[Feat] Heterogeneous Code Part 1: Add Model and Module Code for Chameleon Lumina #377
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| hidden_states = self.norm(hidden_states) | ||
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| if hasattr(self, "output"): | ||
| hidden_states = self.output(hidden_states).float() |
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这里应该不需要把输出额外转fp32类型，我们NaiveAMPModel里有一个参数output_to_fp32来负责做这件事
| k_norm_out = self.k_norm(k_all) | ||
| k = split_forward_gather_backward(k_norm_out, ParallelMode.TENSOR, dim=-2) | ||
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| v = rearrange(v, "b s (h d) -> b s h d", d=self.head_dim) |
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qk_norm这块可能要额外区分一下，如果is_using_isp()wp并行算法的话，就不需要走gather的逻辑了，因为isp算法forward时权重是完整的，算出来的qkv head也是完整的，不需要gather
| k_norm_out = self.k_norm(k_all) | ||
| k = split_forward_gather_backward(k_norm_out, ParallelMode.TENSOR, dim=-2) | ||
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| v = rearrange(v, "b s (h d) -> b s h d", d=self.head_dim) |
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Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation
This PR and next PR series will be the code adding heterogeneous support and Chameleon model for InternEvo.
We plan to merge those PRs:
This PR is the first one: adding Chameleon Model code.
Modification
As described above, this PR is the first of a series of PRs: Adding Chameleon model code.
BC-breaking (Optional)
None
Use cases (Optional)
We will add use case after the configuration PR so that we can show the training use case.
Checklist
Before PR:
After PR: