mirror of https://github.com/llvm/torch-mlir
38 lines
1.7 KiB
MLIR
38 lines
1.7 KiB
MLIR
// RUN: npcomp-opt -lower-shaped-results-to-memref <%s -split-input-file | FileCheck %s --dump-input=fail
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#map0 = affine_map<(d0) -> (d0)>
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// CHECK-LABEL: func @linalg_generic
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func @linalg_generic(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2: tensor<?xindex>) -> tensor<?xf32> {
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// CHECK: %[[LHS:.*]] = tcp.tensor_to_memref %arg0 : tensor<?xf32> -> memref<?xf32>
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// CHECK: %[[RHS:.*]] = tcp.tensor_to_memref %arg1 : tensor<?xf32> -> memref<?xf32>
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// CHECK: %[[DST:.*]] = tcp.alloc_memref %arg2 : memref<?xf32>
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// CHECK: linalg.generic {{.*}} %[[LHS]], %[[RHS]], %[[DST]]
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// CHECK-NOT: tcp.shaped_results
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%0 = tcp.shaped_results %arg2 {
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%0 = linalg.generic {args_in = 2 : i64, args_out = 1 : i64, indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel"]} %arg0, %arg1 {
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^bb0(%arg3: f32, %arg4: f32):
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%8 = addf %arg3, %arg4 : f32
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linalg.yield %8 : f32
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} : tensor<?xf32>, tensor<?xf32> -> tensor<?xf32>
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tcp.yield %0 : tensor<?xf32>
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} : tensor<?xindex> -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// -----
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// CHECK-LABEL: func @tcp_broadcast_to
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func @tcp_broadcast_to(%arg0: tensor<?xf32>, %arg1: tensor<?xindex>) -> tensor<?x?xf32> {
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// Check for two nested loops, but don't look at more detail for now.
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// TODO: This pass should not create loops. Instead it should create a
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// buffer version of tcp.broadcast_to
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// CHECK: scf.for
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// CHECK: scf.for
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// CHECK-NOT: tcp.shaped_results
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%0 = tcp.shaped_results %arg1 {
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%0 = "tcp.broadcast_to"(%arg0, %arg1) : (tensor<?xf32>, tensor<?xindex>) -> 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 %0 : tensor<?x?xf32>
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}
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