[TF:TRT] Add flag TF_TRT_ALLOW_ENGINE_NATIVE_SEGMENT_EXECUTION.

The default value of the flag is True. When the flag value is false, the
bridge will report an error when the native segment of a TRTEngineOp is
executed.

Add test cases.

PiperOrigin-RevId: 317340632
Change-Id: Iacded09b38e63442bbd93076a079d385fb8a77e6

tensorflow/compiler/tf2tensorrt/kernels/trt_engine_op.cc

@@ -45,6 +45,7 @@ limitations under the License.
 #include "tensorflow/core/platform/stream_executor.h"
 #include "tensorflow/core/platform/thread_annotations.h"
 #include "tensorflow/core/platform/types.h"
+#include "tensorflow/core/util/env_var.h"
 #include "tensorflow/stream_executor/lib/statusor.h"
 
 #if GOOGLE_CUDA
@@ -521,6 +522,17 @@ Status TRTEngineOp::VerifyInputShapes(
   return Status::OK();
 }
 
+static bool AllowEngineNativeSegmentExecution() {
+  bool value;
+  Status status =
+      ReadBoolFromEnvVar("TF_TRT_ALLOW_ENGINE_NATIVE_SEGMENT_EXECUTION",
+                         /*default_value=*/true, &value);
+  if (!status.ok()) {
+    LOG(ERROR) << status;
+  }
+  return value;
+}
+
 void TRTEngineOp::ComputeAsync(OpKernelContext* ctx,
                                AsyncOpKernel::DoneCallback done) {
   auto helper = new AsyncHelper(done);
@@ -605,17 +617,31 @@ void TRTEngineOp::ComputeAsync(OpKernelContext* ctx,
 
   EngineContext* engine_context = status.ValueOrDie().first;
   int trt_context_idx = status.ValueOrDie().second;
+  auto may_execute_native_segment = [&] {
+    if (!AllowEngineNativeSegmentExecution()) {
+      ctx->CtxFailure(
+          errors::Aborted("User disallowed engine native segment execution"));
+      return false;
+    }
+    return true;
+  };
   if (!engine_context->cuda_engine) {
-    VLOG(1) << "Engine retrieval for input shapes: "
-            << TensorShapeUtils::ShapeListString(input_concrete_shapes)
-            << " failed. Running native segment for " << name();
-    ExecuteNativeSegment(ctx, helper);
+    LOG_WARNING_WITH_PREFIX
+        << "Engine retrieval for input shapes: "
+        << TensorShapeUtils::ShapeListString(input_concrete_shapes)
+        << " failed. Running native segment for " << name();
+    if (may_execute_native_segment()) {
+      ExecuteNativeSegment(ctx, helper);
+    }
     return;
   }
   Status stat = ExecuteTrtEngine(ctx, engine_context, trt_context_idx);
   if (!stat.ok()) {
     LOG_WARNING_WITH_PREFIX << "Failed to execute engine: " << stat
                             << " Retrying with native segment for " << name();
+    if (!may_execute_native_segment()) {
+      return;
+    }
     // Release any outputs that are allocated, ExecuteNativeSegment will
     // re-allocate them and fail if they are currently allocated.
     for (int i = 0; i < ctx->num_outputs(); i++) {

tensorflow/python/compiler/tensorrt/trt_convert_test.py

@@ -439,6 +439,7 @@ class TrtConvertTest(test_util.TensorFlowTestCase, parameterized.TestCase):
       self,
       input_saved_model_dir,
       input_saved_model_signature_key=_SAVED_MODEL_SIGNATURE_KEY,
+      max_workspace_size_bytes=10 << 20,  # Use a smaller workspace.
       precision_mode=trt_convert.TrtPrecisionMode.FP32,
       is_dynamic_op=True,
       maximum_cached_engines=2):
@@ -446,7 +447,7 @@ class TrtConvertTest(test_util.TensorFlowTestCase, parameterized.TestCase):
         input_saved_model_dir=input_saved_model_dir,
         input_saved_model_signature_key=input_saved_model_signature_key,
         conversion_params=trt_convert.DEFAULT_TRT_CONVERSION_PARAMS._replace(
-            max_workspace_size_bytes=10 << 20,  # Use a smaller workspace.
+            max_workspace_size_bytes=max_workspace_size_bytes,
             precision_mode=precision_mode,
             is_dynamic_op=is_dynamic_op,
             maximum_cached_engines=maximum_cached_engines))
@@ -924,6 +925,36 @@ class TrtConvertTest(test_util.TensorFlowTestCase, parameterized.TestCase):
         # to fall back to TF function.
         self._TestRun(sess, 2)
 
+  @test_util.run_v2_only
+  def testTrtGraphConverter_AllowEngineNativeSegmentExecution(self):
+    if not is_tensorrt_enabled():
+      return
+
+    np_input1, np_input2 = self._RandomInput([4, 1, 1])
+
+    # Create a model and save it.
+    input_saved_model_dir = self.mkdtemp()
+    root = self._GetModelForV2()
+    save.save(root, input_saved_model_dir,
+              {_SAVED_MODEL_SIGNATURE_KEY: root.run})
+
+    def _InputFn():
+      yield np_input1, np_input2
+
+    # Run TRT conversion and request an unreasonably large workspace.
+    converter = self._CreateConverterV2(
+        input_saved_model_dir, max_workspace_size_bytes=10 << 40)
+    converter.convert()
+
+    os.environ["TF_TRT_ALLOW_ENGINE_NATIVE_SEGMENT_EXECUTION"] = "False"
+    with self.assertRaisesRegex(
+        errors.AbortedError,
+        r"User disallowed engine native segment execution"):
+      converter.build(input_fn=_InputFn)
+
+    os.environ["TF_TRT_ALLOW_ENGINE_NATIVE_SEGMENT_EXECUTION"] = "True"
+    converter.build(input_fn=_InputFn)
+
   @test_util.run_v2_only
   def testBackwardCompatibility(self):
     """Load and execute a model that was saved in TF2.0."""