llmcompressor.observers.mse

Bases: Observer

Compute quantization parameters by finding the optimal min/max values which minimize the mean of quantization error squared

mse_quant_error := mean((x - fake_quant(x))**2)
global_scale <- min[min_vals, max_vals, global_scale](mse_quant_error(x))
scale, zp <- min[min_vals, max_vals](mse_quant_error(x, global_scale))

Parameters:

• base_name

  –

  str used to name the observer attribute

• args

  –

  quantization args used to calibrate and quantize the observed value

• module

  –

  optional module with attached quantization parameters. This argument is required to utilize existing qparams such as global_scale or g_idx

• **observer_kwargs

  –

  keyword arguments for observer initialization

  maxshrink: maximum shrink amount (in “grid steps”). The number of search steps is int(maxshrink * grid)

  patience: number of consecutive search steps without improvement before early stopping

  grid: resolution of the shrink search. Larger values give finer granularity in shrink factors

  norm: exponent used when computing the error. norm = 2 approximates MSE

  global_scale: precomputed global scale to use for quantization. Ignored if optimize_global_scale is True

  optimize_global_scale: If True, recompute global_scale from the candidate min/max during each step of the search

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def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)
    observer_kwargs = self.args.observer_kwargs
    self.maxshrink = observer_kwargs.get("maxshrink", 0.20)
    self.patience = observer_kwargs.get("patience", 5)
    self.grid = observer_kwargs.get("grid", 100.0)
    self.norm = observer_kwargs.get("norm", 2.4)

Bases: MovingAverageObserverBase

Compute quantization parameters by finding the optimal min/max values which minimize the mean of quantization error squared.

mse_quant_error := mean((x - fake_quant(x))**2)
global_scale <- min[min_vals, max_vals, global_scale](mse_quant_error(x))
scale, zp <- min[min_vals, max_vals](mse_quant_error(x, global_scale))

Parameters:

• base_name

  –

  str used to name the observer attribute

• args

  –

  quantization args used to calibrate and quantize the observed value

• module

  –

  optional module with attached quantization parameters. This argument is required to utilize existing qparams such as global_scale or g_idx

• **observer_kwargs

  –

  keyword arguments for observer initialization

  maxshrink: maximum shrink amount (in “grid steps”). The number of search steps is int(maxshrink * grid)

  patience: number of consecutive search steps without improvement before early stopping

  grid: resolution of the shrink search. Larger values give finer granularity in shrink factors

  norm: exponent used when computing the error. norm = 2 approximates MSE

  global_scale: precomputed global scale to use for quantization. Ignored if optimize_global_scale is True

  optimize_global_scale: If True, recompute global_scale from the candidate min/max during each step of the search

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def __init__(self, *args, **kwargs):
    super().__init__(*args, **kwargs)
    observer_kwargs = self.args.observer_kwargs
    self.maxshrink = observer_kwargs.get("maxshrink", 0.20)
    self.patience = observer_kwargs.get("patience", 5)
    self.grid = observer_kwargs.get("grid", 100.0)
    self.norm = observer_kwargs.get("norm", 2.4)