mirror of https://github.com/llvm/torch-mlir
82 lines
6.2 KiB
MLIR
82 lines
6.2 KiB
MLIR
// RUN: torch-mlir-opt -torch-decompose-complex-ops -split-input-file %s | FileCheck %s
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// CHECK-LABEL: func.func @matmul_no_decompose
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// CHECK: torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor
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func.func @matmul_no_decompose(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?],f32>) -> !torch.tensor {
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%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?,?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor
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return %0 : !torch.tensor
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}
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// -----
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// CHECK-LABEL: func.func @matmul_decompose_2d
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// CHECK: torch.aten.mm %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.tensor
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func.func @matmul_decompose_2d(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.tensor {
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%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.tensor
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return %0 : !torch.tensor
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}
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// -----
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// CHECK-LABEL: func.func @matmul_decompose_3d(
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// CHECK: torch.aten.bmm %arg0, %arg1 : !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor
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func.func @matmul_decompose_3d(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[?,?,?],f32>) -> !torch.tensor {
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%0 = torch.aten.matmul %arg0, %arg1 : !torch.vtensor<[?,?,?],f32>, !torch.vtensor<[?,?,?],f32> -> !torch.tensor
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return %0 : !torch.tensor
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}
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// -----
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// CHECK-LABEL: func @torch.aten.adaptive_avg_pool2d$non_unit_output_size(
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// CHECK-SAME: %[[SELF:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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// CHECK-DAG: %[[CST0:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[CST1:.*]] = torch.constant.int 1
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// CHECK-DAG: %[[CST2:.*]] = torch.constant.int 2
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// CHECK-DAG: %[[CST3:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[CST6:.*]] = torch.constant.int 6
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// CHECK-DAG: %[[CST7:.*]] = torch.constant.int 7
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// CHECK-DAG: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK-DAG: %[[TRUE:.*]] = torch.constant.bool true
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// CHECK-DAG: %[[NONE:.*]] = torch.constant.none
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// CHECK: %[[DIM2:.*]] = torch.aten.size.int %[[SELF]], %[[CST2]] : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.int
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// CHECK: %[[DIM3:.*]] = torch.aten.size.int %[[SELF]], %[[CST3]] : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.int
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// CHECK: %[[COND1:.*]] = torch.aten.eq.int %[[DIM2]], %[[CST7]] : !torch.int, !torch.int -> !torch.bool
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// CHECK: torch.runtime.assert %[[COND1]], "unimplemented: only support cases where input and output size are equal for non-unit output size"
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// CHECK: %[[T1:.*]] = torch.aten.sub.int %[[DIM2]], %[[CST6]] : !torch.int, !torch.int -> !torch.int
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// CHECK: %[[COND2:.*]] = torch.aten.eq.int %[[DIM3]], %[[CST7]] : !torch.int, !torch.int -> !torch.bool
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// CHECK: torch.runtime.assert %[[COND2]], "unimplemented: only support cases where input and output size are equal for non-unit output size"
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// CHECK: %[[T2:.*]] = torch.aten.sub.int %[[DIM3]], %[[CST6]] : !torch.int, !torch.int -> !torch.int
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// CHECK: %[[KERNEL_SIZE:.*]] = torch.prim.ListConstruct %[[T1]], %[[T2]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[CST1]], %[[CST1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[CST0]], %[[CST0]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[AVG_POOL:.*]] = torch.aten.avg_pool2d %[[SELF]], %[[KERNEL_SIZE]], %[[STRIDE]], %[[PADDING]], %[[FALSE]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32>
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func.func @torch.aten.adaptive_avg_pool2d$non_unit_output_size(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%int7 = torch.constant.int 7
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%output_size = torch.prim.ListConstruct %int7, %int7 : (!torch.int, !torch.int) -> !torch.list<int>
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%0 = torch.aten.adaptive_avg_pool2d %arg0, %output_size : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int> -> !torch.vtensor<[?,?,?,?],f32>
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return %0 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @torch.aten.adaptive_avg_pool2d$unit_output_size(
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// CHECK-SAME: %[[SELF:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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// CHECK-DAG: %[[CST0:.*]] = torch.constant.int 0
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// CHECK-DAG: %[[CST1:.*]] = torch.constant.int 1
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// CHECK-DAG: %[[CST2:.*]] = torch.constant.int 2
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// CHECK-DAG: %[[CST3:.*]] = torch.constant.int 3
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// CHECK-DAG: %[[FALSE:.*]] = torch.constant.bool false
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// CHECK-DAG: %[[TRUE:.*]] = torch.constant.bool true
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// CHECK-DAG: %[[NONE:.*]] = torch.constant.none
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// CHECK: %[[DIM2:.*]] = torch.aten.size.int %[[SELF]], %[[CST2]] : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.int
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// CHECK: %[[DIM3:.*]] = torch.aten.size.int %[[SELF]], %[[CST3]] : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.int
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// CHECK: %[[KERNEL_SIZE:.*]] = torch.prim.ListConstruct %[[DIM2]], %[[DIM3]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[STRIDE:.*]] = torch.prim.ListConstruct %[[CST1]], %[[CST1]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[PADDING:.*]] = torch.prim.ListConstruct %[[CST0]], %[[CST0]] : (!torch.int, !torch.int) -> !torch.list<int>
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// CHECK: %[[AVG_POOL:.*]] = torch.aten.avg_pool2d %[[SELF]], %[[KERNEL_SIZE]], %[[STRIDE]], %[[PADDING]], %[[FALSE]], %[[TRUE]], %[[NONE]] : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?,?],f32>
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func.func @torch.aten.adaptive_avg_pool2d$unit_output_size(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?,?],f32> {
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%int1 = torch.constant.int 1
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%output_size = torch.prim.ListConstruct %int1, %int1 : (!torch.int, !torch.int) -> !torch.list<int>
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%0 = torch.aten.adaptive_avg_pool2d %arg0, %output_size : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int> -> !torch.vtensor<[?,?,?,?],f32>
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return %0 : !torch.vtensor<[?,?,?,?],f32>
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}
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