chore: remove duplicate bert test case

Author: peri044

tests/py/dynamo/models/test_models_export.py

@@ -105,55 +105,6 @@ def test_efficientnet_b0(ir):
     torch._dynamo.reset()
 
 
-@pytest.mark.unit
-def test_bert_base_uncased(ir):
-    model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
-    input = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
-    input2 = torch.randint(0, 1, (1, 14), dtype=torch.int32).to("cuda")
-    model = (
-        transformers_trace(model, input_names=["input_ids", "attention_mask"])
-        .eval()
-        .cuda()
-    )
-
-    compile_spec = {
-        "inputs": [
-            torchtrt.Input(
-                input.shape,
-                dtype=input.dtype,
-                format=torch.contiguous_format,
-            ),
-            torchtrt.Input(
-                input.shape,
-                dtype=input.dtype,
-                format=torch.contiguous_format,
-            ),
-        ],
-        "device": torchtrt.Device("cuda:0"),
-        "enabled_precisions": {torch.float},
-        "truncate_long_and_double": True,
-        "ir": ir,
-        "min_block_size": 10,
-    }
-    trt_mod = torchtrt.compile(model, **compile_spec)
-    model_outputs = model(input, input2)
-    trt_model_outputs = trt_mod(input, input2)
-    assertions.assertTrue(
-        len(model_outputs) == len(trt_model_outputs),
-        msg=f"Number of outputs for BERT model compilation is different with Pytorch {len(model_outputs)} and TensorRT {len(trt_model_outputs)}. Please check the compilation.",
-    )
-    for index, key in enumerate(model_outputs):
-        out, trt_out = model_outputs[key], trt_model_outputs[index]
-        cos_sim = cosine_similarity(out, trt_out)
-        assertions.assertTrue(
-            cos_sim > COSINE_THRESHOLD,
-            msg=f"HF BERT base-uncased TRT outputs don't match with the original model. Cosine sim score: {cos_sim} Threshold: {COSINE_THRESHOLD}",
-        )
-
-    # Clean up model env
-    torch._dynamo.reset()
-
-
 @pytest.mark.unit
 def test_bert_base_uncased(ir):
     model = BertModel.from_pretrained("bert-base-uncased").cuda().eval()
@@ -183,7 +134,6 @@ def test_bert_base_uncased(ir):
         "truncate_long_and_double": True,
         "ir": ir,
         "min_block_size": 15,
-        "debug": True,
     }
     trt_mod = torchtrt.compile(model, **compile_spec)
     model_outputs = model(input, input2)
@@ -204,9 +154,6 @@ def test_bert_base_uncased(ir):
     # Clean up model env
     torch._dynamo.reset()
 
-    with torch.no_grad():
-        torch.cuda.empty_cache()
-
 
 @pytest.mark.unit
 def test_resnet18_half(ir):