Add fleet for transformer benchmark (#5164)

* add fleet, test=develop

PaddleNLP/benchmark/transformer/configs/transformer.big.yaml

@@ -96,4 +96,11 @@ dropout: 0.1
 # Vocabularies in source and target should be same for weight sharing.
 weight_sharing: True
 
+# Use amp or not
+use_amp: False
+scale_loss: 1.0
+
+# Whether to use multi-card/multi-node distributed training.
+is_distributed: True
+
 max_iter: None

PaddleNLP/benchmark/transformer/static/run_pretrain.sh

@@ -0,0 +1,4 @@
+
+python -m paddle.distributed.launch \
+     --gpus="0,1" \
+     train.py

PaddleNLP/benchmark/transformer/static/train.py

@@ -10,6 +10,7 @@
 from pprint import pprint
 
 import paddle
+import paddle.distributed.fleet as fleet
 import paddle.distributed as dist
 
 from paddlenlp.transformers import TransformerModel, CrossEntropyCriterion
@@ -36,8 +37,14 @@ def parse_args():
 
 def do_train(args):
     paddle.enable_static()
-    places = paddle.static.cuda_places() if args.use_gpu else paddle.static.cpu_places()
-    trainer_count = len(places)
+    if args.is_distributed:
+        fleet.init(is_collective=True)
+        gpu_id = int(os.getenv("FLAGS_selected_gpus", "0"))
+        places = paddle.CUDAPlace(gpu_id) if args.use_gpu else paddle.static.cpu_places()
+        trainer_count = 1 if args.use_gpu else len(places)
+    else:
+        places = paddle.static.cuda_places() if args.use_gpu else paddle.static.cpu_places()
+        trainer_count = len(places)
 
     # Set seed for CE
     random_seed = eval(str(args.random_seed))
@@ -88,19 +95,38 @@ def do_train(args):
             epsilon=float(args.eps),
             parameters=transformer.parameters())
 
+        if args.is_distributed:
+            build_strategy = paddle.static.BuildStrategy()
+            exec_strategy = paddle.static.ExecutionStrategy()
+            dist_strategy = fleet.DistributedStrategy()
+            dist_strategy.build_strategy = build_strategy
+            dist_strategy.execution_strategy = exec_strategy
+            dist_strategy.fuse_grad_size_in_MB = 16
+
+            if args.use_amp:
+                dist_strategy.amp = True
+                dist_strategy.amp_configs = {
+                    'custom_white_list': ['softmax', 'layer_norm', 'gelu'],
+                    'init_loss_scaling': args.scale_loss,
+                }
+
+            optimizer = fleet.distributed_optimizer(optimizer, strategy=dist_strategy)
         optimizer.minimize(avg_cost)
 
-    exe = paddle.static.Executor()
+    if args.is_distributed:
+        exe = paddle.static.Executor(places)
+    else:
+        exe = paddle.static.Executor()
+        build_strategy = paddle.static.BuildStrategy()	
+        exec_strategy = paddle.static.ExecutionStrategy()	
+
+        compiled_train_program = paddle.static.CompiledProgram(	
+            train_program).with_data_parallel(	
+                loss_name=avg_cost.name,	
+                build_strategy=build_strategy,	
+                exec_strategy=exec_strategy)
     exe.run(startup_program)
 
-    build_strategy = paddle.static.BuildStrategy()
-    exec_strategy = paddle.static.ExecutionStrategy()
-
-    compiled_train_program = paddle.static.CompiledProgram(
-        train_program).with_data_parallel(
-            loss_name=avg_cost.name,
-            build_strategy=build_strategy,
-            exec_strategy=exec_strategy)
 
     # the best cross-entropy value with label smoothing
     loss_normalizer = -(
@@ -127,13 +153,22 @@ def do_train(args):
                 data = [data]
             train_reader_cost = time.time() - batch_start
 
-            outs = exe.run(compiled_train_program,
-                           feed=[{
-                               'src_word': data[i][0],
-                               'trg_word': data[i][1],
-                               'lbl_word': data[i][2],
-                           } for i in range(trainer_count)],
-                           fetch_list=[sum_cost.name, token_num.name])
+            if args.is_distributed:
+                outs = exe.run(train_program,
+                               feed=[{
+                                   'src_word': data[i][0],
+                                   'trg_word': data[i][1],
+                                   'lbl_word': data[i][2],
+                               } for i in range(trainer_count)],
+                               fetch_list=[sum_cost.name, token_num.name])
+            else:
+                outs = exe.run(compiled_train_program,
+                               feed=[{
+                                   'src_word': data[i][0],
+                                   'trg_word': data[i][1],
+                                   'lbl_word': data[i][2],
+                               } for i in range(trainer_count)],
+                               fetch_list=[sum_cost.name, token_num.name])
             scheduler.step()
 
             train_batch_cost = time.time() - batch_start
@@ -176,7 +211,7 @@ def do_train(args):
                 batch_ips_avg.reset()
 
             if step_idx % args.save_step == 0 and step_idx != 0:
-                if args.save_model:
+                if args.save_model and dist.get_rank() == 0:
                     model_path = os.path.join(
                         args.save_model, "step_" + str(step_idx), "transformer")
                     paddle.static.save(train_program, model_path)