chore: import upstream snapshot with attribution

test/legacy_test/test_fused_stack_transpose_quant_op.py

@@ -0,0 +1,83 @@
+#  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
+from op_test import is_custom_device
+
+import paddle
+import paddle.incubate.nn.functional as F
+from paddle import core
+
+
+@unittest.skipIf(
+    not (core.is_compiled_with_cuda() or is_custom_device()),
+    "core is not compiled with CUDA ",
+)
+class TestFusedStackTransposeQuantOp(unittest.TestCase):
+    def setUp(self):
+        np.random.seed(2025)
+        self.dtype = 'bfloat16'
+        self.transpose = True
+
+    def restore_stack_quant(self, out, scale):
+        # Expand scale to [M, K] shape assuming block size 128 x 128
+        scale = paddle.repeat_interleave(scale, repeats=128, axis=0)
+        scale = paddle.repeat_interleave(scale, repeats=128, axis=1)
+        x = out.astype('float32') * scale
+        return x
+
+    def check_main(self, N, M, K):
+        paddle.disable_static()
+        x_tensor_list = [
+            paddle.randn([M, K], dtype=self.dtype).clip(min=-50, max=50)
+            for _ in range(N)
+        ]
+        x_fp32 = paddle.stack(x_tensor_list).reshape([-1, K]).astype('float32')
+        out, scale = F.fused_stack_transpose_quant(
+            x_tensor_list, transpose=self.transpose
+        )
+        x_restored = self.restore_stack_quant(out, scale)
+        if self.transpose:
+            x_restored = (
+                x_restored.reshape([N, K, M])
+                .transpose([0, 2, 1])
+                .reshape([-1, K])
+            )
+        paddle.enable_static()
+
+        if not (paddle.is_compiled_with_cuda() or is_custom_device()):
+            return
+        np.testing.assert_allclose(
+            x_fp32.numpy(),
+            x_restored.numpy(),
+            rtol=0.01,
+            atol=0.2,
+        )
+
+    def test_fused_stack_transpose_quant(self):
+        self.check_main(1, 2048, 128)
+
+    def test_fused_stack_transpose_quant2(self):
+        self.check_main(4, 2048, 128)
+
+
+class TestFusedStackTransposeQuantOp1(TestFusedStackTransposeQuantOp):
+    def setUp(self):
+        super().setUp()
+        self.transpose = False
+
+
+if __name__ == "__main__":
+    unittest.main()