[CI] green llama tests (#37244)

* green llama tests

* use cleanup instead

* better test comment; cleanup upgrade

* better test comment; cleanup upgrade

benchmark/llama.py

@@ -118,7 +118,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
         with torch.no_grad():
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + num_tokens_to_generate,
@@ -144,7 +144,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + num_tokens_to_generate,
@@ -187,7 +187,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
             # TODO use  decode_one_token(model, input_id.clone(), cache_position) for verification
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + num_tokens_to_generate + 10,
@@ -254,7 +254,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + 128,
@@ -271,7 +271,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + 128,
@@ -287,7 +287,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + 128,
@@ -303,7 +303,7 @@ def run_benchmark(logger: Logger, branch: str, commit_id: str, commit_msg: str,
 
             past_key_values = StaticCache(
                 model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 device=device,
                 dtype=torch.float16,
                 max_cache_len=seq_length + 128,

docs/source/en/llm_optims.md

@@ -93,7 +93,7 @@ model.generation_config.max_new_tokens = 16
 
 past_key_values = StaticCache(
     config=model.config,
-    batch_size=1,
+    max_batch_size=1,
     # If you plan to reuse the cache, make sure the cache length is large enough for all cases
     max_cache_len=prompt_length+(model.generation_config.max_new_tokens*2),
     device=model.device,
@@ -159,7 +159,7 @@ from torch.nn.attention import SDPBackend, sdpa_kernel
 batch_size, seq_length = inputs["input_ids"].shape
 with torch.no_grad():
     past_key_values = StaticCache(
-        config=model.config, batch_size=2, max_cache_len=4096, device=torch_device, dtype=model.dtype
+        config=model.config, max_batch_size=2, max_cache_len=4096, device=torch_device, dtype=model.dtype
     )
     cache_position = torch.arange(seq_length, device=torch_device)
     generated_ids = torch.zeros(

docs/source/ko/llm_optims.md

@@ -99,7 +99,7 @@ model.generation_config.max_new_tokens = 16
 
 past_key_values = StaticCache(
     config=model.config,
-    batch_size=1,
+    max_batch_size=1,
     # 캐시를 재사용할 계획이 있는 경우, 모든 경우에 충분한 캐시 길이를 설정해야 합니다
     max_cache_len=prompt_length+(model.generation_config.max_new_tokens*2),
     device=model.device,
@@ -109,7 +109,7 @@ outputs = model.generate(**input_ids, past_key_values=past_key_values)
 print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
 ['The theory of special relativity states 1. The speed of light is constant in all inertial reference frames. 2']
 
-# 생성된 텍스트와 동일한 캐시 객체를 전달하여, 중단한 곳에서 생성을 계속합니다. 
+# 생성된 텍스트와 동일한 캐시 객체를 전달하여, 중단한 곳에서 생성을 계속합니다.
 # 다중 턴 대화의 경우, 생성된 텍스트에 새로운 사용자 입력을 추가할 수 있습니다.
 new_input_ids = outputs
 outputs = model.generate(new_input_ids, past_key_values=past_key_values)

src/transformers/testing_utils.py

@@ -3075,6 +3075,7 @@ def cleanup(device: str, gc_collect=False):
     if gc_collect:
         gc.collect()
     backend_empty_cache(device)
+    torch._dynamo.reset()
 
 
 # Type definition of key used in `Expectations` class.

tests/generation/test_utils.py

@@ -2285,6 +2285,14 @@ class GenerationTesterMixin:
                     inputs_dict[input_name] = input_data
             main_input = inputs_dict[model_class.main_input_name]
 
+            # FA2 doesn't accept masking in the middle of the sequence for now. We usually generate right-padded
+            # attention masks at test time and, with generate, the mask will be appended with 1s on the right,
+            # resulting in a mask with holes (not supported properly by FA2).
+            if attn_implementation == "flash_attention_2":
+                for input_name in ("attention_mask", "decoder_attention_mask", "encoder_attention_mask"):
+                    if input_name in inputs_dict:
+                        inputs_dict[input_name] = torch.ones_like(inputs_dict[input_name])
+
             # make sure that all models have enough positions for generation
             if hasattr(config, "max_position_embeddings"):
                 config.max_position_embeddings = max_new_tokens + main_input.shape[1] + 1
@@ -2339,8 +2347,6 @@ class GenerationTesterMixin:
     @slow
     def test_eager_matches_fa2_generate(self):
         """Tests that generate has equivalent outputs with FA2 and eager attention implementations."""
-        # TODO (@joao @raushan) -- this test is failing the output checks on most models, investigate. After fixing,
-        # check whether we still need the overwrites
         self._test_attention_implementation("flash_attention_2")
 
     def _check_generate_outputs(self, output, config, use_cache=False, num_return_sequences=1, num_beams=1):
@@ -3974,7 +3980,7 @@ class GenerationIntegrationTests(unittest.TestCase):
         # TODO: We need to raise a warning in case the cache is not set correctly
         # with self.assertRaisesRegex(ValueError, "If you are manually initializing the cache"):
         #     past_key_values = StaticCache(
-        #         config=model.config, batch_size=1, max_cache_len=30, device=torch_device, dtype=model.dtype
+        #         config=model.config, max_batch_size=1, max_cache_len=30, device=torch_device, dtype=model.dtype
         #     )
         #     results = model.generate(input_ids, past_key_values=past_key_values, **generation_kwargs)
 
@@ -3982,7 +3988,7 @@ class GenerationIntegrationTests(unittest.TestCase):
         layer_device_map = {0: 0, 1: 1}
         past_key_values = StaticCache(
             config=model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=30,
             device=torch_device,
             dtype=model.dtype,
@@ -4183,7 +4189,11 @@ class GenerationIntegrationTests(unittest.TestCase):
         batch_size = 2
         query_length = input_ids.shape[-1] - init_input_ids.shape[-1]
         static_cache = StaticCache(
-            config=config, batch_size=batch_size, max_cache_len=max_cache_len, device=torch_device, dtype=torch.float32
+            config=config,
+            max_batch_size=batch_size,
+            max_cache_len=max_cache_len,
+            device=torch_device,
+            dtype=torch.float32,
         )
         static_cache = model(init_input_ids, past_key_values=static_cache).past_key_values
         model_inputs = model.prepare_inputs_for_generation(

tests/models/deepseek_v3/test_modeling_deepseek_v3.py

@@ -21,6 +21,7 @@ from parameterized import parameterized
 
 from transformers import AutoTokenizer, DeepseekV3Config, is_torch_available, set_seed
 from transformers.testing_utils import (
+    cleanup,
     require_read_token,
     require_torch,
     require_torch_accelerator,
@@ -605,6 +606,10 @@ class DeepseekV3IntegrationTest(unittest.TestCase):
             # 8 is for A100 / A10 and 7 for T4
             cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
+    def tearDown(self):
+        # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
+        cleanup(torch_device, gc_collect=False)
+
     @slow
     @require_torch_accelerator
     @require_read_token

tests/models/diffllama/test_modeling_diffllama.py

@@ -25,6 +25,7 @@ from parameterized import parameterized
 from transformers import AutoTokenizer, DiffLlamaConfig, StaticCache, is_torch_available, set_seed
 from transformers.testing_utils import (
     backend_empty_cache,
+    cleanup,
     require_bitsandbytes,
     require_flash_attn,
     require_read_token,
@@ -685,6 +686,10 @@ class DiffLlamaIntegrationTest(unittest.TestCase):
             # 8 is for A100 / A10 and 7 for T4
             cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
+    def tearDown(self):
+        # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
+        cleanup(torch_device, gc_collect=False)
+
     @slow
     @require_torch_accelerator
     @require_read_token
@@ -884,7 +889,7 @@ class Mask4DTestHard(unittest.TestCase):
         max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
         past_key_values = StaticCache(
             config=self.model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=max_cache_len,
             device=torch_device,
             dtype=self.model.dtype,
@@ -932,7 +937,7 @@ class Mask4DTestHard(unittest.TestCase):
         max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
         past_key_values = StaticCache(
             config=self.model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=max_cache_len,
             device=torch_device,
             dtype=self.model.dtype,

tests/models/gemma/test_modeling_gemma.py

@@ -23,6 +23,7 @@ from packaging import version
 from transformers import AutoModelForCausalLM, AutoTokenizer, GemmaConfig, is_torch_available
 from transformers.generation.configuration_utils import GenerationConfig
 from transformers.testing_utils import (
+    cleanup,
     is_flaky,
     require_bitsandbytes,
     require_flash_attn,
@@ -498,6 +499,10 @@ class GemmaIntegrationTest(unittest.TestCase):
             # 8 is for A100 / A10 and 7 for T4
             cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
+    def tearDown(self):
+        # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
+        cleanup(torch_device, gc_collect=False)
+
     @require_read_token
     def test_model_2b_fp16(self):
         model_id = "google/gemma-2b"

tests/models/llama/test_modeling_llama.py

@@ -549,6 +549,13 @@ class LlamaIntegrationTest(unittest.TestCase):
             # 8 is for A100 / A10 and 7 for T4
             cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
 
+    def tearDown(self):
+        # TODO (joao): automatic compilation, i.e. compilation when `cache_implementation="static"` is used, leaves
+        # some memory allocated in the cache, which means some object is not being released properly. This causes some
+        # unoptimal memory usage, e.g. after certain tests a 7B model in FP16 no longer fits in a 24GB GPU.
+        # Investigate the root cause.
+        cleanup(torch_device, gc_collect=False)
+
     @slow
     @require_read_token
     def test_llama_3_1_hard(self):
@@ -748,14 +755,6 @@ class LlamaIntegrationTest(unittest.TestCase):
                 "Simply put, the theory of relativity states that 1) the speed of light is the same for all "
                 "observers, regardless of their location, and 2) the laws of physics are the same for all observers"
             ],
-            "meta-llama/Llama-3.2-3B": [
-                "Simply put, the theory of relativity states that 1. the speed of light is constant, and 2. "
-                "the speed of light is the fastest speed possible"
-            ],
-            "meta-llama/Llama-2-7b-hf": [
-                "Simply put, the theory of relativity states that 1) the speed of light is a constant, and 2) "
-                "the laws of physics are the same for all",
-            ],
         }
 
         for llama_model_ckp, EXPECTED_TEXT_COMPLETION in llama_models.items():
@@ -946,7 +945,7 @@ class Mask4DTestHard(unittest.TestCase):
         max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
         past_key_values = StaticCache(
             config=self.model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=max_cache_len,
             device=torch_device,
             dtype=self.model.dtype,
@@ -994,7 +993,7 @@ class Mask4DTestHard(unittest.TestCase):
         max_cache_len = 16  # note that max_cache_len is greater than the attention_mask.shape[-1]
         past_key_values = StaticCache(
             config=self.model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=max_cache_len,
             device=torch_device,
             dtype=self.model.dtype,

tests/models/phi3/test_modeling_phi3.py

@@ -53,7 +53,7 @@ if is_torch_available():
             self.model = model
             self.cache = StaticCache(
                 config=model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 max_cache_len=max_seq_len,
                 device=self.model.device,
                 dtype=self.model.dtype,

tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py

@@ -227,10 +227,6 @@ class Phi4MultimodalModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.
     def test_flash_attn_2_inference_equivalence_right_padding(self):
         pass
 
-    @unittest.skip(reason="This one tries to use right padding as well")
-    def test_eager_matches_fa2_generate(self):
-        pass
-
     @unittest.skip(reason="Depending on input modalities, some params may not have gradients")
     def test_training_gradient_checkpointing(self):
         pass

tests/models/phimoe/test_modeling_phimoe.py

@@ -52,7 +52,7 @@ if is_torch_available():
             self.model = model
             self.cache = StaticCache(
                 config=model.config,
-                batch_size=batch_size,
+                max_batch_size=batch_size,
                 max_cache_len=max_seq_len,
                 device=self.model.device,
                 dtype=self.model.dtype,

tests/models/t5/test_modeling_t5.py

@@ -24,6 +24,7 @@ from transformers import T5Config, is_torch_available
 from transformers.models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
 from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4
 from transformers.testing_utils import (
+    cleanup,
     require_accelerate,
     require_sentencepiece,
     require_tokenizers,
@@ -1170,6 +1171,10 @@ class T5ModelFp16Tests(unittest.TestCase):
 @require_sentencepiece
 @require_tokenizers
 class T5ModelIntegrationTests(unittest.TestCase):
+    def tearDown(self):
+        # See LlamaIntegrationTest.tearDown(). Can be removed once LlamaIntegrationTest.tearDown() is removed.
+        cleanup(torch_device, gc_collect=False)
+
     @cached_property
     def model(self):
         return T5ForConditionalGeneration.from_pretrained("google-t5/t5-base").to(torch_device)

tests/quantization/aqlm_integration/test_aqlm.py

@@ -226,7 +226,7 @@ class AqlmTest(unittest.TestCase):
         # Setup static KV cache for generation
         past_key_values = StaticCache(
             config=self.quantized_model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=seq_length + self.max_new_tokens + 1,
             device=torch_device,
             dtype=self.quantized_model.config._pre_quantization_dtype,

tests/quantization/spqr_integration/test_spqr.py

@@ -207,7 +207,7 @@ class SpQRTest(unittest.TestCase):
         # Setup static KV cache for generation
         past_key_values = StaticCache(
             config=self.quantized_model.config,
-            batch_size=1,
+            max_batch_size=1,
             max_cache_len=seq_length + self.max_new_tokens + 1,
             device=torch_device,
             dtype=self.quantized_model.config._pre_quantization_dtype,