Adds XLA support for tf.nn.dynamic_rnn

Changes tf.nn.dynamic_rnn to specify `maximum_iterations` argument for the while_loop.

When `maximum_iterations` argument is supplied to tf.while_loop, use this to provide an upper bound on the size of Stacks used for gradient computation.
By specifying the stack limit we can generate gradient code for while loops that uses fixed shape TensorArrays and hence can be compiled with XLA.

PiperOrigin-RevId: 178802710

1 files changed, 2 insertions, 2 deletions

diff --git a/tensorflow/python/profiler/model_analyzer_test.py b/tensorflow/python/profiler/model_analyzer_test.py
index 5d524c8c74..a379bd5236 100644
--- a/tensorflow/python/profiler/model_analyzer_test.py
+++ b/tensorflow/python/profiler/model_analyzer_test.py
@@ -230,12 +230,12 @@ class PrintModelAnalysisTest(test.TestCase):
         with gfile.Open(outfile, 'r') as f:
           lines = f.read().split('\n')
           result = '\n'.join([l[:min(len(l), 80)] for l in lines])
-          self.assertEqual(compat.as_bytes('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/168.85k flops)\n  model_analyzer_testlib.py:63:BuildFullModel (0/1.80k params, 0/45.37k flops)\n    model_analyzer_testlib.py:40:BuildSmallModel (0/0 params, 0/0 flops)\n    model_analyzer_testlib.py:44:BuildSmallModel (0/4 params, 0/8 flops)\n    model_analyzer_testlib.py:48:BuildSmallModel (0/648 params, 0/1.30k flops)\n    model_analyzer_testlib.py:49:BuildSmallModel (0/0 params, 0/23.33k flops)\n    model_analyzer_testlib.py:53:BuildSmallModel (0/1.15k params, 0/2.30k flops)\n    model_analyzer_testlib.py:54:BuildSmallModel (0/0 params, 0/18.43k flops)\n  model_analyzer_testlib.py:63:BuildFullModel (gradient) (0/0 params, 0/67.39k f\n    model_analyzer_testlib.py:49:BuildSmallModel (gradient) (0/0 params, 0/46.66\n    model_analyzer_testlib.py:54:BuildSmallModel (gradient) (0/0 params, 0/20.74\n  model_analyzer_testlib.py:67:BuildFullModel (0/1.04k params, 0/18.57k flops)\n  model_analyzer_testlib.py:67:BuildFullModel (gradient) (0/0 params, 0/37.00k f\n  model_analyzer_testlib.py:69:BuildFullModel (0/0 params, 0/0 flops)\n  model_analyzer_testlib.py:70:BuildFullModel (0/0 params, 0/258 flops)\n  model_analyzer_testlib.py:70:BuildFullModel (gradient) (0/0 params, 0/129 flop\n  model_analyzer_testlib.py:72:BuildFullModel (0/0 params, 0/141 flops)\n'),
+          self.assertEqual(compat.as_bytes('node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/168.86k flops)\n  model_analyzer_testlib.py:63:BuildFullModel (0/1.80k params, 0/45.37k flops)\n    model_analyzer_testlib.py:40:BuildSmallModel (0/0 params, 0/0 flops)\n    model_analyzer_testlib.py:44:BuildSmallModel (0/4 params, 0/8 flops)\n    model_analyzer_testlib.py:48:BuildSmallModel (0/648 params, 0/1.30k flops)\n    model_analyzer_testlib.py:49:BuildSmallModel (0/0 params, 0/23.33k flops)\n    model_analyzer_testlib.py:53:BuildSmallModel (0/1.15k params, 0/2.30k flops)\n    model_analyzer_testlib.py:54:BuildSmallModel (0/0 params, 0/18.43k flops)\n  model_analyzer_testlib.py:63:BuildFullModel (gradient) (0/0 params, 0/67.39k f\n    model_analyzer_testlib.py:49:BuildSmallModel (gradient) (0/0 params, 0/46.66\n    model_analyzer_testlib.py:54:BuildSmallModel (gradient) (0/0 params, 0/20.74\n  model_analyzer_testlib.py:67:BuildFullModel (0/1.04k params, 0/18.58k flops)\n  model_analyzer_testlib.py:67:BuildFullModel (gradient) (0/0 params, 0/37.00k f\n  model_analyzer_testlib.py:69:BuildFullModel (0/0 params, 0/0 flops)\n  model_analyzer_testlib.py:70:BuildFullModel (0/0 params, 0/258 flops)\n  model_analyzer_testlib.py:70:BuildFullModel (gradient) (0/0 params, 0/129 flop\n  model_analyzer_testlib.py:72:BuildFullModel (0/0 params, 0/141 flops)\n'),
                            compat.as_bytes(result))
 
         self.assertLess(0, tfprof_node.total_exec_micros)
         self.assertEqual(2844, tfprof_node.total_parameters)
-        self.assertEqual(168854, tfprof_node.total_float_ops)
+        self.assertEqual(168863, tfprof_node.total_float_ops)
         self.assertEqual(8, len(tfprof_node.children))
         self.assertEqual('_TFProfRoot', tfprof_node.name)
         self.assertEqual(