mirror of
https://github.com/nod-ai/SHARK-Studio.git
synced 2026-01-09 13:57:54 -05:00
Fix GPU benchmarks on PyTorch model tests. (#218)
* Add CUDA_BENCHMARKS option to setup.venv to enable PyTorch benchmarks on CUDA. * Fix PyTorch GPU benchmarks for tank models.
This commit is contained in:
Ean Garvey
6
.github/workflows/test-models.yml
vendored
6
.github/workflows/test-models.yml
vendored
@@ -75,7 +75,7 @@ jobs:
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if: matrix.suite == 'cpu'
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run: |
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cd $GITHUB_WORKSPACE
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PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
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PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
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source shark.venv/bin/activate
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pytest -k 'cpu' --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py
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@@ -83,7 +83,7 @@ jobs:
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if: matrix.suite == 'gpu'
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run: |
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cd $GITHUB_WORKSPACE
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PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
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PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
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source shark.venv/bin/activate
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pytest -k "gpu" --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py
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@@ -91,6 +91,6 @@ jobs:
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if: matrix.suite == 'vulkan'
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run: |
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cd $GITHUB_WORKSPACE
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PYTHON=python${{ matrix.python-version }} IMPORTER=1 ./setup_venv.sh
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PYTHON=python${{ matrix.python-version }} ./setup_venv.sh
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source shark.venv/bin/activate
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pytest -k 'vulkan' --ignore=shark/tests/test_shark_importer.py --ignore=benchmarks/tests/test_hf_benchmark.py --ignore=benchmarks/tests/test_benchmark.py
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@@ -10,3 +10,4 @@ gsutil
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# Testing
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pytest
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pytest-xdist
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Pillow
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@@ -98,7 +98,7 @@ if [[ ! -z "${IMPORTER}" ]]; then
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echo "${Yellow}Installing importer tools.."
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if [[ $(uname -s) = 'Linux' ]]; then
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echo "${Yellow}Linux detected.. installing Linux importer tools"
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$PYTHON -m pip install --upgrade -r "$TD/requirements-importer.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://test.pypi.org/simple/ --extra-index-url https://download.pytorch.org/whl/nightly/cpu
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$PYTHON -m pip install --upgrade -r "$TD/requirements-importer.txt" -f https://github.com/${RUNTIME}/releases --extra-index-url https://test.pypi.org/simple/ --extra-index-url https://download.pytorch.org/whl/nightly/cu116
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elif [[ $(uname -s) = 'Darwin' ]]; then
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echo "${Yellow}macOS detected.. installing macOS importer tools"
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#Conda seems to have some problems installing these packages and hope they get resolved upstream.
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@@ -108,6 +108,16 @@ fi
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$PYTHON -m pip install -e . --extra-index-url https://download.pytorch.org/whl/nightly/cpu -f https://github.com/llvm/torch-mlir/releases -f https://github.com/${RUNTIME}/releases
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if [[ $(uname -s) = 'Linux' ]]; then
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$PYTHON -m pip uninstall -y torch torchvision
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$PYTHON -m pip install --pre torch torchvision --extra-index-url https://download.pytorch.org/whl/nightly/cu116
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if [ $? -eq 0 ];then
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echo "Successfully Installed torch + cu116."
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else
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echo "Could not install torch + cu116." >&2
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fi
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fi
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if [[ -z "${CONDA_PREFIX}" ]]; then
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echo "${Green}Before running examples activate venv with:"
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echo " ${Green}source $VENV_DIR/bin/activate"
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@@ -19,6 +19,7 @@ from shark.iree_utils.benchmark_utils import (
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run_benchmark_module,
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)
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from shark.parser import shark_args
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from tank.model_utils import get_torch_model
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from datetime import datetime
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import time
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import csv
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@@ -59,20 +60,33 @@ class SharkBenchmarkRunner(SharkRunner):
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mlir_dialect=self.mlir_dialect,
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)
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def benchmark_frontend(self, inputs):
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def benchmark_frontend(self, inputs, modelname):
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if self.frontend in ["pytorch", "torch"]:
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return self.benchmark_torch(inputs)
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return self.benchmark_torch(modelname)
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elif self.frontend in ["tensorflow", "tf"]:
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return self.benchmark_tf(inputs)
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return self.benchmark_tf(inputs, modelname)
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def benchmark_torch(self, modelname):
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import torch
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if self.device == "gpu":
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torch.set_default_tensor_type(torch.cuda.FloatTensor)
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else:
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torch.set_default_tensor_type(torch.FloatTensor)
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torch_device = torch.device(
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"cuda:0" if self.device == "gpu" else "cpu"
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)
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HFmodel, input, act_out = get_torch_model(modelname)
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frontend_model = HFmodel.model
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frontend_model.to(torch_device)
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input.to(torch_device)
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def benchmark_torch(self, input_tuple):
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inputs = input_tuple[0]
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for i in range(shark_args.num_warmup_iterations):
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self.frontend_model.forward(inputs)
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frontend_model.forward(input)
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begin = time.time()
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for i in range(shark_args.num_iterations):
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out = self.frontend_model.forward(inputs)
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out = frontend_model.forward(input)
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if i == shark_args.num_iterations - 1:
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end = time.time()
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break
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@@ -84,13 +98,13 @@ class SharkBenchmarkRunner(SharkRunner):
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f"{((end-begin)/shark_args.num_iterations)*1000}",
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]
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def benchmark_tf(self, inputs):
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def benchmark_tf(self, frontend_model, inputs):
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for i in range(shark_args.num_warmup_iterations):
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self.frontend_model.forward(*inputs)
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frontend_model.forward(*inputs)
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begin = time.time()
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for i in range(shark_args.num_iterations):
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out = self.frontend_model.forward(*inputs)
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out = frontend_model.forward(*inputs)
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if i == shark_args.num_iterations - 1:
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end = time.time()
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break
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@@ -162,12 +176,12 @@ class SharkBenchmarkRunner(SharkRunner):
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for p in platforms:
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if p == "frontend":
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bench_result["platform"] = frontend
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bench_result["iter/sec"] = self.benchmark_frontend(inputs)[
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0
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]
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bench_result["ms/iter"] = self.benchmark_frontend(inputs)[
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1
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]
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bench_result["iter/sec"] = self.benchmark_frontend(
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inputs, modelname
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)[0]
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bench_result["ms/iter"] = self.benchmark_frontend(
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inputs, modelname
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)[1]
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elif p == "shark_python":
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bench_result["platform"] = "shark_python"
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bench_result["iter/sec"] = self.benchmark_python(inputs)[0]
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@@ -2,9 +2,27 @@ from shark.shark_inference import SharkInference
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import torch
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import numpy as np
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import sys
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torch.manual_seed(0)
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vision_models = [
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"alexnet",
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"resnet101",
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"resnet18",
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"resnet50",
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"squeezenet1_0",
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"wide_resnet50_2",
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]
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def get_torch_model(modelname):
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if modelname in vision_models:
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return get_vision_model(modelname)
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else:
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return get_hf_model(modelname)
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##################### Hugging Face LM Models ###################################
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@@ -12,7 +30,10 @@ class HuggingFaceLanguage(torch.nn.Module):
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def __init__(self, hf_model_name):
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super().__init__()
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from transformers import AutoModelForSequenceClassification
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import transformers as trf
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transformers_path = trf.__path__[0]
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hf_model_path = f"{transformers_path}/models/{hf_model_name}"
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self.model = AutoModelForSequenceClassification.from_pretrained(
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hf_model_name, # The pretrained model.
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num_labels=2, # The number of output labels--2 for binary classification.
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@@ -1,10 +1,8 @@
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from shark.shark_inference import SharkInference
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from shark.iree_utils._common import check_device_drivers, device_driver_info
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from tank.model_utils import compare_tensors
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from shark.parser import shark_args
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from shark.shark_downloader import download_torch_model
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import torch
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import unittest
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import numpy as np
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import pytest
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@@ -13,20 +11,14 @@ import pytest
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class MiniLMModuleTester:
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def __init__(
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self,
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save_mlir=False,
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save_vmfb=False,
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benchmark=False,
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):
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self.save_mlir = save_mlir
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self.save_vmfb = save_vmfb
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self.benchmark = benchmark
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def create_and_check_module(self, dynamic, device):
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model_mlir, func_name, input, act_out = download_torch_model(
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"microsoft/MiniLM-L12-H384-uncased", dynamic
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)
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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# from shark.shark_importer import SharkImporter
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# mlir_importer = SharkImporter(
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@@ -50,18 +42,9 @@ class MiniLMModuleTester:
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assert True == compare_tensors(act_out, results)
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if self.benchmark == True:
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import torch
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from tank.model_utils import get_hf_model
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torch.manual_seed(0)
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model, input, act_out = get_hf_model(
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"microsoft/MiniLM-L12-H384-uncased"
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)
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shark_module.shark_runner.frontend_model = model
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shark_module.shark_runner.benchmark_all_csv(
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(input,),
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"MiniLM-L12-H384-uncased",
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(input),
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"microsoft/MiniLM-L12-H384-uncased",
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dynamic,
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device,
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"torch",
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@@ -72,8 +55,6 @@ class MiniLMModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester = MiniLMModuleTester(self)
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self.module_tester.save_mlir = pytestconfig.getoption("save_mlir")
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self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
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self.module_tester.benchmark = pytestconfig.getoption("benchmark")
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def test_module_static_cpu(self):
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@@ -1,10 +1,8 @@
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from shark.shark_inference import SharkInference
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from shark.iree_utils._common import check_device_drivers, device_driver_info
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from tank.model_utils import get_hf_model, compare_tensors
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from shark.parser import shark_args
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from tank.model_utils import compare_tensors
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from shark.shark_downloader import download_torch_model
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import torch
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import unittest
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import numpy as np
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import pytest
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@@ -13,20 +11,14 @@ import pytest
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class AlbertModuleTester:
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def __init__(
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self,
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save_mlir=False,
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save_vmfb=False,
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benchmark=False,
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):
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self.save_mlir = save_mlir
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self.save_vmfb = save_vmfb
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self.benchmark = benchmark
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def create_and_check_module(self, dynamic, device):
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model_mlir, func_name, input, act_out = download_torch_model(
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"albert-base-v2", dynamic
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)
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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# from shark.shark_importer import SharkImporter
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# mlir_importer = SharkImporter(
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@@ -50,14 +42,8 @@ class AlbertModuleTester:
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assert True == compare_tensors(act_out, results)
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if self.benchmark == True:
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import torch
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torch.manual_seed(0)
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model, input, act_out = get_hf_model("albert-base-v2")
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shark_module.shark_runner.frontend_model = model
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shark_module.shark_runner.benchmark_all_csv(
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(input,),
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(input),
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"albert-base-v2",
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dynamic,
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device,
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@@ -69,8 +55,6 @@ class AlbertModuleTest(unittest.TestCase):
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
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self.module_tester = AlbertModuleTester(self)
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self.module_tester.save_mlir = pytestconfig.getoption("save_mlir")
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self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
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self.module_tester.benchmark = pytestconfig.getoption("benchmark")
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def test_module_static_cpu(self):
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@@ -1,7 +1,6 @@
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from shark.shark_inference import SharkInference
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from shark.iree_utils._common import check_device_drivers, device_driver_info
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from tank.model_utils import get_vision_model, compare_tensors
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from shark.parser import shark_args
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from tank.model_utils import compare_tensors
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from shark.shark_downloader import download_torch_model
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import unittest
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@@ -12,20 +11,14 @@ import pytest
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class AlexnetModuleTester:
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def __init__(
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self,
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save_mlir=False,
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save_vmfb=False,
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benchmark=False,
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):
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self.save_mlir = save_mlir
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self.save_vmfb = save_vmfb
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self.benchmark = benchmark
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def create_and_check_module(self, dynamic, device):
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model_mlir, func_name, input, act_out = download_torch_model(
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"alexnet", dynamic
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)
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shark_args.save_mlir = self.save_mlir
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shark_args.save_vmfb = self.save_vmfb
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# from shark.shark_importer import SharkImporter
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# mlir_importer = SharkImporter(
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@@ -49,16 +42,8 @@ class AlexnetModuleTester:
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assert True == compare_tensors(act_out, results)
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if self.benchmark == True:
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import torch
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import torchvision.models as models
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torch.manual_seed(0)
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model, input, act_out = get_vision_model(
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models.alexnet(pretrained=True)
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)
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shark_module.shark_runner.frontend_model = model
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shark_module.shark_runner.benchmark_all_csv(
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(input,),
|
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(input),
|
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"alexnet",
|
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dynamic,
|
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device,
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@@ -70,8 +55,6 @@ class AlexnetModuleTest(unittest.TestCase):
|
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@pytest.fixture(autouse=True)
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def configure(self, pytestconfig):
|
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self.module_tester = AlexnetModuleTester(self)
|
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self.module_tester.save_mlir = pytestconfig.getoption("save_mlir")
|
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self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
|
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self.module_tester.benchmark = pytestconfig.getoption("benchmark")
|
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|
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def test_module_static_cpu(self):
|
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|
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1
tank/pytorch/bench_results.csv
Normal file
1
tank/pytorch/bench_results.csv
Normal file
@@ -0,0 +1 @@
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platform,model,dynamic,device,iter/sec,ms/iter,datetime
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|
@@ -1,7 +1,6 @@
|
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from shark.shark_inference import SharkInference
|
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from shark.iree_utils._common import check_device_drivers, device_driver_info
|
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from tank.model_utils import get_hf_model, compare_tensors
|
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from shark.parser import shark_args
|
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from tank.model_utils import compare_tensors
|
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from shark.shark_downloader import download_torch_model
|
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|
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import torch
|
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@@ -25,8 +24,6 @@ class BertBaseUncasedModuleTester:
|
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model_mlir, func_name, input, act_out = download_torch_model(
|
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"bert-base-uncased", dynamic
|
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)
|
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shark_args.save_mlir = self.save_mlir
|
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shark_args.save_vmfb = self.save_vmfb
|
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|
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# from shark.shark_importer import SharkImporter
|
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# mlir_importer = SharkImporter(
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@@ -50,14 +47,8 @@ class BertBaseUncasedModuleTester:
|
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assert True == compare_tensors(act_out, results)
|
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|
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if self.benchmark == True:
|
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import torch
|
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|
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torch.manual_seed(0)
|
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|
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model, input, act_out = get_hf_model("bert-base-uncased")
|
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shark_module.shark_runner.frontend_model = model
|
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shark_module.shark_runner.benchmark_all_csv(
|
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(input,),
|
||||
(input),
|
||||
"bert-base-uncased",
|
||||
dynamic,
|
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device,
|
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@@ -69,8 +60,6 @@ class BertBaseUncasedModuleTest(unittest.TestCase):
|
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@pytest.fixture(autouse=True)
|
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def configure(self, pytestconfig):
|
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self.module_tester = BertBaseUncasedModuleTester(self)
|
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self.module_tester.save_mlir = pytestconfig.getoption("save_mlir")
|
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self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
|
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self.module_tester.benchmark = pytestconfig.getoption("benchmark")
|
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|
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def test_module_static_cpu(self):
|
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|
||||
@@ -1,10 +1,9 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_hf_model, compare_tensors
|
||||
from tank.model_utils import compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
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import torch
|
||||
import unittest
|
||||
import numpy as np
|
||||
import pytest
|
||||
@@ -13,20 +12,14 @@ import pytest
|
||||
class DistilBertModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"distilbert-base-uncased", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -50,14 +43,8 @@ class DistilBertModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
|
||||
torch.manual_seed(0)
|
||||
|
||||
model, input, act_out = get_hf_model("distilbert-base-uncased")
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"distilbert-base-uncased",
|
||||
dynamic,
|
||||
device,
|
||||
@@ -73,19 +60,25 @@ class DistilBertModuleTest(unittest.TestCase):
|
||||
self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
|
||||
self.module_tester.benchmark = pytestconfig.getoption("benchmark")
|
||||
|
||||
@pytest.mark.skip(reason="DistilBert needs to be uploaded to cloud.")
|
||||
@pytest.mark.skip(
|
||||
reason="Fails to lower in torch-mlir. See https://github.com/nod-ai/SHARK/issues/222"
|
||||
)
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="DistilBert needs to be uploaded to cloud.")
|
||||
@pytest.mark.skip(
|
||||
reason="Fails to lower in torch-mlir. See https://github.com/nod-ai/SHARK/issues/222"
|
||||
)
|
||||
def test_module_dynamic_cpu(self):
|
||||
dynamic = True
|
||||
device = "cpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="DistilBert needs to be uploaded to cloud.")
|
||||
@pytest.mark.skip(
|
||||
reason="Fails to lower in torch-mlir. See https://github.com/nod-ai/SHARK/issues/222"
|
||||
)
|
||||
@pytest.mark.skipif(
|
||||
check_device_drivers("gpu"), reason=device_driver_info("gpu")
|
||||
)
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_hf_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from tank.model_utils import compare_tensors
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import torch
|
||||
@@ -13,20 +12,14 @@ import pytest
|
||||
class MobileBertModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"google/mobilebert-uncased", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -50,15 +43,9 @@ class MobileBertModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
|
||||
torch.manual_seed(0)
|
||||
|
||||
model, input, act_out = get_hf_model("google/mobilebert-uncased")
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
"mobilebert-uncased",
|
||||
(input),
|
||||
"google/mobilebert-uncased",
|
||||
dynamic,
|
||||
device,
|
||||
"torch",
|
||||
@@ -69,8 +56,6 @@ class MobileBertModuleTest(unittest.TestCase):
|
||||
@pytest.fixture(autouse=True)
|
||||
def configure(self, pytestconfig):
|
||||
self.module_tester = MobileBertModuleTester(self)
|
||||
self.module_tester.save_mlir = pytestconfig.getoption("save_mlir")
|
||||
self.module_tester.save_vmfb = pytestconfig.getoption("save_vmfb")
|
||||
self.module_tester.benchmark = pytestconfig.getoption("benchmark")
|
||||
|
||||
def test_module_static_cpu(self):
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_vision_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from tank.model_utils import compare_tensors
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import unittest
|
||||
@@ -12,20 +11,14 @@ import pytest
|
||||
class Resnet101ModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"resnet101", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -49,16 +42,8 @@ class Resnet101ModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
import torchvision.models as models
|
||||
|
||||
torch.manual_seed(0)
|
||||
model, input, act_out = get_vision_model(
|
||||
models.resnet101(pretrained=True)
|
||||
)
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"resnet101",
|
||||
dynamic,
|
||||
device,
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_vision_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import unittest
|
||||
@@ -12,22 +11,15 @@ import pytest
|
||||
class Resnet18ModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"resnet18", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
# model,
|
||||
# (input,),
|
||||
@@ -49,16 +41,8 @@ class Resnet18ModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
import torchvision.models as models
|
||||
|
||||
torch.manual_seed(0)
|
||||
model, input, act_out = get_vision_model(
|
||||
models.resnet18(pretrained=True)
|
||||
)
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"resnet18",
|
||||
dynamic,
|
||||
device,
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_vision_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import unittest
|
||||
@@ -12,20 +11,14 @@ import pytest
|
||||
class Resnet50ModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"resnet50", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -49,16 +42,8 @@ class Resnet50ModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
import torchvision.models as models
|
||||
|
||||
torch.manual_seed(0)
|
||||
model, input, act_out = get_vision_model(
|
||||
models.resnet50(pretrained=True)
|
||||
)
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"resnet50",
|
||||
dynamic,
|
||||
device,
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_vision_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import unittest
|
||||
@@ -12,20 +11,14 @@ import pytest
|
||||
class SqueezenetModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"squeezenet1_0", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -49,16 +42,8 @@ class SqueezenetModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
import torchvision.models as models
|
||||
|
||||
torch.manual_seed(0)
|
||||
model, input, act_out = get_vision_model(
|
||||
models.squeezenet1_0(pretrained=True)
|
||||
)
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"squeezenet1_0",
|
||||
dynamic,
|
||||
device,
|
||||
|
||||
@@ -1,7 +1,6 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers, device_driver_info
|
||||
from tank.model_utils import get_vision_model, compare_tensors
|
||||
from shark.parser import shark_args
|
||||
from shark.shark_downloader import download_torch_model
|
||||
|
||||
import unittest
|
||||
@@ -12,20 +11,14 @@ import pytest
|
||||
class WideResnet50ModuleTester:
|
||||
def __init__(
|
||||
self,
|
||||
save_mlir=False,
|
||||
save_vmfb=False,
|
||||
benchmark=False,
|
||||
):
|
||||
self.save_mlir = save_mlir
|
||||
self.save_vmfb = save_vmfb
|
||||
self.benchmark = benchmark
|
||||
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model_mlir, func_name, input, act_out = download_torch_model(
|
||||
"wide_resnet50_2", dynamic
|
||||
)
|
||||
shark_args.save_mlir = self.save_mlir
|
||||
shark_args.save_vmfb = self.save_vmfb
|
||||
|
||||
# from shark.shark_importer import SharkImporter
|
||||
# mlir_importer = SharkImporter(
|
||||
@@ -49,16 +42,8 @@ class WideResnet50ModuleTester:
|
||||
assert True == compare_tensors(act_out, results)
|
||||
|
||||
if self.benchmark == True:
|
||||
import torch
|
||||
import torchvision.models as models
|
||||
|
||||
torch.manual_seed(0)
|
||||
model, input, act_out = get_vision_model(
|
||||
models.wide_resnet50_2(pretrained=True)
|
||||
)
|
||||
shark_module.shark_runner.frontend_model = model
|
||||
shark_module.shark_runner.benchmark_all_csv(
|
||||
(input,),
|
||||
(input),
|
||||
"wide_resnet50_2",
|
||||
dynamic,
|
||||
device,
|
||||
|
||||
@@ -1,90 +0,0 @@
|
||||
from shark.shark_inference import SharkInference
|
||||
from shark.iree_utils._common import check_device_drivers
|
||||
from tank.model_utils_tf import get_TFhf_model, compare_tensors_tf
|
||||
|
||||
import tensorflow as tf
|
||||
import unittest
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
|
||||
class MiniLMTFModuleTester:
|
||||
def create_and_check_module(self, dynamic, device):
|
||||
model, input, act_out = get_TFhf_model(
|
||||
"microsoft/MiniLM-L12-H384-uncased"
|
||||
)
|
||||
shark_module = SharkInference(
|
||||
model, (input,), device=device, dynamic=dynamic, jit_trace=True
|
||||
)
|
||||
shark_module.set_frontend("tensorflow")
|
||||
shark_module.compile()
|
||||
results = shark_module.forward((input))
|
||||
assert True == compare_tensors_tf(act_out, results)
|
||||
|
||||
|
||||
class MiniLMTFModuleTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.module_tester = MiniLMTFModuleTester()
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
def test_module_static_cpu(self):
|
||||
dynamic = False
|
||||
device = "cpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
@pytest.mark.xfail(
|
||||
reason="Language models currently failing for dynamic case"
|
||||
)
|
||||
def test_module_dynamic_cpu(self):
|
||||
dynamic = True
|
||||
device = "cpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
@pytest.mark.skipif(
|
||||
check_device_drivers("gpu"), reason="nvidia-smi not found"
|
||||
)
|
||||
def test_module_static_gpu(self):
|
||||
dynamic = False
|
||||
device = "gpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
@pytest.mark.xfail(
|
||||
reason="Language models currently failing for dynamic case"
|
||||
)
|
||||
@pytest.mark.skipif(
|
||||
check_device_drivers("gpu"), reason="nvidia-smi not found"
|
||||
)
|
||||
def test_module_dynamic_gpu(self):
|
||||
dynamic = True
|
||||
device = "gpu"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
@pytest.mark.skipif(
|
||||
check_device_drivers("vulkan"),
|
||||
reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
|
||||
)
|
||||
def test_module_static_vulkan(self):
|
||||
dynamic = False
|
||||
device = "vulkan"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
@pytest.mark.skip(reason="TF testing temporarily unavailable.")
|
||||
@pytest.mark.xfail(
|
||||
reason="Language models currently failing for dynamic case"
|
||||
)
|
||||
@pytest.mark.skipif(
|
||||
check_device_drivers("vulkan"),
|
||||
reason="vulkaninfo not found, install from https://github.com/KhronosGroup/MoltenVK/releases",
|
||||
)
|
||||
def test_module_dynamic_vulkan(self):
|
||||
dynamic = True
|
||||
device = "vulkan"
|
||||
self.module_tester.create_and_check_module(dynamic, device)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Reference in New Issue
Block a user