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Add Cerebras Integration (microsoft#3585)
* Cerebras Integration * Address feedback * Fix typo * Run formatter
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,270 @@ | ||
| """Create an OpenAI-compatible client using Cerebras's API. | ||
| Example: | ||
| llm_config={ | ||
| "config_list": [{ | ||
| "api_type": "cerebras", | ||
| "model": "llama3.1-8b", | ||
| "api_key": os.environ.get("CEREBRAS_API_KEY") | ||
| }] | ||
| } | ||
| agent = autogen.AssistantAgent("my_agent", llm_config=llm_config) | ||
| Install Cerebras's python library using: pip install --upgrade cerebras_cloud_sdk | ||
| Resources: | ||
| - https://inference-docs.cerebras.ai/quickstart | ||
| """ | ||
|
|
||
| from __future__ import annotations | ||
|
|
||
| import copy | ||
| import os | ||
| import time | ||
| import warnings | ||
| from typing import Any, Dict, List | ||
|
|
||
| from cerebras.cloud.sdk import Cerebras, Stream | ||
| from openai.types.chat import ChatCompletion, ChatCompletionMessageToolCall | ||
| from openai.types.chat.chat_completion import ChatCompletionMessage, Choice | ||
| from openai.types.completion_usage import CompletionUsage | ||
|
|
||
| from autogen.oai.client_utils import should_hide_tools, validate_parameter | ||
|
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| CEREBRAS_PRICING_1K = { | ||
| # Convert pricing per million to per thousand tokens. | ||
| "llama3.1-8b": (0.10 / 1000, 0.10 / 1000), | ||
| "llama3.1-70b": (0.60 / 1000, 0.60 / 1000), | ||
| } | ||
|
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||
|
|
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| class CerebrasClient: | ||
| """Client for Cerebras's API.""" | ||
|
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| def __init__(self, api_key=None, **kwargs): | ||
| """Requires api_key or environment variable to be set | ||
| Args: | ||
| api_key (str): The API key for using Cerebras (or environment variable CEREBRAS_API_KEY needs to be set) | ||
| """ | ||
| # Ensure we have the api_key upon instantiation | ||
| self.api_key = api_key | ||
| if not self.api_key: | ||
| self.api_key = os.getenv("CEREBRAS_API_KEY") | ||
|
|
||
| assert ( | ||
| self.api_key | ||
| ), "Please include the api_key in your config list entry for Cerebras or set the CEREBRAS_API_KEY env variable." | ||
|
|
||
| def message_retrieval(self, response: ChatCompletion) -> List: | ||
| """ | ||
| Retrieve and return a list of strings or a list of Choice.Message from the response. | ||
| NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object, | ||
| since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used. | ||
| """ | ||
| return [choice.message for choice in response.choices] | ||
|
|
||
| def cost(self, response: ChatCompletion) -> float: | ||
| # Note: This field isn't explicitly in `ChatCompletion`, but is injected during chat creation. | ||
| return response.cost | ||
|
|
||
| @staticmethod | ||
| def get_usage(response: ChatCompletion) -> Dict: | ||
| """Return usage summary of the response using RESPONSE_USAGE_KEYS.""" | ||
| # ... # pragma: no cover | ||
| return { | ||
| "prompt_tokens": response.usage.prompt_tokens, | ||
| "completion_tokens": response.usage.completion_tokens, | ||
| "total_tokens": response.usage.total_tokens, | ||
| "cost": response.cost, | ||
| "model": response.model, | ||
| } | ||
|
|
||
| def parse_params(self, params: Dict[str, Any]) -> Dict[str, Any]: | ||
| """Loads the parameters for Cerebras API from the passed in parameters and returns a validated set. Checks types, ranges, and sets defaults""" | ||
| cerebras_params = {} | ||
|
|
||
| # Check that we have what we need to use Cerebras's API | ||
| # We won't enforce the available models as they are likely to change | ||
| cerebras_params["model"] = params.get("model", None) | ||
| assert cerebras_params[ | ||
| "model" | ||
| ], "Please specify the 'model' in your config list entry to nominate the Cerebras model to use." | ||
|
|
||
| # Validate allowed Cerebras parameters | ||
| # https://inference-docs.cerebras.ai/api-reference/chat-completions | ||
| cerebras_params["max_tokens"] = validate_parameter(params, "max_tokens", int, True, None, (0, None), None) | ||
| cerebras_params["seed"] = validate_parameter(params, "seed", int, True, None, None, None) | ||
| cerebras_params["stream"] = validate_parameter(params, "stream", bool, True, False, None, None) | ||
| cerebras_params["temperature"] = validate_parameter( | ||
| params, "temperature", (int, float), True, 1, (0, 1.5), None | ||
| ) | ||
| cerebras_params["top_p"] = validate_parameter(params, "top_p", (int, float), True, None, None, None) | ||
|
|
||
| return cerebras_params | ||
|
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| def create(self, params: Dict) -> ChatCompletion: | ||
|
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| messages = params.get("messages", []) | ||
|
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||
| # Convert AutoGen messages to Cerebras messages | ||
| cerebras_messages = oai_messages_to_cerebras_messages(messages) | ||
|
|
||
| # Parse parameters to the Cerebras API's parameters | ||
| cerebras_params = self.parse_params(params) | ||
|
|
||
| # Add tools to the call if we have them and aren't hiding them | ||
| if "tools" in params: | ||
| hide_tools = validate_parameter( | ||
| params, "hide_tools", str, False, "never", None, ["if_all_run", "if_any_run", "never"] | ||
| ) | ||
| if not should_hide_tools(cerebras_messages, params["tools"], hide_tools): | ||
| cerebras_params["tools"] = params["tools"] | ||
|
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| cerebras_params["messages"] = cerebras_messages | ||
|
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| # We use chat model by default, and set max_retries to 5 (in line with typical retries loop) | ||
| client = Cerebras(api_key=self.api_key, max_retries=5) | ||
|
|
||
| # Token counts will be returned | ||
| prompt_tokens = 0 | ||
| completion_tokens = 0 | ||
| total_tokens = 0 | ||
|
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||
| # Streaming tool call recommendations | ||
| streaming_tool_calls = [] | ||
|
|
||
| ans = None | ||
| try: | ||
| response = client.chat.completions.create(**cerebras_params) | ||
| except Exception as e: | ||
| raise RuntimeError(f"Cerebras exception occurred: {e}") | ||
| else: | ||
|
|
||
| if cerebras_params["stream"]: | ||
| # Read in the chunks as they stream, taking in tool_calls which may be across | ||
| # multiple chunks if more than one suggested | ||
| ans = "" | ||
| for chunk in response: | ||
| # Grab first choice, which _should_ always be generated. | ||
| ans = ans + (chunk.choices[0].delta.content or "") | ||
|
|
||
| if chunk.choices[0].delta.tool_calls: | ||
| # We have a tool call recommendation | ||
| for tool_call in chunk.choices[0].delta.tool_calls: | ||
| streaming_tool_calls.append( | ||
| ChatCompletionMessageToolCall( | ||
| id=tool_call.id, | ||
| function={ | ||
| "name": tool_call.function.name, | ||
| "arguments": tool_call.function.arguments, | ||
| }, | ||
| type="function", | ||
| ) | ||
| ) | ||
|
|
||
| if chunk.choices[0].finish_reason: | ||
| prompt_tokens = chunk.x_cerebras.usage.prompt_tokens | ||
| completion_tokens = chunk.x_cerebras.usage.completion_tokens | ||
| total_tokens = chunk.x_cerebras.usage.total_tokens | ||
| else: | ||
| # Non-streaming finished | ||
| ans: str = response.choices[0].message.content | ||
|
|
||
| prompt_tokens = response.usage.prompt_tokens | ||
| completion_tokens = response.usage.completion_tokens | ||
| total_tokens = response.usage.total_tokens | ||
|
|
||
| if response is not None: | ||
| if isinstance(response, Stream): | ||
| # Streaming response | ||
| if chunk.choices[0].finish_reason == "tool_calls": | ||
| cerebras_finish = "tool_calls" | ||
| tool_calls = streaming_tool_calls | ||
| else: | ||
| cerebras_finish = "stop" | ||
| tool_calls = None | ||
|
|
||
| response_content = ans | ||
| response_id = chunk.id | ||
| else: | ||
| # Non-streaming response | ||
| # If we have tool calls as the response, populate completed tool calls for our return OAI response | ||
| if response.choices[0].finish_reason == "tool_calls": | ||
| cerebras_finish = "tool_calls" | ||
| tool_calls = [] | ||
| for tool_call in response.choices[0].message.tool_calls: | ||
| tool_calls.append( | ||
| ChatCompletionMessageToolCall( | ||
| id=tool_call.id, | ||
| function={"name": tool_call.function.name, "arguments": tool_call.function.arguments}, | ||
| type="function", | ||
| ) | ||
| ) | ||
| else: | ||
| cerebras_finish = "stop" | ||
| tool_calls = None | ||
|
|
||
| response_content = response.choices[0].message.content | ||
| response_id = response.id | ||
| else: | ||
| raise RuntimeError("Failed to get response from Cerebras after retrying 5 times.") | ||
|
|
||
| # 3. convert output | ||
| message = ChatCompletionMessage( | ||
| role="assistant", | ||
| content=response_content, | ||
| function_call=None, | ||
| tool_calls=tool_calls, | ||
| ) | ||
| choices = [Choice(finish_reason=cerebras_finish, index=0, message=message)] | ||
|
|
||
| response_oai = ChatCompletion( | ||
| id=response_id, | ||
| model=cerebras_params["model"], | ||
| created=int(time.time()), | ||
| object="chat.completion", | ||
| choices=choices, | ||
| usage=CompletionUsage( | ||
| prompt_tokens=prompt_tokens, | ||
| completion_tokens=completion_tokens, | ||
| total_tokens=total_tokens, | ||
| ), | ||
| # Note: This seems to be a field that isn't in the schema of `ChatCompletion`, so Pydantic | ||
| # just adds it dynamically. | ||
| cost=calculate_cerebras_cost(prompt_tokens, completion_tokens, cerebras_params["model"]), | ||
| ) | ||
|
|
||
| return response_oai | ||
|
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||
|
|
||
| def oai_messages_to_cerebras_messages(messages: list[Dict[str, Any]]) -> list[dict[str, Any]]: | ||
| """Convert messages from OAI format to Cerebras's format. | ||
| We correct for any specific role orders and types. | ||
| """ | ||
|
|
||
| cerebras_messages = copy.deepcopy(messages) | ||
|
|
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| # Remove the name field | ||
| for message in cerebras_messages: | ||
| if "name" in message: | ||
| message.pop("name", None) | ||
|
|
||
| return cerebras_messages | ||
|
|
||
|
|
||
| def calculate_cerebras_cost(input_tokens: int, output_tokens: int, model: str) -> float: | ||
| """Calculate the cost of the completion using the Cerebras pricing.""" | ||
| total = 0.0 | ||
|
|
||
| if model in CEREBRAS_PRICING_1K: | ||
| input_cost_per_k, output_cost_per_k = CEREBRAS_PRICING_1K[model] | ||
| input_cost = (input_tokens / 1000) * input_cost_per_k | ||
| output_cost = (output_tokens / 1000) * output_cost_per_k | ||
| total = input_cost + output_cost | ||
| else: | ||
| warnings.warn(f"Cost calculation not available for model {model}", UserWarning) | ||
|
|
||
| return total |
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