AmosLewis

[61/62] Running the torch-mlir regression tests
FAIL: TORCH_MLIR :: Conversion/TorchToTosa/basic.mlir (42 of 105)
******************** TEST 'TORCH_MLIR :: Conversion/TorchToTosa/basic.mlir' FAILED ********************
Script:
--
: 'RUN: at line 1';   /home/chi/src/ubuntu20/shark/torch-mlir/build/bin/torch-mlir-opt </home/chi/src/ubuntu20/shark/torch-mlir/test/Conversion/TorchToTosa/basic.mlir -convert-torch-to-tosa -split-input-file -verify-diagnostics | FileCheck /home/chi/src/ubuntu20/shark/torch-mlir/test/Conversion/TorchToTosa/basic.mlir
--
Exit Code: 1

Command Output (stderr):

AmosLewis

(mlir_venv) nod% torch-mlir-opt -convert-torch-to-tosa /tmp/where.mlir         
module {
  func.func @torch.aten.where.self(%arg0: !torch.vtensor<[1,1,5,5],i1>, %arg1: !torch.vtensor<[1,12,5,5],f32>, %arg2: !torch.vtensor<[],f32>) -> !torch.vtensor<[1,12,5,5],f32> {
    %0 = torch_c.to_builtin_tensor %arg0 : !torch.vtensor<[1,1,5,5],i1> -> tensor<1x1x5x5xi1>
    %1 = torch_c.to_builtin_tensor %arg1 : !torch.vtensor<[1,12,5,5],f32> -> tensor<1x12x5x5xf32>
    %2 = torch_c.to_builtin_tensor %arg2 : !torch.vtensor<[],f32> -> tensor<f32>
    %3 = "tosa.select"(%0, %1, %2) : (tensor<1x1x5x5xi1>, tensor<1x12x5x5xf32>, tensor<f32>) -> tensor<1x12x5x5xf32>
    %4 = torch_c.from_builtin_tensor %3 : tensor<1x12x5x5xf32> -> !torch.vtensor<[1,12,5,5],f32>
    return %4 : !torch.vtensor<[1,12,5,5],f32>
  }

AmosLewis

OVERVIEW: MLIR modular optimizer driver

Available Dialects: acc, affine, amdgpu, amx, arith, arm_neon, arm_sve, async, bufferization, builtin, cf, complex, dlti, emitc, func, gpu, linalg, llvm, math, memref, ml_program, nvgpu, nvvm, omp, pdl, pdl_interp, quant, rocdl, scf, shape, sparse_tensor, spirv, tensor, tm_tensor, torch, torch_c, tosa, transform, vector, x86vector
USAGE: torch-mlir-opt [options] <input file>

OPTIONS:

Color Options:

AmosLewis

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

AmosLewis

(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

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>
}

AmosLewis

(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

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

➜  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

AmosLewis

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)