Google Inc.

Benoit Steiner

Jeffrey Adgate Dean

Quoc V Le

Azalia Mirhoseini

Hieu Hy Pham

Vincent Olivier Vanhoucke

Rama Krishna Govindaraju

Nishant P Patil

Matthew Sarett

Naveen Kumar

Samuel Bengio

Mohammad Norouzi

Anna Darling Goldie

Rasmus Munk Larsen

Yuefeng Zhou

A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

Device placement optimization with reinforcement learning

A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices. Determining the placement includes: generating, from the data, a respective operation embedding for each of the operations; grouping the operations into multiple operation groups, comprising processing each of the respective operation embeddings using a grouper neural network having multiple grouper parameters, in which the grouper neural network is configured to, for each of the operations, process the operation embedding for the operation in accordance with first values of the grouper parameters to generate a grouper output that assigns the operation to an operation group from the multiple operation groups; and assigning each of the operation groups to a respective device from the multiple hardware devices.

Hierarchical device placement with reinforcement learning

The present disclosure relates to optimized matrix multiplication using vector multiplication of interleaved matrix values. Two matrices to be multiplied are organized into specially ordered vectors, which are multiplied together to produce a portion of a product matrix.

Optimized matrix multiplication using vector multiplication of interleaved matrix values

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Mountain View, CA, United States of America

Benoit Steiner