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torch-mlir/test/E2E/e2e-basic.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 -pass-pipeline=e2e-lowering-pipeline | FileCheck %s --dump-input=fail
Add ability to run without optimizations. The default is to only do the bare minimum needed for correctness, since that stresses the layering of the system maximally.
2020-06-02 10:30:13 +08:00
// RUN: npcomp-opt <%s -pass-pipeline=e2e-lowering-pipeline{optimize} | FileCheck %s --dump-input=fail
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
Tidy up test/E2E - Make rank1.mlir be the new "basic.mlir", as it is really the simplest case. - Move basic.mlir to mixed-ranks.mlir - Delete starting-from-linalg.mlir, it wasn't really useful anymore.
2020-05-15 05:57:09 +08:00
// This is the simplest case, which is easy to stare at for debugging
// purposes.
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
// CHECK-LABEL: func @rank1
func @rank1(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
%0 = "tcf.add"(%arg0, %arg1) : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
return %0 : tensor<?xf32>
}
Rework reference shape lowering based on upstream shape dialect changes. * Primarily, the upstream shape dialect now uses tensor<?xindex> for non-erroring, immediate shape calculations (and will return this for shape_of of a tensor or memref). * In addition, upstream passes do not yet exist for fully lowering to standard ops, so the passes here need to be extended to handle this new convention. * This should be seen as an intermediate state, necessary to integrate a new LLVM version and needs more work and cleanup for generality. * There is a good deal of awkwardness in these conversions. The hope is that additional upstream work will yield better defined conversion paths once out of this intermediate state.
2020-08-03 13:06:12 +08:00
// -----
// CHECK-LABEL: func @multiple_ops
func @multiple_ops(%arg0: tensor<f32>, %arg1: tensor<?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = "tcf.add"(%arg1, %arg2) : (tensor<?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
%1 = "tcf.add"(%arg0, %0) : (tensor<f32>, tensor<?x?xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}