diff --git a/backends/cadence/aot/compiler.py b/backends/cadence/aot/compiler.py
index a54954a3e3c..438f07ba15f 100644
--- a/backends/cadence/aot/compiler.py
+++ b/backends/cadence/aot/compiler.py
@@ -123,7 +123,7 @@ def prepare_and_convert_pt2(
     assert isinstance(model_gm, torch.fx.GraphModule)
 
     # Prepare
-    prepared_model = prepare_pt2e(model_gm, quantizer)  # pyre-ignore[6]
+    prepared_model = prepare_pt2e(model_gm, quantizer)
 
     # Calibrate
     # If no calibration data is provided, use the inputs
diff --git a/examples/xnnpack/quantization/utils.py b/examples/xnnpack/quantization/utils.py
index deb905ab405..d7648daf5da 100644
--- a/examples/xnnpack/quantization/utils.py
+++ b/examples/xnnpack/quantization/utils.py
@@ -33,7 +33,7 @@ def quantize(
         is_dynamic=is_dynamic,
     )
     quantizer.set_global(operator_config)
-    m = prepare_pt2e(model, quantizer)  # pyre-ignore[6]
+    m = prepare_pt2e(model, quantizer)
     # calibration
     m(*example_inputs)
     m = convert_pt2e(m)
diff --git a/extension/llm/export/builder.py b/extension/llm/export/builder.py
index b0da14e965e..feae9f45861 100644
--- a/extension/llm/export/builder.py
+++ b/extension/llm/export/builder.py
@@ -373,7 +373,7 @@ def pt2e_quantize(self, quantizers: Optional[List[Quantizer]]) -> "LLMEdgeManage
                 ), "Please run export() first"
                 m = prepare_pt2e(
                     self.pre_autograd_graph_module,  # pyre-ignore[6]
-                    composed_quantizer,  # pyre-ignore[6]
+                    composed_quantizer,
                 )
                 logging.info(
                     f"Calibrating with tasks: {self.calibration_tasks}, limit: {self.calibration_limit}, calibration_data: {self.calibration_data}, tokenizer_path: {self.tokenizer_path}, seq_length: {self.calibration_seq_length}"