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insujang.github.io

This post explains flash attention 1 2. More references are also useful to understand flash attention as well 3 4 5. Backgrounds # Attention # $$\text{Attention}(Q, K, V)=\text{softmax}(\frac{QK^T}{\sqrt{d^k}})V$$ This equation can be implemented as: class OPTAttention(nn.Module): def forward(...): # hidden states is an input tenor of Attention layer # Calculate Q, K, and V with linear projections to the input # query_states = self.q_proj(hidden_states) # key_states = self.k_proj(hidden_states) # value_states = self.

sysctl-explorer.net

painless access to linux sysctl reference documentation

lwn.net

In late March, version 78.0.1 of Setuptools — an important Python packaging tool — was released [...]

simplyblock.io

Acelere o OpenShift com o armazenamento NVMe/TCP da simplyblock: rápido, resiliente, escalável e nativo para Kubernetes.

thegeekinthecorner.wordpress.com

Programming odds and ends -- InfiniBand, RDMA, and low-latency networking for now.

marvell.com

Designed for your current needs and future ambitions, Marvell delivers the data infrastructure technology transforming tomorrow’s enterprise, cloud, automotive, and carrier architectures for the better.