Are the TVM dlls (tvm.dll, topi.dll and tvm_runtime.dll) in your PATH
environment var?
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Perhaps it is for compiling efficiency ?
In a NN Graph, there might exist many same structure relay functions (after
relay Graph-Level Opt)。
So during lowering process, when meets a already lowered relay function, no
need to lower again
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I read the code runtime/thread_pool.cc, found that enviroment var
TVM_THREAD_POOL_SPIN_COUNT control the number of iterations to spin before
thread sleep, it's really work in my tests.
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I tried installing tvm on windows but when I try to use
python setup.py install --user
I get the following:
(base) C:\Users\User\Desktop\Incubator-tvm\tvm\python>python setup.py install
--user
Traceback (most recent call last):
File "setup.py", line 61, in
LIB_LIST, __version__ = get_li
I noticed these terms "CCachedKey", "CCachedFunc" in lowering process in /src/relay/backend/compile_engine.cc.
1. Why is there a 'cache'?
2. What's its relationship to the lowering process?
Thanks.
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While post-training quantization from float32 to int8 hidden/cell states
remains an open research topic, one work around we've found is to compute
hidden states at higher precision on CPU rather than on the low-precision
accelerator in order to reach our accuracy requirements.
>From a fronte
OK, Thanks. I guess that on Mac, I would have to use target='llvm' and ignore
the warning "Cannot find config for target=llvm, workload=('dense_nopack.x86''.
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Is converting a model to FP16 with target = "cuda" supported? If so, is there
an example pass I could look at to convert my model?
cc @vinx13 @Hzfengsy
Thanks!
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TRT integration is now working but only on the [AWS forked
repo](https://github.com/neo-ai/tvm). @trevor-m is working hard to make it
upstream. Since the external codegen infra still requires some improvements, it
might take some more time.
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Mac hasn't used nvidia graphic card for a LONG time (>5 years?), which means
your laptop doesn't support a CUDA-enabled device. You'll need a new platform
to try cuda.
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I met the same problem, anyone solved it?
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Hi! Whats the current status on this? Thanks!
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Hi:
I am investigating the capability of TVM primitives (CUDA backend). I take
CUTLASS as a baseline of highly-optimized CUDA library.
I think most of optimization techniques used in CUTLASS like tiling, shared_mem
management are supported by TVM primitives.
Streaming is also an important
I don't know or think if we are exposing CUDA stream abstraction to python
frontend. We typically don't care about cuda stream (we don't support any
concurrency at runtime).
What is your use case?
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Hi:
Thanks for you answer. I will check autotvm to see how it tunes grid/block.
Because based on experience, grid/block dims will affect performance.
And another question is that, I see there is arg for **cuda stream**
```
CUstream strm = static_cast(CUDAThreadEntry::ThreadLocal()->stream);
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