diff --git a/python/paddle/nn/functional/flash_attention.py b/python/paddle/nn/functional/flash_attention.py
index d987e11be3264f..082ec5389a020d 100644
--- a/python/paddle/nn/functional/flash_attention.py
+++ b/python/paddle/nn/functional/flash_attention.py
@@ -1423,6 +1423,12 @@ def scaled_dot_product_attention(
         sdp_func_name = _select_sdp_for_sdpa(
             query, key, attn_mask, dropout_p, is_causal
         )
+        if attn_mask.dtype == paddle.bool:
+            attn_mask = paddle.where(
+                attn_mask,
+                paddle.to_tensor(0.0, dtype=query.dtype),
+                paddle.to_tensor(-float('inf'), dtype=query.dtype),
+            )
         if sdp_func_name == "flash_attn":
             if in_dynamic_or_pir_mode():
                 fixed_seed_offset = None
diff --git a/test/legacy_test/test_scaled_dot_product_attention.py b/test/legacy_test/test_scaled_dot_product_attention.py
new file mode 100644
index 00000000000000..8a0b4b5ccbe459
--- /dev/null
+++ b/test/legacy_test/test_scaled_dot_product_attention.py
@@ -0,0 +1,185 @@
+# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+import paddle.nn.functional as F
+from paddle.nn.functional.flash_attention import (
+    scaled_dot_product_attention,
+    sdp_kernel,
+)
+
+
+def attention_naive(q, k, v, causal=False):
+    qt = paddle.transpose(q, [0, 2, 1, 3])
+    kt = paddle.transpose(k, [0, 2, 1, 3])
+    vt = paddle.transpose(v, [0, 2, 1, 3])
+    scale = 1.0 / np.sqrt(q.shape[-1])
+    s = paddle.matmul(qt * scale, paddle.transpose(kt, [0, 1, 3, 2]))
+    p = (
+        paddle.incubate.softmax_mask_fuse_upper_triangle(s)
+        if causal
+        else F.softmax(s)
+    )
+    o = paddle.matmul(p, vt)
+    return paddle.transpose(o, [0, 2, 1, 3])
+
+
+def attention_naive_with_mask(q, k, v, attn_bias):
+    qt = paddle.transpose(q, [0, 2, 1, 3])
+    kt = paddle.transpose(k, [0, 2, 1, 3])
+    vt = paddle.transpose(v, [0, 2, 1, 3])
+    scale = 1.0 / np.sqrt(q.shape[-1])
+    s = paddle.matmul(qt, paddle.transpose(kt, [0, 1, 3, 2]))
+    s = paddle.scale(s, scale)
+    p = F.softmax(s + attn_bias)
+    o = paddle.matmul(p, vt)
+    return paddle.transpose(o, [0, 2, 1, 3])
+
+
+def attention_naive_with_bool_mask(q, k, v, bool_mask):
+    qt = paddle.transpose(q, [0, 2, 1, 3])
+    kt = paddle.transpose(k, [0, 2, 1, 3])
+    vt = paddle.transpose(v, [0, 2, 1, 3])
+
+    scale = 1.0 / np.sqrt(q.shape[-1])
+    s = paddle.matmul(qt, paddle.transpose(kt, [0, 1, 3, 2]))
+    s = paddle.scale(s, scale)
+
+    float_mask = paddle.where(
+        bool_mask,
+        paddle.to_tensor(0.0, dtype=q.dtype),
+        paddle.to_tensor(-float('inf'), dtype=q.dtype),
+    )
+
+    s = s + float_mask
+    p = F.softmax(s)
+
+    o = paddle.matmul(p, vt)
+    return paddle.transpose(o, [0, 2, 1, 3])
+
+
+@unittest.skipIf(
+    not paddle.is_compiled_with_cuda(),
+    "CUDA is not available, this test requires GPU support.",
+)
+class TestAttentionWithBoolMask(unittest.TestCase):
+    def setUp(self):
+        self.place = paddle.CUDAPlace(0)
+        self.shape = (1, 1, 8, 8)
+        self.dtype = 'float32'
+        self.dropout = 0.0
+        self.causal = False
+
+    def test_dot_scale_product_bool_mask(self):
+        # test dynamic
+        paddle.disable_static()
+
+        query = np.random.random(self.shape)
+        key = np.random.random(self.shape)
+        value = np.random.random(self.shape)
+
+        q = paddle.to_tensor(
+            query, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        k = paddle.to_tensor(
+            key, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        v = paddle.to_tensor(
+            value, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+
+        q_ = paddle.to_tensor(
+            query, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        k_ = paddle.to_tensor(
+            key, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        v_ = paddle.to_tensor(
+            value, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+
+        mask_shape = (self.shape[0], 1, self.shape[1], self.shape[1])
+        bool_mask = np.random.choice([True, False], size=mask_shape)
+
+        m = paddle.to_tensor(
+            bool_mask, place=self.place, dtype=paddle.bool, stop_gradient=False
+        )
+
+        with sdp_kernel(
+            enable_math=True, enable_flash=False, enable_mem_efficient=False
+        ):
+            out = scaled_dot_product_attention(
+                q, k, v, m, self.dropout, self.causal
+            )
+
+        out_ = attention_naive_with_bool_mask(q_, k_, v_, m)
+
+        out.backward()
+        out_.backward()
+
+        np.testing.assert_allclose(out.numpy(), out_, rtol=5e-03, atol=1e-03)
+
+    def test_dot_scale_product_float_mask(self):
+        # test with mask=float
+        paddle.disable_static()
+
+        query = np.random.random(self.shape)
+        key = np.random.random(self.shape)
+        value = np.random.random(self.shape)
+
+        q = paddle.to_tensor(
+            query, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        k = paddle.to_tensor(
+            key, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        v = paddle.to_tensor(
+            value, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+
+        q_ = paddle.to_tensor(
+            query, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        k_ = paddle.to_tensor(
+            key, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+        v_ = paddle.to_tensor(
+            value, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+
+        mask_shape = (self.shape[0], 1, self.shape[1], self.shape[1])
+        mask = np.random.random(mask_shape)
+        m = paddle.to_tensor(
+            mask, place=self.place, dtype=self.dtype, stop_gradient=False
+        )
+
+        with sdp_kernel(
+            enable_math=True, enable_flash=False, enable_mem_efficient=False
+        ):
+            out = scaled_dot_product_attention(
+                q, k, v, m, self.dropout, self.causal
+            )
+
+        out_ = attention_naive_with_mask(q_, k_, v_, m)
+        out.backward()
+        out_.backward()
+        np.testing.assert_allclose(out.numpy(), out_, rtol=5e-03, atol=1e-03)
+
+
+if __name__ == '__main__':
+    unittest.main()