update1.7 save/load & fluid.data (#4283)

PaddleRec/gru4rec/infer_sample_neg.py

@@ -47,8 +47,7 @@ def infer(args, vocab_size, test_reader, use_cuda):
             for epoch in range(start_index, last_index + 1):
                 copy_program = main_program.clone()
                 model_path = model_dir + "/epoch_" + str(epoch)
-                fluid.io.load_params(
-                    executor=exe, dirname=model_path, main_program=copy_program)
+                fluid.load(copy_program, model_path, exe)
                 accum_num_recall = 0.0
                 accum_num_sum = 0.0
                 t0 = time.time()

PaddleRec/gru4rec/train_sample_neg.py

@@ -121,7 +121,7 @@ def train():
         print("epoch:%d num_steps:%d time_cost(s):%f" %
               (epoch_idx, i, total_time / epoch_idx))
         save_dir = "%s/epoch_%d" % (model_dir, epoch_idx)
-        fluid.io.save_params(executor=exe, dirname=save_dir)
+        fluid.save(fluid.default_main_program(), model_path=save_dir)
         print("model saved in %s" % save_dir)
 
     print("finish training")

PaddleRec/multi-task/MMoE/mmoe_train.py

@@ -59,27 +59,14 @@ def mmoe_layer(inputs, expert_num=8, gate_num=3):
 
 
 def model(dict_dim, emb_dim):
-    label_like = fluid.layers.data(
-        name="label_like",
-        shape=[-1, 1],
-        dtype="int64",
-        lod_level=0,
-        append_batch_size=False)
-    label_comment = fluid.layers.data(
-        name="label_comment",
-        shape=[-1, 1],
-        dtype="int64",
-        lod_level=0,
-        append_batch_size=False)
-    label_share = fluid.layers.data(
-        name="label_share",
-        shape=[-1, 1],
-        dtype="int64",
-        lod_level=0,
-        append_batch_size=False)
-
-    a_data = fluid.layers.data(
-        name="a", shape=[-1, 1], dtype="int64", append_batch_size=False)
+    label_like = fluid.data(
+        name="label_like", shape=[-1, 1], dtype="int64", lod_level=0)
+    label_comment = fluid.data(
+        name="label_comment", shape=[-1, 1], dtype="int64", lod_level=0)
+    label_share = fluid.data(
+        name="label_share", shape=[-1, 1], dtype="int64", lod_level=0)
+
+    a_data = fluid.data(name="a", shape=[-1, 1], dtype="int64")
     emb = fluid.layers.embedding(input=a_data, size=[dict_dim, emb_dim])
 
     outs = mmoe_layer(emb, expert_num=8, gate_num=3)

PaddleRec/ssr/cluster_train.py

@@ -121,7 +121,7 @@ def train_loop(main_program, avg_cost, acc, train_input_data, place, args,
         print("epoch:%d num_steps:%d time_cost(s):%f" %
               (epoch_idx, i, total_time / epoch_idx))
         save_dir = "%s/epoch_%d" % (args.model_dir, epoch_idx)
-        fluid.io.save_params(executor=exe, dirname=save_dir)
+        fluid.save(fluid.default_main_program(), save_dir)
         print("model saved in %s" % save_dir)
 
     # only for ce

PaddleRec/ssr/infer.py

@@ -84,8 +84,7 @@ def infer(args, vocab_size, test_reader):
             for epoch in range(start_index, last_index + 1):
                 copy_program = main_program.clone()
                 model_path = model_dir + "/epoch_" + str(epoch)
-                fluid.io.load_params(
-                    executor=exe, dirname=model_path, main_program=copy_program)
+                fluid.load(copy_program, model_path, exe)
                 accum_num_recall = 0.0
                 accum_num_sum = 0.0
                 t0 = time.time()

PaddleRec/ssr/train.py

@@ -121,7 +121,7 @@ def train(args):
         print("epoch:%d num_steps:%d time_cost(s):%f" %
               (epoch_idx, i, total_time / epoch_idx))
         save_dir = "%s/epoch_%d" % (args.model_dir, epoch_idx)
-        fluid.io.save_params(executor=exe, dirname=save_dir)
+        fluid.save(fluid.default_main_program(), model_path=save_dir)
         print("model saved in %s" % save_dir)
 
     # only for ce