# What does this PR do?
You are now able to run a training cycle on CPU. This is useful for
debugging and testing purposes.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
On a Mac machine without CUDA devices:
```
17:00:24.417 [START] /v1/post-training/supervised-fine-tune
DEBUG 2025-02-18 12:00:24,419 torchtune.utils._logging:60: Setting manual seed to local seed 3268931494. Local seed is seed + rank = 3268931494 + 0
INFO 2025-02-18 12:00:24,463 torchtune.utils._logging:64: Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights.
INFO 2025-02-18 12:00:46,699 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:182: Model is initialized with precision torch.bfloat16.
INFO 2025-02-18 12:00:46,784 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:185: Tokenizer is initialized.
INFO 2025-02-18 12:00:46,786 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:188: Optimizer is initialized.
INFO 2025-02-18 12:00:46,786 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:192: Loss is initialized.
INFO 2025-02-18 12:00:48,997 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:209: Dataset and Sampler are initialized.
INFO 2025-02-18 12:00:48,998 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:227: Learning rate scheduler is initialized.
Writing logs to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/log_1739898049.txt
1|1|Loss: 1.7414989471435547: 100% 1/1 [03:46<00:00, 226.21s/it]INFO 2025-02-18 12:04:35,227 llama_stack.providers.inline.post_training.torchtune.recipes.lora_finetuning_single_device:528: Starting checkpoint save...
INFO 2025-02-18 12:04:49,974 torchtune.utils._logging:121: Model checkpoint of size 6.43 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/consolidated.00.pth
INFO 2025-02-18 12:04:49,981 torchtune.utils._logging:132: Adapter checkpoint of size 0.00 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter.pth
model_file_path /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0
1|1|Loss: 1.7414989471435547: 100% 1/1 [04:01<00:00, 241.18s/it]
INFO: ::1:64990 - "POST /v1/post-training/supervised-fine-tune HTTP/1.1" 200 OK
17:04:50.364 [END] /v1/post-training/supervised-fine-tune [StatusCode.OK] (265947.01ms)
17:00:24.419 [DEBUG] Setting manual seed to local seed 3268931494. Local seed is seed + rank = 3268931494 + 0
17:00:24.463 [INFO] Identified model_type = Llama3_2. Ignoring output.weight in checkpoint in favor of the tok_embedding.weight tied weights.
17:00:46.700 [INFO] Model is initialized with precision torch.bfloat16.
17:00:46.784 [INFO] Tokenizer is initialized.
17:00:46.786 [INFO] Optimizer is initialized.
17:00:46.786 [INFO] Loss is initialized.
17:00:48.997 [INFO] Dataset and Sampler are initialized.
17:00:48.998 [INFO] Learning rate scheduler is initialized.
17:04:35.227 [INFO] Starting checkpoint save...
17:04:49.974 [INFO] Model checkpoint of size 6.43 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/consolidated.00.pth
17:04:49.981 [INFO] Adapter checkpoint of size 0.00 GB saved to /Users/ihrachys/.llama/checkpoints/meta-llama/Llama-3.2-3B-Instruct-sft-0/adapter/adapter.pth
```
[//]: # (## Documentation)
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
- **refactor: simplify job status extraction a bit**
- **torchtune: save job status on schedule**
- **refactor: get rid of job_list in torchtune job management code**
# What does this PR do?
A failed job is now registered in API, and one can consult its status.
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
```
$ llama-stack-client post_training status --job-uuid test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73
JobStatusResponse(checkpoints=[], job_uuid='test-jobe244b5b0-5053-4892-a4d9-d8fc8b116e73', status='failed', completed_at=None, resources_allocated=None, scheduled_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 3252), started_at=datetime.datetime(2025, 2, 18, 9, 4, 34, 10688))
```
[//]: # (## Documentation)
---------
Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
llama-models should have extremely minimal cruft. Its sole purpose
should be didactic -- show the simplest implementation of the llama
models and document the prompt formats, etc.
This PR is the complement to
https://github.com/meta-llama/llama-models/pull/279
## Test Plan
Ensure all `llama` CLI `model` sub-commands work:
```bash
llama model list
llama model download --model-id ...
llama model prompt-format -m ...
```
Ran tests:
```bash
cd tests/client-sdk
LLAMA_STACK_CONFIG=fireworks pytest -s -v inference/
LLAMA_STACK_CONFIG=fireworks pytest -s -v vector_io/
LLAMA_STACK_CONFIG=fireworks pytest -s -v agents/
```
Create a fresh venv `uv venv && source .venv/bin/activate` and run
`llama stack build --template fireworks --image-type venv` followed by
`llama stack run together --image-type venv` <-- the server runs
Also checked that the OpenAPI generator can run and there is no change
in the generated files as a result.
```bash
cd docs/openapi_generator
sh run_openapi_generator.sh
```
# What does this PR do?
- Configured ruff linter to automatically fix import sorting issues.
- Set --exit-non-zero-on-fix to ensure non-zero exit code when fixes are
applied.
- Enabled the 'I' selection to focus on import-related linting rules.
- Ran the linter, and formatted all codebase imports accordingly.
- Removed the black dep from the "dev" group since we use ruff
Signed-off-by: Sébastien Han <seb@redhat.com>
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
[//]: # (- [ ] Added a Changelog entry if the change is significant)
Signed-off-by: Sébastien Han <seb@redhat.com>
Lint check in main branch is failing. This fixes the lint check after we
moved to ruff in https://github.com/meta-llama/llama-stack/pull/921. We
need to move to a `ruff.toml` file as well as fixing and ignoring some
additional checks.
Signed-off-by: Yuan Tang <terrytangyuan@gmail.com>
# What does this PR do?
This PR changes our API to follow more idiomatic REST API approaches of
having paths being resources and methods indicating the action being
performed.
Changes made to generator:
1) removed the prefix check of "get" as its not required and is actually
needed for other method types too
2) removed _ check on path since variables can have "_"
## Test Plan
LLAMA_STACK_BASE_URL=http://localhost:5000 pytest -v
tests/client-sdk/agents/test_agents.py
## context
Currently, the GPU memory will be continuously occupied after the
training finishes. In this PR, we explicitly delete the reference and
clean up the memory after training finishes.
## test
Before the change, after training a llama 3.2 3B model, >6GB GPU memory
is still occupied
After the change, after training a llama 3.2 3B model, the GPU memory
drops to ~1GB
<img width="156" alt="Screenshot 2025-01-14 at 6 05 17 PM"
src="https://github.com/user-attachments/assets/45d212b1-a651-49f3-aad9-1c0a27fcebcf"
/>
## context
In this PR, we defined 2 llama stack dataset formats (instruct, dialog)
- For instruct dataset format, the column schema will be
[chat_completion_input, expected_answer], which is consistent with the
eval data format. This dataset format is the abstract of single turn QA
style post training data
- For dialog dataset format, the column schema will be [dialog], which
is a list of user messages and assistant messages that interleave
together. During training, the whole list will be the model input and
the loss is calculated on assistant messages only. This dataset format
is the abstract of multi turn chat style post training data
## changes
- defined the 2 llama stack dataset formats
- an adapter to convert llama stack dataset format to torchtune dataset
format
- move dataset format validation to post training level instead of
torchtune level since it's not specific to torchtune
- add localfs as datasetio provider
## test
instruct format
- use https://huggingface.co/datasets/llamastack/evals as dataset and
the training works as expected
<img width="1443" alt="Screenshot 2025-01-09 at 5 15 14 PM"
src="https://github.com/user-attachments/assets/2c37a936-c67a-4726-90e0-23fa0ba7000f"
/>
- use my generated local dataset and the training works as expected
<img width="1617" alt="Screenshot 2025-01-09 at 5 19 11 PM"
src="https://github.com/user-attachments/assets/0bdccbbf-bac2-472a-a365-15213e49bbfa"
/>
dialog format
- use my generated local dataset and the training works as expected
<img width="1588" alt="Screenshot 2025-01-09 at 5 23 16 PM"
src="https://github.com/user-attachments/assets/893915ba-41a3-4d51-948b-e872060ecede"
/>
## 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"
/>
### 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">
### 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…]()
