I make a lstm step by step,but I found it not same with the offical one.
I got a different results,only input one line data and init with the same params,simple such as:
# official
import torch
import torch.nn as nn
import torch.nn.functional as F



torch.manual_seed(20)
lstm_official = torch.nn.LSTM(6, 2, bidirectional=False, num_layers=1, batch_first=False)
share_weight = torch.randn(lstm_official .weight_ih_l0.shape,dtype = torch.float)
lstm_official .weight_ih_l0 = torch.nn.Parameter(share_weight)
# bias set to zeros
lstm_official .bias_ih_l0 = torch.nn.Parameter(torch.zeros(lstm_official .bias_ih_l0.shape))
lstm_official .weight_hh_l0 = torch.nn.Parameter(torch.ones(lstm_official .weight_hh_l0.shape))
# bias set to zeros
lstm_official .bias_hh_l0 = torch.nn.Parameter(torch.zeros(lstm_official .bias_ih_l0.shape))
x = torch.tensor([[1,2,3,4,5,6],[1,2,3,4,5,6]],dtype=torch.float)
lstm_official_out = lstm_official (x[0].unsqueeze(dim=0).unsqueeze(dim=0))

# implementation step by step
out_shape=2
batchsize=1
i2h = nn.Linear(in_features=6, out_features=8)  #
h2h = nn.Linear(in_features=out_shape, out_features=8)
i2h.weight = torch.nn.Parameter(share_weight)
i2h.bias = torch.nn.Parameter(torch.zeros(i2h.bias.shape))
h2h.weight = torch.nn.Parameter(torch.ones(h2h.weight.shape))
h2h.bias = torch.nn.Parameter(torch.zeros(h2h.bias.shape))
x_i2h = i2h(x[0].unsqueeze(dim=0)) 
prev_h = torch.zeros((batchsize,2))
prev_c = torch.zeros((batchsize,2))
x_h2h = h2h(prev_h)
gates = x_i2h + x_h2h
gates = torch.split(gates,out_shape,-1)
in_gate = torch.sigmoid(gates[0])  
in_transform = torch.tanh(gates[1])
forget_gate = torch.sigmoid(gates[2]) 
out_gate = torch.sigmoid(gates[3]) 
print(in_gate,in_transform,forget_gate,out_gate)
s0 = forget_gate * prev_c
s1 = in_gate * in_transform
next_c = s0 + s1
next_h = out_gate * F.tanh(next_c)  


print(f'official:{lstm_official_out[0]}')
print(f'step:{next_h}')