Comments (2)
I double-checked the compute capability of the K80 and it's actually 3.7, not >= 6.0. Since only atomicAdd<double>
needed the higher compute capability, I've implemented it in terms of compare-and-swap and lowered the requirements so you can use it on the K80. Could you please try it out and let me know if it works for you?
from haste.
Confirmed working! //may be a a good idea to update the docs?
PS Install still required some hacking with Makefile paths to compilers, cuda-libraries, etc. I am fine with this, but some potential users might get turned off by this. // may be a good idea to make a note in the docs that Makefile might require fine-tuning to get up and running? On my systems (including the standard AWS with out-of-the-box TF envs) some voodoo magic was needed for this to compile.
from haste.
Related Issues (20)
- Install on pip on systems without cuda HOT 7
- Segmentation fault on Cuda 10.0 HOT 2
- Support zoneout on lstm cell state and add recurrent dropout HOT 2
- CUDA error: an illegal memory access was encountered HOT 6
- haste_pytorch: Gradient for kernel/recurrent_kernel becomes zero when trained on gpu HOT 4
- How to expose LayerNormGRUCell to python ? HOT 2
- Can't run haste layers in Keras HOT 12
- Biases in final IndRNN layer are 0 HOT 1
- Zoneout remains during eval() HOT 2
- return_state_sequence for tf version
- layer_norm_gru_cell HOT 1
- Can Bidirectional Rnn and multi-layer Rnn be supported? HOT 1
- Activation function in IndRNN HOT 1
- haste_pytorch does not install properly with conda cudatoolkit? HOT 3
- Feature request for cell classes for pytorch HOT 7
- `RNN`s with `zoneout > 0.0` have wrong gradients HOT 1
- haste_tf compilation fails with "‘bfloat16’ in namespace ‘Eigen’ does not name a type"
- Support for PyTorch packed sequences HOT 2
- Supporting RWKV (a RNN that can match transformer LM & zero-shot performance at 1B+ params)
- Nan loss when replace pytorch LSTM with your LSTM or LayerNormLSTM HOT 2
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from haste.