Update README.md

README.md

@@ -1,3 +1,151 @@
----
-license: mit
----
+---
+license: mit
+---
+
+# test rnn
+
+The Models in test rnn is for [Huggingface Candle PR#2542](https://github.com/huggingface/candle/pull/2542) as example test cases.
+
+Test models are refered to Pytorch [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html) and [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html).
+
+Test models are generated by the following codes:
+
+- lstm_test.pt: A simple LSTM model with 1 layer.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.LSTM(10, 20, num_layers=1, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, (hn, cn) = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    state_dict['cn'] = cn
+    torch.save(state_dict, "lstm_test.pt")
+    ```
+
+- gru_test.pt: A simple GRU model with 1 layer.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.GRU(10, 20, num_layers=1, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, hn = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    torch.save(state_dict, "gru_test.pt")
+    ```
+
+- bi_lstm_test.pt: A bidirectional LSTM model with 1 layer.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.LSTM(10, 20, num_layers=1, bidirectional=True, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, (hn, cn) = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    state_dict['cn'] = cn
+    torch.save(state_dict, "bi_lstm_test.pt")
+    ```
+
+- bi_gru_test.pt: A bidirectional GRU model with 1 layer.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.GRU(10, 20, num_layers=1, bidirectional=True, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, hn = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    torch.save(state_dict, "bi_gru_test.pt")
+    ```
+
+- lstm_nlayer_test.pt: A LSTM model with 3 layers.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.LSTM(10, 20, num_layers=3, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, (hn, cn) = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    state_dict['cn'] = cn
+    torch.save(state_dict, "lstm_nlayer_test.pt")
+    ```
+
+- bi_lstm_nlayer_test.pt: A bidirectional LSTM model with 3 layers.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.LSTM(10, 20, num_layers=3, bidirectional=True, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, (hn, cn) = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    state_dict['cn'] = cn
+    torch.save(state_dict, "bi_lstm_nlayer_test.pt")
+    ```
+
+- gru_nlayer_test.pt: A GRU model with 3 layers.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.GRU(10, 20, num_layers=3, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, hn = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    torch.save(state_dict, "gru_nlayer_test.pt")
+    ```
+
+- bi_gru_nlayer_test.pt: A bidirectional GRU model with 3 layers.
+
+    ```python
+    import torch
+    import torch.nn as nn
+
+    rnn = nn.GRU(10, 20, num_layers=3, bidirectional=True, batch_first=True)
+    input = torch.randn(5, 3, 10)
+    output, hn = rnn(input)
+
+    state_dict = rnn.state_dict()
+    state_dict['input'] = input
+    state_dict['output'] = output.contiguous()
+    state_dict['hn'] = hn
+    torch.save(state_dict, "bi_gru_nlayer_test.pt")
+    ```