add gradient accumulate for dygraph

python/paddle/hapi/model.py

@@ -701,7 +701,7 @@ class DynamicGraphAdapter(object):
         self.model.mode = value
 
     # TODO multi device in dygraph mode not implemented at present time
-    def train_batch(self, inputs, labels=None):
+    def train_batch(self, inputs, labels=None, update=True):
         assert self.model._optimizer, \
             "model not ready, please call `model.prepare()` first"
         self.model.network.train()
@@ -729,10 +729,12 @@ class DynamicGraphAdapter(object):
         if self._amp_level != "O0":
             scaled = scaler.scale(final_loss)
             scaled.backward()
+            if update:
                 scaler.minimize(self.model._optimizer, scaled)
                 self.model.network.clear_gradients()
         else:
             final_loss.backward()
+            if update:
                 self.model._optimizer.minimize(final_loss)
                 self.model.network.clear_gradients()
             
@@ -1017,7 +1019,7 @@ class Model(object):
         else:
             self._adapter = StaticGraphAdapter(self)
 
-    def train_batch(self, inputs, labels=None):
+    def train_batch(self, inputs, labels=None, update=True):
         """
         Run one training step on a batch of data.
 
@@ -1062,7 +1064,7 @@ class Model(object):
               loss = model.train_batch([data], [label])
               print(loss)
         """
-        loss = self._adapter.train_batch(inputs, labels)
+        loss = self._adapter.train_batch(inputs, labels, update)
         if fluid.in_dygraph_mode() and self._input_info is None:
             self._update_inputs()
         return loss
@@ -1536,7 +1538,8 @@ class Model(object):
             drop_last=False,
             shuffle=True,
             num_workers=0,
-            callbacks=None, ):
+            callbacks=None, 
+            accumulate=1, ):
         """
         Trains the model for a fixed number of epochs. If `eval_data` is set,
         evaluation will be done at the end of each epoch.
@@ -1579,6 +1582,7 @@ class Model(object):
             callbacks (Callback|None): A list of `Callback` instances to apply
                 during training. If None, `ProgBarLogger` and `ModelCheckpoint`
                 are automatically inserted. Default: None.
+            accumulate (int): The number of steps to accumulate gradident in training process before optimizer update. Using this to mimic large batch size. Default: 1.
             
         Returns:
             None
@@ -1699,6 +1703,7 @@ class Model(object):
 
         do_eval = eval_loader is not None
         self._test_dataloader = eval_loader
+        self._accumulate = accumulate
         
         steps = self._len_data_loader(train_loader)
         cbks = config_callbacks(
@@ -2004,7 +2009,7 @@ class Model(object):
                 model_filename=model_filename,
                 params_filename=params_filename)
 
-    def _run_one_epoch(self, data_loader, callbacks, mode, logs={}):
+    def _run_one_epoch(self, data_loader, callbacks, mode, logs={},):
         outputs = []
         for step, data in enumerate(data_loader):
             # data might come from different types of data_loader and have
@@ -2028,8 +2033,16 @@ class Model(object):
             callbacks.on_batch_begin(mode, step, logs)
 
             if mode != 'predict':
-                outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
-                                                      data[len(self._inputs):])
+                
+                _inputs = [data[:len(self._inputs)], data[len(self._inputs):]]
+                if mode == 'train':
+                    _inputs.append((step + 1) % self._accumulate == 0)
+                    
+                outs = getattr(self, mode + '_batch')(*_inputs)
+                
+                # outs = getattr(self, mode + '_batch')(data[:len(self._inputs)],
+                #                                       data[len(self._inputs):])
+
                 if self._metrics and self._loss:
                     metrics = [[l[0] for l in outs[0]]]
                 elif self._loss: