Merge pull request #13498 from luotao1/for_test

use clone(for_test=True) replace get_inference_program

Author: luotao1

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/__init__.py

@@ -46,7 +46,7 @@
 from .param_attr import ParamAttr, WeightNormParamAttr
 from .data_feeder import DataFeeder
 from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
-from .transpiler import DistributeTranspiler, InferenceTranspiler, \
+from .transpiler import DistributeTranspiler, \
     memory_optimize, release_memory, DistributeTranspilerConfig
 from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
 from . import clip

python/paddle/fluid/io.py

@@ -27,8 +27,7 @@
 
 __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

@@ -437,13 +437,8 @@ def split_data(data, num_part):
     ]
 
 
-def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
+def test_context(test_program, 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])
-
     val_data = DataReader(
         src_vocab_fpath=TrainTaskConfig.src_vocab_fpath,
         trg_vocab_fpath=TrainTaskConfig.trg_vocab_fpath,
@@ -505,7 +500,7 @@ def test(exe=test_exe):
 
 
 def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
-               token_num, predict):
+               token_num, predict, test_program):
     # Initialize the parameters.
     if TrainTaskConfig.ckpt_path:
         lr_scheduler.current_steps = TrainTaskConfig.start_step
@@ -554,7 +549,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                                                                              -1] + label_data_input_fields
 
     if TrainTaskConfig.val_file_pattern is not None:
-        test = test_context(train_progm, avg_cost, train_exe, dev_count,
+        test = test_context(test_program, avg_cost, train_exe, dev_count,
                             data_input_names, sum_cost, token_num)
 
     # the best cross-entropy value with label smoothing
@@ -1647,6 +1642,8 @@ def get_model(is_dist, is_async):
     local_lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
                                                TrainTaskConfig.warmup_steps,
                                                TrainTaskConfig.learning_rate)
+    # Context to do validation.
+    test_program = fluid.default_main_program().clone(for_test=True)
 
     if not is_dist:
         optimizer = fluid.optimizer.Adam(
@@ -1671,7 +1668,7 @@ def get_model(is_dist, is_async):
             epsilon=TrainTaskConfig.eps)
         optimizer.minimize(sum_cost)
 
-    return sum_cost, avg_cost, predict, token_num, local_lr_scheduler
+    return sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program
 
 
 def update_args():
@@ -1705,7 +1702,7 @@ def run_pserver(self, args):
     def run_trainer(self, use_cuda, args):
         place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
         TrainTaskConfig.use_gpu = use_cuda
-        sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
+        sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
             args.is_dist, not args.sync_mode)
 
         if args.is_dist:
@@ -1726,7 +1723,7 @@ def run_trainer(self, use_cuda, args):
         TrainTaskConfig.local = not args.is_dist
 
         train_loop(startup_exe, trainer_prog, 1, sum_cost, avg_cost,
-                   local_lr_scheduler, token_num, predict)
+                   local_lr_scheduler, token_num, predict, test_program)
 
 
 if __name__ == "__main__":