Pytorch-Implement the same model in pytorch and keras but got different results
I am learning pytorch and want to practice it with an keras example (https://keras.io/examples/lstm_seq2seq/), this is a seq2seq 101 example which translate eng to fra on char-level features (no embedding). 

Keras code is below:

from keras.models import Model
from keras.layers import Input, LSTM, Dense
import numpy as np

batch_size = 64  # Batch size for training.
epochs = 100  # Number of epochs to train for.
latent_dim = 256  # Latent dimensionality of the encoding space.
num_samples = 10000  # Number of samples to train on.
# Path to the data txt file on disk.
data_path = 'fra-eng/fra.txt'

# Vectorize the data.
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
    input_text, target_text = line.split('\t')
    # We use "tab" as the "start sequence" character
    # for the targets, and "\n" as "end sequence" character.
    target_text = '\t' + target_text + '\n'
    input_texts.append(input_text)
    target_texts.append(target_text)
    for char in input_text:
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        if char not in target_characters:
            target_characters.add(char)

input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
num_decoder_tokens = len(target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])

print('Number of samples:', len(input_texts))
print('Number of unique input tokens:', num_encoder_tokens)
print('Number of unique output tokens:', num_decoder_tokens)
print('Max sequence length for inputs:', max_encoder_seq_length)
print('Max sequence length for outputs:', max_decoder_seq_length)

input_token_index = dict(
    [(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict(
    [(char, i) for i, char in enumerate(target_characters)])

encoder_input_data = np.zeros(
    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
    dtype='float32')
decoder_input_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')
decoder_target_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')

for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
        encoder_input_data[i, t, input_token_index[char]] = 1.
    for t, char in enumerate(target_text):
        # decoder_target_data is ahead of decoder_input_data by one timestep
        decoder_input_data[i, t, target_token_index[char]] = 1.
        if t &gt; 0:
            # decoder_target_data will be ahead by one timestep
            # and will not include the start character.
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.

# Define an input sequence and process it.
encoder_inputs = Input(shape=(None, num_encoder_tokens))
encoder = LSTM(latent_dim, return_state=True)
encoder_outputs, state_h, state_c = encoder(encoder_inputs)
# We discard `encoder_outputs` and only keep the states.
encoder_states = [state_h, state_c]

# Set up the decoder, using `encoder_states` as initial state.
decoder_inputs = Input(shape=(None, num_decoder_tokens))
# We set up our decoder to return full output sequences,
# and to return internal states as well. We don't use the
# return states in the training model, but we will use them in inference.
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _ = decoder_lstm(decoder_inputs,
                                     initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)

# Define the model that will turn
# `encoder_input_data` &amp; `decoder_input_data` into `decoder_target_data`
model = Model([encoder_inputs, decoder_inputs], decoder_outputs)

# Run training
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.fit([encoder_input_data, decoder_input_data], decoder_target_data,
          batch_size=batch_size,
          epochs=epochs,
          validation_split=0.2)
# Save model
model.save('s2s.h5')

# Next: inference mode (sampling).
# Here's the drill:
# 1) encode input and retrieve initial decoder state
# 2) run one step of decoder with this initial state
# and a "start of sequence" token as target.
# Output will be the next target token
# 3) Repeat with the current target token and current states

# Define sampling models
encoder_model = Model(encoder_inputs, encoder_states)

decoder_state_input_h = Input(shape=(latent_dim,))
decoder_state_input_c = Input(shape=(latent_dim,))
decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]
decoder_outputs, state_h, state_c = decoder_lstm(
    decoder_inputs, initial_state=decoder_states_inputs)
decoder_states = [state_h, state_c]
decoder_outputs = decoder_dense(decoder_outputs)
decoder_model = Model(
    [decoder_inputs] + decoder_states_inputs,
    [decoder_outputs] + decoder_states)

# Reverse-lookup token index to decode sequences back to
# something readable.
reverse_input_char_index = dict(
    (i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
    (i, char) for char, i in target_token_index.items())

def decode_sequence(input_seq):
    # Encode the input as state vectors.
    states_value = encoder_model.predict(input_seq)

    # Generate empty target sequence of length 1.
    target_seq = np.zeros((1, 1, num_decoder_tokens))
    # Populate the first character of target sequence with the start character.
    target_seq[0, 0, target_token_index['\t']] = 1.

    # Sampling loop for a batch of sequences
    # (to simplify, here we assume a batch of size 1).
    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, h, c = decoder_model.predict(
            [target_seq] + states_value)

        # Sample a token
        sampled_token_index = np.argmax(output_tokens[0, -1, :])
        sampled_char = reverse_target_char_index[sampled_token_index]
        decoded_sentence += sampled_char

        # Exit condition: either hit max length
        # or find stop character.
        if (sampled_char == '\n' or
           len(decoded_sentence) &gt; max_decoder_seq_length):
            stop_condition = True

        # Update the target sequence (of length 1).
        target_seq = np.zeros((1, 1, num_decoder_tokens))
        target_seq[0, 0, sampled_token_index] = 1.

        # Update states
        states_value = [h, c]

    return decoded_sentence

for seq_index in range(100):
    # Take one sequence (part of the training set)
    # for trying out decoding.
    input_seq = encoder_input_data[seq_index: seq_index + 1]
    decoded_sentence = decode_sequence(input_seq)
    print('-')
    print('Input sentence:', input_texts[seq_index])
    print('Decoded sentence:', decoded_sentence)


I want to implement this exact same model using pytorch, below is my code:

from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import numpy as np
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data_path = './eng_fra.txt'

# Vectorize the data.
input_texts = []
target_texts = []
input_characters = set()
target_characters = set()
with open(data_path, 'r', encoding='utf-8') as f:
    lines = f.read().split('\n')
for line in lines[: min(num_samples, len(lines) - 1)]:
    #print('line:',line)
    input_text, target_text = line.split('\t')
    # We use "tab" as the "start sequence" character
    # for the targets, and "\n" as "end sequence" character.
    target_text = '\t' + target_text + '\n' # why?
   # print('input_text and target_text:',input_text, target_text)
    input_texts.append(input_text)
    target_texts.append(target_text)
    for char in input_text:
        if char not in input_characters:
            input_characters.add(char)
    for char in target_text:
        if char not in target_characters:
            target_characters.add(char)

input_characters = sorted(list(input_characters))
target_characters = sorted(list(target_characters))
num_encoder_tokens = len(input_characters)
print('input_characters',input_characters)
num_decoder_tokens = len(target_characters)
print('target_characters',target_characters)
max_encoder_seq_length = max([len(txt) for txt in input_texts])
max_decoder_seq_length = max([len(txt) for txt in target_texts])
print('max_encoder_seq_length and max_decoder_seq_length',max_encoder_seq_length,max_decoder_seq_length)

input_token_index = dict(
    [(char, i) for i, char in enumerate(input_characters)])
target_token_index = dict(
    [(char, i) for i, char in enumerate(target_characters)])

# define the shapes
encoder_input_data = np.zeros(
    (len(input_texts), max_encoder_seq_length, num_encoder_tokens),
    dtype='float32')
decoder_input_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')
decoder_target_data = np.zeros(
    (len(input_texts), max_decoder_seq_length, num_decoder_tokens),
    dtype='float32')

# one hot encoding for each word in each sentence
for i, (input_text, target_text) in enumerate(zip(input_texts, target_texts)):
    for t, char in enumerate(input_text):
        encoder_input_data[i, t, input_token_index[char]] = 1.
    for t, char in enumerate(target_text):
        # decoder_target_data is ahead of decoder_input_data by one timestep
        decoder_input_data[i, t, target_token_index[char]] = 1.
        if t &gt; 0:
            # decoder_target_data will be ahead by one timestep
            # and will not include the start character.
            decoder_target_data[i, t - 1, target_token_index[char]] = 1.
encoder_input_data=torch.Tensor(encoder_input_data).to(device)
decoder_input_data=torch.Tensor(decoder_input_data).to(device)
decoder_target_data=torch.Tensor(decoder_target_data).to(device)

class encoder(nn.Module):
  def __init__(self):
    super(encoder,self).__init__()
    self.LSTM=nn.LSTM(input_size=num_encoder_tokens,hidden_size=256,batch_first=True) 
  def forward(self,x):
    out,(h,c)=self.LSTM(x)
    return h,c

class decoder(nn.Module):
  def __init__(self):
    super(decoder,self).__init__()
    self.LSTM=nn.LSTM(input_size=num_decoder_tokens,hidden_size=256,batch_first=True)
    self.FC=nn.Linear(256,num_decoder_tokens)

  def forward(self,x, hidden): 
    out,(h,c)=self.LSTM(x,hidden)
    out=self.FC(out)
    return out,(h,c)

class seq2seq(nn.Module):
  def __init__(self,encoder,decoder):
    super(seq2seq,self).__init__()
    self.encoder=encoder
    self.decoder=decoder    

  def forward(self,encode_input_data,decode_input_data):
    hidden, cell = self.encoder(encode_input_data)
    output, (hidden, cell) = self.decoder(decode_input_data, (hidden, cell))
    return output

encoder=encoder().to(device)
# encoder_loss = nn.CrossEntropyLoss() # CrossEntropyLoss compute softmax internally in pytorch
# encoder_optimizer = torch.optim.Adam(encoder.parameters(), lr=0.001)

decoder=decoder().to(device)
# decoder_loss = nn.CrossEntropyLoss() # CrossEntropyLoss compute softmax internally in pytorch
# decoder_optimizer = torch.optim.Adam(decoder.parameters(), lr=0.001)

model=seq2seq(encoder,decoder).to(device)
optimizer = optim.RMSprop(model.parameters(),lr=0.01)
loss_fun=nn.CrossEntropyLoss()

# model.train()

num_epochs=50
batches=np.array_split(range(decoder_target_data.shape[0]),100)
total_step=len(batches)
for epoch in range(num_epochs):

  for i,batch_ids in enumerate(batches):
    encoder_input=encoder_input_data[batch_ids]
    decoder_input=decoder_input_data[batch_ids]
    decoder_target=decoder_target_data[batch_ids]    

    output = model(encoder_input, decoder_input)
    loss=loss_fun(output.view(-1,93).to(device),decoder_target.view(-1,93).max(dim=1)[1].to(device))    

    # Backward and optimize
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

    if (i+1) % 20 == 0:
        print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
               .format(epoch+1, num_epochs, i+1, total_step, loss.item()))    

# Reverse-lookup token index to decode sequences back to
# something readable.
reverse_input_char_index = dict(
    (i, char) for char, i in input_token_index.items())
reverse_target_char_index = dict(
    (i, char) for char, i in target_token_index.items())

def decode_sequence(input_seq):
    # Encode the input as state vectors.
    h,c=model.encoder(input_seq)

    # Generate empty target sequence of length 1.
    # Populate the first character of target sequence with the start character.
    target_seq = torch.zeros((1, 1, num_decoder_tokens)).to(device)
    target_seq[0, 0, target_token_index['\t']] = 1.

    # Sampling loop for a batch of sequences
    # (to simplify, here we assume a batch of size 1).
    stop_condition = False
    decoded_sentence = ''
    while not stop_condition:
        output_tokens, (h_t, c_t) = model.decoder(target_seq,(h,c))

        # Sample a token
        sampled_token_index = output_tokens.view(-1,93).squeeze(0).max(dim=0)[1].item()
        sampled_char = reverse_target_char_index[sampled_token_index]
        decoded_sentence += sampled_char

        # Exit condition: either hit max length
        # or find stop character.
        if (sampled_char == '\n' or
           len(decoded_sentence) &gt; max_decoder_seq_length):
            stop_condition = True

        # Update the target sequence (of length 1).
        target_seq = torch.zeros((1, 1, num_decoder_tokens)).to(device)
        target_seq[0, 0, sampled_token_index] = 1.

        # Update states
        h,c=h_t,c_t

    return decoded_sentence

for seq_index in range(100):
    # Take one sequence (part of the training set)
    # for trying out decoding.
    input_seq = encoder_input_data[seq_index: seq_index + 1]
    decoded_sentence = decode_sequence(input_seq)
    print('-')
    print('Input sentence:', input_texts[seq_index])
    print('Decoded sentence:', decoded_sentence)


As you can see, I used exactly the same data processing and model structure. My pytorch version can run without error, but the performance seems worse than the original keras version by comparing the translation results. 

One thing might cause error is the loss function (cross_entropy). In pytorch, cross_entropy loss function seems does not support one-hot labels directly, which I need to change the label to integer. However I don't think this should make big difference.

If you want to run the models, the data can be downloaded from:
https://github.com/jinfagang/pytorch_chatbot/blob/master/datasets/eng-fra.txt

Did I do something wrong in my code? Many thanks

One way to look at the issue would be:

Fixing seeds to the same value in both Pytorch and Keras, albeit it cannot really guarantee the same output.
Weight initialization in Pytorch is different from Keras. Make sure they have the same weight initialization functions
I've been using for a problem of mine and I can say that even with 1 and 2 being setup identically, there is a high probability of getting the same results (that could be due to the way how Pytorch is implemented).

Hope that helps! Please update us if you managed to resolve your issue.
