1 files changed, 99 insertions, 0 deletions
diff --git a/tensorflow/compiler/xla/service/gpu/partition_assignment.cc b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc
new file mode 100644
index 0000000000..d0d2deee24
--- /dev/null
+++ b/tensorflow/compiler/xla/service/gpu/partition_assignment.cc
@@ -0,0 +1,99 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+
+#include "tensorflow/compiler/xla/service/gpu/partition_assignment.h"
+
+#include <ostream>
+#include <string>
+
+#include "tensorflow/compiler/xla/map_util.h"
+#include "tensorflow/compiler/xla/ptr_util.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_opcode.h"
+#include "tensorflow/compiler/xla/shape_util.h"
+#include "tensorflow/compiler/xla/types.h"
+#include "tensorflow/compiler/xla/util.h"
+#include "tensorflow/core/lib/core/bits.h"
+#include "tensorflow/core/lib/strings/stringprintf.h"
+#include "tensorflow/core/platform/logging.h"
+
+namespace se = ::perftools::gputools;
+
+namespace xla {
+namespace gpu {
+
+std::ostream& operator<<(std::ostream& out,
+                         const LaunchDimensions& launch_dims) {
+  out << tensorflow::strings::Printf("[block: %lld, thread: %lld]",
+                                     launch_dims.block_count(),
+                                     launch_dims.threads_per_block());
+  return out;
+}
+
+// Calculates the launch dimensions used to invoke `hlo`.
+LaunchDimensions CalculateLaunchDimensions(
+    const Shape& shape, const se::DeviceDescription& device_desc,
+    PartitionStrategy partition_strategy) {
+  int64 warp_size = device_desc.threads_per_warp();
+
+  int64 num_elements = ShapeUtil::ElementsIn(shape);
+  if (num_elements <= 1) {
+    return LaunchDimensions();
+  }
+
+  // Calculate the number of threads per block.
+  // Initialize threads_per_block as the threads-per-block limit.
+  int64 threads_per_block = device_desc.threads_per_block_limit();
+  VLOG(2) << "Initial # of threads per block = " << threads_per_block;
+
+  if (partition_strategy == PartitionStrategy::kLatency) {
+    // Limit the thread count to allow maximum number of registers per thread.
+    // TODO(b/28560520): We don't have to assume the emitted kernel will use up
+    // all the registers. We could use ptxas to examine the actual number of
+    // register used, and set the thread count accordingly.
+    int64 threads_per_block_limit_due_to_registers =
+        device_desc.registers_per_core_limit() /
+        device_desc.registers_per_thread_limit();
+    CHECK_NE(0, threads_per_block_limit_due_to_registers);
+    if (threads_per_block_limit_due_to_registers < threads_per_block) {
+      threads_per_block =
+          // Make `threads_per_block` a multiple of warp size to use GPU
+          // efficiently.
+          warp_size *
+          std::max(1LL, threads_per_block_limit_due_to_registers / warp_size);
+      VLOG(2) << "Update # of threads per block due to register pressure = "
+              << threads_per_block;
+    }
+  }
+
+  if (num_elements < threads_per_block) {
+    threads_per_block = num_elements;
+    VLOG(2) << "Update # of threads per block to the element count ("
+            << threads_per_block << ") because the latter is smaller.";
+  }
+
+  // Calculate the block count. We copy the strategy used by Eigen:
+  // eigen3/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
+  int64 block_count = CeilOfRatio(num_elements, threads_per_block);
+  VLOG(2) << tensorflow::strings::Printf(
+      "Initialized the block count to ceil(# of elements / threads per "
+      "block) = ceil(%lld/%lld) = %lld",
+      num_elements, threads_per_block, block_count);
+
+  return LaunchDimensions(block_count, threads_per_block);
+}
+
+}  // namespace gpu
+}  // namespace xla