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Merge pull request #3 from ROCmSoftwarePlatform/format

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Update to clang-format-10
This commit is contained in:
Chao Liu
2021-07-30 17:16:23 -05:00
committed by GitHub
parent bd27ed6c38 82fae390fb
commit f6edda6119
10 changed files with 38 additions and 50 deletions
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26
composable_kernel/include/tensor_description/dynamic_tensor_descriptor.hpp
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@@ -33,13 +33,11 @@ struct DynamicTensorDescriptor
__host__ __device__ static constexpr index_t GetNumOfHiddenDimension()
{
constexpr auto all_low_dim_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
LowerDimensionIdss{});
constexpr auto all_low_dim_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); }, LowerDimensionIdss{});
constexpr auto all_up_dim_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
UpperDimensionIdss{});
constexpr auto all_up_dim_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); }, UpperDimensionIdss{});
constexpr auto all_dim_ids = merge_sequences(all_low_dim_ids, all_up_dim_ids);
@@ -347,22 +345,22 @@ transform_dynamic_tensor_descriptor(const OldTensorDescriptor& old_tensor_desc,
constexpr auto up_dim_numbers_scan = merge_sequences(
Sequence<0>{}, inclusive_scan_sequence(up_dim_numbers, math::plus<index_t>{}, Number<0>{}));
constexpr auto up_dim_hidden_idss =
generate_tuple([ old_hidden_dim_number, up_dim_numbers_scan ](auto i) constexpr {
constexpr auto up_dim_hidden_idss = generate_tuple(
[ old_hidden_dim_number, up_dim_numbers_scan ](auto i) constexpr {
return
typename arithmetic_sequence_gen<old_hidden_dim_number + up_dim_numbers_scan[i],
old_hidden_dim_number + up_dim_numbers_scan[i + 1],
1>::type{};
},
Number<num_new_transform>{});
Number<num_new_transform>{});
// new visible dimension's hidden ids
constexpr auto unordered_new_visible_dim_hidden_ids =
unpack([](auto... xs) constexpr { return merge_sequences(xs...); }, up_dim_hidden_idss);
constexpr auto unordered_new_visible_dim_hidden_ids = unpack(
[](auto... xs) constexpr { return merge_sequences(xs...); }, up_dim_hidden_idss);
constexpr auto new_visible_dim_unordered2ordered =
unpack([](auto... xs) constexpr { return merge_sequences(xs...); },
NewUpperDimensionNewVisibleIdss{});
constexpr auto new_visible_dim_unordered2ordered = unpack(
[](auto... xs) constexpr { return merge_sequences(xs...); },
NewUpperDimensionNewVisibleIdss{});
constexpr auto new_visible_dim_hidden_ids =
unordered_new_visible_dim_hidden_ids.ReorderGivenOld2New(new_visible_dim_unordered2ordered);

22
composable_kernel/include/tensor_description/tensor_adaptor.hpp
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@@ -106,13 +106,13 @@ struct TensorAdaptor
__host__ __device__ static constexpr index_t GetNumOfHiddenDimension()
{
constexpr auto all_low_dim_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
LowerDimensionHiddenIdss{});
constexpr auto all_low_dim_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); },
LowerDimensionHiddenIdss{});
constexpr auto all_up_dim_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
UpperDimensionHiddenIdss{});
constexpr auto all_up_dim_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); },
UpperDimensionHiddenIdss{});
constexpr auto all_dim_ids = merge_sequences(all_low_dim_ids, all_up_dim_ids);
@@ -418,13 +418,11 @@ __host__ __device__ constexpr auto make_single_stage_tensor_adaptor(const Transf
"wrong!");
// sanity check on LowerDimensionOldTopIdss and UpperDimensionNewTopIdss
constexpr auto all_low_dim_old_top_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
LowerDimensionOldTopIdss{});
constexpr auto all_low_dim_old_top_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); }, LowerDimensionOldTopIdss{});
constexpr auto all_up_dim_new_top_ids =
unpack([](auto&&... xs) constexpr { return merge_sequences(xs...); },
UpperDimensionNewTopIdss{});
constexpr auto all_up_dim_new_top_ids = unpack(
[](auto&&... xs) constexpr { return merge_sequences(xs...); }, UpperDimensionNewTopIdss{});
static_assert(is_valid_sequence_map<decltype(all_low_dim_old_top_ids)>::value &&
is_valid_sequence_map<decltype(all_up_dim_new_top_ids)>::value,

1
composable_kernel/include/tensor_operation/blockwise_gemm_dlops_v3.hpp
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@@ -152,7 +152,6 @@ struct BlockwiseGemmDlops_km_kn_m0m1n0n1_v3
static_for<0, EPerBlock, EPerThreadLoop>{}([&](auto e_begin) {
static_for<0, KPerThread, KPerThreadSubC>{}([&](auto k_begin) {
a_thread_copy_.Run(a_block_mtx,
make_tuple(e_begin, k_begin),
a_block_buf,

2
composable_kernel/include/tensor_operation/threadwise_contraction_dlops.hpp
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@@ -87,7 +87,6 @@ struct ThreadwiseGemmDlops_km0m1_kn0n1_m0m1n0n1
static_for<0, TM1, 1>{}([&](auto tm1) {
static_for<0, TN0, 1>{}([&](auto tn0) {
static_for<0, TN1, 1>{}([&](auto tn1) {
constexpr index_t a_offset =
AThreadDesc_TK0_TM0_TM1_TK1{}.CalculateOffset(
a_origin_idx + make_multi_index(tk, tm0, tm1));
@@ -192,7 +191,6 @@ struct ThreadwiseContractionDlops_A_TK0_TM0_TM1_TK1_B_TK0_TN0_TN1_TK1_C_TM0_TM1_
static_for<0, TM1, 1>{}([&](auto tm1) {
static_for<0, TN0, 1>{}([&](auto tn0) {
static_for<0, TN1, 1>{}([&](auto tn1) {
vector_type<FloatA, TK1> a_vec;
vector_type<FloatB, TK1> b_vec;

1
composable_kernel/include/tensor_operation/threadwise_gemm_dlops_v3.hpp
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@@ -136,7 +136,6 @@ struct ThreadwiseGemmDlops_km_kn_mn_v3
{
static_for<0, H, 1>{}([&](auto h) {
static_for<0, W, 1>{}([&](auto w) {
constexpr index_t b_offset =
BDesc{}.CalculateOffset(b_origin_idx + make_tuple(e, 0, h, w));

3
composable_kernel/include/utility/data_type_enum.hpp
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@@ -4,7 +4,8 @@
namespace ck {
// this enumerate should be synchronized with include/miopen.h
typedef enum {
typedef enum
{
Half = 0,
Float = 1,
Int32 = 2,

12
external/half/include/half.hpp vendored
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@@ -2399,11 +2399,11 @@ unsigned int erf(unsigned int arg)
template <std::float_round_style R, bool L>
unsigned int gamma(unsigned int arg)
{
/* static const double p[] ={ 2.50662827563479526904, 225.525584619175212544, -268.295973841304927459, 80.9030806934622512966, -5.00757863970517583837, 0.0114684895434781459556 };
double t = arg + 4.65, s = p[0];
for(unsigned int i=0; i<5; ++i)
s += p[i+1] / (arg+i);
return std::log(s) + (arg-0.5)*std::log(t) - t;
/* static const double p[] ={ 2.50662827563479526904, 225.525584619175212544,
-268.295973841304927459, 80.9030806934622512966, -5.00757863970517583837,
0.0114684895434781459556 }; double t = arg + 4.65, s = p[0]; for(unsigned int i=0; i<5; ++i)
s += p[i+1] / (arg+i);
return std::log(s) + (arg-0.5)*std::log(t) - t;
*/ static const f31 pi(0xC90FDAA2, 1),
lbe(0xB8AA3B29, 0);
unsigned int abs = arg & 0x7FFF, sign = arg & 0x8000;
@@ -2506,7 +2506,7 @@ unsigned int gamma(unsigned int arg)
template <typename, typename, std::float_round_style>
struct half_caster;
}
} // namespace detail
/// Half-precision floating-point type.
/// This class implements an IEEE-conformant half-precision floating-point type with the usual

15
host/host_tensor/include/host_tensor.hpp
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@@ -39,7 +39,8 @@ std::ostream& LogRangeAsType(std::ostream& os, Range&& range, std::string delim)
return os;
}
typedef enum {
typedef enum
{
Half = 0,
Float = 1,
} DataType_t;
@@ -227,27 +228,23 @@ struct Tensor
{
switch(mDesc.GetNumOfDimension())
{
case 1:
{
case 1: {
auto f = [&](auto i) { (*this)(i) = g(i); };
make_ParallelTensorFunctor(f, mDesc.GetLengths()[0])(num_thread);
break;
}
case 2:
{
case 2: {
auto f = [&](auto i0, auto i1) { (*this)(i0, i1) = g(i0, i1); };
make_ParallelTensorFunctor(f, mDesc.GetLengths()[0], mDesc.GetLengths()[1])(num_thread);
break;
}
case 3:
{
case 3: {
auto f = [&](auto i0, auto i1, auto i2) { (*this)(i0, i1, i2) = g(i0, i1, i2); };
make_ParallelTensorFunctor(
f, mDesc.GetLengths()[0], mDesc.GetLengths()[1], mDesc.GetLengths()[2])(num_thread);
break;
}
case 4:
{
case 4: {
auto f = [&](auto i0, auto i1, auto i2, auto i3) {
(*this)(i0, i1, i2, i3) = g(i0, i1, i2, i3);
};

4
host/online_compilation/hip_utility/kernel_cache.cpp
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@@ -145,9 +145,7 @@ void KernelCache::ClearKernels(const std::string& algorithm, const std::string&
}
const std::pair<std::string, std::string> key = std::make_pair(algorithm, network_config);
auto&& v = this->kernel_map[key];
if(!v.empty())
{
}
if(!v.empty()) {}
v.clear();
}

2
host/online_compilation/hip_utility/logger.cpp
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@@ -40,4 +40,4 @@ ostream& fdt_log(LogLevel level, const char* header, const char* content)
ostream& fdt_log() { return (cerr); };
void fdt_log_flush() { cerr << endl; }
};
}; // namespace olCompile