QuantizedConvTranspose1d

class aimet_torch.nn.QuantizedConvTranspose1d(*args, **kwargs)[source]

Quantized subclass of torch.nn.ConvTranspose1d

forward(self, input: torch.Tensor, output_size: list[int] | None = None) → torch.Tensor

Quantized forward of torch.nn.ConvTranspose1d.

The input(s), parameter(s) (if any), and output(s) will be quantized with self.input_quantizers, self.param_quantizers, and self.output_quantizers respectively.

For more information, see QuantizationMixin.

compute_encodings()

Compute encodings with additional protection against overflow/underflow.

Given

y = round((xW + b) * sx * sw / sy - zy)

HTP implements this equation as either eq 1 or eq 2, depending on hw version and other parameters
y = round(( xW + b ) * sx * sw / sy - zy)

= round(( xW + b ) * s’ - zy) … eq 1 = round(( xW + b - zy/s’ ) * s’ ) ≈ round(( xW + b - round(zy/s’) ) * s’ ) = round(( xW + b’ ) * s’ ) … eq 2

where:

name | description | dtype | equation |

|------|—————————|----------------------|—————————-| | x | input | uint8 or uint16 | round(x_float / sx + zx) | | W | weight | int4, int8, or int16 | round(W_float / sw) | | b | bias | int32 | round(b_float / (sx * sw)) | | y | output | dtype(x) | given above | | sx | input scale | float | - | | zx | input zero_point | dtype(x) | - | | sw | weight scale | float | - | | sy | output scale | float | - | | zy | output zero_point | dtype(y) | - | | s’ | requantization scale | float | sx * sw / sy | | b’ | combined accumulator bias | int32 | b - round(zy / s’) |

This function adjusts the weight scale to prevent 3 possible scenarios of overflow/underflow

  1. Bias Overflow - occurs if: |b| > 2**31 - bad because: Causes severe clipping error when exported as int32

  2. Requantization Scale Underflow - occurs if: s’ <= 2**-24 - bad because: If exponent e <= -24, HexNN misinterprets s’=2**e as 2**(e+32)

    due to internal type casting bug

  3. Accumulator Bias Overflow - occurs if: |b'| > 2**31 - bad because: HexNN internally stores b’ as int32