Fix Mistral OOM again (#26847)

fix
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

tests/models/mistral/test_modeling_mistral.py

@@ -437,6 +437,7 @@ class MistralIntegrationTest(unittest.TestCase):
         input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
         model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
         input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device)
+        with torch.no_grad():
             out = model(input_ids).logits.cpu()
         # Expected mean on dim = -1
         EXPECTED_MEAN = torch.tensor([[-2.5548, -2.5737, -3.0600, -2.5906, -2.8478, -2.8118, -2.9325, -2.7694]])
@@ -457,8 +458,8 @@ class MistralIntegrationTest(unittest.TestCase):
         EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
         prompt = "My favourite condiment is "
         tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", use_fast=False)
-        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(torch_device)
         model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
+        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
 
         # greedy generation outputs
         generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)