[CodeStyle] black -> ruff format migration - part 37 (#74789)

Author: gouzil

.pre-commit-config.yaml

@@ -101,7 +101,7 @@ repos:
 
             # | test/[b-h].+
 
-            # | test/[i-k].+
+            | test/[i-k].+
 
             # | test/l.+
 
@@ -157,7 +157,7 @@ repos:
 
             | test/[b-h].+
 
-            | test/[i-k].+
+            # | test/[i-k].+
 
             | test/l.+
 

test/ipu/test_ipu_strategy_ipu.py

@@ -48,9 +48,9 @@ def test_set_options(self):
             try:
                 ipu_strategy.set_options({option_name: set_value})
                 new_value = ipu_strategy.get_option(option_name)
-                assert (
-                    new_value == set_value
-                ), f"set {option_name} to {set_value} failed"
+                assert new_value == set_value, (
+                    f"set {option_name} to {set_value} failed"
+                )
             except:
                 raise Exception(f"set {option_name} to {set_value} failed")
 
@@ -78,13 +78,13 @@ def test_set_other_options(self):
         for k, v in options.items():
             ipu_strategy.set_options({k: v})
             if isinstance(v, list):
-                assert (
-                    v.sort() == ipu_strategy.get_option(k).sort()
-                ), f"set {k} to {v} failed "
+                assert v.sort() == ipu_strategy.get_option(k).sort(), (
+                    f"set {k} to {v} failed "
+                )
             else:
-                assert v == ipu_strategy.get_option(
-                    k
-                ), f"set {k} to {v} failed "
+                assert v == ipu_strategy.get_option(k), (
+                    f"set {k} to {v} failed "
+                )
 
         # The custom logger need 2 int as inputs
         logger = lambda progress, total: print(

test/ir/inference/auto_scan_test.py

@@ -465,9 +465,9 @@ def run_and_statis(
             report_multiple_bugs=False,
         )
         settings.load_profile("ci")
-        assert (
-            passes is not None
-        ), "Parameter of passes must be defined in function run_and_statis."
+        assert passes is not None, (
+            "Parameter of passes must be defined in function run_and_statis."
+        )
         self.passes = passes
 
         self.add_ignore_pass_case()
@@ -979,7 +979,9 @@ def random_to_skip():
                         assert any(
                             op.name() == "pd_op.tensorrt_engine"
                             for op in trt_program.global_block().ops
-                        ), "trt_program does not contain any tensorrt_engine ops."
+                        ), (
+                            "trt_program does not contain any tensorrt_engine ops."
+                        )
 
                         feed_data = prog_config.get_feed_data()
                         for key, value in feed_data.items():

test/ir/inference/dist_llama_inference_model.py

@@ -191,9 +191,9 @@ def __init__(self, config: FusedMultiTransformerConfig):
 
         self.embed_dim = config.embed_dim
         self.head_dim = config.embed_dim // config.num_heads
-        assert (
-            self.head_dim * config.num_heads == config.embed_dim
-        ), "embed_dim must be divisible by num_heads"
+        assert self.head_dim * config.num_heads == config.embed_dim, (
+            "embed_dim must be divisible by num_heads"
+        )
 
         # tensor model parallel
         if config.nranks > 1:
@@ -406,9 +406,9 @@ def init_weight(self):
 
     def get_attr(self, attrs, idx):
         if isinstance(attrs, (list, tuple)):
-            assert (
-                len(attrs) == self.num_layers
-            ), f"length of attrs is {len(attrs)} is not equal to self.num_layers {self.num_layers}"
+            assert len(attrs) == self.num_layers, (
+                f"length of attrs is {len(attrs)} is not equal to self.num_layers {self.num_layers}"
+            )
             return attrs[idx]
         return attrs
 

test/ir/inference/program_config.py

@@ -66,9 +66,9 @@ def __init__(
             self.dtype = self.data.dtype
             self.shape = self.data.shape
         else:
-            assert (
-                shape is not None
-            ), "While data_gen is not defined, shape must not be None"
+            assert shape is not None, (
+                "While data_gen is not defined, shape must not be None"
+            )
             self.data = np.random.normal(0.0, 1.0, shape).astype(np.float32)
             self.shape = shape
             self.dtype = self.data.dtype
@@ -291,9 +291,9 @@ def __repr__(self):
         return log_str
 
     def set_input_type(self, _type: np.dtype) -> None:
-        assert (
-            _type in self.supported_cast_type or _type is None
-        ), "PaddleTRT only supports FP32 / FP16 IO"
+        assert _type in self.supported_cast_type or _type is None, (
+            "PaddleTRT only supports FP32 / FP16 IO"
+        )
 
         ver = paddle.inference.get_trt_compile_version()
         trt_version = ver[0] * 1000 + ver[1] * 100 + ver[2] * 10
@@ -629,9 +629,9 @@ def create_quant_model(
 
     def _get_op_output_var_names(op):
         """ """
-        assert isinstance(
-            op, (IrNode, Operator)
-        ), "The input op should be IrNode or Operator."
+        assert isinstance(op, (IrNode, Operator)), (
+            "The input op should be IrNode or Operator."
+        )
         var_names = []
         op_name = op.name() if isinstance(op, IrNode) else op.type
         if op_name not in op_real_in_out_name:

test/ir/inference/test_trt_convert_isnan_v2.py

@@ -91,7 +91,9 @@ def generate_input1(dims):
 
             yield program_config
 
-    def sample_predictor_configs(self, program_config) -> Generator[
+    def sample_predictor_configs(
+        self, program_config
+    ) -> Generator[
         tuple[
             paddle_infer.Config, tuple[int, int], tuple[float, float] | float
         ],

test/ir/inference/test_trt_explicit_quantization_resnet.py

@@ -33,9 +33,9 @@ def net(self, input, class_dim=1000, conv1_name='conv1', fc_name=None):
             else self.prefix_name + '_'
         )
         supported_layers = [34, 50, 101, 152]
-        assert (
-            layers in supported_layers
-        ), f"supported layers are {supported_layers} but input layer is {layers}"
+        assert layers in supported_layers, (
+            f"supported layers are {supported_layers} but input layer is {layers}"
+        )
 
         if layers == 34 or layers == 50:
             depth = [3, 4, 6, 3]

test/ir/pir/cinn/performance/test_cinn_large_shape_reduce.py

@@ -41,9 +41,9 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
         if to_static:
             paddle.base.core._set_prim_all_enabled(with_prim)
             if with_cinn:
-                assert (
-                    with_prim
-                ), "with_cinn=True but with_prim=False is unsupported"
+                assert with_prim, (
+                    "with_cinn=True but with_prim=False is unsupported"
+                )
                 net = paddle.jit.to_static(net, backend="CINN", full_graph=True)
             else:
                 net = paddle.jit.to_static(net, backend=None, full_graph=True)

test/ir/pir/cinn/symbolic/test_sub_graph_batch_norm.py

@@ -78,9 +78,9 @@ def train(self, to_static, with_prim=False, with_cinn=False):
         if to_static:
             paddle.base.core._set_prim_all_enabled(with_prim)
             if with_cinn:
-                assert (
-                    with_prim
-                ), "with_cinn=True but with_prim=False is unsupported"
+                assert with_prim, (
+                    "with_cinn=True but with_prim=False is unsupported"
+                )
                 net = paddle.jit.to_static(net, backend="CINN", full_graph=True)
             else:
                 net = paddle.jit.to_static(net, backend=None, full_graph=True)

test/ir/pir/cinn/symbolic/test_sub_graph_chatglm2_0_st.py

@@ -223,9 +223,9 @@ def train(self, net, to_static, with_prim=False, with_cinn=False):
         if to_static:
             paddle.base.core._set_prim_all_enabled(with_prim)
             if with_cinn:
-                assert (
-                    with_prim
-                ), "with_cinn=True but with_prim=False is unsupported"
+                assert with_prim, (
+                    "with_cinn=True but with_prim=False is unsupported"
+                )
                 net = paddle.jit.to_static(net, backend="CINN", full_graph=True)
             else:
                 net = paddle.jit.to_static(net, backend=None, full_graph=True)