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

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[MHLO] Init MHLO integration. (#1083) 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>
2022-07-21 07:18:16 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-mhlo -split-input-file -verify-diagnostics | FileCheck %s
// CHECK-LABEL: func.func @torch.aten.tanh$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.tanh %[[VAL_1]] : tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.tanh$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
[MHLO] Init MHLO basic op conversion (#1092) * [MHLO] Init MHLO basic Op Conversion 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> * [NFC] Remove 'from @llvm-project' annotation Co-authored-by: wujiawei.jw <wujiawei.jw@bytedance.com>
2022-07-27 13:07:51 +08:00
// -----
// CHECK-LABEL: func.func @torch.aten.addscalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 9
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = mhlo.constant dense<9.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = mhlo.add %[[VAL_1]], %[[VAL_4]] : tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.addscalar$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int9 = torch.constant.int 9
%int1 = torch.constant.int 1
%0 = torch.aten.add.Scalar %arg0, %int9, %int1 : !torch.vtensor<[4,64],f32>, !torch.int, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.addtensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.add %[[VAL_2]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.addtensor$basic(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.addtensor$promote(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],si32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],si64>) -> !torch.vtensor<[4,64],si64> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],si32> -> tensor<4x64xi32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],si64> -> tensor<4x64xi64>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<4x64xi32>) -> tensor<4x64xi64>
// CHECK: %[[VAL_6:.*]] = mhlo.add %[[VAL_5]], %[[VAL_3]] : tensor<4x64xi64>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x64xi64> -> !torch.vtensor<[4,64],si64>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,64],si64>
func.func @torch.aten.addtensor$promote(%arg0: !torch.vtensor<[4,64],si32>, %arg1: !torch.vtensor<[4,64],si64>) -> !torch.vtensor<[4,64],si64> {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[4,64],si32>, !torch.vtensor<[4,64],si64>, !torch.int -> !torch.vtensor<[4,64],si64>
return %0 : !torch.vtensor<[4,64],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.addtensor$bcast(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = mhlo.add %[[VAL_5]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.addtensor$bcast(%arg0: !torch.vtensor<[64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.addtensor$alpha(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 2
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<2.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = mhlo.multiply %[[VAL_3]], %[[VAL_5]] : tensor<4x64xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.add %[[VAL_2]], %[[VAL_6]] : tensor<4x64xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %4 : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.addtensor$alpha(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int2 = torch.constant.int 2
%0 = torch.aten.add.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.mulscalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 9
// CHECK: %[[VAL_3:.*]] = mhlo.constant dense<9.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.multiply %[[VAL_1]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.mulscalar$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int9 = torch.constant.int 9
%0 = torch.aten.mul.Scalar %arg0, %int9 : !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.multensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.multiply %[[VAL_2]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.multensor$basic(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32> -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.multensor$bcast(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[8,4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[8,1,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[8,4,64],f32> -> tensor<8x4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[8,1,64],f32> -> tensor<8x1x64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<8x1x64xf32>) -> tensor<8x4x64xf32>
// CHECK: %[[VAL_5:.*]] = mhlo.multiply %[[VAL_2]], %[[VAL_4]] : tensor<8x4x64xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<8x4x64xf32> -> !torch.vtensor<[8,4,64],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[8,4,64],f32>
func.func @torch.aten.multensor$bcast(%arg0: !torch.vtensor<[8,4,64],f32>, %arg1: !torch.vtensor<[8,1,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
%0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[8,4,64],f32>, !torch.vtensor<[8,1,64],f32> -> !torch.vtensor<[8,4,64],f32>
return %0 : !torch.vtensor<[8,4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.subscalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 9
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = mhlo.constant dense<9.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = mhlo.subtract %[[VAL_1]], %[[VAL_4]] : tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.subscalar$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int9 = torch.constant.int 9
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Scalar %arg0, %int9, %int1 : !torch.vtensor<[4,64],f32>, !torch.int, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.subtensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.subtract %[[VAL_2]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.subtensor$basic(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.subtensor$promote(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],si32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],si64>) -> !torch.vtensor<[4,64],si64> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],si32> -> tensor<4x64xi32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],si64> -> tensor<4x64xi64>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = mhlo.convert(%[[VAL_2]]) : (tensor<4x64xi32>) -> tensor<4x64xi64>
// CHECK: %[[VAL_6:.*]] = mhlo.subtract %[[VAL_5]], %[[VAL_3]] : tensor<4x64xi64>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x64xi64> -> !torch.vtensor<[4,64],si64>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,64],si64>
func.func @torch.aten.subtensor$promote(%arg0: !torch.vtensor<[4,64],si32>, %arg1: !torch.vtensor<[4,64],si64>) -> !torch.vtensor<[4,64],si64> {
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[4,64],si32>, !torch.vtensor<[4,64],si64>, !torch.int -> !torch.vtensor<[4,64],si64>
return %0 : !torch.vtensor<[4,64],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.subtensor$bcast(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = mhlo.subtract %[[VAL_5]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.subtensor$bcast(%arg0: !torch.vtensor<[64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.subtensor$alpha(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 2
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<2.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_6:.*]] = mhlo.multiply %[[VAL_3]], %[[VAL_5]] : tensor<4x64xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.subtract %[[VAL_2]], %[[VAL_6]] : tensor<4x64xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %4 : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.subtensor$alpha(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int2 = torch.constant.int 2
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int2 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.divscalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 9
// CHECK: %[[VAL_3:.*]] = mhlo.constant dense<9.000000e+00> : tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.divide %[[VAL_1]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.divscalar$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int9 = torch.constant.int 9
%0 = torch.aten.div.Scalar %arg0, %int9 : !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.divtensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.divide %[[VAL_2]], %[[VAL_3]] : tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.divtensor$basic(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[4,64],f32> -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.divtensor$bcast(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[8,4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[8,1,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[8,4,64],f32> -> tensor<8x4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[8,1,64],f32> -> tensor<8x1x64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<[0, 1, 2]> : tensor<3xi64>} : (tensor<8x1x64xf32>) -> tensor<8x4x64xf32>
// CHECK: %[[VAL_5:.*]] = mhlo.divide %[[VAL_2]], %[[VAL_4]] : tensor<8x4x64xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<8x4x64xf32> -> !torch.vtensor<[8,4,64],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[8,4,64],f32>
func.func @torch.aten.divtensor$bcast(%arg0: !torch.vtensor<[8,4,64],f32>, %arg1: !torch.vtensor<[8,1,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[8,4,64],f32>, !torch.vtensor<[8,1,64],f32> -> !torch.vtensor<[8,4,64],f32>
return %0 : !torch.vtensor<[8,4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.log$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.log %[[VAL_1]] : tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.log$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.log %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.exp$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.exponential %[[VAL_1]] : tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
func.func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.exp %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.clone$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.none
// CHECK: %[[VAL_3:.*]] = "mhlo.copy"(%[[VAL_1]]) : (tensor<4x64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.clone$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%none = torch.constant.none
%0 = torch.aten.clone %arg0, %none : !torch.vtensor<[4,64],f32>, !torch.none -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.vtensor.literal$basic() -> !torch.vtensor<[],f32> {
// CHECK: %[[VAL_0:.*]] = mhlo.constant dense<0.000000e+00> : tensor<f32>
// CHECK: %[[VAL_1:.*]] = torch_c.from_builtin_tensor %[[VAL_0]] : tensor<f32> -> !torch.vtensor<[],f32>
// CHECK: return %[[VAL_1]] : !torch.vtensor<[],f32>
func.func @torch.vtensor.literal$basic() -> !torch.vtensor<[],f32> {
%0 = torch.vtensor.literal(dense<0.0> : tensor<f32>) : !torch.vtensor<[],f32>
return %0 : !torch.vtensor<[],f32>
}
// -----
// CHECK-LABEL: func.func @torch.vtensor.literal$signed() -> !torch.vtensor<[2],si64> {
// CHECK: %[[VAL_0:.*]] = mhlo.constant dense<1> : tensor<2xi64>
// CHECK: %[[VAL_1:.*]] = torch_c.from_builtin_tensor %[[VAL_0]] : tensor<2xi64> -> !torch.vtensor<[2],si64>
// CHECK: return %[[VAL_1]] : !torch.vtensor<[2],si64>
func.func @torch.vtensor.literal$signed() -> !torch.vtensor<[2],si64> {
%0 = torch.vtensor.literal(dense<1> : tensor<2xsi64>) : !torch.vtensor<[2],si64>
return %0 : !torch.vtensor<[2],si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.gt.scalar(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 3
// CHECK: %[[VAL_3:.*]] = mhlo.constant dense<3.000000e+00> : tensor<f32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = "mhlo.compare"(%[[VAL_1]], %[[VAL_4]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<4x64xf32>, tensor<4x64xf32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.gt.scalar(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],i1> {
%int3 = torch.constant.int 3
%0 = torch.aten.gt.Scalar %arg0, %int3 : !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.gt.tensor(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = "mhlo.compare"(%[[VAL_2]], %[[VAL_4]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<4x64xf32>, tensor<4x64xf32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.gt.tensor(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
%0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.gt.tensor$convert(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],si32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],si32> -> tensor<4x64xi32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_4:.*]] = mhlo.convert(%[[VAL_3]]) : (tensor<64xf32>) -> tensor<64xi32>
// CHECK: %[[VAL_5:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_4]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xi32>) -> tensor<4x64xi32>
// CHECK: %[[VAL_6:.*]] = "mhlo.compare"(%[[VAL_2]], %[[VAL_5]]) {compare_type = #mhlo<comparison_type SIGNED>, comparison_direction = #mhlo<comparison_direction GT>} : (tensor<4x64xi32>, tensor<4x64xi32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.gt.tensor$convert(%arg0: !torch.vtensor<[4,64],si32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
%0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],si32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.lt.tensor(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = "mhlo.compare"(%[[VAL_2]], %[[VAL_4]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction LT>} : (tensor<4x64xf32>, tensor<4x64xf32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.lt.tensor(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
%0 = torch.aten.lt.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.eq.tensor(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = "mhlo.compare"(%[[VAL_2]], %[[VAL_4]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction EQ>} : (tensor<4x64xf32>, tensor<4x64xf32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.eq.tensor(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
%0 = torch.aten.eq.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.ne.tensor(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[64],f32> -> tensor<64xf32>
// CHECK: %[[VAL_4:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<4x64xf32>
// CHECK: %[[VAL_5:.*]] = "mhlo.compare"(%[[VAL_2]], %[[VAL_4]]) {compare_type = #mhlo<comparison_type FLOAT>, comparison_direction = #mhlo<comparison_direction NE>} : (tensor<4x64xf32>, tensor<4x64xf32>) -> tensor<4x64xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<4x64xi1> -> !torch.vtensor<[4,64],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[4,64],i1>
func.func @torch.aten.ne.tensor(%arg0: !torch.vtensor<[4,64],f32>, %arg1: !torch.vtensor<[64],f32>) -> !torch.vtensor<[4,64],i1> {
%0 = torch.aten.ne.Tensor %arg0, %arg1 : !torch.vtensor<[4,64],f32>, !torch.vtensor<[64],f32> -> !torch.vtensor<[4,64],i1>
return %0 : !torch.vtensor<[4,64],i1>
}
// -----
// CHECK-LABEL: func.func @torch.aten.batch_norm(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
// CEHCK： %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,3,5,5],f32> -> tensor<2x3x5x5xf32>
// CEHCK： %[[VAL_2:.*]] = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CEHCK： %[[VAL_3:.*]] = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CEHCK： %true = torch.constant.bool true
// CEHCK： %[[VAL_4:.*]] = mhlo.constant dense<0> : tensor<i64>
// CEHCK： %float1.000000e-01 = torch.constant.float 1.000000e-01
// CEHCK： %float1.000000e-05 = torch.constant.float 1.000000e-05
// CEHCK： %int1 = torch.constant.int 1
// CEHCK： %[[VAL_5:.*]] = mhlo.constant dense<1> : tensor<i64>
// CEHCK： %[[VAL_6:.*]] = mhlo.add %[[VAL_4]], %[[VAL_5]] : tensor<i64>
// CEHCK： %[[VAL_7:.*]], %batch_mean, %batch_var = "mhlo.batch_norm_training"(%[[VAL_1]], %[[VAL_3]], %[[VAL_2]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>)
// CEHCK： %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<2x3x5x5xf32> -> !torch.vtensor<[2,3,5,5],f32>
// CEHCK： return %[[VAL_8]] : !torch.vtensor<[2,3,5,5],f32>
func.func @torch.aten.batch_norm(%arg0: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
%0 = torch.vtensor.literal(dense<0.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%1 = torch.vtensor.literal(dense<1.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%true = torch.constant.bool true
%2 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%float1.000000e-01 = torch.constant.float 1.000000e-01
%float1.000000e-05 = torch.constant.float 1.000000e-05
%int1 = torch.constant.int 1
%3 = torch.aten.add.Scalar %2, %int1, %int1 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
%4 = torch.aten.batch_norm %arg0, %1, %0, %0, %1, %true, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor<[2,3,5,5],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[2,3,5,5],f32>
return %4 : !torch.vtensor<[2,3,5,5],f32>
}
// CHECK-LABEL: func.func @torch.aten.batch_norm$none_bias_weight(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
// CEHCK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,3,5,5],f32> -> tensor<2x3x5x5xf32>
// CEHCK: %none = torch.constant.none
// CEHCK: %1 = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CEHCK: %2 = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CEHCK: %true = torch.constant.bool true
// CEHCK: %[[VAL_2:.*]] = mhlo.constant dense<0> : tensor<i64>
// CEHCK: %float1.000000e-01 = torch.constant.float 1.000000e-01
// CEHCK: %float1.000000e-05 = torch.constant.float 1.000000e-05
// CEHCK: %int1 = torch.constant.int 1
// CEHCK: %[[VAL_3:.*]] = mhlo.constant dense<1> : tensor<i64>
// CEHCK: %[[VAL_4:.*]] = mhlo.add %[[VAL_2]], %[[VAL_3]] : tensor<i64>
// CEHCK: %[[VAL_5:.*]] = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CEHCK: %[[VAL_6:.*]] = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CEHCK: %[[VAL_7:.*]], %batch_mean, %batch_var = "mhlo.batch_norm_training"(%[[VAL_1]], %[[VAL_5]], %[[VAL_6]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>)
// CEHCK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<2x3x5x5xf32> -> !torch.vtensor<[2,3,5,5],f32>
// CEHCK: return %[[VAL_8]] : !torch.vtensor<[2,3,5,5],f32>
func.func @torch.aten.batch_norm$none_bias_weight(%arg0: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
%none = torch.constant.none
%0 = torch.vtensor.literal(dense<1.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%1 = torch.vtensor.literal(dense<0.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%true = torch.constant.bool true
%2 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%float1.000000e-01 = torch.constant.float 1.000000e-01
%float1.000000e-05 = torch.constant.float 1.000000e-05
%int1 = torch.constant.int 1
%3 = torch.aten.add.Scalar %2, %int1, %int1 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
%4 = torch.aten.batch_norm %arg0, %none, %none, %1, %0, %true, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor<[2,3,5,5],f32>, !torch.none, !torch.none, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[2,3,5,5],f32>
return %4 : !torch.vtensor<[2,3,5,5],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.batch_norm$inference(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
// CEHCK： %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,3,5,5],f32> -> tensor<2x3x5x5xf32>
// CEHCK： %[[VAL_2:.*]] = mhlo.constant dense<0.000000e+00> : tensor<3xf32>
// CEHCK： %[[VAL_3:.*]] = mhlo.constant dense<1.000000e+00> : tensor<3xf32>
// CEHCK： %true = torch.constant.bool true
// CHECK： %false = torch.constant.bool false
// CEHCK： %[[VAL_4:.*]] = mhlo.constant dense<0> : tensor<i64>
// CEHCK： %float1.000000e-01 = torch.constant.float 1.000000e-01
// CEHCK： %float1.000000e-05 = torch.constant.float 1.000000e-05
// CEHCK： %int1 = torch.constant.int 1
// CEHCK： %[[VAL_5:.*]] = mhlo.constant dense<1> : tensor<i64>
// CEHCK： %[[VAL_6:.*]] = mhlo.add %[[VAL_4]], %[[VAL_5]] : tensor<i64>
// CEHCK： %[[VAL_7:.*]], %batch_mean, %batch_var = "mhlo.batch_norm_training"(%[[VAL_1]], %[[VAL_3]], %[[VAL_2]], %[[VAL_2]], %[[VAL_3]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>) -> (tensor<2x3x5x5xf32>, tensor<3xf32>, tensor<3xf32>)
// CEHCK： %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<2x3x5x5xf32> -> !torch.vtensor<[2,3,5,5],f32>
// CEHCK： return %[[VAL_8]] : !torch.vtensor<[2,3,5,5],f32>
func.func @torch.aten.batch_norm$inference(%arg0: !torch.vtensor<[2,3,5,5],f32>) -> !torch.vtensor<[2,3,5,5],f32> {
%0 = torch.vtensor.literal(dense<0.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%1 = torch.vtensor.literal(dense<1.000000e+00> : tensor<3xf32>) : !torch.vtensor<[3],f32>
%true = torch.constant.bool true
%false = torch.constant.bool false
%2 = torch.vtensor.literal(dense<0> : tensor<si64>) : !torch.vtensor<[],si64>
%float1.000000e-01 = torch.constant.float 1.000000e-01
%float1.000000e-05 = torch.constant.float 1.000000e-05
%int1 = torch.constant.int 1
%3 = torch.aten.add.Scalar %2, %int1, %int1 : !torch.vtensor<[],si64>, !torch.int, !torch.int -> !torch.vtensor<[],si64>
%4 = torch.aten.batch_norm %arg0, %1, %0, %0, %1, %false, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor<[2,3,5,5],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[2,3,5,5],f32>
return %4 : !torch.vtensor<[2,3,5,5],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.relu(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,5],f32>) -> !torch.vtensor<[2,5],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,5],f32> -> tensor<2x5xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.constant dense<0.000000e+00> : tensor<2x5xf32>
// CHECK: %[[VAL_3:.*]] = mhlo.maximum %[[VAL_1]], %[[VAL_2]] : tensor<2x5xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<2x5xf32> -> !torch.vtensor<[2,5],f32>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[2,5],f32>
func.func @torch.aten.relu(%arg0: !torch.vtensor<[2,5],f32>) -> !torch.vtensor<[2,5],f32> {
%0 = torch.aten.relu %arg0 : !torch.vtensor<[2,5],f32> -> !torch.vtensor<[2,5],f32>
return %0 : !torch.vtensor<[2,5],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.relu$int8(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[2,5],si8>) -> !torch.vtensor<[2,5],si8> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[2,5],si8> -> tensor<2x5xi8>
// CHECK: %[[VAL_2:.*]] = mhlo.constant dense<0> : tensor<2x5xi8>
// CHECK: %[[VAL_3:.*]] = mhlo.maximum %[[VAL_1]], %[[VAL_2]] : tensor<2x5xi8>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<2x5xi8> -> !torch.vtensor<[2,5],si8>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[2,5],si8>
func.func @torch.aten.relu$int8(%arg0: !torch.vtensor<[2,5],si8>) -> !torch.vtensor<[2,5],si8> {
%0 = torch.aten.relu %arg0 : !torch.vtensor<[2,5],si8> -> !torch.vtensor<[2,5],si8>
return %0 : !torch.vtensor<[2,5],si8>
}
// -----
// CHECK-LABEL: func.func @torch.aten.reciprocal(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[5,5,5],f32>) -> !torch.vtensor<[5,5,5],f32> {
// CEHCK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0:.*]] : !torch.vtensor<[5,5,5],f32> -> tensor<5x5x5xf32>
// CEHCK: %[[VAL_2:.*]] = mhlo.constant dense<1.000000e+00> : tensor<f32>
// CEHCK: %[[VAL_3:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<> : tensor<0xi64>} : (tensor<f32>) -> tensor<5x5x5xf32>
// CEHCK: %[[VAL_4:.*]] = mhlo.divide %[[VAL_3]], %[[VAL_1]] : tensor<5x5x5xf32>
// CEHCK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<5x5x5xf32> -> !torch.vtensor<[5,5,5],f32>
// CEHCK: return %[[VAL_5]] : !torch.vtensor<[5,5,5],f32>
func.func @torch.aten.reciprocal(%arg0: !torch.vtensor<[5,5,5],f32>) -> !torch.vtensor<[5,5,5],f32> {
%0 = torch.aten.reciprocal %arg0 : !torch.vtensor<[5,5,5],f32> -> !torch.vtensor<[5,5,5],f32>
return %0 : !torch.vtensor<[5,5,5],f32>
}
// CHECK-LABEL: func @torch.aten.native_layer_norm(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,7,4,5],f32>) -> !torch.vtensor<[3,7,4,5],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,7,4,5],f32> -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_2:.*]] = mhlo.constant dense<0.000000e+00> : tensor<4x5xf32>
// CHECK: %[[VAL_3:.*]] = mhlo.constant dense<1.000000e+00> : tensor<4x5xf32>
// CHECK: %int4 = torch.constant.int 4
// CHECK: %int5 = torch.constant.int 5
// CHECK: %float1.000000e-05 = torch.constant.float 1.000000e-05
// CHECK: %true = torch.constant.bool true
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = mhlo.constant dense<[1, 21, 20]> : tensor<3xi64>
// CHECK: %[[VAL_6:.*]] = "mhlo.dynamic_reshape"(%[[VAL_1]], %[[VAL_5]]) : (tensor<3x7x4x5xf32>, tensor<3xi64>) -> tensor<1x21x20xf32>
// CHECK: %[[VAL_7:.*]] = mhlo.constant dense<1.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_8:.*]] = mhlo.constant dense<0.000000e+00> : tensor<21xf32>
// CHECK: %[[VAL_9:.*]], %[[VAL_10:.*]], %[[VAL_11:.*]] = "mhlo.batch_norm_training"(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) {epsilon = 9.99999974E-6 : f32, feature_index = 1 : i64} : (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>) -> (tensor<1x21x20xf32>, tensor<21xf32>, tensor<21xf32>)
// CHECK: %[[VAL_12:.*]] = mhlo.constant dense<[3, 7, 4, 5]> : tensor<4xi64>
// CHECK: %[[VAL_13:.*]] = "mhlo.dynamic_reshape"(%[[VAL_9]], %[[VAL_12]]) : (tensor<1x21x20xf32>, tensor<4xi64>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_14:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64>
// CHECK: %[[VAL_15:.*]] = "mhlo.dynamic_reshape"(%[[VAL_10]], %[[VAL_14]]) : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32>
// CHECK: %[[VAL_16:.*]] = mhlo.constant dense<[3, 7, 1, 1]> : tensor<4xi64>
// CHECK: %[[VAL_17:.*]] = "mhlo.dynamic_reshape"(%[[VAL_11]], %[[VAL_16]]) : (tensor<21xf32>, tensor<4xi64>) -> tensor<3x7x1x1xf32>
// CHECK: %[[VAL_18:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_3]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_19:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_2]]) {broadcast_dimensions = dense<[2, 3]> : tensor<2xi64>} : (tensor<4x5xf32>) -> tensor<3x7x4x5xf32>
// CHECK: %[[VAL_20:.*]] = mhlo.multiply %[[VAL_13]], %[[VAL_18]] : tensor<3x7x4x5xf32>
// CHECK: %[[VAL_21:.*]] = mhlo.add %[[VAL_20]], %[[VAL_19]] : tensor<3x7x4x5xf32>
// CHECK: %[[VAL_22:.*]] = torch_c.from_builtin_tensor %[[VAL_21:.*]] : tensor<3x7x4x5xf32> -> !torch.vtensor<[3,7,4,5],f32>
// CHECK: return %[[VAL_22]] : !torch.vtensor<[3,7,4,5],f32>
func.func @torch.aten.native_layer_norm(%arg0: !torch.vtensor<[3,7,4,5],f32>) -> !torch.vtensor<[3,7,4,5],f32> {
%0 = torch.vtensor.literal(dense<0.000000e+00> : tensor<4x5xf32>) : !torch.vtensor<[4,5],f32>
%1 = torch.vtensor.literal(dense<1.000000e+00> : tensor<4x5xf32>) : !torch.vtensor<[4,5],f32>
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%float1.000000e-05 = torch.constant.float 1.000000e-05
%true = torch.constant.bool true
%2 = torch.prim.ListConstruct %int4, %int5 : (!torch.int, !torch.int) -> !torch.list<int>
%result0, %result1, %result2 = torch.aten.native_layer_norm %arg0, %2, %1, %0, %float1.000000e-05 : !torch.vtensor<[3,7,4,5],f32>, !torch.list<int>, !torch.vtensor<[4,5],f32>, !torch.vtensor<[4,5],f32>, !torch.float -> !torch.vtensor<[3,7,4,5],f32>, !torch.vtensor<[3,7,1,1],f32>, !torch.vtensor<[3,7,1,1],f32>
return %result0 : !torch.vtensor<[3,7,4,5],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.contiguous(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %int0 = torch.constant.int 0
// CHECK: %[[VAL_2:.*]] = torch_c.from_builtin_tensor %[[VAL_1]] : tensor<4x64xf32> -> !torch.vtensor<[4,64],f32>
// CHECK: return %[[VAL_2]] : !torch.vtensor<[4,64],f32>
func.func @torch.aten.contiguous(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[4,64],f32> {
%int0 = torch.constant.int 0
%0 = torch.aten.contiguous %arg0, %int0 : !torch.vtensor<[4,64],f32>, !torch.int -> !torch.vtensor<[4,64],f32>
return %0 : !torch.vtensor<[4,64],f32>
}
// -----
// CEHCK-LABEL: func.func @torch.prim.NumToTensor.Scalar$basic() -> !torch.vtensor<[],si64> {
// CEHCK: %int1 = torch.constant.int 1
// CEHCK: %[[VAL_0:.*]] = mhlo.constant dense<1> : tensor<i64>
// CEHCK: %[[VAL_1:.*]] = torch_c.from_builtin_tensor %[[VAL_0]] : tensor<i64> -> !torch.vtensor<[],si64>
// CEHCK: return %[[VAL_1]] : !torch.vtensor<[],si64>
func.func @torch.prim.NumToTensor.Scalar$basic() -> !torch.vtensor<[], si64> {
%int1 = torch.constant.int 1
%0 = torch.prim.NumToTensor.Scalar %int1 : !torch.int -> !torch.vtensor<[], si64>
return %0 : !torch.vtensor<[], si64>
}
// -----
// CHECK-LABEL: func.func @torch.aten.broadcast_to$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 64
// CHECK: %[[VAL_3:.*]] = torch.constant.int 4
// CHECK: %[[VAL_4:.*]] = torch.constant.int 8
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_6:.*]] = "mhlo.broadcast_in_dim"(%[[VAL_1]]) {broadcast_dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<4x64xf32>) -> tensor<8x4x64xf32>
// CHECK: %[[VAL_7:.*]] = torch_c.from_builtin_tensor %[[VAL_6]] : tensor<8x4x64xf32> -> !torch.vtensor<[8,4,64],f32>
// CHECK: return %[[VAL_7]] : !torch.vtensor<[8,4,64],f32>
func.func @torch.aten.broadcast_to$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[8,4,64],f32> {
%int64 = torch.constant.int 64
%int4 = torch.constant.int 4
%int8 = torch.constant.int 8
%0 = torch.prim.ListConstruct %int8, %int4, %int64 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.broadcast_to %arg0, %0 : !torch.vtensor<[4,64],f32>, !torch.list<int> -> !torch.vtensor<[8,4,64],f32>
return %1 : !torch.vtensor<[8,4,64],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.permute$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,64],f32> -> tensor<4x64xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 0
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = torch.prim.ListConstruct %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int) -> !torch.list<int>
// CHECK: %[[VAL_5:.*]] = "mhlo.transpose"(%[[VAL_1]]) {permutation = dense<[1, 0]> : tensor<2xi64>} : (tensor<4x64xf32>) -> tensor<64x4xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<64x4xf32> -> !torch.vtensor<[64,4],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[64,4],f32>
func.func @torch.aten.permute$basic(%arg0: !torch.vtensor<[4,64],f32>) -> !torch.vtensor<[64,4],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%0 = torch.prim.ListConstruct %int1, %int0 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.permute %arg0, %0 : !torch.vtensor<[4,64],f32>, !torch.list<int> -> !torch.vtensor<[64,4],f32>
return %1 : !torch.vtensor<[64,4],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.transpose$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,3],f32>) -> !torch.vtensor<[3,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,3],f32> -> tensor<4x3xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 0
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = "mhlo.transpose"(%[[VAL_1]]) {permutation = dense<[1, 0]> : tensor<2xi64>} : (tensor<4x3xf32>) -> tensor<3x4xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<3x4xf32> -> !torch.vtensor<[3,4],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[3,4],f32>
func.func @torch.aten.transpose$basic(%arg0: !torch.vtensor<[4,3],f32>) -> !torch.vtensor<[3,4],f32> {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%0 = torch.aten.transpose.int %arg0, %int0, %int1 : !torch.vtensor<[4,3],f32>, !torch.int, !torch.int -> !torch.vtensor<[3,4],f32>
return %0 : !torch.vtensor<[3,4],f32>
}