Cerebras Integration

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -0,0 +1,191 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import AsyncGenerator
+
+from cerebras.cloud.sdk import AsyncCerebras
+
+from llama_models.llama3.api.chat_format import ChatFormat
+
+from llama_models.llama3.api.datatypes import Message
+from llama_models.llama3.api.tokenizer import Tokenizer
+
+from llama_stack.apis.inference import *  # noqa: F403
+
+from llama_models.datatypes import CoreModelId
+
+from llama_stack.providers.utils.inference.model_registry import (
+    build_model_alias,
+    ModelRegistryHelper,
+)
+from llama_stack.providers.utils.inference.openai_compat import (
+    get_sampling_options,
+    process_chat_completion_response,
+    process_chat_completion_stream_response,
+    process_completion_response,
+    process_completion_stream_response,
+)
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    chat_completion_request_to_prompt,
+    completion_request_to_prompt,
+)
+
+from .config import CerebrasImplConfig
+
+
+model_aliases = [
+    build_model_alias(
+        "llama3.1-8b",
+        CoreModelId.llama3_1_8b_instruct.value,
+    ),
+    build_model_alias(
+        "llama3.1-70b",
+        CoreModelId.llama3_1_70b_instruct.value,
+    ),
+]
+
+
+class CerebrasInferenceAdapter(ModelRegistryHelper, Inference):
+    def __init__(self, config: CerebrasImplConfig) -> None:
+        ModelRegistryHelper.__init__(
+            self,
+            model_aliases=model_aliases,
+        )
+        self.config = config
+        self.formatter = ChatFormat(Tokenizer.get_instance())
+
+        self.client = AsyncCerebras(
+            base_url=self.config.base_url, api_key=self.config.api_key
+        )
+
+    async def initialize(self) -> None:
+        return
+
+    async def shutdown(self) -> None:
+        pass
+
+    async def completion(
+        self,
+        model_id: str,
+        content: InterleavedTextMedia,
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
+        request = CompletionRequest(
+            model=model.provider_resource_id,
+            content=content,
+            sampling_params=sampling_params,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+        if stream:
+            return self._stream_completion(
+                request,
+            )
+        else:
+            return await self._nonstream_completion(request)
+
+    async def _nonstream_completion(
+        self, request: CompletionRequest
+    ) -> CompletionResponse:
+        params = self._get_params(request)
+
+        r = await self.client.completions.create(**params)
+
+        return process_completion_response(r, self.formatter)
+
+    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        stream = await self.client.completions.create(**params)
+
+        async for chunk in process_completion_stream_response(stream, self.formatter):
+            yield chunk
+
+    async def chat_completion(
+        self,
+        model_id: str,
+        messages: List[Message],
+        sampling_params: Optional[SamplingParams] = SamplingParams(),
+        tools: Optional[List[ToolDefinition]] = None,
+        tool_choice: Optional[ToolChoice] = ToolChoice.auto,
+        tool_prompt_format: Optional[ToolPromptFormat] = ToolPromptFormat.json,
+        response_format: Optional[ResponseFormat] = None,
+        stream: Optional[bool] = False,
+        logprobs: Optional[LogProbConfig] = None,
+    ) -> AsyncGenerator:
+        model = await self.model_store.get_model(model_id)
+        request = ChatCompletionRequest(
+            model=model.provider_resource_id,
+            messages=messages,
+            sampling_params=sampling_params,
+            tools=tools or [],
+            tool_choice=tool_choice,
+            tool_prompt_format=tool_prompt_format,
+            response_format=response_format,
+            stream=stream,
+            logprobs=logprobs,
+        )
+
+        if stream:
+            return self._stream_chat_completion(request)
+        else:
+            return await self._nonstream_chat_completion(request)
+
+    async def _nonstream_chat_completion(
+        self, request: CompletionRequest
+    ) -> CompletionResponse:
+        params = self._get_params(request)
+
+        r = await self.client.completions.create(**params)
+
+        return process_chat_completion_response(r, self.formatter)
+
+    async def _stream_chat_completion(
+        self, request: CompletionRequest
+    ) -> AsyncGenerator:
+        params = self._get_params(request)
+
+        stream = await self.client.completions.create(**params)
+
+        async for chunk in process_chat_completion_stream_response(
+            stream, self.formatter
+        ):
+            yield chunk
+
+    def _get_params(
+        self, request: Union[ChatCompletionRequest, CompletionRequest]
+    ) -> dict:
+        if request.sampling_params and request.sampling_params.top_k:
+            raise ValueError("`top_k` not supported by Cerebras")
+
+        prompt = ""
+        if type(request) == ChatCompletionRequest:
+            prompt = chat_completion_request_to_prompt(
+                request, self.get_llama_model(request.model), self.formatter
+            )
+        elif type(request) == CompletionRequest:
+            prompt = completion_request_to_prompt(request, self.formatter)
+        else:
+            raise ValueError(f"Unknown request type {type(request)}")
+
+        return {
+            "model": request.model,
+            "prompt": prompt,
+            "stream": request.stream,
+            **get_sampling_options(request.sampling_params),
+        }
+
+    async def embeddings(
+        self,
+        model_id: str,
+        contents: List[InterleavedTextMedia],
+    ) -> EmbeddingsResponse:
+        raise NotImplementedError()