Merge pull request #10342 from Yancey1989/refine_distribute_transpiler_api

Refine distribute transpiler api

benchmark/cluster/vgg16/vgg16_fluid.py

@@ -240,8 +240,6 @@ def main():
 
         t = fluid.DistributeTranspiler()
         t.transpile(
-            optimize_ops,
-            params_grads,
             trainer_id=args.task_index,
             pservers=args.ps_hosts,
             trainers=trainers)

python/paddle/fluid/distribute_transpiler.py

@@ -137,8 +137,6 @@ def split_dense_variable(var_list,
 
 class DistributeTranspiler:
     def transpile(self,
-                  optimize_ops,
-                  params_grads,
                   trainer_id,
                   program=None,
                   pservers="127.0.0.1:6174",
@@ -169,11 +167,6 @@ class DistributeTranspiler:
             4. append ops that should run on current server instance.
             5. add listen_and_serv op
 
-            :param optimize_ops: op list of optimization, should be the
-                                    return value of Optimizer.minimize
-            :type optimize_ops: list
-            :param params_grads: list of tuple(weight, gradient)
-            :type params_grads: list
             :param trainer_id: one unique id for each trainer in a job.
             :type trainer_id: int
             :param program: program to transpile, default is default_main_program
@@ -194,7 +187,6 @@ class DistributeTranspiler:
             program = default_main_program()
         self.origin_program = program
         self.trainer_num = trainers
-        self.optimize_ops = optimize_ops
         self.sync_mode = sync_mode
         # TODO(typhoonzero): currently trainer_id is fetched from cluster system
         # like Kubernetes, we should port this to use etcd later when developing
@@ -202,6 +194,7 @@ class DistributeTranspiler:
         self.trainer_id = trainer_id
         pserver_endpoints = pservers.split(",")
         self.pserver_endpoints = pserver_endpoints
+        self.optimize_ops, params_grads = self._get_optimize_pass()
 
         # process lookup_table_op
         # 1. check all lookup_table_op is distributed
@@ -408,10 +401,7 @@ class DistributeTranspiler:
         # HACK: optimization global ops only used to scale beta1 and beta2
         # replace it with dependency engine.
         for op in self.optimize_ops:
-            if op.type == "scale":
-                for in_name in op.input_arg_names:
-                    if in_name.startswith("beta1_pow_acc") or \
-                            in_name.startswith("beta2_pow_acc"):
+            if self._is_adam_connected_op(op):
                 global_ops.append(op)
 
         def __append_optimize_op__(op, block, grad_to_block_id):
@@ -1147,3 +1137,32 @@ class DistributeTranspiler:
                     # we only need to append op for once
                     break
         return lr_ops
+
+    def _get_optimize_pass(self):
+        block = self.origin_program.global_block()
+        opt_ops = []
+        params_grads = []
+        for op in block.ops:
+            if self._is_opt_op(op):
+                opt_ops.append(op)
+                params_grads.append((self.origin_program.global_block().var(
+                    op.input("Param")[0]),
+                                     self.origin_program.global_block().var(
+                                         op.input("Grad")[0])))
+            elif self._is_adam_connected_op(op):
+                opt_ops.append(op)
+            else:
+                pass
+        return opt_ops, params_grads
+
+    def _is_adam_connected_op(self, op):
+        """
+        A hack function to determinate whether the input operator
+        is connected to optimize operator.
+        """
+        if op.type == "scale":
+            for in_name in op.input_arg_names:
+                if in_name.startswith("beta1_pow_acc") or \
+                        in_name.startswith("beta2_pow_acc"):
+                    return True
+        return False

python/paddle/fluid/tests/book/test_fit_a_line.py

@@ -80,12 +80,7 @@ def train(use_cuda, save_dirname, is_local):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_image_classification.py

@@ -189,12 +189,7 @@ def train(net_type, use_cuda, save_dirname, is_local):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_label_semantic_roles.py

@@ -259,12 +259,7 @@ def train(use_cuda, save_dirname=None, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_machine_translation.py

@@ -231,12 +231,7 @@ def train_main(use_cuda, is_sparse, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -162,12 +162,7 @@ def train(nn_type,
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_recommender_system.py

@@ -261,12 +261,7 @@ def train(use_cuda, save_dirname, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_understand_sentiment.py

@@ -213,12 +213,7 @@ def train(word_dict,
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,

python/paddle/fluid/tests/book/test_word2vec.py

@@ -145,12 +145,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
         trainer_id = int(os.getenv("PADDLE_INIT_TRAINER_ID"))
         training_role = os.getenv("TRAINING_ROLE", "TRAINER")
         t = fluid.DistributeTranspiler()
-        t.transpile(
-            optimize_ops,
-            params_grads,
-            trainer_id,
-            pservers=pserver_endpoints,
-            trainers=trainers)
+        t.transpile(trainer_id, pservers=pserver_endpoints, trainers=trainers)
         if training_role == "PSERVER":
             pserver_prog = t.get_pserver_program(current_endpoint)
             pserver_startup = t.get_startup_program(current_endpoint,