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torch-mlir/test/Conversion/TCFToLinalg/basic.mlir

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[TCP] Replace tcp.matmul with linalg.matmul. This involved adding a `tcp.splatted` op to splat a dynamically sized init tensor. See rationale in TCPOps.td docs. One interesting observation is that when lowering tcf.matmul to linalg.matmul, we need to both 1) create the error checks and 2) calculate a shape transfer function to create the init tensors. Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not sure if this is a conflation of concerns or not. For now, it's not a big burden.
2020-11-10 07:49:22 +08:00
// RUN: npcomp-opt <%s -convert-tcf-to-linalg | FileCheck %s --dump-input=fail
// CHECK-LABEL: func @tcf_matmul(
// CHECK-SAME: %[[LHS:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[RHS:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[C0F32:.*]] = constant 0.000000e+00 : f32
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[C1:.*]] = constant 1 : index
Bump llvm-project to 0524a09cc7e1a0797982feacf505825231efbee7 - renames of OwningRewritePatternList -> RewritePatternSet - also `insert` to `add` - RewritePatternSet holds a context now - memref dialect split from std
2021-03-24 05:16:23 +08:00
// CHECK: %[[LHSK:.*]] = memref.dim %[[LHS]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[RHSK:.*]] = memref.dim %[[RHS]], %[[C0]] : tensor<?x?xf32>
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
// CHECK: %[[KEQUAL:.*]] = cmpi eq, %[[LHSK]], %[[RHSK]] : index
[TCP] Replace tcp.matmul with linalg.matmul. This involved adding a `tcp.splatted` op to splat a dynamically sized init tensor. See rationale in TCPOps.td docs. One interesting observation is that when lowering tcf.matmul to linalg.matmul, we need to both 1) create the error checks and 2) calculate a shape transfer function to create the init tensors. Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not sure if this is a conflation of concerns or not. For now, it's not a big burden.
2020-11-10 07:49:22 +08:00
// CHECK: %[[WINESS:.*]] = shape.cstr_require %[[KEQUAL]], "mismatching contracting dimension for matmul"
// CHECK: %[[RET:.*]] = shape.assuming %[[WINESS]] -> (tensor<?x?xf32>) {
Bump llvm-project to 0524a09cc7e1a0797982feacf505825231efbee7 - renames of OwningRewritePatternList -> RewritePatternSet - also `insert` to `add` - RewritePatternSet holds a context now - memref dialect split from std
2021-03-24 05:16:23 +08:00
// CHECK: %[[LHSROWS:.*]] = memref.dim %[[LHS]], %[[C0]] : tensor<?x?xf32>
// CHECK: %[[RHSCOLS:.*]] = memref.dim %[[RHS]], %[[C1]] : tensor<?x?xf32>
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
// CHECK: %[[SHAPE:.*]] = tensor.from_elements %[[LHSROWS]], %[[RHSCOLS]] : tensor<2xindex>
[TCP] Replace tcp.matmul with linalg.matmul. This involved adding a `tcp.splatted` op to splat a dynamically sized init tensor. See rationale in TCPOps.td docs. One interesting observation is that when lowering tcf.matmul to linalg.matmul, we need to both 1) create the error checks and 2) calculate a shape transfer function to create the init tensors. Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not sure if this is a conflation of concerns or not. For now, it's not a big burden.
2020-11-10 07:49:22 +08:00
// CHECK: %[[INIT_TENSOR:.*]] = tcp.splatted %[[C0F32]], %[[SHAPE]] : (f32, tensor<2xindex>) -> tensor<?x?xf32>
Bump llvm-project to 16c6e9c58e9ae50a775945e6b407f1891f353d2f Changes: - linalg init tensor change (outs+init -> just outs) - IntegerType::get and other builtin types now take the context as the first arg - LLVMType::* is gone. Now LLVM Types are just regular Type's.
2021-01-06 08:12:11 +08:00
// CHECK: %[[MATMUL:.*]] = linalg.matmul ins(%[[LHS]], %[[RHS]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[INIT_TENSOR]] : tensor<?x?xf32>) -> tensor<?x?xf32>
[TCP] Replace tcp.matmul with linalg.matmul. This involved adding a `tcp.splatted` op to splat a dynamically sized init tensor. See rationale in TCPOps.td docs. One interesting observation is that when lowering tcf.matmul to linalg.matmul, we need to both 1) create the error checks and 2) calculate a shape transfer function to create the init tensors. Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not sure if this is a conflation of concerns or not. For now, it's not a big burden.
2020-11-10 07:49:22 +08:00
// CHECK: shape.assuming_yield %[[MATMUL]] : tensor<?x?xf32>
// CHECK: }
// CHECK: return %[[RET:.*]] : tensor<?x?xf32>
func @tcf_matmul(%arg0: tensor<?x?xf32>, %arg1: 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 @tcf_conv_2d_nchw(
// CHECK-SAME: %[[IN:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>
// CHECK-SAME: %[[FILTER:[a-zA-Z0-9]+]]: tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32> {
// CHECK: %[[C0F32:.*]] = constant 0.000000e+00 : f32
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[C2:.*]] = constant 2 : index
// CHECK: %[[C3:.*]] = constant 3 : index
Bump llvm-project to 0524a09cc7e1a0797982feacf505825231efbee7 - renames of OwningRewritePatternList -> RewritePatternSet - also `insert` to `add` - RewritePatternSet holds a context now - memref dialect split from std
2021-03-24 05:16:23 +08:00
// CHECK: %[[CHANNELS:.*]] = memref.dim %[[IN]], %[[C1]] : tensor<?x?x?x?xf32>
// CHECK: %[[HEIGHT:.*]] = memref.dim %[[IN]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK: %[[WIDTH:.*]] = memref.dim %[[IN]], %[[C3]] : tensor<?x?x?x?xf32>
// CHECK: %[[FILTERCHANNELS:.*]] = memref.dim %[[FILTER]], %[[C1]] : tensor<?x?x?x?xf32>
// CHECK: %[[FILTERHEIGHT:.*]] = memref.dim %[[FILTER]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK: %[[FILTERWIDTH:.*]] = memref.dim %[[FILTER]], %[[C3]] : tensor<?x?x?x?xf32>
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
// CHECK: %[[CMPCHANNELS:.*]] = cmpi eq, %[[CHANNELS]], %[[FILTERCHANNELS]] : index
// CHECK: %[[CMPHEIGHT:.*]] = cmpi uge, %[[HEIGHT]], %[[FILTERHEIGHT]] : index
// CHECK: %[[CMPWIDTH:.*]] = cmpi uge, %[[WIDTH]], %[[FILTERWIDTH]] : index
Add TCF convolutional op with bias addition (#137)
2020-12-16 04:53:12 +08:00
// CHECK: %[[CSTRCHANNELS:.*]] = shape.cstr_require %[[CMPCHANNELS]], "input and filter in-channels must be equal"
// CHECK: %[[CSTRHEIGHT:.*]] = shape.cstr_require %[[CMPHEIGHT]], "input height must be greater than or equal to filter KH-dimension"
// CHECK: %[[CSTRWIDTH:.*]] = shape.cstr_require %[[CMPWIDTH]], "input width must be greater than or equal to filter KW-dimension"
// CHECK: %[[WITNESS:.*]] = shape.assuming_all %[[CSTRCHANNELS]], %[[CSTRHEIGHT]], %[[CSTRWIDTH]]
// CHECK: %[[RET:.*]] = shape.assuming %[[WITNESS]] -> (tensor<?x?x?x?xf32>) {
Bump llvm-project to 0524a09cc7e1a0797982feacf505825231efbee7 - renames of OwningRewritePatternList -> RewritePatternSet - also `insert` to `add` - RewritePatternSet holds a context now - memref dialect split from std
2021-03-24 05:16:23 +08:00
// CHECK: %[[BATCH:.*]] = memref.dim %[[IN]], %[[C0]] : tensor<?x?x?x?xf32>
// CHECK: %[[HEIGHT:.*]] = memref.dim %[[IN]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK: %[[WIDTH:.*]] = memref.dim %[[IN]], %[[C3]] : tensor<?x?x?x?xf32>
// CHECK: %[[OUTCHANNELS:.*]] = memref.dim %[[FILTER]], %[[C0]] : tensor<?x?x?x?xf32>
// CHECK: %[[FILTERHEIGHT:.*]] = memref.dim %[[FILTER]], %[[C2]] : tensor<?x?x?x?xf32>
// CHECK: %[[FILTERWIDTH:.*]] = memref.dim %[[FILTER]], %[[C3]] : tensor<?x?x?x?xf32>
Add TCF convolutional op with bias addition (#137)
2020-12-16 04:53:12 +08:00
// CHECK: %[[FILTERHEIGHTM1:.*]] = subi %[[FILTERHEIGHT]], %[[C1]] : index
// CHECK: %[[HEIGHTV0:.*]] = subi %[[HEIGHT]], %[[FILTERHEIGHTM1]] : index
// CHECK: %[[HEIGHTV0M1:.*]] = subi %[[HEIGHTV0]], %[[C1]] : index
// CHECK: %[[OUTHEIGHT:.*]] = addi %[[HEIGHTV0M1]], %[[C1]] : index
// CHECK: %[[FILTERWIDTHM1:.*]] = subi %[[FILTERWIDTH]], %[[C1]] : index
// CHECK: %[[WIDTHV0:.*]] = subi %[[WIDTH]], %[[FILTERWIDTHM1]] : index
// CHECK: %[[WIDTHV0M1:.*]] = subi %[[WIDTHV0]], %[[C1]] : index
// CHECK: %[[OUTWIDTH:.*]] = addi %[[WIDTHV0M1]], %[[C1]] : index
Bump llvm-project to be7352c00d51f4358db3a23ed6a077f7cb48eafd - TensorFromElementsOp -> tensor::FromElementsOp - `cmpi "eq", ...` -> `cmpi eq, ...`. Same for `cmpf` - syntax change for private func ops - some changes to the python bindings
2021-01-21 10:08:28 +08:00
// CHECK: %[[SHAPE:.*]] = tensor.from_elements %[[BATCH]], %[[OUTCHANNELS]], %[[OUTHEIGHT]], %[[OUTWIDTH]] : tensor<4xindex>
Add TCF convolutional op with bias addition (#137)
2020-12-16 04:53:12 +08:00
// CHECK: %[[INIT_TENSOR:.*]] = tcp.splatted %[[C0F32]], %[[SHAPE]] : (f32, tensor<4xindex>) -> tensor<?x?x?x?xf32>
Bump llvm-project to 16c6e9c58e9ae50a775945e6b407f1891f353d2f Changes: - linalg init tensor change (outs+init -> just outs) - IntegerType::get and other builtin types now take the context as the first arg - LLVMType::* is gone. Now LLVM Types are just regular Type's.
2021-01-06 08:12:11 +08:00
// CHECK: %[[CONVNCHW:.*]] = linalg.conv_2d_nchw ins(%[[IN]], %[[FILTER]] : tensor<?x?x?x?xf32>, tensor<?x?x?x?xf32>) outs(%[[INIT_TENSOR]] : tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
Add TCF convolutional op with bias addition (#137)
2020-12-16 04:53:12 +08:00
// CHECK: shape.assuming_yield %[[CONVNCHW]] : tensor<?x?x?x?xf32>
// CHECK: }
// CHECK: return %[[RET:.*]] : tensor<?x?x?x?xf32>
func @tcf_conv_2d_nchw(%arg0: tensor<?x?x?x?xf32>, %arg1: 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>
}