[Core][2/N] Model runner refactoring part 2. Combine prepare prefill / decode to a single API #4681
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vllm/worker/model_runner.py
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| # We should use get_len here because in case of preemption | ||
| # it contains output tokens. | ||
| seq_len = min(seq_data.get_len(), | ||
| context_len + token_chunk_size) |
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token chunk size == query
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change the term in the next PR
vllm/spec_decode/ngram_worker.py
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| @@ -37,7 +38,7 @@ def init_device(self): | |||
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| # Current only support Top1Proposer | |||
| self._proposer = Top1Proposer( | |||
| self, | |||
| weakref.proxy(self), | |||
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This is from #4737. it is needed to run spec decoding test locally for debugging
| @@ -104,6 +103,71 @@ class FlashAttentionMetadata(AttentionMetadataPerStage, | |||
| # Cuda-graph is currently enabled for decoding only. | |||
| # TODO(woosuk): Move `use_cuda_graph` out since it's unrelated to attention. | |||
| use_cuda_graph: bool | |||
| _cached_prefill_metadata: Optional["FlashAttentionMetadata"] = None | |||
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QQ: Can we just use the decorator @lru_cache(maxsize=None)? If not, is it because we need to control memory usage and garbage collection?
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When I checked last time, lru_cache doesn't go well with class instance method; https://stackoverflow.com/questions/14946264/python-lru-cache-decorator-per-instance/14946506#14946506
Lmk if I know it wrong...
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i see! thanks for the explaination
| num_prefill_tokens: int | ||
| num_decode_tokens: int | ||
| num_prefills: int |
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Probably not in this PR, but can these features be turned into cached_property in the future? I think this can reduce the complexity of _prepare_model_input
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For num_prefills & num_decode_tokens, etc.?
I think one tricky part is that we don't know which tokens are prefill/decode once we create a metadata
…/ decode to a single API (vllm-project#4681) This PR combines prepare_prompt and prepare_decode into a single API. This PR also coelsce the attn metadata for prefill/decode to a single class and allow to slice them when running attn backend. It also refactors subquery_start_loc which was not refactored in the previous PR
…/ decode to a single API (vllm-project#4681) This PR combines prepare_prompt and prepare_decode into a single API. This PR also coelsce the attn metadata for prefill/decode to a single class and allow to slice them when running attn backend. It also refactors subquery_start_loc which was not refactored in the previous PR
…/ decode to a single API (vllm-project#4681) This PR combines prepare_prompt and prepare_decode into a single API. This PR also coelsce the attn metadata for prefill/decode to a single class and allow to slice them when running attn backend. It also refactors subquery_start_loc which was not refactored in the previous PR
This PR combines prepare_prompt and prepare_decode into a single API. This PR also coelsce the attn metadata for prefill/decode to a single class and allow to slice them when running attn backend.
It also refactors subquery_start_loc which was not refactored in the previous PR
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