.. index:: pair: page; Quantized ConvTranspose Fusion Patterns
.. _doxid-dev_guide_graph_quantized_convtranspose_fusion_patterns:

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 :ref:`ConvTranspose Fusion Patterns <doxid-dev_guide_graph_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.

.. image:: quantized_convtranspose_pattern.png
	:alt: quantized ConvTranspose pattern



#. Q2F Conversion Subgraph : Converts ``src`` and ``weights`` tensors from quantized to floating-point. It can be one of the following subgraphs, while the second subgraph applies only to ``weights``. See :ref:`Dequantize <doxid-dev_guide_op_dequantize>` and :ref:`Quantize <doxid-dev_guide_op_quantize>` operations in Graph API.
   
   .. image:: q2f_conversion_quantized_convtranspose.png
   	:alt: q2f_conversion_subgraph

#. ConvTranspose Operation : Performs transposed convolution between the ``src`` and ``weights`` tensors. The ``bias`` tensor is optional. See the :ref:`ConvTranspose <doxid-dev_guide_op_convtranspose>` operation in the Graph API for more details.

#. Epilogue Subgraph : Optional and can include the following operations:
   
   * :ref:`BiasAdd <doxid-dev_guide_op_biasadd>` operation.
   
   * Binary and Unary operations: refer to the Note in `Fusion Patterns <graph_fusion_patterns.html>`__.
   
   Combination Rules:
   
   .. image:: epilogue_subgraph_general_2.png
   	:alt: epilogue subgraph
   
   
   
   * BiasAdd : If present, must be the first op in the epilogue subgraph and can only appear once.
   
   * N=20, 0 to 20 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 :ref:`Quantize <doxid-dev_guide_op_quantize>` operation.
   
   .. image:: f2q_conversion_general.png
   	:alt: f2q_conversion_subgraph

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 :ref:`Data Types Guide <doxid-dev_guide_data_types>`.

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.

Implementation Notes
~~~~~~~~~~~~~~~~~~~~

Post-binary Add operations in the epilogue subgraph support in-place operations when the post-binary Add is the last operation in the epilogue subgraph and the ``dst`` output shape is identical and data type size is the same as the binary Add input. In case of an in-place operation, the original input data will be overwritten. Use in-place operations whenever possible for performance.