mirror of https://github.com/llvm/torch-mlir
63 lines
3.7 KiB
MLIR
63 lines
3.7 KiB
MLIR
// RUN: npcomp-opt <%s -convert-torch-to-linalg | FileCheck %s
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// CHECK-LABEL: func @torch.aten.mm$basic(
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// CHECK-SAME: %[[LHS:.*]]: tensor<?x?xf32>,
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// CHECK-SAME: %[[RHS:.*]]: tensor<?x?xf32>) -> tensor<?x2xf32> {
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// CHECK: %[[C0:.*]] = constant 0 : index
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// CHECK: %[[C1:.*]] = constant 1 : index
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// CHECK: %[[CF0:.*]] = constant 0.000000e+00 : f32
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// CHECK: %[[LHS_DIM_0:.*]] = memref.dim %[[LHS]], %[[C0]] : tensor<?x?xf32>
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// CHECK: %[[LHS_DIM_1:.*]] = memref.dim %[[LHS]], %[[C1]] : tensor<?x?xf32>
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// CHECK: %[[RHS_DIM_0:.*]] = memref.dim %[[RHS]], %[[C0]] : tensor<?x?xf32>
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// CHECK: %[[RHS_DIM_1:.*]] = memref.dim %[[RHS]], %[[C1]] : tensor<?x?xf32>
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// CHECK: %[[EQ:.*]] = cmpi eq, %[[LHS_DIM_1]], %[[RHS_DIM_0]] : index
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// CHECK: assert %[[EQ]], "mismatching contracting dimension for torch.aten.mm"
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// CHECK: %[[INIT_TENSOR:.*]] = linalg.init_tensor [%[[LHS_DIM_0]], %[[RHS_DIM_1]]] : tensor<?x?xf32>
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// CHECK: %[[ZEROFILL:.*]] = linalg.fill(%[[INIT_TENSOR]], %[[CF0]]) : tensor<?x?xf32>, f32 -> tensor<?x?xf32>
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// CHECK: %[[MATMUL:.*]] = linalg.matmul ins(%[[LHS]], %[[RHS]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ZEROFILL]] : tensor<?x?xf32>) -> tensor<?x?xf32>
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// CHECK: %[[CASTED:.*]] = tensor.cast %[[MATMUL]] : tensor<?x?xf32> to tensor<?x2xf32>
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// CHECK: return %[[CASTED]] : tensor<?x2xf32>
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func @torch.aten.mm$basic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x2xf32> {
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%0 = torch.aten.mm %arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32> -> tensor<?x2xf32>
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return %0 : tensor<?x2xf32>
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}
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// If the operands are missing dtype, we cannot lower it.
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// CHECK-LABEL: func @torch.aten.mm$no_convert$missing_dtype
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func @torch.aten.mm$no_convert$missing_dtype(%arg0: tensor<*x!numpy.any_dtype>, %arg1: tensor<*x!numpy.any_dtype>) -> tensor<*x!numpy.any_dtype> {
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// CHECK-NEXT: torch.aten.mm
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%0 = torch.aten.mm %arg0, %arg1 : tensor<*x!numpy.any_dtype>, tensor<*x!numpy.any_dtype> -> tensor<*x!numpy.any_dtype>
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return %0 : tensor<*x!numpy.any_dtype>
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}
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// Correctly handle the case that operands are statically the wrong rank
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// (rank 1 vs rank 2 expected for matmul.)
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// CHECK-LABEL: func @torch.aten.mm$no_convert$wrong_rank
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func @torch.aten.mm$no_convert$wrong_rank(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?x?xf32> {
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// CHECK-NEXT: torch.aten.mm
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%0 = torch.aten.mm %arg0, %arg1 : tensor<?xf32>, tensor<?xf32> -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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// If the result is missing dtype, we cannot lower it.
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// CHECK-LABEL: func @torch.aten.mm$no_convert$result_missing_dtype
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func @torch.aten.mm$no_convert$result_missing_dtype(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<*x!numpy.any_dtype> {
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// CHECK-NEXT: torch.aten.mm
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%0 = torch.aten.mm %arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32> -> tensor<*x!numpy.any_dtype>
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return %0 : tensor<*x!numpy.any_dtype>
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}
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// Unary op example.
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// CHECK-LABEL: func @torch.aten.tanh(
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// CHECK-SAME: %[[ARG:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
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// CHECK: %[[RESULT:.*]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%[[ARG]] : tensor<?x?xf32>) outs(%[[ARG]] : tensor<?x?xf32>) {
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// CHECK: ^bb0(%[[BBARG:.*]]: f32, %{{.*}}: f32):
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// CHECK: %[[YIELDED:.*]] = math.tanh %[[BBARG]] : f32
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// CHECK: linalg.yield %[[YIELDED]] : f32
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// CHECK: } -> tensor<?x?xf32>
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// CHECK: return %[[RESULT:.*]] : tensor<?x?xf32>
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func @torch.aten.tanh(%arg0: tensor<?x?xf32>) -> tensor<?x?xf32> {
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%0 = torch.aten.tanh %arg0 : tensor<?x?xf32> -> tensor<?x?xf32>
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return %0 : tensor<?x?xf32>
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}
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