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Enable vision models for Together and Fireworks

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This commit is contained in:
Ashwin Bharambe 2024-11-05 12:29:07 -08:00
parent 8de845a96d
commit 03013dafc1
9 changed files with 297 additions and 35 deletions
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48
llama_stack/providers/adapters/inference/fireworks/fireworks.py
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@ -26,6 +26,8 @@ from llama_stack.providers.utils.inference.openai_compat import (
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
convert_message_to_dict,
request_has_media,
)
from .config import FireworksImplConfig
@ -129,7 +131,10 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
self, request: ChatCompletionRequest, client: Fireworks
) -> ChatCompletionResponse:
params = self._get_params(request)
r = await client.completion.acreate(**params)
if "messages" in params:
r = await client.chat.completions.acreate(**params)
else:
r = await client.completion.acreate(**params)
return process_chat_completion_response(r, self.formatter)
async def _stream_chat_completion(
@ -137,24 +142,44 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
) -> AsyncGenerator:
params = self._get_params(request)
stream = client.completion.acreate(**params)
if "messages" in params:
print(f"Using chat completion endpoint: {params}")
stream = client.chat.completions.acreate(**params)
else:
stream = client.completion.acreate(**params)
async for chunk in process_chat_completion_stream_response(
stream, self.formatter
):
yield chunk
def _get_params(self, request) -> dict:
prompt = ""
if type(request) == ChatCompletionRequest:
prompt = chat_completion_request_to_prompt(request, self.formatter)
elif type(request) == CompletionRequest:
prompt = completion_request_to_prompt(request, self.formatter)
def _get_params(
self, request: Union[ChatCompletionRequest, CompletionRequest]
) -> dict:
input_dict = {}
media_present = request_has_media(request)
if isinstance(request, ChatCompletionRequest):
if media_present:
input_dict["messages"] = [
convert_message_to_dict(m) for m in request.messages
]
else:
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
)
elif isinstance(request, CompletionRequest):
assert (
not media_present
), "Fireworks does not support media for Completion requests"
input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
else:
raise ValueError(f"Unknown request type {type(request)}")
# Fireworks always prepends with BOS
if prompt.startswith("<|begin_of_text|>"):
prompt = prompt[len("<|begin_of_text|>") :]
if "prompt" in input_dict:
if input_dict["prompt"].startswith("<|begin_of_text|>"):
input_dict["prompt"] = input_dict["prompt"][len("<|begin_of_text|>") :]
options = get_sampling_options(request.sampling_params)
options.setdefault("max_tokens", 512)
@ -172,9 +197,10 @@ class FireworksInferenceAdapter(ModelRegistryHelper, Inference):
}
else:
raise ValueError(f"Unknown response format {fmt.type}")
return {
"model": self.map_to_provider_model(request.model),
"prompt": prompt,
**input_dict,
"stream": request.stream,
**options,
}

45
llama_stack/providers/adapters/inference/together/together.py
View file

@ -26,6 +26,8 @@ from llama_stack.providers.utils.inference.openai_compat import (
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
convert_message_to_dict,
request_has_media,
)
from .config import TogetherImplConfig
@ -102,7 +104,7 @@ class TogetherInferenceAdapter(
return process_completion_response(r, self.formatter)
async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
params = self._get_params_for_completion(request)
params = self._get_params(request)
# if we shift to TogetherAsyncClient, we won't need this wrapper
async def _to_async_generator():
@ -131,14 +133,6 @@ class TogetherInferenceAdapter(
return options
def _get_params_for_completion(self, request: CompletionRequest) -> dict:
return {
"model": self.map_to_provider_model(request.model),
"prompt": completion_request_to_prompt(request, self.formatter),
"stream": request.stream,
**self._build_options(request.sampling_params, request.response_format),
}
async def chat_completion(
self,
model: str,
@ -172,7 +166,10 @@ class TogetherInferenceAdapter(
self, request: ChatCompletionRequest
) -> ChatCompletionResponse:
params = self._get_params(request)
r = self._get_client().completions.create(**params)
if "messages" in params:
r = self._get_client().chat.completions.create(**params)
else:
r = self._get_client().completions.create(**params)
return process_chat_completion_response(r, self.formatter)
async def _stream_chat_completion(
@ -182,7 +179,10 @@ class TogetherInferenceAdapter(
# if we shift to TogetherAsyncClient, we won't need this wrapper
async def _to_async_generator():
s = self._get_client().completions.create(**params)
if "messages" in params:
s = self._get_client().chat.completions.create(**params)
else:
s = self._get_client().completions.create(**params)
for chunk in s:
yield chunk
@ -192,10 +192,29 @@ class TogetherInferenceAdapter(
):
yield chunk
def _get_params(self, request: ChatCompletionRequest) -> dict:
def _get_params(
self, request: Union[ChatCompletionRequest, CompletionRequest]
) -> dict:
input_dict = {}
media_present = request_has_media(request)
if isinstance(request, ChatCompletionRequest):
if media_present:
input_dict["messages"] = [
convert_message_to_dict(m) for m in request.messages
]
else:
input_dict["prompt"] = chat_completion_request_to_prompt(
request, self.formatter
)
else:
assert (
not media_present
), "Together does not support media for Completion requests"
input_dict["prompt"] = completion_request_to_prompt(request, self.formatter)
return {
"model": self.map_to_provider_model(request.model),
"prompt": chat_completion_request_to_prompt(request, self.formatter),
**input_dict,
"stream": request.stream,
**self._build_options(request.sampling_params, request.response_format),
}

5
llama_stack/providers/tests/inference/fixtures.py
View file

@ -29,6 +29,11 @@ def inference_model(request):
return request.config.getoption("--inference-model", None)
@pytest.fixture(scope="session")
def vision_inference_model():
return "Llama3.2-11B-Vision-Instruct"
@pytest.fixture(scope="session")
def inference_remote() -> ProviderFixture:
return remote_stack_fixture()

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13
llama_stack/providers/tests/inference/test_inference.py
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@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import itertools
import pytest
@ -15,6 +14,9 @@ from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.datatypes import * # noqa: F403
from .utils import group_chunks
# How to run this test:
#
# pytest -v -s llama_stack/providers/tests/inference/test_inference.py
@ -22,15 +24,6 @@ from llama_stack.distribution.datatypes import * # noqa: F403
# --env FIREWORKS_API_KEY=<your_api_key>
def group_chunks(response):
return {
event_type: list(group)
for event_type, group in itertools.groupby(
response, key=lambda chunk: chunk.event.event_type
)
}
def get_expected_stop_reason(model: str):
return StopReason.end_of_message if "Llama3.1" in model else StopReason.end_of_turn

126
llama_stack/providers/tests/inference/test_vision_inference.py Normal file
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@ -0,0 +1,126 @@
# 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 pathlib import Path
import pytest
from PIL import Image as PIL_Image
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
from .utils import group_chunks
THIS_DIR = Path(__file__).parent
class TestVisionModelInference:
@pytest.mark.asyncio
async def test_vision_chat_completion_non_streaming(
self, vision_inference_model, inference_stack
):
inference_impl, _ = inference_stack
provider = inference_impl.routing_table.get_provider_impl(
vision_inference_model
)
if provider.__provider_spec__.provider_type not in (
"meta-reference",
"remote::together",
"remote::fireworks",
):
pytest.skip("Other inference providers don't support completion() yet")
images = [
ImageMedia(image=PIL_Image.open(THIS_DIR / "pasta.jpeg")),
ImageMedia(
image=URL(
uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
)
),
]
# These are a bit hit-and-miss, need to be careful
expected_strings_to_check = [
["spaghetti"],
["puppy"],
]
for image, expected_strings in zip(images, expected_strings_to_check):
response = await inference_impl.chat_completion(
model=vision_inference_model,
messages=[
SystemMessage(content="You are a helpful assistant."),
UserMessage(
content=[image, "Describe this image in two sentences."]
),
],
stream=False,
)
assert isinstance(response, ChatCompletionResponse)
assert response.completion_message.role == "assistant"
assert isinstance(response.completion_message.content, str)
for expected_string in expected_strings:
assert expected_string in response.completion_message.content
@pytest.mark.asyncio
async def test_vision_chat_completion_streaming(
self, vision_inference_model, inference_stack
):
inference_impl, _ = inference_stack
provider = inference_impl.routing_table.get_provider_impl(
vision_inference_model
)
if provider.__provider_spec__.provider_type not in (
"meta-reference",
"remote::together",
"remote::fireworks",
):
pytest.skip("Other inference providers don't support completion() yet")
images = [
ImageMedia(
image=URL(
uri="https://www.healthypawspetinsurance.com/Images/V3/DogAndPuppyInsurance/Dog_CTA_Desktop_HeroImage.jpg"
)
),
]
expected_strings_to_check = [
["puppy"],
]
for image, expected_strings in zip(images, expected_strings_to_check):
response = [
r
async for r in await inference_impl.chat_completion(
model=vision_inference_model,
messages=[
SystemMessage(content="You are a helpful assistant."),
UserMessage(
content=[image, "Describe this image in two sentences."]
),
],
stream=True,
)
]
assert len(response) > 0
assert all(
isinstance(chunk, ChatCompletionResponseStreamChunk)
for chunk in response
)
grouped = group_chunks(response)
assert len(grouped[ChatCompletionResponseEventType.start]) == 1
assert len(grouped[ChatCompletionResponseEventType.progress]) > 0
assert len(grouped[ChatCompletionResponseEventType.complete]) == 1
content = "".join(
chunk.event.delta
for chunk in grouped[ChatCompletionResponseEventType.progress]
)
for expected_string in expected_strings:
assert expected_string in content

16
llama_stack/providers/tests/inference/utils.py Normal file
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@ -0,0 +1,16 @@
# 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.
import itertools
def group_chunks(response):
return {
event_type: list(group)
for event_type, group in itertools.groupby(
response, key=lambda chunk: chunk.event.event_type
)
}

8
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -46,6 +46,9 @@ def text_from_choice(choice) -> str:
if hasattr(choice, "delta") and choice.delta:
return choice.delta.content
if hasattr(choice, "message"):
return choice.message.content
return choice.text
@ -99,7 +102,6 @@ def process_chat_completion_response(
async def process_completion_stream_response(
stream: AsyncGenerator[OpenAICompatCompletionResponse, None], formatter: ChatFormat
) -> AsyncGenerator:
stop_reason = None
async for chunk in stream:
@ -158,6 +160,10 @@ async def process_chat_completion_stream_response(
break
text = text_from_choice(choice)
if not text:
# Sometimes you get empty chunks from providers
continue
# check if its a tool call ( aka starts with <|python_tag|> )
if not ipython and text.startswith("<|python_tag|>"):
ipython = True

71
llama_stack/providers/utils/inference/prompt_adapter.py
View file

@ -3,10 +3,14 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import base64
import io
import json
from typing import Tuple
from llama_models.llama3.api.chat_format import ChatFormat
from PIL import Image as PIL_Image
from termcolor import cprint
from llama_models.llama3.api.datatypes import * # noqa: F403
@ -24,6 +28,73 @@ from llama_models.sku_list import resolve_model
from llama_stack.providers.utils.inference import supported_inference_models
def content_has_media(content: InterleavedTextMedia):
def _has_media_content(c):
return isinstance(c, ImageMedia)
if isinstance(content, list):
return any(_has_media_content(c) for c in content)
else:
return _has_media_content(content)
def messages_have_media(messages: List[Message]):
return any(content_has_media(m.content) for m in messages)
def request_has_media(request: Union[ChatCompletionRequest, CompletionRequest]):
if isinstance(request, ChatCompletionRequest):
return messages_have_media(request.messages)
else:
return content_has_media(request.content)
def convert_image_media_to_url(media: ImageMedia) -> str:
if isinstance(media.image, PIL_Image.Image):
if media.image.format == "PNG":
format = "png"
elif media.image.format == "GIF":
format = "gif"
elif media.image.format == "JPEG":
format = "jpeg"
else:
raise ValueError(f"Unsupported image format {media.image.format}")
bytestream = io.BytesIO()
media.image.save(bytestream, format=media.image.format)
bytestream.seek(0)
return f"data:image/{format};base64," + base64.b64encode(
bytestream.getvalue()
).decode("utf-8")
else:
assert isinstance(media.image, URL)
return media.image.uri
def convert_message_to_dict(message: Message) -> dict:
def _convert_content(content) -> dict:
if isinstance(content, ImageMedia):
return {
"type": "image_url",
"image_url": {
"url": convert_image_media_to_url(content),
},
}
else:
assert isinstance(content, str)
return {"type": "text", "text": content}
if isinstance(message.content, list):
content = [_convert_content(c) for c in message.content]
else:
content = [_convert_content(message.content)]
return {
"role": message.role,
"content": content,
}
def completion_request_to_prompt(
request: CompletionRequest, formatter: ChatFormat
) -> str: