mirror of https://github.com/llvm/torch-mlir
Remove last mentions of IREE.
parent
9fc059e948
commit
5917f1dc47
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@ -93,7 +93,7 @@ def ConvertTorchToLinalg : Pass<"convert-torch-to-linalg", "FuncOp"> {
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4. All this code operates on ranked tensors, for which using individual
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SSA values for sizes (rather than a "shape type") seems to
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work really well at this level of abstraction based on prior experience
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in IREE. (unranked code tends to benefit from having a discrete
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in other projects. (unranked code tends to benefit from having a discrete
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"shape type" to model shapes).
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We will see if we end up needing something like `shape.assuming`, but for
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@ -88,7 +88,7 @@ LOWERING_PIPELINE = ",".join([
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class RefBackendLinalgOnTensorsBackend(LinalgOnTensorsBackend):
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"""Main entry-point for the backend."""
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"""Main entry-point for the reference backend."""
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def __init__(self):
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super().__init__()
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@ -102,10 +102,8 @@ class RefBackendLinalgOnTensorsBackend(LinalgOnTensorsBackend):
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imported_module: The MLIR module consisting of funcs in the torch
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dialect.
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Returns:
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An opaque, backend specific module object that can be passed to load.
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The object may actually be something more specific to the backend (i.e.
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for IREE, it is a serialized VM flatbuffer) but the contract is that
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it is operated on by methods on this class.
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An opaque, backend specific compiled artifact object that can be
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passed to `load`.
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"""
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with imported_module.context:
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pm = PassManager.parse(LOWERING_PIPELINE)
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