[Model] Adding Granite MoE.

tests/models/decoder_only/language/test_granitemoe.py

@@ -0,0 +1,39 @@
+"""Compare the outputs of HF and vLLM for Granite models using greedy sampling.
+
+Run `pytest tests/models/test_granite.py`.
+"""
+import pytest
+
+from ...utils import check_logprobs_close
+
+MODELS = [
+    "ibm/PowerMoE-3b",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["bfloat16"])
+@pytest.mark.parametrize("max_tokens", [64])
+@pytest.mark.parametrize("num_logprobs", [5])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    num_logprobs: int,
+) -> None:
+    with hf_runner(model, dtype=dtype) 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:
+        vllm_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",
+    )

vllm/model_executor/models/__init__.py

@@ -71,6 +71,9 @@
     "MedusaModel": ("medusa", "Medusa"),
     "EAGLEModel": ("eagle", "EAGLE"),
     "MLPSpeculatorPreTrainedModel": ("mlp_speculator", "MLPSpeculator"),
+    "JambaForCausalLM": ("jamba", "JambaForCausalLM"),
+    "GraniteForCausalLM": ("granite", "GraniteForCausalLM"),
+    "GraniteMoeForCausalLM": ("granitemoe", "GraniteMoeForCausalLM")
 }
 
 _EMBEDDING_MODELS = {

vllm/model_executor/models/granite.py

@@ -404,9 +404,12 @@ def __init__(
                 self.lm_head.weight = self.model.embed_tokens.weight
 
             logit_scale = getattr(config, "logit_scale", 1.0)
+
+            if hasattr(config, "logits_scaling"):
+                logit_scale /= config.logits_scaling
             self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                     config.vocab_size,
-                                                    logit_scale)
+                                                    scale=logit_scale)
             self.sampler = Sampler()
         else:
             self.lm_head = PPMissingLayer()
@@ -428,8 +431,6 @@ def compute_logits(
             sampling_metadata: SamplingMetadata) -> Optional[torch.Tensor]:
         logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
-        if logits is not None:
-            logits /= self.config.logits_scaling
         return logits
 
     def sample(