Add transformer generate square subsequent mask api (#27651)

* add transformer generate square subsequent mask api

* add dtype for input, update doc, use -np.inf

* add dtype for input, update doc, use -np.inf

python/paddle/fluid/tests/unittests/test_transformer_api.py

@@ -609,6 +609,13 @@ class TestTransformer(unittest.TestCase):
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 
+    def test_generate_square_subsequent_mask(self):
+        length = 5
+        d_model, n_head, dim_feedforward = 8, 4, 64
+        transformer = Transformer(
+            d_model, n_head, dim_feedforward=dim_feedforward)
+        mask = transformer.generate_square_subsequent_mask(length)
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/layer/transformer.py

@@ -24,7 +24,9 @@ __all__ = [
 
 import copy
 import collections
+import numpy as np
 
+import paddle
 from .common import Linear, Dropout
 from .norm import LayerNorm
 from .. import functional as F
@@ -1174,3 +1176,39 @@ class Transformer(Layer):
         output = self.decoder(
             tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask)
         return output
+
+    def generate_square_subsequent_mask(self, length):
+        """
+        Generate a square mask for the sequence. The mask ensures that the
+        predictions for position i can depend only on the known outputs at
+        positions less than i.
+
+        Parameters:
+            length (int|Tensor): The length of sequence.
+
+        Returns:
+            Tensor: Generated square mask according to the given length.
+
+        Examples:
+            .. code-block:: python
+
+                import paddle
+                from paddle.nn.layer.transformer import Transformer
+                length = 5
+                d_model, n_head, dim_feedforward = 8, 4, 64
+                transformer_paddle = Transformer(
+                    d_model, n_head, dim_feedforward=dim_feedforward)
+                mask = transformer_paddle.generate_square_subsequent_mask(length)
+                print(mask.numpy())
+
+                # [[  0. -inf -inf -inf -inf]
+                # [  0.   0. -inf -inf -inf]
+                # [  0.   0.   0. -inf -inf]
+                # [  0.   0.   0.   0. -inf]
+                # [  0.   0.   0.   0.   0.]]
+
+        """
+        return paddle.tensor.triu(
+            (paddle.ones(
+                (length, length), dtype=paddle.get_default_dtype()) * -np.inf),
+            1)