Sync from upstream TF. (#90)

tensorflow/lite/micro/kernels/xtensa/softmax_int8_int16.cc

@@ -225,10 +225,10 @@ TfLiteStatus EvalHifi(const XtensaSoftmaxOpData* op_data,
   }
   return kTfLiteOk;
 }
-}  // namespace
-
 #endif  // defined(FUSION_F1) || defined(HIFI5)
 
+}  // namespace
+
 void* XtensaInitSoftmax(TfLiteContext* context, const char* buffer,
                         size_t length) {
 #if defined(HIFIMINI) || defined(FUSION_F1) || defined(HIFI5)

tensorflow/lite/micro/micro_allocator.cc

@@ -242,26 +242,6 @@ TfLiteStatus AllocationInfoBuilder::AddTensors(const SubGraph* subgraph,
       }
     }
   }
-
-  // Sanity check for valid tensor lifetime.
-  for (size_t i = 0; i < tensor_count_; ++i) {
-    AllocationInfo* current = &info_[i];
-    // Even though tensor appears to be read only it may still need to be
-    // allocated.
-    const bool appears_read_only =
-        (current->first_created == -1) && (current->last_used != -1);
-    const bool has_partial_lifetime =
-        !appears_read_only &&
-        ((current->first_created == -1) || (current->last_used == -1));
-    if (has_partial_lifetime && current->needs_allocating) {
-      TF_LITE_REPORT_ERROR(
-          reporter_,
-          "Logic error in memory planner, tensor %d has an invalid lifetime: "
-          "first_created: %d, last_used: %d",
-          i, current->first_created, current->last_used);
-      return kTfLiteError;
-    }
-  }
   return kTfLiteOk;
 }
 

tensorflow/lite/micro/micro_allocator_test.cc

@@ -869,4 +869,35 @@ TF_LITE_MICRO_TEST(TestTypicalFirstOpAndSecondOpWithScratchTensors) {
       0, subgraph_allocations[0].tensors[5].data.uint8 - start);
 }
 
+TF_LITE_MICRO_TEST(TestModelWithUnusedTensors) {
+  tflite::AllOpsResolver op_resolver = tflite::testing::GetOpResolver();
+
+  const tflite::Model* model = tflite::testing::GetModelWithUnusedInputs();
+
+  tflite::ScratchBufferHandle* scratch_buffer_handles = nullptr;
+  constexpr size_t arena_size = 4096;
+  uint8_t arena[arena_size];
+  tflite::MicroAllocator* allocator = tflite::MicroAllocator::Create(
+      arena, arena_size, tflite::GetMicroErrorReporter());
+
+  tflite::SubgraphAllocations* subgraph_allocations =
+      allocator->StartModelAllocation(model);
+  TF_LITE_MICRO_EXPECT(nullptr != subgraph_allocations);
+  TF_LITE_MICRO_EXPECT_EQ(
+      kTfLiteOk, allocator->FinishModelAllocation(model, subgraph_allocations,
+                                                  &scratch_buffer_handles));
+
+  // Unused input tensor should not occupy any space.
+  uint8_t* start = subgraph_allocations[0].tensors[2].data.uint8;
+  TF_LITE_MICRO_EXPECT_EQ(
+      64, subgraph_allocations[0].tensors[0].data.uint8 - start);
+  TF_LITE_MICRO_EXPECT_EQ(
+      0, subgraph_allocations[0].tensors[1].data.uint8 - start);
+  TF_LITE_MICRO_EXPECT_EQ(
+      0, subgraph_allocations[0].tensors[2].data.uint8 - start);
+  // Unused tensor should not occupy any space.
+  TF_LITE_MICRO_EXPECT_EQ(
+      0, subgraph_allocations[0].tensors[3].data.uint8 - start);
+}
+
 TF_LITE_MICRO_TESTS_END

tensorflow/lite/micro/test_helpers.cc

@@ -341,6 +341,72 @@ const Model* BuildModelWithOfflinePlanning(int number_of_tensors,
       node_conn[0].input, node_conn[num_conns - 1].output, num_subgraph_inputs);
 }
 
+const Model* BuildModelWithUnusedInputs() {
+  using flatbuffers::Offset;
+  flatbuffers::FlatBufferBuilder* builder = BuilderInstance();
+
+  constexpr size_t buffers_size = 1;
+  const Offset<Buffer> buffers[buffers_size] = {CreateBuffer(*builder)};
+  constexpr size_t tensor_shape_size = 2;
+  const int32_t tensor_shape[tensor_shape_size] = {1, 64};
+  constexpr size_t tensors_size = 4;
+  const Offset<Tensor> tensors[tensors_size] = {
+      CreateTensor(*builder,
+                   builder->CreateVector(tensor_shape, tensor_shape_size),
+                   TensorType_INT8, 0,
+                   builder->CreateString("test_input_tensor"), 0, false),
+      CreateTensor(*builder,
+                   builder->CreateVector(tensor_shape, tensor_shape_size),
+                   TensorType_INT8, 0,
+                   builder->CreateString("test_unused_input_tensor"), 0, false),
+      CreateTensor(*builder,
+                   builder->CreateVector(tensor_shape, tensor_shape_size),
+                   TensorType_INT8, 0,
+                   builder->CreateString("test_output_tensor"), 0, false),
+      CreateTensor(*builder,
+                   builder->CreateVector(tensor_shape, tensor_shape_size),
+                   TensorType_INT8, 0,
+                   builder->CreateString("test_unused_tensor"), 0, false),
+  };
+  constexpr size_t inputs_size = 2;
+  const int32_t inputs[inputs_size] = {0, 1};
+  constexpr size_t outputs_size = 1;
+  const int32_t outputs[outputs_size] = {2};
+  constexpr size_t operator_inputs_size = 1;
+  const int32_t operator_inputs[operator_inputs_size] = {0};
+  constexpr size_t operator_outputs_size = 1;
+  const int32_t operator_outputs[operator_outputs_size] = {2};
+  constexpr size_t operators_size = 1;
+  const Offset<Operator> operators[operators_size] = {
+      CreateOperator(
+          *builder, 0,
+          builder->CreateVector(operator_inputs, operator_inputs_size),
+          builder->CreateVector(operator_outputs, operator_outputs_size),
+          BuiltinOptions_NONE),
+  };
+  constexpr size_t subgraphs_size = 1;
+  const Offset<SubGraph> subgraphs[subgraphs_size] = {
+      CreateSubGraph(*builder, builder->CreateVector(tensors, tensors_size),
+                     builder->CreateVector(inputs, inputs_size),
+                     builder->CreateVector(outputs, outputs_size),
+                     builder->CreateVector(operators, operators_size),
+                     builder->CreateString("test_subgraph"))};
+  constexpr size_t operator_codes_size = 1;
+  const Offset<OperatorCode> operator_codes[operator_codes_size] = {
+      CreateOperatorCodeDirect(*builder, /*deprecated_builtin_code=*/0,
+                               "mock_custom",
+                               /*version=*/0, BuiltinOperator_CUSTOM)};
+  const Offset<Model> model_offset = CreateModel(
+      *builder, 0, builder->CreateVector(operator_codes, operator_codes_size),
+      builder->CreateVector(subgraphs, subgraphs_size),
+      builder->CreateString("test_model"),
+      builder->CreateVector(buffers, buffers_size));
+  FinishModelBuffer(*builder, model_offset);
+  void* model_pointer = builder->GetBufferPointer();
+  const Model* model = flatbuffers::GetRoot<Model>(model_pointer);
+  return model;
+}
+
 const Model* BuildSimpleMockModel() {
   using flatbuffers::Offset;
   flatbuffers::FlatBufferBuilder* builder = BuilderInstance();
@@ -953,6 +1019,13 @@ AllOpsResolver GetOpResolver() {
                         MultipleInputs::GetMutableRegistration());
   return op_resolver;
 }
+const Model* GetModelWithUnusedInputs() {
+  static Model* model = nullptr;
+  if (!model) {
+    model = const_cast<Model*>(BuildModelWithUnusedInputs());
+  }
+  return model;
+}
 
 const Model* GetSimpleMockModel() {
   static Model* model = nullptr;

tensorflow/lite/micro/test_helpers.h

@@ -126,6 +126,10 @@ const Model* GetModelWithOfflinePlanning(int num_tensors,
                                          int num_conns,
                                          int num_subgraph_inputs = 0);
 
+// Returns a flatbuffer with a single operator, two inputs (one unused) and one
+// output.
+const Model* GetModelWithUnusedInputs();
+
 // Returns a flatbuffer model with `simple_stateful_op`
 const Model* GetSimpleStatefulModel();
 

tensorflow/lite/micro/testing/BUILD

-load("//tensorflow:extra_rules.bzl", "tflm_kernel_friends")
-
 package(
     features = ["-layering_check"],
     licenses = ["notice"],
 )
 
-package_group(
-    name = "kernel_friends",
-    packages = tflm_kernel_friends(),
-)
-
 package_group(
     name = "micro",
     packages = ["//tensorflow/lite/micro/..."],
@@ -26,10 +19,9 @@ cc_library(
         "micro_test.h",
     ],
     visibility = [
-        ":kernel_friends",
         ":micro",
         ":microfrontend",
-    ],  # TODO(b/188226023)
+    ],
     deps = [
         "//tensorflow/lite/c:common",
         "//tensorflow/lite/core/api",