Update lintrunner libraries (pytorch#3963)

Summary: Pull Request resolved: pytorch#3963

Reviewed By: kirklandsign

Differential Revision: D58479009

fbshipit-source-id: 31624ace3ccb6ca8c6de6497f8b239eea8d31ad4

Author: mergennachin

backends/arm/tosa_utils.py

@@ -33,7 +33,7 @@ def dbg_node(node):
     logger.info("  node.meta = ")
     for k, v in node.meta.items():
         logger.info(f"    '{k}' = {v}")
-        if type([]) == type(v):
+        if isinstance(v, list):
             for i in v:
                 logger.info(f"      {i} ")
 

backends/qualcomm/passes/convert_to_linear.py

@@ -192,7 +192,11 @@ def _convert(self, graph_module: torch.fx.GraphModule):
         for _, src_partitions in partitions.items():
             for src_partition in src_partitions:
                 op_cnt = Counter(
-                    [n.target for n in src_partition.nodes if type(n.target) == edge_op]
+                    [
+                        n.target
+                        for n in src_partition.nodes
+                        if isinstance(n.target, edge_op)
+                    ]
                 )
                 if self.linear in op_cnt:
                     continue

backends/qualcomm/passes/fold_qdq.py

@@ -45,7 +45,7 @@ def _fold(self, graph_module: torch.fx.GraphModule) -> torch.fx.GraphModule:
 
             # collecting quant nodes to be removed
             for i in range(1, len(n.args)):
-                if type(n.args[i]) == torch.fx.node.Node:
+                if isinstance(n.args[i], torch.fx.node.Node):
                     to_be_removed.append(n.args[i])
                     # could be a commonly shared attribute between q & dq
                     if n.args[i].target == exir_ops.edge.aten._to_copy.default:

backends/qualcomm/passes/insert_io_qdq.py

@@ -47,7 +47,9 @@ def _ceate_args(self, target: torch.fx.node.Target, quant_attrs: Dict):
             if name == "out_dtype":
                 continue
             value = quant_attrs[name]
-            if type(arg_schema.type) == torch.tensor and type(value) in [int, float]:
+            if isinstance(arg_schema.type, torch.tensor) and (
+                isinstance(value, int) or isinstance(value, float)
+            ):
                 value = torch.tensor(value)
             ret.append(value)
         return ret

backends/qualcomm/passes/utils.py

@@ -32,7 +32,7 @@ def get_quant_attrs(
         attr_n = quant_node.args[i]
 
         value = attr_n
-        if type(attr_n) == torch.fx.node.Node:
+        if isinstance(attr_n, torch.fx.node.Node):
             # could be a commonly shared attribute between q & dq
             if attr_n.target == exir_ops.edge.aten._to_copy.default:
                 value = get_parameter(attr_n.args[0], edge_program)

backends/qualcomm/quantizer/quantizer.py

@@ -295,7 +295,7 @@ def _get_quant_config(self, op: str | OpOverload) -> Optional[QuantizationConfig
             1. is one of use_per_channel_weight_quant_ops
             2. int8 / int16 config
         """
-        if type(op) == str:
+        if isinstance(op, str):
             return
 
         if op in self.use_per_channel_weight_quant_ops:

examples/qualcomm/scripts/dummy_llama2.py

@@ -27,7 +27,7 @@ def create_device_inputs(example_inputs, use_kv_cache):
     input_list = ""
     if use_kv_cache:
         for i, d in enumerate(inputs[0]):
-            if type(d) == list:
+            if isinstance(d, list):
                 d = torch.stack(d)
             d.numpy().tofile(f"{args.artifact}/input_0_0.raw")
             input_list = f"input_0_{i}.raw "

exir/emit/_emitter.py

@@ -285,18 +285,17 @@ def _emit_list(self, val: List[_Argument], val_type: _SchemaType) -> EValue:
 
         NOTE: When symbool and symfloat are supported bool and float lists will be stored boxed.
         """
-        elem_type = type(val_type)
 
-        if elem_type == torch.BoolType:
+        if isinstance(val_type, torch.BoolType):
             return EValue(BoolList(typing.cast(List[bool], val)))
 
-        if elem_type == torch.IntType:
+        if isinstance(val_type, torch.IntType):
             return self._emit_int_list(val)
 
-        if elem_type == torch.FloatType:
+        if isinstance(val_type, torch.FloatType):
             return EValue(DoubleList(typing.cast(List[float], val)))
 
-        if elem_type == torch.TensorType:
+        if isinstance(val_type, torch.TensorType):
             values = []
             for v in val:
                 assert isinstance(v, _AbstractValue)
@@ -308,10 +307,10 @@ def _emit_list(self, val: List[_Argument], val_type: _SchemaType) -> EValue:
                 values.append(v.id)
             return EValue(TensorList(values))
 
-        if elem_type == torch.OptionalType:
+        if isinstance(val_type, torch.OptionalType):
             # refine further
-            actual_type = typing.cast(torch.OptionalType, val_type).getElementType()
-            if type(actual_type) == torch.TensorType:
+            actual_type = val_type.getElementType()
+            if isinstance(actual_type, torch.TensorType):
                 vals = []
                 for v in val:
                     if v is None:
@@ -437,9 +436,9 @@ def _constant_to_evalue(  # noqa: C901
                 val_type = torch.ListType(
                     self._get_list_tuple_jit_type(val)  # pyre-ignore
                 )
-            if type(val_type) == torch.OptionalType:
+            if isinstance(val_type, torch.OptionalType):
                 val_type = val_type.getElementType()
-            assert type(val_type) == torch.ListType
+            assert isinstance(val_type, torch.ListType)
             return self._emit_list(
                 typing.cast(List[_Argument], val),
                 typing.cast(_SchemaType, val_type.getElementType()),

exir/passes/remove_mixed_type_operators.py

@@ -61,7 +61,7 @@ def call_operator(self, op, args, kwargs, meta: NodeMetadata):  # noqa: C901
         )[1]
 
         def try_coerce(value: PyTree, arg: torch.Argument) -> PyTree:
-            if type(arg.type) != torch.TensorType:
+            if not isinstance(arg.type, torch.TensorType):
                 return value
 
             if isinstance(value, ProxyValue):

exir/passes/scalar_to_tensor_pass.py

@@ -24,7 +24,7 @@ def try_coerce(value, arg):
                 return (
                     torch.tensor(value)
                     if isinstance(value, (float, int, bool))
-                    and type(arg.type) == torch.TensorType
+                    and isinstance(arg.type, torch.TensorType)
                     else value
                 )