{"record":{"id":"97fa1517becfce33","repo":"tensorflow/models","slug":"transformerlayer-expects-a-three-dimensional-input-97fa15","errorCode":null,"errorMessage":"TransformerLayer expects a three-dimensional input of shape [batch, sequence, width].","messagePattern":"TransformerLayer expects a three-dimensional input of shape \\[batch, sequence, width\\]\\.","errorType":"exception","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"official/nlp/modeling/layers/transformer_xl.py","lineNumber":128,"sourceCode":"    self._hidden_size = hidden_size\n    self._inner_size = inner_size\n    self._dropout_rate = dropout_rate\n    self._attention_dropout_rate = attention_dropout_rate\n    self._inner_activation = inner_activation\n    self._norm_epsilon = norm_epsilon\n    self._kernel_initializer = kernel_initializer\n    self._inner_dropout = inner_dropout\n    self._two_stream = two_stream\n    if two_stream:\n      self._attention_layer_type = relative_attention.TwoStreamRelativeAttention\n    else:\n      self._attention_layer_type = relative_attention.MultiHeadRelativeAttention\n\n  def build(self, input_shape):\n    input_tensor = input_shape[0] if len(input_shape) == 2 else input_shape\n    input_tensor_shape = tf.TensorShape(input_tensor)\n    if len(input_tensor_shape.as_list()) != 3:\n      raise ValueError(\"TransformerLayer expects a three-dimensional input of \"\n                       \"shape [batch, sequence, width].\")\n    batch_size, sequence_length, hidden_size = input_tensor_shape\n\n    if len(input_shape) == 2:\n      mask_tensor_shape = tf.TensorShape(input_shape[1])\n      expected_mask_tensor_shape = tf.TensorShape(\n          [batch_size, sequence_length, sequence_length])\n      if not expected_mask_tensor_shape.is_compatible_with(mask_tensor_shape):\n        raise ValueError(\"When passing a mask tensor to TransformerXLBlock, \"\n                         \"the mask tensor must be of shape [batch, \"\n                         \"sequence_length, sequence_length] (here %s). Got a \"\n                         \"mask tensor of shape %s.\" %\n                         (expected_mask_tensor_shape, mask_tensor_shape))\n    if hidden_size % self._num_heads != 0:\n      raise ValueError(\n          \"The input size (%d) is not a multiple of the number of attention \"\n          \"heads (%d)\" % (hidden_size, self._num_heads))\n    self._attention_layer = self._attention_layer_type(","sourceCodeStart":110,"sourceCodeEnd":146,"githubUrl":"https://github.com/tensorflow/models/blob/e006f5f0d534913e49c1f1dae87364039fa607e2/official/nlp/modeling/layers/transformer_xl.py#L110-L146","documentation":"Error \"TransformerLayer expects a three-dimensional input of shape [batch, sequence, width].\" thrown in tensorflow/models.","triggerScenarios":"Thrown at official/nlp/modeling/layers/transformer_xl.py:128 when the library encounters an invalid state.","commonSituations":"See trigger scenarios.","solutions":[],"exampleFix":null,"handlingStrategy":null,"validationCode":null,"typeGuard":null,"tryCatchPattern":null,"preventionTips":[],"tags":[],"backgroundTag":null,"analyzedSha":"e006f5f0d534913e49c1f1dae87364039fa607e2","analyzedAt":"2026-08-24T14:09:15.576Z","schemaVersion":2},"datasetVersion":"2026-08-24T17:17:21.512Z"}