Abstract: The transformer architecture has led to revolutionary advancements in NLP. The attention layer within the transformer admits a sequence of input tokens $X$ and makes them interact through pairwise similarities computed as $\texttt{softmax}(XQK^\top X^\top)$, where $(K,Q)$ are the trainable key-query parameters. In this work, we establish a formal equivalence between the optimization geometry of self-attention and a hard-margin SVM problem that separates optimal input tokens from non-optimal tokens using linear constraints on the outer-products of token pairs. This formalism allows us to characterize the implicit bias of 1-layer transformers optimized with gradient descent: (1) Optimizing the attention layer, parameterized by $(K,Q)$, with vanishing regularization, converges in direction to an SVM solution minimizing the nuclear norm of the combined parameter $W:=KQ^\top$. Instead, directly parameterizing by $W$ minimizes a Frobenius norm SVM objective. (2) Complementing this, for $W$-parameterization, we prove the local/global directional convergence of gradient descent under suitable geometric conditions, and propose a more general SVM equivalence that predicts the implicit bias of attention with nonlinear heads/MLPs.