Add minimal vLLM 0.16.1 build repo for BI-V150

vllm/renderers/grok2.py

@@ -0,0 +1,92 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+from vllm.config import VllmConfig
+from vllm.entrypoints.chat_utils import (
+    ChatCompletionMessageParam,
+    ConversationMessage,
+    parse_chat_messages,
+    parse_chat_messages_async,
+)
+from vllm.logger import init_logger
+from vllm.tokenizers import cached_get_tokenizer
+from vllm.tokenizers.grok2 import Grok2Tokenizer
+
+from .base import BaseRenderer
+from .inputs import DictPrompt
+from .inputs.preprocess import parse_dec_only_prompt
+from .params import ChatParams
+
+logger = init_logger(__name__)
+
+
+class Grok2Renderer(BaseRenderer[Grok2Tokenizer]):
+    @classmethod
+    def from_config(  # type: ignore[override]
+        cls,
+        config: VllmConfig,
+        tokenizer_kwargs: dict[str, Any],
+    ) -> "Grok2Renderer":
+        model_config = config.model_config
+        if model_config.skip_tokenizer_init:
+            tokenizer = None
+        else:
+            tokenizer = cached_get_tokenizer(
+                tokenizer_cls=Grok2Tokenizer,
+                **tokenizer_kwargs,
+            )
+
+        return cls(config, tokenizer)
+
+    def render_messages(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        params: ChatParams,
+    ) -> tuple[list[ConversationMessage], DictPrompt]:
+        tokenizer = self.get_tokenizer()
+        conversation, mm_data, mm_uuids = parse_chat_messages(
+            messages,
+            self.model_config,
+            content_format="string",
+        )
+
+        prompt_raw = tokenizer.apply_chat_template(
+            conversation=conversation,
+            messages=messages,
+            **params.get_apply_chat_template_kwargs(),
+        )
+
+        prompt = parse_dec_only_prompt(prompt_raw)
+        if mm_data is not None:
+            prompt["multi_modal_data"] = mm_data
+        if mm_uuids is not None:
+            prompt["multi_modal_uuids"] = mm_uuids
+
+        return conversation, prompt
+
+    async def render_messages_async(
+        self,
+        messages: list[ChatCompletionMessageParam],
+        params: ChatParams,
+    ) -> tuple[list[ConversationMessage], DictPrompt]:
+        tokenizer = self.get_tokenizer()
+        conversation, mm_data, mm_uuids = await parse_chat_messages_async(
+            messages,
+            self.model_config,
+            content_format="string",
+        )
+
+        prompt_raw = tokenizer.apply_chat_template(
+            conversation=conversation,
+            messages=messages,
+            **params.get_apply_chat_template_kwargs(),
+        )
+
+        prompt = parse_dec_only_prompt(prompt_raw)
+        if mm_data is not None:
+            prompt["multi_modal_data"] = mm_data
+        if mm_uuids is not None:
+            prompt["multi_modal_uuids"] = mm_uuids
+
+        return conversation, prompt