add e2e support for torch.log10 (#2479)

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

@@ -2527,6 +2527,51 @@ def Torch_AtenLog2_Op : Torch_Op<"aten.log2_", [
   }];
 }
 
+def Torch_AtenLog10Op : Torch_Op<"aten.log10", [
+    AllowsTypeRefinement,
+    HasValueSemantics,
+    ReadOnly
+  ]> {
+  let summary = "Generated op for `aten::log10 : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLog10Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenLog10Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
+def Torch_AtenLog10_Op : Torch_Op<"aten.log10_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::log10_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let hasCustomAssemblyFormat = 1;
+  let extraClassDefinition = [{
+    ParseResult AtenLog10_Op::parse(OpAsmParser &parser, OperationState &result) {
+      return parseDefaultTorchOp(parser, result, 1, 1);
+    }
+    void AtenLog10_Op::print(OpAsmPrinter &printer) {
+      printDefaultTorchOp(printer, *this, 1, 1);
+    }
+  }];
+}
+
 def Torch_AtenSqrtOp : Torch_Op<"aten.sqrt", [
     AllowsTypeRefinement,
     HasValueSemantics,

lib/Conversion/TorchToLinalg/Uncategorized.cpp

@@ -235,6 +235,10 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return createCalculationForMathOpWithDtypeConversion<math::Log2Op>(
         b, converter, payloadArgs[0], op);
   }
+  if (isa<AtenLog10Op>(op)) {
+    return createCalculationForMathOpWithDtypeConversion<math::Log10Op>(
+        b, converter, payloadArgs[0], op);
+  }
   if (isa<AtenLog1pOp>(op)) {
     return createCalculationForMathOpWithDtypeConversion<math::Log1pOp>(
         b, converter, payloadArgs[0], op);
@@ -1177,7 +1181,7 @@ class ConvertElementwiseOp : public ConversionPattern {
              AtenMinimumOp, AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
              AtenRsubScalarOp, AtenMulScalarOp, AtenLogOp, AtenErfOp,
              AtenSqrtOp, AtenFloorOp, AtenPowScalarOp, AtenPowTensorScalarOp,
-             AtenPowTensorTensorOp, AtenLog2Op, AtenLog1pOp, AtenRsqrtOp,
+             AtenPowTensorTensorOp, AtenLog2Op, AtenLog10Op, AtenLog1pOp, AtenRsqrtOp,
              AtenDivScalarOp, AtenRemainderScalarOp, AtenAbsOp,
              AtenReciprocalOp, AtenBitwiseAndTensorOp, AtenBitwiseOrTensorOp,
              AtenBitwiseXorTensorOp, AtenGtScalarOp, AtenGeScalarOp,
@@ -1712,7 +1716,7 @@ void mlir::torch::torch_to_linalg::populateUncategorizedPatternsAndLegality(
       AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp, AtenLogOp,
       AtenErfOp, AtenSqrtOp, AtenFloorOp, AtenCeilOp, AtenPreluOp,
       AtenPowScalarOp, AtenPowTensorScalarOp, AtenPowTensorTensorOp, AtenLog2Op,
-      AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
+      AtenLog10Op, AtenLog1pOp, AtenRsqrtOp, AtenAbsOp, AtenReciprocalOp,
       AtenBitwiseAndTensorOp, AtenBitwiseOrTensorOp, AtenBitwiseXorTensorOp,
       AtenGtScalarOp, AtenGeScalarOp, AtenEqScalarOp, AtenLtScalarOp,
       AtenLeScalarOp, AtenWhereSelfOp, AtenGtTensorOp, AtenGeTensorOp,

lib/Dialect/Torch/Transforms/AbstractInterpLibrary.cpp

@@ -6322,6 +6322,10 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
 "  }\n"
+"  func.func @\"__torch_mlir_shape_fn.aten.log10\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
+"    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
+"    return %0 : !torch.list<int>\n"
+"  }\n"
 "  func.func @\"__torch_mlir_shape_fn.aten.log1p\"(%arg0: !torch.list<int>) -> !torch.list<int> {\n"
 "    %0 = call @__torch__.torch.jit._shape_functions.unary(%arg0) : (!torch.list<int>) -> !torch.list<int>\n"
 "    return %0 : !torch.list<int>\n"
@@ -8291,6 +8295,11 @@ StringRef mlir::torch::Torch::getAbstractInterpLibrary() {
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
 "    return %1 : !torch.int\n"
 "  }\n"
+"  func.func @\"__torch_mlir_dtype_fn.aten.log10\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
+"    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
+"    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"
+"    return %1 : !torch.int\n"
+"  }\n"
 "  func.func @\"__torch_mlir_dtype_fn.aten.log1p\"(%arg0: !torch.tuple<int, int>) -> !torch.int {\n"
 "    %0:2 = torch.prim.TupleUnpack %arg0 : !torch.tuple<int, int> -> !torch.int, !torch.int\n"
 "    %1 = call @__torch__._get_dtype_of_floating_point_op(%0#1) : (!torch.int) -> !torch.int\n"

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/abstract_interp_lib_gen.py

@@ -122,6 +122,9 @@ def aten〇detach〡shape(self: List[int]) -> List[int]:
 def aten〇log2〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
+def aten〇log10〡shape(self: List[int]) -> List[int]:
+    return upstream_shape_functions.unary(self)
+
 def aten〇log1p〡shape(self: List[int]) -> List[int]:
     return upstream_shape_functions.unary(self)
 
@@ -1438,6 +1441,11 @@ def aten〇log2〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype
     return _get_dtype_of_floating_point_op(self_dtype)
 
+@check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
+def aten〇log10〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
+    self_rank, self_dtype = self_rank_dtype
+    return _get_dtype_of_floating_point_op(self_dtype)
+
 @check_dtype_function(_check_tensors_with_the_same_dtype(num_of_tensors=1))
 def aten〇log1p〡dtype(self_rank_dtype: Tuple[int, int]) -> int:
     self_rank, self_dtype = self_rank_dtype

python/torch_mlir/dialects/torch/importer/jit_ir/build_tools/torch_ods_gen.py

@@ -294,6 +294,7 @@ def emit_with_mutating_variants(key, **kwargs):
             "aten::clamp_max : (Tensor, Scalar) -> (Tensor)",
             "aten::clamp_max.Tensor : (Tensor, Tensor) -> (Tensor)",
             "aten::log2 : (Tensor) -> (Tensor)",
+            "aten::log10 : (Tensor) -> (Tensor)",
             "aten::sqrt : (Tensor) -> (Tensor)",
             "aten::log1p : (Tensor) -> (Tensor)",
             "aten::rsqrt : (Tensor) -> (Tensor)",

python/torch_mlir_e2e_test/test_suite/elementwise.py

@@ -1683,6 +1683,48 @@ def ElementwiseLog2IntModule_basic(module, tu: TestUtils):
     module.forward(tu.randint(3, 4, low=1, high=10).to(torch.int32))
 
 
+# ==============================================================================
+
+class ElementwiseLog10Module(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.log10(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseLog10Module())
+def ElementwiseLog10Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))
+
+
+# ==============================================================================
+
+class ElementwiseLog10IntModule(torch.nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.int32, True),
+    ])
+    def forward(self, a):
+        return torch.log10(a)
+
+
+@register_test_case(module_factory=lambda: ElementwiseLog10IntModule())
+def ElementwiseLog10IntModule_basic(module, tu: TestUtils):
+    module.forward(tu.randint(3, 4, low=1, high=10).to(torch.int32))
+
+
 # ==============================================================================