test=develop, fix test_imperative_transformer and ocr (#18127)

* test=develop, fix test_imperative_transformer and ocr

* test=develop, remove ocr recovery part

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -177,7 +177,6 @@ set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCL
 py_test_modules(test_install_check MODULES test_install_check ENVS
         FLAGS_cudnn_deterministic=1 SERIAL)
 set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST")
-
 if(WITH_DISTRIBUTE)
     py_test_modules(test_dist_train MODULES test_dist_train)
     set_tests_properties(test_listen_and_serv_op PROPERTIES TIMEOUT 20)

python/paddle/fluid/tests/unittests/test_imperative_transformer.py

@@ -313,22 +313,16 @@ sync = False
 batch_num = 5
 
 np.random.seed = 90
-src_word_np = np.random.randint(
-    1,
-    ModelHyperParams.src_vocab_size - 1,
-    size=(TrainTaskConfig.batch_size, seq_len, 1),
-    dtype='int64')
+src_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape(
+    [TrainTaskConfig.batch_size, seq_len, 1]).astype('int64')
 src_pos_np = np.random.randint(
     1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
 src_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,
                                        ModelHyperParams.n_head, seq_len,
                                        seq_len).astype('float32')
 
-trg_word_np = np.random.randint(
-    1,
-    ModelHyperParams.src_vocab_size - 1,
-    size=(TrainTaskConfig.batch_size, seq_len, 1),
-    dtype='int64')
+trg_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape(
+    [TrainTaskConfig.batch_size, seq_len, 1]).astype('int64')
 trg_pos_np = np.random.randint(
     1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
 trg_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

@@ -181,22 +181,16 @@ sync = False
 batch_num = 5
 
 np.random.seed = 90
-src_word_np = np.random.randint(
-    1,
-    ModelHyperParams.src_vocab_size - 1,
-    size=(TrainTaskConfig.batch_size, seq_len, 1),
-    dtype='int64')
+src_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape(
+    [TrainTaskConfig.batch_size, seq_len, 1]).astype('int64')
 src_pos_np = np.random.randint(
     1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
 src_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,
                                        ModelHyperParams.n_head, seq_len,
                                        seq_len).astype('float32')
 
-trg_word_np = np.random.randint(
-    1,
-    ModelHyperParams.src_vocab_size - 1,
-    size=(TrainTaskConfig.batch_size, seq_len, 1),
-    dtype='int64')
+trg_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape(
+    [TrainTaskConfig.batch_size, seq_len, 1]).astype('int64')
 trg_pos_np = np.random.randint(
     1, seq_len, size=(TrainTaskConfig.batch_size, seq_len, 1), dtype='int64')
 trg_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size,