Hello, everyone, I hope you are having a great day. I’m trying to run the first example in tvm, but at the very begining I’m faced with these errors. I have built the tvm with LLVM ON and OpenCL On . installed Intel sdk for opencl applications (opencl2.1) . The build process went smoothly without a hitch! so I guess everything is in place. However I get these errors when I tried to run this snippet from this example: Quick Start Tutorial for Compiling Deep Learning Models :
opt_level = 3
target = tvm.target.intel_graphics()
with relay.build_config(opt_level=opt_level):
graph, lib, params = relay.build_module.build(
mod, target, params=params)
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 3, 224, 224, 'float32'), (64, 3, 7, 7, 'float32'), (2, 2), (3, 3), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 128, 28, 28, 'float32'), (128, 128, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 256, 14, 14, 'float32'), (256, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 512, 7, 7, 'float32'), (512, 512, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=unknown, workload=('dense', (1, 512, 'float32'), (1000, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.
By the way, How should I specify my graphics card model, should I be sending its name the same way it is displayed in TaskManager or Device Manager? which is e.g. Intel(R) Iris(R) Pro Graphics 580
becasue if I do , I’d still get the same error message, this time with the model I provided, i.e :
opt_level = 3
target = tvm.target.intel_graphics(model='Intel(R) Iris(R) Pro Graphics 580')
with relay.build_config(opt_level=opt_level):
graph, lib, params = relay.build_module.build(
mod, target, params=params)
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 3, 224, 224, 'float32'), (64, 3, 7, 7, 'float32'), (2, 2), (3, 3), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (64, 64, 1, 1, 'float32'), (1, 1), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (128, 128, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 64, 56, 56, 'float32'), (128, 64, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (256, 256, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 128, 28, 28, 'float32'), (256, 128, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 3, 3, 'float32'), (2, 2), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 512, 7, 7, 'float32'), (512, 512, 3, 3, 'float32'), (1, 1), (1, 1), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('conv2d', (1, 256, 14, 14, 'float32'), (512, 256, 1, 1, 'float32'), (2, 2), (0, 0), (1, 1), 'NCHW', 'float32'). A fallback configuration is used, which may bring great performance regression.
Cannot find config for target=opencl -device=intel_graphics -model=Intel(R) Iris(R) Pro Graphics 580, workload=('dense', (1, 512, 'float32'), (1000, 512, 'float32'), 0, 'float32'). A fallback configuration is used, which may bring great performance regression.
What should I do ?
Any help is greatly appreciated