add new post-quant methods #32208
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add new post-quant methods #32208
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Thanks for your contribution! |
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请为新增代码补充下单测。 |
juncaipeng
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Apr 13, 2021
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请添加相应单侧，要不然代码覆盖率达不到，参考../tests/下面的单侧示例。
不同方法的实验数据最好贴到pr上，包括量化模型精度、量化过程的时间等。
| batch_size=10, | ||
| batch_nums=None, | ||
| algo="KL", | ||
| hist_perc=0.99999, |
| hist_perc=0.99999, | ||
| quantizable_op_type=["conv2d", "depthwise_conv2d", "mul"], | ||
| is_full_quantize=False, | ||
| bias_correct=False, |
python/paddle/fluid/contrib/slim/quantization/post_training_quantization.py
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| if self._batch_nums and batch_id >= self._batch_nums: | ||
| break | ||
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|
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| if self._algo == 'avg': |
| if self._algo == "abs_max": | ||
| self._sample_abs_max() | ||
| if self._algo in ["avg", "abs_max"]: | ||
| self._sample_abs_max_avg() |
| if mse_loss <= self._best_mse_loss[var_name]: | ||
| self._best_mse_loss[var_name] = mse_loss | ||
| best_scale = scale | ||
| if best_scale > 0.0: |
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这个判断没有必要，self._quantized_threshold[var_name] = best_scale 可以放到if mse_loss <= self._best_mse_loss[var_name]:中
| save_info( | ||
| op_node, out_var_name, self._quantized_threshold, | ||
| argname_index[0] + str(argname_index[1]) + "_threshold", | ||
| "post_absmax") |
| if self._algo == 'avg': | ||
| if (var_name not in self._quantized_var_avg): | ||
| self._quantized_var_avg[var_name] = [] | ||
| abs_avg_value = float(np.mean(np.max(np.abs(var_tensor.reshape(var_tensor.shape[0], -1)), axis=(1)))) |
| quantized_param_v = self._quant( | ||
| param_v, scale_v, self._weight_bits, quant_axis) | ||
| param_v.copy(), scale_v, self._weight_bits, quant_axis) | ||
| if self._bias_correct == True: |
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将bias_correction功能独立为一个函数实现
| if isinstance(scale_v, list): | ||
| if quant_axis == 0: | ||
| for i, s in enumerate(scale_v): | ||
| quantized_param_v[i] = quantized_param_v[i] * s / bnt |
wanghaoshuang
approved these changes
Apr 14, 2021
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PR types
Performance optimization
PR changes
APIs
Describe
1.Add new methods: 'mse', 'hist', 'avg' of getting threshold values of activations for post-training quantization in 'post_training_quantization.py'.
2.Add bias correction method in of https://arxiv.org/abs/1810.05723 for post-training quantization in 'quantization_pass.py'. Bias correction changes the quantized weights by the following formulation:
Experimental results on 6-bit MobileNetV1 which calibrated by a batch of 32 images:
abs_max: 44.99 abs_max+bias_correction: 47.91
avg: 56.34 avg+bias_correction: 56.17
mse: 61.32 mse+bias_correction: 61.42
hist(0.9999): 60.72 hist+bias_correction: 62.44
KL: 53.11 KL+bias_correction: 58.46
Time cost: Abs_max and avg cost about 1 minute; hist and KL cost about 6 minutes; mse cost about 10 minutes. Bias correction cost little time.