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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>
This commit is contained in:
emezh
2025-09-25 21:22:13 -04:00
committed by GitHub
parent ec4d16b991
commit db2524be2d
122 changed files with 1732 additions and 848 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
example/44_elementwise_permute/elementwise_binary_4D_fp16.cpp
View File

@@ -22,6 +22,8 @@ using F32 = float;
using ADataType = F16;
using BDataType = F16;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -73,11 +75,11 @@ int main(int argc, char* argv[])
1};
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 2> as = {Tensor<ADataType>(ab_lengths, ab_strides),
Tensor<ADataType>(ab_lengths, ab_strides)};
std::array<Tensor<ADataType>, 2> as = {Tensor<ADataType>(ab_lengths, ab_strides, NchwLayout{}),
Tensor<ADataType>(ab_lengths, ab_strides, NchwLayout{})};
Tensor<ADataType>& a0 = as[0];
Tensor<ADataType>& a1 = as[1];
Tensor<BDataType> b(ab_lengths, ab_strides);
Tensor<BDataType> b(ab_lengths, ab_strides, NchwLayout{});
float alpha = 3.f;
float beta = 2.f;
a0.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
@@ -134,7 +136,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, ab_strides);
Tensor<BDataType> host_b(ab_lengths, ab_strides, NchwLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<2, ADataType, BDataType, BinaryAddUnaryScaleSquare>;

8
example/44_elementwise_permute/elementwise_permute_4D_fp16.cpp
View File

@@ -22,6 +22,8 @@ using F32 = float;
using ADataType = F16;
using BDataType = F16;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using DeviceElementwisePermuteInstance = ck::tensor_operation::device::DeviceElementwiseImpl<
ck::Tuple<ADataType>, // InDataTypeTuple
@@ -72,9 +74,9 @@ int main(int argc, char* argv[])
static_cast<int>(nhwc[3])};
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides)};
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides, NchwLayout{})};
Tensor<ADataType>& a = as[0];
Tensor<BDataType> b(ab_lengths, b_strides);
Tensor<BDataType> b(ab_lengths, b_strides, NhwcLayout{});
a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
@@ -117,7 +119,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, b_strides);
Tensor<BDataType> host_b(ab_lengths, b_strides, NhwcLayout{});
using ReferenceElementwiseInstance =
ck::tensor_operation::host::ReferenceElementwise<1, ADataType, BDataType, PassThrough>;
auto ref_elementwise = ReferenceElementwiseInstance{};

8
example/44_elementwise_permute/elementwise_permute_4D_fp16_col.cpp
View File

@@ -23,6 +23,8 @@ using F32 = float;
using ADataType = F16;
using BDataType = F16;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -76,9 +78,9 @@ int main(int argc, char* argv[])
static_cast<int>(nhwc[0] * nhwc[1])};
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides)};
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides, NchwLayout{})};
Tensor<ADataType>& a = as[0];
Tensor<BDataType> b(ab_lengths, b_strides);
Tensor<BDataType> b(ab_lengths, b_strides, NhwcLayout{});
float scale = 1.f;
auto i = 0;
std::mt19937 gen(11939);
@@ -137,7 +139,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, b_strides);
Tensor<BDataType> host_b(ab_lengths, b_strides, NhwcLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<1, ADataType, BDataType, UnaryScaleSquare>;
auto ref_elementwise = ReferenceElementwiseInstance{};

9
example/44_elementwise_permute/elementwise_permute_4D_fp16_row.cpp
View File

@@ -22,6 +22,9 @@ using F32 = float;
using ADataType = F16;
using BDataType = F16;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -76,9 +79,9 @@ int main(int argc, char* argv[])
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides)};
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides, NchwLayout{})};
Tensor<ADataType>& a = as[0];
Tensor<BDataType> b(ab_lengths, b_strides);
Tensor<BDataType> b(ab_lengths, b_strides, NhwcLayout{});
float scale = 2.f;
a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
@@ -128,7 +131,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, b_strides);
Tensor<BDataType> host_b(ab_lengths, b_strides, NhwcLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<1, ADataType, BDataType, UnaryScaleSquare>;
auto ref_elementwise = ReferenceElementwiseInstance{};

8
example/44_elementwise_permute/elementwise_permute_4D_fp32_col.cpp
View File

@@ -22,6 +22,8 @@ using F32 = float;
using ADataType = F32;
using BDataType = F32;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -76,9 +78,9 @@ int main(int argc, char* argv[])
static_cast<int>(nhwc[0] * nhwc[1])};
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides)};
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides, NchwLayout{})};
Tensor<ADataType>& a = as[0];
Tensor<BDataType> b(ab_lengths, b_strides);
Tensor<BDataType> b(ab_lengths, b_strides, NhwcLayout{});
float scale = 1.f;
auto i = 0;
@@ -139,7 +141,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, b_strides);
Tensor<BDataType> host_b(ab_lengths, b_strides, NhwcLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<1, ADataType, BDataType, UnaryScaleSquare>;
auto ref_elementwise = ReferenceElementwiseInstance{};

9
example/44_elementwise_permute/elementwise_permute_4D_fp32_row.cpp
View File

@@ -22,6 +22,9 @@ using F32 = float;
using ADataType = F32;
using BDataType = F32;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -76,9 +79,9 @@ int main(int argc, char* argv[])
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides)};
std::array<Tensor<ADataType>, 1> as = {Tensor<ADataType>(ab_lengths, a_strides, NchwLayout{})};
Tensor<ADataType>& a = as[0];
Tensor<BDataType> b(ab_lengths, b_strides);
Tensor<BDataType> b(ab_lengths, b_strides, NhwcLayout{});
float scale = 2.f;
a.GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
@@ -127,7 +130,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, b_strides);
Tensor<BDataType> host_b(ab_lengths, b_strides, NhwcLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<1, ADataType, BDataType, UnaryScaleSquare>;
auto ref_elementwise = ReferenceElementwiseInstance{};

13
example/44_elementwise_permute/elementwise_trinary_4D_fp16.cpp
View File

@@ -22,6 +22,9 @@ using F32 = float;
using ADataType = F16;
using BDataType = F16;
using NchwLayout = ck::tensor_layout::convolution::NCHW;
using NhwcLayout = ck::tensor_layout::convolution::NHWC;
using UnaryScale = ck::tensor_operation::element_wise::Scale;
using UnarySquare = ck::tensor_operation::element_wise::UnarySquare;
using UnaryScaleSquare =
@@ -78,13 +81,13 @@ int main(int argc, char* argv[])
ck::ranges::copy(nchw, ab_lengths.begin());
std::array<Tensor<ADataType>, 3> as = {Tensor<ADataType>(ab_lengths, ab_strides),
Tensor<ADataType>(ab_lengths, ab_strides),
Tensor<ADataType>(ab_lengths, ab_strides)};
std::array<Tensor<ADataType>, 3> as = {Tensor<ADataType>(ab_lengths, ab_strides, NchwLayout{}),
Tensor<ADataType>(ab_lengths, ab_strides, NchwLayout{}),
Tensor<ADataType>(ab_lengths, ab_strides, NchwLayout{})};
Tensor<ADataType>& a0 = as[0];
Tensor<ADataType>& a1 = as[1];
Tensor<ADataType>& a2 = as[2];
Tensor<BDataType> b(ab_lengths, ab_strides);
Tensor<BDataType> b(ab_lengths, ab_strides, NchwLayout{});
float alpha = 3.f;
float beta = 2.f;
float gamma = 4.f;
@@ -149,7 +152,7 @@ int main(int argc, char* argv[])
if(do_verification)
{
Tensor<BDataType> host_b(ab_lengths, ab_strides);
Tensor<BDataType> host_b(ab_lengths, ab_strides, NchwLayout{});
using ReferenceElementwiseInstance = ck::tensor_operation::host::
ReferenceElementwise<3, ADataType, BDataType, TrinaryAddUnaryScaleSquare>;
auto ref_elementwise = ReferenceElementwiseInstance{};