Add support for IBM Granite 3.x models

docs/references/supported_models.md

@@ -29,6 +29,7 @@
 - SmolLM
 - GLM-4
 - Phi-3-Small
+- IBM Granite 3
 
 ## Embedding Models
 

python/sglang/lang/chat_template.py

@@ -320,6 +320,28 @@ def get_chat_template_by_model_path(model_path):
     )
 )
 
+register_chat_template(
+    ChatTemplate(
+        name="granite-3-instruct",
+        default_system_prompt=None,
+        role_prefix_and_suffix={
+            "system": (
+                "<|start_of_role|>system<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+            "user": (
+                "<|start_of_role|>user<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+            "assistant": (
+                "<|start_of_role|>assistant<|end_of_role|>",
+                "<|end_of_text|>",
+            ),
+        },
+        stop_str=("<|end_of_text|>",),
+    )
+)
+
 
 @register_chat_template_matching_function
 def match_dbrx(model_path: str):
@@ -402,6 +424,16 @@ def match_c4ai_command_r(model_path: str):
         return get_chat_template("c4ai-command-r")
 
 
+@register_chat_template_matching_function
+def match_granite_instruct(model_path: str):
+    model_path = model_path.lower()
+    # When future versions of Granite are released, this code may
+    # need to be updated. For now, assume that the Granite 3.0
+    # template works across the board.
+    if "granite" in model_path and "instruct" in model_path:
+        return get_chat_template("granite-3-instruct")
+
+
 if __name__ == "__main__":
     messages = [
         {"role": "system", "content": None},  # None means default

python/sglang/srt/layers/logits_processor.py

@@ -91,9 +91,12 @@ def from_forward_batch(cls, forward_batch: ForwardBatch):
 
 
 class LogitsProcessor(nn.Module):
-    def __init__(self, config, skip_all_gather: bool = False):
+    def __init__(
+        self, config, skip_all_gather: bool = False, logit_scale: Optional[float] = None
+    ):
         super().__init__()
         self.config = config
+        self.logit_scale = logit_scale
         self.do_tensor_parallel_all_gather = (
             not skip_all_gather and get_tensor_model_parallel_world_size() > 1
         )
@@ -240,6 +243,9 @@ def forward(
                 all_logits = self._get_logits(states, lm_head)
                 if self.do_tensor_parallel_all_gather:
                     all_logits = tensor_model_parallel_all_gather(all_logits)
+
+                # The LM head's weights may be zero-padded for parallelism. Remove any
+                # extra logits that this padding may have produced.
                 all_logits = all_logits[:, : self.config.vocab_size].float()
 
                 if hasattr(self.config, "final_logit_softcapping"):
@@ -302,6 +308,10 @@ def _get_logits(
         else:
             # GGUF models
             logits = lm_head.linear_method.apply(lm_head, hidden_states, embedding_bias)
+
+        # Optional scaling factor, backported from vLLM 0.4
+        if self.logit_scale is not None:
+            logits.mul_(self.logit_scale)  # In-place multiply
         return logits