In pjrt runtime client, raise a Python exception if XLA compilation fails.

torch_xla/csrc/BUILD

@@ -126,6 +126,7 @@ ptxla_cc_library(
         ":shape_builder",
         ":shape_helper",
         ":version",
+        "//torch_xla/csrc:thread_pool",
         "//torch_xla/csrc/runtime",
         "//torch_xla/csrc/runtime:stablehlo_helper",
         "//torch_xla/csrc/runtime:xla_util",

torch_xla/csrc/runtime/BUILD

@@ -114,12 +114,10 @@ cc_library(
         ":env_vars",
         ":operation_manager",
         ":pjrt_registry",
-        ":profiler",
         ":stablehlo_helper",
         ":tensor_source",
         ":tf_logging",
         ":xla_coordinator",
-        "//torch_xla/csrc:thread_pool",
         "@com_google_absl//absl/strings",
         "@com_google_absl//absl/synchronization",
         "@com_google_absl//absl/types:span",
@@ -485,15 +483,10 @@ ptxla_cc_test(
     deps = [
         ":computation_client",
         ":pjrt_computation_client",
+        ":operation_manager",
         ":tensor_source",
         "@com_google_absl//absl/status",
-        "@xla//xla/tsl/lib/core:status_test_util",
-        "@tsl//tsl/platform:env",
-        "@tsl//tsl/platform:errors",
-        "@tsl//tsl/platform:logging",
-        "@tsl//tsl/platform:test",
         "@tsl//tsl/platform:test_main",
-        "@tsl//tsl/platform:statusor",
         "@xla//xla:literal",
         "@xla//xla:literal_util",
         "@xla//xla:shape_util",
@@ -502,6 +495,7 @@ ptxla_cc_test(
         "@xla//xla/tests:literal_test_util",
         "@xla//xla/tools:hlo_module_loader",
     ],
+    timeout = "short",
 )
 
 # ptxla_cc_test(

torch_xla/csrc/runtime/pjrt_computation_client.cpp

@@ -1,8 +1,7 @@
 #include "torch_xla/csrc/runtime/pjrt_computation_client.h"
 
 #include <algorithm>
-#include <future>
-#include <unordered_set>
+#include <stdexcept>
 #include <vector>
 
 #include "absl/status/status.h"
@@ -13,34 +12,30 @@
 #include "torch_xla/csrc/runtime/debug_macros.h"
 #include "torch_xla/csrc/runtime/env_hash.h"
 #include "torch_xla/csrc/runtime/env_vars.h"
-#include "torch_xla/csrc/runtime/operation_manager.h"
 #include "torch_xla/csrc/runtime/pjrt_registry.h"
-#include "torch_xla/csrc/runtime/profiler.h"
 #include "torch_xla/csrc/runtime/stablehlo_helper.h"
 #include "torch_xla/csrc/runtime/tensor_source.h"
 #include "torch_xla/csrc/runtime/tf_logging.h"
+#include "torch_xla/csrc/runtime/util.h"
 #include "torch_xla/csrc/runtime/xla_coordinator.h"
-#include "torch_xla/csrc/thread_pool.h"
 #include "tsl/profiler/lib/traceme.h"
 #include "xla/hlo/builder/xla_builder.h"
 #include "xla/hlo/builder/xla_computation.h"
-#include "xla/layout_util.h"
 #include "xla/literal.h"
 #include "xla/pjrt/c/pjrt_c_api_gpu_extension.h"
 #include "xla/pjrt/c/pjrt_c_api_wrapper_impl.h"
 #include "xla/pjrt/pjrt_api.h"
 #include "xla/pjrt/pjrt_c_api_client.h"
 #include "xla/pjrt/pjrt_client.h"
 #include "xla/pjrt/pjrt_executable.h"
-#include "xla/protobuf_util.h"
 #include "xla/service/custom_call_target_registry.h"
 #include "xla/shape.h"
 
-using xla::internal::XlaBuilderFriend;
-
 namespace torch_xla {
 namespace runtime {
 
+using xla::internal::XlaBuilderFriend;
+
 namespace {
 
 // Builds a map from the device's global ordinal to its index in the `devices`
@@ -625,20 +620,27 @@ std::vector<ComputationClient::ComputationPtr> PjRtComputationClient::Compile(
           device_assignment);
     }
 
+    // Compile the computation to an executible. For better user experience, if
+    // the XLA compiler fails for any reason, we raise a Python exception.
     std::unique_ptr<xla::PjRtLoadedExecutable> executable;
     if (runtime::sys_util::GetEnvBool("XLA_STABLEHLO_COMPILE", false)) {
       // Convert HLO to StableHLO for PjRt client compilation.
       mlir::MLIRContext context;
       mlir::ModuleOp mlir_module =
           mlir::ModuleOp::create(mlir::UnknownLoc::get(&context));
       ConvertHloToStableHlo(instance.computation.mutable_proto(), &mlir_module);
-      executable =
-          client_->CompileAndLoad(mlir_module, compile_options).value();
+      executable = util::RaisePythonValueErrorOnFailure([&] {
+        return fake_xla_compile_
+                   ? fake_xla_compile_()
+                   : client_->CompileAndLoad(mlir_module, compile_options);
+      });
       StableHloCompileCounter()->AddValue(1);
     } else {
-      executable =
-          client_->CompileAndLoad(instance.computation, compile_options)
-              .value();
+      executable = util::RaisePythonValueErrorOnFailure([&] {
+        return fake_xla_compile_ ? fake_xla_compile_()
+                                 : client_->CompileAndLoad(instance.computation,
+                                                           compile_options);
+      });
     }
 
     auto memory_stats_status_or = executable->GetCompiledMemoryStats();

torch_xla/csrc/runtime/pjrt_computation_client.h

@@ -27,7 +27,7 @@ namespace runtime {
 class PjRtComputationClient : public ComputationClient {
  public:
   PjRtComputationClient();
-  ~PjRtComputationClient();
+  ~PjRtComputationClient() override;
 
   DataPtr CreateDataPlaceholder(
       std::string device, xla::Shape shape,
@@ -162,6 +162,14 @@ class PjRtComputationClient : public ComputationClient {
                        const std::function<void()>& callback) override;
 
  private:
+  friend class PjRtComputationClientTest;
+
+  // If `function` is not nullptr, makes the client call it instead of the real
+  // XLA compiler when compiling. Used for injecting fault for testing.
+  void FakeXlaCompileForTesting(std::function<absl::Status()> function) {
+    fake_xla_compile_ = std::move(function);
+  }
+
   std::unique_ptr<xla::PjRtClient> client_;
   std::unique_ptr<XlaCoordinator> coordinator_;
   // global_ordinals_ tracks a map from PjRtDeviceId to the device's
@@ -174,6 +182,10 @@ class PjRtComputationClient : public ComputationClient {
       tsl::Env::Default(), "pjrt", std::thread::hardware_concurrency());
   torch::lazy::hash_t comp_env_hash_;
 
+  // If not nullptr, invoke this instead of the actual XLA compilation. Used
+  // only for testing.
+  std::function<absl::Status()> fake_xla_compile_ = nullptr;
+
   xla::PjRtDevice* StringToPjRtDevice(const std::string& device);
 
   struct PjRtData : public Data {

torch_xla/csrc/runtime/pjrt_computation_client_test.cpp

@@ -2,30 +2,48 @@
 
 #include <gtest/gtest.h>
 
+#include <functional>
 #include <memory>
+#include <stdexcept>
 #include <string>
 #include <vector>
 
 #include "absl/status/status.h"
 #include "torch_xla/csrc/runtime/computation_client.h"
 #include "torch_xla/csrc/runtime/pjrt_computation_client.h"
 #include "torch_xla/csrc/runtime/tensor_source.h"
-#include "tsl/platform/env.h"
-#include "tsl/platform/logging.h"
-#include "tsl/platform/statusor.h"
-#include "tsl/platform/test.h"
 #include "xla/hlo/builder/xla_builder.h"
 #include "xla/hlo/builder/xla_computation.h"
 #include "xla/literal.h"
 #include "xla/literal_util.h"
 #include "xla/tests/literal_test_util.h"
-#include "xla/tsl/lib/core/status_test_util.h"
 
 namespace torch_xla {
 namespace runtime {
 
-absl::StatusOr<xla::XlaComputation> MakeComputation() {
-  xla::Shape input_shape =
+class PjRtComputationClientTest : public ::testing::Test {
+ protected:
+  PjRtComputationClientTest() {
+    // Get a CPU client.
+    tsl::setenv("PJRT_DEVICE", "CPU", true);
+    client_ = std::make_unique<PjRtComputationClient>();
+    device_ = client_->GetDefaultDevice();
+  }
+
+  static void FakeXlaCompileForTesting(
+      PjRtComputationClient* client,
+      std::function<absl::Status()> fake_compile) {
+    client->FakeXlaCompileForTesting(std::move(fake_compile));
+  }
+
+  std::unique_ptr<PjRtComputationClient> client_;
+  std::string device_;
+};
+
+// Returns a computation to compute x + y where x and y are both F32[2,2]
+// arrays.
+absl::StatusOr<xla::XlaComputation> MakeAddComputation() {
+  const xla::Shape input_shape =
       xla::ShapeUtil::MakeShape(xla::PrimitiveType::F32, {2, 2});
   xla::XlaBuilder builder("AddComputation");
   xla::XlaOp x = xla::Parameter(&builder, 0, input_shape, "x");
@@ -34,19 +52,51 @@ absl::StatusOr<xla::XlaComputation> MakeComputation() {
   return builder.Build();
 }
 
-TEST(PjRtComputationClientTest, Init) {
-  // Get a CPU client.
-  tsl::setenv("PJRT_DEVICE", "CPU", true);
-  auto client = std::make_unique<PjRtComputationClient>();
-  std::string device = client->GetDefaultDevice();
+TEST_F(PjRtComputationClientTest, ThrowsExpectedExceptionWhenCompileFails) {
+  // Compose a computation to add two matrices.
+  xla::Shape out_shape(xla::F32, {2, 2},
+                       /*dynamic_dimensions=*/{});
+  std::vector<ComputationClient::CompileInstance> instances;
+  instances.push_back(ComputationClient::CompileInstance(
+      std::move(MakeAddComputation().value()), device_,
+      client_->GetCompilationDevices(device_, client_->GetLocalDevices()),
+      &out_shape));
+
+  // Force XLA to fail with the given error when invoked by Compile() below.
+  FakeXlaCompileForTesting(
+      client_.get(), [] { return absl::InvalidArgumentError("invalid arg"); });
+
+  // Compiling the graph should fail, which should throw instead of crashing.
+  EXPECT_THROW(client_->Compile(std::move(instances)), std::invalid_argument);
+}
+
+TEST_F(PjRtComputationClientTest, ThrowsExpectedExceptionWhenCompileThrows) {
+  // Compose a computation to add two matrices.
+  xla::Shape out_shape(xla::F32, {2, 2},
+                       /*dynamic_dimensions=*/{});
+  std::vector<ComputationClient::CompileInstance> instances;
+  instances.push_back(ComputationClient::CompileInstance(
+      std::move(MakeAddComputation().value()), device_,
+      client_->GetCompilationDevices(device_, client_->GetLocalDevices()),
+      &out_shape));
+
+  // Force XLA to throw with the given error when invoked by Compile() below.
+  FakeXlaCompileForTesting(client_.get(), []() -> absl::Status {
+    throw absl::BadStatusOrAccess(absl::InvalidArgumentError("invalid arg"));
+  });
+
+  // Compiling the graph should fail, which should throw instead of crashing.
+  EXPECT_THROW(client_->Compile(std::move(instances)), std::invalid_argument);
+}
 
-  // Compose a computation.
-  auto shape = xla::ShapeUtil::MakeShape(xla::F32, {2, 2});
+TEST_F(PjRtComputationClientTest, Init) {
+  // Compose a computation to add two 2x2 matrices.
+  auto out_shape = xla::ShapeUtil::MakeShape(xla::F32, {2, 2});
   std::vector<ComputationClient::CompileInstance> instances;
   instances.push_back(ComputationClient::CompileInstance(
-      std::move(MakeComputation().value()), device,
-      client->GetCompilationDevices(device, client->GetLocalDevices()),
-      &shape));
+      std::move(MakeAddComputation().value()), device_,
+      client_->GetCompilationDevices(device_, client_->GetLocalDevices()),
+      &out_shape));
 
   // Prepare inputs.
   xla::Literal literal_x =
@@ -56,22 +106,22 @@ TEST(PjRtComputationClientTest, Init) {
 
   // Compile the graph.
   std::vector<ComputationClient::ComputationPtr> computations =
-      client->Compile(std::move(instances));
+      client_->Compile(std::move(instances));
 
   // Copy inputs to device.
   ComputationClient::ExecuteComputationOptions options{};
   std::vector<std::shared_ptr<const TensorSource>> args = {
-      std::make_shared<LiteralSource>(std::move(literal_x), device),
-      std::make_shared<LiteralSource>(std::move(literal_y), device)};
+      std::make_shared<LiteralSource>(std::move(literal_x), device_),
+      std::make_shared<LiteralSource>(std::move(literal_y), device_)};
 
   // Execute the graph.
-  std::vector<ComputationClient::DataPtr> results = client->ExecuteComputation(
-      *computations[0], client->TransferToDevice(absl::MakeConstSpan(args)),
-      device, options);
+  std::vector<ComputationClient::DataPtr> results = client_->ExecuteComputation(
+      *computations[0], client_->TransferToDevice(absl::MakeConstSpan(args)),
+      device_, options);
 
-  // Copy the output from device back to host and assert correctness..
+  // Copy the output from device back to host and assert correctness.
   ASSERT_EQ(results.size(), 1);
-  auto result_literals = client->TransferFromDevice(results);
+  auto result_literals = client_->TransferFromDevice(results);
   ASSERT_THAT(result_literals, ::testing::SizeIs(1));
   EXPECT_TRUE(xla::LiteralTestUtil::Equal(
       xla::LiteralUtil::CreateR2<float>({{6.0f, 8.0f}, {10.0f, 12.0f}}),

torch_xla/csrc/runtime/util.h

@@ -9,10 +9,12 @@
 #include <memory>
 #include <numeric>
 #include <set>
+#include <stdexcept>
 #include <string>
 #include <type_traits>
 #include <vector>
 
+#include "absl/status/statusor.h"
 #include "absl/types/optional.h"
 #include "absl/types/span.h"
 #include "torch_xla/csrc/runtime/types.h"
@@ -128,6 +130,53 @@ T Multiply(const S& input) {
                          std::multiplies<T>());
 }
 
+namespace internal {
+
+// ExtractStatusOrValue<U>::type is T if U is absl::StatusOr<T>, and is
+// undefined otherwise.
+template <typename U>
+struct ExtractStatusOrValue;
+template <typename T>
+struct ExtractStatusOrValue<absl::StatusOr<T>> {
+  using type = T;
+};
+
+}  // namespace internal
+
+// RaisePythonValueErrorOnFailure(func) requires `func` to be a functor that
+// takes no argument and returns an absl::StatusOr<T>. It's a wrapper of
+// `func()` that translates any failure in `func()` to a Python ValueError
+// exception. In particular:
+//
+//   - if `func()` returns an error, throws an std::invalid_argument,
+//     which is translated to a Python ValueError exception;
+//     (https://pybind11.readthedocs.io/en/stable/advanced/exceptions.html).
+//   - if `func()` throws any exception, rethrows it as an
+//     std::invalid_argument so that we get a Python ValueError;
+//   - if `func()` successfully returns a value of type T, returns the value;
+//   - however, if `func()` crashes (e.g. due to a CHECK), we cannot
+//     catch it; therefore we should ensure that `func()` never
+//     crashes (and fix any crash as a bug).
+template <typename Func>
+typename internal::ExtractStatusOrValue<decltype(std::declval<Func>()())>::type
+RaisePythonValueErrorOnFailure(const Func& func) {
+  decltype(std::declval<Func>()()) result;
+  try {
+    result = func();
+  } catch (const std::exception& e) {
+    throw std::invalid_argument(e.what());
+  } catch (...) {
+    throw std::invalid_argument(
+        "Function threw an unknown exception. Please file a bug at "
+        "https://github.com/pytorch/xla/issues with details on how to "
+        "reproduce the error.");
+  }
+  if (result.ok()) {
+    return *std::move(result);
+  }
+  throw std::invalid_argument(std::string(result.status().message()));
+}
+
 }  // namespace util
 }  // namespace runtime
 }  // namespace torch_xla