Set black's target version (#11370)

Description: Set black's target version to be py37 - py310

Motivation and Context

Black by default targets its format for py3.10. Since our project supports python 3.7, we need to target version to all the python versions supported.

Re-ran black. 13 files reformatted.

onnxruntime/python/tools/quantization/operators/activation.py

@@ -79,7 +79,7 @@ class QLinearActivation(QuantOperatorBase):
             qlinear_activation_inputs,
             [qlinear_activation_output],
             qlinear_activation_name,
-            **kwargs
+            **kwargs,
         )
 
         # Create an entry for this quantized value

onnxruntime/python/tools/quantization/operators/binary_op.py

@@ -55,7 +55,7 @@ class QLinearBinaryOp(QuantOperatorBase):
             qlinear_binary_math_inputs,
             [qlinear_binary_math_output],
             qlinear_binary_math_name,
-            **kwargs
+            **kwargs,
         )
         nodes.append(qlinear_binary_math_node)
 

onnxruntime/python/tools/quantization/operators/gavgpool.py

@@ -57,6 +57,6 @@ class QGlobalAveragePool(QuantOperatorBase):
             ],
             [quantized_output_value.q_name],
             qnode_name,
-            **kwargs
+            **kwargs,
         )
         self.quantizer.new_nodes += [qnode]

onnxruntime/python/tools/quantization/operators/pooling.py

@@ -59,7 +59,7 @@ class QLinearPool(QuantOperatorBase):
             ],
             [qlinear_output_name],
             qlinear_node_name,
-            **kwargs
+            **kwargs,
         )
 
         # add all newly created nodes

onnxruntime/test/python/contrib_ops/onnx_contrib_ops_helper.py

@@ -51,7 +51,7 @@ def expect(
     inputs,
     outputs,
     name,
-    **kwargs
+    **kwargs,
 ):  # type: (...) -> None
     present_inputs = [x for x in node.input if (x != "")]
     present_outputs = [x for x in node.output if (x != "")]

onnxruntime/test/python/onnxruntime_test_ort_trainer.py

@@ -374,7 +374,7 @@ class MNISTWrapper:
             ),
             batch_size=args_batch_size,
             shuffle=False,
-            **kwargs
+            **kwargs,
         )
         test_loader = torch.utils.data.DataLoader(
             datasets.MNIST(
@@ -384,7 +384,7 @@ class MNISTWrapper:
             ),
             batch_size=args_test_batch_size,
             shuffle=False,
-            **kwargs
+            **kwargs,
         )
 
         return train_loader, test_loader

onnxruntime/test/testdata/transform/fusion/matmul_scale_gen.py

@@ -52,7 +52,7 @@ def gen(model_path, use_transpose_matmul, scale_input_0, scale_input_1, scale_ou
             matmul_op,
             "",
             matmul_domain,
-            **matmul_attrs
+            **matmul_attrs,
         )
     )
 

orttraining/orttraining/eager/test_model/mnist_fc_training.py

@@ -96,7 +96,7 @@ def main():
         ),
         batch_size=args.batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
     test_loader = torch.utils.data.DataLoader(
         datasets.MNIST(
@@ -106,7 +106,7 @@ def main():
         ),
         batch_size=args.test_batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
 
     # set device

orttraining/orttraining/eager/test_models/mnist_fc_training.py

@@ -97,7 +97,7 @@ def main():
         ),
         batch_size=args.batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
     test_loader = torch.utils.data.DataLoader(
         datasets.MNIST(
@@ -107,7 +107,7 @@ def main():
         ),
         batch_size=args.test_batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
 
     device = torch.device("ort")

orttraining/orttraining/test/python/orttraining_test_transformers.py

@@ -361,7 +361,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_mixed_precision_with_gradient_accumulation(self):
@@ -378,7 +378,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_all(self):
@@ -396,7 +396,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_list_input(self):
@@ -410,7 +410,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_dict_input(self):
@@ -424,7 +424,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_list_and_dict_input(self):
@@ -438,7 +438,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_grad_accumulation_list_input(self):
@@ -452,7 +452,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_grad_accumulation_dict_input(self):
@@ -466,7 +466,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
     def test_for_pretraining_full_precision_grad_accumulation_list_and_dict_input(self):
@@ -480,7 +480,7 @@ class BertModelTest(unittest.TestCase):
             option_fp16,
             option_allreduce_post_accumulation,
             option_gradient_accumulation_steps,
-            option_split_batch
+            option_split_batch,
         )
 
 

orttraining/pytorch_frontend_examples/mnist_training.py

@@ -135,7 +135,7 @@ def main():
         ),
         batch_size=args.batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
     test_loader = torch.utils.data.DataLoader(
         datasets.MNIST(
@@ -145,7 +145,7 @@ def main():
         ),
         batch_size=args.test_batch_size,
         shuffle=True,
-        **kwargs
+        **kwargs,
     )
 
     comm = MPI.COMM_WORLD

pyproject.toml

@@ -2,6 +2,7 @@
 line-length = 120
 # extend-exclude needs to be a regular expression
 extend-exclude = "cmake|onnxruntime/core/flatbuffers/"
+target-version = ["py37", "py38", "py39", "py310"]
 
 [tool.isort]
 profile = "black"

tools/ci_build/get_docker_image.py

@@ -165,7 +165,7 @@ def main():
             full_image_name,
             "--file",
             args.dockerfile,
-            args.context
+            args.context,
         )
 
         if use_container_registry:

tools/python/util/run.py

@@ -18,7 +18,7 @@ def run(
     shell=False,
     env=None,
     check=True,
-    quiet=False
+    quiet=False,
 ):
     """Runs a subprocess.