ROCm/composable_kernel
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Resolution of issue #153: Add compiler warning on comparing int and size_t (#212)

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* Turning compare warnings on

* Cleaning part I

* Cleaning part II

* Explicit static_cast to ck::type_convert

* Resolving large tensor size issue.

* format

* revert change to tensor descriptor; promote lementSpaceSize to 64bit

* use integer value for GEMM test

* Review remarks

* Review remarks + issues with (un)signed arithmetic

* Format fix

* Format

* Clang-format.

* fix 2gb limit issue

Co-authored-by: Chao Liu <chao.liu2@amd.com>
Co-authored-by: Adam Osewski <aosewski@amd.com>

[ROCm/composable_kernel commit: f03a1738d9]
This commit is contained in:
myamlak
2022-05-09 22:06:49 +02:00
committed by GitHub
parent 0bde7aaef1
commit 060938c9a5
30 changed files with 261 additions and 165 deletions
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10
profiler/include/profile_convnd_bwd_data_impl.hpp
View File

@@ -222,7 +222,7 @@ static bool check_out(const Tensor<T>& ref, const Tensor<T>& result)
{
float max_diff = 1e-6;
for(int i = 0; i < ref.mData.size(); ++i)
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float diff = std::abs(double(ref.mData[i]) - double(result.mData[i]));
if(max_diff < diff)
@@ -236,16 +236,16 @@ template <typename DataType>
void show_data_nhwc_layout(Tensor<DataType>& nhwc)
{
std::cout << "[";
for(int n = 0; n < nhwc.mDesc.GetLengths()[0]; n++)
for(int n = 0; n < ck::type_convert<int>(nhwc.mDesc.GetLengths()[0]); n++)
{
std::cout << "[";
for(int hi = 0; hi < nhwc.mDesc.GetLengths()[2]; hi++)
for(int hi = 0; hi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[2]); hi++)
{
std::cout << "[";
for(int wi = 0; wi < nhwc.mDesc.GetLengths()[3]; wi++)
for(int wi = 0; wi < ck::type_convert<int>(nhwc.mDesc.GetLengths()[3]); wi++)
{
std::cout << "[";
for(int c = 0; c < nhwc.mDesc.GetLengths()[1]; c++)
for(int c = 0; c < ck::type_convert<int>(nhwc.mDesc.GetLengths()[1]); c++)
{
std::cout << static_cast<float>(nhwc(n, c, hi, wi)) << " ";
}

22
profiler/include/profile_grouped_gemm_impl.hpp
View File

@@ -50,12 +50,12 @@ void profile_grouped_gemm_impl(int do_verification,
int init_method,
bool do_log,
int nrepeat,
std::vector<int> Ms,
std::vector<int> Ns,
std::vector<int> Ks,
std::vector<int> StrideAs,
std::vector<int> StrideBs,
std::vector<int> StrideCs)
const std::vector<int>& Ms,
const std::vector<int>& Ns,
const std::vector<int>& Ks,
const std::vector<int>& StrideAs,
const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs)
{
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
@@ -71,7 +71,7 @@ void profile_grouped_gemm_impl(int do_verification,
}
};
int group_count = Ms.size();
std::size_t group_count = Ms.size();
if(!(group_count == Ns.size() && group_count == Ks.size() && group_count == StrideAs.size() &&
group_count == StrideBs.size() && group_count == StrideCs.size()))
@@ -83,7 +83,7 @@ void profile_grouped_gemm_impl(int do_verification,
std::vector<Tensor<BDataType>> b_k_n;
std::vector<Tensor<CDataType>> c_m_n_device_results;
for(int i = 0; i < Ms.size(); i++)
for(std::size_t i = 0; i < group_count; i++)
{
a_m_k.push_back(
Tensor<ADataType>(f_host_tensor_descriptor(Ms[i], Ks[i], StrideAs[i], ALayout{})));
@@ -144,7 +144,7 @@ void profile_grouped_gemm_impl(int do_verification,
gemm_shapes.reserve(group_count);
for(int i = 0; i < group_count; i++)
for(std::size_t i = 0; i < group_count; i++)
{
a_device_buf.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_m_k[i].mDesc.GetElementSpace()));
@@ -234,7 +234,7 @@ void profile_grouped_gemm_impl(int do_verification,
float ave_time = invoker_ptr->Run(argument_ptr.get(), nrepeat);
std::size_t flop = 0, num_btype = 0;
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
@@ -258,7 +258,7 @@ void profile_grouped_gemm_impl(int do_verification,
if(do_verification)
{
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());