jcf94 commented on a change in pull request #7313:
URL: https://github.com/apache/tvm/pull/7313#discussion_r586937376
##########
File path: python/tvm/auto_scheduler/measure.py
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@@ -719,6 +720,45 @@ def local_builder_build(inputs, timeout, n_parallel,
build_func="default", verbo
return results
+def _prepare_input_map(args):
+ """This function deals with special task inputs.
+
+ Parameters
+ ----------
+ args : List[Tensor]
+ Input/output Tensor of a TVM subgraph.
+
+ Returns
+ -------
+ A Dict[Tensor, str] that maps the input Tensor to a buffer name.
+
+ Note
+ ----
+ The buffer name is specially designed, and these buffer should be provided
in
+ `SearchTask(..., task_inputs={...})`.
+ """
+ # pylint: disable=import-outside-toplevel
+ from tvm import topi # lazily import to avoid recursive dependency
+
+ # A dict that maps the input tensor arg to a buffer name
+ tensor_input_map = {}
+
+ # Case 0: Check placeholder name
+ for arg in args:
+ if isinstance(arg.op, tvm.te.PlaceholderOp):
+ if arg.op.name != "placeholder":
+ tensor_input_map[arg] = arg.op.name
+
+ # Case 1: Check sparse op
+ sparse_input_map = topi.nn.sparse.try_get_sparse_input(args)
Review comment:
Yeah, I've also had some discussions in our weekly sync while didn't
figure out any better solutions.
There're several reasons:
1. Different ops have different requirements over specific inputs;
2. While the problems is in a subgraph generated in Relay Integration, the
placeholder are all the same, we can not differentiate them from tag, name or
any other way, even the order of inputs are not guaranteed.
Currently approach is to merge all specific inputs checking to this
function, at least they have a same entry here. For the other ops, you have to
add their own check functions below.
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