e4458b2d2b
### What this PR does / why we need it?
Breaking:
1. https://github.com/vllm-project/vllm/pull/33452
2. https://github.com/vllm-project/vllm/pull/33451
3. https://github.com/vllm-project/vllm/pull/32567
4. https://github.com/vllm-project/vllm/pull/32344
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1
---------
Signed-off-by: MrZ20 <2609716663@qq.com>
Signed-off-by: gcanlin <canlinguosdu@gmail.com>
Co-authored-by: MrZ20 <2609716663@qq.com>
52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import torch
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import vllm.v1.worker.utils as utils
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from vllm.model_executor.layers.attention import Attention
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from vllm.v1.worker.utils import defaultdict, extract_layer_index
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# Without this patch, it will raise an exception when initialize kv_cache.
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# TODO To remove the patch, we need check why the original bind_kv_cache raises an NotImplementedError.
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def bind_kv_cache(
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kv_caches: dict[str, torch.Tensor],
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forward_context: dict[str, Attention],
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runner_kv_caches: list[torch.Tensor],
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num_attn_module: int = 1,
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) -> None:
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"""
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Bind the allocated KV cache to both ModelRunner and forward context so
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that the KV cache can be used in the forward pass.
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This function:
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1) Fills the ModelRunner's kv cache list (`runner_kv_caches`) with
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kv_caches.
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2) Associates each attention layer in the `forward_context` with its
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corresponding KV cache in kv_caches.
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Args:
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kv_caches: The allocated kv_caches with layer names as keys.
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forward_context: The global forward context containing all Attention
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layers with layer names as keys.
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runner_kv_caches: The kv_cache declared by ModelRunner.
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"""
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# Bind kv_caches to ModelRunner
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assert len(runner_kv_caches) == 0
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# Convert kv_caches dict to a list of tensors in the order of layer_index.
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index2name = defaultdict(list)
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for layer_name in kv_caches:
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index2name[extract_layer_index(layer_name, num_attn_module)].append(layer_name)
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for layer_index in sorted(index2name.keys()):
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layer_names = index2name[layer_index]
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# remove some codes for the typical case of encoder-decoder model, e.g., bart.
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layer_name = layer_names[0]
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runner_kv_caches.append(kv_caches[layer_name])
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# Bind kv_caches to forward context
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for layer_name, kv_cache in kv_caches.items():
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# NOTE: Use list because of v0 PP virtual engine.
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forward_context[layer_name].kv_cache = [kv_cache]
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utils.bind_kv_cache = bind_kv_cache
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