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#loc = loc(unknown) | |
module attributes {torch.debug_module_name = "_lambda"} { | |
func.func @forward(%arg0: !torch.vtensor<[1,128],si64> loc(unknown)) -> !torch.vtensor<[1,2],f32> { | |
%int1 = torch.constant.int 1 loc(#loc1) | |
%int0 = torch.constant.int 0 loc(#loc2) | |
%int-1 = torch.constant.int -1 loc(#loc3) | |
%true = torch.constant.bool true loc(#loc4) | |
%none = torch.constant.none loc(#loc) | |
%false = torch.constant.bool false loc(#loc5) | |
%int128 = torch.constant.int 128 loc(#loc6) |
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func.func @forward(%arg0: tensor<1x128xi64>) -> tensor<1x2xf32> { | |
%0 = "tosa.const"() {value = dense_resource<__elided__> : tensor<2x768xf32>} : () -> tensor<2x768xf32> | |
%1 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768xf32>} : () -> tensor<768xf32> | |
%2 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072x768xf32>} : () -> tensor<3072x768xf32> | |
%3 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x3072xf32>} : () -> tensor<768x3072xf32> | |
%4 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072xf32>} : () -> tensor<3072xf32> | |
%5 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x768xf32>} : () -> tensor<768x768xf32> | |
%6 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x2304xf32>} : () -> tensor<768x2304xf32> | |
%7 = "tosa.const"() {value = dense_resource<__elided__> : tensor<2304xf32>} : () -> tensor<2304xf32> | |
%8 = "tosa.const"() {value = dense<-3.40282347E+38> : tensor<f32>} : () -> tensor<f32> |
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module attributes {torch.debug_module_name = "_lambda"} { | |
func.func @forward(%arg0: tensor<1x128xi64>) -> tensor<1x2xf32> { | |
%0 = "tosa.const"() {value = dense_resource<__elided__> : tensor<2x768xf32>} : () -> tensor<2x768xf32> | |
%1 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768xf32>} : () -> tensor<768xf32> | |
%2 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072x768xf32>} : () -> tensor<3072x768xf32> | |
%3 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x3072xf32>} : () -> tensor<768x3072xf32> | |
%4 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072xf32>} : () -> tensor<3072xf32> | |
%5 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x768xf32>} : () -> tensor<768x768xf32> | |
%6 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x2304xf32>} : () -> tensor<768x2304xf32> | |
%7 = "tosa.const"() {value = dense_resource<__elided__> : tensor<2304xf32>} : () -> tensor<2304xf32> |
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import torch | |
t = torch.tensor([ | |
[1, 2, 3, 4, 5], | |
[6,7,8,9,10], | |
[11,12,13,14,15], | |
[16,17,18,19,20] | |
]) # 4*5 | |
i = torch.tensor([ | |
[1,2,3], |
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cmake -GNinja -Bbuild \ | |
-DCMAKE_BUILD_TYPE=Debug \ | |
-DCMAKE_C_COMPILER=clang \ | |
-DCMAKE_CXX_COMPILER=clang++ \ | |
-DPython3_FIND_VIRTUALENV=ONLY \ | |
-DLLVM_ENABLE_PROJECTS=mlir \ | |
-DLLVM_EXTERNAL_PROJECTS="torch-mlir;torch-mlir-dialects" \ | |
-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \ | |
-DLLVM_EXTERNAL_TORCH_MLIR_DIALECTS_SOURCE_DIR=`pwd`/externals/llvm-external-projects/torch-mlir-dialects \ | |
-DMLIR_ENABLE_BINDINGS_PYTHON=ON \ |
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func.func @torch.prim.NumToTensor.Scalar() -> !torch.vtensor<[],f64> { | |
%float8.000000e00 = torch.constant.float 8.000000e+00 | |
%1 = "torch.prim.NumToTensor.Scalar"(%float8.000000e00) : (!torch.float) -> !torch.vtensor<[],f64> | |
return %1 : !torch.vtensor<[],f64> | |
} |
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module attributes {torch.debug_module_name = "IndexTensorMultiInputContiguousCenter"} { | |
func.func @forward(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vtensor<[2,2],si64>, %arg2: !torch.vtensor<[2],si64>) -> !torch.vtensor<[?,2,2,?],f32> { | |
%none = torch.constant.none | |
%0 = torch.prim.ListConstruct %none, %arg1, %arg2, %none : (!torch.none, !torch.vtensor<[2,2],si64>, !torch.vtensor<[2],si64>, !torch.none) -> !torch.list<optional<vtensor>> | |
%1 = torch.aten.index.Tensor %arg0, %0 : !torch.vtensor<[?,?,?,?],f32>, !torch.list<optional<vtensor>> -> !torch.vtensor<[?,2,2,?],f32> | |
return %1 : !torch.vtensor<[?,2,2,?],f32> | |
} | |
} |
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func.func @torch.aten.view(%arg0: !torch.vtensor<[1,128],si64>) -> !torch.vtensor<[1,128],si64> { | |
%int-1 = torch.constant.int -1 | |
%int128 = torch.constant.int 128 | |
%0 = torch.prim.ListConstruct %int-1, %int128 : (!torch.int, !torch.int) -> !torch.list<int> | |
%1 = torch.aten.view %arg0, %0 : !torch.vtensor<[1,128],si64>, !torch.list<int> -> !torch.vtensor<[1,128],si64> | |
return %1 : !torch.vtensor<[1,128],si64> | |
} |
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module attributes {torch.debug_module_name = "_lambda"} { | |
func.func @forward(%arg0: tensor<1x128xi64>) -> tensor<1x2xf32> { | |
%0 = "tosa.const"() {value = dense_resource<__elided__> : tensor<2x768xf32>} : () -> tensor<2x768xf32> | |
%1 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072x768xf32>} : () -> tensor<3072x768xf32> | |
%2 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x3072xf32>} : () -> tensor<768x3072xf32> | |
%3 = "tosa.const"() {value = dense_resource<__elided__> : tensor<3072xf32>} : () -> tensor<3072xf32> | |
%4 = "tosa.const"() {value = dense_resource<__elided__> : tensor<768x768xf32>} : () -> tensor<768x768xf32> | |
%5 = "tosa.const"() {value = dense<-3.40282347E+38> : tensor<f32>} : () -> tensor<f32> | |
%6 = "tosa.const"() {value = dense_resource<__elided__> : tensor<1x1x1024x1024xui8>} : () -> tensor<1x1x1024x1024xi8> | |
%7 = "tosa.const"() {value = dense<8.000000e+00> : tensor<f32>} : () -> tensor<f32> |
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#loc = loc(unknown) | |
module attributes {torch.debug_module_name = "SliceNegativeIndexStaticModule"} { | |
func.func @forward(%arg0: !torch.vtensor<[1,128,2],f32> loc(unknown)) -> !torch.vtensor<[1,0,2],f32> { | |
%int1 = torch.constant.int 1 loc(#loc1) | |
%int0 = torch.constant.int 0 loc(#loc2) | |
%int-1 = torch.constant.int -1 loc(#loc3) | |
%0 = torch.aten.slice.Tensor %arg0, %int1, %int-1, %int0, %int1 : !torch.vtensor<[1,128,2],f32>, !torch.int, !torch.int, !torch.int, !torch.int -> !torch.vtensor<[1,0,2],f32> loc(#loc4) | |
return %0 : !torch.vtensor<[1,0,2],f32> loc(#loc) | |
} loc(#loc) | |
} loc(#loc) |