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@@ -201,10 +201,6 @@ class _RemoteMoECall(torch.autograd.Function):
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stacked_alive_outputs = tuple(map(torch.stack, zip(*alive_outputs)))
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flat_average_outputs = tuple(dot_along_first_axis(alive_expert_probs, stacked_out)
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for stacked_out in stacked_alive_outputs)
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- flat_average_outputs_ = tuple(map(
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- lambda *tensors: sum(x * weight for x, weight in zip(tensors, alive_expert_probs)), *alive_outputs))
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- assert torch.allclose(flat_average_outputs_[0], flat_average_outputs[0])
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- assert False
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# 3. save individual outputs for backward pass
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ctx.save_for_backward(expert_logits, alive_ix, alive_expert_probs, *stacked_alive_outputs)
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ctx._alive_contexts = alive_contexts
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justheuristic