fix integration test

tests/models/qwen2_moe/test_modeling_qwen2_moe.py

@@ -549,10 +549,10 @@ def test_model_a2_7b_logits(self):
         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]])
+        EXPECTED_MEAN = torch.tensor([[-4.2125, -3.6416, -4.9136, -4.3005, -4.9938, -3.4393, -3.5195, -4.1621]])
         torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
         # slicing logits[0, 0, 0:30]
-        EXPECTED_SLICE = torch.tensor([-5.8781, -5.8616, -0.1052, -4.7200, -5.8781, -5.8774, -5.8773, -5.8777, -5.8781, -5.8780, -5.8781, -5.8779, -1.0787,  1.7583, -5.8779, -5.8780, -5.8783, -5.8778, -5.8776, -5.8781, -5.8784, -5.8778, -5.8778, -5.8777, -5.8779, -5.8778, -5.8776, -5.8780, -5.8779, -5.8781])  # fmt: skip
+        EXPECTED_SLICE = torch.tensor([2.3013, -0.6595, -0.1389, -1.4095, -1.7381, -1.7609, -2.0449, -2.4289, -3.0271, -2.1351, -0.6568, -4.6012, -1.9102, -0.7475, -3.1377, 4.6904, 7.1936, 7.0991, 6.4414, 6.1720, 6.2617, 5.8751, 5.6997, 5.6011, 5.5828, -3.9505, -0.5384, -0.3392, 1.2445, 2.0714])  # fmt: skip
         print(out[0, 0, :30])
         torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
 
@@ -562,8 +562,8 @@ def test_model_a2_7b_logits(self):
 
     @slow
     def test_model_a2_7b_generation(self):
-        EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
-        prompt = "My favourite condiment is "
+        EXPECTED_TEXT_COMPLETION = """To be or not to be, that is the question. This is the question that has been asked by many people over the"""
+        prompt = "To be or not to"
         tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", use_fast=False)
         model = Qwen2MoeForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", device_map="auto")
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
@@ -633,8 +633,8 @@ def test_model_a2_7b_long_prompt_sdpa(self):
         backend_empty_cache(torch_device)
         gc.collect()
 
-        EXPECTED_TEXT_COMPLETION = """My favourite condiment is 100% ketchup. I love it on everything. I’m not a big"""
-        prompt = "My favourite condiment is "
+        EXPECTED_TEXT_COMPLETION = """To be or not to be, that is the question. This is the question that has been asked by many people over the"""
+        prompt = "To be or not to"
         tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", use_fast=False)
 
         input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device)
@@ -647,9 +647,9 @@ def test_model_a2_7b_long_prompt_sdpa(self):
     @slow
     def test_speculative_generation(self):
         EXPECTED_TEXT_COMPLETION = (
-            "My favourite condiment is 100% Sriracha. I love the heat, the tang and the fact costs"
+            "To be or not to be, that is the question.\nThe answer is to be, of course. But what does it"
         )
-        prompt = "My favourite condiment is "
+        prompt = "To be or not to"
         tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", use_fast=False)
         model = Qwen2MoeForCausalLM.from_pretrained(
             "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", torch_dtype=torch.float16