chore: import upstream snapshot with attribution

python/sglang/srt/entrypoints/openai/chat_encoding.py

@@ -0,0 +1,81 @@
+"""Single home for the chat-encoding dispatch.
+
+Which encoder turns chat messages into prompt tokens is a property of the
+model, so the serving path and offline tools (benchmarks, evals) must resolve
+it here instead of re-deriving it from model architectures themselves.
+"""
+
+from __future__ import annotations
+
+from typing import Any, Dict, List, Optional
+
+
+def resolve_chat_encoding_spec(
+    *,
+    hf_config: Any,
+    tokenizer: Any,
+    tool_call_parser: Optional[str] = None,
+) -> Optional[str]:
+    """Return the chat encoding spec for a model: "dsv4", "dsv32", or None.
+
+    None means the default path (HF chat template).
+    """
+    if tool_call_parser == "deepseekv4":
+        return "dsv4"
+    if tool_call_parser == "deepseekv32":
+        return "dsv32"
+
+    architectures = hf_config.architectures
+    arch = architectures[0] if architectures else ""
+
+    if "DeepseekV4" in arch:
+        return "dsv4"
+
+    has_chat_template = tokenizer is not None and tokenizer.chat_template is not None
+    if "DeepseekV3" in arch and not has_chat_template:
+        return "dsv32"
+    return None
+
+
+def encode_simple_chat(
+    *,
+    tokenizer: Any,
+    spec: Optional[str],
+    messages: List[Dict[str, Any]],
+    thinking_mode: str = "chat",
+) -> List[int]:
+    """Encode a plain-text chat conversation into prompt token ids.
+
+    Minimal encode for offline tools: no tools, no multimodal content, no
+    continue_final_message; the serving path keeps its full request-level
+    pipeline in ``serving_chat``. Like
+    ``serving_chat``, an empty system message is prepended when the
+    conversation does not start with one (for the dsv4/dsv32 encoders this
+    currently renders to zero tokens, but keeping the insertion explicit ties
+    this helper to the serving semantics rather than to that coincidence).
+    """
+    if spec in ("dsv4", "dsv32"):
+        if messages and messages[0]["role"] != "system":
+            messages = [{"role": "system", "content": ""}] + list(messages)
+        if spec == "dsv4":
+            from sglang.srt.entrypoints.openai import encoding_dsv4
+
+            real_input = encoding_dsv4.encode_messages(
+                messages, thinking_mode=thinking_mode
+            )
+        else:
+            from sglang.srt.entrypoints.openai import encoding_dsv32
+
+            real_input = encoding_dsv32.encode_messages(
+                messages, thinking_mode=thinking_mode
+            )
+        return tokenizer.encode(real_input)
+
+    if getattr(tokenizer, "chat_template", None) is None:
+        raise ValueError(
+            "This model has no HF chat template and no custom chat encoder; "
+            f"cannot encode chat messages with {getattr(tokenizer, 'name_or_path', tokenizer)!r}."
+        )
+    return tokenizer.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=True
+    )