Resolve pylint failures

Signed-off-by: Michael Tuttle <[email protected]>

TrainingExtensions/torch/src/python/aimet_torch/quantsim_straight_through_grad.py

@@ -44,7 +44,7 @@
 from aimet_torch.tensor_factory_utils import constant_like
 
 if TYPE_CHECKING:
-    from aimet_torch.tensor_quantizer import LearnedGridTensorQuantizer
+    from aimet_torch.tensor_quantizer import LearnedGridTensorQuantizer # pylint:disable = cyclic-import
 
 
 @dataclass

TrainingExtensions/torch/src/python/aimet_torch/tensor_quantizer.py

@@ -855,7 +855,7 @@ def _set_encoding_min_max_parameters(self, encodings: Union[libpymo.TfEncoding,
         enc_min_param = self.name + '_encoding_min'
         enc_max_param = self.name + '_encoding_max'
         # TODO: refactor to not call internal state of wrapper
-        params = self.wrapper_ref._parameters
+        params = self.wrapper_ref._parameters # pylint:disable = no-member
 
         # TODO: Remove this check when encodings is always a sequence
         if isinstance(encodings, List):
@@ -884,7 +884,7 @@ def freeze_encoding(self):
         enc_min_param = self.name + '_encoding_min'
         enc_max_param = self.name + '_encoding_max'
         # TODO: refactor to not call internal state of wrapper.
-        params = self.wrapper_ref._parameters
+        params = self.wrapper_ref._parameters # pylint:disable = no-member
 
         if params[enc_min_param] is None and params[enc_max_param] is None:
             raise RuntimeError("Encoding can be frozen only when it is not None.")

TrainingExtensions/torch/src/python/aimet_torch/utils.py

@@ -61,7 +61,7 @@
 from aimet_common.utils import profile as _profile, deprecated, _red # pylint:disable = unused-import
 import aimet_common.libpymo as libpymo
 import aimet_torch.v1.nn.modules.custom as aimet_modules
-from aimet_torch.tensor_quantizer import TensorQuantizer, StaticGridPerChannelQuantizer, StaticGridPerTensorQuantizer
+from aimet_torch.tensor_quantizer import TensorQuantizer, StaticGridPerChannelQuantizer, StaticGridPerTensorQuantizer # pylint:disable = cyclic-import
 
 logger = AimetLogger.get_area_logger(AimetLogger.LogAreas.Utils)
 
@@ -953,6 +953,7 @@ def get_all_quantizers(model: torch.nn.Module):
     :param model: Root module
     :returns: List of parameter, input, and output quantizers
     """
+    # pylint:disable = cyclic-import
     from aimet_torch.v2.nn.base import BaseQuantizationMixin# pylint: disable=import-outside-toplevel
     from aimet_torch.qc_quantize_op import QcQuantizeWrapper# pylint: disable=import-outside-toplevel
     from aimet_torch.qc_quantize_recurrent import QcQuantizeRecurrent# pylint: disable=import-outside-toplevel

TrainingExtensions/torch/src/python/aimet_torch/v2/quantsim/quantsim.py

@@ -70,7 +70,7 @@ class QuantizationSimModel(V1QuantizationSimModel):
     """
     Overriden QuantizationSimModel that does off-target quantization simulation using v2 quantsim blocks.
     """
-    def __init__(self, # pylint: disable=too-many-arguments
+    def __init__(self, # pylint: disable=too-many-arguments, too-many-locals
                  model: torch.nn.Module,
                  dummy_input: Union[torch.Tensor, Tuple],
                  quant_scheme: Union[str, QuantScheme] = None, # NOTE: Planned to be deprecated

TrainingExtensions/torch/src/python/aimet_torch/v2/utils.py

@@ -282,7 +282,7 @@ def decorator(fn_or_cls: Callable):
 
 def _map_qmodule(modules, func):
     # pylint: disable=import-outside-toplevel
-    # pylint: disable=protected-access
+    # pylint: disable=protected-access, cyclic-import
     from aimet_torch.v2.nn import BaseQuantizationMixin
     contexts = []
     ctx = _ContextManager(action=lambda: None, cleanup=lambda:[context._cleanup() for context in contexts])