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docs: advanced_apis migration (#3532)
# What does this PR do? <!-- Provide a short summary of what this PR does and why. Link to relevant issues if applicable. --> <!-- If resolving an issue, uncomment and update the line below --> <!-- Closes #[issue-number] --> - Migrates the `advanced_apis/` section of the docs to the new format ## Test Plan - Partial migration <!-- Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.* -->
This commit is contained in:
Alexey Rybak
GitHub
parent
d23865757f
commit
05ff4c4420
19 changed files with 661 additions and 742 deletions
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# Eval Providers
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This section contains documentation for all available providers for the **eval** API.
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- [inline::meta-reference](inline_meta-reference.md)
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- [remote::nvidia](remote_nvidia.md)
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@ -1,25 +0,0 @@
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---
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orphan: true
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---
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# inline::meta-reference
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## Description
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Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `kvstore` | `utils.kvstore.config.RedisKVStoreConfig \| utils.kvstore.config.SqliteKVStoreConfig \| utils.kvstore.config.PostgresKVStoreConfig \| utils.kvstore.config.MongoDBKVStoreConfig` | No | sqlite | |
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## Sample Configuration
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```yaml
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kvstore:
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type: sqlite
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db_path: ${env.SQLITE_STORE_DIR:=~/.llama/dummy}/meta_reference_eval.db
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```
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---
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orphan: true
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---
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# remote::nvidia
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## Description
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NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `evaluator_url` | `<class 'str'>` | No | http://0.0.0.0:7331 | The url for accessing the evaluator service |
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## Sample Configuration
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```yaml
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evaluator_url: ${env.NVIDIA_EVALUATOR_URL:=http://localhost:7331}
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```
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## Evaluation Concepts
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The Llama Stack Evaluation flow allows you to run evaluations on your GenAI application datasets or pre-registered benchmarks.
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We introduce a set of APIs in Llama Stack for supporting running evaluations of LLM applications.
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- `/datasetio` + `/datasets` API
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- `/scoring` + `/scoring_functions` API
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- `/eval` + `/benchmarks` API
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This guide goes over the sets of APIs and developer experience flow of using Llama Stack to run evaluations for different use cases. Checkout our Colab notebook on working examples with evaluations [here](https://colab.research.google.com/drive/10CHyykee9j2OigaIcRv47BKG9mrNm0tJ?usp=sharing).
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The Evaluation APIs are associated with a set of Resources. Please visit the Resources section in our [Core Concepts](../concepts/index.md) guide for better high-level understanding.
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- **DatasetIO**: defines interface with datasets and data loaders.
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- Associated with `Dataset` resource.
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- **Scoring**: evaluate outputs of the system.
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- Associated with `ScoringFunction` resource. We provide a suite of out-of-the box scoring functions and also the ability for you to add custom evaluators. These scoring functions are the core part of defining an evaluation task to output evaluation metrics.
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- **Eval**: generate outputs (via Inference or Agents) and perform scoring.
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- Associated with `Benchmark` resource.
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### Open-benchmark Eval
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#### List of open-benchmarks Llama Stack support
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Llama stack pre-registers several popular open-benchmarks to easily evaluate model perfomance via CLI.
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The list of open-benchmarks we currently support:
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- [MMLU-COT](https://arxiv.org/abs/2009.03300) (Measuring Massive Multitask Language Understanding): Benchmark designed to comprehensively evaluate the breadth and depth of a model's academic and professional understanding
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- [GPQA-COT](https://arxiv.org/abs/2311.12022) (A Graduate-Level Google-Proof Q&A Benchmark): A challenging benchmark of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry.
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- [SimpleQA](https://openai.com/index/introducing-simpleqa/): Benchmark designed to access models to answer short, fact-seeking questions.
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- [MMMU](https://arxiv.org/abs/2311.16502) (A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI)]: Benchmark designed to evaluate multimodal models.
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You can follow this [contributing guide](../references/evals_reference/index.md#open-benchmark-contributing-guide) to add more open-benchmarks to Llama Stack
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#### Run evaluation on open-benchmarks via CLI
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We have built-in functionality to run the supported open-benckmarks using llama-stack-client CLI
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#### Spin up Llama Stack server
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Spin up llama stack server with 'open-benchmark' template
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```
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llama stack run llama_stack/distributions/open-benchmark/run.yaml
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```
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#### Run eval CLI
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There are 3 necessary inputs to run a benchmark eval
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- `list of benchmark_ids`: The list of benchmark ids to run evaluation on
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- `model-id`: The model id to evaluate on
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- `output_dir`: Path to store the evaluate results
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```
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llama-stack-client eval run-benchmark <benchmark_id_1> <benchmark_id_2> ... \
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--model_id <model id to evaluate on> \
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--output_dir <directory to store the evaluate results> \
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```
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You can run
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```
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llama-stack-client eval run-benchmark help
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```
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to see the description of all the flags that eval run-benchmark has
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In the output log, you can find the file path that has your evaluation results. Open that file and you can see you aggregate
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evaluation results over there.
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#### What's Next?
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- Check out our Colab notebook on working examples with running benchmark evaluations [here](https://colab.research.google.com/github/meta-llama/llama-stack/blob/main/docs/notebooks/Llama_Stack_Benchmark_Evals.ipynb#scrollTo=mxLCsP4MvFqP).
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- Check out our [Building Applications - Evaluation](../building_applications/evals.md) guide for more details on how to use the Evaluation APIs to evaluate your applications.
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- Check out our [Evaluation Reference](../references/evals_reference/index.md) for more details on the APIs.
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# Advanced APIs
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## Post-training
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Fine-tunes a model.
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```{toctree}
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:maxdepth: 1
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post_training/index
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```
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## Eval
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Generates outputs (via Inference or Agents) and perform scoring.
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```{toctree}
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:maxdepth: 1
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eval/index
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```
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```{include} evaluation_concepts.md
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:start-after: ## Evaluation Concepts
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```
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## Scoring
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Evaluates the outputs of the system.
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```{toctree}
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:maxdepth: 1
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scoring/index
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```
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---
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orphan: true
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---
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# HuggingFace SFTTrainer
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[HuggingFace SFTTrainer](https://huggingface.co/docs/trl/en/sft_trainer) is an inline post training provider for Llama Stack. It allows you to run supervised fine tuning on a variety of models using many datasets
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## Features
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- Simple access through the post_training API
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- Fully integrated with Llama Stack
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- GPU support, CPU support, and MPS support (MacOS Metal Performance Shaders)
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## Usage
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To use the HF SFTTrainer in your Llama Stack project, follow these steps:
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1. Configure your Llama Stack project to use this provider.
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2. Kick off a SFT job using the Llama Stack post_training API.
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## Setup
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You can access the HuggingFace trainer via the `ollama` distribution:
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```bash
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llama stack build --distro starter --image-type venv
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llama stack run --image-type venv ~/.llama/distributions/ollama/ollama-run.yaml
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```
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## Run Training
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You can access the provider and the `supervised_fine_tune` method via the post_training API:
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```python
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import time
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import uuid
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from llama_stack_client.types import (
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post_training_supervised_fine_tune_params,
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algorithm_config_param,
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)
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def create_http_client():
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from llama_stack_client import LlamaStackClient
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return LlamaStackClient(base_url="http://localhost:8321")
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client = create_http_client()
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# Example Dataset
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client.datasets.register(
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purpose="post-training/messages",
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source={
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"type": "uri",
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"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
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},
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dataset_id="simpleqa",
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)
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training_config = post_training_supervised_fine_tune_params.TrainingConfig(
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data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
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batch_size=32,
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data_format="instruct",
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dataset_id="simpleqa",
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shuffle=True,
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),
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gradient_accumulation_steps=1,
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max_steps_per_epoch=0,
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max_validation_steps=1,
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n_epochs=4,
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)
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algorithm_config = algorithm_config_param.LoraFinetuningConfig( # this config is also currently mandatory but should not be
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alpha=1,
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apply_lora_to_mlp=True,
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apply_lora_to_output=False,
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lora_attn_modules=["q_proj"],
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rank=1,
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type="LoRA",
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)
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job_uuid = f"test-job{uuid.uuid4()}"
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# Example Model
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training_model = "ibm-granite/granite-3.3-8b-instruct"
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start_time = time.time()
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response = client.post_training.supervised_fine_tune(
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job_uuid=job_uuid,
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logger_config={},
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model=training_model,
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hyperparam_search_config={},
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training_config=training_config,
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algorithm_config=algorithm_config,
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checkpoint_dir="output",
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)
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print("Job: ", job_uuid)
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# Wait for the job to complete!
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while True:
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status = client.post_training.job.status(job_uuid=job_uuid)
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if not status:
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print("Job not found")
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break
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print(status)
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if status.status == "completed":
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break
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print("Waiting for job to complete...")
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time.sleep(5)
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end_time = time.time()
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print("Job completed in", end_time - start_time, "seconds!")
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print("Artifacts:")
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print(client.post_training.job.artifacts(job_uuid=job_uuid))
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```
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# Post_Training Providers
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This section contains documentation for all available providers for the **post_training** API.
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- [inline::huggingface](inline_huggingface.md)
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- [inline::torchtune](inline_torchtune.md)
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- [remote::nvidia](remote_nvidia.md)
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---
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orphan: true
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---
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# inline::huggingface
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## Description
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HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `device` | `<class 'str'>` | No | cuda | |
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| `distributed_backend` | `Literal['fsdp', 'deepspeed'` | No | | |
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| `checkpoint_format` | `Literal['full_state', 'huggingface'` | No | huggingface | |
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| `chat_template` | `<class 'str'>` | No | |
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| `model_specific_config` | `<class 'dict'>` | No | {'trust_remote_code': True, 'attn_implementation': 'sdpa'} | |
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| `max_seq_length` | `<class 'int'>` | No | 2048 | |
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| `gradient_checkpointing` | `<class 'bool'>` | No | False | |
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| `save_total_limit` | `<class 'int'>` | No | 3 | |
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| `logging_steps` | `<class 'int'>` | No | 10 | |
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| `warmup_ratio` | `<class 'float'>` | No | 0.1 | |
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| `weight_decay` | `<class 'float'>` | No | 0.01 | |
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| `dataloader_num_workers` | `<class 'int'>` | No | 4 | |
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| `dataloader_pin_memory` | `<class 'bool'>` | No | True | |
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## Sample Configuration
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```yaml
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checkpoint_format: huggingface
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distributed_backend: null
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device: cpu
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```
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[Find more detailed information here!](huggingface.md)
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@ -1,25 +0,0 @@
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---
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orphan: true
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---
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# inline::torchtune
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## Description
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TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.
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## Configuration
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `torch_seed` | `int \| None` | No | | |
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| `checkpoint_format` | `Literal['meta', 'huggingface'` | No | meta | |
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## Sample Configuration
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```yaml
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checkpoint_format: meta
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```
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[Find more detailed information here!](torchtune.md)
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|
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@ -1,163 +0,0 @@
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---
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orphan: true
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---
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# NVIDIA NEMO
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[NVIDIA NEMO](https://developer.nvidia.com/nemo-framework) is a remote post training provider for Llama Stack. It provides enterprise-grade fine-tuning capabilities through NVIDIA's NeMo Customizer service.
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## Features
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- Enterprise-grade fine-tuning capabilities
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- Support for LoRA and SFT fine-tuning
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- Integration with NVIDIA's NeMo Customizer service
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- Support for various NVIDIA-optimized models
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- Efficient training with NVIDIA hardware acceleration
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## Usage
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To use NVIDIA NEMO in your Llama Stack project, follow these steps:
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|
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1. Configure your Llama Stack project to use this provider.
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2. Set up your NVIDIA API credentials.
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3. Kick off a fine-tuning job using the Llama Stack post_training API.
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## Setup
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You'll need to set the following environment variables:
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```bash
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export NVIDIA_API_KEY="your-api-key"
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export NVIDIA_DATASET_NAMESPACE="default"
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export NVIDIA_CUSTOMIZER_URL="your-customizer-url"
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export NVIDIA_PROJECT_ID="your-project-id"
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export NVIDIA_OUTPUT_MODEL_DIR="your-output-model-dir"
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```
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## Run Training
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|
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You can access the provider and the `supervised_fine_tune` method via the post_training API:
|
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|
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```python
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import time
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import uuid
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|
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from llama_stack_client.types import (
|
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post_training_supervised_fine_tune_params,
|
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algorithm_config_param,
|
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)
|
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|
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|
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def create_http_client():
|
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from llama_stack_client import LlamaStackClient
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|
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return LlamaStackClient(base_url="http://localhost:8321")
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|
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client = create_http_client()
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|
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# Example Dataset
|
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client.datasets.register(
|
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purpose="post-training/messages",
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source={
|
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"type": "uri",
|
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"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
|
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},
|
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dataset_id="simpleqa",
|
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)
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training_config = post_training_supervised_fine_tune_params.TrainingConfig(
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data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
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batch_size=8, # Default batch size for NEMO
|
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data_format="instruct",
|
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dataset_id="simpleqa",
|
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shuffle=True,
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),
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n_epochs=50, # Default epochs for NEMO
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optimizer_config=post_training_supervised_fine_tune_params.TrainingConfigOptimizerConfig(
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lr=0.0001, # Default learning rate
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weight_decay=0.01, # NEMO-specific parameter
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),
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# NEMO-specific parameters
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log_every_n_steps=None,
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val_check_interval=0.25,
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sequence_packing_enabled=False,
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hidden_dropout=None,
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attention_dropout=None,
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ffn_dropout=None,
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)
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|
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algorithm_config = algorithm_config_param.LoraFinetuningConfig(
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alpha=16, # Default alpha for NEMO
|
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type="LoRA",
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)
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|
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job_uuid = f"test-job{uuid.uuid4()}"
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# Example Model - must be a supported NEMO model
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training_model = "meta/llama-3.1-8b-instruct"
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start_time = time.time()
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response = client.post_training.supervised_fine_tune(
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job_uuid=job_uuid,
|
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logger_config={},
|
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model=training_model,
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hyperparam_search_config={},
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training_config=training_config,
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algorithm_config=algorithm_config,
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checkpoint_dir="output",
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)
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print("Job: ", job_uuid)
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||||
# Wait for the job to complete!
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||||
while True:
|
||||
status = client.post_training.job.status(job_uuid=job_uuid)
|
||||
if not status:
|
||||
print("Job not found")
|
||||
break
|
||||
|
||||
print(status)
|
||||
if status.status == "completed":
|
||||
break
|
||||
|
||||
print("Waiting for job to complete...")
|
||||
time.sleep(5)
|
||||
|
||||
end_time = time.time()
|
||||
print("Job completed in", end_time - start_time, "seconds!")
|
||||
|
||||
print("Artifacts:")
|
||||
print(client.post_training.job.artifacts(job_uuid=job_uuid))
|
||||
```
|
||||
|
||||
## Supported Models
|
||||
|
||||
Currently supports the following models:
|
||||
- meta/llama-3.1-8b-instruct
|
||||
- meta/llama-3.2-1b-instruct
|
||||
|
||||
## Supported Parameters
|
||||
|
||||
### TrainingConfig
|
||||
- n_epochs (default: 50)
|
||||
- data_config
|
||||
- optimizer_config
|
||||
- log_every_n_steps
|
||||
- val_check_interval (default: 0.25)
|
||||
- sequence_packing_enabled (default: False)
|
||||
- hidden_dropout (0.0-1.0)
|
||||
- attention_dropout (0.0-1.0)
|
||||
- ffn_dropout (0.0-1.0)
|
||||
|
||||
### DataConfig
|
||||
- dataset_id
|
||||
- batch_size (default: 8)
|
||||
|
||||
### OptimizerConfig
|
||||
- lr (default: 0.0001)
|
||||
- weight_decay (default: 0.01)
|
||||
|
||||
### LoRA Config
|
||||
- alpha (default: 16)
|
||||
- type (must be "LoRA")
|
||||
|
||||
Note: Some parameters from the standard Llama Stack API are not supported and will be ignored with a warning.
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
|
||||
# remote::nvidia
|
||||
|
||||
## Description
|
||||
|
||||
NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.
|
||||
|
||||
## Configuration
|
||||
|
||||
| Field | Type | Required | Default | Description |
|
||||
|-------|------|----------|---------|-------------|
|
||||
| `api_key` | `str \| None` | No | | The NVIDIA API key. |
|
||||
| `dataset_namespace` | `str \| None` | No | default | The NVIDIA dataset namespace. |
|
||||
| `project_id` | `str \| None` | No | test-example-model@v1 | The NVIDIA project ID. |
|
||||
| `customizer_url` | `str \| None` | No | | Base URL for the NeMo Customizer API |
|
||||
| `timeout` | `<class 'int'>` | No | 300 | Timeout for the NVIDIA Post Training API |
|
||||
| `max_retries` | `<class 'int'>` | No | 3 | Maximum number of retries for the NVIDIA Post Training API |
|
||||
| `output_model_dir` | `<class 'str'>` | No | test-example-model@v1 | Directory to save the output model |
|
||||
|
||||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
api_key: ${env.NVIDIA_API_KEY:=}
|
||||
dataset_namespace: ${env.NVIDIA_DATASET_NAMESPACE:=default}
|
||||
project_id: ${env.NVIDIA_PROJECT_ID:=test-project}
|
||||
customizer_url: ${env.NVIDIA_CUSTOMIZER_URL:=http://nemo.test}
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -1,125 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
# TorchTune
|
||||
|
||||
[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch.
|
||||
|
||||
## Features
|
||||
|
||||
- Simple access through the post_training API
|
||||
- Fully integrated with Llama Stack
|
||||
- GPU support and single device capabilities.
|
||||
- Support for LoRA
|
||||
|
||||
## Usage
|
||||
|
||||
To use TorchTune in your Llama Stack project, follow these steps:
|
||||
|
||||
1. Configure your Llama Stack project to use this provider.
|
||||
2. Kick off a fine-tuning job using the Llama Stack post_training API.
|
||||
|
||||
## Setup
|
||||
|
||||
You can access the TorchTune trainer by writing your own yaml pointing to the provider:
|
||||
|
||||
```yaml
|
||||
post_training:
|
||||
- provider_id: torchtune
|
||||
provider_type: inline::torchtune
|
||||
config: {}
|
||||
```
|
||||
|
||||
you can then build and run your own stack with this provider.
|
||||
|
||||
## Run Training
|
||||
|
||||
You can access the provider and the `supervised_fine_tune` method via the post_training API:
|
||||
|
||||
```python
|
||||
import time
|
||||
import uuid
|
||||
|
||||
from llama_stack_client.types import (
|
||||
post_training_supervised_fine_tune_params,
|
||||
algorithm_config_param,
|
||||
)
|
||||
|
||||
|
||||
def create_http_client():
|
||||
from llama_stack_client import LlamaStackClient
|
||||
|
||||
return LlamaStackClient(base_url="http://localhost:8321")
|
||||
|
||||
|
||||
client = create_http_client()
|
||||
|
||||
# Example Dataset
|
||||
client.datasets.register(
|
||||
purpose="post-training/messages",
|
||||
source={
|
||||
"type": "uri",
|
||||
"uri": "huggingface://datasets/llamastack/simpleqa?split=train",
|
||||
},
|
||||
dataset_id="simpleqa",
|
||||
)
|
||||
|
||||
training_config = post_training_supervised_fine_tune_params.TrainingConfig(
|
||||
data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
|
||||
batch_size=32,
|
||||
data_format="instruct",
|
||||
dataset_id="simpleqa",
|
||||
shuffle=True,
|
||||
),
|
||||
gradient_accumulation_steps=1,
|
||||
max_steps_per_epoch=0,
|
||||
max_validation_steps=1,
|
||||
n_epochs=4,
|
||||
)
|
||||
|
||||
algorithm_config = algorithm_config_param.LoraFinetuningConfig(
|
||||
alpha=1,
|
||||
apply_lora_to_mlp=True,
|
||||
apply_lora_to_output=False,
|
||||
lora_attn_modules=["q_proj"],
|
||||
rank=1,
|
||||
type="LoRA",
|
||||
)
|
||||
|
||||
job_uuid = f"test-job{uuid.uuid4()}"
|
||||
|
||||
# Example Model
|
||||
training_model = "meta-llama/Llama-2-7b-hf"
|
||||
|
||||
start_time = time.time()
|
||||
response = client.post_training.supervised_fine_tune(
|
||||
job_uuid=job_uuid,
|
||||
logger_config={},
|
||||
model=training_model,
|
||||
hyperparam_search_config={},
|
||||
training_config=training_config,
|
||||
algorithm_config=algorithm_config,
|
||||
checkpoint_dir="output",
|
||||
)
|
||||
print("Job: ", job_uuid)
|
||||
|
||||
# Wait for the job to complete!
|
||||
while True:
|
||||
status = client.post_training.job.status(job_uuid=job_uuid)
|
||||
if not status:
|
||||
print("Job not found")
|
||||
break
|
||||
|
||||
print(status)
|
||||
if status.status == "completed":
|
||||
break
|
||||
|
||||
print("Waiting for job to complete...")
|
||||
time.sleep(5)
|
||||
|
||||
end_time = time.time()
|
||||
print("Job completed in", end_time - start_time, "seconds!")
|
||||
|
||||
print("Artifacts:")
|
||||
print(client.post_training.job.artifacts(job_uuid=job_uuid))
|
||||
```
|
||||
|
|
@ -1,7 +0,0 @@
|
|||
# Scoring Providers
|
||||
|
||||
This section contains documentation for all available providers for the **scoring** API.
|
||||
|
||||
- [inline::basic](inline_basic.md)
|
||||
- [inline::braintrust](inline_braintrust.md)
|
||||
- [inline::llm-as-judge](inline_llm-as-judge.md)
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
|
||||
# inline::basic
|
||||
|
||||
## Description
|
||||
|
||||
Basic scoring provider for simple evaluation metrics and scoring functions.
|
||||
|
||||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
{}
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
|
||||
# inline::braintrust
|
||||
|
||||
## Description
|
||||
|
||||
Braintrust scoring provider for evaluation and scoring using the Braintrust platform.
|
||||
|
||||
## Configuration
|
||||
|
||||
| Field | Type | Required | Default | Description |
|
||||
|-------|------|----------|---------|-------------|
|
||||
| `openai_api_key` | `str \| None` | No | | The OpenAI API Key |
|
||||
|
||||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
openai_api_key: ${env.OPENAI_API_KEY:=}
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -1,17 +0,0 @@
|
|||
---
|
||||
orphan: true
|
||||
---
|
||||
|
||||
# inline::llm-as-judge
|
||||
|
||||
## Description
|
||||
|
||||
LLM-as-judge scoring provider that uses language models to evaluate and score responses.
|
||||
|
||||
## Sample Configuration
|
||||
|
||||
```yaml
|
||||
{}
|
||||
|
||||
```
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue