[TOSA] Add upsample_nearest2d, split_dim, outer, GELU tanh mode and misc #3886
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- Add Torch to TOSA lowering for the following ops: + torch.aten.upsample_nearest2d + torch.aten.upsample_nearest2d.vec + torch.aten.outer + torch.prims.split_dim - Add Tanh approximation mode for GELU lowering - Add different types support for compare ops - Add different input and output types support for linalg vector norm lowering - Update xfail with new e2e results - Add new LIT tests to basic.mlir Signed-off-by: Justin Ngo <[email protected]> Change-Id: I7b1d44d94319cf94fcc9d234cc07708ef9ce321e
sahas3
reviewed
Nov 22, 2024
| // "tanh" approximate | ||
| // GELU(x) = 0.5 * x * (1 + Tanh(sqrt(2/pi) * (x + 0.044715 * x^3)) | ||
| auto selfShape = selfType.getShape(); | ||
| auto numElem = std::accumulate(selfShape.begin(), selfShape.end(), 1, |
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This assumes the shape is all static right? Can you add a failure check for dynamic shape?
Alternatively, can't we rely on broadcasting semantics of TosaOps to correctly expand the shape even for dynamic dims if these constants are defined with size 1?
| if ((isOutputSizeNone && isScaleFactorsNone) || | ||
| (!isOutputSizeNone && !isScaleFactorsNone)) | ||
| return rewriter.notifyMatchFailure( | ||
| op, "Must specified exactly one of output size and scale factors"); |
| self, rewriter.getDenseI64ArrayAttr(reshapedSelfShape)); | ||
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| // Calculate PyTorch-styled gather indices | ||
| SmallVector<int32_t> targetIndicesVec; |
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Should this be int64_t as well since all the other types are int64_t?
| static_cast<double>(outputWidth) / static_cast<double>(selfWidth); | ||
| } | ||
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| if (isOutputSizeNone) { |
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#nit: can be merged with the previous if block since isOutputSizeNone implies !isScaleFactorsNone
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| // ----- | ||
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| // CHECK-LABEL: func.func @torch.aten.outer$basic( |
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Can you also add a LIT test for tanh approximation for Gelu?
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Change-Id: I7b1d44d94319cf94fcc9d234cc07708ef9ce321e