def test_backward_bfs_simple(self):
# Placeholder->ScaleShift->Mul->Add
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'mul_1'),
('mul_1', 'mul_1_data'), ('mul_1_data', 'add_1'),
('add_1', 'add_1_data'),
('add_1_data', 'op_output')])
res = backward_bfs(Node(graph, 'add_1_data'),
['Add', 'ScaleShift', 'Mul'], ['Parameter'])
self.assertTrue(
len(res) == 1 and res[0].id == 'placeholder_1',
'Placeholder operation was not found by bfs')
res = backward_bfs(Node(graph, 'add_1'), [], ['Parameter'],
allowed_all=True)
self.assertTrue(
len(res) == 1 and res[0].id == 'placeholder_1',
'Placeholder operation was not found by bfs')
res = backward_bfs(Node(graph, 'add_1_data'), ['Add'], ['ScaleShift'])
self.assertTrue(
len(res) == 0,
'No one node should be found! But bfs found {} nodes'.format(
len(res)))
res = backward_bfs(Node(graph, 'add_1_data'), ['Add', 'Mul'],
['Parameter', 'ScaleShift'])
self.assertTrue(
len(res) == 1 and res[0].id == 'scaleshift_1',
'BFS should find only one ScaleShift operation')
def fuse_linear_ops(graph: Graph):
"""
This function makes fusing of linear operations (Mul,Add) to Convolution/FC.
"""
fuse_count = 0
# Fusion in backward direction
nodes = graph.pseudo_topological_sort()
for node in nodes:
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_in_port(
node) is not None and node.soft_get('can_be_fused') is True:
fuse_nodes = backward_bfs(
node, [],
['Convolution', 'Deconvolution', 'FullyConnected', 'MatMul'])
is_fused = _fuse_mul(graph, node, fuse_nodes)
if hasattr(graph, 'graph') and 'cmd_params' in graph.graph and \
not graph.graph['cmd_params'].generate_experimental_IR_V10:
# Fuse Add to Convolution/FC
if node.soft_get('op') == 'Add'\
and get_value_in_port(node) is not None\
and node.soft_get('can_be_fused') is True:
fuse_nodes = backward_bfs(node, [], [
'Convolution', 'Deconvolution', 'FullyConnected', 'MatMul'
])
is_fused = _fuse_add(graph, node, fuse_nodes)
fuse_count += is_fused
# Fusion in forward direction
nodes = graph.pseudo_topological_sort(reverse=True)
for node in nodes:
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_in_port(
node) is not None and node.soft_get('can_be_fused') is True:
fuse_nodes = forward_bfs(
node, [],
['Convolution', 'Deconvolution', 'FullyConnected', 'MatMul'])
is_fused = _fuse_mul(graph, node, fuse_nodes, False)
# Fuse Add to Convolution/FC
if hasattr(graph, 'graph') and 'cmd_params' in graph.graph and \
not graph.graph['cmd_params'].generate_experimental_IR_V10:
if node.soft_get('op') == 'Add' and \
get_value_in_port(node) is not None and \
node.soft_get('can_be_fused') is True:
fuse_nodes = forward_bfs(node, [],
['FullyConnected', 'MatMul'])
is_fused = _fuse_add(graph, node, fuse_nodes, False)
fuse_count += is_fused
log.debug("Fused {} nodes".format(fuse_count))
def fuse_linear_ops(graph: nx.MultiDiGraph):
"""
This function makes fusing of linear operations (Mul,Add) to Convolution/FC.
"""
fuse_count = 0
# Fusion in backward direction
nodes = pseudo_topological_sort(graph)
for idx in nodes:
node = Node(graph, idx)
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_id(
node) is not None and node.soft_get('can_be_fused') == True:
fuse_nodes = backward_bfs(
node, [], ['Convolution', 'Deconvolution', 'FullyConnected'])
is_fused = _fuse_mul(graph, node, fuse_nodes)
# Fuse Add to Convolution/FC
if node.soft_get('op') == 'Add' and get_value_id(
node) is not None and node.soft_get('can_be_fused') == True:
fuse_nodes = backward_bfs(
node, [], ['Convolution', 'Deconvolution', 'FullyConnected'])
is_fused = _fuse_add(graph, node, fuse_nodes)
fuse_count += is_fused
# Fusion in forward direction
nodes = pseudo_topological_sort(graph, reverse=True)
for idx in nodes:
node = Node(graph, idx)
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_id(
node) is not None and node.soft_get('can_be_fused') == True:
fuse_nodes = forward_bfs(
node, [], ['Convolution', 'Deconvolution', 'FullyConnected'])
is_fused = _fuse_mul(graph, node, fuse_nodes, False)
# Fuse Add to Convolution/FC
if node.soft_get('op') == 'Add' and get_value_id(
node) is not None and node.soft_get('can_be_fused') == True:
fuse_nodes = forward_bfs(node, [], ['FullyConnected'])
is_fused = _fuse_add(graph, node, fuse_nodes, False)
fuse_count += is_fused
log.debug("Fused {} nodes".format(fuse_count))
def test_backward_bfs_hard(self):
# Placeholder->ScaleShift->Mul1->Add1---->Concat
# `----------->Add2->Mul2--'
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('placeholder_1_data', 'add_2'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'mul_1'),
('mul_1', 'mul_1_data'), ('mul_1_data', 'add_1'),
('add_1', 'add_1_data'), ('add_2', 'add_2_data'),
('add_2_data', 'mul_2'), ('mul_2', 'mul_2_data'),
('add_1_data', 'concat_1'),
('mul_2_data', 'concat_1'),
('concat_1', 'concat_1_data')],
{'concat_1_data': {
'is_output': True
}})
res = backward_bfs(Node(graph, 'concat_1'),
['ScaleShift', 'Mul', 'Add'], ['Placeholder'])
self.assertTrue(len(res) == 0, 'Smth went wrong with bfs')
res = backward_bfs(Node(graph, 'concat_1'), ['Mul'], ['Add'])
self.assertTrue(
len(res) == 2
and all([res[x].id in ['add_1', 'add_2']
for x in range(len(res))]),
'Add operations was not found by bfs')
res = backward_bfs(Node(graph, 'concat_1'), ['ScaleShift'], ['Add'])
self.assertTrue(len(res) == 0, 'BFS shouldn\'t find any operations')
res = backward_bfs(Node(graph, 'concat_1'), [], ['Add'],
allowed_all=True)
self.assertTrue(
len(res) == 2
and all([res[x].id in ['add_1', 'add_2']
for x in range(len(res))]),
'Add operations was not found by bfs')
res = backward_bfs(Node(graph, 'concat_1'), ['ScaleShift'],
['ScaleShift'])
self.assertTrue(
len(res) == 0,
'No one node should be found! But bfs found {} nodes'.format(
len(res)))
def fuse_linear_ops(graph: Graph):
"""
This function makes fusing of linear operations (Mul,Add) to Convolution/FC.
"""
fuse_count = 0
# Fusion in backward direction
nodes = graph.pseudo_topological_sort()
for node in nodes:
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_in_port(
node) is not None and node.has_and_set('can_be_fused'):
fuse_nodes = backward_bfs(
node, [], ['Convolution', 'Deconvolution', 'MatMul'])
is_fused = _fuse_mul(graph, node, fuse_nodes)
fuse_count += is_fused
# Fusion in forward direction
nodes = graph.pseudo_topological_sort(reverse=True)
for node in nodes:
is_fused = False
# Fuse Mul to Convolution/FC
if node.soft_get('op') == 'Mul' and get_value_in_port(
node) is not None and node.has_and_set('can_be_fused'):
fuse_nodes = forward_bfs(
node, [], ['Convolution', 'Deconvolution', 'MatMul'])
is_fused = _fuse_mul(graph, node, fuse_nodes, False)
fuse_count += is_fused
log.debug("Fused {} nodes".format(fuse_count))
def test_backward_bfs_cycle(self):
# Placeholder->ScaleShift->Mul->Add
graph = build_graph(nodes_attributes,
[('placeholder_1', 'placeholder_1_data'),
('placeholder_1_data', 'scaleshift_1'),
('scaleshift_1_w', 'scaleshift_1'),
('scaleshift_1', 'scaleshift_1_data'),
('scaleshift_1_data', 'mul_1'),
('mul_1', 'mul_1_data'),
('mul_1_data', 'add_1'),
('add_1', 'add_1_data'),
('add_1_data', 'placeholder_1'),
('add_1_data', 'op_output')
])
res = backward_bfs(Node(graph, 'add_1_data'), ['Add', 'ScaleShift', 'Mul', 'Parameter'], ['Conv2D'])
self.assertTrue(len(res) == 0, 'Sholdn\'t find any nodes due to cycle in graph')
