def replace_sub_graph(self, graph: Graph, match: dict):
node = match['op']
slice_name = node.soft_get('name', node.id)
slice_node = Slice(graph).create_node()
rename_nodes([(node, slice_name + '/to_be_removed'), (slice_node, slice_name)])
eq_node = Equal(graph, {'name': slice_name + '/equal'}).create_node()
minus_one_node = Const(graph, {'name': slice_name + '/minus_one', 'value': np.array(-1)}).create_node()
int32_max_node = Const(graph, {'name': slice_name + '/int32_max', 'value': np.iinfo(np.int32).max}).create_node()
select_node = Select(graph, {'name': slice_name + '/select'}).create_node()
# node to convert sizes to ends
sum_node = Add(graph, {'name': slice_name + '/end_const'}).create_node()
# reconnect input from tfslice to slice
node.in_port(0).get_source().connect(slice_node.in_port(0))
node.in_port(0).disconnect()
# reconnect begin of tfslice to start of slice
node.in_port(1).get_source().connect(slice_node.in_port(1))
node.in_port(1).disconnect()
# (size -> ends) reconnect begins and sizes to sum to evaluate ends for Slice
# connects begins to slice
slice_node.in_port(1).get_source().connect(sum_node.in_port(0))
node.in_port(2).get_source().connect(sum_node.in_port(1))
node.in_port(2).disconnect()
# if size[i] == -1 when take int32_max as end[i]
sum_node.in_port(1).get_source().connect(eq_node.in_port(0))
minus_one_node.out_port(0).connect(eq_node.in_port(1))
# from equal to 0 port of select
eq_node.out_port(0).connect(select_node.in_port(0))
# from int32_max to 1 of select
int32_max_node.out_port(0).connect(select_node.in_port(1))
# from sum to 2nd of select
sum_node.out_port(0).connect(select_node.in_port(2))
# out of select to end (2nd of slice)
select_node.out_port(0).connect(slice_node.in_port(2))
cast = Cast(graph, dict(name=sum_node.name + '/CastToI64', dst_type=np.int64)).create_node()
select_node.in_port(2).get_connection().insert_node(cast)
node.out_port(0).get_connection().set_source(slice_node.out_port(0))
def replace_pattern(graph: Graph, match: dict):
node = match['op']
if node.name == 'iteration_number_out':
return
# calculate length of context when state of inference becomes meaningful
inputs = []
for n in graph.get_op_nodes(**{'op': 'Parameter'}):
inputs.append(n)
in_nodes = []
for inp in inputs:
for ins in inp.out_port(0).get_destinations():
in_nodes.append(ins.node.name)
context_len = 1
try:
subgraph = invert_sub_graph_between_nodes(
graph, [node.in_port(0).get_source().node.name], in_nodes)
except Error:
return
for n in subgraph:
n_node = Node(graph, n)
if n_node.kind == 'op' and n_node.op == 'Splice':
context_len += len(n_node.context) - 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 = Const(graph, {
'name': 'zero_else',
'value': np.zeros(in_node_shape)
}).create_node()
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_zero_value_with_batch_from_input(
in_node_port, context_len, 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 = Const(graph, {
'name': 'ones',
'value': np.ones([1, 1], dtype=np.int32)
}).create_node()
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 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': np.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)])
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)
