[Model] Add PLaMo2

.buildkite/test-pipeline.yaml

@@ -400,8 +400,9 @@ steps:
     - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
     # V1 Test: https://github.com/vllm-project/vllm/issues/14531
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
     - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'plamo2'
 
 - label: Language Models Test (Standard) # 32min
   #mirror_hardwares: [amd]
@@ -411,6 +412,8 @@ steps:
   - tests/models/embedding/language
   - tests/models/encoder_decoder/language
   commands:
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install causal-conv1d
     - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
     - pytest -v -s models/embedding/language -m core_model
 
@@ -422,6 +425,8 @@ steps:
   - tests/models/embedding/language
   - tests/models/encoder_decoder/language
   commands:
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install causal-conv1d
     - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
     - pytest -v -s models/embedding/language -m 'not core_model'
 

docs/source/models/supported_models.md

@@ -497,6 +497,11 @@ See [this page](#generative-models) for more information on how to use generativ
   * `adept/persimmon-8b-base`, `adept/persimmon-8b-chat`, etc.
   *
   * ✅︎
+- * `Plamo2ForCausalLM`
+  * PLaMo2
+  * `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc.
+  *
+  *
 - * `QWenLMHeadModel`
   * Qwen
   * `Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.

requirements/test.in

@@ -27,6 +27,7 @@ torch==2.6.0
 torchaudio==2.6.0
 torchvision==0.21.0
 transformers_stream_generator # required for qwen-vl test
+mamba_ssm # required for plamo2 test
 matplotlib # required for qwen-vl test
 mistral_common[opencv] >= 1.5.4 # required for pixtral test
 num2words # required for smolvlm test

requirements/test.txt

@@ -111,6 +111,7 @@ einops==0.8.0
     # via
     #   -r requirements/test.in
     #   encodec
+    #   mamba-ssm
     #   vector-quantize-pytorch
     #   vocos
 einx==0.3.0
@@ -233,6 +234,8 @@ lxml==5.3.0
     # via
     #   blobfile
     #   sacrebleu
+mamba-ssm==2.2.4
+    # via -r requirements/test.in
 markdown-it-py==3.0.0
     # via rich
 markupsafe==3.0.2
@@ -268,6 +271,8 @@ mypy-extensions==1.0.0
     # via black
 networkx==3.2.1
     # via torch
+ninja==1.11.1.3
+    # via mamba-ssm
 nltk==3.9.1
     # via rouge-score
 num2words==0.5.14
@@ -360,6 +365,7 @@ packaging==24.1
     #   fastparquet
     #   huggingface-hub
     #   lazy-loader
+    #   mamba-ssm
     #   matplotlib
     #   peft
     #   plotly
@@ -571,6 +577,7 @@ sentencepiece==0.2.0
     # via mistral-common
 setuptools==75.8.0
     # via
+    #   mamba-ssm
     #   pytablewriter
     #   torch
 shellingham==1.5.4
@@ -627,6 +634,7 @@ torch==2.6.0
     #   encodec
     #   fastsafetensors
     #   lm-eval
+    #   mamba-ssm
     #   peft
     #   runai-model-streamer
     #   sentence-transformers
@@ -664,6 +672,7 @@ transformers==4.51.1
     #   -r requirements/test.in
     #   genai-perf
     #   lm-eval
+    #   mamba-ssm
     #   peft
     #   sentence-transformers
     #   transformers-stream-generator

tests/models/decoder_only/language/test_hybrid.py

@@ -9,9 +9,15 @@
 from ...utils import check_outputs_equal
 
 # This test is for the hybrid models
-MODELS = ["ai21labs/Jamba-tiny-dev", "Zyphra/Zamba2-1.2B-instruct"]
+MODELS = [
+    "ai21labs/Jamba-tiny-dev", "Zyphra/Zamba2-1.2B-instruct",
+    "pfnet/plamo-2-1b"
+]
 # Bamba at Fp32 is too big for the CI (L4 GPU).
 # MODELS = ["ai21labs/Jamba-tiny-dev", "ibm-ai-platform/Bamba-9B"]
+# Note: Running Plamo2 in transformers implementation requires to install
+# causal-conv1d package, which is not listed as a test dependency as it's
+# not compatible with pip-compile.
 
 
 @pytest.mark.parametrize("model", MODELS)
@@ -25,21 +31,11 @@ def test_models(
     dtype: str,
     max_tokens: int,
 ) -> None:
-
     # numeric error produces different generation
     if "Bamba" in model:
         example_prompts.pop(3)
 
-    model_kwargs = {
-        "use_mamba_kernels": False,  # mamba kernels are not installed so HF 
-        # don't use them
-    }
-    if "Zamba2" in model:
-        # Zamba2 HF implementation automatically checks if mamba kernels are
-        # installed
-        model_kwargs = {}
-
-    with hf_runner(model, dtype=dtype, model_kwargs=model_kwargs) as hf_model:
+    with hf_runner(model, dtype=dtype) as hf_model:
         hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
 
     with vllm_runner(model, dtype=dtype) as vllm_model:
@@ -94,6 +90,10 @@ def test_mamba_prefill_chunking_with_parallel_sampling(
     # correctly for n > 1 decoding steps inside a
     # chunked prefill forward pass (where we have both prefills
     # and decoding together )
+
+    if 'plamo-2' in model:
+        dtype = "float"  # use a different dtype for plamo
+
     sampling_params = SamplingParams(n=3,
                                      temperature=1,
                                      seed=0,
@@ -125,20 +125,14 @@ def test_mamba_prefill_chunking(hf_runner, vllm_runner, example_prompts,
         example_prompts.pop(3)
         example_prompts.pop(2)
         dtype = "half"  # use a different dtype for Bamba
+
     elif "Zamba2" in model:
         example_prompts.pop(7)
         dtype = "half"
+    elif "plamo-2-1b" in model:
+        example_prompts.pop(7)
 
-    model_kwargs = {
-        "use_mamba_kernels": False,  # mamba kernels are not installed so HF 
-        # don't use them
-    }
-    if "Zamba2" in model:
-        # Zamba2 HF implementation automatically checks if mamba kernels are
-        # installed
-        model_kwargs = {}
-
-    with hf_runner(model, dtype=dtype, model_kwargs=model_kwargs) as hf_model:
+    with hf_runner(model, dtype=dtype) as hf_model:
         non_chunked = hf_model.generate_greedy(example_prompts, max_tokens)
 
     with vllm_runner(model,
@@ -208,7 +202,8 @@ def test_mamba_cache_cg_padding(
     # This test is for verifying that mamba cache is padded to CG captured
     # batch size. If it's not, a torch RuntimeError will be raised because
     # tensor dimensions aren't compatible
-    vllm_config = EngineArgs(model=model).create_engine_config()
+    vllm_config = EngineArgs(model=model,
+                             trust_remote_code=True).create_engine_config()
     while len(example_prompts) == vllm_config.pad_for_cudagraph(
             len(example_prompts)):
         example_prompts.append(example_prompts[0])

tests/models/registry.py

@@ -204,6 +204,8 @@ def check_available_online(
                                             trust_remote_code=True),
     "PhiMoEForCausalLM": _HfExamplesInfo("microsoft/Phi-3.5-MoE-instruct",
                                          trust_remote_code=True),
+    "Plamo2ForCausalLM": _HfExamplesInfo("pfnet/plamo-2-1b",
+                                        trust_remote_code=True),
     "QWenLMHeadModel": _HfExamplesInfo("Qwen/Qwen-7B-Chat",
                                        trust_remote_code=True),
     "Qwen2ForCausalLM": _HfExamplesInfo("Qwen/Qwen2-7B-Instruct",

vllm/config.py

@@ -2838,6 +2838,13 @@ def _get_and_verify_dtype(
             else:
                 torch_dtype = config_dtype
 
+            if config.model_type == "plamo2":
+                logger.info(
+                    "For PLaMo2, we cast models to bfloat16 instead of using "
+                    "float16 by default. This is because float16 does not work."
+                )
+                torch_dtype = torch.bfloat16
+
             from vllm.platforms import current_platform
             if (current_platform.is_cpu()
                     and current_platform.get_cpu_architecture()
@@ -2867,6 +2874,11 @@ def _get_and_verify_dtype(
                     "using float16 by default. Please specify `dtype` if you "
                     "want to use float16.")
                 torch_dtype = torch.bfloat16
+        elif dtype == "float16" and config.model_type == "plamo2":
+            logger.warning(
+                "For PLaMo2, using float16 is unstable and might cause "
+                "unexpected behavior. Please use bfloat16 or float32 instead.")
+            torch_dtype = torch.float16
         else:
             if dtype not in _STR_DTYPE_TO_TORCH_DTYPE:
                 raise ValueError(f"Unknown dtype: {dtype}")