matteosal commented on issue #15767: FullyConnected op with float64 and MKL-DNN fails if gradient are not set in a specific way URL: https://github.com/apache/incubator-mxnet/issues/15767#issuecomment-519040543 I also get the same problem with `RNN`, but setting explicit gradients doesn't help in this case. It seems completely broken on float64: ``` import mxnet as mx sym = mx.sym.RNN( mx.sym.Variable('in'), mx.sym.Variable('par'), mx.sym.Variable('s'), state_size = (2), num_layers = 1, mode = 'rnn_tanh' ) dtype = 'float64' explicit_grad = { 'in': mx.nd.ones([2, 1, 2], dtype=dtype), 'par': mx.nd.ones([12], dtype=dtype), 's': mx.nd.ones([1, 1, 2], dtype=dtype) } args_grad = explicit_grad grad_req = 'write' ex = sym.bind(mx.cpu(), { 'in': mx.nd.ones([2, 1, 2], dtype=dtype), 'par': mx.nd.ones([12], dtype=dtype), 's': mx.nd.ones([1, 1, 2], dtype=dtype) }, args_grad = args_grad, grad_req = grad_req ) ex.forward() print(ex.outputs[0]) ``` Other RNN modes besides 'rnn_tanh' are also affected.
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