use clone(for_test=True) replace get_inference_program

paddle/fluid/API.spec

@@ -73,7 +73,6 @@ paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program',
 paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
 paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
 paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
 paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))

python/paddle/fluid/io.py

@@ -27,8 +27,7 @@ from . import core
 
 __all__ = [
     'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
-    'load_persistables', 'save_inference_model', 'load_inference_model',
-    'get_inference_program'
+    'load_persistables', 'save_inference_model', 'load_inference_model'
 ]
 
 
@@ -504,23 +503,6 @@ def load_persistables(executor, dirname, main_program=None, filename=None):
         filename=filename)
 
 
-def get_inference_program(target_vars, main_program=None):
-    if main_program is None:
-        main_program = default_main_program()
-    if not isinstance(target_vars, list):
-        target_vars = [target_vars]
-    vars = []
-    for var in target_vars:
-        if isinstance(var, Evaluator):
-            vars.extend(var.states)
-            vars.extend(var.metrics)
-        else:
-            vars.append(var)
-    pruned_program = main_program._prune(targets=vars)
-    inference_program = pruned_program._inference_optimize()
-    return inference_program
-
-
 def prepend_feed_ops(inference_program,
                      feed_target_names,
                      feed_holder_name='feed'):

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

@@ -440,9 +440,7 @@ def split_data(data, num_part):
 def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
                  sum_cost, token_num):
     # Context to do validation.
-    test_program = train_progm.clone()
-    with fluid.program_guard(test_program):
-        test_program = fluid.io.get_inference_program([avg_cost])
+    test_program = train_progm.clone(for_test=True)
 
     val_data = DataReader(
         src_vocab_fpath=TrainTaskConfig.src_vocab_fpath,