chengxianbin
4 years ago
1 changed files with 4 additions and 2 deletions
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mindspore/compression/quant/qat.py
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| @@ -56,7 +56,7 @@ def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQ | |||||
| Default: (QuantDtype.INT8, QuantDtype.INT8) | Default: (QuantDtype.INT8, QuantDtype.INT8) | ||||
| per_channel (Union[bool, list, tuple]): Quantization granularity based on layer or on channel. If `True` | per_channel (Union[bool, list, tuple]): Quantization granularity based on layer or on channel. If `True` | ||||
| then base on per channel otherwise base on per layer. The first element represents weights | then base on per channel otherwise base on per layer. The first element represents weights | ||||
| and second element represents data flow. Default: (False, False) | |||||
| and second element represents data flow, and second element must be `False` now. Default: (False, False) | |||||
| symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then | symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then | ||||
| base on symmetric otherwise base on asymmetric. The first element represents weights and second | base on symmetric otherwise base on asymmetric. The first element represents weights and second | ||||
| element represents data flow. Default: (False, False) | element represents data flow. Default: (False, False) | ||||
| @@ -66,6 +66,8 @@ def create_quant_config(quant_observer=(nn.FakeQuantWithMinMaxObserver, nn.FakeQ | |||||
| Returns: | Returns: | ||||
| QuantConfig, Contains the observer type of weight and activation. | QuantConfig, Contains the observer type of weight and activation. | ||||
| """ | """ | ||||
| if per_channel[-1]: | |||||
| raise ValueError("Arg 'per_channel' second element must be 'False'.") | |||||
| weight_observer = quant_observer[0].partial_init(quant_delay=quant_delay[0], quant_dtype=quant_dtype[0], | weight_observer = quant_observer[0].partial_init(quant_delay=quant_delay[0], quant_dtype=quant_dtype[0], | ||||
| per_channel=per_channel[0], symmetric=symmetric[0], | per_channel=per_channel[0], symmetric=symmetric[0], | ||||
| narrow_range=narrow_range[0]) | narrow_range=narrow_range[0]) | ||||
| @@ -130,7 +132,7 @@ class QuantizationAwareTraining(Quantizer): | |||||
| Default: (QuantDtype.INT8, QuantDtype.INT8) | Default: (QuantDtype.INT8, QuantDtype.INT8) | ||||
| per_channel (Union[bool, list, tuple]): Quantization granularity based on layer or on channel. If `True` | per_channel (Union[bool, list, tuple]): Quantization granularity based on layer or on channel. If `True` | ||||
| then base on per channel otherwise base on per layer. The first element represents weights | then base on per channel otherwise base on per layer. The first element represents weights | ||||
| and second element represents data flow. Default: (False, False) | |||||
| and second element represents data flow, and second element must be `False` now. Default: (False, False) | |||||
| symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then | symmetric (Union[bool, list, tuple]): Whether the quantization algorithm is symmetric or not. If `True` then | ||||
| base on symmetric otherwise base on asymmetric. The first element represents weights and second | base on symmetric otherwise base on asymmetric. The first element represents weights and second | ||||
| element represents data flow. Default: (False, False) | element represents data flow. Default: (False, False) | ||||