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Supported and Validated Models

PyTorch HuggingFace Models

PyTorch Language Models Torch-MLIR lowerable SHARK-CPU SHARK-CUDA SHARK-METAL
BERT 💚 (JIT) 💚 💚 💚
Albert 💚 (JIT) 💚 💚 💚
BigBird 💚 (AOT)
dbmdz/ConvBERT 💚 💚 💚 💚
DistilBERT 💔 (JIT)
GPT2 💚 💚 💚 💚
MobileBert 💚 (JIT) 💚 💚 💚
microsoft/beit 💚 💚 💔 💔
facebook/deit 💚 💚 💔 💔
facebook/convnext 💚 💚 💚 💚

Torchvision Models

TORCHVISION Models Torch-MLIR lowerable SHARK-CPU SHARK-CUDA SHARK-METAL
AlexNet 💚 (Script) 💚 💚 💚
MobileNetV2 💚 (Script) 💚 💚 💚
MobileNetV3 💚 (Script) 💚 💚 💚
Unet 💚 (Script) 💚 💚 💚
Resnet18 💚 (Script) 💚 💚 💚
Resnet50 💚 (Script) 💚 💚 💚
Resnet101 💚 (Script) 💚 💚 💚
Resnext50_32x4d 💚 (Script)
SqueezeNet 💚 (Script) 💚 💔 💔
EfficientNet 💚 (Script)
Regnet 💚 (Script)
Resnest 💔 (Script)
Vision Transformer 💚 (Script) 💚 💚 💚
VGG 16 💚 (Script) 💚 💚
Wide Resnet 💚 (Script) 💚 💚 💚
RAFT 💔 (JIT)

For more information refer to MODEL TRACKING SHEET

Tensorflow Models (Inference)

Hugging Face Models tf-mhlo lowerable SHARK-CPU SHARK-CUDA SHARK-METAL
BERT 💚 💚 💚 💚
MiniLM 💚 💚 💚 💚
albert-base-v2 💚 💚 💚 💚
DistilBERT 💚 💚 💚 💚
CamemBert 💚 💚 💚 💚
ConvBert 💚 💚 💚 💚
Deberta
electra 💚 💚 💚 💚
funnel
layoutlm 💚 💚 💚 💚
longformer
mobile-bert 💚 💚 💚 💚
rembert
tapas
flaubert 💔 💚 💚 💚
roberta 💚 💚 💚 💚
xlm-roberta 💚 💚 💚 💚
mpnet 💚 💚 💚 💚

PyTorch Training Models

Models Torch-MLIR lowerable SHARK-CPU SHARK-CUDA SHARK-METAL
BERT 💚 💚
FullyConnected 💚 💚

JAX Models

Models JAX-MHLO lowerable SHARK-CPU SHARK-CUDA SHARK-METAL
DALL-E 💔 💔
FullyConnected 💚 💚
TFLite Models

TFLite Models

Models TOSA/LinAlg SHARK-CPU SHARK-CUDA SHARK-METAL
BERT 💔 💔
FullyConnected 💚 💚
albert 💚 💚
asr_conformer 💚 💚
bird_classifier 💚 💚
cartoon_gan 💚 💚
craft_text 💚 💚
deeplab_v3 💚 💚
densenet 💚 💚
east_text_detector 💚 💚
efficientnet_lite0_int8 💚 💚
efficientnet 💚 💚
gpt2 💚 💚
image_stylization 💚 💚
inception_v4 💚 💚
inception_v4_uint8 💚 💚
lightning_fp16 💚 💚
lightning_i8 💚 💚
lightning 💚 💚
magenta 💚 💚
midas 💚 💚
mirnet 💚 💚
mnasnet 💚 💚
mobilebert_edgetpu_s_float 💚 💚
mobilebert_edgetpu_s_quant 💚 💚
mobilebert 💚 💚
mobilebert_tf2_float 💚 💚
mobilebert_tf2_quant 💚 💚
mobilenet_ssd_quant 💚 💚
mobilenet_v1 💚 💚
mobilenet_v1_uint8 💚 💚
mobilenet_v2_int8 💚 💚
mobilenet_v2 💚 💚
mobilenet_v2_uint8 💚 💚
mobilenet_v3-large 💚 💚
mobilenet_v3-large_uint8 💚 💚
mobilenet_v35-int8 💚 💚
nasnet 💚 💚
person_detect 💚 💚
posenet 💚 💚
resnet_50_int8 💚 💚
rosetta 💚 💚
spice 💚 💚
squeezenet 💚 💚
ssd_mobilenet_v1 💚 💚
ssd_mobilenet_v1_uint8 💚 💚
ssd_mobilenet_v2_fpnlite 💚 💚
ssd_mobilenet_v2_fpnlite_uint8 💚 💚
ssd_mobilenet_v2_int8 💚 💚
ssd_mobilenet_v2 💚 💚
ssd_spaghettinet_large 💚 💚
ssd_spaghettinet_large_uint8 💚 💚
visual_wake_words_i8 💚 💚

Testing and Benchmarks

Run all model tests on CPU/GPU/VULKAN/Metal

For a list of models included in our pytest model suite, see https://github.com/nod-ai/SHARK-Studio/blob/main/tank/all_models.csv

pytest tank/test_models.py

# Models included in the pytest suite can be found listed in all_models.csv.

# If on Linux for multithreading on CPU (faster results):
pytest tank/test_models.py -n auto

Running specific tests

# Search for test cases by including a keyword that matches all or part of the test case's name;
pytest tank/test_models.py -k "keyword" 

# Test cases are named uniformly by format test_module_<model_name_underscores_only>_<torch/tf>_<static/dynamic>_<device>.

# Example: Test all models on nvidia gpu:
pytest tank/test_models.py -k "cuda"

# Example: Test all tensorflow resnet models on Vulkan backend:
pytest tank/test_models.py -k "resnet and tf and vulkan"

# Exclude a test case:
pytest tank/test_models.py -k "not ..."

### Run benchmarks on SHARK tank pytests and generate bench_results.csv with results.

(the following requires source installation with `IMPORTER=1 ./setup_venv.sh`)

```shell
pytest --benchmark tank/test_models.py
  
# Just do static GPU benchmarks for PyTorch tests:
pytest --benchmark tank/test_models.py -k "pytorch and static and cuda"

Benchmark Resnet50, MiniLM on CPU

(requires source installation with IMPORTER=1 ./setup_venv.sh)

# We suggest running the following commands as root before running benchmarks on CPU:
  
cat /sys/devices/system/cpu/cpu*/topology/thread_siblings_list | awk -F, '{print $2}' | sort -n | uniq | ( while read X ; do echo $X ; echo 0 > /sys/devices/system/cpu/cpu$X/online ; done )
echo 1 > /sys/devices/system/cpu/intel_pstate/no_turbo

# Benchmark canonical Resnet50 on CPU via pytest
pytest --benchmark tank/test_models.py -k "resnet50 and tf_static_cpu"

# Benchmark canonical MiniLM on CPU via pytest
pytest --benchmark tank/test_models.py -k "MiniLM and cpu"

# Benchmark MiniLM on CPU via transformer-benchmarks:
git clone --recursive https://github.com/nod-ai/transformer-benchmarks.git
cd transformer-benchmarks
./perf-ci.sh -n
# Check detail.csv for MLIR/IREE results.

To run the fine tuning example, from the root SHARK directory, run:

IMPORTER=1 ./setup_venv.sh
source shark.venv/bin/activate
pip install jupyter tf-models-nightly tf-datasets
jupyter-notebook

if running from a google vm, you can view jupyter notebooks on your local system with:

gcloud compute ssh <YOUR_INSTANCE_DETAILS> --ssh-flag="-N -L localhost:8888:localhost:8888"