[TOPI] Conv2d Schedule for Intel HD Graphics Target fails and produces wrong output

[rajh619]

nnvm.compiler.build() fails for intel_graphics target.
sample model https://s3.amazonaws.com/download.onnx/models/opset_3/resnet50.tar.gz
error log

Traceback (most recent call last):
  File "C:\Users\rg\Documents\Visual Studio 2015\Projects\nnvm_tvm_resnet\src\nnvm_tvm_igpu_.py", line 144, in <module>
    graph, lib, params = nnvm.compiler.build(sym, tvm.target.intel_graphics(), input_dict, params=params)
  File "C:\tvm\nnvm\python\nnvm\compiler\build_module.py", line 294, in build
    graph = graph.apply("GraphFusePartition").apply("GraphFuseCompile")
  File "C:\tvm\nnvm\python\nnvm\graph.py", line 234, in apply
    check_call(_LIB.NNGraphApplyPasses(self.handle, npass, cpass, ctypes.byref(ghandle)))
  File "C:\tvm\nnvm\python\nnvm\_base.py", line 75, in check_call
    raise NNVMError(py_str(_LIB.NNGetLastError()))
nnvm._base.NNVMError: TVMCall CFunc Error:
Traceback (most recent call last):
  File "C:\tvm\python\tvm\_ffi\_ctypes\function.py", line 54, in cfun
    rv = local_pyfunc(*pyargs)
  File "C:\tvm\nnvm\python\nnvm\top\nn.py", line 164, in compute_contrib_conv2d_NCHWc
    strides, padding, layout, out_layout)
  File "<decorator-gen-40>", line 2, in conv2d_NCHWc
  File "C:\tvm\python\tvm\target.py", line 345, in dispatch_func
    return dispatch_dict[k](*args, **kwargs)
TypeError: _decl_conv2d() takes from 6 to 7 positional arguments but 9 were given

call stack hits from here :
https://github.com/dmlc/tvm/blob/fd1a572058aef5a07e1e1032e26e67fe1906f9b2/nnvm/python/nnvm/top/nn.py#L169-L170

https://github.com/dmlc/tvm/blob/fd1a572058aef5a07e1e1032e26e67fe1906f9b2/nnvm/python/nnvm/top/nn.py#L192-L193

and further calls intel_graphics schedules below .
But conv2d implementation for Intel graphics missed " layout," & " out_layout," function parameters.as shown below :
https://github.com/dmlc/tvm/blob/fd1a572058aef5a07e1e1032e26e67fe1906f9b2/topi/python/topi/intel_graphics/conv2d.py#L60

https://github.com/dmlc/tvm/blob/fd1a572058aef5a07e1e1032e26e67fe1906f9b2/topi/python/topi/intel_graphics/conv2d.py#L99

On solving this error by passing default prarmeters as 'layout =None" & " out_layout=None" , It compiled successfully .
But now the prediction output results[inference] turned to be wrong with tvm.target.intel_graphics() !
[ I tested in default mode ie. target='opencl' the prediction result is good ] .
why the Inference output changes when using tvm.target.intel_graphics() ?

[Laurawly]

@rajh619 Are you using opt_level= 3? It seems that a recent PR: #1299 has changed the layout transferring api. I haven't update accordingly yet. But it should work for opt_level = 2. I have tested on Intel Graphics HD500. Also which hardware are you running on?

[tqchen]

Thanks @Laurawly for answering, is it possible for us to move this to http://discuss.tvm.ai/ and when we have actionable items, open issues to resolve this. Thanks!

[rajh619]

@Laurawly Yes you are correct ,i was using opt_level=3 .
I have tried it with opt_level=2 and found it is working . but as i said before while using opt_lvl=2 , the prediction accuracy changes a lot , which is a major concern as the output changes !

I have a test script to test here . If i set target='opencl' the prediction is correct .But if target is set to tvm.target.intel_graphics() ,the prediction is wrong .
Am testing in Intel graphics HD 4600.

@tqchen this issue is w.r.t existing implementation . should we move to the forum ?

Thanks

[Laurawly]

@rajh619 What's your hardware specification? I tested both the performance and accuracy on Intel HD graphics 500 and there's no problem on that hardware.

[tqchen]

@rajh619 the community will always trying to solve the problem together, be it on forum or issue ;) The forum is preferred because issues are for actionable items we aggressively close issues and expect issues to be active actionable items that can get closed in an expected time span (so we won't have pile of issues that get missed).

[rajh619]

@Laurawly my hardware specs are : iGPU -Intel HD 4600 and CPU is - intel core i5 (4590s) ,8GB .

Using opt_lvl=2 , the accuracy changes .
I have tried the resnet50 model mentioned below :
https://s3.amazonaws.com/download.onnx/models/opset_3/resnet50.tar.gz

you can load the model and check the change in accuracy with this script :
https://gist.github.com/rajh619/bbcbb2e776c7c24a016bb61bf1a2d156

[rajh619]

@Laurawly I have an update :
I tested in Intel HD Graphics 520 .The prediction is correct for opt_lvl =2 .
Also i modified the code , to use opt_lvl =3 ,and the prediction is correct.
The problem i think is , I was using Intel HD 4600 which is not "Intel® Processor Graphics Gen9" category . Also i can see improvement in performance too !
It will be great if schedules for other operations like dense ,pooling etc is available.
Thanks

[Laurawly]

@rajh619 Glad to hear that you find the problem. Yeah I agree that the intel_graphics target's schedulers are more suitable for intel integrated graphics cards while when you use independent intel graphics, the original cuda scheduler can do the job better. We'll do more coverage on both the operators and networks.

@tqchen Maybe we are good on closing the issue.

[tqchen]

Thanks for all the discussions, this issue is closed, let us move further discussions to the forum