mirror of https://github.com/llvm/torch-mlir
752abc8d01
The types have different levels of categories: where complex > floating > integral > boolean (> means left hand side has higher category). The operands have different levels of priorities where: dimensioned tensor > 0-dim tensor > scalar == wrapped 0-dim tensor. This is represented by the `ResultTypeState.dimResult`, `ResultTypeState.zeroResult` and `ResultTypeState..wrappedResult` in the source code. For operands of the same priorities, the result type should be the highest categories with sufficient width to hold all operands. By default, only the highest priority operands participate in the type promotion logic. Lower priority operands participate if they are in a higher category than any higher priority operands. For example, <[],f32> (lower priority) and <[1], si64> tensor would result in <[?],f32> tensor because floating > integeral. Another example <[],f64> (lower priority) and <[1], f32> tensor would result in <[?], f32> tensor because f32 and f64 are the same category. The ScalarType enum definition, type promotion table, ResultTypeState struct definition and some helpers are copied from aten/src/ATen/native/TypeProperties.* Other references: - https://pytorch.org/docs/stable/tensor_attributes.html#type-promotion-doc - https://github.com/pytorch/pytorch/issues/9515 Other minor changes: 1. Fix `visitExpandLikeOp` to consider cases where the given sizes list size is larger than the input rank. 2. Add back the somehow deleted `torch.aten.softmax.int` tests in decompose-complex-ops.mlir. |
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