Kernels running on the GPU require all memory accesses to be within a thread or
a block. The file you are looking does not do any thread binding. I suggest
looking at this tutorial:
https://tvm.apache.org/docs/tutorials/optimize/opt_conv_cuda.html
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Because the tutorial only tunes conv2d tasks:
tasks = autotvm.task.extract_from_program(
mod["main"], target=target, params=params,
ops=(relay.op.get("nn.conv2d"),)
)
Although I don't think missing one dense schedule will hurt the performance a
lot, you can modify the code to
@comaniac Hmm, not sure if you're not the right person for this question, but
I'd really appreciate it if you could provide some hints or pointers to others.
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Thanks for the pointer!
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```
with tvm.transform.PassContext(opt_level=3):
lib =relay.build_module.build(mod, target=target, params=params)
```
The Relay build process eventually calls the graph optimization pipeline.
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