Commit Graph
4 Commits
Author SHA1 Message Date
h-guo18 b8a4586702 Refactor: Eagle data loading (#668)
## What does this PR do?

**Type of change:** Refactor <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** 
Jira ticket: https://jirasw.nvidia.com/browse/OMNIML-2955

Main changes :

- Consolidate Eagle data loading with @ChenhanYu 's implementation of
`transformers_dataset.py`

- Refactor: baked the following logics from `example/main.py` to
`modelopt/torch` for cleaner example entrance:
  - default config selecting and merging with custom config
  - tokenizer post-processor (chat template and pad_tok_id)
  - d2t loading 
- Implementation refactor: In HF workflow, reuse base modfel's input
hidden states as input_embedding, instead of calculating from input_ids.
This has two main benefits:
    - Easier VLM support, which has various embedding processing logics.
    - Training effieicy.
    
- Deprecating eagle1 from the example. It is still available by setting
custom config.
  
 - Other minor fixes and readme updates.

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

Tested that training curves after changes (both online&offline) is
identical with original branch:
<img width="1073" height="634" alt="image"
src="https://github.com/user-attachments/assets/abfd7bea-c82c-48a7-8181-68c5a9e4da8d"
/>


## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

## Release Notes

* **New Features**
* Added draft vocabulary cache support for EAGLE model training,
enabling runtime vocabulary customization via `--draft_vocab_cache`
parameter
* Introduced new data loading utilities with sharding, streaming, and
tokenization support for large-scale training
  * Added optional `--log_steps` configuration to training launcher

* **Documentation**
* Updated EAGLE configuration guides with draft vocabulary cache setup
instructions and examples

* **Refactor**
* Restructured data pipeline for offline training with improved dataset
handling and batching
* Updated command-line arguments across training scripts (`--input-data`
replaces `--input-file`)

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
2026-02-18 14:04:06 -08:00
h-guo18 3fd8b804f4 Fix:add vllm dump script (#710)
## What does this PR do?

**Type of change:** ? <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** ?
Missing one file in last
PR:https://github.com/NVIDIA/Model-Optimizer/pull/689/changes
## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->

Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
2025-12-19 12:30:04 -08:00
Ben Shoham Ofir 2a51bbd7cb Optimize calibrate_draft_vocab to read only required lines when calib… (#618)
Optimize calibrate_draft_vocab to read only required lines when
calibrate_size is set

## What does this PR do?

**Type of change:** Performance improvement

**Overview:** 
This PR optimizes the
[calibrate_draft_vocab.py](cci:7://file:///Users/obenshoham/PycharmProjects/TensorRT-Model-Optimizer/examples/speculative_decoding/scripts/calibrate_draft_vocab.py:0:0-0:0)
script to improve memory efficiency and I/O performance when using the
`--calibrate_size` parameter. Previously, the script would read all
lines from the data file into memory before slicing to the specified
`calibrate_size`, causing unnecessary resource usage for large datasets.
The optimization uses `itertools.islice` to read only the required
number of lines when `calibrate_size` is specified.

## Usage

The script usage remains unchanged. When using `--calibrate_size`, the
script now only reads the specified number of lines instead of loading
the entire dataset:

```bash
# Only reads first 1000 lines from the dataset (optimized)
python scripts/calibrate_draft_vocab.py \
    --model meta-llama/Llama-3.2-1B-Instruct \
    --data input_conversations/daring-anteater.jsonl \
    --draft_vocab_size 32000 \
    --calibrate_size 1000 \
    --save_dir draft_vocab_cache

Signed-off-by: Ofir Ben Shoham <ofir_benshoham@intuit.com>
2025-12-19 17:47:08 +00:00
h-guo18 bc52b6cf12 Feat: Support VLLM one-model eagle ckpt; Add unit tests; (#573)
## What does this PR do?

**Type of change:** New feature, new tests; <!-- Use one of the
following: Bug fix, new feature, new example, new tests, documentation.
-->

**Overview:** 
- Add conversion scripts for eagle3 llm-compressor style checkpoint
  - Jira Ticket: https://jirasw.nvidia.com/browse/OMNIML-2866

- Add unit tests for `ar_validate.py`, `export_hf_checkpoint.py`, and
`convert_to_vllm_ckpt.py`.

## Usage
<!-- You can potentially add a usage example below. -->

```python
python scripts/convert_to_vllm_ckpt.py --input <eagle3 ckpt> --verifier <base model> --output <path>
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes/No <!--- If No, explain
why. -->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
Yes/No <!--- Only for new features, API changes, critical bug fixes or
bw breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->

---------

Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com>
2025-11-19 14:26:39 -08:00