[CI] Enable all hf transformers baselines in test_hybrid (#23936)

Signed-off-by: Thomas Parnell <tpa@zurich.ibm.com>

tests/models/language/generation/test_hybrid.py

@@ -34,17 +34,6 @@ HYBRID_MODELS = [
     "LiquidAI/LFM2-1.2B",
 ]
 
-HF_UNSUPPORTED_MODELS = [
-    # The HF transformers implementation of
-    # Mamba2 is buggy for Codestral as it doesn't handle n_groups, so the test
-    # doesn't compare vLLM output with HF output.
-    # See https://github.com/huggingface/transformers/pull/35943
-    "yujiepan/mamba2-codestral-v0.1-tiny-random",
-    # transformers 4.55 is still producing garbage for this model
-    # TODO(tdoublep): follow-up on transformers side
-    "ibm-granite/granite-4.0-tiny-preview"
-]
-
 V1_SUPPORTED_MODELS = [
     "state-spaces/mamba-130m-hf",
     "ai21labs/Jamba-tiny-dev",
@@ -90,20 +79,13 @@ def test_models(
     try:
         model_info = HF_EXAMPLE_MODELS.find_hf_info(model)
         model_info.check_available_online(on_fail="skip")
-        hf_version_check = model_info.check_transformers_version(
-            on_fail="return")
+        model_info.check_transformers_version(on_fail="skip")
     except ValueError:
-        hf_version_check = None
-
-    if hf_version_check is not None:
-        print(f"Skipping transformers comparison because: {hf_version_check}")
+        pass
 
     with hf_runner(model) as hf_model:
-        if model not in HF_UNSUPPORTED_MODELS and hf_version_check is None:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
-        else:
-            hf_outputs = None
 
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "0")
@@ -121,7 +103,7 @@ def test_models(
     else:
         vllm_v1_outputs = None
 
-    if hf_outputs is not None and vllm_v0_outputs is not None:
+    if vllm_v0_outputs is not None:
         check_logprobs_close(
             outputs_0_lst=hf_outputs,
             outputs_1_lst=vllm_v0_outputs,
@@ -130,12 +112,10 @@ def test_models(
         )
 
     if model in V1_SUPPORTED_MODELS:
-        ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
-        assert ref_outputs is not None
         check_logprobs_close(
-            outputs_0_lst=ref_outputs,
+            outputs_0_lst=hf_outputs,
             outputs_1_lst=vllm_v1_outputs,
-            name_0="hf" if hf_outputs is not None else "vllm-v0",
+            name_0="hf",
             name_1="vllm-v1",
         )
 
@@ -402,11 +382,8 @@ def test_full_cuda_graph(
         pass
 
     with hf_runner(model) as hf_model:
-        if model not in HF_UNSUPPORTED_MODELS:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
-        else:
-            hf_outputs = None
 
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "0")
@@ -421,7 +398,7 @@ def test_full_cuda_graph(
         vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
-    if hf_outputs is not None and vllm_v0_outputs is not None:
+    if vllm_v0_outputs is not None:
         check_logprobs_close(
             outputs_0_lst=hf_outputs,
             outputs_1_lst=vllm_v0_outputs,
@@ -429,12 +406,10 @@ def test_full_cuda_graph(
             name_1="vllm-v0",
         )
 
-    ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
-    assert ref_outputs is not None
     check_logprobs_close(
-        outputs_0_lst=ref_outputs,
+        outputs_0_lst=hf_outputs,
         outputs_1_lst=vllm_v1_outputs,
-        name_0="hf" if hf_outputs is not None else "vllm-v0",
+        name_0="hf",
         name_1="vllm-v1",
     )
 
@@ -460,11 +435,8 @@ def test_fp32_state(
         pass
 
     with hf_runner(model) as hf_model:
-        if model not in HF_UNSUPPORTED_MODELS:
         hf_outputs = hf_model.generate_greedy_logprobs_limit(
             example_prompts, max_tokens, num_logprobs)
-        else:
-            hf_outputs = None
 
     with monkeypatch.context() as m:
         m.setenv("VLLM_USE_V1", "0")
@@ -480,7 +452,6 @@ def test_fp32_state(
         vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
-    if hf_outputs is not None:
     check_logprobs_close(
         outputs_0_lst=hf_outputs,
         outputs_1_lst=vllm_v0_outputs,
@@ -488,10 +459,9 @@ def test_fp32_state(
         name_1="vllm-v0",
     )
 
-    ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
     check_logprobs_close(
-        outputs_0_lst=ref_outputs,
+        outputs_0_lst=hf_outputs,
         outputs_1_lst=vllm_v1_outputs,
-        name_0="hf" if hf_outputs is not None else "vllm-v0",
+        name_0="hf",
         name_1="vllm-v1",
     )

tests/models/registry.py

@@ -154,7 +154,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "BailingMoeForCausalLM": _HfExamplesInfo("inclusionAI/Ling-lite-1.5",
                                          trust_remote_code=True),
     "BambaForCausalLM": _HfExamplesInfo("ibm-ai-platform/Bamba-9B-v1",
-                                        min_transformers_version="4.56.0",
+                                        min_transformers_version="4.55.3",
                                         extras={"tiny": "hmellor/tiny-random-BambaForCausalLM"}),  # noqa: E501
     "BloomForCausalLM": _HfExamplesInfo("bigscience/bloom-560m",
                                         {"1b": "bigscience/bloomz-1b1"}),
@@ -208,7 +208,8 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "GptOssForCausalLM": _HfExamplesInfo("lmsys/gpt-oss-20b-bf16"),
     "GraniteForCausalLM": _HfExamplesInfo("ibm/PowerLM-3b"),
     "GraniteMoeForCausalLM": _HfExamplesInfo("ibm/PowerMoE-3b"),
-    "GraniteMoeHybridForCausalLM": _HfExamplesInfo("ibm-granite/granite-4.0-tiny-preview"),  # noqa: E501
+    "GraniteMoeHybridForCausalLM": _HfExamplesInfo("ibm-granite/granite-4.0-tiny-preview", # noqa: E501
+                                                   min_transformers_version="4.55.3"),
     "GraniteMoeSharedForCausalLM": _HfExamplesInfo("ibm-research/moe-7b-1b-active-shared-experts"),  # noqa: E501
     "Grok1ModelForCausalLM": _HfExamplesInfo("hpcai-tech/grok-1",
                                              trust_remote_code=True),
@@ -228,7 +229,7 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
                                             trust_remote_code=True),
     "JAISLMHeadModel": _HfExamplesInfo("inceptionai/jais-13b-chat"),
     "JambaForCausalLM": _HfExamplesInfo("ai21labs/AI21-Jamba-1.5-Mini",
-                                        min_transformers_version="4.56.0",
+                                        min_transformers_version="4.55.3",
                                         extras={
                                             "tiny": "ai21labs/Jamba-tiny-dev",
                                             "random": "ai21labs/Jamba-tiny-random",  # noqa: E501
@@ -244,7 +245,11 @@ _TEXT_GENERATION_EXAMPLE_MODELS = {
     "Llama4ForCausalLM": _HfExamplesInfo("meta-llama/Llama-4-Scout-17B-16E-Instruct", # noqa: E501
                                          is_available_online=False),
     "MambaForCausalLM": _HfExamplesInfo("state-spaces/mamba-130m-hf"),
-    "Mamba2ForCausalLM": _HfExamplesInfo("mistralai/Mamba-Codestral-7B-v0.1"),
+    "Mamba2ForCausalLM": _HfExamplesInfo("mistralai/Mamba-Codestral-7B-v0.1",
+                                         min_transformers_version="4.55.3",
+                                         extras={
+                                            "random": "yujiepan/mamba2-codestral-v0.1-tiny-random", # noqa: E501
+                                         }),
     "FalconMambaForCausalLM": _HfExamplesInfo("tiiuae/falcon-mamba-7b-instruct"),  # noqa: E501
     "MiniCPMForCausalLM": _HfExamplesInfo("openbmb/MiniCPM-2B-sft-bf16",
                                          trust_remote_code=True),