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Posted to discuss-archive@tvm.apache.org by Max Sponner via TVM Discuss <no...@discuss.tvm.ai> on 2020/08/19 13:04:23 UTC
[TVM Discuss] [Questions] Codegeneration for own DLA Instruction Set
I have another problem:
The graph annotation has been defined (by adding my own version at python/tvm/relay/op/contrib/test_dla.py)
But it seems like that is not enough to get the annotation going, as
`mod_t = transform.AnnotateTarget("test_dla")(mod)`
followed by
`mod_t = transform.PartitionGraph()(mod_t)`
results in no change to the representation
what did I miss, to enable my specific annotation?
The graph in question looks like this:
def @main(%x: Tensor[(10, 10), int8], %y: Tensor[(10, 10), int8]) -> Tensor[(10, 10), int8] {
%0 = multiply(%y, %y) /* ty=Tensor[(10, 10), int8] */;
%1 = add(%x, %x) /* ty=Tensor[(10, 10), int8] */;
subtract(%0, %1) /* ty=Tensor[(10, 10), int8] */
}
And my annotations: (I trief replacing qnn.add with add and the same for subtract, but maybe I have forgotten to register my annotation somewhere?)
@tvm.ir.register_op_attr("qnn.add", target_name)
def add(attr, args):
''' check if tensor addition is supported by DLA'''
typ = args[0].checked_type
if typ.dtype != "int8":
return False
#TODO: how to test for equal shapes?
return True
@tvm.ir.register_op_attr("qnn.subtract", target_name)
def sub(attr, args):
''' check if tensor addition is supported by DLA'''
typ = args[0].checked_type
if typ.dtype != "int8":
return False
#TODO: how to test for equal shapes?
return True
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