Enable RMSNorm substitution for Transformers backend
#26353
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This change should enable quant fusions which depend on the `RMSNorm` op being present Signed-off-by: Harry Mellor <[email protected]>
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I've reviewed your changes to enable RMSNorm substitution. The approach to differentiate between RMSNorm and GemmaRMSNorm is clever, but I've identified a critical issue with handling weightless norms and a potential robustness improvement. Please see my detailed comments below.
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Signed-off-by: Harry Mellor <[email protected]>
Signed-off-by: Harry Mellor <[email protected]>
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| if weight_test is not None and torch.all(weight_test == 0): | ||
| return GemmaRMSNorm(**kwargs) |
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Can we simply check the existence of _norm function for GemmaRMSNorm?
https://github.com/huggingface/transformers/blob/50090c3fc82e1e0a06b4da366ea2fb6055d529e9/src/transformers/models/gemma3n/modeling_gemma3n.py#L123-L124
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That would work for Gemma models as they are currently implemented in Transformers. However:
- Custom models may not use this pattern
_normis a private method and so may change under us- A counter-example would be Moshi, which implements
_normbut doesx * winstead ofx * (1 + w)
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Signed-off-by: Harry Mellor <[email protected]>
Signed-off-by: Harry Mellor <[email protected]>
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…to loader * 'loader' of https://github.com/dsxsteven/vllm_splitPR: (778 commits) [torchao] Add support for ModuleFqnToConfig using regex (vllm-project#26001) Add: Support for multiple hidden layers in Eagle3 (vllm-project#26164) Enable `RMSNorm` substitution for Transformers backend (vllm-project#26353) [Model] Gemma3: Fix GGUF loading and quantization (vllm-project#26189) Bump Flashinfer to v0.4.0 (vllm-project#26326) Update Dockerfile and install runai-model-streamer[gcs] package (vllm-project#26464) [Core] Relax the LoRA max rank (vllm-project#26461) [CI/Build] Fix model nightly tests (vllm-project#26466) [Hybrid]: Decouple Kernel Block Size from KV Page Size (vllm-project#24486) [Core][KVConnector] Propagate all tokens on resumed preemptions (vllm-project#24926) [MM][Doc] Add documentation for configurable mm profiling (vllm-project#26200) [Hardware][AMD] Enable FlexAttention backend on ROCm (vllm-project#26439) [Bugfix] Incorrect another MM data format in vllm bench throughput (vllm-project#26462) [Bugfix] Catch and log invalid token ids in detokenizer #2 (vllm-project#26445) [Minor] Change warning->warning_once in preprocess (vllm-project#26455) [Bugfix] Set the minimum python version for gpt-oss (vllm-project#26392) [Misc] Redact ray runtime env before logging (vllm-project#26302) Separate MLAAttention class from Attention (vllm-project#25103) [Attention] Register FLASHMLA_SPARSE (vllm-project#26441) [Kernels] Modular kernel refactor (vllm-project#24812) ...
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This change should enable quant fusions which depend on the
RMSNormop being present