[BugFix] Fix test breakages from transformers 4.45 upgrade

.buildkite/test-pipeline.yaml

@@ -96,7 +96,8 @@ steps:
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/openai
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py
+  - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
   - pytest -v -s entrypoints/test_chat_utils.py
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
@@ -463,7 +464,7 @@ steps:
   # NOTE: don't test llama model here, it seems hf implementation is buggy
   # see https://github.com/vllm-project/vllm/pull/5689 for details
   - pytest -v -s distributed/test_custom_all_reduce.py
-  - TARGET_TEST_SUITE=A100 pytest -v -s distributed/test_basic_distributed_correctness.py
+  - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m distributed_2_gpus
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: LM Eval Large Models # optional

tests/conftest.py

@@ -699,7 +699,6 @@ def generate_w_logprobs(
         if videos is not None:
             for i, video in enumerate(videos):
                 inputs[i]["multi_modal_data"] = {"video": video}
-        print(f"[INPUTS!!!!]: {inputs}, {sampling_params}")
 
         req_outputs = self.model.generate(inputs,
                                           sampling_params=sampling_params)

tests/distributed/test_pipeline_parallel.py

@@ -8,8 +8,6 @@
 import os
 
 import pytest
-from packaging import version
-from transformers import __version__ as transformers_version
 
 from vllm.logger import init_logger
 
@@ -49,11 +47,6 @@ def test_compare_tp(TP_SIZE, PP_SIZE, EAGER_MODE, CHUNKED_PREFILL,
         pytest.skip("Skipping multi-node pipeline parallel test for "
                     "multiprocessing distributed backend")
 
-    # Skip tests that require transformers>=4.45.0
-    if "Qwen2-VL" in MODEL_NAME and version.parse(
-            transformers_version) < version.parse("4.45.0.dev0"):
-        pytest.skip("This test requires transformers>=4.45.0")
-
     pp_args = [
         # use half precision for speed and memory savings in CI environment
         "--dtype",

tests/engine/test_custom_executor.py

@@ -48,9 +48,9 @@ def test_custom_executor_type_checking(model):
 
 
 @pytest.mark.parametrize("model", ["facebook/opt-125m"])
-def test_custom_executor(model, tmpdir):
+def test_custom_executor(model, tmp_path):
     cwd = os.path.abspath(".")
-    os.chdir(tmpdir)
+    os.chdir(tmp_path)
     try:
         assert not os.path.exists(".marker")
 
@@ -68,9 +68,9 @@ def test_custom_executor(model, tmpdir):
 
 
 @pytest.mark.parametrize("model", ["facebook/opt-125m"])
-def test_custom_executor_async(model, tmpdir):
+def test_custom_executor_async(model, tmp_path):
     cwd = os.path.abspath(".")
-    os.chdir(tmpdir)
+    os.chdir(tmp_path)
     try:
         assert not os.path.exists(".marker")
 

tests/entrypoints/openai/test_serving_chat.py

@@ -15,6 +15,11 @@
 BASE_MODEL_PATHS = [BaseModelPath(name=MODEL_NAME, model_path=MODEL_NAME)]
 
 
+@dataclass
+class MockHFConfig:
+    model_type: str = "any"
+
+
 @dataclass
 class MockModelConfig:
     tokenizer = MODEL_NAME
@@ -24,6 +29,7 @@ class MockModelConfig:
     tokenizer_revision = None
     embedding_mode = False
     multimodal_config = MultiModalConfig()
+    hf_config = MockHFConfig()
 
 
 @dataclass

tests/lora/test_tokenizer_group.py

@@ -41,7 +41,7 @@ async def test_tokenizer_group_lora(sql_lora_files, tokenizer_group_type):
             lora_request)
 
 
-def test_get_lora_tokenizer(sql_lora_files, tmpdir):
+def test_get_lora_tokenizer(sql_lora_files, tmp_path):
     lora_request = None
     tokenizer = get_lora_tokenizer(lora_request)
     assert not tokenizer
@@ -50,6 +50,6 @@ def test_get_lora_tokenizer(sql_lora_files, tmpdir):
     tokenizer = get_lora_tokenizer(lora_request)
     assert tokenizer.get_added_vocab()
 
-    lora_request = LoRARequest("1", 1, str(tmpdir))
+    lora_request = LoRARequest("1", 1, str(tmp_path))
     tokenizer = get_lora_tokenizer(lora_request)
     assert not tokenizer

tests/models/decoder_only/language/test_granite.py

@@ -3,7 +3,6 @@
 Run `pytest tests/models/test_granite.py`.
 """
 import pytest
-import transformers
 
 from ...utils import check_logprobs_close
 
@@ -12,9 +11,6 @@
 ]
 
 
-# GraniteForCausalLM will be in transformers >= 4.45
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="granite model test requires transformers >= 4.45")
 @pytest.mark.parametrize("model", MODELS)
 @pytest.mark.parametrize("dtype", ["bfloat16"])
 @pytest.mark.parametrize("max_tokens", [64])

tests/models/decoder_only/vision_language/test_llava_next_video.py

@@ -1,7 +1,6 @@
 from typing import List, Optional, Tuple, Type, overload
 
 import pytest
-import transformers
 from transformers import AutoConfig, AutoModelForVision2Seq, AutoTokenizer
 
 from vllm.multimodal.utils import (rescale_video_size, resize_video,
@@ -158,8 +157,6 @@ def run_test(
         )
 
 
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="Waiting for next transformers release")
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize(
     "size_factors",
@@ -203,8 +200,6 @@ def test_models(hf_runner, vllm_runner, video_assets, model, size_factors,
     )
 
 
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="Waiting for next transformers release")
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize(
     "sizes",

tests/models/decoder_only/vision_language/test_llava_onevision.py

@@ -1,7 +1,6 @@
 from typing import List, Optional, Tuple, Type, overload
 
 import pytest
-import transformers
 from transformers import (AutoConfig, AutoModelForVision2Seq, AutoTokenizer,
                           BatchEncoding)
 
@@ -166,8 +165,6 @@ def process(hf_inputs: BatchEncoding):
         )
 
 
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="Waiting for next transformers release")
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize(
     "size_factors",
@@ -211,8 +208,6 @@ def test_models(hf_runner, vllm_runner, video_assets, model, size_factors,
     )
 
 
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="Waiting for next transformers release")
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize(
     "sizes",
@@ -259,7 +254,9 @@ def run_image_test(
     # max_model_len should be greater than image_feature_size
     with vllm_runner(model,
                      dtype=dtype,
-                     max_model_len=32768,
+                     max_num_seqs=1,
+                     max_model_len=16384,
+                     gpu_memory_utilization=0.98,
                      tensor_parallel_size=tensor_parallel_size,
                      distributed_executor_backend=distributed_executor_backend,
                      enforce_eager=True,
@@ -305,8 +302,6 @@ def process(hf_inputs: BatchEncoding):
         )
 
 
-@pytest.mark.skipif(transformers.__version__ < "4.45",
-                    reason="Waiting for next transformers release")
 @pytest.mark.parametrize("model", models)
 @pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [128])

tests/models/test_registry.py

@@ -1,15 +1,9 @@
 import pytest
-import transformers
 
 from vllm.model_executor.models import _MODELS, ModelRegistry
 
 
 @pytest.mark.parametrize("model_cls", _MODELS)
 def test_registry_imports(model_cls):
-    if (model_cls in ("LlavaOnevisionForConditionalGeneration",
-                      "Qwen2VLForConditionalGeneration")
-            and transformers.__version__ < "4.45"):
-        pytest.skip("Waiting for next transformers release")
-
     # Ensure all model classes can be imported successfully
     ModelRegistry.resolve_model_cls([model_cls])

tests/samplers/test_sampler.py

@@ -1,5 +1,6 @@
 import itertools
 import random
+from dataclasses import dataclass
 from typing import Dict, List, Optional, Tuple
 from unittest.mock import Mock, patch
 
@@ -596,8 +597,19 @@ def test_sampler_top_k_top_p(seed: int, device: str):
     generation_config = GenerationConfig(top_k=top_k,
                                          top_p=top_p,
                                          do_sample=True)
-    warpers = generation_model._get_logits_warper(generation_config, device)
-    assert len(warpers) == 2  # top_p and top_k
+
+    @dataclass
+    class MockConfig:
+        is_encoder_decoder: bool = False
+
+    generation_model.config = MockConfig()  # needed by the following method
+    generation_model._prepare_special_tokens(generation_config, device=device)
+    processors = generation_model._get_logits_processor(generation_config,
+                                                        None,
+                                                        None,
+                                                        None, [],
+                                                        device=device)
+    assert len(processors) == 2  # top_p and top_k
 
     seq_group_metadata_list: List[SequenceGroupMetadata] = []
     seq_lens: List[int] = []
@@ -639,7 +651,7 @@ def mock_sample(probs, *args, **kwargs):
 
     assert sample_probs is not None
 
-    hf_probs = warpers(torch.zeros_like(fake_logits), fake_logits.clone())
+    hf_probs = processors(torch.zeros_like(fake_logits), fake_logits.clone())
     hf_probs = torch.softmax(hf_probs, dim=-1, dtype=torch.float)
     torch.testing.assert_close(hf_probs, sample_probs, rtol=0.0, atol=1e-5)
     assert torch.equal(hf_probs.eq(0), sample_probs.eq(0))

vllm/entrypoints/openai/serving_chat.py

@@ -152,13 +152,13 @@ async def create_chat_completion(
                     **(request.chat_template_kwargs or {}),
                 )
         except Exception as e:
-            logger.error("Error in applying chat template from request: %s", e)
+            logger.exception("Error in applying chat template from request")
             return self.create_error_response(str(e))
 
         try:
             mm_data = await mm_data_future
         except Exception as e:
-            logger.error("Error in loading multi-modal data: %s", e)
+            logger.exception("Error in loading multi-modal data")
             return self.create_error_response(str(e))
 
         # validation for OpenAI tools

vllm/transformers_utils/tokenizer.py

@@ -1,6 +1,7 @@
 import os
 import warnings
 from pathlib import Path
+from types import MethodType
 from typing import Optional, Union
 
 import huggingface_hub
@@ -152,6 +153,27 @@ def get_tokenizer(
             else:
                 raise e
 
+        if type(tokenizer).__name__ == "ChatGLMTokenizer":
+            assert isinstance(tokenizer, PreTrainedTokenizer)
+            orig_pad = tokenizer._pad
+
+            # Patch _pad method to accept `padding_side`
+            def _pad(
+                self: PreTrainedTokenizer,
+                *args,
+                padding_side: Optional[str] = None,
+                **kwargs,
+            ):
+                if (padding_side is not None
+                        and padding_side != self.padding_side):
+                    msg = ("`padding_side` argument is not supported by "
+                           "ChatGLMTokenizer and will be ignored.")
+                    warnings.warn(msg, stacklevel=2)
+
+                return orig_pad(*args, **kwargs)
+
+            tokenizer._pad = MethodType(_pad, tokenizer)
+
         if not isinstance(tokenizer, PreTrainedTokenizerFast):
             logger.warning(
                 "Using a slow tokenizer. This might cause a significant "
@@ -167,7 +189,7 @@ def get_lora_tokenizer(lora_request: LoRARequest, *args,
         return None
     try:
         tokenizer = get_tokenizer(lora_request.lora_path, *args, **kwargs)
-    except OSError as e:
+    except Exception as e:
         # No tokenizer was found in the LoRA folder,
         # use base model tokenizer
         logger.warning(