That means this PR will generate the C code for CUTLASS?
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Yes, different autoTVM/autoscheduler runs on the same network can yield
different implementations.
If you know your model doesnt change, you would autoschedule once and save the
log files of the optimized implementation. If for some reason you need to
recompile your model you would tell TVM t
Ok, thank you for your reply.
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Then you could consider the BYOC flow. You could refer to a recent effort that
integrates NVIDIA CUTLASS with BYOC flow and the C codegen:
https://github.com/apache/tvm/pull/9261
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Because I want to add a new backend in TVM, but there is no LLVM support for
our backend. So I plan to compile the model to c code(it only supports c).
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Why you need to get a model in a C program? In general it's not effective for
compilers to generate human readable C code unless this is the only format that
the corresponding vendor tool chain accepts. For example, TVM does have a
C-like codegen, but it generates OpenCL code for non-NVIDIA GP
Thank you for your reply. I'm confused whether tvm can compile the a tensorflow
model to a c program .
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TVM deals with these in the Relay IR directly. For example, the IR with NCHW16c
and NCHW4c may look like:
```
%1 = nn.conv2d(...) // output layout: NCHW16c
%2 = layout_transform(%1, "NCHW4c") // output layout: NCHW4c
...
```
When compiling the above IR, `layout_tranform` is just an operator li
relay.build doesn't generate C code for execution. It generates LLVM IR (in the
case of CPU) and compiles directly to the executable binary.
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I have been trying to study how TVM does layout transformation during runtime
(eg. NHWC16c -> NHWC4c, etc.). Where in the source code is the required data
copy or move of the data tensor handled?
Also, where is the same for the weights tensor handled?
Is it in the `CopyDataFromTo` function of
Are the times reported by `tvm.runtime.profiler_vm.VirtualMachineProfiler`'s
`profile()` function the actual times observed on running inference on the
target device?
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