fix en doc and test=document_fix (#20441)

* fix en doc train_from_dataset and infer_from_datatset

paddle/fluid/API.spec

@@ -30,9 +30,9 @@ paddle.fluid.load_op_library (ArgSpec(args=['lib_filename'], varargs=None, keywo
 paddle.fluid.Executor ('paddle.fluid.executor.Executor', ('document', '4d963107d87438b5add4a5288855bd04'))
 paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
 paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '90b3268b71a8aceedd0dc9e311921d15'))
-paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period', 'fetch_handler'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100, None)), ('document', '4ff256774ecaeee01c840a5fb5de8f7a'))
+paddle.fluid.Executor.infer_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period', 'fetch_handler'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100, None)), ('document', '67de8ce7fbc618da50037d33cf7a7dbc'))
 paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'de3878f012e60edad05fb24fd88ce910'))
-paddle.fluid.Executor.train_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period', 'fetch_handler'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100, None)), ('document', '73024c79f46b4f14f1060edeaa4919c8'))
+paddle.fluid.Executor.train_from_dataset (ArgSpec(args=['self', 'program', 'dataset', 'scope', 'thread', 'debug', 'fetch_list', 'fetch_info', 'print_period', 'fetch_handler'], varargs=None, keywords=None, defaults=(None, None, None, 0, False, None, None, 100, None)), ('document', 'f35879c6935d87255d4317c7d0d02ab6'))
 paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f65788d9ead293ada47551339df12203'))
 paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', '02fcfc1eda07c03a84ed62422366239c'))
 paddle.fluid.DistributeTranspiler ('paddle.fluid.transpiler.distribute_transpiler.DistributeTranspiler', ('document', 'b2b19821c5dffcd11473d6a4eef089af'))

python/paddle/fluid/executor.py

@@ -1048,11 +1048,17 @@ class Executor(object):
                            print_period=100,
                            fetch_handler=None):
         """
-        The document of infer_from_dataset is almost the same as
-        train_from_dataset, except that in distributed training,
-        push gradients will be disabled in infer_from_dataset.
-        infer_from_dataset() can be used for evaluation in multi-thread
-        very easily.
+        Infer from a pre-defined Dataset. Dataset is defined in paddle.fluid.dataset.
+        Given a program, either a program or compiled program, infer_from_dataset will
+        consume all data samples in dataset. Input scope can be given by users. By default,
+        scope is global_scope(). The total number of thread run in training is `thread`.
+        Thread number used in training will be minimum value of threadnum in Dataset and
+        the value of thread in this interface. Debug can be set so that executor will display
+        Run-Time for all operators and the throughputs of current infer task.
+
+        The document of infer_from_dataset is almost the same as train_from_dataset,
+        except that in distributed training, push gradients will be disabled in infer_from_dataset.
+        infer_from_dataset() can be used for evaluation in multi-threadvery easily.
 
         Args:
             program(Program|CompiledProgram): the program that needs to be run,
@@ -1062,11 +1068,11 @@ class Executor(object):
                 Please check the document of Dataset if needed. default is None
             scope(Scope): the scope used to run this program, you can switch it to different scope
                 for each run. default is global_scope
-            thread(int): number of thread a user wants to run in this function. The actual number
-               of thread will be min(Dataset.thread_num, thread) if thread > 0, default is 0
+            thread(int): number of thread a user wants to run in this function. Default is 0, which
+                means using thread num of dataset
             debug(bool): whether a user wants to run infer_from_dataset, default is False
-            fetch_list(Variable List): fetch variable list, each variable
-                                       will be printed during training, default is None
+            fetch_list(Variable List): fetch variable list, each variable will be printed during
+                training, default is None
             fetch_info(String List): print information for each variable, default is None
             print_period(int): the number of mini-batches for each print, default is 100
             fetch_handler(FetchHandler): a user define class for fetch output.
@@ -1127,13 +1133,14 @@ class Executor(object):
                 Please check the document of Dataset if needed.
             scope(Scope): the scope used to run this program, you can switch it to different scope
                 for each run. default is global_scope
-            thread(int): number of thread a user wants to run in this function. The actual number
-               of thread will be min(Dataset.thread_num, thread)
+            thread(int): number of thread a user wants to run in this function. Default is 0, which
+                means using thread num of dataset
             debug(bool): whether a user wants to run train_from_dataset 
-            fetch_list(Variable List): fetch variable list, each variable
-                                       will be printed during training
-            fetch_info(String List): print information for each variable
-            print_period(int): the number of mini-batches for each print
+            fetch_list(Variable List): fetch variable list, each variable will be printed
+                during training
+            fetch_info(String List): print information for each variable, its length should be equal
+                to fetch_list
+            print_period(int): the number of mini-batches for each print, default is 100
             fetch_handler(FetchHandler): a user define class for fetch output.
 
         Returns: