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c9ab72fa82
# What does this PR do? The current default system prompt for llama3.2 tends to overindex on tool calling and doesn't work well when the prompt does not require tool calling. This PR adds an option to override the default system prompt, and organizes tool-related configs into a new config object. - [ ] Addresses issue (#issue) ## Test Plan python -m unittest llama_stack.providers.tests.inference.test_prompt_adapter ## Sources Please link relevant resources if necessary. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [ ] Ran pre-commit to handle lint / formatting issues. - [ ] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [ ] Wrote necessary unit or integration tests. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/meta-llama/llama-stack/pull/937). * #938 * __->__ #937
283 lines
11 KiB
Python
283 lines
11 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import json
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import warnings
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from typing import AsyncGenerator, Literal, Union
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from groq import Stream
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from groq.types.chat.chat_completion import ChatCompletion
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from groq.types.chat.chat_completion_assistant_message_param import (
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ChatCompletionAssistantMessageParam,
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)
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from groq.types.chat.chat_completion_chunk import ChatCompletionChunk
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from groq.types.chat.chat_completion_message_param import ChatCompletionMessageParam
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from groq.types.chat.chat_completion_message_tool_call import (
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ChatCompletionMessageToolCall,
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)
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from groq.types.chat.chat_completion_system_message_param import (
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ChatCompletionSystemMessageParam,
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)
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from groq.types.chat.chat_completion_tool_param import ChatCompletionToolParam
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from groq.types.chat.chat_completion_user_message_param import (
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ChatCompletionUserMessageParam,
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)
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from groq.types.chat.completion_create_params import CompletionCreateParams
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from groq.types.shared.function_definition import FunctionDefinition
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from llama_models.llama3.api.datatypes import ToolParamDefinition
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from pydantic import BaseModel
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from llama_stack.apis.common.content_types import (
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TextDelta,
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ToolCallDelta,
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ToolCallParseStatus,
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)
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseEvent,
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ChatCompletionResponseEventType,
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ChatCompletionResponseStreamChunk,
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CompletionMessage,
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Message,
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StopReason,
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ToolCall,
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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get_sampling_strategy_options,
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)
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def convert_chat_completion_request(
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request: ChatCompletionRequest,
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) -> CompletionCreateParams:
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"""
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Convert a ChatCompletionRequest to a Groq API-compatible dictionary.
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Warns client if request contains unsupported features.
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"""
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if request.logprobs:
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# Groq doesn't support logprobs at the time of writing
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warnings.warn("logprobs are not supported yet")
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if request.response_format:
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# Groq's JSON mode is beta at the time of writing
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warnings.warn("response_format is not supported yet")
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if request.sampling_params.repetition_penalty != 1.0:
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# groq supports frequency_penalty, but frequency_penalty and sampling_params.repetition_penalty
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# seem to have different semantics
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# frequency_penalty defaults to 0 is a float between -2.0 and 2.0
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# repetition_penalty defaults to 1 and is often set somewhere between 1.0 and 2.0
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# so we exclude it for now
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warnings.warn("repetition_penalty is not supported")
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if request.tool_config.tool_prompt_format != ToolPromptFormat.json:
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warnings.warn("tool_prompt_format is not used by Groq. Ignoring.")
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sampling_options = get_sampling_strategy_options(request.sampling_params)
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return CompletionCreateParams(
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model=request.model,
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messages=[_convert_message(message) for message in request.messages],
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logprobs=None,
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frequency_penalty=None,
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stream=request.stream,
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max_tokens=request.sampling_params.max_tokens or None,
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temperature=sampling_options.get("temperature", 1.0),
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top_p=sampling_options.get("top_p", 1.0),
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tools=[_convert_groq_tool_definition(tool) for tool in request.tools or []],
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tool_choice=(request.tool_config.tool_choice.value if request.tool_config.tool_choice else None),
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)
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def _convert_message(message: Message) -> ChatCompletionMessageParam:
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if message.role == "system":
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return ChatCompletionSystemMessageParam(role="system", content=message.content)
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elif message.role == "user":
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return ChatCompletionUserMessageParam(role="user", content=message.content)
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elif message.role == "assistant":
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return ChatCompletionAssistantMessageParam(role="assistant", content=message.content)
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else:
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raise ValueError(f"Invalid message role: {message.role}")
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def _convert_groq_tool_definition(tool_definition: ToolDefinition) -> dict:
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# Groq requires a description for function tools
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if tool_definition.description is None:
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raise AssertionError("tool_definition.description is required")
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tool_parameters = tool_definition.parameters or {}
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return ChatCompletionToolParam(
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type="function",
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function=FunctionDefinition(
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name=tool_definition.tool_name,
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description=tool_definition.description,
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parameters={key: _convert_groq_tool_parameter(param) for key, param in tool_parameters.items()},
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),
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)
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def _convert_groq_tool_parameter(tool_parameter: ToolParamDefinition) -> dict:
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param = {
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"type": tool_parameter.param_type,
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}
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if tool_parameter.description is not None:
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param["description"] = tool_parameter.description
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if tool_parameter.required is not None:
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param["required"] = tool_parameter.required
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if tool_parameter.default is not None:
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param["default"] = tool_parameter.default
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return param
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def convert_chat_completion_response(
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response: ChatCompletion,
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) -> ChatCompletionResponse:
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# groq only supports n=1 at time of writing, so there is only one choice
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choice = response.choices[0]
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if choice.finish_reason == "tool_calls":
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tool_calls = [_convert_groq_tool_call(tool_call) for tool_call in choice.message.tool_calls]
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if any(isinstance(tool_call, UnparseableToolCall) for tool_call in tool_calls):
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# If we couldn't parse a tool call, jsonify the tool calls and return them
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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stop_reason=StopReason.end_of_message,
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content=json.dumps(tool_calls, default=lambda x: x.model_dump()),
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),
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logprobs=None,
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)
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else:
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# Otherwise, return tool calls as normal
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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tool_calls=tool_calls,
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stop_reason=StopReason.end_of_message,
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# Content is not optional
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content="",
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),
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logprobs=None,
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)
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else:
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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content=choice.message.content,
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stop_reason=_map_finish_reason_to_stop_reason(choice.finish_reason),
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),
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)
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def _map_finish_reason_to_stop_reason(
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finish_reason: Literal["stop", "length", "tool_calls"],
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) -> StopReason:
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"""
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Convert a Groq chat completion finish_reason to a StopReason.
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finish_reason: Literal["stop", "length", "tool_calls"]
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- stop -> model hit a natural stop point or a provided stop sequence
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- length -> maximum number of tokens specified in the request was reached
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- tool_calls -> model called a tool
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"""
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if finish_reason == "stop":
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return StopReason.end_of_turn
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elif finish_reason == "length":
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return StopReason.out_of_tokens
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elif finish_reason == "tool_calls":
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return StopReason.end_of_message
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else:
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raise ValueError(f"Invalid finish reason: {finish_reason}")
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async def convert_chat_completion_response_stream(
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stream: Stream[ChatCompletionChunk],
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) -> AsyncGenerator[ChatCompletionResponseStreamChunk, None]:
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event_type = ChatCompletionResponseEventType.start
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for chunk in stream:
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choice = chunk.choices[0]
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if choice.finish_reason:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.complete,
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delta=TextDelta(text=choice.delta.content or ""),
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logprobs=None,
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stop_reason=_map_finish_reason_to_stop_reason(choice.finish_reason),
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)
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)
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elif choice.delta.tool_calls:
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# We assume there is only one tool call per chunk, but emit a warning in case we're wrong
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if len(choice.delta.tool_calls) > 1:
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warnings.warn("Groq returned multiple tool calls in one chunk. Using the first one, ignoring the rest.")
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# We assume Groq produces fully formed tool calls for each chunk
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tool_call = _convert_groq_tool_call(choice.delta.tool_calls[0])
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if isinstance(tool_call, ToolCall):
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=tool_call,
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parse_status=ToolCallParseStatus.succeeded,
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),
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)
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)
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else:
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# Otherwise it's an UnparseableToolCall - return the raw tool call
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=ToolCallDelta(
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tool_call=tool_call.model_dump_json(),
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parse_status=ToolCallParseStatus.failed,
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),
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)
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)
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else:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=event_type,
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delta=TextDelta(text=choice.delta.content or ""),
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logprobs=None,
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)
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)
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event_type = ChatCompletionResponseEventType.progress
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class UnparseableToolCall(BaseModel):
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"""
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A ToolCall with arguments that are not valid JSON.
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Mirrors the ToolCall schema, but with arguments as a string.
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"""
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call_id: str
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tool_name: str
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arguments: str
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def _convert_groq_tool_call(
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tool_call: ChatCompletionMessageToolCall,
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) -> Union[ToolCall, UnparseableToolCall]:
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"""
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Convert a Groq tool call to a ToolCall.
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Returns an UnparseableToolCall if the tool call is not valid JSON.
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"""
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try:
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arguments = json.loads(tool_call.function.arguments)
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except Exception as e:
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return UnparseableToolCall(
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call_id=tool_call.id,
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tool_name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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)
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return ToolCall(
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call_id=tool_call.id,
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tool_name=tool_call.function.name,
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arguments=arguments,
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)
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