Fix non-existing docstring parameters (#4827)

Author: kit1980

backends/apple/mps/operators/node_visitor.py

@@ -77,7 +77,7 @@ def define_tensor(
         """Defines a tensor value into the MPSGraph serialization schema
 
         Args:
-            tensor (torch.fx.Node): EdgeIR tensor to define into mps_graph
+            node (torch.fx.Node): EdgeIR tensor to define into mps_graph
             mps_graph (MPSGraph): MPSGraph object for serializing into flatbuffer
         """
 
@@ -155,7 +155,7 @@ def define_constant(
         """Defines a scalar value into the MPSGraph serialization schema
 
         Args:
-            tensor (torch.fx.Node): EdgeIR tensor to define into mps_graph
+            constant_tensor (torch.fx.Node): EdgeIR tensor to define into mps_graph
             mps_graph (MPSGraph): MPSGraph object for serializing into flatbuffer
         """
         constant_tensor = constant_tensor.contiguous()
@@ -191,7 +191,6 @@ def define_scalar(
         """Defines a scalar value into the MPSGraph serialization schema
 
         Args:
-            tensor (torch.fx.Node): EdgeIR tensor to define into mps_graph
             mps_graph (MPSGraph): MPSGraph object for serializing into flatbuffer
         """
         assert isinstance(val, int) or isinstance(val, float)
@@ -229,7 +228,7 @@ def get_serialized_buffer(
         index of its placement in the constant buffer
 
         Args:
-            tensor (torch.fx.Node): _description_
+            node (torch.fx.Node): _description_
             mps_graph (MPSGraph): _description_
 
         Returns:
@@ -299,7 +298,7 @@ def get_serialized_id(
         the existent id.
 
         Args:
-            tensor (Union[torch.fx.Node, float]): _description_
+            node (Union[torch.fx.Node, float]): _description_
             mps_graph (MPSGraph): _description_
 
         Returns:

backends/apple/mps/utils/mps_utils.py

@@ -73,7 +73,7 @@ def is_parameter(exp_prog: torch.export.ExportedProgram, node: torch.fx.Node) ->
     are supplied as inputs to the graph.
 
     Args:
-        edge_program (torch.export.ExportedProgram): _description_
+        exp_prog (torch.export.ExportedProgram): _description_
         node (torch.fx.Node): _description_
 
     Returns:

backends/arm/test/runner_utils.py

@@ -275,10 +275,10 @@ def run_tosa_ref_model(
         inputs: Tuple[torch.Tensor],
     ) -> list[torch.Tensor]:
         """
-        Run TOSA reference model using the tosa_refence_model program.
+        Run TOSA reference model using the tosa_reference_model program.
 
         In order to do that we need:
-        1. desc.json, which points to files needed by tosa_refence_model.
+        1. desc.json, which points to files needed by tosa_reference_model.
         2. output.tosa, which is the TOSA buffer that describes the model we're
            trying to run.
 
@@ -287,12 +287,6 @@ def run_tosa_ref_model(
         All these files are saved on disk in self.intermediate_path.
 
         Args:
-            params_input (Tuple[List[str], List[QuantizationParams]]): A tuple
-                containing a list of input node names and a list of their
-                quantization parameters (if model is quantized).
-            param_output (Tuple[str, QuantizationParams]): A tuple containing
-                the output node name and its quantization parameters (if
-                model is quantized).
             inputs (Tuple[torch.Tensor]): The input data to run the TOSA
 
         Returns:
@@ -423,7 +417,7 @@ def save_npy(
     Parameters:
         path: the directory where to save the data.
         data: the data to save.
-        is_quantize: whether to quantize the data before saving it.
+        is_quantized: whether to quantize the data before saving it.
         input_name: the name of the file, without file-ending.
         quant_param: the parameters to use for quantization.
     Returns:
@@ -448,7 +442,7 @@ def save_bytes(
     Parameters:
         path: the directory where to save the data.
         data: the data to save.
-        is_quantize: whether to quantize the data before saving it.
+        is_quantized: whether to quantize the data before saving it.
         input_name: the name of the file, without file-ending.
         quant_param: the parameters to use for quantization.
     Returns:

backends/cadence/aot/quantizer/utils.py

@@ -145,7 +145,7 @@ def get_aten_node_target_partitions(
     """
     Args:
         graph: The graph we want to partition
-        wanted_sources: List of orginal_aten ops (OpOverload)
+        wanted_original_aten_op: List of original_aten ops (OpOverload)
 
     Returns:
         Dictionary mapping aten ops that were given to a list of SourcePartitions

examples/models/llama2/evaluate/eager_eval.py

@@ -99,7 +99,7 @@ def evaluate_model(
 
     Args:
         eval_wrapper (LM): A LM wrapper class compatible with lm-evaluation-harness evaluation
-        task (str): The name of the evaluation task to perform.
+        tasks: Optional[list]: The names of the evaluation tasks to perform.
         limit (Optional[int]): The maximum number of samples to evaluate (None for all available).
 
     Returns:

examples/models/llama2/tokenizer/tiktoken.py

@@ -116,16 +116,16 @@ def encode(
             s (str): The input string to be encoded.
             bos (bool): Whether to prepend the beginning-of-sequence token.
             eos (bool): Whether to append the end-of-sequence token.
-            allowed_tokens ("all"|set[str]): allowed special tokens in string
-            disallowed_tokens ("all"|set[str]): special tokens that raise an error when in string
+            allowed_special ("all"|set[str]): allowed special tokens in string
+            disallowed_special ("all"|set[str]): special tokens that raise an error when in string
 
         Returns:
             list[int]: A list of token IDs.
 
         By default, setting disallowed_special=() encodes a string by ignoring
         special tokens. Specifically:
         - Setting `disallowed_special` to () will cause all text corresponding
-          to special tokens to be encoded as natural text (insteading of raising
+          to special tokens to be encoded as natural text (instead of raising
           an error).
         - Setting `allowed_special` to "all" will treat all text corresponding
           to special tokens to be encoded as special tokens.

examples/qualcomm/oss_scripts/ssd300_vgg16.py

@@ -28,7 +28,6 @@ def create_data_lists(voc07_path, data_size):
     Create lists of images, the bounding boxes and labels of the objects in these images, and save these to file.
 
     :param voc07_path: path to the 'VOC2007' folder
-    :param output_folder: folder where the JSONs must be saved
     """
     from utils import parse_annotation
 

examples/sdk/scripts/export_bundled_program.py

@@ -37,7 +37,7 @@ def save_bundled_program(
     Generates a bundled program from the given ET program and saves it to the specified path.
 
     Args:
-        program: The ExecuTorch program to bundle.
+        executorch_program: The ExecuTorch program to bundle.
         method_test_suites: The MethodTestSuites which contains test cases to include in the bundled program.
         output_path: Path to save the bundled program.
     """

extension/gguf_util/converters/llama_converter.py

@@ -99,8 +99,8 @@ def convert_to_pte(gguf_model_args: GGUFModelArgs, gguf_weights: GGUFWeights) ->
     """Convert a GGUF model into an ExecuTorch program.
 
     Args:
-        model_args: The arguments for the GGUF model.
-        weights: The weights of the GGUF model.
+        gguf_model_args: The arguments for the GGUF model.
+        gguf_weights: The weights of the GGUF model.
     """
 
     assert (

extension/llm/export/builder.py

@@ -227,7 +227,7 @@ def to_backend(self, partitioners: Optional[List[Partitioner]]) -> "LLMEdgeManag
         Partition the model and lower to different backends. The signature is
         aligned with the signature of `to_backend` method of EdgeManager.
         Args:
-            partitioner (Optional[Partitioner]): One or more
+            partitioners (Optional[List[Partitioner]]): One or more
                 partitioner to be sent to EdgeManager.to_backend().
         """
         if partitioners is None:

sdk/bundled_program/config.py

@@ -39,7 +39,7 @@
 """
 All supported types for input/expected output of MethodTestCase.
 
-Namedtuple is also supported and listed implicity since it is a subclass of tuple.
+Namedtuple is also supported and listed implicitly since it is a subclass of tuple.
 """
 
 # pyre-ignore
@@ -59,23 +59,23 @@ def __init__(
         """Single test case for verifying specific method
 
         Args:
-            input: All inputs required by eager_model with specific inference method for one-time execution.
+            inputs: All inputs required by eager_model with specific inference method for one-time execution.
 
                     It is worth mentioning that, although both bundled program and ET runtime apis support setting input
                     other than `torch.tensor` type, only the input in `torch.tensor` type will be actually updated in
                     the method, and the rest of the inputs will just do a sanity check if they match the default value in method.
 
-            expected_output: Expected output of given input for verification. It can be None if user only wants to use the test case for profiling.
+            expected_outputs: Expected output of given input for verification. It can be None if user only wants to use the test case for profiling.
 
         Returns:
             self
         """
         # TODO(gasoonjia): Update type check logic.
-        # pyre-ignore [6]: Misalign data type for between MethodTestCase attribute and sannity check.
+        # pyre-ignore [6]: Misalign data type for between MethodTestCase attribute and sanity check.
         self.inputs: List[ConfigValue] = self._flatten_and_sanity_check(inputs)
         self.expected_outputs: List[ConfigValue] = []
         if expected_outputs is not None:
-            # pyre-ignore [6]: Misalign data type for between MethodTestCase attribute and sannity check.
+            # pyre-ignore [6]: Misalign data type for between MethodTestCase attribute and sanity check.
             self.expected_outputs = self._flatten_and_sanity_check(expected_outputs)
 
     def _flatten_and_sanity_check(

sdk/bundled_program/core.py

@@ -230,7 +230,7 @@ def _assert_valid_bundle(
         Other checks not related to correspondence are done in config.py
 
         Args:
-            program: The program to be bundled.
+            executorch_program: The program to be bundled.
             method_test_suites: The testcases for specific methods to be bundled.
         """
 

sdk/etrecord/_etrecord.py

@@ -185,10 +185,10 @@ def generate_etrecord(
     for SDK tooling usage.
 
     Args:
-        etrecord_path: Path to where the `ETRecord` file will be saved to.
+        et_record: Path to where the `ETRecord` file will be saved to.
         edge_dialect_program: `EdgeProgramManager` for this model returned by the call to to_edge()
         executorch_program: The ExecuTorch program for this model returned by the call to `to_executorch()` or the `BundledProgram` of this model
-        export_modules[Optional]: **Should be ignored by OSS users**. A dictionary of graph modules with the key being the user provided name and the
+        export_modules [Optional]: **Should be ignored by OSS users**. A dictionary of graph modules with the key being the user provided name and the
             value being the corresponding exported module. The exported graph modules can be either the
             output of `torch.export()` or `exir.to_edge()`.