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Implement transform to reduce CPU/GPU code duplication. (#3643)

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* Implement Transform class.
* Add tests for softmax.
* Use Transform in regression, softmax and hinge objectives, except for Cox.
* Mark old gpu objective functions deprecated.
* static_assert for softmax.
* Split up multi-gpu tests.
This commit is contained in:
trivialfis
2018-10-02 15:06:21 +13:00
committed by Rory Mitchell
parent 87aca8c244
commit d594b11f35
31 changed files with 1514 additions and 997 deletions
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77
tests/cpp/common/test_host_device_vector.cu
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@@ -178,18 +178,57 @@ TEST(HostDeviceVector, TestCopy) {
SetCudaSetDeviceHandler(nullptr);
}
// The test is not really useful if n_gpus < 2
TEST(HostDeviceVector, Reshard) {
std::vector<int> h_vec (2345);
for (size_t i = 0; i < h_vec.size(); ++i) {
h_vec[i] = i;
}
HostDeviceVector<int> vec (h_vec);
auto devices = GPUSet::Range(0, 1);
vec.Reshard(devices);
ASSERT_EQ(vec.DeviceSize(0), h_vec.size());
ASSERT_EQ(vec.Size(), h_vec.size());
auto span = vec.DeviceSpan(0); // sync to device
vec.Reshard(GPUSet::Empty()); // pull back to cpu, empty devices.
ASSERT_EQ(vec.Size(), h_vec.size());
ASSERT_TRUE(vec.Devices().IsEmpty());
auto h_vec_1 = vec.HostVector();
ASSERT_TRUE(std::equal(h_vec_1.cbegin(), h_vec_1.cend(), h_vec.cbegin()));
}
TEST(HostDeviceVector, Span) {
HostDeviceVector<float> vec {1.0f, 2.0f, 3.0f, 4.0f};
vec.Reshard(GPUSet{0, 1});
auto span = vec.DeviceSpan(0);
ASSERT_EQ(vec.DeviceSize(0), span.size());
ASSERT_EQ(vec.DevicePointer(0), span.data());
auto const_span = vec.ConstDeviceSpan(0);
ASSERT_EQ(vec.DeviceSize(0), span.size());
ASSERT_EQ(vec.ConstDevicePointer(0), span.data());
}
// Multi-GPUs' test
#if defined(XGBOOST_USE_NCCL)
TEST(HostDeviceVector, MGPU_Reshard) {
auto devices = GPUSet::AllVisible();
if (devices.Size() < 2) {
LOG(WARNING) << "Not testing in multi-gpu environment.";
return;
}
std::vector<int> h_vec (2345);
for (size_t i = 0; i < h_vec.size(); ++i) {
h_vec[i] = i;
}
HostDeviceVector<int> vec (h_vec);
// Data size for each device.
std::vector<size_t> devices_size (devices.Size());
// From CPU to GPUs.
// Assuming we have > 1 devices.
vec.Reshard(devices);
size_t total_size = 0;
for (size_t i = 0; i < devices.Size(); ++i) {
@@ -198,42 +237,26 @@ TEST(HostDeviceVector, Reshard) {
}
ASSERT_EQ(total_size, h_vec.size());
ASSERT_EQ(total_size, vec.Size());
auto h_vec_1 = vec.HostVector();
ASSERT_TRUE(std::equal(h_vec_1.cbegin(), h_vec_1.cend(), h_vec.cbegin()));
vec.Reshard(GPUSet::Empty()); // clear out devices memory
// Reshard from devices to devices with different distribution.
EXPECT_ANY_THROW(
vec.Reshard(GPUDistribution::Granular(devices, 12)));
// Shrink down the number of devices.
vec.Reshard(GPUSet::Range(0, 1));
// All data is drawn back to CPU
vec.Reshard(GPUSet::Empty());
ASSERT_TRUE(vec.Devices().IsEmpty());
ASSERT_EQ(vec.Size(), h_vec.size());
ASSERT_EQ(vec.DeviceSize(0), h_vec.size());
h_vec_1 = vec.HostVector();
ASSERT_TRUE(std::equal(h_vec_1.cbegin(), h_vec_1.cend(), h_vec.cbegin()));
vec.Reshard(GPUSet::Empty()); // clear out devices memory
// Grow the number of devices.
vec.Reshard(devices);
vec.Reshard(GPUDistribution::Granular(devices, 12));
total_size = 0;
for (size_t i = 0; i < devices.Size(); ++i) {
total_size += vec.DeviceSize(i);
ASSERT_EQ(devices_size[i], vec.DeviceSize(i));
devices_size[i] = vec.DeviceSize(i);
}
ASSERT_EQ(total_size, h_vec.size());
ASSERT_EQ(total_size, vec.Size());
h_vec_1 = vec.HostVector();
ASSERT_TRUE(std::equal(h_vec_1.cbegin(), h_vec_1.cend(), h_vec.cbegin()));
}
TEST(HostDeviceVector, Span) {
HostDeviceVector<float> vec {1.0f, 2.0f, 3.0f, 4.0f};
vec.Reshard(GPUSet{0, 1});
auto span = vec.DeviceSpan(0);
ASSERT_EQ(vec.Size(), span.size());
ASSERT_EQ(vec.DevicePointer(0), span.data());
auto const_span = vec.ConstDeviceSpan(0);
ASSERT_EQ(vec.Size(), span.size());
ASSERT_EQ(vec.ConstDevicePointer(0), span.data());
}
#endif
} // namespace common
} // namespace xgboost