I was trying to to compile a tensor expression code for an opencl target, and
while inspecting the kernel generate by using the imported_modules attribute of
the build variable there seemed to be a lot of arguments called stride1,
stride2 and so on being passed to the kernel. Is there a way to
Hey I was trying to execute the functions given in topi
(https://tvm.apache.org/docs/api/python/topi.html) using opencl target but only
the ceil and floor operations seem to work the rest throws an error in tvm.
Here is my code.
> import tvm
>
> from tvm import te, topi
>
> import numpy as n
Hey when generating code for an opencl target is there any way to set the
local_work_size argument (which is set in the clEnqueueNDRangeKernel function)
from the tvm expressions ?.
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Hey @jcf94 and @comaniac thanks for the response , I was just trying to learn
the compiler flow of tvm and how if needed new rules could be added which are
hardware specific, As of now there is no immediate need, but thanks
nevertheless.
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Thanks a lot @comaniac . Where could one get started when trying to add their
own rules to generate sketches for new hardware ?.
as mentioned in the paper (section 4.1)
> On the other hand, the
> derivation-based sketch generation in Ansor is flexible enough
> to generate the required structur
Hey @comaniac thanks for the reference it as very helpful in understanding what
the auto scheduler does. I had a few doubts
1) Is this auto scheduler already implemented in TVM or is it ongoing, as we
were going through some of the files in the auto scheduler folder
([https://github.com/apache
Hey @comaniac Thanks. Are the hardware parameters like (num of threads, cache
size) taken into consideration when auto scheduling is used. Also how does auto
scheduling generate schedules from scratch. Does it look at every loop and try
to figure out the best transformation based on execution
After looking at the tutorial for auto tuning in auto tvm, there seems to two
methods to create a search space, one where the user gives an array if possible
values to search and the other where tvm generates this space. In the latter
case how does tvm model the search space equation, or is ju
Hey @FrozenGene
Could you point me to some documentation on AOCL is intoduces as a device type.
Also in the aocl folder inside src/runtime/opencl/aocl/aocl_device_api.cc , how
exactly is aocl choosing a device as it is not looking through all the
platforms as opencl is ?
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Hey @aurel333 Thanks a ton for that fix. The update you have given will try
choose the first platform even if couldn't find an accelerator device. Would it
be better to continue and check the next platform instead of trying to change
the device_type and look for devices ?
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So I am using device accelerator which uses opencl (pocl). Here when I try to
run opencl code using tvm on this device I have to fetch the context here as
tvm.cl() but it does not use the device type as accelerator unless I set the
dtype = CL_DEVICE_TYPE_ACCELERATOR, in the
tvm/src/runtime/o
Hey @aurel333 thanks , Like you said the llvm which I used to build tvm was the
wrong one.
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Hey @jcf94,
I was looking into the opencl_device_type.cc file and noticed that depending
on which context was used in the python code like (tvm.cpu, tvm.gpu,
tvm.opencl) it chooses a device type and sets the dtype accordingly.
When exactly is the dtype set to the CL_DEVICE_TYPE_ACCELERATOR,
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