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@@ -140,7 +140,7 @@ class RemoteMixtureOfExperts(nn.Module):
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unique_experts = self.network.get_experts(list(set(
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uid for row in beam for uid in row if uid != self.expert_padding)))
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if self._outputs_schema is None:
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- self._output_schema = next(iter(unique_experts)).info['output_schema']
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+ self._outputs_schema = next(iter(unique_experts)).info['outputs_schema']
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unique_experts_by_uid = {expert.uid: expert for expert in unique_experts if expert != self.expert_padding}
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return [[unique_experts_by_uid[uid] for uid in row if uid in unique_experts_by_uid] for row in beam]
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@@ -174,7 +174,7 @@ class RemoteMixtureOfExperts(nn.Module):
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return scores
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@property
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- def output_schema(self):
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+ def outputs_schema(self):
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if self._outputs_schema is None:
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# grab some expert to set ensemble output shape
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dummy_scores = self.proj(torch.randn(1, self.proj.in_features)).split_with_sizes(grid_size, dim=-1)
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justheuristic