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https://github.com/meta-llama/llama-stack.git
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19 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Ashwin Bharambe
|
d9d34433fc | Update spec | ||
|
Ashwin Bharambe
|
9a5803a429 | move all implementations to use updated type | ||
Aidan Do
|
fdcc74fda2
|
[#432] Add Groq Provider - tool calls (#630)
# What does this PR do?
Contributes to issue #432
- Adds tool calls to Groq provider
- Enables tool call integration tests
### PR Train
- https://github.com/meta-llama/llama-stack/pull/609
- https://github.com/meta-llama/llama-stack/pull/630 👈
## Test Plan
Environment:
```shell
export GROQ_API_KEY=<api-key>
# build.yaml and run.yaml files
wget https://raw.githubusercontent.com/aidando73/llama-stack/9165502582cd7cb178bc1dcf89955b45768ab6c1/build.yaml
wget https://raw.githubusercontent.com/aidando73/llama-stack/9165502582cd7cb178bc1dcf89955b45768ab6c1/run.yaml
# Create environment if not already
conda create --prefix ./envs python=3.10
conda activate ./envs
# Build
pip install -e . && llama stack build --config ./build.yaml --image-type conda
# Activate built environment
conda activate llamastack-groq
```
<details>
<summary>Unit tests</summary>
```shell
# Setup
conda activate llamastack-groq
pytest llama_stack/providers/tests/inference/groq/test_groq_utils.py -vv -k groq -s
# Result
llama_stack/providers/tests/inference/groq/test_groq_utils.py .....................
======================================== 21 passed, 1 warning in 0.05s ========================================
```
</details>
<details>
<summary>Integration tests</summary>
```shell
# Run
conda activate llamastack-groq
pytest llama_stack/providers/tests/inference/test_text_inference.py -k groq -s
# Result
llama_stack/providers/tests/inference/test_text_inference.py .sss.s.ss.sss.s...
========================== 8 passed, 10 skipped, 180 deselected, 7 warnings in 2.73s ==========================
```
</details>
<details>
<summary>Manual</summary>
```bash
llama stack run ./run.yaml --port 5001
```
Via this Jupyter notebook:
|
||
Fred Reiss
|
8b2376bfb3
|
Add inline vLLM inference provider to regression tests and fix regressions (#662)
# What does this PR do?
This PR adds the inline vLLM inference provider to the regression tests
for inference providers. The PR also fixes some regressions in that
inference provider in order to make the tests pass.
## Test Plan
Command to run the new tests (from root of project):
```
pytest \
-vvv \
llama_stack/providers/tests/inference/test_text_inference.py \
--providers inference=vllm \
--inference-model meta-llama/Llama-3.2-3B-Instruct \
```
Output of the above command after these changes:
```
/mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/pytest_asyncio/plugin.py:207: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset.
The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session"
warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET))
=================================================================== test session starts ===================================================================
platform linux -- Python 3.12.7, pytest-8.3.4, pluggy-1.5.0 -- /mnt/datadisk1/freiss/llama/env/bin/python3.12
cachedir: .pytest_cache
rootdir: /mnt/datadisk1/freiss/llama/llama-stack
configfile: pyproject.toml
plugins: asyncio-0.25.0, anyio-4.6.2.post1
asyncio: mode=Mode.STRICT, asyncio_default_fixture_loop_scope=None
collected 9 items
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[-vllm] PASSED [ 11%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-vllm] SKIPPED (Other inference providers don't
support completion() yet) [ 22%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_logprobs[-vllm] SKIPPED (Other inference providers
don't support completion() yet) [ 33%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-vllm] SKIPPED (This test is not
quite robust) [ 44%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[-vllm] PASSED [ 55%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[-vllm] SKIPPED (Other inference providers don't
support structured output yet) [ 66%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[-vllm] PASSED [ 77%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[-vllm] PASSED [ 88%]
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[-vllm] PASSED [100%]
======================================================== 5 passed, 4 skipped, 2 warnings in 25.56s ========================================================
Task was destroyed but it is pending!
task: <Task pending name='Task-6' coro=<AsyncLLMEngine.run_engine_loop() running at /mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py:848> cb=[_log_task_completion(error_callback=<bound method...7cfc479440b0>>)() at /mnt/datadisk1/freiss/llama/env/lib/python3.12/site-packages/vllm/engine/async_llm_engine.py:45, shield.<locals>._inner_done_callback() at /mnt/datadisk1/freiss/llama/env/lib/python3.12/asyncio/tasks.py:905]>
[rank0]:[W1219 11:38:34.689424319 ProcessGroupNCCL.cpp:1250] Warning: WARNING: process group has NOT been destroyed before we destruct ProcessGroupNCCL. On normal program exit, the application should call destroy_process_group to ensure that any pending NCCL operations have finished in this process. In rare cases this process can exit before this point and block the progress of another member of the process group. This constraint has always been present, but this warning has only been added since PyTorch 2.4 (function operator())
```
The warning about "asyncio_default_fixture_loop_scope" appears to be due
to my environment having a newer version of pytest-asyncio.
The warning about a pending task appears to be due to a bug in
`vllm.AsyncLLMEngine.shutdown_background_loop()`. It looks like that
method returns without stopping a pending task. I will look into that
issue separately.
## Sources
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [X] Ran pre-commit to handle lint / formatting issues.
- [X] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [ ] Updated relevant documentation.
- [X] Wrote necessary unit or integration tests.
|
||
|
Aidan Do
|
e1f42eb5a5
|
[#432] Add Groq Provider - chat completions (#609)
# What does this PR do?
Contributes towards issue (#432)
- Groq text chat completions
- Streaming
- All the sampling params that Groq supports
A lot of inspiration taken from @mattf's good work at
https://github.com/meta-llama/llama-stack/pull/355
**What this PR does not do**
- Tool calls (Future PR)
- Adding llama-guard model
- See if we can add embeddings
### PR Train
- https://github.com/meta-llama/llama-stack/pull/609 👈
- https://github.com/meta-llama/llama-stack/pull/630
## Test Plan
<details>
<summary>Environment</summary>
```bash
export GROQ_API_KEY=<api_key>
wget https://raw.githubusercontent.com/aidando73/llama-stack/240e6e2a9c20450ffdcfbabd800a6c0291f19288/build.yaml
wget https://raw.githubusercontent.com/aidando73/llama-stack/92c9b5297f9eda6a6e901e1adbd894e169dbb278/run.yaml
# Build and run environment
pip install -e . \
&& llama stack build --config ./build.yaml --image-type conda \
&& llama stack run ./run.yaml \
--port 5001
```
</details>
<details>
<summary>Manual tests</summary>
Using this jupyter notebook to test manually:
|
||
|
Aidan Do
|
5d7b611336
|
Add JSON structured outputs to Ollama Provider (#680)
# What does this PR do? Addresses issue #679 - Adds support for the response_format field for chat completions and completions so users can get their outputs in JSON ## Test Plan <details> <summary>Integration tests</summary> `pytest llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output -k ollama -s -v` ```python llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_8b-ollama] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_3b-ollama] PASSED ================================== 2 passed, 18 deselected, 3 warnings in 41.41s ================================== ``` </details> <details> <summary>Manual Tests</summary> ``` export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct export OLLAMA_INFERENCE_MODEL=llama3.2:3b-instruct-fp16 export LLAMA_STACK_PORT=5000 ollama run $OLLAMA_INFERENCE_MODEL --keepalive 60m llama stack build --template ollama --image-type conda llama stack run ./run.yaml \ --port $LLAMA_STACK_PORT \ --env INFERENCE_MODEL=$INFERENCE_MODEL \ --env OLLAMA_URL=http://localhost:11434 ``` ```python client = LlamaStackClient(base_url=f"http://localhost:{os.environ['LLAMA_STACK_PORT']}") MODEL_ID=meta-llama/Llama-3.2-3B-Instruct prompt =f""" Create a step by step plan to complete the task of creating a codebase that is a web server that has an API endpoint that translates text from English to French. You have 3 different operations you can perform. You can create a file, update a file, or delete a file. Limit your step by step plan to only these operations per step. Don't create more than 10 steps. Please ensure there's a README.md file in the root of the codebase that describes the codebase and how to run it. Please ensure there's a requirements.txt file in the root of the codebase that describes the dependencies of the codebase. """ response = client.inference.chat_completion( model_id=MODEL_ID, messages=[ {"role": "user", "content": prompt}, ], sampling_params={ "max_tokens": 200000, }, response_format={ "type": "json_schema", "json_schema": { "$schema": "http://json-schema.org/draft-07/schema#", "title": "Plan", "description": f"A plan to complete the task of creating a codebase that is a web server that has an API endpoint that translates text from English to French.", "type": "object", "properties": { "steps": { "type": "array", "items": { "type": "string" } } }, "required": ["steps"], "additionalProperties": False, } }, stream=True, ) content = "" for chunk in response: if chunk.event.delta: print(chunk.event.delta, end="", flush=True) content += chunk.event.delta try: plan = json.loads(content) print(plan) except Exception as e: print(f"Error parsing plan into JSON: {e}") plan = {"steps": []} ``` Outputs: ```json { "steps": [ "Update the requirements.txt file to include the updated dependencies specified in the peer's feedback, including the Google Cloud Translation API key.", "Update the app.py file to address the code smells and incorporate the suggested improvements, such as handling errors and exceptions, initializing the Translator object correctly, adding input validation, using type hints and docstrings, and removing unnecessary logging statements.", "Create a README.md file that describes the codebase and how to run it.", "Ensure the README.md file is up-to-date and accurate.", "Update the requirements.txt file to reflect any additional dependencies specified by the peer's feedback.", "Add documentation for each function in the app.py file using docstrings.", "Implement logging statements throughout the app.py file to monitor application execution.", "Test the API endpoint to ensure it correctly translates text from English to French and handles errors properly.", "Refactor the code to follow PEP 8 style guidelines and ensure consistency in naming conventions, indentation, and spacing.", "Create a new folder for logs and add a logging configuration file (e.g., logconfig.json) that specifies the logging level and output destination.", "Deploy the web server on a production environment (e.g., AWS Elastic Beanstalk or Google Cloud Platform) to make it accessible to external users." ] } ``` </details> ## Sources - Ollama api docs: https://github.com/ollama/ollama/blob/main/docs/api.md#generate-a-completion - Ollama structured output docs: https://github.com/ollama/ollama/blob/main/docs/api.md#request-structured-outputs ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Ran pre-commit to handle lint / formatting issues. - [x] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [x] Wrote necessary unit or integration tests. |
||
Xi Yan
|
3c72c034e6
|
[remove import *] clean up import *'s (#689)
# What does this PR do? - as title, cleaning up `import *`'s - upgrade tests to make them more robust to bad model outputs - remove import *'s in llama_stack/apis/* (skip __init__ modules) <img width="465" alt="image" src="https://github.com/user-attachments/assets/d8339c13-3b40-4ba5-9c53-0d2329726ee2" /> - run `sh run_openapi_generator.sh`, no types gets affected ## Test Plan ### Providers Tests **agents** ``` pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "together" --safety-shield meta-llama/Llama-Guard-3-8B --inference-model meta-llama/Llama-3.1-405B-Instruct-FP8 ``` **inference** ```bash # meta-reference torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py # together pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py pytest -v -s -k "together" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py pytest ./llama_stack/providers/tests/inference/test_prompt_adapter.py ``` **safety** ``` pytest -v -s llama_stack/providers/tests/safety/test_safety.py -m together --safety-shield meta-llama/Llama-Guard-3-8B ``` **memory** ``` pytest -v -s llama_stack/providers/tests/memory/test_memory.py -m "sentence_transformers" --env EMBEDDING_DIMENSION=384 ``` **scoring** ``` pytest -v -s -m llm_as_judge_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct pytest -v -s -m basic_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py pytest -v -s -m braintrust_scoring_together_inference llama_stack/providers/tests/scoring/test_scoring.py ``` **datasetio** ``` pytest -v -s -m localfs llama_stack/providers/tests/datasetio/test_datasetio.py pytest -v -s -m huggingface llama_stack/providers/tests/datasetio/test_datasetio.py ``` **eval** ``` pytest -v -s -m meta_reference_eval_together_inference llama_stack/providers/tests/eval/test_eval.py pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio llama_stack/providers/tests/eval/test_eval.py ``` ### Client-SDK Tests ``` LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk ``` ### llama-stack-apps ``` PORT=5000 LOCALHOST=localhost python -m examples.agents.hello $LOCALHOST $PORT python -m examples.agents.inflation $LOCALHOST $PORT python -m examples.agents.podcast_transcript $LOCALHOST $PORT python -m examples.agents.rag_as_attachments $LOCALHOST $PORT python -m examples.agents.rag_with_memory_bank $LOCALHOST $PORT python -m examples.safety.llama_guard_demo_mm $LOCALHOST $PORT python -m examples.agents.e2e_loop_with_custom_tools $LOCALHOST $PORT # Vision model python -m examples.interior_design_assistant.app python -m examples.agent_store.app $LOCALHOST $PORT ``` ### CLI ``` which llama llama model prompt-format -m Llama3.2-11B-Vision-Instruct llama model list llama stack list-apis llama stack list-providers inference llama stack build --template ollama --image-type conda ``` ### Distributions Tests **ollama** ``` llama stack build --template ollama --image-type conda ollama run llama3.2:1b-instruct-fp16 llama stack run ./llama_stack/templates/ollama/run.yaml --env INFERENCE_MODEL=meta-llama/Llama-3.2-1B-Instruct ``` **fireworks** ``` llama stack build --template fireworks --image-type conda llama stack run ./llama_stack/templates/fireworks/run.yaml ``` **together** ``` llama stack build --template together --image-type conda llama stack run ./llama_stack/templates/together/run.yaml ``` **tgi** ``` llama stack run ./llama_stack/templates/tgi/run.yaml --env TGI_URL=http://0.0.0.0:5009 --env INFERENCE_MODEL=meta-llama/Llama-3.1-8B-Instruct ``` ## 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. |
||
Matthew Farrellee
|
2a9b13dd52
|
add test for completion logprobs (#532)
# What does this PR do? adds a test for the completion api's logprobs parameter tbd which providers pass this test ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Ran pre-commit to handle lint / formatting issues. - [x] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [x] Wrote necessary unit or integration tests. |
||
|
Matthew Farrellee
|
b52df5fe5b
|
add completion api support to nvidia inference provider (#533)
# What does this PR do? add the completion api to the nvidia inference provider ## Test Plan while running the meta/llama-3.1-8b-instruct NIM from https://build.nvidia.com/meta/llama-3_1-8b-instruct?snippet_tab=Docker ``` ➜ pytest -s -v --providers inference=nvidia llama_stack/providers/tests/inference/ --env NVIDIA_BASE_URL=http://localhost:8000 -k test_completion --inference-model Llama3.1-8B-Instruct =============================================== test session starts =============================================== platform linux -- Python 3.10.15, pytest-8.3.3, pluggy-1.5.0 -- /home/matt/.conda/envs/stack/bin/python cachedir: .pytest_cache rootdir: /home/matt/Documents/Repositories/meta-llama/llama-stack configfile: pyproject.toml plugins: anyio-4.6.2.post1, asyncio-0.24.0, httpx-0.34.0 asyncio: mode=strict, default_loop_scope=None collected 20 items / 18 deselected / 2 selected llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-nvidia] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-nvidia] SKIPPED ============================= 1 passed, 1 skipped, 18 deselected, 6 warnings in 5.40s ============================= ``` the structured output functionality works but the accuracy fails ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Ran pre-commit to handle lint / formatting issues. - [x] Read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Updated relevant documentation. - [x] Wrote necessary unit or integration tests. |
||
|
Aidan Do
|
095125e463
|
[#391] Add support for json structured output for vLLM (#528)
# What does this PR do?
Addresses issue (#391)
- Adds json structured output for vLLM
- Enables structured output tests for vLLM
> Give me a recipe for Spaghetti Bolognaise:
```json
{
"recipe_name": "Spaghetti Bolognaise",
"preamble": "Ah, spaghetti bolognaise - the quintessential Italian dish that fills my kitchen with the aromas of childhood nostalgia. As a child, I would watch my nonna cook up a big pot of spaghetti bolognaise every Sunday, filling our small Italian household with the savory scent of simmering meat and tomatoes. The way the sauce would thicken and the spaghetti would al dente - it was love at first bite. And now, as a chef, I want to share that same love with you, so you can recreate these warm, comforting memories at home.",
"ingredients": [
"500g minced beef",
"1 medium onion, finely chopped",
"2 cloves garlic, minced",
"1 carrot, finely chopped",
" celery, finely chopped",
"1 (28 oz) can whole peeled tomatoes",
"1 tbsp tomato paste",
"1 tsp dried basil",
"1 tsp dried oregano",
"1 tsp salt",
"1/2 tsp black pepper",
"1/2 tsp sugar",
"1 lb spaghetti",
"Grated Parmesan cheese, for serving",
"Extra virgin olive oil, for serving"
],
"steps": [
"Heat a large pot over medium heat and add a generous drizzle of extra virgin olive oil.",
"Add the chopped onion, garlic, carrot, and celery and cook until the vegetables are soft and translucent, about 5-7 minutes.",
"Add the minced beef and cook until browned, breaking it up with a spoon as it cooks.",
"Add the tomato paste and cook for 1-2 minutes, stirring constantly.",
"Add the canned tomatoes, dried basil, dried oregano, salt, black pepper, and sugar. Stir well to combine.",
"Bring the sauce to a simmer and let it cook for 20-30 minutes, stirring occasionally, until the sauce has thickened and the flavors have melded together.",
"While the sauce cooks, bring a large pot of salted water to a boil and cook the spaghetti according to the package instructions until al dente. Reserve 1 cup of pasta water before draining the spaghetti.",
"Add the reserved pasta water to the sauce and stir to combine.",
"Combine the cooked spaghetti and sauce, tossing to coat the pasta evenly.",
"Serve hot, topped with grated Parmesan cheese and a drizzle of extra virgin olive oil.",
"Enjoy!"
]
}
```
Generated with Llama-3.2-3B-Instruct model - pretty good for a 3B
parameter model 👍
## Test Plan
`pytest -v -s
llama_stack/providers/tests/inference/test_text_inference.py -k
llama_3b-vllm_remote`
With the following setup:
```bash
# Environment
export INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
export INFERENCE_PORT=8000
export VLLM_URL=http://localhost:8000/v1
# vLLM server
sudo docker run --gpus all \
-v $STORAGE_DIR/.cache/huggingface:/root/.cache/huggingface \
--env "HUGGING_FACE_HUB_TOKEN=$(cat ~/.cache/huggingface/token)" \
-p 8000:$INFERENCE_PORT \
--ipc=host \
--net=host \
vllm/vllm-openai:v0.6.3.post1 \
--model $INFERENCE_MODEL
# llama-stack server
llama stack build --template remote-vllm --image-type conda && llama stack run distributions/remote-vllm/run.yaml \
--port 5001 \
--env INFERENCE_MODEL=meta-llama/Llama-3.2-3B-Instruct
```
Results:
```
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completions_structured_output[llama_3b-vllm_remote] SKIPPED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[llama_3b-vllm_remote] PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[llama_3b-vllm_remote] PASSED
================================ 6 passed, 2 skipped, 120 deselected, 2 warnings in 13.26s ================================
```
## Sources
- https://github.com/vllm-project/vllm/discussions/8300
- By default, vLLM uses https://github.com/dottxt-ai/outlines for
structured outputs
[[1](
|
||
Henry Tu
|
64c6df8392
|
Cerebras Inference Integration (#265)
Adding Cerebras Inference as an API provider. ## Testing ### Conda ``` $ llama stack build --template cerebras --image-type conda $ llama stack run ~/.llama/distributions/llamastack-cerebras/cerebras-run.yaml ... Listening on ['::', '0.0.0.0']:5000 INFO: Started server process [12443] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://['::', '0.0.0.0']:5000 (Press CTRL+C to quit) ``` ### Chat Completion ``` $ curl --location 'http://localhost:5000/alpha/inference/chat-completion' --header 'Content-Type: application/json' --data '{ "model_id": "meta-llama/Llama-3.1-8B-Instruct", "messages": [ { "role": "user", "content": "What is the temperature in Seattle right now?" } ], "stream": false, "sampling_params": { "strategy": "top_p", "temperature": 0.5, "max_tokens": 100 }, "tool_choice": "auto", "tool_prompt_format": "json", "tools": [ { "tool_name": "getTemperature", "description": "Gets the current temperature of a location.", "parameters": { "location": { "param_type": "string", "description": "The name of the place to get the temperature from in degress celsius.", "required": true } } } ] }' ``` #### Non-Streaming Response ``` { "completion_message": { "role": "assistant", "content": "", "stop_reason": "end_of_message", "tool_calls": [ { "call_id": "6f42fdcc-6cbb-46ad-a17b-5d20ac64b678", "tool_name": "getTemperature", "arguments": { "location": "Seattle" } } ] }, "logprobs": null } ``` #### Streaming Response ``` data: {"event":{"event_type":"start","delta":"","logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"","parse_status":"started"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"{\"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"type","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\":","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" \"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"function","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\",","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" \"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"name","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\":","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" \"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"get","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"Temperature","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\",","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" \"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"parameters","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\":","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" {\"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"location","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\":","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":" \"","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"Seattle","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":"\"}}","parse_status":"in_progress"},"logprobs":null,"stop_reason":null}} data: {"event":{"event_type":"progress","delta":{"content":{"call_id":"e742df1f-0ae9-40ad-a49e-18e5c905484f","tool_name":"getTemperature","arguments":{"location":"Seattle"}},"parse_status":"success"},"logprobs":null,"stop_reason":"end_of_message"}} data: {"event":{"event_type":"complete","delta":"","logprobs":null,"stop_reason":"end_of_message"}} ``` ### Completion ``` $ curl --location 'http://localhost:5000/alpha/inference/completion' --header 'Content-Type: application/json' --data '{ "model_id": "meta-llama/Llama-3.1-8B-Instruct", "content": "1,2,3,", "stream": true, "sampling_params": { "strategy": "top_p", "temperature": 0.5, "max_tokens": 10 }, "tool_choice": "auto", "tool_prompt_format": "json", "tools": [ { "tool_name": "getTemperature", "description": "Gets the current temperature of a location.", "parameters": { "location": { "param_type": "string", "description": "The name of the place to get the temperature from in degress celsius.", "required": true } } } ] }' ``` #### Non-Streaming Response ``` { "content": "4,5,6,7,8,", "stop_reason": "out_of_tokens", "logprobs": null } ``` #### Streaming Response ``` data: {"delta":"4","stop_reason":null,"logprobs":null} data: {"delta":",","stop_reason":null,"logprobs":null} data: {"delta":"5","stop_reason":null,"logprobs":null} data: {"delta":",","stop_reason":null,"logprobs":null} data: {"delta":"6","stop_reason":null,"logprobs":null} data: {"delta":",","stop_reason":null,"logprobs":null} data: {"delta":"7","stop_reason":null,"logprobs":null} data: {"delta":",","stop_reason":null,"logprobs":null} data: {"delta":"8","stop_reason":null,"logprobs":null} data: {"delta":",","stop_reason":null,"logprobs":null} data: {"delta":"","stop_reason":null,"logprobs":null} data: {"delta":"","stop_reason":"out_of_tokens","logprobs":null} ``` ### Pre-Commit Checks ``` trim trailing whitespace.................................................Passed check python ast.........................................................Passed check for merge conflicts................................................Passed check for added large files..............................................Passed fix end of files.........................................................Passed Insert license in comments...............................................Passed flake8...................................................................Passed Format files with µfmt...................................................Passed ``` ### Testing with `test_inference.py` ``` $ export CEREBRAS_API_KEY=<insert API key here> $ pytest -v -s llama_stack/providers/tests/inference/test_text_inference.py -m "cerebras and llama_8b" /net/henryt-dev/srv/nfs/henryt-data/ws/llama-stack/.venv/lib/python3.12/site-packages/pytest_asyncio/plugin.py:208: PytestDeprecationWarning: The configuration option "asyncio_default_fixture_loop_scope" is unset. The event loop scope for asynchronous fixtures will default to the fixture caching scope. Future versions of pytest-asyncio will default the loop scope for asynchronous fixtures to function scope. Set the default fixture loop scope explicitly in order to avoid unexpected behavior in the future. Valid fixture loop scopes are: "function", "class", "module", "package", "session" warnings.warn(PytestDeprecationWarning(_DEFAULT_FIXTURE_LOOP_SCOPE_UNSET)) =================================================== test session starts =================================================== platform linux -- Python 3.12.3, pytest-8.3.3, pluggy-1.5.0 -- /net/henryt-dev/srv/nfs/henryt-data/ws/llama-stack/.venv/bin/python3.12 cachedir: .pytest_cache rootdir: /net/henryt-dev/srv/nfs/henryt-data/ws/llama-stack configfile: pyproject.toml plugins: anyio-4.6.2.post1, asyncio-0.24.0 asyncio: mode=Mode.STRICT, default_loop_scope=None collected 128 items / 120 deselected / 8 selected llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_model_list[llama_8b-cerebras] Resolved 4 providers inner-inference => cerebras models => __routing_table__ inference => __autorouted__ inspect => __builtin__ Models: meta-llama/Llama-3.1-8B-Instruct served by cerebras PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[llama_8b-cerebras] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completions_structured_output[llama_8b-cerebras] SKIPPED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_non_streaming[llama_8b-cerebras] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_structured_output[llama_8b-cerebras] SKIPPED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_streaming[llama_8b-cerebras] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling[llama_8b-cerebras] PASSED llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_chat_completion_with_tool_calling_streaming[llama_8b-cerebras] PASSED ================================ 6 passed, 2 skipped, 120 deselected, 6 warnings in 3.95s ================================= ``` I ran `python llama_stack/scripts/distro_codegen.py` to run codegen. |
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Matthew Farrellee
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435f34b05e
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reduce the accuracy requirements to pass the chat completion structured output test (#522)
i find `test_structured_output` to be flakey. it's both a functionality
and accuracy test -
```
answer = AnswerFormat.model_validate_json(response.completion_message.content)
assert answer.first_name == "Michael"
assert answer.last_name == "Jordan"
assert answer.year_of_birth == 1963
assert answer.num_seasons_in_nba == 15
```
it's an accuracy test because it checks the value of first/last name,
birth year, and num seasons.
i find that -
- llama-3.1-8b-instruct and llama-3.2-3b-instruct pass the functionality
portion
- llama-3.2-3b-instruct consistently fails the accuracy portion
(thinking MJ was in the NBA for 14 seasons)
- llama-3.1-8b-instruct occasionally fails the accuracy portion
suggestions (not mutually exclusive) -
1. turn the test into functionality only, skip the value checks
2. split the test into a functionality version and an xfail accuracy
version
3. add context to the prompt so the llm can answer without accessing
embedded memory
# What does this PR do?
implements option (3) by adding context to the system prompt.
## Test Plan
`pytest -s -v ... llama_stack/providers/tests/inference/ ... -k
structured_output`
## Before submitting
- [x] Ran pre-commit to handle lint / formatting issues.
- [x] Read the [contributor
guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md),
Pull Request section?
- [x] Updated relevant documentation.
- [x] Wrote necessary unit or integration tests.
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Matthew Farrellee
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4e6c984c26
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add NVIDIA NIM inference adapter (#355)
# What does this PR do? this PR adds a basic inference adapter to NVIDIA NIMs what it does - - chat completion api - tool calls - streaming - structured output - logprobs - support hosted NIM on integrate.api.nvidia.com - support downloaded NIM containers what it does not do - - completion api - embedding api - vision models - builtin tools - have certainty that sampling strategies are correct ## Feature/Issue validation/testing/test plan `pytest -s -v --providers inference=nvidia llama_stack/providers/tests/inference/ --env NVIDIA_API_KEY=...` all tests should pass. there are pydantic v1 warnings. ## Before submitting - [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). - [x] Did you read the [contributor guideline](https://github.com/meta-llama/llama-stack/blob/main/CONTRIBUTING.md), Pull Request section? - [ ] Was this discussed/approved via a Github issue? Please add a link to it if that's the case. - [ ] Did you make sure to update the documentation with your changes? - [x] Did you write any new necessary tests? Thanks for contributing 🎉! |
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Ashwin Bharambe
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d790be28b3 | Don't skip meta-reference for the tests | ||
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Ashwin Bharambe
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05d1ead02f | Update condition in tests to handle llama-3.1 vs llama3.1 (HF names) | ||
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Ashwin Bharambe
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12947ac19e
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Kill "remote" providers and fix testing with a remote stack properly (#435)
# What does this PR do? This PR kills the notion of "pure passthrough" remote providers. You cannot specify a single provider you must specify a whole distribution (stack) as remote. This PR also significantly fixes / upgrades testing infrastructure so you can now test against a remotely hosted stack server by just doing ```bash pytest -s -v -m remote test_agents.py \ --inference-model=Llama3.1-8B-Instruct --safety-shield=Llama-Guard-3-1B \ --env REMOTE_STACK_URL=http://localhost:5001 ``` Also fixed `test_agents_persistence.py` (which was broken) and killed some deprecated testing functions. ## Test Plan All the tests. |
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Dinesh Yeduguru
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fdff24e77a
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Inference to use provider resource id to register and validate (#428)
This PR changes the way model id gets translated to the final model name that gets passed through the provider. Major changes include: 1) Providers are responsible for registering an object and as part of the registration returning the object with the correct provider specific name of the model provider_resource_id 2) To help with the common look ups different names a new ModelLookup class is created. Tested all inference providers including together, fireworks, vllm, ollama, meta reference and bedrock |
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Dinesh Yeduguru
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ec644d3418
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migrate model to Resource and new registration signature (#410)
* resource oriented object design for models * add back llama_model field * working tests * register singature fix * address feedback --------- Co-authored-by: Dinesh Yeduguru <dineshyv@fb.com> |
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Ashwin Bharambe
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7c340f0236 | rename test_inference -> test_text_inference |
Renamed from llama_stack/providers/tests/inference/test_inference.py (Browse further)