Comments (4)
Hi @dacorvo, your understanding is correct, we have multiple NEFFs that needs to be serialized including token generation NEFFs and context encoding NEFFs. However they are not same NEFFs so we cannot simply do
new.compiler_artifacts_path = self.compiler_artifacts_path
There is an internal engineer working on a more general serialization API, which takes care of context encoding models as well.
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Thanks @dacorvo . We will take a look.
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@dacorvo you can also use the Neuron Persistent Cache feature that is enabled now as default in transformers-neuronx in SDK release 2.13: https://awsdocs-neuron.readthedocs-hosted.com/en/latest/libraries/transformers-neuronx/transformers-neuronx-developer-guide.html#neuron-persistent-cache
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Solved in AWS Neuron SDK 2.15.
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Related Issues (20)
- llama-2/codellama benchmark for inf2.xlarge HOT 4
- Mixtral Model support HOT 2
- Vicuna13B model support
- Inf2 Modified Llama 2 Loading Issue HOT 11
- Skipping generation for useless tokens, and modiying cacheids HOT 3
- How to use generate() with inputs_embeds HOT 2
- Mixtral config issue -- not handling null well HOT 8
- Generate Llama 2 from Embeddings HOT 5
- Infering logits from `model.forward` for the entire batch instead of the last forward's output. HOT 5
- Support for MPT model HOT 1
- `stopping_criteria_list(input_ids, probs)` does not check for the correct sequence. HOT 4
- User feedback when compiling and reloading a large model HOT 1
- Issue while compiling Mistral 7B 0.2 Instruct HOT 5
- Backward compatibility with saved llama 2 compiled artifacts HOT 1
- NaN outputs when masking llama model inputs HOT 6
- Improve Neuron model loading time HOT 4
- Add support for `gemma` models HOT 1
- Add support for Baichuan-13B model
- Latest changes introduced for continuous batching break Mixtral model HOT 3
- llava support HOT 3
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