b67cff7cd5
* add m1 mac pipelines as a matrix parameter * Update mac.yml disable java_api because of macos arm64 - Java is not available on macOS arm64 runners * Update mac.yml added always condition for all tests * Update mac.yml * Update mac.yml * Update mac.yml * Update setup.py temp commit * Update tools/openvino_dev/setup.py * use matrix for var * add mxnet to extras only for x86_64 * skip failing tests * use xfail for Python tests; add missing filter for transformations tests * skip CPU func tests on x86_64 mac; skip some tests from CPU func tests on arm mac * Update mac.yml * skip tests on mac arm * skip tests on darwin; apply review * add more skips for python and c++ tests * skip tf tests * skip more tf tests; skip more Python UT stages * rm alwayses, rm triggers, add nightly trigger --------- Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
325 lines
15 KiB
Python
325 lines
15 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import platform
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import numpy as np
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import pytest
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import tensorflow as tf
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from common.tf_layer_test_class import CommonTFLayerTest
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class TestIfFloat(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'cond' in inputs_info, "Test error: inputs_info must contain `cond`"
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assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `y`"
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cond_shape = inputs_info['cond']
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x_shape = inputs_info['x']
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y_shape = inputs_info['y']
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inputs_data = {}
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inputs_data['cond'] = np.random.randint(0, 2, cond_shape).astype(bool)
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inputs_data['x'] = np.random.randint(1, 10, x_shape).astype(np.float32)
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inputs_data['y'] = np.random.randint(-50, 50, y_shape).astype(np.float32)
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return inputs_data
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def create_if_net(self, x_shape, y_shape, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def if_function(cond, x, y):
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def then_branch():
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output1 = tf.add(x, y)
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output2 = tf.multiply(x, y)
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output3 = tf.subtract(x, y)
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return output1, output2, output3
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def else_branch():
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const_two = tf.constant(2.0, dtype=tf.float32)
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output1 = tf.add(y, const_two)
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output2 = tf.multiply(const_two, y)
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output3 = x - y + const_two
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return output1, output2, output3
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if_output = tf.cond(cond, then_branch, else_branch)
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output1 = tf.identity(if_output[0], name='output1')
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output2 = tf.identity(if_output[1], name='output2')
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output3 = tf.identity(if_output[2], name='output3')
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return output1, output2, output3
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tf_if_graph = tf.function(if_function)
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cond = np.random.randint(0, 2, []).astype(bool)
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x = np.random.randint(1, 10, x_shape).astype(np.float32)
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y = np.random.randint(-50, 50, y_shape).astype(np.float32)
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concrete_func = tf_if_graph.get_concrete_function(cond, x, y)
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# lower_control_flow defines representation of If operation
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# in case of lower_control_flow=True it is decomposed into Switch and Merge nodes
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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tf_net = frozen_func.graph.as_graph_def(add_shapes=True)
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return tf_net, None
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test_data_basic = [
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dict(x_shape=[3], y_shape=[2, 3], lower_control_flow=False),
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dict(x_shape=[3], y_shape=[2, 3], lower_control_flow=True),
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dict(x_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=False),
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dict(x_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122716')
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def test_if_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU':
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pytest.xfail('104855')
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self._test(*self.create_if_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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class TestIfInt(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'cond' in inputs_info, "Test error: inputs_info must contain `cond`"
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assert 'ind' in inputs_info, "Test error: inputs_info must contain `ind`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `y`"
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cond_shape = inputs_info['cond']
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ind_shape = inputs_info['ind']
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y_shape = inputs_info['y']
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inputs_data = {}
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inputs_data['cond'] = np.random.randint(0, 2, cond_shape).astype(bool)
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inputs_data['ind'] = np.random.randint(1, 10, ind_shape).astype(np.int32)
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inputs_data['y'] = np.random.randint(-50, 50, y_shape).astype(np.float32)
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return inputs_data
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def create_if_net(self, ind_shape, y_shape, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def if_function(cond, ind, y):
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def then_branch():
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const_one = tf.constant(1, dtype=tf.int32)
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output1 = tf.add(ind, const_one)
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output2 = tf.multiply(tf.cast(output1, tf.float32), y)
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output3 = tf.subtract(tf.cast(output1, tf.float32), y)
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return output1, output2, output3
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def else_branch():
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const_two = tf.constant(2, dtype=tf.int32)
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output1 = tf.add(ind, const_two)
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output2 = tf.multiply(tf.cast(output1, tf.float32), y)
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output3 = tf.cast(output1, tf.float32) - y
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return output1, output2, output3
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if_output = tf.cond(cond, then_branch, else_branch)
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output1 = tf.identity(if_output[0], name='output1')
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output2 = tf.identity(if_output[1], name='output2')
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output3 = tf.identity(if_output[2], name='output3')
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return output1, output2, output3
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tf_if_graph = tf.function(if_function)
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cond = np.random.randint(0, 2, []).astype(bool)
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ind = np.random.randint(1, 10, ind_shape).astype(np.int32)
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y = np.random.randint(-50, 50, y_shape).astype(np.float32)
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concrete_func = tf_if_graph.get_concrete_function(cond, ind, y)
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# lower_control_flow defines representation of If operation
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# in case of lower_control_flow=True it is decomposed into Switch and Merge nodes
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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graph_def = frozen_func.graph.as_graph_def(add_shapes=True)
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return graph_def, None
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test_data_basic = [
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dict(ind_shape=[3], y_shape=[2, 3], lower_control_flow=False),
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dict(ind_shape=[3], y_shape=[2, 3], lower_control_flow=True),
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dict(ind_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=False),
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dict(ind_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122716')
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def test_if_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU':
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pytest.xfail('104855')
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self._test(*self.create_if_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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class TestNestedIf(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'x' in inputs_info, "Test error: inputs_info must contain `cond`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `x`"
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assert 'z' in inputs_info, "Test error: inputs_info must contain `y`"
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x_shape = inputs_info['x']
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y_shape = inputs_info['y']
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z_shape = inputs_info['z']
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inputs_data = {}
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inputs_data['x'] = np.random.randint(0, 6, x_shape).astype(np.int32)
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inputs_data['y'] = np.random.randint(1, 10, y_shape).astype(np.float32)
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inputs_data['z'] = np.random.randint(-50, 50, z_shape).astype(np.float32)
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return inputs_data
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def create_if_net(self, y_shape, z_shape, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def if_function(x, y, z):
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def nested_then_branch():
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add = tf.add(y, z)
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return add
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def nested_else_branch():
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mul = tf.multiply(y, z)
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return mul
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def then_branch():
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output1 = tf.cond(x > 4, nested_then_branch, nested_else_branch)
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output2 = tf.multiply(y, z)
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output3 = tf.subtract(y, z)
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return output1, output2, output3
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def else_branch():
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const_two = tf.constant(2.0, dtype=tf.float32)
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output1 = tf.add(y, const_two)
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output1 = tf.add(output1, z)
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output2 = tf.multiply(z, y)
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output3 = z - y + const_two
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return output1, output2, output3
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if_output = tf.cond(x < 2, then_branch, else_branch)
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output1 = tf.identity(if_output[0], name='output1')
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output2 = tf.identity(if_output[1], name='output2')
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output3 = tf.identity(if_output[2], name='output3')
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return output1, output2, output3
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tf_if_graph = tf.function(if_function)
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x = np.random.randint(0, 8, []).astype(np.int32)
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y = np.random.randint(1, 10, y_shape).astype(np.float32)
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z = np.random.randint(-50, 50, z_shape).astype(np.float32)
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concrete_func = tf_if_graph.get_concrete_function(x, y, z)
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# lower_control_flow defines representation of If operation
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# in case of lower_control_flow=True it is decomposed into Switch and Merge nodes
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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tf_net = frozen_func.graph.as_graph_def(add_shapes=True)
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return tf_net, None
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test_data_basic = [
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dict(y_shape=[3], z_shape=[2, 3], lower_control_flow=False),
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dict(y_shape=[3], z_shape=[2, 3], lower_control_flow=True),
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dict(y_shape=[2, 1, 4], z_shape=[2, 1, 4], lower_control_flow=False),
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dict(y_shape=[2, 1, 4], z_shape=[2, 1, 4], lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122716')
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def test_if_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU':
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pytest.xfail('104855')
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self._test(*self.create_if_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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class TestSequantialIfs(CommonTFLayerTest):
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def _prepare_input(self, inputs_info):
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assert 'cond' in inputs_info, "Test error: inputs_info must contain `cond`"
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assert 'x' in inputs_info, "Test error: inputs_info must contain `x`"
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assert 'y' in inputs_info, "Test error: inputs_info must contain `y`"
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cond_shape = inputs_info['cond']
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x_shape = inputs_info['x']
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y_shape = inputs_info['y']
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inputs_data = {}
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inputs_data['cond'] = np.random.randint(0, 2, cond_shape).astype(bool)
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inputs_data['x'] = np.random.randint(1, 10, x_shape).astype(np.float32)
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inputs_data['y'] = np.random.randint(-50, 50, y_shape).astype(np.float32)
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return inputs_data
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def create_sequential_ifs_net(self, x_shape, y_shape, lower_control_flow):
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from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
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def sequential_ifs(cond, x, y):
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def if1(cond1, x1, y1):
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def then_branch():
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add = tf.add(x1, y1)
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mul = tf.multiply(x1, y1)
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return add, mul
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def else_branch():
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const_two = tf.constant(2.0, dtype=tf.float32)
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add = tf.add(y1, const_two)
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mul = tf.multiply(const_two, y1)
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return add, mul
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if1_op = tf.cond(cond1, then_branch, else_branch)
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output1 = tf.identity(if1_op[0], name='output1')
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output2 = tf.identity(if1_op[1], name='output2')
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return output1, output2
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def if2(cond1, x2, y2):
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def then_branch():
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const_two = tf.constant(2.0, dtype=tf.float32)
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add = tf.add(y2, const_two)
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mul = tf.multiply(const_two, y2)
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return add, mul
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def else_branch():
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add = tf.add(x2, y2)
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mul = tf.multiply(x2, y2)
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return add, mul
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if2_op = tf.cond(cond1, then_branch, else_branch)
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output1 = tf.identity(if2_op[0], name='output1')
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output2 = tf.identity(if2_op[1], name='output2')
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return output1, output2
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output1, output2 = if1(cond, x, y)
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const_ten = tf.constant(10.0, dtype=tf.float32)
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output1 = tf.add(output1, const_ten)
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output1, output2 = if2(cond, output1, output2)
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return output1, output2
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tf_if_graph = tf.function(sequential_ifs)
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cond = np.random.randint(0, 2, []).astype(bool)
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x = np.random.randint(1, 10, x_shape).astype(np.float32)
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y = np.random.randint(-50, 50, y_shape).astype(np.float32)
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concrete_func = tf_if_graph.get_concrete_function(cond, x, y)
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# lower_control_flow defines representation of If operation
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# in case of lower_control_flow=True it is decomposed into Switch and Merge nodes
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frozen_func = convert_variables_to_constants_v2(concrete_func,
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lower_control_flow=lower_control_flow)
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tf_net = frozen_func.graph.as_graph_def(add_shapes=True)
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return tf_net, None
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test_data_basic = [
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dict(x_shape=[3], y_shape=[2, 3], lower_control_flow=False),
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dict(x_shape=[3], y_shape=[2, 3], lower_control_flow=True),
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dict(x_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=False),
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dict(x_shape=[2, 1, 4], y_shape=[2, 1, 4], lower_control_flow=True)
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]
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@pytest.mark.parametrize("params", test_data_basic)
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@pytest.mark.precommit_tf_fe
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@pytest.mark.nightly
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@pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64',
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reason='Ticket - 122716')
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def test_if_basic(self, params, ie_device, precision, ir_version, temp_dir,
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use_new_frontend, use_old_api):
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if ie_device == 'GPU':
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pytest.xfail('104855')
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self._test(*self.create_sequential_ifs_net(**params),
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ie_device, precision, ir_version, temp_dir=temp_dir,
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use_new_frontend=use_new_frontend, use_old_api=use_old_api)
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