Segmentation fault (core dumped) in UnitTestCuVectorAddRowSumMat for large matrix dimensions

[nayakajay]

Tested on: Titan RTX, cuda 11.0, Driver 455.51.05 The system has abundant RAM (>100G) and is a Intel Xeon processor

I have been trying to run the tests provided in cu-matrix-test.cc. I am interested in a particular test, UnitTestCuVectorAddRowSumMat. To run only 1 particular test, I have commented all the other tests in "CudaMatrixUnitTest" function and have modified the "main" function in the test file as

int main() {
  SetVerboseLevel(1);
  int32 loop = 0;

#if HAVE_CUDA == 1
  for (loop = 1; loop < 2; loop++) {
    CuDevice::Instantiate().SetDebugStrideMode(true);
    if (loop == 0)
      CuDevice::Instantiate().SelectGpuId("no");
    else
      CuDevice::Instantiate().SelectGpuId("yes");
#endif

    kaldi::CudaMatrixUnitTest<double>();

    if (loop == 0)
      KALDI_LOG << "Tests without GPU use succeeded.";
    else
      KALDI_LOG << "Tests with GPU use (if available) succeeded.";

#if HAVE_CUDA == 1
  } // No for loop if 'HAVE_CUDA != 1',
  CuDevice::Instantiate().PrintProfile();
#endif
  return 0;
}

As can be seen, I run the test only for double. In the test "UnitTestCuVectorAddRowSumMat", I give X=65000, Y=64360 (well within limits of int32). I am observing segmentation faults in that case. For X=45000, Y=44550, the test runs successfully. Am I doing something wrong?

Sample output

$ ./cu-matrix-test
LOG ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:172) Manually selected to compute on CPU.
Segmentation fault (core dumped)

The GPU code is running fine, I think, the relevant output is (by setting loop=1 in the main shown earlier)

$ ./cu-matrix-test
WARNING ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:247) Not in compute-exclusive mode.  Suggestion: use 'nvidia-smi -c 3' to set compute exclusive mode
LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:446) Selecting from 1 GPUs
LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:461) cudaSetDevice(0): TITAN RTX   free:24048M, used:172M, total:24220M, free/total:0.992899
LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:509) Device: 0, mem_ratio: 0.992899
LOG ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:390) Trying to select device: 0
LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:519) Success selecting device 0 free mem ratio: 0.992899
LOG ([5.5.854~1-403d]:FinalizeActiveGpu():cu-device.cc:346) The active GPU is [0]: TITAN RTX    free:23566M, used:654M, total:24220M, free/total:0.972998 version 7.5
Segmentation fault (core dumped)

[danpovey]

LIkely the problem is that the product of the x dim and y dim is outside of int32. The CPU code might need to be modified to handle that. I would merge a PR if you could make one.

On Thu, Feb 18, 2021 at 4:03 PM Ajay Nayak [email protected] wrote:

Tested on: Titan RTX, cuda 11.0, Driver 455.51.05 The system has abundant RAM (>100G) and is a Intel Xeon processor

I have been trying to run the tests provided in cu-matrix-test.cc. I am interested in a particular test, UnitTestCuVectorAddRowSumMat. To run only 1 particular test, I have commented all the other tests in "CudaMatrixUnitTest" function and have modified the "main" function in the test file as

int main() { SetVerboseLevel(1); int32 loop = 0;

#if HAVE_CUDA == 1 for (loop = 1; loop < 2; loop++) { CuDevice::Instantiate().SetDebugStrideMode(true); if (loop == 0) CuDevice::Instantiate().SelectGpuId("no"); else CuDevice::Instantiate().SelectGpuId("yes"); #endif

kaldi::CudaMatrixUnitTest<double>();

if (loop == 0)
  KALDI_LOG << "Tests without GPU use succeeded.";
else
  KALDI_LOG << "Tests with GPU use (if available) succeeded.";

#if HAVE_CUDA == 1 } // No for loop if 'HAVE_CUDA != 1', CuDevice::Instantiate().PrintProfile(); #endif return 0; }

As can be seen, I run the test only for double. In the test "UnitTestCuVectorAddRowSumMat", I give X=65000, Y=64360 (well within limits of int32). I am observing segmentation faults in that case. For X=45000, Y=44550, the test runs successfully. Am I doing something wrong?

Sample output

$ ./cu-matrix-test LOG ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:172) Manually selected to compute on CPU. Segmentation fault (core dumped)

The GPU code is running fine, I think, the relevant output is (by setting loop=1 in the main shown earlier)

$ ./cu-matrix-test WARNING ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:247) Not in compute-exclusive mode. Suggestion: use 'nvidia-smi -c 3' to set compute exclusive mode LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:446) Selecting from 1 GPUs LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:461) cudaSetDevice(0): TITAN RTX free:24048M, used:172M, total:24220M, free/total:0.992899 LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:509) Device: 0, mem_ratio: 0.992899 LOG ([5.5.854~1-403d]:SelectGpuId():cu-device.cc:390) Trying to select device: 0 LOG ([5.5.854~1-403d]:SelectGpuIdAuto():cu-device.cc:519) Success selecting device 0 free mem ratio: 0.992899 LOG ([5.5.854~1-403d]:FinalizeActiveGpu():cu-device.cc:346) The active GPU is [0]: TITAN RTX free:23566M, used:654M, total:24220M, free/total:0.972998 version 7.5 Segmentation fault (core dumped)

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[nayakajay]

A solution would be, to change MatrixDimT (matrix/matrix-common.h) and MatrixDimT_cuda (cudamatrix/cu-matrixdim.h) from int32 and int32_t to int64 and int64_t?

Edited: Never mind, it can cause problems.

[danpovey]

Yes

On Thu, Feb 18, 2021 at 6:01 PM Ajay Nayak [email protected] wrote:

A solution would be, to change MatrixDimT (matrix/matrix-common.h) and MatrixDimT_cuda (cudamatrix/cu-matrixdim.h) from int32 and int32_t to int64 and int64_t?

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[danpovey]

Wait, no... just change that one function, that is failing, to use larger size types where necessary.

[nayakajay]

The only place I could think where product of x_dim and y_dim will be done is during memory allocation (also a cause of SegFault?). But it seems that is already taken care of with a static_cast. Matrix allocation.

The functions where actual operations happen use cblas_* functions.

[danpovey]

Run it in gdb and get a stack: gdb matrix-lib-test (gdb) r .. crash.. (gdb) bt

On Fri, Feb 19, 2021 at 2:10 PM Ajay Nayak [email protected] wrote:

The only place I could think where product of x_dim and y_dim will be done is during memory allocation (also a cause of SegFault?). But it seems that is already taken care of with a static_cast. Matrix allocation https://github.com/kaldi-asr/kaldi/blob/master/src/matrix/kaldi-matrix.cc#L804 .

The functions where actual operations happen use cblas_* functions.

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[nayakajay]

Solution: In all the trouble causing places, I added a static_cast<size_t>. The test seem to be passing (no ASSERT failure).

Problems: For double type, the first problem was occurring at kaldi-matrix.h. This seemed to get rid of the error. I tried to fit larger data on the GPU by changing the data type to float. On using float type; the another issue showed up kaldi-matrix.cc

The trouble also occurred in the cuda kernel cu-kernels.cu. It threw "illegal memory accessed". Used cuda_memcheck to get that information.

I wanted to know if there are any assumptions w.r.t the _strided_reduction_fused kernel, regarding the dimensions of the matrix passed to it. Can it be rectangular (number of rows <<< number of columns or vice-versa)?

I wanted to get information regarding any real-world applications using this specific kernel. Any hints or suggestions in looking for such applications will be really great.

[danpovey]

Can you please make a pull request with the errors you fixed? Sorry, you'll have to look of that kernel yourself, and where it's called, I didn't write it and am not familiar. This line: int idx = colStart + j * d.stride; concerns me. I'm not sure what int is, it could be 32 bit and that could overflow; could cast to size_t and make sure idx is also of type size_t.

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