[GitHub] [tvm] yuanfz98 opened a new pull request, #12017: [Pytorch] add aten::rnn_tanh, aten::rnn_relu

[GitBox <gi...@apache.org>]

yuanfz98 opened a new pull request, #12017:
URL: https://github.com/apache/tvm/pull/12017

   Hello,
   
   This PR supports aten::rnn_tanh, aten::rnn_relu. The idea is from the previous implementation of GRU and LSTM in relay.
   
   Links to issue #11827
   
   ```
   def test_RNN_torch(num_layers: int,
                                       bidirectional: bool,
                                       use_bias: bool,
                                       hidden_size: int,
                                       input_size: int,
                                       seq_len: int,
                                       batch_first: bool,
                                       batch_size: int):
       r''' 
       Args:
           num_layers (int): num_layers to be passed to torch.nn.RNN
           bidirectional (bool): whether to build bidirectional RNN or not
           use_bias (bool): whether to use bias or not
           hidden_size (int): hidden_size of RNN cells
           input_size (int): Input features
           seq_len (int): Timesteps in input data
           batch_first (bool): Whether batch dimension is first or second dimension in input tensor
           batch_size (int): Batch size of input. If 0, unbatched input will be fed to network
       '''
   
       if batch_first:
           input_shape = (batch_size, seq_len, input_size)
       else:
           input_shape = (seq_len, batch_size, input_size)
       pytorch_net = torch.nn.Sequential(
           torch.nn.RNN(input_size,
                        hidden_size,
                        batch_first=batch_first,
                        num_layers=num_layers,
                        bidirectional=bidirectional,
                        bias=use_bias)
       )
   
       scripted_model = torch.jit.trace(pytorch_net.eval(),
                                        torch.randn(input_shape))
   
       mod, params = relay.frontend.from_pytorch(scripted_model,
                                                 [('input', input_shape)])
       mod = relay.transform.InferType()(mod)
       print(mod.astext())
   
   if __name__ == "__main__":
   
       test_RNN_torch(1,
                      False,
                      True,
                      5,
                      5,
                      15,
                      True,
                      32)
   
   ```
   
   Out:
   
   ```
   #[version = "0.0.5"]
   type List[A] {
     Cons(A, List[A]),
     Nil,
   }
   
   type Option[A] {
     Some(A),
     None,
   }
   
   type Tree[A] {
     Rose(A, List[Tree[A]]),
   }
   
   type tensor_float16_t {
     tensor_nil_float16,
     tensor0_float16(float16),
     tensor1_float16(Tensor[(?), float16]),
     tensor2_float16(Tensor[(?, ?), float16]),
     tensor3_float16(Tensor[(?, ?, ?), float16]),
     tensor4_float16(Tensor[(?, ?, ?, ?), float16]),
     tensor5_float16(Tensor[(?, ?, ?, ?, ?), float16]),
     tensor6_float16(Tensor[(?, ?, ?, ?, ?, ?), float16]),
   }
   
   type tensor_float32_t {
     tensor_nil_float32,
     tensor0_float32(float32),
     tensor1_float32(Tensor[(?), float32]),
   ...
   ```


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[GitHub] [tvm] masahi merged pull request #12017: [Pytorch] add aten::rnn_tanh, aten::rnn_relu

[GitBox <gi...@apache.org>]

masahi merged PR #12017:
URL: https://github.com/apache/tvm/pull/12017


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