Comments (5)
hannanjgaws
commented on August 16, 2024
Thank you for filing the issue. We have found a fix for the problem and it will be available in an upcoming release.
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hannanjgaws
commented on August 16, 2024
Currently continuous batching support has only been officially released with Llama: https://awsdocs-neuron.readthedocs-hosted.com/en/latest/libraries/transformers-neuronx/transformers-neuronx-developer-guide-for-continuous-batching.html#overview-of-continuous-batching-api-and-vllm-support
Mistral/Mixtral are planned for future releases. We will update this ticket when we have released official support for the Mixtral model.
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dacorvo
commented on August 16, 2024
Then Mistral and Mixtral are actually not supported, because static batching with padding (the alternative to continuous batching) has been broken for all models since the introduction of continuous batching: #79. Or has it been fixed ?
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Related Issues (20)
- Llama2 inference overhead time way too long HOT 6
- llama-2/codellama benchmark for inf2.xlarge HOT 4
- Mixtral Model support HOT 2
- Vicuna13B model support HOT 1
- 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 6
- 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 8
- Improve Neuron model loading time HOT 4
- Add support for `gemma` models HOT 1
- Add support for Baichuan-13B model
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