diff --git a/core/conversion/converters/impl/unary.cpp b/core/conversion/converters/impl/unary.cpp
index 52eb17a854..6357f4922e 100644
--- a/core/conversion/converters/impl/unary.cpp
+++ b/core/conversion/converters/impl/unary.cpp
@@ -34,6 +34,36 @@ auto reciprocal_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().
        return true;
      }});
 
+auto log2_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
+    {"aten::log2(Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+       const static float ln2 = 0.693147180559945309; // same constant onnx uses
+       auto in = args[0].ITensorOrFreeze(ctx);
+       auto tensor_type = util::TRTDataTypeToScalarType(in->getType());
+       if (in->getType() == nvinfer1::DataType::kINT32) {
+         // pytorch implicitly casts to float for aten::log2(int)
+         in = castITensor(ctx, in, nvinfer1::DataType::kFLOAT);
+         tensor_type = at::kFloat;
+       }
+
+       auto log_layer = ctx->net->addUnary(*in, nvinfer1::UnaryOperation::kLOG);
+       TORCHTRT_CHECK(log_layer, "Unable to create log layer from node: " << *n);
+       log_layer->setName((util::node_info(n) + "_log").c_str());
+
+       std::vector<int64_t> ln2_dims(in->getDimensions().nbDims, 1);
+       auto ln2_tensor = at::full(ln2_dims, ln2, at::TensorOptions().dtype(tensor_type));
+       auto ln2_itensor = converters::tensor_to_const(ctx, ln2_tensor);
+
+       auto div_layer = add_elementwise(
+           ctx,
+           nvinfer1::ElementWiseOperation::kDIV,
+           log_layer->getOutput(0),
+           ln2_itensor,
+           (util::node_info(n) + "_div").c_str());
+       auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], div_layer->getOutput(0));
+       LOG_DEBUG("Output tensor shape: " << out_tensor->getDimensions());
+       return true;
+     }});
+
 auto logical_not_registration TORCHTRT_UNUSED = RegisterNodeConversionPatterns().pattern(
     {"aten::logical_not(Tensor self) -> Tensor", [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
        auto in = args[0].ITensorOrFreeze(ctx);
diff --git a/tests/core/conversion/converters/test_unary.cpp b/tests/core/conversion/converters/test_unary.cpp
index 2c10e40b42..858fdc69a2 100644
--- a/tests/core/conversion/converters/test_unary.cpp
+++ b/tests/core/conversion/converters/test_unary.cpp
@@ -47,6 +47,21 @@ TEST(Converters, ATenReciprocalIntConvertsCorrectly) {
   ASSERT_TRUE(torch_tensorrt::tests::util::exactlyEqual(jit_results[0], trt_results[0]));
 }
 
+TEST(Converters, ATenLog2IntConvertsCorrectly) {
+  const auto graph = gen_test_graph("log2");
+  auto g = std::make_shared<torch::jit::Graph>();
+  torch::jit::parseIR(graph, g.get());
+
+  auto in = at::tensor({1, 2, 7, 25, 50}, {at::kCUDA}).to(torch::kInt32);
+  auto params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto jit_results = torch_tensorrt::tests::util::RunGraph(g, params, {in});
+
+  in = at::clone(in);
+  params = torch_tensorrt::core::ir::get_static_params(g->inputs(), {});
+  auto trt_results = torch_tensorrt::tests::util::RunGraphEngine(g, params, {in});
+  ASSERT_TRUE(torch_tensorrt::tests::util::almostEqual(jit_results[0], trt_results[0]));
+}
+
 TEST(Converters, ATenSignConvertsCorrectly) {
   const auto graph = gen_test_graph("sign");
   auto g = std::make_shared<torch::jit::Graph>();
@@ -129,6 +144,7 @@ test_unary(abs, Abs);
 test_unary(floor, Floor);
 test_unary(reciprocal, Reciprocal);
 test_unary(log, Log);
+test_unary(log2, Log2);
 test_unary(ceil, Ceil);
 test_unary(sqrt, Sqrt);
 test_unary(exp, Exp);