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[Inference] Update fakequant (#9140)
* add a8w8(fp8) a8w8c8(int8) quant_type support * add llama3.1 and qwen2 ptq config * reformat quantization.md and argument.py * update prepare data method for ceval ptq * fix wint4 config bug * use independent avg/abs_max observer * rename fp8 quant_type * update quantization.md * remove ceval in run_finetune.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import paddle | ||
| from paddle.quantization.factory import ObserverFactory | ||
|
|
||
| from .uniform import UniformObserver | ||
|
|
||
|
|
||
| class AbsmaxObserver(ObserverFactory): | ||
| r""" | ||
| It collects maximum absolute values of target tensor. | ||
| Args: | ||
| bit_length(int, optional): Number of bits to represent an quantized integer in binary. | ||
| dtype(str, optional): The data type of input tensor. | ||
| name (str, optional): This parameter is used by developers to print debugging information. \ | ||
| For details, please refer to :ref:`api_guide_Name`. Default is None. | ||
| Examples: | ||
| .. code-block:: python | ||
| from paddle.quantization import QuantConfig | ||
| from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver | ||
| quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.99) | ||
| q_config = QuantConfig(activation=quanter, weight=quanter) | ||
| """ | ||
|
|
||
| def __init__(self, quant_bits=8): | ||
| super(AbsmaxObserver, self).__init__(quant_bits=quant_bits) | ||
|
|
||
| def _get_class(self): | ||
| return AbsmaxObserverLayer | ||
|
|
||
|
|
||
| class AbsmaxObserverLayer(UniformObserver): | ||
| def __init__( | ||
| self, | ||
| layer, | ||
| quant_bits=8, | ||
| ): | ||
| super(AbsmaxObserverLayer, self).__init__(quant_bits=quant_bits) | ||
| self._quant_bits = quant_bits | ||
| self._layer = layer | ||
| self._scale = None | ||
| self._zero_point = None | ||
| self._min = None | ||
| self._max = paddle.to_tensor(1e-7, dtype="float32") | ||
| self.step = 0 | ||
|
|
||
| def forward(self, inputs): | ||
| """Calculate forward pass.""" | ||
| self._min, self._max = self.cal_min_max(inputs) | ||
| return inputs | ||
|
|
||
| def cal_min_max(self, inputs): | ||
| abs_max_val = paddle.max(paddle.abs(inputs.cast("float32"))) | ||
| abs_max_val = paddle.maximum(abs_max_val, self._max) | ||
| return 0, abs_max_val | ||
|
|
||
| def cal_thresholds(self): | ||
| """Compute thresholds for MAX function.""" | ||
| if self._scale is not None: | ||
| self._zero_point = 0 | ||
| return | ||
| self._scale, self._zero_point = self.cal_scales_zero_points() | ||
|
|
||
| def min_value(self) -> float: | ||
| return self._min | ||
|
|
||
| def max_value(self) -> float: | ||
| return self._max | ||
|
|
||
| def bit_length(self): | ||
| """Return the bit length of quantized data.""" | ||
| return self._quant_bits | ||
|
|
||
| def quant_axis(self): | ||
| """Return quantization axis.""" | ||
| return -1 | ||
|
|
||
| def scales(self): | ||
| """Return output scales.""" | ||
| if self._scale is None: | ||
| self.cal_thresholds() | ||
| return self._scale | ||
|
|
||
| def zero_points(self): | ||
| """Return output zero points.""" | ||
| if self._zero_point is None: | ||
| self.cal_thresholds() | ||
| return self._zero_point |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,102 @@ | ||
| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import paddle | ||
| from paddle.quantization.factory import ObserverFactory | ||
|
|
||
| from .uniform import UniformObserver | ||
|
|
||
|
|
||
| class AVGObserver(ObserverFactory): | ||
| r""" | ||
| It collects maximum absolute values of target tensor. | ||
| Args: | ||
| bit_length(int, optional): Number of bits to represent an quantized integer in binary. | ||
| dtype(str, optional): The data type of input tensor. | ||
| name (str, optional): This parameter is used by developers to print debugging information. \ | ||
| For details, please refer to :ref:`api_guide_Name`. Default is None. | ||
| Examples: | ||
| .. code-block:: python | ||
| from paddle.quantization import QuantConfig | ||
| from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver | ||
| quanter = FakeQuanterWithAbsMaxObserver(moving_rate=0.99) | ||
| q_config = QuantConfig(activation=quanter, weight=quanter) | ||
| """ | ||
|
|
||
| def __init__(self, quant_bits=8): | ||
| super(AVGObserver, self).__init__(quant_bits=quant_bits) | ||
|
|
||
| def _get_class(self): | ||
| return AVGObserverLayer | ||
|
|
||
|
|
||
| class AVGObserverLayer(UniformObserver): | ||
| def __init__( | ||
| self, | ||
| layer, | ||
| quant_bits=8, | ||
| ): | ||
| super(AVGObserverLayer, self).__init__(quant_bits=quant_bits) | ||
| self._quant_bits = quant_bits | ||
| self._avg_list = [] | ||
|
|
||
| def forward(self, inputs): | ||
| """Calculate forward pass.""" | ||
| self._scale = None | ||
| self._zero_point = None | ||
| self._min = None | ||
| self._max = None | ||
| self._avg_min, self._avg_max = self.cal_min_max(inputs) | ||
| self._avg_list.append(self._avg_max) | ||
|
|
||
| return inputs | ||
|
|
||
| def cal_min_max(self, inputs): | ||
| abs_avg_value = paddle.abs(inputs.reshape((inputs.shape[0], -1))) | ||
| abs_avg_value = float(paddle.mean(paddle.max(abs_avg_value, axis=(1)))) | ||
| return 0, abs_avg_value | ||
|
|
||
| def cal_thresholds(self): | ||
| """Compute thresholds for MAX function.""" | ||
| if self._scale is not None: | ||
| self._zero_point = 0 | ||
| return | ||
| self._min, self._max = self._avg_min, paddle.mean(paddle.to_tensor(self._avg_list)) | ||
| self._scale, self._zero_point = self.cal_scales_zero_points() | ||
|
|
||
| def min_value(self) -> float: | ||
| return self._min | ||
|
|
||
| def max_value(self) -> float: | ||
| return self._max | ||
|
|
||
| def bit_length(self): | ||
| """Return the bit length of quantized data.""" | ||
| return self._quant_bits | ||
|
|
||
| def quant_axis(self): | ||
| """Return quantization axis.""" | ||
| return -1 | ||
|
|
||
| def scales(self): | ||
| """Return output scales.""" | ||
| if self._scale is None: | ||
| self.cal_thresholds() | ||
| return self._scale | ||
|
|
||
| def zero_points(self): | ||
| """Return output zero points.""" | ||
| if self._zero_point is None: | ||
| self.cal_thresholds() | ||
| return self._zero_point |
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