I met following error when I try to feed input tensor with dynamic batch size:
(bert model)
File
"/yezhouhai/xtcl-master/baidu/xpu/xmir/python/tvm/relay/frontend/common.py",
line 529, in infer_value
), "All inputs to infer must be available in params."
This is because in function (pytorch.py) expand:
sizes[i] = int(_infer_value(sizes[i], {}).asnumpy())
This sizes[i] will be feeded to input of op.repeat (2nd parameter) which only
accept int parameter.
But in static case, the sizes is constructed by following:
sizes: [Constant(1), Constant(8)]
In dynamic case, the sizes is constructed by following:
sizes: [CallNode(Op(take), [CallNode(Op(shape_of), [Var(input_ids,
ty=TensorType([?, 8], float32))], relay.attrs.ShapeOfAttrs(0x7f169cefa318),
[]), Constant(0)], relay.attrs.TakeAttrs(0x7f169cefabc8), []), Constant(8)]
So in dynamic case, the sizes[0] is a relay.Expr.Call and it can't be inferred
as int.
Two possible solution I can think of:
1) I try to reuse onnx's expand version. But find it's input shape is
relay.Expr, but in our case the shape is a list type. I don't know how to
convert a list to shape.
2) Write a dyn.repeat op? So that it can accpet relay.Expr as its parameter?
Any suggestions? Thanks!
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