.. _op_ai_onnx_QuantizeLinear-10:

QuantizeLinear - version 10
===========================

This page documents version **10** of operator **QuantizeLinear**. See :doc:`QuantizeLinear` for the latest version (since version 25).

- **Domain**: ``ai.onnx``
- **Since version**: 10

The linear per-tensor/layer quantization operator. It consumes a high precision tensor, a scale, a zero point to compute the low precision / quantized tensor.
The quantization formula is y = saturate ((x / y_scale) + y_zero_point). For saturation, it saturates to [0, 255] if it's uint8, or [-128, 127] if it's int8.
For (x / y_scale), it's rounding to the nearest even. Refer to https://en.wikipedia.org/wiki/Rounding for details. 'y_zero_point' and 'y' must have same type.

**Inputs**

- **x** (*T1*): N-D full precision Input tensor to be quantized.
- **y_scale** (*tensor(float)*): Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization.
- **y_zero_point** (*T2*): Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified.

**Outputs**

- **y** (*T2*): N-D quantized output tensor. It has same shape as input 'x'.

**Type Constraints**

- **T1**: Constrain 'x' to float or int32 tensor.
  Allowed types: tensor(float), tensor(int32).
- **T2**: Constrain 'y_zero_point' and 'y' to 8-bit integer tensor.
  Allowed types: tensor(int8), tensor(uint8).