add speed

fluid/neural_machine_translation/transformer_nist_base/nmt_fluid.py

@@ -233,11 +233,16 @@ def main():
                 position_encoding_init(ModelHyperParams.max_length + 1,
                                        ModelHyperParams.d_model), place)
 
-        for pass_id in xrange(TrainTaskConfig.pass_num):
-            pass_start_time = time.time()
+    def train_loop(exe, trainer_prog):
+        for pass_id in xrange(args.pass_num):
+            ts = time.time()
+            total = 0
             for batch_id, data in enumerate(train_reader()):
                 if len(data) != TrainTaskConfig.batch_size:
                     continue
+
+                total += len(data)
+                start_time = time.time()
                 data_input = prepare_batch_input(
                     data, encoder_input_data_names + decoder_input_data_names[:-1] +
                     label_data_names, ModelHyperParams.eos_idx,
@@ -249,15 +254,19 @@ def main():
                                fetch_list=[sum_cost, avg_cost],
                                use_program_cache=True)
                 sum_cost_val, avg_cost_val = np.array(outs[0]), np.array(outs[1])
-                print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f" %
+                print("epoch: %d, batch: %d, sum loss: %f, avg loss: %f, ppl: %f, speed=%.2f /s" %
                       (pass_id, batch_id, sum_cost_val, avg_cost_val,
-                       np.exp([min(avg_cost_val[0], 100)])))
+                       np.exp([min(avg_cost_val[0], 100)]), 
+                       len(data) / (time.time() - start_time)))
             # Validate and save the model for inference.
             #val_avg_cost, val_ppl = test(exe)
             pass_end_time = time.time()
             time_consumed = pass_end_time - pass_start_time
             print("pass_id = " + str(pass_id) + " time_consumed = " +
                   str(time_consumed))
+            print("pass_id = %d cost = %f avg_speed = %.2f sample/s" %
+                  (pass_id, val_cost, total / (time.time() - ts)))
+
             #print("epoch: %d, val avg loss: %f, val ppl: %f, "
             #      "consumed %fs" % (pass_id, val_avg_cost, val_ppl, time_consumed))
             fluid.io.save_inference_model(