mirror of https://github.com/llvm/torch-mlir
158 lines
10 KiB
MLIR
158 lines
10 KiB
MLIR
// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s
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// CHECK-LABEL: func.func @test_resize_sizes_linear
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func.func @test_resize_sizes_linear(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4]
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,si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[generic:.*]] = linalg.generic
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// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32>
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// CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
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// CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
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// CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
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// CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]]
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// CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]]
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// CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]]
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// CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]]
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// CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]]
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// CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]]
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// CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]]
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// CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]]
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// CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]]
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%none = torch.constant.none
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%none_0 = torch.constant.none
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%int0 = torch.constant.int 0
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%false = torch.constant.bool false
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%true = torch.constant.bool true
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%str = torch.constant.str "bilinear"
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%int2 = torch.constant.int 2
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%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
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%int3 = torch.constant.int 3
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%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
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%4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
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%5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
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return %5 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_resize_sizes_nearest
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func.func @test_resize_sizes_nearest(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4],si64>) -> !torch.vtensor<[?,?,?,?],f32> attributes {torch.onnx_meta.ir_version = 7 : si64, torch.onnx_meta.opset_version = 19 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
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// CHECK: %[[GENERIC:.*]] = linalg.generic
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// CHECK: %[[x11:.*]] = linalg.index 0 : index
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// CHECK: %[[x12:.*]] = linalg.index 1 : index
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// CHECK: %[[x13:.*]] = linalg.index 2 : index
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// CHECK: %[[x14:.*]] = linalg.index 3 : index
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// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
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// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
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// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
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// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
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// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
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// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
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// CHECK: %[[x26:.*]] = math.floor %[[x25]] : f32
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// CHECK: %[[x31:.*]] = arith.fptosi %[[x26]] : f32 to i64
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// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
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// CHECK: %[[x16:.*]] = arith.sitofp %[[c4_i64:.*]] : i64 to f32
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// CHECK: %[[x20:.*]] = arith.sitofp %[[x7:.*]] : i64 to f32
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// CHECK: %[[x22:.*]] = arith.divf %[[x20]], %[[x16]] : f32
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// CHECK: %[[x26:.*]] = arith.index_cast %[[x14]] : index to i64
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// CHECK: %[[x27:.*]] = arith.sitofp %[[x26]] : i64 to f32
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// CHECK: %[[x28:.*]] = arith.divf %[[x27]], %[[x22]] : f32
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// CHECK: %[[x29:.*]] = math.floor %[[x28]] : f32
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// CHECK: %[[x33:.*]] = arith.fptosi %[[x29]] : f32 to i64
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// CHECK: %[[x34:.*]] = arith.index_cast %[[x33]] : i64 to index
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// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]]] : tensor<1x1x2x4xf32>
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// CHECK: linalg.yield %[[extracted]] : f32
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%none = torch.constant.none
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%none_0 = torch.constant.none
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%int0 = torch.constant.int 0
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%false = torch.constant.bool false
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%true = torch.constant.bool true
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%str = torch.constant.str "nearest"
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%int2 = torch.constant.int 2
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%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
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%int3 = torch.constant.int 3
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%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
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%4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
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%5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
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return %5 : !torch.vtensor<[?,?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_resize_nearest_1d
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func.func @test_resize_nearest_1d(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> {
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// CHECK: %[[GENERIC:.*]] = linalg.generic
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// CHECK: %[[x11:.*]] = linalg.index 0 : index
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// CHECK: %[[x12:.*]] = linalg.index 1 : index
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// CHECK: %[[x13:.*]] = linalg.index 2 : index
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// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
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// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
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// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
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// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
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// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
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// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
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// CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32
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// CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64
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// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
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// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor<?x?x?xf32>
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// CHECK: linalg.yield %[[extracted]] : f32
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%none = torch.constant.none
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%none_0 = torch.constant.none
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%int0 = torch.constant.int 0
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%false = torch.constant.bool false
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%true = torch.constant.bool true
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%str = torch.constant.str "nearest,floor"
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%int2 = torch.constant.int 2
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%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
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%4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
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%5 = torch.aten.__interpolate.size_list_scale_list %arg0, %4, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?],f32>
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return %5 : !torch.vtensor<[?,?,?],f32>
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}
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// -----
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// CHECK-LABEL: func.func @test_resize_nearest_3d
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func.func @test_resize_nearest_3d(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> {
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// CHECK: %[[GENERIC:.*]] = linalg.generic
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// CHECK: %[[x11:.*]] = linalg.index 0 : index
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// CHECK: %[[x12:.*]] = linalg.index 1 : index
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// CHECK: %[[x13:.*]] = linalg.index 2 : index
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// CHECK: %[[x14:.*]] = linalg.index 3 : index
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// CHECK: %[[index4:.*]] = linalg.index 4 : index
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// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
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// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
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// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
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// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
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// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
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// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
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// CHECK: %[[floor:.*]] = math.floor %[[x25]] : f32
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// CHECK: %[[x31:.*]] = arith.fptosi %[[floor]] : f32 to i64
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// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
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// CHECK: %[[x34:.*]] = arith.index_cast %[[Wfptosi:.*]] : i64 to index
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// CHECK: %[[x35:.*]] = arith.index_cast %[[Dfptosi:.*]] : i64 to index
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// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]], %[[x35]]] : tensor<?x?x?x?x?xf32>
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// CHECK: linalg.yield %[[extracted]] : f32
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%none = torch.constant.none
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%none_0 = torch.constant.none
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%int0 = torch.constant.int 0
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%false = torch.constant.bool false
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%true = torch.constant.bool true
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%str = torch.constant.str "nearest"
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%int2 = torch.constant.int 2
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%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
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%int3 = torch.constant.int 3
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%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
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%int4 = torch.constant.int 4
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%4 = torch.aten.select.int %arg1, %int0, %int4 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
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%5 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
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%6 = torch.prim.ListConstruct %1, %3, %5: (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
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%7 = torch.aten.__interpolate.size_list_scale_list %arg0, %6, %none_0, %str, %false, %none_0, %false : !torch.vtensor<[?,?,?,?,?],f32>, !torch.list<int>, !torch.none, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?,?],f32>
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return %7 : !torch.vtensor<[?,?,?,?,?],f32>
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}
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