Fix timm filter and run timm models in parallel (#23116)

### Details:
 - *Update list of timm models to comply with new version of timm*
- *Run timm models and torchvision models in trace and export model in
parallel*

### Tickets:
 - *ticket-id*

.github/workflows/job_pytorch_models_tests.yml

@@ -106,6 +106,8 @@ jobs:
 
       - name: Install OpenVINO Python wheels
         run: |
+          # To enable pytest parallel features
+          python3 -m pip install pytest-xdist[psutil]
           python3 -m pip install ${INSTALL_DIR}/tools/openvino-*
           python3 -m pip install ${INSTALL_DIR}/openvino_tokenizers-*
 
@@ -118,10 +120,20 @@ jobs:
         env:
           CPLUS_INCLUDE_PATH: ${{ env.Python_ROOT_DIR }}/include/python${{ env.PYTHON_VERSION }}
 
-      - name: PyTorch Models Tests
+      - name: PyTorch Models Tests Timm and Torchvision
         run: |
           export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH
-          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_tests.html --self-contained-html -v
+          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch/ -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_timm_tv_tests.html --self-contained-html -v -n 4 -k "TestTimmConvertModel or TestTorchHubConvertModel"
+        env:
+          TYPE: ${{ inputs.event == 'schedule' && 'nightly' || 'precommit'}}
+          TEST_DEVICE: CPU
+          OP_REPORT_FILE: ${{ env.INSTALL_TEST_DIR }}/TEST-torch_unsupported_ops.log
+
+      - name: PyTorch Models Tests Not Timm or Torchvision
+        if: always()
+        run: |
+          export PYTHONPATH=${MODEL_HUB_TESTS_INSTALL_DIR}:$PYTHONPATH
+          python3 -m pytest ${MODEL_HUB_TESTS_INSTALL_DIR}/pytorch -m ${TYPE} --html=${INSTALL_TEST_DIR}/TEST-torch_model_tests.html --self-contained-html -v -k "not (TestTimmConvertModel or TestTorchHubConvertModel)"
         env:
           TYPE: ${{ inputs.event == 'schedule' && 'nightly' || 'precommit'}}
           TEST_DEVICE: CPU

tests/model_hub_tests/pytorch/hf_transformers_models

@@ -128,7 +128,7 @@ hf-internal-testing/tiny-random-DonutSwinModel,donut-swin
 hf-internal-testing/tiny-random-EfficientFormerForImageClassification,efficientformer
 hf-internal-testing/tiny-random-flaubert,flaubert
 hf-internal-testing/tiny-random-FocalNetModel,focalnet
-hf-internal-testing/tiny-random-GPTBigCodeForCausalLM,gpt_bigcode,xfail,Conversion is failed for: aten::mul
+hf-internal-testing/tiny-random-GPTBigCodeForCausalLM,gpt_bigcode
 hf-internal-testing/tiny-random-GPTJModel,gptj
 hf-internal-testing/tiny-random-groupvit,groupvit
 hf-internal-testing/tiny-random-IBertModel,ibert

tests/model_hub_tests/pytorch/test_aliked.py

@@ -84,7 +84,7 @@ def setup_class(self):
         subprocess.check_call(["sh", "build.sh"], cwd=os.path.join(
             self.repo_dir.name, "custom_ops"))
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         sys.path.append(self.repo_dir.name)
         from nets.aliked import ALIKED
 

tests/model_hub_tests/pytorch/test_detectron2.py

@@ -23,7 +23,7 @@ def setup_class(self):
         subprocess.run([sys.executable, "-m", "pip", "install",
                        "git+https://github.com/facebookresearch/detectron2.git@017abbfa5f2c2a2afa045200c2af9ccf2fc6227f"])
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         from detectron2 import model_zoo, export
         from detectron2.modeling import build_model, PanopticFPN
         from detectron2.checkpoint import DetectionCheckpointer

tests/model_hub_tests/pytorch/test_edsr.py

@@ -38,7 +38,7 @@
 
 
 class TestEdsrConvertModel(TestTorchConvertModel):
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         # image link from https://github.com/eugenesiow/super-image
         url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
         image = Image.open(requests.get(url, stream=True).raw)

tests/model_hub_tests/pytorch/test_gfpgan.py

@@ -28,7 +28,7 @@ def setup_class(self):
         subprocess.check_call(
             ["wget", "-nv", checkpoint_url], cwd=self.repo_dir.name)
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         sys.path.append(self.repo_dir.name)
         from gfpgan import GFPGANer
 

tests/model_hub_tests/pytorch/test_hf_transformers.py

@@ -98,7 +98,7 @@ def setup_class(self):
         self.image = Image.open(requests.get(url, stream=True).raw)
         self.cuda_available, self.gptq_postinit = None, None
 
-    def load_model_impl(self, name, type):
+    def load_model(self, name, type):
         import torch
         name_suffix = ''
         from transformers import AutoConfig

tests/model_hub_tests/pytorch/test_speech-transformer.py

@@ -24,7 +24,7 @@ def setup_class(self):
         checkpoint_url = "https://github.com/foamliu/Speech-Transformer/releases/download/v1.0/speech-transformer-cn.pt"
         subprocess.check_call(["wget", "-nv", checkpoint_url], cwd=self.repo_dir.name)
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         sys.path.append(self.repo_dir.name)
         from transformer.transformer import Transformer
 

tests/model_hub_tests/pytorch/test_timm.py

@@ -6,22 +6,23 @@
 import pytest
 import timm
 import torch
-from models_hub_common.constants import hf_hub_cache_dir
-from models_hub_common.utils import cleanup_dir, get_models_list
+from models_hub_common.utils import get_models_list
 
 from torch_utils import TestTorchConvertModel, process_pytest_marks
-from openvino import convert_model
-from torch.export import export
-from packaging import version
 
 
 def filter_timm(timm_list: list) -> list:
     unique_models = dict()
     filtered_list = []
-    ignore_list = ["base", "xxtiny", "xxs", "pico", "xtiny", "xs", "nano", "tiny", "s", "mini", "small", "lite",
-                   "medium", "m", "big", "large", "l", "xlarge", "xl", "huge", "xxlarge", "gigantic", "giant", "enormous"]
+    ignore_list = ["base", "atto", "femto", "xxtiny", "xxsmall", "xxs", "pico",
+                   "xtiny", "xmall", "xs", "nano", "tiny", "s", "mini", "small",
+                   "lite", "medium", "m", "big", "large", "l", "xlarge", "xl",
+                   "huge", "xxlarge", "gigantic", "giant", "enormous"]
     ignore_set = set(ignore_list)
     for name in sorted(timm_list):
+        if "x_" in name:
+            # x_small or xx_small should be merged to xsmall and xxsmall
+            name.replace("x_", "x")
         # first: remove datasets
         name_parts = name.split(".")
         _name = "_".join(name.split(".")[:-1]) if len(name_parts) > 1 else name
@@ -50,7 +51,7 @@ def get_all_models() -> list:
 
 
 class TestTimmConvertModel(TestTorchConvertModel):
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         m = timm.create_model(model_name, pretrained=True)
         cfg = timm.get_pretrained_cfg(model_name)
         shape = [1] + list(cfg.input_size)
@@ -69,11 +70,6 @@ def infer_fw_model(self, model_obj, inputs):
             fw_outputs = [fw_outputs.numpy(force=True)]
         return fw_outputs
 
-    def teardown_method(self):
-        # remove all downloaded files from cache
-        cleanup_dir(hf_hub_cache_dir)
-        super().teardown_method()
-
     @pytest.mark.parametrize("name", ["mobilevitv2_050.cvnets_in1k",
                                       "poolformerv2_s12.sail_in1k",
                                       "vit_base_patch8_224.augreg_in21k",
@@ -86,8 +82,8 @@ def test_convert_model_precommit(self, name, ie_device):
         self.run(name, None, ie_device)
 
     @pytest.mark.nightly
-    @pytest.mark.parametrize("mode", ["trace"]) # disable "export" for now
     @pytest.mark.parametrize("name", get_all_models())
+    @pytest.mark.parametrize("mode", ["trace", "export"])
     def test_convert_model_all_models(self, mode, name, ie_device):
         self.mode = mode
         self.run(name, None, ie_device)

tests/model_hub_tests/pytorch/test_torchbench.py

@@ -32,7 +32,7 @@ def setup_class(self):
         subprocess.check_call(
             ["git", "checkout", "dbc109791dbb0dfb58775a5dc284fc2c3996cb30"], cwd=self.repo_dir.name)
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         subprocess.check_call([sys.executable, "install.py"] + [model_name], cwd=self.repo_dir.name)
         sys.path.append(self.repo_dir.name)
         from torchbenchmark import load_model_by_name

tests/model_hub_tests/pytorch/test_torchvision_models.py

@@ -53,13 +53,7 @@ def prepare_frames_for_raft(name, frames1, frames2):
 
 
 class TestTorchHubConvertModel(TestTorchConvertModel):
-    def setup_class(self):
-        self.cache_dir = tempfile.TemporaryDirectory()
-        # set temp dir for torch cache
-        if os.environ.get('USE_SYSTEM_CACHE', 'True') == 'False':
-            torch.hub.set_dir(str(self.cache_dir.name))
-
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         m = torch.hub.load("pytorch/vision", model_name,
                            weights='DEFAULT', skip_validation=True)
         m.eval()
@@ -97,11 +91,6 @@ def infer_fw_model(self, model_obj, inputs):
             fw_outputs = [fw_outputs.numpy(force=True)]
         return fw_outputs
 
-    def teardown_method(self):
-        # cleanup tmpdir
-        self.cache_dir.cleanup()
-        super().teardown_method()
-
     @pytest.mark.parametrize("model_name", ["efficientnet_b7", "raft_small", "swin_v2_s"])
     @pytest.mark.precommit
     def test_convert_model_precommit(self, model_name, ie_device):
@@ -114,9 +103,9 @@ def test_convert_model_precommit_export(self, model_name, ie_device):
         self.mode = "export"
         self.run(model_name, None, ie_device)
 
-    @pytest.mark.parametrize("mode", ["trace"]) # disable "export" for now
     @pytest.mark.parametrize("name",
                              process_pytest_marks(os.path.join(os.path.dirname(__file__), "torchvision_models")))
+    @pytest.mark.parametrize("mode", ["trace", "export"])
     @pytest.mark.nightly
     def test_convert_model_all_models(self, mode, name, ie_device):
         self.mode = mode

tests/model_hub_tests/pytorch/test_tpsmm.py

@@ -25,7 +25,7 @@ def setup_class(self):
             ["git", "checkout", "c616878812c9870ed81ac72561be2676fd7180e2"], cwd=self.repo_dir.name)
         # verify model on random weights
 
-    def load_model_impl(self, model_name, model_link):
+    def load_model(self, model_name, model_link):
         sys.path.append(self.repo_dir.name)
         from modules.inpainting_network import InpaintingNetwork
         from modules.keypoint_detector import KPDetector

tests/model_hub_tests/pytorch/timm_models

@@ -26,17 +26,11 @@ convformer_s36.sail_in1k,None
 convit_base.fb_in1k,None,xfail,Trace failed
 convmixer_1024_20_ks9_p14.in1k,None
 convmixer_1536_20.in1k,None
-convnext_atto.d2_in1k,None
 convnext_atto_ols.a2_in1k,None
 convnext_base.clip_laion2b,None
-convnext_femto.d1_in1k,None
-convnext_femto_ols.d1_in1k,None
 convnext_large_mlp.clip_laion2b_augreg,None
-convnext_pico_ols.d1_in1k,None
 convnext_tiny_hnf.a2h_in1k,None
-convnextv2_atto.fcmae,None
 convnextv2_base.fcmae,None
-convnextv2_femto.fcmae,None
 crossvit_15_dagger_240.in1k,None
 crossvit_base_240.in1k,None
 cs3darknet_focus_m.c2ns_in1k,None
@@ -52,10 +46,10 @@ cspresnext50.ra_in1k,None
 darknet53.c2ns_in1k,None
 darknetaa53.c2ns_in1k,None
 davit_base.msft_in1k,None
-deit3_base_patch16_224.fb_in1k,None
-deit3_huge_patch14_224.fb_in1k,None
 deit_base_distilled_patch16_224.fb_in1k,None
 deit_base_patch16_224.fb_in1k,None
+deit3_base_patch16_224.fb_in1k,None
+deit3_huge_patch14_224.fb_in1k,None
 densenet121.ra_in1k,None
 densenet161.tv_in1k,None
 densenet169.tv_in1k,None
@@ -144,11 +138,11 @@ efficientvit_m4.r224_in1k,None
 efficientvit_m5.r224_in1k,None
 ese_vovnet19b_dw.ra_in1k,None
 ese_vovnet39b.ra_in1k,None
+eva_giant_patch14_clip_224.laion400m,None
+eva_large_patch14_196.in22k_ft_in1k,None
 eva02_base_patch14_224.mim_in22k,None
 eva02_base_patch16_clip_224.merged2b,None
 eva02_large_patch14_clip_224.merged2b,None
-eva_giant_patch14_clip_224.laion400m,None
-eva_large_patch14_196.in22k_ft_in1k,None
 fastvit_ma36.apple_dist_in1k,None
 fastvit_s12.apple_dist_in1k,None
 fastvit_sa12.apple_dist_in1k,None
@@ -186,6 +180,14 @@ hardcorenas_c.miil_green_in1k,None
 hardcorenas_d.miil_green_in1k,None
 hardcorenas_e.miil_green_in1k,None
 hardcorenas_f.miil_green_in1k,None
+hgnet_base.ssld_in1k,None
+hgnetv2_b0.ssld_stage1_in22k_in1k,None
+hgnetv2_b1.ssld_stage1_in22k_in1k,None
+hgnetv2_b2.ssld_stage1_in22k_in1k,None
+hgnetv2_b3.ssld_stage1_in22k_in1k,None
+hgnetv2_b4.ssld_stage1_in22k_in1k,None
+hgnetv2_b5.ssld_stage1_in22k_in1k,None
+hgnetv2_b6.ssld_stage1_in22k_in1k,None
 hrnet_w18_small.gluon_in1k,None
 hrnet_w18_small_v2.gluon_in1k,None
 hrnet_w18_ssld.paddle_in1k,None
@@ -245,6 +247,7 @@ mvitv2_base.fb_in1k,None
 mvitv2_base_cls.fb_inw21k,None
 nasnetalarge.tf_in1k,None
 nest_base_jx.goog_in1k,None
+nextvit_base.bd_in1k,None
 nf_regnet_b1.ra2_in1k,None
 nf_resnet50.ra2_in1k,None
 nfnet_l0.ra2_in1k,None