[xla:cpu] Add OneDnnThreadPool based on parallel loop runner

PiperOrigin-RevId: 724703087

xla/backends/cpu/runtime/onednn/BUILD

@@ -0,0 +1,64 @@
+# copybara:uncomment_begin(google-only)
+# load("//xla:xla.bzl", "xla_cc_test")
+# load("//xla/tsl/platform:rules_cc.bzl", "cc_library")
+#
+# package(
+#     # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
+#     default_visibility = [":friends"],
+#     licenses = ["notice"],
+# )
+#
+# package_group(
+#     name = "friends",
+#     includes = [
+#         "//xla:friends",
+#     ],
+# )
+#
+# cc_library(
+#     name = "onednn_interop",
+#     hdrs = ["onednn_interop.h"],
+#     deps = [
+#         "@com_google_absl//absl/base:core_headers",
+#         "@com_google_absl//absl/status",
+#         "@onednn//:mkl_dnn",
+#         "//xla:util",
+#         "//xla/tsl/platform:logging",
+#     ],
+# )
+#
+# cc_library(
+#     name = "onednn_threadpool",
+#     hdrs = ["onednn_threadpool.h"],
+#     deps = [
+#         ":onednn_interop",
+#         "@onednn//:mkl_dnn",
+#         "//xla/backends/cpu/runtime:parallel_loop_runner",
+#     ],
+# )
+#
+# xla_cc_test(
+#     name = "onednn_threadpool_test",
+#     srcs = ["onednn_threadpool_test.cc"],
+#     deps = [
+#         ":onednn_interop",
+#         ":onednn_threadpool",
+#         "@com_google_googletest//:gtest_main",
+#         "@com_google_absl//absl/algorithm:container",
+#         "@com_google_absl//absl/status",
+#         "@com_google_absl//absl/status:statusor",
+#         "@com_google_absl//absl/synchronization",
+#         "@eigen_archive//:eigen3",
+#         "@onednn//:mkl_dnn",
+#         "@pthreadpool",
+#         "//xla/backends/cpu/runtime:parallel_loop_runner",
+#         "//xla/tsl/concurrency:async_value",
+#         "//xla/tsl/lib/core:status_test_util",
+#         "//xla/tsl/platform:env",
+#         "//xla/tsl/platform:statusor",
+#         "//xla/tsl/platform:test",
+#         "//xla/tsl/platform:test_benchmark",
+#         "//xla/tsl/platform:test_main",
+#     ],
+# )
+# copybara:uncomment_end

xla/backends/cpu/runtime/onednn/onednn_interop.h

@@ -0,0 +1,84 @@
+/* Copyright 2025 The OpenXLA Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_INTEROP_H_
+#define XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_INTEROP_H_
+
+#include "oneapi/dnnl/dnnl_graph.hpp"
+#include "absl/base/optimization.h"
+#include "absl/status/status.h"
+#include "xla/tsl/platform/logging.h"
+#include "xla/util.h"
+
+namespace xla::cpu {
+
+#define ONEDNN_RETURN_IF_ERROR(expr)             \
+  do {                                           \
+    absl::Status s = OneDnnStatusToStatus(expr); \
+    if (!s.ok()) {                               \
+      return s;                                  \
+    }                                            \
+  } while (0)
+
+#define ONEDNN_LOG_IF_ERROR(expr)                   \
+  do {                                              \
+    absl::Status s = OneDnnStatusToStatus(expr);    \
+    if (!s.ok()) {                                  \
+      LOG(ERROR) << "DNNL operation failed: " << s; \
+    }                                               \
+  } while (0)
+
+// Statically initializes XNNPACK for the current process.
+absl::Status InitializeXnnPack();
+
+// Converts oneDNN status to absl::Status.
+inline absl::Status OneDnnStatusToStatus(dnnl::graph::status status) {
+  if (ABSL_PREDICT_TRUE(status == dnnl::graph::status::success)) {
+    return absl::OkStatus();
+  }
+
+  auto error_message = [](dnnl::graph::status status) {
+    switch (status) {
+      case dnnl::graph::status::success:
+        return "";
+      case dnnl::graph::status::out_of_memory:
+        return "out of memory";
+      case dnnl::graph::status::invalid_arguments:
+        return "invalid arguments";
+      case dnnl::graph::status::unimplemented:
+        return "unimplemented";
+      case dnnl::graph::status::last_impl_reached:
+        return "last implementation reached";
+      case dnnl::graph::status::runtime_error:
+        return "runtime error";
+      case dnnl::graph::status::not_required:
+        return "not required";
+      case dnnl::graph::status::invalid_graph:
+        return "invalid graph";
+      case dnnl::graph::status::invalid_graph_op:
+        return "invalid graph op";
+      case dnnl::graph::status::invalid_shape:
+        return "invalid shape";
+      case dnnl::graph::status::invalid_data_type:
+        return "invalid data type";
+    }
+  };
+
+  return Internal("DNNL operation failed: %s", error_message(status));
+}
+
+}  // namespace xla::cpu
+
+#endif  // XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_INTEROP_H_

xla/backends/cpu/runtime/onednn/onednn_threadpool.h

@@ -0,0 +1,60 @@
+/* Copyright 2025 The OpenXLA Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#ifndef XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_THREADPOOL_H_
+#define XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_THREADPOOL_H_
+
+#include <cstddef>
+#include <cstdint>
+#include <functional>
+
+#include "oneapi/dnnl/dnnl_threadpool_iface.hpp"
+#include "xla/backends/cpu/runtime/parallel_loop_runner.h"
+
+namespace xla::cpu {
+
+class OneDnnThreadPool final
+    : public dnnl::threadpool_interop::threadpool_iface {
+ public:
+  explicit OneDnnThreadPool(ParallelLoopRunner* runner) : runner_(runner) {}
+
+  int get_num_threads() const final;
+  bool get_in_parallel() const final;
+  uint64_t get_flags() const final;
+
+  void parallel_for(int n, const std::function<void(int, int)>& fn) final;
+
+ private:
+  ParallelLoopRunner* runner_;
+};
+
+inline int OneDnnThreadPool::get_num_threads() const {
+  return runner_->num_threads();
+}
+
+inline bool OneDnnThreadPool::get_in_parallel() const {
+  return runner_->is_in_runner();
+}
+
+inline uint64_t OneDnnThreadPool::get_flags() const { return 0; }
+
+inline void OneDnnThreadPool::parallel_for(
+    int n, const std::function<void(int, int)>& fn) {
+  runner_->Parallelize(n, [fn, n](size_t task_index) { fn(task_index, n); });
+}
+
+}  // namespace xla::cpu
+
+#endif  // XLA_BACKENDS_CPU_RUNTIME_ONEDNN_ONEDNN_THREADPOOL_H_

xla/backends/cpu/runtime/onednn/onednn_threadpool_test.cc

@@ -0,0 +1,118 @@
+/* Copyright 2025 The OpenXLA Authors.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "xla/backends/cpu/runtime/onednn/onednn_threadpool.h"
+
+#include <cmath>
+#include <cstdint>
+#include <vector>
+
+#include "oneapi/dnnl/dnnl.hpp"
+#include "oneapi/dnnl/dnnl_common.hpp"
+#include "oneapi/dnnl/dnnl_graph.hpp"
+#include "oneapi/dnnl/dnnl_threadpool.hpp"
+#include <gtest/gtest.h>
+#include "absl/status/statusor.h"
+#include "xla/backends/cpu/runtime/onednn/onednn_interop.h"
+#include "xla/backends/cpu/runtime/parallel_loop_runner.h"
+#include "xla/tsl/concurrency/async_value_ref.h"
+#include "xla/tsl/platform/env.h"
+#include "xla/tsl/platform/statusor.h"
+#include "xla/tsl/platform/test.h"
+#include "xla/tsl/platform/threadpool.h"
+
+#define EIGEN_USE_THREADS
+#include "unsupported/Eigen/CXX11/Tensor"
+
+namespace xla::cpu {
+namespace {
+
+// Creates a graph with a single Exp operation.
+static absl::StatusOr<dnnl::graph::graph> CreateExpGraph(
+    const dnnl::graph::logical_tensor& src_tensor,
+    const dnnl::graph::logical_tensor& dst_tensor) {
+  dnnl::graph::op exp_op(0, dnnl::graph::op::kind::Exp, {src_tensor},
+                         {dst_tensor});
+
+  dnnl::graph::graph g(dnnl::engine::kind::cpu);
+  ONEDNN_RETURN_IF_ERROR(g.add_op(exp_op));
+  g.finalize();
+
+  return g;
+}
+
+TEST(OneDnnThreadPoolTest, Binary) {
+  tsl::thread::ThreadPool threads(tsl::Env::Default(), "test", 32);
+  Eigen::ThreadPoolDevice device(threads.AsEigenThreadPool(),
+                                 threads.NumThreads());
+
+  ParallelLoopRunner runner(&device);
+  OneDnnThreadPool threadpool(&runner);
+
+  int64_t d0 = 100;
+  int64_t d1 = 1000;
+  int64_t num_elements = d0 * d1;
+
+  // We use row-major layout for both source and destination tensors.
+  dnnl::graph::logical_tensor::dims src_dims = {d0, d1};
+  dnnl::graph::logical_tensor::dims dst_dims = {d0, d1};
+
+  dnnl::graph::logical_tensor::dims src_strides = {d1, 1};
+  dnnl::graph::logical_tensor::dims dst_strides = {d1, 1};
+
+  dnnl::graph::logical_tensor src_tensor(
+      0, dnnl::graph::logical_tensor::data_type::f32, src_dims, src_strides);
+  dnnl::graph::logical_tensor dst_tensor(
+      1, dnnl::graph::logical_tensor::data_type::f32, dst_dims, dst_strides);
+
+  // Compile oneDNN graph with a single Exp operation.
+  TF_ASSERT_OK_AND_ASSIGN(dnnl::graph::graph g,
+                          CreateExpGraph(src_tensor, dst_tensor));
+  std::vector<dnnl::graph::partition> partitions = g.get_partitions();
+
+  // Create oneDNN engine for running the graph on CPU.
+  dnnl::engine engine(dnnl::engine::kind::cpu, 0);
+
+  // Create oneDNN stream backed by parallel loop runner.
+  dnnl::stream stream =
+      dnnl::stream(dnnl::threadpool_interop::make_stream(engine, &threadpool));
+
+  // Compile graph partitions for given engine.
+  std::vector<dnnl::graph::compiled_partition> compiled_partitions;
+  for (const auto& partition : partitions) {
+    compiled_partitions.push_back(
+        partition.compile({src_tensor}, {dst_tensor}, engine));
+  }
+
+  // Create tensors for source and destination.
+  std::vector<float> src_data(num_elements, 1.0f);
+  std::vector<float> dst_data(num_elements, 0.0f);
+
+  dnnl::graph::tensor src(src_tensor, engine, src_data.data());
+  dnnl::graph::tensor dst(dst_tensor, engine, dst_data.data());
+
+  // Execute compiled oneDNN graph on the CPU stream.
+  compiled_partitions[0].execute(stream, {src}, {dst});
+
+  // Wait for the completion of parallel loops scheduled into the runner.
+  tsl::BlockUntilReady(runner.done_event());
+
+  for (int i = 0; i < num_elements; ++i) {
+    EXPECT_NEAR(dst_data[i], std::exp(1.0f), 1e-5);
+  }
+}
+
+}  // namespace
+}  // namespace xla::cpu

xla/backends/cpu/runtime/parallel_loop_runner.cc

@@ -65,6 +65,10 @@ size_t ParallelLoopRunner::num_threads() const {
   return device_.load()->numThreadsInPool();
 }
 
+bool ParallelLoopRunner::is_in_runner() const {
+  return device_.load()->currentThreadId() > -1;
+}
+
 tsl::AsyncValueRef<tsl::Chain> ParallelLoopRunner::TakeDoneEvent(
     ParallelLoopRunner&& runner) {
   return std::move(runner.done_event_);

xla/backends/cpu/runtime/parallel_loop_runner.h

@@ -115,8 +115,12 @@ class ParallelLoopRunner {
   const Eigen::ThreadPoolDevice* device() const { return device_; }
   void set_device(const Eigen::ThreadPoolDevice* device) { device_ = device; }
 
+  // Returns the number of threads in the underlying thread pool.
   size_t num_threads() const;
 
+  // Returns true if the current thread belongs to the underlying thread pool.
+  bool is_in_runner() const;
+
  private:
   // Forward declarations of the parallel tasks.
   struct ParallelTask1D;