Extract the iterated expression of a for loop into a variable to avoid repeated staging.

PiperOrigin-RevId: 188316160

tensorflow/contrib/py2tf/converters/builtin_functions.py

@@ -51,7 +51,7 @@ class BuiltinFunctionTransformer(transformer.Base):
   def visit_Call(self, node):
     self.generic_visit(node)
     # TODO(mdan): This won't work if the function was hidden.
-    if isinstance(node.func, gast.Name) and node.func.id in ('len',):
+    if isinstance(node.func, gast.Name) and node.func.id in ('len', 'range'):
       return self._convert_builtin(node)
     # Print needs to be handled separately because it can be read as statement.
     if isinstance(node.func, gast.Name) and node.func.id == 'print':

tensorflow/contrib/py2tf/converters/for_loops.py

@@ -37,14 +37,18 @@ class ForLoopCanonicalizationTransformer(transformer.Base):
   def visit_For(self, node):
     self.generic_visit(node)
     body_scope = anno.getanno(node, NodeAnno.BODY_SCOPE)
-
+    i_var = self.context.namer.new_symbol('i', body_scope.referenced)
+    n_var = self.context.namer.new_symbol('n', body_scope.referenced)
+    iterated_var = self.context.namer.new_symbol('iterated',
+                                                 body_scope.referenced)
+    # TODO(mdan): Use TensorListFromTensor(loop_iter) here.
     if anno.hasanno(node, 'extra_cond'):
       template = """
         i = 0
-        n = len(loop_iter)
+        iterated = loop_iter
+        n = len(iterated)
         while i < n and extra_cond:
-          # TODO(mdan): Use TensorListFromTensor(loop_iter) here.
-          target = loop_iter[i]
+          target = iterated[i]
           body
           i += 1
       """
@@ -53,17 +57,18 @@ class ForLoopCanonicalizationTransformer(transformer.Base):
           loop_iter=node.iter,
           target=node.target,
           body=node.body,
-          i=self.context.namer.new_symbol('i', body_scope.referenced),
-          n=self.context.namer.new_symbol('n', body_scope.referenced),
+          i=i_var,
+          n=n_var,
+          iterated=iterated_var,
           extra_cond=anno.getanno(node, 'extra_cond'))
     else:
       template = """
         i = 0
-        n = len(loop_iter)
+        iterated = loop_iter
+        n = len(iterated)
         while i < n:
-          # TODO(mdan): Use TensorListFromTensor(loop_iter) here.
-          target = loop_iter[i]
-          body  # pylint:disable=pointless-statement
+          target = iterated[i]
+          body
           i += 1
       """
       repl = templates.replace(
@@ -71,8 +76,9 @@ class ForLoopCanonicalizationTransformer(transformer.Base):
           loop_iter=node.iter,
           target=node.target,
           body=node.body,
-          i=self.context.namer.new_symbol('i', body_scope.referenced),
-          n=self.context.namer.new_symbol('n', body_scope.referenced))
+          i=i_var,
+          n=n_var,
+          iterated=iterated_var)
       return repl
 
   def visit_Continue(self, node):

tensorflow/contrib/py2tf/converters/for_loops_test.py

@@ -42,6 +42,29 @@ class ControlFlowTest(converter_test_base.TestCase):
       l = []
       self.assertEqual(test_fn(l), result.test_fn(l))
 
+  def test_for_with_iterated_expression(self):
+
+    eval_count = [0]
+
+    def count_evals(x):
+      eval_count[0] += 1
+      return x
+
+    def test_fn(n):
+      s = 0
+      for e in count_evals(range(n)):
+        s += e
+      return s
+
+    node = self.parse_and_analyze(test_fn, {'count_evals': count_evals})
+    node = for_loops.transform(node, self.ctx)
+
+    with self.compiled(node) as result:
+      result.count_evals = count_evals
+      self.assertEqual(test_fn(5), result.test_fn(5))
+      # count_evals ran twice, once for test_fn and another for result.test_fn
+      self.assertEqual(eval_count[0], 2)
+
 
 if __name__ == '__main__':
   test.main()

tensorflow/contrib/py2tf/utils/__init__.py

@@ -20,11 +20,13 @@ from __future__ import print_function
 
 from tensorflow.contrib.py2tf.utils.builtins import dynamic_builtin
 from tensorflow.contrib.py2tf.utils.builtins import dynamic_print
+from tensorflow.contrib.py2tf.utils.builtins import dynamic_range
 from tensorflow.contrib.py2tf.utils.context_managers import control_dependency_on_returns
 from tensorflow.contrib.py2tf.utils.misc import alias_tensors
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_cond
 from tensorflow.contrib.py2tf.utils.multiple_dispatch import run_while
 from tensorflow.contrib.py2tf.utils.py_func import wrap_py_func
+from tensorflow.contrib.py2tf.utils.tensor_list import dynamic_list_append
 from tensorflow.contrib.py2tf.utils.testing import fake_tf
 from tensorflow.contrib.py2tf.utils.type_check import is_tensor
 from tensorflow.contrib.py2tf.utils.type_hints import set_element_type