Remove reduce_range as it is not relevant for HTP

Summary:
`reduce_range=True` reduces the available bit width by 1, in cases where quant_min, quant_max are not provided. It was originally intended for intel `fbgemm` kernels but I don't think this quantization setting is relevant for HTP. 
Also, PTQ quantization config doesn't use it, so removing it in all the QAT configs. This helped improve the QAT model quality.

Differential Revision: D82867843

Author: navsud

backends/qualcomm/quantizer/qconfig.py

@@ -205,7 +205,6 @@ def get_16a8w_qnn_qat_config(
         quant_min=torch.iinfo(torch.uint16).min,
         quant_max=torch.iinfo(torch.uint16).max,
         qscheme=torch.per_tensor_affine,
-        reduce_range=True,
         observer=act_observer.with_args(**extra_args),
     )
     act_quantization_spec = QuantizationSpec(
@@ -220,7 +219,6 @@ def get_16a8w_qnn_qat_config(
         quant_min=torch.iinfo(torch.int8).min + 1,
         quant_max=torch.iinfo(torch.int8).max,
         qscheme=torch.per_tensor_symmetric,
-        reduce_range=True,
         observer=MovingAverageMinMaxObserver,
     )
     weight_quantization_spec = QuantizationSpec(
@@ -421,7 +419,6 @@ def get_8a8w_qnn_qat_config(
         quant_min=torch.iinfo(torch.int8).min + 1,
         quant_max=torch.iinfo(torch.int8).max,
         qscheme=torch.per_tensor_symmetric,
-        reduce_range=True,
         observer=MovingAverageMinMaxObserver,
     )
     weight_quantization_spec = QuantizationSpec(
@@ -438,7 +435,6 @@ def get_8a8w_qnn_qat_config(
         quant_min=torch.iinfo(torch.int32).min,
         quant_max=torch.iinfo(torch.int32).max,
         qscheme=torch.per_tensor_symmetric,
-        reduce_range=True,
         observer=MovingAverageMinMaxObserver,
     )
     bias_quantization_spec = QuantizationSpec(
@@ -467,7 +463,6 @@ def get_16a4w_qnn_qat_config(
         quant_min=torch.iinfo(torch.uint16).min,
         quant_max=torch.iinfo(torch.uint16).max,
         qscheme=torch.per_tensor_affine,
-        reduce_range=True,
         observer=act_observer,
     )
     act_quantization_spec = QuantizationSpec(
@@ -484,7 +479,6 @@ def get_16a4w_qnn_qat_config(
         quant_max=7,
         qscheme=torch.per_tensor_symmetric,
         ch_axis=0,
-        reduce_range=True,
         observer=MovingAverageMinMaxObserver,
     )
     weight_quantization_spec = QuantizationSpec(
@@ -501,7 +495,6 @@ def get_16a4w_qnn_qat_config(
         quant_min=torch.iinfo(torch.int32).min,
         quant_max=torch.iinfo(torch.int32).max,
         qscheme=torch.per_tensor_symmetric,
-        reduce_range=True,
         observer=MovingAverageMinMaxObserver,
     )
     bias_quantization_spec = QuantizationSpec(
@@ -551,7 +544,6 @@ def get_qat_per_channel_quant_config(
         act_fake_quant_ctr = FakeQuantize.with_args(
             dtype=torch.int32 if act_dtype == torch.uint16 else act_dtype,
             qscheme=torch.per_tensor_symmetric,
-            reduce_range=True,
             observer=act_observer,
         )
         act_quantization_spec = QuantizationSpec(
@@ -566,7 +558,6 @@ def get_qat_per_channel_quant_config(
             quant_min=torch.iinfo(act_dtype).min,
             quant_max=torch.iinfo(act_dtype).max,
             qscheme=torch.per_tensor_affine,
-            reduce_range=True,
             observer=act_observer,
         )
         act_quantization_spec = QuantizationSpec(