Add log2

e2e_testing/torchscript/elementwise.py

@@ -429,3 +429,19 @@ class ElementwiseToDtypeF32ToI64Module(torch.nn.Module):
 @register_test_case(module_factory=lambda: ElementwiseToDtypeF32ToI64Module())
 def ElementwiseToDtypeF32ToI64Module_basic(module, tu: TestUtils):
     module.forward(tu.rand(3, 5))
+
+class ElementwiseLog2Module(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    @export
+    @annotate_args([
+        None,
+        ([-1, -1], torch.float32, True),
+    ])
+    def forward(self, a):
+        return torch.log2(a)
+
+@register_test_case(module_factory=lambda: ElementwiseLog2Module())
+def ElementwiseLog2Module_basic(module, tu: TestUtils):
+    module.forward(tu.rand(3, 4))

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

@@ -850,6 +850,34 @@ def Torch_AtenClamp_Op : Torch_Op<"aten.clamp_", [
   let assemblyFormat = "$self `,` $min `,` $max attr-dict `:` type($self) `,` type($min) `,` type($max) `->` type($result)";
 }
 
+def Torch_AtenLog2Op : Torch_Op<"aten.log2", [
+    AllowsTypeRefinement,
+    HasValueSemantics
+  ]> {
+  let summary = "Generated op for `aten::log2 : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
+}
+
+def Torch_AtenLog2_Op : Torch_Op<"aten.log2_", [
+    IsTrailingUnderscoreInplaceVariant,
+    AllowsTypeRefinement
+  ]> {
+  let summary = "Generated op for `aten::log2_ : (Tensor) -> (Tensor)`";
+  let arguments = (ins
+    AnyTorchTensorType:$self
+  );
+  let results = (outs
+    AnyTorchTensorType:$result
+  );
+  let assemblyFormat = "$self attr-dict `:` type($self) `->` type($result)";
+}
+
 def Torch_AtenMaximumOp : Torch_Op<"aten.maximum", [
     AllowsTypeRefinement,
     HasValueSemantics

lib/Conversion/TorchToLinalg/TorchToLinalg.cpp

@@ -1284,6 +1284,8 @@ static Value createLinalgPayloadCalculationForElementwiseOp(
     return b.create<math::LogOp>(loc, payloadArgs[0]);
   if (isa<AtenSqrtOp>(op))
     return b.create<math::SqrtOp>(loc, payloadArgs[0]);
+  if (isa<AtenLog2Op>(op))
+    return b.create<math::Log2Op>(loc, payloadArgs[0]);
   if (isa<AtenSigmoidOp>(op)) {
     Type elementType = payloadArgs[0].getType();
     auto one = b.create<arith::ConstantOp>(loc, FloatAttr::get(elementType, 1));
@@ -1700,7 +1702,7 @@ struct ConvertElementwiseOp : ConversionPattern {
              AtenMulTensorOp, AtenDivTensorOp, AtenSubTensorOp,
              AtenLerpTensorOp, AtenSigmoidOp, AtenExpOp, AtenMinimumOp,
              AtenMaximumOp, AtenToDtypeOp, AtenClampOp, AtenRsubScalarOp,
-             AtenLogOp, AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp>(op))
+             AtenLogOp, AtenSqrtOp, AtenFloorOp, AtenPowTensorScalarOp, AtenLog2Op>(op))
       return rewriter.notifyMatchFailure(op, "not a supported elementwise op");
 
     if (failed(verifyLinalgCompatibleTypes(op, rewriter)))
@@ -2861,7 +2863,7 @@ public:
                         AtenLerpTensorOp, AtenSigmoidOp, AtenMinimumOp,
                         AtenMaximumOp, AtenToDtypeOp, AtenClampOp,
                         AtenRsubScalarOp, AtenLogOp, AtenSqrtOp, AtenFloorOp,
-                        AtenPowTensorScalarOp>();
+                        AtenPowTensorScalarOp, AtenLog2Op>();
     patterns.add<ConvertElementwiseOp>(typeConverter, context);
     target.addIllegalOp<AtenUnsqueezeOp>();
     patterns.add<ConvertAtenUnsqueezeOp>(typeConverter, context);

lib/Dialect/Torch/Transforms/RefineTypes.cpp

@@ -230,7 +230,7 @@ public:
             AtenToPrimDeviceOp, AtenCpuOp, AtenContiguousOp, AtenFill_ScalarOp,
             AtenDetachOp, AtenMaskedFill_ScalarOp, AtenCopy_Op, AtenIndexPut_Op,
             AtenCopy_Op, AtenCumsumOp, AtenLayerNormOp, AtenClampOp,
-            AtenRsubScalarOp, AtenLogOp, AtenSqrtOp, AtenFloorOp>(op)) {
+            AtenRsubScalarOp, AtenLogOp, AtenSqrtOp, AtenFloorOp, AtenLog2Op>(op)) {
       return getLatticeElement(op->getResult(0)).join(*operands[0]);
     }
 

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

@@ -465,6 +465,7 @@ def emit_aten_ops(torch_ir_dir: str, registry: Registry):
                 "aten::fmod.Scalar : (Tensor, Scalar) -> (Tensor)",
                 "aten::masked_fill.Scalar : (Tensor, Tensor, Scalar) -> (Tensor)",
                 "aten::clamp : (Tensor, Scalar?, Scalar?) -> (Tensor)",
+                "aten::log2 : (Tensor) -> (Tensor)",
         ]:
             emit_with_mutating_variants(key)
         # Elementwise tensor compute ops that don't have the standard mutating