qtorch.quant package¶
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qtorch.quant.fixed_point_quantize(x, wl, fl, clamp=True, symmetric=False, rounding='stochastic')[source]¶ Quantize a single precision Floating Point into low-precision Fixed Point
- Args:
param: x (torch.Tensor) : the single precision number to be quantized param: wl (int) : word length of the fixed point number being simulated param: fl (int) : fractional length of the fixed point number being simulated param: clamp (bool, optional) : clamp input numbers into representable range. if false, the quantization will only simulate the effect on precision param: symmetric (bool, optional) : discard the minimum representable number to make the representable range symmetric param: rounding (string) : rounding mode, “stochastic” or “nearest” (default: “stochastic”)
- Returns:
- a quantized low-precision block floating point number (torch.Tensor)
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qtorch.quant.block_quantize(x, wl, dim=-1, rounding='stochastic')[source]¶ Quantize a single precision Floating Point into low-precision Block Floating Point
- Args:
param: x (torch.Tensor) : the single precision number to be quantized param: wl (int) : word length of the block floating point number being simulated param: rounding (string) : rounding mode, “stochastic” or “nearest”
- Returns:
- a quantized low-precision block floating point number (torch.Tensor)
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qtorch.quant.float_quantize(x, exp, man, rounding='stochastic')[source]¶ Quantize a single precision Floating Point into low-precision Floating Point
- Args:
attr: x (torch.Tensor) : the single precision number(torch.Tensor) to be quantized attr: exp (int) : number of bits allocated for exponent attr: man (int) : number of bits allocated for mantissa, not counting the virtual bit attr: rounding (string) : rounding mode, “stochastic” or “nearest”
- Returns:
- a quantized low-precision floating point number (torch.Tensor)
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qtorch.quant.quantizer(forward_number=None, backward_number=None, forward_rounding='stochastic', backward_rounding='stochastic', clamping_grad_zero=False, backward_hooks=[])[source]¶ Creates a quantization function to support quantizing forward and backward process differently.
- Args:
param: forward_number (qtorch.Number, optional) : the number format used for forward quantization. if is None, the quantization would be a identity mapping. param: backward_number (qtorch.Number, optional) : the number format used for backward quantization. if is None, the quantization would be a identity mapping. param: forward_rounding (string) : rounding mode, “stochastic” or “nearest” (default: “stochastic”) param: backward_rounding (string) : rounding mode, “stochastic” or “nearest” (default: “stochastic”) param: clamping_grad_zero (bool) : zero out the gradient of numbers that are being clamped during forward propagation. currently requires forward_number to be a fixed point number. param: backward_hooks (iterable) : iterable of functions that will be applied to gradients before backward quantization. For example, this can be used to support custom scaling.
- Returns:
- A quantization function as specified (torch.Tensor -> torch.Tensor)