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Verify HostTensorDescriptor when it is created (#2829)

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* add proper GEMM layout verification

* Handle "auto" strides.

CalculateStrides only called when tensor's strides are empty or all of them are <=0 (auto strides).
CalculateStrides now supports GEMM::ColumnsMajor order. The assumption is still that it applies only to the inner two dims.
ValidateStrides throws if any of the tensor's strides is <=0.
profile_gemm_multiply_add updated to support "auto" strides for tensors.

Manual tests for profile_gemm_multiply_add (matrix B in Row and Col modes)
auto-strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 -1 -1 -1 -1 -1
Note, -1 should be deprecated (use 0 instead)

explicit strides (same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 128
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 128 128 128 128 128

explicit strides (not the same as auto)
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
	bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138

mix of explicit and auto strides
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 0

invalid stride
	bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 64
	terminate called after throwing an instance of 'std::runtime_error'
	  what():  Invalid strides for RowMajor: mLens: 128 128 , mStrides: 64 1
	Aborted (core dumped)

* - add more names to ck::tensor_layout for easier namespace hierarchy checking
- updated convolutional layouts to use explicit ones or BaseConvolutionalLayout where it is not clear which layout to use (TBD) - see include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp

* added handling of partially initialized strides for GEMM. fixed more tests.

* clang-format and more fixes

* replace long dash by a simple hyphen - causes build failure in CK codegen.

* increase sizeof input, otherwise output size becomes zero or negative with large filter size

* select stride based on layout

* specify layout explicitly to avoid errors in HostTensorDescriptor creation

* add validation for higher GEMM tensor dimensions.; Add docstring to `HostTensorDescriptor`

* Not clear why permute test in test/permute_scale/test_permute_scale.cpp uses a lot of invalid strides. Setting layout to BypassLayoutVerification to avoid a lot of errors

* fix test (incl removing invalid config)

* fix moe examples:
- (in .cpp) add layout argument to non-2D tensors
- (in .hpp) fix asserts/failures that show up in Debug mode, specifically addressing 2D tensor by a single index (and 3D tensor by 2d index)

* fix moe_gemm2 example.

* fix profile and wmma examples

* clean-up early mods for ckprofile. verified with:
```
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0
ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138
ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138
#
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 1 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 2 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 3 1 2 0 1 128 128 128 0 0 0
ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 128 128 128
#
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 0 0 0 0
# ckProfiler gemm_add_relu 0 1 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 2 1 1 0 1 128 128 128 0 0 0 0    # not implemented
# ckProfiler gemm_add_relu 0 3 1 1 0 1 128 128 128 0 0 0 0    # not implemented
ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 128 128 128 128
#
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 1 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 2 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 3 1 1 0 0 128 128 128 0 0 0 0 0
ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 130 132 134 136 138
#
example_gemm_add_multiply_dl_fp16
example_gemm_add_multiply_xdl_fp16
#
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 0 0 0
ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 128 128 128
```

* temporary skip first 8 test configs - they throw error

* temporary skip first 8 test configs in wmma too - they throw error

---------

Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>

[ROCm/composable_kernel commit: db2524be2d]
This commit is contained in:
emezh
2025-09-25 21:22:13 -04:00
committed by GitHub
parent 4567c988ca
commit 3c207a18b0
122 changed files with 1732 additions and 848 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_bias_fastgelu_bf16_i8.cpp
View File

@@ -81,10 +81,11 @@ int main(int argc, char* argv[])
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideB1 = 0;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
if(argc == 1)
{
@@ -120,23 +121,31 @@ int main(int argc, char* argv[])
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
auto f_host_tensor_descriptor = [](std::size_t row,
std::size_t col,
ck::index_t& stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
auto desc = HostTensorDescriptor({row, col}, {static_cast<std::size_t>(stride), 1_uz});
if(stride <= 0)
stride = desc.GetStrides()[0];
return desc;
}
else
{
auto desc = HostTensorDescriptor({row, col}, {1_uz, static_cast<std::size_t>(stride)});
if(stride <= 0)
stride = desc.GetStrides()[1];
return desc;
}
};
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, 0, B1Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, StrideB1, B1Layout{}));
Tensor<D0DataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
@@ -196,7 +205,7 @@ int main(int argc, char* argv[])
N,
K,
std::array<ck::index_t, NumATensor>{StrideA},
std::array<ck::index_t, NumBTensor>{StrideB, 0},
std::array<ck::index_t, NumBTensor>{StrideB, StrideB1},
std::array<ck::index_t, NumDTensor>{StrideD},
StrideE,
a_element_op,

45
example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_fastgelu_bf16_i8.cpp
View File

@@ -81,10 +81,11 @@ int main(int argc, char* argv[])
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideB1 = 0;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
if(argc == 1)
{
@@ -120,23 +121,31 @@ int main(int argc, char* argv[])
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
auto f_host_tensor_descriptor = [](std::size_t row,
std::size_t col,
ck::index_t& stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
auto desc = HostTensorDescriptor({row, col}, {static_cast<std::size_t>(stride), 1_uz});
if(stride <= 0)
stride = desc.GetStrides()[0];
return desc;
}
else
{
auto desc = HostTensorDescriptor({row, col}, {1_uz, static_cast<std::size_t>(stride)});
if(stride <= 0)
stride = desc.GetStrides()[1];
return desc;
}
};
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, 0, B1Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, StrideB1, B1Layout{}));
Tensor<D0DataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
@@ -196,7 +205,7 @@ int main(int argc, char* argv[])
N,
K,
std::array<ck::index_t, NumATensor>{StrideA},
std::array<ck::index_t, NumBTensor>{StrideB, 0},
std::array<ck::index_t, NumBTensor>{StrideB, StrideB1},
std::array<ck::index_t, NumDTensor>{},
StrideE,
a_element_op,

47
example/60_gemm_multi_ABD/gemm_multi_ABD_xdl_multiply_bias_fastgelu_bf16_i8.cpp
View File

@@ -80,10 +80,11 @@ int main(int argc, char* argv[])
ck::index_t N = 768;
ck::index_t K = 6144;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
ck::index_t StrideA = K;
ck::index_t StrideB = N;
ck::index_t StrideB1 = 0;
ck::index_t StrideD = 0;
ck::index_t StrideE = N;
if(argc == 1)
{
@@ -119,23 +120,31 @@ int main(int argc, char* argv[])
exit(0);
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
using namespace ck::literals;
auto f_host_tensor_descriptor = [](std::size_t row,
std::size_t col,
ck::index_t& stride,
auto layout) {
using namespace ck::literals;
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor({row, col}, {stride, 1_uz});
}
else
{
return HostTensorDescriptor({row, col}, {1_uz, stride});
}
};
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
auto desc = HostTensorDescriptor({row, col}, {static_cast<std::size_t>(stride), 1_uz});
if(stride <= 0)
stride = desc.GetStrides()[0];
return desc;
}
else
{
auto desc = HostTensorDescriptor({row, col}, {1_uz, static_cast<std::size_t>(stride)});
if(stride <= 0)
stride = desc.GetStrides()[1];
return desc;
}
};
Tensor<A0DataType> a0_m_k(f_host_tensor_descriptor(M, K, StrideA, A0Layout{}));
Tensor<B0DataType> b0_k_n(f_host_tensor_descriptor(K, N, StrideB, B0Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, 0, B1Layout{}));
Tensor<B1DataType> b1_k_n(f_host_tensor_descriptor(K, N, StrideB1, B1Layout{}));
Tensor<D0DataType> d_m_n(f_host_tensor_descriptor(M, N, StrideD, D0Layout{}));
Tensor<EDataType> e_m_n_host_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
Tensor<EDataType> e_m_n_device_result(f_host_tensor_descriptor(M, N, StrideE, ELayout{}));
@@ -196,7 +205,7 @@ int main(int argc, char* argv[])
K,
std::array<ck::index_t, NumATensor>{StrideA},
std::array<ck::index_t, NumBTensor>{StrideB},
std::array<ck::index_t, NumDTensor>{0, StrideD},
std::array<ck::index_t, NumDTensor>{StrideB1, StrideD},
StrideE,
a_element_op,
b_element_op,
@@ -261,7 +270,7 @@ int main(int argc, char* argv[])
{
for(int n = 0; n < N; ++n)
{
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), b1_k_n(0, n), d_m_n(m, n));
cde_element_op(e_m_n_host_result(m, n), c_m_n(m, n), b1_k_n(m, n), d_m_n(m, n));
}
}