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torch-mlir/test/Conversion/TorchToMhlo/pooling.mlir

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[MHLO] Init MHLO pooling-like op conversion (#1141) * [MHLO] Init MHLO pooling-like op conversion and remove 'op' suffix in filenames Co-authored-by: Bairen Yi <yibairen.byron@bytedance.com> Co-authored-by: Jiawei Wu <xremold@gmail.com> Co-authored-by: Tianyou Guo tianyou.gty@alibaba-inc.com Co-authored-by: Xu Yan <yancey.yx@alibaba-inc.com> Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com> See RFC #999
2022-08-04 12:34:22 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -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<?x?x?x?xf32>
// 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<int>
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = mhlo.constant dense<-3.40282347E+38> : tensor<f32>
// CHECK: %[[VAL_7:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_6]]) ({
// CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>):
// CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32>
// CHECK: "mhlo.return"(%[[VAL_10]]) : (tensor<f32>) -> ()
// CHECK: }) {padding = dense<0> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_11:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?x?x?xf32> -> !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<int>
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%4 = torch.aten.max_pool2d %arg0, %0, %1, %2, %3, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %4 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.max_pool2d$padding(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %int2 = torch.constant.int 2
// CHECK: %int1 = torch.constant.int 1
// CHECK: %false = torch.constant.bool false
// CHECK: %[[VAL_2:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<-3.40282347E+38> : tensor<f32>
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[VAL_8:.*]]: tensor<f32>, %[[VAL_9:.*]]: tensor<f32>):
// CHECK: %[[VAL_10:.*]] = mhlo.maximum %[[VAL_8]], %[[VAL_9]] : tensor<f32>
// CHECK: "mhlo.return"(%[[VAL_10]]) : (tensor<f32>) -> ()
// CHECK: })
// CHECK-SAME{LITERAL}: {padding = dense<[[0, 0], [0, 0], [2, 2], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<[1, 1, 2, 1]> : tensor<4xi64>, window_dimensions = dense<[1, 1, 2, 2]> : tensor<4xi64>, window_strides = dense<1> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.max_pool2d$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<int>
%1 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int2, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.aten.max_pool2d %arg0, %0, %1, %2, %2, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %3 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.max_pool2d_with_indices(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?],f32>) -> (!torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],si64>) {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?],f32> -> tensor<?x?x?xf32>
// 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: %[[VAL_2:.*]] = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = mhlo.constant dense<-3.40282347E+38> : tensor<f32>
// CHECK: %[[IDX_0:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[IDX_0]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_8:.*]] = arith.index_cast %[[VAL_7]] : index to i64
// CHECK: %[[IDX_1:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[IDX_1]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = arith.index_cast %[[VAL_9]] : index to i64
// CHECK: %[[IDX_2:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_11:.*]] = tensor.dim %[[VAL_1]], %[[IDX_2]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_12:.*]] = arith.index_cast %[[VAL_11]] : index to i64
// CHECK: %[[VAL_13:.*]] = tensor.from_elements %[[VAL_8]], %[[VAL_10]], %[[VAL_12]] : tensor<3xi64>
// CHECK: %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_10]] : i64
// CHECK: %[[VAL_15:.*]] = tensor.from_elements %[[VAL_8]], %[[VAL_14]] : tensor<2xi64>
// CHECK: %[[VAL_16:.*]] = "mhlo.dynamic_iota"(%[[VAL_15]]) {iota_dimension = 1 : i64} : (tensor<2xi64>) -> tensor<?x?xi64>
// CHECK: %[[VAL_17:.*]] = "mhlo.dynamic_reshape"(%[[VAL_16]], %[[VAL_13]]) : (tensor<?x?xi64>, tensor<3xi64>) -> tensor<?x?x?xi64>
// CHECK: %[[VAL_18:.*]] = mhlo.constant dense<0> : tensor<i64>
// CHECK: %[[VAL_19:.*]]:2 = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_17]], %[[VAL_6]], %[[VAL_18]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<i64>, %[[IVAL_2:.*]]: tensor<f32>, %[[IVAL_3:.*]]: tensor<i64>):
// CHECK: %[[IVAL_4:.*]] = "mhlo.compare"(%[[IVAL_0]], %[[IVAL_2]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
// CHECK: %[[IVAL_5:.*]] = "mhlo.select"(%[[IVAL_4]], %[[IVAL_0]], %[[IVAL_2]]) : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>
// CHECK: %[[IVAL_6:.*]] = "mhlo.compare"(%[[IVAL_0]], %[[IVAL_2]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
// CHECK: %[[IVAL_7:.*]] = mhlo.minimum %[[IVAL_1]], %[[IVAL_3]] : tensor<i64>
// CHECK: %[[IVAL_8:.*]] = "mhlo.select"(%[[IVAL_4]], %[[IVAL_1]], %[[IVAL_3]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: %[[IVAL_9:.*]] = "mhlo.select"(%[[IVAL_6]], %[[IVAL_7]], %[[IVAL_8]]) : (tensor<i1>, tensor<i64>, tensor<i64>) -> tensor<i64>
// CHECK: "mhlo.return"(%[[IVAL_5]], %[[IVAL_9]]) : (tensor<f32>, tensor<i64>) -> ()
// CHECK{LITERAL}: }) {padding = dense<0> : tensor<3x2xi64>, window_dilations = dense<1> : tensor<3xi64>, window_dimensions = dense<[1, 3, 3]> : tensor<3xi64>, window_strides = dense<[1, 2, 2]> : tensor<3xi64>} : (tensor<?x?x?xf32>, tensor<?x?x?xi64>, tensor<f32>, tensor<i64>) -> (tensor<?x?x?xf32>, tensor<?x?x?xi64>)
// CHECK: %[[VAL_20:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#0 : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_19]]#1 : tensor<?x?x?xi64> -> !torch.vtensor<[?,?,?],si64>
// CHECK: return %[[VAL_20]], %[[VAL_21]] : !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<int>
%1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int0, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%result0, %result1 = torch.aten.max_pool2d_with_indices %arg0, %0, %1, %2, %3, %false : !torch.vtensor<[?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !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<?x?x?x?xf32>
// 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<int>
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>):
// CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_2]]) : (tensor<f32>) -> ()
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
// CHECK: %[[IDX_0:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[IDX_0]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_9:.*]] = arith.index_cast %[[VAL_8]] : index to i64
// CHECK: %[[IDX_1:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_10:.*]] = tensor.dim %[[VAL_1]], %[[IDX_1]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_11:.*]] = arith.index_cast %[[VAL_10]] : index to i64
// CHECK: %[[IDX_2:.*]] = arith.constant 2 : index
// CHECK: %[[VAL_12:.*]] = tensor.dim %[[VAL_1]], %[[IDX_2]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_13:.*]] = arith.index_cast %[[VAL_12]] : index to i64
// CHECK: %[[IDX_3:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_1]], %[[IDX_3]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_15:.*]] = arith.index_cast %[[VAL_14]] : index to i64
// CHECK: %[[VAL_16:.*]] = tensor.from_elements %[[VAL_9]], %[[VAL_11]], %[[VAL_13]], %[[VAL_15]] : tensor<4xi64>
// CHECK: %[[VAL_17:.*]] = "mhlo.dynamic_broadcast_in_dim"(%[[VAL_7]], %[[VAL_16]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>, tensor<4xi64>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_18:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK: %[[VAL_19:.*]] = "mhlo.reduce_window"(%[[VAL_17]], %[[VAL_18]]) ({
// CHECK: ^bb0(%[[IVAL_3:.*]]: tensor<f32>, %[[IVAL_4:.*]]: tensor<f32>):
// CHECK: %[[IVAL_5:.*]] = mhlo.add %[[IVAL_3]], %[[IVAL_4]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_5]]) : (tensor<f32>) -> ()
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_20:.*]] = mhlo.divide %[[VAL_6]], %[[VAL_19]] : tensor<?x?x?x?xf32>
// CHECK: %[[VAL_21:.*]] = torch_c.from_builtin_tensor %[[VAL_20]] : tensor<?x?x?x?xf32> -> !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<int>
%1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %false, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32>
return %3 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.avg_pool2d$count_include_pad(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %int3 = torch.constant.int 3
// CHECK: %int2 = torch.constant.int 2
// CHECK: %int1 = torch.constant.int 1
// CHECK: %false = torch.constant.bool false
// CHECK: %none = torch.constant.none
// CHECK: %[[VAL_2:.*]] = torch.prim.ListConstruct %int3, %int3 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK: %[[VAL_6:.*]] = "mhlo.reduce_window"(%[[VAL_1]], %[[VAL_5]]) ({
// CHECK: ^bb0(%[[IVAL_0:.*]]: tensor<f32>, %[[IVAL_1:.*]]: tensor<f32>):
// CHECK: %[[IVAL_2:.*]] = mhlo.add %[[IVAL_0]], %[[IVAL_1]] : tensor<f32>
// CHECK: "mhlo.return"(%[[IVAL_2]]) : (tensor<f32>) -> ()
// CHECK{LITERAL}: }) {padding = dense<[[0, 0], [0, 0], [1, 1], [1, 1]]> : tensor<4x2xi64>, window_dilations = dense<1> : tensor<4xi64>, window_dimensions = dense<[1, 1, 3, 3]> : tensor<4xi64>, window_strides = dense<[1, 1, 2, 2]> : tensor<4xi64>} : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<9> : tensor<i64>
// CHECK: %[[VAL_8:.*]] = mhlo.convert(%[[VAL_7]]) : (tensor<i64>) -> tensor<f32>
// CHECK: %[[VAL_9:.*]] = chlo.broadcast_divide %[[VAL_6]], %[[VAL_8]] : (tensor<?x?x?x?xf32>, tensor<f32>) -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = torch_c.from_builtin_tensor %[[VAL_9]] : tensor<?x?x?x?xf32> -> !torch.vtensor<[?,?,?,?],f32>
// CHECK: return %[[VAL_10]] : !torch.vtensor<[?,?,?,?],f32>
func.func @torch.aten.avg_pool2d$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<int>
%1 = torch.prim.ListConstruct %int2, %int2 : (!torch.int, !torch.int) -> !torch.list<int>
%2 = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
%3 = torch.aten.avg_pool2d %arg0, %0, %1, %2, %false, %true, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32>
return %3 : !torch.vtensor<[?,?,?,?],f32>
}