Add hash table support and frozen graph repairing for from_keras

tests/backend_test_base.py

@@ -70,7 +70,7 @@ def run_onnxcaffe2(self, onnx_graph, inputs):
         results = prepared_backend.run(inputs)
         return results
 
-    def run_onnxruntime(self, model_path, inputs, output_names):
+    def run_onnxruntime(self, model_path, inputs, output_names, use_custom_ops=False):
         """Run test against onnxruntime backend."""
         import onnxruntime as rt
         providers = ['CPUExecutionProvider']
@@ -79,6 +79,9 @@ def run_onnxruntime(self, model_path, inputs, output_names):
             if gpus is None or len(gpus) > 1:
                 providers = ['CUDAExecutionProvider']
         opt = rt.SessionOptions()
+        if use_custom_ops:
+            from onnxruntime_customops import get_library_path
+            opt.register_custom_ops_library(get_library_path())
         # in case of issues with the runtime, one can enable more logging
         # opt.log_severity_level = 0
         # opt.log_verbosity_level = 255
@@ -87,14 +90,14 @@ def run_onnxruntime(self, model_path, inputs, output_names):
         results = m.run(output_names, inputs)
         return results
 
-    def run_backend(self, g, outputs, input_dict, large_model=False, postfix=""):
+    def run_backend(self, g, outputs, input_dict, large_model=False, postfix="", use_custom_ops=False):
         tensor_storage = ExternalTensorStorage() if large_model else None
         model_proto = g.make_model("test", external_tensor_storage=tensor_storage)
         model_path = self.save_onnx_model(model_proto, input_dict, external_tensor_storage=tensor_storage,
                                           postfix=postfix)
 
         if self.config.backend == "onnxruntime":
-            y = self.run_onnxruntime(model_path, input_dict, outputs)
+            y = self.run_onnxruntime(model_path, input_dict, outputs, use_custom_ops)
         elif self.config.backend == "caffe2":
             y = self.run_onnxcaffe2(model_proto, input_dict)
         else:
@@ -299,7 +302,8 @@ def get_dtype(info):
     def run_test_case(self, func, feed_dict, input_names_with_port, output_names_with_port,
                       rtol=1e-07, atol=1e-5, mtol=None, convert_var_to_const=True, constant_fold=True,
                       check_value=True, check_shape=True, check_dtype=True, process_args=None, onnx_feed_dict=None,
-                      graph_validator=None, as_session=False, large_model=False, premade_placeholders=False):
+                      graph_validator=None, as_session=False, large_model=False, premade_placeholders=False,
+                      use_custom_ops=False):
         test_tf = not self.config.skip_tf_tests
         test_tflite = not self.config.skip_tflite_tests
         run_tfl_consistency_test = test_tf and test_tflite and self.config.run_tfl_consistency_test
@@ -339,7 +343,8 @@ def run_test_case(self, func, feed_dict, input_names_with_port, output_names_wit
                                      initialized_tables=initialized_tables,
                                      **process_args)
                 g = optimizer.optimize_graph(g, catch_errors=False)
-                actual = self.run_backend(g, output_names_with_port, onnx_feed_dict, large_model)
+                actual = self.run_backend(g, output_names_with_port, onnx_feed_dict, large_model,
+                                          use_custom_ops=use_custom_ops)
 
             self.assert_results_equal(expected, actual, rtol, atol, mtol, check_value, check_shape, check_dtype)
             self.assert_shapes_correct(g, self.config.allow_missing_shapes, not self.config.skip_onnx_checker)
@@ -369,7 +374,8 @@ def run_test_case(self, func, feed_dict, input_names_with_port, output_names_wit
                                  **tfl_process_args)
             g = optimizer.optimize_graph(g)
             onnx_feed_dict_without_port = {k.split(':')[0]: v for k, v in onnx_feed_dict.items()}
-            onnx_tfl_res = self.run_backend(g, tfl_outputs, onnx_feed_dict_without_port, postfix="_from_tflite")
+            onnx_tfl_res = self.run_backend(g, tfl_outputs, onnx_feed_dict_without_port,
+                                            postfix="_from_tflite", use_custom_ops=use_custom_ops)
 
             self.assert_results_equal(tfl_res, onnx_tfl_res, rtol, atol, mtol, check_value, check_shape, check_dtype)
             self.assert_shapes_correct(g, self.config.allow_missing_shapes, not self.config.skip_onnx_checker)

tests/test_api.py

@@ -10,8 +10,9 @@
 
 import numpy as np
 import tensorflow as tf
+from onnx import helper
 
-from common import check_tf_min_version, unittest_main
+from common import check_tf_min_version, unittest_main, requires_custom_ops, check_opset_min_version
 from backend_test_base import Tf2OnnxBackendTestBase
 import tf2onnx
 
@@ -74,6 +75,48 @@ def test_keras_api(self):
     def test_keras_api_large(self):
         self._test_keras_api(large_model=True)
 
+    @requires_custom_ops()
+    @check_tf_min_version("2.0")
+    @check_opset_min_version(11, "SparseToDense")
+    def test_keras_hashtable(self):
+
+        feature_cols = [
+            tf.feature_column.numeric_column("f_inp", dtype=tf.float32),
+            tf.feature_column.indicator_column(
+                tf.feature_column.categorical_column_with_vocabulary_list("s_inp", ["a", "b", "z"], num_oov_buckets=1)
+            )
+        ]
+        feature_layer = tf.keras.layers.DenseFeatures(feature_cols)
+
+        input_dict = {}
+        input_dict["f_inp"] = tf.keras.Input(name="f_inp", shape=(1,), dtype=tf.float32)
+        input_dict["s_inp"] = tf.keras.Input(name="s_inp", shape=(1,), dtype=tf.string)
+
+        inputs = list(input_dict.values())
+        standard_features = feature_layer(input_dict)
+        hidden1 = tf.keras.layers.Dense(512, activation='relu')(standard_features)
+        output = tf.keras.layers.Dense(10, activation='softmax')(hidden1)
+        model = tf.keras.Model(inputs=inputs, outputs=output)
+        model.compile(optimizer='adam', loss=tf.keras.losses.mean_squared_error)
+
+        inp1 = np.array([[2.], [3.]], dtype=np.float32)
+        inp2 = np.array([["a"], ["b"]], dtype=np.str)
+        k_res = model.predict([inp1, inp2])
+        spec = (tf.TensorSpec((None, 1), dtype=tf.float32, name="f_inp"),
+                tf.TensorSpec((None, 1), tf.string, name="s_inp"))
+        output_path = os.path.join(self.test_data_directory, "model.onnx")
+
+        model_proto, _ = tf2onnx.convert.from_keras(
+            model, input_signature=spec, opset=self.config.opset, output_path=output_path,
+            extra_opset=[helper.make_opsetid("ai.onnx.contrib", 1)])
+        output_names = [n.name for n in model_proto.graph.output]
+
+        o_res = self.run_onnxruntime(output_path, {"f_inp": inp1, "s_inp": inp2}, output_names, use_custom_ops=True)
+        self.assertAllClose(k_res, o_res[0], rtol=0.3, atol=0.1)
+        # make sure the original keras model wasn't trashed
+        k_res2 = model.predict([inp1, inp2])
+        self.assertAllClose(k_res2, o_res[0], rtol=0.3, atol=0.1)
+
     @check_tf_min_version("2.0")
     def test_function(self):
         def func(x, y):

tests/test_string_ops.py

@@ -130,17 +130,8 @@ def func(text):
     def _run_test_case(self, func, output_names_with_port, feed_dict, **kwargs):
         extra_opset = [utils.make_opsetid(constants.CONTRIB_OPS_DOMAIN, 1)]
         process_args = {"extra_opset": extra_opset}
-        return self.run_test_case(func, feed_dict, [], output_names_with_port, process_args=process_args, **kwargs)
-
-    def run_onnxruntime(self, model_path, inputs, output_names):
-        """Run test against onnxruntime backend."""
-        from onnxruntime_customops import get_library_path
-        import onnxruntime as rt
-        opt = rt.SessionOptions()
-        opt.register_custom_ops_library(get_library_path())
-        m = rt.InferenceSession(model_path, opt)
-        results = m.run(output_names, inputs)
-        return results
+        return self.run_test_case(func, feed_dict, [], output_names_with_port,
+                                  use_custom_ops=True, process_args=process_args, **kwargs)
 
     @requires_custom_ops("WordpieceTokenizer")
     @check_tf_min_version("2.0", "tensorflow_text")

tf2onnx/convert.py

@@ -329,7 +329,6 @@ def wrap_call(*args, training=False, **kwargs):
     output_names = [output_tensor.name for output_tensor in concrete_func.outputs
                     if output_tensor.dtype != tf.dtypes.resource]
 
-    initialized_tables = None
     tensors_to_rename = tensor_names_from_structed(concrete_func, input_names, output_names)
     reverse_lookup = {v: k for k, v in tensors_to_rename.items()}
 
@@ -341,7 +340,8 @@ def wrap_call(*args, training=False, **kwargs):
         output_names = [reverse_lookup[out] for out in concrete_func.structured_outputs.keys()]
 
     with tf.device("/cpu:0"):
-        frozen_graph = tf_loader.from_function(concrete_func, input_names, output_names, large_model=large_model)
+        frozen_graph, initialized_tables = \
+            tf_loader.from_trackable(model, concrete_func, input_names, output_names, large_model)
         model_proto, external_tensor_storage = _convert_common(
             frozen_graph,
             name=model.name,

tf2onnx/tf_loader.py

@@ -47,13 +47,12 @@ def not_implemented_tf_placeholder(*args, **kwargs):
 try:
     # pylint: disable=protected-access
     from tensorflow.python.saved_model.load import _RestoredResource as TfRestoredResourceType
+    from tensorflow.python.ops.lookup_ops import StaticHashTable as TfStaticHashTableType
+    from tensorflow.python.training.tracking.base import Trackable as TfTrackableType
 except ImportError:
     TfRestoredResourceType = tuple()  # isinstance(x, tuple()) is always false
-
-try:
-    from tensorflow.python.training.tracking.tracking import AutoTrackable as TfAutoTrackableType
-except ImportError:
-    TfAutoTrackableType = tuple()
+    TfStaticHashTableType = tuple()
+    TfTrackableType = tuple()
 
 if is_tf2():
     convert_variables_to_constants = tf.compat.v1.graph_util.convert_variables_to_constants
@@ -147,6 +146,46 @@ def fix_freezing_errors(graph_def):
     return graph_def
 
 
+def from_trackable(trackable, concrete_func, inputs, outputs, large_model):
+    err_large_model = "model exceeds maximum protobuf size of 2GB. Try setting large_model."
+
+    # Avoid errors due to bug in TF freezing
+    removed_resource_to_placeholder, graph_captures_copy, func_captures_copy = \
+        _remove_non_variable_resources_from_captures(concrete_func)
+
+    try:
+        frozen_graph = from_function(concrete_func, inputs, outputs, large_model)
+    except ValueError as e:
+        if any(msg in str(e) for msg in ["exceeds maximum protobuf size of 2GB", "string too long"]):
+            raise ValueError(err_large_model)
+        raise e
+
+    # We might be returning the concrete_func so let's put it back in working order
+    _restore_captured_resources(concrete_func, graph_captures_copy, func_captures_copy)
+
+    table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
+    placeholder_to_table_info = {}
+    _get_hash_table_info_from_trackable(trackable, table_names, key_dtypes, value_dtypes,
+                                        removed_resource_to_placeholder, placeholder_to_table_info)
+
+    initialized_tables = {}
+    for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
+        h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
+        try:
+            k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
+            initialized_tables[n] = (k.numpy(), v.numpy())
+        except Exception:  # pylint: disable=broad-except
+            logger.warning("Could not initialize table with shared_name = %r", n)
+
+    for placeholder in removed_resource_to_placeholder.values():
+        if placeholder not in placeholder_to_table_info:
+            logger.error("Could not find table resource to replace placeholder %s", placeholder)
+
+    replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
+
+    return frozen_graph, initialized_tables
+
+
 def from_function(func, input_names, output_names, large_model=False):
     if large_model:
         return convert_variables_to_constants_large_model(func)
@@ -327,7 +366,27 @@ def _from_saved_model_v1(sess, model_path, input_names, output_names, tag, signa
 def _get_hash_table_info_from_trackable(trackable, table_names, key_dtypes, value_dtypes,
                                         removed_resource_to_placeholder, placeholder_to_table_info):
     # pylint: disable=protected-access
-    for r in trackable.__dict__.values():
+    stack = [trackable]
+    visited = set()
+    while stack:
+        r = stack.pop()
+        visited.add(id(r))
+        try:
+            for trackable_ref in r._checkpoint_dependencies:
+                if id(trackable_ref.ref) not in visited:
+                    if isinstance(trackable_ref.ref, TfTrackableType):
+                        stack.append(trackable_ref.ref)
+        except Exception:  # pylint: disable=broad-except
+            continue
+        for t in r.__dict__.values() if hasattr(r, '__dict__') else []:
+            if isinstance(t, TfStaticHashTableType) and hasattr(t, '_shared_name'):
+                table_names.append(t._shared_name.encode())
+                key_dtypes.append(t.key_dtype.as_datatype_enum)
+                value_dtypes.append(t.value_dtype.as_datatype_enum)
+                table_handle = id(t.resource_handle)
+                if table_handle in removed_resource_to_placeholder:
+                    table_info = (table_names[-1], key_dtypes[-1], value_dtypes[-1])
+                    placeholder_to_table_info[removed_resource_to_placeholder[table_handle]] = table_info
         if isinstance(r, TfRestoredResourceType) and hasattr(r, '_create_resource'):
             try:
                 table_handle = id(r.resource_handle)
@@ -341,9 +400,6 @@ def _get_hash_table_info_from_trackable(trackable, table_names, key_dtypes, valu
             if table_handle in removed_resource_to_placeholder and len(new_names) == 1:
                 table_info = (new_names[0], new_k_dtypes[0], new_v_dtypes[0])
                 placeholder_to_table_info[removed_resource_to_placeholder[table_handle]] = table_info
-        if isinstance(r, TfAutoTrackableType):
-            _get_hash_table_info_from_trackable(r, table_names, key_dtypes, value_dtypes,
-                                                removed_resource_to_placeholder, placeholder_to_table_info)
 
 
 def _remove_non_variable_resources_from_captures(concrete_func):
@@ -399,7 +455,6 @@ def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_d
     err_index = "Invalid concrete_function value: %i. Valid values are [0 to %i]"
     err_no_sig = "No signatures found in model. Try --concrete_function instead."
     err_sig_nomatch = "Specified signature not in model %s"
-    err_large_model = "model exceeds maximum protobuf size of 2GB. Try running with --large_model flag."
 
     if tag is None:
         tag = ['serve']
@@ -456,39 +511,7 @@ def _from_saved_model_v2(model_path, input_names, output_names, tag, signature_d
         outputs = output_names
         logger.info("Outputs not left as None; will use provided names not structured output names.")
 
-    # Avoid errors due to bug in TF freezing
-    removed_resource_to_placeholder, graph_captures_copy, func_captures_copy = \
-        _remove_non_variable_resources_from_captures(concrete_func)
-
-    try:
-        frozen_graph = from_function(concrete_func, inputs, outputs, large_model)
-    except ValueError as e:
-        if any(msg in str(e) for msg in ["exceeds maximum protobuf size of 2GB", "string too long"]):
-            raise ValueError(err_large_model)
-        raise e
-
-    # We might be returning the concrete_func so let's put it back in working order
-    _restore_captured_resources(concrete_func, graph_captures_copy, func_captures_copy)
-
-    table_names, key_dtypes, value_dtypes = get_hash_table_info(frozen_graph)
-    placeholder_to_table_info = {}
-    _get_hash_table_info_from_trackable(imported, table_names, key_dtypes, value_dtypes,
-                                        removed_resource_to_placeholder, placeholder_to_table_info)
-
-    initialized_tables = {}
-    for n, k_dtype, val_dtype in zip(table_names, key_dtypes, value_dtypes):
-        h = lookup_ops.hash_table_v2(k_dtype, val_dtype, shared_name=n)
-        try:
-            k, v = lookup_ops.lookup_table_export_v2(h, k_dtype, val_dtype)
-            initialized_tables[n] = (k.numpy(), v.numpy())
-        except Exception:  # pylint: disable=broad-except
-            logger.warning("Could not initialize table with shared_name = %r", n)
-
-    for placeholder in removed_resource_to_placeholder.values():
-        if placeholder not in placeholder_to_table_info:
-            logger.error("Could not find table resource to replace placeholder %s", placeholder)
-
-    replace_placeholders_with_tables(frozen_graph, placeholder_to_table_info)
+    frozen_graph, initialized_tables = from_trackable(imported, concrete_func, inputs, outputs, large_model)
 
     return frozen_graph, inputs, outputs, concrete_func, imported, initialized_tables, tensors_to_rename