mirror of
https://github.com/meta-llama/llama-stack.git
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8de8eb03c8
## What does this PR do?
This is a long-pending change and particularly important to get done
now.
Specifically:
- we cannot "localize" (aka download) any URLs from media attachments
anywhere near our modeling code. it must be done within llama-stack.
- `PIL.Image` is infesting all our APIs via `ImageMedia ->
InterleavedTextMedia` and that cannot be right at all. Anything in the
API surface must be "naturally serializable". We need a standard `{
type: "image", image_url: "<...>" }` which is more extensible
- `UserMessage`, `SystemMessage`, etc. are moved completely to
llama-stack from the llama-models repository.
See https://github.com/meta-llama/llama-models/pull/244 for the
corresponding PR in llama-models.
## Test Plan
```bash
cd llama_stack/providers/tests
pytest -s -v -k "fireworks or ollama or together" inference/test_vision_inference.py
pytest -s -v -k "(fireworks or ollama or together) and llama_3b" inference/test_text_inference.py
pytest -s -v -k chroma memory/test_memory.py \
--env EMBEDDING_DIMENSION=384 --env CHROMA_DB_PATH=/tmp/foobar
pytest -s -v -k fireworks agents/test_agents.py \
--safety-shield=meta-llama/Llama-Guard-3-8B \
--inference-model=meta-llama/Llama-3.1-8B-Instruct
```
Updated the client sdk (see PR ...), installed the SDK in the same
environment and then ran the SDK tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=together pytest -s -v agents/test_agents.py
LLAMA_STACK_CONFIG=ollama pytest -s -v memory/test_memory.py
# this one needed a bit of hacking in the run.yaml to ensure I could register the vision model correctly
INFERENCE_MODEL=llama3.2-vision:latest LLAMA_STACK_CONFIG=ollama pytest -s -v inference/test_inference.py
```
286 lines
9 KiB
Python
286 lines
9 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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from typing import AsyncGenerator, Optional
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from llama_models.llama3.api.chat_format import ChatFormat
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from llama_models.llama3.api.datatypes import StopReason
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from llama_stack.apis.inference import * # noqa: F403
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from pydantic import BaseModel
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from llama_stack.apis.common.content_types import ImageContentItem, TextContentItem
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from llama_stack.providers.utils.inference.prompt_adapter import (
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convert_image_content_to_url,
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)
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class OpenAICompatCompletionChoiceDelta(BaseModel):
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content: str
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class OpenAICompatCompletionChoice(BaseModel):
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finish_reason: Optional[str] = None
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text: Optional[str] = None
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delta: Optional[OpenAICompatCompletionChoiceDelta] = None
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class OpenAICompatCompletionResponse(BaseModel):
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choices: List[OpenAICompatCompletionChoice]
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def get_sampling_options(params: SamplingParams) -> dict:
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options = {}
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if params:
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for attr in {"temperature", "top_p", "top_k", "max_tokens"}:
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if getattr(params, attr):
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options[attr] = getattr(params, attr)
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if params.repetition_penalty is not None and params.repetition_penalty != 1.0:
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options["repeat_penalty"] = params.repetition_penalty
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return options
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def text_from_choice(choice) -> str:
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if hasattr(choice, "delta") and choice.delta:
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return choice.delta.content
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if hasattr(choice, "message"):
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return choice.message.content
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return choice.text
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def get_stop_reason(finish_reason: str) -> StopReason:
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if finish_reason in ["stop", "eos"]:
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return StopReason.end_of_turn
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elif finish_reason == "eom":
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return StopReason.end_of_message
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elif finish_reason == "length":
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return StopReason.out_of_tokens
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return StopReason.out_of_tokens
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def process_completion_response(
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response: OpenAICompatCompletionResponse, formatter: ChatFormat
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) -> CompletionResponse:
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choice = response.choices[0]
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# drop suffix <eot_id> if present and return stop reason as end of turn
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if choice.text.endswith("<|eot_id|>"):
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return CompletionResponse(
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stop_reason=StopReason.end_of_turn,
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content=choice.text[: -len("<|eot_id|>")],
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)
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# drop suffix <eom_id> if present and return stop reason as end of message
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if choice.text.endswith("<|eom_id|>"):
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return CompletionResponse(
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stop_reason=StopReason.end_of_message,
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content=choice.text[: -len("<|eom_id|>")],
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)
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return CompletionResponse(
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stop_reason=get_stop_reason(choice.finish_reason),
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content=choice.text,
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)
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def process_chat_completion_response(
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response: OpenAICompatCompletionResponse, formatter: ChatFormat
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) -> ChatCompletionResponse:
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choice = response.choices[0]
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raw_message = formatter.decode_assistant_message_from_content(
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text_from_choice(choice), get_stop_reason(choice.finish_reason)
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)
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return ChatCompletionResponse(
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completion_message=CompletionMessage(
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content=raw_message.content,
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stop_reason=raw_message.stop_reason,
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tool_calls=raw_message.tool_calls,
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),
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logprobs=None,
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)
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async def process_completion_stream_response(
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stream: AsyncGenerator[OpenAICompatCompletionResponse, None], formatter: ChatFormat
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) -> AsyncGenerator:
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stop_reason = None
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async for chunk in stream:
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choice = chunk.choices[0]
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finish_reason = choice.finish_reason
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text = text_from_choice(choice)
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if text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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continue
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elif text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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continue
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yield CompletionResponseStreamChunk(
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delta=text,
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stop_reason=stop_reason,
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)
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if finish_reason:
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if finish_reason in ["stop", "eos", "eos_token"]:
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stop_reason = StopReason.end_of_turn
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elif finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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break
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yield CompletionResponseStreamChunk(
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delta="",
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stop_reason=stop_reason,
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)
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async def process_chat_completion_stream_response(
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stream: AsyncGenerator[OpenAICompatCompletionResponse, None], formatter: ChatFormat
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) -> AsyncGenerator:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.start,
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delta="",
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)
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)
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buffer = ""
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ipython = False
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stop_reason = None
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async for chunk in stream:
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choice = chunk.choices[0]
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finish_reason = choice.finish_reason
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if finish_reason:
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if stop_reason is None and finish_reason in ["stop", "eos", "eos_token"]:
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stop_reason = StopReason.end_of_turn
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elif stop_reason is None and finish_reason == "length":
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stop_reason = StopReason.out_of_tokens
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break
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text = text_from_choice(choice)
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if not text:
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# Sometimes you get empty chunks from providers
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continue
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# check if its a tool call ( aka starts with <|python_tag|> )
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if not ipython and text.startswith("<|python_tag|>"):
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ipython = True
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.started,
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),
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)
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)
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buffer += text
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continue
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if text == "<|eot_id|>":
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stop_reason = StopReason.end_of_turn
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text = ""
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continue
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elif text == "<|eom_id|>":
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stop_reason = StopReason.end_of_message
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text = ""
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continue
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if ipython:
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buffer += text
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delta = ToolCallDelta(
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content=text,
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parse_status=ToolCallParseStatus.in_progress,
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)
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=delta,
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stop_reason=stop_reason,
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)
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)
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else:
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buffer += text
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=text,
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stop_reason=stop_reason,
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)
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)
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# parse tool calls and report errors
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message = formatter.decode_assistant_message_from_content(buffer, stop_reason)
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parsed_tool_calls = len(message.tool_calls) > 0
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if ipython and not parsed_tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content="",
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parse_status=ToolCallParseStatus.failure,
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),
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stop_reason=stop_reason,
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)
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)
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for tool_call in message.tool_calls:
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yield ChatCompletionResponseStreamChunk(
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event=ChatCompletionResponseEvent(
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event_type=ChatCompletionResponseEventType.progress,
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delta=ToolCallDelta(
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content=tool_call,
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parse_status=ToolCallParseStatus.success,
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),
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stop_reason=stop_reason,
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)
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)
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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="",
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stop_reason=stop_reason,
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)
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)
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async def convert_message_to_openai_dict(
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message: Message, download: bool = False
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) -> dict:
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async def _convert_content(content) -> dict:
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if isinstance(content, ImageContentItem):
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return {
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"type": "image_url",
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"image_url": {
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"url": await convert_image_content_to_url(
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content, download=download
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),
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},
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}
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else:
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text = content.text if isinstance(content, TextContentItem) else content
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assert isinstance(text, str)
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return {"type": "text", "text": text}
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if isinstance(message.content, list):
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content = [await _convert_content(c) for c in message.content]
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else:
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content = [await _convert_content(message.content)]
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return {
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"role": message.role,
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"content": content,
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}
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