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

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OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-linalg -split-input-file -verify-diagnostics | FileCheck %s
// CHECK-LABEL: func.func @test_resize_sizes_linear
func.func @test_resize_sizes_linear(%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 = ""} {
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK: %[[generic:.*]] = linalg.generic
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32>
// CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]]
// CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]]
// CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]]
// CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]]
// CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]]
// CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]]
// CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]]
// CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]]
// CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]]
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "bilinear"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%int3 = torch.constant.int 3
%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
%4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
%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>
return %5 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK-LABEL: func.func @test_resize_sizes_nearest
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
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 = ""} {
// CHECK: %[[GENERIC:.*]] = linalg.generic
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
// CHECK: %[[x11:.*]] = linalg.index 0 : index
// CHECK: %[[x12:.*]] = linalg.index 1 : index
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// CHECK: %[[x13:.*]] = linalg.index 2 : index
// CHECK: %[[x14:.*]] = linalg.index 3 : index
[ONNX][TorchToLinalg] Add support for dynamic dims in Interpolate lowering (#3351) Addresses [Shark-Turbine #196](https://github.com/nod-ai/SHARK-TestSuite/issues/196) Related tracker [Shark-Turbine #566](https://github.com/nod-ai/SHARK-Turbine/issues/566) Related onnx.Resize issues [Shark-Turbine #616](https://github.com/nod-ai/SHARK-Turbine/issues/616)
2024-05-18 03:18:57 +08:00
// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK: %[[x26:.*]] = math.floor %[[x25]] : f32
// CHECK: %[[x31:.*]] = arith.fptosi %[[x26]] : f32 to i64
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
// CHECK: %[[x16:.*]] = arith.sitofp %[[c4_i64:.*]] : i64 to f32
// CHECK: %[[x20:.*]] = arith.sitofp %[[x7:.*]] : i64 to f32
// CHECK: %[[x22:.*]] = arith.divf %[[x20]], %[[x16]] : f32
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// CHECK: %[[x26:.*]] = arith.index_cast %[[x14]] : index to i64
// CHECK: %[[x27:.*]] = arith.sitofp %[[x26]] : i64 to f32
// CHECK: %[[x28:.*]] = arith.divf %[[x27]], %[[x22]] : f32
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK: %[[x29:.*]] = math.floor %[[x28]] : f32
// CHECK: %[[x33:.*]] = arith.fptosi %[[x29]] : f32 to i64
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// CHECK: %[[x34:.*]] = arith.index_cast %[[x33]] : i64 to index
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]]] : tensor<1x1x2x4xf32>
OnnxToTorch lowering resize op (#3013) https://github.com/nod-ai/SHARK-Turbine/issues/358 adds a lowering from onnx to linalg for bilinear and nearest resize with support for using scales or sizes to get resize shape. uses coordinate transform half pixel for bilinear mode and asymmetrical for nearest mode. See https://github.com/onnx/onnx/blob/main/docs/Operators.md#Resize. Added two passes -- one for bilinear and the other for nearest.
2024-05-09 05:35:03 +08:00
// CHECK: linalg.yield %[[extracted]] : f32
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "nearest"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%int3 = torch.constant.int 3
%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
%4 = torch.prim.ListConstruct %1, %3 : (!torch.int, !torch.int) -> !torch.list<int>
%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>
return %5 : !torch.vtensor<[?,?,?,?],f32>
}
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
// -----
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK-LABEL: func.func @test_resize_nearest_1d
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
func.func @test_resize_nearest_1d(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[GENERIC:.*]] = linalg.generic
// CHECK: %[[x11:.*]] = linalg.index 0 : index
// CHECK: %[[x12:.*]] = linalg.index 1 : index
// CHECK: %[[x13:.*]] = linalg.index 2 : index
// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
// CHECK: %[[x29:.*]] = math.floor %[[x25]] : f32
// CHECK: %[[x31:.*]] = arith.fptosi %[[x29]] : f32 to i64
// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor<?x?x?xf32>
// CHECK: linalg.yield %[[extracted]] : f32
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
%str = torch.constant.str "nearest,floor"
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
%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>
return %5 : !torch.vtensor<[?,?,?],f32>
}
// -----
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK-LABEL: func.func @test_resize_nearest_3d
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
func.func @test_resize_nearest_3d(%arg0: !torch.vtensor<[?,?,?,?,?],f32>, %arg1: !torch.vtensor<[5],si64>) -> !torch.vtensor<[?,?,?,?,?],f32> {
// CHECK: %[[GENERIC:.*]] = linalg.generic
// CHECK: %[[x11:.*]] = linalg.index 0 : index
// CHECK: %[[x12:.*]] = linalg.index 1 : index
// CHECK: %[[x13:.*]] = linalg.index 2 : index
// CHECK: %[[x14:.*]] = linalg.index 3 : index
// CHECK: %[[index4:.*]] = linalg.index 4 : index
// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
// CHECK: %[[x25:.*]] = arith.divf %[[x24]], %[[x21]] : f32
add resize nearest mode round_prefer_floor, round_prefer_ceil, ceil (#3421)
2024-06-08 03:04:11 +08:00
// CHECK: %[[floor:.*]] = math.floor %[[x25]] : f32
// CHECK: %[[x31:.*]] = arith.fptosi %[[floor]] : f32 to i64
onnx.Resize and aten._interpolate : allow n spatial dims. (#3368) The old lowering only had logic for 2d (i.e. images). this patch allows interpolation for n spatial dims, which is required for some 3d vision models such as - onnx/models/pytorch-3dunet_vaiq_int8 which successfully compiles and runs with this patch.
2024-05-21 04:35:27 +08:00
// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
// CHECK: %[[x34:.*]] = arith.index_cast %[[Wfptosi:.*]] : i64 to index
// CHECK: %[[x35:.*]] = arith.index_cast %[[Dfptosi:.*]] : i64 to index
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]], %[[x34]], %[[x35]]] : tensor<?x?x?x?x?xf32>
// CHECK: linalg.yield %[[extracted]] : f32
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "nearest"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%int3 = torch.constant.int 3
%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%3 = torch.aten.item %2 : !torch.vtensor<[1],si64> -> !torch.int
%int4 = torch.constant.int 4
%4 = torch.aten.select.int %arg1, %int0, %int4 : !torch.vtensor<[5],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%5 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%6 = torch.prim.ListConstruct %1, %3, %5: (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%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>
return %7 : !torch.vtensor<[?,?,?,?,?],f32>
}
onnx.resize: Add support for coordTfMode "half_pixel" (#3441) half_pixel is also the default mode used by ONNX, see https://onnx.ai/onnx/operators/onnx__Resize.html
2024-06-11 02:59:29 +08:00
[ONNX] Fix resize ceil numerics and add half_pixel_symmetric support (#3443) This patch fixes several failing tests in our [external test suite](https://github.com/nod-ai/SHARK-TestSuite/tree/main/iree_tests/onnx/node/generated), and addresses some of the issues discussed in #3420
2024-06-12 11:35:50 +08:00
// -----
// CHECK-LABEL: func.func @test_resize_nearest_ceil
func.func @test_resize_nearest_ceil(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[GENERIC:.*]] = linalg.generic
// CHECK: %[[x11:.*]] = linalg.index 0 : index
// CHECK: %[[x12:.*]] = linalg.index 1 : index
// CHECK: %[[x13:.*]] = linalg.index 2 : index
// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
// CHECK: %[[cst:.*]] = arith.constant 5.000000e-01 : f32
// CHECK: %[[add:.*]] = arith.addf %[[x24]], %[[cst]] : f32
// CHECK: %[[x25:.*]] = arith.divf %[[add]], %[[x21]] : f32
// CHECK: %[[sub:.*]] = arith.subf %[[x25]], %[[cst]] : f32
// CHECK: %[[cst3:.*]] = arith.constant 1.000000e+00 : f32
// CHECK: %[[nM1:.*]] = arith.subf %[[inputsizefp:.*]], %[[cst3]]
// CHECK: %[[ceil:.*]] = math.ceil %[[sub]] : f32
// CHECK: %[[minindex:.*]] = arith.minimumf %[[ceil]], %[[nM1]]
// CHECK: %[[x31:.*]] = arith.fptosi %[[minindex]] : f32 to i64
// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor<?x?x?xf32>
// CHECK: linalg.yield %[[extracted]] : f32
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "nearest_half_pixel,ceil"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
%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>
return %5 : !torch.vtensor<[?,?,?],f32>
}
// -----
// CHECK-LABEL: func.func @test_resize_scales_linear_half_pixel_symmetric
func.func @test_resize_scales_linear_half_pixel_symmetric(%arg0: !torch.vtensor<[1,1,2,4],f32>, %arg1: !torch.vtensor<[4]
,f64>) -> !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 = ""} {
// CHECK: %[[generic:.*]] = linalg.generic
// CHECK: %[[cst7:.*]] = arith.constant 2.0
// CHECK: %[[halfsize:.*]] = arith.divf %[[sizefp:.*]], %[[cst7]]
// CHECK: %[[modifier:.*]] = arith.subf %[[cstOne:.*]], %[[adjustment:.*]]
// CHECK: %[[offset:.*]] = arith.mulf %[[halfsize]], %[[modifier]]
// CHECK: %[[preClip:.*]] = arith.addf %[[offset]], %[[halfpixelbase:.*]]
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x1:.*]], %[[x2:.*]], %[[x3:.*]], %[[x4:.*]]] : tensor<1x1x2x4xf32>
// CHECK: %[[extracted_7:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[extracted_8:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[extracted_9:.*]] = tensor.extract %[[x0]][%[[x1]], %[[x2]]
// CHECK: %[[dx0p00:.*]] = arith.mulf %[[dx0:.*]], %[[extracted]]
// CHECK: %[[dx1p01:.*]] = arith.mulf %[[dx1:.*]], %[[extracted_7]]
// CHECK: %[[sum:.*]] = arith.addf %[[dx0p00]], %[[dx1p01]]
// CHECK: %[[left:.*]] = arith.mulf %[[dy0:.*]], %[[sum]]
// CHECK: %[[dx0p10:.*]] = arith.mulf %[[dx0]], %[[extracted_8]]
// CHECK: %[[dx1p11:.*]] = arith.mulf %[[dx1]], %[[extracted_9]]
// CHECK: %[[sum2:.*]] = arith.addf %[[dx0p10]], %[[dx1p11]]
// CHECK: %[[right:.*]] = arith.mulf %[[dy1:.*]], %[[sum2]]
// CHECK: %[[retval:.*]] = arith.addf %[[left]], %[[right]]
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "bilinear_half_pixel_symmetric"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[4],f64>, !torch.int, !torch.int -> !torch.vtensor<[1],f64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],f64> -> !torch.float
%int3 = torch.constant.int 3
%2 = torch.aten.select.int %arg1, %int0, %int3 : !torch.vtensor<[4],f64>, !torch.int, !torch.int -> !torch.vtensor<[1],f64>
%3 = torch.aten.item %2 : !torch.vtensor<[1],f64> -> !torch.float
%4 = torch.prim.ListConstruct %1, %3 : (!torch.float, !torch.float) -> !torch.list<float>
%5 = torch.aten.__interpolate.size_list_scale_list %arg0, %none_0, %4, %str, %false, %none_0, %false : !torch.vtensor<[1,1,2,4],f32>, !torch.none, !torch.list<float>, !torch.str, !torch.bool, !torch.none, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
return %5 : !torch.vtensor<[?,?,?,?],f32>
}
// -----
// CHECK-LABEL: func.func @test_resize_nearest_half_pixel_round_prefer_floor
onnx.resize: Add support for coordTfMode "half_pixel" (#3441) half_pixel is also the default mode used by ONNX, see https://onnx.ai/onnx/operators/onnx__Resize.html
2024-06-11 02:59:29 +08:00
func.func @test_resize_nearest_half_pixel_round_prefer_floor(%arg0: !torch.vtensor<[?,?,?],f32>, %arg1: !torch.vtensor<[3],si64>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[GENERIC:.*]] = linalg.generic
// CHECK: %[[x11:.*]] = linalg.index 0 : index
// CHECK: %[[x12:.*]] = linalg.index 1 : index
// CHECK: %[[x13:.*]] = linalg.index 2 : index
// CHECK: %[[x15:.*]] = arith.sitofp %[[c2_i64:.*]] : i64 to f32
// CHECK: %[[x19:.*]] = arith.sitofp %[[x6:.*]] : i64 to f32
// CHECK: %[[x21:.*]] = arith.divf %[[x19]], %[[x15]] : f32
// CHECK: %[[x23:.*]] = arith.index_cast %[[x13]] : index to i64
// CHECK: %[[x24:.*]] = arith.sitofp %[[x23]] : i64 to f32
// CHECK: %[[cst:.*]] = arith.constant 5.000000e-01 : f32
// CHECK: %[[add:.*]] = arith.addf %[[x24]], %[[cst]] : f32
// CHECK: %[[x25:.*]] = arith.divf %[[add]], %[[x21]] : f32
// CHECK: %[[sub:.*]] = arith.subf %[[x25]], %[[cst]] : f32
// CHECK: %[[cst3:.*]] = arith.constant 5.000000e-01 : f32
// CHECK: %[[floor:.*]] = math.floor %[[sub]] : f32
// CHECK: %[[ceil:.*]] = math.ceil %[[sub]] : f32
// CHECK: %[[sub2:.*]] = arith.subf %[[sub]], %[[floor]] : f32
// CHECK: %[[cmpf:.*]] = arith.cmpf ule, %[[sub2]], %[[cst3]] : f32
// CHECK: %[[select:.*]] = arith.select %[[cmpf]], %[[floor]], %[[ceil]] : f32
// CHECK: %[[x31:.*]] = arith.fptosi %[[select]] : f32 to i64
// CHECK: %[[x32:.*]] = arith.index_cast %[[x31]] : i64 to index
// CHECK: %[[extracted:.*]] = tensor.extract %[[x0:.*]][%[[x11]], %[[x12]], %[[x32]]] : tensor<?x?x?xf32>
// CHECK: linalg.yield %[[extracted]] : f32
%none = torch.constant.none
%none_0 = torch.constant.none
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%true = torch.constant.bool true
%str = torch.constant.str "nearest_half_pixel,round_prefer_floor"
%int2 = torch.constant.int 2
%0 = torch.aten.select.int %arg1, %int0, %int2 : !torch.vtensor<[3],si64>, !torch.int, !torch.int -> !torch.vtensor<[1],si64>
%1 = torch.aten.item %0 : !torch.vtensor<[1],si64> -> !torch.int
%4 = torch.prim.ListConstruct %1 : (!torch.int) -> !torch.list<int>
%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>
return %5 : !torch.vtensor<[?,?,?],f32>
}
// -----