remove scale loss and coll grads, test=document_fix (#27874)

python/paddle/distributed/fleet/base/fleet_base.py

@@ -630,9 +630,7 @@ class Fleet(object):
 
                 print("loss:", loss.numpy())
 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()
@@ -842,9 +840,7 @@ class Fleet(object):
 
                 print("loss:", loss.numpy())
 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()
@@ -903,9 +899,7 @@ class Fleet(object):
 
                 print("loss:", loss.numpy())
 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()

python/paddle/distributed/parallel.py

@@ -92,9 +92,7 @@ def init_parallel_env():
                 labels = paddle.randn([10, 1], 'float32')
                 loss = loss_fn(outputs, labels)
                 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()

python/paddle/distributed/spawn.py

@@ -314,9 +314,7 @@ def spawn(func, args=(), nprocs=-1, join=True, daemon=False, **options):
                 if print_result is True:
                     print("loss:", loss.numpy())
                 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()

python/paddle/fluid/dygraph/parallel.py

@@ -397,9 +397,7 @@ class DataParallel(layers.Layer):
                 labels = paddle.randn([10, 1], 'float32')
                 loss = loss_fn(outputs, labels)
                 
-                loss = dp_layer.scale_loss(loss)
                 loss.backward()
-                dp_layer.apply_collective_grads()
 
                 adam.step()
                 adam.clear_grad()