Fix _warmstart_var_with_vocab to truly accept either a checkpoint dir or a direct path to checkpoint files.

PiperOrigin-RevId: 181341437

tensorflow/python/estimator/warm_starting_util.py

@@ -121,7 +121,10 @@ class _WarmStartSettings(
   # where ws could be defined as:
 
   # Warm-start all weights in the model (input layer and hidden weights).
+  # Either the directory or a specific checkpoint can be provided (in the case
+  # of the former, the latest checkpoint will be used).
   ws = _WarmStartSettings(ckpt_to_initialize_from="/tmp")
+  ws = _WarmStartSettings(ckpt_to_initialize_from="/tmp/model-1000")
 
   # Warm-start only the embeddings (input layer).
   ws = _WarmStartSettings(ckpt_to_initialize_from="/tmp",
@@ -348,7 +351,7 @@ def _warmstart_var_with_vocab(var,
     # TODO(vihanjain): Support _WarmstartSettings where class vocabularies need
     # remapping too.
     init = checkpoint_ops._load_and_remap_matrix_initializer(
-        ckpt_path=saver.latest_checkpoint(prev_ckpt),
+        ckpt_path=checkpoint_utils._get_checkpoint_filename(prev_ckpt),
         old_tensor_name=prev_tensor_name,
         new_row_vocab_size=current_vocab_size,
         new_col_vocab_size=v_shape[1],

tensorflow/python/estimator/warm_starting_util_test.py

@@ -50,9 +50,7 @@ class WarmStartingUtilTest(test.TestCase):
     sess.run(variables.global_variables_initializer())
     saver = saver_lib.Saver()
     ckpt_prefix = os.path.join(self.get_temp_dir(), "model")
-    ckpt_state_name = "checkpoint"
-    saver.save(
-        sess, ckpt_prefix, global_step=0, latest_filename=ckpt_state_name)
+    saver.save(sess, ckpt_prefix, global_step=0)
 
   def _create_prev_run_var(self,
                            var_name,
@@ -408,6 +406,44 @@ class WarmStartingUtilTest(test.TestCase):
         self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]},
                                   sess)
 
+  def testWarmStart_ExplicitCheckpointFile(self):
+    # Create vocab for sparse column "sc_vocab".
+    vocab_path = self._write_vocab(["apple", "banana", "guava", "orange"],
+                                   "vocab")
+    # Create feature column.
+    sc_vocab = fc.categorical_column_with_vocabulary_file(
+        "sc_vocab", vocabulary_file=vocab_path, vocabulary_size=4)
+
+    # Save checkpoint from which to warm-start.
+    _, prev_vocab_val = self._create_prev_run_var(
+        "linear_model/sc_vocab/weights", shape=[4, 1], initializer=ones())
+
+    partitioner = lambda shape, dtype: [1] * len(shape)
+    # New graph, new session WITHOUT warmstarting.
+    with ops.Graph().as_default() as g:
+      with self.test_session(graph=g) as sess:
+        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
+        sess.run(variables.global_variables_initializer())
+        # Without warmstarting, the weights should be initialized using default
+        # initializer (which is init_ops.zeros_initializer).
+        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [np.zeros([4, 1])]},
+                                  sess)
+
+    # New graph, new session with warmstarting.
+    with ops.Graph().as_default() as g:
+      with self.test_session(graph=g) as sess:
+        cols_to_vars = self._create_linear_model([sc_vocab], partitioner)
+        # Since old vocab is not explicitly set in WarmStartSettings, the old
+        # vocab is assumed to be same as new vocab.
+        ws_util._warmstart(ws_util._WarmStartSettings(
+            # Explicitly provide the file prefix instead of just the dir.
+            os.path.join(self.get_temp_dir(), "model-0"),
+            vars_to_warmstart=".*sc_vocab.*"))
+        sess.run(variables.global_variables_initializer())
+        # Verify weights were correctly warmstarted.
+        self._assert_cols_to_vars(cols_to_vars, {sc_vocab: [prev_vocab_val]},
+                                  sess)
+
   def testWarmStart_SparseColumnVocabularyConstrainedVocabSizes(self):
     # Create old vocabulary, and use a size smaller than the total number of
     # entries.