def test_first_matmul_biasadd_relu_fusion(self):
import numpy as np
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
x_data = np.array([[0.1, 0.2], [0.2, 0.3]])
y_data = np.array([[1, 2], [3, 4]], dtype=np.float)
x = tf.placeholder(tf.float32, shape=[2, 2])
y = tf.constant(y_data, dtype=tf.float32, shape=[2, 2])
z = tf.matmul(x, y)
z = tf.nn.bias_add(z, [1, 2])
z = tf.nn.relu(z)
with tf.Session() as sess:
sess.run(z, feed_dict={x: x_data, y: y_data})
float_graph_def = sess.graph.as_graph_def()
float_graph_def = QuantizeGraphHelper().get_sorted_graph(
float_graph_def, ['Placeholder'], ['Relu'])
worker = FuseNodeStartWithMatmul(float_graph_def, 'MatMul', False,
'MatMul', 'cpu', True)
output_graph = worker.apply_the_transform()
found_quantized_matmul = False
for i in output_graph.node:
if i.op == 'QuantizeV2' and i.name == 'MatMul_eightbit_quantize_Placeholder' and i.attr[
"T"].type == dtypes.quint8:
found_quantized_matmul = True
break
self.assertEqual(found_quantized_matmul, True)
def test_remove_training_nodes_save_last_identity(self):
tf.compat.v1.disable_eager_execution()
input_constant_name = "input_constant"
relu_name = "relu"
float_graph_def = graph_pb2.GraphDef()
input_constant = QuantizeGraphHelper.create_constant_node(
input_constant_name,
value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
dtype=dtypes.float32,
shape=[1, 2, 6, 1])
float_graph_def.node.extend([input_constant])
relu_node = QuantizeGraphHelper.create_node("Relu", relu_name,
[input_constant_name])
QuantizeGraphHelper.set_attr_dtype(relu_node, "T", dtypes.float32)
float_graph_def.node.extend([relu_node])
b_constant_name = "b_constant"
mat_mul_name = "mat_mul"
identity_name = "identity"
b_constant = QuantizeGraphHelper.create_constant_node(
b_constant_name, value=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype=dtypes.float32, shape=[2, 6])
float_graph_def.node.extend([b_constant])
mat_mul_node = QuantizeGraphHelper.create_node("MatMul", mat_mul_name,
[relu_name, b_constant_name])
QuantizeGraphHelper.set_attr_dtype(mat_mul_node, "T", dtypes.float32)
QuantizeGraphHelper.set_attr_bool(mat_mul_node, "transpose_a", False)
QuantizeGraphHelper.set_attr_bool(mat_mul_node, "transpose_b", False)
float_graph_def.node.extend([mat_mul_node])
identity_node = QuantizeGraphHelper.create_node("Identity", identity_name,
[mat_mul_name])
float_graph_def.node.extend([identity_node])
bias_add_name = "bias_add"
offset_constant_name = "offset_constant"
offset_constant = QuantizeGraphHelper.create_constant_node(
offset_constant_name,
value=[1, 2, 3, 4, 5, 6],
dtype=dtypes.float32,
shape=[6])
float_graph_def.node.extend([offset_constant])
bias_add_node = QuantizeGraphHelper.create_node(
"BiasAdd", bias_add_name, [identity_name, offset_constant_name])
QuantizeGraphHelper.set_attr_dtype(bias_add_node, "T", dtypes.float32)
float_graph_def.node.extend([bias_add_node])
post_relu_name = "post_relu"
post_relu_node = QuantizeGraphHelper.create_node("Relu", post_relu_name,
[bias_add_name])
float_graph_def.node.extend([post_relu_node])
last_identity_node_name = 'last_identity'
last_identity_node = QuantizeGraphHelper.create_node("Identity", last_identity_node_name,
[post_relu_name])
float_graph_def.node.extend([last_identity_node])
post_graph = QuantizeGraphHelper().remove_training_nodes(
float_graph_def, protected_nodes=[last_identity_node_name])
found_identity_node_name = []
for i in post_graph.node:
if i.op == 'Identity':
found_identity_node_name.append(i.name)
break
self.assertEqual(found_identity_node_name[0], 'last_identity')