bug fix (#21)

Fix ODPS table io_slicing type error.
Fix restoring checkpoint in distributed evaluation.
Fix hang when enabling amp dynamic loss scale and gradient accumulation.

epl/parallel/graph_editor.py

@@ -117,7 +117,7 @@ def table_io_slicing(self, dataset_api_op):
     """Slicing table to balance data load among all model replicas."""
     slice_id = 0
     all_devices = dataset_api_op.taskgraph.virtual_device.all_devices
-    list.sort(all_devices)
+    all_devices = sorted(all_devices)
     if self._graph.num_constructors > 1:
       total_num_slices = len(all_devices)
       for idx, dev in enumerate(all_devices):

epl/parallel/hooks.py

@@ -210,23 +210,25 @@ def apply_gradients(self, grads_and_vars, *args, **kwargs):
     with ModelPhase(ModelPhase.APPLY):
       if not apply_opt:
         apply_fn = lambda: fn(self, grads_and_vars, *args, **kwargs)
-      elif zero_enabled():
-        apply_fn = lambda: apply_zero(self, fn, grads_and_vars,
-                                      global_step, ga_iters,
-                                      num_apply_group, name)
       elif ga_enabled():
         apply_fn = lambda: apply_ga(self, fn, grads_and_vars,
                                     global_step, ga_iters,
                                     num_apply_group, name)
-      elif num_apply_group > 1:
-        apply_fn = lambda: apply_grad_group(self, fn, grads_and_vars,
-                                            num_apply_group,
-                                            global_step, name=name)
       else:
-        apply_fn = lambda: fn(self, grads_and_vars, *args, **kwargs)
+        if zero_enabled():
+          apply_fn = lambda: apply_zero(self, fn, grads_and_vars,
+                                        global_step, ga_iters,
+                                        num_apply_group, name)
+
+        elif num_apply_group > 1:
+          apply_fn = lambda: apply_grad_group(self, fn, grads_and_vars,
+                                              num_apply_group,
+                                              global_step, name=name)
+        else:
+          apply_fn = lambda: fn(self, grads_and_vars, *args, **kwargs)
 
-      if apply_opt and amp_enabled() and Env.get().config.amp.loss_scale == "dynamic":
-        return amp_update(grads_and_vars, apply_fn, name)
+        if amp_enabled() and Env.get().config.amp.loss_scale == "dynamic":
+          return amp_update(grads_and_vars, apply_fn, name)
       return apply_fn()
   return apply_gradients
 
@@ -578,7 +580,8 @@ def restore(self, sess, save_path):
     # TODO(wangang.wa): This code will be removed after merging
     # variables for split strategy.
     if Graph.get().first_constructor_rank == Env.get().cluster.worker_index or \
-        any(taskgraph.strategy_context.split_strategy is not None for taskgraph in Graph.get().taskgraphs):
+        any(taskgraph.strategy_context.split_strategy is not None for taskgraph in Graph.get().taskgraphs) or \
+        not Graph.get().need_parallel:
       with ModelPhase(ModelPhase.SAVE_AND_RESTORE):
         ret = fn(self, sess, save_path)
     return ret

epl/runtime/gradient_accumulation.py

@@ -33,6 +33,9 @@
 from epl.ir.graph import Graph
 from epl.runtime.optimizer_helper import filter_none_grads, \
   apply_grad_group
+from epl.runtime.amp.loss_scale import amp_update
+from epl.runtime.amp.auto_mixed_precision import amp_enabled
+
 
 def ga_iter_num():
   """Return gradient accumulation iteration number."""
@@ -72,8 +75,12 @@ def apply_accmulation(optimizer, apply_gradients_fn,
     grads_and_vars.append((g, v))
   Graph.get().add_grads_and_vars(grads_and_vars)
   update_ops = []
-  apply_op = apply_grad_group(optimizer, apply_gradients_fn, grads_and_vars,
-                              ngroup, global_step, "epl_apply_grad_ga")
+  apply_fn = lambda: apply_grad_group(optimizer, apply_gradients_fn, grads_and_vars,
+                                      ngroup, global_step, "epl_apply_grad_ga")
+  if amp_enabled() and Env.get().config.amp.loss_scale == "dynamic":
+    apply_op = amp_update(grads_and_vars, apply_fn, 'amp_update')
+  else:
+    apply_op = apply_fn()
   update_ops.append(apply_op)
   with ops.control_dependencies(update_ops):
     clear_ops = [state_ops.assign(s, array_ops.zeros_like(s)) for s in slots]

epl/utils/version.py

@@ -14,4 +14,4 @@
 # =============================================================================
 """EPL version."""
 
-VERSION = "0.3.0"
+VERSION = "0.6.0"

tests/Makefile

@@ -8,8 +8,9 @@ GPU_8 = 0,1,2,3,4,5,6,7
 
 .PHONY: test
 test:
-	PYTHONPATH=../ $(PYTHON) -m epl.utils.launcher --num_workers=2 --gpu_per_worker=1 --debug=True test_launcher.sh
 	CUDA_VISIBLE_DEVICES='' ./launch.sh header_test.py
+	PYTHONPATH=../ $(PYTHON) -m epl.utils.launcher --num_workers=2 --gpu_per_worker=1 --debug=True test_launcher.sh
+	PYTHONPATH=../ $(PYTHON) -m epl.utils.launcher --num_workers=2 --gpu_per_worker=1 --debug=True test_amp_parallel.sh
 	CUDA_VISIBLE_DEVICES=$(GPU_4) ./launch.sh strategy_new_test.py
 	CUDA_VISIBLE_DEVICES=$(GPU_4) ./launch.sh auto_cluster_test.py
 	CUDA_VISIBLE_DEVICES=$(GPU_2) ./launch.sh estimator_test.py

tests/dnn_data_parallel.py

@@ -23,11 +23,19 @@
 import tensorflow as tf
 import epl
 
+tf.logging.set_verbosity(tf.logging.INFO)
 flags = tf.app.flags
 flags.DEFINE_integer("max_steps", 10, "max training step")
+flags.DEFINE_float("learning_rate", 0.001, "learning_rate")
+flags.DEFINE_integer("num_micro_batch", 1, "num_micro_batch")
+flags.DEFINE_string("amp", None, "amp")
 FLAGS = tf.app.flags.FLAGS
-
-epl.init()
+config_json = {}
+if FLAGS.amp:
+  config_json["amp.level"] = "o1"
+  config_json["amp.loss_scale"] = float(FLAGS.amp) if FLAGS.amp != "dynamic" else "dynamic"
+  config_json["pipeline.num_micro_batch"] = FLAGS.num_micro_batch
+epl.init(epl.Config(config_json))
 epl.set_default_strategy(epl.replicate(1))
 
 # dataset
@@ -43,12 +51,26 @@
 loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
 
 global_step = tf.train.get_or_create_global_step()
-optimizer = tf.train.MomentumOptimizer(learning_rate=0.001, momentum=0.9)
+optimizer = tf.train.MomentumOptimizer(FLAGS.learning_rate, momentum=0.9)
 train_op = optimizer.minimize(loss, global_step=global_step)
 
 hooks = [tf.train.StopAtStepHook(last_step=FLAGS.max_steps)]
+max_steps = (FLAGS.max_steps+1) * FLAGS.num_micro_batch
+cum_steps = 0
 with tf.train.MonitoredTrainingSession(hooks=hooks) as sess:
-  while not sess.should_stop():
-    train_loss, _, step = sess.run([loss, train_op, global_step])
-    print("Iteration %s , Loss: %s ." % (step, train_loss))
+  while not sess.should_stop() or cum_steps > max_steps:
+    train_ops = [loss, train_op, global_step]
+    if FLAGS.amp:
+      train_ops.append(epl.Env.get().parallel_information["AMP_LOSS_SCALE"]._num_good_steps) # pylint: disable=protected-access
+      train_ops.append(epl.Env.get().parallel_information["AMP_LOSS_SCALE"]._current_loss_scale) # pylint: disable=protected-access
+    res = sess.run(train_ops)
+    print("Iteration %s , Loss: %s ." % (res[2], res[0]))
+    if FLAGS.amp:
+      num_good_steps = res[3]
+      current_loss_scale = res[4]
+      if FLAGS.learning_rate >= 100:
+        assert num_good_steps <= 1
+        assert res[2] <= 1
+      print('good_steps: {}, current_loss_scale: {}'.format(num_good_steps, current_loss_scale))
+    cum_steps += 1
 print("Train Finished.")

tests/test_amp_parallel.sh

@@ -0,0 +1,4 @@
+python dnn_data_parallel.py \
+    --amp=dynamic \
+    --num_micro_batch=3 \
+    --learning_rate=1000