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New api about checkpoint and models #10878

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merged 39 commits into from
Jun 10, 2018

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seiriosPlus
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@seiriosPlus seiriosPlus commented May 23, 2018

  1. add Checkpoint handle and config in Train.py
  2. update load_model/save_model API
    Related update fluid Train API param_path to checkpoint_config #10828
    Related Incremental Learning Support for Fluid with Distribution #10870

@seiriosPlus seiriosPlus changed the title [WIP] New api about cpkt/params New api about checkpoint and models May 29, 2018
checkpoint_dir = os.getcwd()
raise ValueError("The values of 'checkpoint_dir' should not be None")

if trainer_args and not isinstance(trainer_args, dict):
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what're the details about trainer_args? Shall we need a class instead of a parameter with dict type, it's confusing for users.

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trainer_args not for the user, just for the developers, currently, trainer_args only contains step_id, epoch_id, there maybe have more arguments need to be saved in the checkpoint.

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Please use the clear parameters, it would be confused with other developers, use step_id and epoch_id or a Class as the configuration parameter.

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I write two functions to make it more clearly.

save_trainer_args(cur_dir, trainer_id, trainer_args)

if is_chief:
save_persist_vars_without_grad(executor, cur_dir, main_program)
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It looks all gradient vars are all not persistent, so maybe the function name would shorter for save_persistent_vars? BTW, persist is a verb, we need the adjective one: persistent .

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First, I find arguments named "X@GRAD" are persistent .
Second, save_persistent_vars do not filter RAW arguments.

if "@GRAD" in var.name:
return False

if ".trainer_" in var.name:
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Can you add some comments to explain what's the meaning of the hard code .blcok and .trainer_ ?

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done

_lru_delete(checkpoint_dir, max_num_checkpoints)


def load_checkpoint(executor, checkpoint_dir=None, main_program=None):
def need_load_checkpoint(checkpoint_dir):
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I'm confusing about this function, for need to do ... means the function should return a boolean variable, so that we can use the function as:

if need_load_checkpoint(xxx):
    # resume from the checkpoint
else:
    # train from the beginning

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I renamed need_load_checkpoint to get_latest_checkpoint_serial, make it more meaningful.

"""
if checkpoint_dir is None:
checkpoint_dir = os.getcwd()
raise ValueError("The values of 'checkpoint_dir' should not be None")
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checkpoint_dir should not be None.

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checkpoint_dir should be checked in Trainer.py, A property directory will be given by Trainer.py.

_lru_delete(checkpoint_dir, max_num_checkpoints)


def load_checkpoint(executor, checkpoint_dir=None, main_program=None):
def get_latest_checkpoint_serial(checkpoint_dir):
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Seems we don't need get_latest_checkpoint_serial , do the same thing with _get_latest_checkpoint_dir, or we can rename _get_latest_checkpoint_dir to get_latest_checkpoint_serial.

:param main_program
"""

if checkpoint_dir is None:
checkpoint_dir = os.getcwd()
raise ValueError(
"The values of 'checkpoint_dir' or 'serial' should not be None")
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"The values of 'checkpoint_dir' or 'serial' should not be None")

Seems here only check checkpoint_dir ?

if serial < 0:
return
if main_program is None:
raise ValueError("The values of 'main_program'should not be None")
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The values of 'main_program'should not be None

main_program should not be None.

load_persist_vars_without_grad will load variables from a directory by an executor,
the variable named end with "@GRAD" will not be loaded.

:param executor
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Please add the details comments about the parameters.

@@ -193,14 +253,18 @@ def _dist_transpile_if_necessary(self, optimize_ops, params_grads):
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
# the unique trainer id, starting from 0, needed by trainer
# only
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
self.trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
self.chief = self.trainer_id == 0
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Why hard code trainer_id and chief ere equal to 0 ?

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We will default initialize trainer_id =0 and chief = True as default.
If run PaddlePaddle as local, there is only one trainer, its trainer_id is 0, and it is the chief obviously.
If run PaddlePaddle as distribution, we will get PADDLE_TRAINER_ID from env, there will only have one trainer as the chief.

checkpoint_dir = os.getcwd()
raise ValueError("The values of 'checkpoint_dir' should not be None")

if trainer_args and not isinstance(trainer_args, dict):
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Please use the clear parameters, it would be confused with other developers, use step_id and epoch_id or a Class as the configuration parameter.

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Please add a unit test for the checkpoint feature.

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I have some comments above, please follow them thanks.

if not os.path.isdir(trainer_dir):
os.makedirs(trainer_dir)

return trainer_dir


def _lru_delete(dirname, max_num_checkpoints=3):
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Seems this function does not do implement a real LRU algorithms, scroll_delete would be better.

int(serial)
except ValueError:
serial = _get_dir_serial(cur_dir)
if serial == -1:
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please merge the two condition statement.

@@ -348,6 +423,41 @@ def _get_or_create_parallel_executor(self):
loss_name=self.train_func_outputs[0].name)
return self._get_parallel_executor()

def _clean_checkpoint(self):
if not self.checkpoint:
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use assert instead of return directly, otherwise we don't know whether this function success.

return trainer_args

def _save_checkpoint(self, epoch_id, step_id):
if not self.checkpoint:
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The same reason, use assert please.

@@ -473,79 +478,143 @@ def save_checkpoint(executor,

:param executor
:param checkpoint_dir
:param max_num_checkpoints
:param save_interval_secs
:param trainer_id
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Need more details comments.


class TestCheckpoint(unittest.TestCase):
def setUp(self):
self.dirname = "/tmp/ckpt"
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better to use tempfile instead of the hard code path.

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LGTM, this feature is related to the API for user, please @typhoonzero double check.

:param executor executor for save the value
:param checkpoint_dir the checkpoint directory
:param trainer_id currect trainer id
:param is_chief if the trainer id equals 0, the is_chief will be true
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If have is_chief why still need to pass trainer_id?

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each trainer need to save its arguments practicality.
Only chief need to save variables.

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I have deleted code about chief

def _get_serial_dir(serial, checkpoint_dir):
serial_folder = CHECKPOINT_PREFIX + CHECKPOINT_SEPARATOR + str(serial)
return os.path.join(checkpoint_dir, serial_folder)
def load_persist_vars_without_grad(executor,
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Why this is needed?

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write load_persist_vars_without_grad just because of the filter.
I need to write a new filter to filter variables.

@typhoonzero
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The test seems didn't pass?

@seiriosPlus
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I will check it.

@seiriosPlus seiriosPlus merged commit d896134 into PaddlePaddle:develop Jun 10, 2018
@seiriosPlus seiriosPlus deleted the new_api_about_cpkt branch June 10, 2018 09:13
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3 participants