[Bugfix] Fix standard models tests

docs/source/models/supported_models.md

@@ -322,7 +322,7 @@ See [this page](#generative-models) for more information on how to use generativ
   * ✅︎
 - * `GemmaForCausalLM`
   * Gemma
-  * `google/gemma-2b`, `google/gemma-7b`, etc.
+  * `google/gemma-2b`, `google/gemma-1.1-2b-it`, etc.
   * ✅︎
   * ✅︎
 - * `Gemma2ForCausalLM`

tests/distributed/test_pipeline_parallel.py

@@ -161,12 +161,12 @@ def iter_params(self, model_id: str):
     "deepseek-ai/DeepSeek-V2-Lite-Chat": PPTestSettings.fast(),
     "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct": PPTestSettings.fast(),
     "tiiuae/falcon-7b": PPTestSettings.fast(),
-    "google/gemma-2b": PPTestSettings.fast(),
+    "google/gemma-1.1-2b-it": PPTestSettings.fast(),
     "google/gemma-2-9b": PPTestSettings.fast(),
     "gpt2": PPTestSettings.fast(),
     "bigcode/starcoder": PPTestSettings.fast(),
     "EleutherAI/gpt-j-6b": PPTestSettings.fast(),
-    "EleutherAI/pythia-12b": PPTestSettings.fast(),
+    "EleutherAI/pythia-1.4b": PPTestSettings.fast(),
     "ibm/PowerLM-3b": PPTestSettings.fast(),
     "ibm/PowerMoE-3b": PPTestSettings.fast(),
     # Uses Llama
@@ -195,7 +195,7 @@ def iter_params(self, model_id: str):
     "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(),
     "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(multi_node_only=True, load_format="dummy"),  # noqa: E501
     "Qwen/Qwen-7B-Chat": PPTestSettings.fast(),
-    "Qwen/Qwen2-7B-Instruct": PPTestSettings.fast(),
+    "Qwen/Qwen2.5-0.5B-Instruct": PPTestSettings.fast(),
     "Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(),
     "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(),
     "bigcode/starcoder2-3b": PPTestSettings.fast(),

tests/models/decoder_only/language/test_models.py

@@ -9,6 +9,7 @@
 
 from vllm.platforms import current_platform
 
+from ....utils import large_gpu_mark
 from ...registry import HF_EXAMPLE_MODELS
 from ...utils import check_logprobs_close
 
@@ -26,71 +27,78 @@
 AITER_MODEL_LIST = [
     "meta-llama/Llama-3.2-1B-Instruct",
     "openbmb/MiniCPM3-4B",
-    "Qwen/Qwen-7B",
+    "Qwen/Qwen-7B-Chat",
     "Qwen/Qwen2.5-0.5B-Instruct",
     "ehristoforu/Falcon3-MoE-2x7B-Insruct",
 ]
 
 
 # @maybe_test_rocm_aiter
 @pytest.mark.parametrize(
-    "model_arch",
+    "model",
     [
         pytest.param(
-            "BloomForCausalLM",  # testing alibi slopes
+            "bigscience/bloom-560m",  # bloom - testing alibi slopes
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
         pytest.param(
-            "GPT2LMHeadModel",  # gpt2
+            "openai-community/gpt2",  # gpt2
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
-        pytest.param("GPTJForCausalLM"),
-        pytest.param("GPTBigCodeForCausalLM"),
-        pytest.param("GPTNeoXForCausalLM"),
+        pytest.param("Milos/slovak-gpt-j-405M"),  # gptj
+        pytest.param("bigcode/tiny_starcoder_py"),  # gpt_bigcode
+        pytest.param("EleutherAI/pythia-70m"),  # gpt_neox
         pytest.param(
-            "GemmaForCausalLM",  # gemma
+            "google/gemma-1.1-2b-it",  # gemma
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
-        pytest.param("GlmForCausalLM"),
         pytest.param(
-            "LlamaForCausalLM",
+            "THUDM/chatglm3-6b",  # chatglm (text-only)
+        ),
+        pytest.param(
+            "meta-llama/Llama-3.2-1B-Instruct",  # llama
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
         pytest.param(
-            "MiniCPM3ForCausalLM",
+            "openbmb/MiniCPM3-4B",
             # fused_moe not supported on CPU
-            marks=[pytest.mark.core_model],
+            marks=[pytest.mark.core_model,
+                   large_gpu_mark(min_gb=32)],
         ),
         pytest.param(
-            "OPTForCausalLM",
+            "facebook/opt-125m",  # opt
             marks=[pytest.mark.core_model, pytest.mark.cpu_model],
         ),
         pytest.param(
-            "PhiForCausalLM",
+            "microsoft/phi-2",  # phi
             marks=[pytest.mark.core_model],
         ),
-        pytest.param("QWenLMHeadModel", ),
         pytest.param(
-            "Qwen2ForCausalLM",
+            "Qwen/Qwen-7B-Chat",  # qwen (text-only)
+        ),
+        pytest.param(
+            "Qwen/Qwen2.5-0.5B-Instruct",  # qwen2
             marks=[pytest.mark.core_model],
         ),
-        pytest.param("StableLmForCausalLM"),
-        pytest.param("Starcoder2ForCausalLM"),
+        pytest.param("stabilityai/stablelm-3b-4e1t"),  # stablelm
+        pytest.param("bigcode/starcoder2-3b"),  # starcoder2
         pytest.param(
-            "MixtralForCausalLM",
-            marks=[pytest.mark.cpu_model],
+            "ehristoforu/Falcon3-MoE-2x7B-Insruct",  # mixtral
+            marks=[pytest.mark.cpu_model,
+                   large_gpu_mark(min_gb=48)],
         )
     ])
-@pytest.mark.parametrize("dtype", ["half"])
 @pytest.mark.parametrize("max_tokens", [32])
 @pytest.mark.parametrize("num_logprobs", [5])
 @pytest.mark.parametrize(
     "use_rocm_aiter", [True, False] if current_platform.is_rocm() else [False])
-def test_models(hf_runner, vllm_runner, example_prompts, model_arch: str,
-                dtype: str, max_tokens: int, num_logprobs: int,
-                use_rocm_aiter: bool, monkeypatch) -> None:
+def test_models(hf_runner, vllm_runner, example_prompts, model: str,
+                max_tokens: int, num_logprobs: int, use_rocm_aiter: bool,
+                monkeypatch) -> None:
 
-    model = HF_EXAMPLE_MODELS.get_hf_info(model_arch).default
+    model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
+    model_info.check_available_online(on_fail="skip")
+    model_info.check_transformers_version(on_fail="skip")
 
     if model in REQUIRES_V0:
         monkeypatch.setenv("VLLM_USE_V1", "0")
@@ -104,15 +112,17 @@ def test_models(hf_runner, vllm_runner, example_prompts, model_arch: str,
         # in parts of the operators
         pytest.skip(f"Skipping '{model}' model test with AITER kernel.")
 
-    with hf_runner(model, dtype=dtype) as hf_model:
-        if model.startswith("THUDM/chatglm3"):
-            hf_model.model.get_output_embeddings = lambda: \
-                hf_model.model.transformer.output_layer
-
+    with hf_runner(model) as hf_model:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
 
-    with vllm_runner(model, dtype=dtype) as vllm_model:
+    with vllm_runner(
+            model,
+            tokenizer_name=model_info.tokenizer or model,
+            tokenizer_mode=model_info.tokenizer_mode,
+            trust_remote_code=model_info.trust_remote_code,
+            max_num_seqs=2,
+    ) as vllm_model:
         vllm_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 

tests/models/registry.py

@@ -121,10 +121,11 @@ def check_available_online(
     "BaichuanForCausalLM": _HfExamplesInfo("baichuan-inc/Baichuan2-7B-chat",
                                          trust_remote_code=True),
     "BambaForCausalLM": _HfExamplesInfo("ibm-ai-platform/Bamba-9B"),
-    "BloomForCausalLM": _HfExamplesInfo("bigscience/bloomz-1b1"),
+    "BloomForCausalLM": _HfExamplesInfo("bigscience/bloom-560m",
+                                        {"1b": "bigscience/bloomz-1b1"}),
     "ChatGLMModel": _HfExamplesInfo("THUDM/chatglm3-6b",
                                     trust_remote_code=True,
-                                    max_transformers_version="4.51.1"),
+                                    max_transformers_version="4.48"),
     "ChatGLMForConditionalGeneration": _HfExamplesInfo("thu-coai/ShieldLM-6B-chatglm3",  # noqa: E501
                                                        trust_remote_code=True),
     "CohereForCausalLM": _HfExamplesInfo("CohereForAI/c4ai-command-r-v01",
@@ -142,24 +143,26 @@ def check_available_online(
     "ExaoneForCausalLM": _HfExamplesInfo("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct"),  # noqa: E501
     "Fairseq2LlamaForCausalLM": _HfExamplesInfo("mgleize/fairseq2-dummy-Llama-3.2-1B"),  # noqa: E501
     "FalconForCausalLM": _HfExamplesInfo("tiiuae/falcon-7b"),
-    "GemmaForCausalLM": _HfExamplesInfo("google/gemma-2b"),
+    "GemmaForCausalLM": _HfExamplesInfo("google/gemma-1.1-2b-it"),
     "Gemma2ForCausalLM": _HfExamplesInfo("google/gemma-2-9b"),
-    "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it",
-                                         min_transformers_version="4.50"),
+    "Gemma3ForCausalLM": _HfExamplesInfo("google/gemma-3-1b-it"),
     "GlmForCausalLM": _HfExamplesInfo("THUDM/glm-4-9b-chat-hf"),
     "Glm4ForCausalLM": _HfExamplesInfo(
         "THUDM/GLM-4-32B-0414",
         is_available_online=False,
         min_transformers_version="4.52.dev0"
     ),
-    "GPT2LMHeadModel": _HfExamplesInfo("gpt2"),
-    "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder"),
-    "GPTJForCausalLM": _HfExamplesInfo("EleutherAI/gpt-j-6b"),
-    "GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-160m"),
+    "GPT2LMHeadModel": _HfExamplesInfo("openai-community/gpt2",
+                                       {"alias": "gpt2"}),
+    "GPTBigCodeForCausalLM": _HfExamplesInfo("bigcode/starcoder",
+                                             {"tiny": "bigcode/tiny_starcoder_py"}),  # noqa: E501
+    "GPTJForCausalLM": _HfExamplesInfo("Milos/slovak-gpt-j-405M",
+                                       {"6b": "EleutherAI/gpt-j-6b"}),
+    "GPTNeoXForCausalLM": _HfExamplesInfo("EleutherAI/pythia-70m",
+                                          {"1b": "EleutherAI/pythia-1.4b"}),
     "GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
     "GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
-    "GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts",  # noqa: E501
-                                                   min_transformers_version="4.49"),  # noqa: E501
+    "GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts"),  # noqa: E501
     "Grok1ModelForCausalLM": _HfExamplesInfo("hpcai-tech/grok-1",
                                              trust_remote_code=True),
     "InternLMForCausalLM": _HfExamplesInfo("internlm/internlm-chat-7b",
@@ -187,15 +190,17 @@ def check_available_online(
     "MiniMaxText01ForCausalLM": _HfExamplesInfo("MiniMaxAI/MiniMax-Text-01",
                                                 trust_remote_code=True),
     "MistralForCausalLM": _HfExamplesInfo("mistralai/Mistral-7B-Instruct-v0.1"),
-    "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1"),  # noqa: E501
+    "MixtralForCausalLM": _HfExamplesInfo("mistralai/Mixtral-8x7B-Instruct-v0.1",  # noqa: E501
+                                          {"falcon3": "ehristoforu/Falcon3-MoE-2x7B-Insruct"}),  # noqa: E501
     "QuantMixtralForCausalLM": _HfExamplesInfo("mistral-community/Mixtral-8x22B-v0.1-AWQ"),  # noqa: E501
     "MptForCausalLM": _HfExamplesInfo("mpt", is_available_online=False),
     "MPTForCausalLM": _HfExamplesInfo("mosaicml/mpt-7b"),
     "NemotronForCausalLM": _HfExamplesInfo("nvidia/Minitron-8B-Base"),
     "OlmoForCausalLM": _HfExamplesInfo("allenai/OLMo-1B-hf"),
     "Olmo2ForCausalLM": _HfExamplesInfo("shanearora/OLMo-7B-1124-hf"),
     "OlmoeForCausalLM": _HfExamplesInfo("allenai/OLMoE-1B-7B-0924-Instruct"),
-    "OPTForCausalLM": _HfExamplesInfo("facebook/opt-iml-max-1.3b"),
+    "OPTForCausalLM": _HfExamplesInfo("facebook/opt-125m",
+                                      {"1b": "facebook/opt-iml-max-1.3b"}),
     "OrionForCausalLM": _HfExamplesInfo("OrionStarAI/Orion-14B-Chat",
                                         trust_remote_code=True),
     "PersimmonForCausalLM": _HfExamplesInfo("adept/persimmon-8b-chat"),
@@ -209,8 +214,8 @@ def check_available_online(
                                         trust_remote_code=True),
     "QWenLMHeadModel": _HfExamplesInfo("Qwen/Qwen-7B-Chat",
                                        trust_remote_code=True),
-    "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-7B-Instruct",
-                                        extras={"2.5": "Qwen/Qwen2.5-7B-Instruct"}), # noqa: E501
+    "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-0.5B-Instruct",
+                                        extras={"2.5": "Qwen/Qwen2.5-0.5B-Instruct"}), # noqa: E501
     "Qwen2MoeForCausalLM": _HfExamplesInfo("Qwen/Qwen1.5-MoE-A2.7B-Chat"),
     "Qwen3ForCausalLM": _HfExamplesInfo(
         "Qwen/Qwen3-8B",
@@ -236,8 +241,7 @@ def check_available_online(
     "XverseForCausalLM": _HfExamplesInfo("xverse/XVERSE-7B-Chat",
                                          is_available_online=False,
                                          trust_remote_code=True),
-    "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct",
-                                         min_transformers_version="4.49"),
+    "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"),
     # [Encoder-decoder]
     "BartModel": _HfExamplesInfo("facebook/bart-base"),
     "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"),
@@ -280,9 +284,7 @@ def check_available_online(
     "BertForSequenceClassification": _HfExamplesInfo("cross-encoder/ms-marco-MiniLM-L-6-v2"),  # noqa: E501
     "RobertaForSequenceClassification": _HfExamplesInfo("cross-encoder/quora-roberta-base"),  # noqa: E501
     "XLMRobertaForSequenceClassification": _HfExamplesInfo("BAAI/bge-reranker-v2-m3"),  # noqa: E501
-    "ModernBertForSequenceClassification":
-        _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base",
-                        min_transformers_version="4.49"),
+    "ModernBertForSequenceClassification": _HfExamplesInfo("Alibaba-NLP/gte-reranker-modernbert-base"),  # noqa: E501
 }
 
 _MULTIMODAL_EXAMPLE_MODELS = {
@@ -298,8 +300,7 @@ def check_available_online(
                                                 transformers_version_reason="HF model is not compatible.",  # noqa: E501
                                                hf_overrides={"architectures": ["DeepseekVLV2ForCausalLM"]}),  # noqa: E501
     "FuyuForCausalLM": _HfExamplesInfo("adept/fuyu-8b"),
-    "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it",
-                                                      min_transformers_version="4.50"),
+    "Gemma3ForConditionalGeneration": _HfExamplesInfo("google/gemma-3-4b-it"),
     "GLM4VForCausalLM": _HfExamplesInfo("THUDM/glm-4v-9b",
                                         trust_remote_code=True,
                                         hf_overrides={"architectures": ["GLM4VForCausalLM"]}),  # noqa: E501
@@ -335,7 +336,6 @@ def check_available_online(
                                 extras={"2.6": "openbmb/MiniCPM-V-2_6"},  # noqa: E501
                                 trust_remote_code=True),
     "Mistral3ForConditionalGeneration": _HfExamplesInfo("mistralai/Mistral-Small-3.1-24B-Instruct-2503",  # noqa: E501
-                                                        min_transformers_version="4.50",  # noqa: E501
                                                         extras={"fp8": "nm-testing/Mistral-Small-3.1-24B-Instruct-2503-FP8-dynamic"}),  # noqa: E501
     "MolmoForCausalLM": _HfExamplesInfo("allenai/Molmo-7B-D-0924",
                                         max_transformers_version="4.48",
@@ -361,8 +361,7 @@ def check_available_online(
                                                       hf_overrides={"architectures": ["QwenVLForConditionalGeneration"]}),  # noqa: E501
     "Qwen2AudioForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-Audio-7B-Instruct"),  # noqa: E501
     "Qwen2VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2-VL-2B-Instruct"),  # noqa: E501
-    "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct",  # noqa: E501
-                                                          min_transformers_version="4.49"),  # noqa: E501
+    "Qwen2_5_VLForConditionalGeneration": _HfExamplesInfo("Qwen/Qwen2.5-VL-3B-Instruct"),  # noqa: E501
     "Qwen2_5OmniModel": _HfExamplesInfo("Qwen/Qwen2.5-Omni-7B",  # noqa: E501
                                                                   min_transformers_version="4.52"),  # noqa: E501
     "SkyworkR1VChatModel": _HfExamplesInfo("Skywork/Skywork-R1V-38B"),