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Backport transpose_for_scores function in modeling xlm roberta. #2300

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Backport transpose_for_scores function in modeling xlm roberta. #2300

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13 changes: 11 additions & 2 deletions optimum/habana/transformers/models/xlm_roberta/modeling_xlm_roberta.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,15 @@
logger = logging.get_logger(__name__)


def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
"""
Backported from https://github.com/huggingface/transformers/blob/v4.51.0/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py#L180
"""
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(new_x_shape)
return x.permute(0, 2, 1, 3)


def gaudi_XLMRoberta_Sdpa_SelfAttention_forward(
self,
hidden_states: torch.Tensor,
Expand Down Expand Up @@ -74,8 +83,8 @@ def gaudi_XLMRoberta_Sdpa_SelfAttention_forward(
if is_cross_attention and past_key_value and past_key_value[0].shape[2] == current_states.shape[1]:
key_layer, value_layer = past_key_value
else:
key_layer = self.transpose_for_scores(self.key(current_states))
value_layer = self.transpose_for_scores(self.value(current_states))
key_layer = transpose_for_scores(self, self.key(current_states))
value_layer = transpose_for_scores(self, self.value(current_states))
if past_key_value is not None and not is_cross_attention:
key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
Expand Down
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