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[recipe] feat: DeepSeek-R1-Zero on Ascend NPU - #3427
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This pull request introduces a recipe for training DeepSeek-R1-Zero on Ascend NPUs. The changes are comprehensive, including a new Dockerfile, Python scripts with NPU-specific adaptations, and shell scripts for execution. However, I've identified several critical issues that will likely cause build or runtime failures. These include placeholders and typos in the Dockerfile, NameError exceptions in Python scripts due to undefined variables or missing imports, and a logic error in a model conversion script. Additionally, there are high-severity issues such as inconsistencies between the documentation and the implementation, and a confusing exit code in a shell script. Addressing these issues is crucial for the recipe to be functional and usable.
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Hi @FightingZhen @ji-huazhong , could you please help review this PR when you have a moment? |
| if self.inference_engine.llm_engine.model_executor.driver_worker.worker.cache_engine is None: | ||
| self.inference_engine.llm_engine.model_executor.driver_worker.worker._init_cache_engine() | ||
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| def onload_model_weights(self): |
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Please add a comment explaining why we need to manually on/off load the weights instead of directly using VLLM's sleep mode, to help other users understand this change.
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Thanks for the suggestion. I will add a comment for it.
| attn_impl.value_cache = None | ||
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| gc.collect() | ||
| torch.npu.empty_cache() |
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please use aggressive_empty_cache instead.
| @@ -0,0 +1,69 @@ | |||
| # Adapted from | |||
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Suggest moving it below copyright with blank line split, same advice in following files :)
### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.
This recipe is a training example based on the Deepseek-V3-Base model
after RLHF (Reinforcement Learning from Human Feedback) training on
Ascend A3 NPU, utilizing GRPO and rule-based rewards with the deepscaler
dataset.
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Co-authored-by: ZhangHZ <zhuoer33@gmail.com>
### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.
This recipe is a training example based on the Deepseek-V3-Base model
after RLHF (Reinforcement Learning from Human Feedback) training on
Ascend A3 NPU, utilizing GRPO and rule-based rewards with the deepscaler
dataset.
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---------
Co-authored-by: ZhangHZ <zhuoer33@gmail.com>
### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.
This recipe is a training example based on the Deepseek-V3-Base model
after RLHF (Reinforcement Learning from Human Feedback) training on
Ascend A3 NPU, utilizing GRPO and rule-based rewards with the deepscaler
dataset.
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---------
Co-authored-by: ZhangHZ <zhuoer33@gmail.com>
### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.
This recipe is a training example based on the Deepseek-V3-Base model
after RLHF (Reinforcement Learning from Human Feedback) training on
Ascend A3 NPU, utilizing GRPO and rule-based rewards with the deepscaler
dataset.
### Checklist Before Starting
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---------
Co-authored-by: ZhangHZ <zhuoer33@gmail.com>
### What does this PR do?
> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.
This recipe is a training example based on the Deepseek-V3-Base model
after RLHF (Reinforcement Learning from Human Feedback) training on
Ascend A3 NPU, utilizing GRPO and rule-based rewards with the deepscaler
dataset.
### Checklist Before Starting
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# Add code snippet or script demonstrating how to use this
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Co-authored-by: ZhangHZ <zhuoer33@gmail.com>
What does this PR do?
Checklist Before Starting
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API and Usage Example
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