update xfails for PyTorch DistilBERT (huggingface#355)

Author: monorimet

tank/pytorch/torch_model_list.csv

@@ -3,7 +3,6 @@ microsoft/MiniLM-L12-H384-uncased,True,hf,True,66M,"nlp;bert-variant;transformer
 albert-base-v2,True,hf,True,11M,"nlp;bert-variant;transformer-encoder","12 layers; 128 embedding dim; 768 hidden dim; 12 attention heads; Smaller than BERTbase (11M params vs 109M params); Uses weight sharing to reduce # params but computational cost is similar to BERT."
 bert-base-uncased,True,hf,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
 bert-base-cased,True,hf,True,109M,"nlp;bert-variant;transformer-encoder","12 layers; 768 hidden; 12 attention heads"
-distilbert-base-uncased,True,hf,True,66M,"nlp;bert-variant;transformer-encoder","Smaller and faster than BERT with 97percent retained accuracy."
 google/mobilebert-uncased,True,hf,True,25M,"nlp,bert-variant,transformer-encoder,mobile","24 layers, 512 hidden size, 128 embedding"
 alexnet,False,vision,True,61M,"cnn,parallel-layers","The CNN that revolutionized computer vision (move away from hand-crafted features to neural networks),10 years old now and probably no longer used in prod."
 resnet18,False,vision,True,11M,"cnn,image-classification,residuals,resnet-variant","1 7x7 conv2d and the rest are 3x3 conv2d"

tank/test_models.py

@@ -259,6 +259,11 @@ def test_module(self, dynamic, device, config):
         self.module_tester.tf32 = self.pytestconfig.getoption("tf32")
         self.module_tester.ci = self.pytestconfig.getoption("ci")
         self.module_tester.ci_sha = self.pytestconfig.getoption("ci_sha")
+        if (
+            config["model_name"] == "distilbert-base-uncased"
+            and config["framework"] == "torch"
+        ):
+            pytest.xfail(reason="https://github.com/nod-ai/SHARK/issues/354")
         if (
             config["model_name"] == "facebook/convnext-tiny-224"
             and device == "cuda"