[quant] Use PlaceholderObserver as default dynamic quant observer

torch/quantization/observer.py

@@ -475,82 +475,6 @@ def forward(self, x_orig):
         self.max_val.copy_(max_val)
         return x_orig
 
-
-class MinMaxDynamicQuantObserver(MinMaxObserver):
-    r"""Observer module for computing the quantization parameters based on the
-    tensor min and max values in dynamic quantization.
-
-    This observer will mimic the quantization steps followed in the operator
-    to compute the activation tensor quantization parameters at run-time.
-
-    Args:
-        dtype: Quantized data type
-        qscheme: Quantization scheme to be used
-        reduce_range: Reduces the range of the quantized data type by 1 bit
-
-    .. warning:: Only works with ``torch.per_tensor_symmetric`` quantization scheme
-
-    .. warning:: :attr:`dtype` can only take ``torch.qint8`` or ``torch.quint8``.
-
-    .. note:: If the running minimum equals to the running maximum, the scale
-              and zero_point are set to 0.1 and 0.
-    """
-
-    @torch.jit.export
-    def calculate_qparams(self):
-        r"""Calculates the quantization parameters."""
-
-        if self.max_val == float('-inf') and self.min_val == float('inf'):
-            return torch.tensor([1.0]), torch.tensor([0])
-
-        assert self.min_val <= self.max_val, "min {} should be less than max {}".format(
-            self.min_val, self.max_val
-        )
-
-        if self.dtype == torch.qint8:
-            if self.reduce_range:
-                qmin, qmax = -64, 63
-            else:
-                qmin, qmax = -128, 127
-        else:  # dtype == torch.quint8
-            if self.reduce_range:
-                qmin, qmax = 0, 127
-            else:
-                qmin, qmax = 0, 255
-
-        max_val, min_val = self.max_val.to(dtype=torch.float), self.min_val.to(dtype=torch.float)
-
-        # Extend the min_val and max_val to ensure that it contains 0.
-        min_val = torch.min(min_val, torch.tensor(0.).to(dtype=torch.float))
-        max_val = torch.max(max_val, torch.tensor(0.).to(dtype=torch.float))
-
-        scale = (max_val.to(dtype=torch.double) - min_val) / float(qmax - qmin)
-
-        if scale == 0.0 or torch.isinf(1.0 / scale):
-            scale = torch.tensor(0.1).to(dtype=torch.float)
-            zero_point = 0
-
-        zero_point_from_min = qmin - min_val / scale.to(dtype=torch.double)
-        zero_point_from_max = qmax - max_val / scale.to(dtype=torch.double)
-        zero_point_from_min_error = abs(qmin) - abs(min_val / scale.to(dtype=torch.double))
-        zero_point_from_max_error = abs(qmax) - abs(max_val / scale.to(dtype=torch.double))
-
-        if zero_point_from_min_error < zero_point_from_max_error:
-            initial_zero_point = zero_point_from_min
-        else:
-            initial_zero_point = zero_point_from_max
-
-        nudged_zero_point = 0
-
-        if initial_zero_point < qmin:
-            nudged_zero_point = qmin
-        elif initial_zero_point > qmax:
-            nudged_zero_point = qmax
-        else:
-            nudged_zero_point = int(initial_zero_point.round())
-
-        return scale.to(dtype=torch.float), torch.tensor([nudged_zero_point])
-
 class PerChannelMinMaxObserver(_ObserverBase):
     r"""Observer module for computing the quantization parameters based on the
     running per channel min and max values.
@@ -1099,7 +1023,7 @@ def calculate_qparams(self):
 default_weight_observer = MinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_tensor_symmetric)
 default_histogram_observer = HistogramObserver.with_args(reduce_range=True)
 default_per_channel_weight_observer = PerChannelMinMaxObserver.with_args(dtype=torch.qint8, qscheme=torch.per_channel_symmetric)
-default_dynamic_quant_observer = MinMaxDynamicQuantObserver
+default_dynamic_quant_observer = PlaceholderObserver.with_args(dtype=torch.float)
 default_float_qparams_observer = PerChannelMinMaxObserver.with_args(dtype=torch.quint8,
                                                                     qscheme=torch.per_channel_affine_float_qparams,
                                                                     ch_axis=0)