Drop torch 1.8.2 support (#1736)

examples/post_training_quantization/openvino/yolov8_quantize_with_accuracy_control/main.py

@@ -114,7 +114,7 @@ def prepare_validation(model: YOLO, args: Any) -> Tuple[Validator, torch.utils.d
 def benchmark_performance(model_path, config) -> float:
     command = f"benchmark_app -m {model_path} -d CPU -api async -t 30"
     command += f' -shape "[1,3,{config.imgsz},{config.imgsz}]"'
-    cmd_output = subprocess.check_output(command, shell=True) # nosec
+    cmd_output = subprocess.check_output(command, shell=True)  # nosec
 
     match = re.search(r"Throughput\: (.+?) FPS", str(cmd_output))
     return float(match.group(1))

examples/torch/common/model_loader.py

@@ -110,6 +110,6 @@ def download_checkpoint(url):
     if not download_path.exists():
         print("Downloading checkpoint ...")
         checkpoint = requests.get(url)
-        with open(download_path, 'wb') as f:
+        with open(download_path, "wb") as f:
             f.write(checkpoint.content)
     return str(download_path)

examples/torch/requirements.txt

@@ -6,5 +6,5 @@ defusedxml>=0.7.0rc1
 mlflow>=1.12.1
 returns>0.14
 opencv-python>=4.4.0.46
-torchvision>=0.9.2,<0.15  # the minor version should always match the torch minor version that is installed via NNCF's `pip install nncf[torch]`; TV minor version is torch minor version +1
+torchvision>=0.10.0,<0.15  # the minor version should always match the torch minor version that is installed via NNCF's `pip install nncf[torch]`; TV minor version is torch minor version +1
 efficientnet_pytorch

nncf/experimental/common/graph/netron.py

@@ -11,7 +11,7 @@
  limitations under the License.
 """
 # Since we are not reading XML, but creating it, the package security message is irrelevant
-import xml.etree.ElementTree as ET # nosec
+import xml.etree.ElementTree as ET  # nosec
 from typing import Callable, Dict, List, Optional, Tuple
 
 from nncf.common.graph import NNCFGraph

nncf/torch/binarization/binarize_functions.py

@@ -14,26 +14,13 @@
 from typing import Any
 
 import torch
-from torch import _C  # pylint:disable=protected-access
+from torch.onnx.symbolic_helper import _is_constant  # pylint:disable=protected-access
 
 from nncf.common.logging import nncf_logger
 from nncf.torch.binarization.extensions import BinarizedFunctionsCUDA
 from nncf.torch.utils import add_domain
 
 
-def _is_value(x: Any) -> bool:
-    return isinstance(x, _C.Value)
-
-
-# Implementation is copy-pasted from torch.onnx.symbolic_helper.
-# It's need to support torch < 1.9, since there's no such function in such versions of torch.
-def _is_constant(value: Any) -> bool:
-    return not _is_value(value) or value.node().kind() in {
-        "onnx::Constant",
-        "prim::Constant",
-    }
-
-
 def _unsqueeze_helper(g, input_, axes_i):
     # Unsqueeze handling for different opsets inspired by torch.onnx.symbolic_helper._unsqueeze_helper
     # The original unsqueeze_helper cannot be used in 1.13 since it references

setup.py

@@ -133,7 +133,7 @@ def find_version(*file_paths):
 ]
 
 TORCH_EXTRAS = [
-    "torch>=1.8.2,<1.14",
+    "torch>=1.9.1,<1.14",
 ]
 
 ONNX_EXTRAS = ["onnx~=1.13.1", "onnxruntime~=1.14.1"]

tests/torch/experimental/search_building_blocks/test_transformer_blocks.py

@@ -18,7 +18,6 @@
 import numpy as np
 import pytest
 import torch
-from pkg_resources import parse_version
 from torch import nn
 from transformers import AutoModelForAudioClassification
 from transformers import AutoModelForImageClassification
@@ -123,10 +122,6 @@ def fixture_transformer_search_params_desc(request):
     return request.param
 
 
-@pytest.mark.skipif(
-    parse_version(torch.__version__) < parse_version("1.9"),
-    reason=f"torch {torch.__version__} is not compatible with installed transformers package",
-)
 def test_transformer_building_blocks(desc: TransformerSearchBBlockParamsCase):
     model = desc.model_creator()
     move_model_to_cuda_if_available(model)

tests/torch/nas/test_elastic_depth.py

@@ -485,10 +485,6 @@ def forward(self, x):
         return x * self.dummy + x
 
 
-@pytest.mark.skipif(
-    parse_version(torch.__version__) < parse_version("1.9"),
-    reason="Test uses torch.permute attribute, which is not presented in the current torch version",
-)
 @pytest.mark.parametrize("model_creator", (TwoPermute, ChunkConcat, TwoBranchesBeforeInput, TwoBranchesAfterInput))
 def test_can_skip_trivial_block(model_creator):
     model = model_creator()

tests/torch/nas/test_sanity_sample.py

@@ -15,8 +15,6 @@
 from typing import Dict
 
 import pytest
-import torch
-from pkg_resources import parse_version
 
 from nncf.common.initialization.batchnorm_adaptation import BatchnormAdaptationAlgorithm
 from nncf.experimental.torch.nas.bootstrapNAS.elasticity.elastic_depth import ElasticDepthHandler
@@ -101,13 +99,6 @@ def fixture_nas_desc(request, dataset_dir):
 
 
 def test_e2e_supernet_training(nas_desc: NASSampleTestDescriptor, tmp_path, mocker):
-    if parse_version(torch.__version__) < parse_version("1.9") and (
-        "efficient_net" in nas_desc.config_name_ or "mobilenet_v3" in nas_desc.config_name_
-    ):
-        pytest.skip(
-            f"Test exports model with hardsigmoid operator to ONNX opset version 13.\n"
-            f"It is not supported in the current torch version: {torch.__version__}"
-        )
     validator = nas_desc.get_validator()
     args = validator.get_default_args(tmp_path)
     validator.validate_sample(args, mocker)