Support storage of large parameters

python/paddle/fluid/io.py

@@ -24,7 +24,7 @@
 from functools import reduce
 
 import numpy as np
-
+import math
 import paddle
 from paddle.fluid import layers
 from paddle.fluid.executor import Executor, global_scope
@@ -1710,6 +1710,52 @@ def _exist(var):
     load_vars(executor=executor, dirname=dirname, vars=var_list)
 
 
+def _unpack_saved_dict(saved_obj):
+    temp_saved_obj = {}
+    unpack_infor = {}
+    for key, value in saved_obj.items():
+        if isinstance(value, np.ndarray):
+            MAX_NUMBER_OF_ELEMENT = 2**22
+            num_element = np.prod(value.shape)
+            if num_element > MAX_NUMBER_OF_ELEMENT:
+                unpack_infor[key] = {}
+                unpack_infor[key]["OriginShape"] = value.shape
+                unpack_infor[key]["slices"] = []
+                value = value.flatten()
+                for i in range(
+                        int(
+                            math.ceil(num_element * 1.0 /
+                                      MAX_NUMBER_OF_ELEMENT))):
+                    part_name = key + "@@." + str(i)
+                    unpack_infor[key]["slices"].append(part_name)
+                    temp_saved_obj[part_name] = value[
+                        i * MAX_NUMBER_OF_ELEMENT:MAX_NUMBER_OF_ELEMENT * (i + 1
+                                                                           )]
+
+    if unpack_infor:
+        for key, value in unpack_infor.items():
+            if key in saved_obj:
+                saved_obj.pop(key)
+                for part in value['slices']:
+                    saved_obj[part] = temp_saved_obj[part]
+        saved_obj['UnpackBigParamInfor@@'] = unpack_infor
+    return saved_obj
+
+
+def _pack_loaded_dict(load_obj):
+    unpack_info = 'UnpackBigParamInfor@@'
+    if unpack_info in load_obj:
+        removes = []
+        for key, value in load_obj[unpack_info].items():
+            slices = [load_obj[part] for part in value["slices"]]
+            load_obj[key] = np.concatenate(slices).reshape(value["OriginShape"])
+            removes += value["slices"]
+        for key in removes:
+            load_obj.pop(key)
+        load_obj.pop(unpack_info)
+    return load_obj
+
+
 @static_only
 def save(program, model_path):
     """
@@ -1762,6 +1808,7 @@ def get_tensor(var):
 
     parameter_list = list(filter(is_parameter, program.list_vars()))
     param_dict = {p.name: get_tensor(p) for p in parameter_list}
+    param_dict = _unpack_saved_dict(param_dict)
     with open(model_path + ".pdparams", 'wb') as f:
         pickle.dump(param_dict, f, protocol=2)
 
@@ -1935,6 +1982,7 @@ def set_var(var, ndarray):
     with open(parameter_file_name, 'rb') as f:
         load_dict = pickle.load(f) if six.PY2 else pickle.load(
             f, encoding='latin1')
+        load_dict = _pack_loaded_dict(load_dict)
     for v in parameter_list:
         assert v.name in load_dict, \
             "Can not find [{}] in model file [{}]".format(

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

@@ -695,7 +695,8 @@ set_tests_properties(test_nearest_interp_v2_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_trilinear_interp_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_bicubic_interp_v2_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_gather_op PROPERTIES TIMEOUT 120)
-set_tests_properties(test_static_save_load PROPERTIES TIMEOUT 120)
+set_tests_properties(test_static_save_load PROPERTIES TIMEOUT 200)
+set_tests_properties(test_paddle_save_load PROPERTIES TIMEOUT 150)
 set_tests_properties(test_imperative_selected_rows_to_lod_tensor PROPERTIES TIMEOUT 120)
 set_tests_properties(test_index_select_op PROPERTIES TIMEOUT 120)
 set_tests_properties(test_parallel_ssa_graph_inference_feed_partial_data PROPERTIES TIMEOUT 120)

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

@@ -28,6 +28,8 @@
 IMAGE_SIZE = 784
 CLASS_NUM = 10
 
+LARGE_PARAM = 2**26
+
 
 def random_batch_reader():
     def _get_random_inputs_and_labels():
@@ -57,6 +59,16 @@ def forward(self, x):
         return self._linear(x)
 
 
+class LayerWithLargeParameters(paddle.nn.Layer):
+    def __init__(self):
+        super(LayerWithLargeParameters, self).__init__()
+        self._l = paddle.nn.Linear(10, LARGE_PARAM)
+
+    def forward(self, x):
+        y = self._l(x)
+        return y
+
+
 def train(layer, loader, loss_fn, opt):
     for epoch_id in range(EPOCH_NUM):
         for batch_id, (image, label) in enumerate(loader()):
@@ -67,6 +79,26 @@ def train(layer, loader, loss_fn, opt):
             opt.clear_grad()
 
 
+class TestSaveLoadLargeParameters(unittest.TestCase):
+    def setUp(self):
+        pass
+
+    def test_large_parameters_paddle_save(self):
+        # enable dygraph mode
+        paddle.disable_static()
+        # create network
+        layer = LayerWithLargeParameters()
+        save_dict = layer.state_dict()
+
+        path = "test_paddle_save_load_large_param_save/layer" + ".pdparams"
+        paddle.save(layer.state_dict(), path)
+        dict_load = paddle.load(path)
+        # compare results before and after saving
+        for key, value in save_dict.items():
+            self.assertTrue(
+                np.sum(np.abs(dict_load[key] - value.numpy())) < 1e-15)
+
+
 class TestSaveLoad(unittest.TestCase):
     def setUp(self):
         # enable dygraph mode

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

@@ -1199,6 +1199,39 @@ def check_in_static(self, main_program, base_map):
                 self.assertTrue(np.array_equal(new_t, base_t))
 
 
+class TestStaticSaveLoadLargeParameters(unittest.TestCase):
+    def test_large_parameters_static_save(self):
+        # enable static mode
+        paddle.enable_static()
+        LARGE_PARAM = 2**26
+        with new_program_scope():
+            # create network
+            x = paddle.static.data(
+                name="static_save_load_large_x",
+                shape=[None, 10],
+                dtype='float32')
+            z = paddle.static.nn.fc(x, LARGE_PARAM)
+            place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(paddle.static.default_startup_program())
+            prog = paddle.static.default_main_program()
+
+            inputs = np.random.randn(1, 10).astype("float32")
+            result_z = exe.run(program=prog,
+                               feed={"static_save_load_large_x": inputs},
+                               fetch_list=[z.name])
+            path = "test_static_save_load_large_param/static_save"
+            paddle.fluid.save(prog, path)
+
+            paddle.fluid.load(prog, path)
+            result_load = exe.run(program=prog,
+                                  feed={"static_save_load_large_x": inputs},
+                                  fetch_list=[z.name])
+            # compare results before and after saving
+            self.assertTrue(
+                np.sum(np.abs(result_z[0] - result_load[0])) < 1e-15)
+
+
 class TestProgramStateOldSaveSingleModel(unittest.TestCase):
     def test_ptb_rnn_cpu_float32(self):
         seed = 90

python/paddle/framework/io.py

@@ -25,6 +25,7 @@
 # deprecated module import
 from paddle import fluid
 from paddle.fluid import core
+from paddle.fluid.io import _unpack_saved_dict, _pack_loaded_dict
 from paddle.fluid.framework import Variable, _varbase_creator, _dygraph_tracer
 from paddle.fluid.dygraph.jit import _SaveLoadConfig
 from paddle.fluid.dygraph.io import _construct_program_holders, _construct_params_and_buffers
@@ -259,6 +260,7 @@ def save(obj, path):
 
     # TODO(chenweihang): supports save other object
     saved_obj = _build_saved_state_dict(obj)
+    saved_obj = _unpack_saved_dict(saved_obj)
 
     with open(path, 'wb') as f:
         pickle.dump(saved_obj, f, protocol=2)
@@ -338,7 +340,7 @@ def load(path, **configs):
         with open(path, 'rb') as f:
             load_result = pickle.load(f) if six.PY2 else pickle.load(
                 f, encoding='latin1')
-
+        load_result = _pack_loaded_dict(load_result)
         if not config.keep_name_table and "StructuredToParameterName@@" in load_result:
             del load_result["StructuredToParameterName@@"]
     else: