quantize

aimet_torch.v2.quantization.affine.quantize(tensor, scale, offset, *args, **kwargs)[source]

Applies quantization to the input.

Precisely,

\[out = clamp\left(\left\lceil\frac{input}{scale}\right\rfloor - offset, qmin, qmax\right)\]

If block size \(B = \begin{pmatrix} B_0 & B_1 & \cdots & B_{D-1} \end{pmatrix}\) is specified, this equation will be further generalized as

\[ \begin{align}\begin{aligned}\begin{split}out_{j_0 \cdots j_{D-1}} & = clamp\left( \left\lceil\frac{input_{j_0 \cdots j_{D-1}}}{scale_{i_0 \cdots i_{D-1}}}\right\rfloor - offset_{i_0 \cdots i_{D-1}}, qmin, qmax\right)\\\end{split}\\\text{where} \quad \forall_{0 \leq d < D} \quad i_d = \left\lfloor \frac{j_d}{B_d} \right\rfloor\end{aligned}\end{align} \]

This function is overloaded with the signatures listed below:

aimet_torch.v2.quantization.affine.quantize(tensor, scale, offset, bitwidth, signed=False, block_size=None)[source]

Equivalent to:

\[\begin{split}qmin= \begin{cases} -\left\lceil\frac{2^{bitwidth}-1}{2}\right\rceil,& \text{if } signed\\ 0, & \text{otherwise (default)} \end{cases} qmax= \begin{cases} \left\lfloor\frac{2^{bitwidth}-1}{2}\right\rfloor,& \text{if } signed\\ 2^{bitwidth}-1, & \text{otherwise (default)} \end{cases}\end{split}\]

Parameters

tensor (Tensor) – Tensor to quantize
scale (Tensor) – Scale for quantization
offset (Tensor) – Offset for quantization
bitwidth (int) – Bitwidth of quantized tensor based on which \(qmin\) and \(qmax\) will be derived
signed (bool) – If false, the output will be mapped to positive integers only. Otherwise, it will range over both positive and negative integers.
block_size (Tuple[int, ...], optional) – Block size

aimet_torch.v2.quantization.affine.quantize(tensor, scale, offset, *, num_steps, signed=False, block_size=None)[source]

Equivalent to:

\[\begin{split}qmin= \begin{cases} -\left\lceil\frac{num\_steps}{2}\right\rceil,& \text{if } signed\\ 0, & \text{otherwise (default)} \end{cases} qmax= \begin{cases} \left\lfloor\frac{num\_steps}{2}\right\rfloor,& \text{if } signed\\ num\_steps, & \text{otherwise (default)} \end{cases}\end{split}\]

Parameters

tensor (Tensor) – Tensor to quantize
scale (Tensor) – Scale for quantization
offset (Tensor) – Offset for quantization
num_steps (int) – The number of steps in the quantization range based on which \(qmin\) and \(qmax\) will be derived
signed (bool) – If false, the output will be mapped to positive integers only. Otherwise, it will range over both positive and negative integers.
block_size (Tuple[int, ...], optional) – Block size

aimet_torch.v2.quantization.affine.quantize(tensor, scale, offset, *, qmin, qmax, block_size=None)[source]

Parameters

tensor (Tensor) – Tensor to quantize
scale (Tensor) – Scale for quantization
offset (Tensor) – Offset for quantization
qmin (int) – Minimum value of the quantization range
qmax (int) – Maximum value of the quantization range
block_size (Tuple[int, ...], optional) – Block size

Examples

>>> import aimet_torch.v2.quantization as Q
>>> input = torch.arange(start=-0.3, end=1.3, step=0.05)
>>> print(input)
tensor([-3.0000e-01, -2.5000e-01, -2.0000e-01, -1.5000e-01, -1.0000e-01,
        -5.0000e-02, -1.1921e-08,  5.0000e-02,  1.0000e-01,  1.5000e-01,
        2.0000e-01,  2.5000e-01,  3.0000e-01,  3.5000e-01,  4.0000e-01,
        4.5000e-01,  5.0000e-01,  5.5000e-01,  6.0000e-01,  6.5000e-01,
        7.0000e-01,  7.5000e-01,  8.0000e-01,  8.5000e-01,  9.0000e-01,
        9.5000e-01,  1.0000e+00,  1.0500e+00,  1.1000e+00,  1.1500e+00,
        1.2000e+00,  1.2500e+00])
>>> scale = torch.tensor(1/15)
>>> offset = torch.tensor(0.0)
>>> Q.affine.quantize(input, scale, offset, bitwidth=4)
tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
         6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
         15., 15., 15., 15.])
>>> Q.affine.quantize(input, scale, offset, num_steps=15)
tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
         6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
         15., 15., 15., 15.])
>>> Q.affine.quantize(input, scale, offset, qmin=0, qmax=15)
tensor([ 0.,  0.,  0.,  0.,  0.,  0., -0.,  1.,  2.,  2.,  3.,  4.,  4.,  5.,
         6.,  7.,  7.,  8.,  9., 10., 10., 11., 12., 13., 13., 14., 15., 15.,
         15., 15., 15., 15.])