Allow passing model parameters for PyTorch server

/kind feature

Describe the solution you’d like Currently we can only pass ModelClassName on Pytorch spec, however most of time ModelClass requires additional parameters such as EmbeddingSize and Layers etc.

This is an example

class TorchTextClassifier(nn.Module):
    
    def __init__(self, vocab_size, embedding_dim, seq_length, num_classes, 
                 num_filters, kernel_size, pool_size, dropout_rate):
        super(TorchTextClassifier, self).__init__()

        self.embeddings = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)
        
        self.conv1 = nn.Conv1d(seq_length, num_filters, kernel_size)
        self.max_pool1 = nn.MaxPool1d(pool_size)
        self.conv2 = nn.Conv1d(num_filters, num_filters*2, kernel_size)
        
        self.dropout = nn.Dropout(dropout_rate)
        self.dense = nn.Linear(num_filters*2, num_classes)

Anything else you would like to add: The proposal is to support passing additional parameters on pytorch spec

  default:
    predictor:
      pytorch:
        modelClassName: TorchTextClassifier
        modelClassKwargs: {'embedding_size': 128, 'n_layers': 6}

[zhangrongguo]

@yuzisun @WangAllen851018 @nlarusstone

Hi, WangAllen851018 and I are in the same team. Now we have the solution of this issue, and we want to contribute our code to kfserving project. But we don’t know how to do that (We already have the CLA).

[yuzisun]

@alexcoca currently we are doing torch.save(model.state_dict(), PATH) since this is suggested way on pytorch website https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-model-for-inference. Saving the entire model seems unsafe.