Merge branch 'master' into upstream-staging-norocm-tag-1

tensorflow/api_template.__init__.py

@@ -26,14 +26,28 @@ import sys as _sys
 
 # API IMPORTS PLACEHOLDER
 
+# Make sure directory containing top level submodules is in
+# the __path__ so that "from tensorflow.foo import bar" works.
+# We're using bitwise, but there's nothing special about that.
+_API_MODULE = bitwise  # pylint: disable=undefined-variable
+_current_module = _sys.modules[__name__]
+_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
+if not hasattr(_current_module, '__path__'):
+  __path__ = [_tf_api_dir]
+elif _tf_api_dir not in __path__:
+  __path__.append(_tf_api_dir)
+
 # pylint: disable=g-bad-import-order
 from tensorflow.python.tools import component_api_helper as _component_api_helper
+_component_api_helper.package_hook(
+    parent_package_str=__name__,
+    child_package_str=('tensorboard.summary._tf.summary'),
+    error_msg="Limited tf.summary API due to missing TensorBoard installation")
 _component_api_helper.package_hook(
     parent_package_str=__name__,
     child_package_str=(
         'tensorflow_estimator.python.estimator.api._v2.estimator'))
 
-_current_module = _sys.modules[__name__]
 if not hasattr(_current_module, 'estimator'):
   _component_api_helper.package_hook(
       parent_package_str=__name__,
@@ -42,14 +56,6 @@ if not hasattr(_current_module, 'estimator'):
 _component_api_helper.package_hook(
     parent_package_str=__name__,
     child_package_str=('tensorflow.python.keras.api._v2.keras'))
-# Make sure directory containing top level submodules is in
-# the __path__ so that "from tensorflow.foo import bar" works.
-# We're using bitwise, but there's nothing special about that.
-_tf_api_dir = _os.path.dirname(_os.path.dirname(bitwise.__file__))  # pylint: disable=undefined-variable
-if not hasattr(_current_module, '__path__'):
-  __path__ = [_tf_api_dir]
-elif _tf_api_dir not in __path__:
-  __path__.append(_tf_api_dir)
 
 # Enable TF2 behaviors
 from tensorflow.python.compat import v2_compat as _compat  # pylint: disable=g-import-not-at-top

tensorflow/api_template_v1.__init__.py

@@ -70,7 +70,7 @@ _API_MODULE = app  # pylint: disable=undefined-variable
 
 # Make sure directory containing top level submodules is in
 # the __path__ so that "from tensorflow.foo import bar" works.
-_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))  # pylint: disable=undefined-variable
+_tf_api_dir = _os.path.dirname(_os.path.dirname(_API_MODULE.__file__))
 if not hasattr(_current_module, '__path__'):
   __path__ = [_tf_api_dir]
 elif _tf_api_dir not in __path__:

tensorflow/cc/BUILD

@@ -150,6 +150,7 @@ cc_library_with_android_deps(
         "//tensorflow/core:framework",
         "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
+        "//tensorflow/core:ops",
         "//tensorflow/core:protos_all_cc",
     ],
 )
@@ -586,6 +587,25 @@ tf_gen_op_wrappers_cc(
     pkg = "//tensorflow/core",
 )
 
+tf_gen_op_wrappers_cc(
+    name = "tpu_ops",
+    include_internal_ops = 1,
+    op_lib_names = [
+        "tpu_configuration_ops",
+        "tpu_cross_replica_ops",
+        "tpu_embedding_ops",
+        "tpu_functional_ops",
+        "tpu_heartbeat_ops",
+        "tpu_host_compute_ops",
+        "tpu_infeed_ops",
+        "tpu_outfeed_ops",
+        "tpu_ordinal_selector_ops",
+        "tpu_replication_ops",
+    ],
+    pkg = "//tensorflow/core",
+    visibility = ["//tensorflow:internal"],
+)
+
 cc_library_with_android_deps(
     name = "cc_op_gen_main",
     srcs = [

tensorflow/cc/saved_model/BUILD

@@ -81,6 +81,7 @@ cc_library(
     ] + if_not_mobile([
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:lib",
+        "//tensorflow/core:ops",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:tensorflow",
     ]) + if_android([

tensorflow/compat_template.__init__.py

@@ -22,11 +22,16 @@ import os as _os
 import sys as _sys
 
 # pylint: disable=g-bad-import-order
-from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
 
 # API IMPORTS PLACEHOLDER
 
 from tensorflow.python.tools import component_api_helper as _component_api_helper
+_component_api_helper.package_hook(
+    parent_package_str=__name__,
+    child_package_str=('tensorboard.summary._tf.summary'),
+    error_msg=(
+        "Limited tf.compat.v2.summary API due to missing TensorBoard "
+        "installation"))
 _component_api_helper.package_hook(
     parent_package_str=__name__,
     child_package_str=(

tensorflow/compiler/jit/encapsulate_subgraphs_pass_test.cc

@@ -307,22 +307,6 @@ REGISTER_OP("XlaHostCompute")
     .Attr("shapes: list(shape) >= 0")
     .SetShapeFn(::tensorflow::shape_inference::UnknownShape);
 
-REGISTER_OP("_XlaSendFromHost")
-    .Input("inputs: Tinputs")
-    .Input("dynamic_key: string")
-    .Attr("Tinputs: list(type) >= 0")
-    .Attr("key: string")
-    .Attr("device_ordinal: int")
-    .SetShapeFn(::tensorflow::shape_inference::UnknownShape);
-
-REGISTER_OP("_XlaRecvAtHost")
-    .Input("dynamic_key: string")
-    .Output("outputs: Toutputs")
-    .Attr("Toutputs: list(type) >= 0")
-    .Attr("key: string")
-    .Attr("device_ordinal: int")
-    .SetShapeFn(::tensorflow::shape_inference::UnknownShape);
-
 REGISTER_OP("InputTest")
     .Output("o: float")
     .SetShapeFn([](::tensorflow::shape_inference::InferenceContext* c) {

tensorflow/compiler/jit/encapsulate_xla_computations_pass.cc

@@ -200,7 +200,7 @@ Status RewriteSubgraph(const std::vector<OutputTensor>& arg_source_tensors,
   auto serialized = absl::make_unique<char[]>(size);
   TF_RET_CHECK(SerializeToBufferDeterministic(gdef, serialized.get(), size));
   uint64 fingerprint = Fingerprint64(absl::string_view(serialized.get(), size));
-  LOG(INFO) << "Subgraph fingerprint:" << fingerprint;
+  VLOG(1) << "Subgraph fingerprint:" << fingerprint;
   call_def->set_op(absl::StrCat(call_def->op(), "_", fingerprint));
   return Status::OK();
 }

tensorflow/compiler/tests/BUILD

@@ -250,6 +250,29 @@ tf_xla_py_test(
     ],
 )
 
+tf_xla_py_test(
+    name = "self_adjoint_eig_op_test",
+    size = "medium",
+    srcs = ["self_adjoint_eig_op_test.py"],
+    # TODO(kuny): remove it after b/124377352 is fixed.
+    disabled_backends = [
+        "cpu",
+        "gpu",
+        "cpu_ondemand",
+    ],
+    tags = ["optonly"],
+    deps = [
+        ":xla_test",
+        "//tensorflow/python:array_ops",
+        "//tensorflow/python:framework",
+        "//tensorflow/python:map_fn",
+        "//tensorflow/python:math_ops",
+        "//tensorflow/python:platform_test",
+        "//tensorflow/python:training",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
 tf_xla_py_test(
     name = "matrix_triangular_solve_op_test",
     size = "small",

tensorflow/compiler/tests/self_adjoint_eig_op_test.py

@@ -0,0 +1,62 @@
+# Copyright 2019 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.
+# ==============================================================================
+"""Tests for tensorflow.ops.self_adjoint_eig."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import itertools
+from absl.testing import parameterized
+import numpy as np
+
+from tensorflow.compiler.tests import xla_test
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import linalg_ops
+from tensorflow.python.platform import test
+
+
+class SelfAdjointEigOpTest(xla_test.XLATestCase, parameterized.TestCase):
+
+  def _test(self, dtype, shape):
+    np.random.seed(1)
+    x_np = np.random.uniform(
+        low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype)
+    x_np = x_np + np.swapaxes(x_np, -1, -2)
+    n = shape[-1]
+
+    e_np, _ = np.linalg.eigh(x_np)
+    with self.cached_session() as sess:
+      x_tf = array_ops.placeholder(dtype)
+      with self.test_scope():
+        e, v = linalg_ops.self_adjoint_eig(x_tf)
+      e_val, v_val = sess.run([e, v], feed_dict={x_tf: x_np})
+
+      v_diff = np.matmul(v_val, np.swapaxes(v_val, -1, -2)) - np.eye(n)
+      self.assertAlmostEqual(np.mean(v_diff**2), 0.0, delta=1e-6)
+      self.assertAlmostEqual(np.mean((e_val - e_np)**2), 0.0, delta=1e-6)
+
+  SIZES = [1, 2, 5, 10, 32]
+  DTYPES = [np.float32]
+  PARAMS = itertools.product(SIZES, DTYPES)
+
+  @parameterized.parameters(*PARAMS)
+  def testSelfAdjointEig(self, n, dtype):
+    for batch_dims in [(), (3,)] + [(3, 2)] * (n < 10):
+      self._test(dtype, batch_dims + (n, n))
+
+
+if __name__ == "__main__":
+  test.main()

tensorflow/compiler/tests/tensor_list_ops_test.py

@@ -102,7 +102,7 @@ class ListOpsTest(xla_test.XLATestCase):
       _, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
       with self.assertRaisesRegexp(errors.InvalidArgumentError,
                                    "Set the max number of elements"):
-        self.assertEqual(sess.run(e), 1.0 * np.ones((7, 15)))
+        self.assertAllEqual(sess.run(e), 1.0 * np.ones((7, 15)))
 
   def testEmptyTensorListMax(self):
     with self.cached_session() as sess, self.test_scope():
@@ -136,6 +136,17 @@ class ListOpsTest(xla_test.XLATestCase):
       t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
       self.assertAllEqual(t, [3.0, 2.0])
 
+  def testSetDoesNotUpdatePushIndex(self):
+    with self.cached_session(), self.test_scope():
+      l = list_ops.empty_tensor_list(
+          element_shape=[], element_dtype=dtypes.float32, max_num_elements=2)
+      # SetItem should not change the push index.
+      l = list_ops.tensor_list_set_item(l, 1, 3.)
+      l = list_ops.tensor_list_push_back(l, 5.)
+      l = list_ops.tensor_list_push_back(l, 7.)
+      t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
+      self.assertAllEqual(t, [5., 7.])
+
   def testGetSetReserved(self):
     with self.cached_session(), self.test_scope():
       l = list_ops.tensor_list_reserve(
@@ -146,6 +157,25 @@ class ListOpsTest(xla_test.XLATestCase):
       t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
       self.assertAllEqual(t, [3.0, 0.0])
 
+  def testSetStackReservedUnknownElementShape(self):
+    with self.cached_session(), self.test_scope():
+      l = list_ops.tensor_list_reserve(
+          element_dtype=dtypes.float32, element_shape=None, num_elements=2)
+      l = list_ops.tensor_list_set_item(l, 0, [3.0, 4.0])
+      t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32)
+      self.assertAllEqual(t, [[3.0, 4.0], [0., 0.]])
+
+  def testPushInEmptyListWithUnknownElementShape(self):
+    with self.cached_session(), self.test_scope():
+      l = list_ops.empty_tensor_list(
+          element_dtype=dtypes.float32, element_shape=None, max_num_elements=2)
+      l = list_ops.tensor_list_push_back(l, [3.0, 4.0])
+      # Pushing an element with a different shape should raise an error.
+      with self.assertRaisesRegexp(errors.InvalidArgumentError, "Shape"):
+        l = list_ops.tensor_list_push_back(l, 5.)
+        self.evaluate(
+            list_ops.tensor_list_stack(l, element_dtype=dtypes.float32))
+
   def testGetSetReservedNonScalar(self):
     with self.cached_session() as sess, self.test_scope():
       l = list_ops.tensor_list_reserve(

tensorflow/compiler/tests/unary_ops_test.py

@@ -72,6 +72,7 @@ class UnaryOpsTest(xla_test.XLATestCase):
         output = op(pinp)
       result = session.run(output, {pinp: inp})
       if equality_test is None:
+        self.assertEqual(output.dtype, expected.dtype)
         self.assertAllCloseAccordingToType(
             result, expected, rtol=rtol, atol=atol, bfloat16_rtol=0.03)
       else:
@@ -260,7 +261,8 @@ class UnaryOpsTest(xla_test.XLATestCase):
       self._assertOpOutputMatchesExpected(
           math_ops.log1p,
           np.array([[1e-14, 1e-15, 0.6]], dtype=dtype),
-          expected=np.log1p(np.array([[1e-14, 1e-15, 0.6]], dtype=dtype)),
+          expected=np.log1p(np.array([[1e-14, 1e-15, 0.6]],
+                                     dtype=dtype)).astype(dtype),
           rtol=1e-4,
           atol=1e-6)
 
@@ -710,7 +712,7 @@ class UnaryOpsTest(xla_test.XLATestCase):
       self._assertOpOutputMatchesExpected(
           math_ops.abs,
           np.array([[2, -1]], dtype=dtype),
-          expected=np.array([[2, 1]], dtype=dtype))
+          expected=np.array([[2, 1]], dtype=np.real(dtype(0)).dtype))
 
       self._assertOpOutputMatchesExpected(
           math_ops.negative,
@@ -880,6 +882,17 @@ class UnaryOpsTest(xla_test.XLATestCase):
           np.array([[-1], [1], [4]], dtype=dtype),
           expected=np.int32(3))
 
+  def testSizeWithInt64OutType(self):
+
+    def size_op(x):
+      return array_ops.size_internal(x, optimize=False, out_type=np.int64)
+
+    for dtype in self.numeric_types:
+      self._assertOpOutputMatchesExpected(
+          size_op,
+          np.array([[-1], [1], [4]], dtype=dtype),
+          expected=np.int64(3))
+
   def testUnpack(self):
     self._assertOpOutputMatchesExpected(
         array_ops.unstack,
@@ -989,7 +1002,7 @@ class UnaryOpsTest(xla_test.XLATestCase):
   def _assertSoftplusMatchesExpected(self, features, dtype):
     features = np.array(features, dtype=dtype)
     zero = np.asarray(0).astype(dtype)
-    expected = np.logaddexp(zero, features)
+    expected = np.logaddexp(zero, features).astype(dtype)
     self._assertOpOutputMatchesExpected(
         nn_ops.softplus, features, expected=expected, rtol=1e-6, atol=9.1e-6)
 

tensorflow/compiler/tf2tensorrt/BUILD

@@ -171,13 +171,11 @@ tf_cuda_library(
     name = "trt_resources",
     srcs = [
         "utils/trt_int8_calibrator.cc",
-        "utils/trt_resource_manager.cc",
         "utils/trt_resources.cc",
     ],
     hdrs = [
         "utils/trt_int8_calibrator.h",
         "utils/trt_lru_cache.h",
-        "utils/trt_resource_manager.h",
         "utils/trt_resources.h",
     ],
     deps = [
@@ -266,7 +264,6 @@ tf_cuda_library(
         "//tensorflow/core:framework_lite",
         "//tensorflow/core:gpu_runtime",
         "//tensorflow/core:graph",
-        "//tensorflow/core:lib",
         "//tensorflow/core:lib_internal",
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core/grappler:devices",
@@ -362,11 +359,12 @@ cc_library(
     ],
 )
 
-tf_cc_test(
+tf_cuda_cc_test(
     name = "segment_test",
     size = "small",
     srcs = ["segment/segment_test.cc"],
     tags = [
+        "no_cuda_on_cpu_tap",
         "no_windows",
         "nomac",
     ],
@@ -432,7 +430,7 @@ cc_library(
     copts = tf_copts(),
     deps = [
         "//tensorflow/core:framework",
-        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_proto_parsing",
     ],
 )
 
@@ -441,7 +439,7 @@ cc_library(
     srcs = ["utils/test_utils.cc"],
     hdrs = ["utils/test_utils.h"],
     deps = [
-        "//tensorflow/core:lib",
+        "//tensorflow/core:lib_proto_parsing",
         "@com_googlesource_code_re2//:re2",
     ],
 )

tensorflow/compiler/tf2tensorrt/convert/convert_graph.cc

@@ -30,7 +30,6 @@ limitations under the License.
 #include "tensorflow/compiler/tf2tensorrt/plugin/trt_plugin_factory.h"
 #include "tensorflow/compiler/tf2tensorrt/segment/segment.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/test_utils.h"
-#include "tensorflow/compiler/tf2tensorrt/utils/trt_resource_manager.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_resources.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_id.h"
 #include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h"
@@ -106,6 +105,7 @@ Status TrtCandidateSelector::IsTensorRTCandidate(const tensorflow::Node* node) {
       "ExpandDims",
       "FusedBatchNorm",
       "FusedBatchNormV2",
+      "GatherV2",
       "Identity",
       "LeakyRelu",
       "Log",
@@ -190,55 +190,6 @@ tensorflow::Status BuildNodeMap(
 
 }  // namespace
 
-// Function to get calibration from ResourceMgr and put them into nodedef.
-tensorflow::Status ConvertCalibGraphToInferGraph(
-    const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* infer_graph,
-    bool is_dyn_op) {
-  LOG(INFO) << "Starting Calib Conversion";
-  *infer_graph = graph_def;
-  auto trt_rm = TRTResourceManager::instance();
-  auto calib_rm = trt_rm->getManager("TRTCalibration");
-  int num_nodes = infer_graph->node_size();
-  if (!is_dyn_op) {
-    LOG(WARNING) << "Construction of static int8 engine is not implemented "
-                    "yet!. Dynamic engine will be constructed";
-  }
-  for (int i = 0; i < num_nodes; ++i) {
-    auto n = infer_graph->mutable_node(i);
-    if (n->op() == "TRTEngineOp") {
-      VLOG(1) << "Processing " << n->name();
-      const string& container_name = n->attr().at("segment_funcdef_name").s();
-      TRTCalibrationResource* cres = nullptr;
-      auto status = calib_rm->Lookup(container_name, "Calibrator", &cres);
-      if (!status.ok()) {
-        LOG(ERROR) << "Could not get Calibration information. Did you run with "
-                      "calibration data?";
-        return tensorflow::errors::FailedPrecondition(
-            "Need to run graph with calibration data first!");
-      }
-      tensorflow::core::ScopedUnref calib_sc(cres);
-      if (cres->calibrator_) {
-        cres->calibrator_->waitAndSetDone();
-        cres->thr_->join();
-        const auto& calibration_table =
-            cres->calibrator_->getCalibrationTableAsString();
-        if (calibration_table.empty()) {
-          LOG(ERROR) << "Calibration table is empty";
-          return tensorflow::errors::Unknown(
-              "Calibration table is missing. This shouldn't have happened!");
-        }
-        n->mutable_attr()->at("calibration_data").set_s(calibration_table);
-      } else {
-        LOG(ERROR) << "Can't get TRTCalibrator from resource manager!";
-        return tensorflow::errors::Unknown(
-            "Can't get TRTCalibrator from resource manager!");
-      }
-      TF_RETURN_IF_ERROR(calib_rm->Cleanup(container_name));
-    }
-  }
-  return tensorflow::Status::OK();
-}
-
 tensorflow::Status ConvertGraphDefToTensorRT(
     const tensorflow::GraphDef& graph_def,
     const std::vector<string>& output_names, size_t max_batch_size,

tensorflow/compiler/tf2tensorrt/convert/convert_graph.h

@@ -85,12 +85,6 @@ struct ConversionParams {
   std::vector<int> cached_engine_batches;  // list of cached engines
 };
 
-// This method extracts calibration information from the resource managers
-// and puts them in to engine nodedefs.
-tensorflow::Status ConvertCalibGraphToInferGraph(
-    const tensorflow::GraphDef& graph_def, tensorflow::GraphDef* new_graph_def,
-    bool is_dyn_op);
-
 // - max_batch_size: maximum batch size which can be used for inference for
 //   optimization targets inference run with max batch size.
 // - max_workspace_size_bytes: The upper bound of memory allowance for engine

tensorflow/compiler/tf2tensorrt/convert/convert_nodes.cc

@@ -30,7 +30,6 @@ limitations under the License.
 #include "tensorflow/compiler/tf2tensorrt/convert/utils.h"
 #include "tensorflow/compiler/tf2tensorrt/plugin/trt_plugin_factory.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_logger.h"
-#include "tensorflow/compiler/tf2tensorrt/utils/trt_resource_manager.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_resources.h"
 #include "tensorflow/core/framework/node_def.pb.h"  // NOLINT
 #include "tensorflow/core/framework/node_def_builder.h"
@@ -379,6 +378,32 @@ tensorflow::Status CreateBroadcastableScalarConstant(
   return Status::OK();
 }
 
+// Convert an axis from TF format to TRT format while validating. TF format
+// includes the batch dimension, while TRT does not. TF can also use negative
+// indices.
+// TODO(tmorris): Use this method in more ops.
+tensorflow::Status ConvertAxis(int tf_axis, int trt_nb_dims,
+                               absl::string_view node_name, int* trt_axis) {
+  const int tf_nb_dims = trt_nb_dims + 1;
+  // Check bounds.
+  if (tf_axis < -tf_nb_dims || tf_axis >= tf_nb_dims) {
+    return tensorflow::errors::InvalidArgument(
+        "Axis value of ", tf_axis, " is out of bounds, must be in range [",
+        -tf_nb_dims, ", ", tf_nb_dims, "), at ", node_name);
+  }
+  // Make negative axis positive.
+  if (tf_axis < 0) tf_axis += tf_nb_dims;
+  // Don't allow axis to be the batch dimension.
+  if (tf_axis == 0) {
+    return tensorflow::errors::Unimplemented(
+        "TensorRT does not allow manipulation of the batch dimension, at ",
+        node_name);
+  }
+  // Remove batch dimension.
+  *trt_axis = tf_axis - 1;
+  return Status::OK();
+}
+
 inline bool DimsEqual(const nvinfer1::Dims& dim_l,
                       const nvinfer1::Dims& dim_r) {
   if (dim_l.nbDims != dim_r.nbDims) {
@@ -3413,6 +3438,29 @@ tensorflow::Status ConvertFusedBatchNorm(OpConverterParams* params) {
   return tensorflow::Status::OK();
 }
 
+tensorflow::Status ConvertGather(OpConverterParams* params) {
+  const auto& inputs = params->inputs;
+  const auto& node_def = params->node_def;
+  TF_RETURN_IF_ERROR(CheckInputsWeights(
+      *params, {{"params", false}, {"indices", false}, {"axis", true}}));
+  absl::Span<const int> axis = inputs.at(2).weights().GetSpan<int>();
+  if (axis.size() != 1) {
+    return tensorflow::errors::InvalidArgument(
+        "Axis for GatherV2 must be a scalar, at ", node_def.name());
+  }
+  int trt_axis = 0;
+  TF_RETURN_IF_ERROR(ConvertAxis(axis[0], inputs.at(0).GetTrtDims().nbDims,
+                                 node_def.name(), &trt_axis));
+  if (params->validation_only) return Status::OK();
+
+  nvinfer1::IGatherLayer* layer = params->converter->network()->addGather(
+      *const_cast<nvinfer1::ITensor*>(inputs.at(0).tensor()),
+      *const_cast<nvinfer1::ITensor*>(inputs.at(1).tensor()), trt_axis);
+  TFTRT_RETURN_ERROR_IF_NULLPTR(layer, node_def.name());
+  params->outputs->push_back(TRT_TensorOrWeights(layer->getOutput(0)));
+  return Status::OK();
+}
+
 tensorflow::Status ConvertMatMulHelper(OpConverterParams* params,
                                        TRT_TensorOrWeights tensor_input,
                                        TRT_ShapedWeights weights_raw,
@@ -3643,6 +3691,7 @@ static void RegisterValidatableOpConverters(
   (*registration)["Conv2DBackpropInput"] = ConvertConv2DBackpropInput;
   (*registration)["DepthwiseConv2dNative"] = ConvertConv2DDepthwise;
   (*registration)["ExpandDims"] = ConvertExpandDims;
+  (*registration)["GatherV2"] = ConvertGather;
   (*registration)["LeakyRelu"] = ConvertLeakyRelu;
   (*registration)["MatMul"] = ConvertMatMul;
   (*registration)["Pad"] = ConvertPad;

tensorflow/compiler/tf2tensorrt/convert/convert_nodes.h

@@ -190,6 +190,11 @@ class TRT_ShapedWeights {
 
   string DebugString() const;
 
+  template <typename T>
+  absl::Span<const T> GetSpan() const {
+    return absl::Span<const T>(tensor_.flat<T>().data(), count());
+  }
+
   // TODO(aaroey): make these private.
   nvinfer1::Dims shape_;  // Note: shape.type[] is not used.
   tensorflow::DataType type_;

tensorflow/compiler/tf2tensorrt/convert/convert_nodes_test.cc

@@ -3129,6 +3129,126 @@ TEST_F(OpConverterTest, ConvertTopK) {
   }
 }
 
+template <DataType dtype>
+void TestConvertGather(OpConverterTest* test) {
+  typedef typename EnumToDataType<dtype>::Type CType;
+
+  // Get the NodeDef for GatherV2.
+  Scope s = Scope::NewRootScope();
+  auto params = ops::Placeholder(s.WithOpName("params"), dtype);
+  auto indices = ops::Placeholder(s.WithOpName("indices"), DT_INT32);
+  auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
+  auto gather = ops::GatherV2(s.WithOpName("my_gather"), params, indices, axis);
+  const NodeDef& node_def = gather.operation.node()->def();
+
+  struct TestParams {
+    std::vector<int> params_dims;
+    std::vector<int> indices_dims;
+    std::vector<int> indices;
+    int axis;
+    std::vector<int> expected_output_dims;
+    std::vector<int> expected_output;
+  };
+
+  // Input is the same {1, 2, 3, 4, 5, 6} for all cases.
+  const int kGatherOKCases = 5;
+  TestParams ok_params[kGatherOKCases] = {
+      // Vector indices (output is rank(params)).
+      TestParams{{1, 2, 3}, {1}, {0}, 3, {1, 2, 1}, {1, 4}},
+      TestParams{{1, 2, 3}, {1}, {1}, 3, {1, 2, 1}, {2, 5}},
+      TestParams{{1, 2, 3}, {1}, {2}, -1, {1, 2, 1}, {3, 6}},
+      TestParams{{1, 2, 3}, {3}, {2, 0, 1}, 3, {1, 2, 3}, {3, 1, 2, 6, 4, 5}},
+      // Higher rank indices (output is rank(params) + rank(indices) - 1).
+      TestParams{{1, 2, 3}, {1, 1}, {0}, 2, {1, 1, 1, 3}, {1, 2, 3}},
+  };
+
+  // Ok.
+  for (int i = 0; i < kGatherOKCases; i++) {
+    test->Reset();
+    test->AddTestTensor("params", ok_params[i].params_dims, 1,
+                        TfDataTypeToTrt(dtype));
+    test->AddTestTensor("indices", ok_params[i].indices_dims, 1,
+                        nvinfer1::DataType::kINT32);
+    test->AddTestWeights<int32>("axis", {1}, {ok_params[i].axis});
+    test->RunValidationAndConversion(node_def);
+    TRT_TensorOrWeights output;
+    TF_EXPECT_OK(test->GetTensorOrWeights("my_gather", &output));
+    EXPECT_TRUE(output.is_tensor());
+    ExpectTrtDimsEqualsArray(ok_params[i].expected_output_dims,
+                             output.tensor()->getDimensions());
+
+    // Create input in CType and convert expected output to CType.
+    std::vector<CType> inputs = {CType(1), CType(2), CType(3),
+                                 CType(4), CType(5), CType(6)};
+    std::vector<CType> converted_expected_output(
+        ok_params[i].expected_output.begin(),
+        ok_params[i].expected_output.end());
+
+    const DataVec input_data{
+        {"params", test::AsTensor<CType>(inputs)},
+        {"indices", test::AsTensor<int32>(ok_params[i].indices)}};
+    DataVec output_data{
+        {"my_gather",
+         ConstructTensor<CType>(ok_params[i].expected_output.size())}};
+    test->BuildAndRun(input_data, &output_data);
+    EXPECT_THAT(GetSpanForData<CType>(output_data[0]),
+                ElementsAreArray(converted_expected_output));
+  }
+}
+
+TEST_F(OpConverterTest, ConvertGather) {
+  {
+    // Input list is empty, should fail.
+    NodeDef node_def = MakeNodeDef("my_gather", "GatherV2", {});
+    RunValidationAndConversion(
+        node_def, error::INVALID_ARGUMENT,
+        "GatherV2 got 0 inputs but expected 3, at my_gather");
+  }
+
+  // Get the NodeDef for GatherV2.
+  Scope s = Scope::NewRootScope();
+  auto params = ops::Placeholder(s.WithOpName("params"), DT_FLOAT);
+  auto indices = ops::Placeholder(s.WithOpName("indices"), DT_INT32);
+  auto axis = ops::Placeholder(s.WithOpName("axis"), DT_INT32);
+  auto gather = ops::GatherV2(s.WithOpName("my_gather"), params, indices, axis);
+  const NodeDef& node_def = gather.operation.node()->def();
+  {
+    // Axis is a tensor, should fail.
+    Reset();
+    AddTestTensor("params", {1, 2, 3});
+    AddTestTensor("indices", {2});
+    AddTestTensor("axis", {1});
+    RunValidationAndConversion(
+        node_def, error::UNIMPLEMENTED,
+        "The input \"axis\" for GatherV2 must be a constant, at my_gather");
+  }
+  {
+    // Axis is out of bounds, should fail.
+    Reset();
+    AddTestTensor("params", {1, 2, 3});
+    AddTestTensor("indices", {2});
+    AddTestWeights<int32>("axis", {1}, {4});
+    RunValidationAndConversion(node_def, error::INVALID_ARGUMENT,
+                               "Axis value of 4 is out of bounds, must be in "
+                               "range [-4, 4), at my_gather");
+  }
+  {
+    // Axis is batch dimension, should fail.
+    Reset();
+    AddTestTensor("params", {1, 2, 3});
+    AddTestTensor("indices", {2});
+    AddTestWeights<int32>("axis", {1}, {0});
+    RunValidationAndConversion(node_def, error::UNIMPLEMENTED,
+                               "TensorRT does not allow manipulation of the "
+                               "batch dimension, at my_gather");
+  }
+
+  Reset();
+  TestConvertGather<DT_FLOAT>(this);
+  TestConvertGather<DT_HALF>(this);
+  TestConvertGather<DT_INT32>(this);
+}
+
 }  // namespace convert
 }  // namespace tensorrt
 }  // namespace tensorflow

tensorflow/compiler/tf2tensorrt/kernels/trt_engine_op.cc

@@ -25,7 +25,6 @@ limitations under the License.
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_allocator.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_logger.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_lru_cache.h"
-#include "tensorflow/compiler/tf2tensorrt/utils/trt_resource_manager.h"
 #include "tensorflow/compiler/tf2tensorrt/utils/trt_resources.h"
 #include "tensorflow/core/framework/function.h"
 #include "tensorflow/core/framework/graph_to_functiondef.h"
@@ -295,27 +294,6 @@ void TRTEngineOp::ExecuteCalibration(OpKernelContext* ctx,
             return this->AllocateCalibrationResources(ctx, cr);
           }}));
   tensorflow::core::ScopedUnref calib_sc(calib_res);
-  // TODO(aaroey): here we also add the resource to the ResourceMgr singleton.
-  // This is needed before we migrate all uses of calib_graph_to_infer_graph()
-  // to the new calibration workflow. After that we'll remove this block.
-  {
-    auto deprecated_rm =
-        TRTResourceManager::instance()->getManager("TRTCalibration");
-    TRTCalibrationResource* copied_resource = nullptr;
-    // Check whether the resource exists, and create it if not.
-    if (deprecated_rm->Lookup(funcdef_name_, "Calibrator", &copied_resource)
-            .ok()) {
-      // Do nothing if the resource exists.
-      copied_resource->Unref();
-    } else {
-      copied_resource = calib_res;
-      // Increase the refcount by 1 then transfer the ownership of that refcount
-      // to the ResourceMgr singleton.
-      copied_resource->Ref();
-      OP_REQUIRES_OK(ctx, deprecated_rm->Create(funcdef_name_, "Calibrator",
-                                                copied_resource));
-    }
-  }
   int num_inputs = ctx->num_inputs();
   // Pass input data to calibrator
   std::unordered_map<string, void*> input_data;

tensorflow/compiler/tf2tensorrt/python/ops/trt_ops.py

@@ -38,16 +38,23 @@ def load_trt_ops():
     if _trt_ops_so:
       return
 
+    try:
+      # pylint: disable=g-import-not-at-top,unused-variable
+      # This registers the TRT ops, it doesn't require loading TRT library.
+      from tensorflow.compiler.tf2tensorrt.ops.gen_trt_ops import trt_engine_op
+      # pylint: enable=g-import-not-at-top,unused-variable
+    except ImportError as e:
+      print("**** Failed to import TF-TRT ops. This is because the binary was "
+            "not built with CUDA or TensorRT enabled. ****")
+      raise e
+
     # TODO(laigd): we should load TF-TRT kernels here as well after removing the
     # swig binding.
     try:
-      # TODO(lagid): It is not known why these unused imports were introduced.
+      # pylint: disable=g-import-not-at-top
-      # Investigate and get rid of these, if not required.
-      # pylint: disable=unused-import,g-import-not-at-top,unused-variable
-      from tensorflow.compiler.tf2tensorrt.ops.gen_trt_ops import trt_engine_op
       from tensorflow.python.framework import load_library
       from tensorflow.python.platform import resource_loader
-      # pylint: enable=unused-import,g-import-not-at-top,unused-variable
+      # pylint: enable=g-import-not-at-top
 
       _trt_ops_so = load_library.load_op_library(
           resource_loader.get_path_to_datafile("_trt_ops.so"))

tensorflow/compiler/tf2tensorrt/utils/test_utils.cc

@@ -19,7 +19,9 @@ limitations under the License.
 #include <vector>
 
 #include "re2/re2.h"
+#include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
+#include "tensorflow/core/platform/types.h"
 
 namespace tensorflow {
 namespace tensorrt {

tensorflow/compiler/tf2tensorrt/utils/test_utils.h

@@ -16,8 +16,7 @@ limitations under the License.
 #ifndef TENSORFLOW_COMPILER_TF2TENSORRT_UTILS_TEST_UTILS_H_
 #define TENSORFLOW_COMPILER_TF2TENSORRT_UTILS_TEST_UTILS_H_
 
-#include "tensorflow/core/lib/core/errors.h"
+#include "tensorflow/core/platform/types.h"
-#include "tensorflow/core/lib/core/status.h"
 
 namespace tensorflow {
 namespace tensorrt {

tensorflow/compiler/tf2tensorrt/utils/trt_resource_manager.cc

@@ -1,45 +0,0 @@
-/* Copyright 2018 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/tf2tensorrt/utils/trt_resource_manager.h"
-#include "tensorflow/core/platform/logging.h"
-
-namespace tensorflow {
-namespace tensorrt {
-
-std::shared_ptr<TRTResourceManager>
-tensorflow::tensorrt::TRTResourceManager::instance() {
-  static std::shared_ptr<TRTResourceManager> instance_(new TRTResourceManager);
-  return instance_;
-}
-
-std::shared_ptr<tensorflow::ResourceMgr>
-tensorflow::tensorrt::TRTResourceManager::getManager(const string& op_name) {
-  // mutex is held for lookup only. Most instantiations where mutex will be held
-  // longer will be during op creation and should be ok.
-  tensorflow::mutex_lock lock(map_mutex_);
-  auto s = managers_.find(op_name);
-  if (s == managers_.end()) {
-    auto it = managers_.emplace(
-        op_name, std::make_shared<tensorflow::ResourceMgr>(op_name));
-    VLOG(1) << "Returning a new manager " << op_name;
-    return it.first->second;
-  }
-  VLOG(1) << "Returning old manager " << op_name;
-  return s->second;
-}
-
-}  // namespace tensorrt
-}  // namespace tensorflow

tensorflow/compiler/tf2tensorrt/utils/trt_resource_manager.h

@@ -1,45 +0,0 @@
-/* Copyright 2018 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.
-==============================================================================*/
-
-#ifndef TENSORFLOW_COMPILER_TF2TENSORRT_UTILS_TRT_RESOURCE_MANAGER_H_
-#define TENSORFLOW_COMPILER_TF2TENSORRT_UTILS_TRT_RESOURCE_MANAGER_H_
-#include <memory>
-
-#include <string>
-#include <unordered_map>
-#include "tensorflow/core/framework/resource_mgr.h"
-#include "tensorflow/core/platform/mutex.h"
-
-namespace tensorflow {
-namespace tensorrt {
-
-class TRTResourceManager {
-  TRTResourceManager() = default;
-
- public:
-  static std::shared_ptr<TRTResourceManager> instance();
-  // returns a manager for given op, if it doesn't exists it creates one
-  std::shared_ptr<tensorflow::ResourceMgr> getManager(const string& op_name);
-
- private:
-  std::unordered_map<string, std::shared_ptr<tensorflow::ResourceMgr>>
-      managers_;
-  tensorflow::mutex map_mutex_;
-};
-
-}  // namespace tensorrt
-}  // namespace tensorflow
-
-#endif  // TENSORFLOW_COMPILER_TF2TENSORRT_UTILS_TRT_RESOURCE_MANAGER_H_

tensorflow/compiler/tf2xla/BUILD

@@ -24,7 +24,7 @@ package(
 )
 
 load("@local_config_cuda//cuda:build_defs.bzl", "if_cuda_is_configured")
-load("//tensorflow/compiler/xla:xla.bzl", "xla_proto_library")
+load("//tensorflow/compiler/xla:xla.bzl", "xla_proto_library", "xla_py_proto_library")
 
 cc_library(
     name = "tf2xla_supported_ops_lib",
@@ -60,6 +60,14 @@ xla_proto_library(
     ],
 )
 
+xla_py_proto_library(
+    name = "tf2xla_py",
+    has_services = False,
+    api_version = 2,
+    visibility = ["//visibility:public"],
+    deps = [":tf2xla_proto"],
+)
+
 xla_proto_library(
     name = "host_compute_metadata_proto",
     srcs = ["host_compute_metadata.proto"],
@@ -283,6 +291,7 @@ tf_cc_test(
         "//tensorflow/core:protos_all_cc",
         "//tensorflow/core:test",
         "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
     ],
 )
 

tensorflow/compiler/tf2xla/kernels/BUILD

@@ -107,11 +107,13 @@ tf_kernel_library(
         "xla_pad_op.cc",
         "xla_reduce_op.cc",
         "xla_select_and_scatter_op.cc",
+        "xla_self_adjoint_eig_op.cc",
     ],
     hdrs = [
         "index_ops.h",
         "shape_util.h",
     ],
+    tags = ["optonly"],
     deps = [
         ":conv_op_helpers",
         ":if_op",
@@ -143,6 +145,7 @@ tf_kernel_library(
         "//tensorflow/compiler/xla/client/lib:prng",
         "//tensorflow/compiler/xla/client/lib:qr",
         "//tensorflow/compiler/xla/client/lib:quantize",
+        "//tensorflow/compiler/xla/client/lib:self_adjoint_eig",
         "//tensorflow/compiler/xla/client/lib:sorting",
         "//tensorflow/core:bitwise_ops_op_lib",
         "//tensorflow/core:control_flow_ops_op_lib",

tensorflow/compiler/tf2xla/kernels/shape_op.cc

@@ -104,7 +104,7 @@ class SizeOp : public XlaOpKernel {
     for (int64 i = 0; i < rank; ++i) {
       size = xla::Mul(size, xla::GetDimensionSize(ctx->Input(0), i));
     }
-    size = xla::ConvertElementType(size, xla::S32);
+    size = xla::ConvertElementType(size, ctx->output_xla_type(0));
     ctx->SetOutput(0, size);
   }
 };

tensorflow/compiler/tf2xla/kernels/tensor_list_ops.cc

@@ -26,6 +26,7 @@ limitations under the License.
 #include "tensorflow/compiler/tf2xla/xla_helpers.h"
 #include "tensorflow/compiler/tf2xla/xla_op_kernel.h"
 #include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/xla_builder.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/core/framework/bounds_check.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -35,6 +36,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor_types.h"
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/kernels/concat_lib.h"
+#include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/platform/types.h"
 
@@ -69,6 +71,43 @@ class TensorListLengthOp : public XlaOpKernel {
 
 REGISTER_XLA_OP(Name("TensorListLength"), TensorListLengthOp);
 
+// Creates an empty list with size (leading_dim, *element_shape) if
+// element_shape is known at compile time. Otherwise creates one with size
+// (leading_dim, 0) which gets initialized later in `GetInitializedList`.
+Status CreateZerosList(XlaOpKernelContext* ctx, int element_shape_index,
+                       int64 leading_dim, DataType dtype, xla::XlaOp* list) {
+  TensorShape list_shape;
+  list_shape.AddDim(leading_dim);
+  xla::XlaOp element_shape_handle = ctx->Input(element_shape_index);
+  TF_ASSIGN_OR_RETURN(
+      bool is_element_shape_compile_time_const,
+      element_shape_handle.builder()->IsConstant(element_shape_handle));
+  PartialTensorShape partial_element_shape;
+  if (is_element_shape_compile_time_const) {
+    TF_RETURN_IF_ERROR(ctx->ConstantInputAsPartialShape(
+        element_shape_index, &partial_element_shape));
+  }
+  if (is_element_shape_compile_time_const &&
+      partial_element_shape.IsFullyDefined()) {
+    TensorShape element_shape;
+    partial_element_shape.AsTensorShape(&element_shape);
+    list_shape.AppendShape(element_shape);
+  } else {
+    // If element_shape is not a compile time constant or if it is not fully
+    // defined we will have to wait for the first write call to fully allocate
+    // the array.
+    // TODO(srbs): We are using element_shape of [0] as a proxy to denote an
+    // uninitialized list. A better implementation may be to represent the
+    // list as a 3-tuple containining an explicit "initialized" flag. However,
+    // we would still need to create a dummy tensor for the first tuple
+    // element.
+    list_shape.AddDim(0);
+  }
+  *list = xla::Broadcast(XlaHelpers::Zero(ctx->builder(), dtype),
+                         list_shape.dim_sizes());
+  return Status::OK();
+}
+
 class TensorListReserveOp : public XlaOpKernel {
  public:
   explicit TensorListReserveOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
@@ -76,20 +115,15 @@ class TensorListReserveOp : public XlaOpKernel {
   }
 
   void Compile(XlaOpKernelContext* ctx) override {
-    TensorShape element_shape;
-    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &element_shape));
     int64 num_elements;
     OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(1, &num_elements));
 
-    TensorShape tensor_shape;
+    xla::XlaOp list;
-    tensor_shape.AddDim(num_elements);
+    OP_REQUIRES_OK(ctx, CreateZerosList(ctx, 0, num_elements, dtype_, &list));
-    tensor_shape.AppendShape(element_shape);
 
     xla::XlaBuilder* b = ctx->builder();
     ctx->SetTensorListOutput(
-        0, xla::Tuple(b, {xla::Broadcast(XlaHelpers::Zero(b, dtype_),
+        0, xla::Tuple(b, {list, xla::ConstantR0<int32>(b, num_elements)}));
-                                         tensor_shape.dim_sizes()),
-                          xla::ConstantR0<int32>(b, num_elements)}));
   }
 
  private:
@@ -110,8 +144,6 @@ class EmptyTensorListOp : public XlaOpKernel {
   }
 
   void Compile(XlaOpKernelContext* ctx) override {
-    TensorShape element_shape;
-    OP_REQUIRES_OK(ctx, ctx->ConstantInputAsShape(0, &element_shape));
     int64 max_num_elements;
     OP_REQUIRES_OK(ctx, ctx->ConstantInputAsIntScalar(1, &max_num_elements));
     OP_REQUIRES(
@@ -119,15 +151,13 @@ class EmptyTensorListOp : public XlaOpKernel {
         errors::InvalidArgument("XLA compilation requires a fixed tensor list "
                                 "size. Set the max number of elements."));
 
-    TensorShape tensor_shape;
+    xla::XlaOp list;
-    tensor_shape.AddDim(max_num_elements);
+    OP_REQUIRES_OK(ctx,
-    tensor_shape.AppendShape(element_shape);
+                   CreateZerosList(ctx, 0, max_num_elements, dtype_, &list));
 
     xla::XlaBuilder* b = ctx->builder();
     ctx->SetTensorListOutput(
-        0, xla::Tuple(b, {xla::Broadcast(XlaHelpers::Zero(b, dtype_),
+        0, xla::Tuple(b, {list, xla::ConstantR0<int32>(b, 0)}));
-                                         tensor_shape.dim_sizes()),
-                          xla::ConstantR0<int32>(b, 0)}));
   }
 
  private:
@@ -274,6 +304,36 @@ REGISTER_XLA_OP(
     Name("TensorListFromTensor").CompileTimeConstantInput("element_shape"),
     TensorListFromTensorOp);
 
+// Returns the 0'th element of `tuple` containing the list tensor if it has been
+// initialized already else creates one lazily. This allows lazy initialization
+// of the list on the first call to SetItem or PushBack.
+Status GetInitializedList(XlaOpKernelContext* ctx, const xla::XlaOp& tuple,
+                          const TensorShape& element_shape, DataType dtype,
+                          xla::XlaOp* list) {
+  *list = xla::GetTupleElement(tuple, 0);
+  TensorShape list_shape;
+  TF_RETURN_IF_ERROR(GetTensorListShape(ctx->builder(), tuple, &list_shape));
+  int64 leading_dim = list_shape.dim_size(0);
+  TensorShape list_element_shape = list_shape;
+  list_element_shape.RemoveDim(0);
+  // This checks for the lazy initialization contract set by CreateEmptyList.
+  // In TensorListReserve if the element_shape is not known at compile time,
+  // it creates a list with shape [leading_dim, 0].
+  if (element_shape != list_element_shape) {
+    if (list_element_shape.num_elements() != 0) {
+      return errors::InvalidArgument(
+          "Invalid shape of value in TensorListSetItem. Expected: ",
+          list_element_shape.DebugString(),
+          " Actual: ", element_shape.DebugString());
+    }
+    list_shape = element_shape;
+    list_shape.InsertDim(0, leading_dim);
+    *list = xla::Broadcast(XlaHelpers::Zero(ctx->builder(), dtype),
+                           list_shape.dim_sizes());
+  }
+  return Status::OK();
+}
+
 class TensorListSetItemOp : public XlaOpKernel {
  public:
   explicit TensorListSetItemOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
@@ -285,7 +345,9 @@ class TensorListSetItemOp : public XlaOpKernel {
     xla::XlaOp tl = ctx->Input(0);
     TensorShape elem_shape = ctx->InputShape(2);
 
-    xla::XlaOp ta = xla::GetTupleElement(tl, 0);
+    xla::XlaOp list;
+    OP_REQUIRES_OK(ctx, GetInitializedList(ctx, tl, elem_shape, dtype_, &list));
+
     xla::XlaOp index = ctx->Input(1);
     xla::XlaOp value = ctx->Input(2);
 
@@ -299,8 +361,8 @@ class TensorListSetItemOp : public XlaOpKernel {
     auto update = xla::Reshape(value, slice_shape.dim_sizes());
 
     ctx->SetTensorListOutput(
-        0, xla::Tuple(b, {xla::DynamicUpdateSlice(ta, update, start_indices),
+        0, xla::Tuple(b, {xla::DynamicUpdateSlice(list, update, start_indices),
-                          index + xla::ConstantR0<int32>(b, 1)}));
+                          xla::GetTupleElement(tl, 1)}));
   }
 
  private:
@@ -319,11 +381,14 @@ class TensorListPushBackOp : public XlaOpKernel {
 
   void Compile(XlaOpKernelContext* ctx) override {
     xla::XlaBuilder* b = ctx->builder();
-    xla::XlaOp tl = ctx->Input(0);
+    xla::XlaOp list_tuple = ctx->Input(0);
     TensorShape elem_shape = ctx->InputShape(1);
 
-    xla::XlaOp ta = xla::GetTupleElement(tl, 0);
+    xla::XlaOp list;
-    xla::XlaOp index = xla::GetTupleElement(tl, 1);
+    OP_REQUIRES_OK(
+        ctx, GetInitializedList(ctx, list_tuple, elem_shape, dtype_, &list));
+
+    xla::XlaOp index = xla::GetTupleElement(list_tuple, 1);
     xla::XlaOp value = ctx->Input(1);
 
     // start_indices of the DynamicUpdateSlice are [index, 0, 0, ..., 0].
@@ -336,7 +401,7 @@ class TensorListPushBackOp : public XlaOpKernel {
     auto update = xla::Reshape(value, slice_shape.dim_sizes());
 
     ctx->SetTensorListOutput(
-        0, xla::Tuple(b, {xla::DynamicUpdateSlice(ta, update, start_indices),
+        0, xla::Tuple(b, {xla::DynamicUpdateSlice(list, update, start_indices),
                           index + xla::ConstantR0<int32>(b, 1)}));
   }
 

tensorflow/compiler/tf2xla/kernels/xla_self_adjoint_eig_op.cc

@@ -0,0 +1,66 @@
+/* Copyright 2019 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/tf2xla/xla_op_kernel.h"
+#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
+#include "tensorflow/compiler/xla/client/lib/self_adjoint_eig.h"
+#include "tensorflow/core/lib/core/bits.h"
+
+namespace tensorflow {
+namespace {
+
+class XlaSelfAdjointEigOp : public XlaOpKernel {
+ public:
+  explicit XlaSelfAdjointEigOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("lower", &lower_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("max_iter", &max_iter_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("epsilon", &epsilon_));
+  }
+  void Compile(XlaOpKernelContext* ctx) override {
+    auto result =
+        xla::SelfAdjointEig(ctx->Input(0), lower_, max_iter_, epsilon_);
+    ctx->SetOutput(0, result.w);
+    ctx->SetOutput(1, result.v);
+  }
+
+ private:
+  bool lower_;
+  int32 max_iter_;
+  float epsilon_;
+};
+
+class SelfAdjointEigV2Op : public XlaOpKernel {
+ public:
+  explicit SelfAdjointEigV2Op(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
+  void Compile(XlaOpKernelContext* ctx) override {
+    const TensorShape input_shape = ctx->InputShape("input");
+    int n = input_shape.dim_size(input_shape.dims() - 1);
+    // This is based on heuristics that approx log(n) sweep updates are needed.
+    // Note: the heuristics provides no theoretical guarantee, max_iter=100 and
+    // epsilon should be used to determine exit condition.
+    int max_iter = 2 * tensorflow::Log2Ceiling(n);
+    auto result = xla::SelfAdjointEig(ctx->Input(0), true, max_iter, 1e-6);
+    ctx->SetOutput(0, result.w);
+    ctx->SetOutput(1, result.v);
+  }
+};
+
+REGISTER_XLA_OP(Name("XlaSelfAdjointEig").TypeConstraint("T", kFloatTypes),
+                XlaSelfAdjointEigOp);
+REGISTER_XLA_OP(Name("SelfAdjointEigV2").TypeConstraint("T", kFloatTypes),
+                SelfAdjointEigV2Op);
+
+}  // namespace
+}  // namespace tensorflow

tensorflow/compiler/tf2xla/ops/xla_ops.cc

@@ -56,6 +56,41 @@ lhs_output: the broadcasted LHS tensor
 rhs_output: the broadcasted RHS tensor
 )doc");
 
+REGISTER_OP("XlaSelfAdjointEig")
+    .Input("a: T")
+    .Attr("lower: bool")
+    .Attr("max_iter: int")
+    .Attr("epsilon: float")
+    .Output("w: T")
+    .Output("v: T")
+    .SetShapeFn(shape_inference::UnknownShape)
+    .Attr("T: numbertype")
+    .Doc(R"doc(
+Computes the eigen decomposition of a batch of self-adjoint matrices
+(Note: Only real inputs are supported).
+
+Computes the eigenvalues and eigenvectors of the innermost N-by-N matrices in
+tensor such that tensor[...,:,:] * v[..., :,i] = e[..., i] * v[...,:,i], for
+i=0...N-1.
+
+a: the input tensor.
+
+lower: a boolean specifies whether the calculation is done with the lower
+  triangular part or the upper triangular part.
+
+max_iter: maximum number of sweep update, i.e., the whole lower triangular
+  part or upper triangular part based on parameter lower. Heuristically, it has
+  been argued that approximatly logN sweeps are needed in practice (Ref: Golub &
+  van Loan "Matrix Computation").
+
+epsilon: the tolerance ratio.
+
+w: The eigenvalues in ascending order, each repeated according to its
+  multiplicity.
+v: The column v[..., :, i] is the normalized eigenvector corresponding to the
+  eigenvalue w[..., i].
+)doc");
+
 REGISTER_OP("XlaConv")
     .Input("lhs: T")
     .Input("rhs: T")

tensorflow/compiler/tf2xla/python/xla.py

@@ -291,6 +291,10 @@ def dot_general(lhs, rhs, dimension_numbers, precision_config=None, name=None):
       name=name)
 
 
+def self_adjoint_eig(a, lower, max_iter, epsilon):
+  return gen_xla_ops.xla_self_adjoint_eig(a, lower, max_iter, epsilon)
+
+
 dynamic_slice = gen_xla_ops.xla_dynamic_slice
 dynamic_update_slice = gen_xla_ops.xla_dynamic_update_slice
 

tensorflow/compiler/tf2xla/xla_compiler.cc

@@ -185,9 +185,10 @@ Status BuildComputation(
   std::vector<xla::XlaOp> elems;
   elems.reserve(retvals.size());
 
-  // Keeps track of which retvals have layout to update. The first element is
+  // Keeps track of the layout of each retval. If a retval is not in this list,
-  // the output index, second element is the new layout.
+  // a descending layout is used. The first element is the output index, second
-  std::vector<std::pair<int64, xla::Layout>> retval_to_update_layout;
+  // element is the new layout.
+  std::vector<std::pair<int64, xla::Layout>> retval_index_and_layout;
   for (int i = 0; i < retvals.size(); ++i) {
     XlaCompiler::OutputDescription& output = (*outputs)[i];
     const XlaExpression& retval = retvals[i];
@@ -216,7 +217,7 @@ Status BuildComputation(
           TF_ASSIGN_OR_RETURN(xla::Shape shape, shape_representation_fn(
                                                     output.shape, output.type));
           value = xla::Reshape(value, xla::AsInt64Slice(shape.dimensions()));
-          retval_to_update_layout.emplace_back(elems.size(), shape.layout());
+          retval_index_and_layout.emplace_back(elems.size(), shape.layout());
         } else if (it != retval_cores.end()) {
           // Apply the sharding to the output, if there is a core assignment.
           value = identity_op(value);
@@ -289,6 +290,11 @@ Status BuildComputation(
       // Ensures the correct sharding is applied to the output.
       handle = identity_op(handle);
 
+      // Set layout of the retval to device representation layout.
+      if (resource->representation_shape().has_value()) {
+        retval_index_and_layout.emplace_back(
+            elems.size(), resource->representation_shape()->layout());
+      }
       elems.push_back(handle);
     }
   }
@@ -318,15 +324,15 @@ Status BuildComputation(
                       computation->GetProgramShape());
   *output_shape = program_shape.result();
   // Update the output layout to the layout of retval.
-  for (auto& update : retval_to_update_layout) {
+  for (auto& index_and_layout : retval_index_and_layout) {
     if (!always_return_tuple && elems.size() == 1) {
-      *output_shape->mutable_layout() = update.second;
+      *output_shape->mutable_layout() = index_and_layout.second;
       continue;
     }
 
-    xla::Shape* output_sub_shape =
+    xla::Shape* output_sub_shape = xla::ShapeUtil::GetMutableSubshape(
-        xla::ShapeUtil::GetMutableSubshape(output_shape, {update.first});
+        output_shape, {index_and_layout.first});
-    *output_sub_shape->mutable_layout() = update.second;
+    *output_sub_shape->mutable_layout() = index_and_layout.second;
   }
   return Status::OK();
 }

tensorflow/compiler/tf2xla/xla_compiler_test.cc

@@ -277,6 +277,97 @@ TEST_F(XlaCompilerTest, OutOfOrderGraph) {
   EXPECT_TRUE(xla::LiteralTestUtil::Equal(param0_literal, actual_literal));
 }
 
+// Tests that the compiler can correctly propagate the layout assigned by
+// shape_representation_fn_ to return types.
+TEST_F(XlaCompilerTest, HonorShapeRepresentationFnForRetVal) {
+  Scope scope = Scope::NewRootScope().ExitOnError();
+  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
+  auto var = ops::_Arg(scope.WithOpName("V"), DT_RESOURCE, 1);
+  // Adds an identity op around the resource to make sure identity ops propagate
+  // resources correctly.
+  auto identity = ops::Identity(scope.WithOpName("VIdentity"), var);
+  auto write = ops::AssignAddVariableOp(scope, identity, a);
+  auto read = ops::ReadVariableOp(
+      scope.WithControlDependencies(std::vector<Operation>{write}), var,
+      DT_INT32);
+  auto read_plus_one = ops::Add(scope, read, ops::Const<int32>(scope, 1));
+  auto d = ops::_Retval(scope.WithOpName("D"), read_plus_one, 0);
+  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
+  TF_ASSERT_OK(scope.ToGraph(graph.get()));
+
+  // Builds a description of the arguments.
+  std::vector<XlaCompiler::Argument> args(2);
+  args[0].kind = XlaCompiler::Argument::kParameter;
+  args[0].type = DT_INT32;
+  args[0].shape = TensorShape({2, 3});
+  args[1].kind = XlaCompiler::Argument::kResource;
+  args[1].resource_kind = XlaResource::kVariable;
+  args[1].initialized = true;
+  args[1].type = DT_INT32;
+  args[1].shape = TensorShape({2, 3});
+
+  auto options = DefaultOptions();
+  options.shape_representation_fn =
+      [](const TensorShape& shape, DataType dt) -> xla::StatusOr<xla::Shape> {
+    xla::Shape xla_shape;
+    TF_RETURN_IF_ERROR(TensorShapeToXLAShape(dt, shape, &xla_shape));
+    *xla_shape.mutable_layout() = xla::LayoutUtil::MakeLayout({0, 1});
+    return xla_shape;
+  };
+  // Compiles the graph.
+  XlaCompiler compiler(options);
+
+  XlaCompiler::CompilationResult result;
+  TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(), "add",
+                                     std::move(graph), args, &result));
+  xla::Shape transposed =
+      xla::ShapeUtil::MakeShapeWithLayout(xla::S32, {2, 3}, {0, 1});
+  // Check that the return shapes are correctly tranposed.
+  EXPECT_EQ(result.xla_output_shape,
+            xla::ShapeUtil::MakeTupleShape({transposed, transposed}));
+}
+
+// The layout of resource variable shouldn't change after transpose
+TEST_F(XlaCompilerTest, TransposeVariables) {
+  Scope scope = Scope::NewRootScope().ExitOnError();
+  auto a = ops::_Arg(scope.WithOpName("A"), DT_INT32, 0);
+  auto var = ops::_Arg(scope.WithOpName("V"), DT_RESOURCE, 1);
+  // Adds an identity op around the resource to make sure identity ops propagate
+  // resources correctly.
+  auto identity = ops::Identity(scope.WithOpName("VIdentity"), var);
+  auto write = ops::AssignAddVariableOp(scope, identity, a);
+  auto read = ops::ReadVariableOp(
+      scope.WithControlDependencies(std::vector<Operation>{write}), var,
+      DT_INT32);
+  auto transposed_read = ops::Transpose(scope, read, {1, 0});
+  auto reshape = ops::Reshape(scope, transposed_read, {2, 3});
+  auto d = ops::_Retval(scope.WithOpName("D"), reshape, 0);
+  std::unique_ptr<Graph> graph(new Graph(OpRegistry::Global()));
+  TF_ASSERT_OK(scope.ToGraph(graph.get()));
+
+  // Builds a description of the arguments.
+  std::vector<XlaCompiler::Argument> args(2);
+  args[0].kind = XlaCompiler::Argument::kParameter;
+  args[0].type = DT_INT32;
+  args[0].shape = TensorShape({2, 3});
+  args[1].kind = XlaCompiler::Argument::kResource;
+  args[1].resource_kind = XlaResource::kVariable;
+  args[1].initialized = true;
+  args[1].type = DT_INT32;
+  args[1].shape = TensorShape({2, 3});
+  // Compiles the graph.
+  XlaCompiler compiler(DefaultOptions());
+
+  XlaCompiler::CompilationResult result;
+  TF_ASSERT_OK(compiler.CompileGraph(XlaCompiler::CompileOptions(), "transpose",
+                                     std::move(graph), args, &result));
+  xla::Shape transposed =
+      xla::ShapeUtil::MakeShapeWithLayout(xla::S32, {2, 3}, {1, 0});
+  // Check that the return shapes are correctly tranposed.
+  EXPECT_EQ(result.xla_output_shape,
+            xla::ShapeUtil::MakeTupleShape({transposed, transposed}));
+}
+
 // Tests that the compiler doesn't reorder the parameters.
 TEST_F(XlaCompilerTest, MixedOrderArguments) {
   for (bool swap_order : {false, true}) {

tensorflow/compiler/tf2xla/xla_op_kernel.cc

@@ -319,6 +319,27 @@ Status XlaOpKernelContext::ConstantInputAsShape(int index, TensorShape* shape) {
   return Status::OK();
 }
 
+Status XlaOpKernelContext::ConstantInputAsPartialShape(
+    int index, PartialTensorShape* shape) {
+  xla::Literal literal;
+  TF_RETURN_IF_ERROR(ConstantInput(index, &literal));
+  // If `literal` is a scalar it's value must be -1.
+  if (literal.shape().rank() == 0) {
+    int64 shape_val;
+    TF_RETURN_IF_ERROR(LiteralToInt64Scalar(literal, &shape_val));
+    if (shape_val != -1) {
+      return errors::InvalidArgument(
+          "Cannot convert value to PartialTensorShape: ", shape_val);
+    }
+    *shape = PartialTensorShape();  // Shape with unknown rank.
+    return Status::OK();
+  }
+  std::vector<int64> dims;
+  TF_RETURN_IF_ERROR(LiteralToInt64Vector(literal, &dims));
+  *shape = PartialTensorShape(dims);
+  return Status::OK();
+}
+
 Status XlaOpKernelContext::InputList(absl::string_view name,
                                      std::vector<xla::XlaOp>* handles,
                                      std::vector<TensorShape>* shapes) {
@@ -447,6 +468,16 @@ void XlaOpKernelContext::SetOutputExpression(int index,
   }
 }
 
+xla::PrimitiveType XlaOpKernelContext::output_xla_type(int index) {
+  xla::PrimitiveType type;
+  Status status = DataTypeToPrimitiveType(expected_output_dtype(index), &type);
+  if (!status.ok()) {
+    SetStatus(status);
+    return xla::PRIMITIVE_TYPE_INVALID;
+  }
+  return type;
+}
+
 void XlaOpKernelContext::SetOutput(int index, const xla::XlaOp& handle) {
   SetOutputExpression(
       index,
@@ -503,6 +534,7 @@ Status AssignVariableTensor(const Tensor& tensor, DataType type,
     handle = xla::Reshape(handle,
                           xla::AsInt64Slice(representation_shape.dimensions()));
   }
+  variable->SetRepresentationShape(representation_shape);
   return variable->SetValue(handle);
 }
 

tensorflow/compiler/tf2xla/xla_op_kernel.h

@@ -138,6 +138,10 @@ class XlaOpKernelContext {
   // Converts a constant 1D int32 or int64 tensor into a TensorShape.
   Status ConstantInputAsShape(int index, TensorShape* shape);
 
+  // Converts a constant 1D int32 or int64 tensor, or a scalar with value -1
+  // into a PartialTensorShape.
+  Status ConstantInputAsPartialShape(int index, PartialTensorShape* shape);
+
   // Returns the named list-valued immutable input in "list", as
   // defined in the OpDef.  If the named output is not list-valued,
   // returns a one-element list.
@@ -155,6 +159,11 @@ class XlaOpKernelContext {
     return context_->expected_output_dtype(index);
   }
 
+  // Returns the type of output `index` as an xla::PrimitiveType. If the type
+  // is not representable as an XLA type, sets an error status and returns
+  // xla::PRIMITIVE_TYPE_INVALID.
+  xla::PrimitiveType output_xla_type(int index);
+
   // Sets output `index` to the XlaOp `handle`.
   // All outputs should be set using SetOutput and SetConstantOutput, not
   // via the underlying OpKernelContext.

tensorflow/compiler/tf2xla/xla_resource.h

@@ -86,6 +86,12 @@ class XlaResource {
   // variables have new values that need to be written back.
   const xla::XlaOp& initial_value() const { return initial_value_; }
 
+  // An xla shape that indicates how this resource variable is represented on
+  // device.
+  const absl::optional<xla::Shape>& representation_shape() const {
+    return representation_shape_;
+  }
+
   // A variable is initialized if it has a value.
   bool initialized() const { return value_.valid(); }
 
@@ -100,6 +106,11 @@ class XlaResource {
   // Sets the current value of the resource to an all-zero value.
   Status SetZeroValue(xla::XlaBuilder* builder);
 
+  // Sets the representational shape of the resource on device.
+  void SetRepresentationShape(const xla::Shape& shape) {
+    representation_shape_ = absl::make_optional(shape);
+  }
+
   // Looks up the gradient for `source`, or creates it if it does not already
   // exist. The call target must be an initialized TensorArray resource. A
   // TensorArray can have multiple named gradients; see the operator
@@ -160,6 +171,10 @@ class XlaResource {
   xla::XlaOp value_;
   xla::XlaOp initial_value_;
 
+  // An xla shape that indicates how this resource variable is represented on
+  // device.
+  absl::optional<xla::Shape> representation_shape_;
+
   int64 max_array_size_ = -1;
   bool tensor_array_multiple_writes_aggregate_ = false;
 

tensorflow/compiler/xla/client/lib/BUILD

@@ -452,11 +452,12 @@ cc_library(
 )
 
 cc_library(
-    name = "self_adjoint_eigen",
+    name = "self_adjoint_eig",
-    srcs = ["self_adjoint_eigen.cc"],
+    srcs = ["self_adjoint_eig.cc"],
-    hdrs = ["self_adjoint_eigen.h"],
+    hdrs = ["self_adjoint_eig.h"],
     deps = [
         ":arithmetic",
+        ":comparators",
         ":constants",
         ":loops",
         ":math",
@@ -473,9 +474,12 @@ cc_library(
 )
 
 xla_test(
-    name = "self_adjoint_eigen_test",
+    name = "self_adjoint_eig_test",
-    size = "medium",
+    srcs = ["self_adjoint_eig_test.cc"],
-    srcs = ["self_adjoint_eigen_test.cc"],
+    blacklisted_backends = [
+        "cpu",
+        "gpu",
+    ],
     real_hardware_only = True,
     shard_count = 10,
     tags = ["optonly"],
@@ -483,7 +487,7 @@ xla_test(
         ":arithmetic",
         ":constants",
         ":matrix",
-        ":self_adjoint_eigen",
+        ":self_adjoint_eig",
         "//tensorflow/compiler/xla:array2d",
         "//tensorflow/compiler/xla:array3d",
         "//tensorflow/compiler/xla:literal",

tensorflow/compiler/xla/client/lib/self_adjoint_eig.cc

@@ -13,12 +13,13 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/compiler/xla/client/lib/self_adjoint_eigen.h"
+#include "tensorflow/compiler/xla/client/lib/self_adjoint_eig.h"
 
 #include <memory>
 #include <vector>
 
 #include "tensorflow/compiler/xla/client/lib/arithmetic.h"
+#include "tensorflow/compiler/xla/client/lib/comparators.h"
 #include "tensorflow/compiler/xla/client/lib/constants.h"
 #include "tensorflow/compiler/xla/client/lib/loops.h"
 #include "tensorflow/compiler/xla/client/lib/math.h"
@@ -42,7 +43,6 @@ namespace {
 struct SymmetricSchurDecomposition {
   XlaOp c;          // cosine.
   XlaOp s;          // sine.
-  XlaOp reduction;  // Reduction in the off diagonal after applying G.
 };
 
 // JacobiUpdate holds the intermediate orthogonal matrix, Jacobi-rotated matrix
@@ -51,7 +51,11 @@ struct SymmetricSchurDecomposition {
 struct JacobiUpdate {
   XlaOp v;
   XlaOp w;
+};
+
+struct FrobeniusNorms {
   XlaOp off_diagonal_norm;
+  XlaOp total_norm;
 };
 
 // Given an n-by-n symmetric A and integers p and q that satisfy 0 <= p < q < n,
@@ -79,10 +83,6 @@ StatusOr<SymmetricSchurDecomposition> SymmetricShurDecomposition2x2(XlaOp a,
   XlaBuilder* builder = a.builder();
   TF_ASSIGN_OR_RETURN(Shape a_shape, builder->GetShape(a));
 
-  PrimitiveType type = a_shape.element_type();
-
-  const int64 num_dims = a_shape.rank();
-
   auto zero = ScalarLike(a, 0.0);
   auto one = ScalarLike(a, 1.0);
   auto two = ScalarLike(a, 2.0);
@@ -110,9 +110,7 @@ StatusOr<SymmetricSchurDecomposition> SymmetricShurDecomposition2x2(XlaOp a,
 
   schur.c = c * rnorm;
   schur.s = s * rnorm;
-  schur.reduction =
+
-      Reduce(two * Square(pqs), zero, CreateScalarAddComputation(type, builder),
-             {num_dims - 2, num_dims - 1});
   return schur;
 }
 
@@ -196,12 +194,32 @@ StatusOr<JacobiUpdate> Update(JacobiUpdate jacobi_update, XlaOp p, XlaOp q,
   jacobi_update.v =
       DynamicUpdateSliceInMinorDims(jacobi_update.v, slice_q_new, {zero, q});
 
-  jacobi_update.off_diagonal_norm = Sqrt(
-      Max(Square(jacobi_update.off_diagonal_norm) - schur.reduction, pq_zero));
-
   return jacobi_update;
 }
 
+StatusOr<FrobeniusNorms> ComputeFrobeniusNorms(XlaOp w) {
+  XlaBuilder* builder = w.builder();
+  TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(w));
+  const int64 num_dims = shape.rank();
+  auto frobenius_norm =
+      Sqrt(Reduce(Square(w), ScalarLike(w, 0.0),
+                  CreateScalarAddComputation(shape.element_type(), builder),
+                  {num_dims - 2, num_dims - 1}));
+  auto diag = GetMatrixDiagonal(w);
+  auto diag_square =
+      Reduce(Square(diag), ScalarLike(w, 0.0),
+             CreateScalarAddComputation(shape.element_type(), builder),
+             {num_dims - 2});
+
+  FrobeniusNorms frobenius_norms;
+
+  frobenius_norms.off_diagonal_norm =
+      Sqrt(Max(Square(frobenius_norm) - diag_square, ScalarLike(w, 0.0)));
+  frobenius_norms.total_norm = frobenius_norm;
+
+  return frobenius_norms;
+}
+
 StatusOr<std::vector<XlaOp>> WhileLoopFn(
     absl::Span<const XlaOp> initial_values,  //
     int matrix_dimension,                    //
@@ -212,62 +230,108 @@ StatusOr<std::vector<XlaOp>> WhileLoopFn(
   auto while_cond_fn = [&](absl::Span<const XlaOp> values,
                            XlaBuilder* cond_builder) -> StatusOr<XlaOp> {
     auto k = values[0];
-    auto off_diagonal_norm = values[5];
-    // tol = frobenius_norm * epsilon.
-    auto tol = values[6] * values[7];
-
     auto max_sweeps = ScalarLike(k, max_sweep_updates);
-
     auto sweep_update_cond = Gt(max_sweeps, k);
 
-    auto tol_cond = ReduceAll(Lt(tol, off_diagonal_norm),
+    auto norms = ComputeFrobeniusNorms(values[2]).ValueOrDie();
+    auto tol = norms.total_norm * values[3];
+    auto tol_cond = ReduceAll(Lt(tol, norms.off_diagonal_norm),
                               xla::ConstantR0<bool>(cond_builder, false),
                               CreateScalarOrComputation(PRED, cond_builder));
-    return And(tol_cond, sweep_update_cond);
+
+    return And(sweep_update_cond, tol_cond);
   };
 
   auto while_body_fn =
       [&](absl::Span<const XlaOp> values,
           XlaBuilder* body_builder) -> StatusOr<std::vector<XlaOp>> {
-    auto zero = Zero(body_builder, index_type);
+    auto while_cond_fn_inner =
-    auto one = One(body_builder, index_type);
+        [&](absl::Span<const XlaOp> values_inner,
-    auto end_index = ScalarLike(one, matrix_dimension);
+            XlaBuilder* inner_cond_builder) -> StatusOr<XlaOp> {
+      auto p = values_inner[0];
+      return Lt(p, ScalarLike(p, matrix_dimension - 1));
+    };
 
+    auto while_body_fn_inner =
+        [&](absl::Span<const XlaOp> values_inner,
+            XlaBuilder* inner_body_builder) -> StatusOr<std::vector<XlaOp>> {
+      auto while_cond_fn_innermost =
+          [&](absl::Span<const XlaOp> values_innermost,
+              XlaBuilder* innermost_cond_builder) -> StatusOr<XlaOp> {
+        auto q = values_innermost[1];
+        return Lt(q, ScalarLike(q, matrix_dimension));
+      };
+      auto while_body_fn_innermost =
+          [&](absl::Span<const XlaOp> values_innermost,
+              XlaBuilder* innermost_body_builder)
+          -> StatusOr<std::vector<XlaOp>> {
+        auto p = values_innermost[0];
+        auto q = values_innermost[1];
+
+        JacobiUpdate jacobi_update;
+        jacobi_update.v = values_innermost[2];
+        jacobi_update.w = values_innermost[3];
+
+        auto tol = values_innermost[4];
+
+        TF_ASSIGN_OR_RETURN(jacobi_update,
+                            Update(jacobi_update, p, q, tol, matrix_dimension));
+
+        std::vector<XlaOp> updated_values_innermost;
+        updated_values_innermost.reserve(values_innermost.size());
+
+        updated_values_innermost.push_back(p);
+        updated_values_innermost.push_back(q + ScalarLike(q, 1));
+        updated_values_innermost.push_back(jacobi_update.v);
+        updated_values_innermost.push_back(jacobi_update.w);
+        updated_values_innermost.push_back(tol);
+
+        return updated_values_innermost;
+      };
+
+      std::vector<XlaOp> values_innermost(5);
+      auto p = values_inner[0];
+      auto q = p + ScalarLike(p, 1);
+      values_innermost[0] = p;                // index p.
+      values_innermost[1] = q;                // index q.
+      values_innermost[2] = values_inner[1];  // v.
+      values_innermost[3] = values_inner[2];  // w.
+      values_innermost[4] = values_inner[3];  // tol.
+      TF_ASSIGN_OR_RETURN(
+          values_innermost,
+          WhileLoopHelper(while_cond_fn_innermost, while_body_fn_innermost,
+                          values_innermost, absl::StrCat(name, "-Innermost"),
+                          inner_body_builder));
+
+      std::vector<XlaOp> updated_values_inner;
+      updated_values_inner.reserve(values_inner.size());
+
+      updated_values_inner.push_back(p + ScalarLike(p, 1));
+      updated_values_inner.push_back(values_innermost[2]);
+      updated_values_inner.push_back(values_innermost[3]);
+      updated_values_inner.push_back(values_innermost[4]);
+      return updated_values_inner;
+    };
     // Indexes.
     XlaOp k = values[0];
-    XlaOp p = values[1];
-    XlaOp q = values[2];
 
-    JacobiUpdate jacobi_update;
+    std::vector<XlaOp> values_inner(4);
-    jacobi_update.v = values[3];
+    values_inner[0] = ScalarLike(k, 0);  // index p.
-    jacobi_update.w = values[4];
+    values_inner[1] = values[1];         // v.
-    jacobi_update.off_diagonal_norm = values[5];
+    values_inner[2] = values[2];         // w.
-
+    values_inner[3] = values[3];         // tol.
-    XlaOp frobenius_norm = values[6];
+    TF_ASSIGN_OR_RETURN(
-    XlaOp tol = values[7];
+        values_inner,
-
+        WhileLoopHelper(while_cond_fn_inner, while_body_fn_inner, values_inner,
-    TF_ASSIGN_OR_RETURN(jacobi_update,
+                        absl::StrCat(name, "-Inner"), body_builder));
-                        Update(jacobi_update, p, q, tol, matrix_dimension));
 
     std::vector<XlaOp> updated_values;
-    updated_values.reserve(values.size());
+    updated_values.reserve(values_inner.size());
 
-    q = q + one;
+    updated_values.push_back(k + ScalarLike(k, 1));
-    p = Select(Eq(q, end_index), p + one, p);
+    updated_values.push_back(values_inner[1]);
-    k = Select(Eq(p, end_index - one), k + one, k);
+    updated_values.push_back(values_inner[2]);
-    p = Select(Eq(p, end_index - one), zero, p);
+    updated_values.push_back(values_inner[3]);
-    q = Select(Eq(q, end_index), p + one, q);
-
-    updated_values.push_back(k);
-    updated_values.push_back(p);
-    updated_values.push_back(q);
-
-    updated_values.push_back(jacobi_update.v);
-    updated_values.push_back(jacobi_update.w);
-    updated_values.push_back(jacobi_update.off_diagonal_norm);
-
-    updated_values.push_back(frobenius_norm);
-    updated_values.push_back(tol);
 
     return updated_values;
   };
@@ -278,6 +342,27 @@ StatusOr<std::vector<XlaOp>> WhileLoopFn(
   return values;
 }
 
+StatusOr<SelfAdjointEigResult> SortByEigenvalues(SelfAdjointEigResult result) {
+  XlaBuilder* builder = result.v.builder();
+  TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(result.v));
+  const int64 num_dims = shape.rank();
+  auto dimensions = shape.dimensions();
+
+  std::vector<int64> broadcast_dims(num_dims - 1);
+  std::iota(broadcast_dims.begin(), broadcast_dims.end(), 0);
+  broadcast_dims[num_dims - 2] = num_dims - 1;
+  result.w = BroadcastInDim(result.w, dimensions, broadcast_dims);
+
+  XlaOp sort_result =
+      Sort({result.w, result.v},
+           CreateScalarLtComputation(
+               {shape.element_type(), shape.element_type()}, builder),
+           num_dims - 1);
+  result.w = GetMatrixDiagonal(GetTupleElement(sort_result, 0));
+  result.v = GetTupleElement(sort_result, 1);
+  return result;
+}
+
 }  // namespace
 
 // This is the cyclic Jacobi iteration. Please note that the eigenvalues are
@@ -286,31 +371,35 @@ StatusOr<std::vector<XlaOp>> WhileLoopFn(
 //  def jacobi(A):
 //      n, _ = A.shape
 //      V = np.eye(n)
-//      nfrob = np.sum(A ** 2)
+//      frobenius_norm = np.linalg.norm(A)
-//      ndiag = np.sum(np.diag(A) ** 2)
+//      diag_norm = np.linalg.norm(np.diag(A))
-//      off = nfrob - ndiag
+//      off_diag_norm = np.sqrt(
-//      while off > 1e-6 * nfrob:
+//          frobenius_norm - diag_norm) * np.sqrt(frobenius_norm + diag_norm)
+//      while off_diag_norm > 1e-6 * frobenius_norm:
 //          for p in range(n - 1):
 //              for q in range(p + 1, n):
-//                  if off > 1e-6 * nfrob:
+//                  c, s = sym_schur2x2(A, p, q)
-//                      c, s = sym_schur2x2(A, p, q)
+//                  A[[p, q], :] = np.matmul(np.array([[c, -s], [s, c]]),
-//                      off = off - 2 * A[p, q] ** 2
+//                                           A[[p, q], :])
-//                      A[[p, q], :] = np.matmul(np.array([[c, -s], [s, c]]),
+//                  A[:, [p, q]] = np.matmul(A[:, [p, q]],
-//                                               A[[p, q], :])
+//                                           np.array([[c, s], [-s, c]]))
-//                      A[:, [p, q]] = np.matmul(A[:, [p, q]],
+//                  V[:, [p, q]] = np.matmul(V[:, [p, q]],
-//                                               np.array([[c, s], [-s, c]]))
-//                      V[:, [p, q]] = np.matmul(V[:, [p, q]],
 //                                               np.array([[c, s], [-s, c]]))
+//          frobenius_norm_sq = np.linalg.norm(A)
+//          diag_square_sum = np.linalg.norm(np.diag(A))
+//          off_diag_norm = np.sqrt(
+//              frobenius_norm - diag_norm) * np.sqrt(
+//                  frobenius_norm + diag_norm)
 //
 //      return A, V
 //
 // TODO(kuny): Implement parallel order Jacobi.
 //
-SelfAdjointEigenResult SelfAdjointEigen(XlaOp a, bool lower, int64 max_iter,
+SelfAdjointEigResult SelfAdjointEig(XlaOp a, bool lower, int64 max_iter,
-                                        float epsilon) {
+                                    float epsilon) {
   XlaBuilder* builder = a.builder();
   auto return_error = [&](const Status& status) {
-    SelfAdjointEigenResult result;
+    SelfAdjointEigResult result;
     result.v = builder->ReportError(status);
     result.w = builder->ReportError(status);
     return result;
@@ -348,33 +437,17 @@ SelfAdjointEigenResult SelfAdjointEigen(XlaOp a, bool lower, int64 max_iter,
     batch_dims[i] = ShapeUtil::GetDimension(a_shape, i);
   }
 
-  auto zero = ScalarLike(a, 0.0);
   auto tol = ScalarLike(a, epsilon);
 
   auto v_init = Broadcast(IdentityMatrix(builder, type, m, m), batch_dims);
   auto w_init = Triangle(a, lower);
   w_init = w_init + TransposeInMinorDims(w_init) - w_init * v_init;
 
-  auto frobenius_norm = Sqrt(Reduce(Square(w_init), zero,
-                                    CreateScalarAddComputation(type, builder),
-                                    {num_dims - 2, num_dims - 1}));
-  auto diag = GetMatrixDiagonal(w_init);
-  auto diag_square =
-      Reduce(Square(diag), zero, CreateScalarAddComputation(type, builder),
-             {num_dims - 2});
-
-  auto off_diagonal_init =
-      Sqrt(Max(Square(frobenius_norm) - diag_square, zero));
-
   auto output_with_status = WhileLoopFn(
       {
           Zero(builder, S32),  // k
-          Zero(builder, S32),  // p
+          v_init,              // v
-          One(builder, S32),   // q
+          w_init,              // w
-          v_init,              //
-          w_init,              //
-          off_diagonal_init,   //
-          frobenius_norm,      //
           tol,                 //
       },                       //
       n,                       //
@@ -388,11 +461,11 @@ SelfAdjointEigenResult SelfAdjointEigen(XlaOp a, bool lower, int64 max_iter,
 
   auto output = output_with_status.ValueOrDie();
 
-  SelfAdjointEigenResult result;
+  SelfAdjointEigResult result;
-  result.v = output[3];
+  result.v = output[1];
-  result.w = GetMatrixDiagonal(output[4]);
+  result.w = GetMatrixDiagonal(output[2]);
 
-  return result;
+  return SortByEigenvalues(result).ValueOrDie();
 }
 
 }  // namespace xla

tensorflow/compiler/xla/client/lib/self_adjoint_eig.h

@@ -13,8 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIGEN_H_
+#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIG_H_
-#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIGEN_H_
+#define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIG_H_
 
 #include "tensorflow/compiler/xla/client/xla_builder.h"
 #include "tensorflow/compiler/xla/xla_data.pb.h"
@@ -23,20 +23,18 @@ namespace xla {
 
 // The eigenvalue decomposition of a symmetric matrix, the original matrix is
 // recovered by v * w * v_t.
-struct SelfAdjointEigenResult {
+struct SelfAdjointEigResult {
   // The i-th column is the normalized eigenvector corresponding to the
   // eigenvalue w[i]. Will return a matrix object if a is a matrix object.
   XlaOp v;
-  // TODO(kuny): Sort the eigenvalues.
   // The eigenvalues in ascending order, each repeated according to its
   // multiplicity.
   XlaOp w;
 };
 
-SelfAdjointEigenResult SelfAdjointEigen(XlaOp a, bool lower = true,
+SelfAdjointEigResult SelfAdjointEig(XlaOp a, bool lower = true,
-                                        int64 max_iter = 100,
+                                    int64 max_iter = 100, float epsilon = 1e-6);
-                                        float epsilon = 1e-6);
 
 }  // namespace xla
 
-#endif  // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIGEN_H_
+#endif  // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_SELF_ADJOINT_EIG_H_

tensorflow/compiler/xla/client/lib/self_adjoint_eig_test.cc

@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
-#include "tensorflow/compiler/xla/client/lib/self_adjoint_eigen.h"
+#include "tensorflow/compiler/xla/client/lib/self_adjoint_eig.h"
 
 #include "tensorflow/compiler/xla/array2d.h"
 #include "tensorflow/compiler/xla/array3d.h"
@@ -32,7 +32,7 @@ limitations under the License.
 
 namespace xla {
 
-class SelfAdjointEigenTest : public ClientLibraryTestBase {
+class SelfAdjointEigTest : public ClientLibraryTestBase {
  protected:
   void SetUp() override {
     ClientLibraryTestBase::SetUp();
@@ -71,7 +71,7 @@ class SelfAdjointEigenTest : public ClientLibraryTestBase {
   }
   void TearDown() override { ClientLibraryTestBase::TearDown(); }
 
-  Array3D<float> get_unit_matrix_3d(const Array3D<float>& matrix) {
+  Array3D<float> GetUnitMatrix3D(const Array3D<float>& matrix) {
     Array3D<float> result(matrix.n1(), matrix.n2(), matrix.n3(), 0.0);
     for (int i = 0; i < matrix.n1(); ++i) {
       for (int j = 0; j < matrix.n2(); ++j) {
@@ -100,7 +100,7 @@ class SelfAdjointEigenTest : public ClientLibraryTestBase {
     return result;
   }
 
-  XlaOp ComputeMatmulVWVt(SelfAdjointEigenResult result, XlaBuilder* builder) {
+  XlaOp ComputeMatmulVWVt(SelfAdjointEigResult result, XlaBuilder* builder) {
     Shape shape = builder->GetShape(result.v).ValueOrDie();
     std::vector<int64> out_dims = shape.dimensions();
     std::vector<int64> broadcast_dims(shape.rank() - 1);
@@ -140,69 +140,69 @@ class SelfAdjointEigenTest : public ClientLibraryTestBase {
   Array2D<int> wrong_type_4x4_;
 };
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_VWVt_EQ_A_2x4x4) {
+XLA_TEST_F(SelfAdjointEigTest, Test_VWVt_EQ_A_2x4x4) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR3Parameter<float>(batch_3d_4x4_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   ComputeMatmulVWVt(result, &builder);
 
   ComputeAndCompareR3<float>(&builder, batch_3d_4x4_, {a_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_VWVt_EQ_A_Lower_2x4x4) {
+XLA_TEST_F(SelfAdjointEigTest, Test_VWVt_EQ_A_Lower_2x4x4) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR3Parameter<float>(
       ExtractTriangularMatrix(batch_3d_4x4_, true), 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   ComputeMatmulVWVt(result, &builder);
 
   ComputeAndCompareR3<float>(&builder, batch_3d_4x4_, {a_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_VWVt_EQ_A_Upper_2x4x4) {
+XLA_TEST_F(SelfAdjointEigTest, Test_VWVt_EQ_A_Upper_2x4x4) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR3Parameter<float>(
       ExtractTriangularMatrix(batch_3d_4x4_, false), 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a, false);
+  auto result = SelfAdjointEig(a, false);
   ComputeMatmulVWVt(result, &builder);
 
   ComputeAndCompareR3<float>(&builder, batch_3d_4x4_, {a_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_Orthogonality_2x4x4) {
+XLA_TEST_F(SelfAdjointEigTest, Test_Orthogonality_2x4x4) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR3Parameter<float>(batch_3d_4x4_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   BatchDot(result.v, TransposeInMinorDims(result.v), PrecisionConfig::HIGHEST);
 
-  ComputeAndCompareR3<float>(&builder, get_unit_matrix_3d(batch_3d_4x4_),
+  ComputeAndCompareR3<float>(&builder, GetUnitMatrix3D(batch_3d_4x4_),
                              {a_data.get()}, ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_VtWV_EQ_A_Rank_Deficient_4x4) {
+XLA_TEST_F(SelfAdjointEigTest, Test_VtWV_EQ_A_Rank_Deficient_4x4) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(low_rank_4x4_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   ComputeMatmulVWVt(result, &builder);
 
   ComputeAndCompareR2<float>(&builder, low_rank_4x4_, {a_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_Eigen_8x8) {
+XLA_TEST_F(SelfAdjointEigTest, Test_Eigen_8x8) {
   XlaBuilder builder(TestName());
 
   // This is computed by numpy.linalg.eigh with float32.
@@ -211,21 +211,21 @@ XLA_TEST_F(SelfAdjointEigenTest, Test_Eigen_8x8) {
 
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(matrix2d_8x8_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
-  Sort(result.w);
+  Add(result.w, ZerosLike(result.w));
 
   ComputeAndCompareR1<float>(&builder, expected, {a_data.get()},
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Test_Orthogonality_8x8) {
+XLA_TEST_F(SelfAdjointEigTest, Test_Orthogonality_8x8) {
   XlaBuilder builder(TestName());
 
   float expected_vals = 1e-3;
 
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(matrix2d_8x8_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   // np.sum(norm(eye(n) - matmul(conj(T(v)), v)) / n**2
   GetAverageAbsoluteError(IdentityMatrix(&builder, F32, 8, 8),
                           BatchDot(TransposeInMinorDims(result.v), result.v),
@@ -235,66 +235,79 @@ XLA_TEST_F(SelfAdjointEigenTest, Test_Orthogonality_8x8) {
                              ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Wrong_Type_Int) {
+XLA_TEST_F(SelfAdjointEigTest, Wrong_Type_Int) {
   XlaBuilder builder(TestName());
 
   XlaOp a;
   auto a_data = CreateR2Parameter<int>(wrong_type_4x4_, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   EXPECT_FALSE(result.v.valid());
   EXPECT_FALSE(result.w.valid());
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Various_Size_Random_Matrix_8x8) {
+XLA_TEST_F(SelfAdjointEigTest, Various_Size_Random_Matrix_8x8) {
   XlaBuilder builder(TestName());
   int size = 8;
   Array2D<float> a_val = GenerateRandomSymmetricMatrix(size);
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(a_val, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   GetAverageAbsoluteError(ComputeMatmulVWVt(result, &builder), a, &builder);
 
   ComputeAndCompareR0<float>(&builder, 1e-3, {a_data.get()},
-                             ErrorSpec(1e-2, 1e-2));
+                             ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Various_Size_Random_Matrix_16x16) {
+XLA_TEST_F(SelfAdjointEigTest, Various_Size_Random_Matrix_16x16) {
   XlaBuilder builder(TestName());
   int size = 16;
   Array2D<float> a_val = GenerateRandomSymmetricMatrix(size);
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(a_val, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   GetAverageAbsoluteError(ComputeMatmulVWVt(result, &builder), a, &builder);
 
   ComputeAndCompareR0<float>(&builder, 1e-3, {a_data.get()},
-                             ErrorSpec(1e-2, 1e-2));
+                             ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Various_Size_Random_Matrix_32x32) {
+XLA_TEST_F(SelfAdjointEigTest, Various_Size_Random_Matrix_32x32) {
   XlaBuilder builder(TestName());
   int size = 32;
   Array2D<float> a_val = GenerateRandomSymmetricMatrix(size);
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(a_val, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   GetAverageAbsoluteError(ComputeMatmulVWVt(result, &builder), a, &builder);
 
   ComputeAndCompareR0<float>(&builder, 1e-3, {a_data.get()},
-                             ErrorSpec(1e-2, 1e-2));
+                             ErrorSpec(1e-3, 1e-3));
 }
 
-XLA_TEST_F(SelfAdjointEigenTest, Various_Size_Random_Matrix_64x64) {
+XLA_TEST_F(SelfAdjointEigTest, Various_Size_Random_Matrix_256x256) {
   XlaBuilder builder(TestName());
-  int size = 64;
+  int size = 256;
   Array2D<float> a_val = GenerateRandomSymmetricMatrix(size);
   XlaOp a;
   auto a_data = CreateR2Parameter<float>(a_val, 0, "a", &builder, &a);
-  auto result = SelfAdjointEigen(a);
+  auto result = SelfAdjointEig(a);
   GetAverageAbsoluteError(ComputeMatmulVWVt(result, &builder), a, &builder);
 
   ComputeAndCompareR0<float>(&builder, 1e-3, {a_data.get()},
-                             ErrorSpec(1e-2, 1e-2));
+                             ErrorSpec(1e-3, 1e-3));
+}
+
+XLA_TEST_F(SelfAdjointEigTest, Various_Size_Random_Matrix_512x512) {
+  XlaBuilder builder(TestName());
+  int size = 512;
+  Array2D<float> a_val = GenerateRandomSymmetricMatrix(size);
+  XlaOp a;
+  auto a_data = CreateR2Parameter<float>(a_val, 0, "a", &builder, &a);
+  auto result = SelfAdjointEig(a);
+  GetAverageAbsoluteError(ComputeMatmulVWVt(result, &builder), a, &builder);
+
+  ComputeAndCompareR0<float>(&builder, 1e-3, {a_data.get()},
+                             ErrorSpec(1e-3, 1e-3));
 }
 
 }  // namespace xla

tensorflow/compiler/xla/client/lib/sorting.cc

@@ -36,7 +36,8 @@ XlaOp TopK(XlaOp input, int64 k) {
     XlaOp sort_result =
         Sort({Neg(input), iota_s32},
              CreateScalarLtComputation({input_shape.element_type(), S32},
-                                       iota_s32.builder()));
+                                       iota_s32.builder()),
+             last_dim, /*is_stable=*/true);
     std::vector<int64> start_indices(input_shape.dimensions_size(), 0);
     std::vector<int64> limit_indices(input_dims.begin(), input_dims.end());
     limit_indices[last_dim] = k;

tensorflow/compiler/xla/client/lib/sorting_test.cc

@@ -81,9 +81,7 @@ XLA_TEST_F(SortingTest, TopKFullSort) {
   ComputeAndCompareR1<float>(&builder, inputs, {});
 }
 
-// TODO(b/122298745): Enable this test when the GPU backend supports stable
+XLA_TEST_F(SortingTest, TopKFullSortWithDuplicates) {
-// sorting.
-XLA_TEST_F(SortingTest, DISABLED_ON_GPU(TopKFullSortWithDuplicates)) {
   XlaBuilder builder(TestName());
   XlaOp a;
   auto a_data = CreateR1Parameter<int>({1, 1, 2, 2, 1}, 0, "a", &builder, &a);

tensorflow/compiler/xla/client/xla_builder.cc

@@ -1663,14 +1663,16 @@ XlaOp XlaBuilder::Sort(const XlaOp& keys, absl::Span<const XlaOp> values,
     Lt(first_lhs_param, first_rhs_param);
 
     TF_ASSIGN_OR_RETURN(auto comparator, b->Build());
-    return Sort(operands, comparator, dimension);
+    return Sort(operands, comparator, dimension, /*is_stable=*/false);
   });
 }
 
 XlaOp XlaBuilder::Sort(absl::Span<const XlaOp> operands,
-                       const XlaComputation& comparator, int64 dimension) {
+                       const XlaComputation& comparator, int64 dimension,
+                       bool is_stable) {
   return ReportErrorOrReturn([&]() -> StatusOr<XlaOp> {
     HloInstructionProto instr;
+    instr.set_is_stable(is_stable);
     std::vector<const Shape*> operand_shape_ptrs;
     TF_ASSIGN_OR_RETURN(std::vector<Shape> operand_shapes,
                         GetOperandShapes(operands));
@@ -3320,8 +3322,9 @@ XlaOp Sort(const XlaOp& keys, absl::Span<const XlaOp> values, int64 dimension) {
 }
 
 XlaOp Sort(absl::Span<const XlaOp> operands, const XlaComputation& comparator,
-           int64 dimension) {
+           int64 dimension, bool is_stable) {
-  return operands[0].builder()->Sort(operands, comparator, dimension);
+  return operands[0].builder()->Sort(operands, comparator, dimension,
+                                     is_stable);
 }
 
 XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max) {

tensorflow/compiler/xla/client/xla_builder.h

@@ -505,7 +505,7 @@ class XlaBuilder {
   XlaOp Sort(const XlaOp& keys, absl::Span<const XlaOp> values = {},
              int64 dimension = -1);
   XlaOp Sort(absl::Span<const XlaOp> operands, const XlaComputation& comparator,
-             int64 dimension = -1);
+             int64 dimension = -1, bool is_stable = false);
 
   XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max);
 
@@ -923,7 +923,8 @@ class XlaBuilder {
   friend XlaOp Sort(const XlaOp& keys, absl::Span<const XlaOp> values,
                     int64 dimension);
   friend XlaOp Sort(absl::Span<const XlaOp> operands,
-                    const XlaComputation& comparator, int64 dimension);
+                    const XlaComputation& comparator, int64 dimension,
+                    bool is_stable);
   friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max);
   friend XlaOp Map(XlaBuilder* builder, absl::Span<const XlaOp> operands,
                    const XlaComputation& computation,
@@ -1695,7 +1696,8 @@ XlaOp Sort(const XlaOp& keys, absl::Span<const XlaOp> values = {},
            int64 dimension = -1);
 
 // Enqueues a sort instruction onto the computation, using 'comparator' for
-// comparisons. 'comparator' needs to define a strict weak order.
+// comparisons. 'comparator' needs to define a strict weak order. 'is_stable'
+// determines whether the stable sorting should be used.
 // If only one operand is provided:
 // * If the operand is a rank-1 tensor (an array), the result is a sorted array.
 //   The resulting sorting order has the property that for all index positions
@@ -1718,7 +1720,7 @@ XlaOp Sort(const XlaOp& keys, absl::Span<const XlaOp> values = {},
 //   correspond to the value of operand i at two index positions.
 // Default comparator computations can be found in lib/comparators.h
 XlaOp Sort(absl::Span<const XlaOp> operands, const XlaComputation& comparator,
-           int64 dimension = -1);
+           int64 dimension = -1, bool is_stable = false);
 
 // Enqueues a clamp instruction onto the computation.
 XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max);

tensorflow/compiler/xla/executable_run_options.cc

@@ -77,7 +77,7 @@ ExecutionProfile* ExecutableRunOptions::execution_profile() const {
 }
 
 ExecutableRunOptions& ExecutableRunOptions::set_device_assignment(
-    DeviceAssignment* device_assignment) {
+    const DeviceAssignment* device_assignment) {
   device_assignment_ = device_assignment;
   return *this;
 }

tensorflow/compiler/xla/executable_run_options.h

@@ -74,7 +74,7 @@ class ExecutableRunOptions {
   ExecutableRunOptions& set_execution_profile(ExecutionProfile* profile);
 
   ExecutableRunOptions& set_device_assignment(
-      DeviceAssignment* device_assignment);
+      const DeviceAssignment* device_assignment);
   const DeviceAssignment* device_assignment() const;
 
   ExecutableRunOptions& set_rng_seed(int rng_seed);
@@ -83,7 +83,7 @@ class ExecutableRunOptions {
  private:
   DeviceMemoryAllocator* allocator_ = nullptr;
   int device_ordinal_ = -1;
-  DeviceAssignment* device_assignment_ = nullptr;
+  const DeviceAssignment* device_assignment_ = nullptr;
   stream_executor::Stream* stream_ = nullptr;
   const Eigen::ThreadPoolDevice* intra_op_thread_pool_ = nullptr;
   ExecutionProfile* execution_profile_ = nullptr;

tensorflow/compiler/xla/python/BUILD

@@ -77,6 +77,7 @@ cc_library(
         "//tensorflow/compiler/xla/client/lib:cholesky",
         "//tensorflow/compiler/xla/client/lib:math",
         "//tensorflow/compiler/xla/client/lib:qr",
+        "//tensorflow/compiler/xla/service:computation_placer",
         "//tensorflow/compiler/xla/service:platform_util",
         "//tensorflow/compiler/xla/service:shaped_buffer",
         "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry",

tensorflow/compiler/xla/python/local_computation_builder.cc

@@ -32,6 +32,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/executable_run_options.h"
 #include "tensorflow/compiler/xla/literal.h"
 #include "tensorflow/compiler/xla/literal_util.h"
+#include "tensorflow/compiler/xla/service/computation_placer.h"
 #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h"
 #include "tensorflow/compiler/xla/service/platform_util.h"
 #include "tensorflow/compiler/xla/shape_util.h"
@@ -53,74 +54,6 @@ namespace swig {
 // TODO(b/118641336): Factor out XRT parts into a small c++ library of their
 // own.
 
-// TODO(b/34473877) Ideally XLA would support AllReduce among arbitrary sets of
-// device handles instead of needing to set the number of replicas at XLA
-// service initialization time.
-tensorflow::mutex g_local_client_mutex(tensorflow::LINKER_INITIALIZED);
-int g_replica_count GUARDED_BY(g_local_client_mutex) = 1;
-LocalClient* g_local_client GUARDED_BY(g_local_client_mutex) = nullptr;
-
-string* GetPlatformNameString() {
-  static string* platform_name_string PT_GUARDED_BY(g_local_client_mutex) =
-      new string("Host");
-  return platform_name_string;
-}
-
-Status InitializeReplicaCount(int replica_count) {
-  if (replica_count < 1) {
-    return InvalidArgument("Replica count must be >= 1; got %d.",
-                           replica_count);
-  }
-  tensorflow::mutex_lock lock(g_local_client_mutex);
-  if (g_local_client != nullptr) {
-    return FailedPrecondition(
-        "Attempted to set the replica count to %d, but a local XLA service was "
-        "previously created with a replica count of %d.",
-        replica_count, g_replica_count);
-  }
-  g_replica_count = replica_count;
-  return Status::OK();
-}
-
-Status InitializePlatformName(const string& platform_name) {
-  string* g_platform_name = GetPlatformNameString();
-  tensorflow::mutex_lock lock(g_local_client_mutex);
-  if (g_local_client != nullptr) {
-    return FailedPrecondition(
-        "Attempted to set the platform name to %s, but a local XLA service was "
-        "previously created with a platform name of %s.",
-        platform_name, *g_platform_name);
-  }
-  TF_ASSIGN_OR_RETURN(se::Platform * platform,
-                      PlatformUtil::GetPlatform(platform_name));
-  if (platform->VisibleDeviceCount() <= 0) {
-    return InvalidArgument("Platform %s has no visible devices.",
-                           platform_name);
-  }
-  *g_platform_name = platform_name;
-  return Status::OK();
-}
-
-int GetReplicaCount() {
-  tensorflow::mutex_lock lock(g_local_client_mutex);
-  return g_replica_count;
-}
-
-StatusOr<LocalClient*> GetOrCreateLocalClient() {
-  string* platform_name = GetPlatformNameString();
-  tensorflow::mutex_lock lock(g_local_client_mutex);
-  if (g_local_client != nullptr) {
-    return g_local_client;
-  }
-  LocalClientOptions options;
-  options.set_platform(PlatformUtil::GetPlatform(*platform_name).ValueOrDie());
-  options.set_number_of_replicas(g_replica_count);
-  TF_ASSIGN_OR_RETURN(g_local_client,
-                      ClientLibrary::GetOrCreateLocalClient(options));
-  CHECK(g_local_client != nullptr);
-  return g_local_client;
-}
-
 Status RegisterCpuCustomCallTarget(const string& fn_name, PyObject* capsule) {
   const char* name = "xla._CPU_CUSTOM_CALL_TARGET";
   if (!PyCapsule_IsValid(capsule, name)) {
@@ -135,62 +68,66 @@ Status RegisterCpuCustomCallTarget(const string& fn_name, PyObject* capsule) {
   return Status::OK();
 }
 
-Status TransferToInfeedLocal(const Literal& literal) {
+LocalClient::LocalClient(xla::LocalClient* client) : client_(client) {}
-  VLOG(1) << "Infeeding literal without replica number; shape: "
+
-          << literal.shape();
+/* static */ StatusOr<LocalClient> LocalClient::Get(
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+    const string& platform_name) {
-  return client->TransferToInfeedLocal(literal, /*device_ordinal=*/0);
+  TF_ASSIGN_OR_RETURN(se::Platform * platform,
+                      PlatformUtil::GetPlatform(platform_name));
+  if (platform->VisibleDeviceCount() <= 0) {
+    return InvalidArgument("Platform %s has no visible devices.",
+                           platform_name);
+  }
+  LocalClientOptions options;
+  options.set_platform(platform);
+  TF_ASSIGN_OR_RETURN(xla::LocalClient * client,
+                      ClientLibrary::GetOrCreateLocalClient(options));
+  CHECK(client != nullptr);
+  return LocalClient(client);
 }
 
-Status TransferToInfeedLocalReplica(const Literal& literal,
+// Returns the number of devices known to the XLA client.
-                                    int replica_number) {
+int LocalClient::DeviceCount() const { return client_->device_count(); }
-  VLOG(1) << "Infeeding shape " << literal.shape()
+
-          << " to replica number: " << replica_number;
+Status LocalClient::TransferToInfeed(const Literal& literal,
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+                                     int device_ordinal) {
-  TF_ASSIGN_OR_RETURN(int device_ordinal,
+  VLOG(1) << "Infeeding literal to device " << device_ordinal
-                      client->ReplicaNumberToDeviceOrdinal(replica_number));
+          << "; shape: " << literal.shape();
-  return client->TransferToInfeedLocal(literal, device_ordinal);
+  return client_->TransferToInfeed(literal, device_ordinal);
 }
 
-StatusOr<Literal> TransferFromOutfeedLocalReplica(const Shape& shape,
+StatusOr<Literal> LocalClient::TransferFromOutfeed(const Shape& shape,
-                                                  int replica_number) {
+                                                   int device_ordinal) {
-  VLOG(1) << "Outfeeding literal from replica number: " << replica_number
+  VLOG(1) << "Outfeeding literal from device " << device_ordinal
-          << " shape: " << shape;
+          << "; shape: " << shape;
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+  return client_->TransferFromOutfeed(&shape, device_ordinal);
-  TF_ASSIGN_OR_RETURN(int device_ordinal,
-                      client->ReplicaNumberToDeviceOrdinal(replica_number));
-  return client->TransferFromOutfeedLocal(shape, device_ordinal);
-}
-
-static StatusOr<ScopedShapedBuffer> ToBuffer(LocalClient* client,
-                                             int device_ordinal,
-                                             const Literal& arg) {
-  return client->LiteralToShapedBuffer(arg, device_ordinal,
-                                       client->backend().memory_allocator());
 }
 
 /* static */
 StatusOr<LocalShapedBuffer*> LocalShapedBuffer::FromLiteral(
     const Literal& argument, const absl::optional<Shape>& shape_with_layout,
-    int replica_number) {
+    const LocalClient& client, int device_ordinal) {
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+  VLOG(1) << "Creating shaped buffer from literal on device ordinal: "
-  TF_ASSIGN_OR_RETURN(int device_ordinal,
+          << device_ordinal;
-                      client->ReplicaNumberToDeviceOrdinal(replica_number));
+  auto literal_to_buffer = [&](const Literal& arg) {
-  VLOG(1) << "Creating shaped buffer from literal on replica/ordinal: "
+    return client.client()->LiteralToShapedBuffer(
-          << replica_number << "/" << device_ordinal;
+        arg, device_ordinal, client.client()->backend().memory_allocator());
+  };
+
   StatusOr<ScopedShapedBuffer> buf = [&] {
     if (shape_with_layout) {
       Literal relaid = argument.Relayout(shape_with_layout.value());
-      return ToBuffer(client, device_ordinal, relaid);
+      return literal_to_buffer(relaid);
     }
-    return ToBuffer(client, device_ordinal, argument);
+    return literal_to_buffer(argument);
   }();
   TF_RETURN_IF_ERROR(buf.status());
-  return new LocalShapedBuffer(std::move(buf).ValueOrDie());
+  return new LocalShapedBuffer(std::move(buf).ValueOrDie(), client.client());
 }
 
-LocalShapedBuffer::LocalShapedBuffer(ScopedShapedBuffer shaped_buffer)
+LocalShapedBuffer::LocalShapedBuffer(ScopedShapedBuffer shaped_buffer,
-    : shaped_buffer_(std::move(shaped_buffer)) {}
+                                     xla::LocalClient* client)
+    : shaped_buffer_(std::move(shaped_buffer)), client_(client) {}
 
 const ScopedShapedBuffer* LocalShapedBuffer::shaped_buffer() const {
   return &shaped_buffer_;
@@ -203,8 +140,7 @@ const Shape& LocalShapedBuffer::shape() const {
 }
 
 StatusOr<Literal> LocalShapedBuffer::ToLiteral() const {
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+  return client_->ShapedBufferToLiteral(*shaped_buffer());
-  return client->ShapedBufferToLiteral(*shaped_buffer());
 }
 
 LocalShapedBufferTuple::LocalShapedBufferTuple(
@@ -235,6 +171,51 @@ StatusOr<LocalShapedBuffer*> LocalShapedBufferTuple::Release(int i) {
 
 int64 LocalShapedBufferTuple::size() const { return elements_.size(); }
 
+StatusOr<LocalShapedBufferTuple*> LocalShapedBuffer::DestructureTuple() {
+  const Shape tuple_shape = shape();
+
+  if (!tuple_shape.IsTuple()) {
+    return InvalidArgument(
+        "Attemped to destructure a LocalShapedBuffer that did not have a tuple "
+        "shape; shape: %s",
+        ShapeUtil::HumanString(tuple_shape));
+  }
+
+  DeviceMemoryAllocator* allocator = shaped_buffer()->memory_allocator();
+  ShapedBuffer tuple_buffer = Release();
+
+  // Extract some metadata we use to construct scoped buffers.
+  const se::Platform* platform = tuple_buffer.platform();
+  int device_ordinal = tuple_buffer.device_ordinal();
+
+  ShapeTree<se::DeviceMemoryBase>& shape_tree = tuple_buffer.buffers();
+  std::vector<LocalShapedBuffer*> results;
+  for (int64 i = 0; i < ShapeUtil::TupleElementCount(tuple_shape); ++i) {
+    // Create a shaped buffer for this destructured tuple element.
+    const Shape& subshape = ShapeUtil::GetSubshape(tuple_shape, {i});
+    VLOG(3) << "Starting tuple element " << i << " subshape: " << subshape;
+    ShapedBuffer shaped_buffer(subshape, subshape, platform, device_ordinal);
+
+    ShapeUtil::ForEachSubshape(
+        subshape, [&](const Shape& s, const ShapeIndex& index) {
+          ShapeIndex original(index);
+          original.push_front(i);
+          se::DeviceMemoryBase* device_memory =
+              shape_tree.mutable_element(original);
+          shaped_buffer.set_buffer(*device_memory, index);
+          *device_memory = se::DeviceMemoryBase();
+        });
+
+    VLOG(3) << "Completed tuple element: " << i;
+    results.push_back(new LocalShapedBuffer(
+        ScopedShapedBuffer(std::move(shaped_buffer), allocator), client_));
+  }
+  // Deallocate the root buffer.
+  se::DeviceMemoryBase root_buffer = tuple_buffer.root_buffer();
+  TF_RETURN_IF_ERROR(allocator->Deallocate(device_ordinal, root_buffer));
+  return new LocalShapedBufferTuple(std::move(results));
+}
+
 XrtAllocation::XrtAllocation(int64 handle, Shape shape,
                              const string& session_target)
     : handle_(handle), shape_(shape), session_target_(session_target) {}
@@ -332,23 +313,32 @@ StatusOr<XrtAllocation*> XrtAllocationTuple::Release(int i) {
 
 int64 XrtAllocationTuple::size() const { return elements_.size(); }
 
-CompiledLocalComputation::CompiledLocalComputation(
+LocalExecutable::LocalExecutable(
-    std::unique_ptr<LocalExecutable> executable)
+    std::unique_ptr<xla::LocalExecutable> executable,
-    : executable_(std::move(executable)) {}
+    xla::DeviceAssignment device_assignment, xla::LocalClient* client)
+    : executable_(std::move(executable)),
+      device_assignment_(std::move(device_assignment)),
+      client_(client) {}
 
-StatusOr<LocalShapedBuffer*> CompiledLocalComputation::Execute(
+std::vector<int> LocalExecutable::DeviceOrdinals() const {
+  int num_replicas = device_assignment_.replica_count();
+  std::vector<int> device_ordinals;
+  device_ordinals.reserve(num_replicas);
+  for (int i = 0; i < num_replicas; ++i) {
+    device_ordinals.push_back(device_assignment_(i, 0));
+  }
+  return device_ordinals;
+}
+
+StatusOr<LocalShapedBuffer*> LocalExecutable::Execute(
     absl::Span<LocalShapedBuffer* const> argument_handles) {
   if (num_replicas() != 1) {
     return InvalidArgument(
         "Attempted to execute computation with %d replicas using Execute()",
         num_replicas());
   }
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
-  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
-                      client->backend().computation_placer()->AssignDevices(
-                          1, /*computation_count=*/1));
   StatusOr<ScopedShapedBuffer> result_buffer_status;
-  const int device_ordinal = device_assignment(0, 0);
+  const int device_ordinal = device_assignment_(0, 0);
   VLOG(3) << "Replica 0 mapped to device ordinal for execution: "
           << device_ordinal;
 
@@ -360,10 +350,10 @@ StatusOr<LocalShapedBuffer*> CompiledLocalComputation::Execute(
 
   ExecutableRunOptions options;
   options.set_device_ordinal(device_ordinal);
-  options.set_allocator(client->backend().memory_allocator());
+  options.set_allocator(client_->backend().memory_allocator());
   options.set_intra_op_thread_pool(
-      client->backend().eigen_intra_op_thread_pool_device());
+      client_->backend().eigen_intra_op_thread_pool_device());
-  options.set_device_assignment(&device_assignment);
+  options.set_device_assignment(&device_assignment_);
 
   result_buffer_status = executable_->Run(argument_buffers, options);
 
@@ -373,13 +363,13 @@ StatusOr<LocalShapedBuffer*> CompiledLocalComputation::Execute(
         "%s.",
         result_buffer_status.status().ToString());
   }
-  return new LocalShapedBuffer(std::move(result_buffer_status).ValueOrDie());
+  return new LocalShapedBuffer(std::move(result_buffer_status).ValueOrDie(),
+                               client_);
 }
 
-StatusOr<LocalShapedBufferTuple*> CompiledLocalComputation::ExecutePerReplica(
+StatusOr<LocalShapedBufferTuple*> LocalExecutable::ExecutePerReplica(
     absl::Span<const std::vector<LocalShapedBuffer*>> argument_handles) {
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
+  const int num_devices = client_->device_count();
-  const int num_devices = client->device_count();
 
   if (argument_handles.size() != num_replicas()) {
     return InvalidArgument(
@@ -394,14 +384,9 @@ StatusOr<LocalShapedBufferTuple*> CompiledLocalComputation::ExecutePerReplica(
 
   VLOG(1) << "Executing with " << num_replicas() << " replicas.";
 
-  TF_ASSIGN_OR_RETURN(DeviceAssignment device_assignment,
-                      client->backend().computation_placer()->AssignDevices(
-                          num_replicas(), /*computation_count=*/1));
-
   std::vector<StatusOr<ScopedShapedBuffer>> results(num_replicas());
-  auto execute = [this, client, &device_assignment, &argument_handles,
+  auto execute = [this, &argument_handles, &results](int replica) {
-                  &results](int replica) {
+    const int device_ordinal = device_assignment_(replica, 0);
-    const int device_ordinal = device_assignment(replica, 0);
     VLOG(3) << "Replica " << replica
             << " mapped to device ordinal for execution: " << device_ordinal;
 
@@ -413,10 +398,10 @@ StatusOr<LocalShapedBufferTuple*> CompiledLocalComputation::ExecutePerReplica(
 
     ExecutableRunOptions options;
     options.set_device_ordinal(device_ordinal);
-    options.set_allocator(client->backend().memory_allocator());
+    options.set_allocator(client_->backend().memory_allocator());
     options.set_intra_op_thread_pool(
-        client->backend().eigen_intra_op_thread_pool_device());
+        client_->backend().eigen_intra_op_thread_pool_device());
-    options.set_device_assignment(&device_assignment);
+    options.set_device_assignment(&device_assignment_);
     StatusOr<ScopedShapedBuffer> result_buffer_status =
         executable_->Run(argument_buffers, options);
 
@@ -448,26 +433,19 @@ StatusOr<LocalShapedBufferTuple*> CompiledLocalComputation::ExecutePerReplica(
           replica, statusor.status().ToString());
     }
     wrapped_results[replica] =
-        new LocalShapedBuffer(std::move(statusor).ValueOrDie());
+        new LocalShapedBuffer(std::move(statusor).ValueOrDie(), client_);
   }
 
   return new LocalShapedBufferTuple(std::move(wrapped_results));
 }
 
-static StatusOr<Shape> GetReturnValueShape(const XlaComputation& computation) {
+XrtExecutable::XrtExecutable(const ProgramShape& program_shape, int64 handle,
-  TF_ASSIGN_OR_RETURN(ProgramShape program_shape,
+                             const string& session_target)
-                      computation.GetProgramShape());
-  return std::move(*program_shape.mutable_result());
-}
-
-CompiledXrtComputation::CompiledXrtComputation(
-    const ProgramShape& program_shape, int64 handle,
-    const string& session_target)
     : program_shape_(program_shape),
       handle_(handle),
       session_target_(session_target) {}
 
-CompiledXrtComputation::~CompiledXrtComputation() {
+XrtExecutable::~XrtExecutable() {
   tensorflow::Scope root = tensorflow::Scope::NewRootScope();
   auto computation_handle =
       tensorflow::ops::Placeholder(root, tensorflow::DT_INT64);
@@ -489,7 +467,7 @@ CompiledXrtComputation::~CompiledXrtComputation() {
   }
 }
 
-StatusOr<XrtAllocation*> CompiledXrtComputation::Execute(
+StatusOr<XrtAllocation*> XrtExecutable::Execute(
     absl::Span<XrtAllocation* const> argument_handles) {
   const int num_expected_arguments = program_shape().parameters().size();
 
@@ -528,36 +506,41 @@ StatusOr<XrtAllocation*> CompiledXrtComputation::Execute(
   return new XrtAllocation(output, program_shape().result(), session_target_);
 }
 
-const ProgramShape& CompiledXrtComputation::program_shape() const {
+const ProgramShape& XrtExecutable::program_shape() const {
   return program_shape_;
 }
 
-int64 CompiledXrtComputation::handle() const { return handle_; }
+int64 XrtExecutable::handle() const { return handle_; }
 
-LocalComputation::LocalComputation(XlaComputation computation)
+Computation::Computation(XlaComputation computation)
     : computation_(std::move(computation)) {}
 
-StatusOr<CompiledLocalComputation*> LocalComputation::Compile(
+StatusOr<LocalExecutable*> Computation::Compile(
     const std::vector<Shape>& argument_shapes,
-    const ExecutableBuildOptions* build_options) {
+    const ExecutableBuildOptions* build_options, const LocalClient& client) {
   std::vector<const Shape*> argument_shape_pointers;
   argument_shape_pointers.reserve(argument_shapes.size());
   for (auto& argument_shape : argument_shapes) {
     argument_shape_pointers.push_back(&argument_shape);
   }
 
-  TF_ASSIGN_OR_RETURN(LocalClient * client, GetOrCreateLocalClient());
   ExecutableBuildOptions options;
   if (build_options != nullptr) {
     options = *build_options;
   }
   TF_ASSIGN_OR_RETURN(
       auto local_executable,
-      client->Compile(computation_, argument_shape_pointers, options));
+      client.client()->Compile(computation_, argument_shape_pointers, options));
-  return new CompiledLocalComputation(std::move(local_executable));
+  TF_ASSIGN_OR_RETURN(
+      DeviceAssignment device_assignment,
+      client.client()->backend().computation_placer()->AssignDevices(
+          options.num_replicas(), /*computation_count=*/1));
+
+  return new LocalExecutable(std::move(local_executable),
+                             std::move(device_assignment), client.client());
 }
 
-StatusOr<CompiledXrtComputation*> LocalComputation::CompileForXrt(
+StatusOr<XrtExecutable*> Computation::CompileForXrt(
     const std::vector<Shape>& argument_shapes, const string& session_target) {
   tensorflow::Scope root = tensorflow::Scope::NewRootScope();
   auto program = tensorflow::ops::Placeholder(root, tensorflow::DT_STRING);
@@ -585,14 +568,12 @@ StatusOr<CompiledXrtComputation*> LocalComputation::CompileForXrt(
   TF_ASSIGN_OR_RETURN(ProgramShape program_shape,
                       computation().GetProgramShape());
   int64 handle = outputs[0].scalar<int64>()();
-  return new CompiledXrtComputation(program_shape, handle, session_target);
+  return new XrtExecutable(program_shape, handle, session_target);
 }
 
-const XlaComputation& LocalComputation::computation() const {
+const XlaComputation& Computation::computation() const { return computation_; }
-  return computation_;
-}
 
-string LocalComputation::GetSerializedProto() const {
+string Computation::GetSerializedProto() const {
   string result;
   if (!computation_.proto().SerializeToString(&result)) {
     LOG(ERROR) << "Failed to serialize the HloModuleProto.";
@@ -601,101 +582,103 @@ string LocalComputation::GetSerializedProto() const {
   return result;
 }
 
-StatusOr<Shape> LocalComputation::GetReturnValueShape() const {
+StatusOr<ProgramShape> Computation::GetProgramShape() const {
-  return swig::GetReturnValueShape(computation_);
+  return computation_.GetProgramShape();
+}
+
+StatusOr<Shape> Computation::GetReturnValueShape() const {
+  TF_ASSIGN_OR_RETURN(ProgramShape shape, computation_.GetProgramShape());
+  return std::move(*shape.mutable_result());
 }
 
 LocalOp::LocalOp(const XlaOp& op) : op_(op) {}
 
 const XlaOp& LocalOp::op() const { return op_; }
 
-LocalComputationBuilder::LocalComputationBuilder(const string& computation_name)
+ComputationBuilder::ComputationBuilder(const string& computation_name)
     : builder_(computation_name) {}
 
-void LocalComputationBuilder::SetOpMetadata(const OpMetadata& metadata) {
+void ComputationBuilder::SetOpMetadata(const OpMetadata& metadata) {
   builder_.SetOpMetadata(metadata);
 }
 
-void LocalComputationBuilder::ClearOpMetadata() { builder_.ClearOpMetadata(); }
+void ComputationBuilder::ClearOpMetadata() { builder_.ClearOpMetadata(); }
 
-StatusOr<LocalComputation*> LocalComputationBuilder::Build() {
+StatusOr<Computation*> ComputationBuilder::Build() {
   TF_ASSIGN_OR_RETURN(XlaComputation computation, builder_.Build());
-  return new LocalComputation(std::move(computation));
+  return new Computation(std::move(computation));
 }
 
-LocalOp LocalComputationBuilder::Parameter(int64 parameter_number,
+LocalOp ComputationBuilder::Parameter(int64 parameter_number,
-                                           const Shape& shape,
+                                      const Shape& shape, const string& name) {
-                                           const string& name) {
   return xla::Parameter(&builder_, parameter_number, shape, name);
 }
 
-StatusOr<LocalComputation*> LocalComputationBuilder::BuildWithRoot(
+StatusOr<Computation*> ComputationBuilder::BuildWithRoot(const LocalOp& root) {
-    const LocalOp& root) {
   TF_ASSIGN_OR_RETURN(XlaComputation computation, builder_.Build(root.op()));
-  return new LocalComputation(std::move(computation));
+  return new Computation(std::move(computation));
 }
 
-StatusOr<Shape> LocalComputationBuilder::GetShape(const LocalOp& operand) {
+StatusOr<Shape> ComputationBuilder::GetShape(const LocalOp& operand) {
   return builder_.GetShape(operand.op());
 }
 
-StatusOr<Shape> LocalComputationBuilder::GetReturnValueShape() {
+StatusOr<Shape> ComputationBuilder::GetReturnValueShape() {
   TF_ASSIGN_OR_RETURN(ProgramShape program_shape, builder_.GetProgramShape());
   return program_shape.result();
 }
 
-LocalOp LocalComputationBuilder::Infeed(const Shape& shape) {
+LocalOp ComputationBuilder::Infeed(const Shape& shape) {
   return xla::Infeed(&builder_, shape);
 }
 
-void LocalComputationBuilder::Outfeed(const LocalOp& operand,
+void ComputationBuilder::Outfeed(const LocalOp& operand, const Shape& shape,
-                                      const Shape& shape,
+                                 const string& outfeed_config) {
-                                      const string& outfeed_config) {
   xla::Outfeed(operand.op(), shape, outfeed_config);
 }
 
-LocalOp LocalComputationBuilder::ConstantLiteral(const Literal& literal) {
+LocalOp ComputationBuilder::ConstantLiteral(const Literal& literal) {
   return xla::ConstantLiteral(&builder_, literal);
 }
 
-LocalOp LocalComputationBuilder::Iota(PrimitiveType element_type, int64 size) {
+LocalOp ComputationBuilder::Iota(PrimitiveType element_type, int64 size) {
   return xla::Iota(&builder_, element_type, size);
 }
 
-LocalOp LocalComputationBuilder::BroadcastedIota(const Shape& shape,
+LocalOp ComputationBuilder::BroadcastedIota(const Shape& shape,
-                                                 int64 dimension) {
+                                            int64 dimension) {
   return xla::Iota(&builder_, shape, dimension);
 }
 
-LocalOp LocalComputationBuilder::Broadcast(
+LocalOp ComputationBuilder::Broadcast(const LocalOp& operand,
-    const LocalOp& operand, absl::Span<const int64> broadcast_sizes) {
+                                      absl::Span<const int64> broadcast_sizes) {
   return xla::Broadcast(operand.op(), broadcast_sizes);
 }
 
-LocalOp LocalComputationBuilder::BroadcastInDim(
+LocalOp ComputationBuilder::BroadcastInDim(
     const LocalOp& operand, absl::Span<const int64> out_dim_sizes,
     absl::Span<const int64> broadcast_dimensions) {
   return xla::BroadcastInDim(operand.op(), out_dim_sizes, broadcast_dimensions);
 }
 
-LocalOp LocalComputationBuilder::Pad(const LocalOp& operand,
+LocalOp ComputationBuilder::Pad(const LocalOp& operand,
-                                     const LocalOp& padding_value,
+                                const LocalOp& padding_value,
-                                     const PaddingConfig& padding_config) {
+                                const PaddingConfig& padding_config) {
   return xla::Pad(operand.op(), padding_value.op(), padding_config);
 }
 
-LocalOp LocalComputationBuilder::Reshape(const LocalOp& operand,
+LocalOp ComputationBuilder::Reshape(const LocalOp& operand,
-                                         absl::Span<const int64> dimensions,
+                                    absl::Span<const int64> dimensions,
-                                         absl::Span<const int64> new_sizes) {
+                                    absl::Span<const int64> new_sizes) {
   return xla::Reshape(operand.op(), dimensions, new_sizes);
 }
 
-LocalOp LocalComputationBuilder::Collapse(const LocalOp& operand,
+LocalOp ComputationBuilder::Collapse(const LocalOp& operand,
-                                          absl::Span<const int64> dimensions) {
+                                     absl::Span<const int64> dimensions) {
   return xla::Collapse(operand.op(), dimensions);
 }
 
-LocalOp LocalComputationBuilder::AllToAll(
+LocalOp ComputationBuilder::AllToAll(
     const LocalOp& operand, int64 split_dimension, int64 concat_dimension,
     int64 split_count, absl::Span<const ReplicaGroup> replica_groups) {
   std::vector<ReplicaGroup> rg(replica_groups.size());
@@ -706,39 +689,38 @@ LocalOp LocalComputationBuilder::AllToAll(
                        split_count, rg);
 }
 
-LocalOp LocalComputationBuilder::CrossReplicaSum(
+LocalOp ComputationBuilder::CrossReplicaSum(
     const LocalOp& operand, absl::Span<const ReplicaGroup> replica_groups) {
   return xla::CrossReplicaSum(operand.op(), replica_groups);
 }
 
-LocalOp LocalComputationBuilder::Slice(const LocalOp& operand,
+LocalOp ComputationBuilder::Slice(const LocalOp& operand,
-                                       absl::Span<const int64> start_indices,
+                                  absl::Span<const int64> start_indices,
-                                       absl::Span<const int64> limit_indices,
+                                  absl::Span<const int64> limit_indices,
-                                       absl::Span<const int64> strides) {
+                                  absl::Span<const int64> strides) {
   return xla::Slice(operand.op(), start_indices, limit_indices, strides);
 }
 
-LocalOp LocalComputationBuilder::SliceInDim(const LocalOp& operand,
+LocalOp ComputationBuilder::SliceInDim(const LocalOp& operand,
-                                            int64 start_index,
+                                       int64 start_index, int64 limit_index,
-                                            int64 limit_index, int64 stride,
+                                       int64 stride, int64 dimno) {
-                                            int64 dimno) {
   return xla::SliceInDim(operand.op(), start_index, limit_index, stride, dimno);
 }
 
-LocalOp LocalComputationBuilder::DynamicSlice(
+LocalOp ComputationBuilder::DynamicSlice(const LocalOp& operand,
-    const LocalOp& operand, const LocalOp& start_indices,
+                                         const LocalOp& start_indices,
-    absl::Span<const int64> slice_sizes) {
+                                         absl::Span<const int64> slice_sizes) {
   return xla::DynamicSlice(operand.op(), start_indices.op(), slice_sizes);
 }
 
-LocalOp LocalComputationBuilder::DynamicUpdateSlice(
+LocalOp ComputationBuilder::DynamicUpdateSlice(const LocalOp& operand,
-    const LocalOp& operand, const LocalOp& update,
+                                               const LocalOp& update,
-    const LocalOp& start_indices) {
+                                               const LocalOp& start_indices) {
   return xla::DynamicUpdateSlice(operand.op(), update.op(), start_indices.op());
 }
 
-LocalOp LocalComputationBuilder::ConcatInDim(absl::Span<const LocalOp> operands,
+LocalOp ComputationBuilder::ConcatInDim(absl::Span<const LocalOp> operands,
-                                             int64 dimension) {
+                                        int64 dimension) {
   std::vector<XlaOp> xla_ops;
   xla_ops.reserve(operands.size());
   for (const auto& op : operands) {
@@ -747,18 +729,18 @@ LocalOp LocalComputationBuilder::ConcatInDim(absl::Span<const LocalOp> operands,
   return xla::ConcatInDim(&builder_, xla_ops, dimension);
 }
 
-LocalOp LocalComputationBuilder::SelectAndScatterWithGeneralPadding(
+LocalOp ComputationBuilder::SelectAndScatterWithGeneralPadding(
-    const LocalOp& operand, const LocalComputation& select,
+    const LocalOp& operand, const Computation& select,
     absl::Span<const int64> window_dimensions,
     absl::Span<const int64> window_strides,
     absl::Span<const std::pair<int64, int64>> padding, const LocalOp& source,
-    const LocalOp& init_value, const LocalComputation& scatter) {
+    const LocalOp& init_value, const Computation& scatter) {
   return xla::SelectAndScatterWithGeneralPadding(
       operand.op(), select.computation(), window_dimensions, window_strides,
       padding, source.op(), init_value.op(), scatter.computation());
 }
 
-LocalOp LocalComputationBuilder::Tuple(absl::Span<const LocalOp> elements) {
+LocalOp ComputationBuilder::Tuple(absl::Span<const LocalOp> elements) {
   std::vector<XlaOp> xla_ops;
   xla_ops.reserve(elements.size());
   for (const auto& op : elements) {
@@ -768,22 +750,22 @@ LocalOp LocalComputationBuilder::Tuple(absl::Span<const LocalOp> elements) {
   return xla::Tuple(&builder_, xla_ops);
 }
 
-LocalOp LocalComputationBuilder::GetTupleElement(const LocalOp& tuple_data,
+LocalOp ComputationBuilder::GetTupleElement(const LocalOp& tuple_data,
-                                                 int64 index) {
+                                            int64 index) {
   return xla::GetTupleElement(tuple_data.op(), index);
 }
 
-LocalOp LocalComputationBuilder::Dot(const LocalOp& lhs, const LocalOp& rhs) {
+LocalOp ComputationBuilder::Dot(const LocalOp& lhs, const LocalOp& rhs) {
   return xla::Dot(lhs.op(), rhs.op());
 }
 
-LocalOp LocalComputationBuilder::DotGeneral(
+LocalOp ComputationBuilder::DotGeneral(
     const LocalOp& lhs, const LocalOp& rhs,
     const DotDimensionNumbers& dimension_numbers) {
   return xla::DotGeneral(lhs.op(), rhs.op(), dimension_numbers);
 }
 
-LocalOp LocalComputationBuilder::ConvGeneralDilated(
+LocalOp ComputationBuilder::ConvGeneralDilated(
     const LocalOp& lhs, const LocalOp& rhs,
     absl::Span<const int64> window_strides,
     absl::Span<const std::pair<int64, int64>> padding,
@@ -795,18 +777,18 @@ LocalOp LocalComputationBuilder::ConvGeneralDilated(
                                  feature_group_count);
 }
 
-LocalOp LocalComputationBuilder::ConvertElementType(
+LocalOp ComputationBuilder::ConvertElementType(const LocalOp& operand,
-    const LocalOp& operand, PrimitiveType new_element_type) {
+                                               PrimitiveType new_element_type) {
   return xla::ConvertElementType(operand.op(), new_element_type);
 }
 
-LocalOp LocalComputationBuilder::BitcastConvertType(
+LocalOp ComputationBuilder::BitcastConvertType(const LocalOp& operand,
-    const LocalOp& operand, PrimitiveType new_element_type) {
+                                               PrimitiveType new_element_type) {
   return xla::BitcastConvertType(operand.op(), new_element_type);
 }
 
-LocalOp LocalComputationBuilder::Call(const LocalComputation& local_computation,
+LocalOp ComputationBuilder::Call(const Computation& local_computation,
-                                      absl::Span<const LocalOp> operands) {
+                                 absl::Span<const LocalOp> operands) {
   std::vector<XlaOp> xla_ops;
   xla_ops.reserve(operands.size());
   for (const auto& op : operands) {
@@ -815,7 +797,7 @@ LocalOp LocalComputationBuilder::Call(const LocalComputation& local_computation,
   return xla::Call(&builder_, local_computation.computation(), xla_ops);
 }
 
-LocalOp LocalComputationBuilder::CustomCall(
+LocalOp ComputationBuilder::CustomCall(
     const string& call_target_name, absl::Span<const LocalOp> operands,
     const Shape& shape_with_layout,
     const std::vector<Shape>& operand_shapes_with_layout,
@@ -830,19 +812,19 @@ LocalOp LocalComputationBuilder::CustomCall(
                                    operand_shapes_with_layout, opaque);
 }
 
-LocalOp LocalComputationBuilder::Transpose(
+LocalOp ComputationBuilder::Transpose(const LocalOp& operand,
-    const LocalOp& operand, absl::Span<const int64> permutation) {
+                                      absl::Span<const int64> permutation) {
   return xla::Transpose(operand.op(), permutation);
 }
 
-LocalOp LocalComputationBuilder::Rev(const LocalOp& operand,
+LocalOp ComputationBuilder::Rev(const LocalOp& operand,
-                                     absl::Span<const int64> dimensions) {
+                                absl::Span<const int64> dimensions) {
   return xla::Rev(operand.op(), dimensions);
 }
 
-LocalOp LocalComputationBuilder::Map(absl::Span<const LocalOp> operands,
+LocalOp ComputationBuilder::Map(absl::Span<const LocalOp> operands,
-                                     const LocalComputation& local_computation,
+                                const Computation& local_computation,
-                                     absl::Span<const int64> dimensions) {
+                                absl::Span<const int64> dimensions) {
   std::vector<XlaOp> xla_ops;
   xla_ops.reserve(operands.size());
   for (const auto& op : operands) {
@@ -853,17 +835,17 @@ LocalOp LocalComputationBuilder::Map(absl::Span<const LocalOp> operands,
                   dimensions);
 }
 
-LocalOp LocalComputationBuilder::Reduce(
+LocalOp ComputationBuilder::Reduce(
     const LocalOp& operand, const LocalOp& init_value,
-    const LocalComputation& local_computation,
+    const Computation& local_computation,
     absl::Span<const int64> dimensions_to_reduce) {
   return xla::Reduce(operand.op(), init_value.op(),
                      local_computation.computation(), dimensions_to_reduce);
 }
 
-LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding(
+LocalOp ComputationBuilder::ReduceWindowWithGeneralPadding(
     const LocalOp& operand, const LocalOp& init_value,
-    const LocalComputation& local_computation,
+    const Computation& local_computation,
     absl::Span<const int64> window_dimensions,
     absl::Span<const int64> window_strides,
     absl::Span<const int64> base_dilations,
@@ -875,51 +857,50 @@ LocalOp LocalComputationBuilder::ReduceWindowWithGeneralPadding(
       padding);
 }
 
-LocalOp LocalComputationBuilder::RngNormal(const LocalOp& mu,
+LocalOp ComputationBuilder::RngNormal(const LocalOp& mu, const LocalOp& sigma,
-                                           const LocalOp& sigma,
+                                      const Shape& shape) {
-                                           const Shape& shape) {
   return xla::RngNormal(mu.op(), sigma.op(), shape);
 }
 
-LocalOp LocalComputationBuilder::RngUniform(const LocalOp& a, const LocalOp& b,
+LocalOp ComputationBuilder::RngUniform(const LocalOp& a, const LocalOp& b,
-                                            const Shape& shape) {
+                                       const Shape& shape) {
   return xla::RngUniform(a.op(), b.op(), shape);
 }
 
-LocalOp LocalComputationBuilder::While(const LocalComputation& condition,
+LocalOp ComputationBuilder::While(const Computation& condition,
-                                       const LocalComputation& body,
+                                  const Computation& body,
-                                       const LocalOp& init) {
+                                  const LocalOp& init) {
   return xla::While(condition.computation(), body.computation(), init.op());
 }
 
-LocalOp LocalComputationBuilder::Conditional(
+LocalOp ComputationBuilder::Conditional(const LocalOp& predicate,
-    const LocalOp& predicate, const LocalOp& true_operand,
+                                        const LocalOp& true_operand,
-    const LocalComputation& true_computation, const LocalOp& false_operand,
+                                        const Computation& true_computation,
-    const LocalComputation& false_computation) {
+                                        const LocalOp& false_operand,
+                                        const Computation& false_computation) {
   return xla::Conditional(predicate.op(), true_operand.op(),
                           true_computation.computation(), false_operand.op(),
                           false_computation.computation());
 }
 
-StatusOr<bool> LocalComputationBuilder::IsConstant(const LocalOp& operand) {
+StatusOr<bool> ComputationBuilder::IsConstant(const LocalOp& operand) {
   return builder_.IsConstant(operand.op());
 }
 
-LocalOp LocalComputationBuilder::Sort(const LocalOp& operand, int64 dimension) {
+LocalOp ComputationBuilder::Sort(const LocalOp& operand, int64 dimension) {
   return xla::Sort(operand.op(), {}, dimension);
 }
 
-LocalOp LocalComputationBuilder::SortKeyVal(const LocalOp& keys,
+LocalOp ComputationBuilder::SortKeyVal(const LocalOp& keys,
-                                            const LocalOp& values,
+                                       const LocalOp& values, int64 dimension) {
-                                            int64 dimension) {
   return xla::Sort(keys.op(), {values.op()}, dimension);
 }
 
-LocalOp LocalComputationBuilder::Cholesky(const LocalOp& a) {
+LocalOp ComputationBuilder::Cholesky(const LocalOp& a) {
   return xla::Cholesky(a.op());
 }
 
-LocalOp LocalComputationBuilder::QR(const LocalOp& a, bool full_matrices) {
+LocalOp ComputationBuilder::QR(const LocalOp& a, bool full_matrices) {
   XlaBuilder* builder = a.op().builder();
   return builder->ReportErrorOrReturn([&]() -> StatusOr<XlaOp> {
     TF_ASSIGN_OR_RETURN(auto qr, xla::QRDecomposition(a.op(), full_matrices));
@@ -927,17 +908,16 @@ LocalOp LocalComputationBuilder::QR(const LocalOp& a, bool full_matrices) {
   });
 }
 
-LocalOp LocalComputationBuilder::TriangularSolve(const LocalOp& a,
+LocalOp ComputationBuilder::TriangularSolve(const LocalOp& a, const LocalOp& b,
-                                                 const LocalOp& b,
+                                            bool left_side, bool lower,
-                                                 bool left_side, bool lower,
+                                            bool unit_diagonal,
-                                                 bool unit_diagonal,
+                                            int transpose_a) {
-                                                 int transpose_a) {
   return xla::TriangularSolve(
       a.op(), b.op(), left_side, lower, unit_diagonal,
       xla::TriangularSolveOptions::Transpose(transpose_a));
 }
 
-LocalOp LocalComputationBuilder::Gather(
+LocalOp ComputationBuilder::Gather(
     const LocalOp& input, const LocalOp& start_indices,
     const GatherDimensionNumbers& dimension_numbers,
     absl::Span<const int64> slice_sizes) {
@@ -945,24 +925,24 @@ LocalOp LocalComputationBuilder::Gather(
                      slice_sizes);
 }
 
-LocalOp LocalComputationBuilder::Scatter(
+LocalOp ComputationBuilder::Scatter(
     const LocalOp& input, const LocalOp& scatter_indices,
-    const LocalOp& updates, const LocalComputation& update_computation,
+    const LocalOp& updates, const Computation& update_computation,
     const ScatterDimensionNumbers& dimension_numbers) {
   return xla::Scatter(input.op(), scatter_indices.op(), updates.op(),
                       update_computation.computation(), dimension_numbers);
 }
 
-StatusOr<LocalComputation*> LocalComputationBuilder::BuildConstantSubGraph(
+StatusOr<Computation*> ComputationBuilder::BuildConstantSubGraph(
     const LocalOp& operand) {
   TF_ASSIGN_OR_RETURN(XlaComputation computation,
                       builder_.BuildConstantSubGraph(operand.op()));
-  return new LocalComputation(std::move(computation));
+  return new Computation(std::move(computation));
 }
 
-#define _FORWARD(method_name, return_sig, args_sig, args)    \
+#define _FORWARD(method_name, return_sig, args_sig, args) \
-  return_sig LocalComputationBuilder::method_name args_sig { \
+  return_sig ComputationBuilder::method_name args_sig {   \
-    return xla::method_name args;                            \
+    return xla::method_name args;                         \
   }
 
 #define _FORWARD_UNOP(method_name) \
@@ -1051,64 +1031,11 @@ void DeleteLocalShapedBuffer(LocalShapedBuffer* local_shaped_buffer) {
 
 void DeleteXrtAllocation(XrtAllocation* allocation) { delete allocation; }
 
-void DeleteCompiledLocalComputation(CompiledLocalComputation* computation) {
+void DeleteLocalExecutable(LocalExecutable* computation) { delete computation; }
-  delete computation;
-}
 
-void DeleteCompiledXrtComputation(CompiledXrtComputation* computation) {
+void DeleteXrtExecutable(XrtExecutable* computation) { delete computation; }
-  delete computation;
-}
 
-void DeleteLocalComputation(LocalComputation* computation) {
+void DeleteComputation(Computation* computation) { delete computation; }
-  delete computation;
-}
-
-StatusOr<LocalShapedBufferTuple*> DestructureLocalShapedBufferTuple(
-    LocalShapedBuffer* local_shaped_buffer) {
-  const Shape tuple_shape = local_shaped_buffer->shape();
-
-  if (!tuple_shape.IsTuple()) {
-    return InvalidArgument(
-        "Attemped to destructure a LocalShapedBuffer that did not have a tuple "
-        "shape; shape: %s",
-        ShapeUtil::HumanString(tuple_shape));
-  }
-
-  DeviceMemoryAllocator* allocator =
-      local_shaped_buffer->shaped_buffer()->memory_allocator();
-  ShapedBuffer tuple_buffer = local_shaped_buffer->Release();
-
-  // Extract some metadata we use to construct scoped buffers.
-  const se::Platform* platform = tuple_buffer.platform();
-  int device_ordinal = tuple_buffer.device_ordinal();
-
-  ShapeTree<se::DeviceMemoryBase>& shape_tree = tuple_buffer.buffers();
-  std::vector<LocalShapedBuffer*> results;
-  for (int64 i = 0; i < ShapeUtil::TupleElementCount(tuple_shape); ++i) {
-    // Create a shaped buffer for this destructured tuple element.
-    const Shape& subshape = ShapeUtil::GetSubshape(tuple_shape, {i});
-    VLOG(3) << "Starting tuple element " << i << " subshape: " << subshape;
-    ShapedBuffer shaped_buffer(subshape, subshape, platform, device_ordinal);
-
-    ShapeUtil::ForEachSubshape(
-        subshape, [&](const Shape& s, const ShapeIndex& index) {
-          ShapeIndex original(index);
-          original.push_front(i);
-          se::DeviceMemoryBase* device_memory =
-              shape_tree.mutable_element(original);
-          shaped_buffer.set_buffer(*device_memory, index);
-          *device_memory = se::DeviceMemoryBase();
-        });
-
-    VLOG(3) << "Completed tuple element: " << i;
-    results.push_back(new LocalShapedBuffer(
-        ScopedShapedBuffer(std::move(shaped_buffer), allocator)));
-  }
-  // Deallocate the root buffer.
-  se::DeviceMemoryBase root_buffer = tuple_buffer.root_buffer();
-  TF_RETURN_IF_ERROR(allocator->Deallocate(device_ordinal, root_buffer));
-  return new LocalShapedBufferTuple(std::move(results));
-}
 
 StatusOr<XrtAllocationTuple*> DestructureXrtAllocationTuple(
     XrtAllocation* allocation, const string& session_target) {

tensorflow/compiler/xla/python/local_computation_builder.h

@@ -35,42 +35,42 @@ limitations under the License.
 namespace xla {
 namespace swig {
 
-// Initializes the number of replicas that XLA will be initialized with (when
-// first obtaining a handle to the local XLA service). If this is called after
-// the handle to the local XLA service has been established, then an error is
-// returned.
-Status InitializeReplicaCount(int replica_count);
-
-// Initializes the platform name that XLA will be initialized with (when
-// first obtaining a handle to the local XLA service). If this is called after
-// the handle to the local XLA service has been established, then an error is
-// returned.
-Status InitializePlatformName(const string& platform_name);
-
-// Returns the replica count that is currently set, regardless of whether the
-// local XLA service has been instantiated yet or not.
-int GetReplicaCount();
-
 // Registers a 'fn_capsule' as a CPU custom call target.
 // 'fn_capsule' is a void* pointer encapsulated in a PyCapsule object, with name
 // "xla._CPU_CUSTOM_CALL_TARGET".
 Status RegisterCpuCustomCallTarget(const string& name, PyObject* fn_capsule);
 
-// Wraps the local client's infeed-transfer function.
+// Wrapper around an xla::LocalClient.
-//
+class LocalClient {
-// The default device ordinal (0) is used.
+ public:
-Status TransferToInfeedLocal(const Literal& literal);
+  // Initializes a local XLA client for `platform_name`. Returns an error if no
+  /// such platform exists, or if the platform has no visible devices.
+  static StatusOr<LocalClient> Get(const string& platform_name);
 
-// Transfers the given literal to the infeed of the given replica.
+  // Copyable and moveable; the class is just a wrapper around a
-//
+  // xla::LocalClient pointer for convenient SWIG wrapping.
-// The replica number is resolved to an appropriate device ordinal.
-Status TransferToInfeedLocalReplica(const Literal& literal, int replica_number);
 
-// Transfers a literal of the given shape from the outfeed of the given replica.
+  // Returns the number of devices known to the XLA client.
-//
+  int DeviceCount() const;
-// The replica number is resolved to an appropriate device ordinal.
+
-StatusOr<Literal> TransferFromOutfeedLocalReplica(const Shape& shape,
+  // Wraps the local client's infeed-transfer function.
-                                                  int replica_number);
+  //
+  // The default device ordinal (0) is used.
+  Status TransferToInfeed(const Literal& literal, int device_ordinal);
+
+  // Transfers a literal of the given shape from the outfeed of the given
+  // replica.
+  StatusOr<Literal> TransferFromOutfeed(const Shape& shape, int device_ordinal);
+
+  xla::LocalClient* client() const { return client_; }
+
+ private:
+  LocalClient(xla::LocalClient* client);
+
+  xla::LocalClient* client_;
+};
+
+class LocalShapedBufferTuple;
 
 // Represents a reference to literals that live in a device-allocated buffer via
 // XLA. Specifically, wraps a ScopedShapedBuffer produced by transferring a
@@ -79,9 +79,9 @@ class LocalShapedBuffer {
  public:
   static StatusOr<LocalShapedBuffer*> FromLiteral(
       const Literal& argument, const absl::optional<Shape>& shape_with_layout,
-      int replica_number);
+      const LocalClient& client, int device_ordinal);
 
-  LocalShapedBuffer(ScopedShapedBuffer shaped_buffer);
+  LocalShapedBuffer(ScopedShapedBuffer shaped_buffer, xla::LocalClient* client);
   StatusOr<Literal> ToLiteral() const;
   const Shape& shape() const;
   const ScopedShapedBuffer* shaped_buffer() const;
@@ -90,8 +90,13 @@ class LocalShapedBuffer {
   // analogous to std::unique_ptr::release().
   ShapedBuffer Release();
 
+  // Destructures a tuple-valued LocalShapedBuffer into its constitutent
+  // elements in LocalShapedBufferTuple form.
+  StatusOr<LocalShapedBufferTuple*> DestructureTuple();
+
  private:
   ScopedShapedBuffer shaped_buffer_;
+  xla::LocalClient* client_;
 };
 
 // Result of a tuple destructuring operation on a LocalShapedBuffer -- this
@@ -117,11 +122,6 @@ class LocalShapedBufferTuple {
   std::vector<LocalShapedBuffer*> elements_;
 };
 
-// Destructures a tuple-valued LocalShapedBuffer into its constitutent elements
-// in LocalShapedBufferTuple form.
-StatusOr<LocalShapedBufferTuple*> DestructureLocalShapedBufferTuple(
-    LocalShapedBuffer* local_shaped_buffer);
-
 // Represents a reference to literals that live in a device-allocated buffer via
 // XRT. Specifically, wraps an int64 handle produced by running the allocation
 // graph, and an XLA shape to track the referent's shape.
@@ -176,14 +176,19 @@ StatusOr<XrtAllocationTuple*> DestructureXrtAllocationTuple(
 
 // Represents a compiled computation that can be executed given handles to
 // device-allocated literals. Specifically, wraps an XLA LocalExecutable.
-class CompiledLocalComputation {
+class LocalExecutable {
  public:
-  CompiledLocalComputation(std::unique_ptr<LocalExecutable> executable);
+  LocalExecutable(std::unique_ptr<xla::LocalExecutable> executable,
+                  xla::DeviceAssignment device_assignment,
+                  xla::LocalClient* client);
 
   int num_replicas() const {
     return executable_->build_options().num_replicas();
   }
 
+  // Returns the device ordinals to which each replica is assigned.
+  std::vector<int> DeviceOrdinals() const;
+
   StatusOr<LocalShapedBuffer*> Execute(
       absl::Span<LocalShapedBuffer* const> argument_handles);
 
@@ -194,18 +199,22 @@ class CompiledLocalComputation {
       absl::Span<const std::vector<LocalShapedBuffer*> > argument_handles);
 
  private:
-  std::unique_ptr<LocalExecutable> executable_;
+  const std::unique_ptr<xla::LocalExecutable> executable_;
+  const xla::DeviceAssignment device_assignment_;
+  xla::LocalClient* const client_;
 };
 
 // Represents a compiled computation that can be executed given handles to
 // device-allocated literals. Specifically, wraps an XRT computation handle.
-class CompiledXrtComputation {
+class XrtExecutable {
  public:
   // Accepts a `session_target` argument, used in constructing the
   // `tensorflow::ClientSession` instance in which the execution graph is run.
-  CompiledXrtComputation(const ProgramShape& program_shape, int64 handle,
+  XrtExecutable(const ProgramShape& program_shape, int64 handle,
-                         const string& session_target);
+                const string& session_target);
-  ~CompiledXrtComputation();
+  ~XrtExecutable();
+
+  std::vector<int> DeviceOrdinals() const { return {0}; }
 
   StatusOr<XrtAllocation*> Execute(
       absl::Span<XrtAllocation* const> argument_handles);
@@ -219,21 +228,21 @@ class CompiledXrtComputation {
   const string session_target_;
 };
 
-// Wraps a XlaComputation produced by a LocalComputationBuilder. The
+// Wraps a XlaComputation produced by a ComputationBuilder. The
 // Compile method compiles the computation to a (local) executable via
 // the client library's local client. This class is intended to be
 // made available to Python via SWIG.
-class LocalComputation {
+class Computation {
  public:
-  LocalComputation(XlaComputation computation);
+  Computation(XlaComputation computation);
 
-  StatusOr<CompiledLocalComputation*> Compile(
+  StatusOr<LocalExecutable*> Compile(
       const std::vector<Shape>& argument_shapes,
-      const ExecutableBuildOptions* build_options);
+      const ExecutableBuildOptions* build_options, const LocalClient& client);
 
   // Accepts a `session_target` argument, used in constructing the
   // `tensorflow::ClientSession` instance in which the compilation graph is run.
-  StatusOr<CompiledXrtComputation*> CompileForXrt(
+  StatusOr<XrtExecutable*> CompileForXrt(
       const std::vector<Shape>& argument_shapes, const string& session_target);
 
   const XlaComputation& computation() const;
@@ -243,6 +252,9 @@ class LocalComputation {
   // string on failure.
   string GetSerializedProto() const;
 
+  // Returns the program shape for this computation.
+  StatusOr<ProgramShape> GetProgramShape() const;
+
   // Returns the return-value shape for this computation.
   StatusOr<Shape> GetReturnValueShape() const;
 
@@ -250,7 +262,7 @@ class LocalComputation {
   XlaComputation computation_;
 };
 
-// Wraps a XlaOp produced by a LocalComputationBuilder. This class is intended
+// Wraps a XlaOp produced by a ComputationBuilder. This class is intended
 // to be made available to Python via SWIG.
 class LocalOp {
  public:
@@ -267,20 +279,20 @@ class LocalOp {
 //   Python.
 // - Set up the underlying builder to use the client library's
 //   LocalClient.
-// - Wrap Computations in LocalComputations for Python access.
+// - Wrap Computations in Computations for Python access.
-// - Correspondingly unwrap incoming LocalComputations.
+// - Correspondingly unwrap incoming Computations.
-class LocalComputationBuilder {
+class ComputationBuilder {
  public:
-  LocalComputationBuilder(const string& computation_name);
+  ComputationBuilder(const string& computation_name);
 
   void SetOpMetadata(const OpMetadata& metadata);
   void ClearOpMetadata();
 
-  // Returns an owned LocalComputation to the caller on success.
+  // Returns an owned Computation to the caller on success.
-  StatusOr<LocalComputation*> Build();
+  StatusOr<Computation*> Build();
 
-  // Returns an owned LocalComputation to the caller on success with given root.
+  // Returns an owned Computation to the caller on success with given root.
-  StatusOr<LocalComputation*> BuildWithRoot(const LocalOp& root);
+  StatusOr<Computation*> BuildWithRoot(const LocalOp& root);
 
   LocalOp Parameter(int64 parameter_number, const Shape& shape,
                     const string& name);
@@ -339,11 +351,11 @@ class LocalComputationBuilder {
   LocalOp ConcatInDim(absl::Span<const LocalOp> operands, int64 dimension);
 
   LocalOp SelectAndScatterWithGeneralPadding(
-      const LocalOp& operand, const LocalComputation& select,
+      const LocalOp& operand, const Computation& select,
       absl::Span<const int64> window_dimensions,
       absl::Span<const int64> window_strides,
       absl::Span<const std::pair<int64, int64> > padding, const LocalOp& source,
-      const LocalOp& init_value, const LocalComputation& scatter);
+      const LocalOp& init_value, const Computation& scatter);
 
   LocalOp Tuple(absl::Span<const LocalOp> elements);
 
@@ -369,7 +381,7 @@ class LocalComputationBuilder {
   LocalOp BitcastConvertType(const LocalOp& operand,
                              PrimitiveType new_element_type);
 
-  LocalOp Call(const LocalComputation& local_computation,
+  LocalOp Call(const Computation& local_computation,
                absl::Span<const LocalOp> operands);
 
   LocalOp CustomCall(const string& call_target_name,
@@ -384,16 +396,16 @@ class LocalComputationBuilder {
   LocalOp Rev(const LocalOp& operand, absl::Span<const int64> dimensions);
 
   LocalOp Map(absl::Span<const LocalOp> operands,
-              const LocalComputation& local_computation,
+              const Computation& local_computation,
               absl::Span<const int64> dimensions);
 
   LocalOp Reduce(const LocalOp& operand, const LocalOp& init_value,
-                 const LocalComputation& local_computation,
+                 const Computation& local_computation,
                  absl::Span<const int64> dimensions_to_reduce);
 
   LocalOp ReduceWindowWithGeneralPadding(
       const LocalOp& operand, const LocalOp& init_value,
-      const LocalComputation& local_computation,
+      const Computation& local_computation,
       absl::Span<const int64> window_dimensions,
       absl::Span<const int64> window_strides,
       absl::Span<const int64> base_dilations,
@@ -405,13 +417,13 @@ class LocalComputationBuilder {
 
   LocalOp RngUniform(const LocalOp& a, const LocalOp& b, const Shape& shape);
 
-  LocalOp While(const LocalComputation& condition, const LocalComputation& body,
+  LocalOp While(const Computation& condition, const Computation& body,
                 const LocalOp& init);
 
   LocalOp Conditional(const LocalOp& predicate, const LocalOp& true_operand,
-                      const LocalComputation& true_computation,
+                      const Computation& true_computation,
                       const LocalOp& false_operand,
-                      const LocalComputation& false_computation);
+                      const Computation& false_computation);
 
   StatusOr<bool> IsConstant(const LocalOp& operand);
 
@@ -435,11 +447,10 @@ class LocalComputationBuilder {
                  absl::Span<const int64> slice_sizes);
 
   LocalOp Scatter(const LocalOp& input, const LocalOp& scatter_indices,
-                  const LocalOp& updates,
+                  const LocalOp& updates, const Computation& update_computation,
-                  const LocalComputation& update_computation,
                   const ScatterDimensionNumbers& dimension_numbers);
 
-  StatusOr<LocalComputation*> BuildConstantSubGraph(const LocalOp& operand);
+  StatusOr<Computation*> BuildConstantSubGraph(const LocalOp& operand);
 
 #define _FORWARD(method_name, return_sig, args_sig) \
   return_sig method_name args_sig;
@@ -529,9 +540,9 @@ class LocalComputationBuilder {
 // Functions for freeing resources from the Python side.
 void DeleteLocalShapedBuffer(LocalShapedBuffer* local_shaped_buffer);
 void DeleteXrtAllocation(XrtAllocation* allocation);
-void DeleteCompiledLocalComputation(CompiledLocalComputation* computation);
+void DeleteLocalExecutable(LocalExecutable* computation);
-void DeleteCompiledXrtComputation(CompiledXrtComputation* computation);
+void DeleteXrtExecutable(XrtExecutable* computation);
-void DeleteLocalComputation(LocalComputation* computation);
+void DeleteComputation(Computation* computation);
 
 }  // namespace swig
 }  // namespace xla

tensorflow/compiler/xla/python/local_computation_builder.i

@@ -23,11 +23,13 @@ limitations under the License.
 //    C++                                  Python
 // -------------------------------------+---------------------------------------
 //  Span<int64>                        <-  sequence of int
+//  vector<int>                        ->  sequence of int
 //  Span<LocalOp>                      <-  sequence of LocalOp
 //  Literal                            <-> (nested tuple of) numpy ndarray
 //  std::vector<Literal>               <-  sequence of (nested tuple of) ndarray
 //  Shape                               -> pair holding (dtype, dimensions)
 //                                     <-  object duck-typed as xla_client.Shape
+//  ProgramShape                       ->  pair of ([arg_shapes], ret_shape)
 //  std::vector<Shape>                 <-  sequence of xla_client.Shape objects
 //  PrimitiveType                      <-  int
 //  Span<pair<int64, in64>>            <-  sequence of int pairs
@@ -97,7 +99,7 @@ limitations under the License.
 // wrapped in a Python class (xla_client.Shape) so as not to expose
 // the raw pair externally.
 //
-// Other SWIG object wrappers (e.g. of LocalComputation) are further
+// Other SWIG object wrappers (e.g. of Computation) are further
 // wrapped by xla_client in order to set up a custom destructor that
 // triggers memory deallocation on the C++ side.
 
@@ -214,6 +216,15 @@ tensorflow::ImportNumpy();
 
 // Basic types
 
+
+%typemap(out) std::vector<int> {
+  PyObject* out = PyList_New($1.size());
+  for (int i = 0; i < $1.size(); ++i) {
+    PyList_SET_ITEM(out, i, PyInt_FromLong($1[i]));
+  }
+  $result = out;
+}
+
 %typemap(out) StatusOr<bool> {
   if ($1.ok()) {
     $result = PyBool_FromLong($1.ConsumeValueOrDie());
@@ -287,12 +298,12 @@ tensorflow::ImportNumpy();
 
 // Computation and buffer/allocation types
 
-%typemap(out) StatusOr<xla::swig::CompiledLocalComputation*> {
+%typemap(out) StatusOr<xla::swig::LocalClient> {
   if ($1.ok()) {
-    auto* value = $1.ValueOrDie();
+    xla::swig::LocalClient value = $1.ValueOrDie();
     {
-      auto* $1 = value;
+      auto $1 = value;
-      $typemap(out, xla::swig::CompiledLocalComputation*)
+      $typemap(out, xla::swig::LocalClient)
     }
   } else {
     PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
@@ -300,12 +311,25 @@ tensorflow::ImportNumpy();
   }
 }
 
-%typemap(out) StatusOr<xla::swig::CompiledXrtComputation*> {
+%typemap(out) StatusOr<xla::swig::LocalExecutable*> {
   if ($1.ok()) {
     auto* value = $1.ValueOrDie();
     {
       auto* $1 = value;
-      $typemap(out, xla::swig::CompiledXrtComputation*)
+      $typemap(out, xla::swig::LocalExecutable*)
+    }
+  } else {
+    PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
+    SWIG_fail;
+  }
+}
+
+%typemap(out) StatusOr<xla::swig::XrtExecutable*> {
+  if ($1.ok()) {
+    auto* value = $1.ValueOrDie();
+    {
+      auto* $1 = value;
+      $typemap(out, xla::swig::XrtExecutable*)
     }
   } else {
     PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
@@ -365,12 +389,12 @@ tensorflow::ImportNumpy();
   }
 }
 
-%typemap(out) StatusOr<xla::swig::LocalComputation*> {
+%typemap(out) StatusOr<xla::swig::Computation*> {
   if ($1.ok()) {
     auto* value = $1.ValueOrDie();
     {
       auto* $1 = value;
-      $typemap(out, xla::swig::LocalComputation*)
+      $typemap(out, xla::swig::Computation*)
     }
   } else {
     PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
@@ -519,18 +543,30 @@ tensorflow::ImportNumpy();
 // Shape
 
 %typemap(out) const Shape& {
-  $result = numpy::PyShapeInfoFromXlaShape(*$1);
+  $result = numpy::PyShapeInfoFromXlaShape(*$1).release();
 }
 
 %typemap(out) StatusOr<Shape> {
   if ($1.ok()) {
-    $result = numpy::PyShapeInfoFromXlaShape($1.ConsumeValueOrDie());
+    $result = numpy::PyShapeInfoFromXlaShape($1.ConsumeValueOrDie()).release();
   } else {
     PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
     SWIG_fail;
   }
 }
 
+
+%typemap(out) StatusOr<ProgramShape> {
+  if ($1.ok()) {
+    $result = numpy::PyProgramShapeInfoFromXlaProgramShape(
+        $1.ConsumeValueOrDie()).release();
+  } else {
+    PyErr_SetString(PyExc_RuntimeError, $1.status().ToString().c_str());
+    SWIG_fail;
+  }
+}
+
+
 %typemap(in) const Shape& (Shape temp) {
   StatusOr<Shape> statusor = numpy::XlaShapeFromPyShape($input);
   if (!statusor.ok()) {
@@ -558,7 +594,7 @@ tensorflow::ImportNumpy();
 }
 
 %typemap(out) std::unique_ptr<Shape> {
-  $result = numpy::PyShapeInfoFromXlaShape(*$1);
+  $result = numpy::PyShapeInfoFromXlaShape(*$1).release();
 }
 
 %typemap(in) const std::vector<Shape>& (std::vector<Shape> temps) {
@@ -966,17 +1002,17 @@ tensorflow::ImportNumpy();
 %ignoreall
 %unignore xla;
 %unignore xla::swig;
-%unignore xla::swig::InitializeReplicaCount;
-%unignore xla::swig::InitializePlatformName;
-%unignore xla::swig::GetReplicaCount;
 %unignore xla::swig::RegisterCpuCustomCallTarget;
-%unignore xla::swig::TransferToInfeedLocal;
+%unignore xla::swig::LocalClient;
-%unignore xla::swig::TransferToInfeedLocalReplica;
+%unignore xla::swig::LocalClient::Get;
-%unignore xla::swig::TransferFromOutfeedLocalReplica;
+%unignore xla::swig::LocalClient::DeviceCount;
+%unignore xla::swig::LocalClient::TransferToInfeed;
+%unignore xla::swig::LocalClient::TransferFromOutfeed;
 %unignore xla::swig::LocalShapedBuffer;
 %unignore xla::swig::LocalShapedBuffer::FromLiteral;
 %unignore xla::swig::LocalShapedBuffer::ToLiteral;
 %unignore xla::swig::LocalShapedBuffer::shape;
+%unignore xla::swig::LocalShapedBuffer::DestructureTuple;
 %unignore xla::swig::LocalShapedBufferTuple;
 %unignore xla::swig::LocalShapedBufferTuple::Release;
 %unignore xla::swig::LocalShapedBufferTuple::size;
@@ -987,139 +1023,141 @@ tensorflow::ImportNumpy();
 %unignore xla::swig::XrtAllocationTuple;
 %unignore xla::swig::XrtAllocationTuple::Release;
 %unignore xla::swig::XrtAllocationTuple::size;
-%unignore xla::swig::CompiledLocalComputation;
+%unignore xla::swig::LocalExecutable;
-%unignore xla::swig::CompiledLocalComputation::Execute;
+%unignore xla::swig::LocalExecutable::DeviceOrdinals;
-%unignore xla::swig::CompiledLocalComputation::ExecutePerReplica;
+%unignore xla::swig::LocalExecutable::Execute;
-%unignore xla::swig::CompiledXrtComputation;
+%unignore xla::swig::LocalExecutable::ExecutePerReplica;
-%unignore xla::swig::CompiledXrtComputation::Execute;
+%unignore xla::swig::XrtExecutable;
-%unignore xla::swig::LocalComputation;
+%unignore xla::swig::XrtExecutable::DeviceOrdinals;
-%unignore xla::swig::LocalComputation::Compile;
+%unignore xla::swig::XrtExecutable::Execute;
-%unignore xla::swig::LocalComputation::CompileForXrt;
+%unignore xla::swig::Computation;
-%unignore xla::swig::LocalComputation::GetReturnValueShape;
+%unignore xla::swig::Computation::Compile;
-%unignore xla::swig::LocalComputation::GetSerializedProto;
+%unignore xla::swig::Computation::CompileForXrt;
+%unignore xla::swig::Computation::GetProgramShape;
+%unignore xla::swig::Computation::GetReturnValueShape;
+%unignore xla::swig::Computation::GetSerializedProto;
 %unignore xla::swig::LocalOp;
-%unignore xla::swig::LocalComputationBuilder;
+%unignore xla::swig::ComputationBuilder;
-%unignore xla::swig::LocalComputationBuilder::LocalComputationBuilder;
+%unignore xla::swig::ComputationBuilder::ComputationBuilder;
-%unignore xla::swig::LocalComputationBuilder::Build;
+%unignore xla::swig::ComputationBuilder::Build;
-%unignore xla::swig::LocalComputationBuilder::BuildWithRoot;
+%unignore xla::swig::ComputationBuilder::BuildWithRoot;
-%unignore xla::swig::LocalComputationBuilder::SetOpMetadata;
+%unignore xla::swig::ComputationBuilder::SetOpMetadata;
-%unignore xla::swig::LocalComputationBuilder::ClearOpMetadata;
+%unignore xla::swig::ComputationBuilder::ClearOpMetadata;
-%unignore xla::swig::LocalComputationBuilder::Parameter;
+%unignore xla::swig::ComputationBuilder::Parameter;
-%unignore xla::swig::LocalComputationBuilder::GetShape;
+%unignore xla::swig::ComputationBuilder::GetShape;
-%unignore xla::swig::LocalComputationBuilder::GetReturnValueShape;
+%unignore xla::swig::ComputationBuilder::GetReturnValueShape;
-%unignore xla::swig::LocalComputationBuilder::Infeed;
+%unignore xla::swig::ComputationBuilder::Infeed;
-%unignore xla::swig::LocalComputationBuilder::Outfeed;
+%unignore xla::swig::ComputationBuilder::Outfeed;
-%unignore xla::swig::LocalComputationBuilder::ConstantLiteral;
+%unignore xla::swig::ComputationBuilder::ConstantLiteral;
-%unignore xla::swig::LocalComputationBuilder::ConstantR0;
+%unignore xla::swig::ComputationBuilder::ConstantR0;
-%unignore xla::swig::LocalComputationBuilder::Iota;
+%unignore xla::swig::ComputationBuilder::Iota;
-%unignore xla::swig::LocalComputationBuilder::BroadcastedIota;
+%unignore xla::swig::ComputationBuilder::BroadcastedIota;
-%unignore xla::swig::LocalComputationBuilder::Broadcast;
+%unignore xla::swig::ComputationBuilder::Broadcast;
-%unignore xla::swig::LocalComputationBuilder::BroadcastInDim;
+%unignore xla::swig::ComputationBuilder::BroadcastInDim;
-%unignore xla::swig::LocalComputationBuilder::Pad;
+%unignore xla::swig::ComputationBuilder::Pad;
-%unignore xla::swig::LocalComputationBuilder::Reshape;
+%unignore xla::swig::ComputationBuilder::Reshape;
-%unignore xla::swig::LocalComputationBuilder::Collapse;
+%unignore xla::swig::ComputationBuilder::Collapse;
-%unignore xla::swig::LocalComputationBuilder::AllToAll;
+%unignore xla::swig::ComputationBuilder::AllToAll;
-%unignore xla::swig::LocalComputationBuilder::CrossReplicaSum;
+%unignore xla::swig::ComputationBuilder::CrossReplicaSum;
-%unignore xla::swig::LocalComputationBuilder::Slice;
+%unignore xla::swig::ComputationBuilder::Slice;
-%unignore xla::swig::LocalComputationBuilder::SliceInDim;
+%unignore xla::swig::ComputationBuilder::SliceInDim;
-%unignore xla::swig::LocalComputationBuilder::DynamicSlice;
+%unignore xla::swig::ComputationBuilder::DynamicSlice;
-%unignore xla::swig::LocalComputationBuilder::DynamicUpdateSlice;
+%unignore xla::swig::ComputationBuilder::DynamicUpdateSlice;
-%unignore xla::swig::LocalComputationBuilder::ConcatInDim;
+%unignore xla::swig::ComputationBuilder::ConcatInDim;
-%unignore xla::swig::LocalComputationBuilder::SelectAndScatterWithGeneralPadding;
+%unignore xla::swig::ComputationBuilder::SelectAndScatterWithGeneralPadding;
-%unignore xla::swig::LocalComputationBuilder::Select;
+%unignore xla::swig::ComputationBuilder::Select;
-%unignore xla::swig::LocalComputationBuilder::Tuple;
+%unignore xla::swig::ComputationBuilder::Tuple;
-%unignore xla::swig::LocalComputationBuilder::GetTupleElement;
+%unignore xla::swig::ComputationBuilder::GetTupleElement;
-%unignore xla::swig::LocalComputationBuilder::ConvertElementType;
+%unignore xla::swig::ComputationBuilder::ConvertElementType;
-%unignore xla::swig::LocalComputationBuilder::BitcastConvertType;
+%unignore xla::swig::ComputationBuilder::BitcastConvertType;
-%unignore xla::swig::LocalComputationBuilder::Call;
+%unignore xla::swig::ComputationBuilder::Call;
-%unignore xla::swig::LocalComputationBuilder::Transpose;
+%unignore xla::swig::ComputationBuilder::Transpose;
-%unignore xla::swig::LocalComputationBuilder::Rev;
+%unignore xla::swig::ComputationBuilder::Rev;
-%unignore xla::swig::LocalComputationBuilder::Clamp;
+%unignore xla::swig::ComputationBuilder::Clamp;
-%unignore xla::swig::LocalComputationBuilder::Map;
+%unignore xla::swig::ComputationBuilder::Map;
-%unignore xla::swig::LocalComputationBuilder::Reduce;
+%unignore xla::swig::ComputationBuilder::Reduce;
-%unignore xla::swig::LocalComputationBuilder::ReduceWindowWithGeneralPadding;
+%unignore xla::swig::ComputationBuilder::ReduceWindowWithGeneralPadding;
-%unignore xla::swig::LocalComputationBuilder::RngNormal;
+%unignore xla::swig::ComputationBuilder::RngNormal;
-%unignore xla::swig::LocalComputationBuilder::RngUniform;
+%unignore xla::swig::ComputationBuilder::RngUniform;
-%unignore xla::swig::LocalComputationBuilder::RngBernoulli;
+%unignore xla::swig::ComputationBuilder::RngBernoulli;
-%unignore xla::swig::LocalComputationBuilder::While;
+%unignore xla::swig::ComputationBuilder::While;
-%unignore xla::swig::LocalComputationBuilder::Conditional;
+%unignore xla::swig::ComputationBuilder::Conditional;
-%unignore xla::swig::LocalComputationBuilder::IsConstant;
+%unignore xla::swig::ComputationBuilder::IsConstant;
-%unignore xla::swig::LocalComputationBuilder::Eq;
+%unignore xla::swig::ComputationBuilder::Eq;
-%unignore xla::swig::LocalComputationBuilder::Ne;
+%unignore xla::swig::ComputationBuilder::Ne;
-%unignore xla::swig::LocalComputationBuilder::Ge;
+%unignore xla::swig::ComputationBuilder::Ge;
-%unignore xla::swig::LocalComputationBuilder::Gt;
+%unignore xla::swig::ComputationBuilder::Gt;
-%unignore xla::swig::LocalComputationBuilder::Lt;
+%unignore xla::swig::ComputationBuilder::Lt;
-%unignore xla::swig::LocalComputationBuilder::Le;
+%unignore xla::swig::ComputationBuilder::Le;
-%unignore xla::swig::LocalComputationBuilder::Dot;
+%unignore xla::swig::ComputationBuilder::Dot;
-%unignore xla::swig::LocalComputationBuilder::DotGeneral;
+%unignore xla::swig::ComputationBuilder::DotGeneral;
-%unignore xla::swig::LocalComputationBuilder::ConvGeneralDilated;
+%unignore xla::swig::ComputationBuilder::ConvGeneralDilated;
-%unignore xla::swig::LocalComputationBuilder::Add;
+%unignore xla::swig::ComputationBuilder::Add;
-%unignore xla::swig::LocalComputationBuilder::Sub;
+%unignore xla::swig::ComputationBuilder::Sub;
-%unignore xla::swig::LocalComputationBuilder::Mul;
+%unignore xla::swig::ComputationBuilder::Mul;
-%unignore xla::swig::LocalComputationBuilder::Div;
+%unignore xla::swig::ComputationBuilder::Div;
-%unignore xla::swig::LocalComputationBuilder::Rem;
+%unignore xla::swig::ComputationBuilder::Rem;
-%unignore xla::swig::LocalComputationBuilder::Max;
+%unignore xla::swig::ComputationBuilder::Max;
-%unignore xla::swig::LocalComputationBuilder::Min;
+%unignore xla::swig::ComputationBuilder::Min;
-%unignore xla::swig::LocalComputationBuilder::And;
+%unignore xla::swig::ComputationBuilder::And;
-%unignore xla::swig::LocalComputationBuilder::Or;
+%unignore xla::swig::ComputationBuilder::Or;
-%unignore xla::swig::LocalComputationBuilder::Xor;
+%unignore xla::swig::ComputationBuilder::Xor;
-%unignore xla::swig::LocalComputationBuilder::ShiftLeft;
+%unignore xla::swig::ComputationBuilder::ShiftLeft;
-%unignore xla::swig::LocalComputationBuilder::ShiftRightArithmetic;
+%unignore xla::swig::ComputationBuilder::ShiftRightArithmetic;
-%unignore xla::swig::LocalComputationBuilder::ShiftRightLogical;
+%unignore xla::swig::ComputationBuilder::ShiftRightLogical;
-%unignore xla::swig::LocalComputationBuilder::Not;
+%unignore xla::swig::ComputationBuilder::Not;
-%unignore xla::swig::LocalComputationBuilder::Abs;
+%unignore xla::swig::ComputationBuilder::Abs;
-%unignore xla::swig::LocalComputationBuilder::Exp;
+%unignore xla::swig::ComputationBuilder::Exp;
-%unignore xla::swig::LocalComputationBuilder::Expm1;
+%unignore xla::swig::ComputationBuilder::Expm1;
-%unignore xla::swig::LocalComputationBuilder::Floor;
+%unignore xla::swig::ComputationBuilder::Floor;
-%unignore xla::swig::LocalComputationBuilder::Ceil;
+%unignore xla::swig::ComputationBuilder::Ceil;
-%unignore xla::swig::LocalComputationBuilder::Round;
+%unignore xla::swig::ComputationBuilder::Round;
-%unignore xla::swig::LocalComputationBuilder::Log;
+%unignore xla::swig::ComputationBuilder::Log;
-%unignore xla::swig::LocalComputationBuilder::Log1p;
+%unignore xla::swig::ComputationBuilder::Log1p;
-%unignore xla::swig::LocalComputationBuilder::Sign;
+%unignore xla::swig::ComputationBuilder::Sign;
-%unignore xla::swig::LocalComputationBuilder::Cos;
+%unignore xla::swig::ComputationBuilder::Cos;
-%unignore xla::swig::LocalComputationBuilder::Sin;
+%unignore xla::swig::ComputationBuilder::Sin;
-%unignore xla::swig::LocalComputationBuilder::Tanh;
+%unignore xla::swig::ComputationBuilder::Tanh;
-%unignore xla::swig::LocalComputationBuilder::Atan2;
+%unignore xla::swig::ComputationBuilder::Atan2;
-%unignore xla::swig::LocalComputationBuilder::IsFinite;
+%unignore xla::swig::ComputationBuilder::IsFinite;
-%unignore xla::swig::LocalComputationBuilder::Pow;
+%unignore xla::swig::ComputationBuilder::Pow;
-%unignore xla::swig::LocalComputationBuilder::Neg;
+%unignore xla::swig::ComputationBuilder::Neg;
-%unignore xla::swig::LocalComputationBuilder::Sort;
+%unignore xla::swig::ComputationBuilder::Sort;
-%unignore xla::swig::LocalComputationBuilder::SortKeyVal;
+%unignore xla::swig::ComputationBuilder::SortKeyVal;
-%unignore xla::swig::LocalComputationBuilder::Sqrt;
+%unignore xla::swig::ComputationBuilder::Sqrt;
-%unignore xla::swig::LocalComputationBuilder::Rsqrt;
+%unignore xla::swig::ComputationBuilder::Rsqrt;
-%unignore xla::swig::LocalComputationBuilder::Square;
+%unignore xla::swig::ComputationBuilder::Square;
-%unignore xla::swig::LocalComputationBuilder::Reciprocal;
+%unignore xla::swig::ComputationBuilder::Reciprocal;
-%unignore xla::swig::LocalComputationBuilder::Erfc;
+%unignore xla::swig::ComputationBuilder::Erfc;
-%unignore xla::swig::LocalComputationBuilder::Erf;
+%unignore xla::swig::ComputationBuilder::Erf;
-%unignore xla::swig::LocalComputationBuilder::ErfInv;
+%unignore xla::swig::ComputationBuilder::ErfInv;
-%unignore xla::swig::LocalComputationBuilder::Lgamma;
+%unignore xla::swig::ComputationBuilder::Lgamma;
-%unignore xla::swig::LocalComputationBuilder::Digamma;
+%unignore xla::swig::ComputationBuilder::Digamma;
-%unignore xla::swig::LocalComputationBuilder::Acos;
+%unignore xla::swig::ComputationBuilder::Acos;
-%unignore xla::swig::LocalComputationBuilder::Asin;
+%unignore xla::swig::ComputationBuilder::Asin;
-%unignore xla::swig::LocalComputationBuilder::Atan;
+%unignore xla::swig::ComputationBuilder::Atan;
-%unignore xla::swig::LocalComputationBuilder::Tan;
+%unignore xla::swig::ComputationBuilder::Tan;
-%unignore xla::swig::LocalComputationBuilder::Acosh;
+%unignore xla::swig::ComputationBuilder::Acosh;
-%unignore xla::swig::LocalComputationBuilder::Asinh;
+%unignore xla::swig::ComputationBuilder::Asinh;
-%unignore xla::swig::LocalComputationBuilder::Atanh;
+%unignore xla::swig::ComputationBuilder::Atanh;
-%unignore xla::swig::LocalComputationBuilder::Cosh;
+%unignore xla::swig::ComputationBuilder::Cosh;
-%unignore xla::swig::LocalComputationBuilder::Sinh;
+%unignore xla::swig::ComputationBuilder::Sinh;
-%unignore xla::swig::LocalComputationBuilder::Real;
+%unignore xla::swig::ComputationBuilder::Real;
-%unignore xla::swig::LocalComputationBuilder::Imag;
+%unignore xla::swig::ComputationBuilder::Imag;
-%unignore xla::swig::LocalComputationBuilder::Conj;
+%unignore xla::swig::ComputationBuilder::Conj;
-%unignore xla::swig::LocalComputationBuilder::Complex;
+%unignore xla::swig::ComputationBuilder::Complex;
-%unignore xla::swig::LocalComputationBuilder::Cholesky;
+%unignore xla::swig::ComputationBuilder::Cholesky;
-%unignore xla::swig::LocalComputationBuilder::QR;
+%unignore xla::swig::ComputationBuilder::QR;
-%unignore xla::swig::LocalComputationBuilder::TriangularSolve;
+%unignore xla::swig::ComputationBuilder::TriangularSolve;
-%unignore xla::swig::LocalComputationBuilder::CustomCall;
+%unignore xla::swig::ComputationBuilder::CustomCall;
-%unignore xla::swig::LocalComputationBuilder::Gather;
+%unignore xla::swig::ComputationBuilder::Gather;
-%unignore xla::swig::LocalComputationBuilder::Scatter;
+%unignore xla::swig::ComputationBuilder::Scatter;
-%unignore xla::swig::DeleteLocalComputation;
+%unignore xla::swig::DeleteComputation;
-%unignore xla::swig::DestructureLocalShapedBufferTuple;
 %unignore xla::swig::DestructureXrtAllocationTuple;
 %unignore xla::swig::DeleteLocalShapedBuffer;
 %unignore xla::swig::DeleteXrtAllocation;
-%unignore xla::swig::DeleteCompiledLocalComputation;
+%unignore xla::swig::DeleteLocalExecutable;
-%unignore xla::swig::DeleteCompiledXrtComputation;
+%unignore xla::swig::DeleteXrtExecutable;
 
 %thread;
 %include "tensorflow/compiler/xla/python/local_computation_builder.h"

tensorflow/compiler/xla/python/numpy_bridge.cc

@@ -127,28 +127,42 @@ bool NumpyTypeIsValid(int np_type) {
   }
 }
 
-PyObject* PyShapeInfoFromXlaShape(const Shape& shape) {
+Safe_PyObjectPtr PyShapeInfoFromXlaShape(const Shape& shape) {
   int np_typenum = PrimitiveTypeToNumpyType(shape.element_type());
   PyArray_Descr* np_dtype = PyArray_DescrFromType(np_typenum);
 
-  PyObject* dimensions;
+  Safe_PyObjectPtr dimensions;
   if (shape.IsTuple()) {
     int num_elements = ShapeUtil::TupleElementCount(shape);
-    dimensions = PyTuple_New(ShapeUtil::TupleElementCount(shape));
+    dimensions = make_safe(PyTuple_New(ShapeUtil::TupleElementCount(shape)));
     for (int i = 0; i < num_elements; ++i) {
       PyTuple_SET_ITEM(
-          dimensions, i,
+          dimensions.get(), i,
-          PyShapeInfoFromXlaShape(ShapeUtil::GetTupleElementShape(shape, i)));
+          PyShapeInfoFromXlaShape(ShapeUtil::GetTupleElementShape(shape, i))
+              .release());
     }
   } else {
     int rank = shape.rank();
-    dimensions = PyTuple_New(rank);
+    dimensions = make_safe(PyTuple_New(rank));
     for (int i = 0; i < rank; ++i) {
-      PyTuple_SET_ITEM(dimensions, i,
+      PyTuple_SET_ITEM(dimensions.get(), i,
                        LongToPyIntOrPyLong(ShapeUtil::GetDimension(shape, i)));
     }
   }
-  return PyTuple_Pack(2, np_dtype, dimensions);
+  return make_safe(PyTuple_Pack(2, np_dtype, dimensions.release()));
+}
+
+Safe_PyObjectPtr PyProgramShapeInfoFromXlaProgramShape(
+    const ProgramShape& shape) {
+  Safe_PyObjectPtr arg_shapes = make_safe(PyTuple_New(shape.parameters_size()));
+  for (int i = 0; i < shape.parameters_size(); ++i) {
+    PyTuple_SET_ITEM(arg_shapes.get(), i,
+                     PyShapeInfoFromXlaShape(shape.parameters(i)).release());
+  }
+
+  Safe_PyObjectPtr result_shape = PyShapeInfoFromXlaShape(shape.result());
+  return make_safe(
+      PyTuple_Pack(2, arg_shapes.release(), result_shape.release()));
 }
 
 // Precondition: o->ob_type == &PyArrayDescr_Type

tensorflow/compiler/xla/python/numpy_bridge.h

@@ -64,7 +64,13 @@ bool NumpyTypeIsValid(int np_type);
 // providing the array dimensions.
 //
 // The return value is a new reference.
-PyObject* PyShapeInfoFromXlaShape(const Shape& shape);
+Safe_PyObjectPtr PyShapeInfoFromXlaShape(const Shape& shape);
+
+// Returns a pair of (arg_shapes, result_shape), where arg_shapes is a tuple
+// of argument shapes and result_shape is the result shape. Each shape is as
+// described in in PyShapeInfoFromXlaShape's comment.
+Safe_PyObjectPtr PyProgramShapeInfoFromXlaProgramShape(
+    const ProgramShape& shape);
 
 // Converts a Python object with a method interface mathing that of
 // xla_client.Shape into an XLA Shape object.

tensorflow/compiler/xla/python/xla_client.py

@@ -36,7 +36,7 @@ from tensorflow.compiler.xla.service import hlo_pb2
 
 
 # Most functions are snake_case for consistency with other modules, whereas
-# method names of ComputationBuilder and LocalComputation are CamelCase for
+# method names of ComputationBuilder and Computation are CamelCase for
 # consistency with XLA.
 # pylint: disable=invalid-name
 
@@ -50,7 +50,7 @@ from tensorflow.compiler.xla.service import hlo_pb2
 # which case we need to be able to detect when incompatible versions are
 # installed.
 def version():
-  return (0, 1, 7)
+  return (0, 1, 8)
 
 
 _OP_METADATA_FIELDS = [
@@ -66,6 +66,10 @@ OpMetadata = collections.namedtuple('OpMetadata', _OP_METADATA_FIELDS)
 class Backend(object):
   """Abstract base class for XLA backends."""
 
+  @abc.abstractmethod
+  def device_count(self):
+    """Returns the number of devices known to the backend."""
+
   @abc.abstractmethod
   def buffer_from_pyval(self, pyval, device=0):
     """Allocates a fresh buffer and populates it with `pyval`."""
@@ -95,25 +99,39 @@ class Backend(object):
     """Runs an executable in a replicated manner."""
 
 
+def _maybe_encode_string(s):
+  if six.PY3:
+    return s.encode('utf-8')
+  else:
+    return s
+
+
 class XlaLocalBackend(Backend):
   """XLA backend implemented using the in-process xla::LocalClient API."""
 
+  def __init__(self, platform=None):
+    platform = platform or _get_default_platform_name()
+    self.client = c_api.LocalClient.Get(_maybe_encode_string(platform))
+
+  def device_count(self):
+    return self.client.DeviceCount()
+
   def buffer_from_pyval(self, pyval, device=0):
-    return c_api.LocalShapedBuffer.FromLiteral(pyval, None, device)
+    return c_api.LocalShapedBuffer.FromLiteral(pyval, None, self.client, device)
 
   def delete_buffer(self, c_buffer):
     c_api.DeleteLocalShapedBuffer(c_buffer)
 
   def destructure_tuple(self, c_buffer):
-    result = c_api.DestructureLocalShapedBufferTuple(c_buffer)
+    result = c_buffer.DestructureTuple()
     return [result.Release(i) for i in xrange(result.size())]
 
   def compile(self, c_computation, argument_shapes, compile_options):
-    return c_computation.Compile(argument_shapes, compile_options)
+    return c_computation.Compile(argument_shapes, compile_options, self.client)
 
   def delete_executable(self, executable):
-    assert isinstance(executable, c_api.CompiledLocalComputation)
+    assert isinstance(executable, c_api.LocalExecutable)
-    c_api.DeleteCompiledLocalComputation(executable)
+    c_api.DeleteLocalExecutable(executable)
 
   def execute(self, executable, args):
     return executable.Execute(args)
@@ -130,6 +148,9 @@ class XrtBackend(Backend):
   def __init__(self, target):
     self.target = target
 
+  def device_count(self):
+    return 1  # Multidevice execution not implemented.
+
   def buffer_from_pyval(self, pyval, device=0):
     if device != 0:
       raise NotImplementedError(
@@ -150,8 +171,8 @@ class XrtBackend(Backend):
                                        _maybe_encode_string(self.target))
 
   def delete_executable(self, executable):
-    assert isinstance(executable, c_api.CompiledXrtComputation)
+    assert isinstance(executable, c_api.XrtExecutable)
-    c_api.DeleteCompiledXrtComputation(executable)
+    c_api.DeleteXrtExecutable(executable)
 
   def execute(self, executable, args):
     return executable.Execute(args)
@@ -163,7 +184,20 @@ class XrtBackend(Backend):
     return [executable.Execute(per_replica_args[0])]
 
 
-XLA_LOCAL_BACKEND = XlaLocalBackend()
+_default_platform_name = 'Host'
+_default_backend = None
+
+
+def _get_default_platform_name():
+  return _default_platform_name
+
+
+def _get_default_local_backend():
+  global _default_backend
+  global _default_platform_name
+  if _default_backend is None:
+    _default_backend = XlaLocalBackend(_default_platform_name)
+  return _default_backend
 
 
 class BackendType(enum.Enum):
@@ -174,7 +208,7 @@ class BackendType(enum.Enum):
 def BackendSpec(backend, target):
   """Compatibility wrapper to support older clients. Do not use in new code."""
   if backend == BackendType.XLA_LOCAL:
-    return XLA_LOCAL_BACKEND
+    return _get_default_local_backend()
   elif backend == BackendType.XRT:
     return XrtBackend(target)
   else:
@@ -201,13 +235,6 @@ def CurrentSourceInfoMetadata(op_type=None, op_name=None, skip_frames=1):
       source_line=lineno)
 
 
-def _maybe_encode_string(s):
-  if six.PY3:
-    return s.encode('utf-8')
-  else:
-    return s
-
-
 class PaddingType(enum.Enum):
   VALID = 1
   SAME = 2
@@ -346,22 +373,18 @@ class LocalBuffer(object):
   means the referent is in device memory.
   """
 
-  def __init__(self, c_buffer, backend, replica):
+  def __init__(self, c_buffer, backend, device):
     self.c_buffer = c_buffer
     self._backend = backend
-    self._replica = replica
+    self._device = device
 
   @staticmethod
-  def from_pyval(pyval, replica=0, backend=XLA_LOCAL_BACKEND):
+  def from_pyval(pyval, device=0, backend=None):
     """Allocate and copy to XLA the given python value."""
+    backend = backend or _get_default_local_backend()
     pyval = require_numpy_array_layout(pyval)
-    num_replicas = get_replica_count()
+    cbuf = backend.buffer_from_pyval(pyval, device)
-    if not 0 <= replica < num_replicas:
+    return LocalBuffer(cbuf, backend, device)
-      raise ValueError(
-          'Attempt to place buffer on replica {} when the replica count is {}'
-          .format(replica, num_replicas))
-    cbuf = backend.buffer_from_pyval(pyval, replica)
-    return LocalBuffer(cbuf, backend, replica)
 
   def to_py(self):
     return self.c_buffer.ToLiteral()
@@ -369,8 +392,8 @@ class LocalBuffer(object):
   def shape(self):
     return _wrap_shape(self.c_buffer.shape())
 
-  def replica(self):
+  def device(self):
-    return self._replica
+    return self._device
 
   def delete(self):
     if self.c_buffer is not None:
@@ -383,7 +406,7 @@ class LocalBuffer(object):
     result = self._backend.destructure_tuple(self.c_buffer)
     self.delete()
     return tuple(
-        LocalBuffer(sub_buffer, replica=self._replica, backend=self._backend)
+        LocalBuffer(sub_buffer, device=self._device, backend=self._backend)
         for sub_buffer in result)
 
   def is_deleted(self):
@@ -533,6 +556,16 @@ class Shape(object):
     updated._check_minor_to_major()  # pylint: disable=protected-access
     return updated
 
+  def with_major_to_minor_layout_if_absent(self):
+    """Returns a copy of a shape with missing layouts set to major-to-minor."""
+
+    def f(a):
+      if a.minor_to_major():
+        return None
+      return a.update_minor_to_major(tuple(xrange(a.rank() - 1, -1, -1)))
+
+    return self.map_leaves(f)
+
   def serialize(self, proto):
     """Serializes 'shape' into proto."""
     if self.is_tuple():
@@ -548,6 +581,10 @@ class Shape(object):
         proto.layout.minor_to_major.extend(self.minor_to_major())
 
 
+ProgramShape = collections.namedtuple('ProgramShape',
+                                      ('parameter_shapes', 'result_shape'))
+
+
 def _wrap_shape(shape_info):
   dtype, dims = shape_info
   element_type = DTYPE_TO_XLA_ELEMENT_TYPE[str(dtype)]
@@ -581,7 +618,7 @@ class CompileOptions(object):
     self.num_replicas = get_replica_count()
 
 
-def transfer_to_infeed(value, replica_number=None):
+def transfer_to_infeed(value, device_ordinal=0):
   """Transfers the given value into the XLA infeed queue.
 
   XLA's infeed queue is a single queue that feeds the "XLA virtual machine" with
@@ -591,52 +628,49 @@ def transfer_to_infeed(value, replica_number=None):
   Args:
     value: the value that the caller would like to enqueue into the XLA infeed
       queue
-    replica_number: the replica number to infeed the value to -- if not
+    device_ordinal: the device to infeed the value to. Each device has a
-      provided, then the default replica (trivially replica 0) is used.
+      distinct infeed queue.
   """
-  if replica_number is None:
+  # TODO(phawkins): support non-default backends.
-    c_api.TransferToInfeedLocal(require_numpy_array_layout(value))
+  backend = _get_default_local_backend()
-  else:
+  backend.client.TransferToInfeed(
-    c_api.TransferToInfeedLocalReplica(
+      require_numpy_array_layout(value), device_ordinal)
-        require_numpy_array_layout(value), replica_number)
 
 
-def transfer_from_outfeed(shape, replica_number=None):
+def transfer_from_outfeed(shape, device_ordinal=0):
-  """Transfers a literal of the given shape from replica_number's outfeed.
+  """Transfers a literal of the given shape from `device_ordinal`'s outfeed.
 
   Args:
     shape: The shape of the value to transfer from outfeed.
-    replica_number: The replica number ordinal to transfer the outfeed value
+    device_ordinal: The device ordinal to transfer the outfeed value from. Each
-      from. (Each replica has a distinct outfeed queue.)
+      device has a distinct outfeed queue..
 
   Returns:
     The literal value that is produced from the outfeed queue.
   """
-  return c_api.TransferFromOutfeedLocalReplica(shape, replica_number or 0)
+  # TODO(phawkins): support non-default backends.
+  backend = _get_default_local_backend()
+  return backend.client.TransferFromOutfeed(shape, device_ordinal)
 
 
-class LocalComputation(object):
+class Computation(object):
-  """Python wrapper for a local XLA Computation.
+  """Python wrapper for an XLA Computation.
 
-  A LocalComputation can be executed if it is compiled. Otherwise, it
+  A Computation can be compiled to form an Executable, or used as a
-  can still be used as a Computation where required by the
+  subcomputation in ComputationBuilder methods.
-  ComputationBuilder methods.
   """
 
-  def __init__(self, c_computation, is_compiled, backend=XLA_LOCAL_BACKEND):
+  def __init__(self, c_computation, backend=None):
     self._c_computation = c_computation
+    # The backend argument is deprecated. Pass a backend to Compile() instead.
     self._backend = backend
-    self._is_compiled = is_compiled
 
   @property
   def computation(self):
-    if self._is_compiled:
-      raise ValueError(
-          'Attempt to read the XLA computation of a compiled LocalComputation.')
     return self._c_computation
 
   def GetProto(self):
-    """Get the HloModuleProto proto object in this local computation.
+    """Get the HloModuleProto proto object in this computation.
 
     Returns:
        An HloModuleProto proto object that has the whole-graph information.
@@ -645,30 +679,25 @@ class LocalComputation(object):
     proto = hlo_pb2.HloModuleProto.FromString(serialized)
     return proto
 
-  def Compile(self, argument_shapes=(), compile_options=None, layout_fn=None):
+  def Compile(self, argument_shapes=(), compile_options=None, layout_fn=None,
-    """Compiles an un-compiled local computation.
+              backend=None):
+    """Compiles a computation.
 
-    Local computations are the result of a "LocalComputationBuild'ing" process
+    Computations are the result of a "ComputationBuild'ing" process.
-    -- they start in uncompiled form, and via a call to Compile() turn into a
-    compiled local computation.
-
-    Raises:
-      ValueError: if this is already a compiled local computation.
 
     Arguments:
       argument_shapes: parameter shapes -- they are first laid out by layout_fn
         if layout_fn is provided. Otherwise, the default layout for those shapes
         will be used.
-      compile_options: options to use for compilation, includes an optional
+      compile_options: options to use for compilation, includes an optional laid
-        laid out result shape for the computation.
+        out result shape for the computation.
       layout_fn: lambda that is used to lay out the argument/result shapes.
+      backend: a `Backend` for which an executable should be generated.
 
     Returns:
-      A newly *compiled* local computation instance.
+      A Executable instance.
     """
-    if self._is_compiled:
+    backend = backend or self._backend or _get_default_local_backend()
-      raise ValueError('Attempt to compile a compiled local XLA computation.')
-
     result_shape = _wrap_shape(self.computation.GetReturnValueShape())
 
     if layout_fn:
@@ -681,29 +710,55 @@ class LocalComputation(object):
 
     compile_options = compile_options or CompileOptions()
     compile_options.result_shape = result_shape
-    c = self._backend.compile(self.computation, argument_shapes,
+    c = backend.compile(self.computation, argument_shapes, compile_options)
-                              compile_options)
+    return Executable(c, backend=backend)
-    return LocalComputation(c, is_compiled=True, backend=self._backend)
 
   def CompileWithExampleArguments(self,
                                   arguments=(),
                                   compile_options=None,
-                                  layout_fn=None):
+                                  layout_fn=None,
+                                  backend=None):
     return self.Compile(
         argument_shapes=[Shape.from_pyval(arg) for arg in arguments],
         compile_options=compile_options,
-        layout_fn=layout_fn)
+        layout_fn=layout_fn,
+        backend=backend)
+
+  def GetProgramShape(self):
+    (arg_shapes, result_shape) = self._c_computation.GetProgramShape()
+    return ProgramShape([_wrap_shape(arg) for arg in arg_shapes],
+                        _wrap_shape(result_shape))
 
   def GetReturnValueShape(self):
     return _wrap_shape(self._c_computation.GetReturnValueShape())
 
+  def __del__(self):
+    # Python may have freed c_api first.
+    if c_api and self._c_computation:
+      assert isinstance(self._c_computation, c_api.Computation)
+      c_api.DeleteComputation(self._c_computation)
+
+
+class Executable(object):
+  """Python wrapper for an XLA Executable."""
+
+  def __init__(self, c_executable, backend=None):
+    self._c_executable = c_executable
+    self._device_ordinals = c_executable.DeviceOrdinals()
+    self._backend = backend
+
+  def DeviceOrdinals(self):
+    """Returns a list containing the device ordinals for each replica."""
+    return self._device_ordinals
+
   def Execute(self, arguments=(), check_for_deleted_args=True):
     """Execute on one replica with LocalBuffer arguments and return value."""
     if check_for_deleted_args and any(arg.is_deleted() for arg in arguments):
       raise ValueError('Executing with deleted local buffer argument')
     raw_args = [arg.c_buffer for arg in arguments]
-    output_buffer = self._backend.execute(self._c_computation, raw_args)
+    output_buffer = self._backend.execute(self._c_executable, raw_args)
-    return LocalBuffer(output_buffer, backend=self._backend, replica=0)
+    return LocalBuffer(
+        output_buffer, backend=self._backend, device=self._device_ordinals[0])
 
   def ExecutePerReplica(self, arguments=None):
     """Execute on many replicas with LocalBuffer arguments and return value.
@@ -713,14 +768,12 @@ class LocalComputation(object):
         sequence comprises the arguments for execution on the i'th replica.
 
     Returns:
-      A list of the computation's outputs on each replica, as a LocalBuffer. If
+      A list of the computation's outputs for each replica, as a LocalBuffer. If
       a shallow sequence of arguments was passed in for `arguments`, then the
       sole, zero'th replica's output is returned instead, as a LocalBuffer.
     """
-    if not self._is_compiled:
-      raise ValueError('Cannot execute an uncompiled local XLA computation.')
     if arguments is None:
-      arguments = ((),) * get_replica_count()
+      arguments = ((),) * len(self._device_ordinals)
     else:
       arguments = [list(replica_args) for replica_args in arguments]
 
@@ -729,30 +782,35 @@ class LocalComputation(object):
       for arg in replica_args:
         if arg.is_deleted():
           raise ValueError('Executing with deleted local buffer argument')
-        if arg.replica() != replica:
+        if arg.device() != self._device_ordinals[replica]:
           raise ValueError(
-              'Executing on replica {} with argument from replica {}'.format(
+              'Executing on device {} with argument from device {}'.format(
-                  replica, arg.replica()))
+                  self._device_ordinals[replica], arg.device()))
 
     # Pull out argument buffer handles
+    # pylint: disable=g-complex-comprehension
     stripped_args = [
         [arg.c_buffer for arg in replica_args] for replica_args in arguments
     ]
 
     # Execute
-    output_buffers = self._backend.execute_replicated(
+    output_buffers = self._backend.execute_replicated(self._c_executable,
-        self._c_computation, stripped_args)
+                                                      stripped_args)
 
     # Wrap output handles in LocalBuffer instances
     return tuple(
-        LocalBuffer(output_buffer, backend=self._backend, replica=replica)
+        LocalBuffer(
+            output_buffer,
+            backend=self._backend,
+            device=self._device_ordinals[replica])
         for replica, output_buffer in enumerate(output_buffers))
 
   def ExecuteWithPythonValues(self, arguments=()):
     """Execute on one replica with Python values as arguments and output."""
 
     def put(arg):
-      return LocalBuffer.from_pyval(arg, backend=self._backend)
+      return LocalBuffer.from_pyval(
+          arg, device=self._device_ordinals[0], backend=self._backend)
 
     arguments = [put(arg) for arg in arguments]
     return self.Execute(arguments).to_py()
@@ -760,22 +818,19 @@ class LocalComputation(object):
   def ExecuteWithPythonValuesPerReplica(self, arguments):
     """Execute on many replicas with Python values as arguments and output."""
 
-    def put(arg, replica):
+    def put(arg, device):
-      return LocalBuffer.from_pyval(arg, replica, backend=self._backend)
+      return LocalBuffer.from_pyval(arg, device, backend=self._backend)
 
-    arguments = [[put(arg, replica)
+    # pylint: disable=g-complex-comprehension
-                  for arg in replica_args]
+    arguments = [[
-                 for replica, replica_args in enumerate(arguments)]
+        put(arg, self._device_ordinals[replica]) for arg in replica_args
+    ] for replica, replica_args in enumerate(arguments)]
     return [out.to_py() for out in self.ExecutePerReplica(arguments)]
 
   def __del__(self):
     # Python may have freed c_api first.
-    if c_api and self._c_computation:
+    if c_api and self._c_executable:
-      if self._is_compiled:
+      self._backend.delete_executable(self._c_executable)
-        self._backend.delete_executable(self._c_computation)
-      else:
-        assert isinstance(self._c_computation, c_api.LocalComputation)
-        c_api.DeleteLocalComputation(self._c_computation)
 
 
 def _make_replica_group_proto(replica_group):
@@ -788,8 +843,8 @@ class ComputationBuilder(object):
   """XLA computation builder.
 
   Enqueues XLA ops in sequence and in order to build a
-  LocalComputation, which in turn can be compiled into a
+  Computation, which in turn can be compiled into a
-  CompiledLocalComputation, which in turn can be locally executed.
+  LocalExecutable, which in turn can be locally executed.
   """
 
   # The methods of this class map 1-to-1 onto the XLA C++
@@ -800,16 +855,23 @@ class ComputationBuilder(object):
   # pylint: disable=g-doc-args
 
   def __init__(self, name):
-    self._client = c_api.LocalComputationBuilder(name.encode('utf8'))
+    self._client = c_api.ComputationBuilder(name.encode('utf8'))
     self._parameter_numbering = itertools.count()
 
-  def Build(self, root=None, backend=XLA_LOCAL_BACKEND):
+  def Build(self, root=None, backend=None):
+    """Builds a `Computation` from the contents of the builder.
+
+    Args:
+      root: if not None, the operator containing the return value of the
+        computation.
+      backend: deprecated. Pass a `backend` to `Computation.Compile` instead.
+    Returns:
+      A `Computation`.
+    """
     if root is not None:
-      return LocalComputation(
+      return Computation(self._client.BuildWithRoot(root), backend=backend)
-          self._client.BuildWithRoot(root), is_compiled=False, backend=backend)
     else:
-      return LocalComputation(
+      return Computation(self._client.Build(), backend=backend)
-          self._client.Build(), is_compiled=False, backend=backend)
 
   def SetOpMetadata(self, op_metadata):
     """Set metadata for operations that are about to be enqueued."""
@@ -1461,7 +1523,7 @@ class ComputationBuilder(object):
 
     Args:
       operand: a LocalOp to test.
-    Returns: a LocalComputation that is rooted on the given `operand` which is a
+    Returns: a Computation that is rooted on the given `operand` which is a
       compile-time constant.
     """
     return self._client.BuildConstantSubGraph(operand)
@@ -1662,7 +1724,7 @@ def _forward_methods_to_local_builder():
 
   Set up methods, corresponding to unary and binary XLA operations,
   whose calls are forwarded in a boilerplate manner to the underlying
-  LocalComputationBuilder C-extension API.
+  ComputationBuilder C-extension API.
   """
 
   def forward_to_local_builder_with_handles(target_method, is_binop=False):
@@ -1682,13 +1744,13 @@ def _forward_methods_to_local_builder():
 
   for method_name in _UNARY_OPS:
     forward = forward_to_local_builder_with_handles(
-        getattr(c_api.LocalComputationBuilder, method_name))
+        getattr(c_api.ComputationBuilder, method_name))
     forward.__name__ = method_name
     setattr(ComputationBuilder, method_name, forward)
 
   for method_name in _BINARY_OPS:
     forward = forward_to_local_builder_with_handles(
-        getattr(c_api.LocalComputationBuilder, method_name), is_binop=True)
+        getattr(c_api.ComputationBuilder, method_name), is_binop=True)
     forward.__name__ = method_name
     setattr(ComputationBuilder, method_name, forward)
 
@@ -1696,8 +1758,14 @@ def _forward_methods_to_local_builder():
 _forward_methods_to_local_builder()
 
 
+_default_replica_count = 1
+
+
 def initialize_replica_count(replica_count):
-  """Initializes the desired replica count to use on XLA service init.
+  """Initializes the default replica count to use.
+
+  Deprecated; pass `num_replicas` as an option to `Computation.Compile()`
+  instead.
 
   Args:
     replica_count: number of replicas that are desired for set up during XLA
@@ -1706,31 +1774,30 @@ def initialize_replica_count(replica_count):
   Raises:
     A runtime exception if the XLA service has already been initialized.
   """
-  c_api.InitializeReplicaCount(replica_count)
+  global _default_replica_count
-
+  _default_replica_count = replica_count
-
-def initialize_platform_name(platform_name):
-  """Initializes the desired platform name to use on XLA service init.
-
-  Args:
-    platform_name: string name of platform.
-
-  Raises:
-    A runtime exception if the XLA service has already been initialized.
-    A runtime exception if the platform does not exist, or there are no devices
-    with that platform.
-  """
-  platform_name = _maybe_encode_string(platform_name)
-  c_api.InitializePlatformName(platform_name)
 
 
 def get_replica_count():
-  """Returns the current replica count used for the XLA service.
+  """Returns the default replica count.
 
-  Note: this will return a value whether the XLA service has been initialized
+  Deprecated; pass `num_replicas` as an option to `Computation.Compile()`
-  yet or not.
+  instead.
   """
-  return c_api.GetReplicaCount()
+  return _default_replica_count
+
+
+def initialize_platform_name(platform_name):
+  """Initializes the default platform name to use for XLA.
+
+  Args:
+    platform_name: string name of platform.
+  """
+  global _default_platform_name
+  _default_platform_name = platform_name
+
+  # Make sure the platform is valid by trying to instantiate it.
+  _get_default_local_backend()
 
 
 def register_cpu_custom_call_target(name, fn):

tensorflow/compiler/xla/python/xla_client_test.py

@@ -29,7 +29,7 @@ from tensorflow.compiler.xla.python import xla_client
 import unittest
 
 
-class LocalComputationTest(unittest.TestCase):
+class ComputationTest(unittest.TestCase):
   """Base class for running an XLA Computation through the local client."""
 
   def _NewComputation(self, name=None):
@@ -85,7 +85,7 @@ def NumpyArrayBool(*args, **kwargs):
   return np.array(*args, dtype=np.bool, **kwargs)
 
 
-class ComputationsWithConstantsTest(LocalComputationTest):
+class ComputationsWithConstantsTest(ComputationTest):
   """Tests focusing on Constant ops."""
 
   def testConstantScalarSumS8(self):
@@ -304,7 +304,7 @@ class ComputationsWithConstantsTest(LocalComputationTest):
     self._ExecuteAndCompareClose(c, expected=0.75)
 
 
-class ParametersTest(LocalComputationTest):
+class ParametersTest(ComputationTest):
   """Tests focusing on Parameter ops and argument-passing."""
 
   def setUp(self):
@@ -384,7 +384,7 @@ class ParametersTest(LocalComputationTest):
         expected=[-4.3, 1.3, -6.3, 3.3])
 
 
-class LocalBufferTest(LocalComputationTest):
+class LocalBufferTest(ComputationTest):
   """Tests focusing on execution with LocalBuffers."""
 
   def _Execute(self, c, arguments):
@@ -482,7 +482,7 @@ class LocalBufferTest(LocalComputationTest):
     self.assertEqual(np.dtype(xla_shape.element_type()), np.dtype(np.float32))
 
 
-class SingleOpTest(LocalComputationTest):
+class SingleOpTest(ComputationTest):
   """Tests for single ops.
 
   The goal here is smoke testing - to exercise the most basic functionality of
@@ -1175,7 +1175,7 @@ class SingleOpTest(LocalComputationTest):
     np.testing.assert_allclose(g, expected, rtol=1e-4)
 
 
-class EmbeddedComputationsTest(LocalComputationTest):
+class EmbeddedComputationsTest(ComputationTest):
   """Tests for XLA graphs with embedded computations (such as maps)."""
 
   def _CreateConstantS32Computation(self):
@@ -1639,7 +1639,7 @@ class EmbeddedComputationsTest(LocalComputationTest):
     self._ExecuteAndCompareClose(c, expected=expected)
 
 
-class ErrorTest(LocalComputationTest):
+class ErrorTest(ComputationTest):
 
   def setUp(self):
     self.f32_scalar_2 = NumpyArrayF32(2.0)
@@ -1656,7 +1656,7 @@ class ErrorTest(LocalComputationTest):
         lambda: c.Build().CompileWithExampleArguments([self.f32_scalar_2]))
 
 
-class ComputationRootTest(LocalComputationTest):
+class ComputationRootTest(ComputationTest):
   """Tests related to setting the root of the computation."""
 
   def testComputationRootDifferentFromLastOp(self):

tensorflow/compiler/xla/service/BUILD

@@ -3529,6 +3529,37 @@ tf_cc_test(
     ],
 )
 
+cc_library(
+    name = "stable_sort_expander",
+    srcs = ["stable_sort_expander.cc"],
+    hdrs = ["stable_sort_expander.h"],
+    deps = [
+        ":hlo",
+        ":hlo_casting_utils",
+        ":hlo_pass",
+        ":op_expander_pass",
+        "//tensorflow/compiler/xla:statusor",
+        "@com_google_absl//absl/container:flat_hash_map",
+        "@com_google_absl//absl/container:flat_hash_set",
+    ],
+)
+
+tf_cc_test(
+    name = "stable_sort_expander_test",
+    srcs = ["stable_sort_expander_test.cc"],
+    deps = [
+        ":algebraic_simplifier",
+        ":hlo_matchers",
+        ":hlo_parser",
+        ":pattern_matcher",
+        ":pattern_matcher_gmock",
+        ":stable_sort_expander",
+        "//tensorflow/compiler/xla:test",
+        "//tensorflow/compiler/xla/tests:hlo_test_base",
+        "//tensorflow/core:test",
+    ],
+)
+
 cc_library(
     name = "tuple_util",
     srcs = ["tuple_util.cc"],

tensorflow/compiler/xla/service/algebraic_simplifier.cc

@@ -280,15 +280,51 @@ class AlgebraicSimplifierVisitor : public DfsHloVisitorWithDefault {
         hlo));
   }
 
-  // Helper method to perform and add reduction in a single dimension.
+  // Converts to primitive type if the input hlo is not that type, otherwise
-  HloInstruction* AddReduce(HloInstruction* hlo, int64 dim) {
+  // returns the original hlo.
+  HloInstruction* AsType(HloInstruction* hlo,
+                         const PrimitiveType element_type) {
+    if (hlo->shape().element_type() == element_type) {
+      return hlo;
+    }
+    return computation_->AddInstruction(HloInstruction::CreateConvert(
+        ShapeUtil::ChangeElementType(hlo->shape(), element_type), hlo));
+  }
+
+  // Transposes a dot operand such that the batch dimensions are the msot major,
+  // and the contracting dimensions are most minor.
+  StatusOr<HloInstruction*> NormalizeDotOperandToBatchMajorAndContractingMinor(
+      HloInstruction* dot_operand, absl::Span<const int64> batch_dimensions,
+      absl::Span<const int64> contracting_dimensions) {
+    std::vector<int64> transpose_dimensions(batch_dimensions.begin(),
+                                            batch_dimensions.end());
+    for (int64 i = 0; i < dot_operand->shape().rank(); ++i) {
+      if (!(absl::c_linear_search(batch_dimensions, i) ||
+            absl::c_linear_search(contracting_dimensions, i))) {
+        transpose_dimensions.push_back(i);
+      }
+    }
+    transpose_dimensions.insert(transpose_dimensions.end(),
+                                contracting_dimensions.begin(),
+                                contracting_dimensions.end());
+    return MakeTransposeHlo(dot_operand, transpose_dimensions);
+  }
+
+  // Helper method to perform and add reduction on a list of dimensions.
+  HloInstruction* AddReduce(HloInstruction* hlo, absl::Span<const int64> dims) {
     HloInstruction* zero =
         computation_->AddInstruction(HloInstruction::CreateConstant(
             LiteralUtil::Zero(hlo->shape().element_type()).Clone()));
     HloComputation* AddReduce_computation = GetOrCreateScalarAddComputation();
-    Shape shape = ShapeUtil::DeleteDimension(dim, hlo->shape());
+    Shape shape = ShapeUtil::FilterDimensions(
+        [&](int64 dim) { return !absl::c_linear_search(dims, dim); },
+        hlo->shape());
     return computation_->AddInstruction(HloInstruction::CreateReduce(
-        shape, hlo, zero, {dim}, AddReduce_computation));
+        shape, hlo, zero, dims, AddReduce_computation));
+  }
+
+  HloInstruction* AddReduce(HloInstruction* hlo, int64 dim) {
+    return AddReduce(hlo, std::vector<int64>{dim});
   }
 
   // Convenience method for replacing an instruction with a bitcast. If operand
@@ -1120,16 +1156,8 @@ StatusOr<bool> AlgebraicSimplifierVisitor::HandleDotStrengthReduction(
     std::swap(rhs_collapsing_dim, rhs_kept_dim);
   }
 
-  auto as_type = [&](HloInstruction* hlo, const PrimitiveType element_type) {
-    if (hlo->shape().element_type() == element_type) {
-      return hlo;
-    }
-    return computation_->AddInstruction(HloInstruction::CreateConvert(
-        ShapeUtil::ChangeElementType(hlo->shape(), element_type), hlo));
-  };
-
   auto reshape_if_necessary = [&](HloInstruction* hlo) {
-    hlo = as_type(hlo, dot->shape().element_type());
+    hlo = AsType(hlo, dot->shape().element_type());
     if (!ShapeUtil::SameDimensions(hlo->shape(), dot->shape())) {
       hlo = computation_->AddInstruction(
           HloInstruction::CreateReshape(dot->shape(), hlo));
@@ -1138,7 +1166,7 @@ StatusOr<bool> AlgebraicSimplifierVisitor::HandleDotStrengthReduction(
   };
 
   auto add_reduce_in_f32 = [&](HloInstruction* hlo, const int64 dim) {
-    return AddReduce(as_type(hlo, F32), dim);
+    return AddReduce(AsType(hlo, F32), dim);
   };
 
   auto broadcast = [&](HloInstruction* hlo, const Shape& shape,
@@ -1247,8 +1275,8 @@ StatusOr<bool> AlgebraicSimplifierVisitor::HandleDotStrengthReduction(
     return dims;
   };
 
-  // If the contracting dimension is 1, remove the degnerate dimnesions from the
+  // If the contracting dimension is 1, remove the degnerate dimnensions from
-  // lhs and rhs, broadcast each to the result shape and multiply.
+  // the lhs and rhs, broadcast each to the result shape and multiply.
   if (lhs->shape().dimensions(lhs_collapsing_dim) == 1 &&
       (rhs_kept_dim == rhs_rank - 1 ||
        (rhs_collapsing_dim == rhs_rank - 1 && rhs_kept_dim == rhs_rank - 2))) {
@@ -1608,34 +1636,26 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) {
   // If there are no contracting dimensions, a dot can be rewritten as
   // mul(broadcast(transpose(x)),broadcast(transpose(y)))
   if (dot->dot_dimension_numbers().lhs_contracting_dimensions_size() == 0) {
-    std::vector<int64> lhs_transpose(
+    TF_ASSIGN_OR_RETURN(
-        dot->dot_dimension_numbers().lhs_batch_dimensions().begin(),
+        HloInstruction * new_lhs,
-        dot->dot_dimension_numbers().lhs_batch_dimensions().end());
+        NormalizeDotOperandToBatchMajorAndContractingMinor(
-    for (int64 i = 0; i < lhs->shape().rank(); ++i) {
+            lhs,
-      if (!absl::c_linear_search(
+            AsInt64Slice(dot->dot_dimension_numbers().lhs_batch_dimensions()),
-              dot->dot_dimension_numbers().lhs_batch_dimensions(), i)) {
+            AsInt64Slice(
-        lhs_transpose.push_back(i);
+                dot->dot_dimension_numbers().lhs_contracting_dimensions())));
-      }
-    }
-    TF_ASSIGN_OR_RETURN(HloInstruction * new_lhs,
-                        MakeTransposeHlo(lhs, lhs_transpose));
     if (dot->shape().rank() != lhs->shape().rank()) {
       std::vector<int64> lhs_broadcast_dims(lhs->shape().rank());
       absl::c_iota(lhs_broadcast_dims, 0);
       new_lhs = computation_->AddInstruction(HloInstruction::CreateBroadcast(
           dot->shape(), new_lhs, lhs_broadcast_dims));
     }
-    std::vector<int64> rhs_transpose(
+    TF_ASSIGN_OR_RETURN(
-        dot->dot_dimension_numbers().rhs_batch_dimensions().begin(),
+        HloInstruction * new_rhs,
-        dot->dot_dimension_numbers().rhs_batch_dimensions().end());
+        NormalizeDotOperandToBatchMajorAndContractingMinor(
-    for (int64 i = 0; i < rhs->shape().rank(); ++i) {
+            rhs,
-      if (!absl::c_linear_search(
+            AsInt64Slice(dot->dot_dimension_numbers().rhs_batch_dimensions()),
-              dot->dot_dimension_numbers().rhs_batch_dimensions(), i)) {
+            AsInt64Slice(
-        rhs_transpose.push_back(i);
+                dot->dot_dimension_numbers().rhs_contracting_dimensions())));
-      }
-    }
-    TF_ASSIGN_OR_RETURN(HloInstruction * new_rhs,
-                        MakeTransposeHlo(rhs, rhs_transpose));
     if (dot->shape().rank() != rhs->shape().rank()) {
       std::vector<int64> rhs_broadcast_dims(
           dot->dot_dimension_numbers().lhs_batch_dimensions_size());
@@ -1651,6 +1671,78 @@ Status AlgebraicSimplifierVisitor::HandleDot(HloInstruction* dot) {
                                           new_lhs, new_rhs));
   }
 
+  // If the lhs or rhs have only batch and contracting dimensions, a dot can be
+  // rewritten as reduce(mul(broadcast(transpose(x)),broadcast(transpose(y))))
+  if ((dot->dot_dimension_numbers().lhs_batch_dimensions_size() +
+           dot->dot_dimension_numbers().lhs_contracting_dimensions_size() ==
+       lhs->shape().rank()) ||
+      (dot->dot_dimension_numbers().rhs_contracting_dimensions_size() +
+           dot->dot_dimension_numbers().rhs_batch_dimensions_size() ==
+       rhs->shape().rank())) {
+    TF_ASSIGN_OR_RETURN(
+        HloInstruction * new_lhs,
+        NormalizeDotOperandToBatchMajorAndContractingMinor(
+            lhs,
+            AsInt64Slice(dot->dot_dimension_numbers().lhs_batch_dimensions()),
+            AsInt64Slice(
+                dot->dot_dimension_numbers().lhs_contracting_dimensions())));
+    TF_ASSIGN_OR_RETURN(
+        HloInstruction * new_rhs,
+        NormalizeDotOperandToBatchMajorAndContractingMinor(
+            rhs,
+            AsInt64Slice(dot->dot_dimension_numbers().rhs_batch_dimensions()),
+            AsInt64Slice(
+                dot->dot_dimension_numbers().rhs_contracting_dimensions())));
+
+    int64 lhs_outer_dims =
+        lhs->shape().rank() -
+        (dot->dot_dimension_numbers().lhs_batch_dimensions_size() +
+         dot->dot_dimension_numbers().lhs_contracting_dimensions_size());
+    int64 rhs_outer_dims =
+        rhs->shape().rank() -
+        (dot->dot_dimension_numbers().rhs_batch_dimensions_size() +
+         dot->dot_dimension_numbers().rhs_contracting_dimensions_size());
+    CHECK(lhs_outer_dims == 0 || rhs_outer_dims == 0);
+    if (rhs_outer_dims > 0) {
+      std::vector<int64> lhs_broadcast_dims(
+          dot->dot_dimension_numbers().lhs_batch_dimensions_size());
+      absl::c_iota(lhs_broadcast_dims, 0);
+      lhs_broadcast_dims.resize(lhs->shape().rank());
+      std::iota(lhs_broadcast_dims.begin() +
+                    dot->dot_dimension_numbers().lhs_batch_dimensions_size(),
+                lhs_broadcast_dims.end(),
+                dot->dot_dimension_numbers().lhs_batch_dimensions_size() +
+                    rhs_outer_dims);
+      new_lhs = computation_->AddInstruction(HloInstruction::CreateBroadcast(
+          new_rhs->shape(), new_lhs, lhs_broadcast_dims));
+    } else if (lhs_outer_dims > 0) {
+      std::vector<int64> rhs_broadcast_dims(
+          dot->dot_dimension_numbers().rhs_batch_dimensions_size());
+      absl::c_iota(rhs_broadcast_dims, 0);
+      rhs_broadcast_dims.resize(rhs->shape().rank());
+      std::iota(rhs_broadcast_dims.begin() +
+                    dot->dot_dimension_numbers().rhs_batch_dimensions_size(),
+                rhs_broadcast_dims.end(),
+                dot->dot_dimension_numbers().rhs_batch_dimensions_size() +
+                    lhs_outer_dims);
+      new_rhs = computation_->AddInstruction(HloInstruction::CreateBroadcast(
+          new_lhs->shape(), new_rhs, rhs_broadcast_dims));
+    }
+
+    TF_ASSIGN_OR_RETURN(HloInstruction * new_dot,
+                        MakeBinaryHlo(HloOpcode::kMultiply, new_lhs, new_rhs));
+    std::vector<int64> reduce_dims(
+        dot->dot_dimension_numbers().lhs_contracting_dimensions_size());
+    new_dot = AsType(new_dot, F32);
+    const int64 outer_dims = std::max(rhs_outer_dims, lhs_outer_dims);
+    absl::c_iota(
+        reduce_dims,
+        outer_dims + dot->dot_dimension_numbers().lhs_batch_dimensions_size());
+    new_dot = AddReduce(new_dot, reduce_dims);
+    new_dot = AsType(new_dot, dot->shape().element_type());
+    return ReplaceInstruction(dot, new_dot);
+  }
+
   if (lhs->shape().rank() > 2 || rhs->shape().rank() > 2 ||
       dot->shape().rank() > 2) {
     if (options_.enable_dot_strength_reduction() &&

tensorflow/compiler/xla/service/algebraic_simplifier_test.cc

@@ -2753,8 +2753,9 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSort) {
   Shape keys_shape = ShapeUtil::MakeShape(F32, {1});
   auto keys = builder.AddInstruction(
       HloInstruction::CreateParameter(0, keys_shape, "keys"));
-  TF_ASSERT_OK(
+  TF_ASSERT_OK(MakeSortHlo(keys_shape, {keys}, 0, /*is_stable=*/false, &builder,
-      MakeSortHlo(keys_shape, {keys}, 0, &builder, module.get()).status());
+                           module.get())
+                   .status());
   HloComputation* computation = module->AddEntryComputation(builder.Build());
   AlgebraicSimplifier simplifier(default_options_);
   ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
@@ -2775,7 +2776,8 @@ TEST_F(AlgebraicSimplifierTest, ReplaceEffectiveScalarKeyValueSortWithTuple) {
       HloInstruction::CreateParameter(2, values_shape, "values1"));
   TF_ASSERT_OK(MakeSortHlo(ShapeUtil::MakeTupleShape(
                                {keys_shape, values_shape, values_shape}),
-                           {keys, values0, values1}, 0, &builder, module.get())
+                           {keys, values0, values1}, 0, /*is_stable=*/false,
+                           &builder, module.get())
                    .status());
   HloComputation* computation = module->AddEntryComputation(builder.Build());
   AlgebraicSimplifier simplifier(default_options_);
@@ -3712,8 +3714,8 @@ TEST_F(AlgebraicSimplifierTest, IteratorInvalidation) {
   HloInstruction* y =
       builder.AddInstruction(HloInstruction::CreateParameter(1, r1f32, "y"));
   DotDimensionNumbers dot_dnums;
-  dot_dnums.add_lhs_contracting_dimensions(1);
+  dot_dnums.add_lhs_batch_dimensions(0);
-  dot_dnums.add_rhs_contracting_dimensions(0);
+  dot_dnums.add_rhs_batch_dimensions(0);
   builder.AddInstruction(HloInstruction::CreateDot(r1f32, x, y, dot_dnums,
                                                    DefaultPrecisionConfig(2)));
   std::unique_ptr<HloComputation> dot_computation(builder.Build());
@@ -4220,12 +4222,24 @@ TEST_P(BatchDotStrengthReductionTest, BatchDotStrengthReduction) {
   int m, k, n;
   PrimitiveType element_type;
   std::tie(m, k, n, element_type) = GetParam();
-
+  std::vector<int64> lhs_dims = {1, 3, 5};
-  Shape dot_shape = ShapeUtil::MakeShape(element_type, {1, 3, 5, m, n});
+  std::vector<int64> rhs_dims = lhs_dims;
-  Shape lhs_shape = k > 0 ? ShapeUtil::MakeShape(element_type, {1, 3, 5, m, k})
+  std::vector<int64> output_dims = lhs_dims;
-                          : ShapeUtil::MakeShape(element_type, {1, 3, 5, m});
+  if (m > 0) {
-  Shape rhs_shape = k > 0 ? ShapeUtil::MakeShape(element_type, {1, 3, 5, k, n})
+    lhs_dims.push_back(m);
-                          : ShapeUtil::MakeShape(element_type, {1, 3, 5, n});
+    output_dims.push_back(m);
+  }
+  if (k > 0) {
+    lhs_dims.push_back(k);
+    rhs_dims.push_back(k);
+  }
+  if (n > 0) {
+    rhs_dims.push_back(n);
+    output_dims.push_back(n);
+  }
+  Shape dot_shape = ShapeUtil::MakeShape(element_type, output_dims);
+  Shape lhs_shape = ShapeUtil::MakeShape(element_type, lhs_dims);
+  Shape rhs_shape = ShapeUtil::MakeShape(element_type, rhs_dims);
   HloComputation::Builder builder(TestName());
 
   auto lhs = builder.AddInstruction(
@@ -4240,7 +4254,7 @@ TEST_P(BatchDotStrengthReductionTest, BatchDotStrengthReduction) {
   dot_dnums.add_rhs_batch_dimensions(1);
   dot_dnums.add_rhs_batch_dimensions(2);
   if (k > 0) {
-    dot_dnums.add_lhs_contracting_dimensions(4);
+    dot_dnums.add_lhs_contracting_dimensions(m > 0 ? 4 : 3);
     dot_dnums.add_rhs_contracting_dimensions(3);
   }
   builder.AddInstruction(HloInstruction::CreateDot(
@@ -4248,9 +4262,9 @@ TEST_P(BatchDotStrengthReductionTest, BatchDotStrengthReduction) {
   auto computation = module->AddEntryComputation(builder.Build());
   AlgebraicSimplifier simplifier(default_options_);
   TF_ASSERT_OK_AND_ASSIGN(bool changed, simplifier.Run(module.get()));
-  const bool dot_should_be_transformed = m == 1 || k == 1 || n == 1 || k == -1;
+  const bool dot_should_be_transformed =
-  const bool computation_should_be_modified = dot_should_be_transformed;
+      m == 1 || k == 1 || n == 1 || m == -1 || k == -1 || n == -1;
-  EXPECT_EQ(changed, computation_should_be_modified);
+  EXPECT_EQ(changed, dot_should_be_transformed);
   bool has_no_dot = true;
   for (const auto& hlo : computation->instructions()) {
     if (hlo->opcode() == HloOpcode::kDot) {
@@ -4261,10 +4275,12 @@ TEST_P(BatchDotStrengthReductionTest, BatchDotStrengthReduction) {
   EXPECT_EQ(has_no_dot, dot_should_be_transformed);
 }
 
-INSTANTIATE_TEST_SUITE_P(
+INSTANTIATE_TEST_SUITE_P(BatchDotStrengthReductionTestInstantiation,
-    BatchDotStrengthReductionTestInstantiation, BatchDotStrengthReductionTest,
+                         BatchDotStrengthReductionTest,
-    ::testing::Combine(::testing::Values(1, 2), ::testing::Values(-1, 1, 2),
+                         ::testing::Combine(::testing::Values(-1, 1, 2),
-                       ::testing::Values(1, 2), ::testing::Values(F32, BF16)));
+                                            ::testing::Values(-1, 1, 2),
+                                            ::testing::Values(-1, 1, 2),
+                                            ::testing::Values(F32, BF16)));
 
 class DotStrengthReductionTest
     : public AlgebraicSimplifierTest,

tensorflow/compiler/xla/service/ar_crs_combiner.cc

@@ -32,15 +32,13 @@ limitations under the License.
 
 namespace xla {
 
-namespace {
-
 namespace m = match;
 
 // Checks if the argument instruction is an AllReduce, followed by a certain
 // sequence of instructions and then a CRS. It must be possible to move
 // the AR past each instruction in the sequence. Returns the CRS, which is the
 // last instruction in the sequence.
-absl::optional<HloInstruction*> MatchesArCrsPattern(
+absl::optional<ArCrsCombiner::ArCrsPair> ArCrsCombiner::MatchesArCrsPattern(
     HloInstruction* instruction) {
   auto can_ar_move_past_instruction = [](HloInstruction* instruction) -> bool {
     if (instruction->user_count() != 1) {
@@ -77,23 +75,23 @@ absl::optional<HloInstruction*> MatchesArCrsPattern(
     return absl::nullopt;
   }
   auto next = instruction->users()[0];
+  int64 distance = 1;
   while (!next->IsCrossReplicaAllReduce()) {
     if (can_ar_move_past_instruction(next)) {
       next = next->users()[0];
     } else {
       return absl::nullopt;
     }
+    ++distance;
   }
   if (!Cast<HloAllReduceInstruction>(next)->IsNoop() &&
       computation_is_addition(next->called_computations()[0])) {
-    return absl::optional<HloInstruction*>(next);
+    return absl::optional<ArCrsPair>(ArCrsPair(instruction, next, distance));
   } else {
     return absl::nullopt;
   }
 }
 
-}  // namespace
-
 absl::optional<HloInstruction*> ArCrsCombiner::WhileFromBodyParameter(
     HloInstruction* instruction) {
   CHECK_EQ(HloOpcode::kParameter, instruction->opcode());
@@ -235,15 +233,55 @@ bool ArCrsCombiner::InstructionsComputeSameValue(
 }
 
 void ArCrsCombiner::GroupAllReducesById(HloModule* module) {
+  // Say that two or more ARs lead to the same CRS: (AR1, CRS), (AR2, CRS),
+  // ... , (ARn, CRS).
+  // If as we traverse the HLO graph we start tracking the pair (AR2, CRS),
+  // and later find that AR1's distance from the CRS is longer, we discard
+  // AR2 and start tracking AR1. We put the discarded ids in this set, in order
+  // to skip processing of short paths when we encounter the other ARs that
+  // have the same id as AR2.
+  absl::flat_hash_set<int64> discarded_ar_ids;
   for (HloComputation* computation : module->MakeNonfusionComputations()) {
     for (HloInstruction* instruction : computation->instructions()) {
-      auto maybe_crs = MatchesArCrsPattern(instruction);
+      auto maybe_pair = MatchesArCrsPattern(instruction);
-      if (maybe_crs) {
+      if (maybe_pair) {
-        auto crs = *maybe_crs;
+        auto pair = *maybe_pair;
         int64 ar_id = *(instruction->all_reduce_id());
-        if (crs_reserved_map_.find(crs) == crs_reserved_map_.end()) {
+        if (discarded_ar_ids.find(ar_id) != discarded_ar_ids.end()) {
-          all_reduce_map_[ar_id].push_back(instruction);
+          continue;
-          crs_reserved_map_[crs] = ar_id;
+        }
+        auto it = crs_reserved_map_.find(pair.crs);
+        if (it != crs_reserved_map_.end()) {
+          auto prev_ar_id = it->second;
+          // Since there is another AR paired with CRS,
+          // all_reduce_map_[prev_ar_id] should exist, but
+          // all_reduce_map_[ar_id] shouldn't.
+          CHECK(all_reduce_map_.find(ar_id) == all_reduce_map_.end());
+          CHECK_NE(prev_ar_id, ar_id);
+          auto prev_pair = all_reduce_map_[prev_ar_id].back();
+          int64 prev_distance = prev_pair.distance;
+          if (prev_distance < pair.distance) {
+            // The current AR's distance to CRS is longer than the previously
+            // tracked AR, so we discard the previous AR.
+            all_reduce_map_.erase(prev_ar_id);
+            discarded_ar_ids.insert(prev_ar_id);
+            all_reduce_map_[ar_id].push_back(pair);
+            crs_reserved_map_[pair.crs] = ar_id;
+          } else {
+            // Discard the current AR id because we are keeping the previously
+            // tracked AR.
+            discarded_ar_ids.insert(ar_id);
+          }
+        } else {
+          if (all_reduce_map_.find(ar_id) != all_reduce_map_.end()) {
+            int64 prev_distance = all_reduce_map_[ar_id].back().distance;
+            CHECK_EQ(prev_distance, pair.distance)
+                << "All ARs with the same AR ID must have the same distance "
+                   "from the corresponding CRSs. Found: "
+                << prev_distance << " and " << pair.distance;
+          }
+          all_reduce_map_[ar_id].push_back(pair);
+          crs_reserved_map_[pair.crs] = ar_id;
         }
       }
     }
@@ -253,11 +291,11 @@ void ArCrsCombiner::GroupAllReducesById(HloModule* module) {
 void ArCrsCombiner::KeepProvablyEqualInstructionGroups() {
   for (auto it : all_reduce_map_) {
     auto all_reduce_id = it.first;
-    auto instruction_vec = it.second;
+    auto pairs_vec = it.second;
-    CHECK_EQ(instruction_vec.size(), num_spatial_partitions_);
+    CHECK_EQ(pairs_vec.size(), num_spatial_partitions_);
-    auto instr_0 = instruction_vec[0];
+    auto instr_0 = pairs_vec[0].ar;
-    for (int i = 1; i < instruction_vec.size(); ++i) {
+    for (int i = 1; i < pairs_vec.size(); ++i) {
-      auto instr_i = instruction_vec[i];
+      auto instr_i = pairs_vec[i].ar;
       auto next_0 = instr_0->users()[0];
       auto next_i = instr_i->users()[0];
       absl::flat_hash_map<int64, int64> visited_pairs;
@@ -281,8 +319,9 @@ StatusOr<bool> ArCrsCombiner::RewriteGraph() {
     return false;
   }
   for (auto it : all_reduce_map_) {
-    auto instruction_vec = it.second;
+    auto pairs_vec = it.second;
-    for (auto all_reduce : instruction_vec) {
+    for (auto pair : pairs_vec) {
+      auto all_reduce = pair.ar;
       auto parent_computation = all_reduce->parent();
       auto all_reduce_id = all_reduce->all_reduce_id();
       auto prev = all_reduce->mutable_operand(0);
@@ -303,16 +342,23 @@ StatusOr<bool> ArCrsCombiner::RewriteGraph() {
                                      ? next->operands()[1]
                                      : next->operands()[0];
             // To move the AR past the addition/subtraction, we need to divide
-            // other_operand by the number of spatial partitions.
+            // other_operand by the number of spatial partitions, except if
-            auto shape = other_operand->shape();
+            // other_operand is a cross-module AR, which can be eliminated.
-            Literal lit(shape);
+            if (other_operand->IsCrossModuleAllReduce() &&
-            lit.PopulateWithValue<float>(num_spatial_partitions_);
+                other_operand->user_count() == 1) {
-            auto divisor = parent_computation->AddInstruction(
+              TF_CHECK_OK(other_operand->ReplaceAllUsesWith(
-                HloInstruction::CreateConstant(lit.Clone()));
+                  other_operand->mutable_operand(0)));
-            auto division =
+            } else {
-                parent_computation->AddInstruction(HloInstruction::CreateBinary(
+              auto shape = other_operand->shape();
-                    shape, HloOpcode::kDivide, other_operand, divisor));
+              Literal lit(shape);
-            TF_CHECK_OK(other_operand->ReplaceUseWith(next, division));
+              lit.PopulateWithValue<float>(num_spatial_partitions_);
+              auto divisor = parent_computation->AddInstruction(
+                  HloInstruction::CreateConstant(lit.Clone()));
+              auto division = parent_computation->AddInstruction(
+                  HloInstruction::CreateBinary(shape, HloOpcode::kDivide,
+                                               other_operand, divisor));
+              TF_CHECK_OK(other_operand->ReplaceUseWith(next, division));
+            }
             break;
           }
           default:

tensorflow/compiler/xla/service/ar_crs_combiner.h

@@ -26,11 +26,47 @@ limitations under the License.
 namespace xla {
 
 // When the HLO graph contains a cross-module AllReduce, followed by some simple
-// linear operations, followed by a cross-replica AllReduce, we can combine the
+// linear operations, followed by a cross-replica AllReduce (also known as
-// CMAR and the CRAR, to use an efficient AllReduce implementation that fully
+// cross-replica sum, or CRS), we can combine the CMAR and the CRAR, to use an
-// utilizes the interconnect bandwidth.
+// efficient AllReduce implementation that fully utilizes the interconnect
+// bandwidth.
 // Such sequences appear in spatially partitioned models.
-// This pass must run right after spatial partitioning.
+// This pass must run right after spatial partitioning, when the code is still
+// in a single HLO module.
+//
+// The steps are:
+// 1) Find CMARs followed by simple ops followed by CRARs.
+// 2) Group CMARs by all_reduce_id. They must all be rewritten.
+// 3) Prove that the CMAR patterns in each core produce the same result.
+// 4) Eliminate the CMAR, and if it feeds an addition/subtraction, divide the
+//    other operand by the number of spatial partitions.
+// 5) Turn the CRAR into an all-core AllReduce.
+//
+// The pass also handles the case where multiple CMARs lead to the same CRAR,
+// and eliminates all CMARs. This graph:
+//
+//        Y
+//        |
+//  X   CMAR_2   Z
+//  |      \    /
+// CMAR_1     +
+//    \     /
+//       +
+//       |
+//     CRAR
+//
+// gets rewritten to:
+//
+//           Z   num_partitions
+//            \  /
+//       Y    div
+//        \   /
+//    X     +
+//     \   /
+//       +
+//       |
+//  all-core AR
+//
 class ArCrsCombiner : public HloModulePass {
  public:
   ArCrsCombiner(int num_spatial_partitions)
@@ -43,6 +79,28 @@ class ArCrsCombiner : public HloModulePass {
                                                HloInstruction* i2);
 
  private:
+  // We used this struct because multiple ARs could be paired with the same CRS.
+  // In this case, we want to select the AR that is furthest from the CRS,
+  // because it makes it easier to eliminate all ARs during RewriteGraph.
+  struct ArCrsPair {
+    HloInstruction* ar;
+    HloInstruction* crs;
+    // The length of the path from AR to CRS in the HLO graph.
+    int64 distance;
+
+    ArCrsPair(HloInstruction* all_reduce, HloInstruction* cross_replica_sum,
+              int64 dist)
+        : ar(all_reduce), crs(cross_replica_sum), distance(dist) {}
+
+    string ToString() {
+      return absl::StrCat("(AR: ", ar->name(), ", CRS: ", crs->name(),
+                          ", distance: ", distance, ")");
+    }
+  };
+
+  absl::optional<ArCrsCombiner::ArCrsPair> MatchesArCrsPattern(
+      HloInstruction* instruction);
+
   // If the passed instruction is a while parameter, and the while body is only
   // called by a single while instruction, return the while instruction.
   absl::optional<HloInstruction*> WhileFromBodyParameter(
@@ -80,8 +138,8 @@ class ArCrsCombiner : public HloModulePass {
 
   int num_spatial_partitions_;
 
-  // Map from all-reduce ids to the all reduce instructions.
+  // Map from all-reduce ids to the AR/CRS pairs.
-  absl::flat_hash_map<int64, std::vector<HloInstruction*>> all_reduce_map_;
+  absl::flat_hash_map<int64, std::vector<ArCrsPair>> all_reduce_map_;
 
   // Map from a CRS instruction to the all-reduce ID of the AR paired with the
   // CRS. Sometimes, several ARs in the code could be paired with the same CRS.

tensorflow/compiler/xla/service/ar_crs_combiner_test.cc

@@ -1005,11 +1005,11 @@ ENTRY %entrycomp (p: f32[]) -> (f32[], f32[]) {
               op::Tuple(op::AllReduce(op::Add(
                             op::Add(op::Parameter(),
                                     op::Divide(op::Constant(), op::Constant())),
-                            op::Divide(op::AllReduce(), op::Constant()))),
+                            op::Parameter())),
                         op::AllReduce(op::Add(
                             op::Add(op::Parameter(),
                                     op::Divide(op::Constant(), op::Constant())),
-                            op::Divide(op::AllReduce(), op::Constant())))));
+                            op::Parameter()))));
   auto crs_after =
       module->entry_computation()->root_instruction()->operands()[0];
   auto replica_groups_after = crs_after->replica_groups();
@@ -1093,15 +1093,17 @@ ENTRY %entrycomp (p: f32[]) -> (f32[], f32[]) {
   ArCrsCombiner combiner(2);
   auto changed = combiner.Run(module.get()).ValueOrDie();
   EXPECT_TRUE(changed);
-  EXPECT_THAT(module->entry_computation()->root_instruction(),
+  EXPECT_THAT(
-              op::Tuple(op::AllReduce(op::Add(
+      module->entry_computation()->root_instruction(),
-                            op::Parameter(),
+      op::Tuple(op::AllReduce(op::Add(
-                            op::Divide(op::Add(op::AllReduce(), op::Constant()),
+                    op::Parameter(),
-                                       op::Constant()))),
+                    op::Add(op::Parameter(),
-                        op::AllReduce(op::Add(
+                            op::Divide(op::Constant(), op::Constant())))),
-                            op::Parameter(),
+                op::AllReduce(op::Add(
-                            op::Divide(op::Add(op::AllReduce(), op::Constant()),
+                    op::Parameter(),
-                                       op::Constant())))));
+                    op::Add(op::Parameter(),
+                            op::Divide(op::Constant(), op::Constant()))))));
+
   auto crs_after =
       module->entry_computation()->root_instruction()->operands()[0];
   auto replica_groups_after = crs_after->replica_groups();

tensorflow/compiler/xla/service/bfloat16_normalization_test.cc

@@ -286,7 +286,8 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleSort) {
   TF_ASSERT_OK_AND_ASSIGN(
       auto* sort,
       MakeSortHlo(ShapeUtil::MakeTupleShape({bf16_shape, s32_shape}),
-                  {key, value}, 0, &builder, module.get()));
+                  {key, value}, 0, /*is_stable=*/false, &builder,
+                  module.get()));
   HloInstruction* gte = builder.AddInstruction(
       HloInstruction::CreateGetTupleElement(bf16_shape, sort, 0));
 
@@ -314,7 +315,8 @@ TEST_F(BFloat16NormalizationTest, ResolveMixedPrecisionTupleSortRoot) {
   TF_ASSERT_OK_AND_ASSIGN(
       auto* sort,
       MakeSortHlo(ShapeUtil::MakeTupleShape({bf16_shape, f32_shape}),
-                  {key, value}, 0, &builder, module.get()));
+                  {key, value}, 0, /*is_stable=*/false, &builder,
+                  module.get()));
 
   auto computation = module->AddEntryComputation(builder.Build());
 

tensorflow/compiler/xla/service/cpu/cpu_compiler.cc

@@ -673,9 +673,9 @@ StatusOr<std::unique_ptr<Executable>> CpuCompiler::RunBackend(
   if (embed_ir_in_executable) {
     ir_module_string = llvm_ir::DumpModuleToString(*llvm_module);
   }
-  TF_RETURN_IF_ERROR(VerifyLlvmModule(*llvm_module));
 
   XLA_VLOG_LINES(2, "LLVM IR:\n" + llvm_ir::DumpModuleToString(*llvm_module));
+  TF_RETURN_IF_ERROR(VerifyLlvmModule(*llvm_module));
 
   // JIT compile the LLVM IR module to in-memory machine code.
   jit->AddModule(std::move(llvm_module));

tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc

@@ -963,8 +963,8 @@ Status EmitBatchDotOperation(
   KernelSupportLibrary ksl(b);
 
   return ksl.ForWithStatus(
-      "bdot", /*start=*/0, /*end=*/batch_count, /*step=*/1,
+      llvm_ir::IrName(&dot, "bdot"), /*start=*/0, /*end=*/batch_count,
-      [&](llvm::Value* indvar) {
+      /*step=*/1, [&](llvm::Value* indvar) {
         DotDimensionNumbers adjusted_dim_numbers = dot.dot_dimension_numbers();
         adjusted_dim_numbers.clear_lhs_batch_dimensions();
         adjusted_dim_numbers.clear_rhs_batch_dimensions();

tensorflow/compiler/xla/service/cpu/ir_emitter.cc

@@ -583,7 +583,7 @@ Status IrEmitter::HandleSort(HloInstruction* hlo) {
       b_.getVoidTy(),
       {b_.getInt64Ty(), b_.getInt64Ty(), b_.getInt64Ty(),
        b_.getInt8PtrTy()->getPointerTo(), b_.getInt32Ty(),
-       b_.getInt32Ty()->getPointerTo(), b_.getInt8PtrTy(),
+       b_.getInt32Ty()->getPointerTo(), b_.getInt1Ty(), b_.getInt8PtrTy(),
        b_.getInt64Ty()->getPointerTo(), less_than_function->getType()},
       /*isVarArg=*/false);
   auto* key_value_sort_func = llvm::dyn_cast<llvm::Function>(
@@ -616,8 +616,8 @@ Status IrEmitter::HandleSort(HloInstruction* hlo) {
        {b_.getInt64(higher_dimensions), b_.getInt64(sort_dimension_elements),
         b_.getInt64(lower_dimensions), values,
         b_.getInt32(sort->operand_count()), sizes,
-        GetExecutableRunOptionsArgument(), GetProfileCountersArgument(),
+        b_.getInt1(sort->is_stable()), GetExecutableRunOptionsArgument(),
-        less_than_function});
+        GetProfileCountersArgument(), less_than_function});
 
   if (sort->values_count() > 0) {
     llvm_ir::EmitTuple(GetIrArrayFor(sort), destination_addresses, &b_,

tensorflow/compiler/xla/service/cpu/runtime_key_value_sort.cc

@@ -32,8 +32,8 @@ using tensorflow::int64;
 
 TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_KeyValueSort(
     int64 a, int64 b, int64 c, char** values, int32 values_count,
-    int32* values_primitive_type_size_in_bytes, char* run_options,
+    int32* values_primitive_type_size_in_bytes, bool is_stable,
-    int64* prof_counters,
+    char* run_options, int64* prof_counters,
     void (*less_than)(char*, char*, char**, char**, tensorflow::int64*)) {
   // 'values' and 'values_primitive_type_size_in_bytes' are managed by the JIT
   // code, so msan can't tell they are initialized.
@@ -69,22 +69,27 @@ TF_ATTRIBUTE_NO_SANITIZE_MEMORY void __xla_cpu_runtime_KeyValueSort(
     int64 base_offset =
         index % sort_dimension_offset +
         (index - index % sort_dimension_offset) * sort_dimension_elements;
-    std::stable_sort(
+    auto compare_function = [&](int64 a, int64 b) -> bool {
-        indices.get(), indices.get() + sort_dimension_elements,
+      int64 memory_index_lhs = (base_offset + a * sort_dimension_offset) *
-        [&](int64 a, int64 b) -> bool {
+                               values_primitive_type_size_in_bytes[0];
-          int64 memory_index_lhs = (base_offset + a * sort_dimension_offset) *
+      int64 memory_index_rhs = (base_offset + b * sort_dimension_offset) *
-                                   values_primitive_type_size_in_bytes[0];
+                               values_primitive_type_size_in_bytes[0];
-          int64 memory_index_rhs = (base_offset + b * sort_dimension_offset) *
+      for (int32 i = 0; i < values_count; ++i) {
-                                   values_primitive_type_size_in_bytes[0];
+        comparison_values[i * 2] = values[i] + memory_index_lhs;
-          for (int32 i = 0; i < values_count; ++i) {
+        comparison_values[i * 2 + 1] = values[i] + memory_index_rhs;
-            comparison_values[i * 2] = values[i] + memory_index_lhs;
+      }
-            comparison_values[i * 2 + 1] = values[i] + memory_index_rhs;
+      char result = 0;  // Overwritten by less_than.
-          }
+      less_than(&result, run_options, comparison_values.get(), nullptr,
-          char result = 0;  // Overwritten by less_than.
+                prof_counters);
-          less_than(&result, run_options, comparison_values.get(), nullptr,
+      return result != 0u;
-                    prof_counters);
+    };
-          return result != 0u;
+    if (is_stable) {
-        });
+      std::stable_sort(indices.get(), indices.get() + sort_dimension_elements,
+                       compare_function);
+    } else {
+      std::sort(indices.get(), indices.get() + sort_dimension_elements,
+                compare_function);
+    }
 
     // Reorder the values according to the order defined by 'indices'.
     for (int32 idx = 0; idx < values_count; ++idx) {

tensorflow/compiler/xla/service/cpu/runtime_key_value_sort.h

@@ -22,15 +22,14 @@ limitations under the License.
 extern "C" {
 
 // Each entry in 'values' represents a 3-dimensional shape with dimensions
-// [a, b, c]. The 'b' dimension of the first shape is sorted into ascending
+// [a, b, c]. The 'b' dimension of each shape is sorted into ascending order
-// order according to the results of comparisons using the provided 'less_than'
+// according to the results of comparisons using the provided 'less_than'
 // function. 'values_count' must be > 0 and specifies the number of entries in
 // 'values' and 'values_primitive_type_size_in_bytes'. The size of the primitive
 // type of the i-th shape has exactly 'values_primitive_type_size_in_bytes[i]'
-// bytes. The elements in each 'values' shape are reordered in the same way
+// bytes. 'is_stable' specifies whether the sorting should be stable.
-// according to the comparisons using the first shape. 'run_options' and
+// 'run_options' and 'prof_counters' are passed through to the less-than
-// 'prof_counters' are passed through to the less-than function, which expects
+// function, which expects the following arguments:
-// the following arguments:
 // - pointer to the return value buffer (char*)
 // - xla::ExecutableRunOptions = 'run_options' (char*)
 // - pointers to the parameter buffers (char**)
@@ -39,8 +38,8 @@ extern "C" {
 extern void __xla_cpu_runtime_KeyValueSort(
     tensorflow::int64 a, tensorflow::int64 b, tensorflow::int64 c,
     char** values, tensorflow::int32 values_count,
-    tensorflow::int32* values_primitive_type_size_in_bytes, char* run_options,
+    tensorflow::int32* values_primitive_type_size_in_bytes, bool is_stable,
-    tensorflow::int64* prof_counters,
+    char* run_options, tensorflow::int64* prof_counters,
     void (*less_than)(char*, char*, char**, char**, tensorflow::int64*));
 }
 

tensorflow/compiler/xla/service/cpu/tiled_dot_emitter.cc

@@ -938,6 +938,53 @@ void TiledSmallGemmEmitter::EmitTiledGemm(
   });
 }
 
+llvm::Type* GetPointerToElementType(llvm::Type* pointer_type) {
+  llvm::Type* type =
+      llvm::cast<llvm::PointerType>(pointer_type)->getElementType();
+  while (auto* array_type = llvm::dyn_cast<llvm::ArrayType>(type)) {
+    type = array_type->getElementType();
+  }
+
+  return type->getPointerTo();
+}
+
+struct GemvBuffersWithCanonicalType {
+  llvm::Value* lhs_canonicalized;
+  llvm::Value* rhs_canonicalized;
+  llvm::Value* addend_canonicalized;
+  llvm::Value* result_canonicalized;
+};
+
+GemvBuffersWithCanonicalType GetGemvBuffersWithCanonicalType(
+    llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend,
+    llvm::Value* result, llvm::IRBuilder<>* b) {
+  // We characterize a GEMV operation via M and K, since N is implicitly 1.
+  // This means the GEMV that multiplies (say) [5,6] with [6,1] is implemented
+  // by the same GEMV that multiplies [5,6] with [1,6].  However, the
+  // `llvm::Types` for the inputs to the two GEMVs don't match (in a trivial
+  // sense -- the in memory representations are the same) since they're computed
+  // from the `xla::Shape`s.  Since we want to be able to call the same
+  // `llvm::Function` for the two GEMVs we canonicalize the types of the GEMV
+  // inputs here into the same type.
+  GemvBuffersWithCanonicalType buffers_with_canonical_type;
+  llvm::Type* lhs_type = lhs->getType();
+  llvm::Type* rhs_type = rhs->getType();
+  llvm::Type* addend_type = addend ? addend->getType() : nullptr;
+  llvm::Type* result_type = result->getType();
+
+  buffers_with_canonical_type.lhs_canonicalized =
+      b->CreateBitCast(lhs, GetPointerToElementType(lhs_type));
+  buffers_with_canonical_type.rhs_canonicalized =
+      b->CreateBitCast(rhs, GetPointerToElementType(rhs_type));
+  buffers_with_canonical_type.addend_canonicalized =
+      addend ? b->CreateBitCast(addend, GetPointerToElementType(addend_type))
+             : nullptr;
+  buffers_with_canonical_type.result_canonicalized =
+      b->CreateBitCast(result, GetPointerToElementType(result_type));
+
+  return buffers_with_canonical_type;
+}
+
 }  // namespace
 
 void EmitRowMajorGemv(PrimitiveType scalar_type, int64 tile_rows,
@@ -950,12 +997,18 @@ void EmitRowMajorGemv(PrimitiveType scalar_type, int64 tile_rows,
       /*tile_rows=*/tile_rows, /*tile_cols=*/tile_cols,
       /*m=*/m, /*k=*/k, /*has_addend=*/addend != nullptr);
 
+  GemvBuffersWithCanonicalType canonical_inputs =
+      GetGemvBuffersWithCanonicalType(lhs, rhs, addend, result, b);
+
   KernelSupportLibrary::EmitAndCallOutlinedKernel(
       /*enable_fast_math=*/enable_fast_math,
-      /*optimize_for_size=*/optimize_for_size, b, config.GetCacheKey(), lhs,
+      /*optimize_for_size=*/optimize_for_size, b, config.GetCacheKey(),
-      rhs, addend, result,
+      canonical_inputs.lhs_canonicalized, canonical_inputs.rhs_canonicalized,
-      [&](llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend,
+      canonical_inputs.addend_canonicalized,
-          llvm::Value* result) {
+      canonical_inputs.result_canonicalized,
+      [&config, b, &canonical_inputs](llvm::Value* lhs, llvm::Value* rhs,
+                                      llvm::Value* addend,
+                                      llvm::Value* result) {
         RowMajorMatrixVectorProductEmitter emitter(config, lhs, rhs, addend,
                                                    result, b);
         emitter.Emit();
@@ -972,12 +1025,18 @@ void EmitColumnMajorGemv(PrimitiveType scalar_type, int64 tile_rows,
       /*tile_rows=*/tile_rows, /*tile_cols=*/tile_cols,
       /*m=*/m, /*k=*/k, /*has_addend=*/addend != nullptr);
 
+  GemvBuffersWithCanonicalType canonical_inputs =
+      GetGemvBuffersWithCanonicalType(lhs, rhs, addend, result, b);
+
   KernelSupportLibrary::EmitAndCallOutlinedKernel(
       /*enable_fast_math=*/enable_fast_math,
-      /*optimize_for_size=*/optimize_for_size, b, config.GetCacheKey(), lhs,
+      /*optimize_for_size=*/optimize_for_size, b, config.GetCacheKey(),
-      rhs, addend, result,
+      canonical_inputs.lhs_canonicalized, canonical_inputs.rhs_canonicalized,
-      [&](llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend,
+      canonical_inputs.addend_canonicalized,
-          llvm::Value* result) {
+      canonical_inputs.result_canonicalized,
+      [&config, b, &canonical_inputs](llvm::Value* lhs, llvm::Value* rhs,
+                                      llvm::Value* addend,
+                                      llvm::Value* result) {
         ColumnMajorMatrixVectorProductEmitter emitter(config, lhs, rhs, addend,
                                                       result, b);
         emitter.Emit();

tensorflow/compiler/xla/service/elemental_ir_emitter.cc

@@ -1367,26 +1367,69 @@ StatusOr<llvm::Value*> ElementalIrEmitter::ConvertValueForDistribution(
       llvm_ir::PrimitiveTypeToIrType(elem_prim_ty, module_);
   llvm::Type* raw_value_ty = raw_value->getType();
 
-  // Convert raw integer to float in range [0, 1) if the element is a float.
+  // If we're generating a floating-point value, convert the raw integer R (i.e.
+  // `raw_value`) to a float in the range [0, 1).
+  //
+  // The basic approach is to choose a significand and exponent such that the
+  // significand is uniformly distributed and the exponent is distributed, well,
+  // exponentially (it's more likely to be close to 0 than far from 0).
+  //
+  // An easy way to do this is to say that the significand is the first S bits
+  // of R, and the exponent is determined by the number of trailing zeroes in R,
+  // exp = 2^-(cttz(R) + 1).  (+1 because the largest exponent should be -1;
+  // this way the largest value we can return is 1.999... * 2^-1 = 1-ε.)
+  //
+  // This results in a small bias.  Namely, if R has enough trailing zeroes, the
+  // significand and exponent will "overlap".  As a concrete example, consider
+  //
+  //         20 X's                 12 zeroes
+  //   R = 0bXXXXXXXXXXXXXXXXXXXX000000000000
+  //
+  // Here the exponent is 2^-13 because R has 12 trailing zeroes.  The
+  // significand is made up of the first 23 most-significant bits of R, which we
+  // observe contain 3 zeroes.  This is biased because any random value with
+  // exponent 2^-12 will have a significand which ends in `000`.
+  //
+  // For f32s, this problem occurs only when there are more than 32-23 = 9
+  // trailing zeros, which happens with probability 0.5^10 = ~0.1%. Moreover the
+  // probability of a large bias (i.e. many trailing 0s in the significand) is
+  // exponentially low.  So we deem this acceptable.
   llvm::Value* elem_value = raw_value;
   if (elem_ir_ty->isFloatingPointTy()) {
-    unsigned raw_value_size_in_bits = raw_value_ty->getPrimitiveSizeInBits();
+    const auto& dest_flt_semantics = elem_ir_ty->getFltSemantics();
-    CHECK(raw_value_size_in_bits == 32 || raw_value_size_in_bits == 64);
+    const int bits = raw_value_ty->getPrimitiveSizeInBits();
-    // Perform the division using the float type with the same number of bits
+    CHECK_GE(bits, llvm::APFloat::semanticsSizeInBits(dest_flt_semantics));
-    // as the raw value to avoid overflow.
-    if (raw_value_size_in_bits == 32) {
-      elem_value = UIToFP(elem_value, b_->getFloatTy());
-      elem_value = FDiv(elem_value,
-                        llvm::ConstantFP::get(b_->getFloatTy(), std::exp2(32)));
-    } else {
-      elem_value = UIToFP(elem_value, b_->getDoubleTy());
-      elem_value = FDiv(
-          elem_value, llvm::ConstantFP::get(b_->getDoubleTy(), std::exp2(64)));
-    }
 
-    if (elem_ir_ty != elem_value->getType()) {
+    // Subtract 1 because semanticsPrecision includes the "hidden bit", i.e. the
-      elem_value = FPTrunc(elem_value, elem_ir_ty);
+    // implicit "1." at the beginning of the significand.
-    }
+    const int significand_bits =
+        llvm::APFloat::semanticsPrecision(dest_flt_semantics) - 1;
+
+    llvm::Value* cttz = llvm_ir::EmitCallToIntrinsic(
+        llvm::Intrinsic::cttz, {raw_value, /*is_zero_undef=*/b_->getFalse()},
+        {raw_value->getType()}, b_);
+    llvm::Value* significand = LShr(raw_value, bits - significand_bits);
+
+    // Exponent bias is -127 for f32, meaning that if the exponent is E and the
+    // significand is S, then the value of the number is 2^(E - 127) * (1.S).
+    //
+    // We want cttz == 0 to correspond to 2^-1, so our exponent is computed as
+    // E = 126 - cttz.
+    //
+    // For f64, this is all the same, except the bias is -1023.
+    //
+    // In IEEE floating point, the absolute value of the exponent bias equals
+    // the value of the largest possible exponent.
+    const int bias = -llvm::APFloat::semanticsMaxExponent(dest_flt_semantics);
+    llvm::Value* exponent =
+        Sub(llvm::ConstantInt::get(cttz->getType(), -bias - 1), cttz);
+
+    // Now just slot everything into place!  The `Trunc` is here because
+    // raw_value may be larger than our float destination.
+    elem_value =
+        BitCast(Trunc(Or(Shl(exponent, significand_bits), significand),
+                      b_->getIntNTy(elem_ir_ty->getPrimitiveSizeInBits())),
+                elem_ir_ty);
   }
 
   // Convert the value for the requested distribution.

tensorflow/compiler/xla/service/elemental_ir_emitter.h

@@ -216,8 +216,11 @@ class ElementalIrEmitter : public IrBuilderMixin<ElementalIrEmitter> {
   llvm_ir::ElementGenerator MakePhiloxRngElementGenerator(
       const HloInstruction* hlo,
       const HloToElementGeneratorMap& operand_to_generator);
+
   // Converts the raw value generated by a random number generation algorithm
   // to the distribution requested by the RNG HloInstruction.
+  //
+  // Precondition: raw_value has at least as many bits as hlo's element type.
   StatusOr<llvm::Value*> ConvertValueForDistribution(
       const HloInstruction* hlo,
       const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator,

tensorflow/compiler/xla/service/gpu/BUILD

@@ -765,6 +765,7 @@ cc_library(
         "//tensorflow/compiler/xla/service:reduce_precision_insertion",
         "//tensorflow/compiler/xla/service:reshape_mover",
         "//tensorflow/compiler/xla/service:sort_simplifier",
+        "//tensorflow/compiler/xla/service:stable_sort_expander",
         "//tensorflow/compiler/xla/service:transpose_folding",
         "//tensorflow/compiler/xla/service:tuple_simplifier",
         "//tensorflow/compiler/xla/service:while_loop_constant_sinking",

tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc

@@ -82,6 +82,7 @@ limitations under the License.
 #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h"
 #include "tensorflow/compiler/xla/service/reshape_mover.h"
 #include "tensorflow/compiler/xla/service/sort_simplifier.h"
+#include "tensorflow/compiler/xla/service/stable_sort_expander.h"
 #include "tensorflow/compiler/xla/service/transpose_folding.h"
 #include "tensorflow/compiler/xla/service/tuple_simplifier.h"
 #include "tensorflow/compiler/xla/service/while_loop_constant_sinking.h"
@@ -195,6 +196,8 @@ Status OptimizeHloModule(HloModule* hlo_module, se::StreamExecutor* stream_exec,
     pipeline.AddPass<ConvolutionGroupConverter>(
         cost_model,
         /*convert_batch_groups_only=*/true);
+    // Expand the sort op to support stable sorting if required.
+    pipeline.AddPass<StableSortExpander>();
     // Convert BF16 operations to F32 operations so that the GPU backend can
     // support BF16 operations without directly implementing a BF16 lowering for
     // most ops.

tensorflow/compiler/xla/service/hlo.proto

@@ -34,7 +34,7 @@ import "tensorflow/compiler/xla/xla_data.proto";
 option cc_enable_arenas = true;
 
 // Serialization of HloInstruction.
-// Next ID: 60
+// Next ID: 61
 message HloInstructionProto {
   reserved 10;
   reserved "parameter_name";
@@ -175,6 +175,9 @@ message HloInstructionProto {
   // partners.
   bool is_host_transfer = 47;
 
+  // Whether this Sort instruction should be stable.
+  bool is_stable = 60;
+
   xla.ScatterDimensionNumbers scatter_dimension_numbers = 48;
 
   // Precision configuration for the instruction. Has backend-specific meaning.

tensorflow/compiler/xla/service/hlo_creation_utils.cc

@@ -275,7 +275,7 @@ StatusOr<HloInstruction*> MakeSelectHlo(HloInstruction* pred,
 
 StatusOr<HloInstruction*> MakeSortHlo(
     const Shape& sort_shape, absl::Span<HloInstruction* const> operands,
-    int64 dimension_to_sort, HloComputation::Builder* builder,
+    int64 dimension_to_sort, bool is_stable, HloComputation::Builder* builder,
     HloModule* module) {
   CHECK(!operands.empty()) << "Sort Hlo requires at least one operand.";
   HloComputation* compare_computation;
@@ -293,7 +293,7 @@ StatusOr<HloInstruction*> MakeSortHlo(
   compare_computation =
       module->DeepCloneComputation(new_module->entry_computation(), &context);
   return builder->AddInstruction(HloInstruction::CreateSort(
-      sort_shape, dimension_to_sort, operands, compare_computation));
+      sort_shape, dimension_to_sort, operands, compare_computation, is_stable));
 }
 
 StatusOr<HloInstruction*> CollapseFirstNDims(HloInstruction* operand, int64 n) {

tensorflow/compiler/xla/service/hlo_creation_utils.h

@@ -126,10 +126,10 @@ StatusOr<HloInstruction*> MakeSelectHlo(HloInstruction* pred,
 // Creates a Sort HLO instruction and adds it to the computation containing the
 // operands. All operands must be in the same computation. Also creates a
 // default compare sub-computation which sorts the first operand into ascending
-// order.
+// order. 'is_stable' specifies whether the sorting should be stable.
 StatusOr<HloInstruction*> MakeSortHlo(
     const Shape& sort_shape, absl::Span<HloInstruction* const> operands,
-    int64 dimension_to_sort, HloComputation::Builder* builder,
+    int64 dimension_to_sort, bool is_stable, HloComputation::Builder* builder,
     HloModule* module);
 
 // Creates an R1 Constant HLO instruction of the given PrimitiveType with the

tensorflow/compiler/xla/service/hlo_dataflow_analysis_test.cc

@@ -2363,7 +2363,8 @@ TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) {
   auto keys = builder.AddInstruction(
       HloInstruction::CreateParameter(0, keys_shape, "keys"));
   TF_ASSERT_OK_AND_ASSIGN(
-      auto* sort, MakeSortHlo(keys_shape, {keys}, -1, &builder, module_.get()));
+      auto* sort, MakeSortHlo(keys_shape, {keys}, -1, /*is_stable=*/false,
+                              &builder, module_.get()));
 
   computation_ = module_->AddEntryComputation(builder.Build());
   RunAnalysis();
@@ -2385,7 +2386,8 @@ TEST_F(CanShareOperandBufferWithUserTest, SortCanShareWithTupleUser) {
   TF_ASSERT_OK_AND_ASSIGN(
       auto* sort,
       MakeSortHlo(ShapeUtil::MakeTupleShape({keys_shape, values_shape}),
-                  {keys, values}, 0, &builder, module_.get()));
+                  {keys, values}, 0, /*is_stable=*/false, &builder,
+                  module_.get()));
 
   computation_ = module_->AddEntryComputation(builder.Build());
   RunAnalysis();

tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h

@@ -2670,12 +2670,25 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault {
         const Literal& high =
             parent_->GetEvaluatedLiteralFor(random->operand(1));
 
-        std::uniform_real_distribution<NativeT> generator(
+        // std::uniform_real_distribution(a, b) can sometimes return a value
-            low.Get<NativeT>({}), high.Get<NativeT>({}));
+        // equal to b.  Unclear if this is a spec bug or an implementation bug
-
+        // or WAI [0] [1] [2].  Anyway for our purposes we want a half-open
+        // interval, so we have to re-sample if we get `b` out.
+        //
+        // [0] https://gcc.gnu.org/bugzilla/show_bug.cgi?id=63176
+        // [1] https://bugs.llvm.org/show_bug.cgi?id=18767
+        // [2] http://open-std.org/JTC1/SC22/WG21/docs/lwg-active.html#2524
+        auto low_val = low.Get<NativeT>({});
+        auto high_val = high.Get<NativeT>({});
+        std::uniform_real_distribution<NativeT> generator(low_val, high_val);
         TF_RETURN_IF_ERROR(
             result.Populate<NativeT>([&](absl::Span<const int64> /*indexes*/) {
-              return generator(parent_->engine_);
+              while (true) {
+                NativeT v = generator(parent_->engine_);
+                if (v != high_val) {
+                  return v;
+                }
+              }
             }));
         break;
       }

tensorflow/compiler/xla/service/hlo_instruction.cc

@@ -214,7 +214,7 @@ StatusOr<std::unique_ptr<HloInstruction>> HloInstruction::CreateFromProto(
           << proto.called_computation_ids_size();
       auto sort_operands = all_operands();
       instruction = CreateSort(shape, proto.dimensions(0), all_operands(),
-                               computations(0));
+                               computations(0), proto.is_stable());
       break;
     }
     case HloOpcode::kTranspose:
@@ -1170,9 +1170,10 @@ HloInstruction::CreateBroadcastSequence(
 
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateSort(
     const Shape& shape, int64 dimension,
-    absl::Span<HloInstruction* const> operands, HloComputation* compare) {
+    absl::Span<HloInstruction* const> operands, HloComputation* compare,
+    bool is_stable) {
   return absl::make_unique<HloSortInstruction>(shape, dimension, operands,
-                                               compare);
+                                               compare, is_stable);
 }
 
 /* static */ std::unique_ptr<HloInstruction> HloInstruction::CreateFusion(

tensorflow/compiler/xla/service/hlo_instruction.h

@@ -384,6 +384,14 @@ class HloInstruction {
 
   // Creates a random number generation instruction that fills a shape with
   // random numbers from a given distribution.
+  //
+  // The parameters to the instruction are interpreted as follows:
+  //
+  //  - If `distribution` is RNG_UNIFORM, generates a number in range
+  //    [param0, param1).
+  //
+  //  - If `distribution` is RNG_NORMAL, generates a normally-distributed value
+  //    with mean `param0` and standard deviation `param1`.
   static std::unique_ptr<HloInstruction> CreateRng(
       const Shape& shape, RandomDistribution distribution,
       absl::Span<HloInstruction* const> parameters);
@@ -678,10 +686,11 @@ class HloInstruction {
   // comparisons in the sorting algorithm. 'compare' gets 2 * n parameters,
   // where parameters 2 * i and 2 * i + 1 are the values of the i-th operand at
   // specific index positions which should be compared, and should return a
-  // PRED.
+  // PRED. 'is_stable' specifies whether stable sorting is required.
   static std::unique_ptr<HloInstruction> CreateSort(
       const Shape& shape, int64 dimension,
-      absl::Span<HloInstruction* const> operands, HloComputation* compare);
+      absl::Span<HloInstruction* const> operands, HloComputation* compare,
+      bool is_stable);
 
   // Creates a while instruction, given a condition computation, a body
   // computation, and the initial value for the input of the computations. For
@@ -1286,6 +1295,9 @@ class HloInstruction {
     backend_config_ = std::move(config_str);
   }
 
+  bool is_default_config() const { return is_default_config_; }
+  void set_default_config() { is_default_config_ = true; }
+
   // Returns a string representation of a proto in the format used by
   // raw_backend_config_string.
   //
@@ -1734,6 +1746,10 @@ class HloInstruction {
   // HLO. See the documentation on backend_config().
   string backend_config_;
 
+  // This field is assigned to true when backend_config_ is assigned to
+  // a default configuration.
+  bool is_default_config_ = false;
+
   // String identifier for instruction.
   string name_;
 

tensorflow/compiler/xla/service/hlo_instructions.cc

@@ -659,8 +659,11 @@ std::unique_ptr<HloInstruction> HloReduceInstruction::CloneWithNewOperandsImpl(
 
 HloSortInstruction::HloSortInstruction(
     const Shape& shape, int64 dimension,
-    absl::Span<HloInstruction* const> operands, HloComputation* compare)
+    absl::Span<HloInstruction* const> operands, HloComputation* compare,
-    : HloInstruction(HloOpcode::kSort, shape), dimensions_({dimension}) {
+    bool is_stable)
+    : HloInstruction(HloOpcode::kSort, shape),
+      dimensions_({dimension}),
+      is_stable_(is_stable) {
   for (auto* value : operands) {
     AppendOperand(value);
   }
@@ -672,12 +675,18 @@ HloInstructionProto HloSortInstruction::ToProto() const {
   for (int64 dimension : dimensions_) {
     proto.add_dimensions(dimension);
   }
+  proto.set_is_stable(is_stable());
   return proto;
 }
 
 std::vector<string> HloSortInstruction::ExtraAttributesToStringImpl(
     const HloPrintOptions& options) const {
-  return {StrCat("dimensions={", StrJoin(dimensions(), ","), "}")};
+  std::vector<string> attrs;
+  attrs.push_back(StrCat("dimensions={", StrJoin(dimensions(), ","), "}"));
+  if (is_stable()) {
+    attrs.push_back("is_stable=true");
+  }
+  return attrs;
 }
 
 bool HloSortInstruction::IdenticalSlowPath(
@@ -688,14 +697,17 @@ bool HloSortInstruction::IdenticalSlowPath(
   if (dimensions() != casted_other.dimensions()) {
     return false;
   }
+  if (is_stable() != casted_other.is_stable()) {
+    return false;
+  }
   return eq_computations(to_apply(), other.to_apply());
 }
 
 std::unique_ptr<HloInstruction> HloSortInstruction::CloneWithNewOperandsImpl(
     const Shape& shape, absl::Span<HloInstruction* const> new_operands,
     HloCloneContext* context) const {
-  return absl::make_unique<HloSortInstruction>(shape, dimensions(0),
+  return absl::make_unique<HloSortInstruction>(
-                                               new_operands, to_apply());
+      shape, dimensions(0), new_operands, to_apply(), is_stable());
 }
 
 HloTransposeInstruction::HloTransposeInstruction(

tensorflow/compiler/xla/service/hlo_instructions.h

@@ -447,7 +447,7 @@ class HloSortInstruction : public HloInstruction {
  public:
   explicit HloSortInstruction(const Shape& shape, int64 dimension,
                               absl::Span<HloInstruction* const> operands,
-                              HloComputation* compare);
+                              HloComputation* compare, bool is_stable);
   // Returns the dimension sizes or numbers associated with this instruction.
   const std::vector<int64>& dimensions() const override { return dimensions_; }
   int64 dimensions(int64 index) const override { return dimensions()[index]; }
@@ -460,6 +460,7 @@ class HloSortInstruction : public HloInstruction {
   HloInstruction* mutable_keys() { return mutable_operand(0); }
   // Returns the number of value operands.
   int64 values_count() const { return operand_count() - 1; }
+  bool is_stable() const { return is_stable_; }
 
  private:
   std::vector<string> ExtraAttributesToStringImpl(
@@ -474,6 +475,7 @@ class HloSortInstruction : public HloInstruction {
       HloCloneContext* context) const override;
 
   std::vector<int64> dimensions_;
+  bool is_stable_;
 };
 
 class HloTransposeInstruction : public HloInstruction {

tensorflow/compiler/xla/service/hlo_parser.cc

@@ -895,6 +895,8 @@ bool HloParser::ParseInstructionRhs(HloComputation::Builder* builder,
       optional<std::vector<int64>> dimensions;
       attrs["dimensions"] = {/*required=*/true, AttrTy::kBracedInt64List,
                              &dimensions};
+      optional<bool> is_stable = false;
+      attrs["is_stable"] = {/*required=*/false, AttrTy::kBool, &is_stable};
       optional<HloComputation*> to_apply;
       attrs["to_apply"] = {/*required=*/true, AttrTy::kHloComputation,
                            &to_apply};
@@ -902,8 +904,9 @@ bool HloParser::ParseInstructionRhs(HloComputation::Builder* builder,
           dimensions->size() != 1) {
         return false;
       }
-      instruction = builder->AddInstruction(HloInstruction::CreateSort(
+      instruction = builder->AddInstruction(
-          shape, dimensions->at(0), operands, to_apply.value()));
+          HloInstruction::CreateSort(shape, dimensions->at(0), operands,
+                                     to_apply.value(), is_stable.value()));
       break;
     }
     case HloOpcode::kTuple: {

tensorflow/compiler/xla/service/hlo_parser_test.cc

@@ -1145,6 +1145,24 @@ ENTRY Sort {
   ROOT sorted = (f32[1024,16]{0,1}, s32[1024,16]{0,1}, u32[1024,16]{0,1}, f32[1024,16]{0,1}) sort(keys, values.0, values.1, values.2), dimensions={0}, to_apply=compare
 }
 
+)"
+},
+// Sort (Key) is_stable=true
+{
+"SortKeyStable",
+R"(HloModule sort
+
+compare {
+  p.0.lhs = f32[] parameter(0)
+  p.0.rhs = f32[] parameter(1)
+  ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+}
+
+ENTRY Sort {
+  x = f32[1024]{0} parameter(0)
+  ROOT sorted = f32[1024]{0} sort(x), dimensions={0}, is_stable=true, to_apply=compare
+}
+
 )"
 },
 // Conditional

tensorflow/compiler/xla/service/hlo_runner.cc

@@ -176,7 +176,7 @@ StatusOr<Literal> HloRunner::Execute(
                       TransferLiteralsToDevice(arguments));
   TF_ASSIGN_OR_RETURN(ScopedShapedBuffer result,
                       ExecuteWithDeviceBuffers(
-                          /*module=*/std::move(executable),
+                          /*executable=*/executable.get(),
                           /*arguments=*/argument_buffers,
                           /*profile=*/profile));
   return TransferLiteralFromDevice(result);
@@ -235,7 +235,7 @@ StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
 }
 
 StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
-    std::unique_ptr<Executable> executable,
+    Executable* executable,
     const absl::Span<const ShapedBuffer* const> arguments,
     ExecutionProfile* profile) {
   // Get service run options.
@@ -254,7 +254,7 @@ StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
 }
 
 StatusOr<ScopedShapedBuffer> HloRunner::ExecuteWithDeviceBuffers(
-    std::unique_ptr<Executable> executable,
+    Executable* executable,
     const absl::Span<const ScopedShapedBuffer> arguments,
     ExecutionProfile* profile) {
   std::vector<const ShapedBuffer*> argument_pointers;

tensorflow/compiler/xla/service/hlo_runner.h

@@ -144,13 +144,16 @@ class HloRunner {
       const absl::Span<const ScopedShapedBuffer> arguments,
       bool run_hlo_passes = true, ExecutionProfile* profile = nullptr);
 
+  // In the following two calls, "executable" is not a unique_ptr to allow
+  // reuse of the Executable.  This call may update the profile information in
+  // *executable.
   StatusOr<ScopedShapedBuffer> ExecuteWithDeviceBuffers(
-      std::unique_ptr<Executable> executable,
+      Executable* executable,
       const absl::Span<const ShapedBuffer* const> arguments,
       ExecutionProfile* profile = nullptr);
 
   StatusOr<ScopedShapedBuffer> ExecuteWithDeviceBuffers(
-      std::unique_ptr<Executable> executable,
+      Executable* executable,
       const absl::Span<const ScopedShapedBuffer> arguments,
       ExecutionProfile* profile = nullptr);
 

tensorflow/compiler/xla/service/op_expander_pass.cc

@@ -36,6 +36,9 @@ StatusOr<bool> OpExpanderPass::Run(HloModule* module) {
   for (HloInstruction* inst : matching_instructions) {
     TF_ASSIGN_OR_RETURN(HloInstruction * expanded_root,
                         ExpandInstruction(inst));
+    if (expanded_root == nullptr) {
+      continue;
+    }
     TF_RETURN_IF_ERROR(inst->parent()->ReplaceInstruction(inst, expanded_root));
   }
 

tensorflow/compiler/xla/service/op_expander_pass.h

@@ -33,7 +33,9 @@ class OpExpanderPass : public HloModulePass {
   // Returns `true` if `instruction` should be expanded by this pass.
   virtual bool InstructionMatchesPattern(HloInstruction* instruction) = 0;
 
-  // Returns a replacement for `instruction`.
+  // Returns a replacement for `instruction`, or nullptr if no replacement is
+  // neeeded (e.g. only the to_apply subcomputation of the instruction was
+  // modified).
   virtual StatusOr<HloInstruction*> ExpandInstruction(
       HloInstruction* instruction) = 0;
 };

tensorflow/compiler/xla/service/stable_sort_expander.cc

@@ -0,0 +1,204 @@
+/* Copyright 2019 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/stable_sort_expander.h"
+
+#include <limits>
+#include <memory>
+#include <vector>
+
+#include "absl/container/flat_hash_map.h"
+#include "absl/container/flat_hash_set.h"
+#include "tensorflow/compiler/xla/service/hlo_casting_utils.h"
+#include "tensorflow/compiler/xla/service/hlo_computation.h"
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_instructions.h"
+#include "tensorflow/compiler/xla/service/op_expander_pass.h"
+#include "tensorflow/compiler/xla/statusor.h"
+
+namespace xla {
+
+// Looks for a iota operand that can be used as tie breaker in the computation.
+// If no matching iota operand is found, a iota operand is added to Sort. The
+// comparison computation is adjusted to break ties using the values from the
+// iota operand.
+StatusOr<HloInstruction*> StableSortExpander::ExpandInstruction(
+    HloInstruction* instruction) {
+  auto* sort = Cast<HloSortInstruction>(instruction);
+  HloComputation* computation = sort->parent();
+
+  HloInstruction* expanded_sort = nullptr;
+  absl::flat_hash_set<int64> used_indices;
+  int64 iota_index = -1;
+  for (const HloInstruction* operand : sort->operands()) {
+    // We can only use the iota operand if it has an iota dimension which is the
+    // same as the dimension to sort. Also it should have an integral type that
+    // is large enough for the number of elements in the sort dimension. For
+    // now, we only allow S32, because we expect to find a S32 iota operand for
+    // all Sort ops which are created by TopK.
+    // TODO(b/122298745): Also support other types.
+    if (operand->opcode() == HloOpcode::kIota &&
+        Cast<HloIotaInstruction>(operand)->iota_dimension() ==
+            sort->sort_dimension() &&
+        operand->shape().element_type() == S32) {
+      iota_index = sort->operand_index(operand);
+      break;
+    }
+  }
+
+  // If there is currently no iota operand which we could use for making the
+  // sort stable, we will have to add a new such operand.
+  if (iota_index == -1) {
+    Shape iota_shape = sort->operand(0)->shape();
+    // We might need to use S64 if the number of elements in the sort dimension
+    // is bigger than 2^31 - 1.
+    // TODO(b/122298745): Handle Sort ops where S32 is too small for the number
+    // of elements in the sort dimension.
+    if (iota_shape.dimensions(sort->sort_dimension()) >
+        std::numeric_limits<int32>::max()) {
+      return Unimplemented(
+          "Stable sorting of more than 2^31-1 elements is not implemented");
+    }
+    iota_shape.set_element_type(S32);
+    auto iota = computation->AddInstruction(
+        HloInstruction::CreateIota(iota_shape, sort->sort_dimension()));
+
+    // Create a new comparator.
+    auto comparator = sort->to_apply();
+    absl::flat_hash_map<const HloInstruction*, std::unique_ptr<HloInstruction>>
+        replacements;
+    std::vector<std::unique_ptr<HloInstruction>> extra_parameters;
+    std::vector<HloInstruction*> extra_parameter_ptrs;
+    Shape scalar_shape = ShapeUtil::MakeShape(S32, {});
+    extra_parameters.push_back(HloInstruction::CreateParameter(
+        sort->operand_count() * 2, scalar_shape,
+        absl::StrCat("p.", sort->operand_count(), ".lhs")));
+    extra_parameter_ptrs.push_back(extra_parameters.back().get());
+    extra_parameters.push_back(HloInstruction::CreateParameter(
+        sort->operand_count() * 2 + 1, scalar_shape,
+        absl::StrCat("p.", sort->operand_count(), ".rhs")));
+    extra_parameter_ptrs.push_back(extra_parameters.back().get());
+    sort->set_to_apply(sort->GetModule()->AddEmbeddedComputation(
+        comparator->CloneWithReplacements(std::move(replacements),
+                                          extra_parameter_ptrs)));
+
+    // Replace the original sort op.
+    std::vector<HloInstruction*> new_operands(sort->operands().begin(),
+                                              sort->operands().end());
+    new_operands.push_back(iota);
+    std::vector<Shape> new_shapes = sort->operand_count() == 1
+                                        ? std::vector<Shape>{sort->shape()}
+                                        : sort->shape().tuple_shapes();
+    new_shapes.push_back(iota_shape);
+    Shape new_sort_shape = ShapeUtil::MakeTupleShape(new_shapes);
+    HloInstruction* new_sort = computation->AddInstruction(
+        sort->CloneWithNewOperands(new_sort_shape, new_operands));
+
+    // Add a "wrapper" around the new sort op to make sure we have the same
+    // shape as before. For the rank 1 case, we only need a GetTupleElement,
+    // otherwise we create a Tuple consisting of GetTupleElements of the new
+    // sort.
+    std::vector<HloInstruction*> tuple_elements;
+    tuple_elements.reserve(sort->operand_count());
+    for (int64 i = 0; i < sort->operand_count(); ++i) {
+      tuple_elements.push_back(
+          computation->AddInstruction(HloInstruction::CreateGetTupleElement(
+              sort->operand(i)->shape(), new_sort, i)));
+    }
+    expanded_sort = tuple_elements[0];
+    if (tuple_elements.size() > 1) {
+      expanded_sort = computation->AddInstruction(
+          HloInstruction::CreateTuple(tuple_elements));
+    }
+    sort = Cast<HloSortInstruction>(new_sort);
+    iota_index = sort->operand_count() - 1;
+  }
+
+  // Modify the computation to break ties using the iota operand.
+  auto comparator = sort->to_apply();
+  std::vector<HloInstruction*> instructions_postorder =
+      comparator->MakeInstructionPostOrder();
+  absl::flat_hash_map<HloInstruction*, HloInstruction*> replacements;
+  // Look up instr in the replacements map, and return either the replacement,
+  // or instr, if the replacement isn't present.
+  auto replace = [&](HloInstruction* instr) {
+    auto it = replacements.find(instr);
+    if (it == replacements.end()) {
+      return instr;
+    }
+    return it->second;
+  };
+  HloInstruction* old_root = comparator->root_instruction();
+  // The comparison computation gets 2 * n parameters (n being the number of
+  // operands of Sort), where parameters 2 * i and 2 * i + 1 correspond to two
+  // different scalars of operand i of Sort which are to be compared. The
+  // comparison computation should induce a strict weak order, so if
+  // to_apply(p1.lhs, p1.rhs, ..., pn.lhs, pn.rhs) is equal to
+  // to_apply(p1.rhs, p1.lhs, ..., pn.rhs, pn.lhs), we can conclude that the
+  // values to be compared are equivalent, and perform a tie-breaker comparison.
+  //
+  // We clone each instruction with at least one operand, but use as new
+  // operands of the instruction the replacements of the original operands.
+  // Parameter 2 * i is replaced by parameter 2 * i + 1 and vice versa. This
+  // should make sure that the cloned root instruction gives the result of the
+  // comparison computation when being called with each scalar pair reversed.
+  // parameters corresponding to the iota operand.
+  for (int64 i = 0; i < comparator->num_parameters(); ++i) {
+    replacements[comparator->parameter_instruction(i)] =
+        comparator->parameter_instruction(i ^ 1);
+  }
+  HloInstruction* cloned_root = nullptr;
+  for (HloInstruction* inst : instructions_postorder) {
+    if (inst->operand_count() == 0) {
+      continue;
+    }
+    std::vector<HloInstruction*> new_operands;
+    new_operands.reserve(inst->operand_count());
+    for (HloInstruction* operand : inst->operands()) {
+      new_operands.push_back(replace(operand));
+    }
+    auto new_instruction =
+        inst->CloneWithNewOperands(inst->shape(), new_operands);
+    replacements[inst] = new_instruction.get();
+    if (inst == old_root) {
+      cloned_root = new_instruction.get();
+    }
+    comparator->AddInstruction(std::move(new_instruction));
+  }
+  CHECK_NE(cloned_root, nullptr);
+  Shape scalar_pred = ShapeUtil::MakeShape(PRED, {});
+  HloInstruction* same =
+      comparator->AddInstruction(HloInstruction::CreateBinary(
+          scalar_pred, HloOpcode::kEq, old_root, cloned_root));
+  HloInstruction* tie_breaker =
+      comparator->AddInstruction(HloInstruction::CreateBinary(
+          scalar_pred, HloOpcode::kLt,
+          comparator->parameter_instruction(2 * iota_index),
+          comparator->parameter_instruction(2 * iota_index + 1)));
+  HloInstruction* new_root =
+      comparator->AddInstruction(HloInstruction::CreateTernary(
+          ShapeUtil::MakeShape(PRED, {}), HloOpcode::kSelect, same, tie_breaker,
+          old_root));
+  comparator->set_root_instruction(new_root);
+
+  return expanded_sort;
+}
+
+bool StableSortExpander::InstructionMatchesPattern(
+    HloInstruction* instruction) {
+  return instruction->opcode() == HloOpcode::kSort &&
+         Cast<HloSortInstruction>(instruction)->is_stable();
+}
+}  // namespace xla

tensorflow/compiler/xla/service/stable_sort_expander.h

@@ -0,0 +1,42 @@
+/* Copyright 2019 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.
+==============================================================================*/
+
+#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_STABLE_SORT_EXPANDER_H_
+#define TENSORFLOW_COMPILER_XLA_SERVICE_STABLE_SORT_EXPANDER_H_
+
+#include "tensorflow/compiler/xla/service/hlo_instruction.h"
+#include "tensorflow/compiler/xla/service/hlo_module.h"
+#include "tensorflow/compiler/xla/service/hlo_pass_interface.h"
+#include "tensorflow/compiler/xla/service/op_expander_pass.h"
+#include "tensorflow/compiler/xla/statusor.h"
+
+namespace xla {
+
+// HLO pass which expands Sort ops that have the is_stable field set to true
+// into equivalent Sort ops which guarantee stable sorting without relying on
+// the is_stable field.
+class StableSortExpander : public OpExpanderPass {
+ public:
+  absl::string_view name() const override { return "stable-sort-expander"; }
+
+ private:
+  bool InstructionMatchesPattern(HloInstruction* instruction) override;
+  StatusOr<HloInstruction*> ExpandInstruction(
+      HloInstruction* instruction) override;
+};
+
+}  // namespace xla
+
+#endif  // TENSORFLOW_COMPILER_XLA_SERVICE_STABLE_SORT_EXPANDER_H_

tensorflow/compiler/xla/service/stable_sort_expander_test.cc

@@ -0,0 +1,358 @@
+/* Copyright 2019 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/stable_sort_expander.h"
+
+#include "tensorflow/compiler/xla/service/algebraic_simplifier.h"
+#include "tensorflow/compiler/xla/service/hlo_matchers.h"
+#include "tensorflow/compiler/xla/service/hlo_parser.h"
+#include "tensorflow/compiler/xla/service/pattern_matcher.h"
+#include "tensorflow/compiler/xla/service/pattern_matcher_gmock.h"
+#include "tensorflow/compiler/xla/test.h"
+#include "tensorflow/compiler/xla/tests/hlo_test_base.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+
+namespace xla {
+namespace {
+
+namespace m = match;
+
+using StableSortExpanderTest = HloTestBase;
+
+// Checks whether 'a' and 'b' are roots of equivalent computations, except that
+// parameters 2 * i and 2 * i + 1 are switched.
+bool IsSameComputationExceptParams(const HloInstruction* a,
+                                   const HloInstruction* b) {
+  if (a->opcode() != b->opcode() || a->operand_count() != b->operand_count()) {
+    return false;
+  }
+  if (a->opcode() == HloOpcode::kParameter) {
+    // Check that parameters were switched.
+    return a->parameter_number() == (b->parameter_number() ^ 1);
+  }
+  // If the operation has no operands, it should actually be the same.
+  if (a->operand_count() == 0) {
+    return a == b;
+  }
+  // Otherwise recursively compare all operands.
+  for (int64 i = 0; i < a->operand_count(); ++i) {
+    if (!IsSameComputationExceptParams(a->operand(i), b->operand(i))) {
+      return false;
+    }
+  }
+  return true;
+}
+
+// Check that the comparison computation has been modified to add a tie breaker
+// using 'iota_parameter'.
+void CheckComputationHasTieBreaker(const HloInstruction* root,
+                                   int64 iota_parameter) {
+  // With the tie breaker, the root instruction should be
+  //   Select(Eq(Comp(), CompReverse()), Lt(), Comp())
+  // with Comp() being the original comparison function, and CompReverse() being
+  // the copied comparison function where the parameters are reversed. Lt() is
+  // the tie breaker comparison using the Iota operand.
+  ASSERT_EQ(root->opcode(), HloOpcode::kSelect);
+  ASSERT_EQ(root->operand(0)->opcode(), HloOpcode::kEq);
+
+  // Check that the tie breaker instruction is correct.
+  EXPECT_THAT(root->operand(1),
+              GmockMatch(m::Lt(m::Parameter(iota_parameter * 2),
+                               m::Parameter(iota_parameter * 2 + 1))));
+  EXPECT_EQ(root->operand(2), root->operand(0)->operand(0));
+
+  // Check that Comp() and CompReverse() are equivalent except that
+  // CompReverse() has reversed parameters.
+  EXPECT_TRUE(IsSameComputationExceptParams(root->operand(0)->operand(0),
+                                            root->operand(0)->operand(1)));
+}
+
+TEST_F(StableSortExpanderTest, StabilizeSortReuseIotaOperand) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = s32[] parameter(2)
+     p.1.rhs = s32[] parameter(3)
+     ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = s32[64,8732]{1,0} iota(), iota_dimension=1
+     sort = (f32[64,8732]{1,0}, s32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=true
+     ROOT gte = f32[64,8732]{1,0} get-tuple-element(sort), index=0
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota()), 0)));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->to_apply()->root_instruction(), /*iota_parameter=*/1);
+}
+
+TEST_F(StableSortExpanderTest,
+       StabilizeSortReuseIotaOperandComplicatedComparison) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = s32[] parameter(2)
+     p.1.rhs = s32[] parameter(3)
+     max = u32[] constant(2147483647)
+     zero = s32[] constant(0)
+     lhs.signed = s32[] bitcast-convert(p.0.lhs)
+     lhs.unsigned = u32[] bitcast-convert(p.0.lhs)
+     lhs.flipped = u32[] subtract(max, lhs.unsigned)
+     lhs.flipped.signed = s32[] bitcast-convert(lhs.flipped)
+     lhs.is_negative = pred[] less-than(lhs.flipped.signed, zero)
+     lhs.converted = s32[] select(lhs.is_negative, lhs.flipped.signed, lhs.signed)
+     rhs.signed = s32[] bitcast-convert(p.0.rhs)
+     rhs.unsigned = u32[] bitcast-convert(p.0.rhs)
+     rhs.flipped = u32[] subtract(max, rhs.unsigned)
+     rhs.flipped.signed = s32[] bitcast-convert(rhs.flipped)
+     rhs.is_negative = pred[] less-than(rhs.flipped.signed, zero)
+     rhs.converted = s32[] select(rhs.is_negative, rhs.flipped.signed, rhs.signed)
+     ROOT lt = pred[] less-than(lhs.converted, rhs.converted)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = s32[64,8732]{1,0} iota(), iota_dimension=1
+     sort = (f32[64,8732]{1,0}, s32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=true
+     ROOT gte = f32[64,8732]{1,0} get-tuple-element(sort), index=0
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota()), 0)));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->to_apply()->root_instruction(), /*iota_parameter=*/1);
+}
+
+TEST_F(StableSortExpanderTest, StabilizeSortAddIotaOperandAndChangeRoot) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = s32[] parameter(2)
+     p.1.rhs = s32[] parameter(3)
+     ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = s32[64,8732]{1,0} parameter(1)
+     ROOT sort = (f32[64,8732]{1,0}, s32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=true
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(
+      root, GmockMatch(m::Tuple(
+                m::GetTupleElement(
+                    m::Sort(m::Parameter(0), m::Parameter(1), m::Iota()), 0),
+                m::GetTupleElement(
+                    m::Sort(m::Parameter(0), m::Parameter(1), m::Iota()), 1))));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->operand(0)->to_apply()->root_instruction(),
+      /*iota_parameter=*/2);
+}
+
+TEST_F(StableSortExpanderTest, HonorIsStableFlag) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = s32[] parameter(2)
+     p.1.rhs = s32[] parameter(3)
+     ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = s32[64,8732]{1,0} iota(), iota_dimension=1
+     sort = (f32[64,8732]{1,0}, s32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=false
+     ROOT gte = f32[64,8732]{1,0} get-tuple-element(sort), index=0
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_FALSE(stabilizer.Run(module.get()).ValueOrDie());
+}
+
+TEST_F(StableSortExpanderTest,
+       StabilizeSortDontReuseIotaOperandWrongDimension) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = s32[] parameter(2)
+     p.1.rhs = s32[] parameter(3)
+     ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = s32[64,8732]{1,0} iota(), iota_dimension=0
+     sort = (f32[64,8732]{1,0}, s32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=true
+     ROOT gte = f32[64,8732]{1,0} get-tuple-element(sort), index=0
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  // Simplify away the "wrapper" tuple around the new sort.
+  AlgebraicSimplifier simplifier(AlgebraicSimplifierOptions(
+      [](const Shape&, const Shape&) { return false; }));
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota(), m::Iota()), 0)));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->to_apply()->root_instruction(),
+      /*iota_parameter=*/2);
+}
+
+TEST_F(StableSortExpanderTest, StabilizeSortDontReuseIotaOperandWrongType) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = f32[] parameter(0)
+     p.0.rhs = f32[] parameter(1)
+     p.1.lhs = f32[] parameter(2)
+     p.1.rhs = f32[] parameter(3)
+     ROOT lt = pred[] less-than(p.0.lhs, p.0.rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = f32[64,8732]{1,0} parameter(0)
+     values = f32[64,8732]{1,0} iota(), iota_dimension=1
+     sort = (f32[64,8732]{1,0}, f32[64,8732]{1,0}) sort(keys, values),
+       dimensions={1}, to_apply=compare, is_stable=true
+     ROOT gte = f32[64,8732]{1,0} get-tuple-element(sort), index=0
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  // Simplify away the "wrapper" tuple around the new sort.
+  AlgebraicSimplifier simplifier(AlgebraicSimplifierOptions(
+      [](const Shape&, const Shape&) { return false; }));
+  ASSERT_TRUE(simplifier.Run(module.get()).ValueOrDie());
+
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota(), m::Iota()), 0)));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->to_apply()->root_instruction(),
+      /*iota_parameter=*/2);
+}
+
+TEST_F(StableSortExpanderTest, StabilizeSortR1) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = s32[] parameter(0)
+     p.0.rhs = s32[] parameter(1)
+     mask = s32[] constant(65535)
+     lhs = s32[] and(p.0.lhs, mask)
+     rhs = s32[] and(p.0.rhs, mask)
+     ROOT lt = pred[] less-than(lhs, rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = s32[64,8732]{1,0} parameter(0)
+     ROOT sort = s32[64,8732]{1,0} sort(keys), dimensions={0}, to_apply=compare,
+       is_stable=true
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota()), 0)));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->to_apply()->root_instruction(), /*iota_parameter=*/1);
+}
+
+TEST_F(StableSortExpanderTest, StabilizeSortR1NoRoot) {
+  const char* hlo_string = R"(
+   HloModule permutation_sort
+
+   compare {
+     p.0.lhs = s32[] parameter(0)
+     p.0.rhs = s32[] parameter(1)
+     mask = s32[] constant(65535)
+     lhs = s32[] and(p.0.lhs, mask)
+     rhs = s32[] and(p.0.rhs, mask)
+     ROOT lt = pred[] less-than(lhs, rhs)
+   }
+
+   ENTRY sort_computation {
+     keys = s32[64,8732]{1,0} parameter(0)
+     sort = s32[64,8732]{1,0} sort(keys), dimensions={0}, to_apply=compare,
+       is_stable=true
+     ROOT neg = s32[64,8732]{1,0} negate(sort)
+   })";
+  TF_ASSERT_OK_AND_ASSIGN(auto module,
+                          ParseAndReturnVerifiedModule(hlo_string));
+
+  StableSortExpander stabilizer;
+  EXPECT_TRUE(stabilizer.Run(module.get()).ValueOrDie());
+  auto root = module->entry_computation()->root_instruction();
+  EXPECT_THAT(root, GmockMatch(m::Negate(m::GetTupleElement(
+                        m::Sort(m::Parameter(0), m::Iota()), 0))));
+  CheckComputationHasTieBreaker(
+      root->operand(0)->operand(0)->to_apply()->root_instruction(),
+      /*iota_parameter=*/1);
+}
+
+}  // namespace
+}  // namespace xla

tensorflow/compiler/xla/service/tuple_points_to_analysis_test.cc

@@ -1072,7 +1072,8 @@ TEST_F(CanShareOperandBufferWithUserTest, SortCanShare) {
   auto keys = builder.AddInstruction(
       HloInstruction::CreateParameter(0, keys_shape, "keys"));
   TF_ASSERT_OK_AND_ASSIGN(
-      auto* sort, MakeSortHlo(keys_shape, {keys}, 0, &builder, module_.get()));
+      auto* sort, MakeSortHlo(keys_shape, {keys}, 0, /*is_stable=*/false,
+                              &builder, module_.get()));
 
   computation_ = module_->AddEntryComputation(builder.Build());
   RunAnalysis();
@@ -1094,7 +1095,8 @@ TEST_F(CanShareOperandBufferWithUserTest, SortCanShareWithTupleUser) {
   TF_ASSERT_OK_AND_ASSIGN(
       auto* sort,
       MakeSortHlo(ShapeUtil::MakeTupleShape({keys_shape, values_shape}),
-                  {keys, values}, 0, &builder, module_.get()));
+                  {keys, values}, 0, /*is_stable=*/false, &builder,
+                  module_.get()));
 
   computation_ = module_->AddEntryComputation(builder.Build());
   RunAnalysis();

tensorflow/compiler/xla/tests/BUILD

@@ -1146,7 +1146,7 @@ xla_test(
 xla_test(
     name = "reduce_test",
     srcs = ["reduce_test.cc"],
-    shard_count = 40,
+    shard_count = 31,
     tags = [
         "optonly",
     ],

tensorflow/compiler/xla/tests/dot_operation_test.cc

@@ -1188,6 +1188,8 @@ std::vector<EinsumParamType> GetEinsumTestCases() {
       p{v{8, 55, 11, 3}, v{55, 11, 3, 29}, "mkBC,kBCn->BCnm"},
       p{v{5, 6}, v{6, 7}, "ab,cd->dcba"},
       p{v{6}, v{6, 7}, "b,bc->c"},
+      p{v{5, 6, 7}, v{5, 6, 7}, "abc,abc->ab"},
+      p{v{5, 6, 7}, v{7, 6, 5}, "abc,cba->ca"},
       p{v{77}, v{77}, "a,a->a"},
       p{v{77}, v{77, 55}, "a,ab->ba"},
       p{v{2, 3, 77}, v{77, 2, 3, 55}, "ija,aijb->baij"},
@@ -1265,5 +1267,51 @@ ENTRY %test {
   EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{4e-3, 4e-3}));
 }
 
+XLA_TEST_F(DotOperationTextTest, CpuTiledDotEmitterCachingBug_1) {
+  // Tests for a caching bug in the XLA CPU backend.
+  absl::string_view hlo_string =
+      R"(
+HloModule CpuTiledDotEmitterCachingBug
+
+ENTRY main {
+  lhs = f32[20,40] parameter(0)
+  rhs_0 = f32[40,1] parameter(2)
+  rhs_1 = f32[1,40] parameter(1)
+
+  dot_0 = f32[20,1] dot(lhs, rhs_0), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  dot_1 = f32[20,1] dot(lhs, rhs_1), lhs_contracting_dims={1}, rhs_contracting_dims={1}
+
+  ROOT result = f32[20,1] divide(dot_0, dot_1)
+}
+)";
+
+  EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{4e-3, 4e-3}));
+}
+
+XLA_TEST_F(DotOperationTextTest, CpuTiledDotEmitterCachingBug_2) {
+  // Tests for a caching bug in the XLA CPU backend.
+  absl::string_view hlo_string =
+      R"(
+HloModule CpuTiledDotEmitterCachingBug
+
+ENTRY main {
+  lhs_0 = f32[20,40] parameter(0)
+  rhs_0 = f32[40,1] parameter(1)
+  lhs_1 = f32[1,40] parameter(2)
+  rhs_1 = f32[20,40] parameter(3)
+
+  dot_0 = f32[20,1] dot(lhs_0, rhs_0), lhs_contracting_dims={1}, rhs_contracting_dims={0}
+  dot_1 = f32[1,20] dot(lhs_1, rhs_1), lhs_contracting_dims={1}, rhs_contracting_dims={1}
+
+  dot_0_reshaped = f32[20] reshape(dot_0)
+  dot_1_reshaped = f32[20] reshape(dot_1)
+
+  ROOT result = f32[20] divide(dot_0_reshaped, dot_1_reshaped)
+}
+)";
+
+  EXPECT_TRUE(RunAndCompare(hlo_string, ErrorSpec{4e-3, 4e-3}));
+}
+
 }  // namespace
 }  // namespace xla

tensorflow/compiler/xla/tests/hlo_test_base.cc

@@ -205,6 +205,17 @@ Literal HloTestBase::ExecuteAndTransfer(std::unique_ptr<HloModule> module,
   return test_runner_.Execute(std::move(module), arguments).ValueOrDie();
 }
 
+StatusOr<std::vector<Literal>> HloTestBase::ExecuteReplicated(
+    std::unique_ptr<HloModule> module, absl::Span<Literal* const> arguments,
+    int64 num_replicas) {
+  HloRunner::ReplicatedExecuteOptions options;
+  options.num_replicas = num_replicas;
+  for (auto argument : arguments) {
+    options.arguments.push_back(argument);
+  }
+  return test_runner_.ExecuteReplicated(std::move(module), options);
+}
+
 StatusOr<std::unique_ptr<HloModule>> HloTestBase::MakeReferenceModule(
     const HloModule& test_module,
     const std::function<void(HloModule*)>& reference_preprocessor) {

tensorflow/compiler/xla/tests/hlo_test_base.h

@@ -173,6 +173,11 @@ class HloTestBase : public ::testing::Test {
   Literal ExecuteAndTransfer(std::unique_ptr<HloModule> module,
                              absl::Span<Literal* const> arguments);
 
+  // Executes the given module on multiple replicas.
+  StatusOr<std::vector<Literal>> ExecuteReplicated(
+      std::unique_ptr<HloModule> module, absl::Span<Literal* const> arguments,
+      int64 num_replicas);
+
   // Executes the given hlo module on two backends and compares results.
   //
   // 'arguments': the input of the hlo module.

tensorflow/compiler/xla/xla.bzl

@@ -30,6 +30,11 @@ def xla_proto_library(name, srcs = [], deps = [], visibility = None, testonly =
         **kwargs
     )
 
+def xla_py_proto_library(**kwargs):
+    # Note: we don't currently define a proto library target for Python in OSS.
+    _ignore = kwargs
+    pass
+
 def xla_py_grpc_library(**kwargs):
     # Note: we don't currently define any special targets for Python GRPC in OSS.
     _ignore = kwargs

tensorflow/compiler/xrt/kernels/xrt_state_ops.cc

@@ -122,6 +122,17 @@ REGISTER_KERNEL_BUILDER(Name("XRTReadLiteralAndRelease")
                             .HostMemory("literal"),
                         XRTReadLiteralOp<true, XRTGenericDeviceAccessor>);
 
+REGISTER_KERNEL_BUILDER(Name("XRTReadToTensor")
+                            .Device(DEVICE_XLA_GPU)
+                            .HostMemory("handles")
+                            .HostMemory("tensors"),
+                        XRTReadToTensorOp<XRTGenericDeviceAccessor>);
+REGISTER_KERNEL_BUILDER(Name("XRTReadToTensor")
+                            .Device(DEVICE_XLA_CPU)
+                            .HostMemory("handles")
+                            .HostMemory("tensors"),
+                        XRTReadToTensorOp<XRTGenericDeviceAccessor>);
+
 REGISTER_KERNEL_BUILDER(Name("XRTReleaseAllocationHandle")
                             .Device(DEVICE_XLA_GPU)
                             .HostMemory("handle"),

tensorflow/compiler/xrt/kernels/xrt_state_ops.h

@@ -25,6 +25,7 @@ limitations under the License.
 
 #include "tensorflow/compiler/tf2xla/literal_util.h"
 #include "tensorflow/compiler/tf2xla/shape_util.h"
+#include "tensorflow/compiler/tf2xla/type_util.h"
 #include "tensorflow/compiler/xla/client/local_client.h"
 #include "tensorflow/compiler/xla/layout_util.h"
 #include "tensorflow/compiler/xla/literal.h"
@@ -40,6 +41,7 @@ limitations under the License.
 #include "tensorflow/core/framework/tensor.h"
 #include "tensorflow/core/framework/tensor_shape.h"
 #include "tensorflow/core/framework/types.pb.h"
+#include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/core/refcount.h"
 #include "tensorflow/core/lib/core/status.h"
 #include "tensorflow/core/lib/gtl/cleanup.h"
@@ -215,27 +217,29 @@ class XRTAllocateFromTensorOp : public OpKernel {
     OP_REQUIRES_OK(ctx, ctx->GetAttr("shapes", &tf_shapes_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("dtypes", &dtypes_));
     OP_REQUIRES_OK(ctx, ctx->GetAttr("make_tuple", &make_tuple));
+    std::vector<int64> minor_to_major;
     if (ctx->HasAttr("layouts")) {
-      OP_REQUIRES_OK(ctx, ctx->GetAttr("layouts", &minor_to_major_));
+      OP_REQUIRES_OK(ctx, ctx->GetAttr("layouts", &minor_to_major));
     }
     OP_REQUIRES(
         ctx, tf_shapes_.size() == dtypes_.size(),
         errors::InvalidArgument("shapes and dtypes must be the same length"));
     std::vector<xla::Shape> xla_shapes;
+    xla_shapes.reserve(tf_shapes_.size());
     for (int i = 0; i < tf_shapes_.size(); i++) {
       xla::Shape xla_shape;
       OP_REQUIRES_OK(
           ctx, TensorShapeToXLAShape(dtypes_[i], tf_shapes_[i], &xla_shape));
-      xla_shapes.push_back(xla_shape);
+      xla_shapes.push_back(std::move(xla_shape));
     }
     if (xla_shapes.size() > 1 || make_tuple) {
       shape_ = xla::ShapeUtil::MakeTupleShape(xla_shapes);
     } else {
       shape_.Swap(&xla_shapes.front());
     }
-    if (!minor_to_major_.empty()) {
+    if (!minor_to_major.empty()) {
       xla::Shape shape_with_layouts;
-      OP_REQUIRES_OK(ctx, GetShapeWithLayout(shape_, minor_to_major_,
+      OP_REQUIRES_OK(ctx, GetShapeWithLayout(shape_, minor_to_major,
                                              /*layout_func=*/nullptr,
                                              &shape_with_layouts));
       shape_.Swap(&shape_with_layouts);
@@ -304,7 +308,6 @@ class XRTAllocateFromTensorOp : public OpKernel {
  private:
   std::vector<TensorShape> tf_shapes_;
   DataTypeVector dtypes_;
-  std::vector<int64> minor_to_major_;
   xla::Shape shape_;
 };
 
@@ -487,7 +490,7 @@ class XRTReadLiteralOp : public OpKernel {
     OP_REQUIRES_OK(ctx, DeviceAccessor::InitScopedRef(
                             ctx, allocation->device_ordinal(), &device_ref));
 
-    xla::Literal literal;
+    xla::Literal literal(allocation->on_host_shape());
     OP_REQUIRES_OK(
         ctx, allocation->ToLiteral(device_ref.backend(),
                                    device_ref.device_ordinal(), &literal));
@@ -499,6 +502,96 @@ class XRTReadLiteralOp : public OpKernel {
   }
 };
 
+// Op that reads a device-resident tuple to host memory and returns it as a
+// literal.
+template <class DeviceAccessor>
+class XRTReadToTensorOp : public OpKernel {
+ public:
+  explicit XRTReadToTensorOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("release_handles", &discard_));
+    OP_REQUIRES_OK(ctx, ctx->GetAttr("dtypes", &dtypes_));
+  }
+  ~XRTReadToTensorOp() override = default;
+  XRTReadToTensorOp(const XRTReadToTensorOp&) = delete;
+  XRTReadToTensorOp& operator=(const XRTReadToTensorOp&) = delete;
+
+  void Compute(OpKernelContext* ctx) override {
+    VLOG(1) << "XRTReadToTensorOp::Compute";
+
+    const Tensor& handle_tensor = ctx->input(0);
+    // TODO(phawkins,dlibenzi): accept multiple handles (i.e., vectors, not
+    // just scalars.)
+    OP_REQUIRES(
+        ctx, TensorShapeUtils::IsScalar(handle_tensor.shape()),
+        errors::Internal("computation input should be an int64 scalar"));
+    int64 allocation_handle = handle_tensor.scalar<int64>()();
+
+    ResourceMgr* rm;
+    OP_REQUIRES_OK(ctx, DeviceAccessor::GetResourceManager(ctx, &rm));
+
+    XRTTupleAllocation* allocation;
+    OP_REQUIRES_OK(
+        ctx, XRTTupleAllocation::Lookup(rm, allocation_handle, &allocation));
+    core::ScopedUnref allocation_unref(allocation);
+
+    if (discard_) {
+      VLOG(2) << "Releasing handle " << allocation_handle;
+      OP_REQUIRES_OK(ctx, XRTTupleAllocation::DeleteFromResourceManager(
+                              rm, allocation_handle));
+    }
+
+    // We are guaranteed that the underlying device object won't be deleted out
+    // from under us, while the ScopedRef is live.
+    class DeviceAccessor::ScopedRef device_ref;
+    OP_REQUIRES_OK(ctx, DeviceAccessor::InitScopedRef(
+                            ctx, allocation->device_ordinal(), &device_ref));
+
+    xla::Shape shape = allocation->on_host_shape();
+    int output = 0;
+    Status status = xla::ShapeUtil::ForEachMutableSubshapeWithStatus(
+        &shape,
+        [&](xla::Shape* subshape, const xla::ShapeIndex& index) -> Status {
+          if (subshape->IsTuple()) return Status::OK();
+
+          xla::PrimitiveType xla_type;
+          TF_RETURN_IF_ERROR(DataTypeToPrimitiveType(
+              ctx->expected_output_dtype(output), &xla_type));
+          if (xla_type != subshape->element_type()) {
+            return errors::InvalidArgument(
+                "Type mismatch between buffer type (", subshape->ToString(),
+                ") and tensor type (",
+                DataTypeString(ctx->expected_output_dtype(output)),
+                ") for output tensor ", output);
+          }
+
+          TensorShape output_shape;
+          TF_RETURN_IF_ERROR(XLAShapeToTensorShape(*subshape, &output_shape));
+
+          Tensor* output_tensor;
+          TF_RETURN_IF_ERROR(
+              ctx->allocate_output(output, output_shape, &output_tensor));
+
+          XRTTupleAllocation* sub;
+          TF_RETURN_IF_ERROR(XRTTupleAllocation::MakeSubBuffer(
+              allocation, index, &sub, /*alias_parent_allocation=*/true));
+          core::ScopedUnref sub_unref(sub);
+
+          xla::MutableBorrowingLiteral literal;
+          TF_RETURN_IF_ERROR(HostTensorToMutableBorrowingLiteral(
+              xla::LayoutUtil::GetWithDefaultLayout(*subshape), output_tensor,
+              &literal));
+          TF_RETURN_IF_ERROR(sub->ToLiteral(
+              device_ref.backend(), device_ref.device_ordinal(), &literal));
+
+          ++output;
+          return Status::OK();
+        });
+    OP_REQUIRES_OK(ctx, status);
+  }
+  bool discard_;
+  DataTypeVector dtypes_;
+};
+
 // Op that writes a new literal value into device-resident memory.
 template <class DeviceAccessor>
 class XRTWriteLiteralOp : public OpKernel {

tensorflow/compiler/xrt/ops/xrt_state_ops.cc

@@ -151,6 +151,27 @@ releases the handle.
 'literal' is a serialized xla::LiteralProto proto.
 )");
 
+REGISTER_OP("XRTReadToTensor")
+    .Input("handles: int64")
+    .Attr("release_handles: bool = False")
+    .Attr("dtypes: list(type)")
+    .Output("tensors: dtypes")
+    .SetShapeFn(tensorflow::shape_inference::UnknownShape)
+    .Doc(
+        R"(
+Copies allocated values from device memory and returns them as zero or more
+Tensors. If a handle refers to a non-tuple buffer, a single tensor is returned.
+In general, the tensors returned for a handle correspond to an in-order traversal
+of a the tuple-tree value referenced by the handle.
+
+'handles' contains ids returned from Ops that produced on-device allocations.
+At present, only a single (scalar) handle is supported.
+'dtypes' are the expected types for each `Tensor` to be returned. If the
+expected and actual tensor types do not match, an error is returned.
+'release_handles': if True, `handles` are released.
+'tensors' are the output Tensors.
+)");
+
 REGISTER_OP("XRTReleaseAllocationHandle")
     .Input("handle: int64")
     .SetShapeFn(tensorflow::shape_inference::NoOutputs)

tensorflow/compiler/xrt/xrt_state.cc

@@ -220,7 +220,7 @@ XRTTupleAllocation::~XRTTupleAllocation() {
 }
 
 Status XRTTupleAllocation::ToLiteral(xla::Backend* backend, int device_ordinal,
-                                     xla::Literal* literal) {
+                                     xla::MutableLiteralBase* literal) {
   auto transfer_manager = backend->transfer_manager();
   TF_ASSIGN_OR_RETURN(auto stream, backend->BorrowStream(device_ordinal));
 
@@ -234,9 +234,8 @@ Status XRTTupleAllocation::ToLiteral(xla::Backend* backend, int device_ordinal,
                                      " has been released");
     }
   }
-  TF_ASSIGN_OR_RETURN(*literal, transfer_manager->TransferLiteralFromDevice(
+  return transfer_manager->TransferLiteralFromDevice(stream.get(),
-                                    stream.get(), shaped_buffer));
+                                                     shaped_buffer, *literal);
-  return Status::OK();
 }
 
 Status XRTTupleAllocation::WriteLiteral(xla::Backend* backend,

tensorflow/compiler/xrt/xrt_state.h

@@ -147,7 +147,7 @@ class XRTTupleAllocation : public ResourceBase {
 
   // Copies the allocation from device to host and returns it in literal.
   Status ToLiteral(xla::Backend* backend, int device_ordinal,
-                   xla::Literal* literal);
+                   xla::MutableLiteralBase* literal);
 
   // Write a new literal value to the allocation.
   Status WriteLiteral(xla::Backend* backend, const xla::Literal& literal);

tensorflow/contrib/BUILD

@@ -218,7 +218,6 @@ cc_library(
         "//tensorflow/contrib/tensor_forest:stats_ops_op_lib",
         "//tensorflow/contrib/tensor_forest:tensor_forest_ops_op_lib",
         "//tensorflow/contrib/text:all_ops",
-        "//tensorflow/contrib/tpu:all_ops",
     ] + select({
         "//tensorflow:android": [],
         "//tensorflow:ios": [],