def test(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**const('depth', int64_array([2])),
**regular_op('onehot', {'type': 'OneHot', 'kind': 'op', 'op': 'OneHot'}),
**regular_op('reshape', {'type': 'Reshape', 'kind': 'op', 'op': 'Reshape'}),
**const('reshape_dims', int64_array([])),
**result('result'),
}
edges = [('input', 'onehot'),
('depth', 'onehot'),
('onehot', 'result'),
]
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [('input', 'onehot'),
('depth', 'reshape'),
('reshape_dims', 'reshape'),
('reshape', 'onehot'),
('onehot', 'result'),
]
graph_ref = build_graph(nodes, edges_ref)
OneHotDepthNormalizer().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_one(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('some_op', {'type': 'SomeOp', 'name': 'some_op_name'}),
**regular_op('fake_output', {'type': None, 'kind': 'op', 'op': 'FakeOutput', 'name': 'my_output_name'}),
**result('result'),
}
edges = [('input', 'some_op'),
('some_op', 'fake_output'),
('fake_output', 'result'),
]
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [('input', 'some_op'),
('some_op', 'result'),
]
graph_ref = build_graph(nodes, edges_ref, {'some_op': {'name': 'my_output_name'}})
FakeOutputResolver().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_swish_with_sigmoid_without_beta_different_tensors(self):
graph = build_graph_with_edge_attrs(
{
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('sigmoid', {'op': 'Sigmoid'}),
**regular_op('mul', {
'op': 'Mul',
'name': 'final_mul'
}),
**result('result'),
}, [('input_2', 'mul', {
'in': 0,
'out': 0
}), ('input', 'sigmoid', {
'in': 0,
'out': 0
}), ('sigmoid', 'mul', {
'in': 1,
'out': 0
}), ('mul', 'result', {
'in': 0,
'out': 0
})], {})
graph_ref = graph.copy()
graph.stage = 'front'
SwishWithSigmoidWithoutBeta().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
class SoftplusFusionTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('exp', {'op': 'Exp'}),
**const('const_1', float_array([1.0])),
**regular_op('add', {'op': 'Add'}),
**regular_op('ln', {
'op': 'Log',
'name': 'final_log'
}),
**result('result'),
}
edges = [('input', 'exp', {
'in': 0,
'out': 0
}), ('const_1', 'add', {
'in': 0,
'out': 0
}), ('exp', 'add', {
'in': 1,
'out': 0
}), ('add', 'ln', {
'in': 0,
'out': 0
}), ('ln', 'result', {
'in': 0,
'out': 0
})]
def test_softplus_fusion_test(self):
graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
SoftplusFusion().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
len(graph.get_op_nodes(name='final_log')) == 1
and graph.get_op_nodes(name='final_log')[0].op == 'SoftPlus')
def test_softplus_fusion_test_wrong_const(self):
graph = build_graph_with_edge_attrs(
self.nodes, self.edges,
{'const_1': {
'value': float_array([0.9999])
}})
graph_ref = graph.copy()
graph.stage = 'front'
SoftplusFusion().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
class SwishWithSigmoidWithBetaTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('beta', {'type': 'Parameter'}),
**regular_op('mul_beta', {'op': 'Mul'}),
**regular_op('sigmoid', {'op': 'Sigmoid'}),
**regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
**result('result'),
}
edges = [('input', 'mul_beta', {'in': 0, 'out': 0}),
('input', 'mul_2', {'in': 0, 'out': 0}),
('beta', 'mul_beta', {'in': 1, 'out': 0}),
('mul_beta', 'sigmoid', {'in': 0, 'out': 0}),
('sigmoid', 'mul_2', {'in': 1, 'out': 0}),
('mul_2', 'result', {'in': 0, 'out': 0})]
def test_swish_with_sigmoid_with_beta_test(self):
graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
new_ref_nodes = ref_nodes.copy()
new_ref_nodes.update(**regular_op('beta', {'type': 'Parameter'}))
graph_ref = build_graph(new_ref_nodes, ref_edges + [('beta', 'swish')])
graph.stage = 'front'
SwishWithSigmoidWithBeta().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(len(graph.get_op_nodes(name='final_mul')) == 1 and
graph.get_op_nodes(name='final_mul')[0].op == 'Swish')
def test_swish_with_sigmoid_with_beta_different_tensors(self):
graph = build_graph_with_edge_attrs({
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('beta', {'type': 'Parameter'}),
**regular_op('mul_beta', {'op': 'Mul'}),
**regular_op('sigmoid', {'op': 'Sigmoid'}),
**regular_op('mul_2', {'op': 'Mul', 'name': 'final_mul'}),
**result('result'),
}, [('input', 'mul_beta', {'in': 0, 'out': 0}),
('input_2', 'mul_2', {'in': 0, 'out': 0}),
('beta', 'mul_beta', {'in': 1, 'out': 0}),
('mul_beta', 'sigmoid', {'in': 0, 'out': 0}),
('sigmoid', 'mul_2', {'in': 1, 'out': 0}),
('mul_2', 'result', {'in': 0, 'out': 0})], {})
graph_ref = graph.copy()
graph.stage = 'front'
SwishWithSigmoidWithBeta().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def setUp(self):
self.start_node_name = 'StatefulPartitionedCall/Preprocessor/unstack'
self.end_node_name = 'StatefulPartitionedCall/Preprocessor/stack'
self.end_node_name2 = 'StatefulPartitionedCall/Preprocessor/stack2'
self.loop_start_node_name = 'prefix/map/while/Preprocessor/unstack'
self.loop_end_node_name = 'prefix/map/while/Preprocessor/stack'
self.mul_const = float32_array([0.025, 0.374, -0.45])
self.sub_const = float32_array([2.0, 3.0, 4.0])
self.nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('mul', {'op': 'Mul', 'type': 'Multiply', 'name': 'my_mul'}),
**regular_op('sub', {'op': 'Sub', 'type': 'Subtract', 'name': 'my_sub'}),
**const('mul_const', self.mul_const),
**const('sub_const', self.sub_const),
**regular_op(self.start_node_name, {'op': 'Identity'}),
**regular_op(self.end_node_name, {'op': 'Identity'}),
**regular_op(self.end_node_name2, {'op': 'Identity'}),
**regular_op('loop', {'op': 'Loop', 'body': None}),
**regular_op('resize', {'type': 'Interpolate'}),
**result('result'),
}
self.replacement_desc = {'start_nodes': [self.start_node_name],
'end_nodes': [self.end_node_name, self.end_node_name2]}
def generate_offsets():
offset_edges = []
offset_nodes = {}
for i, t in enumerate(time_offsets):
offset_nodes.update(**regular_op('memoryoffset_' +
str(i), {'type': None}))
if t != 0:
offset_edges.append(
('placeholder', 'memoryoffset_' + str(i), {
'out': 0,
'in': 0
}))
offset_edges.append(('memoryoffset_' + str(i), 'concat', {
'out': 0,
'in': i
}))
else:
offset_edges.append(('placeholder', 'concat', {
'out': 0,
'in': i
}))
return offset_nodes, offset_edges
def test(self):
nodes = {
**const('weights_inp', np.random.randn(100, 2)),
**regular_op('indices_inp', {'type': 'Parameter'}),
**regular_op('offsets_inp', {'type': 'Parameter'}),
**regular_op(
'aten', {
'type': None,
'kind': 'op',
'op': 'ATen',
'operator': 'embedding_bag',
'mode': 0,
'name': 'my_aten'
}),
**regular_op(
'emb_bag', {
'type': 'EmbeddingBagOffsetsSum',
'kind': 'op',
'op': 'EmbeddingBagOffsetsSum'
}),
**result('result'),
}
edges = [
('weights_inp', 'aten'),
('indices_inp', 'aten'),
('offsets_inp', 'aten'),
('aten', 'result'),
]
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [
('weights_inp', 'emb_bag'),
('indices_inp', 'emb_bag'),
('offsets_inp', 'emb_bag'),
('emb_bag', 'result'),
]
graph_ref = build_graph(nodes, edges_ref)
AtenToEmbeddingBag().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_hsigmoid_with_relu_mul_different_tensors(self):
graph = build_graph_with_edge_attrs(
{
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('add', {'op': 'Add'}),
**regular_op('max', {'op': 'Maximum'}),
**regular_op('min', {'op': 'Minimum'}),
**regular_op('mul', {'op': 'Mul'}),
**regular_op('mul_2', {
'op': 'Mul',
'name': 'final_mul'
}),
**const('const_0', float_array([0.0])),
**const('const_3', float_array([3.0])),
**const('const_6', float_array([6.0])),
**const('const_1_6', float_array([1.0 / 6.0])),
**result('result'),
}, [('input_2', 'mul', {
'in': 1,
'out': 0
}), ('input', 'add', {
'in': 0,
'out': 0
}), ('const_3', 'add', {
'in': 1,
'out': 0
}), ('add', 'max', {
'in': 0,
'out': 0
}), ('const_0', 'max', {
'in': 1,
'out': 0
}), ('max', 'min', {
'in': 0,
'out': 0
}), ('const_6', 'min', {
'in': 1,
'out': 0
}), ('min', 'mul', {
'in': 0,
'out': 0
}), ('mul', 'mul_2', {
'in': 0,
'out': 0
}), ('const_1_6', 'mul_2', {
'in': 1,
'out': 0
}), ('mul_2', 'result', {
'in': 0,
'out': 0
})])
graph_ref = graph.copy()
graph.stage = 'front'
HSigmoidWithReluMul().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_reduce_axis_is_None(self):
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.stage = 'front'
ReduceAxisNormalizer().find_and_replace_pattern(graph)
ref_nodes = nodes.copy()
ref_nodes.update({
**regular_op('rank', {
'op': 'Rank',
'type': None
}),
**regular_op('range', {
'op': 'Range',
'type': 'Range'
}),
**regular_op('begin', {
'type': 'Const',
'value': int64_array([0])
}),
**regular_op('step', {
'type': 'Const',
'value': int64_array([1])
}),
})
graph_ref = build_graph(ref_nodes, [
*edges,
*connect_front('parameter:0', 'rank'),
*connect_front('begin:0', '0:range'),
*connect_front('rank:0', '1:range'),
*connect_front('step:0', '2:range'),
*connect_front('range:0', '1:reduce'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('shape', {'type': 'ShapeOf', 'kind': 'op', 'op': 'ShapeOf'}),
**regular_op('random_uniform', {'type': 'RandomUniform', 'kind': 'op', 'op': 'RandomUniform',
'name': 'dropout/RU'}),
**regular_op('mul', {'type': 'Mul', 'kind': 'op', 'op': 'Mul'}),
**regular_op('add', {'type': 'Add', 'kind': 'op', 'op': 'Add'}),
**regular_op('add2', {'type': 'Add', 'kind': 'op', 'op': 'Add'}),
**regular_op('floor', {'type': 'Floor', 'kind': 'op', 'op': 'Floor'}),
'add_const': {'kind': 'op', 'op': 'Const', 'value': np.array(0.0), 'data_type': np.float32},
**result('result'),
# new nodes to be added
'broadcast_const': {'kind': 'op', 'op': 'Const', 'value': np.array(0.5), 'data_type': np.float32},
**regular_op('broadcast', {'type': 'Broadcast', 'kind': 'op', 'op': 'Broadcast'}),
}
edges = [('input', 'shape'),
('shape', 'random_uniform'),
('random_uniform', 'mul'),
('mul', 'add'),
('add_const', 'add'),
('add', 'add2'),
('add2', 'floor'),
('floor', 'result')]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
DropoutWithRandomUniformReplacer().find_and_replace_pattern(graph)
edges_ref = [('input', 'shape'),
('broadcast_const', 'broadcast'),
('shape', 'broadcast'),
('broadcast', 'mul'),
('mul', 'add'),
('add_const', 'add'),
('add', 'add2'),
('add2', 'floor'),
('floor', 'result')]
graph_ref = build_graph(nodes, edges_ref, nodes_with_edges_only=True)
# check graph structure after the transformation and output name
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(graph.node[graph.get_nodes_with_attributes(op='Broadcast')[0]]['name'] == 'dropout/RU')
def test_mish_fusion_different_source(self):
# check case when different tensors goes to Mul and SoftPlus
graph = build_graph_with_edge_attrs(
{
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('softplus', {'op': 'SoftPlus'}),
**regular_op('tanh', {'op': 'Tanh'}),
**regular_op('mul', {
'op': 'Mul',
'name': 'final_mul'
}),
**result('result'),
}, [('input', 'softplus', {
'in': 0,
'out': 0
}), ('input_2', 'mul', {
'in': 0,
'out': 0
}), ('softplus', 'tanh', {
'in': 0,
'out': 0
}), ('tanh', 'mul', {
'in': 1,
'out': 0
}), ('mul', 'result', {
'in': 0,
'out': 0
})], {})
graph_ref = graph.copy()
graph.stage = 'front'
MishFusion().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_swish_with_sigmoid_with_beta_test(self):
graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
new_ref_nodes = ref_nodes.copy()
new_ref_nodes.update(**regular_op('beta', {'type': 'Parameter'}))
graph_ref = build_graph(new_ref_nodes, ref_edges + [('beta', 'swish')])
graph.stage = 'front'
SwishWithSigmoidWithBeta().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(len(graph.get_op_nodes(name='final_mul')) == 1 and
graph.get_op_nodes(name='final_mul')[0].op == 'Swish')
def build_body_graph(pre_processing: str):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('mul', {'op': 'Mul', 'type': 'Multiply', 'name': 'my_body_mul'}),
**regular_op('sub', {'op': 'Sub', 'type': 'Subtract', 'name': 'my_body_sub'}),
**const('body_mul_const', self.mul_const),
**const('body_sub_const', self.sub_const),
**regular_op(self.loop_start_node_name, {'op': 'Identity'}),
**regular_op(self.loop_end_node_name, {'op': 'Identity'}),
**regular_op('resize', {'type': 'Interpolate'}),
**result('result'),
}
edges = None
if pre_processing == 'no':
edges = [*connect_front('input', self.loop_start_node_name),
*connect_front(self.loop_start_node_name, 'resize'),
*connect_front('resize', self.loop_end_node_name),
*connect_front(self.loop_end_node_name, 'result'),
]
elif pre_processing == 'trailing':
edges = [*connect_front('input', self.loop_start_node_name),
*connect_front(self.loop_start_node_name, 'resize'),
*connect_front('resize', self.loop_end_node_name),
*connect_front(self.loop_end_node_name, '0:mul'),
*connect_front('body_mul_const', '1:mul'),
*connect_front('body_sub_const', '0:sub'),
*connect_front('mul', '1:sub'),
*connect_front('sub', 'result'),
]
else:
edges = [*connect_front('input', '0:mul'),
*connect_front('body_mul_const', '1:mul'),
*connect_front('body_sub_const', '0:sub'),
*connect_front('mul', '1:sub'),
*connect_front('sub', self.loop_start_node_name),
*connect_front(self.loop_start_node_name, 'resize'),
*connect_front('resize', self.loop_end_node_name),
*connect_front(self.loop_end_node_name, 'result'),
]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.stage = 'front'
return graph
def test_multi(self):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('some_op', {'type': 'SomeOp', 'name': 'some_op_name'}),
**regular_op('fake_output1', {'type': None, 'kind': 'op', 'op': 'FakeOutput', 'name': 'my_output_name1'}),
**regular_op('fake_output2', {'type': None, 'kind': 'op', 'op': 'FakeOutput', 'name': 'my_output_name2'}),
**const('const1', int64_array(0)),
**const('const2', int64_array(0)),
**regular_op('add1', {'type': None, 'kind': 'op', 'op': 'Add', 'name': 'my_output_name1'}),
**regular_op('add2', {'type': None, 'kind': 'op', 'op': 'Add', 'name': 'my_output_name2'}),
**result('result1'),
**result('result2'),
}
edges = [('input', 'some_op'),
('some_op', 'fake_output1'),
('some_op', 'fake_output2'),
('fake_output1', 'result1'),
('fake_output2', 'result2'),
]
graph = build_graph(nodes, edges)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [('input', 'some_op'),
('some_op', 'add1'),
('const1', 'add1'),
('some_op', 'add2'),
('const2', 'add2'),
('add1', 'result1'),
('add2', 'result2'),
]
graph_ref = build_graph(nodes, edges_ref)
FakeOutputResolver().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result1')
self.assertTrue(flag, resp)
class MishFusionTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('softplus', {'op': 'SoftPlus'}),
**regular_op('tanh', {'op': 'Tanh'}),
**regular_op('mul', {
'op': 'Mul',
'name': 'final_mul'
}),
**result('result'),
}
edges = [('input', 'softplus', {
'in': 0,
'out': 0
}), ('input', 'mul', {
'in': 0,
'out': 0
}), ('softplus', 'tanh', {
'in': 0,
'out': 0
}), ('tanh', 'mul', {
'in': 1,
'out': 0
}), ('mul', 'result', {
'in': 0,
'out': 0
})]
def test_mish_fusion(self):
graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
MishFusion().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'Mish')
def test_mish_fusion_different_source(self):
# check case when different tensors goes to Mul and SoftPlus
graph = build_graph_with_edge_attrs(
{
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('softplus', {'op': 'SoftPlus'}),
**regular_op('tanh', {'op': 'Tanh'}),
**regular_op('mul', {
'op': 'Mul',
'name': 'final_mul'
}),
**result('result'),
}, [('input', 'softplus', {
'in': 0,
'out': 0
}), ('input_2', 'mul', {
'in': 0,
'out': 0
}), ('softplus', 'tanh', {
'in': 0,
'out': 0
}), ('tanh', 'mul', {
'in': 1,
'out': 0
}), ('mul', 'result', {
'in': 0,
'out': 0
})], {})
graph_ref = graph.copy()
graph.stage = 'front'
MishFusion().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
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.
"""
import unittest
from extensions.front.Mish_fusion import MishFusion
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op, result, build_graph_with_edge_attrs
ref_nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('mish', {
'type': 'Mish',
'name': 'final_mul'
}),
**result('result')
}
ref_edges = [('input', 'mish'), ('mish', 'result')]
class MishFusionTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('softplus', {'op': 'SoftPlus'}),
**regular_op('tanh', {'op': 'Tanh'}),
**regular_op('mul', {
# Copyright (C) 2018-2021 Intel Corporation
# SPDX-License-Identifier: Apache-2.0
import unittest
import numpy as np
from extensions.front.reduce_axis_normalizer import ReduceAxisNormalizer
from mo.front.common.partial_infer.utils import int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, result, connect_front, regular_op
nodes = {
**regular_op('parameter', {'type': 'Parameter'}),
**regular_op('reduce', {
'op': 'ReduceSum',
'axis': None
}),
**regular_op('axis', {
'op': 'Const',
'type': 'Const',
'value': int64_array([1])
}),
**result(),
}
edges = [
*connect_front('parameter:0', '0:reduce'),
*connect_front('reduce', 'output'),
]
def test_per_sample_weights(self):
nodes = {
**const('weights_inp', np.random.randn(100, 2)),
**regular_op('indices_inp', {'type': 'Parameter'}),
**regular_op('offsets_inp', {'type': 'Parameter'}),
**regular_op('per_sample_weights', {'type': 'Parameter'}),
**regular_op(
'aten', {
'type': None,
'kind': 'op',
'op': 'ATen',
'operator': 'embedding_bag',
'mode': 0,
'name': 'my_aten'
}),
**regular_op(
'emb_bag', {
'type': 'EmbeddingBagOffsetsSum',
'kind': 'op',
'op': 'EmbeddingBagOffsetsSum'
}),
**regular_op('WeightsRank', {
'type': None,
'kind': 'op',
'op': 'Rank'
}),
**regular_op('WeightsRank/axis', {
'type': 'Add',
'kind': 'op',
'op': 'Add'
}),
**regular_op('gather1', {
'type': 'Gather',
'kind': 'op',
'op': 'Gather'
}),
**regular_op('gather2', {
'type': 'Gather',
'kind': 'op',
'op': 'Gather'
}),
**regular_op('WeightsShape', {
'type': 'ShapeOf',
'kind': 'op',
'op': 'ShapeOf'
}),
**regular_op('Broadcast', {
'type': 'Broadcast',
'kind': 'op',
'op': 'Broadcast'
}),
**regular_op('Unsqueeze', {
'type': 'Unsqueeze',
'kind': 'op',
'op': 'Unsqueeze'
}),
**const('WeightsShape/Axis', int64_array(0)),
**const('zero1', int64_array(0)),
**const('zero2', int64_array(0)),
**const('Unsqueeze/value', int64_array(0)),
**const('Broadcast/value', int64_array(0)),
**const('neg', int64_array(-1)),
**regular_op('Concat', {
'type': 'Concat',
'kind': 'op',
'op': 'Concat'
}),
**result('result'),
}
edges = [
('weights_inp', 'aten'),
('indices_inp', 'aten'),
('offsets_inp', 'aten'),
('per_sample_weights', 'aten'),
('aten', 'result'),
]
graph = build_graph(nodes, edges, nodes_with_edges_only=True)
graph.graph['layout'] = 'NCHW'
graph.stage = 'front'
edges_ref = [
('weights_inp', 'Concat', {
'in': 0,
'out': 0
}),
('weights_inp', 'WeightsShape', {
'in': 0,
'out': 0
}),
('weights_inp', 'WeightsRank', {
'in': 0,
'out': 0
}),
('WeightsRank', 'WeightsRank/axis'),
('neg', 'WeightsRank/axis'),
('WeightsShape', 'gather1', {
'in': 0,
'out': 0
}),
('WeightsRank/axis', 'gather1'),
('WeightsShape/Axis', 'gather1'),
('WeightsShape', 'gather2', {
'in': 0,
'out': 0
}),
('zero1', 'gather2'),
('zero2', 'gather2'),
('Broadcast/value', 'Broadcast'),
('gather1', 'Broadcast'),
('Broadcast', 'Unsqueeze'),
('Unsqueeze/value', 'Unsqueeze'),
('Unsqueeze', 'Concat'),
('Concat', 'emb_bag'),
('indices_inp', 'emb_bag'),
('offsets_inp', 'emb_bag'),
('gather2', 'emb_bag'),
('per_sample_weights', 'emb_bag'),
('emb_bag', 'result'),
]
graph_ref = build_graph(nodes, edges_ref, nodes_with_edges_only=True)
AtenToEmbeddingBag().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_tdnnreplacer(self, weights, biases, time_offsets):
def generate_offsets():
offset_edges = []
offset_nodes = {}
for i, t in enumerate(time_offsets):
offset_nodes.update(**regular_op('memoryoffset_' +
str(i), {'type': None}))
if t != 0:
offset_edges.append(
('placeholder', 'memoryoffset_' + str(i), {
'out': 0,
'in': 0
}))
offset_edges.append(('memoryoffset_' + str(i), 'concat', {
'out': 0,
'in': i
}))
else:
offset_edges.append(('placeholder', 'concat', {
'out': 0,
'in': i
}))
return offset_nodes, offset_edges
offset_nodes, ref_offset_edges = generate_offsets()
nodes = {
**offset_nodes,
**regular_op('placeholder', {'type': 'Parameter'}),
**regular_op(
'tdnncomponent', {
'op': 'tdnncomponent',
'weights': np.array(weights),
'biases': np.array(biases),
'time_offsets': np.array(time_offsets)
}),
**const('weights', np.array(weights)),
**const('biases', np.array(biases)),
**regular_op('concat', {
'type': 'Concat',
'axis': 1
}),
**regular_op('memoryoffset_0', {'type': None}),
**regular_op('memoryoffset_1', {'type': None}),
**regular_op('memoryoffset_2', {'type': None}),
**regular_op('fully_connected', {'type': 'FullyConnected'}),
**result('result'),
}
graph = build_graph(nodes, [
*connect_front('placeholder', 'tdnncomponent'),
*connect_front('tdnncomponent', 'result')
],
nodes_with_edges_only=True)
graph.stage = 'front'
ref_graph = build_graph(nodes, [
*ref_offset_edges, *connect_front('concat', '0:fully_connected'),
*connect_front('weights', '1:fully_connected'),
*connect_front('biases', '2:fully_connected'),
*connect_front('fully_connected', 'result')
],
nodes_with_edges_only=True)
TdnnComponentReplacer().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph,
ref_graph,
'result',
check_op_attrs=True)
self.assertTrue(flag, resp)
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.
"""
import unittest
from extensions.front.Softplus_fusion import SoftplusFusion
from mo.front.common.partial_infer.utils import float_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, const, regular_op, result, build_graph_with_edge_attrs
ref_nodes = {**regular_op('input', {'type': 'Parameter'}),
**regular_op('softplus', {'type': 'SoftPlus', 'name': 'final_log'}),
**result('result')
}
ref_edges = [('input', 'softplus'), ('softplus', 'result')]
class SoftplusFusionTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('exp', {'op': 'Exp'}),
**const('const_1', float_array([1.0])),
**regular_op('add', {'op': 'Add'}),
**regular_op('ln', {'op': 'Log', 'name': 'final_log'}),
**result('result'),
}
class GeLUMergerErfTest(unittest.TestCase):
nodes = {
**regular_op('input', {
'op': 'Parameter',
'type': 'Parameter'
}),
**regular_op('mul', {'op': 'Mul'}),
**regular_op('mul0', {
'op': 'Mul',
'name': 'final_mul'
}),
**regular_op('div', {'op': 'Div'}),
**regular_op('erf', {'op': 'Erf'}),
**regular_op('add', {'op': 'Add'}),
**const('mul_param', float_array([0.5])),
**const('div_param', float_array([sqrt(2.)])),
**const('add_param', int64_array([1])),
**result('result'),
}
def test_gelu_p1(self):
edges = [('input', 'mul'), ('mul', 'mul0'), ('input', 'div'),
('div', 'erf'), ('erf', 'add'), ('add', 'mul0'),
('mul_param', 'mul'), ('div_param', 'div'),
('add_param', 'add'), ('mul0', 'result')]
graph = build_graph(self.nodes, edges)
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
GeLUMergerErf().find_and_replace_pattern(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
graph.get_op_nodes(op='Gelu')[0].approximation == 'erf')
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'Gelu')
def test_gelu_p2(self):
edges = [('input', 'mul'), ('div', 'erf'), ('erf', 'add'),
('add', 'mul'), ('mul', 'mul0'), ('mul_param', 'mul0'),
('div_param', 'div'), ('add_param', 'add'),
('mul0', 'result')]
graph = build_graph(self.nodes, edges)
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
GeLUMergerErf().find_and_replace_pattern(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
graph.get_op_nodes(op='Gelu')[0].approximation == 'erf')
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'Gelu')
def test_gelu_p3(self):
edges = [('input', 'mul'), ('div', 'erf'), ('erf', 'add'),
('add', 'mul'), ('mul', 'mul0'), ('mul_param', 'mul'),
('div_param', 'div'), ('add_param', 'add'),
('mul0', 'result')]
graph = build_graph(self.nodes, edges)
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
GeLUMergerErf().find_and_replace_pattern(graph)
graph.clean_up()
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
graph.get_op_nodes(op='Gelu')[0].approximation == 'erf')
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'Gelu')
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.
"""
import unittest
from math import sqrt
from extensions.front.GeLUMerger_Erf import GeLUMergerErf
from mo.front.common.partial_infer.utils import float_array, int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, const, regular_op, result, build_graph
ref_nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('gelu', {
'type': 'Gelu',
'approximation': 'erf',
'name': 'final_mul'
}),
**result('result')
}
ref_edges = [('input', 'gelu'), ('gelu', 'result')]
class GeLUMergerErfTest(unittest.TestCase):
nodes = {
**regular_op('input', {
'op': 'Parameter',
'type': 'Parameter'
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.
"""
import unittest
from extensions.front.HSigmoid_fusion import HSigmoidWithClamp, HSigmoidWithMinMax, HSigmoidWithReluDiv, \
HSigmoidWithReluMul
from mo.front.common.partial_infer.utils import float_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, const, regular_op, result, build_graph_with_edge_attrs
ref_nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('hsigmoid', {
'type': 'HSigmoid',
'name': 'final_mul'
}),
**result('result')
}
ref_edges = [('input', 'hsigmoid'), ('hsigmoid', 'result')]
class HSigmoidWithClampTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('add', {'op': 'Add'}),
**regular_op('relu6', {'op': 'Clamp'}),
**regular_op('mul_2', {
class HSigmoidWithReluMulTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('add', {'op': 'Add'}),
**regular_op('relu', {'op': 'ReLU'}),
**regular_op('min', {'op': 'Minimum'}),
**regular_op('mul', {
'op': 'Mul',
'name': 'final_mul'
}),
**const('add_const', float_array([3.0])),
**const('min_const', float_array([6.0])),
**const('mul_const', float_array([1.0 / 6.0])),
**result('result'),
}
edges = [('input', 'add', {
'in': 0,
'out': 0
}), ('add_const', 'add', {
'in': 1,
'out': 0
}), ('add', 'relu', {
'in': 0,
'out': 0
}), ('relu', 'min', {
'in': 0,
'out': 0
}), ('min_const', 'min', {
'in': 1,
'out': 0
}), ('min', 'mul', {
'in': 0,
'out': 0
}), ('mul_const', 'mul', {
'in': 1,
'out': 0
}), ('mul', 'result', {
'in': 0,
'out': 0
})]
def test_hsigmoid_with_relu_mul(self):
graph = build_graph_with_edge_attrs(self.nodes, self.edges, {})
graph_ref = build_graph(ref_nodes, ref_edges)
graph.stage = 'front'
HSigmoidWithReluMul().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
self.assertTrue(
len(graph.get_op_nodes(name='final_mul')) == 1
and graph.get_op_nodes(name='final_mul')[0].op == 'HSigmoid')
self.assertTrue(
graph.get_op_nodes(
name='final_mul')[0].out_nodes()[0].node == 'result')
def test_hsigmoid_with_relu_mul_wrong_constant(self):
graph = build_graph_with_edge_attrs(
self.nodes, self.edges,
{'add_const': {
'value': float_array([0.00001])
}})
graph_ref = graph.copy()
graph.stage = 'front'
HSigmoidWithReluMul().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
def test_hsigmoid_with_relu_mul_different_tensors(self):
graph = build_graph_with_edge_attrs(
{
**regular_op('input', {'type': 'Parameter'}),
**regular_op('input_2', {'type': 'Parameter'}),
**regular_op('add', {'op': 'Add'}),
**regular_op('max', {'op': 'Maximum'}),
**regular_op('min', {'op': 'Minimum'}),
**regular_op('mul', {'op': 'Mul'}),
**regular_op('mul_2', {
'op': 'Mul',
'name': 'final_mul'
}),
**const('const_0', float_array([0.0])),
**const('const_3', float_array([3.0])),
**const('const_6', float_array([6.0])),
**const('const_1_6', float_array([1.0 / 6.0])),
**result('result'),
}, [('input_2', 'mul', {
'in': 1,
'out': 0
}), ('input', 'add', {
'in': 0,
'out': 0
}), ('const_3', 'add', {
'in': 1,
'out': 0
}), ('add', 'max', {
'in': 0,
'out': 0
}), ('const_0', 'max', {
'in': 1,
'out': 0
}), ('max', 'min', {
'in': 0,
'out': 0
}), ('const_6', 'min', {
'in': 1,
'out': 0
}), ('min', 'mul', {
'in': 0,
'out': 0
}), ('mul', 'mul_2', {
'in': 0,
'out': 0
}), ('const_1_6', 'mul_2', {
'in': 1,
'out': 0
}), ('mul_2', 'result', {
'in': 0,
'out': 0
})])
graph_ref = graph.copy()
graph.stage = 'front'
HSigmoidWithReluMul().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'result')
self.assertTrue(flag, resp)
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.
"""
import unittest
from extensions.front.output_cut import OutputCut
from mo.graph.graph import Node
from mo.utils.unittest.graph import build_graph, regular_op
nodes = {
**regular_op('Parameter1', {
'type': 'Parameter',
'kind': 'op',
'op': 'Parameter'
}),
**regular_op('Op1', {
'type': 'Op1',
'kind': 'op',
'op': 'Op1'
}),
**regular_op('Op2', {
'type': 'Op2',
'kind': 'op',
'op': 'Op2'
}),
**regular_op(
'FakeOutput1', {
'type': 'Identity',
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.
"""
import unittest
from mo.graph.graph import Node
from mo.utils.unittest.graph import build_graph, regular_op
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('Op1', {
'type': 'Op1',
'kind': 'op',
'op': 'Op1'
}),
**regular_op('Op2', {
'type': 'Op2',
'kind': 'op',
'op': 'Op2'
}),
**regular_op('Op3', {
'type': 'Op3',
'kind': 'op',
'op': 'Op3'
}),
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.
"""
import unittest
from extensions.back.ResultRename import ResultRename
from mo.graph.graph import Node
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op, result
nodes = {
**regular_op('Op1', {
'type': 'Op1',
'kind': 'op',
'op': 'Op1'
}),
**regular_op('Op2', {
'type': 'Op2',
'kind': 'op',
'op': 'Op2'
}),
**result('result1'),
**result('result2'),
'Op1_data': {
'kind': 'data',
'fw_tensor_debug_info': [('Op1', 0, 'Op1_tensor')]
},
'Op2_data': {
'kind': 'data',
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.
"""
import unittest
from extensions.front.Swish_fusion import SwishWithSigmoidWithoutBeta, SwishWithSigmoidWithBeta
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, regular_op, result, build_graph_with_edge_attrs
ref_nodes = {**regular_op('input', {'type': 'Parameter'}),
**regular_op('swish', {'type': 'Swish', 'name': 'final_mul'}),
**result('result')
}
ref_edges = [('input', 'swish'), ('swish', 'result')]
class SwishWithSigmoidWithoutBetaTest(unittest.TestCase):
nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('sigmoid', {'op': 'Sigmoid'}),
**regular_op('mul', {'op': 'Mul', 'name': 'final_mul'}),
**result('result'),
}
edges = [('input', 'mul', {'in': 0, 'out': 0}),
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.
"""
import unittest
from math import sqrt
from extensions.front.GeLUMerger_Erf import GeLUMergerErf
from mo.front.common.partial_infer.utils import float_array, int64_array
from mo.utils.ir_engine.compare_graphs import compare_graphs
from mo.utils.unittest.graph import build_graph, const, regular_op, result, build_graph
ref_nodes = {
**regular_op('input', {'type': 'Parameter'}),
**regular_op('gelu', {
'type': 'Gelu',
'name': 'final_mul'
}),
**result('result')
}
ref_edges = [('input', 'gelu'), ('gelu', 'result')]
class GeLUMergerErfTest(unittest.TestCase):
nodes = {
**regular_op('input', {
'op': 'Parameter',
'type': 'Parameter'
}),