hallazie opened a new issue #12493: ctc_loss with large alphabet size raises CUDA error URL: https://github.com/apache/incubator-mxnet/issues/12493 ##Enviroment: python2.7/windows7_64bit mxnet-1.2.0 Nvidia Driver Version 397.31 ## Error Message: [15:41:28] G:\deeplearn\mxnet\dmlc-core\include\dmlc/logging.h:308: [15:41:28] g:\deeplearn\mxnet\mshadow\mshadow\./stream_gpu-inl.h:62: Check failed: e == cudaSuccess CUDA: unknown error [15:41:28] G:\deeplearn\mxnet\dmlc-core\include\dmlc/logging.h:308: [15:41:28] g:\deeplearn\mxnet\src\engine\./threaded_engine.h:370: [15:41:28] g:\deeplearn\mxnet\mshadow\mshadow\./stream_gpu-inl.h:62: Check failed: e == cudaSuccess CUDA: unknown error ## Minimum reproducible example ```python import mxnet as mx import numpy as np ctx=mx.gpu(0) alphabet_size=3000 in_var = mx.sym.Variable('data') labels_var = mx.sym.Variable('label') ctc = mx.sym.contrib.ctc_loss(in_var, labels_var) loss = mx.symbol.MakeLoss(ctc) arg_shapes,_,_ = loss.infer_shape(data=(6,2,alphabet_size), label=(2,3)) arg_array = [mx.nd.normal(shape=shape, ctx=ctx) for shape in arg_shapes] exe = loss.bind(ctx=ctx, args=arg_array) exe.forward(is_train=True) exe.backward() outTest = exe.outputs[0] print '%s'%(outTest.asnumpy()) ``` when `alphabet_size=200` the code works fine, when `alphabet_size=3000` (for chinese ocr task) the code crashes.
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