LFM2

CMakeLists.txt

@@ -79,6 +79,13 @@ endif()
 #
 find_package(Torch REQUIRED)
 
+#
+# Ignore nvToolsExt for cuda-12.9
+#
+if (NOT TARGET CUDA::nvToolsExt)
+    add_library(CUDA::nvToolsExt INTERFACE IMPORTED)
+endif()
+
 # Supported NVIDIA architectures.
 # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
 if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND

docs/models/supported_models.md

@@ -356,6 +356,7 @@ Specified using `--task generate`.
 | `InternLM3ForCausalLM` | InternLM3 | `internlm/internlm3-8b-instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `JAISLMHeadModel` | Jais | `inceptionai/jais-13b`, `inceptionai/jais-13b-chat`, `inceptionai/jais-30b-v3`, `inceptionai/jais-30b-chat-v3`, etc. | | ✅︎ | ✅︎ |
 | `JambaForCausalLM` | Jamba | `ai21labs/AI21-Jamba-1.5-Large`, `ai21labs/AI21-Jamba-1.5-Mini`, `ai21labs/Jamba-v0.1`, etc. | ✅︎ | ✅︎ | |
+| `Lfm2ForCausalLM`  | LFM2  | `LiquidAI/LFM2-1.2B`, `LiquidAI/LFM2-700M`, `LiquidAI/LFM2-350M`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `LlamaForCausalLM` | Llama 3.1, Llama 3, Llama 2, LLaMA, Yi | `meta-llama/Meta-Llama-3.1-405B-Instruct`, `meta-llama/Meta-Llama-3.1-70B`, `meta-llama/Meta-Llama-3-70B-Instruct`, `meta-llama/Llama-2-70b-hf`, `01-ai/Yi-34B`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `MambaForCausalLM` | Mamba | `state-spaces/mamba-130m-hf`, `state-spaces/mamba-790m-hf`, `state-spaces/mamba-2.8b-hf`, etc. | | ✅︎ | |
 | `Mamba2ForCausalLM` | Mamba2 | `mistralai/Mamba-Codestral-7B-v0.1`, etc. | | ✅︎ | ✅︎ |

docs/usage/v1_guide.md

@@ -114,6 +114,10 @@ Models that combine Mamba-2 layers with standard attention layers are also suppo
 these models currently require enforcing eager mode, disabling prefix caching, and using the FlashInfer attention
 backend in V1.
 
+Hybrid models that share similar sub-components to Mamba2 layers, e.g. ShortConv layers in LFM2, are also supported.
+As above, they also require enforcing eager mode, disabling prefix caching, and using the FlashInfer attention
+backend in V1.
+
 #### Encoder-Decoder Models
 
 Models requiring cross-attention between separate encoder and decoder (e.g., `BartForConditionalGeneration`, `MllamaForConditionalGeneration`)

tests/models/language/generation/test_hybrid.py

@@ -35,6 +35,7 @@
     "nvidia/Nemotron-H-8B-Base-8K",
     "ibm-granite/granite-4.0-tiny-preview",
     "tiiuae/Falcon-H1-0.5B-Base",
+    "LiquidAI/LFM2-1.2B"
 ]
 
 HF_UNSUPPORTED_MODELS = [
@@ -53,17 +54,21 @@
 ]
 
 V1_SUPPORTED_MODELS = [
-    "mistralai/Mamba-Codestral-7B-v0.1",
-    "ibm-ai-platform/Bamba-9B-v1",
-    "Zyphra/Zamba2-1.2B-instruct",
-    "nvidia/Nemotron-H-8B-Base-8K",
-    "ibm-granite/granite-4.0-tiny-preview",
-    "tiiuae/Falcon-H1-0.5B-Base",
+    "mistralai/Mamba-Codestral-7B-v0.1", "ibm-ai-platform/Bamba-9B-v1",
+    "Zyphra/Zamba2-1.2B-instruct", "nvidia/Nemotron-H-8B-Base-8K",
+    "ibm-granite/granite-4.0-tiny-preview", "tiiuae/Falcon-H1-0.5B-Base",
+    "LiquidAI/LFM2-1.2B"
 ]
 
 # Avoid OOM
 MAX_NUM_SEQS = 4
 
+# To be removed once implemented
+V1_HYBRID_UNSUPPORTED_ARGS = {
+    "enforce_eager": True,
+    "enable_prefix_caching": False,
+}
+
 
 @pytest.mark.parametrize("model", SSM_MODELS + HYBRID_MODELS)
 @pytest.mark.parametrize("max_tokens", [64])
@@ -104,8 +109,7 @@ def test_models(
                 m.setenv("VLLM_ATTENTION_BACKEND", "FLASHINFER")
             with vllm_runner(model,
                              max_num_seqs=MAX_NUM_SEQS,
-                             enforce_eager=True,
-                             enable_prefix_caching=False) as vllm_model:
+                             **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
                 vllm_v1_outputs = vllm_model.generate_greedy_logprobs(
                     example_prompts, max_tokens, num_logprobs)
     else:
@@ -183,13 +187,15 @@ def test_chunked_prefill(
 
     with vllm_runner(model,
                      enable_chunked_prefill=True,
+                     **V1_HYBRID_UNSUPPORTED_ARGS,
                      max_num_batched_tokens=max_num_batched_tokens,
                      max_num_seqs=max_num_seqs) as vllm_model:
         chunked = vllm_model.generate_greedy_logprobs(example_prompts,
                                                       max_tokens, num_logprobs)
 
     with vllm_runner(model,
                      enable_chunked_prefill=False,
+                     **V1_HYBRID_UNSUPPORTED_ARGS,
                      max_num_seqs=max_num_seqs) as vllm_model:
         non_chunked = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
@@ -230,6 +236,7 @@ def test_chunked_prefill_with_parallel_sampling(
             # forces prefill chunks with decoding
             max_num_batched_tokens=MAX_NUM_SEQS * 3,
             max_num_seqs=MAX_NUM_SEQS,
+            **V1_HYBRID_UNSUPPORTED_ARGS,
     ) as vllm_model:
         vllm_model.generate(example_prompts, sampling_params)
 
@@ -254,7 +261,7 @@ def test_mamba_cache_cg_padding(
         example_prompts.append(example_prompts[0])
 
     try:
-        with vllm_runner(model) as vllm_model:
+        with vllm_runner(model, **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
             vllm_model.generate_greedy(example_prompts, max_tokens)
     except RuntimeError:
         pytest.fail(
@@ -274,7 +281,9 @@ def test_models_preemption_recompute(
     """
     Tests that outputs are identical with and w/o preemptions (recompute).
     """
-    with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+    with vllm_runner(model,
+                     max_num_seqs=MAX_NUM_SEQS,
+                     **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
         scheduler = vllm_model.model.llm_engine.scheduler[0]
         scheduler.ENABLE_ARTIFICIAL_PREEMPT = True
         preempt_vllm_outputs = vllm_model.generate_greedy(
@@ -307,7 +316,9 @@ def test_fail_upon_inc_requests_and_finished_requests_lt_available_blocks(
     a single step.
     """
     try:
-        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+        with vllm_runner(model,
+                         max_num_seqs=MAX_NUM_SEQS,
+                         **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
             vllm_model.generate_greedy([example_prompts[0]] * 100, 10)
     except ValueError:
         pytest.fail("Hybrid inner state wasn't cleaned up properly between"
@@ -327,7 +338,9 @@ def test_state_cleanup(
     If its not cleaned, an error would be expected.
     """
     try:
-        with vllm_runner(model, max_num_seqs=MAX_NUM_SEQS) as vllm_model:
+        with vllm_runner(model,
+                         max_num_seqs=MAX_NUM_SEQS,
+                         **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
             for _ in range(10):
                 vllm_model.generate_greedy([example_prompts[0]] * 100, 1)
     except ValueError:
@@ -343,13 +356,17 @@ def test_multistep_correctness(
     model: str,
     max_tokens: int,
 ) -> None:
-    with vllm_runner(model, num_scheduler_steps=8,
-                     max_num_seqs=2) as vllm_model:
+    with vllm_runner(model,
+                     num_scheduler_steps=8,
+                     max_num_seqs=2,
+                     **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
         vllm_outputs_multistep = vllm_model.generate_greedy(
             example_prompts, max_tokens)
 
-    with vllm_runner(model, num_scheduler_steps=1,
-                     max_num_seqs=2) as vllm_model:
+    with vllm_runner(model,
+                     num_scheduler_steps=1,
+                     max_num_seqs=2,
+                     **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
         vllm_outputs_single_step = vllm_model.generate_greedy(
             example_prompts, max_tokens)
 
@@ -372,13 +389,17 @@ def test_distributed_correctness(
     max_tokens: int,
     num_logprobs: int,
 ) -> None:
-    with vllm_runner(model, tensor_parallel_size=1,
-                     max_num_seqs=2) as vllm_model:
+    with vllm_runner(model,
+                     tensor_parallel_size=1,
+                     max_num_seqs=2,
+                     **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
         vllm_outputs_tp_1 = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 
-    with vllm_runner(model, tensor_parallel_size=2,
-                     max_num_seqs=2) as vllm_model:
+    with vllm_runner(model,
+                     tensor_parallel_size=2,
+                     max_num_seqs=2,
+                     **V1_HYBRID_UNSUPPORTED_ARGS) as vllm_model:
         vllm_outputs_tp_2 = vllm_model.generate_greedy_logprobs(
             example_prompts, max_tokens, num_logprobs)
 

tests/models/registry.py

@@ -207,6 +207,8 @@ def check_available_online(
     "JAISLMHeadModel": _HfExamplesInfo("inceptionai/jais-13b-chat"),
     "JambaForCausalLM": _HfExamplesInfo("ai21labs/AI21-Jamba-1.5-Mini",
                                         extras={"tiny": "ai21labs/Jamba-tiny-dev"}),  # noqa: E501
+    "Lfm2ForCausalLM": _HfExamplesInfo("LiquidAI/LFM2-1.2B",
+                                       min_transformers_version="4.54"),
     "LlamaForCausalLM": _HfExamplesInfo("meta-llama/Llama-3.2-1B-Instruct",
                                         extras={"guard": "meta-llama/Llama-Guard-3-1B",  # noqa: E501
                                                 "hermes": "NousResearch/Hermes-3-Llama-3.1-8B", # noqa: E501

vllm/config.py

@@ -1373,6 +1373,13 @@ def get_num_layers_by_block_type(
             # Hybrid model Jamba
             layers_block_type_value = getattr(self.hf_config,
                                               "layers_block_type", None)
+
+            # Hybrid models in transformers >= 4.54.0.dev0
+            # populate a `layer_types` attribute
+            if layers_block_type_value is None:
+                layers_block_type_value = getattr(self.hf_text_config,
+                                                  "layer_types", None)
+
             if layers_block_type_value is not None:
                 if hasattr(self.hf_text_config,
                            "model_type") and (self.hf_text_config.model_type
@@ -1382,8 +1389,14 @@ def get_num_layers_by_block_type(
                                    for t in layers_block_type_value[start:end])
                     else:
                         return self.get_num_layers(parallel_config)
-                return sum(t == block_type.value
-                           for t in layers_block_type_value[start:end])
+
+                # Support with hybrid transformers configs >= 4.54.0.dev0
+                if attn_block_type:
+                    return sum(t in ("full_attention", "attention")
+                               for t in layers_block_type_value[start:end])
+                else:
+                    return sum(t == block_type.value
+                               for t in layers_block_type_value[start:end])
 
             # Hybrid model Minimax
             attn_type_list = getattr(self.hf_config, "attn_type_list", None)
@@ -1630,9 +1643,10 @@ class CacheConfig:
     checkpoint if available. Otherwise, the scales will default to 1.0."""
     cpu_kvcache_space_bytes: Optional[int] = None
     """(CPU backend only) CPU key-value cache space."""
-    mamba_page_size_padded: Optional[int] = None
-    """ Optional override for mamba page size; used by hybrid mamba/attention
-    models to ensure exact alignment with attention page size."""
+    static_cache_page_size_padded: Optional[int] = None
+    """ Optional override for static cache page size; used by hybrid static
+    cache (e.g. mamba, short-conv) / attention models to ensure exact alignment
+    with attention page size."""
 
     # Will be set after profiling.
     num_gpu_blocks: Optional[int] = field(default=None, init=False)
@@ -4823,13 +4837,14 @@ def try_verify_and_update_config(self):
             return
 
         from vllm.model_executor.models.config import (
-            MODELS_CONFIG_MAP, HybridAttentionMambaModelConfig)
+            MODELS_CONFIG_MAP, HybridAttentionStaticCacheModelConfig)
         cls = MODELS_CONFIG_MAP.get(architecture, None)
         if cls is not None:
             cls.verify_and_update_config(self)
 
         if self.model_config.is_hybrid:
-            HybridAttentionMambaModelConfig.verify_and_update_config(self)
+            HybridAttentionStaticCacheModelConfig.verify_and_update_config(
+                self)
 
         if self.model_config.task == "classify":
             # Maybe convert ForCausalLM into ForSequenceClassification model.