def extract(cls, node):
param = node.pb.inner_product_param
pb_model = node.model_pb
attrs = {
'out-size': param.num_output,
'transpose_weights': not param.transpose,
}
attrs.update(weights_biases(param.bias_term, pb_model))
FullyConnected.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
attr = get_mxnet_layer_attrs(node.symbol_dict)
num_hidden = attr.int('num_hidden', None)
assert num_hidden is not None, "{} node with no `num_hidden` parameter found".format(cls.op)
attrs = {
'out-size': num_hidden,
'transpose_weights': True,
}
FullyConnected.update_node_stat(node, attrs)
return cls.enabled
def extract(cls, node):
pb = node.parameters
collect_until_token(pb, b'<Params>')
weights, weights_shape = read_binary_matrix(pb)
mapping_rule = {
'out-size': weights_shape[0],
'transpose_weights': True,
}
embed_input(mapping_rule, 1, 'weights', weights)
FullyConnected.update_node_stat(node, mapping_rule)
return cls.enabled
def replace_pattern(graph: Graph, match: dict):
node = match['matmul']
name = node.soft_get('name', node.id)
A_shape = node.in_port(0).data.get_shape()
B_shape = node.in_port(1).data.get_shape()
out_shape = node.out_port(0).data.get_shape()
assert A_shape is not None and B_shape is not None and out_shape is not None
B_value = node.in_port(1).data.get_value()
if (B_value is not None or node.in_port(1).get_source().node.has_and_set('stop_value_propagation')) and B_shape[
B_shape != 1].size <= 2:
# transferring from MatMul representation: [B, I, K] * [B, K, O] = [B, I, O]
# to FullyConnected representation: [I, K] * [O, K] = [I, O]
B, I, K, O, aligned_A_shape, aligned_B_shape = MatMulToFullyConnected.get_matmul_BIKO(node)
# weights normalization
if not node.transpose_b:
# FullyConnected weights layout is OI
# MatMul second input layout is (B)IO
transpose_order = list(range(B_shape.size))
transpose_order[-1], transpose_order[-2] = transpose_order[-2], transpose_order[-1]
order = Const(graph, {'value': int64_array(transpose_order)}).create_node()
transpose = Transpose(graph, {'name': name + '/weights_transpose'}).create_node()
weights_source = node.in_port(1).get_source()
node.in_port(1).get_connection().set_source(transpose.out_port(0))
transpose.in_port(0).connect(weights_source)
transpose.in_port(1).connect(order.out_port(0))
order.infer(order)
transpose.infer(transpose)
if node.in_port(1).data.get_shape().size != 2:
const = Const(graph, {'value': int64_array([-1, K])}).create_node()
reshape = Reshape(graph, {'name': name + '/weights_reshape'}).create_node()
weights_source = node.in_port(1).get_source()
node.in_port(1).get_connection().set_source(reshape.out_port(0))
reshape.in_port(0).connect(weights_source)
reshape.in_port(1).connect(const.out_port(0))
const.infer(const)
reshape.infer(reshape)
assert np.all(np.array_equal(node.in_port(1).data.get_shape(), int64_array([O, K]))), \
"MatMul `{}` was not converted to FullyConnected: wrong weights shape: {}, " \
"B={}, I={}, K={}, O={}".format(name, node.in_port(1).data.get_shape(), B, I, K, O)
node.in_port(1).bin = 'weights'
del node['transpose_b']
# input normalization
if node.transpose_a:
transpose_order = list(range(A_shape.size))
transpose_order[-1], transpose_order[-2] = transpose_order[-2], transpose_order[-1]
order = Const(graph, {'value': int64_array(transpose_order)}).create_node()
transpose = Transpose(graph, {'name': name + '/input_transpose'}).create_node()
input_source = node.in_port(0).get_source()
node.in_port(0).get_connection().set_source(transpose.out_port(0))
transpose.in_port(0).connect(input_source)
transpose.in_port(1).connect(order.out_port(0))
order.infer(order)
transpose.infer(transpose)
if A_shape.size != 2:
const = Const(graph, {'value': int64_array([-1, K])}).create_node()
reshape = Reshape(graph, {'name': name + '/input_reshape'}).create_node()
input_source = node.in_port(0).get_source()
node.in_port(0).get_connection().set_source(reshape.out_port(0))
reshape.in_port(0).connect(input_source)
reshape.in_port(1).connect(const.out_port(0))
const.infer(const)
reshape.infer(reshape)
assert np.all(np.array_equal(node.in_port(0).data.get_shape(), int64_array([np.prod(B) * I, K]))), \
"MatMul `{}` wasn't converted to FullyConnected: wrong input shape: {}, " \
"B={}, I={}, K={}, O={}".format(name, node.in_port(0).data.get_shape(), B, I, K, O)
del node['transpose_a']
FullyConnected.update_node_stat(node, {'out-size': O})
# output normalization
if out_shape.size != 2:
const = Const(graph, {'value': int64_array([*B, I, O])}).create_node()
reshape = Reshape(graph, {'name': name + '/output_reshape'}).create_node()
dst = node.out_port(0).get_destination()
node.out_port(0).get_connection().set_destination(reshape.in_port(0))
const.out_port(0).connect(reshape.in_port(1))
reshape.out_port(0).connect(dst)
node.infer(node)
const.infer(const)
reshape.infer(reshape)
else:
assert A_shape.size == out_shape.size
assert B_shape.size <= out_shape.size
if B_shape.size != out_shape.size:
unsqueeze_dim = Const(graph, {'value': int64_array(list(range(out_shape.size - B_shape.size)))
}).create_node()
unsqueeze = Unsqueeze(graph, {}).create_node()
B_source = node.in_port(1).get_source()
node.in_port(1).get_connection().set_source(unsqueeze.out_port(0))
unsqueeze.in_port(0).connect(B_source)
unsqueeze.in_port(1).connect(unsqueeze_dim.out_port(0))
unsqueeze_dim.infer(unsqueeze_dim)
unsqueeze.infer(unsqueeze)
Gemm.update_node_stat(node, {
'transpose_a': node.has_and_set('transpose_a'),
'transpose_b': node.has_and_set('transpose_b'),
})