paddlepaddle版本：2.2.1
pytorch版本：1.10.0+cu113

torch.nn.lstm和paddle.nn.lstm在经过相同的修改属性名操作后，前向会有diff

代码如下：

import paddle
import paddle.nn as nn
import torch
from torch.nn import Module
import numpy as np
import warnings
warnings.filterwarnings("ignore")

# 有diff的版本

class TorchWeightDropout(Module):
    def __init__(self,module,layer_names='weight_hh_l0'):
        super(TorchWeightDropout, self).__init__()
        self.module,self.layer_names = module,[layer_names]
        for layer in self.layer_names:
            w = getattr(self.module,layer)
            delattr(self.module,layer)
            self.register_parameter(f'{layer}_raw',torch.nn.Parameter(w.data))
            setattr(self.module,layer,w.clone())

    def _setweights(self):
        for layer in self.layer_names:
            raw_w = getattr(self,f'{layer}_raw')
            w = raw_w.clone()
            setattr(self.module,layer,w)

    def forward(self,*args):
        self._setweights()
        return self.module(*args)

class PaddleWeightDropout(nn.Layer):
    def __init__(self,module,layer_names='weight_hh_l0'):
        super(PaddleWeightDropout, self).__init__()
        self.module, self.layer_names = module, [layer_names]
        for layer in self.layer_names:
            w = getattr(self.module, layer)
            delattr(self.module, layer)
            parameter = paddle.create_parameter(shape=w.detach().shape, dtype=str(w.detach().numpy().dtype),
                                                default_initializer=nn.initializer.Assign(w.detach()))
            self.add_parameter(f'{layer}_raw', parameter)
            setattr(self.module, layer, w.clone())

    def _setweights(self):
        for layer in self.layer_names:
            raw_w = getattr(self, f'{layer}_raw')
            w = raw_w.clone()
            setattr(self.module, layer, w)

    def forward(self, *args):
        self._setweights()
        return self.module(*args) 

# 预构建lstm和对应权重

n_in = 400
n_out = 1152
paddle_lstm = paddle.nn.LSTM(n_in,n_out,1,time_major=False,direction="forward")
torch_lstm = torch.nn.LSTM(n_in,n_out,1,batch_first=True,bidirectional=False)

weight1 = np.random.randint(1,100,size=(4608,400))
weight2 = np.random.randint(1,100,size=(4608,1152))
weight3 = np.random.randint(1,100,size=(4608))
weight4 = np.random.randint(1,100,size=(4608))

# 注意：此处将原权重值名改为了weight_hh_l0_raw

weight_dict = {"module.weight_ih_l0":weight1,"weight_hh_l0_raw":weight2,"module.bias_ih_l0":weight3,"module.bias_hh_l0":weight4}
paddle_dict = {}
torch_dict = {}
for key in weight_dict.keys():
    paddle_dict[key] = paddle.to_tensor(weight_dict[key],dtype=paddle.float32)
    torch_dict[key] = torch.tensor(weight_dict[key],dtype=torch.float32)

paddle_model = PaddleWeightDropout(paddle_lstm)
torch_model = TorchWeightDropout(torch_lstm)
paddle_model.load_dict(paddle_dict)
torch_model.load_state_dict(torch_dict)
paddle_model.eval()
torch_model.eval()

x = np.random.randn(1,4,400).astype(np.float32)
paddle_x = paddle.to_tensor(x)
torch_x = torch.tensor(x)

paddle_out = paddle_model(paddle_x)[0].detach().numpy()
torch_out = torch_model(torch_x)[0].cpu().detach().numpy()
diff = np.allclose(paddle_out,torch_out,atol = 1e-6)
diff2 = np.mean(np.abs(paddle_out - torch_out))

# 有diff

print(diff,diff2)