forked from phoenix-oss/llama-stack-mirror
4 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Sixian Yi
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c79b087552
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[test automation] support run tests on config file (#730)
# Context For test automation, the end goal is to run a single pytest command from root test directory (llama_stack/providers/tests/.) such that we execute push-blocking tests The work plan: 1) trigger pytest from llama_stack/providers/tests/. 2) use config file to determine what tests and parametrization we want to run # What does this PR do? 1) consolidates the "inference-models" / "embedding-model" / "judge-model" ... options in root conftest.py. Without this change, we will hit into error when trying to run `pytest /Users/sxyi/llama-stack/llama_stack/providers/tests/.` because of duplicated `addoptions` definitions across child conftest files. 2) Add a `config` option to specify test config in YAML. (see [`ci_test_config.yaml`](https://gist.github.com/sixianyi0721/5b37fbce4069139445c2f06f6e42f87e) for example config file) For provider_fixtures, we allow users to use either a default fixture combination or define their own {api:provider} combinations. ``` memory: .... fixtures: provider_fixtures: - default_fixture_param_id: ollama // use default fixture combination with param_id="ollama" in [providers/tests/memory/conftest.py](https://fburl.com/mtjzwsmk) - inference: sentence_transformers memory: faiss - default_fixture_param_id: chroma ``` 3) generate tests according to the config. Logic lives in two places: a) in `{api}/conftest.py::pytest_generate_tests`, we read from config to do parametrization. b) after test collection, in `pytest_collection_modifyitems`, we filter the tests to include only functions listed in config. ## Test Plan 1) `pytest /Users/sxyi/llama-stack/llama_stack/providers/tests/. --collect-only --config=ci_test_config.yaml` Using `--collect-only` tag to print the pytests listed in the config file (`ci_test_config.yaml`). output: [gist](https://gist.github.com/sixianyi0721/05145e60d4d085c17cfb304beeb1e60e) 2) sanity check on `--inference-model` option ``` pytest -v -s -k "ollama" --inference-model="meta-llama/Llama-3.1-8B-Instruct" ./llama_stack/providers/tests/inference/test_text_inference.py ``` ## 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. |
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Xi Yan
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3c72c034e6
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[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. |
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Botao Chen
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c294a01c4b
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[2/n][torchtune integration] implement job management and return training artifacts (#593)
### 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"> |
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Botao Chen
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aeb76390fc
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[1/n] torchtune <> llama-stack integration skeleton (#540)
### 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…]() |