Quantized ConvTranspose Fusion Patterns#
Overview#
oneDNN supports both floating-point and quantized ConvTranspose fusion patterns to optimize performance and reduce memory bandwidth requirements. This document describes the supported quantized fusion patterns for ConvTranspose. For floating-point ConvTranspose fusion patterns, refer to ConvTranspose Fusion Patterns for more details.
Pattern Structure#
oneDNN defines quantized ConvTranspose fusion patterns as follows. The blue nodes are required when defining a quantized ConvTranspose fusion pattern while the brown nodes are optional.

Q2F Conversion Subgraph : Converts
src
andweights
tensors from quantized to floating-point. It can be one of the following subgraphs, while the second subgraph applies only toweights
. See Dequantize and Quantize operations in Graph API.ConvTranspose Operation : Performs transposed convolution between the
src
andweights
tensors. Thebias
tensor is optional. See the ConvTranspose operation in the Graph API for more details.Epilogue Subgraph : Optional and can include the following operations:
BiasAdd operation.
Binary and Unary operations: refer to the Note in Fusion Patterns.
Combination Rules:
BiasAdd : If present, must be the first op in the epilogue subgraph and can only appear once.
0 to 4 Binary or Unary operations are supported in the epilogue subgraph.
F2Q Conversion Subgraph : Converts the output tensor from floating-point to quantized data type. It is constructed by a Quantize operation.
Data Types#
oneDNN supports the following combinations of data types for src, weights, bias and dst:
src |
weights |
bias |
dst |
---|---|---|---|
u8,s8 |
s8,f32 |
f32,bf16,f16 |
u8,s8,bf16,f16,f32 |
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#
GPU
Dequantize and Quantize in Q2F and F2Q Conversion Subgraphs only support zps values as all zeros.
Quantize in F2Q Conversion Subgraph only supports per_tensor quantization type, and its scales values should be all ones.