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torch-mlir/test/Dialect/Torch/reduce-op-variants.mlir

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// RUN: torch-mlir-opt -torch-reduce-op-variants --split-input-file %s | FileCheck %s
// CHECK-LABEL: func.func @convert_to_value_semantic_tensors(
// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
// CHECK: %[[OPERAND_TENSOR:.*]] = torch.copy.to_vtensor %[[ARG]] : !torch.vtensor<[],f32>
// CHECK: %[[RESULT_TENSOR:.*]] = torch.aten.tanh %[[OPERAND_TENSOR]] : !torch.vtensor<[],f32> -> !torch.vtensor<[],f32>
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[RESULT_TENSOR]] : !torch.tensor<[],f32>
// CHECK: return %[[RET]] : !torch.tensor<[],f32>
func.func @convert_to_value_semantic_tensors(%arg0: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
%0 = torch.aten.tanh %arg0 : !torch.tensor<[],f32> -> !torch.tensor<[],f32>
return %0 : !torch.tensor<[],f32>
}
// -----
// CHECK-LABEL: func.func @convert_to_value_semantic_tensors_list(
// CHECK-SAME: %[[VT0:.*]]: !torch.vtensor, %[[VT1:.*]]: !torch.vtensor,
// CHECK-SAME: %[[VT2:.*]]: !torch.vtensor) -> !torch.tensor {
// CHECK: %[[T0:.*]] = torch.copy.to_tensor %[[VT0]] : !torch.tensor
// CHECK: %[[T1:.*]] = torch.copy.to_tensor %[[VT1]] : !torch.tensor
// CHECK: %[[T2:.*]] = torch.copy.to_tensor %[[VT2]] : !torch.tensor
// CHECK: %[[DIM:.*]] = torch.constant.int 1
// CHECK: %[[LIST_ORIG:.*]] = torch.prim.ListConstruct %[[T0]], %[[T1]], %[[T2]] :
// CHECK-SAME: (!torch.tensor, !torch.tensor, !torch.tensor) -> !torch.list<tensor>
// CHECK: %[[VT0_COPY:.*]] = torch.copy.to_vtensor %[[T0]] : !torch.vtensor
// CHECK: %[[VT1_COPY:.*]] = torch.copy.to_vtensor %[[T1]] : !torch.vtensor
// CHECK: %[[VT2_COPY:.*]] = torch.copy.to_vtensor %[[T2]] : !torch.vtensor
// CHECK: %[[LIST_NEW:.*]] = torch.prim.ListConstruct
// CHECK-SAME: %[[VT0_COPY]], %[[VT1_COPY]], %[[VT2_COPY]] :
// CHECK-SAME: (!torch.vtensor, !torch.vtensor, !torch.vtensor) -> !torch.list<vtensor>
// CHECK: %[[VRET:.*]] = torch.aten.cat %[[LIST_NEW]], %[[DIM]] :
// CHECK-SAME: !torch.list<vtensor>, !torch.int -> !torch.vtensor
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
// CHECK: return %[[RET]] : !torch.tensor
func.func @convert_to_value_semantic_tensors_list(%vt0: !torch.vtensor, %vt1: !torch.vtensor, %vt2: !torch.vtensor) -> !torch.tensor {
%t0 = torch.copy.to_tensor %vt0 : !torch.tensor
%t1 = torch.copy.to_tensor %vt1 : !torch.tensor
%t2 = torch.copy.to_tensor %vt2 : !torch.tensor
%int1 = torch.constant.int 1
%list = torch.prim.ListConstruct %t0, %t1, %t2 : (!torch.tensor, !torch.tensor, !torch.tensor) -> !torch.list<tensor>
%ret = torch.aten.cat %list, %int1 : !torch.list<tensor>, !torch.int -> !torch.tensor
return %ret : !torch.tensor
}
// -----
// CHECK-LABEL: func.func @convert_to_value_semantic_tensors_optional(
// CHECK-SAME: %[[INPUT:.*]]: !torch.tensor, %[[FLOAT_TENSOR:.*]]: !torch.tensor<[4],f32>,
// CHECK-SAME: %[[TRAINING:.*]]: !torch.bool, %[[CUDNN_ENABLE:.*]]: !torch.bool,
// CHECK-SAME: %[[FLOAT:.*]]: !torch.float) -> !torch.tensor {
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[FLOAT_TENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_TENSOR]] :
// CHECK-SAME: !torch.tensor<[4],f32> to !torch.optional<tensor>
// CHECK: %[[BIAS_NONE_OPTIONAL:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<tensor>
// CHECK: %[[VINPUT:.*]] = torch.copy.to_vtensor %[[INPUT]] : !torch.vtensor
// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
// CHECK: %[[WEIGHTS_TENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<vtensor<[4],f32>>
// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
// CHECK: %[[MEAN_VTENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<vtensor<[4],f32>>
// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
// CHECK: %[[VAR_VTENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<vtensor<[4],f32>>
// CHECK: %[[VRET:.*]] = torch.aten.batch_norm %[[VINPUT]], %[[WEIGHTS_TENSOR_OPTIONAL]],
// CHECK-SAME: %[[BIAS_NONE_OPTIONAL]], %[[MEAN_VTENSOR_OPTIONAL]], %[[VAR_VTENSOR_OPTIONAL]],
// CHECK-SAME: %[[TRAINING]], %[[FLOAT]], %[[FLOAT]], %[[CUDNN_ENABLE]] :
// CHECK-SAME: !torch.vtensor, !torch.optional<vtensor<[4],f32>>, !torch.optional<tensor>,
// CHECK-SAME: !torch.optional<vtensor<[4],f32>>, !torch.optional<vtensor<[4],f32>>,
// CHECK-SAME: !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
// CHECK: return %[[RET]] : !torch.tensor
// CHECK: }
func.func @convert_to_value_semantic_tensors_optional(%t: !torch.tensor,
%ft: !torch.tensor<[4],f32>,
%training: !torch.bool,
%cudnn_enable: !torch.bool,
%f : !torch.float) -> !torch.tensor {
%none = torch.constant.none
%tensor_optional = torch.derefine %ft: !torch.tensor<[4],f32> to !torch.optional<tensor>
%none_optional = torch.derefine %none : !torch.none to !torch.optional<tensor>
%ret = torch.aten.batch_norm %t, %tensor_optional, %none_optional, %tensor_optional,
%tensor_optional, %training, %f, %f, %cudnn_enable:
!torch.tensor, !torch.optional<tensor>, !torch.optional<tensor>,
!torch.optional<tensor>, !torch.optional<tensor>,
!torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.tensor
return %ret: !torch.tensor
}
// -----
// CHECK-LABEL: func.func @reduce_trailing_underscore_inplace_variant(
// CHECK-SAME: %[[ARG0:.*]]: !torch.tensor<[2,2],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.tensor<[2,2],f32>) -> (!torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>) {
// CHECK: %[[C1:.*]] = torch.constant.int 1
// CHECK: %[[TENSOR0:.*]] = torch.copy.to_vtensor %[[ARG0]] : !torch.vtensor<[2,2],f32>
// CHECK: %[[TENSOR1:.*]] = torch.copy.to_vtensor %[[ARG1]] : !torch.vtensor<[2,2],f32>
// CHECK: %[[TENSOR_RESULT:.*]] = torch.aten.add.Tensor %[[TENSOR0]], %[[TENSOR1]], %[[C1]] : !torch.vtensor<[2,2],f32>, !torch.vtensor<[2,2],f32>, !torch.int -> !torch.vtensor<[2,2],f32>
// Note: This somewhat redundant conversion back and forth
// (which is cleaned up by canonicalization) is an artifact of two patterns
// being applied in sequence.
// CHECK: %[[ARRAY_RESULT:.*]] = torch.copy.to_tensor %[[TENSOR_RESULT]] : !torch.tensor<[2,2],f32>
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: %[[DTYPE:.*]] = torch.constant.int 6
// CHECK: %[[DTYPE_RESULT:.*]] = torch.aten.to.dtype %[[ARRAY_RESULT]], %[[DTYPE]], %[[FALSE]], %[[FALSE]], %[[NONE]] : !torch.tensor<[2,2],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.tensor<[2,2],f32>
// CHECK: %[[TENSOR_AGAIN:.*]] = torch.copy.to_vtensor %[[DTYPE_RESULT]] : !torch.vtensor<[2,2],f32>
// CHECK: torch.overwrite.tensor.contents %[[TENSOR_AGAIN]] overwrites %[[ARG0]] : !torch.vtensor<[2,2],f32>, !torch.tensor<[2,2],f32>
// CHECK: return %[[ARG0]], %[[ARG0]] : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>
func.func @reduce_trailing_underscore_inplace_variant(%arg0: !torch.tensor<[2,2],f32>, %arg1: !torch.tensor<[2,2],f32>) -> (!torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>) {
%c1 = torch.constant.int 1
%0 = torch.aten.add_.Tensor %arg0, %arg1, %c1 : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>, !torch.int -> !torch.tensor<[2,2],f32>
return %0, %arg0 : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>
}
// -----
// CHECK-LABEL: func.func @torch.tensor.literal() -> !torch.tensor {
// CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<0.000000e+00> : tensor<7xf32>) : !torch.vtensor<[7],f32>
// CHECK: %[[SIZES_ERASED:.*]] = torch.tensor_static_info_cast %[[VTENSOR]] : !torch.vtensor<[7],f32> to !torch.vtensor
// CHECK: %[[TENSOR:.*]] = torch.copy.to_tensor %[[SIZES_ERASED]] : !torch.tensor
// CHECK: return %[[TENSOR]] : !torch.tensor
func.func @torch.tensor.literal() -> !torch.tensor {
%0 = torch.tensor.literal(dense<0.0> : tensor<7xf32>) : !torch.tensor
return %0 : !torch.tensor
}
// -----
// CHECK-LABEL: func.func @convert_to_value_semantic_tensors_optional_list(
// CHECK-SAME: %[[SELF:.*]]: !torch.tensor<[5],f32>,
// CHECK-SAME: %[[INDICES:.*]]: !torch.tensor<[2,3],si64>) -> !torch.tensor {
// CHECK: %[[INDICES_OPTIONAL_LIST:.*]] = torch.prim.ListConstruct %[[INDICES]] :
// CHECK-SAME: (!torch.tensor<[2,3],si64>) -> !torch.list<optional<tensor<[2,3],si64>>>
// CHECK: %[[SELF_VTENSOR:.*]] = torch.copy.to_vtensor %[[SELF]] : !torch.vtensor<[5],f32>
// CHECK: %[[INDICES_VTENSOR:.*]] = torch.copy.to_vtensor %[[INDICES]] : !torch.vtensor<[2,3],si64>
// CHECK: %[[INDICES_LIST:.*]] = torch.prim.ListConstruct %[[INDICES_VTENSOR]] : (!torch.vtensor<[2,3],si64>) -> !torch.list<optional<vtensor<[2,3],si64>>>
// CHECK: %[[VRET:.*]] = torch.aten.index.Tensor %[[SELF_VTENSOR]], %[[INDICES_LIST]] : !torch.vtensor<[5],f32>, !torch.list<optional<vtensor<[2,3],si64>>> -> !torch.vtensor
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
// CHECK: return %[[RET]] : !torch.tensor
func.func @convert_to_value_semantic_tensors_optional_list(%self: !torch.tensor<[5],f32>, %indices: !torch.tensor<[2,3],si64>) -> !torch.tensor {
%tensor_optional_list = torch.prim.ListConstruct %indices : (!torch.tensor<[2,3],si64>) -> !torch.list<optional<tensor<[2,3],si64>>>
%ret = torch.aten.index.Tensor %self, %tensor_optional_list : !torch.tensor<[5],f32>, !torch.list<optional<tensor<[2,3],si64>>> -> !torch.tensor
return %ret : !torch.tensor
}
// -----
// CHECK-LABEL: func.func @convert_to_value_semantic_tensors_optional_list_nones_and_tensors(
// CHECK-SAME: %[[SELF:.*]]: !torch.tensor<[5],f32>,
// CHECK-SAME: %[[INDICES_0:.*]]: !torch.tensor<[2,3],si64>,
// CHECK-SAME: %[[INDICES_1:.*]]: !torch.tensor<[3],si64>) -> !torch.tensor {
// CHECK: %[[NONE:.*]] = torch.constant.none
// CHECK: %[[INDICES_OPTIONAL_LIST:.*]] = torch.prim.ListConstruct %[[NONE]], %[[INDICES_0]], %[[INDICES_1]] :
// CHECK-SAME: (!torch.none, !torch.tensor<[2,3],si64>, !torch.tensor<[3],si64>) -> !torch.list<optional<tensor>>
// CHECK: %[[SELF_VTENSOR:.*]] = torch.copy.to_vtensor %[[SELF]] : !torch.vtensor<[5],f32>
// CHECK: %[[INDICES_VTENSOR_0:.*]] = torch.copy.to_vtensor %[[INDICES_0]] : !torch.vtensor<[2,3],si64>
// CHECK: %[[INDICES_VTENSOR_1:.*]] = torch.copy.to_vtensor %[[INDICES_1]] : !torch.vtensor<[3],si64>
// CHECK: %[[INDICES_LIST:.*]] = torch.prim.ListConstruct %[[NONE]], %[[INDICES_VTENSOR_0]], %[[INDICES_VTENSOR_1]] : (!torch.none, !torch.vtensor<[2,3],si64>, !torch.vtensor<[3],si64>) -> !torch.list<optional<vtensor>>
// CHECK: %[[VRET:.*]] = torch.aten.index.Tensor %[[SELF_VTENSOR]], %[[INDICES_LIST]] : !torch.vtensor<[5],f32>, !torch.list<optional<vtensor>> -> !torch.vtensor
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
// CHECK: return %[[RET]] : !torch.tensor
func.func @convert_to_value_semantic_tensors_optional_list_nones_and_tensors(%self: !torch.tensor<[5],f32>, %indices0: !torch.tensor<[2,3],si64>, %indices1: !torch.tensor<[3],si64>) -> !torch.tensor {
%none = torch.constant.none
%tensor_optional_list = torch.prim.ListConstruct %none, %indices0, %indices1 : (!torch.none, !torch.tensor<[2,3],si64>, !torch.tensor<[3],si64>) -> !torch.list<optional<tensor>>
%ret = torch.aten.index.Tensor %self, %tensor_optional_list : !torch.tensor<[5],f32>, !torch.list<optional<tensor>> -> !torch.tensor
return %ret : !torch.tensor
}
// -----
// CHECK-LABEL: func.func @torch.aten.bernoulli_.float(
// CHECK-SAME: %[[T:.*]]: !torch.tensor) -> !torch.tensor {
// CHECK: %[[GENERATOR:.*]] = torch.constant.none
// CHECK: %[[P:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[T_VTENSOR:.*]] = torch.copy.to_vtensor %[[T]] : !torch.vtensor
// CHECK: %[[VRET:.*]] = torch.valsem.aten.bernoulli.float %[[T_VTENSOR]], %[[P]], %[[GENERATOR]] : !torch.vtensor, !torch.float, !torch.none -> !torch.vtensor
// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
// CHECK: %[[COPY_VTENSOR:.*]] = torch.copy.to_vtensor %[[RET]] : !torch.vtensor
// CHECK: torch.overwrite.tensor.contents %[[COPY_VTENSOR]] overwrites %[[T]] : !torch.vtensor, !torch.tensor
// CHECK: return %[[T]] : !torch.tensor
func.func @torch.aten.bernoulli_.float(%t: !torch.tensor) -> !torch.tensor {
%generator = torch.constant.none
%p = torch.constant.float 5.000000e-01
%ret = torch.aten.bernoulli_.float %t, %p, %generator : !torch.tensor, !torch.float, !torch.none -> !torch.tensor
return %ret : !torch.tensor
}
// -----
// CHECK-LABEL: func.func @scaled_dot_product_flash_attention_for_cpu
// CHECK-SAME: %[[ARG0:.+]]: !torch.vtensor<[1,1,5,5],f32>, %[[ARG1:.+]]: !torch.vtensor<[1,1,5,5],f32>, %[[ARG2:.+]]: !torch.vtensor<[1,1,5,5],f32>
// CHECK: %[[ZERO:.+]] = torch.constant.float 0.000000e+00
// CHECK: %[[FALSE:.+]] = torch.constant.bool false
// CHECK: %[[NONE0:.+]] = torch.constant.none
// CHECK: %[[NONE1:.+]] = torch.constant.none
// CHECK: %[[ATTEN:.+]] = torch.aten.scaled_dot_product_attention %[[ARG0]], %[[ARG1]], %[[ARG2]], %[[NONE0]], %[[ZERO]], %[[FALSE]], %[[NONE1]], %[[FALSE]]
// CHECK: return %[[ATTEN]]
func.func @scaled_dot_product_flash_attention_for_cpu(%arg0: !torch.vtensor<[1,1,5,5],f32>, %arg1: !torch.vtensor<[1,1,5,5],f32>, %arg2: !torch.vtensor<[1,1,5,5],f32>) -> !torch.vtensor<[1,1,5,5],f32> {
%float0.000000e00 = torch.constant.float 0.000000e+00
%false = torch.constant.bool false
%none = torch.constant.none
%none_0 = torch.constant.none
%0:2 = torch.operator "torch.aten._scaled_dot_product_flash_attention_for_cpu"(%arg0, %arg1, %arg2, %float0.000000e00, %false, %none, %none_0) : (!torch.vtensor<[1,1,5,5],f32>, !torch.vtensor<[1,1,5,5],f32>, !torch.vtensor<[1,1,5,5],f32>, !torch.float, !torch.bool, !torch.none, !torch.none) -> (!torch.vtensor<[1,1,5,5],f32>, !torch.vtensor<[1,1,5],f32>)
return %0#0 : !torch.vtensor<[1,1,5,5],f32>
}
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