Comments (4)
Hi @Tracin! Thanks a lot for bringing the issue to our attention. We have fixed the problem in #8 , please have a look! Once again, thank you for your interest in AWQ.
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Hi @ys-2020! Very appreciated for your MR for this. It is very impressive both the AWQ method and this fast kernel.
However I am afraid there is still something wrong in it. When I continue doubling the M up to 2048 * 32
, I met a CUDA error: an illegal memory access was encountered
.
I believe you understand it is 32 batchsize and 2k sentence length which is quite often used for LLM inference.
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Hi @Tracin , we have reproduced the illegal memory access issue and are trying to fix it. Please stay tuned for our updates. Thanks!
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I got this error today as well for long generation. Do you know what the issue is, and if there is a patch / fix we can do locally?
Thanks!
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Related Issues (20)
- illegal memory access when input tokens < 8
- Grok-1 AWQ
- can awq support 3-bit,2-bit, 8-bit quantization? HOT 1
- awq_inference_engine is missing from source, so quantizing custom models fails HOT 2
- Support for Qwen models HOT 2
- AWQ for non-Transfomer Implementation HOT 3
- Error while Quantizing OWLv2 model
- No module named 'awq_inference_engine' HOT 2
- No such file or directory: "VILA1.5-13b-AWQ/llm/model-00001-of-00006.safetensors" HOT 7
- tinychat.serve.model_worker_new.py AWQ model in training mode
- how to support to custom module like mla in deep-seek-v2
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- AWQ kernel Issue
- Can you provide examples code to run inference on video QA? HOT 1
- AWQ and VILA dependency compatible issue HOT 2
- google.protobuf.message.DecodeError: Error parsing message HOT 1
- Is this a bug for the quantization phase? HOT 1
- Rocm support request
- Invalid Characters
- Memory increases significantly during inference
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