def test_dequantize(self):
original_type = np.float32
nodes = nodes_dict(original_type, np.int8)
graph = build_graph(nodes, [
*connect('weights:0', '0:cast'),
*connect('cast:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True)
error_message = 'Unexpected number of {} nodes {} CompressQuantizeWeights.dequantize_data call `{}`'
fq_nodes = graph.get_op_nodes(type='FakeQuantize')
cast_nodes = graph.get_op_nodes(name='cast')
self.assertEqual(
len(fq_nodes), 1,
error_message.format('FakeQuantize', 'before', len(fq_nodes)))
self.assertEqual(
len(cast_nodes), 1,
error_message.format('Convert', 'before', len(cast_nodes)))
cast_nodes[0]['need_shape_inference'] = True
CompressQuantizeWeights.dequantize_data(fq_nodes[0], original_type,
np.int8)
graph.clean_up()
fq_nodes = graph.get_op_nodes(type='FakeQuantize')
self.assertEqual(
len(fq_nodes), 0,
error_message.format('FakeQuantize', 'after', len(fq_nodes)))
graph_ref = build_graph(nodes, [
*connect('int_weights:0', '0:cast'),
*connect('cast:0', '0:sub'),
*connect('zp:0', '1:sub'),
*connect('sub:0', '0:mul'),
*connect('scale:0', '1:mul'),
*connect('mul:0', 'output'),
], {'cast': {
'dst_type': original_type
}},
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_data_type(self, model_dtype, original, transformed=None):
if transformed is None:
transformed = original
nodes = nodes_dict(original, transformed)
graph = build_graph(nodes, [
*connect('weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True,
cli=Namespace(data_type=model_dtype,
static_shape=True))
CompressQuantizeWeights().find_and_replace_pattern(graph)
graph.clean_up()
graph_ref = build_graph(nodes, [
*connect('int_weights:0', '0:cast'),
*connect('cast:0', '0:sub'),
*connect('zp:0', '1:sub'),
*connect('sub:0', '0:mul'),
*connect('scale:0', '1:mul'),
*connect('mul:0', 'output'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_quantize(self):
original_type = np.float32
nodes = nodes_dict(original_type)
graph = build_graph(nodes, [
*connect('weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True)
error_message = 'Unexpected number of FakeQuantize nodes {} CompressQuantizeWeights.quantize_data call `{}`'
fq_nodes = graph.get_op_nodes(type='FakeQuantize')
self.assertEqual(len(fq_nodes), 1,
error_message.format('before', len(fq_nodes)))
fake_quantize = fq_nodes[0]
CompressQuantizeWeights.quantize_data(fake_quantize, original_type,
np.int8, "signed")
graph.clean_up()
fq_nodes = graph.get_op_nodes(type='FakeQuantize')
self.assertEqual(len(fq_nodes), 1,
error_message.format('after', len(fq_nodes)))
self.assertEqual(
fq_nodes[0].in_port(0).get_source().node.soft_get('type'), 'Const')
self.assertEqual(fq_nodes[0].in_port(0).get_source().node.data_type,
np.int8)
graph_ref = build_graph(nodes, [
*connect('int_weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_accuracy(self, data, in_low, in_high, out_low, out_high, levels):
nodes = nodes_dict(np.float32, None, levels, data, in_low, in_high,
out_low, out_high)
graph = build_graph(nodes, [
*connect('weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
],
nodes_with_edges_only=True)
graph_ref = graph.copy()
CompressQuantizeWeights().find_and_replace_pattern(graph)
for node in graph.get_op_nodes() + graph_ref.get_op_nodes():
node['stop_value_propagation'] = False
node['need_shape_inference'] = node.soft_get(
'need_shape_inference', True)
graph.clean_up()
graph_ref.clean_up()
const_result_graph = build_graph(
{
**shaped_const_with_data('weights',
np.array(data).shape),
**result()
}, [*connect('weights', 'output')],
nodes_with_edges_only=True)
(flag, resp) = compare_graphs(graph,
const_result_graph,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
(flag, resp) = compare_graphs(graph_ref,
const_result_graph,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
# as this two graphs calculated the same data through different constant folding functions, they resulted in
# constants of different data type since FakeQuantize always have f32 output dtype, but eltwises use numpy
# for folding which doesn't have such restriction
const_node = graph.get_op_nodes(type='Const')
self.assertEqual(len(const_node), 1)
if const_node[0].data_type == np.float64:
const_node[0].data_type = np.float32
(flag, resp) = compare_graphs(graph,
graph_ref,
'output',
check_op_attrs=True)
self.assertTrue(flag, resp)
def test_negative_fq_unacceptable_levels(self, levels):
nodes = nodes_dict(np.float32, None, levels)
graph = build_graph(nodes, [
*connect('weights:0', '0:FQ'),
*connect('il:0', '1:FQ'),
*connect('ih:0', '2:FQ'),
*connect('ol:0', '3:FQ'),
*connect('oh:0', '4:FQ'),
*connect('FQ:0', 'output'),
], nodes_with_edges_only=True)
graph_ref = graph.copy()
CompressQuantizeWeights().find_and_replace_pattern(graph)
(flag, resp) = compare_graphs(graph, graph_ref, 'output', check_op_attrs=True)
self.assertTrue(flag, resp)
def compress_weights(model: Graph):
"""Apply transformations to save model weights to INT8."""
CompressQuantizeWeights().find_and_replace_pattern(model)
model.clean_up()
ForceStrictPrecision().find_and_replace_pattern(model)
model.clean_up()