// RUN: torch-mlir-opt <%s -convert-torch-to-stablehlo -split-input-file -verify-diagnostics | FileCheck %s // ----- // CHECK-LABEL: func.func @torch.aten.max_pool2d( // CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { // CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor // CHECK: %int2 = torch.constant.int 2 // CHECK: %int1 = torch.constant.int 1 // CHECK: %int0 = torch.constant.int 0 // CHECK: %false = torch.constant.bool false // CHECK: %[[VAL_2:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_6:.*]] = stablehlo.constant dense<0xFF800000> : tensor // CHECK: %[[VAL_7:.*]] = "stablehlo.reduce_window"(%[[VAL_1]], %[[VAL_6]]) <{padding = dense<0> : tensor<4x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[VAL_8:.*]]: tensor, %[[VAL_9:.*]]: tensor): // CHECK: %[[VAL_10:.*]] = stablehlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor // CHECK: stablehlo.return %[[VAL_10]] : tensor // CHECK: }) : (tensor, tensor) -> tensor // CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor -> !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[VAL_11]] : !torch.vtensor<[?,?,?,?],f32> func.func @torch.aten.max_pool2d(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { %int2 = torch.constant.int 2 %int1 = torch.constant.int 1 %int0 = torch.constant.int 0 %false = torch.constant.bool false %0 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list %1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list %2 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list %3 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list %4 = torch.aten.max_pool2d %arg0, %0, %1, %2, %3, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.list, !torch.list, !torch.list, !torch.list, !torch.bool -> !torch.vtensor<[?,?,?,?],f32> return %4 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @torch.aten.max_pool2d$padding( // CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { // CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor // CHECK: %int2 = torch.constant.int 2 // CHECK: %int1 = torch.constant.int 1 // CHECK: %false = torch.constant.bool false // CHECK: %[[VAL_2:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_5:.*]] = stablehlo.constant dense<0xFF800000> : tensor // CHECK: %[[VAL_6:.*]] = "stablehlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) // CHECK{LITERAL}: <{padding = dense<[[0, 0], [0, 0], [2, 2], [1, 1]]> : tensor<4x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[VAL_8:.*]]: tensor, %[[VAL_9:.*]]: tensor): // CHECK: %[[VAL_10:.*]] = stablehlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor // CHECK: stablehlo.return %[[VAL_10]] : tensor // CHECK: }) : (tensor, tensor) -> tensor // CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor -> !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[VAL_7]] : !torch.vtensor<[?,?,?,?],f32> func.func @torch.aten.max_pool2d$padding(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { %int2 = torch.constant.int 2 %int1 = torch.constant.int 1 %false = torch.constant.bool false %0 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list %1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list %2 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list %3 = torch.aten.max_pool2d %arg0, %0, %1, %2, %2, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.list, !torch.list, !torch.list, !torch.list, !torch.bool -> !torch.vtensor<[?,?,?,?],f32> return %3 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @torch.aten.max_pool2d_with_indices( // CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?],f32>) -> (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64>) { // CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?],f32> -> tensor // CHECK: %[[INT3:.*]] = torch.constant.int 3 // CHECK: %[[INT2:.*]] = torch.constant.int 2 // CHECK: %[[FALSE:.*]] = torch.constant.bool false // CHECK: %[[INT0:.*]] = torch.constant.int 0 // CHECK: %[[INT1:.*]] = torch.constant.int 1 // CHECK: %[[T1:.*]] = torch.prim.ListConstruct %[[INT3]], %[[INT3]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T2:.*]] = torch.prim.ListConstruct %[[INT2]], %[[INT2]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T3:.*]] = torch.prim.ListConstruct %[[INT0]], %[[INT0]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T4:.*]] = torch.prim.ListConstruct %[[INT1]], %[[INT1]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T5:.*]] = stablehlo.constant dense<0xFF800000> : tensor // CHECK: %[[C0:.*]] = arith.constant 0 : index // CHECK: %[[DIM:.*]] = tensor.dim %[[T0]], %[[C0]] : tensor // CHECK: %[[C1:.*]] = arith.constant 1 : index // CHECK: %[[DIM_0:.*]] = tensor.dim %[[T0]], %[[C1]] : tensor // CHECK: %[[C2:.*]] = arith.constant 2 : index // CHECK: %[[DIM_1:.*]] = tensor.dim %[[T0]], %[[C2]] : tensor // CHECK: %[[FROM_ELEMENTS:.*]] = tensor.from_elements %[[DIM]], %[[DIM_0]], %[[DIM_1]] : tensor<3xindex> // CHECK: %[[T9:.*]] = arith.muli %[[DIM_1]], %[[DIM_0]] : index // CHECK: %[[FROM_ELEMENTS_2:.*]] = tensor.from_elements %[[DIM]], %[[T9]] : tensor<2xindex> // CHECK: %[[T10:.*]] = stablehlo.dynamic_iota %[[FROM_ELEMENTS_2]], dim = 1 : (tensor<2xindex>) -> tensor // CHECK: %[[T11:.*]] = stablehlo.dynamic_reshape %[[T10]], %[[FROM_ELEMENTS]] : (tensor, tensor<3xindex>) -> tensor // CHECK: %[[T12:.*]] = stablehlo.constant dense<0> : tensor // CHECK: %[[T13:.*]]:2 = "stablehlo.reduce_window"(%[[T0]], %[[T11]], %[[T5]], %[[T12]]) <{padding = dense<0> : tensor<3x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[ARG1:.*]]: tensor, %[[ARG2:.*]]: tensor, %[[ARG3:.*]]: tensor, %[[ARG4:.*]]: tensor): // CHECK: %[[T16:.*]] = stablehlo.compare GE, %[[ARG1]], %[[ARG3]], FLOAT : (tensor, tensor) -> tensor // CHECK: %[[T17:.*]] = stablehlo.select %[[T16]], %[[ARG1]], %[[ARG3]] : tensor, tensor // CHECK: %[[T18:.*]] = stablehlo.compare EQ, %[[ARG1]], %[[ARG3]], FLOAT : (tensor, tensor) -> tensor // CHECK: %[[T19:.*]] = stablehlo.minimum %[[ARG2]], %[[ARG4]] : tensor // CHECK: %[[T20:.*]] = stablehlo.select %[[T16]], %[[ARG2]], %[[ARG4]] : tensor, tensor // CHECK: %[[T21:.*]] = stablehlo.select %[[T18]], %[[T19]], %[[T20]] : tensor, tensor // CHECK: stablehlo.return %[[T17]], %[[T21]] : tensor, tensor // CHECK: }) : (tensor, tensor, tensor, tensor) -> (tensor, tensor) // CHECK-DAG: %[[T14:.*]] = torch_c.from_builtin_tensor %[[T13]]#0 : tensor -> !torch.vtensor<[?,?,?],f32> // CHECK-DAG: %[[T15:.*]] = torch_c.from_builtin_tensor %[[T13]]#1 : tensor -> !torch.vtensor<[?,?,?],si64> // CHECK: return %[[T14]], %[[T15]] : !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64> func.func @torch.aten.max_pool2d_with_indices(%arg0: !torch.vtensor<[?,?,?],f32>) -> (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64>) { %int3 = torch.constant.int 3 %int2 = torch.constant.int 2 %false = torch.constant.bool false %int0 = torch.constant.int 0 %int1 = torch.constant.int 1 %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list %1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list %2 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list %3 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list %result0, %result1 = torch.aten.max_pool2d_with_indices %arg0, %0, %1, %2, %3, %false : !torch.vtensor<[?,?,?],f32>, !torch.list, !torch.list, !torch.list, !torch.list, !torch.bool -> !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64> return %result0, %result1 : !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64> } // ----- // CHECK-LABEL: func.func @torch.aten.avg_pool2d( // CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { // CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor // CHECK: %int3 = torch.constant.int 3 // CHECK: %int2 = torch.constant.int 2 // CHECK: %int1 = torch.constant.int 1 // CHECK: %false = torch.constant.bool false // CHECK: %none = torch.constant.none // CHECK: %[[VAL_2:.*]] = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[VAL_5:.*]] = stablehlo.constant dense<0.000000e+00> : tensor // CHECK: %[[VAL_6:.*]] = "stablehlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) // CHECK{LITERAL}: <{padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[IVAL_0:.*]]: tensor, %[[IVAL_1:.*]]: tensor): // CHECK: %[[IVAL_2:.*]] = stablehlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor // CHECK: stablehlo.return %[[IVAL_2]] : tensor // CHECK: }) : (tensor, tensor) -> tensor // CHECK: %[[VAL_7:.*]] = stablehlo.constant dense<1.000000e+00> : tensor // CHECK: %[[IDX_0:.*]] = arith.constant 0 : index // CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[IDX_0]] : tensor // CHECK: %[[IDX_1:.*]] = arith.constant 1 : index // CHECK: %[[VAL_10:.*]] = tensor.dim %[[VAL_1]], %[[IDX_1]] : tensor // CHECK: %[[IDX_2:.*]] = arith.constant 2 : index // CHECK: %[[VAL_12:.*]] = tensor.dim %[[VAL_1]], %[[IDX_2]] : tensor // CHECK: %[[IDX_3:.*]] = arith.constant 3 : index // CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_1]], %[[IDX_3]] : tensor // CHECK: %[[VAL_16:.*]] = tensor.from_elements %[[VAL_8]], %[[VAL_10]], %[[VAL_12]], %[[VAL_14]] : tensor<4xindex> // CHECK: %[[VAL_17:.*]] = stablehlo.dynamic_broadcast_in_dim %[[VAL_7]], %[[VAL_16]], dims = [] : (tensor, tensor<4xindex>) -> tensor // CHECK: %[[VAL_18:.*]] = stablehlo.constant dense<0.000000e+00> : tensor // CHECK: %[[VAL_19:.*]] = "stablehlo.reduce_window"(%[[VAL_17]], %[[VAL_18]]) // CHECK{LITERAL}: <{padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[IVAL_3:.*]]: tensor, %[[IVAL_4:.*]]: tensor): // CHECK: %[[IVAL_5:.*]] = stablehlo.add %[[IVAL_3]], %[[IVAL_4]] : tensor // CHECK: stablehlo.return %[[IVAL_5]] : tensor // CHECK: }) : (tensor, tensor) -> tensor // CHECK: %[[VAL_20:.*]] = stablehlo.divide %[[VAL_6]], %[[VAL_19]] : tensor // CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_20]] : tensor -> !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[VAL_21]] : !torch.vtensor<[?,?,?,?],f32> func.func @torch.aten.avg_pool2d(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { %int3 = torch.constant.int 3 %int2 = torch.constant.int 2 %int1 = torch.constant.int 1 %false = torch.constant.bool false %none = torch.constant.none %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list %1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list %2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list %3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %false, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.list, !torch.list, !torch.list, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32> return %3 : !torch.vtensor<[?,?,?,?],f32> } // ----- // CHECK-LABEL: func.func @torch.aten.avg_pool2d$count_include_pad( // CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { // CHECK: %[[T0:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor // CHECK: %[[INT3:.*]] = torch.constant.int 3 // CHECK: %[[INT2:.*]] = torch.constant.int 2 // CHECK: %[[INT1:.*]] = torch.constant.int 1 // CHECK: %[[FALSE:.*]] = torch.constant.bool false // CHECK: %[[TRUE:.*]] = torch.constant.bool true // CHECK: %[[NONE:.*]] = torch.constant.none // CHECK: %[[T1:.*]] = torch.prim.ListConstruct %[[INT3]], %[[INT3]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T2:.*]] = torch.prim.ListConstruct %[[INT2]], %[[INT2]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T3:.*]] = torch.prim.ListConstruct %[[INT1]], %[[INT1]] : (!torch.int, !torch.int) -> !torch.list // CHECK: %[[T4:.*]] = stablehlo.constant dense<0.000000e+00> : tensor // CHECK: %[[T5:.*]] = "stablehlo.reduce_window"(%[[T0]], %[[T4]]) // CHECK{LITERAL}: <{padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = array, window_dimensions = array, window_strides = array}> ({ // CHECK: ^bb0(%[[ARG1:.*]]: tensor, %[[ARG2:.*]]: tensor): // CHECK: %[[T10:.*]] = stablehlo.add %[[ARG1]], %[[ARG2]] : tensor // CHECK: stablehlo.return %[[T10]] : tensor // CHECK: }) : (tensor, tensor) -> tensor // CHECK: %[[T6:.*]] = stablehlo.constant dense<9> : tensor // CHECK: %[[T7:.*]] = stablehlo.convert %[[T6]] : (tensor) -> tensor // CHECK: %[[T8:.*]] = chlo.broadcast_divide %[[T5]], %[[T7]] : (tensor, tensor) -> tensor // CHECK: %[[T9:.*]] = torch_c.from_builtin_tensor %[[T8]] : tensor -> !torch.vtensor<[?,?,?,?],f32> // CHECK: return %[[T9]] : !torch.vtensor<[?,?,?,?],f32> func.func @torch.aten.avg_pool2d$count_include_pad(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> { %int3 = torch.constant.int 3 %int2 = torch.constant.int 2 %int1 = torch.constant.int 1 %false = torch.constant.bool false %true = torch.constant.bool true %none = torch.constant.none %0 = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list %1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list %2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list %3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %true, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.list, !torch.list, !torch.list, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32> return %3 : !torch.vtensor<[?,?,?,?],f32> }