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@@ -198,10 +198,9 @@ class _RemoteMoECall(torch.autograd.Function):
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alive_ix = torch.as_tensor(alive_ix, device=expert_logits.device)
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alive_expert_probs = torch.softmax(expert_logits[alive_ix], dim=0)
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- stacked_alive_outputs = tuple(map(torch.stack, list(zip(*alive_outputs))))
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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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- print('!' * 50, [x.shape for x in flat_average_outputs])
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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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@@ -232,7 +231,7 @@ class _RemoteMoECall(torch.autograd.Function):
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grad_wrt_probs = sum(tuple(
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torch.sum(grad_out[None, ...] * stacked_avive_out[backward_survivors_in_alive_ix],
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dim=tuple(range(1, stacked_avive_out.ndim)))
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- for grad_out, stacked_avive_out in zip(grad_outputs_flat, stacked_alive_outputs)
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+ for grad_out, stacked_avive_out in zip(grad_outputs_flat, zip(*stacked_alive_outputs))
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))
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softmax_jacobian = torch.diagflat(survived_probas) - torch.ger(survived_probas, survived_probas)
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grad_wrt_logits = grad_wrt_probs @ softmax_jacobian
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justheuristic