[SOLVED] Allow_missing Device API gpu is not enabled

Questions
1

Hi, @srkreddy1238 @tqchen, I am not able to deploy Non cpu(Cuda, OpenCL) tvm compiled model in c++ statically i am facing below error.

terminate called after throwing an instance of ‘dmlc::Error’ what(): [11:13:16] ../../src/runtime/c_runtime_api.cc:69: Check failed: allow_missing Device API gpu is not enabled.

even though i enabled Cuda and OpenCL support while building tvm, but i am able to deploy llvm tvm compiled model.

2

What’s your configuration for USE_CUDA?

3

@junrushao You mean H/W configuration ?

4

To use cuda I believe you should turn on this line

5

Yes i built tvm after enabling Cuda, and OpenCL support only/

6

I went through your error message and found that the name is “gpu”, which caused this issue. Could you try changing “gpu” to “cuda” or “opencl”?

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7

@srkreddy1238 @junrushao is below DLContext are correct for Cuda?

constexpr int dtype_code = kDLFloat;
constexpr int dtype_bits = 32;
constexpr int dtype_lanes = 1;
constexpr int device_type = kDLGPU;
constexpr int device_id = 0;
constexpr int in_ndim = 4;

Based on dlpack header file

8

Sorry i misread. From your error message i see that “device_api.gpu” is not registered, which should have been registered here, so may I confirm with you if the file is compiled and properly linked?

9

yes same working in python.

10

It’s weird…Could you print all elements in out_array provided by TVMFuncListGlobalNames?

11

int dtype_code = kDLFloat;
int dtype_bits = 32;
int dtype_lanes = 1;
int device_type = kDLCPU;
int device_id = 0;
int in_ndim = 4;

DLTensor* input;
TVMArrayAlloc(in_shape, in_ndim, dtype_code, dtype_bits, dtype_lanes, device_type, device_id, &input);
memset(input->data, 0,in_size);
when i do memset it is giving segmentation fault.

12

@junrushao Any suggestions on above comments ?

13

Thank you @junrushao for your response, issue is resoled by using TVMArrayCopyFromBytes().

14

Glad that you find a way to deal with the issue :slight_smile:

15

Thanks a lot. This solved my problem. I was using the opencl backend with intel_graphics . and using tvm.opencl() instead of tvm.gpu() actually solved my issue :slight_smile:

1 Like