[GitHub] [tvm] trevor-m commented on a change in pull request #8074: [Frontend] [Tensorflow2] Added test infrastructure for TF2 frozen models

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trevor-m commented on a change in pull request #8074:
URL: https://github.com/apache/tvm/pull/8074#discussion_r635654606



##########
File path: tests/python/frontend/tensorflow2/test_functional_models.py
##########
@@ -0,0 +1,368 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test: tests basic examples"""
+
+import tempfile
+import tensorflow as tf
+import numpy as np
+import pytest
+from common import compare_tf_tvm
+from common import run_tf_code
+
+
+class AddOne(tf.Module):

Review comment:
       For all the tests in this file, I think it would be best to encapsulate the tf module class(es) inside the test function. For example:
   
   ```
   def test_expand_dims():
       class ExpandDims(tf.Module):
           def get_input(self):
               return np.ones((1, 30), dtype=np.float32)
   
           @tf.function(input_signature=[tf.TensorSpec(shape=(1, 30), dtype=tf.float32)])
           def func(self, x):
               return tf.expand_dims(x, axis=2)
   
       run_all(ExpandDims)
   ```

##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+    if isinstance(o, tvm.nd.NDArray):
+        out = o.asnumpy().tolist()
+    elif isinstance(o, tvm.runtime.container.ADT):
+        result = []
+        for f in o:
+            result.append(vmobj_to_list(f))
+        out = result
+    else:
+        raise RuntimeError("Unknown object type: %s" % type(o))
+    return out
+
+
+def run_tf_code(func, input_):
+    if type(func) is Function:
+        out = func(input_)
+        if isinstance(out, list):
+            a = [x.numpy() for x in out]
+        else:
+            a = out.numpy()
+    else:
+        a = func(tf.constant(input_))
+        if type(a) is dict:
+            a = [x.numpy() for x in a.values()]
+            if len(a) == 1:
+                a = a[0]
+        elif type(a) is list:
+            a = [x.numpy() for x in a]
+            if len(a) == 1:
+                a = a[0]
+        else:
+            a = a.numpy()
+    return a
+
+
+def compile_graph_runtime(

Review comment:
       Graph Runtime is now Graph Executor, may want to rename these functions

##########
File path: tests/python/frontend/tensorflow2/test_sequential_models.py
##########
@@ -0,0 +1,118 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test: testing models built with tf.keras.Sequential()"""
+
+import tempfile
+import numpy as np
+import pytest
+import tensorflow as tf
+from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
+
+from common import compare_tf_tvm
+from common import run_tf_code
+
+
+def run_sequential_model(model_fn, input_shape):
+    def get_input(shape):
+        _input = np.random.uniform(0, 1, shape).astype(dtype="float32")
+        return _input
+
+    def save_and_reload(_model):
+        with tempfile.TemporaryDirectory() as model_path:
+            tf.saved_model.save(_model, model_path)
+            loaded = tf.saved_model.load(model_path)
+            func = loaded.signatures["serving_default"]
+            frozen_func = convert_variables_to_constants_v2(func)
+        return frozen_func
+
+    def model_graph(model, input_shape):
+        _input = get_input(input_shape)
+        f = save_and_reload(model(input_shape))
+        _output = run_tf_code(f, _input)
+        gdef = f.graph.as_graph_def(add_shapes=True)
+        return gdef, _input, _output
+
+    compare_tf_tvm(*model_graph(model_fn, input_shape), vm=True, output_sig=None)
+
+
+def dense_model(input_shape, num_units=128):

Review comment:
       Same here, I would put these functions inside the test functions which they are used.

##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+    if isinstance(o, tvm.nd.NDArray):
+        out = o.asnumpy().tolist()
+    elif isinstance(o, tvm.runtime.container.ADT):
+        result = []
+        for f in o:
+            result.append(vmobj_to_list(f))
+        out = result
+    else:
+        raise RuntimeError("Unknown object type: %s" % type(o))
+    return out
+
+
+def run_tf_code(func, input_):
+    if type(func) is Function:
+        out = func(input_)
+        if isinstance(out, list):
+            a = [x.numpy() for x in out]
+        else:
+            a = out.numpy()
+    else:
+        a = func(tf.constant(input_))
+        if type(a) is dict:
+            a = [x.numpy() for x in a.values()]
+            if len(a) == 1:
+                a = a[0]
+        elif type(a) is list:
+            a = [x.numpy() for x in a]
+            if len(a) == 1:
+                a = a[0]
+        else:
+            a = a.numpy()
+    return a
+
+
+def compile_graph_runtime(
+    mod, params, target="llvm", target_host="llvm", opt_level=3, output_sig=None
+):
+    with tvm.transform.PassContext(opt_level):
+        lib = relay.build(mod, target=target, target_host=target_host, params=params)
+    return lib
+
+
+def compile_vm(
+    mod, params, target="llvm", target_host="llvm", opt_level=3, disabled_pass=None, output_sig=None
+):
+    with tvm.transform.PassContext(opt_level, disabled_pass=disabled_pass):
+        mod = relay.transform.InferType()(mod)

Review comment:
       I don't think InferType is required here

##########
File path: tests/python/frontend/tensorflow2/common.py
##########
@@ -0,0 +1,120 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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.
+# pylint: disable=import-self, invalid-name, unused-argument, too-many-lines, len-as-condition, broad-except
+# pylint: disable=import-outside-toplevel, redefined-builtin
+"""TF2 to relay converter test utilities"""
+
+import tvm
+from tvm import relay
+
+from tvm.runtime.vm import VirtualMachine
+import tvm.contrib.graph_executor as runtime
+from tvm.relay.frontend.tensorflow import from_tensorflow
+import tvm.testing
+
+import tensorflow as tf
+from tensorflow.python.eager.def_function import Function
+
+
+def vmobj_to_list(o):
+    if isinstance(o, tvm.nd.NDArray):
+        out = o.asnumpy().tolist()
+    elif isinstance(o, tvm.runtime.container.ADT):
+        result = []
+        for f in o:
+            result.append(vmobj_to_list(f))
+        out = result
+    else:
+        raise RuntimeError("Unknown object type: %s" % type(o))
+    return out
+
+
+def run_tf_code(func, input_):
+    if type(func) is Function:
+        out = func(input_)
+        if isinstance(out, list):
+            a = [x.numpy() for x in out]
+        else:
+            a = out.numpy()
+    else:
+        a = func(tf.constant(input_))
+        if type(a) is dict:
+            a = [x.numpy() for x in a.values()]
+            if len(a) == 1:
+                a = a[0]
+        elif type(a) is list:
+            a = [x.numpy() for x in a]
+            if len(a) == 1:
+                a = a[0]
+        else:
+            a = a.numpy()
+    return a
+
+
+def compile_graph_runtime(
+    mod, params, target="llvm", target_host="llvm", opt_level=3, output_sig=None

Review comment:
       `output_sig` is unused here and in `compile_vm()`, is this intended? What is `output_sig`?




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