add regularizer

examples/quantization_aware_training/cifar10/basecase/main.py

@@ -5,6 +5,7 @@
 import time
 import warnings
 from enum import Enum
+import math
 
 import torch
 import torch.nn as nn
@@ -27,7 +28,7 @@
     raise NotImplementedError("This example should run on a GPU device.")  # 确定在GPU上运行
 
 
-config = "qconfig_lsq.yaml"  # QAT配置文件——包括量化方式（dorefa/lsq），权重和激活值的量化bit数等
+config = "qconfig_lsq_dampen.yaml"  # QAT配置文件——包括量化方式（dorefa/lsq），权重和激活值的量化bit数等
 workers = 4
 epochs = 200
 start_epoch = 0
@@ -38,8 +39,7 @@
 print_freq = 100
 pretrained = ""
 qconfig = parse_qconfig(config)
-is_pact = qconfig.A.QUANTIZER.TYPE == "pact"
-regularizer_lambda = 1e-4
+regularizer_schedule = "cosine" if qconfig.REGULARIZER.TYPE == "dampen" else "keep"
 
 model = resnet20(num_classes=10)  # 以resnet20作为基础模型
 if pretrained:  # 可以采用pretrained中保存的模型参数
@@ -109,21 +109,8 @@
     optimizer, milestones=[100, 150], last_epoch=start_epoch - 1
 )
 
-# PACT算法中对 alpha 增加 L2-regularization
-def get_pact_regularizer_loss(model):
-    loss = 0
-    for n, p in model.named_parameters():
-        if "alpha" in n:
-            loss += (p ** 2).sum()
-    return loss
-
-def get_regularizer_loss(model, scale=0):
-    if is_pact:
-        return get_pact_regularizer_loss(model) * scale
-    else:
-        return torch.tensor(0.).cuda()
 
-def train(train_loader, model, criterion, optimizer, epoch):
+def train(train_loader, model, criterion, optimizer, epoch, schedule_value=1.0):
     batch_time = AverageMeter("Time", ":6.3f")
     data_time = AverageMeter("Data", ":6.3f")
     losses = AverageMeter("Loss", ":.4e")
@@ -151,7 +138,7 @@ def train(train_loader, model, criterion, optimizer, epoch):
         # compute output
         output = model(images)
         ce_loss = criterion(output, target)
-        regular_loss = get_regularizer_loss(model, scale=regularizer_lambda)
+        regular_loss = model.get_regularizer_loss() * schedule_value
         loss = ce_loss + regular_loss
 
         # measure accuracy and record loss
@@ -311,12 +298,18 @@ def accuracy(output, target, topk=(1,)):
 best_acc1 = 0
 for epoch in range(start_epoch, epochs):
     # train for one epoch
+    if regularizer_schedule == "cosine":
+        coeff = (epoch - start_epoch + 1) / (epochs - start_epoch)
+        schedule_value = 1 - 0.5 * (1.0 + math.cos(math.pi * coeff))
+    else:
+        schedule_value = 1.0
     train(
         trainloader,
         model,
         criterion,
         optimizer,
         epoch,
+        schedule_value=schedule_value,
     )
 
     # evaluate on validation set

examples/quantization_aware_training/cifar10/basecase/qconfig_pact.yaml

@@ -9,3 +9,7 @@ A:
   QUANTIZER:
     TYPE: pact
     BIT: 4
+REGULARIZER:
+  ENABLE: True
+  TYPE: pact
+  LAMBDA: 0.0001

sparsebit/quantization/quant_config.py

@@ -38,6 +38,11 @@
 _C.A.OBSERVER.LAYOUT = "NCHW"  # NCHW / NLC
 _C.A.SPECIFIC = []
 
+_C.REGULARIZER = CN()
+_C.REGULARIZER.ENABLE = False
+_C.REGULARIZER.TYPE = ""
+_C.REGULARIZER.LAMBDA = 0.0
+
 
 def parse_qconfig(cfg_file):
     qconfig = _parse_config(cfg_file, default_cfg=_C)

sparsebit/quantization/quant_model.py

@@ -20,6 +20,7 @@
 from sparsebit.quantization.quantizers import Quantizer
 from sparsebit.quantization.tools import QuantizationErrorProfiler
 from sparsebit.quantization.converters import simplify, fuse_operations
+from sparsebit.quantization.regularizers import build_regularizer
 
 
 __all__ = ["QuantModel"]
@@ -34,6 +35,7 @@ def __init__(self, model: nn.Module, config):
         self._run_simplifiers()
         self._convert2quantmodule()
         self._build_quantizer()
+        self._build_regularizer()
         self._run_fuse_operations()
 
     def _convert2quantmodule(self):
@@ -119,11 +121,17 @@ def _sub_build(src, module_name):
                         update_config(_config, "A", _sub_build(self.cfg.A, node.target))
                         identity_module.build_quantizer(_config)
 
+    def _build_regularizer(self):
+        if self.cfg.REGULARIZER.ENABLE:
+            self.regularizer = build_regularizer(self.cfg)
+        else:
+            self.regularizer = None
+
     def _run_simplifiers(self):
         self.model = simplify(self.model)
 
     def _run_fuse_operations(self):
-        if self.cfg.SCHEDULE.BN_TUNING: # first disable fuse bn
+        if self.cfg.SCHEDULE.BN_TUNING:  # first disable fuse bn
             update_config(self.cfg.SCHEDULE, "FUSE_BN", False)
         self.model = fuse_operations(self.model, self.cfg.SCHEDULE)
         self.model.graph.print_tabular()
@@ -144,7 +152,9 @@ def batchnorm_tuning(self):
         yield
         self.model.eval()
         update_config(self.cfg.SCHEDULE, "FUSE_BN", True)
-        self.model = fuse_operations(self.model, self.cfg.SCHEDULE, custom_fuse_list=["fuse_bn"])
+        self.model = fuse_operations(
+            self.model, self.cfg.SCHEDULE, custom_fuse_list=["fuse_bn"]
+        )
         self.set_quant(w_quant=False, a_quant=False)
 
     def prepare_calibration(self):
@@ -210,6 +220,12 @@ def set_quant(self, w_quant=False, a_quant=False):
             if isinstance(m, QuantOpr):
                 m.set_quant(w_quant, a_quant)
 
+    def get_regularizer_loss(self):
+        if self.regularizer is None:
+            return torch.tensor(0.).to(self.device)
+        else:
+            return self.regularizer(self.model)
+
     def export_onnx(
         self,
         dummy_data,

sparsebit/quantization/regularizers/__init__.py

@@ -7,7 +7,7 @@ def register_regularizer(regularizer):
 
 
 from .base import Regularizer
-from . import dampen
+from . import dampen, pact
 
 
 def build_regularizer(config):