diff --git a/codegen/xla_native_functions.yaml b/codegen/xla_native_functions.yaml
index 0f510975d982..9a8ba258b0cd 100644
--- a/codegen/xla_native_functions.yaml
+++ b/codegen/xla_native_functions.yaml
@@ -304,7 +304,7 @@ supported:
   - _propagate_xla_data
   - put_
   - _pdist_forward
-  - qr
+  - linalg_qr
   - random_
   - random_.from
   - random_.to
diff --git a/test/cpp/test_aten_xla_tensor_2.cpp b/test/cpp/test_aten_xla_tensor_2.cpp
index 0047ae123fab..86a861900405 100644
--- a/test/cpp/test_aten_xla_tensor_2.cpp
+++ b/test/cpp/test_aten_xla_tensor_2.cpp
@@ -1,6 +1,8 @@
 #include <gtest/gtest.h>
 
+#include <algorithm>
 #include <iostream>
+#include <string>
 #include <tuple>
 
 #include <torch/torch.h>
@@ -560,19 +562,39 @@ TEST_F(AtenXlaTensorTest, TestQR) {
   static const int dims[] = {4, 7};
   for (auto m : dims) {
     for (auto n : dims) {
-      torch::Tensor a =
-          torch::rand({m, n}, torch::TensorOptions(torch::kFloat));
-      auto b = torch::qr(a);
-      ForEachDevice([&](const torch::Device& device) {
-        torch::Tensor xla_a = CopyToDevice(a, device);
-        auto xla_b = torch::qr(xla_a);
-        AllClose(std::get<0>(b).abs(), std::get<0>(xla_b).abs(), /*rtol=*/1e-3,
-                 /*atol=*/1e-4);
-        AllClose(std::get<1>(b).abs(), std::get<1>(xla_b).abs(), /*rtol=*/1e-3,
-                 /*atol=*/1e-4);
-      });
+      for (const auto mode : {"reduced", "complete"}) {
+        torch::Tensor a =
+            torch::rand({m, n}, torch::TensorOptions(torch::kFloat));
+        auto b = torch::linalg_qr(a, mode);
+        ForEachDevice([&](const torch::Device& device) {
+          torch::Tensor xla_a = CopyToDevice(a, device);
+          auto xla_b = torch::linalg_qr(xla_a, mode);
+          int64_t k = mode == std::string("complete") ? m : std::min(m, n);
+          EXPECT_EQ(std::get<0>(xla_b).size(0), m);
+          EXPECT_EQ(std::get<0>(xla_b).size(1), k);
+          EXPECT_EQ(std::get<1>(xla_b).size(0), k);
+          EXPECT_EQ(std::get<1>(xla_b).size(1), n);
+          AllClose(std::get<0>(b).abs(), std::get<0>(xla_b).abs(),
+                   /*rtol=*/1e-3, /*atol=*/1e-4);
+          AllClose(std::get<1>(b).abs(), std::get<1>(xla_b).abs(),
+                   /*rtol=*/1e-3, /*atol=*/1e-4);
+        });
+      }
     }
   }
+
+  ForEachDevice([&](const torch::Device& device) {
+    torch::Tensor xla_a =
+        CopyToDevice(torch::rand({4, 7}, torch::TensorOptions(torch::kFloat)),
+                     device);
+    try {
+      torch::linalg_qr(xla_a, "raw");
+      FAIL() << "Expected torch::linalg_qr to reject unsupported QR mode";
+    } catch (const c10::Error& error) {
+      EXPECT_NE(std::string(error.what()).find("mode='raw'"),
+                std::string::npos);
+    }
+  });
 }
 
 TEST_F(AtenXlaTensorTest, TestCholesky) {
diff --git a/test/test_ops.py b/test/test_ops.py
index 2e71948b76a1..02702c6ef1e8 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -364,8 +364,6 @@ def get_allowed_ops_map(
     # AllowedOpInfoEntry('norm'),
     # AllowedOpInfoEntry('t'),
     # AllowedOpInfoEntry('logdet'), xla::lodget does not handle empty input
-    # AllowedOpInfoEntry('qr'),  # Slice dim size 1 greater than dynamic slice dimension: 0
-
     # Worked locally (but failing on CI both CPU)
     # app.circleci.com/pipelines/github/pytorch/xla/9130/workflows/71c74f3d-1735-4328-81b5-784d6e6744da/jobs/17998
     # AllowedOpInfoEntry('var_mean'),
diff --git a/torch_xla/csrc/aten_xla_type.cpp b/torch_xla/csrc/aten_xla_type.cpp
index 1c855ca82397..f418b72236c7 100644
--- a/torch_xla/csrc/aten_xla_type.cpp
+++ b/torch_xla/csrc/aten_xla_type.cpp
@@ -3118,9 +3118,18 @@ at::Tensor& XLANativeFunctions::put_(at::Tensor& self, const at::Tensor& index,
   return self;
 }
 
-std::tuple<at::Tensor, at::Tensor> XLANativeFunctions::qr(
-    const at::Tensor& self, bool some) {
-  TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
+std::tuple<at::Tensor, at::Tensor> XLANativeFunctions::linalg_qr(
+    const at::Tensor& self, c10::string_view mode) {
+  TORCH_LAZY_FN_COUNTER_TIMED_TRACING("xla::");
+  bool some = true;
+  if (mode == "reduced") {
+    some = true;
+  } else if (mode == "complete") {
+    some = false;
+  } else {
+    TORCH_CHECK(false, "linalg_qr on XLA only supports modes 'reduced' and "
+                       "'complete', but got mode='", mode, "'");
+  }
   XLA_ASSIGN_OR_THROW(XLATensorPtr xla_self, bridge::GetXlaTensor(self));
   auto results = tensor_methods::qr(xla_self, some);
   return std::make_tuple(bridge::AtenFromXlaTensor(std::get<0>(results)),
diff --git a/torch_xla/csrc/autocast_mode.cpp b/torch_xla/csrc/autocast_mode.cpp
index 4db31feb6ba9..b63c7200e41d 100644
--- a/torch_xla/csrc/autocast_mode.cpp
+++ b/torch_xla/csrc/autocast_mode.cpp
@@ -144,11 +144,10 @@ TORCH_LIBRARY_IMPL(aten, AutocastXLA, m) {
   // KERNEL_XLA(fake_quantize_per_tensor_affine, fp32)
   // KERNEL_XLA(geqrf, fp32)
   // KERNEL_XLA(_lu_with_info, fp32)
-  KERNEL_XLA(qr, fp32)
   KERNEL_XLA(svd, fp32)
   KERNEL_XLA(triangular_solve, fp32)
   KERNEL_XLA(multilabel_margin_loss_forward, fp32)
-  // KERNEL_XLA(linalg_qr, fp32)
+  KERNEL_XLA(linalg_qr, fp32)
   // KERNEL_XLA(linalg_cholesky_ex, fp32)
   KERNEL_XLA(linalg_svd, fp32)
   // KERNEL_XLA(linalg_eig, fp32)
diff --git a/torch_xla/csrc/ops/qr.cpp b/torch_xla/csrc/ops/qr.cpp
index a067a77d41d6..0370d7d7c828 100644
--- a/torch_xla/csrc/ops/qr.cpp
+++ b/torch_xla/csrc/ops/qr.cpp
@@ -44,7 +44,7 @@ xla::Shape NodeOutputShape(const torch::lazy::Value& input, bool some) {
 
 QR::QR(const torch::lazy::Value& input, bool some)
     : XlaNode(
-          torch::lazy::OpKind(at::aten::qr), {input},
+          torch::lazy::OpKind(at::aten::linalg_qr), {input},
           [&]() { return NodeOutputShape(input, some); },
           /*num_outputs=*/2, torch::lazy::MHash(some)),
       some_(some) {}