Re-enable tflite runtime unit tests with guard

src/runtime/contrib/tflite/tflite_runtime.cc

@@ -177,5 +177,8 @@ Module TFLiteRuntimeCreate(const std::string& tflite_model_bytes, TVMContext ctx
 TVM_REGISTER_GLOBAL("tvm.tflite_runtime.create").set_body([](TVMArgs args, TVMRetValue* rv) {
   *rv = TFLiteRuntimeCreate(args[0], args[1]);
 });
+
+TVM_REGISTER_GLOBAL("target.runtime.tflite")
+.set_body_typed(TFLiteRuntime);
 }  // namespace runtime
 }  // namespace tvm

src/runtime/module.cc

@@ -129,6 +129,8 @@ bool RuntimeEnabled(const std::string& target) {
     f_name = "device_api.opencl";
   } else if (target == "mtl" || target == "metal") {
     f_name = "device_api.metal";
+  } else if (target == "tflite") {
+    f_name = "target.runtime.tflite";
   } else if (target == "vulkan") {
     f_name = "device_api.vulkan";
   } else if (target == "stackvm") {

tests/python/contrib/test_tflite_runtime.py

@@ -14,92 +14,117 @@
 # KIND, either express or implied.  See the License for the
 # specific language governing permissions and limitations
 # under the License.
+import pytest
+
 import tvm
 from tvm import te
 import numpy as np
 from tvm import rpc
 from tvm.contrib import util, tflite_runtime
-# import tensorflow as tf
-# import tflite_runtime.interpreter as tflite
-
-
-def skipped_test_tflite_runtime():
-
-    def create_tflite_model():
-        root = tf.Module()
-        root.const = tf.constant([1., 2.], tf.float32)
-        root.f = tf.function(lambda x: root.const * x)
-
-        input_signature = tf.TensorSpec(shape=[2,  ], dtype=tf.float32)
-        concrete_func = root.f.get_concrete_function(input_signature)
-        converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
-        tflite_model = converter.convert()
-        return tflite_model
-
-
-    def check_local():
-        tflite_fname = "model.tflite"
-        tflite_model = create_tflite_model()
-        temp = util.tempdir()
-        tflite_model_path = temp.relpath(tflite_fname)
-        open(tflite_model_path, 'wb').write(tflite_model)
-
-        # inference via tflite interpreter python apis
-        interpreter = tflite.Interpreter(model_path=tflite_model_path)
-        interpreter.allocate_tensors()
-        input_details = interpreter.get_input_details()
-        output_details = interpreter.get_output_details()
-
-        input_shape = input_details[0]['shape']
-        tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.float32)
-        interpreter.set_tensor(input_details[0]['index'], tflite_input)
-        interpreter.invoke()
-        tflite_output = interpreter.get_tensor(output_details[0]['index'])
-
-        # inference via tvm tflite runtime
-        with open(tflite_model_path, 'rb') as model_fin:
-            runtime = tflite_runtime.create(model_fin.read(), tvm.cpu(0))
-            runtime.set_input(0, tvm.nd.array(tflite_input))
-            runtime.invoke()
-            out = runtime.get_output(0)
-            np.testing.assert_equal(out.asnumpy(), tflite_output)
-
-
-    def check_remote():
-        tflite_fname = "model.tflite"
-        tflite_model = create_tflite_model()
-        temp = util.tempdir()
-        tflite_model_path = temp.relpath(tflite_fname)
-        open(tflite_model_path, 'wb').write(tflite_model)
-
-        # inference via tflite interpreter python apis
-        interpreter = tflite.Interpreter(model_path=tflite_model_path)
-        interpreter.allocate_tensors()
-        input_details = interpreter.get_input_details()
-        output_details = interpreter.get_output_details()
-
-        input_shape = input_details[0]['shape']
-        tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.float32)
-        interpreter.set_tensor(input_details[0]['index'], tflite_input)
-        interpreter.invoke()
-        tflite_output = interpreter.get_tensor(output_details[0]['index'])
-
-        # inference via remote tvm tflite runtime
-        server = rpc.Server("localhost")
-        remote = rpc.connect(server.host, server.port)
-        ctx = remote.cpu(0)
-        a = remote.upload(tflite_model_path)
-
-        with open(tflite_model_path, 'rb') as model_fin:
-            runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0))
-            runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0)))
-            runtime.invoke()
-            out = runtime.get_output(0)
-            np.testing.assert_equal(out.asnumpy(), tflite_output)
-
-    check_local()
-    check_remote()
+
+
+def _create_tflite_model():
+    root = tf.Module()
+    root.const = tf.constant([1., 2.], tf.float32)
+    root.f = tf.function(lambda x: root.const * x)
+
+    input_signature = tf.TensorSpec(shape=[2,  ], dtype=tf.float32)
+    concrete_func = root.f.get_concrete_function(input_signature)
+    converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+    tflite_model = converter.convert()
+    return tflite_model
+
+
+@pytest.mark.skip('skip because accessing output tensor is flakey')
+def test_local():
+    if not tvm.runtime.enabled("tflite"):
+        print("skip because tflite runtime is not enabled...")
+        return
+    if not tvm.get_global_func("tvm.tflite_runtime.create", True):
+        print("skip because tflite runtime is not enabled...")
+        return
+
+    try:
+        import tensorflow as tf
+    except ImportError:
+        print('skip because tensorflow not installed...')
+        return
+
+    tflite_fname = "model.tflite"
+    tflite_model = _create_tflite_model()
+    temp = util.tempdir()
+    tflite_model_path = temp.relpath(tflite_fname)
+    open(tflite_model_path, 'wb').write(tflite_model)
+
+    # inference via tflite interpreter python apis
+    interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    input_shape = input_details[0]['shape']
+    tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+    interpreter.set_tensor(input_details[0]['index'], tflite_input)
+    interpreter.invoke()
+    tflite_output = interpreter.get_tensor(output_details[0]['index'])
+
+    # inference via tvm tflite runtime
+    with open(tflite_model_path, 'rb') as model_fin:
+        runtime = tflite_runtime.create(model_fin.read(), tvm.cpu(0))
+        runtime.set_input(0, tvm.nd.array(tflite_input))
+        runtime.invoke()
+        out = runtime.get_output(0)
+        np.testing.assert_equal(out.asnumpy(), tflite_output)
+
+
+def test_remote():
+    if not tvm.runtime.enabled("tflite"):
+        print("skip because tflite runtime is not enabled...")
+        return
+    if not tvm.get_global_func("tvm.tflite_runtime.create", True):
+        print("skip because tflite runtime is not enabled...")
+        return
+
+    try:
+        import tensorflow as tf
+    except ImportError:
+        print('skip because tensorflow not installed...')
+        return
+
+    tflite_fname = "model.tflite"
+    tflite_model = _create_tflite_model()
+    temp = util.tempdir()
+    tflite_model_path = temp.relpath(tflite_fname)
+    open(tflite_model_path, 'wb').write(tflite_model)
+
+    # inference via tflite interpreter python apis
+    interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
+    interpreter.allocate_tensors()
+    input_details = interpreter.get_input_details()
+    output_details = interpreter.get_output_details()
+
+    input_shape = input_details[0]['shape']
+    tflite_input = np.array(np.random.random_sample(input_shape), dtype=np.float32)
+    interpreter.set_tensor(input_details[0]['index'], tflite_input)
+    interpreter.invoke()
+    tflite_output = interpreter.get_tensor(output_details[0]['index'])
+
+    # inference via remote tvm tflite runtime
+    server = rpc.Server("localhost")
+    remote = rpc.connect(server.host, server.port)
+    ctx = remote.cpu(0)
+    a = remote.upload(tflite_model_path)
+
+    with open(tflite_model_path, 'rb') as model_fin:
+        runtime = tflite_runtime.create(model_fin.read(), remote.cpu(0))
+        runtime.set_input(0, tvm.nd.array(tflite_input, remote.cpu(0)))
+        runtime.invoke()
+        out = runtime.get_output(0)
+        np.testing.assert_equal(out.asnumpy(), tflite_output)
+
+    server.terminate()
+
 
 if __name__ == "__main__":
-    # skipped_test_tflite_runtime()
-    pass
+    test_local()
+    test_remote()

tests/scripts/task_config_build_cpu.sh

@@ -38,3 +38,6 @@ echo set\(CMAKE_CXX_FLAGS -Werror\) >> config.cmake
 echo set\(HIDE_PRIVATE_SYMBOLS ON\) >> config.cmake
 echo set\(USE_VTA_TSIM ON\) >> config.cmake
 echo set\(USE_VTA_FSIM ON\) >> config.cmake
+echo set\(USE_TFLITE ON\) >> config.cmake
+echo set\(USE_TENSORFLOW_PATH \"/tensorflow\"\) >> config.cmake
+echo set\(USE_FLATBUFFERS_PATH \"/flatbuffers\"\) >> config.cmake