lint

Author: xadupre

_unittests/ut_helpers/test_cache_helper.py

@@ -69,7 +69,7 @@ def test_replace_by(self):
         )
 
         DYN = torch.export.Dim.DYNAMIC
-        nargs, nkwargs, nds = convert_dynamic_axes_into_dynamic_shapes(
+        _nargs, _nkwargs, nds = convert_dynamic_axes_into_dynamic_shapes(
             None, args=tuple(), kwargs=kwargs, dynamic_axes=dynamic_shapes
         )
         self.assertEqual(dynamic_shapes, nds)

_unittests/ut_torch_export_patches/test_patch_serialization_transformers.py

@@ -164,7 +164,7 @@ def forward(self, cache):
     def test_base_model_output_unflatten_flatten(self):
         bo = BaseModelOutput(last_hidden_state=torch.rand((4, 4, 4)))
         with torch_export_patches(patch_transformers=True):
-            flat, _spec = torch.utils._pytree.tree_flatten(bo)
+            _flat, _spec = torch.utils._pytree.tree_flatten(bo)
             unflat = flatten_unflatten_for_dynamic_shapes(bo, use_dict=True)
             self.assertIsInstance(unflat, list)
             self.assertEqual("#1[T1r3]", self.string_type(unflat))

_unittests/ut_torch_models/test_tiny_llms_bypassed.py

@@ -32,7 +32,7 @@ def debug():
                 print("***", data["dynamic_shapes"])
                 import torch.export._draft_export
 
-                ep, report = torch.export._draft_export.draft_export(
+                _ep, report = torch.export._draft_export.draft_export(
                     model,
                     (),
                     kwargs=inputs,

_unittests/ut_xrun_doc/test_documentation_examples.py

@@ -47,7 +47,7 @@ def run_test(self, fold: str, name: str, verbose=0) -> int:
             cmds = [sys.executable, "-u", os.path.join(fold, name)]
             p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
             res = p.communicate()
-            out, err = res
+            _out, err = res
             st = err.decode("ascii", errors="ignore")
             if st and "Traceback" in st:
                 if '"dot" not found in path.' in st:

_unittests/ut_xrun_doc/test_documentation_recipes.py

@@ -46,7 +46,7 @@ def run_test(self, fold: str, name: str, verbose=0) -> int:
             cmds = [sys.executable, "-u", os.path.join(fold, name)]
             p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
             res = p.communicate()
-            out, err = res
+            _out, err = res
             st = err.decode("ascii", errors="ignore")
             if st and "Traceback" in st:
                 if '"dot" not found in path.' in st:

_unittests/ut_xrun_doc/test_documentation_technical.py

@@ -41,7 +41,7 @@ def run_test(self, fold: str, name: str, verbose=0) -> int:
             cmds = [sys.executable, "-u", os.path.join(fold, name)]
             p = subprocess.Popen(cmds, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
             res = p.communicate()
-            out, err = res
+            _out, err = res
             st = err.decode("ascii", errors="ignore")
             if st and "Traceback" in st:
                 if '"dot" not found in path.' in st:

onnx_diagnostic/helpers/onnx_helper.py

@@ -1186,7 +1186,7 @@ def shadowing_names(
                 shadow |= set(i.name for i in g.input) & shadow_context
                 shadow |= set(i.name for i in g.initializer) & shadow_context
                 shadow |= set(i.name for i in g.sparse_initializer) & shadow_context
-                s, ps, c = shadowing_names(
+                s, _ps, c = shadowing_names(
                     g.node, verbose=verbose, existing=existing, shadow_context=existing
                 )
                 shadow |= s

onnx_diagnostic/helpers/torch_helper.py

@@ -543,7 +543,7 @@ def __init__(self, embedding_dim: int = 16, context_size: int = 256):
             )
 
         def forward(self, x):
-            B, T, C = x.shape
+            _B, T, C = x.shape
 
             query = self.query(x)
             key = self.key(x)
@@ -866,7 +866,7 @@ def torch_tensor_size(value: Any) -> Any:
     if value.__class__.__name__ == "MambaCache":
         return torch_tensor_size(value.conv_states) + torch_tensor_size(value.ssm_states)
     if value.__class__ in torch.utils._pytree.SUPPORTED_NODES:
-        args, spec = torch.utils._pytree.tree_flatten(value)
+        args, _spec = torch.utils._pytree.tree_flatten(value)
         return sum(torch_tensor_size(a) for a in args)
 
     # We should have a code using serialization, deserialization assuming a model

onnx_diagnostic/reference/ops/op_scan.py

@@ -26,11 +26,11 @@ def _run(
     ):
         (
             num_loop_state_vars,
-            num_scan_outputs,
-            output_directions,
-            max_dir_out,
-            output_axes,
-            max_axe_out,
+            _num_scan_outputs,
+            _output_directions,
+            _max_dir_out,
+            _output_axes,
+            _max_axe_out,
             state_names_in,
             state_names_out,
             scan_names_in,

onnx_diagnostic/reference/ort_evaluator.py

@@ -562,7 +562,7 @@ def _run_if(
         if key in self._cache:
             sess = self._cache[key][1]
         else:
-            self._cache[key] = onx, sess = self._get_sess_if(node, name, inputs, results)
+            self._cache[key] = _onx, sess = self._get_sess_if(node, name, inputs, results)
 
         assert hasattr(sess, "run"), f"Missing method run for type {type(sess)}"
         feeds = {name: results[name] for name in sess.input_names}
@@ -616,7 +616,7 @@ def _run_scan(
         if key in self._cache:
             sess = self._cache[key][1]
         else:
-            self._cache[key] = onx, sess = self._get_sess_scan(node, name, inputs, results)
+            self._cache[key] = _onx, sess = self._get_sess_scan(node, name, inputs, results)
 
         assert hasattr(sess, "run"), f"Missing method run for type {type(sess)}"
         feeds = {name: results[name] for name in sess.input_names}