Merge pull request #43358 from tensorflow/mm-patch-r2.3

Patch for TF 2.3.1

tensorflow/c/eager/dlpack.cc

@@ -248,21 +248,36 @@ void TFE_CallDLManagedTensorDeleter(void* dlm_ptr) {
 }
 
 void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
+  auto tf_dlm_context = GetDlContext(h, status);
+  if (!status->status.ok()) {
+    return nullptr;
+  }
+
+  auto* tf_dlm_data = TFE_TensorHandleDevicePointer(h, status);
+  if (!status->status.ok()) {
+    return nullptr;
+  }
+
   const Tensor* tensor = GetTensorFromHandle(h, status);
   TF_DataType data_type = static_cast<TF_DataType>(tensor->dtype());
-  TensorReference tensor_ref(*tensor);  // This will call buf_->Ref()
 
+  auto tf_dlm_type = GetDlDataType(data_type, status);
+  if (!status->status.ok()) {
+    return nullptr;
+  }
+
+  TensorReference tensor_ref(*tensor);  // This will call buf_->Ref()
   auto* tf_dlm_tensor_ctx = new TfDlManagedTensorCtx(tensor_ref);
   tf_dlm_tensor_ctx->reference = tensor_ref;
 
   DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
   dlm_tensor->manager_ctx = tf_dlm_tensor_ctx;
   dlm_tensor->deleter = &DLManagedTensorDeleter;
-  dlm_tensor->dl_tensor.ctx = GetDlContext(h, status);
+  dlm_tensor->dl_tensor.ctx = tf_dlm_context;
   int ndim = tensor->dims();
   dlm_tensor->dl_tensor.ndim = ndim;
-  dlm_tensor->dl_tensor.data = TFE_TensorHandleDevicePointer(h, status);
+  dlm_tensor->dl_tensor.data = tf_dlm_data;
-  dlm_tensor->dl_tensor.dtype = GetDlDataType(data_type, status);
+  dlm_tensor->dl_tensor.dtype = tf_dlm_type;
 
   std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
   std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
@@ -275,13 +290,14 @@ void* TFE_HandleToDLPack(TFE_TensorHandle* h, TF_Status* status) {
     (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];
   }
 
-  dlm_tensor->dl_tensor.shape = &(*shape_arr)[0];
+  dlm_tensor->dl_tensor.shape = shape_arr->data();
   // There are two ways to represent compact row-major data
   // 1) nullptr indicates tensor is compact and row-majored.
   // 2) fill in the strides array as the real case for compact row-major data.
   // Here we choose option 2, since some frameworks didn't handle the strides
   // argument properly.
-  dlm_tensor->dl_tensor.strides = &(*stride_arr)[0];
+  dlm_tensor->dl_tensor.strides = stride_arr->data();
+
   dlm_tensor->dl_tensor.byte_offset =
       0;  // TF doesn't handle the strides and byte_offsets here
   return static_cast<void*>(dlm_tensor);

tensorflow/cc/saved_model/loader.cc

@@ -21,6 +21,7 @@ limitations under the License.
 #include "tensorflow/cc/saved_model/loader_util.h"
 #include "tensorflow/cc/saved_model/reader.h"
 #include "tensorflow/core/framework/attr_value.pb.h"
+#include "tensorflow/core/framework/function.proto.h"
 #include "tensorflow/core/framework/node_def.pb.h"
 #include "tensorflow/core/framework/tensor.pb.h"
 #include "tensorflow/core/lib/io/path.h"
@@ -72,8 +73,7 @@ uint64 GetLatencyMicroseconds(const uint64 start_microseconds) {
 // Ensure that constant tensors loaded from the saved model have valid shape.
 // Also ensure that constant nodes have a value assigned to them.
 // TODO(b/154763635): this is temporary and will be replaced with a better audit
-static Status ValidateSavedTensors(const GraphDef& graph_def) {
+static Status ValidateNode(const NodeDef& node) {
-  for (const auto& node : graph_def.node()) {
   const auto node_iterator = node.attr().find("value");
   if (node_iterator != node.attr().end()) {
     AttrValue node_value = node_iterator->second;
@@ -81,8 +81,8 @@ static Status ValidateSavedTensors(const GraphDef& graph_def) {
       const PartialTensorShape node_shape(node_value.tensor().tensor_shape());
       if (node_shape.num_elements() < 0) {
         return errors::FailedPrecondition(
-              "Saved model contains node \"", node.name(), "\" (op \"",
+            "Saved model contains node \"", node.name(), "\" (op \"", node.op(),
-              node.op(), "\") which initializes from a tensor with ",
+            "\") which initializes from a tensor with ",
             node_shape.num_elements(), " elements");
       }
     }
@@ -91,7 +91,23 @@ static Status ValidateSavedTensors(const GraphDef& graph_def) {
         "Saved model contains node \"", node.name(),
         "\" which is a constant tensor but no value has been provided");
   }
+  return Status::OK();
+}
+
+static Status ValidateSavedTensors(const GraphDef& graph_def) {
+  for (const auto& node : graph_def.node()) {
+    TF_RETURN_IF_ERROR(ValidateNode(node));
   }
+
+  if (graph_def.has_library()) {
+    const FunctionDefLibrary& library = graph_def.library();
+    for (const auto& function : library.function()) {
+      for (const auto& node : function.node_def()) {
+	TF_RETURN_IF_ERROR(ValidateNode(node));
+      }
+    }
+  }
+
   return Status::OK();
 }
 

tensorflow/core/common_runtime/eager/kernel_and_device.cc

@@ -307,7 +307,12 @@ Status KernelAndDeviceOp::Run(
   if (outputs != nullptr) {
     outputs->clear();
     for (int i = 0; i < context.num_outputs(); ++i) {
-      outputs->push_back(Tensor(*context.mutable_output(i)));
+      const auto* output_tensor = context.mutable_output(i);
+      if (output_tensor != nullptr) {
+        outputs->push_back(Tensor(*output_tensor));
+      } else {
+        outputs->push_back(Tensor());
+      }
     }
   }
   return Status::OK();

tensorflow/core/kernels/BUILD

@@ -6085,6 +6085,24 @@ tf_kernel_library(
     deps = STRING_DEPS,
 )
 
+tf_cc_test(
+    name = "as_string_op_test",
+    size = "small",
+    srcs = ["as_string_op_test.cc"],
+    deps = [
+        ":as_string_op",
+        ":ops_testutil",
+        ":ops_util",
+        "//tensorflow/core:core_cpu",
+        "//tensorflow/core:framework",
+        "//tensorflow/core:lib",
+        "//tensorflow/core:protos_all_cc",
+        "//tensorflow/core:test",
+        "//tensorflow/core:test_main",
+        "//tensorflow/core:testlib",
+    ],
+)
+
 tf_kernel_library(
     name = "unicode_ops",
     prefix = "unicode_ops",

tensorflow/core/kernels/as_string_op.cc

@@ -65,9 +65,26 @@ class AsStringOp : public OpKernel {
     OP_REQUIRES(ctx, !(scientific && shortest),
                 errors::InvalidArgument(
                     "Cannot select both scientific and shortest notation"));
+
     format_ = "%";
+    if (!fill_string.empty()) {
+      switch (fill_string[0]) {
+        case ' ':
+        case '+':
+        case '-':
+        case '0':
+        case '#':
+          strings::Appendf(&format_, "%s", fill_string.c_str());
+          break;
+        default:
+          bool fill_not_supported = true;
+          OP_REQUIRES(ctx, !fill_not_supported,
+                      errors::InvalidArgument("Fill argument not supported: \"",
+                                              fill_string, "\""));
+      }
+    }
     if (width > -1) {
-      strings::Appendf(&format_, "%s%d", fill_string.c_str(), width);
+      strings::Appendf(&format_, "%d", width);
     }
     if (precision > -1) {
       strings::Appendf(&format_, ".%d", precision);

tensorflow/core/kernels/as_string_op_test.cc

@@ -0,0 +1,245 @@
+/* Copyright 2020 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/core/framework/fake_input.h"
+#include "tensorflow/core/framework/node_def_builder.h"
+#include "tensorflow/core/framework/tensor.h"
+#include "tensorflow/core/framework/tensor_testutil.h"
+#include "tensorflow/core/framework/types.h"
+#include "tensorflow/core/kernels/ops_testutil.h"
+#include "tensorflow/core/kernels/ops_util.h"
+#include "tensorflow/core/lib/core/status_test_util.h"
+
+namespace tensorflow {
+namespace {
+
+class AsStringGraphTest : public OpsTestBase {
+ protected:
+  Status Init(DataType input_type, const string& fill = "", int width = -1,
+              int precision = -1, bool scientific = false,
+              bool shortest = false) {
+    TF_CHECK_OK(NodeDefBuilder("op", "AsString")
+                    .Input(FakeInput(input_type))
+                    .Attr("fill", fill)
+                    .Attr("precision", precision)
+                    .Attr("scientific", scientific)
+                    .Attr("shortest", shortest)
+                    .Attr("width", width)
+                    .Finalize(node_def()));
+    return InitOp();
+  }
+};
+
+TEST_F(AsStringGraphTest, Int8) {
+  TF_ASSERT_OK(Init(DT_INT8));
+
+  AddInputFromArray<int8>(TensorShape({3}), {-42, 0, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(&expected, {"-42", "0", "42"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, Int64) {
+  TF_ASSERT_OK(Init(DT_INT64));
+
+  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(&expected, {"-42", "0", "42"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FloatDefault) {
+  TF_ASSERT_OK(Init(DT_FLOAT));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(
+      &expected, {"-42.000000", "0.000000", "3.141590", "42.000000"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FloatScientific) {
+  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
+                    /*scientific=*/true));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(&expected, {"-4.200000e+01", "0.000000e+00",
+                                        "3.141590e+00", "4.200000e+01"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FloatShortest) {
+  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
+                    /*scientific=*/false, /*shortest=*/true));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(&expected, {"-42", "0", "3.14159", "42"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FloatPrecisionOnly) {
+  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/2));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(&expected, {"-42.00", "0.00", "3.14", "42.00"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FloatWidthOnly) {
+  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/5));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(
+      &expected, {"-42.000000", "0.000000", "3.141590", "42.000000"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, Float_5_2_Format) {
+  TF_ASSERT_OK(Init(DT_FLOAT, /*fill=*/"", /*width=*/5, /*precision=*/2));
+
+  AddInputFromArray<float>(TensorShape({4}), {-42, 0, 3.14159, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({4}));
+  test::FillValues<tstring>(&expected, {"-42.00", " 0.00", " 3.14", "42.00"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, Complex) {
+  TF_ASSERT_OK(Init(DT_COMPLEX64, /*fill=*/"", /*width=*/5, /*precision=*/2));
+
+  AddInputFromArray<complex64>(TensorShape({3}), {{-4, 2}, {0}, {3.14159, -1}});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(
+      &expected, {"(-4.00, 2.00)", "( 0.00, 0.00)", "( 3.14,-1.00)"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, Bool) {
+  TF_ASSERT_OK(Init(DT_BOOL));
+
+  AddInputFromArray<bool>(TensorShape({2}), {true, false});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({2}));
+  test::FillValues<tstring>(&expected, {"true", "false"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, String) {
+  Status s = Init(DT_STRING);
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(absl::StrContains(
+      s.error_message(),
+      "Value for attr 'T' of string is not in the list of allowed values"));
+}
+
+TEST_F(AsStringGraphTest, OnlyOneOfScientificAndShortest) {
+  Status s = Init(DT_FLOAT, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
+                  /*scientific=*/true, /*shortest=*/true);
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(
+      absl::StrContains(s.error_message(),
+                        "Cannot select both scientific and shortest notation"));
+}
+
+TEST_F(AsStringGraphTest, NoShortestForNonFloat) {
+  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
+                  /*scientific=*/false, /*shortest=*/true);
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(absl::StrContains(
+      s.error_message(),
+      "scientific and shortest format not supported for datatype"));
+}
+
+TEST_F(AsStringGraphTest, NoScientificForNonFloat) {
+  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/-1,
+                  /*scientific=*/true);
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(absl::StrContains(
+      s.error_message(),
+      "scientific and shortest format not supported for datatype"));
+}
+
+TEST_F(AsStringGraphTest, NoPrecisionForNonFloat) {
+  Status s = Init(DT_INT32, /*fill=*/"", /*width=*/-1, /*precision=*/5);
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(absl::StrContains(s.error_message(),
+                                "precision not supported for datatype"));
+}
+
+TEST_F(AsStringGraphTest, LongFill) {
+  Status s = Init(DT_INT32, /*fill=*/"asdf");
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(absl::StrContains(s.error_message(),
+                                "Fill string must be one or fewer characters"));
+}
+
+TEST_F(AsStringGraphTest, FillWithZero) {
+  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/"0", /*width=*/4));
+
+  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(&expected, {"-042", "0000", "0042"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FillWithSpace) {
+  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/" ", /*width=*/4));
+
+  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(&expected, {" -42", "   0", "  42"});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FillWithChar1) {
+  TF_ASSERT_OK(Init(DT_INT64, /*fill=*/"-", /*width=*/4));
+
+  AddInputFromArray<int64>(TensorShape({3}), {-42, 0, 42});
+  TF_ASSERT_OK(RunOpKernel());
+  Tensor expected(allocator(), DT_STRING, TensorShape({3}));
+  test::FillValues<tstring>(&expected, {"-42 ", "0   ", "42  "});
+  test::ExpectTensorEqual<tstring>(expected, *GetOutput(0));
+}
+
+TEST_F(AsStringGraphTest, FillWithChar3) {
+  Status s = Init(DT_INT32, /*fill=*/"s");
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(
+      absl::StrContains(s.error_message(), "Fill argument not supported"));
+}
+
+TEST_F(AsStringGraphTest, FillWithChar4) {
+  Status s = Init(DT_INT32, /*fill=*/"n");
+  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
+  ASSERT_TRUE(
+      absl::StrContains(s.error_message(), "Fill argument not supported"));
+}
+
+}  // end namespace
+}  // end namespace tensorflow

tensorflow/core/kernels/banded_triangular_solve_op.cc

@@ -193,7 +193,8 @@ struct LaunchBatchBandedTriangularSolve {
 
     Shard(worker_threads.num_threads, worker_threads.workers, batch_size,
           cost_per_unit,
-          [&in_x, &in_y, adjoint, lower, &bcast, out](int start, int limit) {
+          [&in_x, &in_y, adjoint, lower, &bcast, out](int64 start,
+                                                      int64 limit) {
             SequentialBandedTriangularSolveKernel<Scalar>::Run(
                 in_x, in_y, lower, adjoint, bcast, out, start, limit);
           });

tensorflow/core/kernels/boosted_trees/prediction_ops.cc

@@ -121,7 +121,7 @@ class BoostedTreesTrainingPredictOp : public OpKernel {
       auto do_work = [&resource, &bucketized_features, &cached_tree_ids,
                       &cached_node_ids, &output_partial_logits,
                       &output_node_ids, latest_tree,
-                      this](int32 start, int32 end) {
+                      this](int64 start, int64 end) {
         for (int32 i = start; i < end; ++i) {
           int32 tree_id = cached_tree_ids(i);
           int32 node_id = cached_node_ids(i);
@@ -237,7 +237,7 @@ class BoostedTreesPredictOp : public OpKernel {
 
     const int32 last_tree = resource->num_trees() - 1;
     auto do_work = [&resource, &bucketized_features, &output_logits, last_tree,
-                    this](int32 start, int32 end) {
+                    this](int64 start, int64 end) {
       for (int32 i = start; i < end; ++i) {
         std::vector<float> tree_logits(logits_dimension_, 0.0);
         int32 tree_id = 0;
@@ -340,7 +340,7 @@ class BoostedTreesExampleDebugOutputsOp : public OpKernel {
     // path. Note: feature_ids has one less value than logits_path because the
     // first value of each logit path will be the bias.
     auto do_work = [&resource, &bucketized_features, &output_debug_info,
-                    last_tree](int32 start, int32 end) {
+                    last_tree](int64 start, int64 end) {
       for (int32 i = start; i < end; ++i) {
         // Proto to store debug outputs, per example.
         boosted_trees::DebugOutput example_debug_info;

tensorflow/core/kernels/count_ops.cc

@@ -178,10 +178,30 @@ class SparseCount : public OpKernel {
     const Tensor& weights = context->input(3);
     bool use_weights = weights.NumElements() > 0;
 
+    OP_REQUIRES(context, TensorShapeUtils::IsMatrix(indices.shape()),
+                errors::InvalidArgument(
+                    "Input indices must be a 2-dimensional tensor. Got: ",
+                    indices.shape().DebugString()));
+
+    if (use_weights) {
+      OP_REQUIRES(
+          context, weights.shape() == values.shape(),
+          errors::InvalidArgument(
+              "Weights and values must have the same shape. Weight shape: ",
+              weights.shape().DebugString(),
+              "; values shape: ", values.shape().DebugString()));
+    }
+
     bool is_1d = shape.NumElements() == 1;
     int num_batches = is_1d ? 1 : shape.flat<int64>()(0);
     int num_values = values.NumElements();
 
+    OP_REQUIRES(context, num_values == indices.shape().dim_size(0),
+                errors::InvalidArgument(
+                    "Number of values must match first dimension of indices.",
+                    "Got ", num_values,
+                    " values, indices shape: ", indices.shape().DebugString()));
+
     const auto indices_values = indices.matrix<int64>();
     const auto values_values = values.flat<T>();
     const auto weight_values = weights.flat<W>();
@@ -235,12 +255,33 @@ class RaggedCount : public OpKernel {
     bool use_weights = weights.NumElements() > 0;
     bool is_1d = false;
 
+    if (use_weights) {
+      OP_REQUIRES(
+          context, weights.shape() == values.shape(),
+          errors::InvalidArgument(
+              "Weights and values must have the same shape. Weight shape: ",
+              weights.shape().DebugString(),
+              "; values shape: ", values.shape().DebugString()));
+    }
+
     const auto splits_values = splits.flat<int64>();
     const auto values_values = values.flat<T>();
     const auto weight_values = weights.flat<W>();
     int num_batches = splits.NumElements() - 1;
     int num_values = values.NumElements();
 
+    OP_REQUIRES(
+        context, num_batches > 0,
+        errors::InvalidArgument(
+            "Must provide at least 2 elements for the splits argument"));
+    OP_REQUIRES(context, splits_values(0) == 0,
+                errors::InvalidArgument("Splits must start with 0, not with ",
+                                        splits_values(0)));
+    OP_REQUIRES(context, splits_values(num_batches) == num_values,
+                errors::InvalidArgument(
+                    "Splits must end with the number of values, got ",
+                    splits_values(num_batches), " instead of ", num_values));
+
     auto per_batch_counts = BatchedMap<W>(num_batches);
     T max_value = 0;
     int batch_idx = 0;

tensorflow/core/kernels/crop_and_resize_op.cc

@@ -223,7 +223,7 @@ struct CropAndResize<CPUDevice, T> {
     const int depth = crops.dimension(3);
 
     // Sharding across boxes.
-    auto CropAndResizePerBox = [&](int start_box, int limit_box) {
+    auto CropAndResizePerBox = [&](int64 start_box, int64 limit_box) {
       for (int b = start_box; b < limit_box; ++b) {
         const float y1 = boxes(b, 0);
         const float x1 = boxes(b, 1);
@@ -449,7 +449,7 @@ struct CropAndResizeBackpropImage<CPUDevice, T> {
 
     grads_image.setZero();
 
-    auto CropAndResizeBackImgPerBox = [&](int start_box, int limit_box) {
+    auto CropAndResizeBackImgPerBox = [&](int64 start_box, int64 limit_box) {
       for (int b = start_box; b < limit_box; ++b) {
         const float y1 = boxes(b, 0);
         const float x1 = boxes(b, 1);

tensorflow/core/kernels/nth_element_op.cc

@@ -95,7 +95,8 @@ struct NthElementFunctor<CPUDevice, T> {
     const int last_dim = input_tensor.dim_size(input_tensor.dims() - 1);
 
     // Allocate each row to different shard.
-    auto SubNthElement = [&, input, output, last_dim, n](int start, int limit) {
+    auto SubNthElement = [&, input, output, last_dim, n](int64 start,
+                                                         int64 limit) {
       // std::nth_element would rearrange the array, so we need a new buffer.
       std::vector<T> buf(last_dim);
 

tensorflow/core/kernels/parameterized_truncated_normal_op.cc

@@ -70,8 +70,8 @@ struct TruncatedNormalFunctor<CPUDevice, T> {
 
     auto do_work = [samples_per_batch, num_elements, &ctx, &means, &stddevs,
                     &minvals, &maxvals, &gen, &output,
-                    kStdDevsInsideBoundsToUseRandnSampler](int start_batch,
+                    kStdDevsInsideBoundsToUseRandnSampler](int64 start_batch,
-                                                           int limit_batch) {
+                                                           int64 limit_batch) {
       // Capturing "gen" by-value would only make a copy for the _shared_
       // lambda.  Since we want to let each worker have its own copy, we pass
       // "gen" by reference and explicitly do a copy assignment here.
@@ -333,8 +333,8 @@ struct TruncatedNormalFunctorV2<CPUDevice, T> {
 
     auto do_work = [num_batches, samples_per_batch, &ctx, &bcast, &means,
                     &stddevs, &minvals, &maxvals, &gen, &output,
-                    kStdDevsInsideBoundsToUseRandnSampler](int start_output,
+                    kStdDevsInsideBoundsToUseRandnSampler](int64 start_output,
-                                                           int limit_output) {
+                                                           int64 limit_output) {
       // Capturing "gen" by-value would only make a copy for the _shared_
       // lambda.  Since we want to let each worker have its own copy, we pass
       // "gen" by reference and explicitly do a copy assignment here.

tensorflow/core/kernels/random_binomial_op.cc

@@ -182,7 +182,7 @@ struct RandomBinomialFunctor<CPUDevice, T, U> {
     // the sample shape and [H1, ... Hm] for the batch shape of the samples.
     // We have B1 * ... * Bk samples per batch member we need.
     auto DoWork = [num_batches, samples_per_batch, &bcast, &counts, &probs,
-                   &gen, &output](int start_output, int limit_output) {
+                   &gen, &output](int64 start_output, int64 limit_output) {
       // Vectorized intermediate calculations for uniform rejection sampling.
       // We always generate at most 4 samples.
       Eigen::array<T, 4> z;

tensorflow/core/kernels/random_op.cc

@@ -205,7 +205,7 @@ class RandomGammaOp : public OpKernel {
     // avoid a couple flops which can be done on a per-alpha basis.
 
     auto DoWork = [samples_per_alpha, num_alphas, &rng, samples_flat,
-                   alpha_flat](int start_output, int limit_output) {
+                   alpha_flat](int64 start_output, int64 limit_output) {
       using Eigen::numext::exp;
       using Eigen::numext::log;
       using Eigen::numext::log1p;

tensorflow/core/kernels/random_poisson_op.cc

@@ -97,7 +97,7 @@ struct PoissonFunctor<CPUDevice, T, U> {
     typedef random::UniformDistribution<random::PhiloxRandom, CT> Uniform;
 
     auto DoWork = [num_samples, num_rate, &rng, samples_flat, rate_flat](
-                      int start_output, int limit_output) {
+                      int64 start_output, int64 limit_output) {
       // Capturing "rng" by value would only make a copy for the _shared_
       // lambda.  Since we want to let each worker have its own copy, we pass
       // "rng" by reference and explicitly do a copy assignment.

tensorflow/core/kernels/session_ops.cc

@@ -16,6 +16,7 @@ limitations under the License.
 // See docs in ../ops/data_flow_ops.cc.
 
 #include <limits.h>
+
 #include <vector>
 
 #include "tensorflow/core/common_runtime/device.h"
@@ -27,6 +28,7 @@ limitations under the License.
 #include "tensorflow/core/framework/types.h"
 #include "tensorflow/core/lib/core/errors.h"
 #include "tensorflow/core/lib/gtl/map_util.h"
+#include "tensorflow/core/platform/errors.h"
 #include "tensorflow/core/platform/logging.h"
 #include "tensorflow/core/platform/macros.h"
 #include "tensorflow/core/platform/mutex.h"
@@ -42,7 +44,11 @@ class GetSessionHandleOp : public OpKernel {
 
   void Compute(OpKernelContext* ctx) override {
     const Tensor& val = ctx->input(0);
-    int64 id = ctx->session_state()->GetNewId();
+    auto session_state = ctx->session_state();
+    OP_REQUIRES(ctx, session_state != nullptr,
+                errors::FailedPrecondition(
+                    "GetSessionHandle called on null session state"));
+    int64 id = session_state->GetNewId();
     TensorStore::TensorAndKey tk{val, id, requested_device()};
     OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(name(), tk));
 

tensorflow/core/kernels/sparse_fill_empty_rows_op.cc

@@ -232,6 +232,9 @@ class SparseFillEmptyRowsGradOp : public OpKernel {
         context, TensorShapeUtils::IsVector(reverse_index_map_t->shape()),
         errors::InvalidArgument("reverse_index_map must be a vector, saw: ",
                                 reverse_index_map_t->shape().DebugString()));
+    OP_REQUIRES(context, TensorShapeUtils::IsVector(grad_values_t->shape()),
+                errors::InvalidArgument("grad_values must be a vector, saw: ",
+                                        grad_values_t->shape().DebugString()));
 
     const auto reverse_index_map = reverse_index_map_t->vec<int64>();
     const auto grad_values = grad_values_t->vec<T>();
@@ -260,8 +263,13 @@ class SparseFillEmptyRowsGradOp : public OpKernel {
       // Locate the index of the output of the forward prop associated
       // with this location in the input of the forward prop.  Copy
       // the gradient into it.  Mark it as visited.
-      d_values(i) = grad_values(reverse_index_map(i));
+      int64 reverse_index = reverse_index_map(i);
-      visited(reverse_index_map(i)) = true;
+      OP_REQUIRES(
+          context, 0 <= reverse_index && reverse_index < N_full,
+          errors::InvalidArgument("Elements in reverse index must be in [0, ",
+                                  N_full, ") but got ", reverse_index));
+      d_values(i) = grad_values(reverse_index);
+      visited(reverse_index) = true;
     }
     for (int j = 0; j < N_full; ++j) {
       // The default value gradient gets the accumulated remainder of

tensorflow/core/kernels/stateless_random_ops.cc

@@ -252,7 +252,7 @@ class StatelessRandomGammaOp : public StatelessRandomOpBase {
     // avoid a couple flops which can be done on a per-alpha basis.
 
     auto DoWork = [samples_per_alpha, num_alphas, &random, samples_flat,
-                   alpha_flat](int start_output, int limit_output) {
+                   alpha_flat](int64 start_output, int64 limit_output) {
       // Capturing "random" by-value would only make a copy for the _shared_
       // lambda.  Since we want to let each worker have its own copy, we pass
       // "random" by reference and explicitly do a copy assignment.

tensorflow/core/kernels/string_ngrams_op.cc

@@ -19,6 +19,7 @@ limitations under the License.
 #include "absl/strings/ascii.h"
 #include "absl/strings/str_cat.h"
 #include "tensorflow/core/framework/op_kernel.h"
+#include "tensorflow/core/platform/errors.h"
 
 namespace tensorflow {
 namespace text {
@@ -60,6 +61,18 @@ class StringNGramsOp : public tensorflow::OpKernel {
     OP_REQUIRES_OK(context, context->input("data_splits", &splits));
     const auto& splits_vec = splits->flat<SPLITS_TYPE>();
 
+    // Validate that the splits are valid indices into data
+    const int input_data_size = data->flat<tstring>().size();
+    const int splits_vec_size = splits_vec.size();
+    for (int i = 0; i < splits_vec_size; ++i) {
+      bool valid_splits = splits_vec(i) >= 0;
+      valid_splits = valid_splits && (splits_vec(i) <= input_data_size);
+      OP_REQUIRES(
+          context, valid_splits,
+          errors::InvalidArgument("Invalid split value ", splits_vec(i),
+                                  ", must be in [0,", input_data_size, "]"));
+    }
+
     int num_batch_items = splits_vec.size() - 1;
     tensorflow::Tensor* ngrams_splits;
     OP_REQUIRES_OK(

tensorflow/core/kernels/topk_op.cc

@@ -136,7 +136,7 @@ struct TopKFunctor<CPUDevice, T> {
       return Status::OK();
     }
 
-    auto SortIndices = [&](int start_batch, int limit_batch) {
+    auto SortIndices = [&](int64 start_batch, int64 limit_batch) {
       for (int32 b = start_batch; b < limit_batch; ++b) {
         const T* input_data = &input(b, 0);
         const auto stable_comp = [input_data](const int32 a, const int32 b) {

tensorflow/lite/core/subgraph.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #include <algorithm>
 
 #include "tensorflow/lite/arena_planner.h"
+#include "tensorflow/lite/builtin_ops.h"
 #include "tensorflow/lite/c/common.h"
 #include "tensorflow/lite/context_util.h"
 #include "tensorflow/lite/core/api/tensor_utils.h"
@@ -567,6 +568,33 @@ TfLiteStatus Subgraph::CheckTensorIndices(const char* label, const int* indices,
   return kTfLiteOk;
 }
 
+// We have two arrays and we need to check that elements from one array don't
+// show up in the other. We could sort both arrays and then iterate with two
+// pointers from start to finish always increasing the smaller one but since
+// these arrays are usually short (<25 elements for inputs, usually <3 for
+// outputs), this might be slower than the naive approach (if arrays have size n
+// and m, with n >> m ~ O(1), first approach is O(nlogn) whereas the other is
+// O(n)). Plus, sorting the input and output arrays might not be something we
+// want as it destroys ordering of elements.
+//
+// If it turns out that this is an issue, we can switch to the other algorithm.
+TfLiteStatus Subgraph::CheckInputAndOutputForOverlap(const int* input_indices,
+                                                     int num_inputs,
+                                                     const int* output_indices,
+                                                     int num_outputs) {
+  for (int i = 0; i < num_inputs; i++) {
+    for (int j = 0; j < num_outputs; j++) {
+      if (input_indices[i] == output_indices[j]) {
+        ReportError("Tensor %d is both input %d and output %d\n",
+                    input_indices[i], i, j);
+        consistent_ = false;
+        return kTfLiteError;
+      }
+    }
+  }
+  return kTfLiteOk;
+}
+
 namespace {
 // Multiply two sizes and return true if overflow occurred;
 // This is based off tensorflow/overflow.h but is simpler as we already
@@ -688,6 +716,16 @@ TfLiteStatus Subgraph::AddNodeWithParameters(
       &context_,
       CheckTensorIndices("node outputs", outputs.data(), outputs.size()));
 
+  // For builtin ops, inputs and outputs must not overlap. Custom ops must do
+  // this check by themselves if they don't support overlapping tensors. This
+  // distinction is to allow custom ops to just forward a tensor, reusing it as
+  // both input and output.
+  if (builtin_data != nullptr) {
+    TF_LITE_ENSURE_OK(&context_, CheckInputAndOutputForOverlap(
+                                     inputs.data(), inputs.size(),
+                                     outputs.data(), outputs.size()));
+  }
+
   int new_node_index = nodes_and_registration_.size();
   if (node_index) *node_index = new_node_index;
   nodes_and_registration_.resize(nodes_and_registration_.size() + 1);
@@ -934,6 +972,19 @@ TfLiteStatus Subgraph::Invoke() {
           tensor->data_is_stale) {
         TF_LITE_ENSURE_STATUS(EnsureTensorDataIsReadable(tensor_index));
       }
+      if (tensor->data.raw == nullptr && tensor->bytes > 0) {
+        if (registration.builtin_code == kTfLiteBuiltinReshape && i == 1) {
+          // In general, having a tensor here with no buffer will be an error.
+          // However, for the reshape operator, the second input tensor is only
+          // used for the shape, not for the data. Thus, null buffer is ok.
+          continue;
+        } else {
+          // In all other cases, we need to return an error as otherwise we will
+          // trigger a null pointer dereference (likely).
+          ReportError("Input tensor %d lacks data", tensor_index);
+          return kTfLiteError;
+        }
+      }
     }
 
     if (check_cancelled_func_ != nullptr &&

tensorflow/lite/core/subgraph.h

@@ -433,6 +433,15 @@ class Subgraph {
   TfLiteStatus CheckTensorIndices(const char* label, const int* indices,
                                   int length);
 
+  // Check that the input indices and the output indices don't overlap.
+  // This is needed because same tensor must not be used both as input and
+  // output for an operator.
+  // NOTE: this changes consistent_ to be false if indices are out of bounds.
+  TfLiteStatus CheckInputAndOutputForOverlap(const int* input_indices,
+                                             int num_inputs,
+                                             const int* output_indices,
+                                             int num_outputs);
+
   // Compute the number of bytes required to represent a tensor with dimensions
   // specified by the array dims (of length dims_size). Returns the status code
   // and bytes.

tensorflow/lite/interpreter_builder.cc

@@ -609,7 +609,12 @@ TfLiteStatus InterpreterBuilder::operator()(
   auto* buffers = model_->buffers();
 
   if (subgraphs->size() == 0) {
-    error_reporter_->Report("No subgraph in the model.\n");
+    TF_LITE_REPORT_ERROR(error_reporter_, "No subgraph in the model.\n");
+    return cleanup_and_error();
+  }
+
+  if (!buffers) {
+    TF_LITE_REPORT_ERROR(error_reporter_, "No buffers in the model.\n");
     return cleanup_and_error();
   }
 
@@ -630,9 +635,9 @@ TfLiteStatus InterpreterBuilder::operator()(
         (*interpreter)->subgraph(subgraph_index);
     auto operators = subgraph->operators();
     auto tensors = subgraph->tensors();
-    if (!operators || !tensors || !buffers) {
+    if (!operators || !tensors) {
-      error_reporter_->Report(
+      TF_LITE_REPORT_ERROR(error_reporter_,
-          "Did not get operators, tensors, or buffers in subgraph %d.\n",
+                           "Did not get operators or tensors in subgraph %d.\n",
                            subgraph_index);
       return cleanup_and_error();
     }

tensorflow/lite/kernels/internal/reference/reduce.h

@@ -70,6 +70,9 @@ inline bool ResolveAxis(const int num_dims, const int* axis,
     // eg: For num_dims=3, [0, 1, 2] is the same as [-3, -2, -1]  */
     int current = axis[idx] < 0 ? (axis[idx] + num_dims) : axis[idx];
     TFLITE_DCHECK(current >= 0 && current < num_dims);
+    if (current < 0 || current >= num_dims) {
+      return false;
+    }
     bool is_dup = false;
     for (int j = 0; j < *out_num_axis; ++j) {
       if (out_axis[j] == current) {

tensorflow/lite/kernels/internal/types.h

@@ -432,7 +432,7 @@ int MatchingArraySize(const ArrayType1& array1, int index1,
 inline int MatchingDim(const RuntimeShape& shape1, int index1,
                        const RuntimeShape& shape2, int index2) {
   TFLITE_DCHECK_EQ(shape1.Dims(index1), shape2.Dims(index2));
-  return shape1.Dims(index1);
+  return std::min(shape1.Dims(index1), shape2.Dims(index2));
 }
 
 template <typename... Args>

tensorflow/lite/kernels/kernel_util.h

@@ -30,27 +30,48 @@ inline int SizeOfDimension(const TfLiteTensor* t, int dim) {
 }
 inline const TfLiteTensor* GetInput(const TfLiteContext* context,
                                     const TfLiteNode* node, int index) {
-  return &context->tensors[node->inputs->data[index]];
+  const int tensor_index = node->inputs->data[index];
+  if (tensor_index < 0) {
+    return nullptr;
+  }
+  return &context->tensors[tensor_index];
 }
 // Note: You must check if result is not null:
 // TfLiteTensor* my_tensor = GetVariableInput(context, node, kMyTensorIdx);
 // TF_LITE_ENSURE(context, my_tensor != nullptr);
 inline TfLiteTensor* GetVariableInput(TfLiteContext* context,
                                       const TfLiteNode* node, int index) {
-  TfLiteTensor* tensor = &context->tensors[node->inputs->data[index]];
+  const int tensor_index = node->inputs->data[index];
+  if (tensor_index < 0) {
+    return nullptr;
+  }
+  TfLiteTensor* tensor = &context->tensors[tensor_index];
   return (tensor->is_variable) ? tensor : nullptr;
 }
 inline TfLiteTensor* GetOutput(TfLiteContext* context, const TfLiteNode* node,
                                int index) {
-  return &context->tensors[node->outputs->data[index]];
+  const int tensor_index = node->outputs->data[index];
+  if (tensor_index < 0) {
+    return nullptr;
+  }
+  return &context->tensors[tensor_index];
 }
 inline TfLiteTensor* GetTemporary(TfLiteContext* context,
                                   const TfLiteNode* node, int index) {
-  return &context->tensors[node->temporaries->data[index]];
+  const int tensor_index = node->temporaries->data[index];
+  if (tensor_index < 0) {
+    return nullptr;
+  }
+  return &context->tensors[tensor_index];
 }
+
 inline const TfLiteTensor* GetIntermediates(TfLiteContext* context,
                                             const TfLiteNode* node, int index) {
-  return &context->tensors[node->intermediates->data[index]];
+  const int tensor_index = node->intermediates->data[index];
+  if (tensor_index < 0) {
+    return nullptr;
+  }
+  return &context->tensors[tensor_index];
 }
 inline int NumInputs(const TfLiteNode* node) { return node->inputs->size; }
 inline int NumOutputs(const TfLiteNode* node) { return node->outputs->size; }
@@ -73,12 +94,7 @@ inline int64_t NumElements(const TfLiteTensor* t) {
 inline const TfLiteTensor* GetOptionalInputTensor(TfLiteContext* context,
                                                   const TfLiteNode* node,
                                                   int index) {
-  const bool use_tensor = index < node->inputs->size &&
+  return GetInput(context, node, index);
-                          node->inputs->data[index] != kTfLiteOptionalTensor;
-  if (use_tensor) {
-    return &context->tensors[node->inputs->data[index]];
-  }
-  return nullptr;
 }
 
 // Determines whether tensor is constant.

tensorflow/lite/kernels/segment_sum.cc

@@ -34,11 +34,24 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context,
                                 const TfLiteTensor* data,
                                 const TfLiteTensor* segment_ids,
                                 TfLiteTensor* output) {
-  int max_index = -1;
+  // Segment ids should be of same cardinality as first input dimension and they
+  // should be increasing by at most 1, from 0 (e.g., [0, 0, 1, 2, 3] is valid)
   const int segment_id_size = segment_ids->dims->data[0];
-  if (segment_id_size > 0) {
+  TF_LITE_ENSURE_EQ(context, segment_id_size, data->dims->data[0]);
-    max_index = segment_ids->data.i32[segment_id_size - 1];
+  int previous_segment_id = -1;
+  for (int i = 0; i < segment_id_size; i++) {
+    const int current_segment_id = GetTensorData<int32_t>(segment_ids)[i];
+    if (i == 0) {
+      TF_LITE_ENSURE_EQ(context, current_segment_id, 0);
+    } else {
+      int delta = current_segment_id - previous_segment_id;
+      TF_LITE_ENSURE(context, delta == 0 || delta == 1);
     }
+    previous_segment_id = current_segment_id;
+  }
+
+  const int max_index = previous_segment_id;
+
   const int data_rank = NumDimensions(data);
   TfLiteIntArray* output_shape = TfLiteIntArrayCreate(NumDimensions(data));
   output_shape->data[0] = max_index + 1;

tensorflow/lite/kernels/segment_sum_test.cc

@@ -110,5 +110,37 @@ TEST(SegmentSumOpModelTest, Float32Test_ThreeDimensions) {
   EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 1}));
 }
 
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotSorted) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotConsecutive) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 3, 5});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNegative) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {3}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {-1, 0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
+TEST(SegmentSumOpModelTest, TestFailIfSegmentsAreNotTheRightCardinality) {
+  SegmentSumOpModel<int32_t> model({TensorType_INT32, {3, 2}},
+                                   {TensorType_INT32, {2}});
+  model.PopulateTensor<int32_t>(model.data(), {1, 2, 3, 4, 5, 6});
+  model.PopulateTensor<int32_t>(model.segment_ids(), {0, 1});
+  ASSERT_EQ(model.InvokeUnchecked(), kTfLiteError);
+}
+
 }  // namespace
 }  // namespace tflite

tensorflow/python/dlpack/dlpack_test.py

@@ -20,9 +20,11 @@ from __future__ import print_function
 from absl.testing import parameterized
 import numpy as np
 
+
 from tensorflow.python.dlpack import dlpack
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.platform import test
 
@@ -95,6 +97,12 @@ class DLPackTest(parameterized.TestCase, test.TestCase):
     self.assertRaisesRegex(Exception, ".* is not supported by dlpack",
                            UnsupportedComplex64)
 
+  def testMustPassTensorArgumentToDLPack(self):
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        "The argument to `to_dlpack` must be a TF tensor, not Python object"):
+      dlpack.to_dlpack([1])
+
 
 if __name__ == "__main__":
   ops.enable_eager_execution()

tensorflow/python/kernel_tests/control_flow_ops_py_test.py

@@ -4581,6 +4581,14 @@ class ControlFlowTest(test.TestCase, parameterized.TestCase):
       result = control_flow_ops.merge([v_f, v_t])
       self.evaluate(result)
 
+  def testSwitchEagerMode(self):
+    if not context.executing_eagerly():
+      return
+    input_data = [1, 2, 3, 4]
+    vf, vt = control_flow_ops.switch(input_data, False)
+    self.assertAllEqual(vf, input_data)
+    self.assertAllEqual(vt, [])
+
   @test_util.run_deprecated_v1
   def testQIntArgAndRet(self):
 

tensorflow/python/ops/bincount_ops_test.py

@@ -25,7 +25,9 @@ from tensorflow.python.eager import context
 from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.framework import test_util
 from tensorflow.python.ops import bincount_ops
+from tensorflow.python.ops import gen_count_ops
 from tensorflow.python.ops import sparse_ops
 from tensorflow.python.ops.ragged import ragged_factory_ops
 from tensorflow.python.ops.ragged import ragged_tensor
@@ -834,5 +836,121 @@ class TestSparseCountFailureModes(test.TestCase):
       self.evaluate(bincount_ops.sparse_bincount(x, weights=weights, axis=-1))
 
 
+@test_util.run_all_in_graph_and_eager_modes
+@test_util.disable_tfrt
+class RawOpsTest(test.TestCase, parameterized.TestCase):
+
+  def testSparseCountSparseOutputBadIndicesShape(self):
+    indices = [[[0], [0]], [[0], [1]], [[1], [0]], [[1], [2]]]
+    values = [1, 1, 1, 10]
+    weights = [1, 2, 4, 6]
+    dense_shape = [2, 3]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Input indices must be a 2-dimensional tensor"):
+      self.evaluate(
+          gen_count_ops.SparseCountSparseOutput(
+              indices=indices,
+              values=values,
+              dense_shape=dense_shape,
+              weights=weights,
+              binary_output=False))
+
+  def testSparseCountSparseOutputBadWeightsShape(self):
+    indices = [[0, 0], [0, 1], [1, 0], [1, 2]]
+    values = [1, 1, 1, 10]
+    weights = [1, 2, 4]
+    dense_shape = [2, 3]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Weights and values must have the same shape"):
+      self.evaluate(
+          gen_count_ops.SparseCountSparseOutput(
+              indices=indices,
+              values=values,
+              dense_shape=dense_shape,
+              weights=weights,
+              binary_output=False))
+
+  def testSparseCountSparseOutputBadNumberOfValues(self):
+    indices = [[0, 0], [0, 1], [1, 0]]
+    values = [1, 1, 1, 10]
+    weights = [1, 2, 4, 6]
+    dense_shape = [2, 3]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        "Number of values must match first dimension of indices"):
+      self.evaluate(
+          gen_count_ops.SparseCountSparseOutput(
+              indices=indices,
+              values=values,
+              dense_shape=dense_shape,
+              weights=weights,
+              binary_output=False))
+
+  def testRaggedCountSparseOutput(self):
+    splits = [0, 4, 7]
+    values = [1, 1, 2, 1, 2, 10, 5]
+    weights = [1, 2, 3, 4, 5, 6, 7]
+    output_indices, output_values, output_shape = self.evaluate(
+        gen_count_ops.RaggedCountSparseOutput(
+            splits=splits, values=values, weights=weights, binary_output=False))
+    self.assertAllEqual([[0, 1], [0, 2], [1, 2], [1, 5], [1, 10]],
+                        output_indices)
+    self.assertAllEqual([7, 3, 5, 7, 6], output_values)
+    self.assertAllEqual([2, 11], output_shape)
+
+  def testRaggedCountSparseOutputBadWeightsShape(self):
+    splits = [0, 4, 7]
+    values = [1, 1, 2, 1, 2, 10, 5]
+    weights = [1, 2, 3, 4, 5, 6]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Weights and values must have the same shape"):
+      self.evaluate(
+          gen_count_ops.RaggedCountSparseOutput(
+              splits=splits,
+              values=values,
+              weights=weights,
+              binary_output=False))
+
+  def testRaggedCountSparseOutputEmptySplits(self):
+    splits = []
+    values = [1, 1, 2, 1, 2, 10, 5]
+    weights = [1, 2, 3, 4, 5, 6, 7]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        "Must provide at least 2 elements for the splits argument"):
+      self.evaluate(
+          gen_count_ops.RaggedCountSparseOutput(
+              splits=splits,
+              values=values,
+              weights=weights,
+              binary_output=False))
+
+  def testRaggedCountSparseOutputBadSplitsStart(self):
+    splits = [1, 7]
+    values = [1, 1, 2, 1, 2, 10, 5]
+    weights = [1, 2, 3, 4, 5, 6, 7]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Splits must start with 0"):
+      self.evaluate(
+          gen_count_ops.RaggedCountSparseOutput(
+              splits=splits,
+              values=values,
+              weights=weights,
+              binary_output=False))
+
+  def testRaggedCountSparseOutputBadSplitsEnd(self):
+    splits = [0, 5]
+    values = [1, 1, 2, 1, 2, 10, 5]
+    weights = [1, 2, 3, 4, 5, 6, 7]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Splits must end with the number of values"):
+      self.evaluate(
+          gen_count_ops.RaggedCountSparseOutput(
+              splits=splits,
+              values=values,
+              weights=weights,
+              binary_output=False))
+
+
 if __name__ == "__main__":
   test.main()

tensorflow/python/ops/raw_ops_test.py

@@ -18,16 +18,22 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+from absl.testing import parameterized
+
 from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
+from tensorflow.python.framework import errors
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
+from tensorflow.python.ops import gen_data_flow_ops
 from tensorflow.python.ops import gen_math_ops
+from tensorflow.python.ops import gen_string_ops
 from tensorflow.python.platform import test
 
 
 @test_util.run_all_in_graph_and_eager_modes
-class RawOpsTest(test.TestCase):
+@test_util.disable_tfrt
+class RawOpsTest(test.TestCase, parameterized.TestCase):
 
   def testSimple(self):
     x = constant_op.constant(1)
@@ -58,6 +64,29 @@ class RawOpsTest(test.TestCase):
         gen_math_ops.Any(input=x, axis=0),
         gen_math_ops.Any(input=x, axis=0, keep_dims=False))
 
+  @parameterized.parameters([[0, 8]], [[-1, 6]])
+  def testStringNGramsBadDataSplits(self, splits):
+    data = ["aa", "bb", "cc", "dd", "ee", "ff"]
+    with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                "Invalid split value"):
+      self.evaluate(
+          gen_string_ops.string_n_grams(
+              data=data,
+              data_splits=splits,
+              separator="",
+              ngram_widths=[2],
+              left_pad="",
+              right_pad="",
+              pad_width=0,
+              preserve_short_sequences=False))
+
+  def testGetSessionHandle(self):
+    if context.executing_eagerly():
+      with self.assertRaisesRegex(
+          errors.FailedPreconditionError,
+          "GetSessionHandle called on null session state"):
+        gen_data_flow_ops.GetSessionHandle(value=[1])
+
 
 if __name__ == "__main__":
   ops.enable_eager_execution()

tensorflow/python/ops/sparse_ops_test.py

@@ -21,6 +21,7 @@ from __future__ import print_function
 from absl.testing import parameterized
 import numpy as np
 
+from tensorflow.python.eager import context
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
@@ -29,6 +30,7 @@ from tensorflow.python.framework import test_util
 # Need array_grad to register gradient for Identity.
 from tensorflow.python.ops import array_grad  # pylint: disable=unused-import
 from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import gradient_checker_v2 as gradient_checker
 from tensorflow.python.ops import math_ops
 # Need sparse_grad to register gradient for SparseToDense.
@@ -181,5 +183,57 @@ class SparseOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
     self.assertAllEqual(expected, result)
 
 
+@test_util.run_all_in_graph_and_eager_modes
+class RawOpsTest(test_util.TensorFlowTestCase, parameterized.TestCase):
+
+  def testSparseFillEmptyRowsGrad(self):
+    reverse_index_map = [2, 1]
+    grad_values = [0, 1, 2, 3]
+    d_values, d_default_value = self.evaluate(
+        gen_sparse_ops.SparseFillEmptyRowsGrad(
+            reverse_index_map=reverse_index_map, grad_values=grad_values))
+    self.assertAllEqual([2, 1], d_values)
+    self.assertEqual(3, d_default_value)
+
+  def testSparseFillEmptyRowsGradNegativeIndexMapValue(self):
+    reverse_index_map = [2, -1]
+    grad_values = [0, 1, 2, 3]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        r'Elements in reverse index must be in \[0, 4\)'):
+      self.evaluate(
+          gen_sparse_ops.SparseFillEmptyRowsGrad(
+              reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+  def testSparseFillEmptyRowsGradLargeIndexMapValue(self):
+    reverse_index_map = [2, 10]
+    grad_values = [0, 1, 2, 3]
+    with self.assertRaisesRegex(
+        errors.InvalidArgumentError,
+        r'Elements in reverse index must be in \[0, 4\)'):
+      self.evaluate(
+          gen_sparse_ops.SparseFillEmptyRowsGrad(
+              reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+  def testSparseFillEmptyRowsGradMatrix(self):
+    reverse_index_map = [0, 1]
+    grad_values = [[0, 1], [2, 3]]
+    # Note: Eager mode and graph mode throw different errors here. Graph mode
+    # will fail with a ValueError from the shape checking logic, while Eager
+    # will fail with an InvalidArgumentError from the kernel itself.
+    if context.executing_eagerly():
+      with self.assertRaisesRegex(errors.InvalidArgumentError,
+                                  r'grad_values must be a vector'):
+        self.evaluate(
+            gen_sparse_ops.SparseFillEmptyRowsGrad(
+                reverse_index_map=reverse_index_map, grad_values=grad_values))
+    else:
+      with self.assertRaisesRegex(ValueError,
+                                  r'Shape must be rank 1 but is rank 2'):
+        self.evaluate(
+            gen_sparse_ops.SparseFillEmptyRowsGrad(
+                reverse_index_map=reverse_index_map, grad_values=grad_values))
+
+
 if __name__ == '__main__':
   googletest.main()

tensorflow/python/tfe_wrapper.cc

@@ -1129,9 +1129,16 @@ PYBIND11_MODULE(_pywrap_tfe, m) {
   // DLPack functions
   m.def("TFE_ToDlpackCapsule", [](py::handle& o) {
     PyObject* eager_tensor_pyobject_ptr = o.ptr();
-    TFE_TensorHandle* thandle = EagerTensor_Handle(eager_tensor_pyobject_ptr);
     tensorflow::Safe_TF_StatusPtr status =
         tensorflow::make_safe(TF_NewStatus());
+
+    if (!EagerTensor_CheckExact(eager_tensor_pyobject_ptr)) {
+      status->status = tensorflow::errors::InvalidArgument(
+          "The argument to `to_dlpack` must be a TF tensor, not Python object");
+      tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());
+    }
+
+    TFE_TensorHandle* thandle = EagerTensor_Handle(eager_tensor_pyobject_ptr);
     void* dlm_ptr = tensorflow::TFE_HandleToDLPack(thandle, status.get());
     tensorflow::MaybeRaiseRegisteredFromTFStatus(status.get());