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@@ -33,5 +33,15 @@
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# # [IMPORTANT] maybe first create an op for one batch, then a wrapper that split into batches
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# return torch.cat(output_batches, dim=0)
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#
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-# def backward(ctx, grad_outputs):
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-# return TODO(ctx, )
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+# def backward(ctx, *grad_outputs: torch.Tensor):
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+# return TODO_think_through(ctx, *grad_outputs)
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+
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+# class RemoteSequentialInferenceSession:
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+# """An interface to a multi-step *inference* session for a sequence of remote transformer blocks"""
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+# # form sequence, maintain past inputs for all stages; if any stage breaks, fix the same way as in forward:
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+# # patch = RemoteSequenceManager[spans[BROKEN_INDEX].start : spans[BROKEN_INDEX].end].form_sequence()
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+# # feed_past_key_values(into=patch, inputs=past_key_values[BROKEN_INDEX])
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+# # self.TODO_replace_in_metadata(remove=BROKEN_INDEX, replace_with=patch)
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+# #
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+# TODO_think()
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+#
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justheuristic