Add AMAX, AVG, NORM1, NORM2, MUL, MUL_NO_ZEROS reduction modes#325
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rsuderman wants to merge 3 commits intoiree-org:mainfrom
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Add AMAX, AVG, NORM1, NORM2, MUL, MUL_NO_ZEROS reduction modes#325rsuderman wants to merge 3 commits intoiree-org:mainfrom
rsuderman wants to merge 3 commits intoiree-org:mainfrom
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Enable the remaining cuDNN reduction modes in ReductionAttr and add the corresponding MLIR schemas to the asm emitter: - NORM1 lowers to abs + sum.dim_IntList. - AMAX lowers to abs + amax. - AVG lowers to mean.dim (float dtypes only — torch.aten.mean.dim is not defined on integer tensors, so the sample skips int32 for AVG). - NORM2 lowers to mul + sum.dim_IntList + sqrt. - MUL lowers directly to torch.prims.prod. - MUL_NO_ZEROS uses aten.ne.Scalar to build an i1 mask, then aten.where.ScalarOther to substitute 1 for zero entries before feeding the result to torch.prims.prod, so zero inputs are excluded from the product. Extend samples/reduction/reduction_ops.cpp to exercise every new mode. Input data is built by a per-mode generateReductionInputData helper so MUL/MUL_NO_ZEROS get a non-trivial pattern (mostly 1s with a 2 and a 3, plus injected zeros for MUL_NO_ZEROS) that stays in range for fp16/int32, and the expected value is computed by the existing reference reduction loop rather than hardcoded. Add lit tests for each new mode under tests/lit/ and register them in tests/CMakeLists.txt. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Signed-off-by: Rob Suderman <rob.suderman@gmail.com>
Signed-off-by: Rob Suderman <rob.suderman@gmail.com> # Conflicts: # include/fusilli/support/asm_emitter.h # samples/reduction/reduction_ops.cpp
Signed-off-by: Rob Suderman <rob.suderman@gmail.com>
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Might need rebase / CI seems unclean.
| #define FUSILLI_REDUCTION_MODES(OP) \ | ||
| OP(NOT_SET) \ | ||
| OP(SUM) \ | ||
| /* OP(ADD) */ \ |
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Is ADD dropped because it's equivalent to SUM or some other reason?
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| permuteX, // {0} | ||
| dimListOss.str(), // {1} | ||
| suffix, // {2} | ||
| getResultNamesAsm(), // {3} | ||
| getOperandNamesAsm(), // {4} | ||
| getOperandTypesAsm(), // {5} | ||
| getResultTypesAsm(), // {6} | ||
| permuteY, // {7} | ||
| boolType // {8} |
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hyper nit: Use /* {0} */ style comments in format string placeholders for consistency with the rest.
| {0} | ||
| {1} | ||
| %dtype_{2} = torch.constant.none | ||
| {3}_{2}_perm = torch.prims.prod {4}, %reduction_dims_{2}, %dtype_{2} : {5}, !torch.list<int>, !torch.none -> {6} |
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Claude tells me this is not registered in the torch dialect. Is that true?
Do we need to use torch.aten.prod.dim_int (with chaining for multiple reduction dims)?
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Enable the remaining cuDNN reduction modes in ReductionAttr and add the corresponding MLIR schemas to the asm emitter:
Extend samples/reduction/reduction_ops.cpp to exercise every new mode. Input data is built by a per-mode generateReductionInputData helper so MUL/MUL_NO_ZEROS get a non-trivial pattern (mostly 1s with a 2 and a 3, plus injected zeros for MUL_NO_ZEROS) that stays in range for fp16/int32, and the expected value is computed by the existing reference reduction loop rather than hardcoded.
Add lit tests for each new mode under tests/lit/ and register them in tests/CMakeLists.txt.