Cudnn RNN v2 kernels with autotune capability

CudnnRNN V2 kernels run all applicable cudnn rnn algorithms and pick the best one for following runs.
* To enable autotune, TF_CUDNN_RNN_USE_AUTOTUNE and TF_CUDNN_RNN_USE_V2 need to be set to {"1" or unset}.
* TF_CUDNN_RNN_USE_AUTOTUNE does not work with existing CudnnRNN kernels.
* V2 kernels work with existing cudnn checkpoints, since it doesn't change persistence format.

This change
* Introduces v2 kernels as templates inheriting the v1 kernels.
* Profiles fwd and bak runs in v2 kernel (forward pass)
* Exposes the chosen algorithm as fwd op output and bak op input.
* Changes rnn descriptor cache key to include AlgorithmDesc (since cudnn rnn descriptor can't be reused across different algorithms)
* Updates unittests s.t. it tests both v1 and v2 kernels. When testing v2 kernels, autotune is turned on.

PiperOrigin-RevId: 194333948

1 files changed, 4 insertions, 3 deletions

diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc
index 2e1adeb31e..20579790ef 100644
--- a/tensorflow/stream_executor/stream_executor_pimpl.cc
+++ b/tensorflow/stream_executor/stream_executor_pimpl.cc
@@ -350,7 +350,7 @@ bool StreamExecutor::GetBlasGemmAlgorithms(
 
 port::StatusOr<std::unique_ptr<dnn::RnnDescriptor>>
 StreamExecutor::createRnnDescriptor(
-    int num_layers, int hidden_size, int input_size,
+    int num_layers, int hidden_size, int input_size, int batch_size,
     dnn::RnnInputMode input_mode, dnn::RnnDirectionMode direction_mode,
     dnn::RnnMode rnn_mode, dnn::DataType data_type,
     const dnn::AlgorithmConfig &algorithm_config, float dropout, uint64 seed,
@@ -361,8 +361,9 @@ StreamExecutor::createRnnDescriptor(
                         "Fail to find the dnn implementation.");
   }
   return dnn_support->createRnnDescriptor(
-      num_layers, hidden_size, input_size, input_mode, direction_mode, rnn_mode,
-      data_type, algorithm_config, dropout, seed, state_allocator);
+      num_layers, hidden_size, input_size, batch_size, input_mode,
+      direction_mode, rnn_mode, data_type, algorithm_config, dropout, seed,
+      state_allocator);
 }
 
 port::StatusOr<std::unique_ptr<dnn::RnnSequenceTensorDescriptor>>