print_param_sizes.py

import torch
import torch.nn as nn
from torch.autograd import Variable

torch.backends.cudnn.enabled=True

seq_size = 2
hidden_size = 3
inp_size = 4
batch_size = 5

num_layers = 2
bias = False
batch_first = False
dropout = 0.0
bidirectional = True

print("Seq_size", seq_size)
print("hidden_size", hidden_size)
print("inp_size", inp_size)
print("batch_size", batch_size)
print("num_layers", num_layers)

inp = torch.cuda.FloatTensor(seq_size, batch_size, inp_size).uniform_()

pytorch_lstm = nn.LSTM(inp_size, hidden_size, num_layers, bias, batch_first, dropout, bidirectional).cuda()


pyt_out, (pyt_hx, pyt_cx) = pytorch_lstm(Variable(inp))

pyt_out.sum().backward()


for layer in pytorch_lstm._all_weights:
    for weight in layer:
        print(weight, getattr(pytorch_lstm, weight).size())