[CodeStyle] black -> ruff format migration - part 32

.pre-commit-config.yaml

@@ -89,7 +89,7 @@ repos:
 
             # | python/paddle/j.+
 
-            # | python/paddle/[k-n].+
+            | python/paddle/[k-n].+
 
             # | python/paddle/[o-t].+
 
@@ -145,7 +145,7 @@ repos:
 
             | python/paddle/j.+
 
-            | python/paddle/[k-n].+
+            # | python/paddle/[k-n].+
 
             | python/paddle/[o-t].+
 

python/paddle/nn/functional/activation.py

@@ -603,9 +603,9 @@ def prelu(
                [-1.25000000,  6.        ,  7.        , -2.        ],
                [ 6.        ,  7.        ,  8.        ,  9.        ]]]])
     """
-    assert (
-        len(weight.shape) == 0 or len(weight.shape) == 1
-    ), "The dim count of weight shape should be 0 or 1 in prelu()."
+    assert len(weight.shape) == 0 or len(weight.shape) == 1, (
+        "The dim count of weight shape should be 0 or 1 in prelu()."
+    )
 
     mode = 'all'
     if len(weight.shape) == 1 and weight.shape[0] > 1:
@@ -626,19 +626,19 @@ def prelu(
 
         data_format = 'NCHW' if data_format[1] == 'C' else 'NHWC'
 
-        assert (
-            len(x.shape) > 1
-        ), "The dim count of x should be equal or larger than 2 in prelu() when weight shape is not [1]."
+        assert len(x.shape) > 1, (
+            "The dim count of x should be equal or larger than 2 in prelu() when weight shape is not [1]."
+        )
 
         # NOTE(GuoxiaWang): support NHWC data format
         if data_format == 'NHWC':
-            assert (
-                weight.shape[0] == x.shape[-1]
-            ), "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
+            assert weight.shape[0] == x.shape[-1], (
+                "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
+            )
         else:
-            assert (
-                weight.shape[0] == x.shape[1]
-            ), "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
+            assert weight.shape[0] == x.shape[1], (
+                "The weight size should be equal to x input channel in prelu() when weight shape is not [1]."
+            )
         mode = 'channel'
 
     if in_dynamic_or_pir_mode():

python/paddle/nn/functional/common.py

@@ -601,9 +601,9 @@ def _is_list_or_tuple_(data):
                 if isinstance(dim_size, (Variable, paddle.pir.Value)):
                     contain_var = True
                     continue
-                assert (
-                    dim_size > 0
-                ), "Each dimension size given in out_shape must be greater than 0."
+                assert dim_size > 0, (
+                    "Each dimension size given in out_shape must be greater than 0."
+                )
 
             if contain_var:
                 new_size_tensor = []
@@ -2068,7 +2068,9 @@ def pad(
         'replicate',
         'constant',
         'circular',
-    ], f"mode should be one of constant, reflect, replicate, circular, but got {mode}."
+    ], (
+        f"mode should be one of constant, reflect, replicate, circular, but got {mode}."
+    )
 
     x_dim = len(x.shape)
     if in_dynamic_mode():
@@ -2162,9 +2164,9 @@ def pad(
         4: ["NCHW", "NHWC"],
         5: ["NCDHW", "NDHWC"],
     }
-    assert (
-        data_format in supported_format_map[x_dim]
-    ), f"input tensor dimension is {x_dim}, it's data format should be in {supported_format_map[x_dim]} but got {data_format}"
+    assert data_format in supported_format_map[x_dim], (
+        f"input tensor dimension is {x_dim}, it's data format should be in {supported_format_map[x_dim]} but got {data_format}"
+    )
 
     unsqueezed_dim = []
 
@@ -2831,40 +2833,40 @@ def fold(
     )
 
     assert len(x.shape) == 3, "input should be the format of [N, C, L]"
-    assert (
-        math.prod(x.shape) >= 0
-    ), "The number of elements must greater or equal than zero."
+    assert math.prod(x.shape) >= 0, (
+        "The number of elements must greater or equal than zero."
+    )
 
     def _is_list_or_tuple_(data):
         return isinstance(data, (list, tuple))
 
     if isinstance(output_sizes, int):
         output_sizes = [output_sizes, output_sizes]
     else:
-        assert _is_list_or_tuple_(output_sizes) and (
-            len(output_sizes) == 2
-        ), "output_sizes should either be an integer or a list/tuple of two integers"
+        assert _is_list_or_tuple_(output_sizes) and (len(output_sizes) == 2), (
+            "output_sizes should either be an integer or a list/tuple of two integers"
+        )
 
     if isinstance(kernel_sizes, int):
         kernel_sizes = [kernel_sizes, kernel_sizes]
     else:
-        assert _is_list_or_tuple_(kernel_sizes) and (
-            len(kernel_sizes) == 2
-        ), "kernel_sizes should either be an integer or a list/tuple of two integers"
+        assert _is_list_or_tuple_(kernel_sizes) and (len(kernel_sizes) == 2), (
+            "kernel_sizes should either be an integer or a list/tuple of two integers"
+        )
 
     if isinstance(strides, int):
         strides = [strides, strides]
     else:
-        assert _is_list_or_tuple_(strides) and (
-            len(strides) == 2
-        ), "strides should either be an integer or a list/tuple of two integers"
+        assert _is_list_or_tuple_(strides) and (len(strides) == 2), (
+            "strides should either be an integer or a list/tuple of two integers"
+        )
 
     if isinstance(dilations, int):
         dilations = [dilations, dilations]
     else:
-        assert _is_list_or_tuple_(dilations) and (
-            len(dilations) == 2
-        ), "dilations should either be an integer or a list/tuple of two integers"
+        assert _is_list_or_tuple_(dilations) and (len(dilations) == 2), (
+            "dilations should either be an integer or a list/tuple of two integers"
+        )
 
     if isinstance(paddings, int):
         paddings = [paddings] * 4

python/paddle/nn/functional/conv.py

@@ -272,9 +272,9 @@ def _conv_nd(
                     attrs={'axis': -1},
                 )
             else:
-                assert len(x_shape) > len(
-                    y_shape
-                ), 'The length of pre_bias must greater than the length of bias'
+                assert len(x_shape) > len(y_shape), (
+                    'The length of pre_bias must greater than the length of bias'
+                )
                 padding = len(x_shape) - len(y_shape) - channel_dim
                 bias = reshape(
                     bias, [1] * channel_dim + y_shape + [1] * padding
@@ -1336,9 +1336,9 @@ def conv2d_transpose(
                     attrs={'axis': -1},
                 )
             else:
-                assert len(x_shape) > len(
-                    y_shape
-                ), 'The length of pre_bias must greater than the length of bias'
+                assert len(x_shape) > len(y_shape), (
+                    'The length of pre_bias must greater than the length of bias'
+                )
                 padding = len(x_shape) - len(y_shape) - channel_dim
                 bias = reshape(
                     bias, [1] * channel_dim + y_shape + [1] * padding

python/paddle/nn/functional/flash_attention.py

@@ -508,30 +508,30 @@ def flash_attention(
             fa_version = paddle.base.framework.get_flags(
                 ["FLAGS_flash_attn_version"]
             )["FLAGS_flash_attn_version"]
-        assert (
-            in_dynamic_or_pir_mode() or fa_version == 2
-        ), "flash attention 3 only support dynamic or pir mode"
-        assert (
-            dropout == 0.0 or fa_version == 2
-        ), "flash attention 3 does not support dropout"
-        assert (
-            not return_softmax or fa_version == 2
-        ), "flash attention 3 does not support return softmax"
-        assert (
-            fixed_seed_offset is None or fa_version == 2
-        ), "flash attention 3 does not support return softmax"
-        assert (
-            rng_name == "" or fa_version == 2
-        ), "flash attention 3 does not support setting rng_name"
-        assert (
-            training or fa_version == 2
-        ), "flash attention 3 does not support setting training"
-        assert (
-            name is None or fa_version == 2
-        ), "flash attention 3 does not support setting name"
-        assert (
-            softmax_scale is None or fa_version == 3
-        ), "flash attention 2 does not support setting softmax_scale"
+        assert in_dynamic_or_pir_mode() or fa_version == 2, (
+            "flash attention 3 only support dynamic or pir mode"
+        )
+        assert dropout == 0.0 or fa_version == 2, (
+            "flash attention 3 does not support dropout"
+        )
+        assert not return_softmax or fa_version == 2, (
+            "flash attention 3 does not support return softmax"
+        )
+        assert fixed_seed_offset is None or fa_version == 2, (
+            "flash attention 3 does not support return softmax"
+        )
+        assert rng_name == "" or fa_version == 2, (
+            "flash attention 3 does not support setting rng_name"
+        )
+        assert training or fa_version == 2, (
+            "flash attention 3 does not support setting training"
+        )
+        assert name is None or fa_version == 2, (
+            "flash attention 3 does not support setting name"
+        )
+        assert softmax_scale is None or fa_version == 3, (
+            "flash attention 2 does not support setting softmax_scale"
+        )
         if in_dynamic_or_pir_mode():
             if fa_version == 2:
                 (result_attention, result_softmax, _, _) = _C_ops.flash_attn(
@@ -1142,9 +1142,9 @@ def flash_attn_varlen_func(
             >>> output = paddle.nn.functional.flash_attention.flash_attention_v3_varlen(q, q, q, cu_seqlens_q, cu_seqlens_q, max_seqlen_q=max_seq_len_q, max_seqlen_k=max_seq_len_q, causal=True)
             >>> # doctest: -SKIP
     """
-    assert (
-        "xpu" not in paddle.get_device()
-    ), "flash_attn_varlen_func is not supported on xpu"
+    assert "xpu" not in paddle.get_device(), (
+        "flash_attn_varlen_func is not supported on xpu"
+    )
 
     assert not paddle.get_flags(["FLAGS_cudnn_deterministic"])[
         "FLAGS_cudnn_deterministic"
@@ -1157,9 +1157,9 @@ def flash_attn_varlen_func(
         == 3
     ), "FLAGS_flash_attn_version is 2, conflicts with flash_attn_varlen_func"
 
-    assert (
-        in_dynamic_or_pir_mode()
-    ), "flash_attn_varlen_func only support dynamic or pir mode"
+    assert in_dynamic_or_pir_mode(), (
+        "flash_attn_varlen_func only support dynamic or pir mode"
+    )
 
     assert qv is None, "flash_attn_varlen_func does not support setting qv"
 
@@ -2203,9 +2203,9 @@ def flashmask_attention(
             window_size = (window_size, window_size)
         sq = query.shape[1]
         bsz = query.shape[0]
-        assert (
-            startend_row_indices is None
-        ), "can't use window_size with startend_row_indices"
+        assert startend_row_indices is None, (
+            "can't use window_size with startend_row_indices"
+        )
         if causal:
             startend_row_indices = paddle.arange(
                 window_size[0] + 1, sq + window_size[0] + 1, dtype="int32"
@@ -2246,24 +2246,26 @@ def flashmask_attention(
         )
 
     else:
-        assert (
-            startend_row_indices.dtype == paddle.int32
-        ), f"startend_row_indices.dtype must be paddle.int32, but got {startend_row_indices.dtype}"
-        assert (
-            len(startend_row_indices.shape) == 4
-        ), f"startend_row_indices rank must be 4,but got {startend_row_indices.shape}"
-
-        assert (
-            startend_row_indices.shape[0] == key.shape[0]
-        ), f"startend_row_indices.shape[0] must be equal to batch_size, but got {startend_row_indices.shape[0]} and {key.shape[0]}"
-
-        assert (
-            startend_row_indices.shape[2] == key.shape[1]
-        ), f"startend_row_indices.shape[2] must be equal to seqlen_k, but got {startend_row_indices.shape[2]} and {key.shape[2]}"
+        assert startend_row_indices.dtype == paddle.int32, (
+            f"startend_row_indices.dtype must be paddle.int32, but got {startend_row_indices.dtype}"
+        )
+        assert len(startend_row_indices.shape) == 4, (
+            f"startend_row_indices rank must be 4,but got {startend_row_indices.shape}"
+        )
+
+        assert startend_row_indices.shape[0] == key.shape[0], (
+            f"startend_row_indices.shape[0] must be equal to batch_size, but got {startend_row_indices.shape[0]} and {key.shape[0]}"
+        )
+
+        assert startend_row_indices.shape[2] == key.shape[1], (
+            f"startend_row_indices.shape[2] must be equal to seqlen_k, but got {startend_row_indices.shape[2]} and {key.shape[2]}"
+        )
         assert startend_row_indices.shape[1] in [
             1,
             key.shape[2],
-        ], "startend_row_indices head_num must be equal to 1(broadcast) or head_num_k."
+        ], (
+            "startend_row_indices head_num must be equal to 1(broadcast) or head_num_k."
+        )
 
         if causal:
             if startend_row_indices.shape[-1] == 1:
@@ -2383,9 +2385,9 @@ def calc_reduced_attention_scores(
             >>> )
             >>> # doctest: -SKIP
     """
-    assert (
-        query.stop_gradient and key.stop_gradient
-    ), 'calc_reduced_attention_scores() is for inference only.'
+    assert query.stop_gradient and key.stop_gradient, (
+        'calc_reduced_attention_scores() is for inference only.'
+    )
 
     if in_dynamic_or_pir_mode():
         reduced_scores = _C_ops.calc_reduced_attn_scores(

python/paddle/nn/functional/loss.py

@@ -94,9 +94,9 @@ def dice_loss(
     """
     assert input.dtype in (paddle.float32, paddle.float64)
     assert label.dtype in (paddle.int32, paddle.int64)
-    assert (
-        len(input.shape) >= 2
-    ), "The rank of input should be greater than or equal to 2."
+    assert len(input.shape) >= 2, (
+        "The rank of input should be greater than or equal to 2."
+    )
     assert len(input.shape) == len(label.shape), (
         "The rank of input and label should be equal, "
         f"but received input: {len(input.shape)}, label: {len(label.shape)}."
@@ -105,9 +105,9 @@ def dice_loss(
         "The last dimension of label should be 1, "
         f"but received {label.shape[-1]}."
     )
-    assert (
-        input.shape[:-1] == label.shape[:-1]
-    ), "All dimensions should be equal except the last one."
+    assert input.shape[:-1] == label.shape[:-1], (
+        "All dimensions should be equal except the last one."
+    )
 
     label = paddle.squeeze(label, [-1])
     label = paddle.nn.functional.one_hot(label, input.shape[-1])

python/paddle/nn/functional/pooling.py

@@ -704,9 +704,9 @@ def max_pool1d(
 
 
 def _unpool_output_size(x, kernel_size, stride, padding, output_size):
-    assert output_size is None or isinstance(
-        output_size, (list, tuple)
-    ), f"Required output_size is None|list|tuple, but received {output_size}"
+    assert output_size is None or isinstance(output_size, (list, tuple)), (
+        f"Required output_size is None|list|tuple, but received {output_size}"
+    )
     input_size = x.shape
     default_size = []
     for d in range(len(kernel_size)):

python/paddle/nn/initializer/bilinear.py

@@ -96,7 +96,9 @@ def forward(
         """
         assert not (
             isinstance(var, framework.EagerParamBase) and var.is_dist()
-        ), "Currently, Bilinear initializer not support lazy init for dist param."
+        ), (
+            "Currently, Bilinear initializer not support lazy init for dist param."
+        )
         block = self._check_block(block)
 
         if not isinstance(var, (framework.Variable, pir.core.ParameterMeta)):