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Used share_memory param in tf.Graph decoder. (#18747)

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Anastasiia Pnevskaia 2023-07-25 11:16:17 +02:00 committed by GitHub
parent be02d1a3c9
commit 5aad9ee652
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6 changed files with 65 additions and 15 deletions
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9
src/bindings/python/src/openvino/frontend/tensorflow/graph_iterator.py
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@ -10,12 +10,13 @@ from openvino.frontend.tensorflow.py_tensorflow_frontend import _FrontEndPyGraph
class GraphIteratorTFGraph(GraphIterator):
def __init__(self, tf_graph: tf.Graph, inner_graph: bool = False):
def __init__(self, tf_graph: tf.Graph, share_weights: bool, inner_graph: bool = False):
GraphIterator.__init__(self)
self.m_graph = tf_graph
self.m_node_index = 0
self.m_decoders = []
self.m_inner_graph = inner_graph
self.m_share_weights = share_weights
self.m_vars = None
if hasattr(tf_graph, "variables"):
@ -24,7 +25,7 @@ class GraphIteratorTFGraph(GraphIterator):
self.m_vars = tf_graph.variables
for op in tf_graph.get_operations():
self.m_decoders.append(TFGraphNodeDecoder(op, inner_graph))
self.m_decoders.append(TFGraphNodeDecoder(op, share_weights, inner_graph))
self.m_iterators = {}
for func_name, _ in self.m_graph._functions.items():
@ -85,5 +86,7 @@ class GraphIteratorTFGraph(GraphIterator):
if func_name not in self.m_iterators:
return None
if self.m_iterators[func_name] is None:
self.m_iterators[func_name] = GraphIteratorTFGraph(self.m_graph._functions[func_name].graph, True)
self.m_iterators[func_name] = GraphIteratorTFGraph(self.m_graph._functions[func_name].graph,
self.m_share_weights,
True)
return self.m_iterators[func_name]

6
src/bindings/python/src/openvino/frontend/tensorflow/node_decoder.py
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@ -47,7 +47,7 @@ def tf_attr_to_ov(attr):
class TFGraphNodeDecoder(DecoderBase):
def __init__(self, operation: tf.Operation, inner_graph: bool):
def __init__(self, operation: tf.Operation, share_weights: bool, inner_graph: bool):
DecoderBase.__init__(self)
assert isinstance(operation, tf.Operation), "Unknown operation type. " \
"Expected tf.Operation, got {}".format(type(operation))
@ -57,9 +57,7 @@ class TFGraphNodeDecoder(DecoderBase):
# Copies value from inner buffer of TF_Operation to NodeDef class.
self.m_node_def = self.m_operation.node_def
# TODO: Create parameter in convert_model() for turning on/off shared memory, ticket: 114971
self.m_shared_memory = True
self.m_shared_memory = share_weights
if self.m_operation.type == "Const":
self.m_data_type = tf.dtypes.DType(self.m_node_def.attr["dtype"].type).name

6
src/bindings/python/src/openvino/frontend/tensorflow/utils.py
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@ -143,7 +143,7 @@ def type_supported_by_tf_fe(input_model):
return False
def create_tf_graph_iterator(input_model, placeholder_shapes, placeholder_data_types, example_input):
def create_tf_graph_iterator(input_model, placeholder_shapes, placeholder_data_types, example_input, share_weights):
input_model = trace_tf_model_if_needed(input_model, placeholder_shapes, placeholder_data_types, example_input)
import tensorflow as tf
@ -151,9 +151,9 @@ def create_tf_graph_iterator(input_model, placeholder_shapes, placeholder_data_t
if model_is_graph_iterator(input_model):
return input_model
if isinstance(input_model, tf.Graph):
return GraphIteratorTFGraph(input_model)
return GraphIteratorTFGraph(input_model, share_weights)
elif isinstance(input_model, tf.types.experimental.ConcreteFunction):
return GraphIteratorTFGraph(input_model.graph)
return GraphIteratorTFGraph(input_model.graph, share_weights)
raise Exception("Could not wrap model of type {} to GraphIteratorTFGraph.".format(type(input_model)))

53
tests/layer_tests/mo_python_api_tests/test_mo_convert_tf.py
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@ -688,7 +688,7 @@ class TestMoConvertTF(CommonMOConvertTest):
def test_zero_copy(self, ie_device, precision, ir_version, temp_dir):
import tensorflow as tf
from openvino.tools.mo import convert_model
from openvino.tools.ovc import convert_model
from openvino.runtime import compile_model
class LayerModel(tf.Module):
def __init__(self):
@ -710,7 +710,7 @@ class TestMoConvertTF(CommonMOConvertTest):
test_input = np.array(7.).astype(np.float32)
# Convert model to OV
ov_model = convert_model(keras_model, input_shape=[1])
ov_model = convert_model(keras_model, input=[1], share_weights=True)
cmp_model = compile_model(ov_model)
# Check model inference
@ -734,8 +734,55 @@ class TestMoConvertTF(CommonMOConvertTest):
assert np.array_equal(ov_infer2['Identity:0'], fw_infer2)
assert np.array_equal(ov_infer2['Identity:0'], [ 0., 8., 16.])
def test_turn_off_sharing(self, ie_device, precision, ir_version, temp_dir):
import tensorflow as tf
from openvino.tools.ovc import convert_model
from openvino.runtime import compile_model
class LayerModel(tf.Module):
def __init__(self):
super(LayerModel, self).__init__()
self.var1 = tf.Variable([7., 5., 6.], name='var1')
self.var2 = tf.Variable([5., 7., 3.], name='var2')
@tf.function
def sub_function(self, input):
return input * self.var1 + self.var2
@tf.function()
def __call__(self, input):
return self.sub_function(input)
# Create TF model with variables
keras_model = LayerModel()
test_input = np.array(7.).astype(np.float32)
# Convert model to OV
ov_model = convert_model(keras_model, input=[1], share_weights=False)
cmp_model = compile_model(ov_model)
# Check model inference
ov_infer1 = cmp_model(test_input, ie_device)
fw_infer1 = keras_model(test_input).numpy()
assert np.array_equal(ov_infer1['Identity:0'], fw_infer1)
assert np.array_equal(ov_infer1['Identity:0'], [54., 42., 45.])
# Change value of variables in original model
for val in keras_model.variables:
arr = val.value().__array__()
arr[0] = 0
arr[1] = 1
arr[2] = 2
# Check model inference
ov_infer2 = cmp_model(test_input)
fw_infer2 = keras_model(test_input).numpy()
# Check model inference calculated with old constant values
assert not np.array_equal(ov_infer2['Identity:0'], fw_infer2)
assert np.array_equal(ov_infer2['Identity:0'], [54., 42., 45.])
def test_memory_loss(self, ie_device, precision, ir_version, temp_dir):
# This test checks that the memory allocated for constants
# is not lost after returning the model from convert_model() method.
@ -823,7 +870,7 @@ class TestTFLoadByModel(unittest.TestCase):
return tf_net
from openvino.frontend.tensorflow.graph_iterator import GraphIteratorTFGraph
from openvino.frontend import FrontEndManager
model = GraphIteratorTFGraph(simple_tf_model())
model = GraphIteratorTFGraph(simple_tf_model(), True)
fem = FrontEndManager()
fe = fem.load_by_model(model)
assert fe is not None

3
tools/mo/openvino/tools/mo/convert_impl.py
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@ -396,7 +396,8 @@ def prepare_ir(argv: argparse.Namespace):
argv.input_model = create_tf_graph_iterator(argv.input_model,
argv.placeholder_shapes,
argv.placeholder_data_types,
getattr(argv, "example_input", None))
getattr(argv, "example_input", None),
argv.share_weights)
try:
t.send_event("mo", "conversion_method", moc_front_end.get_name() + "_frontend")
moc_front_end.add_extension(TelemetryExtension("mo", t.send_event, t.send_error, t.send_stack_trace))

3
tools/ovc/openvino/tools/ovc/convert_impl.py
View File

@ -136,7 +136,8 @@ def prepare_ir(argv: argparse.Namespace):
argv.input_model = create_tf_graph_iterator(argv.input_model,
argv.placeholder_shapes,
argv.placeholder_data_types,
getattr(argv, "example_input", None))
getattr(argv, "example_input", None),
argv.share_weights)
t.send_event("mo", "conversion_method", moc_front_end.get_name() + "_frontend")
moc_front_end.add_extension(TelemetryExtension("mo", t.send_event, t.send_error, t.send_stack_trace))
if new_extensions_used(argv):