XeGPU RFC update: Add mem_desc and operations for share local memory access #1092
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Jianhui-Li
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Jianhui-Li
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Garra1980
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Jul 8, 2025
docs/rfcs/XeGPU.md
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| ## XeGPU operations to access share local memory | ||
| Users must create `matrix_desc` to hold a matrix in the share local memory. The matrix must be row-major. The matrix can attach a attribute for its memory layout, for example, a blocked layout or just original non-blocked row-major layout (aka. linear layout). | ||
| User can get a subview of an existing `matrix_desc` to get a new `matrix_desc`, potentially having a stride. Then user can use load_matrix and store_matrix to move the matrix data between share local memory and vectors (registers). The matrix is typically 2d and but can be multi-dimension. XeGPU's load_matrix and store_matrix works at workgroup level only. It uses xegpu.layout to describe how the matrix is decomposed to data fragments and maps to work items. The workgroup level operation loads the entire matrix to vector. |
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Since we're talking about WG-level here I think #1033 should me merged before this one
akroviakov
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Jul 11, 2025
akroviakov
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Jul 11, 2025
chencha3
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Aug 14, 2025
docs/rfcs/XeGPU.md
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| Users create a `matrix_desc` to represent a matrix stored in shared local memory (SLM). The operation takes a memory buffer (1D int8 memref with empty layout) and create a structured representation of the share local memory. The result matrix_desc has proper information including shape, element type, and memory layout attributes (@block and @strides). The @block attribute indicates that the matrix follows a blocked layout, enabling optimized lowering to 1D block loads. The @strides attribute specifies the logical strides of each dimension and is typically used to support chunked loads. | ||
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| When there is no input memref operand, it allocates SLM for the matrix, assuming a row-major contiguous layout. |
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What is the purpose of making memref operand optional?
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removed. was there to ensure consistent with the earlier definition.
Garra1980
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Aug 15, 2025
Jianhui-Li
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cc @dchigarev |
Garra1980
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Sep 29, 2025
docs/rfcs/XeGPU.md
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| #dpas_wg = {sg_layout = [8, 4], sg_data= [32, 32], order=[1, 0] } | ||
| %at = xegpu.load_nd %tdesc: tensor_desc<32x256xf16, #Coop_t_wg> -> vector<32x256xf16> | ||
| %a = vector.transpose %1 {layout_result_0 = #Coop_wg}: vector<32x256xf16> to vector<256x32xf16> |
Garra1980
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Sep 30, 2025
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| The code is transformed to use store_matrix and load_matrix to implement the transpose cooperatively in shared local memory. Note that both load_nd and store_matrix use smaller sg_data values, meaning each subgroup processes a smaller fragment, enabling a cooperative transpose across threads. | ||
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| It is generally preferable to detect and fuse the “transpose + convert_layout” pattern at the workgroup level early in the compilation pipeline. Early fusion directly influences the blocking strategy for `load_matrix` and `store_matrix`, which are the lowered forms of logical layout conversion and transpose. If this fusion is not performed at the workgroup level, later fusion passes may only fuse transpose with load at the subgroup level, potentially missing the most optimized code sequence. |
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Is it correct that "transpose+convert_layout" pattern should be generated by higher dialects?
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