mirror of https://github.com/llvm/torch-mlir
115 lines
8.5 KiB
MLIR
115 lines
8.5 KiB
MLIR
// RUN: torch-mlir-opt -torch-reduce-op-variants %s | FileCheck %s
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// CHECK-LABEL: func @convert_to_value_semantic_tensors(
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// CHECK-SAME: %[[ARG:.*]]: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
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// CHECK: %[[OPERAND_TENSOR:.*]] = torch.copy.to_vtensor %[[ARG]] : !torch.vtensor<[],f32>
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// CHECK: %[[RESULT_TENSOR:.*]] = torch.aten.tanh %[[OPERAND_TENSOR]] : !torch.vtensor<[],f32> -> !torch.vtensor<[],f32>
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// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[RESULT_TENSOR]] : !torch.tensor<[],f32>
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// CHECK: return %[[RET]] : !torch.tensor<[],f32>
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func @convert_to_value_semantic_tensors(%arg0: !torch.tensor<[],f32>) -> !torch.tensor<[],f32> {
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%0 = torch.aten.tanh %arg0 : !torch.tensor<[],f32> -> !torch.tensor<[],f32>
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return %0 : !torch.tensor<[],f32>
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}
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// CHECK-LABEL: func @convert_to_value_semantic_tensors_list(
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// CHECK-SAME: %[[VT0:.*]]: !torch.vtensor, %[[VT1:.*]]: !torch.vtensor,
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// CHECK-SAME: %[[VT2:.*]]: !torch.vtensor) -> !torch.tensor {
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// CHECK: %[[T0:.*]] = torch.copy.to_tensor %[[VT0]] : !torch.tensor
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// CHECK: %[[T1:.*]] = torch.copy.to_tensor %[[VT1]] : !torch.tensor
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// CHECK: %[[T2:.*]] = torch.copy.to_tensor %[[VT2]] : !torch.tensor
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// CHECK: %[[DIM:.*]] = torch.constant.int 1
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// CHECK: %[[LIST_ORIG:.*]] = torch.prim.ListConstruct %[[T0]], %[[T1]], %[[T2]] :
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// CHECK-SAME: (!torch.tensor, !torch.tensor, !torch.tensor) -> !torch.list<!torch.tensor>
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// CHECK: %[[VT0_COPY:.*]] = torch.copy.to_vtensor %[[T0]] : !torch.vtensor
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// CHECK: %[[VT1_COPY:.*]] = torch.copy.to_vtensor %[[T1]] : !torch.vtensor
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// CHECK: %[[VT2_COPY:.*]] = torch.copy.to_vtensor %[[T2]] : !torch.vtensor
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// CHECK: %[[LIST_NEW:.*]] = torch.prim.ListConstruct
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// CHECK-SAME: %[[VT0_COPY]], %[[VT1_COPY]], %[[VT2_COPY]] :
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// CHECK-SAME: (!torch.vtensor, !torch.vtensor, !torch.vtensor) -> !torch.list<!torch.vtensor>
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// CHECK: %[[VRET:.*]] = torch.aten.cat %[[LIST_NEW]], %[[DIM]] :
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// CHECK-SAME: !torch.list<!torch.vtensor>, !torch.int -> !torch.vtensor
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// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
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// CHECK: return %[[RET]] : !torch.tensor
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func @convert_to_value_semantic_tensors_list(%vt0: !torch.vtensor, %vt1: !torch.vtensor, %vt2: !torch.vtensor) -> !torch.tensor {
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%t0 = torch.copy.to_tensor %vt0 : !torch.tensor
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%t1 = torch.copy.to_tensor %vt1 : !torch.tensor
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%t2 = torch.copy.to_tensor %vt2 : !torch.tensor
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%int1 = torch.constant.int 1
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%list = torch.prim.ListConstruct %t0, %t1, %t2 : (!torch.tensor, !torch.tensor, !torch.tensor) -> !torch.list<!torch.tensor>
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%ret = torch.aten.cat %list, %int1 : !torch.list<!torch.tensor>, !torch.int -> !torch.tensor
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return %ret : !torch.tensor
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}
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// CHECK-LABEL: func @convert_to_value_semantic_tensors_optional(
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// CHECK-SAME: %[[INPUT:.*]]: !torch.tensor, %[[FLOAT_TENSOR:.*]]: !torch.tensor<[4],f32>,
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// CHECK-SAME: %[[TRAINING:.*]]: !torch.bool, %[[CUDNN_ENABLE:.*]]: !torch.bool,
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// CHECK-SAME: %[[FLOAT:.*]]: !torch.float) -> !torch.tensor {
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// CHECK: %[[NONE:.*]] = torch.constant.none
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// CHECK: %[[FLOAT_TENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_TENSOR]] :
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// CHECK-SAME: !torch.tensor<[4],f32> to !torch.optional<!torch.tensor>
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// CHECK: %[[BIAS_NONE_OPTIONAL:.*]] = torch.derefine %[[NONE]] : !torch.none to !torch.optional<!torch.tensor>
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// CHECK: %[[VINPUT:.*]] = torch.copy.to_vtensor %[[INPUT]] : !torch.vtensor
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// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
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// CHECK: %[[WEIGHTS_TENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
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// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<!torch.vtensor<[4],f32>>
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// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
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// CHECK: %[[MEAN_VTENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
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// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<!torch.vtensor<[4],f32>>
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// CHECK: %[[FLOAT_VTENSOR:.*]] = torch.copy.to_vtensor %[[FLOAT_TENSOR]] : !torch.vtensor<[4],f32>
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// CHECK: %[[VAR_VTENSOR_OPTIONAL:.*]] = torch.derefine %[[FLOAT_VTENSOR]] :
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// CHECK-SAME: !torch.vtensor<[4],f32> to !torch.optional<!torch.vtensor<[4],f32>>
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// CHECK: %[[VRET:.*]] = torch.aten.batch_norm %[[VINPUT]], %[[WEIGHTS_TENSOR_OPTIONAL]],
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// CHECK-SAME: %[[BIAS_NONE_OPTIONAL]], %[[MEAN_VTENSOR_OPTIONAL]], %[[VAR_VTENSOR_OPTIONAL]],
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// CHECK-SAME: %[[TRAINING]], %[[FLOAT]], %[[FLOAT]], %[[CUDNN_ENABLE]] :
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// CHECK-SAME: !torch.vtensor, !torch.optional<!torch.vtensor<[4],f32>>, !torch.optional<!torch.tensor>,
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// CHECK-SAME: !torch.optional<!torch.vtensor<[4],f32>>, !torch.optional<!torch.vtensor<[4],f32>>,
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// CHECK-SAME: !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor
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// CHECK: %[[RET:.*]] = torch.copy.to_tensor %[[VRET]] : !torch.tensor
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// CHECK: return %[[RET]] : !torch.tensor
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// CHECK: }
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func @convert_to_value_semantic_tensors_optional(%t: !torch.tensor,
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%ft: !torch.tensor<[4],f32>,
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%training: !torch.bool,
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%cudnn_enable: !torch.bool,
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%f : !torch.float) -> !torch.tensor {
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%none = torch.constant.none
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%tensor_optional = torch.derefine %ft: !torch.tensor<[4],f32> to !torch.optional<!torch.tensor>
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%none_optional = torch.derefine %none : !torch.none to !torch.optional<!torch.tensor>
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%ret = torch.aten.batch_norm %t, %tensor_optional, %none_optional, %tensor_optional,
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%tensor_optional, %training, %f, %f, %cudnn_enable:
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!torch.tensor, !torch.optional<!torch.tensor>, !torch.optional<!torch.tensor>,
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!torch.optional<!torch.tensor>, !torch.optional<!torch.tensor>,
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!torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.tensor
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return %ret: !torch.tensor
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}
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// CHECK-LABEL: func @reduce_trailing_underscore_inplace_variant(
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// CHECK-SAME: %[[ARG0:.*]]: !torch.tensor<[2,2],f32>,
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// CHECK-SAME: %[[ARG1:.*]]: !torch.tensor<[2,2],f32>) -> (!torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>) {
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// CHECK: %[[C1:.*]] = torch.constant.int 1
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// CHECK: %[[TENSOR0:.*]] = torch.copy.to_vtensor %[[ARG0]] : !torch.vtensor<[2,2],f32>
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// CHECK: %[[TENSOR1:.*]] = torch.copy.to_vtensor %[[ARG1]] : !torch.vtensor<[2,2],f32>
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// 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>
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// Note: This somewhat redundant conversion back and forth
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// (which is cleaned up by canonicalization) is an artifact of two patterns
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// being applied in sequence.
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// CHECK: %[[ARRAY_RESULT:.*]] = torch.copy.to_tensor %[[TENSOR_RESULT]] : !torch.tensor<[2,2],f32>
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// CHECK: %[[TENSOR_AGAIN:.*]] = torch.copy.to_vtensor %[[ARRAY_RESULT]] : !torch.vtensor<[2,2],f32>
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// CHECK: torch.overwrite.tensor %[[TENSOR_AGAIN]] overwrites %[[ARG0]] : !torch.vtensor<[2,2],f32>, !torch.tensor<[2,2],f32>
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// CHECK: return %[[ARG0]], %[[ARG0]] : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>
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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>) {
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%c1 = torch.constant.int 1
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%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>
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return %0, %arg0 : !torch.tensor<[2,2],f32>, !torch.tensor<[2,2],f32>
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}
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// CHECK-LABEL: func @torch.tensor.literal() -> !torch.tensor {
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// CHECK: %[[VTENSOR:.*]] = torch.vtensor.literal(dense<0.000000e+00> : tensor<7xf32>) : !torch.vtensor<[7],f32>
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// CHECK: %[[SIZES_ERASED:.*]] = torch.tensor_static_info_cast %[[VTENSOR]] : !torch.vtensor<[7],f32> to !torch.vtensor
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// CHECK: %[[TENSOR:.*]] = torch.copy.to_tensor %[[SIZES_ERASED]] : !torch.tensor
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// CHECK: return %[[TENSOR]] : !torch.tensor
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func @torch.tensor.literal() -> !torch.tensor {
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%0 = torch.tensor.literal(dense<0.0> : tensor<7xf32>) : !torch.tensor
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return %0 : !torch.tensor
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}
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