Merge branch 'distributed' (#144)

tensorpack/callbacks/base.py

@@ -36,6 +36,8 @@ class Callback(object):
     .. automethod:: _after_train
     """
 
+    _chief_only = True
+
     def setup_graph(self, trainer):
         self._steps_per_epoch = trainer.config.steps_per_epoch
         self.trainer = trainer
@@ -162,6 +164,19 @@ class Callback(object):
     def local_step(self):
         return self.trainer.local_step
 
+    @property
+    def chief_only(self):
+        """
+        Only run this callback on chief training process.
+
+        Returns: bool
+        """
+        return self._chief_only
+
+    @chief_only.setter
+    def chief_only(self, v):
+        self._chief_only = v
+
     def __str__(self):
         return type(self).__name__
 

tensorpack/callbacks/graph.py

@@ -17,13 +17,15 @@ class RunOp(Callback):
     """ Run an Op. """
 
     def __init__(self, setup_func,
-                 run_before=True, run_as_trigger=True, run_step=False):
+                 run_before=True, run_as_trigger=True,
+                 run_step=False, verbose=False):
         """
         Args:
             setup_func: a function that returns the Op in the graph
             run_before (bool): run the Op before training
             run_as_trigger (bool): run the Op on every trigger
             run_step (bool): run the Op every step (along with training)
+            verbose (bool): pring logs when the op is run.
 
         Examples:
             The `DQN Example
@@ -34,27 +36,38 @@ class RunOp(Callback):
         self.run_before = run_before
         self.run_as_trigger = run_as_trigger
         self.run_step = run_step
+        self.verbose = verbose
 
     def _setup_graph(self):
         self._op = self.setup_func()
 
     def _before_train(self):
         if self.run_before:
+            self._print()
             self._op.run()
 
     def _trigger(self):
         if self.run_as_trigger:
+            self._print()
             self._op.run()
 
     def _before_run(self, _):
         if self.run_step:
+            self._print()
             return [self._op]
 
+    def _print(self):
+        if self.verbose:
+            logger.info("Running Op {} ...".format(self._op.name))
+
 
 class RunUpdateOps(RunOp):
     """
     Run ops from the collection UPDATE_OPS every step
     """
+
+    _chief_only = False
+
     def __init__(self, collection=tf.GraphKeys.UPDATE_OPS):
         def f():
             ops = tf.get_collection(collection)

tensorpack/callbacks/inference_runner.py

@@ -90,6 +90,7 @@ class InferenceRunnerBase(Callback):
         def fn(_):
             in_tensors = self._input_source.get_input_tensors()
             self.trainer.model.build_graph(in_tensors)
+        with tf.variable_scope(self.trainer.vs_name_for_predictor, reuse=True):
             PredictorTowerBuilder(fn, self._prefix).build(self._predict_tower_id)
 
         self._hooks = [self._build_hook(inf) for inf in self.infs]

tensorpack/callbacks/param.py

@@ -72,7 +72,7 @@ class GraphVarParam(HyperParam):
                 self.var = v
                 break
         else:
-            raise ValueError("{} is not a VARIABLE in the graph!".format(self.var_name))
+            raise ValueError("{} is not a GLOBAL_VARIABLE in the graph!".format(self.var_name))
 
     def set_value(self, v):
         """ Assign the variable a new value. """

tensorpack/callbacks/saver.py

@@ -43,6 +43,7 @@ class ModelSaver(Callback):
         vars = []
         for key in self.var_collections:
             vars.extend(tf.get_collection(key))
+        vars = list(set(vars))
         self.path = os.path.join(self.checkpoint_dir, 'model')
         if get_tf_version_number() <= 1.1:
             self.saver = tf.train.Saver(

tensorpack/callbacks/steps.py

@@ -55,13 +55,14 @@ class MaintainStepCounter(Callback):
         # ensure it exists
         gs_var = get_global_step_var()
         with tf.name_scope(None):
-            self.gs_incr_var = tf.assign_add(
+            with tf.device(gs_var.device):
+                self.gs_incr_op = tf.assign_add(
                     gs_var, 1,
-                name=GLOBAL_STEP_INCR_OP_NAME)
+                    name=GLOBAL_STEP_INCR_OP_NAME).op
             # tf.mod(
             #     self.gs_incr_var, self.trainer.config.steps_per_epoch,
             #     name=LOCAL_STEP_OP_NAME)
-        self._fetches = tf.train.SessionRunArgs(self.gs_incr_var)
+        self._fetches = tf.train.SessionRunArgs(self.gs_incr_op)
 
     def _before_train(self):
         gs_val = get_global_step_value()
@@ -81,6 +82,8 @@ class MaintainStepCounter(Callback):
 class ProgressBar(Callback):
     """ A progress bar based on tqdm. Enabled by default. """
 
+    _chief_only = False
+
     def __init__(self, names=[]):
         """
         Args:

tensorpack/models/common.py

@@ -136,7 +136,7 @@ def layer_register(
                         # log shape info and add activation
                         logger.info("{} output: {}".format(
                             scope.name, get_shape_str(outputs)))
-                        _LAYER_LOGGED.add(scope.name)
+                        _LAYER_LOGGED.add(scope_name)
             else:
                 # run the actual function
                 outputs = func(*args, **actual_args)

tensorpack/models/regularize.py

@@ -47,7 +47,7 @@ def regularize_cost(regex, func, name='regularize_cost'):
     for p in params:
         para_name = p.name
         # in replicated mode, only regularize variables inside this tower
-        if ctx.has_own_variables and (not para_name.startswith(ctx.vs_name)):
+        if ctx.has_own_variables and ctx.vs_name and (not para_name.startswith(ctx.vs_name)):
             continue
         if re.search(regex, para_name):
             costs.append(func(p))

tensorpack/tfutils/common.py

@@ -39,9 +39,11 @@ def get_default_sess_config(mem_fraction=0.99):
     conf.inter_op_parallelism_threads = 0
 
     conf.gpu_options.per_process_gpu_memory_fraction = mem_fraction
+    if get_tf_version_number() >= 1.2:
+        conf.gpu_options.force_gpu_compatible = True
+
     conf.gpu_options.allocator_type = 'BFC'
     conf.gpu_options.allow_growth = True
-    # force gpu compatible?
 
     conf.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
     return conf

tensorpack/tfutils/summary.py

@@ -154,11 +154,13 @@ def add_moving_summary(v, *args, **kwargs):
     for x in v:
         assert isinstance(x, tf.Tensor), x
         assert x.get_shape().ndims == 0, x.get_shape()
-    # TODO will produce tower0/xxx?
+    # TODO will produce variable tower0/xxx?
+    # TODO not saved under distributed
     # TODO use zero_debias
-    with tf.name_scope(None):
+    gs = get_global_step_var()
+    with tf.name_scope(None), tf.device(gs.device):
         averager = tf.train.ExponentialMovingAverage(
-            decay, num_updates=get_global_step_var(), name='EMA')
+            decay, num_updates=gs, name='EMA')
         avg_maintain_op = averager.apply(v)
 
         for c in v:

tensorpack/tfutils/tower.py

@@ -17,13 +17,16 @@ class TowerContext(object):
 
     def __init__(self, tower_name,
                  device=None, is_training=None,
-                 var_strategy='shared'):
+                 var_strategy='shared',
+                 vs_name=None):
         """
         Args:
             tower_name (str): 'tower0', 'towerp0', or ''
             device (str or device function): the device to use. Defaults to either cpu0 or gpu0.
             is_training (bool): if None, automatically determine from tower_name.
             var_strategy (str): either 'shared' or 'replicated'.
+            vs_name (str): the variable scope name to open. Only valid in
+                'replicated' mode. Defaults to be tower_name.
         """
         self._name = tower_name
         if device is None:
@@ -38,6 +41,13 @@ class TowerContext(object):
         self._var_strategy = var_strategy
         if self._var_strategy == 'replicated':
             assert self._name
+            if vs_name is None:
+                self._vs_name = self._name
+            else:
+                self._vs_name = vs_name
+        else:
+            assert vs_name is None, "vs_name is only valid in 'replicated' mode!"
+            self._vs_name = ''
 
     @property
     def is_main_training_tower(self):
@@ -62,12 +72,7 @@ class TowerContext(object):
     # variable_scope name
     @property
     def vs_name(self):
-        if self.has_own_variables:
-            # do not open new variable scope for the main tower,
-            # just use '', so that Saver & PredictTower know what to do
-            if self.index > 0:
-                return self._name
-        return ""
+        return self._vs_name
 
     @property
     def index(self):
@@ -113,13 +118,16 @@ class TowerContext(object):
         self._ctxs = []
         if len(self._name):
             if self.has_own_variables:
-                if self.vs_name:
+                if len(self.vs_name):
                     self._ctxs.append(tf.variable_scope(self.vs_name))
             else:
-                # use existing variable scope
-                reuse = self.index > 0 or (not self.is_training)
+                if self.is_training:
+                    reuse = self.index > 0
+                    if reuse is True:
+                        self._ctxs.append(tf.name_scope(None))
                         self._ctxs.append(tf.variable_scope(
-                    tf.get_variable_scope(), reuse=reuse))
+                            tf.get_variable_scope(), reuse=True))
+                # if not training, should handle vs outside (TODO not good)
                 self._ctxs.append(tf.name_scope(self._name))
         self._ctxs.append(tf.device(self._device))
         for c in self._ctxs:

tensorpack/tfutils/varmanip.py

@@ -160,7 +160,7 @@ def get_checkpoint_path(model_path):
         new_path = model_path.split('.index')[0]
     if new_path != model_path:
         logger.warn(
-            "[SaverRestore] {} is corrected to {} when restoring the model.".format(model_path, new_path))
+            "Checkpoint path {} is auto-corrected to {}.".format(model_path, new_path))
         model_path = new_path
     assert os.path.isfile(model_path) or os.path.isfile(model_path + '.index'), model_path
     return model_path
@@ -183,7 +183,8 @@ def dump_chkpt_vars(model_path):
 
 def is_training_name(name):
     """
-    This is a hack temporarily used to improve logging. Do not use this function.
+    Guess if a name belongs to a training-only variables.
+    Only used internally to avoid too many logging. Do not use it.
 
     Returns:
         bool: Guess whether this tensor is something only used in training.

tensorpack/train/base.py

@@ -9,8 +9,6 @@ import six
 from six.moves import range
 
 import tensorflow as tf
-from tensorflow.python.training.monitored_session \
-    import _HookedSession as HookedSession
 
 from .predict import PredictorFactory
 from .config import TrainConfig
@@ -21,6 +19,7 @@ from ..callbacks.monitor import Monitors, TrainingMonitor
 from ..tfutils import get_global_step_value
 from ..tfutils.model_utils import describe_model
 from ..tfutils.sesscreate import ReuseSessionCreator
+from ..tfutils.sessinit import JustCurrentSession
 
 __all__ = ['Trainer', 'StopTraining']
 
@@ -46,6 +45,9 @@ class Trainer(object):
         local_step (int): the number of steps that have finished in the current epoch.
         global_step (int): the number of steps that have finished.
     """
+    # step attr only available after before_train?
+
+    is_chief = True
 
     def __init__(self, config):
         """
@@ -79,12 +81,18 @@ class Trainer(object):
         assert isinstance(cb, Callback), cb
         assert not isinstance(self._callbacks, Callbacks), \
             "Cannot register more callbacks after trainer was setup!"
+        if not self.is_chief and cb.chief_only:
+            logger.warn("Callback {} is chief-only, skipped.".format(str(cb)))
+        else:
             self._callbacks.append(cb)
 
     def register_monitor(self, mon):
         assert isinstance(mon, TrainingMonitor), mon
         assert not isinstance(self.monitors, Monitors), \
             "Cannot register more monitors after trainer was setup!"
+        if not self.is_chief and mon.chief_only:
+            logger.warn("Callback {} is chief-only, skipped.".format(str(mon)))
+        else:
             self.monitors.append(mon)
             self.register_callback(mon)
 
@@ -110,6 +118,7 @@ class Trainer(object):
         self.monitors = Monitors(self.monitors)
         self.register_callback(self.monitors)
 
+        # TODO cache per graph, avoid describing all towers
         describe_model()
 
         # some final operations that might modify the graph
@@ -117,21 +126,28 @@ class Trainer(object):
         self._callbacks = Callbacks(self._callbacks)
         self._callbacks.setup_graph(weakref.proxy(self))
 
-        # create session
         logger.info("Creating the session ...")
-        self.sess = self.config.session_creator.create_session()
-        self._monitored_sess = tf.train.MonitoredSession(
-            session_creator=ReuseSessionCreator(self.sess), hooks=None)
+        self._create_session()
 
+        if self.is_chief:
             logger.info("Initializing the session ...")
-        # init session
             self.config.session_init.init(self.sess)
+        else:
+            assert isinstance(self.config.session_init, JustCurrentSession), \
+                "session_init is only valid for chief worker session!"
 
         self.sess.graph.finalize()
         logger.info("Graph Finalized.")
 
+    def _create_session(self):
+        """
+        Setup self.sess (the raw tf.Session)
+        and self.hooked_sess (the session with hooks and coordinator)
+        """
         hooks = self._callbacks.get_hooks()
-        self.hooked_sess = HookedSession(self.sess, hooks)
+        self.sess = self.config.session_creator.create_session()
+        self.hooked_sess = tf.train.MonitoredSession(
+            session_creator=ReuseSessionCreator(self.sess), hooks=hooks)
 
     @abstractmethod
     def _setup(self):
@@ -154,12 +170,14 @@ class Trainer(object):
             self._starting_step = get_global_step_value()
             try:
                 self._callbacks.before_train()
+                # refresh global step (might have changed by callbacks) TODO ugly
+                self._starting_step = get_global_step_value()
                 for self.epoch_num in range(
                         self.config.starting_epoch, self.config.max_epoch + 1):
                     logger.info("Start Epoch {} ...".format(self.epoch_num))
                     start_time = time.time()
                     for self.local_step in range(self.config.steps_per_epoch):
-                        if self._monitored_sess.should_stop():
+                        if self.hooked_sess.should_stop():
                             return
                         self.run_step()  # implemented by subclass
                         self._callbacks.trigger_step()
@@ -169,6 +187,7 @@ class Trainer(object):
                     # trigger epoch outside the timing region.
                     self._trigger_epoch()
                     self._callbacks.trigger_epoch()
+                logger.info("Training has finished!")
             except (StopTraining, tf.errors.OutOfRangeError):
                 logger.info("Training was stopped.")
             except KeyboardInterrupt:
@@ -177,7 +196,14 @@ class Trainer(object):
                 raise
             finally:
                 self._callbacks.after_train()
-                self._monitored_sess.close()
+                self.hooked_sess.close()
+
+    @property
+    def vs_name_for_predictor(self):
+        """
+        The variable scope name a predictor should be built in.
+        """
+        return ""
 
     # Predictor related methods:    TODO
     def get_predictor(self, input_names, output_names, tower=0):

tensorpack/train/feedfree.py

@@ -4,6 +4,7 @@
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
 import tensorflow as tf
+from six.moves import zip
 
 from ..tfutils.tower import TowerContext, get_current_tower_context
 from .input_source import QueueInput, FeedfreeInput
@@ -64,20 +65,18 @@ class SingleCostFeedfreeTrainer(FeedfreeTrainerBase):
         """ get the cost and gradient"""
         self.build_train_tower()
         cost = self.model.get_cost()    # assume single cost
-        # opt may be created under first-tower variable scope (which is '')
-        opt = self.model.get_optimizer()
-        # GATE_NONE faster?
         varlist = tf.trainable_variables()
         ctx = get_current_tower_context()
         if ctx is not None and ctx.has_own_variables and ctx.vs_name:
             # only optimize w.r.t vars in this tower
-            # TODO assumption on the first-tower empty variable scope
+            # TODO use ctx.vars?
             varlist = [v for v in varlist if v.op.name.startswith(ctx.vs_name + '/')]
-        grads = opt.compute_gradients(
+        grads = tf.gradients(
             cost,
-            var_list=varlist,
-            gate_gradients=tf.train.Optimizer.GATE_NONE,
+            varlist,
+            gate_gradients=False,
             colocate_gradients_with_ops=True)
+        grads = list(zip(grads, varlist))
         return cost, grads
 
 

tensorpack/train/input_source.py

@@ -241,7 +241,9 @@ class QueueInput(FeedfreeInput):
 
     def setup_training(self, trainer):
         super(QueueInput, self).setup_training(trainer)
-        trainer.register_callback(StartProcOrThread(self.thread))
+        cb = StartProcOrThread(self.thread)
+        cb.chief_only = False
+        trainer.register_callback(cb)
 
     def get_input_tensors(self):
         with tf.device('/cpu:0'):
@@ -365,6 +367,7 @@ class DummyConstantInput(TensorInput):
         def fn():
             tlist = []
             ctx = get_current_tower_context()
+            assert ctx is not None
             assert len(self.shapes) == len(self.input_placehdrs)
             for idx, p in enumerate(self.input_placehdrs):
                 tlist.append(tf.get_variable(

tensorpack/train/multigpu.py

@@ -49,13 +49,17 @@ def apply_prefetch_policy(config, use_stage=True):
 class MultiGPUTrainerBase(Trainer):
     """ Base class for multi-gpu training"""
     @staticmethod
-    def build_on_multi_tower(towers, func, devices=None, var_strategy='shared'):
+    def build_on_multi_tower(
+            towers, func,
+            devices=None, var_strategy='shared',
+            vs_names=None):
         """
         Args:
             towers: list of gpu relative ids
             func: a lambda to be called inside each tower
             devices: a list of devices to be used. By default will use GPUs in towers.
-            var_strategy (str):
+            var_strategy (str): 'shared' or 'replicated'
+            vs_names (list[str]): list of variable scope names to use.
 
         Returns:
             List of outputs of ``func``, evaluated on each tower.
@@ -70,15 +74,20 @@ class MultiGPUTrainerBase(Trainer):
 
         keys_to_freeze = TOWER_FREEZE_KEYS[:]
         if var_strategy == 'replicated':        # TODO ugly
-            logger.info("UPDATE_OPS from all GPUs will be kept in the collection.")
+            logger.info("In replicated mode, UPDATE_OPS from all GPUs will be run.")
             keys_to_freeze.remove(tf.GraphKeys.UPDATE_OPS)
+        else:
+            assert vs_names is None
+        if vs_names is None:
+            vs_names = [None] * len(towers)
 
         for idx, t in enumerate(towers):
             device = devices[idx] if devices is not None else '/gpu:{}'.format(t)
             with TowerContext(
                     'tower{}'.format(idx),
                     device=device, is_training=True,
-                    var_strategy=var_strategy):
+                    var_strategy=var_strategy,
+                    vs_name=vs_names[idx]):
                 if idx == t:
                     logger.info("Building graph for training tower {}...".format(idx))
                 else:
@@ -248,7 +257,9 @@ class SyncMultiGPUTrainerReplicated(MultiGPUTrainerBase, SingleCostFeedfreeTrain
         grad_list = MultiGPUTrainerBase.build_on_multi_tower(
             self.config.tower,
             lambda: self._get_cost_and_grad()[1],
-            var_strategy='replicated')
+            var_strategy='replicated',
+            # use no variable scope for the first tower
+            vs_names=[''] + [None] * (self.config.nr_tower - 1))
         grads = self._allreduce_grads(grad_list)
 
         train_ops = []
@@ -261,7 +272,7 @@ class SyncMultiGPUTrainerReplicated(MultiGPUTrainerBase, SingleCostFeedfreeTrain
         self.train_op = tf.group(*train_ops, name='train_op')
         self.register_callback(RunOp(
             SyncMultiGPUTrainerReplicated.get_post_init_ops,
-            run_before=True, run_as_trigger=True))
+            run_before=True, run_as_trigger=True, verbose=True))
 
 
 # Adopt from https://github.com/tensorflow/benchmarks/blob/master/scripts/tf_cnn_benchmarks/variable_mgr.py
@@ -279,7 +290,7 @@ class SyncMultiGPUTrainerReplicated(MultiGPUTrainerBase, SingleCostFeedfreeTrain
             split_name = split_name[1:]
             copy_from = var_by_name['/'.join(split_name)]
             post_init_ops.append(v.assign(copy_from.read_value()))
-        return tf.group(*post_init_ops, name='init_sync_vars')
+        return tf.group(*post_init_ops, name='sync_variables_from_tower0')
 
 
 class AsyncMultiGPUTrainer(MultiGPUTrainerBase,

tensorpack/train/predict.py

@@ -3,6 +3,7 @@
 # File: predict.py
 # Author: Yuxin Wu <ppwwyyxxc@gmail.com>
 
+import tensorflow as tf
 from ..predict import (OnlinePredictor,
                        PredictorTowerBuilder)
 
@@ -19,6 +20,7 @@ class PredictorFactory(object):
         """
         self.model = trainer.model
         self.towers = trainer.config.predict_tower
+        self.vs_name = trainer.vs_name_for_predictor
 
         def fn(_):
             self.model.build_graph(self.model.get_reused_placehdrs())
@@ -34,6 +36,7 @@ class PredictorFactory(object):
         """
         tower = self.towers[tower]
         # just ensure the tower exists. won't rebuild (memoized)
+        with tf.variable_scope(self.vs_name, reuse=True):
             self._tower_builder.build(tower)
 
         placeholder_names = set([k.name for k in self.model.get_inputs_desc()])