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torch-mlir/test/Dialect/TCF/ops.mlir

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Initial TCF/TCP E2E seed. Very much WIP. This is enough to get tcf.add down to approximately the "linalg.generic on buffers" level of abstraction. (but there are nuances)
2020-05-07 09:41:54 +08:00
// RUN: npcomp-opt <%s | FileCheck %s --dump-input=fail
func @f(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) {
// CHECK: "tcf.add"
%0 = "tcf.add"(%arg0, %arg1) : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
return
}
[RefE2E] Add support for matmul. I'm pretty happy with how this turned out. It looks pretty much like it should -- one change at each layer. This particular op bottoms out on linalg which takes care of the rest. - Add tcf.matmul - Add tcp.matmul - Add TCF->TCP lowering - Add tcp.matmul shape transfer function (BypassShapes.cpp) - Add tcp.matmul -> linalg.matmul lowering (LowerShapedResultsToMemref.cpp) - Add support to LowerShapeConstraints for lowering the new shape.cstr_require This matmul op is pretty limited in its capabilities. There is no batching and no multidimensional contraction. Certainly more design work will be needed to find the right abstractions that aren't too general but also help to canonicalize many cases from frontends. This is mainly to show that adding a new op needn't be very "scary" once we have the e2e infra in place. Also, - this clears out some exploratory cruft from the TCF dialect now that this is starting to become real.
2020-09-18 09:56:01 +08:00
func @matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: tcf.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
%0 = tcf.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
return %0 : tensor<?x?xf32>
}