def sliced_rows_batch_scaled_add(stream, embd_rows_indxs, nrows, ncols, alpha,
dense_matrices, embd_nrows, embd_ncols,
embd_matrix):
status = gpu_matrix_kernels._slicedRowsBatchScaledAdd(
stream, embd_rows_indxs, nrows, ncols, alpha, dense_matrices,
embd_nrows, embd_ncols, embd_matrix)
cudart.check_cuda_status(status)
def add_scaled_columns_slice(stream, nrows, ncols, alpha, dense_matrix,
embedding_column_indxs, embedding_matrix):
status = gpu_matrix_kernels._addScaledColumnsSlice(stream, nrows, ncols,
alpha, dense_matrix,
embedding_column_indxs,
embedding_matrix)
cudart.check_cuda_status(status)
def batch_horizontal_split(stream, n, nrows, x_ncols, y_ncols, matrices,
x_matrices, y_matrices):
status = gpu_matrix_kernels._batchHorizontalSplit(stream, n, nrows,
x_ncols, y_ncols,
matrices, x_matrices,
y_matrices)
cudart.check_cuda_status(status)
def softmax_ce_derivative(stream, batchSize, num_classes, probs,
target_classes, derivatives):
status = gpu_matrix_kernels._softmaxCeDerivative(stream, batchSize,
num_classes, probs,
target_classes,
derivatives)
cudart.check_cuda_status(status)
def slice_rows_int(stream, embedding_matrix_nrows, embedding_row_indxs,
embedding_matrix, nrows, ncols, dense_matrix):
status = gpu_matrix_kernels._sliceRowsInt(stream, embedding_matrix_nrows,
embedding_row_indxs,
embedding_matrix, nrows, ncols,
dense_matrix)
cudart.check_cuda_status(status)
def sigmoid_der(stream, nelems, data, sigmoid_data, derivative):
status = nonlinearities._sigmoidDer(stream, nelems, data, sigmoid_data,
derivative)
cudart.check_cuda_status(status)
def tanh_sigm_der(stream, axis, nrows, ncols, data, tanh_sigm_data, derivatve):
status = nonlinearities._tanhSigmDer(stream, axis, nrows, ncols, data,
tanh_sigm_data, derivatve)
cudart.check_cuda_status(status)
def add_scaled_columns_slice(stream, nrows, ncols, alpha, dense_matrix, embedding_column_indxs, embedding_matrix):
status = gpu_matrix_kernels._addScaledColumnsSlice(stream, nrows, ncols, alpha, dense_matrix, embedding_column_indxs, embedding_matrix)
cudart.check_cuda_status(status)
def assign_sum(stream, nelems, matrices, n, s):
status = gpu_matrix_kernels._assign_sum(stream, nelems, matrices, n, s)
cudart.check_cuda_status(status)
def mask_column_numbers_row_wise(stream, nrows, ncols, numbers, out):
status = gpu_matrix_kernels._maskColumnNumbersRowWise(stream, nrows, ncols, numbers, out)
cudart.check_cuda_status(status)
def assign_masked_addition_column_broadcasted(stream, nrows, ncols, mask, a, b, out):
status = gpu_matrix_kernels._assignMaskedAdditionColumnBroadcasted(stream, nrows, ncols, mask, a, b, out)
cudart.check_cuda_status(status)
def assign_masked_addition(stream, nelems, mask, a, b, out):
status = gpu_matrix_kernels._assignMaskedAddition(stream, nelems, mask, a, b, out)
cudart.check_cuda_status(status)
def add_mask_zeros(stream, nelems, a, b, out):
status = gpu_matrix_kernels._addMaskZeros(stream, nelems, a, b, out)
cudart.check_cuda_status(status)
def dropout(stream, nelems, dropout_prob, data, uniform_data, out):
status = gpu_matrix_kernels._dropout(stream, nelems, dropout_prob, data, uniform_data, out)
cudart.check_cuda_status(status)
def add_scaled_subtraction(stream, nelems, alpha, a, b, out):
status = gpu_matrix_kernels._addScaledSubtraction(stream, nelems, alpha, a, b, out)
cudart.check_cuda_status(status)
def add_softmax_ce_derivative(stream, batchSize, num_classes, probs, target_classes, derivatives):
status = gpu_matrix_kernels._addSoftmaxCeDerivative(stream, batchSize, num_classes, probs, target_classes, derivatives)
cudart.check_cuda_status(status)
def add_mask_zeros(stream, nelems, a, b, out):
status = gpu_matrix_kernels._addMaskZeros(stream, nelems, a, b, out)
cudart.check_cuda_status(status)
def assign_masked_addition_column_broadcasted(stream, nrows, ncols, mask, a, b, out):
status = gpu_matrix_kernels._assignMaskedAdditionColumnBroadcasted(stream, nrows, ncols, mask, a, b, out)
cudart.check_cuda_status(status)
def add_hprod_one_minus_mask_column_broadcasted(stream, nrows, ncols, mask, a, out):
status = gpu_matrix_kernels._addHprodOneMinusMaskColumnBroadcasted(stream, nrows, ncols, mask, a, out)
cudart.check_cuda_status(status)
def add_hprod_one_minus_mask(stream, nelems, mask, a, out):
status = gpu_matrix_kernels._addHprodOneMinusMask(stream, nelems, mask, a, out)
cudart.check_cuda_status(status)
def repeat_along_col(stream, repeats, nrows, ncols, a, out):
status = gpu_matrix_kernels._repeatAlongCol(stream, repeats, nrows, ncols, a, out)
cudart.check_cuda_status(status)
def add_hprod_one_minus_mask_column_broadcasted(stream, nrows, ncols, mask, a, out):
status = gpu_matrix_kernels._addHprodOneMinusMaskColumnBroadcasted(stream, nrows, ncols, mask, a, out)
cudart.check_cuda_status(status)
def clip(stream, nelems, min_value, max_value, data, out):
status = gpu_matrix_kernels._clip(stream, nelems, min_value, max_value, data, out)
cudart.check_cuda_status(status)
def matrix_vector_column_hprod(stream, nrows, ncols, matrix, vector, out):
status = gpu_matrix_kernels._matrixVectorColumnHprod(stream, nrows, ncols, matrix, vector, out)
cudart.check_cuda_status(status)
def callback(stream, status, user_data):
cudart.check_cuda_status(status)
args, kwargs = ct.cast(user_data, ct_py_object_p).contents.value
function(*args, **kwargs)
GpuContext._user_data[ct.cast(stream, ct.c_void_p).value].popleft()
def mask_column_numbers_row_wise(stream, nrows, ncols, numbers, out):
status = gpu_matrix_kernels._maskColumnNumbersRowWise(stream, nrows, ncols, numbers, out)
cudart.check_cuda_status(status)
def relu_der(stream, nelems, data, relu_data, derivative):
status = nonlinearities._reluDer(stream, nelems, data, relu_data,
derivative)
cudart.check_cuda_status(status)
def assign_sum(stream, nelems, matrices, n, s):
status = gpu_matrix_kernels._assign_sum(stream, nelems, matrices, n, s)
cudart.check_cuda_status(status)
def tanh_der(stream, nelems, data, tanh_data, derivative):
status = nonlinearities._tanhDer(stream, nelems, data, tanh_data,
derivative)
cudart.check_cuda_status(status)
def repeat_along_col(stream, repeats, nrows, ncols, a, out):
status = gpu_matrix_kernels._repeatAlongCol(stream, repeats, nrows, ncols, a, out)
cudart.check_cuda_status(status)
def add_scaled_subtraction(stream, nelems, alpha, a, b, out):
status = gpu_matrix_kernels._addScaledSubtraction(stream, nelems, alpha, a, b, out)
cudart.check_cuda_status(status)
def add_repeat_along_col_derivative(stream, repeats, a, nrows, ncols, derivative):
status = gpu_matrix_kernels._addRepeatAlongColDerivative(stream, repeats, a, nrows, ncols, derivative)
cudart.check_cuda_status(status)
def dropout(stream, nelems, dropout_prob, data, uniform_data, out):
status = gpu_matrix_kernels._dropout(stream, nelems, dropout_prob, data, uniform_data, out)
cudart.check_cuda_status(status)
def add_scaled_div_sqrt(stream, nelems, alpha, a, b, epsilon, c):
status = gpu_matrix_kernels._addScaledDivSqrt(stream, nelems, alpha, a, b, epsilon, c)
cudart.check_cuda_status(status)
def assign_masked_addition(stream, nelems, mask, a, b, out):
status = gpu_matrix_kernels._assignMaskedAddition(stream, nelems, mask, a, b, out)
cudart.check_cuda_status(status)
def clip(stream, nelems, min_value, max_value, data, out):
status = gpu_matrix_kernels._clip(stream, nelems, min_value, max_value, data, out)
cudart.check_cuda_status(status)
def add_hprod_one_minus_mask(stream, nelems, mask, a, out):
status = gpu_matrix_kernels._addHprodOneMinusMask(stream, nelems, mask, a, out)
cudart.check_cuda_status(status)
def transpose_int(stream, nrows, ncols, in_, out):
status = gpu_matrix_kernels._transposeInt(stream, nrows, ncols, in_, out)
cudart.check_cuda_status(status)
def matrix_vector_column_hprod(stream, nrows, ncols, matrix, vector, out):
status = gpu_matrix_kernels._matrixVectorColumnHprod(stream, nrows, ncols, matrix, vector, out)
cudart.check_cuda_status(status)
def add_hadamard_product_2(stream, nelems, a, b, alpha, c):
status = gpu_matrix_kernels._addHadamardProduct2(stream, nelems, a, b, alpha, c)
cudart.check_cuda_status(status)
def batch_horizontal_split(stream, n, nrows, x_ncols, y_ncols, matrices, x_matrices, y_matrices):
status = gpu_matrix_kernels._batchHorizontalSplit(stream, n, nrows, x_ncols, y_ncols, matrices, x_matrices, y_matrices)
cudart.check_cuda_status(status)
def assign_sequential_sum_pooling(stream, nrows, ncols, matrices, n, out):
status = gpu_matrix_kernels._assignSequentialSumPooling(stream, nrows, ncols, matrices, n, out)
cudart.check_cuda_status(status)
def add_repeat_along_col_derivative(stream, repeats, a, nrows, ncols, derivative):
status = gpu_matrix_kernels._addRepeatAlongColDerivative(stream, repeats, a, nrows, ncols, derivative)
cudart.check_cuda_status(status)
def assign_sequential_weighted_sum(stream, nrows, ncols, matrices, weights, n, out):
status = gpu_matrix_kernels._assignSequentialWeightedSum(stream, nrows, ncols, matrices, weights, n, out)
cudart.check_cuda_status(status)
def add_scaled_div_sqrt(stream, nelems, alpha, a, b, epsilon, c):
status = gpu_matrix_kernels._addScaledDivSqrt(stream, nelems, alpha, a, b, epsilon, c)
cudart.check_cuda_status(status)
def sequentially_tile(stream, nelems, a, matrices, n):
status = gpu_matrix_kernels._sequentiallyTile(stream, nelems, a, matrices, n)
cudart.check_cuda_status(status)
def transpose_int(stream, nrows, ncols, in_, out):
status = gpu_matrix_kernels._transposeInt(stream, nrows, ncols, in_, out)
cudart.check_cuda_status(status)
def assign_dL_dpre_a(stream, nrows, ncols, matrices, derivative, weights, n, out):
status = gpu_matrix_kernels._assignDLDprea(stream, nrows, ncols, matrices, derivative, weights, n, out)
cudart.check_cuda_status(status)
def callback(stream, status, user_data):
cudart.check_cuda_status(status)
args, kwargs = ct.cast(user_data, ct_py_object_p).contents.value
function(*args, **kwargs)
GpuContext._user_data[ct.cast(stream, ct.c_void_p).value].popleft()
def add_attention_derivative(stream, nrows, ncols, matrices, derivative, n, out):
status = gpu_matrix_kernels._addAttentionDerivative(stream, nrows, ncols, matrices, derivative, n, out)
cudart.check_cuda_status(status)
def tanh_sigm(stream, axis, nrows, ncols, data, tanh_sigm_data):
status = nonlinearities._tanhSigm(stream, axis, nrows, ncols, data,
tanh_sigm_data)
cudart.check_cuda_status(status)
def add_attention_tile(stream, nrows, ncols, derivative, a, dL_dpre_a, u, n, matrices_derivs):
status = gpu_matrix_kernels._addAttentionTile(stream, nrows, ncols, derivative, a, dL_dpre_a, u, n, matrices_derivs)
cudart.check_cuda_status(status)
def relu(stream, nelems, data, relu_data):
status = nonlinearities._relu(stream, nelems, data, relu_data)
cudart.check_cuda_status(status)
def slice_rows_batch(stream, embd_rows_indxs, nrows, ncols, embd_matrix, embd_nrows, embd_ncols, dense_matrices):
status = gpu_matrix_kernels._sliceRowsBatch(stream, embd_rows_indxs, nrows, ncols, embd_matrix, embd_nrows, embd_ncols, dense_matrices)
cudart.check_cuda_status(status)
def sigmoid(stream, nelems, data, sigmoid_data):
status = nonlinearities._sigmoid(stream, nelems, data, sigmoid_data)
cudart.check_cuda_status(status)
def sliced_rows_batch_scaled_add(stream, embd_rows_indxs, nrows, ncols, alpha, dense_matrices, embd_nrows, embd_ncols, embd_matrix):
status = gpu_matrix_kernels._slicedRowsBatchScaledAdd(stream, embd_rows_indxs, nrows, ncols, alpha, dense_matrices, embd_nrows, embd_ncols, embd_matrix)
cudart.check_cuda_status(status)
def tanh(stream, nelems, data, tanh_data):
status = nonlinearities._tanh(stream, nelems, data, tanh_data)
cudart.check_cuda_status(status)
def assign_scaled_addition(stream, nelems, alpha, a, b, out):
status = gpu_matrix_kernels._assignScaledAddition(stream, nelems, alpha, a, b, out)
cudart.check_cuda_status(status)
def test_dependencies(cuda_stream, node_id, blocking_nodes, blocking_nodes_num, execution_checklist, test_results):
status = test_events._testDependencies(cuda_stream, node_id, blocking_nodes, blocking_nodes_num, execution_checklist, test_results)
cudart.check_cuda_status(status)
def masked_fill(stream, nelems, value, mask_data, true_value, out_data):
status = gpu_matrix_kernels.\
_maskedFill(stream, nelems, value, mask_data, true_value, out_data)
cudart.check_cuda_status(status)
