// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s // CHECK-LABEL: func.func @test_resize_sizes_linear func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4] ,si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} { // CHECK: %[[generic:.*]] = linalg.generic // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32> // CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]] // CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]] // CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]] // CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]] // CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]] // CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]] // CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]] // CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]] // CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]] %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "bilinear" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %int3 = torch.constant.int 3 %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int %4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32> return %5 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_sizes_nearest func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} { // CHECK: %[[GENERIC:.*]] = linalg.generic // CHECK: %[[x11:.*]] = linalg.index 0 : index // CHECK: %[[x12:.*]] = linalg.index 1 : index // CHECK: %[[x13:.*]] = linalg.index 2 : index // CHECK: %[[x14:.*]] = linalg.index 3 : index // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32 // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32 // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32 // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64 // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32 // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32 // CHECK: %[[x26:.*]] = math.floor %[[x25]] : f32 // CHECK: %[[x31:.*]] = arith.fptosi %[[x26]] : f32 to i64 // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index // CHECK: %[[x16:.*]] = arith.sitofp %[[c4_i64:.*]] : i64 to f32 // CHECK: %[[x20:.*]] = arith.sitofp %[[x7:.*]] : i64 to f32 // CHECK: %[[x22:.*]] = arith.divf %[[x20]], %[[x16]] : f32 // CHECK: %[[x26:.*]] = arith.index_cast %[[x14]] : index to i64 // CHECK: %[[x27:.*]] = arith.sitofp %[[x26]] : i64 to f32 // CHECK: %[[x28:.*]] = arith.divf %[[x27]], %[[x22]] : f32 // CHECK: %[[x29:.*]] = math.floor %[[x28]] : f32 // CHECK: %[[x33:.*]] = arith.fptosi %[[x29]] : f32 to i64 // CHECK: %[[x34:.*]] = arith.index_cast %[[x33]] : i64 to index // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]]] : tensor<1x1x2x4xf32> // CHECK: linalg.yield %[[extracted]] : f32 %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "nearest" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %int3 = torch.constant.int 3 %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int %4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32> return %5 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_nearest_1d func.func @test_resize_nearest_1d(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> { // CHECK: %[[GENERIC:.*]] = linalg.generic // CHECK: %[[x11:.*]] = linalg.index 0 : index // CHECK: %[[x12:.*]] = linalg.index 1 : index // CHECK: %[[x13:.*]] = linalg.index 2 : index // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32 // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32 // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32 // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64 // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32 // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32 // CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32 // CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64 // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor // CHECK: linalg.yield %[[extracted]] : f32 %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "nearest,floor" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?],f32> return %5 : !torch.vtensor<[?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_nearest_3d func.func @test_resize_nearest_3d(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> { // CHECK: %[[GENERIC:.*]] = linalg.generic // CHECK: %[[x11:.*]] = linalg.index 0 : index // CHECK: %[[x12:.*]] = linalg.index 1 : index // CHECK: %[[x13:.*]] = linalg.index 2 : index // CHECK: %[[x14:.*]] = linalg.index 3 : index // CHECK: %[[index4:.*]] = linalg.index 4 : index // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32 // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32 // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32 // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64 // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32 // CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32 // CHECK: %[[floor:.*]] = math.floor %[[x25]] : f32 // CHECK: %[[x31:.*]] = arith.fptosi %[[floor]] : f32 to i64 // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index // CHECK: %[[x34:.*]] = arith.index_cast %[[Wfptosi:.*]] : i64 to index // CHECK: %[[x35:.*]] = arith.index_cast %[[Dfptosi:.*]] : i64 to index // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]], %[[x35]]] : tensor // CHECK: linalg.yield %[[extracted]] : f32 %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "nearest" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %int3 = torch.constant.int 3 %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int %int4 = torch.constant.int 4 %4 = torch.aten.select.int %arg1, %int0, %int4 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %5 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %6 = torch.prim.ListConstruct %1, %3, %5: (!torch.int, !torch.int, !torch.int) -> !torch.list %7 = torch.aten.__interpolate.size_list_scale_list %arg0, %6, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?,?,?],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?,?],f32> return %7 : !torch.vtensor<[?,?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_nearest_ceil func.func @test_resize_nearest_ceil(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> { // CHECK: %[[GENERIC:.*]] = linalg.generic // CHECK: %[[x11:.*]] = linalg.index 0 : index // CHECK: %[[x12:.*]] = linalg.index 1 : index // CHECK: %[[x13:.*]] = linalg.index 2 : index // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32 // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32 // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32 // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64 // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32 // CHECK: %[[cst:.*]] = arith.constant 5.000000e-01 : f32 // CHECK: %[[add:.*]] = arith.addf %[[x24]], %[[cst]] : f32 // CHECK: %[[x25:.*]] = arith.divf %[[add]], %[[x21]] : f32 // CHECK: %[[sub:.*]] = arith.subf %[[x25]], %[[cst]] : f32 // CHECK: %[[cst3:.*]] = arith.constant 1.000000e+00 : f32 // CHECK: %[[nM1:.*]] = arith.subf %[[inputsizefp:.*]], %[[cst3]] // CHECK: %[[ceil:.*]] = math.ceil %[[sub]] : f32 // CHECK: %[[minindex:.*]] = arith.minimumf %[[ceil]], %[[nM1]] // CHECK: %[[x31:.*]] = arith.fptosi %[[minindex]] : f32 to i64 // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor // CHECK: linalg.yield %[[extracted]] : f32 %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "nearest_half_pixel,ceil" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?],f32> return %5 : !torch.vtensor<[?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_scales_linear_half_pixel_symmetric func.func @test_resize_scales_linear_half_pixel_symmetric(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4] ,f64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} { // CHECK: %[[generic:.*]] = linalg.generic // CHECK: %[[cst7:.*]] = arith.constant 2.0 // CHECK: %[[halfsize:.*]] = arith.divf %[[sizefp:.*]], %[[cst7]] // CHECK: %[[modifier:.*]] = arith.subf %[[cstOne:.*]], %[[adjustment:.*]] // CHECK: %[[offset:.*]] = arith.mulf %[[halfsize]], %[[modifier]] // CHECK: %[[preClip:.*]] = arith.addf %[[offset]], %[[halfpixelbase:.*]] // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32> // CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]] // CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]] // CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]] // CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]] // CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]] // CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]] // CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]] // CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]] // CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]] // CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]] %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "bilinear_half_pixel_symmetric" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],f64>, !torch.int, !torch.int -> !torch.vtensor<[1],f64> %1 = torch.aten.item %0 : !torch.vtensor<[1],f64> -> !torch.float %int3 = torch.constant.int 3 %2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],f64>, !torch.int, !torch.int -> !torch.vtensor<[1],f64> %3 = torch.aten.item %2 : !torch.vtensor<[1],f64> -> !torch.float %4 = torch.prim.ListConstruct %1, %3 : (!torch.float, !torch.float) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %none_0, %4, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.list, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32> return %5 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @test_resize_nearest_half_pixel_round_prefer_floor func.func @test_resize_nearest_half_pixel_round_prefer_floor(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> { // CHECK: %[[GENERIC:.*]] = linalg.generic // CHECK: %[[x11:.*]] = linalg.index 0 : index // CHECK: %[[x12:.*]] = linalg.index 1 : index // CHECK: %[[x13:.*]] = linalg.index 2 : index // CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32 // CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32 // CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32 // CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64 // CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32 // CHECK: %[[cst:.*]] = arith.constant 5.000000e-01 : f32 // CHECK: %[[add:.*]] = arith.addf %[[x24]], %[[cst]] : f32 // CHECK: %[[x25:.*]] = arith.divf %[[add]], %[[x21]] : f32 // CHECK: %[[sub:.*]] = arith.subf %[[x25]], %[[cst]] : f32 // CHECK: %[[cst3:.*]] = arith.constant 5.000000e-01 : f32 // CHECK: %[[floor:.*]] = math.floor %[[sub]] : f32 // CHECK: %[[ceil:.*]] = math.ceil %[[sub]] : f32 // CHECK: %[[sub2:.*]] = arith.subf %[[sub]], %[[floor]] : f32 // CHECK: %[[cmpf:.*]] = arith.cmpf ule, %[[sub2]], %[[cst3]] : f32 // CHECK: %[[select:.*]] = arith.select %[[cmpf]], %[[floor]], %[[ceil]] : f32 // CHECK: %[[x31:.*]] = arith.fptosi %[[select]] : f32 to i64 // CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index // CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor // CHECK: linalg.yield %[[extracted]] : f32 %none = torch.constant.none %none_0 = torch.constant.none %int0 = torch.constant.int 0 %false = torch.constant.bool false %true = torch.constant.bool true %str = torch.constant.str "nearest_half_pixel,round_prefer_floor" %int2 = torch.constant.int 2 %0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64> %1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int %4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list %5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?],f32>, !torch.list, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?],f32> return %5 : !torch.vtensor<[?,?,?],f32> } // -----