def quantize_APoT(self, tensor2quantize: Tensor):
result = torch.tensor([])
# map float_to_apot over tensor2quantize elements
result = tensor2quantize.apply_(lambda x: float_to_apot(
x, self.quantization_levels, self.level_indices))
return result
def quant_levels_visualization(self, obs_result, filename):
xs = [float(x) / 1000.0 for x in range(1000)]
ys = [apot_to_float(float_to_apot(x, obs_result[1], obs_result[2]),
obs_result[1], obs_result[2]).item() for x in xs]
f = plt.figure(figsize=(15, 10))
plt.plot(xs, ys)
plt.title("APoT Quantization Plot")
plt.xlabel("Full Precision")
plt.ylabel("Quantized")
plt.show()
def quantize(self, tensor2quantize: Tensor):
result = torch.tensor([])
# map float_to_apot over tensor2quantize elements
tensor2quantize = tensor2quantize.apply_(lambda x: float_to_apot(
x, self.quantization_levels, self.level_indices, self.alpha))
from torch.ao.quantization.experimental.APoT_tensor import TensorAPoT
result = TensorAPoT(self, tensor2quantize)
return result
def quant_levels_visualization(self, signed=False):
alpha, gamma, quantization_levels, level_indices = self.calculate_qparams(signed)
xs = [float(x) / 1000.0 for x in range(1000)]
ys = [apot_to_float(float_to_apot(x, quantization_levels, level_indices, alpha),
quantization_levels, level_indices).item() for x in xs]
f = plt.figure(figsize=(15, 10))
plt.plot(xs, ys)
plt.title("APoT Quantization Plot")
plt.xlabel("Full Precision")
plt.ylabel("Quantized")
plt.show()