fixup
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@@ -49,7 +49,7 @@ def TritonGPUCoalescedEncodingAttr : TritonGPU_Attr<"TritonGPUCoalescedEncoding"
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let description = [{
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An encoding where each warp owns a contiguous portion of the target tensor. This is typically the kind of data layout
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consumed (and returned) by LoadInst.
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For example, a row-major coalesced layout may distribute a 32x16 tensor over 2 warps (i.e. 64 threads) as follows:
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For example, a row-major coalesced layout may distribute a 64x16 tensor over 2 warps (i.e. 64 threads) as follows:
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thread tile size 2
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- - - - - - /\ - - - - - -
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@@ -61,12 +61,13 @@ size } ....
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-----------------------------/\-----------------------------------
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block tile size 8
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A_{16, 0}[T0] A_{16, 1}[T0] ... A_{16, 6}[T3] A_{16, 7}[T3] A_{16, 8}[T0] A_{16, 9}[T0] ... A_{16, 14}[T3] A_{16, 15}[T3]
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A_{17, 0}[T4] A_{17, 1}[T4] ... A_{17, 6}[T7] A_{17, 7}[T7] A_{17, 8}[T4] A_{17, 9}[T4] ... A_{17, 14}[T7] A_{17, 15}[T7]
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....
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A_{30, 0}[T56] A_{30, 1}[T56] ... A_{30, 6}[T59] A_{30, 7}[T59] A_{30, 8}[T56] A_{30, 9}[T56] ... A_{30, 14}[T59] A_{30, 15}[T59]
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A_{31, 0}[T60] A_{31, 1}[T60] ... A_{31, 6}[T63] A_{31, 7}[T63] A_{31, 8}[T60] A_{31, 9}[T60] ... A_{31, 14}[T63] A_{31, 15}[T63]
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A_{32, 0}[T0] A_{32, 1}[T0] ... A_{32, 6}[T3] A_{32, 7}[T3] A_{32, 8}[T0] A_{32, 9}[T0] ... A_{32, 14}[T3] A_{32, 15}[T3]
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A_{33, 0}[T0] A_{33, 1}[T0] ... A_{33, 6}[T3] A_{33, 7}[T3] A_{33, 8}[T0] A_{33, 9}[T0] ... A_{33, 14}[T3] A_{33, 15}[T3]
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A_{62, 0}[T60] A_{62, 1}[T60] ... A_{62, 6}[T63] A_{62, 7}[T63] A_{62, 8}[T60] A_{62, 9}[T60] ... A_{62, 14}[T63] A_{62, 15}[T63]
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A_{63, 0}[T60] A_{63, 1}[T60] ... A_{63, 6}[T63] A_{63, 7}[T63] A_{63, 8}[T60] A_{63, 9}[T60] ... A_{63, 14}[T63] A_{63, 15}[T63]
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And the associated TritonGPU MLIR
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#SMEM = #triton_gpu.encoding<{
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threadTileSize = {2, 2}
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