Merge pull request PaddlePaddle#7 from zhaoyinglia/new_api_valid

[Auto Parallel] Add valid after training

python/paddle/distributed/auto_parallel/constants.py

@@ -47,6 +47,9 @@ def set_field_default_config(category, field, default_value):
 set_field_default_config(BASE, "all_ranks", False)
 set_field_default_config(BASE, "split_data", False)
 set_field_default_config(BASE, "seed", None)
+set_field_default_config(
+    BASE, "reinit", False
+)  # Only for debug, and must be set with seed at the same time when the value is True.
 
 #########################################
 # recompute configuration

python/paddle/distributed/auto_parallel/dist_context.py

@@ -120,7 +120,7 @@ def __init__(self,
         self._backup_serial_main_program_stack = []
         self._backup_serial_startup_program_stack = []
 
-        # # flag whether scale gradient with dp size
+        # flag whether scale gradient with dp size
         self._gradient_scale = True
 
         # A flag indicates whether the used parallelism is data parallel

python/paddle/distributed/auto_parallel/dist_saver.py

@@ -59,6 +59,14 @@ def __init__(self):
 
     def save(self, path, serial_program, dist_main_program, dist_context):
 
+        def _save_state(program, path, mode="param"):
+            state = {
+                k: np.array(v)
+                for k, v in program.state_dict(mode).items()
+            }
+            with open(path, "wb") as f:
+                pickle.dump(state, f)
+
         dirname, filename = _process_path(path)
 
         rank_id = paddle.distributed.get_rank()
@@ -76,82 +84,76 @@ def save(self, path, serial_program, dist_main_program, dist_context):
         with open(dist_model_path, "wb") as f:
             f.write(dist_main_program.desc.serialize_to_string())
 
-        # save distributed params
-        dist_param_filename = filename + "_dist" + str(rank_id) + ".pdparams"
-        dist_param_path = os.path.join(dirname, dist_param_filename)
-        dist_param = {
-            k: np.array(v)
-            for k, v in dist_main_program.state_dict().items()
-        }
-        with open(dist_param_path, "wb") as f:
-            pickle.dump(dist_param, f)
-
         # save distributed attribute
         dist_attr_filename = filename + "_dist" + str(rank_id) + ".pdattr"
         dist_attr_path = os.path.join(dirname, dist_attr_filename)
         dist_attrs = get_dist_attr(dist_main_program, dist_context)
         with open(dist_attr_path, "wb") as f:
             pickle.dump(dist_attrs, f)
 
+        # save distributed params
+        dist_param_filename = filename + "_dist" + str(rank_id) + ".pdparams"
+        dist_param_path = os.path.join(dirname, dist_param_filename)
+        _save_state(dist_main_program, dist_param_path)
+
+        # save distributed opt states
+        dist_opt_filename = filename + "_dist" + str(rank_id) + ".pdopt"
+        dist_opt_path = os.path.join(dirname, dist_opt_filename)
+        _save_state(dist_main_program, dist_opt_path, "opt")
+
         # TODO:save cluster.json
 
-    def load(self,
-             path,
-             program,
-             dist_context,
-             strict=True,
-             load_optimizer=True):
+    def load(self, path, load_optimizer=True):
         # TODO: if `program` is None, load `path.pdmodel`.
+        def _load_file(filename, dirname, suffix="pdparams"):
+            file_list = []
+            for file in os.listdir(dirname):
+                if check_filename('{}(.*)_dist(.*).{}'.format(filename, suffix),
+                                  file):
+                    file_list.append(os.path.join(dirname, file))
+            file_list.sort()
+            return file_list
+
+        def _load_state(filename, dirname, suffix="pdparams"):
+            file_list = _load_file(filename, dirname, suffix)
+            state_dict = {}
+            for file in file_list:
+                with open(file, 'rb') as f:
+                    state_dict_info = pickle.load(f, encoding='latin1')
+                for name, value in state_dict_info.items():
+                    if name in state_dict:
+                        state_dict[name].append(np.array(value))
+                    else:
+                        state_dict[name] = [np.array(value)]
+            self._logger.info("Load param file: {}".format(file_list))
+            return state_dict
+
         filename = os.path.basename(path)
         if filename == "":
             raise ValueError(
                 "path should be of 'dirname/filename' format, but received filename is empty string"
             )
         dirname = os.path.dirname(path)
-        # load path.pdparam
-        param_file_list = []
-        for param_file in os.listdir(dirname):
-            if check_filename('{}(.*)_dist(.*).pdparams'.format(filename),
-                              param_file):
-                param_file_list.append(os.path.join(dirname, param_file))
-        param_file_list.sort()
-        self._logger.info(
-            "Load distributed attribute file: {}".format(param_file_list))
-        param_dict = {}
-        for param_file in param_file_list:
-            with open(param_file, 'rb') as f:
-                state_dict_info = pickle.load(f, encoding='latin1')
-            for name, value in state_dict_info.items():
-                if name in param_dict:
-                    param_dict[name].append(np.array(value))
-                else:
-                    param_dict[name] = [np.array(value)]
+
+        # load path.pdparam and path.pdopt
+        param_state_dict = _load_state(filename, dirname)
+        opt_state_dict = _load_state(filename, dirname,
+                                     "pdopt") if load_optimizer else {}
+        state_dict = dict(param_state_dict, **opt_state_dict)
 
         # load path.pdattr
-        dist_attr_file_list = []
-        for dist_attr_file in os.listdir(dirname):
-            if check_filename('{}(.*)_dist(.*).pdattr'.format(filename),
-                              dist_attr_file):
-                dist_attr_file_list.append(os.path.join(dirname,
-                                                        dist_attr_file))
-        dist_attr_file_list.sort()
+        dist_attr_file_list = _load_file(filename, dirname, "pdattr")
         self._logger.info(
             "Load distributed attribute file: {}".format(dist_attr_file_list))
-        pre_dist_attr = {}
+        dist_attr = {}
         for dist_attr_file in dist_attr_file_list:
             with open(dist_attr_file, 'rb') as f:
-                dist_attr = pickle.load(f, encoding='latin1')
-            for name, attr in dist_attr.items():
-                if name not in pre_dist_attr:
-                    pre_dist_attr[name] = attr
-
-        # get current dist_attr
-        cur_dist_attr = get_dist_attr(program, dist_context)
-
-        # param convert
-        converter = Converter(param_dict, pre_dist_attr, cur_dist_attr)
-        param_dict = converter.convert(strict=strict)
-        program.set_state_dict(param_dict)
+                dist_attr_info = pickle.load(f, encoding='latin1')
+            for name, attr in dist_attr_info.items():
+                if name not in dist_attr:
+                    dist_attr[name] = attr
+
+        return state_dict, dist_attr
 
     def save_inference_model(self, path, feed_vars, fetch_vars, exe, **kwargs):