When I try to rebuild a Transformer model, got 'Expected tensor for argument #1 'indices' to have scalar type Long, but got torch.IntTensor instead'

NLPnewcomer: <p>`When I try to rebuild a Transformer model based on HarvardNLP paper ’ The Annotated Transformer’ and try to train that model, I got a problem ‘RuntimeError: Expected tensor for argument <span class="hashtag">#1</span> ‘indices’ to have scalar type Long; but got torch.IntTensor instead (while checking arguments for embedding)’.</p>
<p>But in the paper’s Colab, that problem did not occur.</p>

K_Frank: <p>Hello 老师!</p>
<aside class="quote no-group" data-username="NLPnewcomer" data-post="1" data-topic="40265" data-full="true">
<div class="title">
<div class="quote-controls"></div>
<img loading="lazy" alt="" width="24" height="24" src="https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/nlpnewcomer/48/10150_2.png" class="avatar"> NLPnewcomer:</div>
<blockquote>
<p>`When I try to rebuild a Transformer model based on HarvardNLP paper ’ The Annotated Transformer’ and try to train that model, I got a problem ‘RuntimeError: Expected tensor for argument <span class="hashtag-raw">#1</span> ‘indices’ to have scalar type Long; but got torch.IntTensor instead (while checking arguments for embedding)’.</p>
<p>But in the paper’s Colab, that problem did not occur.</p>
</blockquote>
</aside>
<p>Without seeing the code, I don’t know the specifics.</p>
<p>But, yes, there are pytorch functions that expect <code>LongTensor</code>s,<br>
and won’t convert their inputs for you, so you have to convert<br>
them yourself.  Depending on the specific situation, this might<br>
be as simple as:</p>
<pre><code class="lang-auto">funcRequiringLongTensor (myIntTensor.to (torch.long))
</code></pre>
<p>Best.</p>
<p>K. Frank</p>

NLPnewcomer: <p>Sorry, it’s code about ’ The Annotated Transformer’.<br>
And the bug is focused on <strong>run_epoch</strong>  , <strong>EncoderDecoder</strong>.<br>
In the end, I will update the RuntimeError information.</p>
<pre><code class="lang-auto">import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math, copy, time
from torch.autograd import Variable
import matplotlib.pyplot as plt
import seaborn

seaborn.set_context(context="talk")

class EncoderDecoder(nn.Module):
    '''
    构建一个标准的Encoder-Decoder架构
    可以发现里面的encode和decode处理的不同，在前面说过，decoder比encoder多了一个context attention。
    '''

    def __init__(self, encoder, decoder, src_embed, tgt_embed, generator):
        super(EncoderDecoder, self).__init__()
        self.encoder = encoder
        self.decoder = decoder
        self.src_embed = src_embed
        self.tgt_embed = tgt_embed
        self.generator = generator

    def encode(self, src, src_mask):
        return self.encoder(self.tgt_embed(src), src_mask)

    def decode(self, memory, src_mask, tgt, tgt_mask):
        return self.decoder(self.tgt_embed(tgt), memory, src_mask, tgt_mask)

    def forward(self, src, tgt, src_mask, tgt_mask):
        return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)


class Generator(nn.Module):
    '''
    定义标准的线性+softmax生成步骤
    这是在8. Embeddings和Softmax中
    '''

    def __init__(self, d_model, vocab):
        super(Generator, self).__init__()
        self.proj = nn.Linear(d_model, vocab)

    def forward(self, x):
        return F.log_softmax(self.proj(x), dim=1)





def clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])


class LayerNorm(nn.Module):
    def __init__(self, features, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a_2 = nn.Parameter(torch.ones(features))
        self.b_2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        return self.a_2 * (x - mean) / (std + self.eps) + self.b_2


class Encoder(nn.Module):
    '''
    核心encoder是N层构成的堆
    '''

    def __init__(self, layer, N):
        super(Encoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, mask):
        for layer in self.layers:
            x = layer(x, mask)
        return self.norm(x)



class SublayerConnection(nn.Module):
    def __init__(self, size, dropout):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x, sublayer):
        '''
        将残差连接以同样的size到任意一个子层
        :param x: 
        :param sublayer: 
        :return: 
        '''
        return x + self.dropout(sublayer(self.norm(x)))



class EncoderLayer(nn.Module):
    def __init__(self, size, self_attn, feed_forward, dropout):
        super(EncoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.sublayer = clones(SublayerConnection(size, dropout), 2)
        self.size = size

    def forward(self, x, mask):
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))
        return self.sublayer[1](x, self.feed_forward)



class Decoder(nn.Module):
    def __init__(self, layer, N):
        super(Decoder, self).__init__()
        self.layers = clones(layer, N)
        self.norm = LayerNorm(layer.size)

    def forward(self, x, memory, src_mask, tgt_mask):
        for layer in self.layers:
            x = layer(x, memory, src_mask, tgt_mask)
        return self.norm(x)



class DecoderLayer(nn.Module):
    def __init__(self, size, self_attn, src_attn, feed_forward, dropout):
        super(DecoderLayer, self).__init__()
        self.self_attn = self_attn
        self.feed_forward = feed_forward
        self.src_attn = src_attn
        self.sublayer = clones(SublayerConnection(size, dropout), 3)
        self.size = size

    def forward(self, x, memory, src_mask, tgt_mask):
        m = memory
        x = self.sublayer[0](x, lambda x: self.self_attn(x, x, x, tgt_mask))
        x = self.sublayer[1](x, lambda x: self.src_attn(x, m, m, src_mask))
        return self.sublayer[2](x, self.feed_forward)


def subsequent_mask(size):
    "Mask out subsequent positions."
    attn_shape = (1, size, size)
    subsequent_mask = np.triu(np.ones(attn_shape), k=1).astype('uint8')
    return torch.from_numpy(subsequent_mask) == 0



def attention(query, key, value, mask=None, dropout=None):
    d_k = query.size(-1)
    scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)
    if mask is not None:
        scores = scores.masked_fill(mask == 0, -1e9)
    p_attn = F.softmax(scores, dim=-1)
    if dropout is not None:
        p_attn = dropout(p_attn)
    return torch.matmul(p_attn, value), p_attn


class MultiHeadedAttention(nn.Module):
    def __init__(self, h, d_model, dropout=0.1):
        super(MultiHeadedAttention, self).__init__()
        assert d_model % h == 0
        self.d_k = d_model // h
        self.h = h
        self.linears = clones(nn.Linear(d_model, d_model), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        if mask is not None:
            mask = mask.unsqueeze(1)
        nbatches = query.size(0)

        # 1) Do all the linear projections in batch from d_model =&gt; h x d_k
        query, key, value = [l(x).view(nbatches, -1, self.h, self.d_k).transpose(1, 2) for l, x in
                             zip(self.linears, (query, key, value))]
        # 2) Apply attention on all the projected vectors in batch.
        x, self.attn = attention(query, key, value, mask=mask, dropout=self.dropout)
        # 3) "Concat" using a view and apply a final linear.
        x = x.transpose(1, 2).contiguous().view(nbatches, -1, self.h * self.d_k)

        return self.linears[-1](x)



class PositionwiseFeedForward(nn.Module):
    def __init__(self, d_model, d_ff, dropout=0.1):
        super(PositionwiseFeedForward, self).__init__()
        self.w_1 = nn.Linear(d_model, d_ff)
        self.w_2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.w_2(self.dropout(F.relu(self.w_1(x))))

class Embeddings(nn.Module):
    def __init__(self, d_model, vocab):
        super(Embeddings, self).__init__()
        self.lut = nn.Embedding(vocab, d_model)
        self.d_model = d_model

    def forward(self, x):
        return self.lut(x) * math.sqrt(self.d_model)




class PositionalEncoding(nn.Module):
    "Implement the PE function."

    def __init__(self, d_model, dropout, max_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        # Compute the positional encodings once in log space.
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0., max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0., d_model, 2) *
                             -(math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + Variable(self.pe[:, :x.size(1)],
                         requires_grad=False)
        return self.dropout(x)

def make_model(src_vocab, tgt_vocab, N=6, d_model=512, h=8, d_ff=2048, dropout=0.1):
    c = copy.deepcopy
    attn = MultiHeadedAttention(h, d_model)
    ff = PositionwiseFeedForward(d_model, d_ff, dropout)
    position = PositionalEncoding(d_model, dropout)
    model = EncoderDecoder(
        Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),
        Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),
        nn.Sequential(Embeddings(d_model, src_vocab), c(position)),
        nn.Sequential(Embeddings(d_model, tgt_vocab), c(position)),
        Generator(d_model, tgt_vocab)
    )
    for p in model.parameters():
        if p.dim() &gt; 1:
            nn.init.xavier_uniform_(p)
    return model


class Batch:
    def __init__(self, src, trg=None, pad=0):
        self.src = src
        self.src_mask = (src != pad).unsqueeze(-2)
        if trg is not None:
            self.trg = trg[:, :-1]
            self.trg_y = trg[:, 1:]
            self.trg_mask = self.make_std_mask(self.trg, pad)
            self.ntokens = (self.trg_y != pad).data.sum()

    @staticmethod
    def make_std_mask(tgt, pad):
        "构建一个mask用于隐藏padding和未来的词"
        tgt_mask = (tgt != pad).unsqueeze(-2)
        tgt_mask = tgt_mask &amp; Variable(subsequent_mask(tgt.size(-1)).type_as(tgt_mask.data))
        return tgt_mask




def run_epoch(data_iter, model, loss_compute):
    start = time.time()
    total_tokens = 0
    total_loss = 0
    tokens = 0
    for i, batch in enumerate(data_iter):
        out = model.forward(batch.src, batch.trg, batch.src_mask, batch.trg_mask)
        loss = loss_compute(out, batch.trg_y, batch.ntokens)
        total_loss += loss
        total_tokens += batch.ntokens
        tokens += batch.ntokens

        if i % 50 == 1:
            elapsed = time.time() - start
            print("Epoch Step: %d Loss: %f Tokens per Sec: %f" % (i, loss / batch.ntokens, tokens / elapsed))
            start = time.time()
            tokens = 0
    return total_loss / total_tokens


global max_src_in_batch, max_tgt_in_batch


def batch_size_fn(new, count, sofar):
    "Keep augmenting batch and calculate total number of tokens + padding."
    global max_src_in_batch, max_tgt_in_batch
    if count == 1:
        max_src_in_batch = 0
        max_tgt_in_batch = 0
    max_src_in_batch = max(max_src_in_batch, len(new.src))
    max_tgt_in_batch = max(max_tgt_in_batch, len(new.trg) + 2)
    src_elements = count * max_src_in_batch
    tgt_elements = count * max_tgt_in_batch
    return max(src_elements, tgt_elements)



class NoamOpt:
    "Optim wrapper that implements rate."

    def __init__(self, model_size, factor, warmup, optimizer):
        self.optimizer = optimizer
        self._step = 0
        self.warmup = warmup
        self.factor = factor
        self.model_size = model_size
        self._rate = 0

    def step(self):
        "Update parameters and rate"
        self._step += 1
        rate = self.rate()
        for p in self.optimizer.param_groups:
            p['lr'] = rate
        self._rate = rate
        self.optimizer.step()

    def rate(self, step=None):
        "Implement `lrate` above"
        if step is None:
            step = self._step
        return self.factor * \
               (self.model_size ** (-0.5) *
                min(step ** (-0.5), step * self.warmup ** (-1.5)))


def get_std_opt(model):
    return NoamOpt(model.src_embed[0].d_model, 2, 4000,
                   torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))



class LabelSmoothing(nn.Module):
    "Implement label smoothing."

    def __init__(self, size, padding_idx, smoothing=0.0):
        super(LabelSmoothing, self).__init__()
        self.criterion = nn.KLDivLoss(size_average=False)
        self.padding_idx = padding_idx
        self.confidence = 1.0 - smoothing
        self.smoothing = smoothing
        self.size = size
        self.true_dist = None

    def forward(self, x, target):
        assert x.size(1) == self.size
        true_dist = x.data.clone()
        true_dist.fill_(self.smoothing / (self.size - 2))
        true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
        true_dist[:, self.padding_idx] = 0
        mask = torch.nonzero(target.data == self.padding_idx)
        if mask.dim() &gt; 0:
            true_dist.index_fill_(0, mask.squeeze(), 0.0)
        self.true_dist = true_dist
        return self.criterion(x, Variable(true_dist, requires_grad=False))




def data_gen(V, batch, nbatches):
    '''
    为src-tgt复制任务生成随机数据
    :param V: 
    :param batch: 
    :param nbatches: 
    :return: 
    '''
    for i in range(nbatches):
        data = torch.from_numpy(np.random.randint(1, V, size=(batch, 10)))
        data[:, 0] = 1
        src = Variable(data, requires_grad=False)
        tgt = Variable(data, requires_grad=False)
        yield Batch(src, tgt, 0)



class SimpleLossCompute:
    def __init__(self, generator, criterion, opt=None):
        self.generator = generator
        self.criterion = criterion
        self.opt = opt

    def __call__(self, x, y, norm):
        x = self.generator(x)
        loss = self.criterion(x.contiguous().view(-1, x.size(-1)),
                              y.contiguous().view(-1)) / norm.float()
        loss.backward()
        if self.opt is not None:
            self.opt.step()
            self.opt.optimizer.zero_grad()
        return loss.item() * norm.float().item()


if __name__ == '__main__':

    V = 10
    criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)
    model = make_model(V, V, N=2)
    model_opt = NoamOpt(model.src_embed[0].d_model, 1, 400,
                        torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))

    for epoch in range(10):
        model.train()
        run_epoch(data_gen(V, 30, 20), model, SimpleLossCompute(model.generator, criterion, model_opt))
        model.eval()
        print(run_epoch(data_gen(V, 30, 5), model, SimpleLossCompute(model.generator, criterion, None)))
</code></pre>
<blockquote>
<p>Traceback (most recent call last):<br>
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 664, in <br>
run_epoch(data_gen(V, 30, 20), model, SimpleLossCompute(model.generator, criterion, model_opt))<br>
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 433, in run_epoch<br>
out = model.forward(batch.src, batch.trg, batch.src_mask, batch.trg_mask)<br>
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 42, in forward<br>
return self.decode(self.encode(src, src_mask), src_mask, tgt, tgt_mask)<br>
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 36, in encode<br>
return self.encoder(self.tgt_embed(src), src_mask)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in <strong>call</strong><br>
result = self.forward(*input, **kwargs)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\container.py”, line 91, in forward<br>
input = module(input)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in <strong>call</strong><br>
result = self.forward(*input, **kwargs)<br>
File “C:/Users/01/Desktop/机器学习作业/AllenNLP/[NLP]23AllenNLP08Transformer.py”, line 319, in forward<br>
return self.lut(x) * math.sqrt(self.d_model)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\module.py”, line 491, in <strong>call</strong><br>
result = self.forward(*input, **kwargs)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\modules\sparse.py”, line 108, in forward<br>
self.norm_type, self.scale_grad_by_freq, self.sparse)<br>
File “C:\ProgramData\Anaconda3\envs\python36\lib\site-packages\torch\nn\functional.py”, line 1076, in embedding<br>
return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)<br>
RuntimeError: Expected tensor for argument <span class="hashtag-raw">#1</span> ‘indices’ to have scalar type Long; but got CPUIntTensor instead (while checking arguments for embedding)</p>
</blockquote>
<p>Best<br>
Alex</p>

baalimago: <p>I have the same issue.</p>
<p>Possible solution is to download the version of pytorch from the Prelims section of document ( <code>!pip install http://download.pytorch.org/whl/cu80/torch-0.3.0.post4-cp36-cp36m-linux_x86_64.whl ...</code>), meaning pytorch version 0.3.0 on cuda version 8.</p>
<p>I haven’t confirmed this works, though. Downgrading proved a bit too much of a headache for me to think it’s worth it. Trying to find alternative solutions.</p>