Chi_Liu AmosLewis
AmosLewis
/ gather.mlir
Created
November 17, 2022 22:08
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| func.func @torch.aten.gather(%arg0: !torch.vtensor<[12,128,512],f32>, %arg1: !torch.vtensor<[1,128,128],si64>) -> !torch.vtensor<[12,128,128],f32> { | |
| %int-1 = torch.constant.int -1 | |
| %false = torch.constant.bool false | |
| %0 = torch.aten.gather %arg0, %int-1, %arg1, %false : !torch.vtensor<[12,128,512],f32>, !torch.int, !torch.vtensor<[1,128,128],si64>, !torch.bool -> !torch.vtensor<[12,128,128],f32> | |
| return %0 : !torch.vtensor<[12,128,128],f32> | |
| } |
AmosLewis
/ debert_torch_ir_print.txt
Created
November 15, 2022 17:42
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| ➜ SHARK git:(main) ✗ torch-mlir-opt -pass-pipeline='builtin.module(torch-backend-to-tosa-backend-pipeline)' /tmp/_lambda.mlir -mlir-print-ir-after-all -mlir-disable-threading --debug | |
| Args: /home/chi/src/ubuntu20/shark/torch-mlir/build/bin/torch-mlir-opt -pass-pipeline=builtin.module(torch-backend-to-tosa-backend-pipeline) /tmp/_lambda.mlir -mlir-print-ir-after-all -mlir-disable-threading --debug | |
| Load new dialect in Context builtin | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::SubElementTypeInterface) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::ShapedType) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::MemRefLayoutAttrInterface) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::SubElementAttrInterface) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::ElementsAttr) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::TypedAttr) | |
| ImplicitTypeIDRegistry::lookupOrInsert(mlir::SymbolOpInterface) |
AmosLewis
/ deberta_torch_small.mlir
Created
November 14, 2022 17:17
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| func.func @forward(%arg0: !torch.vtensor<[1,128],si64>) -> !torch.vtensor<[1,128,1],f32>{ | |
| %int1 = torch.constant.int 1 | |
| %int32 = torch.constant.int 32 | |
| %int128 = torch.constant.int 128 | |
| %float1.000000e00 = torch.constant.float 1.000000e+00 | |
| %0 = torch.vtensor.literal(dense<0.000000e+00> : tensor<2xf32>) : !torch.vtensor<[2],f32> | |
| %1 = torch.vtensor.literal(dense_resource<__elided__> : tensor<2x32xf32>) : !torch.vtensor<[2,32],f32> | |
| %2 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x32xf32>) : !torch.vtensor<[32,32],f32> | |
| %3 = torch.vtensor.literal(dense_resource<__elided__> : tensor<32x37xf32>) : !torch.vtensor<[32,37],f32> | |
| %4 = torch.vtensor.literal(dense_resource<__elided__> : tensor<37x32xf32>) : !torch.vtensor<[37,32],f32> |
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| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| import torch | |
| from torch.fx.experimental.proxy_tensor import make_fx | |
| from torch._decomp import get_decompositions | |
| import tempfile | |
| import torch_mlir | |
| def prepare_sentence_tokens(hf_model: str, sentence: str): | |
| tokenizer = AutoTokenizer.from_pretrained(hf_model) |
AmosLewis
/ deberta_debug.txt
Created
November 10, 2022 06:06
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| ➜ SHARK git:(main) ✗ python tank/pytorch/deberta/deberta_tosa.py | |
| Some weights of the model checkpoint at hf-internal-testing/tiny-random-deberta were not used when initializing DebertaForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.decoder.weight', 'qa_outputs.bias', 'cls.predictions.transform.LayerNorm.weight', 'qa_outputs.weight', 'cls.predictions.decoder.bias'] | |
| - This IS expected if you are initializing DebertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model). | |
| - This IS NOT expected if you are initializing DebertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). | |
| model(test |
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| template <typename AtenOpT> | |
| class ConvertSelectiveAtenOpToTosaCustom : public OpConversionPattern<AtenOpT> { | |
| public: | |
| using OpConversionPattern<AtenOpT>::OpConversionPattern; | |
| using OpAdaptor = typename AtenOpT::Adaptor; | |
| LogicalResult | |
| matchAndRewrite(AtenOpT op, OpAdaptor adaptor, | |
| ConversionPatternRewriter &rewriter) const override { | |
| ValueRange operands = adaptor.getOperands(); |
AmosLewis
/ custom-pass-debug-print.txt
Last active
October 28, 2022 18:36
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| (mlir_venv) nod% python -m e2e_testing.main --config=tosa -v | |
| [W TensorShape.cpp:3154] Warning: The use of `x.T` on tensors of dimension other than 2 to reverse their shape is deprecated and it will throw an error in a future release. Consider `x.mT` to transpose batches of matrices or `x.permute(*torch.arange(x.ndim - 1, -1, -1))` to reverse the dimensions of a tensor. (function operator()) | |
| XFAIL - "AdaptiveAvgPool2dNonUnitOutputSizeDynamicModule_basic" | |
| XFAIL - "AdaptiveAvgPool2dNonUnitOutputSizeStaticModule_basic" | |
| XFAIL - "AdaptiveAvgPool2dUnitOutputSizeDynamicModule_basic" | |
| PASS - "AdaptiveAvgPool2dUnitOutputSizeStaticModule_basic" | |
| PASS - "AddCDivModule_basic" | |
| PASS - "AddCMulModule_basic" | |
| XFAIL - "AddIntModule_basic" | |
| XFAIL - "AddSizeIntModule_basic" |
AmosLewis
/ softmax_nodecompose.txt
Last active
October 25, 2022 19:14
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| func.func @torch.aten.softmax.int$cst_dim(%t: !torch.vtensor<[2,3],f32>) -> !torch.vtensor<[2,3],f32> { | |
| %none = torch.constant.none | |
| %dim = torch.constant.int 1 | |
| %ret = torch.aten.softmax.int %t, %dim, %none : !torch.vtensor<[2,3],f32>, !torch.int, !torch.none -> !torch.vtensor<[2,3],f32> | |
| return %ret : !torch.vtensor<[2,3],f32> | |
| } |
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| (mlir_venv) nod% cmake --build build --target tools/torch-mlir/all | |
| [54/72] Building CXX object tools/torch-mlir/lib/Conversion/TorchToTosa/CMakeFiles/obj.TorchMLIRTorchToTosa.dir/TorchToTosa.cpp.o | |
| FAILED: tools/torch-mlir/lib/Conversion/TorchToTosa/CMakeFiles/obj.TorchMLIRTorchToTosa.dir/TorchToTosa.cpp.o | |
| /usr/lib/ccache/clang++ -DGTEST_HAS_RTTI=0 -DTORCH_MLIR_ENABLE_MHLO -D_DEBUG -D_GNU_SOURCE -D__STDC_CONSTANT_MACROS -D__STDC_FORMAT_MACROS -D__STDC_LIMIT_MACROS -I/home/chi/src/ubuntu20/shark/torch-mlir/build/tools/torch-mlir/lib/Conversion/TorchToTosa -I/home/chi/src/ubuntu20/shark/torch-mlir/lib/Conversion/TorchToTosa -I/home/chi/src/ubuntu20/shark/torch-mlir/build/include -I/home/chi/src/ubuntu20/shark/torch-mlir/externals/llvm-project/llvm/include -I/home/chi/src/ubuntu20/shark/torch-mlir/externals/mlir-hlo/include -I/home/chi/src/ubuntu20/shark/torch-mlir/build/tools/torch-mlir/mlir-hlo/include -I/home/chi/src/ubuntu20/shark/torch-mlir/externals/llvm-project/llvm/../mlir/include -I/home/chi/src/ubuntu |
AmosLewis
/ softmax_decompose.mlir
Created
October 20, 2022 01:19
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| func.func @torch.aten.softmax.int$cst_dim(%arg0: !torch.vtensor<[2,3],f32>) -> !torch.vtensor<[2,3],f32> { | |
| %none = torch.constant.none | |
| %int1 = torch.constant.int 1 | |
| %true = torch.constant.bool true | |
| %values, %indices = torch.aten.max.dim %arg0, %int1, %true : !torch.vtensor<[2,3],f32>, !torch.int, !torch.bool -> !torch.vtensor<[2,1],f32>, !torch.vtensor<[2,1],si64> | |
| %float1.000000e00 = torch.constant.float 1.000000e+00 | |
| %0 = torch.aten.sub.Tensor %arg0, %values, %float1.000000e00 : !torch.vtensor<[2,3],f32>, !torch.vtensor<[2,1],f32>, !torch.float -> !torch.vtensor<[2,3],f32> | |
| %1 = torch.aten.exp %0 : !torch.vtensor<[2,3],f32> -> !torch.vtensor<[2,3],f32> | |
| %2 = torch.prim.ListConstruct %int1 : (!torch.int) -> !torch.list<int> | |
| %true_0 = torch.constant.bool true |