def test_identify_input_output(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
inputs, outputs = g.get_graph_input_output()
self.assertEqual(inputs, self.inputs)
self.assertEqual(outputs, self.outputs)
def test_no_input_output_config(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
float_graph_def = g.dump_graph()
from lpot import Quantization, common
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy', shape=(20, 224, 224, 3), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=2)
quantizer.eval_dataloader = common.DataLoader(dataset, batch_size=2)
quantizer.model = float_graph_def
output_graph = quantizer()
self.assertGreater(len(output_graph.graph_def.node), 0)
def test_replace_node(self):
graph_analyzer = GraphAnalyzer()
graph_analyzer.graph = copy.deepcopy(self.graph_def)
graph_analyzer.parse_graph()
new_add_node = node_def_pb2.NodeDef()
new_add_node.op = "Add"
new_add_node.name = "add1"
new_add_node.input.extend(
[self.input0_node.name, self.input1_node.name])
graph_analyzer.replace_node(new_add_node, self.add_node.name,
[self.mul_node.name])
result_graph = graph_analyzer.dump_graph()
assert self.add_node not in list(result_graph.node)
assert new_add_node in list(result_graph.node)
def test_invalid_input_output_config(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
float_graph_def = g.dump_graph()
from lpot import Quantization, common
quantizer = Quantization('fake_yaml_2.yaml')
dataset = quantizer.dataset('dummy', shape=(20, 224, 224, 3), label=True)
quantizer.calib_dataloader = common.DataLoader(dataset, batch_size=2)
quantizer.eval_dataloader = common.DataLoader(dataset, batch_size=2)
quantizer.model = float_graph_def
model = quantizer()
# will detect the right inputs/outputs
self.assertNotEqual(model.input_node_names, ['x'])
self.assertNotEqual(model.output_node_names, ['op_to_store'])
def test_replace_constant_graph_with_constant_node(self):
graph_analyzer = GraphAnalyzer()
graph_analyzer.graph = copy.deepcopy(self.graph_def)
graph_analyzer.parse_graph()
new_constant_value = np.random.random([4, 1])
new_constant_type = tf.as_dtype(np.float32(new_constant_value).dtype)
new_constant_node = GraphRewriterHelper.create_constant_node(
self.add_node.name + "_const", new_constant_value,
new_constant_type)
assert graph_analyzer.replace_constant_graph_with_constant_node(
new_constant_node, self.add_node.name)
result_graph = graph_analyzer.dump_graph()
assert len(list(result_graph.node)) == 10
new_constant_value = np.random.random([4, 1])
new_constant_type = tf.as_dtype(np.float32(new_constant_value).dtype)
new_constant_node = GraphRewriterHelper.create_constant_node(
self.mul_node.name + "_const", new_constant_value,
new_constant_type)
assert graph_analyzer.replace_constant_graph_with_constant_node(
new_constant_node, self.mul_node.name)
result_graph = graph_analyzer.dump_graph()
assert len(list(result_graph.node)) == 8
new_constant_value = np.random.random([4, 1])
new_constant_type = tf.as_dtype(np.float32(new_constant_value).dtype)
new_constant_node = GraphRewriterHelper.create_constant_node(
self.sqrt_node.name + "_const", new_constant_value,
new_constant_type)
assert graph_analyzer.replace_constant_graph_with_constant_node(
new_constant_node, self.sqrt_node.name)
result_graph = graph_analyzer.dump_graph()
assert len(list(result_graph.node)) == 7
new_constant_value = np.random.random([4, 1])
new_constant_type = tf.as_dtype(np.float32(new_constant_value).dtype)
new_constant_node = GraphRewriterHelper.create_constant_node(
self.block_node.name + "_const", new_constant_value,
new_constant_type)
assert not graph_analyzer.replace_constant_graph_with_constant_node(
new_constant_node, self.block_node.name)
def test_identify_input_output(self):
g = GraphAnalyzer()
g.graph = self.input_graph
g.parse_graph()
inputs, outputs = g.get_graph_input_output()
self.assertEqual(inputs, self.inputs)
self.assertEqual(outputs, self.outputs)
input_graph = tf.compat.v1.GraphDef()
with open('model_1.pb', "rb") as f:
input_graph.ParseFromString(f.read())
g = GraphAnalyzer()
g.graph = input_graph
g.parse_graph()
inputs, outputs = g.get_graph_input_output()
self.assertEqual(inputs, ['sub'])
self.assertEqual(outputs, ['op_to_store'])
input_graph = tf.compat.v1.GraphDef()
with open('model_2.pb', "rb") as f:
input_graph.ParseFromString(f.read())
g = GraphAnalyzer()
g.graph = input_graph
g.parse_graph()
inputs, outputs = g.get_graph_input_output()
self.assertEqual(inputs, [])
self.assertEqual(outputs, [])
input_graph = tf.compat.v1.GraphDef()
with open('model_3.pb', "rb") as f:
input_graph.ParseFromString(f.read())
g = GraphAnalyzer()
g.graph = input_graph
g.parse_graph()
inputs, outputs = g.get_graph_input_output()
self.assertEqual(inputs, [])
self.assertEqual(outputs, [])
def test_tensorflow_concat_quantization(self):
output_graph_def = read_graph(self.pb_path)
from lpot import Quantization
quantizer = Quantization('fake_yaml.yaml')
dataset = quantizer.dataset('dummy',
shape=(100, 299, 299, 3),
label=True)
dataloader = quantizer.dataloader(dataset)
output_graph = quantizer(output_graph_def,
q_dataloader=dataloader,
eval_dataloader=dataloader)
found_quantized_concat_node = False
target_concat_node_name = 'v0/cg/incept_v3_a0/concat_eightbit_quantized_concatv2'
from lpot.adaptor.tf_utils.graph_rewriter.graph_util import GraphAnalyzer
cur_graph = GraphAnalyzer()
cur_graph.graph = output_graph.as_graph_def()
graph_info = cur_graph.parse_graph()
found_quantized_concat_node = target_concat_node_name in graph_info
self.assertEqual(found_quantized_concat_node, True)
min_out, max_out = [], []
for input_conv_name in graph_info[
target_concat_node_name].node.input[:4]:
# print (input_conv_name, graph_info[input_conv_name].node.input)
min_freezed_out_name = graph_info[input_conv_name].node.input[-2]
max_freezed_out_name = graph_info[input_conv_name].node.input[-1]
min_freezed_out_value = (graph_info[min_freezed_out_name].node.
attr['value'].tensor.float_val)[0]
max_freezed_out_value = (graph_info[max_freezed_out_name].node.
attr['value'].tensor.float_val)[0]
min_out.append(min_freezed_out_value)
max_out.append(max_freezed_out_value)
self.assertEqual(len(set(min_out)), 1)
self.assertEqual(len(set(max_out)), 1)
class PreOptimization(object):
def __init__(self, model, inputs, outputs):
self.output_node_names = list(
set([output.split(":")[0] for output in outputs]))
self.input_graph = get_graph_def(model, self.output_node_names)
if 'MakeIterator' in [node.op for node in self.input_graph.node]:
self.output_node_names.append('MakeIterator')
self.analyzer = GraphAnalyzer()
self.analyzer.graph = self.input_graph
self.analyzer.parse_graph()
self.input_node_names = inputs
self.logger = logging.getLogger()
self._tmp_graph_def = None
self._excluded_node_names = []
if not self.input_node_names or not self.output_node_names:
self.input_node_names, self.output_node_names = self.analyzer.get_graph_input_output(
)
def get_excluded_node_names(self):
"""Get the excluded node name
Returns:
string list: the excluded ops' name
"""
return self._excluded_node_names
@dump_elapsed_time("Pass Pre Optimization")
def get_optimized_graphdef(self):
"""Executed the non-precision dependant graph optimization.
The input graph will be optimized with following passes:
1. Remove the training nodes like Identity Op.
2. Split the shared nodes like weights node for multi-Conv2d.
3. Fold Constant Nodes as less as possible.
4. Fuse the Mul node into the previous Conv2D/MatMul if possible.
5. Strip the useless nodes.
6. Do the Common sequence elimation optimization on the graph.
7. Fold the BN node into the previous Conv2D if possible.
Returns:
[graphdef]: the optimized graphdef object.
"""
self.logger.debug("Start to pre optimize input model...")
self._tmp_graph_def = ConvertLayoutOptimizer(
self.input_graph, self.output_node_names).do_transformation()
self._tmp_graph_def = RemoveTrainingNodesOptimizer(
self._tmp_graph_def,
protected_nodes=self.output_node_names).do_transformation()
self._tmp_graph_def = SplitSharedInputOptimizer(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = GraphFoldConstantOptimizer(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = FuseColumnWiseMulOptimizer(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = StripUnusedNodesOptimizer(
self._tmp_graph_def, self.input_node_names,
self.output_node_names).do_transformation()
self._tmp_graph_def = GraphCseOptimizer(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = FoldBatchNormNodesOptimizer(
self._tmp_graph_def).do_transformation()
#TODO we should handle all control ops elegantly not bypass it.
self._tmp_graph_def, excluded_node_names = UpdateEnterOptimizer(
self._tmp_graph_def).do_transformation()
self._excluded_node_names.extend(excluded_node_names)
self._tmp_graph_def.library.CopyFrom(self.input_graph.library)
return self._tmp_graph_def
def get_matched_nodes(self, patterns):
"""Searche the matched nodes with the specified patterns
Args:
patterns ([string list]): The patterns should be illustrated as below.
[['MatMul'], ("BiasAdd"), ("Relu",)]
Returns:
[string list]: It will return the list that contains the matched nodes name
and pattern. ['matched_node_a_name', 'matched_node_a_name',['MatMul','BiasAdd']]
"""
self.analyzer.graph = self._tmp_graph_def
self.analyzer.parse_graph()
res = []
for sub_pattern in patterns:
res.extend(self.analyzer.query_fusion_pattern_nodes(sub_pattern))
return res
def has_positive_input(self, node_name):
"""Check the specified node has the positive input or not.
Args:
node_name ([string]): node's name
Returns:
[bool]: True if the node has the positive input data,
False if the node has the negative input data.
"""
return self.analyzer.has_positive_input(node_name)
class PreOptimization():
def __init__(self, model, optimization):
self.model = model
self.optimization = optimization
self.output_node_names = model.output_node_names
self.input_node_names = model.input_node_names
if model.iter_op is not None:
self.output_node_names.append('MakeIterator')
self.analyzer = GraphAnalyzer()
self.analyzer.graph = model.graph_def
self.analyzer.parse_graph()
self.logger = logging.getLogger()
self._tmp_graph_def = None
self._excluded_node_names = []
def get_excluded_node_names(self):
"""Get the excluded node name
Returns:
string list: the excluded ops' name
"""
return self._excluded_node_names
@dump_elapsed_time("Pass Pre Optimization")
def get_optimized_model(self):
"""Executed the non-precision dependant graph optimization.
The input graph will be optimized with following passes:
1. Remove the training nodes like Identity Op.
2. Split the shared nodes like weights node for multi-Conv2d.
3. Fold Constant Nodes as less as possible.
4. Fuse the Mul node into the previous Conv2D/MatMul if possible.
5. Strip the useless nodes.
6. Do the Common sequence elimation optimization on the graph.
7. Fold the BN node into the previous Conv2D if possible.
Returns:
[graphdef]: the optimized graphdef object.
"""
self.logger.debug("Start to pre optimize input model...")
origin_model = TensorflowModel(self.model._model,
self.model.framework_specific_info,
**self.model.kwargs)
self._tmp_graph_def = ConvertLayoutOptimizer(
self.model.graph_def, self.output_node_names).do_transformation()
self._tmp_graph_def = GrapplerOptimizer(
self._tmp_graph_def, self.output_node_names, self.optimization).do_transformation()
self._tmp_graph_def = RemoveTrainingNodesOptimizer(
self._tmp_graph_def, protected_nodes=self.output_node_names).do_transformation()
self._tmp_graph_def = SplitSharedInputOptimizer(self._tmp_graph_def).do_transformation()
self._tmp_graph_def = GraphFoldConstantOptimizer(self._tmp_graph_def).do_transformation()
self._tmp_graph_def = FuseColumnWiseMulOptimizer(self._tmp_graph_def).do_transformation()
self._tmp_graph_def = StripUnusedNodesOptimizer(self._tmp_graph_def,
self.input_node_names, self.output_node_names).do_transformation()
self._tmp_graph_def = FuseGeluOptimizer(self._tmp_graph_def).do_transformation()
self._tmp_graph_def = GraphCseOptimizer(self._tmp_graph_def).do_transformation()
self._tmp_graph_def = FoldBatchNormNodesOptimizer(
self._tmp_graph_def).do_transformation()
#TODO we should handle all control ops elegantly not bypass it.
self._tmp_graph_def, excluded_node_names = UpdateEnterOptimizer(
self._tmp_graph_def).do_transformation()
#TODO we need to remove below optimizer once the TF enabled the single
# matmul op quantization
self._tmp_graph_def = InjectDummyBiasAddOptimizer(
self._tmp_graph_def).do_transformation()
self._tmp_graph_def = FuseTransposeReshapeOptimizer(
self._tmp_graph_def).do_transformation()
self._excluded_node_names.extend(excluded_node_names)
self._tmp_graph_def.library.CopyFrom(self.model.graph_def.library)
origin_model.graph_def = self._tmp_graph_def
return origin_model
def get_matched_nodes(self, patterns):
"""Searche the matched nodes with the specified patterns
Args:
patterns ([string list]): The patterns should be illustrated as below.
[['MatMul'], ("BiasAdd"), ("Relu",)]
Returns:
[string list]: It will return the list that contains the matched nodes name
and pattern. ['matched_node_a_name', 'matched_node_a_name',['MatMul','BiasAdd']]
"""
self.analyzer.graph = self._tmp_graph_def
self.analyzer.parse_graph()
res = []
for sub_pattern in patterns:
res.extend([i for i in self.analyzer.query_fusion_pattern_nodes(
sub_pattern) if i not in res])
return res
def has_positive_input(self, node_name):
"""Check the specified node has the positive input or not.
Args:
node_name ([string]): node's name
Returns:
[bool]: True if the node has the positive input data,
False if the node has the negative input data.
"""
return self.analyzer.has_positive_input(node_name)
def test_graph_cse(self):
tf.compat.v1.disable_eager_execution()
input_constant_name = "input_constant"
relu_name = "relu"
float_graph_def = graph_pb2.GraphDef()
input_constant = QuantizeGraphHelper.create_constant_node(
input_constant_name,
value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
dtype=dtypes.float32,
shape=[1, 2, 6, 1])
float_graph_def.node.extend([input_constant])
relu_node = QuantizeGraphHelper.create_node("Relu", relu_name,
[input_constant_name])
QuantizeGraphHelper.set_attr_dtype(relu_node, "T", dtypes.float32)
float_graph_def.node.extend([relu_node])
b_constant_name = "b_constant"
mat_mul_name = "mat_mul"
b_constant = QuantizeGraphHelper.create_constant_node(
b_constant_name,
value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
dtype=dtypes.float32,
shape=[2, 6])
float_graph_def.node.extend([b_constant])
mat_mul_node = QuantizeGraphHelper.create_node(
"MatMul", mat_mul_name, [relu_name, b_constant_name])
QuantizeGraphHelper.set_attr_dtype(mat_mul_node, "T", dtypes.float32)
QuantizeGraphHelper.set_attr_bool(mat_mul_node, "transpose_a", False)
QuantizeGraphHelper.set_attr_bool(mat_mul_node, "transpose_b", False)
float_graph_def.node.extend([mat_mul_node])
bias_add_name = "bias_add"
offset_constant_name = "offset_constant"
offset_constant = QuantizeGraphHelper.create_constant_node(
offset_constant_name,
value=[1, 2, 3, 4, 5, 6],
dtype=dtypes.float32,
shape=[6])
float_graph_def.node.extend([offset_constant])
bias_add_node = QuantizeGraphHelper.create_node(
"BiasAdd", bias_add_name, [mat_mul_name, offset_constant_name])
QuantizeGraphHelper.set_attr_dtype(bias_add_node, "T", dtypes.float32)
float_graph_def.node.extend([bias_add_node])
post_relu_name = "post_relu"
post_relu_node = QuantizeGraphHelper.create_node(
"Relu", post_relu_name, [bias_add_name])
float_graph_def.node.extend([post_relu_node])
last_identity_node_name = 'last_identity'
last_identity_node = QuantizeGraphHelper.create_node(
"Identity", last_identity_node_name, [post_relu_name])
float_graph_def.node.extend([last_identity_node])
analyzer = GraphAnalyzer()
analyzer.graph = float_graph_def
analyzer.parse_graph()
res = analyzer.query_fusion_pattern_nodes([['MatMul'], ("BiasAdd"),
("Relu")])
self.assertEqual(3, len(res[0][-1]))