wehub-resource-sync
97 lines
3.7 KiB
C++
97 lines
3.7 KiB
C++
/* Copyright 2023 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <memory>
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#include <string_view>
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#include <utility>
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#include <vector>
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#include <gtest/gtest.h>
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#include "tensorflow/compiler/jit/flags.h"
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#include "tensorflow/compiler/tf2xla/xla_op_registry.h"
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#include "xla/tsl/lib/core/status_test_util.h"
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#include "tensorflow/core/framework/tensor_testutil.h"
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namespace tensorflow {
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namespace {
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static bool Initialized = [] {
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auto& rollout_config = GetXlaOpsCommonFlags()->tf_xla_use_device_api;
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rollout_config.enabled_for_xla_launch_ = true;
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rollout_config.enabled_for_compile_on_demand_ = true;
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tensorflow::GetXlaDeviceFlags()->tf_xla_enable_xla_devices = true;
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return true;
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}();
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class DeviceContextTest : public ::testing::Test {
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public:
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void SetDevice(const std::string& device_type) {
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auto& rollout_config = GetXlaOpsCommonFlags()->tf_xla_use_device_api;
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rollout_config.AllowForDeviceInXlaLaunch(DeviceType(device_type));
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rollout_config.AllowForDeviceInXlaCompileOnDemand(DeviceType(device_type));
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auto device_factory = DeviceFactory::GetFactory(device_type);
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SessionOptions options;
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std::vector<std::unique_ptr<Device>> devices;
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absl::Status s = device_factory->CreateDevices(
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options, "/job:worker/replica:0/task:0", &devices);
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device_ = std::move(devices[0]);
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tensorflow::AllocatorAttributes host_alloc_attr;
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host_alloc_attr.set_on_host(true);
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host_allocator_ = device_->GetAllocator(host_alloc_attr);
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tensorflow::AllocatorAttributes device_alloc_attr;
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device_alloc_attr.set_on_host(false);
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device_allocator_ = device_->GetAllocator(device_alloc_attr);
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tensorflow::DeviceContext* device_context;
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auto status = device_->TryGetDeviceContext(&device_context);
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TF_EXPECT_OK(status);
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device_context_.reset(device_context);
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}
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std::unique_ptr<Device> device_;
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tensorflow::core::RefCountPtr<DeviceContext> device_context_;
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tensorflow::Allocator* host_allocator_;
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tensorflow::Allocator* device_allocator_;
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};
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#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
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TEST_F(DeviceContextTest, TestXlaGpuRoundTripTransferWithDeviceApi) {
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SetDevice(DEVICE_XLA_GPU);
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tensorflow::Tensor origin_cpu_tensor(host_allocator_, tensorflow::DT_FLOAT,
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tensorflow::TensorShape({2, 2}));
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tensorflow::test::FillValues<float>(&origin_cpu_tensor, {1.2, 2.3, 3.4, 4.5});
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tensorflow::Tensor device_tensor(device_allocator_, tensorflow::DT_FLOAT,
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tensorflow::TensorShape({2, 2}));
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tensorflow::Tensor dest_cpu_tensor(host_allocator_, tensorflow::DT_FLOAT,
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tensorflow::TensorShape({2, 2}));
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TF_ASSERT_OK(device_context_->CopyCPUTensorToDeviceSync(
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&origin_cpu_tensor, device_.get(), &device_tensor));
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TF_ASSERT_OK(device_context_->CopyDeviceTensorToCPUSync(
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&device_tensor, "", device_.get(), &dest_cpu_tensor));
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LOG(INFO) << "H2D - D2H roundtrip completes. tensor: "
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<< dest_cpu_tensor.DebugString(4);
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tensorflow::test::ExpectClose(origin_cpu_tensor, dest_cpu_tensor);
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}
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#endif
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} // namespace
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} // namespace tensorflow
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