %0 = torch.aten.mul.Tensor %cst_7, %cst_11 : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32> -> !torch.vtensor<[4],f32>
   return %0 : !torch.vtensor<[4],f32>
 }
+
+// -----
+
+// CHECK-LABEL: @fold_aten_rsub_scalar_int
+func.func @fold_aten_rsub_scalar_int() -> !torch.vtensor<[4],si64> {
+  // CHECK: torch.vtensor.literal(dense<-4> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %cst_2 = torch.constant.int 2
+  %cst_3 = torch.vtensor.literal(dense<3> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %0 = torch.aten.rsub.Scalar %cst_3, %cst_2, %cst_2: !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[4],si64>
+  return %0 : !torch.vtensor<[4],si64>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_rsub_scalar_float
+func.func @fold_aten_rsub_scalar_float() -> !torch.vtensor<[4],f32> {
+  // CHECK: torch.vtensor.literal(dense<-4.000000e+00> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %cst_2 = torch.constant.float 2.0
+  %cst_3 = torch.vtensor.literal(dense<3.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %0 = torch.aten.rsub.Scalar %cst_3, %cst_2, %cst_2: !torch.vtensor<[4],f32>, !torch.float, !torch.float -> !torch.vtensor<[4],f32>
+  return %0 : !torch.vtensor<[4],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_remainder_scalar_int
+func.func @fold_aten_remainder_scalar_int() -> !torch.vtensor<[4],si64> {
+  // CHECK: torch.vtensor.literal(dense<1> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %cst_2 = torch.constant.int 2
+  %cst_3 = torch.vtensor.literal(dense<3> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %0 = torch.aten.remainder.Scalar %cst_3, %cst_2 : !torch.vtensor<[4],si64>, !torch.int -> !torch.vtensor<[4],si64>
+  return %0 : !torch.vtensor<[4],si64>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_remainder_scalar_float
+func.func @fold_aten_remainder_scalar_float() -> !torch.vtensor<[4],f32> {
+  // CHECK: torch.vtensor.literal(dense<1.000000e+00> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %cst_2 = torch.constant.float 2.0
+  %cst_3 = torch.vtensor.literal(dense<3.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %0 = torch.aten.remainder.Scalar %cst_3, %cst_2 : !torch.vtensor<[4],f32>, !torch.float -> !torch.vtensor<[4],f32>
+  return %0 : !torch.vtensor<[4],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_int_tensor_int
+func.func @fold_aten_int_tensor_int() -> !torch.int {
+  // CHECK: %int3 = torch.constant.int 3
+  %cst_3 = torch.vtensor.literal(dense<3> : tensor<si64>) : !torch.vtensor<[],si64>
+  %0 = torch.aten.Int.Tensor %cst_3 : !torch.vtensor<[],si64> -> !torch.int
+  return %0 : !torch.int
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_int_tensor_bool
+func.func @fold_aten_int_tensor_bool() -> !torch.int {
+  // CHECK: %int1 = torch.constant.int 1
+  %cst_false = torch.vtensor.literal(dense<true> : tensor<i1>) : !torch.vtensor<[],i1>
+  %0 = torch.aten.Int.Tensor %cst_false : !torch.vtensor<[],i1> -> !torch.int
+  return %0 : !torch.int
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_int_tensor_float
+func.func @fold_aten_int_tensor_float() -> !torch.int {
+  // CHECK: %int3 = torch.constant.int 3
+  %cst_3 = torch.vtensor.literal(dense<3.1> : tensor<f32>) : !torch.vtensor<[],f32>
+  %0 = torch.aten.Int.Tensor %cst_3 : !torch.vtensor<[],f32> -> !torch.int
+  return %0 : !torch.int
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_div_tensor_mode_int
+func.func @fold_aten_div_tensor_mode_int() -> !torch.vtensor<[4],si64> {
+  // CHECK: torch.vtensor.literal(dense<4> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %cst_8 = torch.vtensor.literal(dense<8> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %cst_2 = torch.vtensor.literal(dense<2> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %trunc = torch.constant.str "trunc"
+  %0 = torch.aten.div.Tensor_mode %cst_8, %cst_2, %trunc : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>, !torch.str -> !torch.vtensor<[4],si64>
+  return %0 : !torch.vtensor<[4],si64>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_div_tensor_mode_float
+func.func @fold_aten_div_tensor_mode_float() -> !torch.vtensor<[4],f32> {
+  // CHECK: torch.vtensor.literal(dense<3.000000e+00> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %cst_8 = torch.vtensor.literal(dense<8.0> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %cst_2 = torch.vtensor.literal(dense<2.1> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %floor = torch.constant.str "floor"
+  %0 = torch.aten.div.Tensor_mode %cst_8, %cst_2, %floor : !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.str -> !torch.vtensor<[4],f32>
+  return %0 : !torch.vtensor<[4],f32>
+}
+
+// -----
+
+// CHECK-LABEL: @fold_aten_div_tensor_mode_none
+func.func @fold_aten_div_tensor_mode_none() -> !torch.vtensor<[4],f32> {
+  // CHECK: torch.vtensor.literal(dense<2.66666675> : tensor<4xf32>) : !torch.vtensor<[4],f32>
+  %cst_8 = torch.vtensor.literal(dense<8> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %cst_3 = torch.vtensor.literal(dense<3> : tensor<4xsi64>) : !torch.vtensor<[4],si64>
+  %none = torch.constant.none
+  %0 = torch.aten.div.Tensor_mode %cst_8, %cst_3, %none : !torch.vtensor<[4],si64>, !torch.vtensor<[4],si64>, !torch.none -> !torch.vtensor<[4],f32>
+  return %0 : !torch.vtensor<[4],f32>
+}