add checkpoint functions for graph. test=develop

python/paddle/fluid/contrib/slim/tests/test_graph.py

@@ -15,6 +15,7 @@
 from __future__ import print_function
 import os
 import six
+import numpy as np
 import unittest
 import paddle
 import paddle.fluid as fluid
@@ -53,6 +54,7 @@ class TestGraph(unittest.TestCase):
     def graph_apis(self, use_cuda=False, for_ci=True):
         main = fluid.Program()
         startup = fluid.Program()
+        with fluid.unique_name.guard():
             with fluid.program_guard(main, startup):
                 feeds, loss = conv_block()
                 opt = fluid.optimizer.Adam(learning_rate=0.001)
@@ -77,16 +79,39 @@ class TestGraph(unittest.TestCase):
             paddle.dataset.mnist.train(), batch_size=batch_size)
         feeder = fluid.DataFeeder(feed_list=feeds, place=place)
 
-        def train(binary):
+        def _train(binary):
             for _ in range(iters):
                 data = next(train_reader())
                 loss_v = exe.run(binary,
                                  feed=feeder.feed(data),
                                  fetch_list=[loss.name])
+                if not for_ci:
                     print('{}: {}'.format('loss', loss_v))
 
-        train(origin_binary)
-        train(backup_binary)
+        _train(origin_binary)
+        _train(backup_binary)
+
+        checkponit_dir = "checkpoint_gpu" if use_cuda else "checkpoint_cpu"
+
+        def _set_zero(var_name, scope, place):
+            var = scope.find_var(var_name).get_tensor()
+            var_array = np.zeros(var._get_dims()).astype("float32")
+            var.set(var_array, place)
+
+        sum_before = np.sum(
+            np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
+            )))
+        fluid.io._save_persistable_nodes(exe, checkponit_dir, graph)
+        _set_zero('conv2d_1.w_0', fluid.global_scope(), place)
+        set_after = np.sum(
+            np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
+            )))
+        self.assertEqual(set_after, 0)
+        fluid.io._load_persistable_nodes(exe, checkponit_dir, graph)
+        sum_after = np.sum(
+            np.array(fluid.global_scope().find_var('conv2d_1.w_0').get_tensor(
+            )))
+        self.assertEqual(sum_before, sum_after)
 
         marked_nodes = set()
         for op in graph.all_op_nodes():

python/paddle/fluid/io.py

@@ -20,6 +20,7 @@ import warnings
 import time
 import shutil
 import six
+import logging
 from functools import reduce
 
 from paddle.fluid import layers
@@ -29,12 +30,17 @@ from paddle.fluid.framework import Program, Parameter, default_main_program, def
 from . import reader
 from .reader import *
 from . import core
+from .. import compat as cpt
 
 __all__ = [
     'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
     'load_persistables', 'save_inference_model', 'load_inference_model'
 ] + reader.__all__
 
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
 
 def is_parameter(var):
     """
@@ -1181,3 +1187,80 @@ def get_parameter_value_by_name(name, executor, program=None):
         program = default_main_program()
     var = program.global_block().var(name)
     return get_parameter_value(var, executor)
+
+
+def _save_persistable_nodes(executor, dirname, graph):
+    """
+    Save persistable nodes to the given directory by the executor.
+
+    Args:
+        executor(Executor): The executor to run for saving node values.
+        dirname(str): The directory path.
+        graph(IrGraph): All the required persistable nodes in the graph will be saved.
+    """
+    persistable_node_names = set()
+    persistable_nodes = []
+    all_persistable_nodes = graph.all_persistable_nodes()
+    for node in all_persistable_nodes:
+        name = cpt.to_text(node.name())
+        if name not in persistable_node_names:
+            persistable_node_names.add(name)
+            persistable_nodes.append(node)
+    program = Program()
+    var_list = []
+    for node in persistable_nodes:
+        var_desc = node.var()
+        if var_desc.type() == core.VarDesc.VarType.RAW or \
+                var_desc.type() == core.VarDesc.VarType.READER:
+            continue
+        var = program.global_block().create_var(
+            name=var_desc.name(),
+            shape=var_desc.shape(),
+            dtype=var_desc.dtype(),
+            type=var_desc.type(),
+            lod_level=var_desc.lod_level(),
+            persistable=var_desc.persistable())
+        var_list.append(var)
+    save_vars(executor=executor, dirname=dirname, vars=var_list)
+
+
+def _load_persistable_nodes(executor, dirname, graph):
+    """
+    Load persistable node values from the given directory by the executor.
+
+    Args:
+        executor(Executor): The executor to run for loading node values.
+        dirname(str): The directory path.
+        graph(IrGraph): All the required persistable nodes in the graph will be loaded.
+    """
+    persistable_node_names = set()
+    persistable_nodes = []
+    all_persistable_nodes = graph.all_persistable_nodes()
+    for node in all_persistable_nodes:
+        name = cpt.to_text(node.name())
+        if name not in persistable_node_names:
+            persistable_node_names.add(name)
+            persistable_nodes.append(node)
+    program = Program()
+    var_list = []
+
+    def _exist(var):
+        return os.path.exists(os.path.join(dirname, var.name))
+
+    for node in persistable_nodes:
+        var_desc = node.var()
+        if var_desc.type() == core.VarDesc.VarType.RAW or \
+                var_desc.type() == core.VarDesc.VarType.READER:
+            continue
+        var = program.global_block().create_var(
+            name=var_desc.name(),
+            shape=var_desc.shape(),
+            dtype=var_desc.dtype(),
+            type=var_desc.type(),
+            lod_level=var_desc.lod_level(),
+            persistable=var_desc.persistable())
+        if _exist(var):
+            var_list.append(var)
+        else:
+            _logger.warn("Cannot find the var %s!!!" % (node.name()))
+    load_vars(executor=executor, dirname=dirname, vars=var_list)