Chi_Liu AmosLewis
AmosLewis
/ opt_torchbackend.mlir
Created
January 24, 2023 21:05
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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> { | |
| %float1.000000e00 = torch.constant.float 1.000000e+00 loc(#loc1) | |
| %int0 = torch.constant.int 0 loc(#loc2) | |
| %int1 = torch.constant.int 1 loc(#loc3) | |
| %int-1 = torch.constant.int -1 loc(#loc4) | |
| %true = torch.constant.bool true loc(#loc5) | |
| %none = torch.constant.none loc(#loc) | |
| %false = torch.constant.bool false loc(#loc) |
AmosLewis
/ distilgpt2_fx_g.graph.txt
Created
January 24, 2023 04:47
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| fx_g.graph: | |
| graph(): | |
| %arg0_1 : [#users=1] = placeholder[target=arg0_1] | |
| %view : [#users=1] = call_function[target=torch.ops.aten.view.default](args = (%arg0_1, [-1, 128]), kwargs = {}) | |
| %arange : [#users=1] = call_function[target=torch.ops.aten.arange.start](args = (0, 128), kwargs = {dtype: torch.int64, device: cpu, pin_memory: False}) | |
| %unsqueeze : [#users=1] = call_function[target=torch.ops.aten.unsqueeze.default](args = (%arange, 0), kwargs = {}) | |
| %view_1 : [#users=1] = call_function[target=torch.ops.aten.view.default](args = (%unsqueeze, [-1, 128]), kwargs = {}) | |
| %_param_constant0 : [#users=1] = get_attr[target=_param_constant0] | |
| %embedding : [#users=1] = call_function[target=torch.ops.aten.embedding.default](args = (%_param_constant0, %view), kwargs = {}) | |
| %_param_constant1 : [#users=1] = get_attr[target=_param_constant1] |
AmosLewis
/ SliceNegativeIndexStaticModule.mlir
Created
January 20, 2023 06:21
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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) |
AmosLewis
/ distilgpt_torch_raw_transformers4.21.2_elided_torchbackend_tosabackend_elide_Canonicalizer.mlir
Created
January 17, 2023 19:27
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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> |
AmosLewis
/ view_torchbackend.mlir
Created
January 17, 2023 18:55
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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> | |
| } |
AmosLewis
/ IndexTensorMultiInputContiguousCenter.mlir
Created
January 17, 2023 07:35
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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> | |
| } | |
| } |
AmosLewis
/ NumToTensor.mlir
Created
January 16, 2023 07:26
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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> | |
| } |
AmosLewis
/ torch_mlir_debug_command.txt
Last active
March 20, 2025 04:27
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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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| 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], |
AmosLewis
/ distilgpt2_tosa.mlir
Created
January 3, 2023 21:48
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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> |