# What does this PR do?
- See https://github.com/meta-llama/llama-stack/pull/666 &
https://github.com/meta-llama/llama-stack/pull/668
- Refactor BaseScoringFn to be just a minimal interface, add new
RegistrableBaseScoring
- Refactor data schema check
- To separately evaluate retrieval component in RAG, we will have
scoring functions needing "context" column additionally.
- Refactor braintrust eval (more scoring fn added & tested in following
PR)
## Test Plan
```
pytest -v -s -m llm_as_judge_scoring_together_inference scoring/test_scoring.py --judge-model meta-llama/Llama-3.2-3B-Instruct
pytest -v -s -m basic_scoring_together_inference scoring/test_scoring.py
pytest -v -s -m braintrust_scoring_together_inference scoring/test_scoring.py
```
<img width="847" alt="image"
src="https://github.com/user-attachments/assets/d099cb2d-6f9c-4bdf-9d0d-f388cf758c0f"
/>
```
pytest -v -s -m meta_reference_eval_together_inference eval/test_eval.py
pytest -v -s -m meta_reference_eval_together_inference_huggingface_datasetio eval/test_eval.py
```
<img width="850" alt="image"
src="https://github.com/user-attachments/assets/dce28fc3-0493-4d34-820a-567260873cc8"
/>
## 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.
# What does this PR do?
Contributes to issue (#407)
tl;dr - @subramen was getting a 500 error because llama-stack called
code_interpreter when it never was defined as a tool.
Prevents failures like:
<img width="544" alt="image"
src="https://github.com/user-attachments/assets/392683d2-4670-414c-aaba-07ebc006d748"
/>
```
# Server side
Traceback (most recent call last):
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/distribution/server/server.py", line 206, in sse_generator
async for item in await event_gen:
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/agents/agents.py", line 138, in _create_agent_turn_streaming
async for event in agent.create_and_execute_turn(request):
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/agents/agent_instance.py", line 179, in create_and_execute_turn
async for chunk in self.run(
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/agents/agent_instance.py", line 252, in run
async for res in self._run(
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/agents/agent_instance.py", line 560, in _run
result_messages = await execute_tool_call_maybe(
File "/opt/conda/envs/llamastack-vllm-stack/lib/python3.10/site-packages/llama_stack/providers/impls/meta_reference/agents/agent_instance.py", line 824, in execute_tool_call_maybe
assert name in tools_dict, f"Tool {name} not found"
AssertionError: Tool code_interpreter not found
```
Instead, if the model hallucinates, we just let it hallucinate and let
the client know.
<img width="544" alt="image"
src="https://github.com/user-attachments/assets/d2418583-d45a-48db-b476-45a584f2986f"
/>
## Test Plan
<details>
<summary>pytest llama_stack/providers/tests/agents/test_agents.py -k
ollama</summary>
```
llama stack build --template ollama --image-type conda
conda activate llamastack-ollama
```
```
llama_stack/providers/tests/agents/test_agents.py ..Fss [100%]
======================================================================= FAILURES =======================================================================
_________________________________________ TestAgents.test_rag_agent_as_attachments[--ollama][ollama] __________________________________________
llama_stack/providers/tests/agents/test_agents.py:261: in test_rag_agent_as_attachments
turn_response = [
llama_stack/providers/tests/agents/test_agents.py:261: in <listcomp>
turn_response = [
llama_stack/providers/inline/agents/meta_reference/agents.py:153: in _create_agent_turn_streaming
async for event in agent.create_and_execute_turn(request):
llama_stack/providers/inline/agents/meta_reference/agent_instance.py:179: in create_and_execute_turn
async for chunk in self.run(
llama_stack/providers/inline/agents/meta_reference/agent_instance.py:250: in run
async for res in self._run(
llama_stack/providers/inline/agents/meta_reference/agent_instance.py:363: in _run
rag_context, bank_ids = await self._retrieve_context(
llama_stack/providers/inline/agents/meta_reference/agent_instance.py:698: in _retrieve_context
bank_id = await self._ensure_memory_bank(session_id)
llama_stack/providers/inline/agents/meta_reference/agent_instance.py:653: in _ensure_memory_bank
await self.memory_banks_api.register_memory_bank(
llama_stack/providers/utils/telemetry/trace_protocol.py:101: in async_wrapper
result = await method(self, *args, **kwargs)
llama_stack/distribution/routers/routing_tables.py:312: in register_memory_bank
raise ValueError(
E ValueError: Embeddings are now served via Inference providers. Please upgrade your run.yaml to include inline::sentence-transformer as an additional inference provider. See https://github.com/meta-llama/llama-stack/blob/main/llama_stack/templates/together/run.yaml for an example.
=============================================================== short test summary info ================================================================
FAILED llama_stack/providers/tests/agents/test_agents.py::TestAgents::test_rag_agent_as_attachments[--ollama] - ValueError: Embeddings are now served via Inference providers. Please upgrade your run.yaml to include inline::sentence-transformer as an additiona...
========================================== 1 failed, 2 passed, 2 skipped, 20 deselected, 5 warnings in 14.24s ==========================================
```
Unrelated test is failing (also failing on main)
</details>
<details>
<summary>Manual</summary>
Using this client code:
7ebc257b27/client.py
<img width="544" alt="Screenshot 2024-12-16 at 17 41 31"
src="https://github.com/user-attachments/assets/7425deaf-c94a-4dda-a635-922728e373f1"
/>
</details>
## 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.
- [ ] Wrote necessary unit or integration tests.
# 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.
This fixes the pre-commit check when running locally (not sure why this
was not caught on CI check):
```
> pre-commit run --show-diff-on-failure --color=always --all-files
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...................................................................Failed
- hook id: flake8
- exit code: 1
llama_stack/distribution/ui/page/evaluations/app_eval.py:132:65: E226 missing whitespace around arithmetic operator
llama_stack/distribution/ui/page/evaluations/native_eval.py:235:61: E226 missing whitespace around arithmetic operator
llama_stack/providers/utils/telemetry/trace_protocol.py:56:78: E226 missing whitespace around arithmetic operator
```
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
- fix interleaved_content_convert_to_raw in prompt_adapter to correctly
convert ImageContentItem to RawMediaItem with raw data bytes
## Test Plan
```
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
```
**Before**
<img width="844" alt="image"
src="https://github.com/user-attachments/assets/f2784b42-2e36-4477-9041-903d5d628a68"
/>
**After**
<img width="836" alt="image"
src="https://github.com/user-attachments/assets/362b6e47-29f7-4119-bcf3-f75db842735f"
/>
## 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.
# What does this PR do?
- Fix broken pytest for meta-reference's agents
- Safety model needs to be registered to a different provider id from
inference model in order to be recognized
## Test Plan
```
torchrun $CONDA_PREFIX/bin/pytest -v -s llama_stack/providers/tests/agents/test_agents.py -m "meta_reference" --safety-shield meta-llama/Llama-Guard-3-1B --inference-model meta-llama/Llama-3.1-8B-Instruct
```
**Before**
<img width="845" alt="image"
src="https://github.com/user-attachments/assets/83818fe1-2179-4e9c-a753-bf1472a2f01d"
/>
**After**
<img width="851" alt="image"
src="https://github.com/user-attachments/assets/1cf8124b-14e2-47bf-80fd-ef8b4b3f6fd9"
/>
**Other test not broken**
```
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
```
## 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.
# What does this PR do?
- vision inference via image as binary bytes fails with serialization
error
- add custom serialization for "bytes" in `_URLOrData`
## Test Plan
```
pytest -v -s -k "fireworks" --inference-model="meta-llama/Llama-3.2-11B-Vision-Instruct" ./llama_stack/providers/tests/inference/test_vision_inference.py::TestVisionModelInference::test_vision_chat_completion_non_streaming
```
**Before**
<img width="1020" alt="image"
src="https://github.com/user-attachments/assets/3803fcee-32ee-4b8e-ba46-47848e1a6247"
/>
**After**
<img width="1018" alt="image"
src="https://github.com/user-attachments/assets/f3e3156e-88ce-40fd-ad1b-44b87f376e03"
/>
<img width="822" alt="image"
src="https://github.com/user-attachments/assets/1898696f-95c0-4694-8a47-8f51c7de0e86"
/>
## 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.
# What does this PR do?
- Makes Llama 70B 3.3 available for fireworks
## Test Plan
```shell
pip install -e . \
&& llama stack build --config distributions/fireworks/build.yaml --image-type conda \
&& llama stack run distributions/fireworks/run.yaml \
--port 5000
```
```python
response = client.inference.chat_completion(
model_id="Llama3.3-70B-Instruct",
messages=[
{"role": "user", "content": "hello world"},
],
)
```
## 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.
- [ ] Wrote necessary unit or integration tests.
# What does this PR do?
In short, provide a summary of what this PR does and why. Usually, the
relevant context should be present in a linked issue.
- [x] Addresses issue (#issue)
```
from .nvidia import NVIDIAInferenceAdapter
File "/localhome/local-cdgamarose/llama-stack/llama_stack/providers/remote/inference/nvidia/nvidia.py", line 37, in <module>
from .openai_utils import (
File "/localhome/local-cdgamarose/llama-stack/llama_stack/providers/remote/inference/nvidia/openai_utils.py", line 11, in <module>
from llama_models.llama3.api.datatypes import (
ImportError: cannot import name 'CompletionMessage' from 'llama_models.llama3.api.datatypes' (/localhome/local-cdgamarose/.local/lib/python3.10/site-packages/llama_models/llama3/api/datatypes.py)
++ error_handler 62
```
## Test Plan
Deploy NIM using docker from
https://build.nvidia.com/meta/llama-3_1-8b-instruct?snippet_tab=Docker
```
(lsmyenv) local-cdgamarose@a4u8g-0006:~/llama-stack$ python3 -m 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 3.10.16, pytest-8.3.4, pluggy-1.5.0 -- /localhome/local-cdgamarose/anaconda3/envs/lsmyenv/bin/python3
cachedir: .pytest_cache
rootdir: /localhome/local-cdgamarose/llama-stack
configfile: pyproject.toml
plugins: anyio-4.7.0, asyncio-0.25.0
asyncio: mode=strict, asyncio_default_fixture_loop_scope=None
collected 24 items / 21 deselected / 3 selected
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion[-nvidia] Initializing NVIDIAInferenceAdapter(http://localhost:8000)...
Checking NVIDIA NIM health...
Checking NVIDIA NIM health...
PASSED
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_logprobs[-nvidia] SKIPPED (Other inference providers don't support completion() yet)
llama_stack/providers/tests/inference/test_text_inference.py::TestInference::test_completion_structured_output[-nvidia] SKIPPED (This test is not quite robust)
====================================================================== 1 passed, 2 skipped, 21 deselected, 2 warnings in 1.57s =======================================================================
```
## 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.
# What does this PR do?
Remove unused code since this now exists in the meta reference provider
as a sink
## Test Plan
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
## What does this PR do?
In this PR, we refactor the meta reference inference logic to support
- load the model during registering model instead of during spinning up
server
- support inference finetuned model checkpoint on top of native llama
model
## Why need these changes
To solve the existing pain points that
- user cannot lazy load the model and hot switch the inference
checkpoint after spinning up the server
- this blocks us doing inference and eval on the same sever for a
finetuned checkpoint after post training
- user cannot do inference on a finetuned checkpoint on top of native
llama models
## Expect user experience change
- The inference model won't be loaded when spinning up server. Instead,
it will be loaded during register model. If user add the model as models
resource in run.yaml, it will be registered and loaded automatically
when starting server. There is an optional flag 'skip_initialize' in
model metadata to skip model loading during registration.
- There is an optional flag 'llama_model' in model metadata to identify
the base model of the Model class for validation and initialize model
arch. model identifier no longer needs to be a native llama model
- the default inference model name updates from
'meta-llama/Llama-3.2-3B-Instruct' to 'Llama3.2-3B-Instruct'
- It aligns with the checkpoint folder name after running 'llama model
download'
- It aligns with the descriptor name defined in llama-models SKU list
bf5b0c4fe7/models/datatypes.py (L95)
## test
run python llama_stack/scripts/distro_codegen.py
**run unit test**
- torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference"
--inference-model="Llama3.1-8B-Instruct"
./llama_stack/providers/tests/inference/test_text_inference.py
- torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference"
--inference-model="Llama3.1-8B-Instruct"
./llama_stack/providers/tests/inference/test_model_registration.py
**test post training experience**
on server side run: llama stack run
llama_stack/templates/experimental-post-training/run.yaml
server is spinning up without model loaded
<img width="812" alt="Screenshot 2024-12-17 at 1 24 50 PM"
src="https://github.com/user-attachments/assets/ce1f606b-3b6f-452f-b48e-b3761ffd90f3"
/>
on client side, run: llama-stack-client --endpoint
http://devgpu018.nha2.facebook.com:5000 models register
Llama3.2-3B-Instruct
register model successfully and the model is loaded
<img width="1111" alt="Screenshot 2024-12-17 at 1 26 30 PM"
src="https://github.com/user-attachments/assets/56e02131-cf7d-4de5-8f63-fbdcb8c55c26"
/>
<img width="1541" alt="Screenshot 2024-12-17 at 1 26 09 PM"
src="https://github.com/user-attachments/assets/a83255a1-20f5-40a2-af51-55641410a115"
/>
if add "skip_initialize" in metadata, model is registered but isn't
loaded
on client side, run: llama-stack-client --endpoint
http://devgpu018.nha2.facebook.com:5000 inference chat-completion
--message "hello, what model are you?"
Inference the model succesfully
<img width="1121" alt="Screenshot 2024-12-17 at 1 27 33 PM"
src="https://github.com/user-attachments/assets/8e708545-3fe7-4a73-8754-1470fa5f1e75"
/>
**test inference experience**
run: llama stack run llama_stack/templates/meta-reference-gpu/run.yaml
model is loaded since the model is in resouce list in run.yaml
<img width="1537" alt="Screenshot 2024-12-17 at 1 30 19 PM"
src="https://github.com/user-attachments/assets/5c8af817-66eb-43f8-bf4c-f5e24b0a12c6"
/>
on client side, run: llama-stack-client --endpoint
http://devgpu018.nha2.facebook.com:5000 inference chat-completion
--message "hello, what model are you?"
inference successfully
<img width="1123" alt="Screenshot 2024-12-17 at 1 31 08 PM"
src="https://github.com/user-attachments/assets/471809aa-c65e-46dc-a37e-7094fb857f97"
/>
## inference on a finetuned model
**register a finetuned model that finetuned by post training api
(torchtune)**
- the model is registered and loaded successfully
- the model is shown up in the model list
<img width="974" alt="Screenshot 2024-12-18 at 3 56 33 PM"
src="https://github.com/user-attachments/assets/2994b4f5-4fa9-40c6-acc6-4b971479f3e2"
/>
**run inference**
<img width="977" alt="Screenshot 2024-12-18 at 3 57 59 PM"
src="https://github.com/user-attachments/assets/d117abbc-b2a0-41d8-a028-1a13128787b2"
/>
# What does this PR do?
Serialize objects to built in types to avoid otel warnings
## Test Plan
╰─❯ llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
# What does this PR do?
Cerebras is rolling out support for llama 3.3 70b and deprecating llama
3.1 70b. This PR updates the documentation, config, and internal mapping
to reflect this change.
cc: @ashwinb @raghotham
## 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
```
# What does this PR do?
**Why**
- When AgentConfig has no `input_shields` / `output_shields` defined, we
still outputs a shield_call step with violation=None. This is impossible
to distinguish the case b/w (1) no violation from running shields v.s.
(2) no shields call
**What**
- We should not have a shield_call step when no `input_shields` /
`output_shields` are defined.
- Also removes a never reached try/catch code block in agent loop.
`run_multiple_shields` is never called in the try block (verified by
stacktrace print)
**Side Note**
- pre-commit fix
## Test Plan
Tested w/ DirectClient via:
https://gist.github.com/yanxi0830/b48f2a53b6f5391b9ff1e39992bc05b3
**No Shields**
<img width="858" alt="image"
src="https://github.com/user-attachments/assets/67319370-329f-4954-bd16-d21ce54c6ebf"
/>
**With Input + Output Shields**
<img width="854" alt="image"
src="https://github.com/user-attachments/assets/75ab1bee-3ba9-4549-ab51-23210be83da7"
/>
**Input Shields Only**
<img width="858" alt="image"
src="https://github.com/user-attachments/assets/1897206b-13dd-4ea5-92c2-b39bf68e9286"
/>
E2E pytest
```
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk/agents/test_agents.py
```
## 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.
We cannot use recursive types because not only does our OpenAPI
generator not like them, even if it did, it is not easy for all client
languages to automatically construct proper APIs (especially considering
garbage collection) around them. For now, we can return a `Dict[str,
SpanWithStatus]` instead of `SpanWithChildren` and rely on the client to
reconstruct the tree.
Also fixed a super subtle issue with the OpenAPI generation process
(monkey-patching of json_schema_type wasn't working because of import
reordering.)
# What does this PR do?
**Why**
- Clean up examples which we will not maintain; reduce the surface area
to the minimal showcases
**What**
- Delete `client.py` in /apis/*
- Move all scripts to unit tests
- SDK sync in the future will just require running pytests
**Side notes**
- `bwrap` not available on Mac so code_interpreter will not work
## Test Plan
```
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v ./tests/client-sdk
```
<img width="725" alt="image"
src="https://github.com/user-attachments/assets/36bfe537-628d-43c3-8479-dcfcfe2e4035"
/>
## 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.
### Context
In this PR, we
- Implement the post training job management and get training artifacts
apis
- get_training_jobs
- get_training_job_status
- get_training_job_artifacts
- get_training_job_logstream is deleted since the trace can be directly
accessed by UI with Jaeger
https://llama-stack.readthedocs.io/en/latest/building_applications/telemetry.html#jaeger-to-visualize-traces
- Refactor the post training and training types definition to make them
more intuitive.
- Rewrite the checkpointer to make it compatible with llama-stack file
system and can be recognized during inference
### Test
Unit test
`pytest llama_stack/providers/tests/post_training/test_post_training.py
-m "torchtune_post_training_huggingface_datasetio" -v -s --tb=short
--disable-warnings`
<img width="1506" alt="Screenshot 2024-12-10 at 4 06 17 PM"
src="https://github.com/user-attachments/assets/16225029-bdb7-48c4-9d13-e580cc769c0a">
e2e test with client side call
<img width="888" alt="Screenshot 2024-12-10 at 4 09 44 PM"
src="https://github.com/user-attachments/assets/de375e4c-ef67-4dcc-a045-4037d9489191">
# What does this PR do?
Adds the sentence transformer provider and the `all-MiniLM-L6-v2`
embedding model to the default models to register in the run.yaml for
all providers.
## Test Plan
llama stack build --template together --image-type conda
llama stack run
~/.llama/distributions/llamastack-together/together-run.yaml
### Context
This is the 1st of series PRs that integrate torchtune with llama-stack
as meta reference post-training implementation. For MVP, we will focus
on single device LoRA SFT.
Though this PR is still WIP, we want to get early feedback on the high
level design of this skeleton while still working on several details
### Scope
To limit the scope of this PR, we focus on the skeleton of the
implementation.
**What are included?**
- refine the post-training SFT apis
- skeleton of supervised_fine_tune implementation. We verified that we
can call the supervised_fine_tune API successfully from llama stack
client SDK (client side PR:
https://github.com/meta-llama/llama-stack-client-python/pull/51)
- a very basic single device LoRA training recipe based on torchtune
core components
- parity check with torchtune library and post training api unit test
**What are not includes?**
- implementation of other job management, get training artifacts apis
(separate PR)
- refactor the meta reference inference logic to support eval on
finetuned model (separate PR)
- several necessary functionality in the training recipe such as
logging, validation etc (separate PR)
- interop with telemetry for tracing and metrics logging, currently
temporarily log to local disk (separate PR)
### Testing
**e2e test**
Although we haven't added detailed testing and numerical parity check
with torchtune yet, we did a simple E2E test from client to server
1. setup server with` llama stack build --template
experimental-post-training --image-type conda` and `llama stack run
experimental-post-training `
2. On client, run `llama-stack-client --endpoint
http://devgpu018.nha2.facebook.com:5000 post_training
supervised_fine_tune`
3. Training finishes successfully. On server side, get the finetune
checkpoints under output dir. On client side, get the job uuid
server
<img width="1110" alt="Screenshot 2024-12-02 at 5 52 32 PM"
src="https://github.com/user-attachments/assets/b548eb90-7a9b-4edc-a858-ee237cc4361d">
client
<img width="807" alt="Screenshot 2024-12-02 at 5 52 37 PM"
src="https://github.com/user-attachments/assets/1138ffa8-4698-40fa-b190-3d7b99646838">
**parity check**
torchtune dataloader output and llama-stack post training dataloader
output are same
<img width="1116" alt="Screenshot 2024-12-04 at 8 18 46 PM"
src="https://github.com/user-attachments/assets/5e295cdc-4c24-4ea6-82c0-ca96ef1bd6ee">
torchtune LoRA SFT and llama-stack post training LoRA SFT on alpaca
dataset with llama3.2 3B instruct model are numerical match
<img width="860" alt="Screenshot 2024-12-04 at 8 17 01 PM"
src="https://github.com/user-attachments/assets/c05cf0a8-c674-4d2e-9f0a-c5d01b2dca99">
<img width="1049" alt="Screenshot 2024-12-04 at 8 17 06 PM"
src="https://github.com/user-attachments/assets/b911d4e2-e7b1-41a9-b62c-d75529b6d443">
**unit test **
![Uploading Screenshot 2024-12-09 at 1.35.10 PM.png…]()
# 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.
This PR does the following:
1) adds the ability to generate embeddings in all supported inference
providers.
2) Moves all the memory providers to use the inference API and improved
the memory tests to setup the inference stack correctly and use the
embedding models
This is a merge from #589 and #598
# What does this PR do?
**Why**
- huggingface datasets could have extra unused columns, some of these
columns (e.g. images) is unable to be casted as JSON over http requests
for datasetio.
- it is also inefficient to create a new dataset that's a subset of
columns
**Solution**
- drop columns not specified by dataset schema
## Test Plan
Tested with script:
https://gist.github.com/yanxi0830/23be5725e0d82d79e24cc5dd1d21b571
## 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.
