Figure 1: The fourth Laplacian eigenfunction on the elephant (left) is separated into components residing on complementary parts (right) by the action of a discrete Hamiltonian operator whose step potential has support only on the elephant's body (i.e. head excluded). The separation from the Laplacian eigenbasis is the result of continuous perturbations of the potential energy and is captured by Hamiltonian functional maps, with a bi-diagonal slanted structure. When the potential is low, a Hamiltonian map is simply the identity (bottom left). Increasing the energy the diagonal separates (center map) and at convergence the bi-diagonal structure emerges (top right).

Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network architectures and learning-based methods to solve this issue, which are expensive and require changes to the MT model, trading inference speed at the cost of the translation quality. In this paper, we propose to address the problem from the point of view of decoding algorithms, as a less explored but rather compelling direction. We propose to reframe the standard greedy autoregressive decoding of MT with a parallel formulation leveraging Jacobi and Gauss-Seidel fixed-point iteration methods for fast inference. This formulation allows to speed up existing models without training or modifications while retaining translation quality. We present three parallel decoding algorithms and test them on different languages and models showing how the parallelization introduces a speedup up to 38% w.r.t. the standard autoregressive decoding and nearly 2x when scaling the method on parallel resources. Finally, we introduce a decoding dependency graph visualizer (DDGviz) that let us see how the model has learned the conditional dependence between tokens and inspect the decoding procedure.

No abstract

No abstract