Pool Fusion Patterns

Pool Fusion Patterns#

Overview#

The Pool category includes operations such as: AvgPool and MaxPool.

oneDNN supports various pool fusion patterns to optimize performance and reduce memory bandwidth requirements. This document describes the supported fusion patterns for Pool operations.

Pattern Structure#

oneDNN defines floating-point Pool fusion patterns as follows. The blue nodes are required when defining a Pool fusion pattern while the brown nodes are optional.

Pool pattern

  1. Pool Operation : Performs the corresponding pool operation for the src tensor. See the AvgPool and MaxPool operations in the Graph API for more details.

  2. Epilogue Subgraph : Optional and can include the following operations:

    Combination Rules:

    epilogue subgraph

    • 0 to 4 Binary or Unary operations are supported in the epilogue subgraph.

Data Types#

oneDNN supports the following combinations of data types for src and dst:

src

dst

f32,bf16,f16

f32,bf16,f16

The definition of the data types and support status on different CPU and GPU platforms follow the general description in the Data Types Guide.

Implementation Limitations#

  1. The AvgPool operation doesn’t support the combination of rounding_type equal to ceil and exclude_pad equal to false.

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