mirror of https://github.com/llvm/torch-mlir
33 lines
1.3 KiB
MLIR
33 lines
1.3 KiB
MLIR
// RUN: npcomp-opt <%s | npcomp-opt | FileCheck %s --dump-input=fail
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// CHECK-LABEL: tcp.global @foo dense<0.0{{.*}}> : tensor<10xf32>
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tcp.global @foo dense<0.0> : tensor<10xf32>
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func @f(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2: i32) {
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// CHECK: tcp.add
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%0 = tcp.add %arg0, %arg1 : (tensor<?xf32>, tensor<?xf32>) -> tensor<?xf32>
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%1 = tcp.get_global_memref @foo : memref<10xf32>
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return
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}
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func @matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
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// CHECK: tcp.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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%0 = tcp.matmul %arg0, %arg1 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// CHECK-LABEL: func @g
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// CHECK-NEXT: %[[RET:.*]] = tcp.shaped_results %arg1 {
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// CHECK-NEXT: %[[VAL:.*]] =
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// CHECK-NEXT: tcp.yield %[[VAL]] : tensor<?x?xf32>
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// CHECK-NEXT: } : tensor<?xindex> -> tensor<?x?xf32>
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// CHECK-NEXT: return %[[RET]] : tensor<?x?xf32>
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// CHECK-NEXT: }
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func @g(%arg0: tensor<?x?xf32>, %arg1: tensor<?xindex>) -> tensor<?x?xf32> {
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%add = tcp.shaped_results %arg1 {
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%0 = tcp.add %arg0, %arg0 : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
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tcp.yield %0 : tensor<?x?xf32>
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} : tensor<?xindex> -> tensor<?x?xf32>
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return %add : tensor<?x?xf32>
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}
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