Feat (activation_calibration): speed-up by skipping quantization

src/brevitas/core/quant/float.py

@@ -64,11 +64,10 @@ def __init__(
         if dtype is None:
             dtype = torch.get_default_dtype()
         self.eps = torch.finfo(dtype).tiny
+        self.observer_only = brevitas.jit.Attribute(False, bool)
 
     @brevitas.jit.script_method
-    def quantize(self, x: torch.Tensor):
-        scale = self.scaling_impl(x)
-
+    def quantize(self, x: torch.Tensor, scale: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         if self.float_scaling_impl is not None:
             float_scaling_impl_value = self.float_scaling_impl(
                 self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias())
@@ -86,10 +85,15 @@ def dequantize(self, y, scale):
 
     @brevitas.jit.script_method
     def forward(self, x):
-        y, scale = self.quantize(x)
-        # after quantizing, clamp to special cases like NaN/inf if they are set
-        y, saturating, inf_values, nan_values = self.float_clamp_impl(
-            y, self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias())
-        y = self.dequantize(y, scale)
+        scale = self.scaling_impl(x)
+        if self.observer_only:
+            y = x
+            saturating, inf_values, nan_values = self.float_clamp_impl.saturating, self.float_clamp_impl.inf_values, self.float_clamp_impl.nan_values
+        else:
+            y, scale = self.quantize(x, scale)
+            # after quantizing, clamp to special cases like NaN/inf if they are set
+            y, saturating, inf_values, nan_values = self.float_clamp_impl(
+                y, self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias())
+            y = self.dequantize(y, scale)
         # This is to respect the current interface of proxies
         return y, scale, self.zero_point_impl(), self.exponent_bit_width(), self.mantissa_bit_width(), self.exponent_bias(), saturating, inf_values, nan_values

src/brevitas/core/quant/int.py

@@ -145,6 +145,7 @@ def __init__(
         self.int_scaling_impl = int_scaling_impl
         self.zero_point_impl = zero_point_impl
         self.msb_clamp_bit_width_impl = bit_width_impl
+        self.observer_only = brevitas.jit.Attribute(False, bool)
 
     @brevitas.jit.script_method
     def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
@@ -153,7 +154,10 @@ def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor]:
         int_threshold = self.int_scaling_impl(bit_width)
         scale = threshold / int_threshold
         zero_point = self.zero_point_impl(x, scale, bit_width)
-        y = self.int_quant(scale, zero_point, bit_width, x)
+        if self.observer_only:
+            y = x
+        else:
+            y = self.int_quant(scale, zero_point, bit_width, x)
         return y, scale, zero_point, bit_width
 
 
@@ -176,6 +180,7 @@ def __init__(
         self.pre_zero_point_impl = pre_zero_point_impl
         self.zero_point_impl = zero_point_impl
         self.msb_clamp_bit_width_impl = bit_width_impl
+        self.observer_only = brevitas.jit.Attribute(False, bool)
 
     @brevitas.jit.script_method
     def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Tensor]:
@@ -187,7 +192,10 @@ def forward(self, x: Tensor) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor, Te
         threshold = self.scaling_impl(x)
         scale = threshold / int_threshold
         zero_point = self.zero_point_impl(x, scale, bit_width)
-        y = self.decoupled_int_quant(pre_scale, pre_zero_point, scale, zero_point, bit_width, x)
+        if self.observer_only:
+            y = x
+        else:
+            y = self.decoupled_int_quant(pre_scale, pre_zero_point, scale, zero_point, bit_width, x)
         return y, scale, zero_point, bit_width, pre_scale, pre_zero_point
 
 
@@ -253,5 +261,8 @@ def forward(self, x: Tensor, input_bit_width: Tensor,
         threshold = self.scaling_impl(x)
         scale = threshold / int_threshold
         zero_point = self.zero_point_impl(x, scale, bit_width)
-        y = self.decoupled_int_quant(pre_scale, pre_zero_point, scale, zero_point, bit_width, x)
+        if self.observer_only:
+            y = x
+        else:
+            y = self.decoupled_int_quant(pre_scale, pre_zero_point, scale, zero_point, bit_width, x)
         return y, scale, zero_point, bit_width, pre_scale, pre_zero_point

src/brevitas/core/stats/stats_op.py

@@ -442,6 +442,19 @@ def _set_local_loss_mode(module, enabled):
             m.local_loss_mode = enabled
 
 
+def _set_observer_mode(module, enabled, previous_observer_mode):
+    for m in module.modules():
+        if hasattr(m, 'observer_only'):
+            previous_observer_mode[m] = m.observer_only
+            m.observer_only = enabled
+
+
+def _restore_observer_mode(module, previous_observer_mode):
+    for m in module.modules():
+        if hasattr(m, 'observer_only'):
+            m.observer_only = previous_observer_mode[m]
+
+
 class MSE(torch.nn.Module):
     # References:
     # https://github.com/cornell-zhang/dnn-quant-ocs/blob/master/distiller/quantization/clip.py
@@ -459,7 +472,12 @@ def __init__(
         self.mse_init_op = mse_init_op
         self.input_view_shape_impl = inner_stats_input_view_shape_impl
         self.proxy_forward = proxy_module.forward
+        self.previous_observer_mode = dict()
         self.set_local_loss_mode = lambda enabled: _set_local_loss_mode(proxy_module, enabled)
+        self.set_observer_mode = lambda enabled: _set_observer_mode(
+            proxy_module, enabled, self.previous_observer_mode)
+        self.restore_observer_mode = lambda: _restore_observer_mode(
+            proxy_module, self.previous_observer_mode)
         self.internal_candidate = None
         self.num = mse_iters
         self.search_method = mse_search_method
@@ -480,10 +498,12 @@ def evaluate_loss(self, x, candidate):
         self.internal_candidate = candidate
         # Set to local_loss_mode before calling the proxy
         self.set_local_loss_mode(True)
+        self.set_observer_mode(False)
         quant_value = self.proxy_forward(x)
         quant_value = _unpack_quant_tensor(quant_value)
         loss = self.mse_loss_fn(x, quant_value)
         self.set_local_loss_mode(False)
+        self.restore_observer_mode()
         return loss
 
     def mse_grid_search(self, xl, x):
@@ -571,7 +591,12 @@ def __init__(
         self.hqo_init_op = hqo_init_op_scale
         self.input_view_shape_impl = inner_stats_input_view_shape_impl
         self.proxy_forward = proxy_module.forward
+        self.previous_observer_mode = dict()
         self.set_local_loss_mode = lambda enabled: _set_local_loss_mode(proxy_module, enabled)
+        self.set_observer_mode = lambda enabled: _set_observer_mode(
+            proxy_module, enabled, self.previous_observer_mode)
+        self.restore_observer_mode = lambda: _restore_observer_mode(
+            proxy_module, self.previous_observer_mode)
         self.internal_candidate = None
         self.hqo_iters = hqo_iters_scale
         self.stats_reduce_dim = stats_reduce_dim
@@ -598,8 +623,10 @@ def parameter_search(self, xl, x):
             for i in range(0, self.hqo_iters):
                 self.internal_candidate = candidate
                 self.set_local_loss_mode(True)
+                self.set_observer_mode(False)
                 quant_tensor = self.proxy_forward(x).detach()
                 self.set_local_loss_mode(False)
+                self.restore_observer_mode()
                 loss = torch.abs(quant_tensor.value - x).mean()
 
                 best_candidate = torch.where(loss < best_loss, candidate, best_candidate)
@@ -670,7 +697,12 @@ def __init__(
         self.hqo_init_op_zp = hqo_init_op_zp
         self.input_view_shape_impl = inner_stats_input_view_shape_impl
         self.proxy_forward = proxy_module.forward
+        self.previous_observer_mode = dict()
         self.set_local_loss_mode = lambda enabled: _set_local_loss_mode(proxy_module, enabled)
+        self.set_observer_mode = lambda enabled: _set_observer_mode(
+            proxy_module, enabled, self.previous_observer_mode)
+        self.restore_observer_mode = lambda: _restore_observer_mode(
+            proxy_module, self.previous_observer_mode)
         self.internal_candidate = None
         self.stats_reduce_dim = stats_reduce_dim
         self.local_loss_mode: bool = False
@@ -688,8 +720,10 @@ def parameter_search(self, xl, x):
             for i in range(0, self.hqo_iters):
                 self.internal_candidate = candidate
                 self.set_local_loss_mode(True)
+                self.set_observer_mode(False)
                 quant_tensor = self.proxy_forward(x).detach()
                 self.set_local_loss_mode(False)
+                self.restore_observer_mode()
                 qt_value = self.input_view_shape_impl(quant_tensor.value)
                 qt_scale = self.input_view_shape_impl(quant_tensor.scale)
                 qt_zp = self.input_view_shape_impl(quant_tensor.zero_point)

src/brevitas/graph/calibrate.py

@@ -200,8 +200,9 @@ def disable_act_quantization(self, model, is_training):
             if isinstance(module, ActQuantProxyFromInjectorBase):
                 module.train(is_training)
                 if self.call_act_quantizer_impl:
-                    hook = module.register_forward_hook(self.disable_act_quant_hook)
-                    self.disable_act_quant_hooks.append(hook)
+                    for m in module.modules():
+                        if hasattr(m, 'observer_only'):
+                            m.observer_only = True
                 else:
                     module.disable_quant = True
             elif isinstance(module, _ACC_PROXIES):
@@ -228,9 +229,9 @@ def enable_act_quantization(self, model, is_training):
             elif isinstance(module, ActQuantProxyFromInjectorBase):
                 module.disable_quant = False
                 module.train(is_training)
-        for hook in self.disable_act_quant_hooks:
-            hook.remove()
-        self.disable_act_quant_hooks = []
+                for m in module.modules():
+                    if hasattr(m, 'observer_only'):
+                        m.observer_only = False
 
     def enable_param_quantization(self, model, is_training):
         for module in model.modules():

tests/brevitas/core/test_float_quant.py

@@ -98,8 +98,8 @@ def test_float_to_quant_float(inp, minifloat_format):
             signed=signed,
             float_clamp_impl=float_clamp)
         expected_out, *_ = float_quant(inp)
-
-        out_quant, scale = float_quant.quantize(inp)
+        scale = float_quant.scaling_impl(inp)
+        out_quant, scale = float_quant.quantize(inp, scale)
         exponent_bit_width, mantissa_bit_width, exponent_bias  = torch.tensor(exponent_bit_width, dtype=torch.float), torch.tensor(mantissa_bit_width, dtype=torch.float), torch.tensor(exponent_bias, dtype=torch.float)
         out_quant, *_ = float_quant.float_clamp_impl(
             out_quant, exponent_bit_width, mantissa_bit_width, exponent_bias)
@@ -142,7 +142,8 @@ def test_scaling_impls_called_once(inp, minifloat_format):
             scaling_impl=scaling_impl,
             float_scaling_impl=float_scaling_impl,
             float_clamp_impl=float_clamp)
-        _ = float_quant.quantize(inp)
+        scale = float_quant.scaling_impl(inp)
+        _ = float_quant.quantize(inp, scale)
         # scaling implementations should be called exaclty once on the input
         float_scaling_impl.assert_called_once_with(
             torch.tensor(exponent_bit_width),