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Commits
| Author | SHA1 | Message | Date | |
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 h-guo18
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a34d613d3c |
Feat: Speculatice Decoding export with quantization support (#913)
## What does this PR do? **Type of change:** ? <!-- Use one of the following: Bug fix, new feature, new example, new tests, documentation. --> **Overview:** Main changes: - Refactored speculative decoding export logics into `class EagleExporter` to improve cohesion; - Separated speculative decoding export entrance with quantization export (`export_hf_checkpoint()`) due to their fundamental differences: - Quantization export base model's state_dict and config, while speculative decoding only export drafter's. - Most of the model-specific logics of quantization export (e.g. diffusers, vlms) are not needed for speculative decoding export. - Quantization export produce different format than speculative decoding checkpoint. (The former produce tokenizer config, generation config, e.t.c, while the later does not need. ) ## Usage <!-- You can potentially add a usage example below. --> To export an regular bf16 eagle checkpoint without quantization, the commands are the same: ```python python scripts/export_hf_checkpoint.py --model_path <x> --export_path <x> ``` To run PTQ on online-trained eagle checkpoint and export it: ```python python hf_ptq.py --pyt_ckpt_path <x> --qformat fp8 --export_path <x> ``` The above two commands will produce drafter ckpt for deployment, in the same foramt. ## Testing <!-- Mention how have you tested your change if applicable. --> Tested setting: - Base model: llama3.1-8b - Algorithms: eagle - Export path tested: - (Unquantized online ckpt) `python scripts/export_hf_checkpoint.py --model_path <x> --export_path <x>` - (PTQ) export `python hf_ptq.py --pyt_ckpt_path <x> --qformat fp8 --export_path <x>` - Tested deployment on vllm. Got normal AR. ## 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 export functionality for speculative decoding-optimized models * Support for multiple speculative decoding architectures with pre-configured deployment templates * Enhanced model export detection and automatic routing for optimized models * **Tests** * Updated export validation tests for speculative decoding models <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: h-guo18 <67671475+h-guo18@users.noreply.github.com> |
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h-guo18
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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> |
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h-guo18
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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> |