Do not use feed

fluid/neural_machine_translation/transformer/train.py

@@ -113,6 +113,21 @@ def prepare_batch_input(insts, data_input_names, util_input_names, src_pad_idx,
     return data_input_dict, util_input_dict
 
 
+def read_multiple(reader, count):
+    def __impl__():
+        res = []
+        for item in reader():
+            res.append(item)
+            if len(res) == count:
+                yield res
+                res = []
+
+        if len(res) == count:
+            yield res
+
+    return __impl__
+
+
 def main():
     place = fluid.CUDAPlace(0) if TrainTaskConfig.use_gpu else fluid.CPUPlace()
     exe = fluid.Executor(place)
@@ -169,15 +184,8 @@ def main():
         test_ppl = np.exp([min(test_avg_cost, 100)])
         return test_avg_cost, test_ppl
 
-    def set_util_input(input_name_value):
-        tensor = fluid.global_scope().find_var(input_name_value[0]).get_tensor()
-        tensor.set(input_name_value[1], place)
-
     # Initialize the parameters.
     exe.run(fluid.framework.default_startup_program())
-    for pos_enc_param_name in pos_enc_param_names:
-        set_util_input((pos_enc_param_name, position_encoding_init(
-            ModelHyperParams.max_length + 1, ModelHyperParams.d_model)))
 
     data_input_names = encoder_data_input_fields + decoder_data_input_fields[:
                                                                              -1] + label_data_names
@@ -188,19 +196,37 @@ def main():
         loss_name=avg_cost.name
         if TrainTaskConfig.use_avg_cost else sum_cost.name)
 
+    local_scopes = train_exe.executor.local_scopes()
+    dev_count = fluid.core.get_cuda_device_count()
+
+    for pos_enc_param_name in pos_enc_param_names:
+        tensor = position_encoding_init(ModelHyperParams.max_length + 1, ModelHyperParams.d_model)
+        for place_id, local_scope in enumerate(local_scopes):
+            local_scope.find_var(pos_enc_param_name).get_tensor().set(tensor, fluid.CUDAPlace(place_id))
+
+    train_data = read_multiple(reader=train_data, count=dev_count)
     for pass_id in xrange(TrainTaskConfig.pass_num):
         pass_start_time = time.time()
         for batch_id, data in enumerate(train_data()):
-            data_input_dict, util_input_dict = prepare_batch_input(
-                data, data_input_names, util_input_names,
-                ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
-                ModelHyperParams.n_head, ModelHyperParams.d_model)
-            map(set_util_input,
-                zip(util_input_dict.keys() + [lr_scheduler.learning_rate.name],
-                    util_input_dict.values() +
-                    [lr_scheduler.update_learning_rate()]))
-            outs = train_exe.run(feed_dict=data_input_dict,
-                                 fetch_list=[sum_cost.name, token_num.name])
+            for place_id, data_buffer, local_scope in zip(range(len(data)), data, local_scopes):
+                data_input_dict, util_input_dict = prepare_batch_input(
+                    data_buffer, data_input_names, util_input_names,
+                    ModelHyperParams.eos_idx, ModelHyperParams.eos_idx,
+                    ModelHyperParams.n_head, ModelHyperParams.d_model)
+
+                local_scope.find_var(lr_scheduler.learning_rate.name).get_tensor().set(
+                    lr_scheduler.update_learning_rate(),
+                    fluid.CUDAPlace(place_id))
+
+                for var_name in data_input_dict:
+                    local_scope.find_var(var_name).get_tensor().set(data_input_dict[var_name],
+                                                                    fluid.CUDAPlace(place_id))
+
+                for var_name in util_input_dict:
+                    local_scope.find_var(var_name).get_tensor().set(util_input_dict[var_name],
+                                                                    fluid.CUDAPlace(place_id))
+
+            outs = train_exe.run(fetch_list=[sum_cost.name, token_num.name])
             sum_cost_val, token_num_val = np.array(outs[0]), np.array(outs[1])
             total_sum_cost = sum_cost_val.sum(
             )  # sum the cost from multi devices