[S-TIR][Dlight] Add layered fall back strategy to handle missing attr max_shared_memory_per_block#19453
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This pull request introduces a robust fallback mechanism for determining the maximum shared memory per block across various GPU targets. It adds the get_max_shared_memory_per_block utility function in common_analysis.py, which provides default values for CUDA, ROCm, Metal, OpenCL, and Vulkan when the target attribute is not explicitly defined. The gemv and low_batch_gemv schedules were updated to utilize this new function. Feedback suggests moving the hardcoded mapping dictionary to a module-level constant to optimize performance and improve code structure.
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mshr-h
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Apr 28, 2026
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Apr 29, 2026
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Thanks to @mshr-h 😄 |
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Hi Committers,
This PR is trying to fix issues #19419. Any suggestions would be appreciated if you are available.
Root Cause
max_shared_memory_per_blockand it would causeKeyErrorSolutions
max_shared_memory_per_block