batch_kernels_test.cc

/* Copyright 2021 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/kernels/batch_kernels.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include <gtest/gtest.h>
#include "absl/strings/match.h"
#include "tensorflow/core/common_runtime/rendezvous_mgr.h"
#include "tensorflow/core/framework/function.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/kernels/batch_kernel_test_util.h"
#include "tensorflow/core/kernels/batching_util/warmup.h"
#include "tensorflow/core/lib/core/status_test_util.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/tsl/platform/blocking_counter.h"
#include "tensorflow/tsl/platform/status.h"

namespace tensorflow {

using PerModelData = serving::WarmupStateRegistry::PerModelData;

class BatchFunctionKernelTest : public BatchFunctionKernelTestBase {};

TEST_P(BatchFunctionKernelTest, EnableAdaptiveScheduler) {
  TF_EXPECT_OK(Init());
  BatchFunctionKernel *batch_kernel =
      dynamic_cast<BatchFunctionKernel *>(op_kernel());
  EXPECT_EQ(internal::BatchFunctionKernelTestAccess(batch_kernel)
                .enable_adaptive_batch_threads(),
            enable_adaptive_scheduler());
}

INSTANTIATE_TEST_SUITE_P(Params, BatchFunctionKernelTest, ::testing::Bool());

class BatchFunctionKernelParallelWarmupTestState : public OpsTestBase {
 public:
  // Init test fixture with a batch kernel instance.
  Status Init(bool enable_splitting, bool check_output_shape = true) {
    static auto *const cpu_device = []() {
      auto device =
          DeviceFactory::NewDevice("CPU", {}, "/job:a/replica:0/task:0");
      return device.release();
    }();

    // Overriding the per-test/per-op device with a global device so that it can
    // be shared between ops.
    device_ = cpu_device;

    std::vector<DataType> input_dtypes({DataType::DT_INT64});
    std::vector<NodeDefBuilder::NodeOut> inputs(
        {NodeDefBuilder::NodeOut({"n1", 0, DataType::DT_INT64})});

    NameAttrList f;
    f.set_name("func_to_batch");
    tensorflow::FunctionDefHelper::Node node_info = {
        {"output1"}, "Identity", {"input1"}, {{"T", DT_INT64}}};
    if (check_output_shape) {
      node_info = {{"output1"},
                   "EnsureShape",
                   {"input1"},
                   {{"T", DT_INT64}, {"shape", TensorShape({2})}}};
    }
    TF_RETURN_IF_ERROR(flib_def_->AddFunctionDef(FunctionDefHelper::Define(
        /*Function*/ "func_to_batch",
        /*Inputs*/ {"input1:int64"},
        /*Outputs*/ {"output1:int64"},
        /*Attribute*/ {},
        // Node info
        {node_info})));

    pflr_ = std::make_unique<ProcessFunctionLibraryRuntime>(
        device_mgr_.get(), Env::Default(), /*config=*/nullptr,
        TF_GRAPH_DEF_VERSION, flib_def_.get(), OptimizerOptions(),
        /*thread_pool=*/nullptr, /*parent=*/nullptr,
        /*session_metadata=*/nullptr,
        Rendezvous::Factory{[](const int64, const DeviceMgr *device_mgr,
                               tsl::core::RefCountPtr<Rendezvous> *r) {
          *r = tsl::core::RefCountPtr<Rendezvous>(
              new IntraProcessRendezvous(device_mgr));
          return OkStatus();
        }});

    TF_CHECK_OK(NodeDefBuilder("BatchTPUInput", "BatchFunction")
                    .Attr("max_batch_size", enable_splitting ? 16 : 8)
                    .Attr("num_batch_threads", 8)
                    .Attr("allowed_batch_sizes", {2, 4, 8})
                    .Attr("batch_timeout_micros", 100000)
                    .Attr("max_enqueued_batches", 10)
                    .Attr("enable_large_batch_splitting", true)
                    .Attr("low_priority_max_batch_size", 64)
                    .Attr("low_priority_batch_timeout_micros", 8000)
                    .Attr("low_priority_allowed_batch_sizes", {32, 64})
                    .Attr("low_priority_max_enqueued_batches", 1000)
                    .Attr("Tin", input_dtypes)
                    .Input(inputs)
                    .Attr("Tcaptured", std::vector<DataType>{})
                    .Input(std::vector<NodeDefBuilder::NodeOut>{})
                    .Attr("Tout", std::vector<DataType>{DT_INT64})
                    .Attr("f", f)
                    .Finalize(node_def()));
    return InitOp();
  }

  void TestBody() override {}
};

class BatchFunctionKernelParallelWarmupTest
    : public ::testing::TestWithParam<bool> {};

TEST_P(BatchFunctionKernelParallelWarmupTest, ParallelWarmup) {
  SessionMetadata session_metadata;
  session_metadata.set_name("test_model");
  session_metadata.set_version(123);
  serving::WarmupStateRegistry::Key key(session_metadata.name(),
                                        session_metadata.version());

  int num_requests = 16;

  bool enable_splitting = GetParam();

  {
    // Setting the state to warmup disables batching in the BatchFunction op. We
    // are checking this behavior by checking the tensor shape inside batch
    // function is the same as the input tensor shape using EnsureShape op.
    auto per_model_data = std::make_unique<PerModelData>();
    auto handle = serving::GetGlobalWarmupStateRegistry().Register(
        key, std::move(per_model_data));
    tsl::BlockingCounter blocking_counter(num_requests);
    for (int i = 0; i < num_requests; ++i) {
      Env::Default()->SchedClosure([&]() {
        BatchFunctionKernelParallelWarmupTestState test;
        test.set_session_metadata(session_metadata);
        TF_CHECK_OK(test.Init(enable_splitting));
        test.AddInputFromList<int64_t>(TensorShape({2}), {123, 456});
        TF_CHECK_OK(test.RunOpKernel());

        test::ExpectTensorEqual<int64_t>(*test.GetOutput(0),
                                         test::AsTensor<int64_t>({123, 456}));
        blocking_counter.DecrementCount();
      });
    }
    // Note this times out after 60s, so `batch_timeout_micros` and `batch_size`
    // need to be set accordingly.
    blocking_counter.Wait();
  }

  EXPECT_FALSE(serving::GetGlobalWarmupStateRegistry().Lookup(key));
  {
    tsl::BlockingCounter blocking_counter(num_requests);
    for (int i = 0; i < num_requests; ++i) {
      Env::Default()->SchedClosure([&]() {
        BatchFunctionKernelParallelWarmupTestState test;
        test.set_session_metadata(session_metadata);
        TF_CHECK_OK(test.Init(enable_splitting));
        test.AddInputFromList<int64_t>(TensorShape({2}), {123, 456});
        // We expect requests to be batched together when the warm-up mode is
        // turned off, which will make the execution fail at `EnsureShape`.
        EXPECT_FALSE(test.RunOpKernel().ok());

        blocking_counter.DecrementCount();
      });
    }
    blocking_counter.Wait();
  }
}

TEST_P(BatchFunctionKernelParallelWarmupTest, ParallelWarmupAutoBatch) {
  SessionMetadata session_metadata;
  session_metadata.set_name("test_model");
  session_metadata.set_version(123);
  serving::WarmupStateRegistry::Key key(session_metadata.name(),
                                        session_metadata.version());

  int num_requests = 16;

  bool enable_splitting = GetParam();
  {
    auto per_model_data = std::make_unique<PerModelData>();
    per_model_data->warmup_all_batch_sizes = true;
    auto handle = serving::GetGlobalWarmupStateRegistry().Register(
        key, std::move(per_model_data));

    tsl::BlockingCounter blocking_counter(num_requests);
    for (int i = 0; i < num_requests; ++i) {
      Env::Default()->SchedClosure([&]() {
        BatchFunctionKernelParallelWarmupTestState test;
        test.set_session_metadata(session_metadata);
        TF_CHECK_OK(test.Init(enable_splitting));
        test.AddInputFromList<int64_t>(TensorShape({2}), {123, 456});
        auto status = test.RunOpKernel();
        ASSERT_FALSE(status.ok());
        // This proves the kernel is executed with batch sizes other than 2.
        EXPECT_TRUE(absl::StrContains(status.message(),
                                      "is not compatible with expected shape"));
        blocking_counter.DecrementCount();
      });
    }
    blocking_counter.Wait();
  }

  {
    EXPECT_FALSE(serving::GetGlobalWarmupStateRegistry().Lookup(key));
    auto per_model_data = std::make_unique<PerModelData>();
    per_model_data->warmup_all_batch_sizes = true;
    auto handle = serving::GetGlobalWarmupStateRegistry().Register(
        key, std::move(per_model_data));

    tsl::BlockingCounter blocking_counter(num_requests);
    for (int i = 0; i < num_requests; ++i) {
      Env::Default()->SchedClosure([&]() {
        BatchFunctionKernelParallelWarmupTestState test;
        test.set_session_metadata(session_metadata);
        // Error free when the EnsureShapeOp is replaced with an Identity op.
        TF_CHECK_OK(test.Init(enable_splitting, /*check_output_shape=*/false));
        test.AddInputFromList<int64_t>(TensorShape({2}), {123, 456});
        auto status = test.RunOpKernel();
        TF_CHECK_OK(test.RunOpKernel());

        test::ExpectTensorEqual<int64_t>(*test.GetOutput(0),
                                         test::AsTensor<int64_t>({123, 456}));

        blocking_counter.DecrementCount();
      });
    }

    blocking_counter.Wait();
  }
}

INSTANTIATE_TEST_SUITE_P(BatchFunctionKernelParallelWarmupTestSuite,
                         BatchFunctionKernelParallelWarmupTest,
                         ::testing::Bool());

}  // namespace tensorflow