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mindspore/tests/ut/python/dataset/test_callbacks.py

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# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from builtins import range, super
import time
import pytest
from mindspore import context
from mindspore import log as logger
from mindspore.dataset.callback import DSCallback, WaitedDSCallback
from mindspore.train import Model
from mindspore.train.callback import Callback
import mindspore.dataset as ds
import mindspore.nn as nn
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
class BaseCallback(DSCallback):
def __init__(self, step_size=1, events=None, cb_id=0):
super().__init__(step_size)
self.events = events
self.cb_id = cb_id
def append(self, event_name, ds_run_context):
event = [event_name, ds_run_context.cur_epoch_num,
ds_run_context.cur_step_num_in_epoch, ds_run_context.cur_step_num]
event = '_'.join([str(e) for e in event])
index = -1
for i, e in enumerate(self.events):
if e[0] == event:
index = i
break
if index != -1:
self.events[index][1].append(self.cb_id)
else:
self.events.append((event, [self.cb_id]))
class Begin(BaseCallback):
def ds_begin(self, ds_run_context):
self.append("begin", ds_run_context)
class EpochBegin(BaseCallback):
def ds_epoch_begin(self, ds_run_context):
self.append("epoch_begin", ds_run_context)
class EpochEnd(BaseCallback):
def ds_epoch_end(self, ds_run_context):
self.append("epoch_end", ds_run_context)
class StepBegin(BaseCallback):
def ds_step_begin(self, ds_run_context):
self.append("step_begin", ds_run_context)
class StepEnd(BaseCallback):
def ds_step_end(self, ds_run_context):
self.append("step_end", ds_run_context)
class MyDSCallback(Begin, EpochBegin, EpochEnd, StepBegin, StepEnd):
pass
def verify_events(events, epoch_num, step_num, step_size=1, map_num=1, repeat=1):
'''
Make sure that the events are in correct order.
* begin is the first
* epoch x begin before epoch x end
* epoch x end before epoch x+1 begin
* step x begin before step x end
* step x begin before step x+1 begin
* step x end before step x+1 end
'''
assert events[0][0] == "begin_0_0_0"
epochs = list(filter(lambda e: 'epoch' in e[0], events))
i = 0
while i < len(epochs):
epoch_num = epochs[i][0].split('_')[2]
e_type = epochs[i][0].split('_')[1]
assert str(i // 2 + 1) == epoch_num
assert e_type == "begin"
i += 1
epoch_num = epochs[i][0].split('_')[2]
e_type = epochs[i][0].split('_')[1]
assert str(i // 2 + 1) == epoch_num
assert e_type == "end"
i += 1
steps = list(filter(lambda e: 'step' in e[0], events))
steps = [(s[0].split('_')[1], s[0].split('_')[-1]) for s in steps]
steps_map = {}
max_step = 0
for s in steps:
if s[1] in steps_map:
assert steps_map[s[1]] == 'begin'
assert s[0] == 'end'
else:
assert s[0] == 'begin'
steps_map[s[1]] = 'begin'
assert int(s[1]) > max_step
max_step = max(max_step, int(s[1]))
def generate_expected(epoch_num, step_num, step_size=1, map_num=1, repeat=1):
events = []
cb_id = list(range(map_num))
def append(name, e, s):
event = [name, e + 1, s + 1, e * step_num * repeat + s + 1]
event = '_'.join([str(ev) for ev in event])
events.append((event, cb_id))
events.append(("begin_0_0_0", cb_id))
for e in range(epoch_num):
append("epoch_begin", e, -1)
for s in range(step_num * repeat):
if s % step_size == 0:
append("step_begin", e, s)
append("step_end", e, s)
append("epoch_end", e, step_num * repeat - 1)
return events
def build_test_case_1cb(epochs, steps, step_size=1, repeat=1):
events = []
arr = list(range(1, steps + 1))
data = ds.NumpySlicesDataset(arr, shuffle=False)
my_cb = MyDSCallback(step_size=step_size, events=events)
data = data.map(operations=(lambda x: x), callbacks=my_cb)
if repeat != 1:
if repeat % 2 == 0 and repeat != 2:
data = data.repeat(2)
data = data.map(operations=(lambda x: x))
data = data.repeat(repeat // 2)
else:
data = data.repeat(repeat)
itr = data.create_tuple_iterator(num_epochs=epochs)
for _ in range(epochs):
for _ in itr:
pass
expected_events = generate_expected(epochs, steps, step_size, 1, repeat)
expected_events = [e[0] for e in expected_events]
verify_events(events, epochs, steps, step_size, repeat)
events = [e[0] for e in events]
expected_events.sort()
events.sort()
assert expected_events == events
def build_test_case_2cbs(epochs, steps):
events1 = []
events2 = []
my_cb1 = MyDSCallback(events=events1)
my_cb2 = MyDSCallback(events=events2)
arr = list(range(1, steps + 1))
data = ds.NumpySlicesDataset(arr, shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=[my_cb1, my_cb2])
itr = data.create_tuple_iterator(num_epochs=epochs)
for _ in range(epochs):
for _ in itr:
pass
expected_events = generate_expected(epochs, steps)
expected_events.sort()
events1.sort()
events2.sort()
assert expected_events == events1
assert expected_events == events2
def build_test_case_2maps(epochs, steps):
events = []
my_cb1 = MyDSCallback(events=events, cb_id=0)
my_cb2 = MyDSCallback(events=events, cb_id=1)
arr = list(range(1, steps + 1))
data = ds.NumpySlicesDataset(arr, shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb1)
data = data.map(operations=(lambda x: x), callbacks=my_cb2)
itr = data.create_tuple_iterator(num_epochs=epochs)
for _ in range(epochs):
for _ in itr:
pass
expected_events = generate_expected(epochs, steps, map_num=2)
assert expected_events[1:] == events[1:]
for event in events:
assert len(event) == 2
event, cb_ids = event
if event != "begin_0_0_0":
assert cb_ids[0] == 0
assert cb_ids[1] == 1
def test_callbacks_all_methods():
"""
Feature: Callback
Description: Test Map op with 1 callback with various num_epochs and num_steps args combinations
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_all_methods")
build_test_case_1cb(1, 1)
build_test_case_1cb(1, 2)
build_test_case_1cb(1, 3)
build_test_case_1cb(1, 4)
build_test_case_1cb(2, 1)
build_test_case_1cb(2, 2)
build_test_case_1cb(2, 3)
build_test_case_1cb(2, 4)
build_test_case_1cb(3, 1)
build_test_case_1cb(3, 2)
build_test_case_1cb(3, 3)
build_test_case_1cb(3, 4)
def test_callbacks_var_step_size():
"""
Feature: Callback
Description: Test Map op with 1 callback with step_size=2 and various num_epochs and num_steps args combinations
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_var_step_size")
build_test_case_1cb(1, 2, 2)
build_test_case_1cb(1, 3, 2)
build_test_case_1cb(1, 4, 2)
build_test_case_1cb(2, 2, 2)
build_test_case_1cb(2, 3, 2)
build_test_case_1cb(2, 4, 2)
build_test_case_1cb(3, 2, 2)
build_test_case_1cb(3, 3, 2)
build_test_case_1cb(3, 4, 2)
def test_callbacks_all_2cbs():
"""
Feature: Callback
Description: Test Map op with 2 callbacks with various num_epochs and num_steps args combinations
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_all_2cbs")
build_test_case_2cbs(4, 1)
build_test_case_2cbs(4, 2)
build_test_case_2cbs(4, 3)
build_test_case_2cbs(4, 4)
class MyWaitedCallback(WaitedDSCallback):
def __init__(self, events, step_size=1):
super().__init__(step_size)
self.events = events
def sync_epoch_begin(self, train_run_context, ds_run_context):
event = f"ds_epoch_begin_{ds_run_context.cur_epoch_num}_{ds_run_context.cur_step_num}"
self.events.append(event)
def sync_step_begin(self, train_run_context, ds_run_context):
event = f"ds_step_begin_{ds_run_context.cur_epoch_num}_{ds_run_context.cur_step_num}"
self.events.append(event)
class MyMSCallback(Callback):
def __init__(self, events):
self.events = events
def epoch_end(self, run_context):
cb_params = run_context.original_args()
event = f"ms_epoch_end_{cb_params.cur_epoch_num}_{cb_params.cur_step_num}"
self.events.append(event)
def step_end(self, run_context):
cb_params = run_context.original_args()
event = f"ms_step_end_{cb_params.cur_epoch_num}_{cb_params.cur_step_num}"
self.events.append(event)
class Net(nn.Cell):
def construct(self, x, y):
return x
def test_callbacks_non_sink():
"""
Feature: Callback
Description: Test callbacks with dataset_sink_mode=False in train
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_non_sink")
events = []
my_cb1 = MyWaitedCallback(events, 1)
my_cb2 = MyMSCallback(events)
arr = [1, 2, 3, 4]
data = ds.NumpySlicesDataset((arr, arr), column_names=["c1", "c2"], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb1)
net = Net()
model = Model(net)
model.train(2, data, dataset_sink_mode=False, callbacks=[my_cb2, my_cb1])
expected_synced_events = ['ms_step_end_1_1', 'ds_step_begin_1_2', 'ms_step_end_1_2', 'ds_step_begin_1_3',
'ms_step_end_1_3', 'ds_step_begin_1_4', 'ms_step_end_1_4',
'ms_epoch_end_1_4', 'ds_epoch_begin_2_4',
'ds_step_begin_2_5', 'ms_step_end_2_5', 'ds_step_begin_2_6',
'ms_step_end_2_6', 'ds_step_begin_2_7', 'ms_step_end_2_7', 'ds_step_begin_2_8',
'ms_step_end_2_8', 'ms_epoch_end_2_8']
assert events[:18] == expected_synced_events
def test_callbacks_non_sink_batch_size2():
"""
Feature: Callback
Description: Test callbacks with dataset_sink_mode=False in train after batch(2) is applied to the dataset
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_non_sink_batch_size2")
events = []
my_cb1 = MyWaitedCallback(events, 2)
my_cb2 = MyMSCallback(events)
arr = [1, 2, 3, 4]
data = ds.NumpySlicesDataset((arr, arr), column_names=["c1", "c2"], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb1)
data = data.batch(2)
net = Net()
model = Model(net)
model.train(2, data, dataset_sink_mode=False, callbacks=[my_cb2, my_cb1])
expected_synced_events = ['ms_step_end_1_1', 'ds_step_begin_1_3',
'ms_step_end_1_2',
'ms_epoch_end_1_2', 'ds_epoch_begin_2_4',
'ds_step_begin_2_5', 'ms_step_end_2_3', 'ds_step_begin_2_7',
'ms_step_end_2_4', 'ms_epoch_end_2_4']
assert events[:10] == expected_synced_events
def test_callbacks_non_sink_mismatch_size():
"""
Feature: Callback
Description: Test callbacks with dataset_sink_mode=False in train with mismatch size
Expectation: Exception is raised as expected
"""
logger.info("test_callbacks_non_sink_mismatch_size")
default_timeout = ds.config.get_callback_timeout()
ds.config.set_callback_timeout(1)
events = []
my_cb1 = MyWaitedCallback(events, 2)
my_cb2 = MyMSCallback(events)
arr = [1, 2, 3, 4]
data = ds.NumpySlicesDataset((arr, arr), column_names=["c1", "c2"], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb1)
data = data.batch(3)
net = Net()
model = Model(net)
with pytest.raises(Exception) as err:
model.train(2, data, dataset_sink_mode=False, callbacks=[my_cb2, my_cb1])
assert "RuntimeError: ds_step_begin timed out after 1 second(s)" in str(err.value)
ds.config.set_callback_timeout(default_timeout)
def test_callbacks_validations():
"""
Feature: Callback
Description: Test callbacks param in Map op with invalid argument
Expectation: Exception is raised as expected
"""
logger.info("test_callbacks_validations")
with pytest.raises(Exception) as err:
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data.map(operations=(lambda x: x), callbacks=0)
assert "Argument callbacks with value 0 is not " in str(err.value)
with pytest.raises(Exception) as err:
my_cb1 = MyDSCallback()
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data.map(operations=(lambda x: x), callbacks=[my_cb1, 0])
assert "Argument callbacks[1] with value 0 is not " in str(err.value)
with pytest.raises(Exception) as err:
class BadCB(DSCallback):
pass
my_cb = BadCB()
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb)
for _ in data:
pass
assert "Inheriting Callback class without overriding any methods" in str(err.value)
def test_callbacks_sink_simulation():
"""
Feature: Callback
Description: Test callbacks under sink simulation
Expectation: Output is equal to the expected output
"""
logger.info("test_callback_sink_simulation")
events = []
epochs = 2
my_cb = MyWaitedCallback(events, 1)
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=my_cb)
data = data.device_que()
data.send(num_epochs=epochs)
for e in range(epochs):
for s in range(4):
time.sleep(0.5)
events.append(f"ms_step_end_{e + 1}_{e * 4 + s + 1}")
my_cb.step_end(run_context=0)
events.append(f"ms_epoch_end_{e + 1}_{(e + 1) * 4}")
my_cb.epoch_end(run_context=0)
expected_synced_events = ['ms_step_end_1_1', 'ds_step_begin_1_2', 'ms_step_end_1_2', 'ds_step_begin_1_3',
'ms_step_end_1_3', 'ds_step_begin_1_4', 'ms_step_end_1_4',
'ms_epoch_end_1_4', 'ds_epoch_begin_2_4',
'ds_step_begin_2_5', 'ms_step_end_2_5', 'ds_step_begin_2_6',
'ms_step_end_2_6', 'ds_step_begin_2_7', 'ms_step_end_2_7', 'ds_step_begin_2_8',
'ms_step_end_2_8', 'ms_epoch_end_2_8']
assert events == expected_synced_events
def test_callbacks_repeat():
"""
Feature: Callback
Description: Test Map op with 1 callback with various num_epochs, num_steps, step_size, and repeat args combinations
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_repeat")
build_test_case_1cb(epochs=2, steps=2, step_size=1, repeat=2)
build_test_case_1cb(epochs=2, steps=2, step_size=1, repeat=3)
build_test_case_1cb(epochs=2, steps=2, step_size=2, repeat=3)
build_test_case_1cb(epochs=3, steps=2, step_size=4, repeat=3)
build_test_case_1cb(epochs=2, steps=2, step_size=1, repeat=2)
build_test_case_1cb(epochs=2, steps=2, step_size=1, repeat=4)
build_test_case_1cb(epochs=2, steps=2, step_size=2, repeat=8)
build_test_case_1cb(epochs=3, steps=2, step_size=4, repeat=16)
def test_callbacks_exceptions():
logger.info("test_callbacks_exceptions")
class BadCB(DSCallback):
def ds_begin(self, ds_run_context):
raise RuntimeError("Bad begin")
with pytest.raises(Exception) as err:
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=BadCB())
for _ in data:
pass
assert "RuntimeError: Bad begin" in str(err.value)
def test_callbacks_train_end():
"""
Feature: Callback
Description: Test callback end op under sink simulation
Expectation: Runs successfully
"""
logger.info("test_callback_sink_simulation")
# No asserts are needed, just test there is no deadlock or exceptions
events = []
epochs = 2
my_cb = MyWaitedCallback(events, 1)
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
data = data.map(operations=(lambda x: x), callbacks=[my_cb])
data = data.device_que()
data.send(num_epochs=epochs)
time.sleep(0.5)
my_cb.end(run_context={})
time.sleep(0.5)
def test_callbacks_one_cb():
"""
Feature: Callback
Description: Test callbacks with Begin, EpochBegin, EpochEnd, StepBegin, and StepEnd as the args
Expectation: Output is equal to the expected output
"""
logger.info("test_callbacks_one_cb")
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
events1 = []
events2 = []
events3 = []
my_begin = Begin(events=events1, cb_id=1)
my_epoch_begin = EpochBegin(events=events2, cb_id=2)
my_epoch_end = EpochEnd(events=events3, cb_id=3)
my_step_begin = StepBegin(events=events3, cb_id=3)
my_step_end = StepEnd(events=events2, cb_id=2)
data = data.map(operations=(lambda x: x), callbacks=my_begin)
data = data.map(operations=(lambda x: x), callbacks=[my_epoch_begin, my_step_end])
data = data.map(operations=(lambda x: x), callbacks=[my_epoch_end, my_step_begin])
itr = data.create_tuple_iterator(num_epochs=2)
for _ in range(2):
for _ in itr:
pass
expected_events1 = [('begin_0_0_0', [1])]
expected_events2 = [('epoch_begin_1_0_0', [2]), ('step_end_1_1_1', [2]), ('step_end_1_2_2', [2]),
('step_end_1_3_3', [2]), ('step_end_1_4_4', [2]), ('epoch_begin_2_0_4', [2]),
('step_end_2_1_5', [2]), ('step_end_2_2_6', [2]), ('step_end_2_3_7', [2]),
('step_end_2_4_8', [2])]
expected_events3 = [('step_begin_1_1_1', [3]), ('step_begin_1_2_2', [3]), ('step_begin_1_3_3', [3]),
('step_begin_1_4_4', [3]), ('epoch_end_1_4_4', [3]), ('step_begin_2_1_5', [3]),
('step_begin_2_2_6', [3]), ('step_begin_2_3_7', [3]), ('step_begin_2_4_8', [3]),
('epoch_end_2_4_8', [3])]
events1.sort()
events2.sort()
events3.sort()
expected_events1.sort()
expected_events2.sort()
expected_events3.sort()
assert events1 == expected_events1
assert events2 == expected_events2
assert events3 == expected_events3
def test_clear_callback():
"""
Feature: Callback
Description: Test callback is removed for get_dataset_size and output_shape/type
Expectation: Output is equal to the expected output
"""
logger.info("test_clear_callback")
# this test case will test that callback is removed for get_dataset_size and output_shape/type
class FlagCallback(DSCallback):
def __init__(self):
super().__init__(step_size=1)
self.flag = False
self.row_cnt = 0
def ds_begin(self, ds_run_context):
# if callback isn't removed in getter pass, this function will be called
self.flag = True
def ds_step_begin(self, ds_run_context):
self.row_cnt += 1
data = ds.NumpySlicesDataset([1, 2, 3, 4], shuffle=False)
cb = FlagCallback()
# make sure variables are properly initialized before testing
assert not cb.flag and cb.row_cnt == 0
data = data.map(operations=(lambda x: x), callbacks=cb)
assert data.get_dataset_size() == 4
assert data.output_shapes() == [[]]
# make sure callback is never called by checking flag and row_cnt
assert not cb.flag and cb.row_cnt == 0
for _ in data.create_dict_iterator(num_epochs=1):
pass
# this ensure that callback is indeed called
assert cb.flag and cb.row_cnt == 4
if __name__ == '__main__':
test_callbacks_all_2cbs()
test_callbacks_all_methods()
test_callbacks_exceptions()
test_callbacks_repeat()
test_callbacks_sink_simulation()
test_callbacks_validations()
test_callbacks_var_step_size()
test_callbacks_non_sink_batch_size2()
test_callbacks_non_sink()
test_callbacks_one_cb()
test_callbacks_non_sink_mismatch_size()
test_callbacks_train_end()
test_clear_callback()
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