Sambanova Systems, Inc.

Adi Fuchs

Raghu Prabhakar

Sumti Jairath

Ram Sivaramakrishnan

Arvind Krishna Sujeeth

Matheen Musaddiq

Junjue Wang

Tejas Nagendra Babu Nama

Kaizhao Liang

Ruddhi Arun Chaphekar

Ruddhi Chaphekar

Disclosed is a method that includes sectioning a graph into a sequence of sections, the sequence of sections including at least a first section followed by a second section. The first section is configured to generate a first output in a first target tiling configuration in response to processing a first input in a first input tiling configuration. The graph is configured to reconfigure the first output in the first target tiling configuration to a second input in a second input tiling configuration. The second section is configured to generate a second output in a second target tiling configuration in response to processing the second input in the second input tiling configuration.

Lossless tiling in convolution networks—tiling configuration between two sections

Disclosed is a method that includes generating by an output processing node of a first section of a processing graph, a plurality of output tiles of an output tensor. The plurality of output tiles of the output tensor is written in a memory, where the writing includes zero-padding the plurality of output tiles of the output tensor in the memory. The zero-padded plurality of output tiles of the output tensor are tiled, to generate a plurality of input tiles of an input tensor. The plurality of input tiles of the input tensor is processed in a second section of the processing graph.

Lossless tiling in convolution networks—padding and re-tilling at section boundaries

A data processing system includes memory and reconfigurable processors, operatively coupled to the memory, configured to execute a sequence of subgraphs of a graph. The sequence of subgraphs includes a preceding subgraph and a succeeding subgraph. The data processing system also includes data flow logic, operatively coupled to the reconfigurable processors and the memory, configured to store a tiled output of the preceding subgraph as a composed input in the memory and make available parts of the composed input for processing by the succeeding subgraph.

Lossless tiling in convolution networks—data flow logic

A data processing system receives a graph that includes a sequence of layers and executes graph cuts between a preceding layer in the graph and a succeeding layer in the graph that succeeds the preceding layer. The preceding layer generates a set of tiles on a tile-by-tile basis and the succeeding layer processes a tensor that includes multiple tiles in the set of tiles. Thus the graph is partitioned into a sequence of subgraphs, and a subgraph in the sequence of subgraphs including a sub-sequence of layers in the sequence of layers. One or more configuration files is generated to configure runtime logic to execute the sequence of subgraphs and the one or more configuration files are stored on a computer-readable media.

Lossless tiling in convolution networks—section cuts

A data processing system includes compile time logic to section a graph into a sequence of sections, including a first section followed by a second section. The compile time logic configured the first section to generate a first output in a first non-overlapping target configuration in response to processing an input in a first overlapping input configuration, and configures the second section to generate a second output in a second non-overlapping target configuration in response to processing the first output in a second overlapping input configuration. The compile time logic also creates a set of computer instructions to execute the first section and the second section on a target processing system.

Lossless tiling in convolution networks—resetting overlap factor to zero at section boundaries

Disclosed is a data processing system that includes a plurality of reconfigurable processors and processor memory. Runtime logic, operatively coupled to the plurality of reconfigurable processors and the processor memory, is configured to configure at least one reconfigurable processor in the plurality of reconfigurable processors with a first subgraph in a sequence of subgraphs of a graph; load an input onto the processor memory; on a tile-by-tile basis, process a first set of input tiles from the input through the first subgraph and generate a first set of intermediate tiles, load the first set of intermediate tiles onto the processor memory, and process the first set of intermediate tiles through the first subgraph and generate a first set of output tiles; and compose output tiles in the first set of output tiles into a first composed input, and load the first composed input onto the processor memory.

Lossless tiling in convolution networks—materialization of tensors

A processing graph of an application with a sequence of processing nodes is obtained which processes an input and generates an intermediate representation a further intermediate representation, and an output representation of the input at stages in the sequence of processing nodes. Graph metadata is generated that specifies a non-overlapping target tiling configuration for the output representation, an overlapping tiling configuration for the input, an overlapping tiling configuration for the intermediate representation, and a third tiling configuration for the further intermediate representation. The processing graph is modified based on the graph metadata to conform to the parameters specified by the graph metadata. A set of computer instructions is then created to execute the modified processing graph on a target processing system.

Lossless tiling in convolution networks—graph metadata generation

Disclosed is a data processing system that includes compile time logic to section a graph into a sequence of sections including a first section and a second section. The compile time logic is to configure the first section with a first topology of tiling configurations in which to tile inputs, intermediate outputs, and final outputs of the first section, and configure the second section with a second topology of tiling configurations in which to tile inputs, intermediate outputs, and final outputs of the second section. The data processing system further includes runtime logic configured with the compile time logic to execute the first section to generate the inputs, intermediate outputs, and final outputs of the first section in the first topology of tiling configurations, and execute the second section to generate the inputs, intermediate outputs, and final outputs of the second section in the second topology of tiling configurations.

Lossless tiling in convolution networks—tiling configuration for a sequence of sections of a graph

Disclosed is a data processing system to receive a processing graph of an application. A compile time logic is configured to modify the processing graph and generate a modified processing graph. The modified processing graph is configured to apply a post-padding tiling after applying a cumulative input padding that confines padding to an input. The cumulative input padding pads the input into a padded input. The post-padding tiling tiles the padded input into a set of pre-padded input tiles with a same tile size, tiles intermediate representation of the input into a set of intermediate tiles with a same tile size, and tiles output representation of the input into a set of non-overlapping output tiles with a same tile size. Runtime logic is configured with the compile time logic to execute the modified processing graph to execute the application.

Lossless tiling in convolution networks-backward pass

Lossless tiling in convolution networks&#x2014;padding before tiling, location-based tiling, and zeroing-out

Disclosed is a data processing system which includes compile time logic configured to section a graph into a sequence of subgraphs, the sequence of subgraphs including at least a first subgraph. The compile time logic configures the first subgraph to generate a plurality of output tiles of an output tensor. A runtime logic configured with the compile time logic is to execute the sequence of subgraphs to generate, at the output of the first subgraph, the plurality of output tiles of the output tensor, and write the plurality of output tiles in a memory in an overlapping configuration. In an example, an overlapping region between any two neighboring output tiles of the plurality of output tiles comprises a summation of a corresponding region of a first neighboring output tile and a corresponding region of a second neighboring output tile.

Lossless tiling in convolution networks&#x2014;read-modify-write in backward pass

Disclosed is a data processing system that includes compile time logic configured to process a processing graph to generate a modified processing graph, which includes a plurality of forward processing nodes of a forward pass and a plurality of backward processing nodes of a backward pass. The data processing system also includes runtime logic configured with the compile time logic to execute the modified processing graph to generate, at a backward processing node of the plurality of backward processing nodes, a plurality of partial weight gradients, based on processing a corresponding plurality of gradient tiles of a gradient tensor, and generate, based on the plurality of partial weight gradients, a final weight gradient corresponding to the gradient tensor.

Lossless tiling in convolution networks&#x2014;weight gradient calculation

Disclosed is a data processing system that includes compile time logic to section a graph into a sequence of sections, configure a first section to generate a first set of output tiles in a first target tiling configuration in response to processing a first set of input tiles in a first input tiling configuration, and configure a second section to generate a second set of output tiles in a second target tiling configuration in response to processing the first set of output tiles in a second input tiling configuration. Runtime logic is configured to pad a first input into a first padded input, read the first set of input tiles from the first padded input in the first input tiling configuration, and process the first set of input tiles through the first section to generate the first set of output tiles in the first target tiling configuration.

Lossless tiling in convolution networks&#x2014;section boundaries

Disclosed is a data processing system that includes compile time logic configured to section a graph into a sequence of sections, and configure each section of the sequence of sections such that an input layer of a section processes an input, one or more intermediate layers of the corresponding section processes corresponding one or more intermediate outputs, and a final layer of the corresponding section generates a final output. The final output has a non-overlapping final tiling configuration, the one or more intermediate outputs have corresponding one or more overlapping intermediate tiling configurations, and the input has an overlapping input tiling configuration. The compile time logic is further to determine the various tiling configurations by starting from the final layer and reverse traversing through the one or more intermediate layers, and ending with the input layer.

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West Windsor, NJ, United States of America

Adi Fuchs