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

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// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s
// CHECK-LABEL: func.func @torch.aten.mm$basic(
// CHECK-SAME: %[[LHS_VTENSOR:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[RHS_VTENSOR:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,2],f32> {
// CHECK: %[[LHS:.*]] = torch_c.to_builtin_tensor %[[LHS_VTENSOR]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RHS:.*]] = torch_c.to_builtin_tensor %[[RHS_VTENSOR]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[LHS_DIM_0:.*]] = tensor.dim %[[LHS]], %[[C0]] : tensor<?x?xf32>
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[LHS_DIM_1:.*]] = tensor.dim %[[LHS]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[RHS_DIM_0:.*]] = tensor.dim %[[RHS]], %[[C0]] : tensor<?x?xf32>
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[RHS_DIM_1:.*]] = tensor.dim %[[RHS]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[EQ:.*]] = arith.cmpi eq, %[[LHS_DIM_1]], %[[RHS_DIM_0]] : index
// CHECK: assert %[[EQ]], "mismatching contracting dimension for torch.aten.mm"
// CHECK: %[[INIT_TENSOR:.*]] = tensor.empty(%[[LHS_DIM_0]], %[[RHS_DIM_1]]) : tensor<?x?xf32>
// CHECK: %[[CF0:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[ZEROFILL:.*]] = linalg.fill ins(%[[CF0]] : f32) outs(%[[INIT_TENSOR]] : tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[MATMUL:.*]] = linalg.matmul ins(%[[LHS]], %[[RHS]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ZEROFILL]] : tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[CASTED:.*]] = tensor.cast %[[MATMUL]] : tensor<?x?xf32> to tensor<?x2xf32>
// CHECK: %[[RESULT_VTENSOR:.*]] = torch_c.from_builtin_tensor %[[CASTED]] : tensor<?x2xf32> -> !torch.vtensor<[?,2],f32>
// CHECK: return %[[RESULT_VTENSOR]] : !torch.vtensor<[?,2],f32>
func.func @torch.aten.mm$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,2],f32> {
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,2],f32>
return %0 : !torch.vtensor<[?,2],f32>
}
// -----
// If the operands are missing dtype, we cannot lower it.
func.func @torch.aten.mm$no_convert$missing_dtype(%arg0: !torch.vtensor, %arg1: !torch.vtensor) -> !torch.vtensor {
// expected-error@+1 {{failed to legalize}}
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor, !torch.vtensor -> !torch.vtensor
return %0 : !torch.vtensor
}
// -----
// Correctly handle the case that operands are statically the wrong rank
// (rank 1 vs rank 2 expected for matmul.)
func.func @torch.aten.mm$no_convert$wrong_rank(%arg0: !torch.vtensor<[?],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?],f32> {
// expected-error@+1 {{failed to legalize}}
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?],f32>, !torch.vtensor<[?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// If the result is missing dtype, we cannot lower it.
func.func @torch.aten.mm$no_convert$result_missing_dtype(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor {
// expected-error@+1 {{failed to legalize}}
%0 = torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor
return %0 : !torch.vtensor
}
// -----
// CHECK-LABEL: func.func @torch.aten.Int.Tensor$zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[],si64>) -> !torch.int {
// CHECK: %[[I:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[],si64> -> tensor<i64>
// CHECK: %[[EXT:.*]] = tensor.extract %[[I]][] : tensor<i64>
// CHECK: %[[RET:.*]] = torch_c.from_i64 %[[EXT]]
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.Int.Tensor$zero_rank(%arg0: !torch.vtensor<[],si64>) -> !torch.int {
%0 = torch.aten.Int.Tensor %arg0 : !torch.vtensor<[],si64> -> !torch.int
return %0 : !torch.int
}
// -----
// CHECK-LABEL: func.func @torch.aten.Int.Tensor$non_zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[?,?],si64>) -> !torch.int {
// CHECK: %[[I:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],si64> -> tensor<?x?xi64>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[DIM0:.*]] = tensor.dim %[[I]], %[[C0]] : tensor<?x?xi64>
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[DIM1:.*]] = tensor.dim %[[I]], %[[C1]] : tensor<?x?xi64>
// CHECK: %[[ONE:.*]] = arith.constant 1 : i64
// CHECK: %[[DIM0_INDEX:.*]] = arith.index_cast %[[DIM0]] : index to i64
// CHECK: %[[PRED0:.*]] = arith.cmpi eq, %[[DIM0_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED0]], "mismatching contracting dimension"
// CHECK: %[[DIM1_INDEX:.*]] = arith.index_cast %[[DIM1]] : index to i64
// CHECK: %[[PRED1:.*]] = arith.cmpi eq, %[[DIM1_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED1]], "mismatching contracting dimension"
// CHECK: %[[ZERO:.*]] = arith.constant 0 : index
// CHECK: %[[EXT:.*]] = tensor.extract %[[I]][%[[ZERO]], %[[ZERO]]] : tensor<?x?xi64>
// CHECK: %[[RET:.*]] = torch_c.from_i64 %[[EXT]]
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.Int.Tensor$non_zero_rank(%arg0: !torch.vtensor<[?,?],si64>) -> !torch.int {
%0 = torch.aten.Int.Tensor %arg0 : !torch.vtensor<[?,?],si64> -> !torch.int
return %0 : !torch.int
}
// -----
// CHECK-LABEL: func.func @torch.aten.Int.Tensor$zero_rank$byte_dtype
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[],ui8>) -> !torch.int {
// CHECK: %[[I:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[],ui8> -> tensor<i8>
// CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[EXTRACT:.*]] = tensor.extract %[[I]][] : tensor<i8>
// CHECK: %[[RES:.*]] = arith.extui %[[EXTRACT]] : i8 to i64
// CHECK: %[[RET:.*]] = torch_c.from_i64 %[[RES]]
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.Int.Tensor$zero_rank$byte_dtype(%arg0: !torch.vtensor<[],ui8>) -> !torch.int {
%0 = torch.aten.Int.Tensor %arg0 : !torch.vtensor<[],ui8> -> !torch.int
return %0 : !torch.int
}
// -----
// CHECK-LABEL: func.func @torch.aten.Int.Tensor$zero_rank$char_dtype
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[],si8>) -> !torch.int {
// CHECK: %[[I:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[],si8> -> tensor<i8>
// CHECK: %[[C1_I64:.*]] = arith.constant 1 : i64
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[EXTRACT:.*]] = tensor.extract %[[I]][] : tensor<i8>
// CHECK: %[[RES:.*]] = arith.extsi %[[EXTRACT]] : i8 to i64
// CHECK: %[[RET:.*]] = torch_c.from_i64 %[[RES]]
// CHECK: return %[[RET]] : !torch.int
func.func @torch.aten.Int.Tensor$zero_rank$char_dtype(%arg0: !torch.vtensor<[],si8>) -> !torch.int {
%0 = torch.aten.Int.Tensor %arg0 : !torch.vtensor<[],si8> -> !torch.int
return %0 : !torch.int
}
// -----
// CHECK-LABEL: func.func @torch.aten.Float.Tensor$zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[],f64>) -> !torch.float {
// CHECK: %[[F:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[],f64> -> tensor<f64>
// CHECK: %[[EXT:.*]] = tensor.extract %[[F]][] : tensor<f64>
// CHECK: %[[RET:.*]] = torch_c.from_f64 %[[EXT]]
// CHECK: return %[[RET]] : !torch.float
func.func @torch.aten.Float.Tensor$zero_rank(%arg0: !torch.vtensor<[],f64>) -> !torch.float {
%0 = torch.aten.Float.Tensor %arg0 : !torch.vtensor<[],f64> -> !torch.float
return %0 : !torch.float
}
// -----
// CHECK-LABEL: func.func @torch.aten.Float.Tensor$non_zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[?,?],f64>) -> !torch.float {
// CHECK: %[[F:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f64> -> tensor<?x?xf64>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[DIM0:.*]] = tensor.dim %[[F]], %[[C0]] : tensor<?x?xf64>
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[DIM1:.*]] = tensor.dim %[[F]], %[[C1]] : tensor<?x?xf64>
// CHECK: %[[ONE:.*]] = arith.constant 1 : i64
// CHECK: %[[DIM0_INDEX:.*]] = arith.index_cast %[[DIM0]] : index to i64
// CHECK: %[[PRED0:.*]] = arith.cmpi eq, %[[DIM0_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED0]], "mismatching contracting dimension"
// CHECK: %[[DIM1_INDEX:.*]] = arith.index_cast %[[DIM1]] : index to i64
// CHECK: %[[PRED1:.*]] = arith.cmpi eq, %[[DIM1_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED1]], "mismatching contracting dimension"
// CHECK: %[[ZERO:.*]] = arith.constant 0 : index
// CHECK: %[[EXT:.*]] = tensor.extract %[[F]][%[[ZERO]], %[[ZERO]]] : tensor<?x?xf64>
// CHECK: %[[RET:.*]] = torch_c.from_f64 %[[EXT]]
// CHECK: return %[[RET]] : !torch.float
func.func @torch.aten.Float.Tensor$non_zero_rank(%arg0: !torch.vtensor<[?,?],f64>) -> !torch.float {
%0 = torch.aten.Float.Tensor %arg0 : !torch.vtensor<[?,?],f64> -> !torch.float
return %0 : !torch.float
}
// -----
// CHECK-LABEL: func.func @torch.aten.Bool.Tensor$zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[],i1>) -> !torch.bool {
// CHECK: %[[B:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[],i1> -> tensor<i1>
// CHECK: %[[EXT:.*]] = tensor.extract %[[B]][] : tensor<i1>
// CHECK: %[[RES:.*]] = torch_c.from_i1 %[[EXT]]
// CHECK: return %[[RES]] : !torch.bool
func.func @torch.aten.Bool.Tensor$zero_rank(%arg0: !torch.vtensor<[],i1>) -> !torch.bool {
%0 = torch.aten.Bool.Tensor %arg0 : !torch.vtensor<[],i1> -> !torch.bool
return %0 : !torch.bool
}
// -----
// CHECK-LABEL: func.func @torch.aten.Bool.Tensor$non_zero_rank
// CHECK-SAME: (%[[ARG:.*]]: !torch.vtensor<[?,?],i1>) -> !torch.bool {
// CHECK: %[[B:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],i1> -> tensor<?x?xi1>
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[DIM0:.*]] = tensor.dim %[[B]], %[[C0]] : tensor<?x?xi1>
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[DIM1:.*]] = tensor.dim %[[B]], %[[C1]] : tensor<?x?xi1>
// CHECK: %[[ONE:.*]] = arith.constant 1 : i64
// CHECK: %[[DIM0_INDEX:.*]] = arith.index_cast %[[DIM0]] : index to i64
// CHECK: %[[PRED0:.*]] = arith.cmpi eq, %[[DIM0_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED0]], "mismatching contracting dimension"
// CHECK: %[[DIM1_INDEX:.*]] = arith.index_cast %[[DIM1]] : index to i64
// CHECK: %[[PRED1:.*]] = arith.cmpi eq, %[[DIM1_INDEX]], %[[ONE]] : i64
// CHECK: assert %[[PRED1]], "mismatching contracting dimension"
// CHECK: %[[ZERO:.*]] = arith.constant 0 : index
// CHECK: %[[EXT:.*]] = tensor.extract %[[I]][%[[ZERO]], %[[ZERO]]] : tensor<?x?xi1>
// CHECK: %[[RET:.*]] = torch_c.from_i1 %[[EXT]]
// CHECK: return %[[RET]] : !torch.bool
func.func @torch.aten.Bool.Tensor$non_zero_rank(%arg0: !torch.vtensor<[?,?],i1>) -> !torch.bool {
%0 = torch.aten.Bool.Tensor %arg0 : !torch.vtensor<[?,?],i1> -> !torch.bool
return %0 : !torch.bool
}
// -----
// CHECK: func.func @torch.prim.NumToTensor.Scalar$basic(%[[IN:.*]]: !torch.int) -> !torch.vtensor<[],si64> {
// CHECK: %[[INI64:.*]] = torch_c.to_i64 %[[IN]]
// CHECK: %[[NEWVEC:.*]] = tensor.empty() : tensor<i64>
// CHECK: %[[FILLVEC:.*]] = linalg.fill ins(%[[INI64]] : i64) outs(%[[NEWVEC]] : tensor<i64>) -> tensor<i64>
// CHECK: %[[OUTVEC:.*]] = torch_c.from_builtin_tensor %[[FILLVEC]] : tensor<i64> -> !torch.vtensor<[],si64>
// CHECK: return %[[OUTVEC]] : !torch.vtensor<[],si64>
func.func @torch.prim.NumToTensor.Scalar$basic(%arg0: !torch.int) -> !torch.vtensor<[],si64> {
%0 = torch.prim.NumToTensor.Scalar %arg0 : !torch.int -> !torch.vtensor<[],si64>
return %0 : !torch.vtensor<[],si64>
}
// -----
// CHECK-LABEL: func.func @torch.tensor_static_info_cast$basic(
// CHECK-SAME: %[[VALUE_T:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[4],f32> {
// CHECK: %[[T:.*]] = torch_c.to_builtin_tensor %[[VALUE_T]] : !torch.vtensor<[?],f32> -> tensor<?xf32>
// CHECK: %[[T_CAST:.*]] = tensor.cast %[[T]] : tensor<?xf32> to tensor<4xf32>
// CHECK: %[[VALUE_T_CAST:.*]] = torch_c.from_builtin_tensor %[[T_CAST]] : tensor<4xf32> -> !torch.vtensor<[4],f32>
// CHECK: return %[[VALUE_T_CAST]] : !torch.vtensor<[4],f32>
func.func @torch.tensor_static_info_cast$basic(%t: !torch.vtensor<[?],f32>) -> !torch.vtensor<[4],f32> {
%t_cast = torch.tensor_static_info_cast %t : !torch.vtensor<[?],f32> to !torch.vtensor<[4],f32>
return %t_cast : !torch.vtensor<[4],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.neg
// CHECK: linalg.generic {{.*}} {
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: f32, %{{.*}}: f32):
// CHECK-NEXT: %[[NEG:.*]] = arith.negf %[[LHS]] : f32
// CHECK-NEXT: linalg.yield %[[NEG]] : f32
// CHECK-NEXT: } -> tensor<?x?xf32>
func.func @torch.aten.neg(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func.func @torch.aten.neg.bf16
// CHECK: linalg.generic {{.*}} {
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: bf16, %{{.*}}: bf16):
// CHECK-NEXT: %[[NEG:.*]] = arith.negf %[[LHS]] : bf16
// CHECK-NEXT: linalg.yield %[[NEG]] : bf16
// CHECK-NEXT: } -> tensor<?x?xbf16>
func.func @torch.aten.neg.bf16(%arg0: !torch.vtensor<[?,?],bf16>) -> !torch.vtensor<[?,?],bf16> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],bf16> -> !torch.vtensor<[?,?],bf16>
return %0 : !torch.vtensor<[?,?],bf16>
}
// -----
// CHECK-LABEL: func.func @torch.aten.neg.f16
// CHECK: linalg.generic {{.*}} {
// CHECK-NEXT: ^bb0(%[[LHS:.*]]: f16, %{{.*}}: f16):
// CHECK-NEXT: %[[NEG:.*]] = arith.negf %[[LHS]] : f16
// CHECK-NEXT: linalg.yield %[[NEG]] : f16
// CHECK-NEXT: } -> tensor<?x?xf16>
func.func @torch.aten.neg.f16(%arg0: !torch.vtensor<[?,?],f16>) -> !torch.vtensor<[?,?],f16> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f16> -> !torch.vtensor<[?,?],f16>
return %0 : !torch.vtensor<[?,?],f16>
}
// -----
// CHECK-LABEL: func.func @torch.aten.index.Tensor
// CHECK-SAME: (%[[INPUT:.*]]: !torch.vtensor<[?,?,?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?,1],si64>, %[[ARG2:.*]]: !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[T:.*]] = torch_c.to_builtin_tensor %[[INPUT]] : !torch.vtensor<[?,?,?],f32> -> tensor<?x?x?xf32>
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[INDICES:.*]] = torch.prim.ListConstruct %[[ARG1]], %[[NONE]], %[[ARG2]] : (!torch.vtensor<[?,1],si64>, !torch.none, !torch.vtensor<[?],si64>) -> !torch.list<optional<vtensor>>
// CHECK: %[[INDEX1:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,1],si64> -> tensor<?x1xi64>
// CHECK: %[[INDEX2:.*]] = torch_c.to_builtin_tensor %[[ARG2]] : !torch.vtensor<[?],si64> -> tensor<?xi64>
// CHECK: %[[CST0:.*]] = arith.constant 0 : index
// CHECK: %[[DIM0:.*]] = tensor.dim %[[INDEX1]], %[[CST0]] : tensor<?x1xi64>
// CHECK: %[[CST0_0:.*]] = arith.constant 0 : index
// CHECK: %[[DIM1:.*]] = tensor.dim %[[INDEX2]], %[[CST0_0]] : tensor<?xi64>
// CHECK: %[[CST1:.*]] = arith.constant 1 : index
// CHECK: %[[DIM2:.*]] = tensor.dim %[[T]], %[[CST1]] : tensor<?x?x?xf32>
// CHECK: %[[OUT_T:.*]] = tensor.empty(%[[DIM0]], %[[DIM1]], %[[DIM2]]) : tensor<?x?x?xf32>
// CHECK: %[[OUT:.*]] = linalg.generic {indexing_maps = [#map0, #map1, #map2], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[INDEX1]], %[[INDEX2]] : tensor<?x1xi64>, tensor<?xi64>) outs(%[[OUT_T]] : tensor<?x?x?xf32>) {
// CHECK: ^bb0(%[[IN1:.*]]: i64, %[[IN2:.*]]: i64, %[[IN3:.*]]: f32):
// CHECK: %[[INDEX_1:.*]] = arith.index_cast %[[IN1]] : i64 to index
// CHECK: %[[INDEX_2:.*]] = linalg.index 2 : index
// CHECK: %[[INDEX_3:.*]] = arith.index_cast %[[IN2]] : i64 to index
// CHECK: %[[RESULT:.*]] = tensor.extract %[[T]][%[[INDEX_1]], %[[INDEX_2]], %[[INDEX_3]]] : tensor<?x?x?xf32>
// CHECK: linalg.yield %[[RESULT]] : f32
// CHECK: } -> tensor<?x?x?xf32>
// CHECK: %[[OUT_CAST:.*]] = tensor.cast %[[OUT]] : tensor<?x?x?xf32> to tensor<?x?x?xf32>
// CHECK: %[[VALUE_OUT_CAST:.*]] = torch_c.from_builtin_tensor %[[OUT_CAST]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[VALUE_OUT_CAST]] : !torch.vtensor<[?,?,?],f32>
func.func @torch.aten.index.Tensor(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[?,1],si64>, %arg2: !torch.vtensor<[?],si64>) -> !torch.vtensor<[?,?,?],f32> {
%none = torch.constant.none
%1 = torch.prim.ListConstruct %arg1, %none, %arg2 : (!torch.vtensor<[?,1],si64>, !torch.none, !torch.vtensor<[?],si64>) -> !torch.list<optional<vtensor>>
%2 = torch.aten.index.Tensor %arg0, %1 : !torch.vtensor<[?,?,?],f32>, !torch.list<optional<vtensor>> -> !torch.vtensor<[?,?,?],f32>
return %2 : !torch.vtensor<[?,?,?],f32>
}
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