def extract(cls, node):
Equal.update_node_stat(node)
return cls.enabled
def insert_select(graph: Graph, node: Node):
context_len = node.frame_time + 1
if context_len == 1:
return
in_node_port = node.in_port(0).get_source()
in_node_shape = node.in_port(0).data.get_shape()
node.in_port(0).disconnect()
# add Select before saving state to avoid saving garbage
select_node = Select(graph, {
'name': 'select_' + node.name
}).create_node()
zero_else = create_const_with_batch_from_input(in_node_port,
in_node_shape[1])
select_node.in_port(1).connect(in_node_port)
select_node.in_port(2).connect(zero_else.out_port(0))
# check if we have already appropriate iteration counter
existing_counters = find_pattern_matches(
graph,
nodes=[('mem_in', dict(op='ReadValue')),
('mem_in_data', dict(shape=int64_array([context_len]))),
('crop_mem_in',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([1]),
dim=int64_array([context_len - 1]))),
('crop_mem_in_data', dict()),
('concat', dict(op='Concat', axis=1)),
('concat_data', dict()), ('const_1', dict(op='Const')),
('const_1_data', dict()), ('mem_out', dict(op='Assign')),
('crop_out',
dict(op='Crop',
axis=int64_array([1]),
offset=int64_array([0]),
dim=int64_array([1]))), ('crop_out_data', dict()),
('select', dict(op='Select'))],
edges=[('mem_in', 'mem_in_data'), ('mem_in_data', 'crop_mem_in'),
('crop_mem_in', 'crop_mem_in_data'),
('crop_mem_in_data', 'concat', {
'in': 0
}), ('const_1', 'const_1_data'),
('const_1_data', 'concat', {
'in': 1
}), ('concat', 'concat_data'), ('concat_data', 'mem_out'),
('concat_data', 'crop_out'), ('crop_out', 'crop_out_data'),
('crop_out_data', 'select')])
counter_match = next(existing_counters, None)
if counter_match is not None:
ones = Node(graph, inverse_dict(counter_match)['const_1'])
input_port = Node(
graph,
inverse_dict(counter_match)['crop_out']).out_port(0)
else:
init_value_mem_out = create_const_with_batch_from_input(
in_node_port, context_len, precision=np.int32)
mem_out = ReadValue(
graph, {
'name': 'iteration_number',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_out.in_port(0).connect(init_value_mem_out.out_port(0))
cut_first = Crop(
graph, {
'name': 'cut_first',
'axis': int64_array([1]),
'offset': int64_array([1]),
'dim': int64_array([context_len - 1])
}).create_node()
cut_first.in_port(0).connect(mem_out.out_port(0))
ones = create_const_with_batch_from_input(in_node_port, 1, 1,
np.int32)
concat = Concat(graph, {
'name': 'concat_ones',
'in_ports_count': 2,
'axis': 1
}).create_node()
concat.in_port(0).connect(cut_first.out_port(0))
concat.in_port(1).connect(ones.out_port(0))
mem_in = Assign(
graph, {
'name': 'iteration_number_out',
'variable_id': 'iteration_' + node.name
}).create_node()
mem_in.in_port(0).connect(concat.out_port(0))
res = Result(graph, {}).create_node()
mem_in.out_port(0).connect(res.in_port(0))
cut_last = Crop(
graph, {
'name': 'cut_last',
'axis': int64_array([1]),
'offset': int64_array([0]),
'dim': int64_array([1])
}).create_node()
cut_last.in_port(0).connect(concat.out_port(0))
input_port = cut_last.out_port(0)
# Check if data from memory is 1
# if it is True, we have correct data and should proceed with saving it to memory
# else we have not gathered context and have garbage here, shouldn't change initial state of memory
cast_in = Equal(graph, {
'name': input_port.node.name + '/cast_to_bool'
}).create_node()
cast_in.in_port(0).connect(ones.out_port(0))
cast_in.in_port(1).connect(input_port)
select_node.in_port(0).connect(cast_in.out_port(0))
select_node.out_port(0).connect(node.in_port(0))
select_node.out_port(0).data.set_shape(in_node_shape)
def find_and_replace_pattern(self, graph: Graph):
for embedding_segments_mean in graph.get_op_nodes(
op='EmbeddingSegmentsMean'):
embedding_segments_mean_name = embedding_segments_mean.soft_get(
'name', embedding_segments_mean.id)
embedding_table_input = embedding_segments_mean.in_port(0)
segment_ids_input = embedding_segments_mean.in_port(2)
num_segments_input = embedding_segments_mean.in_port(3)
# TODO: support EmbeddingSegmentsMean with specified weights vector.
# now this case has not appeared in models so far so EmbeddingSegmentsOperation fusion
# transformations do not handle it either
if embedding_segments_mean.is_in_port_connected(5):
return
# 1. compute indices membership matrix, i.e. which indices belong to some object
# the shape of this matrix is [num_segments, num_indices]
non_norm_range_1_to_num_segments = create_op_with_const_inputs(
graph, Range, {
0: int64_array(0),
2: int64_array(1)
}, {
'name':
embedding_segments_mean_name + '/Range1ToNumSegments',
'output_type': np.int64
})
num_segments_input.get_connection().add_destination(
non_norm_range_1_to_num_segments.in_port(1))
range_1_to_num_segments = ConvertLike(graph, {
'name':
embedding_segments_mean_name + '/Range1ToNumSegmentsNorm'
}).create_node()
range_1_to_num_segments.in_port(0).connect(
non_norm_range_1_to_num_segments.out_port(0))
num_segments_input.get_connection().add_destination(
range_1_to_num_segments.in_port(1))
unsqueeze_range_1_to_num_segments = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(1)}, {
'name':
embedding_segments_mean_name +
'/Range1ToNumSegmentsUnsqueeze'
})
unsqueeze_range_1_to_num_segments.in_port(0).connect(
range_1_to_num_segments.out_port(0))
unsqueeze_segment_ids = create_op_with_const_inputs(
graph, Unsqueeze, {1: int64_array(0)}, {
'name':
embedding_segments_mean_name + '/SegmentIdsUnsqueeze'
})
segment_ids_input.get_connection().add_destination(
unsqueeze_segment_ids.in_port(0))
boolean_membership_matrix = Equal(graph, {
'name':
embedding_segments_mean_name + '/BooleanMembershipMatrix'
}).create_node()
boolean_membership_matrix.in_port(0).connect(
unsqueeze_range_1_to_num_segments.out_port(0))
boolean_membership_matrix.in_port(1).connect(
unsqueeze_segment_ids.out_port(0))
shape_of_membership_matrix = Shape(graph, {
'name':
embedding_segments_mean_name + '/ShapeOfMembershipMatrix'
}).create_node([boolean_membership_matrix])
one_scalar_constant = Const(
graph, {
'name': embedding_segments_mean_name + '/OneScalar',
'value': int64_array([1])
}).create_node()
one_constant = Broadcast(graph, {
'name':
embedding_segments_mean_name + '/One'
}).create_node([one_scalar_constant, shape_of_membership_matrix])
zero_constant = Const(
graph, {
'name': embedding_segments_mean_name + '/Zero',
'value': int64_array(0)
}).create_node()
membership_matrix = Select(
graph, {
'name': embedding_segments_mean_name + '/MembershipMatrix',
'auto_broadcast': 'numpy'
}).create_node(
[boolean_membership_matrix, one_constant, zero_constant])
# 2. compute a number of indices belong to each object from the batch
# it computes the normalization coefficients
num_indices_per_object = create_op_with_const_inputs(
graph, ReduceSum, {1: int64_array(1)}, {
'name':
embedding_segments_mean_name + '/NumIndicesPerObject'
})
num_indices_per_object.in_port(0).connect(
membership_matrix.out_port(0))
# 3. replace zero coefficient (zero number of indices belong to an object) with one
# because for such object the single default embedding vector is used
where_zero_number = Equal(graph, {
'name':
embedding_segments_mean_name + '/WhereZeroIndicesNumber'
}).create_node([num_indices_per_object, zero_constant])
normalized_num_indices_per_object = Select(
graph, {
'name':
embedding_segments_mean_name + '/NormNumIndicesPerObject',
'auto_broadcast': 'numpy'
}).create_node([
where_zero_number, one_scalar_constant,
num_indices_per_object
])
# 4. cast normalized_num_indices_per_object to the same type as embedding vector table
norm_coefficients = ConvertLike(
graph, {
'name': embedding_segments_mean_name + '/NormCoefficients'
}).create_node()
norm_coefficients.in_port(0).connect(
normalized_num_indices_per_object.out_port(0))
embedding_table_input.get_connection().add_destination(
norm_coefficients.in_port(1))
# 5. replace EmbeddingSegmentMean with EmbeddingSegmentSum
embedding_segments_sum = EmbeddingSegmentsSum(
graph, {
'name':
embedding_segments_mean_name + '/EmbeddingSegmentsSum'
}).create_node()
for in_port in embedding_segments_mean.in_ports():
if embedding_segments_mean.is_in_port_connected(in_port):
embedding_segments_mean.in_port(
in_port).get_connection().set_destination(
embedding_segments_sum.in_port(in_port))
# 6. normalize EmbeddingSegmentSum results by computed coefficients
result_node = Div(graph, {
'name': embedding_segments_mean_name + '/Div'
}).create_node([embedding_segments_sum, norm_coefficients])
embedding_segments_mean.out_port(0).get_connection().set_source(
result_node.out_port(0))
rename_nodes([(embedding_segments_mean,
embedding_segments_mean_name + '/AbandonedName'),
(result_node, embedding_segments_mean_name)])
graph.remove_nodes_from([embedding_segments_mean.id])
def dequantize_data(fake_quantize: Node, dst_type: type,
quantized_type: type) -> Node:
graph = fake_quantize.graph
quantized_data = fake_quantize.in_port(0).get_source().node
name = fake_quantize.soft_get('name', fake_quantize.id)
assert quantized_data.soft_get('type') == 'Convert' and quantized_data.dst_type == quantized_type, \
'Weights aren`t compressed as expected for node {}'.format(fake_quantize.soft_get('name', fake_quantize.id))
dequantizing_cast = Cast(
graph,
dict(name=quantized_data.name +
"/to_{}".format(np_data_type_to_destination_type(dst_type)),
dst_type=dst_type,
stop_value_propagation=True)).create_node()
fake_quantize.in_port(0).get_connection().set_destination(
dequantizing_cast.in_port(0))
# limits of dequantize
in_low = fake_quantize.in_port(1).get_source()
in_high = fake_quantize.in_port(2).get_source()
out_low = fake_quantize.in_port(3).get_source()
out_high = fake_quantize.in_port(4).get_source()
# scale calculation
output_range = Sub(graph, {
'name': name + '/output_range'
}).create_node()
output_range.in_port(0).connect(out_high)
output_range.in_port(1).connect(out_low)
input_range = Sub(graph, {'name': name + '/input_range'}).create_node()
input_range.in_port(0).connect(in_high)
input_range.in_port(1).connect(in_low)
scale = Div(graph, {'name': name + '/scale'}).create_node()
scale.in_port(0).connect(output_range.out_port(0))
scale.in_port(1).connect(input_range.out_port(0))
# shift calculation
descaled_output_low = Div(graph, {
'name': name + '/descaled_output_low'
}).create_node()
descaled_output_low.in_port(0).connect(out_low)
descaled_output_low.in_port(1).connect(scale.out_port(0))
shift = Sub(graph, {'name': name + '/shift'}).create_node()
shift.in_port(0).connect(in_low)
shift.in_port(1).connect(descaled_output_low.out_port(0))
zero = Const(graph, {
'name': name + '/zero',
'value': mo_array(0, dtype=dst_type)
}).create_node()
scale_eq_zero = Equal(graph, {
'name': name + '/scale_eq_zero'
}).create_node()
scale_eq_zero.in_port(0).connect(scale.out_port(0))
scale_eq_zero.in_port(1).connect(zero.out_port(0))
zero_point = Select(graph, {
'name': name + '/zero_point'
}).create_node()
zero_point.in_port(0).connect(scale_eq_zero.out_port(0))
zero_point.in_port(1).connect(zero.out_port(0))
zero_point.in_port(2).connect(shift.out_port(0))
# DeQuantize(x) == Mul(Sub(x, zero_point), scale)
sub_zp = Sub(graph, {'name': name + '/minus_zp'}).create_node()
sub_zp.in_port(0).connect(dequantizing_cast.out_port(0))
sub_zp.in_port(1).connect(zero_point.out_port(0))
mul_scale = Mul(graph, {
'name': name + '/mulpiply_by_scale'
}).create_node()
mul_scale.in_port(0).connect(sub_zp.out_port(0))
mul_scale.in_port(1).connect(scale.out_port(0))
fake_quantize.out_port(0).get_connection().set_source(
mul_scale.out_port(0))
graph.remove_nodes_from([fake_quantize.id, fake_quantize.out_node(0)])