Add example code to save JAX program and run using C++ runtime.

examples/jax_cpp/BUILD

@@ -0,0 +1,21 @@
+load(
+    "@org_tensorflow//tensorflow:tensorflow.bzl",
+    "tf_cc_binary",
+)
+
+tf_cc_binary(
+    name = "main",
+    srcs = ["main.cc"],
+    deps = [
+      "@org_tensorflow//tensorflow/compiler/xla/tools:hlo_module_loader",
+      "@org_tensorflow//tensorflow/compiler/xla/pjrt:pjrt_client",
+      "@org_tensorflow//tensorflow/compiler/xla/pjrt:cpu_device",
+      "@org_tensorflow//tensorflow/compiler/jit:xla_cpu_jit",
+      "@org_tensorflow//tensorflow/compiler/xla:status",
+      "@org_tensorflow//tensorflow/compiler/xla:statusor",
+      "@org_tensorflow//tensorflow/compiler/xla:literal",
+      "@org_tensorflow//tensorflow/compiler/xla:literal_util",
+      "@org_tensorflow//tensorflow/compiler/xla:shape_util",
+      "@org_tensorflow//tensorflow/compiler/xla/service:hlo_proto_cc",
+    ],
+)

examples/jax_cpp/main.cc

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+// An example for reading a HloModule from a HloProto file and execute the
+// module on PJRT CPU client.
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "tensorflow/compiler/xla/literal.h"
+#include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/pjrt/cpu_device.h"
+#include "tensorflow/compiler/xla/pjrt/pjrt_client.h"
+#include "tensorflow/compiler/xla/status.h"
+#include "tensorflow/compiler/xla/statusor.h"
+#include "tensorflow/compiler/xla/tools/hlo_module_loader.h"
+#include "tensorflow/core/platform/init_main.h"
+#include "tensorflow/core/platform/logging.h"
+
+int main(int argc, char** argv) {
+  tensorflow::port::InitMain("", &argc, &argv);
+
+  // Load HloModule from file.
+  std::string hlo_filename = "/tmp/fn_hlo.txt";
+  std::function<void(xla::HloModuleConfig*)> config_modifier_hook =
+      [](xla::HloModuleConfig* config) { config->set_seed(42); };
+  std::unique_ptr<xla::HloModule> test_module =
+      LoadModuleFromFile(hlo_filename, xla::hlo_module_loader_details::Config(),
+                         "txt", config_modifier_hook)
+          .ValueOrDie();
+  const xla::HloModuleProto test_module_proto = test_module->ToProto();
+
+  // Run it using JAX C++ Runtime (PJRT).
+
+  // Get a CPU client.
+  std::unique_ptr<xla::PjRtClient> client =
+      xla::GetCpuClient(/*asynchronous=*/true).ValueOrDie();
+
+  // Compile XlaComputation to PjRtExecutable.
+  xla::XlaComputation xla_computation(test_module_proto);
+  xla::CompileOptions compile_options;
+  std::unique_ptr<xla::PjRtExecutable> executable =
+      client->Compile(xla_computation, compile_options).ValueOrDie();
+
+  // Prepare inputs.
+  xla::Literal literal_x =
+      xla::LiteralUtil::CreateR2<float>({{1.0f, 2.0f}, {3.0f, 4.0f}});
+  xla::Literal literal_y =
+      xla::LiteralUtil::CreateR2<float>({{1.0f, 1.0f}, {1.0f, 1.0f}});
+  std::unique_ptr<xla::PjRtBuffer> param_x =
+      client->BufferFromHostLiteral(literal_x, client->local_devices()[0])
+          .ValueOrDie();
+  std::unique_ptr<xla::PjRtBuffer> param_y =
+      client->BufferFromHostLiteral(literal_y, client->local_devices()[0])
+          .ValueOrDie();
+
+  // Execute on CPU.
+  xla::ExecuteOptions execute_options;
+  // One vector<buffer> for each device.
+  std::vector<std::vector<std::unique_ptr<xla::PjRtBuffer>>> results =
+      executable->Execute({{param_x.get(), param_y.get()}}, execute_options)
+          .ValueOrDie();
+
+  // Get result.
+  std::shared_ptr<xla::Literal> result_literal =
+      results[0][0]->ToLiteral().ValueOrDie();
+  LOG(INFO) << "result = " << *result_literal;
+  return 0;
+}

examples/jax_cpp/prog.py

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+import jax.numpy as jnp
+
+def fn(x, y, z):
+    return jnp.dot(x, y) / z