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examples/common_subexpression_elimination/cse_demo.py

@@ -6,7 +6,7 @@
 class Model(torch.nn.Module):
     def __init__(self):
         """Initializes the Model class with a single LayerNorm layer of embedding dimension 10."""
-        super(Model, self).__init__()
+        super().__init__()
         embedding_dim = 10
         self.layer_norm = nn.LayerNorm(embedding_dim)
 

onnxslim/cli/_main.py

@@ -11,9 +11,9 @@ def slim(model: Union[str, onnx.ModelProto], *args, **kwargs):
     from onnxslim.core import (
         convert_data_format,
         freeze,
+        input_modification,
         input_shape_modification,
         optimize,
-        input_modification,
         output_modification,
         shape_infer,
     )
@@ -29,20 +29,20 @@ def slim(model: Union[str, onnx.ModelProto], *args, **kwargs):
         summarize_model,
     )
 
-    output_model = args[0] if len(args) > 0 else kwargs.get('output_model', None)
-    model_check = kwargs.get('model_check', False)
-    input_shapes = kwargs.get('input_shapes', None)
-    inputs = kwargs.get('inputs', None)
-    outputs = kwargs.get('outputs', None)
-    no_shape_infer = kwargs.get('no_shape_infer', False)
-    no_constant_folding = kwargs.get('no_constant_folding', False)
-    dtype = kwargs.get('dtype', None)
-    skip_fusion_patterns = kwargs.get('skip_fusion_patterns', None)
-    inspect = kwargs.get('inspect', False)
-    dump_to_disk = kwargs.get('dump_to_disk', False)
-    save_as_external_data = kwargs.get('save_as_external_data', False)
-    model_check_inputs = kwargs.get('model_check_inputs', None)
-    verbose = kwargs.get('verbose', False)
+    output_model = args[0] if len(args) > 0 else kwargs.get("output_model", None)
+    model_check = kwargs.get("model_check", False)
+    input_shapes = kwargs.get("input_shapes", None)
+    inputs = kwargs.get("inputs", None)
+    outputs = kwargs.get("outputs", None)
+    no_shape_infer = kwargs.get("no_shape_infer", False)
+    no_constant_folding = kwargs.get("no_constant_folding", False)
+    dtype = kwargs.get("dtype", None)
+    skip_fusion_patterns = kwargs.get("skip_fusion_patterns", None)
+    inspect = kwargs.get("inspect", False)
+    dump_to_disk = kwargs.get("dump_to_disk", False)
+    save_as_external_data = kwargs.get("save_as_external_data", False)
+    model_check_inputs = kwargs.get("model_check_inputs", None)
+    verbose = kwargs.get("verbose", False)
 
     logger = init_logging(verbose)
 

onnxslim/misc/tabulate.py

@@ -222,7 +222,7 @@ def make_header_line(is_header, colwidths, colaligns):
         alignment = {"left": "<", "right": ">", "center": "^", "decimal": ">"}
         # use the column widths generated by tabulate for the asciidoc column width specifiers
         asciidoc_alignments = zip(colwidths, [alignment[colalign] for colalign in colaligns])
-        asciidoc_column_specifiers = ["{:d}{}".format(width, align) for width, align in asciidoc_alignments]
+        asciidoc_column_specifiers = [f"{width:d}{align}" for width, align in asciidoc_alignments]
         header_list = ['cols="' + (",".join(asciidoc_column_specifiers)) + '"']
 
         # generate the list of options (currently only "header")
@@ -2484,7 +2484,7 @@ def _wrap_chunks(self, chunks):
         """
         lines = []
         if self.width <= 0:
-            raise ValueError("invalid width %r (must be > 0)" % self.width)
+            raise ValueError(f"invalid width {self.width!r} (must be > 0)")
         if self.max_lines is not None:
             indent = self.subsequent_indent if self.max_lines > 1 else self.initial_indent
             if self._len(indent) + self._len(self.placeholder.lstrip()) > self.width:

onnxslim/third_party/onnx_graphsurgeon/exporters/base_exporter.py

@@ -18,7 +18,7 @@
 from onnxslim.third_party.onnx_graphsurgeon.ir.graph import Graph
 
 
-class BaseExporter(object):
+class BaseExporter:
     @staticmethod
     def export_graph(graph: Graph):
         """

onnxslim/third_party/onnx_graphsurgeon/exporters/onnx_exporter.py

@@ -52,12 +52,7 @@ def check_duplicate_node_names(nodes: Sequence[Node], level=G_LOGGER.WARNING):
         if not node.name:
             continue
         if node.name in name_map:
-            msg = "Found distinct Nodes that share the same name:\n[id: {:}]:\n {:}---\n[id: {:}]:\n {:}\n".format(
-                id(name_map[node.name]),
-                name_map[node.name],
-                id(node),
-                node,
-            )
+            msg = f"Found distinct Nodes that share the same name:\n[id: {id(name_map[node.name])}]:\n {name_map[node.name]}---\n[id: {id(node)}]:\n {node}\n"
             G_LOGGER.log(msg, level)
         else:
             name_map[node.name] = node
@@ -146,9 +141,7 @@ def export_value_info_proto(tensor: Tensor, do_type_check: bool) -> onnx.ValueIn
         """Creates an ONNX ValueInfoProto from a Tensor, optionally checking for dtype information."""
         if do_type_check and tensor.dtype is None:
             G_LOGGER.critical(
-                "Graph input and output tensors must include dtype information. Please set the dtype attribute for: {:}".format(
-                    tensor
-                )
+                f"Graph input and output tensors must include dtype information. Please set the dtype attribute for: {tensor}"
             )
 
         if tensor.dtype is None:

onnxslim/third_party/onnx_graphsurgeon/graph_pattern/graph_pattern.py

@@ -104,7 +104,7 @@ def __init__(self) -> None:
         self.op = None  # op (str)
         self.check_func = None  # callback function for single node
         # pattern node name -> GraphPattern nodes(single or subpattern)
-        self.nodes: Dict[str, "GraphPattern"] = {}
+        self.nodes: Dict[str, GraphPattern] = {}
         # pattern node name -> input tensors
         self.node_inputs: Dict[str, List[int]] = {}
         # pattern node name -> output tensors
@@ -239,15 +239,13 @@ def _single_node_match(self, onnx_node: Node) -> bool:
         with G_LOGGER.indent():
             if self.op != onnx_node.op:
                 G_LOGGER.info(
-                    "No match because: Op did not match. Node op was: {:} but pattern op was: {:}.".format(
-                        onnx_node.op, self.op
-                    )
+                    f"No match because: Op did not match. Node op was: {onnx_node.op} but pattern op was: {self.op}."
                 )
                 return False
             if self.check_func is not None and not self.check_func(onnx_node):
                 G_LOGGER.info("No match because: check_func returned false.")
                 return False
-            G_LOGGER.info("Single node is matched: {:}, {:}".format(self.op, onnx_node.name))
+            G_LOGGER.info(f"Single node is matched: {self.op}, {onnx_node.name}")
         return True
 
     def _get_tensor_index_for_node(self, node: str, tensor_id: int, is_node_input: bool):
@@ -330,7 +328,7 @@ def _match_node(
     ) -> bool:
         """Matches ONNX nodes to the graph pattern starting from a specific node and tensor context."""
         with G_LOGGER.indent():
-            G_LOGGER.info("Checking node: {:} against pattern node: {:}.".format(onnx_node.name, node_name))
+            G_LOGGER.info(f"Checking node: {onnx_node.name} against pattern node: {node_name}.")
         tensor_index_for_node = self._get_tensor_index_for_node(node_name, from_tensor, is_node_input=from_inbound)
         subgraph_mapping = self.nodes[node_name].match(
             onnx_node,

onnxslim/third_party/onnx_graphsurgeon/importers/base_importer.py

@@ -18,7 +18,7 @@
 from onnxslim.third_party.onnx_graphsurgeon.ir.graph import Graph
 
 
-class BaseImporter(object):
+class BaseImporter:
     @staticmethod
     def import_graph(graph) -> Graph:
         """

onnxslim/third_party/onnx_graphsurgeon/importers/onnx_importer.py

@@ -278,13 +278,11 @@ def process_attr(attr_str: str):
                     attr_dict[attr.name] = process_attr(attr_str)
                 else:
                     G_LOGGER.warning(
-                        "Attribute of type {:} is currently unsupported. Skipping attribute.".format(attr_str)
+                        f"Attribute of type {attr_str} is currently unsupported. Skipping attribute."
                     )
             else:
                 G_LOGGER.warning(
-                    "Attribute type: {:} was not recognized. Was the graph generated with a newer IR version than the installed `onnx` package? Skipping attribute.".format(
-                        attr.type
-                    )
+                    f"Attribute type: {attr.type} was not recognized. Was the graph generated with a newer IR version than the installed `onnx` package? Skipping attribute."
                 )
         return attr_dict
 
@@ -315,9 +313,7 @@ def get_tensor(name: str, check_outer_graph=True):
                 return Variable.empty()
 
             G_LOGGER.verbose(
-                "Tensor: {:} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor.".format(
-                    name
-                )
+                f"Tensor: {name} was not generated during shape inference, or shape inference was not run on this model. Creating a new Tensor."
             )
             subgraph_tensor_map[name] = Variable(name)
             return subgraph_tensor_map[name]