Support Fake GroupWise Quant (PaddlePaddle#61900)

python/paddle/nn/quant/format.py

@@ -46,7 +46,14 @@ def from_quanter(quanter):
 
 
 class LinearQuanter(Layer):
-    def __init__(self, scales, zero_point=None, quant_axis=None, bit_length=8):
+    def __init__(
+        self,
+        scales,
+        zero_point=None,
+        quant_axis=None,
+        bit_length=8,
+        group_size=128,
+    ):
         super().__init__()
         scales = paddle.to_tensor(scales, dtype="float32")
         scale_attr = paddle.framework.ParamAttr(
@@ -65,9 +72,21 @@ def __init__(self, scales, zero_point=None, quant_axis=None, bit_length=8):
         )
         self._quant_axis = -1 if quant_axis is None else quant_axis
         self._bit_length = bit_length
+        self._group_size = group_size
 
     def forward(self, input):
         if in_dynamic_mode():
+            if len(self._scales.shape) > 1:
+                bnt = (1 << (self._bit_length - 1)) - 1
+                new_s = paddle.repeat_interleave(
+                    self._scales, self._group_size, 0
+                )
+                quant_weight = paddle.clip(
+                    paddle.round(input.cast('float32') / new_s * bnt),
+                    -bnt - 1,
+                    bnt,
+                )
+                return quant_weight.cast(input.dtype)
             return _C_ops.quantize_linear(
                 input.cast('float32'),
                 self._scales,
@@ -105,7 +124,14 @@ def from_quanter(quanter):
 
 
 class LinearDequanter(Layer):
-    def __init__(self, scales, zero_point=None, quant_axis=None, bit_length=8):
+    def __init__(
+        self,
+        scales,
+        zero_point=None,
+        quant_axis=None,
+        bit_length=8,
+        group_size=128,
+    ):
         super().__init__()
         scales = paddle.to_tensor(scales, dtype="float32")
         scale_attr = paddle.framework.ParamAttr(
@@ -124,9 +150,18 @@ def __init__(self, scales, zero_point=None, quant_axis=None, bit_length=8):
         )
         self._quant_axis = -1 if quant_axis is None else quant_axis
         self._bit_length = bit_length
+        self._group_size = group_size
 
     def forward(self, input):
         if in_dynamic_mode():
+            if len(self._scales.shape) > 1:
+                bnt = (1 << (self._bit_length - 1)) - 1
+                new_s = paddle.repeat_interleave(
+                    self._scales, self._group_size, 0
+                )
+                quant_dequant_weight = input.cast('float32') / bnt * new_s
+                return quant_dequant_weight.cast(input.dtype)
+
             return _C_ops.dequantize_linear(
                 input.cast('float32'),
                 self._scales,

python/paddle/quantization/observers/__init__.py

@@ -14,5 +14,6 @@
 # limitations under the License.
 
 from .abs_max import AbsmaxObserver
+from .groupwise import GroupWiseWeightObserver
 
-__all__ = ["AbsmaxObserver"]
+__all__ = ["AbsmaxObserver", "GroupWiseWeightObserver"]

python/paddle/quantization/observers/groupwise.py

@@ -0,0 +1,113 @@
+# Copyright (c) 2023 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 numpy as np
+
+import paddle
+
+from ..base_observer import BaseObserver
+from ..factory import ObserverFactory
+
+
+class GroupWiseWeightObserver(ObserverFactory):
+    r"""
+    It collects channel-wise maximum absolute values of target weights.
+    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 AbsMaxChannelWiseWeightObserver
+            quanter = AbsMaxChannelWiseWeightObserver()
+            q_config = QuantConfig(activation=None, weight=quanter)
+    """
+
+    def __init__(self, quant_bits=8, group_size=128):
+        super().__init__(quant_bits=quant_bits)
+
+    def _get_class(self):
+        return GroupWiseWeightObserverLayer
+
+
+class GroupWiseWeightObserverLayer(BaseObserver):
+    def __init__(self, layer, quant_bits=8, group_size=128):
+        super().__init__()
+        self.quant_bits = quant_bits
+        self.group_size = group_size
+        self._layer = layer
+        self._max = None
+        self._scale = None
+        self._zero_point = None
+
+    def forward(self, inputs):
+        self._max = self._cal_abs_max(inputs)
+        return inputs
+
+    def _cal_abs_max(self, inputs):
+        """Use group_size to group the input, then use the
+        absmax method to calculate the scale
+        """
+        input_shape = inputs.shape
+        assert (
+            self.group_size == 64 or self.group_size == 128
+        ), "group_size only support 64 or 128"
+        assert (
+            inputs.shape[0] % self.group_size == 0
+        ), "group_size must be a factor of input channels"
+        assert len(inputs.shape) == 2, "Currently only support 2D tensor"
+        input_processed = inputs.transpose([1, 0]).reshape(
+            [input_shape[1], input_shape[0] // self.group_size, self.group_size]
+        )
+
+        abs_max_values = paddle.max(paddle.abs(input_processed), axis=2).cast(
+            "float32"
+        )
+        abs_max_values = paddle.where(
+            abs_max_values == np.float32(0), np.float32(1e-8), abs_max_values
+        )
+        abs_max_values = abs_max_values.transpose([1, 0])
+        return abs_max_values
+
+    def min_value(self) -> float:
+        return 0.0
+
+    def max_value(self) -> float:
+        return self._max
+
+    def bit_length(self):
+        return self._quant_bits
+
+    def quant_axis(self):
+        return -1
+
+    def cal_thresholds(self):
+        """Compute thresholds for MAX function."""
+        if self._scale is None:
+            self._scale = self._max
+        self._zero_point = paddle.zeros_like(self._scale)
+
+    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

test/quantization/test_groupwise.py

@@ -0,0 +1,69 @@
+# copyright (c) 2023 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 os
+import tempfile
+import unittest
+
+import paddle
+from paddle.nn import Linear, Sequential
+from paddle.quantization import PTQ, QuantConfig
+from paddle.quantization.observers import GroupWiseWeightObserver
+
+
+class LinearDygraph(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.fc = Sequential(
+            Linear(128, 128), Linear(128, 128), Linear(128, 128)
+        )
+
+    def forward(self, inputs):
+        out = self.fc(inputs)
+        return out
+
+
+class TestPTQGroupWise(unittest.TestCase):
+    def setUp(self):
+        self.temp_dir = tempfile.TemporaryDirectory()
+        self.path = os.path.join(self.temp_dir.name, 'ptq')
+
+    def tearDown(self):
+        self.temp_dir.cleanup()
+
+    def _get_model_for_ptq(self):
+        observer = GroupWiseWeightObserver(quant_bits=4, group_size=128)
+        model = LinearDygraph()
+        model.eval()
+        q_config = QuantConfig(activation=None, weight=observer)
+        ptq = PTQ(q_config)
+        quant_model = ptq.quantize(model)
+        return quant_model, ptq
+
+    def _count_layers(self, model, layer_type):
+        count = 0
+        for _layer in model.sublayers(True):
+            if isinstance(_layer, layer_type):
+                count += 1
+        return count
+
+    def test_quantize(self):
+        ptq_model, _ = self._get_model_for_ptq()
+        inputs = paddle.rand([128, 128], dtype="float32")
+        out = ptq_model(inputs)
+        self.assertIsNotNone(out)
+
+
+if __name__ == '__main__':
+    unittest.main()