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Posted to dev@tvm.apache.org by MajorTom via Apache TVM Discuss <no...@discuss.tvm.ai> on 2021/12/06 16:15:01 UTC
[Apache TVM Discuss] [Development/pre-RFC] Tvm integration with external DSP accelerator API
Hello,
I have a task for implementing tvm codegen generating code from relay operators to an external DSP accelerator with has a C API.
The accelerator API include c functions API, such as:
Void f1 (int* a, int* b char* c);
Void f2 (float* a, int_64* b char* c);
Each operator can be matched with one or several DSP API functions, or alternatively Several relay operators can be matched with one or several API accelerator function.
The issue is that the implementation must be **modular**.
It should be relatively easy for the user to register relay ops to the corresponding DSP C API functions.
Therefore, I would like to create some sort of data structure/dictionary that will map operators to corresponding DSP C API functions.
the API can be in python for example. and the user will be able to add a new entry for relay op and
map it to the external API.
It should also include a function per operator that returns true, depending on input and output type, if it can be implemented with the aforementioned API.
This data structure/dictionary with be used in the codegen.
**Can someone advice how to define create such a data structure/dictionary?**
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[Apache TVM Discuss] [Development/pre-RFC] Tvm integration with external DSP accelerator API
Posted by Ziheng Jiang via Apache TVM Discuss <no...@discuss.tvm.ai>.
Sounds like a good case for https://github.com/tlc-pack/relax/issues/46
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[Apache TVM Discuss] [Development/pre-RFC] Tvm integration with external DSP accelerator API
Posted by Andrew Reusch via Apache TVM Discuss <no...@discuss.tvm.ai>.
@MajorTom would BYOC + pattern matching against Relay ops work for your use case?
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[Apache TVM Discuss] [Development/pre-RFC] Tvm integration with external DSP accelerator API
Posted by Junru Shao via Apache TVM Discuss <no...@discuss.tvm.ai>.
Looks like it could be abstracted as calling a packed function...On the low-level you may use `call_packed` as demonstrated [here](https://github.com/apache/tvm/blob/main/tests/python/unittest/test_te_tensor.py#L187); CC @yuchenj on the high-level IR
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