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nuudlmannuudlman
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Fix test
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‎mlir/test/Dialect/Linalg/winograd-conv2d-rewrite.mlir‎

Lines changed: 98 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -145,3 +145,101 @@ func.func @conv2d_type_promotion(%arg0: tensor<2x6x6x5xf16>, %arg1: tensor<2x3x3
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%7 = linalg.winograd_output_transform fmr(F_4_3) ins(%expanded : tensor<6x6x1x1x2x2xf32>) outs(%arg3 : tensor<2x4x4x2xf32>) -> tensor<2x4x4x2xf32>
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return %7 : tensor<2x4x4x2xf32>
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}
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// CHECK: #[[$ATTR_0:.+]] = affine_map<(d0) -> (d0 * 4)>
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// CHECK: #[[$ATTR_1:.+]] = affine_map<(d0, d1) -> ()>
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// CHECK: #[[$ATTR_2:.+]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-LABEL: func.func @conv2d_type_promotion(
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// CHECK-SAME: %[[ARG0:.*]]: tensor<2x6x6x5xf16>,
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// CHECK-SAME: %[[ARG1:.*]]: tensor<2x3x3x5xf16>,
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// CHECK-SAME: %[[ARG2:.*]]: tensor<1xf32>,
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// CHECK-SAME: %[[ARG3:.*]]: tensor<2x4x4x2xf32>) -> tensor<2x4x4x2xf32> {
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// CHECK-DAG: %[[VAL_0:.*]] = arith.constant 1.024000e+03 : f32
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// CHECK-DAG: %[[VAL_1:.*]] = arith.constant dense<{{\[\[}}1.250000e-01, 0.000000e+00, 0.000000e+00, 0.000000e+00], [2.500000e-01, -2.500000e-01, 2.500000e-01, -2.500000e-01], [2.500000e-01, 2.500000e-01, 2.500000e-01, 2.500000e-01], [1.250000e-01, -2.500000e-01, 5.000000e-01, -1.000000e+00], [1.250000e-01, 2.500000e-01, 5.000000e-01, 1.000000e+00], [0.000000e+00, 0.000000e+00, 0.000000e+00, 5.000000e-01]]> : tensor<6x4xf32>
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<{{\[\[}}1.250000e-01, 2.500000e-01, 2.500000e-01, 1.250000e-01, 1.250000e-01, 0.000000e+00], [0.000000e+00, -2.500000e-01, 2.500000e-01, -2.500000e-01, 2.500000e-01, 0.000000e+00], [0.000000e+00, 2.500000e-01, 2.500000e-01, 5.000000e-01, 5.000000e-01, 0.000000e+00], [0.000000e+00, -2.500000e-01, 2.500000e-01, -1.000000e+00, 1.000000e+00, 5.000000e-01]]> : tensor<4x6xf32>
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant dense<{{\[\[}}2.500000e-01, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00], [0.000000e+00, 2.500000e-01, -2.500000e-01, 2.500000e-01, -2.500000e-01, 2.500000e-01], [-3.125000e-01, -2.500000e-01, -2.500000e-01, -1.250000e-01, -1.250000e-01, 0.000000e+00], [0.000000e+00, -6.250000e-02, 6.250000e-02, -2.500000e-01, 2.500000e-01, -3.125000e-01], [6.250000e-02, 6.250000e-02, 6.250000e-02, 1.250000e-01, 1.250000e-01, 0.000000e+00], [0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00, 6.250000e-02]]> : tensor<6x6xf32>
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant dense<{{\[\[}}2.500000e-01, 0.000000e+00, -3.125000e-01, 0.000000e+00, 6.250000e-02, 0.000000e+00], [0.000000e+00, 2.500000e-01, -2.500000e-01, -6.250000e-02, 6.250000e-02, 0.000000e+00], [0.000000e+00, -2.500000e-01, -2.500000e-01, 6.250000e-02, 6.250000e-02, 0.000000e+00], [0.000000e+00, 2.500000e-01, -1.250000e-01, -2.500000e-01, 1.250000e-01, 0.000000e+00], [0.000000e+00, -2.500000e-01, -1.250000e-01, 2.500000e-01, 1.250000e-01, 0.000000e+00], [0.000000e+00, 2.500000e-01, 0.000000e+00, -3.125000e-01, 0.000000e+00, 6.250000e-02]]> : tensor<6x6xf32>
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant dense<{{\[\[}}1.000000e+00, -0.333333343, -0.333333343, 0.0833333358, 0.0833333358, 0.000000e+00], [0.000000e+00, 0.333333343, -0.333333343, -0.166666672, 0.166666672, 0.000000e+00], [0.000000e+00, -0.333333343, -0.333333343, 0.333333343, 0.333333343, 1.000000e+00]]> : tensor<3x6xf32>
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<{{\[\[}}1.000000e+00, 0.000000e+00, 0.000000e+00], [-0.333333343, 0.333333343, -0.333333343], [-0.333333343, -0.333333343, -0.333333343], [0.0833333358, -0.166666672, 0.333333343], [0.0833333358, 0.166666672, 0.333333343], [0.000000e+00, 0.000000e+00, 1.000000e+00]]> : tensor<6x3xf32>
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// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0.000000e+00 : f16
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// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 5 : index
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// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 2 : index
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// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK: %[[VAL_13:.*]] = tensor.empty() : tensor<6x6x5x2xf16>
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// CHECK-NEXT: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_8]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (tensor<6x6x5x2xf16>) {
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// CHECK-NEXT: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_8]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (tensor<6x6x5x2xf16>) {
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// CHECK-NEXT: %[[VAL_20:.*]] = tensor.extract_slice %[[ARG1]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_11]], %[[VAL_18]]] [1, 3, 3, 1] [1, 1, 1, 1] : tensor<2x3x3x5xf16> to tensor<3x3xf16>
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// CHECK-NEXT: %[[VAL_21:.*]] = tensor.empty() : tensor<6x3xf16>
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// CHECK-NEXT: %[[VAL_22:.*]] = linalg.fill ins(%[[VAL_7]] : f16) outs(%[[VAL_21]] : tensor<6x3xf16>) -> tensor<6x3xf16>
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// CHECK-NEXT: %[[VAL_23:.*]] = linalg.matmul ins(%[[VAL_6]], %[[VAL_20]] : tensor<6x3xf32>, tensor<3x3xf16>) outs(%[[VAL_22]] : tensor<6x3xf16>) -> tensor<6x3xf16>
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// CHECK-NEXT: %[[VAL_24:.*]] = tensor.empty() : tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_25:.*]] = linalg.fill ins(%[[VAL_7]] : f16) outs(%[[VAL_24]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_26:.*]] = linalg.matmul ins(%[[VAL_23]], %[[VAL_5]] : tensor<6x3xf16>, tensor<3x6xf32>) outs(%[[VAL_25]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_27:.*]] = tensor.insert_slice %[[VAL_26]] into %[[VAL_19]]{{\[}}%[[VAL_11]], %[[VAL_11]], %[[VAL_18]], %[[VAL_15]]] [6, 6, 1, 1] [1, 1, 1, 1] : tensor<6x6xf16> into tensor<6x6x5x2xf16>
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// CHECK-NEXT: scf.yield %[[VAL_27]] : tensor<6x6x5x2xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_17]] : tensor<6x6x5x2xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: %[[VAL_28:.*]] = tensor.empty() : tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: %[[VAL_29:.*]] = scf.for %[[VAL_30:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_8]] iter_args(%[[VAL_31:.*]] = %[[VAL_28]]) -> (tensor<6x6x1x1x2x5xf16>) {
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// CHECK-NEXT: %[[VAL_32:.*]] = scf.for %[[VAL_33:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_8]] iter_args(%[[VAL_34:.*]] = %[[VAL_31]]) -> (tensor<6x6x1x1x2x5xf16>) {
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// CHECK-NEXT: %[[VAL_35:.*]] = scf.for %[[VAL_36:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_8]] iter_args(%[[VAL_37:.*]] = %[[VAL_34]]) -> (tensor<6x6x1x1x2x5xf16>) {
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// CHECK-NEXT: %[[VAL_38:.*]] = scf.for %[[VAL_39:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_8]] iter_args(%[[VAL_40:.*]] = %[[VAL_37]]) -> (tensor<6x6x1x1x2x5xf16>) {
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// CHECK-NEXT: %[[VAL_41:.*]] = affine.apply #[[$ATTR_0]](%[[VAL_30]])
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// CHECK-NEXT: %[[VAL_42:.*]] = affine.apply #[[$ATTR_0]](%[[VAL_33]])
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// CHECK-NEXT: %[[VAL_43:.*]] = tensor.extract_slice %[[ARG0]]{{\[}}%[[VAL_36]], %[[VAL_41]], %[[VAL_42]], %[[VAL_39]]] [1, 6, 6, 1] [1, 1, 1, 1] : tensor<2x6x6x5xf16> to tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_44:.*]] = tensor.empty() : tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_45:.*]] = linalg.fill ins(%[[VAL_7]] : f16) outs(%[[VAL_44]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_46:.*]] = linalg.matmul ins(%[[VAL_4]], %[[VAL_43]] : tensor<6x6xf32>, tensor<6x6xf16>) outs(%[[VAL_45]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_47:.*]] = tensor.empty() : tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_48:.*]] = linalg.fill ins(%[[VAL_7]] : f16) outs(%[[VAL_47]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_49:.*]] = linalg.matmul ins(%[[VAL_46]], %[[VAL_3]] : tensor<6x6xf16>, tensor<6x6xf32>) outs(%[[VAL_48]] : tensor<6x6xf16>) -> tensor<6x6xf16>
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// CHECK-NEXT: %[[VAL_50:.*]] = tensor.insert_slice %[[VAL_49]] into %[[VAL_40]][0, 0, %[[VAL_30]], %[[VAL_33]], %[[VAL_36]], %[[VAL_39]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1] : tensor<6x6xf16> into tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: scf.yield %[[VAL_50]] : tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_38]] : tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_35]] : tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_32]] : tensor<6x6x1x1x2x5xf16>
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// CHECK-NEXT: }
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// CHECK-NEXT: %[[VAL_51:.*]] = tensor.collapse_shape %[[VAL_14]] {{\[\[}}0, 1], [2], [3]] : tensor<6x6x5x2xf16> into tensor<36x5x2xf16>
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// CHECK-NEXT: %[[VAL_52:.*]] = tensor.collapse_shape %[[VAL_29]] {{\[\[}}0, 1], [2, 3, 4], [5]] : tensor<6x6x1x1x2x5xf16> into tensor<36x2x5xf16>
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// CHECK-NEXT: %[[VAL_53:.*]] = tensor.empty() : tensor<36x2x2xf32>
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// CHECK-NEXT: %[[VAL_54:.*]] = linalg.fill ins(%[[VAL_12]] : f32) outs(%[[VAL_53]] : tensor<36x2x2xf32>) -> tensor<36x2x2xf32>
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// CHECK-NEXT: %[[VAL_55:.*]] = linalg.batch_matmul ins(%[[VAL_52]], %[[VAL_51]] : tensor<36x2x5xf16>, tensor<36x5x2xf16>) outs(%[[VAL_54]] : tensor<36x2x2xf32>) -> tensor<36x2x2xf32>
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// CHECK-NEXT: %[[VAL_56:.*]] = tensor.expand_shape %[[VAL_55]] {{\[\[}}0, 1], [2, 3, 4], [5]] output_shape [6, 6, 1, 1, 2, 2] : tensor<36x2x2xf32> into tensor<6x6x1x1x2x2xf32>
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// CHECK-NEXT: %[[VAL_57:.*]] = scf.for %[[VAL_58:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_8]] iter_args(%[[VAL_59:.*]] = %[[ARG3]]) -> (tensor<2x4x4x2xf32>) {
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// CHECK-NEXT: %[[VAL_60:.*]] = scf.for %[[VAL_61:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_8]] iter_args(%[[VAL_62:.*]] = %[[VAL_59]]) -> (tensor<2x4x4x2xf32>) {
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// CHECK-NEXT: %[[VAL_63:.*]] = scf.for %[[VAL_64:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_8]] iter_args(%[[VAL_65:.*]] = %[[VAL_62]]) -> (tensor<2x4x4x2xf32>) {
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// CHECK-NEXT: %[[VAL_66:.*]] = scf.for %[[VAL_67:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_8]] iter_args(%[[VAL_68:.*]] = %[[VAL_65]]) -> (tensor<2x4x4x2xf32>) {
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// CHECK-NEXT: %[[VAL_69:.*]] = tensor.extract_slice %[[VAL_56]][0, 0, %[[VAL_58]], %[[VAL_61]], %[[VAL_64]], %[[VAL_67]]] [6, 6, 1, 1, 1, 1] [1, 1, 1, 1, 1, 1] : tensor<6x6x1x1x2x2xf32> to tensor<6x6xf32>
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// CHECK-NEXT: %[[VAL_70:.*]] = affine.apply #[[$ATTR_0]](%[[VAL_58]])
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// CHECK-NEXT: %[[VAL_71:.*]] = affine.apply #[[$ATTR_0]](%[[VAL_61]])
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// CHECK-NEXT: %[[VAL_72:.*]] = tensor.extract_slice %[[VAL_68]]{{\[}}%[[VAL_64]], %[[VAL_70]], %[[VAL_71]], %[[VAL_67]]] [1, 4, 4, 1] [1, 1, 1, 1] : tensor<2x4x4x2xf32> to tensor<4x4xf32>
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// CHECK-NEXT: %[[VAL_73:.*]] = tensor.empty() : tensor<4x6xf32>
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// CHECK-NEXT: %[[VAL_74:.*]] = linalg.fill ins(%[[VAL_12]] : f32) outs(%[[VAL_73]] : tensor<4x6xf32>) -> tensor<4x6xf32>
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// CHECK-NEXT: %[[VAL_75:.*]] = linalg.matmul ins(%[[VAL_2]], %[[VAL_69]] : tensor<4x6xf32>, tensor<6x6xf32>) outs(%[[VAL_74]] : tensor<4x6xf32>) -> tensor<4x6xf32>
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// CHECK-NEXT: %[[VAL_76:.*]] = tensor.empty() : tensor<4x4xf32>
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// CHECK-NEXT: %[[VAL_77:.*]] = linalg.fill ins(%[[VAL_12]] : f32) outs(%[[VAL_76]] : tensor<4x4xf32>) -> tensor<4x4xf32>
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// CHECK-NEXT: %[[VAL_78:.*]] = linalg.matmul ins(%[[VAL_75]], %[[VAL_1]] : tensor<4x6xf32>, tensor<6x4xf32>) outs(%[[VAL_77]] : tensor<4x4xf32>) -> tensor<4x4xf32>
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// CHECK-NEXT: %[[VAL_79:.*]] = linalg.generic {indexing_maps = [#[[$ATTR_1]], #[[$ATTR_2]], #[[$ATTR_2]]], iterator_types = ["parallel", "parallel"]} ins(%[[VAL_0]], %[[VAL_78]] : f32, tensor<4x4xf32>) outs(%[[VAL_72]] : tensor<4x4xf32>) {
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// CHECK-NEXT: ^bb0(%[[VAL_80:.*]]: f32, %[[VAL_81:.*]]: f32, %[[VAL_82:.*]]: f32):
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// CHECK-NEXT: %[[VAL_83:.*]] = arith.mulf %[[VAL_80]], %[[VAL_81]] : f32
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// CHECK-NEXT: %[[VAL_84:.*]] = arith.addf %[[VAL_83]], %[[VAL_82]] : f32
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// CHECK-NEXT: linalg.yield %[[VAL_84]] : f32
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// CHECK-NEXT: } -> tensor<4x4xf32>
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// CHECK-NEXT: %[[VAL_85:.*]] = tensor.insert_slice %[[VAL_79]] into %[[VAL_68]]{{\[}}%[[VAL_64]], %[[VAL_70]], %[[VAL_71]], %[[VAL_67]]] [1, 4, 4, 1] [1, 1, 1, 1] : tensor<4x4xf32> into tensor<2x4x4x2xf32>
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// CHECK-NEXT: scf.yield %[[VAL_85]] : tensor<2x4x4x2xf32>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_66]] : tensor<2x4x4x2xf32>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_63]] : tensor<2x4x4x2xf32>
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// CHECK-NEXT: }
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// CHECK-NEXT: scf.yield %[[VAL_60]] : tensor<2x4x4x2xf32>
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// CHECK-NEXT: }
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// CHECK-NEXT: return %[[VAL_57]] : tensor<2x4x4x2xf32>
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// CHECK-NEXT: }

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