Update PyTorch and XLA pin.

.github/workflows/_tpu_ci.yml

@@ -51,10 +51,14 @@ jobs:
           pip install fsspec
           pip install rich
 
+          # Test dependencies 
+          pip install --upgrade protobuf
+          pip install flax
+
           # PyTorch/XLA Optional Dependencies
           # =================================
           #
-          # Install `JAX` and `libtpu` dependencies for pallas and TPU tests.
+          # Install `jax` and `libtpu` dependencies for pallas and TPU tests.
           #
           # Note that we might need to install pre-release versions of both, in
           # external artifact repositories.
@@ -70,18 +74,6 @@ jobs:
           pip install "$WHL[pallas]" --pre --index-url $INDEX --find-links $LINKS
           pip install "$WHL[tpu]"    --pre --index-url $INDEX --find-links $LINKS
 
-          pip install --upgrade protobuf
-
-          # Flax Pin
-          # ========
-          #
-          # Be careful when bumping the `flax` version, since it can cause tests that
-          # depend on `jax` to start breaking.
-          #
-          # Newer `flax` versions might pull newer `jax` versions, which might be incompatible
-          # with the current version of PyTorch/XLA.
-          pip install flax==0.11.2
-
       - name: Run Tests (${{ matrix.test_script }})
         if: inputs.has_code_changes == 'true'
         env:

.torch_commit

@@ -1,2 +1,2 @@
-# 2025-09-17
-928ac57c2ab03f9f79376f9995553eea2e6f4ca8
+# 2025-09-29
+21fec65781bebe867faf209f89bb687ffd236ca4

BUILD

@@ -1,5 +1,5 @@
-load("@python//:defs.bzl", "compile_pip_requirements")
 load("@python_version_repo//:py_version.bzl", "REQUIREMENTS")
+load("@rules_python//python:pip.bzl", "compile_pip_requirements")
 
 compile_pip_requirements(
     name = "requirements",

WORKSPACE

@@ -52,7 +52,7 @@ new_local_repository(
 
 # To build PyTorch/XLA with a new revison of OpenXLA, update the xla_hash to
 # the openxla git commit hash and note the date of the commit.
-xla_hash = '92f7b5952dd585c5be17c9a5caad27407005b513'  # Committed on 2025-08-15.
+xla_hash = '9a9aa0e11e4fcda8d6a9c3267dca6776ddbdb0ca'  # Committed on 2025-10-01.
 
 http_archive(
     name = "xla",
@@ -63,6 +63,7 @@ http_archive(
     patch_tool = "patch",
     patches = [
         "//openxla_patches:no_fortify.diff",
+        "//openxla_patches:if_constexpr_static_assert.diff",
     ],
     strip_prefix = "xla-" + xla_hash,
     urls = [

openxla_patches/if_constexpr_static_assert.diff

@@ -0,0 +1,40 @@
+diff --git a/xla/python/ifrt/attribute_map.h b/xla/python/ifrt/attribute_map.h
+index a8c9f11c8d..e5bb70bcf8 100644
+--- a/xla/python/ifrt/attribute_map.h
++++ b/xla/python/ifrt/attribute_map.h
+@@ -106,7 +106,9 @@ class AttributeMap {
+     } else if constexpr (std::is_same_v<T, float>) {
+       return Get<T, FloatValue>(key);
+     } else {
+-      static_assert(false, "Unsupported type for AttributeMap::Get");
++    // Same as: static_assert(false).
++    // Make it compileable by GCC version < 13.
++      static_assert(!sizeof(T), "Unsupported type for AttributeMap::Get");
+     }
+   }
+
+diff --git a/xla/stream_executor/plugin_registry.cc b/xla/stream_executor/plugin_registry.cc
+index f16a4f6707..8bfd51b238 100644
+--- a/xla/stream_executor/plugin_registry.cc
++++ b/xla/stream_executor/plugin_registry.cc
+@@ -41,7 +41,9 @@ PluginKind GetPluginKind() {
+   } else if constexpr (std::is_same_v<FactoryT, PluginRegistry::FftFactory>) {
+     return PluginKind::kFft;
+   } else {
+-    static_assert(false, "Unsupported factory type");
++    // Same as: static_assert(false).
++    // Make it compileable by GCC version < 13.
++    static_assert(!sizeof(FactoryT), "Unsupported factory type");
+   }
+ }
+ template <typename FactoryT>
+@@ -53,7 +55,9 @@ absl::string_view GetPluginName() {
+   } else if constexpr (std::is_same_v<FactoryT, PluginRegistry::FftFactory>) {
+     return "FFT";
+   } else {
+-    static_assert(false, "Unsupported factory type");
++    // Same as: static_assert(false).
++    // Make it compileable by GCC version < 13.
++    static_assert(!sizeof(FactoryT), "Unsupported factory type");
+   }
+ }

setup.py

@@ -112,12 +112,12 @@
 
 USE_NIGHTLY = True  # Whether to use nightly or stable libtpu and JAX.
 
-_libtpu_version = '0.0.21'
-_libtpu_date = '20250813'
+_libtpu_version = '0.0.24'
+_libtpu_date = '20250929'
 
-_jax_version = '0.7.1'
-_jaxlib_version = '0.7.1'
-_jax_date = '20250813'  # Date for jax and jaxlib.
+_jax_version = '0.8.0'
+_jaxlib_version = '0.8.0'
+_jax_date = '20251001'  # Date for jax and jaxlib.
 
 if USE_NIGHTLY:
   _libtpu_version += f".dev{_libtpu_date}+nightly"

test/spmd/test_fsdp_v2.py

@@ -55,7 +55,7 @@ def test_fsdp_v2_basic(self):
     # Make sure optimization barrier is applied.
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([model.fc2.weight.grad])
     self.assertIn(
-        'opt-barrier.38 = (f32[1,64]{0,1}, f32[1]{0}, f32[16,64]{1,0}) opt-barrier((f32[1,64]{0,1}, f32[1]{0}, f32[16,64]{1,0}) %tuple.37',
+        'opt-barrier.1 = (f32[1,64]{0,1}, f32[1]{0}, f32[16,64]{1,0}) opt-barrier((f32[1,64]{0,1}, f32[1]{0}, f32[16,64]{1,0}) %tuple.2',
         hlo)
 
     # Make sure the model can execute without error.

test/spmd/test_xla_sharding.py

@@ -613,7 +613,7 @@ def test_inplace_add_with_sharding(self):
     self.assertEqual(sharding_spec, torch_xla._XLAC._get_xla_sharding_spec(xt))
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xt])
     self.assertIn(
-        '%custom-call.7 = f32[2,2]{1,0} custom-call(f32[2,2]{1,0} %add.6), custom_call_target="Sharding", sharding=',
+        '%custom-call.1 = f32[2,2]{1,0} custom-call(f32[2,2]{1,0} %add.1), custom_call_target="Sharding", sharding=',
         hlo)
 
   # avoid calling xr.addressable_device_count here otherwise it will init the test
@@ -713,7 +713,8 @@ def test_xla_sharded_hlo_dump(self):
                             partition_spec)
     xst2 = xst1 + 5
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xst2.global_tensor])
-    self.assertIn('%p1.3 = f32[1,8]{1,0} parameter(1), sharding', hlo)
+    print(hlo)
+    self.assertIn('%p1.1 = f32[1,8]{1,0} parameter(1), sharding', hlo)
     if torch_xla._XLAC._xla_get_auto_sharding():
       # scalar 5 should be implicitly replicated, so the pre-optimization HLO
       # shouldn't mark it with sharding.
@@ -828,13 +829,13 @@ def test_mark_sharding_ir(self):
                               (0, 1))
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([actual.global_tensor])
     self.assertIn(
-        '%custom-call.7 = f32[1,128]{1,0} custom-call(f32[1,128]{1,0} %add.6), custom_call_target="Sharding", sharding=',
+        '%custom-call.1 = f32[1,128]{1,0} custom-call(f32[1,128]{1,0} %add.1), custom_call_target="Sharding", sharding=',
         hlo)
 
     actual += 0
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([actual.global_tensor])
     self.assertIn(
-        '%add.12 = f32[1,128]{1,0} add(f32[1,128]{1,0} %custom-call.9, f32[1,128]{1,0} %broadcast.11)',
+        '%add.3 = f32[1,128]{1,0} add(f32[1,128]{1,0} %custom-call.1, f32[1,128]{1,0} %broadcast.3)',
         hlo)
 
     self.assertTrue(torch.allclose(expected, actual.cpu()))
@@ -1141,7 +1142,7 @@ def test_backward_optimization_barrier(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([model.fc2.weight.grad])
     self.assertIn(
-        '%opt-barrier.37 = (f32[1,64]{0,1}, f32[1]{0}, f32[2,64]{1,0}) opt-barrier((f32[1,64]{0,1}, f32[1]{0}, f32[2,64]{1,0}) %tuple.36)',
+        '%opt-barrier.1 = (f32[1,64]{0,1}, f32[1]{0}, f32[2,64]{1,0}) opt-barrier((f32[1,64]{0,1}, f32[1]{0}, f32[2,64]{1,0}) %tuple.2)',
         hlo)
 
   def test_mark_shard_scalar(self):
@@ -1198,7 +1199,7 @@ def test_spmd_full_to_shard_shape(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xx])
     self.assertEqual(xx.shape, (8, 8 // self.n_devices))
-    self.assertIn(f'%custom-call.2 = f32[8,{8//self.n_devices}]{{1,0}}', hlo)
+    self.assertIn(f'%custom-call.1 = f32[8,{8//self.n_devices}]{{1,0}}', hlo)
     self.assertIn(
         f'custom_call_target="SPMDFullToShardShape", sharding={{manual}}', hlo)
     self.assertEqual(torch_xla._XLAC._get_xla_sharding_spec(xx), "{manual}")
@@ -1215,7 +1216,7 @@ def test_spmd_full_to_shard_shape(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xx])
     self.assertEqual(xx.shape, (8, 4))
-    self.assertIn(f'%custom-call.2 = f32[8,4]{{1,0}}', hlo)
+    self.assertIn(f'%custom-call.1 = f32[8,4]{{1,0}}', hlo)
     self.assertIn(
         f'custom_call_target="SPMDFullToShardShape", sharding={{manual}}', hlo)
     self.assertEqual(torch_xla._XLAC._get_xla_sharding_spec(xx), "{manual}")
@@ -1246,7 +1247,7 @@ def test_spmd_shard_to_full_shape(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([xx])
     self.assertEqual(xx.shape, x.shape)
-    self.assertIn('%custom-call.9 = f32[8,8]{1,0}', hlo)
+    self.assertIn('%custom-call.5 = f32[8,8]{1,0}', hlo)
     self.assertIn(
         'custom_call_target="SPMDShardToFullShape", sharding={replicated}', hlo)
     self.assertEqual(torch_xla._XLAC._get_xla_sharding_spec(xx), "{replicated}")
@@ -1297,7 +1298,7 @@ def test_spmd_reduce_scatter(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([x])
     self.assertIn(
-        f"reduce-scatter(f32[8,8]{{1,0}} %custom-call.2), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, dimensions={{0}}, to_apply=%AddComputation.3",
+        f"reduce-scatter(f32[8,8]{{1,0}} %custom-call.3), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, dimensions={{0}}, to_apply=%AddComputation.1",
         hlo)
 
     expected_x = torch.ones(8 // self.n_devices, 8) * self.n_devices
@@ -1318,7 +1319,7 @@ def test_spmd_reduce_scatter_canonical_index(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([x])
     self.assertIn(
-        f"reduce-scatter(f32[8,8]{{1,0}} %custom-call.2), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, dimensions={{1}}, to_apply=%AddComputation.3",
+        f"reduce-scatter(f32[8,8]{{1,0}} %custom-call.3), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, dimensions={{1}}, to_apply=%AddComputation.1",
         hlo)
 
     expected_x = torch.ones(8, 8 // self.n_devices) * self.n_devices
@@ -1338,7 +1339,7 @@ def test_spmd_all_reduce(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([x])
     self.assertIn(
-        f"all-reduce(f32[8,8]{{1,0}} %custom-call.2), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, to_apply=%AddComputation.3",
+        f"all-reduce(f32[8,8]{{1,0}} %custom-call.3), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, to_apply=%AddComputation.1",
         hlo)
 
     expected_x = torch.ones(8, 8) * self.n_devices
@@ -1359,7 +1360,7 @@ def test_spmd_all_reduce_scale(self):
 
     hlo = torch_xla._XLAC._get_xla_tensors_hlo([x])
     self.assertIn(
-        f"all-reduce(f32[8,8]{{1,0}} %custom-call.2), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, to_apply=%AddComputation.3",
+        f"all-reduce(f32[8,8]{{1,0}} %custom-call.3), channel_id=1, replica_groups={{{{{','.join([str(x) for x in self.device_ids])}}}}}, use_global_device_ids=true, to_apply=%AddComputation.1",
         hlo)
 
     expected_x = torch.ones(8, 8) * int(self.n_devices * scale)
@@ -1713,7 +1714,7 @@ def test_annotate_custom_sharding(self):
         f'%p0.1 = f32[2,4,64,64]{{3,2,1,0}} parameter(0), sharding={original_sharding_spec}',
         hlo)
     self.assertIn(
-        f'%custom-call.2 = f32[2,4,64,64]{{3,2,1,0}} custom-call(f32[2,4,64,64]{{3,2,1,0}} %p0.1), custom_call_target="Sharding", sharding={custom_sharding_spec}',
+        f'%custom-call.1 = f32[2,4,64,64]{{3,2,1,0}} custom-call(f32[2,4,64,64]{{3,2,1,0}} %p0.1), custom_call_target="Sharding", sharding={custom_sharding_spec}',
         hlo)
     xm.mark_step()
     # Ensure that the resulting sharding spec is preserved

torch_xla/csrc/lowering_context.h

@@ -124,8 +124,8 @@ class LoweringContext : public torch::lazy::LoweringContext {
   };
 
   // Reports an XLA builder error for the given node.
-  TF_ATTRIBUTE_NORETURN void ReportBuilderError(const torch::lazy::Node& node,
-                                                absl::string_view error_msg);
+  ABSL_ATTRIBUTE_NORETURN void ReportBuilderError(const torch::lazy::Node& node,
+                                                  absl::string_view error_msg);
 
   xla::XlaBuilder builder_;
   std::unordered_map<torch::lazy::BackendData::Handle, Parameter>

torch_xla/csrc/runtime/BUILD

@@ -381,18 +381,34 @@ cc_test(
     ],
 )
 
+cc_library(
+    name = "tsl_platform_logging",
+    srcs = ["tsl_platform_logging.cpp"],
+    hdrs = ["tsl_platform_logging.h"],
+    deps = [
+        "@xla//xla/tsl/platform:env_time",
+        "@xla//xla/tsl/platform:logging",
+        "@xla//xla/tsl/platform:macros",
+        "@xla//xla/tsl/platform:types",
+        "@com_google_absl//absl/base:core_headers",
+        "@com_google_absl//absl/base:log_severity",
+        "@com_google_absl//absl/container:flat_hash_map",
+        "@com_google_absl//absl/strings:str_format",
+        "@com_google_absl//absl/strings:string_view",
+    ],
+)
+
 cc_library(
     name = "tf_logging",
     srcs = ["tf_logging.cpp"],
     hdrs = ["tf_logging.h"],
     deps = [
+        ":tsl_platform_logging",
         "//torch_xla/csrc:status",
         "@torch//:headers",
         "@torch//:runtime_headers",
-        "@tsl//tsl/platform:stacktrace",
-        "@tsl//tsl/platform:statusor",
-        "@xla//xla/service:platform_util",
         "@com_google_absl//absl/base:log_severity",
+        "@com_google_absl//absl/log:absl_log",
     ],
 )
 

torch_xla/csrc/runtime/pjrt_registry.cpp

@@ -12,12 +12,12 @@
 #include "torch_xla/csrc/runtime/xla_coordinator.h"
 #include "torch_xla/csrc/status.h"
 #include "xla/pjrt/c/pjrt_c_api.h"
+#include "xla/pjrt/cpu/cpu_client.h"
 #include "xla/pjrt/distributed/client.h"
 #include "xla/pjrt/distributed/distributed.h"
 #include "xla/pjrt/distributed/in_memory_key_value_store.h"
 #include "xla/pjrt/pjrt_api.h"
 #include "xla/pjrt/pjrt_c_api_client.h"
-#include "xla/pjrt/tfrt_cpu_pjrt_client.h"
 
 namespace torch_xla {
 namespace runtime {