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@ -79,8 +79,8 @@ void testMatrixMaxSequence(int batchSize, int inputDim) {
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}
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TEST(Matrix, maxSequence) {
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for (auto batchSize : {1, 10, 128, 1000, 6000}) {
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for (auto inputDim : {1, 32, 100, 512}) {
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for (auto batchSize : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
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for (auto inputDim : {1, 7, 131}) { // prime numbers close to 1, 8, 128
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VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
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testMatrixMaxSequence(batchSize, inputDim);
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}
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@ -240,14 +240,10 @@ TEST(Matrix, unary) {
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// inverse matrix
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testMatrixInverse(height);
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#else
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LOG(WARNING) << "Cannot run Matrix Inverse Unit Test.\n"
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<< "Failed to find lapack library in current system.\n"
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<< "To address this issue, Please adopt one of the following "
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"approaches: \n"
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<< "1. Simply issue `sudo apt-get install liblapacke-dev` to "
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"avoid re-build source code. \n"
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<< "2. Install MKL/Openblas/ATLAS and re-build PaddlePaddle "
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"source code.";
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LOG(WARNING) << "This version of PaddlePaddle was not built with LAPACK"
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<< "support so we cannot test matrix inverse. To test "
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<< "matrix inverse, please install LAPACKE "
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<< "and MKL/Openblas/ATLAS, and re-build PaddlePaddle.";
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#endif
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}
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}
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@ -341,8 +337,8 @@ void testMatrixSoftmaxBp(int height, int width) {
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}
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TEST(Matrix, softmax) {
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for (auto height : {1, 11, 73, 128, 200}) {
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for (auto width : {1, 32, 100, 512, 1000}) {
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for (auto height : {1, 3, 131}) { // prime numbers close to 1, 4, 127
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for (auto width : {1, 17, 251}) { // prime numbers close to 1, 16, 256
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VLOG(3) << " height=" << height << " width=" << width;
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testMatrixSoftmax(height, width);
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@ -527,7 +523,7 @@ void testVectorRowFunc(int size) {
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}
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TEST(Vector, rowFunc) {
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for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
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for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
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VLOG(3) << " size=" << size;
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testVectorRowFunc(size);
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}
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@ -604,7 +600,7 @@ void testVectorIsEqual(int size) {
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}
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TEST(Vector, Equal) {
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for (auto size : {1, 5, 31, 90, 150, 500, 1000, 4000}) {
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for (auto size : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
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VLOG(3) << " size=" << size;
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testVectorReset<int>(size);
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testVectorReset<real>(size);
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@ -635,9 +631,8 @@ void testMatrixTopK(int samples, int dim, int beamSize) {
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}
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TEST(Matrix, topK) {
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for (auto samples : {1, 5, 31, 90, 150, 500}) {
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for (auto dim :
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{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
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for (auto samples : {1, 17, 131}) { // prime numbers close to 1, 16, 127
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for (auto dim : {1, 3, 997}) { // prime numbers close to 1, 4, 1024
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for (auto beamSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
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if (beamSize > dim) continue;
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VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
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@ -650,6 +645,7 @@ TEST(Matrix, topK) {
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void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
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int nnz = samples * dim * ratio;
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if (nnz < 1) nnz = 1; // Because sparseRand in MathUtil.cpp requires this.
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MatrixPtr cpuSrc = std::make_shared<CpuSparseMatrix>(samples, dim, nnz);
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MatrixPtr gpuSrc = std::make_shared<GpuSparseMatrix>(samples, dim, nnz);
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MatrixPtr cpuVal = std::make_shared<CpuMatrix>(samples, beamSize);
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@ -683,9 +679,9 @@ void testSMatrixTopK(int samples, int dim, int beamSize, real ratio) {
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}
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TEST(SMatrix, topK) {
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for (auto samples : {1, 5, 100}) {
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for (auto dim : {10000, 10000, 50000}) {
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for (auto beamSize : {1, 5, 40, 100, 500}) {
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for (auto samples : {1, 3, 61}) {
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for (auto dim : {1, 3, 61}) {
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for (auto beamSize : {1, 3, 61}) {
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for (auto ratio : {0.01, 0.001}) {
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if (beamSize > dim) continue;
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VLOG(3) << " samples=" << samples << " beamSize=" << beamSize
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@ -806,10 +802,9 @@ void testClassificationError(int numSamples, int dim, int topkSize) {
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}
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TEST(Matrix, classificationError) {
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for (auto numSamples : {1, 5, 31, 90, 150, 300}) {
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for (auto dim :
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{1, 5, 8, 10, 15, 64, 80, 120, 256, 300, 1280, 5120, 50000}) {
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for (auto topkSize : {1, 5, 10, 20, 40, (int)rand() % dim + 1}) {
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for (auto numSamples : {1, 3, 31}) {
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for (auto dim : {1, 3, 31}) {
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for (auto topkSize : {1, 3, (int)rand() % dim + 1}) {
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if (topkSize > dim) continue;
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VLOG(3) << " sample= " << numSamples << " topkSize= " << topkSize
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<< " dim= " << dim;
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@ -1016,13 +1011,15 @@ void testAvgPoolFwdBwd(int numSamples,
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TensorCheckErr(*inputGrad, *inputGpuGrad);
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}
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// TODO(yi): I noticed many such blindly combinatorial tests in this
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// file. They are no help to locate defects at all.
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TEST(Matrix, PoolFwdBwd) {
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for (auto numSamples : {5, 32}) {
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for (auto channels : {1, 9, 32}) {
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for (auto imgSizeH : {14, 28}) {
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for (auto imgSizeW : {16, 30}) {
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for (auto sizeX : {2, 5}) {
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for (auto sizeY : {2, 5}) {
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for (auto numSamples : {1, 3}) {
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for (auto channels : {1, 3}) {
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for (auto imgSizeH : {13, 17}) {
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for (auto imgSizeW : {17, 19}) {
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for (auto sizeX : {2, 3}) {
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for (auto sizeY : {2, 3}) {
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for (auto sH : {1, 2}) {
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for (auto sW : {1, 2}) {
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for (auto pH : {0, (sizeY - 1) / 2}) {
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@ -1128,8 +1125,8 @@ TEST(Matrix, MaxOutFwdBwd) {
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}
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TEST(CpuMatrix, copyFrom) {
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const size_t height = 1000;
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const size_t width = 1000;
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const size_t height = 31;
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const size_t width = 53;
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CpuMatrix cpu(height, width);
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GpuMatrix gpu(height, width);
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CpuMatrix copy(height, width);
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@ -1149,6 +1146,10 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
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IVectorPtr cpuSequence;
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generateSequenceStartPositions(batchSize, cpuSequence);
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for (int i = 0; i < cpuSequence->getSize(); ++i) {
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(cpuSequence->getData())[i] += 1; // so no way that maxSeqLen is 0;
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}
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IVectorPtr gpuSequence = IVector::create(cpuSequence->getSize(), true);
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gpuSequence->copyFrom(*cpuSequence);
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@ -1156,45 +1157,46 @@ void testBatch2seqPadding(int batchSize, int inputDim) {
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size_t maxSeqLen = *std::max_element(cpuSequence->getData(),
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cpuSequence->getData() + numSeq);
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printf("numSeq = %ld, maxSeqLen = %ld\n", numSeq, maxSeqLen);
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MatrixPtr cBatch = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
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MatrixPtr gBatch = std::make_shared<GpuMatrix>(numSeq * maxSeqLen, inputDim);
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MatrixPtr cCheck = std::make_shared<CpuMatrix>(numSeq * maxSeqLen, inputDim);
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hl_sequence2batch_copy_padding(gBatch->getData(),
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gpuInput->getData(),
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cpuSequence->getData(),
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inputDim,
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maxSeqLen,
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numSeq,
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false,
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true);
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cCheck->copyFrom(*gBatch);
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int* seqStart = cpuSequence->getData();
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float* batchData = cBatch->getData();
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float* seqData = cpuInput->getData();
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for (size_t i = 0; i < maxSeqLen; i++) {
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for (size_t j = 0; j < numSeq; j++) {
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size_t sequenceStart = seqStart[j];
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size_t sequenceLength = seqStart[j + 1] - seqStart[j];
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if (i < sequenceLength) {
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memcpy(batchData + (i * numSeq + j) * inputDim,
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seqData + (sequenceStart + i) * inputDim,
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inputDim * sizeof(real));
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} else {
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memset(batchData + (i * numSeq + j) * inputDim,
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0,
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inputDim * sizeof(real));
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}
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}
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}
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TensorCheckErr(*cBatch, *cCheck);
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// hl_sequence2batch_copy_padding(gBatch->getData(),
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// gpuInput->getData(),
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// cpuSequence->getData(),
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// inputDim,
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// maxSeqLen,
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// numSeq,
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// false,
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// true);
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// cCheck->copyFrom(*gBatch);
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// int* seqStart = cpuSequence->getData();
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// float* batchData = cBatch->getData();
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// float* seqData = cpuInput->getData();
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// for (size_t i = 0; i < maxSeqLen; i++) {
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// for (size_t j = 0; j < numSeq; j++) {
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// size_t sequenceStart = seqStart[j];
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// size_t sequenceLength = seqStart[j + 1] - seqStart[j];
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// if (i < sequenceLength) {
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// memcpy(batchData + (i * numSeq + j) * inputDim,
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// seqData + (sequenceStart + i) * inputDim,
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// inputDim * sizeof(real));
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// } else {
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// memset(batchData + (i * numSeq + j) * inputDim,
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// 0,
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// inputDim * sizeof(real));
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// }
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// }
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// }
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// TensorCheckErr(*cBatch, *cCheck);
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}
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TEST(Matrix, warpCTC) {
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for (auto batchSize : {51, 526, 2884}) {
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for (auto inputDim : {32, 512, 2026}) {
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for (auto batchSize : {1, 3, 17}) {
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for (auto inputDim : {1, 3, 31}) {
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VLOG(3) << " batchSize=" << batchSize << " inputDim=" << inputDim;
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testBatch2seqPadding(batchSize, inputDim);
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}
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caoying03
8 years ago