def test_RunContext():
"""Test RunContext."""
context_err = 666
with pytest.raises(TypeError):
RunContext(context_err)
cb_params = _InternalCallbackParam()
cb_params.member1 = 1
cb_params.member2 = "abc"
run_context = RunContext(cb_params)
run_context.original_args()
assert cb_params.member1 == 1
assert cb_params.member2 == "abc"
run_context.request_stop()
should_stop = run_context.get_stop_requested()
assert should_stop
def _train_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None):
"""
Training process. The data would be passed to network directly.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
dataset_helper, _ = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=False)
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
for next_element in dataset_helper:
len_element = len(next_element)
if self._loss_fn and len_element != 2:
raise ValueError(
"when loss_fn is not None, train_dataset should"
"return two elements, but got {}".format(len_element))
cb_params.cur_step_num += 1
list_callback.step_begin(run_context)
overflow = False
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update(
):
scaling_sens = self._get_scaling_sens()
next_element = tuple(next_element) + (Tensor(
scaling_sens, mstype.float32), )
cb_params.train_dataset_element = next_element
outputs = self._train_network(*next_element)
cb_params.net_outputs = outputs
if self._loss_scale_manager and self._loss_scale_manager.get_drop_overflow_update(
):
_, overflow, _ = outputs
overflow = np.all(overflow.asnumpy())
self._loss_scale_manager.update_loss_scale(overflow)
list_callback.step_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
train_dataset.reset()
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
def _train_dataset_sink_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None,
sink_size=-1):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
sink_size (int): Control the amount of data each sink. Default: -1.
"""
if sink_size == -1:
epoch_num = epoch
else:
epoch_num = math.ceil(epoch * sink_size /
train_dataset.get_dataset_size())
iter_first_order = self._frequency - 1
iter_second_order = 1
train_dataset.__loop_size__ = iter_second_order
dataset_helper, train_network = self._exec_preprocess(
self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num,
iter_first_order=iter_first_order)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
has_do_dataset_init = False
switch_branch_one = True
train_network_init_flag = True
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
if _need_to_full():
inputs = _to_full_tensor(inputs, self._device_number,
self._global_rank)
list_callback.step_begin(run_context)
if switch_branch_one:
cb_params.cur_step_num += dataset_helper.sink_size()
if train_network_init_flag:
self._train_network.add_flags_recursive(thor=True)
self._train_network.phase = 'train0'
else:
cb_params.cur_step_num += iter_first_order
if train_network_init_flag:
self._train_network.add_flags_recursive(thor=False)
train_network_init_flag = False
self._train_network.phase = 'train1'
if not has_do_dataset_init:
_exec_datagraph(train_dataset,
iter_first_order,
phase='train1_dataset')
has_do_dataset_init = True
switch_branch_one = not switch_branch_one
outputs = self._train_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
dataset_helper.stop_send()
list_callback.end(run_context)
def _train_dataset_sink_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (_ListCallback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
# remove later to deal with loop sink
iter_first_order = 277
iter_second_order = 1
train_dataset.__loop_size__ = iter_second_order
need_wrap = False
if not hasattr(train_dataset, '__ME_INITED__') and context.get_context("enable_loop_sink") \
and not context.get_context("enable_ge"):
need_wrap = True
dataset_helper = DatasetHelper(train_dataset, iter_first_order)
# remove later to deal with loop sink
if need_wrap:
self._train_network = nn.DataWrapper(
self._train_network, *(dataset_helper.types_shapes()),
train_dataset.__ME_INITED__)
cb_params.train_network = self._train_network
self._train_network.set_train()
cb_params.cur_step_num = 0
loop_size = dataset_helper.loop_size()
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
has_do_train1_dataset = False
checkpoint_branch_one = True
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
list_callback.step_begin(run_context)
if checkpoint_branch_one:
cb_params.cur_step_num += loop_size
self._train_network.set_second_order(True)
self._train_network.phase = 'train0'
else:
cb_params.cur_step_num += iter_first_order
self._train_network.set_second_order(False)
self._train_network.phase = 'train1'
if not has_do_train1_dataset:
_exec_datagraph(train_dataset,
iter_first_order,
phase='train1_dataset')
has_do_train1_dataset = True
checkpoint_branch_one = not checkpoint_branch_one
outputs = self._train_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)
def _train_dataset_sink_process(self,
epoch,
train_dataset,
list_callback=None,
cb_params=None,
sink_size=-1):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiple data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned. The data and label would be passed to the network and loss
function respectively.
list_callback (Callback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
sink_size (int): Control the amount of data in each sink. Default: -1.
"""
if sink_size == -1:
epoch_num = epoch
else:
epoch_num = math.ceil(epoch * sink_size /
train_dataset.get_dataset_size())
iter_update_order = 1
iter_accu_order = self._frequency - 1
if context.get_context("device_target") == "GPU":
train_dataset.__loop_size__ = 1
else:
train_dataset.__loop_size__ = iter_accu_order
dataset_helper, train_network = self._exec_preprocess(
self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
sink_size=sink_size,
epoch_num=epoch_num,
iter_update_order=iter_update_order)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
run_context = RunContext(cb_params)
list_callback.begin(run_context)
for i in range(epoch):
cb_params.cur_epoch_num = i + 1
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
for inputs in dataset_helper:
if _need_to_full() and context.get_context(
"device_target") == "GPU":
inputs = _to_full_tensor(inputs, self._device_number,
self._global_rank)
list_callback.step_begin(run_context)
if context.get_context("device_target") == "GPU":
self._train_gpu_sink_step(cb_params, inputs, list_callback,
iter_accu_order, run_context)
else:
self._train_ascend_sink_step(cb_params, train_dataset,
iter_accu_order, inputs,
list_callback, run_context)
list_callback.epoch_end(run_context)
self.should_stop = self.should_stop or run_context.get_stop_requested(
)
if self.should_stop:
break
dataset_helper.stop_send()
list_callback.end(run_context)
def _train_dataset_sink_process(self, epoch, train_dataset, list_callback=None, cb_params=None):
"""
Training process. The data would be passed to network through dataset channel.
Args:
epoch (int): Total number of iterations on the data.
train_dataset (Dataset): A training dataset iterator. If there is no
loss_fn, a tuple with multiply data (data1, data2, data3, ...) should be
returned and passed to the network. Otherwise, a tuple (data, label) should
be returned, and the data and label are passed to the network and loss
function respectively.
list_callback (_ListCallback): Executor of callback list. Default: None.
cb_params (_InternalCallbackParam): Callback parameters. Default: None.
"""
iter_first_order = self._frequency - 1
iter_second_order = 1
train_dataset.__loop_size__ = iter_second_order
dataset_helper, train_network = self._exec_preprocess(self._train_network,
is_train=True,
phase='train',
dataset=train_dataset,
dataset_sink_mode=True,
iter_first_order=iter_first_order)
self._train_network = train_network
cb_params.train_network = self._train_network
cb_params.cur_step_num = 0
loop_size = dataset_helper.loop_size()
run_context = RunContext(cb_params)
list_callback.begin(run_context)
# used to stop training for early stop, such as stopAtTIme or stopATStep
should_stop = False
switch_branch_one = True
# has_do_dataset_init1 = False
for i in range(epoch):
global index_epoch
cb_params.cur_epoch_num = i + 1 + index_epoch
list_callback.epoch_begin(run_context)
# for data sink dataset_helper only iter once, other wise iter epoch_size times.
epoch = i + index_epoch
index_epoch += 1
for inputs in dataset_helper:
# if epoch < self._stop_epoch:
list_callback.step_begin(run_context)
if switch_branch_one:
cb_params.cur_step_num += loop_size
self._train_network.add_flags_recursive(thor=True)
self._train_network.phase = 'train0'
else:
cb_params.cur_step_num += iter_first_order
self._train_network.add_flags_recursive(thor=False)
self._train_network.phase = 'train1'
if not self._has_do_dataset_init:
_exec_datagraph(train_dataset, iter_first_order, phase='train1_dataset')
self._has_do_dataset_init = True
switch_branch_one = not switch_branch_one
outputs = self._train_network(*inputs)
cb_params.net_outputs = outputs
list_callback.step_end(run_context)
# else:
# iter_first_order = 5004
# cb_params.cur_step_num += iter_first_order
# self._train_network.add_flags_recursive(thor=False)
# self._train_network.phase = 'train2'
# if not has_do_dataset_init1:
# _exec_datagraph(train_dataset, iter_first_order, phase='train2_dataset')
# has_do_dataset_init1 = True
# outputs = self._train_network(*inputs)
# cb_params.net_outputs = outputs
# list_callback.step_end(run_context)
# break
list_callback.epoch_end(run_context)
should_stop = should_stop or run_context.get_stop_requested()
if should_stop:
break
list_callback.end(run_context)