chore: import upstream snapshot with attribution

2026-07-13 13:24:13 +08:00
commit 1037506f2e
6050 changed files with 1731598 additions and 0 deletions
@@ -0,0 +1,49 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+#
+# This source code is licensed under the MIT license found in the
+# LICENSE file in the root directory of this source tree.
+
+import torch
+import torch.nn as nn
+
+
+class BeamableMM(nn.Module):
+    """This module provides an optimized MM for beam decoding with attention.
+
+    It leverage the fact that the source-side of the input is replicated beam
+    times and the target-side of the input is of width one. This layer speeds up
+    inference by replacing the inputs {(bsz x 1 x nhu), (bsz x sz2 x nhu)}
+    with smaller inputs {(bsz/beam x beam x nhu), (bsz/beam x sz2 x nhu)}.
+    """
+
+    def __init__(self, beam_size=None):
+        super(BeamableMM, self).__init__()
+        self.beam_size = beam_size
+
+    def forward(self, input1, input2):
+        if (
+            not self.training
+            and self.beam_size is not None  # test mode
+            and input1.dim() == 3  # beam size is set
+            and input1.size(1)  # only support batched input
+            == 1  # single time step update
+        ):
+            bsz, beam = input1.size(0), self.beam_size
+
+            # bsz x 1 x nhu --> bsz/beam x beam x nhu
+            input1 = input1[:, 0, :].unfold(0, beam, beam).transpose(2, 1)
+
+            # bsz x sz2 x nhu --> bsz/beam x sz2 x nhu
+            input2 = input2.unfold(0, beam, beam)[:, :, :, 0]
+
+            # use non batched operation if bsz = beam
+            if input1.size(0) == 1:
+                output = torch.mm(input1[0, :, :], input2[0, :, :])
+            else:
+                output = input1.bmm(input2)
+            return output.view(bsz, 1, -1)
+        else:
+            return input1.bmm(input2)
+
+    def set_beam_size(self, beam_size):
+        self.beam_size = beam_size