add regularizer

examples/quantization_aware_training/cifar10/basecase/qconfig_lsq_dampen.yaml

@@ -0,0 +1,15 @@
+BACKEND: virtual
+W:
+  QSCHEME: per-channel-symmetric
+  QUANTIZER: 
+    TYPE: lsq
+    BIT: 4
+A:
+  QSCHEME: per-tensor-affine
+  QUANTIZER:
+    TYPE: lsq
+    BIT: 4
+REGULARIZER:
+  ENABLE: True
+  TYPE: dampen
+  LAMBDA: 0.01

sparsebit/quantization/regularizers/__init__.py

@@ -0,0 +1,15 @@
+REGULARIZERS_MAP = {}
+
+
+def register_regularizer(regularizer):
+    REGULARIZERS_MAP[regularizer.TYPE.lower()] = regularizer
+    return regularizer
+
+
+from .base import Regularizer
+from . import dampen
+
+
+def build_regularizer(config):
+    regularizer = REGULARIZERS_MAP[config.REGULARIZER.TYPE.lower()](config)
+    return regularizer

sparsebit/quantization/regularizers/base.py

@@ -0,0 +1,6 @@
+class Regularizer(object):
+    def __init__(self, config):
+        self.config = config
+
+    def __call__(self):
+        pass

sparsebit/quantization/regularizers/dampen.py

@@ -0,0 +1,49 @@
+import torch
+
+from sparsebit.quantization.regularizers import Regularizer as BaseRegularizer
+from sparsebit.quantization.regularizers import register_regularizer
+
+
+@register_regularizer
+class Regularizer(BaseRegularizer):
+    TYPE = "Dampen"
+
+    def __init__(self, config):
+        super(Regularizer, self).__init__(config)
+        self.config = config
+        self._lambda = config.REGULARIZER.LAMBDA
+
+    def _get_loss(self, x, quantizer):
+
+        x_q = quantizer(x)
+
+        qmin, qmax = quantizer.qdesc.qrange
+
+        scale, zero_point = quantizer._qparams_preprocess(x)
+
+        scale = scale.detach()
+        zero_point = zero_point.detach()
+
+        min_val = (qmin - zero_point) * scale
+
+        max_val = (qmax - zero_point) * scale
+
+        x_c = torch.min(torch.max(x, min_val), max_val)
+
+        loss = (x_q - x_c) ** 2
+
+        loss = loss.sum()
+
+        return loss
+
+    def __call__(self, model):
+        loss = 0.0
+        for n, m in model.named_modules():
+            if (
+                hasattr(m, "weight")
+                and hasattr(m, "weight_quantizer")
+                and m.weight_quantizer
+                and m.weight_quantizer.is_enable
+            ):
+                loss += self._get_loss(m.weight, m.weight_quantizer)
+        return loss * self._lambda

sparsebit/quantization/regularizers/pact.py

@@ -0,0 +1,21 @@
+import torch
+
+from sparsebit.quantization.regularizers import Regularizer as BaseRegularizer
+from sparsebit.quantization.regularizers import register_regularizer
+
+
+@register_regularizer
+class Regularizer(BaseRegularizer):
+    TYPE = "Pact"
+
+    def __init__(self, config):
+        super(Regularizer, self).__init__(config)
+        self.config = config
+        self._lambda = config.REGULARIZER.LAMBDA
+
+    def __call__(self, model):
+        loss = 0.0
+        for n, p in model.named_parameters():
+            if "alpha" in n:
+                loss += (p ** 2).sum()
+        return loss * self._lambda