Update model definition to support Flash-Decoding #177
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masahi
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Jan 30, 2024
| @@ -402,6 +403,10 @@ class BuildArgs: | |||
| "action": "store_true", | |||
| }, | |||
| ) | |||
| paged_kv_cache_type: str = field( | |||
| default="vllm", | |||
| metadata={"help": "The type of paged KV cache, either vllm or flash-decoding"}, | |||
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This new option makes --use_vllm_attention obsolete. Since removing it is a breaking change, I'll do that later when I integrate Flash-Decoding into mlc-serve. @sunggg
vinx13
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Lunderberg
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The repetition penalty (introduced in [CTRL](https://arxiv.org/abs/1909.05858)) can help prevent the LLM from generating repetitive tokens. This PR implements the repetition penalty. Note: Previous the logits softmax is performed on GPU, this PR moves it to CPU to accommodate the repetition penalty.
sunggg
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This was referenced Jan 31, 2024
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This PR integrates Flash-Decoding support from apache/tvm#16474. This is a drop-in replacement for the vLLM kernel. The only difference with the vLLM-based build is the shape of KV cache blocks. In particular, the block size for vLLM is 16 while for Flash-Decoding it is 256.
In addition, it supports decoding with multiple, fixed length queries per request, which is necessary for speculative decoding.
evaluate_multi_queryfrom #156 can also be used for this purpose, but it supports variable-length queries per request and piggy-backs to the prefill attention, which is not efficient when the number of queries is fixed and small. The changes inrun_llama_batched_vllm.pydemonstrates that the new Relax function,decode_multi_query, can do exactly the same thing asevaluate_multi_querywhen the query length is fixed.This PR only updates the model definition and
run_llama_batched_vllm.pyexample script. I'll follow up with the integration intomlc-servenext.Need the latest https://github.com/octoml/tvm/tree/for-mlc-serve-jan12
@sunggg @yelite @vinx13