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Verify HostTensorDescriptor when it is created (#2829)
* 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
@@ -203,8 +203,11 @@ make_input_host_tensor_descriptor_g_n_c_wis_packed(const ck::utils::conv::ConvPa
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}
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return transpose_host_tensor_descriptor_given_new2old(
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HostTensorDescriptor(physical_lengths),
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detail::get_layout_transpose_gnchw_to_old<InLayout>());
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// TBD: specify explicit conv layout rather than base one
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HostTensorDescriptor(physical_lengths,
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ck::tensor_layout::convolution::BaseConvolutionLayout{}),
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detail::get_layout_transpose_gnchw_to_old<InLayout>(),
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InLayout{});
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}
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// make tensor descriptor for packed weight tensor, and order the dimension in the order of GKCYX
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@@ -296,8 +299,10 @@ make_weight_host_tensor_descriptor_g_k_c_xs_packed(const ck::utils::conv::ConvPa
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}
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return transpose_host_tensor_descriptor_given_new2old(
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HostTensorDescriptor(physical_lengths),
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detail::get_layout_transpose_gnchw_to_old<WeiLayout>());
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HostTensorDescriptor(physical_lengths,
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ck::tensor_layout::convolution::BaseConvolutionLayout{}),
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detail::get_layout_transpose_gnchw_to_old<WeiLayout>(),
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WeiLayout{});
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}
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// make tensor descriptor for packed output tensor, and order the dimension in the order of GNKHW
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@@ -386,8 +391,10 @@ make_output_host_tensor_descriptor_g_n_k_wos_packed(const ck::utils::conv::ConvP
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}
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return transpose_host_tensor_descriptor_given_new2old(
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HostTensorDescriptor(physical_lengths),
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detail::get_layout_transpose_gnchw_to_old<OutLayout>());
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HostTensorDescriptor(physical_lengths,
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ck::tensor_layout::convolution::BaseConvolutionLayout{}),
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detail::get_layout_transpose_gnchw_to_old<OutLayout>(),
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OutLayout{});
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}
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} // namespace conv
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@@ -21,6 +21,8 @@
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#include "ck/library/utility/ranges.hpp"
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#include "ck/library/utility/thread.hpp"
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#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
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template <typename Range>
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std::ostream& LogRange(std::ostream& os, Range&& range, std::string delim)
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{
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@@ -97,59 +99,455 @@ auto construct_f_unpack_args(F, T args)
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return construct_f_unpack_args_impl<F>(args, std::make_index_sequence<N>{});
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}
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/**
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* @brief A descriptor class for host tensors that manages tensor dimensions, strides, and layout.
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*
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* The HostTensorDescriptor provides a comprehensive interface for describing multi-dimensional
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* tensors with configurable layouts and automatic stride calculation capabilities.
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*
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* @section stride_handling Stride Handling
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*
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* The descriptor supports multiple stride specification modes:
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*
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* 1. **Explicit Strides**: When strides are provided explicitly, they are validated against
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* the specified layout to ensure memory access patterns are correct.
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*
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* 2. **Auto-calculated Strides**: When strides are empty or all-zero, they are automatically
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* calculated based on the tensor layout:
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* - For RowMajor layout: rightmost dimension has stride 1, others calculated as cumulative
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* products
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* - For ColumnMajor layout: similar to RowMajor but with swapped stride positions for last two
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* dimensions
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*
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* 3. **Partial Stride Specification**: For GEMM layouts, unknown strides (represented as 0 or
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* negative values) in the last two dimensions can be auto-calculated while preserving higher
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* dimension strides.
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*
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* 4. **Bypass**: When using `BypassLayoutVerification` layout, no stride calculation or validation
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* is performed. That allows to pass in any arbitrary strides including 0.
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*
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* For more details see `CalculateStrides` method.
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*
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* @section layout_support Layout Support
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*
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* - **GEMM Layouts**: Supports RowMajor and ColumnMajor layouts with full validation
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* - **Convolution Layouts**: Recognized but validation is not yet implemented
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* - **Abstract Layouts**: BaseTensorLayout will attempt automatic layout detection for 2D tensors
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*
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* @section limitations Limitations
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*
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* 1. **Layout Detection**: Automatic layout detection only works reliably for 2D tensors.
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* This is done mostly for legacy GEMM cases to avoid modifying many existing GEMM tests to pass
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* RowMajor/ColumnMajor explicitly. Higher-dimensional tensors with BaseTensorLayout will throw
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* validation errors. For more details see `HandleDefaultLayout` method.
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*
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* 2. **Stride Validation**: Only GEMM layouts (RowMajor/ColumnMajor) have full stride validation.
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* Convolution layouts are accepted but not validated. For more details see `ValidateStrides`.
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*
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* 3. **GEMM Assumptions**: For tensors with more than 2 dimensions, GEMM layout validation
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* assumes the last two dimensions represent the height-width pattern (e.g., BHW or BWH for
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* batched GEMM).
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*
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* 4. **Negative Stride Handling**: Negative stride values are interpreted as "unknown" and
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* converted to auto-calculated values only for supported layouts.
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*
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* @section thread_safety Thread Safety
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* This class is not thread-safe. External synchronization is required for concurrent access.
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*
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* @section examples Usage Examples
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*
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* ```cpp
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* // Auto-calculate strides for RowMajor layout
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* HostTensorDescriptor desc1({4, 3}, ck::tensor_layout::gemm::RowMajor{});
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*
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* // Explicit strides with validation
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* HostTensorDescriptor desc2({4, 3}, {3, 1}, ck::tensor_layout::gemm::RowMajor{});
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*
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* // Partial stride specification (auto-calculate unknown dimension)
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* HostTensorDescriptor desc3({4, 3}, {0, 1}, ck::tensor_layout::gemm::RowMajor{});
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* ```
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*/
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struct HostTensorDescriptor
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{
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HostTensorDescriptor() = default;
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using BaseTensorLayout = ck::tensor_layout::BaseTensorLayout;
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using DefaultLayout = BaseTensorLayout;
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void CalculateStrides();
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template <typename X, typename = std::enable_if_t<std::is_convertible_v<X, std::size_t>>>
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HostTensorDescriptor(const std::initializer_list<X>& lens) : mLens(lens.begin(), lens.end())
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// Runtime tag describing which layout is picked when layout is not specified explicitly at
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// construction time.
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enum class ChosenLayout
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{
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this->CalculateStrides();
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Original,
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RowMajor,
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ColumnMajor
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};
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// Master constructor
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template <typename Layout>
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HostTensorDescriptor(std::vector<std::size_t> lens,
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std::vector<std::size_t> strides,
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const Layout& layout = DefaultLayout())
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: mLens(std::move(lens)), mStrides(std::move(strides))
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{
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// To support legacy use cases, when layout is not passed in
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const auto new_layout = HandleDefaultLayout(layout);
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if(dbg)
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{
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std::cout << "Original Lens: [";
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LogRange(std::cout, mLens, ", ") << "] and Strides: [";
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LogRange(std::cout, mStrides, ", ") << "]" << std::endl;
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std::cout << "Layout: " << layout << " --> " << new_layout << std::endl;
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}
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// Handling the strides and validation based on the chosen layout
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DispatchChosenLayout(new_layout, layout, [&](auto selected_layout) {
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this->CalculateStrides(selected_layout);
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this->ValidateStrides(selected_layout);
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});
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}
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HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens)
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: mLens(lens.begin(), lens.end())
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HostTensorDescriptor() : HostTensorDescriptor({}, {}, DefaultLayout()){};
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// Helper that invokes a callable with a concrete layout object whose type
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// matches the chosen tag (so template code depending on the layout type
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// can still leverage if constexpr branches).
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template <typename F, typename OrigLayout>
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void DispatchChosenLayout(ChosenLayout tag, const OrigLayout& orig, F&& f) const
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{
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this->CalculateStrides();
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switch(tag)
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{
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case ChosenLayout::RowMajor: f(ck::tensor_layout::gemm::RowMajor{}); break;
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case ChosenLayout::ColumnMajor: f(ck::tensor_layout::gemm::ColumnMajor{}); break;
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case ChosenLayout::Original:
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default: f(orig); break;
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}
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}
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template <typename Layout>
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ChosenLayout HandleDefaultLayout(const Layout&)
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{
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if constexpr(!std::is_same_v<Layout, DefaultLayout>)
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{
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return ChosenLayout::Original;
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}
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else
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{
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if(mStrides.empty())
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{
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// No strides provided -> assume RowMajor
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return ChosenLayout::RowMajor;
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}
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const auto rank = mLens.size();
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if(rank > 2)
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{
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// Keep as-is - validation will warn/throw later
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return ChosenLayout::Original;
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}
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if(rank == 0)
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{
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// Keep as-is - validation will warn/throw later
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return ChosenLayout::Original;
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}
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if(rank == 1)
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{
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// Treat 1D tensor as RowMajor
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return ChosenLayout::RowMajor;
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}
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// rank == 2
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if(mStrides.size() == 2)
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{
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// RowMajor pattern (?, 1)
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if(mStrides[1] == 1)
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{
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return ChosenLayout::RowMajor;
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}
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// ColumnMajor pattern (1, ?)
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if(mStrides[0] == 1)
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{
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return ChosenLayout::ColumnMajor;
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}
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}
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// Fallback: leave as-is
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return ChosenLayout::Original;
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}
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}
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template <typename Layout>
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void CalculateStrides(const Layout& layout)
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{
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if constexpr(std::is_same_v<Layout, ck::tensor_layout::BypassLayoutVerification>)
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return;
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// This is a workaround if the original stride value is -1 (which means "unknown") has been
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// passed in and casted to size_t (unsigned).
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auto strides_int = AsInt(mStrides);
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// case of empty strides or all-zero: auto-calculate based on layout and tensor dimensions
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if(mStrides.empty() || std::all_of(strides_int.begin(), strides_int.end(), [](int stride) {
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return stride <= 0;
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}))
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{
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if constexpr(!(std::is_same_v<ck::tensor_layout::gemm::RowMajor, Layout> ||
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std::is_same_v<ck::tensor_layout::gemm::ColumnMajor, Layout>))
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{
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std::cerr << "Only RowMajor and ColumnMajor layouts are supported for empty "
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"strides, got "
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<< layout << ". Will calculate strides as RowMajor." << std::endl;
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}
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mStrides.clear();
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mStrides.resize(mLens.size(), 0);
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if(mStrides.empty())
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return;
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mStrides.back() = 1;
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std::partial_sum(mLens.rbegin(),
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mLens.rend() - 1,
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mStrides.rbegin() + 1,
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std::multiplies<std::size_t>());
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if constexpr(std::is_same_v<ck::tensor_layout::gemm::ColumnMajor, Layout>)
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{
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// swap the last two strides
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if(mStrides.size() >= 2)
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std::swap(mStrides[mStrides.size() - 1], mStrides[mStrides.size() - 2]);
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}
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}
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// The other case is if one of the strides is unknown
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// Currently, only GEMM RowMajor and ColumnMajor layouts are supported and only in the lower
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// two dimensions, e.g. {..., 0, N} or {..., M, 0}. The higher dimensions are left
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// untouched.
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else if constexpr(std::is_same_v<ck::tensor_layout::gemm::RowMajor, Layout> ||
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std::is_same_v<ck::tensor_layout::gemm::ColumnMajor, Layout>)
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{
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auto rank = mStrides.size();
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if(mLens.size() >= 2 && rank >= 2)
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{
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const auto inner_idx =
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std::is_same_v<ck::tensor_layout::gemm::RowMajor, Layout> ? rank - 1 : rank - 2;
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const auto outer_idx = inner_idx == rank - 1 ? rank - 2 : rank - 1;
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if(mStrides[inner_idx] <= 0)
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{
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mStrides[inner_idx] = 1;
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}
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if(mStrides[outer_idx] <= 0)
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{
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mStrides[outer_idx] = mLens[inner_idx] * mStrides[inner_idx];
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}
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}
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}
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}
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template <typename Layout>
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void ValidateStrides(const Layout& layout) const
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{
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if constexpr(std::is_same_v<ck::tensor_layout::BypassLayoutVerification, Layout>)
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{
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return;
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}
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if(mLens.empty())
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{
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throw std::runtime_error(
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"HostTensorDescriptor::ValidateStrides: empty tensor dimensions is not allowed.");
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}
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const int rank = mLens.size();
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if(rank == 1) // skip any 1D tensors
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{
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return;
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}
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if constexpr(std::is_same_v<ck::tensor_layout::BaseTensorLayout, Layout>)
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{
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// Any legacy code that doesn't pass layout to HostTensorDescriptor ctor will
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// hit this case (unless it is a special case - see `HandleDefaultLayout`).
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throw std::runtime_error("HostTensorDescriptor::ValidateStrides: Abstract tensor "
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"layout BaseTensorLayout can't be verified. Pls "
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"pass specific tensor layout to HostTensorDescriptor (or "
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"ck::tensor_layout::BypassLayoutVerification)");
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}
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// GEMM cases
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if constexpr(std::is_base_of_v<ck::tensor_layout::gemm::BaseGemmLayout, Layout>)
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{
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if(mLens.size() != mStrides.size())
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{
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std::ostringstream oss;
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oss << "HostTensorDescriptor::ValidateStrides: mismatch between tensor rank and "
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"size of strides: "
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<< *this;
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throw std::runtime_error(oss.str());
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}
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// in GEMM, strides must be all positive or all zeros (auto-derived from tensor
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// dimensions)
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auto strides_int = AsInt(mStrides);
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if(std::any_of(
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strides_int.begin(), strides_int.end(), [](int stride) { return stride <= 0; }))
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{
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std::ostringstream oss;
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oss << "Stride values must be positive or all-zeros (auto-derived from tensor "
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"dimensions). Instead got ";
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std::copy(
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strides_int.begin(), strides_int.end(), std::ostream_iterator<int>(oss, " "));
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throw std::runtime_error(oss.str());
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}
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if constexpr(std::is_same_v<ck::tensor_layout::gemm::RowMajor, Layout> ||
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std::is_same_v<ck::tensor_layout::gemm::ColumnMajor, Layout>)
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{
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// The logic here assumes the GEMM with tensor of more than 2 dims, will always have
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// HW dimesnsions as the inner ones e.g. batched GEMM is either BHW or BWH
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const auto inner_idx =
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std::is_same_v<ck::tensor_layout::gemm::RowMajor, Layout> ? rank - 1 : rank - 2;
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||||
const auto outer_idx = inner_idx == rank - 1 ? rank - 2 : rank - 1;
|
||||
|
||||
if(mStrides[outer_idx] < mLens[inner_idx] * mStrides[inner_idx])
|
||||
{
|
||||
std::ostringstream oss;
|
||||
oss << "Invalid strides for " << layout << ": " << *this;
|
||||
throw std::runtime_error(oss.str());
|
||||
}
|
||||
|
||||
// For higher dimensions, validate strides assuming RowMajor
|
||||
for(int i = 1; i < rank - 2; ++i)
|
||||
{
|
||||
if(mStrides[i - 1] < mStrides[i] * mLens[i])
|
||||
{
|
||||
std::ostringstream oss;
|
||||
oss << "Invalid strides for higher dimensions in " << layout << ": "
|
||||
<< *this;
|
||||
throw std::runtime_error(oss.str());
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
std::ostringstream oss;
|
||||
oss << "Error: Unsupported GEMM layout: " << layout;
|
||||
throw std::runtime_error(oss.str());
|
||||
}
|
||||
}
|
||||
// Convolution cases
|
||||
else if constexpr(std::is_base_of_v<ck::tensor_layout::convolution::BaseConvolutionLayout,
|
||||
Layout>)
|
||||
{
|
||||
// TBD: implement verification for Conv layouts
|
||||
// For now, just print warning and return
|
||||
std::cerr << "Warning: Tensor layout verification for ck::tensor_layout::convolution "
|
||||
"layouts is not supported yet. Skipping..."
|
||||
<< std::endl;
|
||||
return;
|
||||
}
|
||||
else
|
||||
{
|
||||
std::ostringstream oss;
|
||||
oss << "Error: Tensor layout verification for " << layout << " is not supported yet.";
|
||||
throw std::runtime_error(oss.str());
|
||||
}
|
||||
}
|
||||
|
||||
template <typename X,
|
||||
typename Layout = DefaultLayout,
|
||||
typename = std::enable_if_t<std::is_convertible_v<X, std::size_t> &&
|
||||
std::is_convertible_v<Layout, BaseTensorLayout>>>
|
||||
HostTensorDescriptor(const std::initializer_list<X>& lens, const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()), {}, layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
template <typename Layout = DefaultLayout,
|
||||
typename = std::enable_if_t<std::is_convertible_v<Layout, BaseTensorLayout>>>
|
||||
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens,
|
||||
const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()), {}, layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
template <typename Lengths,
|
||||
typename = std::enable_if_t<
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> ||
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t>>>
|
||||
HostTensorDescriptor(const Lengths& lens) : mLens(lens.begin(), lens.end())
|
||||
typename Layout = DefaultLayout,
|
||||
typename = std::enable_if_t<
|
||||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> ||
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t>) &&
|
||||
std::is_convertible_v<Layout, BaseTensorLayout>>>
|
||||
HostTensorDescriptor(const Lengths& lens, const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()), {}, layout)
|
||||
{
|
||||
this->CalculateStrides();
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
template <typename X,
|
||||
typename Y,
|
||||
typename = std::enable_if_t<std::is_convertible_v<X, std::size_t> &&
|
||||
std::is_convertible_v<Y, std::size_t>>>
|
||||
typename = std::enable_if_t<std::is_convertible_v<X, std::size_t> &&
|
||||
std::is_convertible_v<Y, std::size_t>>,
|
||||
typename Layout = DefaultLayout>
|
||||
HostTensorDescriptor(const std::initializer_list<X>& lens,
|
||||
const std::initializer_list<Y>& strides)
|
||||
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
|
||||
const std::initializer_list<Y>& strides,
|
||||
const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()),
|
||||
std::vector<std::size_t>(strides.begin(), strides.end()),
|
||||
layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
// HostTensorDescriptor({row, col}, {row_stride, col_stride})
|
||||
template <typename Layout = DefaultLayout>
|
||||
HostTensorDescriptor(const std::initializer_list<ck::long_index_t>& lens,
|
||||
const std::initializer_list<ck::long_index_t>& strides)
|
||||
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
|
||||
const std::initializer_list<ck::long_index_t>& strides,
|
||||
const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()),
|
||||
std::vector<std::size_t>(strides.begin(), strides.end()),
|
||||
layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
// HostTensorDescriptor({row, col}, strides)
|
||||
template <typename Strides, typename Layout = DefaultLayout>
|
||||
HostTensorDescriptor(const std::initializer_list<std::size_t>& lens,
|
||||
const Strides& strides,
|
||||
const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()),
|
||||
std::vector<std::size_t>(strides.begin(), strides.end()),
|
||||
layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
template <typename Lengths,
|
||||
typename Strides,
|
||||
typename = std::enable_if_t<
|
||||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>) ||
|
||||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t> &&
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Strides>, ck::long_index_t>)>>
|
||||
HostTensorDescriptor(const Lengths& lens, const Strides& strides)
|
||||
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
|
||||
typename Layout = DefaultLayout,
|
||||
typename = std::enable_if_t<
|
||||
((std::is_convertible_v<ck::ranges::range_value_t<Lengths>, std::size_t> &&
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Strides>, std::size_t>) ||
|
||||
(std::is_convertible_v<ck::ranges::range_value_t<Lengths>, ck::long_index_t> &&
|
||||
std::is_convertible_v<ck::ranges::range_value_t<Strides>, ck::long_index_t>)) &&
|
||||
std::is_convertible_v<Layout, BaseTensorLayout>>>
|
||||
HostTensorDescriptor(const Lengths& lens,
|
||||
const Strides& strides,
|
||||
const Layout& layout = Layout{})
|
||||
: HostTensorDescriptor(std::vector<std::size_t>(lens.begin(), lens.end()),
|
||||
std::vector<std::size_t>(strides.begin(), strides.end()),
|
||||
layout)
|
||||
{
|
||||
if(dbg)
|
||||
std::cout << "HostTensorDescriptor ctor (" << __LINE__ << ")" << std::endl;
|
||||
}
|
||||
|
||||
std::size_t GetNumOfDimension() const;
|
||||
@@ -173,15 +571,34 @@ struct HostTensorDescriptor
|
||||
}
|
||||
|
||||
friend std::ostream& operator<<(std::ostream& os, const HostTensorDescriptor& desc);
|
||||
friend std::ostream& operator<<(std::ostream& os, ChosenLayout tag);
|
||||
|
||||
private:
|
||||
std::vector<std::size_t> mLens;
|
||||
std::vector<std::size_t> mStrides;
|
||||
static constexpr bool dbg = false;
|
||||
|
||||
/**
|
||||
* @brief Converts a vector of size_t values to a vector of int values.
|
||||
*
|
||||
* @param vec The input vector of size_t values to be converted.
|
||||
* @return std::vector<int> A vector containing the converted int values.
|
||||
*/
|
||||
std::vector<int> AsInt(const std::vector<size_t>& vec) const
|
||||
{
|
||||
std::vector<int> strides_int(vec.size());
|
||||
std::transform(vec.begin(), vec.end(), strides_int.begin(), [](std::size_t stride) {
|
||||
return static_cast<int>(stride);
|
||||
});
|
||||
return strides_int;
|
||||
}
|
||||
};
|
||||
|
||||
template <typename New2Old>
|
||||
HostTensorDescriptor transpose_host_tensor_descriptor_given_new2old(const HostTensorDescriptor& a,
|
||||
const New2Old& new2old)
|
||||
template <typename New2Old, typename NewLayout = HostTensorDescriptor::BaseTensorLayout>
|
||||
HostTensorDescriptor
|
||||
transpose_host_tensor_descriptor_given_new2old(const HostTensorDescriptor& a,
|
||||
const New2Old& new2old,
|
||||
const NewLayout& new_layout = NewLayout())
|
||||
{
|
||||
std::vector<std::size_t> new_lengths(a.GetNumOfDimension());
|
||||
std::vector<std::size_t> new_strides(a.GetNumOfDimension());
|
||||
@@ -192,7 +609,7 @@ HostTensorDescriptor transpose_host_tensor_descriptor_given_new2old(const HostTe
|
||||
new_strides[i] = a.GetStrides()[new2old[i]];
|
||||
}
|
||||
|
||||
return HostTensorDescriptor(new_lengths, new_strides);
|
||||
return HostTensorDescriptor(new_lengths, new_strides, new_layout);
|
||||
}
|
||||
|
||||
struct joinable_thread : std::thread
|
||||
@@ -300,6 +717,36 @@ struct Tensor
|
||||
{
|
||||
}
|
||||
|
||||
template <typename X, typename... Rest, std::enable_if_t<(sizeof...(Rest) > 0), int> = 0>
|
||||
Tensor(std::initializer_list<X> lens, Rest&&... rest)
|
||||
: mDesc(lens, std::forward<Rest>(rest)...), mData(GetElementSpaceSize())
|
||||
{
|
||||
}
|
||||
|
||||
template <typename X,
|
||||
typename Y,
|
||||
typename... Rest,
|
||||
std::enable_if_t<(sizeof...(Rest) > 0), int> = 0>
|
||||
Tensor(std::initializer_list<X> lens, std::initializer_list<Y> strides, Rest&&... rest)
|
||||
: mDesc(lens, strides, std::forward<Rest>(rest)...), mData(GetElementSpaceSize())
|
||||
{
|
||||
}
|
||||
|
||||
template <typename Lengths, typename... Rest, std::enable_if_t<(sizeof...(Rest) > 0), int> = 0>
|
||||
Tensor(const Lengths& lens, Rest&&... rest)
|
||||
: mDesc(lens, std::forward<Rest>(rest)...), mData(GetElementSpaceSize())
|
||||
{
|
||||
}
|
||||
|
||||
template <typename Lengths,
|
||||
typename Strides,
|
||||
typename... Rest,
|
||||
std::enable_if_t<(sizeof...(Rest) > 0), int> = 0>
|
||||
Tensor(const Lengths& lens, const Strides& strides, Rest&&... rest)
|
||||
: mDesc(lens, strides, std::forward<Rest>(rest)...), mData(GetElementSpaceSize())
|
||||
{
|
||||
}
|
||||
|
||||
Tensor(const Descriptor& desc) : mDesc(desc), mData(GetElementSpaceSize()) {}
|
||||
|
||||
template <typename OutT>
|
||||
|
||||
@@ -1,50 +0,0 @@
|
||||
// SPDX-License-Identifier: MIT
|
||||
// Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
#include <type_traits>
|
||||
#include "ck/ck.hpp"
|
||||
#include "ck/utility/type.hpp"
|
||||
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
|
||||
|
||||
namespace ck {
|
||||
namespace utils {
|
||||
|
||||
template <typename Layout>
|
||||
inline void
|
||||
validate_gemm_stride(int M, int N, int stride, const std::string& stride_name = "Stride")
|
||||
{
|
||||
if(ck::is_same_v<Layout, ck::tensor_layout::gemm::ColumnMajor>)
|
||||
{
|
||||
if(stride < M)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"Error: For ColumnMajor layout, " + stride_name + " (" + std::to_string(stride) +
|
||||
") must be greater than or equal to dim (" + std::to_string(M) + ")");
|
||||
}
|
||||
}
|
||||
else // RowMajor
|
||||
{
|
||||
if(stride < N)
|
||||
{
|
||||
throw std::runtime_error(
|
||||
"Error: For RowMajor layout, " + stride_name + " (" + std::to_string(stride) +
|
||||
") must be greater than or equal to dim (" + std::to_string(N) + ")");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Convenience functions for common GEMM patterns
|
||||
template <typename ALayout, typename BLayout, typename CLayout>
|
||||
inline void validate_gemm_strides_abc(int M, int N, int K, int StrideA, int StrideB, int StrideC)
|
||||
{
|
||||
validate_gemm_stride<ALayout>(M, K, StrideA, "StrideA");
|
||||
validate_gemm_stride<BLayout>(K, N, StrideB, "StrideB");
|
||||
validate_gemm_stride<CLayout>(M, N, StrideC, "StrideC");
|
||||
}
|
||||
|
||||
} // namespace utils
|
||||
} // namespace ck
|
||||
Reference in New Issue
Block a user