Extend support to varying block sizes on both dimensions for 2D matrices #2302
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…ces (pytorch#2302) Summary: Pull Request resolved: pytorch#2302 Extend support for reducing across individual dimensions on 2-dimensional matrices by allowing for varying block sizes on both the `M` (first) and `N` (second) dimensions. The existing kernel performed a simplified reduction, assuming that the entire reduction dimension fit within one thread block. The new kernel implementation removes the need for this assumption, allowing both the reduction and the non-reduction dimensions to fit in multiple thread blocks. This implementation also enables autotuning on block sizes for both the `M` and `N` dimensions. For 1D results, add a `sum_then_buffer` configuration which decides which kernel configuration to run. `Sum_then_buffer` sums individual blocks of input and adds these sums into a buffer. `Buffer_then_sum` adds blocks of raw input into a buffer, then reduces the buffer. Reviewed By: davidberard98 Differential Revision: D58313958
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…ces (pytorch#2302) Summary: Pull Request resolved: pytorch#2302 Extend support for reducing across individual dimensions on 2-dimensional matrices by allowing for varying block sizes on both the `M` (first) and `N` (second) dimensions. The existing kernel performed a simplified reduction, assuming that the entire reduction dimension fit within one thread block. The new kernel implementation removes the need for this assumption, allowing both the reduction and the non-reduction dimensions to fit in multiple thread blocks. This implementation also enables autotuning on block sizes for both the `M` and `N` dimensions. For 1D results, add a `sum_then_buffer` configuration which decides which kernel configuration to run. `Sum_then_buffer` sums individual blocks of input and adds these sums into a buffer. `Buffer_then_sum` adds blocks of raw input into a buffer, then reduces the buffer. Reviewed By: davidberard98 Differential Revision: D58313958
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Summary:
Extend support for reducing across individual dimensions on 2-dimensional matrices by allowing for varying block sizes on both the
M(first) andN(second) dimensions.The existing kernel performed a simplified reduction, assuming that the entire reduction dimension fit within one thread block. The new kernel implementation removes the need for this assumption, allowing both the reduction and the non-reduction dimensions to fit in multiple thread blocks. This implementation also enables autotuning on block sizes for both the
MandNdimensions.For 1D results, add a
sum_then_bufferconfiguration which decides which kernel configuration to run.Sum_then_buffersums individual blocks of input and adds these sums into a buffer.Buffer_then_sumadds blocks of raw input into a buffer, then reduces the buffer.Reviewed By: davidberard98
Differential Revision: D58313958