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

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[RefE2E] Add support for unary ops exp and tanh This is fairly mechanical.
2020-09-25 08:14:21 +08:00
// RUN: npcomp-opt <%s | npcomp-opt | 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
[RefE2E] Add support for "max". This cleans up the lowering pipeline to easily allow extending to multiple binary ops. It looks fairly repetitive at multiple levels, but I don't want to prematurely generalize. I think that in principle we could derive a large swatch of TCF + TCP from a single linalg-style specification. Another direction is to use an OpInterface (something like "buildLinalgGenericBody"). I'm keeping my eye on it. In a subsequent commit, I'll mechanically add a set of binary ops modeled off of the std arithmetic ops.
2020-09-22 10:14:27 +08:00
// CHECK-LABEL: func @binary_elementwise
func @binary_elementwise(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) {
[RefE2E] Add assemblyFormat for TCF and TCP ops and tidy up.
2020-09-19 05:05:36 +08:00
// CHECK: tcf.add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
[RefE2E] Add support for "max". This cleans up the lowering pipeline to easily allow extending to multiple binary ops. It looks fairly repetitive at multiple levels, but I don't want to prematurely generalize. I think that in principle we could derive a large swatch of TCF + TCP from a single linalg-style specification. Another direction is to use an OpInterface (something like "buildLinalgGenericBody"). I'm keeping my eye on it. In a subsequent commit, I'll mechanically add a set of binary ops modeled off of the std arithmetic ops.
2020-09-22 10:14:27 +08:00
// CHECK: tcf.max %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
[RefE2E] Add support for unary ops exp and tanh This is fairly mechanical.
2020-09-25 08:14:21 +08:00
// CHECK: tcf.exp %arg0 : tensor<?xf32>
[RefE2E] Add assemblyFormat for TCF and TCP ops and tidy up.
2020-09-19 05:05:36 +08:00
%0 = tcf.add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
[RefE2E] Add support for "max". This cleans up the lowering pipeline to easily allow extending to multiple binary ops. It looks fairly repetitive at multiple levels, but I don't want to prematurely generalize. I think that in principle we could derive a large swatch of TCF + TCP from a single linalg-style specification. Another direction is to use an OpInterface (something like "buildLinalgGenericBody"). I'm keeping my eye on it. In a subsequent commit, I'll mechanically add a set of binary ops modeled off of the std arithmetic ops.
2020-09-22 10:14:27 +08:00
%1 = tcf.max %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
[RefE2E] Add support for unary ops exp and tanh This is fairly mechanical.
2020-09-25 08:14:21 +08:00
%2 = tcf.exp %arg0 : tensor<?xf32>
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
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
[RefE2E] Add support for "max". This cleans up the lowering pipeline to easily allow extending to multiple binary ops. It looks fairly repetitive at multiple levels, but I don't want to prematurely generalize. I think that in principle we could derive a large swatch of TCF + TCP from a single linalg-style specification. Another direction is to use an OpInterface (something like "buildLinalgGenericBody"). I'm keeping my eye on it. In a subsequent commit, I'll mechanically add a set of binary ops modeled off of the std arithmetic ops.
2020-09-22 10:14:27 +08:00
// CHECK-LABEL: func @matmul
[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>
}
Add TCF convolutional op with bias addition (#137)
2020-12-16 04:53:12 +08:00
// CHECK-LABEL: func @conv_2d_nchw
func @conv_2d_nchw(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
// CHECK: tcf.conv_2d_nchw %arg0, %arg1 : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
%0 = tcf.conv_2d_nchw %arg0, %arg1 : (tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
return %0 : tensor<?x?x?x?xf32>
}