# What does this PR do?
- there's no value in keeping data schema validation logic in a
DataSchemaValidatorMixin
- move into data schema validation logic into standalone utils
## 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
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
```
## 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?
- Change to support llama3.1 8B instruct model other than llama3 8B
model as llama3.1 8B instruct model is a better model to finetune on top
of
- Make the copy files logic in checkpointer safer in case the file be
copied doesn't exist in source path
## test
issue a post training request from client and verify training works as
expect
<img width="1101" alt="Screenshot 2025-01-02 at 12 18 45 PM"
src="https://github.com/user-attachments/assets/47cc4df9-3edc-4afd-b5dd-abe1f039f1ed"
/>
<img width="782" alt="Screenshot 2025-01-02 at 12 18 52 PM"
src="https://github.com/user-attachments/assets/b9435274-ef1d-4570-bd8e-0880c3a4b2e9"
/>
## what does this PR do?
The current code hardcode the validation steps to run (forgot to change
it after testing). in this PR, we make it configurable by training
config
## test
On client side, issue a post training request with 20 validation steps,
server side logging shows that it runs 20 validation steps successfully
<img width="1128" alt="Screenshot 2025-01-02 at 8 21 06 PM"
src="https://github.com/user-attachments/assets/7a757516-c6ba-41d4-85c5-361a80ecf46e"
/>
# 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?
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?
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.)
### 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">
