[v1] Support mamba2

tests/models/language/generation/test_hybrid.py

@@ -17,9 +17,10 @@
     "state-spaces/mamba-130m-hf",
     "tiiuae/falcon-mamba-tiny-dev",
     # TODO: Compare to a Mamba2 model. The HF transformers implementation of
-    # Mamba2 is buggy for Codestral as it doesn't handle n_groups.
+    # 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
-    # "mistralai/Mamba-Codestral-7B-v0.1",
+    "mistralai/Mamba-Codestral-7B-v0.1",
 ]
 
 HYBRID_MODELS = [
@@ -35,6 +36,10 @@
     "hmellor/tiny-random-BambaForCausalLM",
 ]
 
+V1_SUPPORTED_MODELS = [
+    "mistralai/Mamba-Codestral-7B-v0.1",
+]
+
 # Avoid OOM
 MAX_NUM_SEQS = 4
 
@@ -46,24 +51,50 @@ def test_models(
     hf_runner,
     vllm_runner,
     example_prompts,
+    monkeypatch,
     model: str,
     max_tokens: int,
     num_logprobs: int,
 ) -> None:
     with hf_runner(model) as hf_model:
-        hf_outputs = hf_model.generate_greedy_logprobs_limit(
-            example_prompts, max_tokens, num_logprobs)
+        if model != "mistralai/Mamba-Codestral-7B-v0.1":
+            hf_outputs = hf_model.generate_greedy_logprobs_limit(
+                example_prompts, max_tokens, num_logprobs)
+        else:
+            hf_outputs = None
 
     with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
-        vllm_outputs = vllm_model.generate_greedy_logprobs(
+        vllm_v0_outputs = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
-    check_logprobs_close(
-        outputs_0_lst=hf_outputs,
-        outputs_1_lst=vllm_outputs,
-        name_0="hf",
-        name_1="vllm",
-    )
+    if model in V1_SUPPORTED_MODELS:
+        with monkeypatch.context() as m:
+            m.setenv("VLLM_USE_V1", "1")
+            with vllm_runner(model,
+                             max_num_seqs=MAX_NUM_SEQS,
+                             enforce_eager=True,
+                             enable_prefix_caching=False) as vllm_model:
+                vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
+                    example_prompts, max_tokens, num_logprobs)
+    else:
+        vllm_v1_outputs = None
+
+    if hf_outputs is not None:
+        check_logprobs_close(
+            outputs_0_lst=hf_outputs,
+            outputs_1_lst=vllm_v0_outputs,
+            name_0="hf",
+            name_1="vllm-v0",
+        )
+
+    if model in V1_SUPPORTED_MODELS:
+        ref_outputs = hf_outputs if hf_outputs is not None else vllm_v0_outputs
+        check_logprobs_close(
+            outputs_0_lst=ref_outputs,
+            outputs_1_lst=vllm_v1_outputs,
+            name_0="hf" if hf_outputs is not None else "vllm-v0",
+            name_1="vllm-v1",
+        )
 
 
 @pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)

tests/v1/test_oracle.py

@@ -12,7 +12,7 @@
 UNSUPPORTED_MODELS_V1 = [
     "openai/whisper-large-v3",  # transcription
     "facebook/bart-large-cnn",  # encoder decoder
-    "mistralai/Mamba-Codestral-7B-v0.1",  # mamba
+    "state-spaces/mamba-130m-hf",  # mamba1
     "hmellor/tiny-random-BambaForCausalLM",  # hybrid
     "BAAI/bge-m3",  # embedding
 ]

vllm/engine/arg_utils.py

@@ -1354,12 +1354,17 @@ def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
                                recommend_to_remove=False)
             return False
 
-        # No Mamba or Encoder-Decoder so far.
+        # No Encoder-Decoder, not all Mamba so far.
         if not model_config.is_v1_compatible:
             _raise_or_fallback(feature_name=model_config.architectures,
                                recommend_to_remove=False)
             return False
 
+        # V1 mamba models are unoptimized.
+        if model_config.has_inner_state and _warn_or_fallback(
+                feature_name="Mamba"):
+            return False
+
         # No Concurrent Partial Prefills so far.
         if (self.max_num_partial_prefills
                 != SchedulerConfig.max_num_partial_prefills