[cherry pick]perfect 'var_list' of static.load/fluid.load (#30457) (#30479)

完善static.load的var_list参数。
当加载的是多个小文件时，Tensor列表可以是所有加载文件中Tensor的子集。
原始PR：#30457

python/paddle/fluid/io.py

@@ -1895,6 +1895,12 @@ def load(program, model_path, executor=None, var_list=None):
             raise ValueError(
                 "executor is required when loading model file saved with [ save_params, save_persistables, save_vars ]"
             )
+
+        if var_list is not None:
+            var_list_names = [var.name for var in var_list]
+        else:
+            var_list_names = None
+
         if os.path.isdir(model_path):
             binary_file_set = set()
             for root, dirs, files in os.walk(model_path, topdown=False):
@@ -1905,7 +1911,8 @@ def load(program, model_path, executor=None, var_list=None):
             loaded_var_list = []
             for var in program_var_list:
                 var_path = os.path.join(model_path, var.name).replace("\\", "/")
-                if var_path in binary_file_set:
+                load_condition = var_list_names is None or var.name in var_list_names
+                if var_path in binary_file_set and load_condition:
                     loaded_var_list.append(var)
                     binary_file_set.remove(var_path)
             if len(binary_file_set) > 0:

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

@@ -794,6 +794,9 @@ class TestLoadFromOldInterface(unittest.TestCase):
         if os.path.exists("test_path.pdparams"):
             os.remove("test_path.pdparams")
 
+        if os.path.exists("test_static_load_var_list.pdparams"):
+            os.remove("test_static_load_var_list.pdparams")
+
     def test_load_from_old_interface(self):
         seed = 90
         hidden_size = 10
@@ -910,6 +913,117 @@ class TestLoadFromOldInterface(unittest.TestCase):
 
             fluid.load(test_clone_program, "test_path", exe)
 
+    def test_load_from_old_interface_var_list(self):
+        seed = 90
+        hidden_size = 10
+        vocab_size = 1000
+        num_layers = 1
+        num_steps = 3
+        init_scale = 0.1
+        batch_size = 4
+        batch_num = 200
+
+        with new_program_scope():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+            ptb_model = PtbModel(
+                "ptb_model",
+                hidden_size=hidden_size,
+                vocab_size=vocab_size,
+                num_layers=num_layers,
+                num_steps=num_steps,
+                init_scale=init_scale)
+
+            place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
+            ) else fluid.CUDAPlace(0)
+            exe = fluid.Executor(place)
+            sgd = Adam(learning_rate=1e-3)
+            x = fluid.layers.data(
+                name="x", shape=[-1, num_steps], dtype='int64')
+            y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
+            init_hidden = fluid.layers.data(
+                name="init_hidden", shape=[1], dtype='float32')
+            init_cell = fluid.layers.data(
+                name="init_cell", shape=[1], dtype='float32')
+
+            static_loss, static_last_hidden, static_last_cell = ptb_model(
+                x, y, init_hidden, init_cell)
+
+            test_clone_program = fluid.default_main_program().clone()
+            sgd.minimize(static_loss)
+            static_param_updated = dict()
+            static_param_init = dict()
+
+            out = exe.run(framework.default_startup_program())
+
+            static_loss_value = None
+            static_last_cell_value = None
+            static_last_hidden_value = None
+            for i in range(batch_num):
+                x_data = np.arange(12).reshape(4, 3).astype('int64')
+                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, 1))
+                init_hidden_data = np.zeros(
+                    (num_layers, batch_size, hidden_size), dtype='float32')
+                init_cell_data = np.zeros(
+                    (num_layers, batch_size, hidden_size), dtype='float32')
+                fetch_list = [static_loss, static_last_hidden, static_last_cell]
+                out = exe.run(fluid.default_main_program(),
+                              feed={
+                                  "x": x_data,
+                                  "y": y_data,
+                                  "init_hidden": init_hidden_data,
+                                  "init_cell": init_cell_data
+                              },
+                              fetch_list=fetch_list)
+                static_loss_value = out[0]
+                static_last_hidden_value = out[1]
+                static_last_cell_value = out[2]
+
+            # get value before save
+            main_program = framework.default_main_program()
+            base_map = {}
+            for var in main_program.list_vars():
+                if isinstance(var, framework.Parameter) or var.persistable:
+                    t = np.array(fluid.global_scope().find_var(var.name)
+                                 .get_tensor())
+                    # make sure all the paramerter or optimizer var have been update
+                    self.assertTrue(np.sum(np.abs(t)) != 0)
+                    base_map[var.name] = t
+
+            #fluid.save(main_program, "./test_1")
+            fluid.io.save_persistables(exe, "test_static_load_var_list",
+                                       main_program)
+
+            # set var to zero            
+            var_list = []
+            for i, var in enumerate(main_program.list_vars()):
+                if isinstance(var, framework.Parameter) or var.persistable:
+                    if i % 2 == 0:
+                        var_list.append(var)
+                    ten = fluid.global_scope().find_var(var.name).get_tensor()
+                    ten.set(np.zeros_like(np.array(ten)), place)
+
+                    new_t = np.array(fluid.global_scope().find_var(var.name)
+                                     .get_tensor())
+                    # make sure all the paramerter or optimizer var have been set to zero
+                    self.assertTrue(np.sum(np.abs(new_t)) == 0)
+
+            fluid.load(main_program, "test_static_load_var_list", exe, var_list)
+            var_list_names = [var.name for var in var_list]
+            for var in main_program.list_vars():
+                if isinstance(var, framework.Parameter) or var.persistable:
+                    new_t = np.array(fluid.global_scope().find_var(var.name)
+                                     .get_tensor())
+                    if var.name in var_list_names:
+                        # loaded vars
+                        base_t = base_map[var.name]
+                        self.assertTrue(np.array_equal(new_t, base_t))
+                    else:
+                        #not loaded vars
+                        self.assertTrue(np.sum(np.abs(new_t)) == 0)
+
 
 class TestLoadFromOldInterfaceSingleFile(unittest.TestCase):
     def test_load_from_old_interface(self):