Feat (graph): flag to enable/disable stats collection with disabled q…

…uant (#641)

* Fix (act_quant): flag to enable/disable stats collection

* review

src/brevitas/graph/calibrate.py

@@ -62,7 +62,7 @@ class calibration_mode:
     def __init__(self, model, enabled=True):
         self.model = model
         self.previous_training_state = model.training
-        self.disable_quant_inference = DisableEnableQuantization()
+        self.disable_quant_inference = DisableEnableQuantization(call_act_quantizer_impl=True)
         self.enabled = enabled
 
     def __enter__(self):
@@ -111,9 +111,10 @@ def apply(self, model):
 
 class DisableEnableQuantization(Transform):
 
-    def __init__(self):
+    def __init__(self, call_act_quantizer_impl=False):
         super(DisableEnableQuantization, self).__init__()
         self.disable_act_quant_hooks = []
+        self.call_act_quantizer_impl = call_act_quantizer_impl
 
     def unpack_input(self, inp):
         if isinstance(inp, tuple):
@@ -136,11 +137,17 @@ def disable_act_quant_hook(self, module, inp, output):
         return QuantTensor(value=inp, training=module.training)
 
     def disable_act_quantization(self, model, is_training):
+        # If self.call_act_quantizer_impl is set to True, the quantization will be performed but the output
+        # will be discarded through the hook. It is useful for collecting activation stats,
+        # for example during activation calibration in PTQ
         for module in model.modules():
             if isinstance(module, ActQuantProxyFromInjector):
-                hook = module.register_forward_hook(self.disable_act_quant_hook)
                 module.train(is_training)
-                self.disable_act_quant_hooks.append(hook)
+                if self.call_act_quantizer_impl:
+                    hook = module.register_forward_hook(self.disable_act_quant_hook)
+                    self.disable_act_quant_hooks.append(hook)
+                else:
+                    module.disable_quant = True
             elif isinstance(module, _ACC_PROXIES):
                 module.train(is_training)
                 module.disable_quant = True
@@ -163,6 +170,7 @@ def enable_act_quantization(self, model, is_training):
                 module.train(is_training)
                 module.disable_quant = False
             elif isinstance(module, ActQuantProxyFromInjector):
+                module.disable_quant = False
                 module.train(is_training)
         for hook in self.disable_act_quant_hooks:
             hook.remove()

src/brevitas/proxy/runtime_quant.py

@@ -92,7 +92,7 @@ def __init__(self, quant_layer, quant_injector):
 
     @property
     def is_quant_enabled(self):
-        return self._is_quant_enabled
+        return self._is_quant_enabled and not self.disable_quant
 
     @is_quant_enabled.setter
     def is_quant_enabled(self, is_quant_enabled):
@@ -142,9 +142,12 @@ def forward(self, x: Union[Tensor, QuantTensor]) -> QuantTensor:
             y = x
             if isinstance(y, QuantTensor):
                 y = y.value
+
             if self.export_mode:
                 y = self.fused_activation_quant_proxy.activation_impl(y)
                 y = self.export_handler(y)
+            elif not self.is_quant_enabled:
+                y = self.fused_activation_quant_proxy.activation_impl(y)
             else:
                 y = self.fused_activation_quant_proxy(y)
             # If y is an empty QuantTensor, we need to check if this is a passthrough proxy,
@@ -156,6 +159,8 @@ def forward(self, x: Union[Tensor, QuantTensor]) -> QuantTensor:
                     y = y[0]
                 return QuantTensor(y, x.scale, x.zero_point, x.bit_width, x.signed, self.training)
             else:
+                if isinstance(y, tuple):
+                    y = y[0]
                 return QuantTensor(y, training=self.training)
         else:
             if isinstance(x, QuantTensor):  # passthrough