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

Browse Source 
* 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>
This commit is contained in:
myamlak
2022-05-09 22:06:49 +02:00
committed by GitHub
parent 968bd93285
commit f03a1738d9
30 changed files with 261 additions and 165 deletions
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2
cmake/EnableCompilerWarnings.cmake
View File

@@ -66,7 +66,7 @@ else()
-Wunreachable-code
-Wunused
-Wno-sign-compare
-Wsign-compare
-Wno-extra-semi-stmt
)
if (CMAKE_${COMPILER}_COMPILER_ID MATCHES "Clang")

2
example/12_reduce/reduce_blockwise.cpp
View File

@@ -140,7 +140,7 @@ class SimpleAppArgs
int processArgs(int argc, char* argv[])
{
unsigned int ch;
int ch;
while(1)
{

4
example/13_pool2d_fwd/pool2d_fwd.cpp
View File

@@ -80,8 +80,8 @@ static void pool_host_verify(const Tensor<InDataType>& in,
for(int x = 0; x < window_spatial_lengths[1]; ++x)
{
int wi = wo * window_strides[1] + x - in_left_pads[1];
if(hi >= 0 && hi < in.mDesc.GetLengths()[2] && wi >= 0 &&
wi < in.mDesc.GetLengths()[3])
if(hi >= 0 && hi < ck::type_convert<int>(in.mDesc.GetLengths()[2]) && wi >= 0 &&
wi < ck::type_convert<int>(in.mDesc.GetLengths()[3]))
{
AccDataType currVal = static_cast<AccDataType>(in(n, c, hi, wi));

6
example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp
View File

@@ -131,7 +131,7 @@ int main(int argc, char* argv[])
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++)
{
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
@@ -168,7 +168,7 @@ int main(int argc, char* argv[])
}
}
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
@@ -213,7 +213,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
auto ref_gemm = ReferenceGemmInstance{};

30
include/ck/tensor_description/tensor_descriptor_helper.hpp
View File

@@ -1,6 +1,4 @@
#ifndef CK_TENSOR_DESCRIPTOR_HELPER_HPP
#define CK_TENSOR_DESCRIPTOR_HELPER_HPP
#pragma once
#include "common_header.hpp"
#include "tensor_descriptor.hpp"
#include "multi_index_transform_helper.hpp"
@@ -35,6 +33,12 @@ __host__ __device__ constexpr auto calculate_element_space_size_impl(const Lengt
}
#endif
// Lengths..., Strides... could be:
// 1) index_t, which is known at run-time, or
// 2) Number<>, which is known at compile-time
// element_space_size could be:
// 1) long_index_t, or
// 2) LongNumber<>
template <typename... Lengths,
typename... Strides,
typename enable_if<sizeof...(Lengths) == sizeof...(Strides), bool>::type = false>
@@ -68,10 +72,10 @@ __host__ __device__ constexpr auto make_naive_tensor_descriptor(const Tuple<Leng
}
};
const auto element_space_size = f(f, Number<0>{}, Number<1>{});
const auto element_space_size = f(f, Number<0>{}, LongNumber<1>{});
#else
const auto element_space_size =
calculate_element_space_size_impl(lengths, strides, Number<0>{}, Number<1>{});
calculate_element_space_size_impl(lengths, strides, Number<0>{}, LongNumber<1>{});
#endif
return TensorDescriptor<remove_cv_t<decltype(transforms)>,
@@ -82,9 +86,12 @@ __host__ __device__ constexpr auto make_naive_tensor_descriptor(const Tuple<Leng
element_space_size};
}
// Lengths... can be:
// 1) index_t, which is known at run-time
// Lengths... could be:
// 1) index_t, which is known at run-time, or
// 2) Number<>, which is known at compile-time
// element_space_size could be:
// 1) long_index_t, or
// 2) LongNumber<>
template <typename... Lengths>
__host__ __device__ constexpr auto
make_naive_tensor_descriptor_packed(const Tuple<Lengths...>& lengths)
@@ -100,7 +107,7 @@ make_naive_tensor_descriptor_packed(const Tuple<Lengths...>& lengths)
constexpr auto visible_dim_hidden_ids = typename arithmetic_sequence_gen<1, N + 1, 1>::type{};
const auto element_space_size = container_reduce(lengths, math::multiplies{}, Number<1>{});
const auto element_space_size = container_reduce(lengths, math::multiplies{}, LongNumber<1>{});
return TensorDescriptor<remove_cv_t<decltype(transforms)>,
remove_cv_t<decltype(low_dim_hidden_idss)>,
@@ -110,6 +117,12 @@ make_naive_tensor_descriptor_packed(const Tuple<Lengths...>& lengths)
element_space_size};
}
// Lengths... could be:
// 1) index_t, which is known at run-time, or
// 2) Number<>, which is known at compile-time
// align could be:
// 1) index_t, or
// 2) Number<>
template <typename... Lengths, typename Align>
__host__ __device__ constexpr auto
make_naive_tensor_descriptor_aligned(const Tuple<Lengths...>& lengths, Align align)
@@ -146,4 +159,3 @@ make_naive_tensor_descriptor_aligned(const Tuple<Lengths...>& lengths, Align ali
}
} // namespace ck
#endif

11
include/ck/tensor_operation/gpu/device/device_batched_gemm_reduce_xdl_cshuffle.hpp
View File

@@ -635,11 +635,12 @@ struct DeviceBatchedGemmReduce_Xdl_CShuffle : public DeviceGemmReduce<AElementwi
d_grid_desc_m_{DeviceOp::MakeDGridDescriptor_M(MRaw)},
c_grid_desc_mblock_mperblock_nblock_nperblock_{},
d_grid_desc_mblock_mperblock_{},
compute_base_ptr_of_batch_{a_grid_desc_ak0_m_ak1_.GetElementSpaceSize(),
b_grid_desc_bk0_n_bk1_.GetElementSpaceSize(),
c_grid_desc_m_n_.GetElementSpaceSize(),
d_grid_desc_m_.GetElementSpaceSize(),
d_grid_desc_m_.GetElementSpaceSize()},
compute_base_ptr_of_batch_{
type_convert<index_t>(a_grid_desc_ak0_m_ak1_.GetElementSpaceSize()),
type_convert<index_t>(b_grid_desc_bk0_n_bk1_.GetElementSpaceSize()),
type_convert<index_t>(c_grid_desc_m_n_.GetElementSpaceSize()),
type_convert<index_t>(d_grid_desc_m_.GetElementSpaceSize()),
type_convert<index_t>(d_grid_desc_m_.GetElementSpaceSize())},
block_2_ctile_map_{},
a_element_op_{a_element_op},
b_element_op_{b_element_op},

7
include/ck/tensor_operation/gpu/device/device_batched_gemm_xdl.hpp
View File

@@ -384,9 +384,10 @@ struct DeviceBatchedGemmXdl
DeviceBatchedGemmXdl::MakeBGridDescriptor_K0_N_K1(K, N, StrideB)},
c_grid_desc_m_n_{DeviceBatchedGemmXdl::MakeCGridDescriptor_M_N(M, N, StrideC)},
c_grid_desc_m0_n0_m1_n1_m2_m3_m4_n2_{},
compute_ptr_offset_of_batch_{a_grid_desc_k0_m_k1_.GetElementSpaceSize(),
b_grid_desc_k0_n_k1_.GetElementSpaceSize(),
c_grid_desc_m_n_.GetElementSpaceSize()},
compute_ptr_offset_of_batch_{
type_convert<index_t>(a_grid_desc_k0_m_k1_.GetElementSpaceSize()),
type_convert<index_t>(b_grid_desc_k0_n_k1_.GetElementSpaceSize()),
type_convert<index_t>(c_grid_desc_m_n_.GetElementSpaceSize())},
block_2_ctile_map_{},
M01_{M01},
N01_{N01},

2
include/ck/tensor_operation/gpu/device/device_conv2d_bwd_data_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -697,7 +697,7 @@ struct DeviceConv2dBwdDataXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
}
// Gridwise GEMM size
for(int i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
for(std::size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
{
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],

2
include/ck/tensor_operation/gpu/device/device_convnd_bwd_data_xdl_ndhwc_kzyxc_ndhwk.hpp
View File

@@ -1412,7 +1412,7 @@ struct DeviceConvndBwdDataXdl_Input_N_Di_Hi_Wi_C_Weight_K_Z_Y_X_C_Output_N_Do_Ho
}
// Gridwise GEMM size
for(int i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
for(std::size_t i = 0; i < arg.a_grid_desc_k0_m_k1_container_.size(); i++)
{
if(!GridwiseGemm::CheckValidity(arg.a_grid_desc_k0_m_k1_container_[i],
arg.b_grid_desc_k0_n_k1_container_[i],

14
include/ck/tensor_operation/gpu/device/device_convnd_fwd_xdl_nhwc_kyxc_nhwk.hpp
View File

@@ -861,17 +861,11 @@ struct DeviceConvNDFwdXdl_Input_N_Hi_Wi_C_Weight_K_Y_X_C_Output_N_Ho_Wo_K
static bool IsSupportedArgument(const Argument& arg)
{
// Input tensors can't be bigger than 2GB each.
constexpr std::size_t GB2 = 2 * 1e9;
constexpr ck::long_index_t GB2 = (ck::long_index_t{1} << 31);
if(arg.a_grid_desc_k0_m_k1_.GetElementSpaceSize() > GB2)
{
return false;
}
if(arg.b_grid_desc_k0_n_k1_.GetElementSpaceSize() > GB2)
{
return false;
}
if(arg.c_grid_desc_m_n_.GetElementSpaceSize() > GB2)
if(arg.a_grid_desc_k0_m_k1_.GetElementSpaceSize() * sizeof(ADataType) > GB2 ||
arg.b_grid_desc_k0_n_k1_.GetElementSpaceSize() * sizeof(BDataType) > GB2 ||
arg.c_grid_desc_m_n_.GetElementSpaceSize() * sizeof(CDataType) > GB2)
{
return false;
}

11
include/ck/tensor_operation/gpu/device/device_grouped_gemm_xdl.hpp
View File

@@ -372,17 +372,18 @@ struct DeviceGroupedGemmXdl
{
grid_size_ = 0;
group_count_ = static_cast<int>(gemm_shapes.size());
group_count_ = ck::type_convert<ck::index_t>(gemm_shapes.size());
if(!(group_count_ == p_a.size() && group_count_ == p_b.size() &&
group_count_ == p_c.size()))
if(!(group_count_ == ck::type_convert<ck::index_t>(p_a.size()) &&
group_count_ == ck::type_convert<ck::index_t>(p_b.size()) &&
group_count_ == ck::type_convert<ck::index_t>(p_c.size())))
{
throw std::runtime_error("wrong! group_count_ != P_a/b/c.size");
}
gemm_desc_kernel_arg_.reserve(group_count_);
for(index_t i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
const index_t M = gemm_shapes[i].M;
const index_t N = gemm_shapes[i].N;
@@ -563,7 +564,7 @@ struct DeviceGroupedGemmXdl
static bool IsSupportedArgument(const Argument& arg)
{
if(arg.gemm_desc_kernel_arg_.size() != arg.group_count_)
if(ck::type_convert<ck::index_t>(arg.gemm_desc_kernel_arg_.size()) != arg.group_count_)
return false;
else
return true;

3
include/ck/utility/number.hpp
View File

@@ -8,5 +8,8 @@ namespace ck {
template <index_t N>
using Number = integral_constant<index_t, N>;
template <index_t N>
using LongNumber = integral_constant<long_index_t, N>;
} // namespace ck
#endif

6
include/ck/utility/static_buffer.hpp
View File

@@ -158,5 +158,11 @@ __host__ __device__ constexpr auto make_static_buffer(Number<N>)
return StaticBuffer<AddressSpace, T, N, true>{};
}
template <AddressSpaceEnum AddressSpace, typename T, long_index_t N>
__host__ __device__ constexpr auto make_static_buffer(LongNumber<N>)
{
return StaticBuffer<AddressSpace, T, N, true>{};
}
} // namespace ck
#endif

7
library/include/ck/library/host_tensor/host_reduction.hpp
View File

@@ -211,7 +211,8 @@ struct ReductionHost
AccDataType accuVal = ReduceOpZeroVal<AccDataType, ReduceOpId>();
IndexDataType accuIndex = 0;
for(IndexDataType i = 0; i < reduce_dim_indexes.size(); i++)
for(IndexDataType i = 0; i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size());
i++)
{
auto offset_reduce =
get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);
@@ -246,7 +247,9 @@ struct ReductionHost
auto offset_invariant =
get_offset_from_index<NumInvariantDim>(invariantStrides, invariant_index);
for(IndexDataType i = 0; i < reduce_dim_indexes.size(); i++)
for(IndexDataType i = 0;
i < ck::type_convert<IndexDataType>(reduce_dim_indexes.size());
i++)
{
auto offset_reduce =
get_offset_from_index<NumReduceDim>(reduceStrides, reduce_dim_indexes[i]);

4
library/include/ck/library/host_tensor/host_tensor.hpp
View File

@@ -154,7 +154,7 @@ struct ParallelTensorFunctor
{
std::array<std::size_t, NDIM> indices;
for(int idim = 0; idim < NDIM; ++idim)
for(std::size_t idim = 0; idim < NDIM; ++idim)
{
indices[idim] = i / mStrides[idim];
i -= indices[idim] * mStrides[idim];
@@ -316,7 +316,7 @@ float check_error(const Tensor<T>& ref, const Tensor<T>& result)
constexpr float eps = 1e-10;
for(int i = 0; i < ref.mData.size(); ++i)
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float ref_v = ck::type_convert<float>(ref.mData[i]);
float result_v = ck::type_convert<float>(result.mData[i]);

25
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_backward_weight.hpp
View File

@@ -70,18 +70,25 @@ struct ReferenceConvBwdWeight : public device::BaseOperator
constexpr auto I1 = Number<1>{};
auto f_kcyx = [&](auto k, auto c, auto y, auto x) {
float v_acc = 0;
for(int n = 0; n < arg.out_n_k_ho_wo_.mDesc.GetLengths()[0]; ++n)
for(std::size_t n = 0; n < arg.out_n_k_ho_wo_.mDesc.GetLengths()[0]; ++n)
{
for(int ho = 0; ho < arg.out_n_k_ho_wo_.mDesc.GetLengths()[2]; ++ho)
for(std::size_t ho = 0; ho < arg.out_n_k_ho_wo_.mDesc.GetLengths()[2]; ++ho)
{
int hi = ho * arg.conv_strides_[I0] + y * arg.conv_dilations_[I0] -
arg.in_left_pads_[I0];
for(int wo = 0; wo < arg.out_n_k_ho_wo_.mDesc.GetLengths()[3]; ++wo)
auto hi = ck::type_convert<ck::long_index_t>(ho * arg.conv_strides_[I0]) +
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[I0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[I0]);
for(std::size_t wo = 0; wo < arg.out_n_k_ho_wo_.mDesc.GetLengths()[3]; ++wo)
{
int wi = wo * arg.conv_strides_[I1] + x * arg.conv_dilations_[I1] -
arg.in_left_pads_[I1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
auto wi =
ck::type_convert<ck::long_index_t>(wo * arg.conv_strides_[I1]) +
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[I1]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[I1]);
if(hi >= 0 &&
ck::type_convert<std::size_t>(hi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] &&
wi >= 0 &&
ck::type_convert<std::size_t>(wi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
float v_out;
float v_in;

87
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_bwd_data.hpp
View File

@@ -78,15 +78,18 @@ struct ReferenceConvBwdData : public device::BaseOperator
AccDataType v_acc = 0;
for(int x = 0; x < X; ++x)
for(std::size_t x = 0; x < X; ++x)
{
int w_tmp = wi + arg.in_left_pads_[0] - x * arg.conv_dilations_[0];
auto w_tmp = ck::type_convert<ck::long_index_t>(wi) +
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]) -
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[0]);
if(w_tmp % arg.conv_strides_[0] == 0)
{
int wo = w_tmp / arg.conv_strides_[0];
if(wo >= 0 && wo < Wo)
auto wo = ck::type_convert<ck::long_index_t>(w_tmp) /
ck::type_convert<ck::long_index_t>(arg.conv_strides_[0]);
if(wo >= 0 && ck::type_convert<std::size_t>(wo) < Wo)
{
for(int k = 0; k < K; ++k)
for(std::size_t k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;
@@ -128,24 +131,32 @@ struct ReferenceConvBwdData : public device::BaseOperator
AccDataType v_acc = 0;
for(int y = 0; y < Y; ++y)
for(std::size_t y = 0; y < Y; ++y)
{
int h_tmp = hi + arg.in_left_pads_[0] - y * arg.conv_dilations_[0];
auto h_tmp = ck::type_convert<ck::long_index_t>(hi) +
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]) -
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[0]);
if(h_tmp % arg.conv_strides_[0] == 0)
{
int ho = h_tmp / arg.conv_strides_[0];
if(ho >= 0 && ho < Ho)
auto ho = ck::type_convert<ck::long_index_t>(h_tmp) /
ck::type_convert<ck::long_index_t>(arg.conv_strides_[0]);
if(ho >= 0 && ck::type_convert<std::size_t>(ho) < Ho)
{
for(int x = 0; x < X; ++x)
for(std::size_t x = 0; x < X; ++x)
{
int w_tmp =
wi + arg.in_left_pads_[1] - x * arg.conv_dilations_[1];
auto w_tmp =
ck::type_convert<ck::long_index_t>(wi) +
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]) -
ck::type_convert<ck::long_index_t>(x *
arg.conv_dilations_[1]);
if(w_tmp % arg.conv_strides_[1] == 0)
{
int wo = w_tmp / arg.conv_strides_[1];
if(wo >= 0 && wo < Wo)
auto wo = ck::type_convert<ck::long_index_t>(w_tmp) /
ck::type_convert<ck::long_index_t>(
arg.conv_strides_[1]);
if(wo >= 0 && ck::type_convert<std::size_t>(wo) < Wo)
{
for(int k = 0; k < K; ++k)
for(std::size_t k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;
@@ -194,33 +205,49 @@ struct ReferenceConvBwdData : public device::BaseOperator
AccDataType v_acc = 0;
for(int z = 0; z < Z; ++z)
for(std::size_t z = 0; z < Z; ++z)
{
int d_tmp = di + arg.in_left_pads_[0] - z * arg.conv_dilations_[0];
auto d_tmp = ck::type_convert<ck::long_index_t>(di) +
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]) -
ck::type_convert<ck::long_index_t>(z * arg.conv_dilations_[0]);
if(d_tmp % arg.conv_strides_[0] == 0)
{
int do_ = d_tmp / arg.conv_strides_[0];
if(do_ >= 0 && do_ < Do)
auto do_ = ck::type_convert<ck::long_index_t>(d_tmp) /
ck::type_convert<ck::long_index_t>(arg.conv_strides_[0]);
if(do_ >= 0 && ck::type_convert<std::size_t>(do_) < Do)
{
for(int y = 0; y < Y; ++y)
for(std::size_t y = 0; y < Y; ++y)
{
int h_tmp =
hi + arg.in_left_pads_[1] - y * arg.conv_dilations_[1];
auto h_tmp =
ck::type_convert<ck::long_index_t>(hi) +
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]) -
ck::type_convert<ck::long_index_t>(y *
arg.conv_dilations_[1]);
if(h_tmp % arg.conv_strides_[1] == 0)
{
int ho = h_tmp / arg.conv_strides_[1];
if(ho >= 0 && ho < Ho)
auto ho = ck::type_convert<ck::long_index_t>(h_tmp) /
ck::type_convert<ck::long_index_t>(
arg.conv_strides_[1]);
if(ho >= 0 && ck::type_convert<std::size_t>(ho) < Ho)
{
for(int x = 0; x < X; ++x)
for(std::size_t x = 0; x < X; ++x)
{
int w_tmp = wi + arg.in_left_pads_[2] -
x * arg.conv_dilations_[2];
auto w_tmp =
ck::type_convert<ck::long_index_t>(wi) +
ck::type_convert<ck::long_index_t>(
arg.in_left_pads_[2]) -
ck::type_convert<ck::long_index_t>(
x * arg.conv_dilations_[2]);
if(w_tmp % arg.conv_strides_[2] == 0)
{
int wo = w_tmp / arg.conv_strides_[2];
if(wo >= 0 && wo < Wo)
auto wo =
ck::type_convert<ck::long_index_t>(w_tmp) /
ck::type_convert<ck::long_index_t>(
arg.conv_strides_[2]);
if(wo >= 0 &&
ck::type_convert<std::size_t>(wo) < Wo)
{
for(int k = 0; k < K; ++k)
for(std::size_t k = 0; k < K; ++k)
{
AccDataType v_out = 0;
AccDataType v_wei = 0;

79
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd.hpp
View File

@@ -88,13 +88,16 @@ struct ReferenceConvFwd : public device::BaseOperator
auto f_ncw = [&](auto n, auto k, auto wo) {
float v_acc = 0;
for(int c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
for(std::size_t c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
{
for(int x = 0; x < arg.weight_.mDesc.GetLengths()[2]; ++x)
for(std::size_t x = 0; x < arg.weight_.mDesc.GetLengths()[2]; ++x)
{
int wi = wo * arg.conv_strides_[0] + x * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
if(wi >= 0 && wi < arg.input_.mDesc.GetLengths()[2])
auto wi =
ck::type_convert<ck::long_index_t>(wo * arg.conv_strides_[0]) +
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]);
if(wi >= 0 &&
ck::type_convert<std::size_t>(wi) < arg.input_.mDesc.GetLengths()[2])
{
float v_in;
float v_wei;
@@ -128,18 +131,26 @@ struct ReferenceConvFwd : public device::BaseOperator
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v_acc = 0;
for(int c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
for(std::size_t c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.weight_.mDesc.GetLengths()[2]; ++y)
for(std::size_t y = 0; y < arg.weight_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.weight_.mDesc.GetLengths()[3]; ++x)
auto hi =
ck::type_convert<ck::long_index_t>(ho * arg.conv_strides_[0]) +
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]);
for(std::size_t x = 0; x < arg.weight_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.input_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.input_.mDesc.GetLengths()[3])
auto wi =
ck::type_convert<ck::long_index_t>(wo * arg.conv_strides_[1]) +
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[1]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]);
if(hi >= 0 &&
ck::type_convert<std::size_t>(hi) <
arg.input_.mDesc.GetLengths()[2] &&
wi >= 0 &&
ck::type_convert<std::size_t>(wi) <
arg.input_.mDesc.GetLengths()[3])
{
float v_in;
float v_wei;
@@ -174,23 +185,37 @@ struct ReferenceConvFwd : public device::BaseOperator
auto f_nchw = [&](auto n, auto k, auto d_o, auto ho, auto wo) {
float v_acc = 0;
for(int c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
for(std::size_t c = 0; c < arg.weight_.mDesc.GetLengths()[1]; ++c)
{
for(int z = 0; z < arg.weight_.mDesc.GetLengths()[2]; ++z)
for(std::size_t z = 0; z < arg.weight_.mDesc.GetLengths()[2]; ++z)
{
int di = d_o * arg.conv_strides_[0] + z * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int y = 0; y < arg.weight_.mDesc.GetLengths()[3]; ++y)
auto di =
ck::type_convert<ck::long_index_t>(d_o * arg.conv_strides_[0]) +
ck::type_convert<ck::long_index_t>(z * arg.conv_dilations_[0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]);
for(std::size_t y = 0; y < arg.weight_.mDesc.GetLengths()[3]; ++y)
{
int hi = ho * arg.conv_strides_[1] + y * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
for(int x = 0; x < arg.weight_.mDesc.GetLengths()[4]; ++x)
auto hi =
ck::type_convert<ck::long_index_t>(ho * arg.conv_strides_[1]) +
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[1]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]);
for(std::size_t x = 0; x < arg.weight_.mDesc.GetLengths()[4]; ++x)
{
int wi = wo * arg.conv_strides_[2] +
x * arg.conv_dilations_[2] - arg.in_left_pads_[2];
if(di >= 0 && di < arg.input_.mDesc.GetLengths()[2] &&
hi >= 0 && hi < arg.input_.mDesc.GetLengths()[3] &&
wi >= 0 && wi < arg.input_.mDesc.GetLengths()[4])
auto wi =
ck::type_convert<ck::long_index_t>(wo *
arg.conv_strides_[2]) +
ck::type_convert<ck::long_index_t>(x *
arg.conv_dilations_[2]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[2]);
if(di >= 0 &&
ck::type_convert<std::size_t>(di) <
arg.input_.mDesc.GetLengths()[2] &&
hi >= 0 &&
ck::type_convert<std::size_t>(hi) <
arg.input_.mDesc.GetLengths()[3] &&
wi >= 0 &&
ck::type_convert<std::size_t>(wi) <
arg.input_.mDesc.GetLengths()[4])
{
float v_in;
float v_wei;

25
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation.hpp
View File

@@ -73,18 +73,25 @@ struct ReferenceConvFwd_Bias_Activation : public device::BaseOperator
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v_acc = 0;
for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
for(std::size_t c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
for(std::size_t y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
auto hi = ck::type_convert<ck::long_index_t>(ho * arg.conv_strides_[0]) +
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]);
for(std::size_t x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
auto wi =
ck::type_convert<ck::long_index_t>(wo * arg.conv_strides_[1]) +
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[1]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]);
if(hi >= 0 &&
ck::type_convert<std::size_t>(hi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] &&
wi >= 0 &&
ck::type_convert<std::size_t>(wi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
float v_in;
float v_wei;

25
library/include/ck/library/reference_tensor_operation/cpu/reference_conv_fwd_bias_activation_add.hpp
View File

@@ -76,18 +76,25 @@ struct ReferenceConvFwd_Bias_Activation_Add : public device::BaseOperator
auto f_nchw = [&](auto n, auto k, auto ho, auto wo) {
float v_acc = 0;
for(int c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
for(std::size_t c = 0; c < arg.wei_k_c_y_x_.mDesc.GetLengths()[1]; ++c)
{
for(int y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
for(std::size_t y = 0; y < arg.wei_k_c_y_x_.mDesc.GetLengths()[2]; ++y)
{
int hi = ho * arg.conv_strides_[0] + y * arg.conv_dilations_[0] -
arg.in_left_pads_[0];
for(int x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
auto hi = ck::type_convert<ck::long_index_t>(ho * arg.conv_strides_[0]) +
ck::type_convert<ck::long_index_t>(y * arg.conv_dilations_[0]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[0]);
for(std::size_t x = 0; x < arg.wei_k_c_y_x_.mDesc.GetLengths()[3]; ++x)
{
int wi = wo * arg.conv_strides_[1] + x * arg.conv_dilations_[1] -
arg.in_left_pads_[1];
if(hi >= 0 && hi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] && wi >= 0 &&
wi < arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
auto wi =
ck::type_convert<ck::long_index_t>(wo * arg.conv_strides_[1]) +
ck::type_convert<ck::long_index_t>(x * arg.conv_dilations_[1]) -
ck::type_convert<ck::long_index_t>(arg.in_left_pads_[1]);
if(hi >= 0 &&
ck::type_convert<std::size_t>(hi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[2] &&
wi >= 0 &&
ck::type_convert<std::size_t>(wi) <
arg.in_n_c_hi_wi_.mDesc.GetLengths()[3])
{
float v_in;
float v_wei;

4
library/src/host_tensor/host_tensor.cpp
View File

@@ -25,7 +25,7 @@ std::size_t HostTensorDescriptor::GetElementSize() const
std::size_t HostTensorDescriptor::GetElementSpace() const
{
std::size_t space = 1;
for(int i = 0; i < mLens.size(); ++i)
for(std::size_t i = 0; i < mLens.size(); ++i)
{
space += (mLens[i] - 1) * mStrides[i];
}
@@ -68,7 +68,7 @@ void ostream_HostTensorDescriptor(const HostTensorDescriptor& desc, std::ostream
// FIXME: remove
void bf16_to_f32_(const Tensor<ck::bhalf_t>& src, Tensor<float>& dst)
{
for(int i = 0; i < src.mData.size(); ++i)
for(std::size_t i = 0; i < src.mData.size(); ++i)
dst.mData[i] = ck::type_convert<float>(src.mData[i]);
}
#endif

22
library/src/utility/conv_fwd_util.cpp
View File

@@ -71,11 +71,12 @@ ConvParams::ConvParams(ck::index_t n_dim,
input_left_pads(left_pads),
input_right_pads(right_pads)
{
if(filter_spatial_lengths.size() != num_dim_spatial ||
input_spatial_lengths.size() != num_dim_spatial ||
conv_filter_strides.size() != num_dim_spatial ||
conv_filter_dilations.size() != num_dim_spatial ||
input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
if(ck::type_convert<ck::index_t>(filter_spatial_lengths.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_spatial_lengths.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(conv_filter_strides.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(conv_filter_dilations.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_left_pads.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_right_pads.size()) != num_dim_spatial)
{
throw(
std::runtime_error("ConvParams::GetOutputSpatialLengths: "
@@ -85,11 +86,12 @@ ConvParams::ConvParams(ck::index_t n_dim,
std::vector<ck::index_t> ConvParams::GetOutputSpatialLengths() const
{
if(filter_spatial_lengths.size() != num_dim_spatial ||
input_spatial_lengths.size() != num_dim_spatial ||
conv_filter_strides.size() != num_dim_spatial ||
conv_filter_dilations.size() != num_dim_spatial ||
input_left_pads.size() != num_dim_spatial || input_right_pads.size() != num_dim_spatial)
if(ck::type_convert<ck::index_t>(filter_spatial_lengths.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_spatial_lengths.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(conv_filter_strides.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(conv_filter_dilations.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_left_pads.size()) != num_dim_spatial ||
ck::type_convert<ck::index_t>(input_right_pads.size()) != num_dim_spatial)
{
throw(
std::runtime_error("ConvParams::GetOutputSpatialLengths: "

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());

2
profiler/src/profile_reduce.cpp
View File

@@ -186,7 +186,7 @@ class AppArgs
int processArgs(int argc, char* argv[])
{
unsigned int ch;
int ch;
optind++; // to skip the "reduce" module name

2
test/gemm_split_k/gemm_split_k.cpp
View File

@@ -45,7 +45,7 @@ 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)

6
test/grouped_gemm/grouped_gemm_fp16.cpp
View File

@@ -104,7 +104,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
b_tensors_device.reserve(group_count);
c_tensors_device.reserve(group_count);
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
a_tensors.emplace_back(Tensor<ADataType>(f_host_tensor_descriptor(
gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{})));
@@ -119,7 +119,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
}
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize()));
@@ -147,7 +147,7 @@ bool TestGroupedGemm(DeviceGroupedGemmPtr_& groupedGemmPtr)
invoker_ptr->Run(argument_ptr.get());
for(int i = 0; i < gemm_shapes.size(); i++)
for(std::size_t i = 0; i < gemm_shapes.size(); i++)
{
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());

2
test/reduce/reduce_no_index.cpp
View File

@@ -460,7 +460,7 @@ class SimpleAppArgs
int processArgs(int argc, char* argv[])
{
unsigned int ch;
int ch;
while(1)
{

2
test/reduce/reduce_util.hpp
View File

@@ -9,7 +9,7 @@ namespace reduce_util {
template <typename T>
void to_f32_vector(const Tensor<T>& src, Tensor<float>& dst)
{
for(int i = 0; i < src.mData.size(); ++i)
for(std::size_t i = 0; i < src.mData.size(); ++i)
dst.mData[i] = type_convert<float>(src.mData[i]);
}

2
test/reduce/reduce_with_index.cpp
View File

@@ -463,7 +463,7 @@ class SimpleAppArgs
int processArgs(int argc, char* argv[])
{
unsigned int ch;
int ch;
while(1)
{