chore: import upstream snapshot with attribution

nemo/collections/asr/modules/ssl_modules/multi_softmax_decoder.py

@@ -0,0 +1,84 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  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.
+
+
+from collections import OrderedDict
+
+import torch
+
+from nemo.collections.asr.parts.submodules.jasper import init_weights
+from nemo.core.classes import NeuralModule, typecheck
+from nemo.core.neural_types import AcousticEncodedRepresentation, LogprobsType, NeuralType
+
+
+class MultiSoftmaxDecoder(NeuralModule):
+    """
+    A linear decoder that takes encoder output and produces log probabilities, which also handles the
+    case where each frame has multiple output targets. This can be used together with
+    `nemo.collections.asr.losses.ssl_losses.MultiMLMLoss` to train a model with multiple targets per frame.
+    """
+
+    @property
+    def input_types(self):
+        return OrderedDict({"encoder_output": NeuralType(('B', 'D', 'T'), AcousticEncodedRepresentation())})
+
+    @property
+    def output_types(self):
+        if self.squeeze_single and self.num_decoders == 1:
+            return OrderedDict({"logprobs": NeuralType(('B', 'T', 'C'), LogprobsType())})
+        return OrderedDict({"logprobs": NeuralType(('B', 'T', 'C', 'H'), LogprobsType())})
+
+    def __init__(
+        self,
+        feat_in: int,
+        num_classes: int,
+        num_decoders: int = 1,
+        init_mode: str = "xavier_uniform",
+        use_bias: bool = False,
+        squeeze_single: bool = False,
+    ) -> None:
+        """
+        Args:
+            feat_in: input feature dimension
+            num_classes: number of classes
+            num_decoders: number of decoders
+            init_mode: initialization mode
+            use_bias: whether to use bias
+            squeeze_single: if True, squeeze codebook dimension if num_books is 1
+        """
+        super().__init__()
+        self.feat_in = feat_in
+        self.num_classes = num_classes
+        self.num_decoders = num_decoders
+        self.squeeze_single = squeeze_single
+
+        self.decoder_layers = torch.nn.Sequential(
+            torch.nn.Conv1d(self.feat_in, self.num_classes * self.num_decoders, kernel_size=1, bias=use_bias)
+        )
+        self.apply(lambda x: init_weights(x, mode=init_mode))
+
+    @typecheck()
+    def forward(self, encoder_output):
+        """
+        Args:
+            encoder_output: output from the encoder of shape (B, D, T)
+        Returns:
+            logprobs: log probabilities of shape (B, T, C, H), or (B, T, C) if squeeze_single is True
+        """
+        logits = self.decoder_layers(encoder_output).transpose(1, 2)
+        logits = logits.reshape(logits.shape[0], logits.shape[1], self.num_classes, self.num_decoders)
+        if self.squeeze_single and self.num_decoders == 1:
+            logits = logits.squeeze(-1)
+
+        return torch.nn.functional.log_softmax(logits, dim=2)