cache_ops.reshape_and_cache_flash cuda的内部实现在: cache_kernels.cu中的reshape_and_cache_flash 核心代码就两行: k_cache[tgt_value_idx] = key[src_key_idx]; v_cache[tgt_value_idx] = value[src_value_idx]; 至于寻址,需要理解slot_mapping和blockManager的相关操作。 简单来说就是把新计算出来的...
核心点1:CachedBlockAllocator实现的是通用的Cache功能,不区分是否为Prefix还是Generate阶段,只要产生了KV Cache,就会被先放到cached_blocks table中缓存,key为block_hash,value为block_id。 核心点2:无论是Prefix还是Generate阶段,也只会调用allocate接口,也只有这个接口。 vLLM CachedBlockAllocator: Prefix + Generated...
核心点1:CachedBlockAllocator实现的是通用的Cache功能,不区分是否为Prefix还是Generate阶段,只要产生了KV Cache,就会被先放到cached_blocks table中缓存,key为block_hash,value为block_id。 核心点2:无论是Prefix还是Generate阶段,也只会调用allocate接口,也只有这个接口。 vLLM CachedBlockAllocator: Prefix + Generated...
./vllm/vllm/model_executor/layers/attention.py中: from vllm._C import ops # 注意这里的包的名字:vllm._C。后面会补充说明怎么来的。 from vllm._C import cache_ops # 省略部分 ops.paged_attention_v2( output, exp_sums, max_logits, tmp_output, query, key_cache, value_cache, num_kv_he...
[rank0]: AttributeError: '_OpNamespace' '_C_cache_ops' object has no attribute 'reshape_and_cache_flash' I tried docker images: nvcr.io/nvidia/pytorch:24.03-py3 and pytorch/pytorch:2.3.0-cuda12.1-cudnn8-devel vllm was installed in an editable mode:pip install -e . ...
cache_ops.reshape_and_cache( key[:num_valid_tokens], value[:num_valid_tokens], key_cache, value_cache, input_metadata.slot_mapping, ) # Single Query Attention self.single_query_cached_kv_attention( output[num_prompt_tokens:num_valid_tokens], ...
cuda-programmingtransformer-modelskv-cachellmvllmllm-inferencetriton-kernels UpdatedMar 13, 2025 Python Make Discord your LLM frontend ● Supports any OpenAI compatible API (Ollama, LM Studio, vLLM, OpenRouter, xAI, Mistral, Groq and more) ...
OpenShift AI è una piattaforma MLOps, flessibile e scalabile, dotata di strumenti per creare, distribuire e gestire le applicazioni basate sull'intelligenza artificiale. OpenShift AI supporta l'intero ciclo di vita dei modelli e degli esperimenti di AI/ML, sia on premise che nel cloud pu...
Proposal Establish baseline system requirements by: Stand up an AI Gateway and GitLab environment, could be areference environment(with support from@grantyoung) Choose 3-4 representative OS models and set them up in our GCP area The machines to be tested are: ...
vLLM is fast with: State-of-the-art serving throughput Efficient management of attention key and value memory with PagedAttention Continuous batching of incoming requests Fast model execution with CUDA/HIP graph Quantization: GPTQ, AWQ, SqueezeLLM, FP8 KV Cache ...