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@@ -199,8 +199,8 @@ class _RemoteMoECall(torch.autograd.Function):
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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, alive_outputs))
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- flat_average_outputs = sum(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(dot_along_first_axis(alive_expert_probs, stacked_out)
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+ for stacked_out in stacked_alive_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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@@ -215,7 +215,7 @@ class _RemoteMoECall(torch.autograd.Function):
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""" Like normal backward, but we ignore any experts that failed during backward pass """
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expert_logits, alive_ix, alive_expert_probas, *stacked_alive_outputs = ctx.saved_tensors
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alive_contexts, k_min, timeout = ctx._alive_contexts, ctx._backward_k_min, ctx._backward_timeout
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- print(grad_outputs_flat)
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+
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jobs = [partial(cls._run_expert_backward, ctx, prob, *grad_outputs_flat)
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for ctx, prob in zip(alive_contexts, alive_expert_probas.split(1))]
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results = run_and_await_k(jobs, k=k_min, timeout_after_k=None, timeout_total=timeout)
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justheuristic