Make XRT CPU/GPU use MaybeOwning buffer interface, so the new copy protection CL won't break aliasing.

PiperOrigin-RevId: 317700747
Change-Id: Ie7b5bb1989cd4359b30ad86a450de5bff0962c31

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

@@ -299,12 +299,11 @@ StatusOr<ExecutionOutput> CpuExecutable::ExecuteAsyncOnStream(
       const Shape& expected_shape =
           entry_comp->parameter_instruction(i)->shape();
       const Shape& actual_shape = arguments[i].Buffers().shape();
-      CHECK(
-          Shape::Equal().IgnoreDynamicDimension()(expected_shape, actual_shape))
-          << absl::StreamFormat(
-                 "Shape mismatch on argument %d.  Expected %s, but was %s.", i,
-                 expected_shape.ToString(/*print_layout=*/true),
-                 actual_shape.ToString(/*print_layout=*/true));
+      TF_RET_CHECK(
+          ShapeUtil::DynamicShapeIsCompatible(actual_shape, expected_shape))
+          << "Shape mismatch on argument " << i << ", "
+          << expected_shape.ToString(/*print_layout=*/true) << " vs. "
+          << actual_shape.ToString(/*print_layout=*/true);
     }
   }
 

tensorflow/compiler/xla/service/executable.cc

@@ -28,10 +28,57 @@ limitations under the License.
 #include "tensorflow/core/lib/io/path.h"
 #include "tensorflow/core/lib/strings/proto_serialization.h"
 #include "tensorflow/core/platform/env.h"
+#include "tensorflow/core/platform/errors.h"
 #include "tensorflow/stream_executor/device_description.h"
 
 namespace xla {
 
+ExecutionInput::~ExecutionInput() {
+  for (auto& index : unowned_indices_) {
+    auto buffer = buffers_.mutable_element(index)->Release();
+    if (buffer) {
+      buffer->Release();
+    }
+  }
+}
+
+Status ExecutionInput::SetDynamicShape(Shape dynamic_shape) {
+  const Shape& input_shape = shape();
+  if (!ShapeUtil::DynamicShapeIsCompatible(input_shape, dynamic_shape)) {
+    return tensorflow::errors::InvalidArgument(
+        "Cannot set dynamic shape: ", input_shape.DebugString(), " vs. ",
+        dynamic_shape.DebugString());
+  }
+  dynamic_shape_ = absl::make_unique<Shape>(std::move(dynamic_shape));
+  return Status::OK();
+}
+
+void ExecutionInput::SetUnownedBuffer(const ShapeIndex& index,
+                                      MaybeOwningDeviceMemory buffer) {
+  *buffers_.mutable_element(index) = std::move(buffer);
+  unowned_indices_.insert(index);
+}
+
+xla::StatusOr<xla::ShapedBuffer> ExecutionInput::ToShapedBuffer(
+    se::DeviceMemoryAllocator* allocator, int device_ordinal) const {
+  const Shape& input_shape = shape();
+  xla::ShapedBuffer shaped_buffer(input_shape, input_shape,
+                                  allocator->platform(), device_ordinal);
+  for (const auto& index_buffer : Buffers()) {
+    const tensorflow::se::OwningDeviceMemory* mem =
+        index_buffer.second.AsOwningDeviceMemory();
+    if (mem != nullptr && (mem->allocator() != allocator ||
+                           mem->device_ordinal() != device_ordinal)) {
+      return tensorflow::errors::InvalidArgument(
+          "Device buffer at index ", index_buffer.first.ToString(),
+          " has mismatching allocator/device");
+    }
+    shaped_buffer.set_buffer(index_buffer.second.AsDeviceMemoryBase(),
+                             index_buffer.first);
+  }
+  return std::move(shaped_buffer);
+}
+
 StatusOr<ScopedShapedBuffer> Executable::ExecuteOnStream(
     const ServiceExecutableRunOptions* run_options,
     absl::Span<const ShapedBuffer* const> arguments,

tensorflow/compiler/xla/service/executable.h

@@ -17,6 +17,7 @@ limitations under the License.
 #define TENSORFLOW_COMPILER_XLA_SERVICE_EXECUTABLE_H_
 
 #include <memory>
+#include <set>
 #include <utility>
 #include <vector>
 
@@ -65,31 +66,32 @@ class ExecutionInput {
       : buffers_(std::move(buffers)) {}
   ExecutionInput(ExecutionInput&&) = default;
 
-  ~ExecutionInput() {
-    for (auto& index : unowned_indices_) {
-      auto buffer = buffers_.mutable_element(index)->Release();
-      if (buffer) {
-        buffer->Release();
-      }
-    }
-  }
+  ~ExecutionInput();
 
   ExecutionInput& operator=(ExecutionInput&&) = default;
 
-  const Shape& shape() const { return buffers_.shape(); }
+  const Shape& shape() const {
+    return dynamic_shape_ != nullptr ? *dynamic_shape_ : buffers_.shape();
+  }
+
+  Status SetDynamicShape(Shape dynamic_shape);
+
+  xla::StatusOr<xla::ShapedBuffer> ToShapedBuffer(
+      se::DeviceMemoryAllocator* allocator, int device_ordinal) const;
 
   void SetBuffer(const ShapeIndex& index, MaybeOwningDeviceMemory buffer) {
     *buffers_.mutable_element(index) = std::move(buffer);
   }
 
   void SetUnownedBuffer(const ShapeIndex& index,
-                        MaybeOwningDeviceMemory buffer) {
-    *buffers_.mutable_element(index) = std::move(buffer);
-    unowned_indices_.push_back(index);
-  }
+                        MaybeOwningDeviceMemory buffer);
 
   void SetUnownedIndex(const ShapeIndex& index) {
-    unowned_indices_.push_back(index);
+    unowned_indices_.insert(index);
+  }
+
+  void ClearUnownedIndex(const ShapeIndex& index) {
+    unowned_indices_.erase(index);
   }
 
   const ShapeTree<MaybeOwningDeviceMemory>& Buffers() const { return buffers_; }
@@ -106,9 +108,10 @@ class ExecutionInput {
 
  private:
   ShapeTree<MaybeOwningDeviceMemory> buffers_;
-  // (Unordered) set of indices of buffers that should be returned to the
-  // caller if an error occurs when enqueuing the computation.
-  std::vector<ShapeIndex> unowned_indices_;
+  // Set of indices of buffers that should be returned to the caller if an error
+  // occurs when enqueuing the computation.
+  std::set<ShapeIndex> unowned_indices_;
+  std::unique_ptr<Shape> dynamic_shape_;
 };
 
 // ExecutionOutput encapsulates the output buffers of a execution and the
@@ -145,7 +148,6 @@ class ExecutionOutput {
     to_be_released_.push_back(std::move(mem));
   }
 
-
   // Should be called once it is known that the execute operation succeeded,
   // before returning the ExecutionOutput to the caller.
   ExecutionOutput& Commit() {

tensorflow/compiler/xla/service/maybe_owning_device_memory.cc

@@ -14,7 +14,9 @@ limitations under the License.
 ==============================================================================*/
 
 #include "tensorflow/compiler/xla/service/maybe_owning_device_memory.h"
+
 #include "absl/types/variant.h"
+
 namespace xla {
 
 tensorflow::se::DeviceMemoryBase MaybeOwningDeviceMemory::AsDeviceMemoryBase()
@@ -38,4 +40,10 @@ MaybeOwningDeviceMemory::Release() {
   return std::move(absl::get<tensorflow::se::OwningDeviceMemory>(mem_));
 }
 
+const tensorflow::se::OwningDeviceMemory*
+MaybeOwningDeviceMemory::AsOwningDeviceMemory() const {
+  return HasOwnership() ? &absl::get<tensorflow::se::OwningDeviceMemory>(mem_)
+                        : nullptr;
+}
+
 }  // namespace xla

tensorflow/compiler/xla/service/maybe_owning_device_memory.h

@@ -57,6 +57,10 @@ class MaybeOwningDeviceMemory {
   // A nullopt is returned if the HasOwnership() == false;
   absl::optional<tensorflow::se::OwningDeviceMemory> Release();
 
+  // If the device memory is owned, returns a pointer to the internal
+  // OwningDeviceMemory, otherwise nullptr is returned.
+  const tensorflow::se::OwningDeviceMemory* AsOwningDeviceMemory() const;
+
   // Returns true if the device_memory has ownership over underlying memory.
   bool HasOwnership() const;
 

tensorflow/compiler/xla/shape_util.cc

@@ -1461,7 +1461,7 @@ ShapeUtil::ReshapeLeavesDimensionsUnmodified(
   return shape;
 }
 
-/* static */ bool ShapeUtil::DynamicShapeIsCompatible(
+/* static */ bool ShapeUtil::DynamicArrayShapeIsCompatible(
     const xla::Shape& dynamic_shape, const xla::Shape& bounded_shape) {
   if (dynamic_shape.rank() != bounded_shape.rank()) {
     return false;
@@ -1474,6 +1474,36 @@ ShapeUtil::ReshapeLeavesDimensionsUnmodified(
   return true;
 }
 
+/* static */ bool ShapeUtil::DynamicShapeIsCompatible(
+    const xla::Shape& dynamic_shape, const xla::Shape& bounded_shape) {
+  bool compatible = true;
+  xla::ShapeUtil::ForEachSubshape(dynamic_shape, [&](const Shape& sub_shape,
+                                                     const ShapeIndex& index) {
+    if (compatible) {
+      auto subshape_result = TryGetSubshape(bounded_shape, index);
+      if (subshape_result.ok()) {
+        const Shape* bounded_sub_shape = subshape_result.ConsumeValueOrDie();
+        if (sub_shape.IsTuple()) {
+          if (!bounded_sub_shape->IsTuple()) {
+            compatible = false;
+          }
+        } else {
+          if (bounded_sub_shape->IsTuple()) {
+            compatible = false;
+          } else if (!sub_shape.is_static() &&
+                     !DynamicArrayShapeIsCompatible(sub_shape,
+                                                    *bounded_sub_shape)) {
+            compatible = false;
+          }
+        }
+      } else {
+        compatible = false;
+      }
+    }
+  });
+  return compatible;
+}
+
 /* static */ Shape ShapeUtil::FilterDimensions(
     const std::function<bool(int64)>& p, Shape shape) {
   CHECK(shape.IsArray());

tensorflow/compiler/xla/shape_util.h

@@ -657,7 +657,11 @@ class ShapeUtil {
                                 Shape shape);
 
   // Returns true if `dynamic_shape` has dimensions that are less-equal to the
-  // "bounded_shape".
+  // "bounded_shape". Shapes must be arrays.
+  static bool DynamicArrayShapeIsCompatible(const xla::Shape& dynamic_shape,
+                                            const xla::Shape& bounded_shape);
+
+  // Same as DynamicArrayShapeIsCompatible() but supports tuples.
   static bool DynamicShapeIsCompatible(const xla::Shape& dynamic_shape,
                                        const xla::Shape& bounded_shape);
 

tensorflow/compiler/xrt/BUILD

@@ -74,6 +74,7 @@ cc_library(
         "//tensorflow/compiler/xla/client:local_client",
         "//tensorflow/compiler/xla/service:backend",
         "//tensorflow/compiler/xla/service:executable",
+        "//tensorflow/compiler/xla/service:hlo",
         "//tensorflow/compiler/xla/service:shaped_buffer",
         "//tensorflow/core:core_cpu_internal",
         "//tensorflow/core:framework",

tensorflow/compiler/xrt/kernels/xrt_execute_op.cc

@@ -51,12 +51,6 @@ namespace tensorflow {
 
 namespace {
 
-struct InputBuffers {
-  std::vector<RefPtr<XRTTupleAllocation>> input_tuples;
-  std::vector<xla::ShapedBuffer> input_allocations;
-  std::vector<xla::ShapedBuffer*> input_pointers;
-};
-
 uint32 InitialRandomSeed() {
   // Support plumbing the TF seed through to XLA is being worked on.
   // If a user wants deterministic behavior, their best option
@@ -80,75 +74,51 @@ uint32 GetXLARandomSeed() {
   return counter.fetch_add(2);
 }
 
-xla::StatusOr<InputBuffers> GetInputBuffers(
-    XRTMemoryManager::WorkingSet* working_set, xla::Backend* backend,
-    const std::vector<InputCoords>& input_coords, bool release_inputs) {
-  InputBuffers input_buffers;
-  input_buffers.input_tuples.reserve(input_coords.size());
-  input_buffers.input_allocations.reserve(input_coords.size());
-  input_buffers.input_pointers.reserve(input_coords.size());
-  for (size_t i = 0; i < input_coords.size(); ++i) {
-    TF_RETURN_IF_ERROR(
-        working_set->LookupAndPin(backend, input_coords[i].handle));
-    auto tuple = working_set->PinnedTuples().back();
-    input_buffers.input_tuples.emplace_back(tuple);
-    if (release_inputs) {
-      // We are holding a reference to the tuple, so we can safely delete it
-      // from the resource manager here.
-      TF_RETURN_IF_ERROR(
-          working_set->MemoryManager()->Release(input_coords[i].handle));
-      VLOG(2) << "Released allocation handle " << input_coords[i].handle;
-    }
-    if (input_coords[i].index.empty()) {
-      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer shaped_buffer,
-                          tuple->ToShapedBuffer());
-      input_buffers.input_allocations.emplace_back(std::move(shaped_buffer));
-    } else {
-      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer shaped_buffer,
-                          tuple->ToShapedBuffer());
-      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer sub_shaped_buffer,
-                          shaped_buffer.SubShapedBuffer(input_coords[i].index));
-      input_buffers.input_allocations.emplace_back(
-          std::move(sub_shaped_buffer));
-    }
+std::vector<bool> GetDynamicInputInfo(
+    const xla::ComputationLayout& computation_layout) {
+  std::vector<bool> input_is_dynamic;
+  input_is_dynamic.reserve(computation_layout.parameter_count());
+  for (int64 i = 0; i < computation_layout.parameter_count(); ++i) {
+    input_is_dynamic.push_back(
+        !computation_layout.parameter_shape(i).is_static());
   }
-  for (size_t i = 0; i < input_buffers.input_allocations.size(); ++i) {
-    input_buffers.input_pointers.push_back(&input_buffers.input_allocations[i]);
-  }
-  return std::move(input_buffers);
+  return input_is_dynamic;
 }
 
-xla::StatusOr<InputBuffers> GetChainedOpInputs(
+xla::StatusOr<std::vector<RefPtr<XRTTupleAllocation>>> GetInputTuples(
+    xla::LocalExecutable* executable, XRTMemoryManager::WorkingSet* working_set,
+    xla::Backend* backend, const std::vector<InputCoords>& input_coords,
+    bool release_inputs) {
+  const xla::ComputationLayout& computation_layout =
+      executable->executable()->module_config().entry_computation_layout();
+
+  return GetInputTupleAllocations(
+      input_coords, working_set, backend, computation_layout.parameter_count(),
+      [&](int64 i) { return computation_layout.parameter_shape(i); },
+      release_inputs);
+}
+
+xla::StatusOr<std::vector<RefPtr<XRTTupleAllocation>>> GetChainedOpInputTuples(
     const xrt::XRTChainedExecuteOp& op,
     absl::Span<const RefPtr<XRTTupleAllocation>> op_inputs) {
-  InputBuffers input_buffers;
-  input_buffers.input_tuples.reserve(op.inputs_size());
-  input_buffers.input_allocations.reserve(op.inputs_size());
-  input_buffers.input_pointers.reserve(op.inputs_size());
+  std::vector<RefPtr<XRTTupleAllocation>> input_tuples;
+  input_tuples.reserve(op.inputs_size());
   for (int i = 0; i < op.inputs_size(); ++i) {
     auto& input = op.inputs(i);
-    input_buffers.input_tuples.emplace_back(op_inputs[i]);
     // Thanks to the greatness of proto3, there is no way to query for
     // explicitly set fields, so the default for output_index (zero) means no
     // sub-index. As consequence, the real index is output_index - 1.
     if (input.output_index() == 0) {
-      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer shaped_buffer,
-                          input_buffers.input_tuples.back()->ToShapedBuffer());
-      input_buffers.input_allocations.emplace_back(std::move(shaped_buffer));
+      input_tuples.emplace_back(op_inputs[i]);
     } else {
-      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer shaped_buffer,
-                          input_buffers.input_tuples.back()->ToShapedBuffer());
-      TF_ASSIGN_OR_RETURN(
-          xla::ShapedBuffer sub_shaped_buffer,
-          shaped_buffer.SubShapedBuffer({input.output_index() - 1}));
-      input_buffers.input_allocations.emplace_back(
-          std::move(sub_shaped_buffer));
+      XRTTupleAllocation* sub_tuple;
+      TF_RETURN_IF_ERROR(XRTTupleAllocation::MakeSubBuffer(
+          op_inputs[i].get(), {input.output_index() - 1}, &sub_tuple,
+          /*alias_parent_allocation=*/true));
+      input_tuples.emplace_back(sub_tuple);
     }
   }
-  for (size_t i = 0; i < input_buffers.input_allocations.size(); ++i) {
-    input_buffers.input_pointers.push_back(&input_buffers.input_allocations[i]);
-  }
-  return std::move(input_buffers);
+  return input_tuples;
 }
 
 // Given a shape, returns a byte array representing the shape metadata of the
@@ -228,12 +198,11 @@ Status UpdateMetadata(se::Stream* stream, se::DeviceMemory<uint8>* buffer,
 // As we can't expand the size of an existing memory allocation, a reallocation
 // is required. A list of new allocations are returned after this function. The
 // caller is reponsible for maintaining those allocations.
-xla::StatusOr<std::vector<se::OwningDeviceMemory>> UpdateDynamicInputs(
+Status UpdateDynamicInputs(
     se::Stream* stream, se::DeviceMemoryAllocator* allocator,
-    std::vector<xla::ShapedBuffer*> runtime_inputs,
+    std::vector<xla::ExecutionInput>* execution_inputs,
     const std::vector<xla::ShapeLayout>& compile_time_shapes) {
-  std::vector<se::OwningDeviceMemory> new_allocations;
-  TF_RET_CHECK(runtime_inputs.size() == compile_time_shapes.size());
+  TF_RET_CHECK(execution_inputs->size() == compile_time_shapes.size());
   TF_ASSIGN_OR_RETURN(auto compiler, xla::Compiler::GetForPlatform(
                                          stream->parent()->platform()));
   auto shape_size_fn = compiler->ShapeSizeBytesFunction();
@@ -242,57 +211,61 @@ xla::StatusOr<std::vector<se::OwningDeviceMemory>> UpdateDynamicInputs(
     if (compile_time_shape.is_static()) {
       continue;
     }
-    auto* runtime_input = runtime_inputs[i];
-
+    xla::ExecutionInput* execution_input = &(*execution_inputs)[i];
     bool element_modified = false;
     TF_RETURN_IF_ERROR(xla::ShapeUtil::ForEachSubshapeWithStatus(
         compile_time_shape,
-        [&](const xla::Shape& compile_time_shape,
+        [&](const xla::Shape& sub_shape,
             const xla::ShapeIndex& index) -> Status {
-          if (compile_time_shape.IsTuple() || compile_time_shape.is_static()) {
+          if (sub_shape.IsTuple() || sub_shape.is_static()) {
             return Status::OK();
           }
-          const xla::Shape& runtime_shape = xla::ShapeUtil::GetSubshape(
-              runtime_input->on_device_shape(), index);
-          TF_RET_CHECK(!runtime_shape.IsTuple());
-          TF_RET_CHECK(xla::ShapeUtil::DynamicShapeIsCompatible(
-              runtime_shape, compile_time_shape));
-          se::DeviceMemoryBase* static_input =
-              runtime_input->buffers().mutable_element(index);
           TF_ASSIGN_OR_RETURN(
-              auto dynamic_input,
+              const xla::Shape* runtime_shape,
+              xla::ShapeUtil::TryGetSubshape(execution_input->shape(), index));
+          TF_RET_CHECK(!runtime_shape->IsTuple());
+          TF_RET_CHECK(xla::ShapeUtil::DynamicArrayShapeIsCompatible(
+              *runtime_shape, sub_shape));
+          TF_ASSIGN_OR_RETURN(
+              se::OwningDeviceMemory dynamic_input,
               allocator->Allocate(stream->parent()->device_ordinal(),
-                                  shape_size_fn(compile_time_shape)));
-          new_allocations.emplace_back(std::move(dynamic_input));
-          se::DeviceMemory<uint8>* dynamic_input_base =
-              new_allocations.back().ptr();
+                                  shape_size_fn(sub_shape)));
+
+          se::DeviceMemoryBase static_input =
+              execution_input->Buffer(index).AsDeviceMemoryBase();
+          se::DeviceMemory<uint8>* dynamic_input_base = dynamic_input.ptr();
           // Send the original data to the new location.
-          stream->ThenMemcpyD2D(dynamic_input_base, *static_input,
-                                static_input->size());
+          stream->ThenMemcpyD2D(dynamic_input_base, static_input,
+                                static_input.size());
           TF_RETURN_IF_ERROR(UpdateMetadata(stream, dynamic_input_base,
-                                            compile_time_shape, runtime_shape));
+                                            sub_shape, *runtime_shape));
           // Modify the memory location in the input shape tree to point to the
           // new input.
-          runtime_input->set_buffer(*dynamic_input_base, index);
+          execution_input->SetBuffer(
+              index, xla::MaybeOwningDeviceMemory(std::move(dynamic_input)));
+          execution_input->ClearUnownedIndex(index);
           element_modified = true;
           return Status::OK();
         }));
     if (element_modified) {
-      runtime_input->set_shapes(compile_time_shape, compile_time_shape);
+      TF_RETURN_IF_ERROR(execution_input->SetDynamicShape(compile_time_shape));
+      TF_ASSIGN_OR_RETURN(xla::ShapedBuffer shaped_buffer,
+                          execution_input->ToShapedBuffer(
+                              allocator, stream->parent()->device_ordinal()));
       // The input location has been modified, need to fix tuple table to
       // point to the correct address.
       TF_ASSIGN_OR_RETURN(
           auto transfer_manager,
           xla::TransferManager::GetForPlatform(stream->parent()->platform()));
       TF_RETURN_IF_ERROR(
-          transfer_manager->WriteTupleIndexTablesAsync(stream, *runtime_input));
+          transfer_manager->WriteTupleIndexTablesAsync(stream, shaped_buffer));
     }
   }
-  return std::move(new_allocations);
+  return Status::OK();
 }
 
 xla::StatusOr<xla::Literal> ReadMetadataLiteral(
-    se::Stream* stream, se::DeviceMemoryBase* buffer,
+    se::Stream* stream, se::DeviceMemoryBase buffer,
     const xla::Shape& buffer_shape, xla::TransferManager* transfer_manager) {
   TF_ASSIGN_OR_RETURN(auto compiler, xla::Compiler::GetForPlatform(
                                          stream->parent()->platform()));
@@ -302,7 +275,7 @@ xla::StatusOr<xla::Literal> ReadMetadataLiteral(
   const int64 offset = shape_size_fn(buffer_shape_static);
   int64 metadata_size = shape_size_fn(buffer_shape) - offset;
   TF_RET_CHECK(metadata_size != 0);
-  auto buffer_8 = se::DeviceMemory<uint8>(*buffer);
+  auto buffer_8 = se::DeviceMemory<uint8>(buffer);
   auto metadata_buffer =
       stream->parent()->GetSubBuffer(&buffer_8, offset, metadata_size);
   return transfer_manager->TransferArrayFromDevice(
@@ -315,7 +288,7 @@ xla::StatusOr<xla::Literal> ReadMetadataLiteral(
 // dimension sizes from the metadata, and update output shapes. The result shape
 // is a static and concrete shape.
 xla::Status UpdateDynamicOutputs(se::Stream* stream,
-                                 xla::ShapedBuffer* shaped_buffer,
+                                 const xla::ShapedBuffer& shaped_buffer,
                                  xla::Shape* output_host_shape,
                                  xla::Shape* output_device_shape) {
   DCHECK(output_device_shape->is_dynamic());
@@ -323,8 +296,8 @@ xla::Status UpdateDynamicOutputs(se::Stream* stream,
       auto transfer_manager,
       xla::TransferManager::GetForPlatform(stream->parent()->platform()));
   TF_RETURN_IF_ERROR(stream->BlockHostUntilDone());
-  TF_RETURN_IF_ERROR(shaped_buffer->buffers().ForEachMutableElementWithStatus(
-      [&](const xla::ShapeIndex& index, se::DeviceMemoryBase* buffer) {
+  TF_RETURN_IF_ERROR(shaped_buffer.buffers().ForEachElementWithStatus(
+      [&](const xla::ShapeIndex& index, const se::DeviceMemoryBase& buffer) {
         const xla::Shape& buffer_shape =
             xla::ShapeUtil::GetSubshape(*output_device_shape, index);
         if (buffer_shape.IsTuple()) {
@@ -352,19 +325,18 @@ xla::Status UpdateDynamicOutputs(se::Stream* stream,
   return Status::OK();
 }
 
-// Create output tuple from run_result.
 xla::StatusOr<RefPtr<XRTTupleAllocation>> CreateOutputTuple(
-    se::Stream* stream, xla::ScopedShapedBuffer run_result,
-    xla::Backend* backend, int device_ordinal) {
+    se::Stream* stream, xla::ExecutionOutput run_result, xla::Backend* backend,
+    int device_ordinal) {
   XRTTupleAllocation* output_tuple;
-  xla::ShapedBuffer shaped_buffer = run_result.release();
+  const xla::ScopedShapedBuffer& shaped_buffer = run_result.Result();
   if (shaped_buffer.on_device_shape().is_dynamic()) {
     // Update dynamic shapes from output buffer, and create a XRT tensor with
     // dimension sizes read from metadata.
     xla::Shape output_host_shape = shaped_buffer.on_host_shape();
     xla::Shape output_device_shape = shaped_buffer.on_device_shape();
     TF_RETURN_IF_ERROR(UpdateDynamicOutputs(
-        stream, &shaped_buffer, &output_host_shape, &output_device_shape));
+        stream, shaped_buffer, &output_host_shape, &output_device_shape));
     TF_RETURN_IF_ERROR(XRTTupleAllocation::CreateFromBuffer(
         shaped_buffer, output_host_shape, output_device_shape, backend,
         device_ordinal, &output_tuple));
@@ -373,15 +345,27 @@ xla::StatusOr<RefPtr<XRTTupleAllocation>> CreateOutputTuple(
     TF_RETURN_IF_ERROR(XRTTupleAllocation::CreateFromBuffer(
         shaped_buffer, backend, device_ordinal, &output_tuple));
   }
+  // After the output tuple is created, we can release the output result
+  // buffers, to make sure they won't be cleared by its destructor.
+  (void)run_result.ConsumeResult().release();
   return RefPtr<XRTTupleAllocation>(output_tuple);
 }
 
 xla::StatusOr<RefPtr<XRTTupleAllocation>> RunExecutable(
     OpKernelContext* context, XRTGenericDeviceAccessor::ScopedRef* device_ref,
-    xla::LocalExecutable* executable, const InputBuffers& input_buffers,
-    se::Stream* stream, int rng_seed,
+    xla::LocalExecutable* executable,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    bool release_inputs, se::Stream* stream, int rng_seed,
     const xrt::CommonExecutionConfig& config) {
-  VLOG(2) << "Executing computation.";
+  const xla::ComputationLayout& computation_layout =
+      executable->executable()->module_config().entry_computation_layout();
+  std::vector<bool> input_is_dynamic = GetDynamicInputInfo(computation_layout);
+  TF_ASSIGN_OR_RETURN(
+      std::vector<xla::ExecutionInput> execution_inputs,
+      GetArgumentsBuffers(
+          executable->executable()->module().input_output_alias_config(),
+          input_tuples, input_is_dynamic, release_inputs));
+
   xla::ExecutableRunOptions run_options;
   run_options.set_stream(stream);
   run_options.set_allocator(device_ref->backend()->memory_allocator());
@@ -419,51 +403,28 @@ xla::StatusOr<RefPtr<XRTTupleAllocation>> RunExecutable(
   }
   run_options.set_gpu_executable_run_options(&gpu_options);
 
-  Env* env = Env::Default();
-  auto start_time = env->NowMicros();
   const std::vector<xla::ShapeLayout>& shape_layouts =
       executable->executable()
           ->module_config()
           .entry_computation_layout()
           .parameter_layouts();
-  TF_ASSIGN_OR_RETURN(auto new_allocations,
-                      UpdateDynamicInputs(stream, run_options.allocator(),
-                                          input_buffers.input_pointers,
-                                          shape_layouts));
-  auto new_allocations_ptr =
-      std::make_shared<std::vector<se::OwningDeviceMemory>>(
-          std::move(new_allocations));
+  TF_RETURN_IF_ERROR(UpdateDynamicInputs(stream, run_options.allocator(),
+                                         &execution_inputs, shape_layouts));
   TF_ASSIGN_OR_RETURN(
-      xla::ScopedShapedBuffer run_result,
-      executable->Run(input_buffers.input_pointers, run_options));
-  // Retain the new allocation for input memory until the end of execution.
-  stream->ThenDoHostCallback([new_allocations_ptr]() { return Status::OK(); });
-
-  auto elapsed = env->NowMicros() - start_time;
-  VLOG(2) << "Elapsed time: " << elapsed << "us";
+      xla::ExecutionOutput run_result,
+      executable->Run(std::move(execution_inputs), run_options));
 
   TF_ASSIGN_OR_RETURN(
       RefPtr<XRTTupleAllocation> output_tuple_ptr,
       CreateOutputTuple(stream, std::move(run_result), device_ref->backend(),
                         device_ref->device_ordinal()));
-
   // The ScopedShapedBuffer returned by the executable Run() API, in case of
   // input/output buffer aliasing, might have holes in it, which need to be
   // filled using the proper input tuples buffers which are the source of
   // aliasing.
-  const xla::HloInputOutputAliasConfig& input_output_alias =
-      executable->executable()->module().input_output_alias_config();
-  auto alias_function =
-      [&](const xla::ShapeIndex& output_index,
-          const xla::HloInputOutputAliasConfig::Alias& alias) -> Status {
-    TF_RET_CHECK(alias.parameter_number < input_buffers.input_tuples.size());
-    return alias.kind == xla::HloInputOutputAliasConfig::AliasKind::kUserAlias
-               ? output_tuple_ptr->AliasBufferFrom(
-                     *input_buffers.input_tuples[alias.parameter_number],
-                     alias.parameter_index, output_index)
-               : Status::OK();
-  };
-  TF_RETURN_IF_ERROR(input_output_alias.ForEachAliasWithStatus(alias_function));
+  TF_RETURN_IF_ERROR(RebuildOutputAliases(
+      output_tuple_ptr, input_tuples,
+      executable->executable()->module().input_output_alias_config()));
 
   return std::move(output_tuple_ptr);
 }
@@ -471,12 +432,13 @@ xla::StatusOr<RefPtr<XRTTupleAllocation>> RunExecutable(
 xla::StatusOr<RefPtr<XRTTupleAllocation>> ExecuteComputation(
     OpKernelContext* context, XRTMemoryManager* memory_manager,
     XRTGenericDeviceAccessor::ScopedRef* device_ref,
-    xla::LocalExecutable* executable, const InputBuffers& input_buffers,
-    se::Stream* stream, int rng_seed,
+    xla::LocalExecutable* executable,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    bool release_inputs, se::Stream* stream, int rng_seed,
     const xrt::CommonExecutionConfig& config) {
   auto runfn = [&]() {
-    return RunExecutable(context, device_ref, executable, input_buffers, stream,
-                         rng_seed, config);
+    return RunExecutable(context, device_ref, executable, input_tuples,
+                         release_inputs, stream, rng_seed, config);
   };
 
   // We pass zero as requested_free_size as there is no simple way to get the
@@ -495,12 +457,13 @@ xla::StatusOr<RefPtr<XRTTupleAllocation>> ExecuteComputation(
     se::Stream* stream, int rng_seed,
     const xrt::CommonExecutionConfig& config) {
   XRTMemoryManager::WorkingSet working_set(memory_manager);
-  TF_ASSIGN_OR_RETURN(InputBuffers input_buffers,
-                      GetInputBuffers(&working_set, device_ref->backend(),
-                                      input_coords, release_inputs));
+  TF_ASSIGN_OR_RETURN(
+      std::vector<RefPtr<XRTTupleAllocation>> input_tuples,
+      GetInputTuples(executable, &working_set, device_ref->backend(),
+                     input_coords, release_inputs));
   return ExecuteComputation(context, memory_manager.get(), device_ref,
-                            executable, input_buffers, stream, rng_seed,
-                            config);
+                            executable, input_tuples, release_inputs, stream,
+                            rng_seed, config);
 }
 
 // XRTExecuteOp
@@ -653,16 +616,16 @@ Status XRTExecuteChainedOp::DoWork(OpKernelContext* context) {
   auto execute_op = [&](const xrt::XRTChainedExecuteOp& op,
                         absl::Span<const RefPtr<XRTTupleAllocation>> op_inputs)
       -> xla::StatusOr<RefPtr<XRTTupleAllocation>> {
-    TF_ASSIGN_OR_RETURN(InputBuffers input_buffers,
-                        GetChainedOpInputs(op, op_inputs));
-
     std::unique_ptr<XRTCompilationCacheEntryRef> entry;
     TF_RETURN_IF_ERROR(cache->Lookup(op.computation_handle(), &entry));
     xla::LocalExecutable* executable = entry->get().get_executable();
 
-    return ExecuteComputation(context, memory_manager.get(), &device_ref,
-                              executable, input_buffers, stream, rng_seed,
-                              config.common_config());
+    TF_ASSIGN_OR_RETURN(std::vector<RefPtr<XRTTupleAllocation>> input_tuples,
+                        GetChainedOpInputTuples(op, op_inputs));
+
+    return ExecuteComputation(
+        context, memory_manager.get(), &device_ref, executable, input_tuples,
+        /*release_inputs=*/false, stream, rng_seed, config.common_config());
   };
 
   return ExecuteChained(context, memory_manager, device_ref.backend(),

tensorflow/compiler/xrt/xrt_util.cc

@@ -221,6 +221,140 @@ xla::StatusOr<std::vector<InputCoords>> GetComputationInputs(
   return std::move(input_coords);
 }
 
+bool InputShapeMatches(const xla::Shape& parameter_shape,
+                       const xla::Shape& input_shape) {
+  auto shape_checker = [&](const xla::Shape& pshape,
+                           const xla::ShapeIndex& index) {
+    if (pshape.IsArray()) {
+      TF_ASSIGN_OR_RETURN(const xla::Shape* ishape,
+                          xla::ShapeUtil::TryGetSubshape(input_shape, index));
+      if (pshape.rank() != ishape->rank() ||
+          pshape.element_type() != ishape->element_type()) {
+        return errors::InvalidArgument("Mismatching shapes");
+      }
+      if (pshape.is_static() && pshape.layout() != ishape->layout()) {
+        return errors::InvalidArgument("Mismatching layouts");
+      }
+      for (int64 dim = 0; dim < pshape.rank(); ++dim) {
+        if (pshape.is_dynamic_dimension(dim)) {
+          if (pshape.dimensions(dim) < ishape->dimensions(dim)) {
+            return errors::InvalidArgument("Mismatching shapes");
+          }
+        } else if (pshape.dimensions(dim) != ishape->dimensions(dim)) {
+          return errors::InvalidArgument("Mismatching shapes");
+        }
+      }
+    }
+    return Status::OK();
+  };
+  return xla::ShapeUtil::ForEachSubshapeWithStatus(parameter_shape,
+                                                   shape_checker)
+      .ok();
+}
+
+xla::StatusOr<std::vector<RefPtr<XRTTupleAllocation>>> GetInputTupleAllocations(
+    const std::vector<InputCoords>& input_coords,
+    XRTMemoryManager::WorkingSet* working_set, xla::Backend* backend,
+    int64 num_input_shapes,
+    const std::function<xla::Shape(int64)>& shape_getter, bool release_inputs) {
+  if (input_coords.size() != num_input_shapes) {
+    return errors::InvalidArgument(
+        "Number of inputs does not match executable proto input shapes: ",
+        input_coords.size(), " vs. ", num_input_shapes);
+  }
+  std::vector<RefPtr<XRTTupleAllocation>> input_tuples;
+  input_tuples.reserve(input_coords.size());
+  for (size_t i = 0; i < input_coords.size(); ++i) {
+    TF_RETURN_IF_ERROR(
+        working_set->LookupAndPin(backend, input_coords[i].handle));
+    auto tuple = working_set->PinnedTuples().back();
+    if (release_inputs) {
+      // We are holding a reference to the tuple, so we can safely delete it
+      // from the resource manager here.
+      TF_RETURN_IF_ERROR(
+          working_set->MemoryManager()->Release(input_coords[i].handle));
+      VLOG(2) << "Released allocation handle " << input_coords[i].handle;
+    }
+    xla::Shape input_shape = shape_getter(i);
+    if (!InputShapeMatches(input_shape, tuple->on_host_shape())) {
+      return errors::InvalidArgument(
+          "Run-time shape mismatch for XRTExecute argument[", i, "] (",
+          input_coords[i].handle, "). Expected ", input_shape.DebugString(),
+          "; got ", tuple->on_host_shape().DebugString());
+    }
+    if (input_coords[i].index.empty()) {
+      input_tuples.emplace_back(std::move(tuple));
+    } else {
+      XRTTupleAllocation* sub_tuple;
+      TF_RETURN_IF_ERROR(XRTTupleAllocation::MakeSubBuffer(
+          tuple.get(), input_coords[i].index, &sub_tuple,
+          /*alias_parent_allocation=*/true));
+      input_tuples.emplace_back(sub_tuple);
+    }
+  }
+  return std::move(input_tuples);
+}
+
+Status RebuildOutputAliases(
+    const RefPtr<XRTTupleAllocation>& output_tuple,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    const xla::HloInputOutputAliasConfig& input_output_alias) {
+  auto alias_function =
+      [&](const xla::ShapeIndex& output_index,
+          const xla::HloInputOutputAliasConfig::Alias& alias) -> Status {
+    TF_RET_CHECK(alias.parameter_number < input_tuples.size());
+    return alias.kind == xla::HloInputOutputAliasConfig::AliasKind::kUserAlias
+               ? output_tuple->AliasBufferFrom(
+                     *input_tuples[alias.parameter_number],
+                     alias.parameter_index, output_index)
+               : Status::OK();
+  };
+  return input_output_alias.ForEachAliasWithStatus(alias_function);
+}
+
+xla::StatusOr<std::vector<xla::ExecutionInput>> GetArgumentsBuffers(
+    const xla::HloInputOutputAliasConfig& input_output_alias,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    const std::vector<bool>& input_is_dynamic, bool release_inputs) {
+  auto is_dynamic = [&](size_t arg) {
+    return arg < input_is_dynamic.size() && input_is_dynamic[arg];
+  };
+  std::vector<xla::ExecutionInput> arguments;
+  // Don't alias dynamic input -- Due to the underlying implementation,
+  // aliased inputs have two owners: XRTAllocation and return value of
+  // this function. If an argument is dynamic and the ownership is
+  // released to output of this function, TPUExecute will free it and
+  // reallocate a new one, which creates a double freeing issue where
+  // XRTAllocation also attempts to release the buffer.
+  bool alias_outputs = release_inputs && input_tuples.size() == 1 &&
+                       input_tuples[0]->IsExclusiveOwner() && !is_dynamic(0);
+  arguments.reserve(input_tuples.size());
+  for (int64 i = 0; i < input_tuples.size(); ++i) {
+    auto alias_checker =
+        [&](const xla::ShapeIndex& index) -> xla::StatusOr<bool> {
+      // Only the buffers which the caller explicitly marked as aliased
+      // (kUserAlias), should create aliases.
+      // The XLA compiler might create opportunistic aliases (kSystemAlias)
+      // which need a different handling. With a system alias we know that XLA
+      // is going to reuse a given input parameter buffer for a given output, so
+      // unless it is known at call site that the input buffer has no more uses,
+      // a copy needs to be made at call site. With user specified alias the
+      // caller tells us that he expects a given output to land over the buffers
+      // of a given parametter.
+      if (input_output_alias.ParameterAliasKind(i, index) ==
+          xla::HloInputOutputAliasConfig::AliasKind::kUserAlias) {
+        TF_RET_CHECK(!is_dynamic(i));
+        return true;
+      }
+      return alias_outputs;
+    };
+    TF_ASSIGN_OR_RETURN(xla::ExecutionInput exec_input,
+                        input_tuples[i]->ToExecutionInput(alias_checker));
+    arguments.emplace_back(std::move(exec_input));
+  }
+  return std::move(arguments);
+}
+
 Status CreateExecuteOutput(OpKernelContext* context,
                            XRTMemoryManager* memory_manager,
                            RefPtr<XRTTupleAllocation> output_tuple,

tensorflow/compiler/xrt/xrt_util.h

@@ -23,6 +23,8 @@ limitations under the License.
 #include <vector>
 
 #include "tensorflow/compiler/xla/service/backend.h"
+#include "tensorflow/compiler/xla/service/hlo_input_output_alias_config.h"
+#include "tensorflow/compiler/xla/shape.h"
 #include "tensorflow/compiler/xla/shape_util.h"
 #include "tensorflow/compiler/xla/statusor.h"
 #include "tensorflow/compiler/xla/xla.pb.h"
@@ -69,6 +71,25 @@ xla::DebugOptions BuildXlaDebugOptions(const xla::DebugOptions& ref_options);
 xla::StatusOr<std::vector<InputCoords>> GetComputationInputs(
     OpKernelContext* context, const char* input_name);
 
+bool InputShapeMatches(const xla::Shape& parameter_shape,
+                       const xla::Shape& input_shape);
+
+xla::StatusOr<std::vector<RefPtr<XRTTupleAllocation>>> GetInputTupleAllocations(
+    const std::vector<InputCoords>& input_coords,
+    XRTMemoryManager::WorkingSet* working_set, xla::Backend* backend,
+    int64 num_input_shapes,
+    const std::function<xla::Shape(int64)>& shape_getter, bool release_inputs);
+
+Status RebuildOutputAliases(
+    const RefPtr<XRTTupleAllocation>& output_tuple,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    const xla::HloInputOutputAliasConfig& input_output_alias);
+
+xla::StatusOr<std::vector<xla::ExecutionInput>> GetArgumentsBuffers(
+    const xla::HloInputOutputAliasConfig& input_output_alias,
+    absl::Span<const RefPtr<XRTTupleAllocation>> input_tuples,
+    const std::vector<bool>& input_is_dynamic, bool release_inputs);
+
 // Create the XRT execute output tensor given the computation result
 // (output_tuple). The return_exploded_tuple tells whether a tuple result should
 // be returned as vector of handles representing each tuple child.