[Model] add minicpm #3893
[Model] add minicpm #3893
Conversation
| rope_scaling=rope_scaling, | ||
| ) | ||
| # set rope as fp32 instead of bf16 | ||
| self.rotary_emb.cos_sin_cache = self.rotary_emb._compute_cos_sin_cache( |
There was a problem hiding this comment.
Could you elaborate more why fp32 cache is needed?
There was a problem hiding this comment.
We observe slight loss of benchmark accuracy when using bf16 cache. We compare forward precision and find a mismatch here with our training code.
| ]) | ||
| self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) | ||
|
|
||
| def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: |
There was a problem hiding this comment.
Will support vision model here? Looking forward to that. :)
There was a problem hiding this comment.
We are actively working on it and will create a pr about minicpm-v soon.
There was a problem hiding this comment.
Will support vision model here? Looking forward to that. :)
We've add vision model PR to vllm #4087.
There was a problem hiding this comment.
Will support vision model here? Looking forward to that. :)
We've add vision model PR to vllm #4087.
We've add vision model PR to vllm in #4087, would you review it please? @esmeetu @youkaichao @ywang96
I have added them in the latest commit, please review. |
SUDA-HLT-ywfang
force-pushed
the
add_minicpm
branch
from
05c6172 to
5f5c3a0
Compare
add minicpm and the moe variation of minicpm.
BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]for bug fixes.[CI/Build]for build or continuous integration improvements.[Doc]for documentation fixes and improvements.[Model]for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]For changes on the vLLM frontend (e.g., OpenAI API server,LLMclass, etc.)[Kernel]for changes affecting CUDA kernels or other compute kernels.[Core]for changes in the core vLLM logic (e.g.,LLMEngine,AsyncLLMEngine,Scheduler, etc.)[Hardware][Vendor]for hardware-specific changes. Vendor name should appear in the prefix (e.g.,[Hardware][AMD]).[Misc]for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:
format.shto format your code.docs/source/if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-requiredand might not go through the PR.What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
action-requiredlabel on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!