MXNet: from mxnet import np, npx
from mxnet.gluon import rnn
from d2l import mxnet as d2l
npx.set_np()
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_lstm_params(vocab_size, num_hiddens, device):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return np.random.normal(scale=0.01, size=shape, ctx=device)
    def three():
        return (normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), np.zeros(num_hiddens, ctx=device))
    W_xi, W_hi, b_i = three()
    W_xf, W_hf, b_f = three()
    W_xo, W_ho, b_o = three()
    W_xc, W_hc, b_c = three()
    W_hq = normal((num_hiddens, num_outputs))
    b_q = np.zeros(num_outputs, ctx=device)
    params = [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q]
    for param in params:
        param.attach_grad()
    return params
def init_lstm_state(batch_size, num_hiddens, device):
    return (np.zeros((batch_size, num_hiddens), ctx=device), np.zeros((batch_size, num_hiddens), ctx=device))
def lstm(inputs, state, params):
    [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c,
    W_hq, b_q] = params
    (H, C) = state
    outputs = []
    for X in inputs:
        I = npx.sigmoid(np.dot(X, W_xi) + np.dot(H, W_hi) + b_i)
        F = npx.sigmoid(np.dot(X, W_xf) + np.dot(H, W_hf) + b_f)
        O = npx.sigmoid(np.dot(X, W_xo) + np.dot(H, W_ho) + b_o)
        C_tilda = np.tanh(np.dot(X, W_xc) + np.dot(H, W_hc) + b_c)
        C = F * C + I * C_tilda
        H = O * np.tanh(C)
        Y = np.dot(H, W_hq) + b_q
        outputs.append(Y)
    return np.concatenate(outputs, axis=0), (H, C)
vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1
model = d2l.RNNModelScratch(len(vocab), num_hiddens, device, get_lstm_params, init_lstm_state, lstm)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)
lstm_layer = rnn.LSTM(num_hiddens)
model = d2l.RNNModel(lstm_layer, len(vocab))
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)

Paddle: import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn.functional as Function
from paddle import nn
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_lstm_params(vocab_size, num_hiddens):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return paddle.randn(shape=shape)*0.01
    def three():
        return (normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), paddle.zeros([num_hiddens]))
    W_xi, W_hi, b_i = three()
    W_xf, W_hf, b_f = three()
    W_xo, W_ho, b_o = three()
    W_xc, W_hc, b_c = three()
    W_hq = normal((num_hiddens, num_outputs))
    b_q = paddle.zeros([num_outputs])
    params = [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q]
    for param in params:
        param.stop_gradient = False
    return params
def init_lstm_state(batch_size, num_hiddens):
    return (paddle.zeros([batch_size, num_hiddens]), paddle.zeros([batch_size, num_hiddens]))
def lstm(inputs, state, params):
    [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c,
    W_hq, b_q] = params
    (H, C) = state
    outputs = []
    for X in inputs:
        I = Function.sigmoid((X @ W_xi) + (H @ W_hi) + b_i)
        F = Function.sigmoid((X @ W_xf) + (H @ W_hf) + b_f)
        O = Function.sigmoid((X @ W_xo) + (H @ W_ho) + b_o)
        C_tilda = paddle.tanh((X @ W_xc) + (H @ W_hc) + b_c)
        C = F * C + I * C_tilda
        H = O * paddle.tanh(C)
        Y = (H @ W_hq) + b_q
        outputs.append(Y)
    return paddle.concat(outputs, axis=0), (H, C)
vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1.0
model = d2l.RNNModelScratch(len(vocab), num_hiddens, get_lstm_params, init_lstm_state, lstm)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)
num_inputs = vocab_size
lstm_layer = nn.LSTM(num_inputs, num_hiddens, time_major=True)
model = d2l.RNNModel(lstm_layer, len(vocab))
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)