Unusual behaviour with PyTorch transformer decoder layer gpt

specialized_boy: <p>I was turning the decoder model code with pytorch transformer decoder layer an I am getting different loss even though I tried to match the implementation.</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
from torch.nn import functional as F

# hyperparameters
batch_size = 16 # how many independent sequences will we process in parallel?
block_size = 32 # what is the maximum context length for predictions?
max_iters = 5000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 64
n_head = 4
n_layer = 4
dropout = 0.0
# ------------

torch.manual_seed(1337)

# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
with open('input.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# here are all the unique characters that occur in this text
chars = sorted(list(set(text)))
vocab_size = len(chars)
# create a mapping from characters to integers
stoi = { ch:i for i,ch in enumerate(chars) }
itos = { i:ch for i,ch in enumerate(chars) }
encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string

# Train and test splits
data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9*len(data)) # first 90% will be train, rest val
train_data = data[:n]
val_data = data[n:]

# data loading
def get_batch(split):
    # generate a small batch of data of inputs x and targets y
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i+block_size] for i in ix])
    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
    x, y = x.to(device), y.to(device)
    return x, y

u/torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

class Head(nn.Module):
    """ one head of self-attention """

    def __init__(self, head_size):
        super().__init__()
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B,T,C = x.shape
        k = self.key(x)   # (B,T,C)
        q = self.query(x) # (B,T,C)
        # compute attention scores ("affinities")
        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -&gt; (B, T, T)
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
        wei = F.softmax(wei, dim=-1) # (B, T, T)
        wei = self.dropout(wei)
        # perform the weighted aggregation of the values
        v = self.value(x) # (B,T,C)
        out = wei @ v # (B, T, T) @ (B, T, C) -&gt; (B, T, C)
        return out

class MultiHeadAttention(nn.Module):
    """ multiple heads of self-attention in parallel """

    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(n_embd, n_embd)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)
        out = self.dropout(self.proj(out))
        return out

class FeedFoward(nn.Module):
    """ a simple linear layer followed by a non-linearity """

    def __init__(self, n_embd):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(n_embd, 4 * n_embd),
            nn.ReLU(),
            nn.Linear(4 * n_embd, n_embd),
            nn.Dropout(dropout),
        )

    def forward(self, x):
        return self.net(x)

class Block(nn.Module):
    """ Transformer block: communication followed by computation """

    def __init__(self, n_embd, n_head):
        # n_embd: embedding dimension, n_head: the number of heads we'd like
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = FeedFoward(n_embd)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        x = x + self.ffwd(self.ln2(x))
        return x

# super simple bigram model
class TransformerDecoder(nn.Module):

    def __init__(self):
        super().__init__()
        # each token directly reads off the logits for the next token from a lookup table
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape

        # idx and targets are both (B,T) tensor of integers
        tok_emb = self.token_embedding_table(idx) # (B,T,C)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
        x = tok_emb + pos_emb # (B,T,C)
        x = self.blocks(x) # (B,T,C)
        x = self.ln_f(x) # (B,T,C)
        logits = self.lm_head(x) # (B,T,vocab_size)

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets)

        return logits, loss

    def generate(self, idx, max_new_tokens):
        # idx is (B, T) array of indices in the current context
        for _ in range(max_new_tokens):
            # crop idx to the last block_size tokens
            idx_cond = idx[:, -block_size:]
            # get the predictions
            logits, loss = self(idx_cond)
            # focus only on the last time step
            logits = logits[:, -1, :] # becomes (B, C)
            # apply softmax to get probabilities
            probs = F.softmax(logits, dim=-1) # (B, C)
            # sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
            # append sampled index to the running sequence
            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
        return idx

model = TransformerDecoder()
m = model.to(device)
# print the number of parameters in the model
print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')

# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

for iter in range(max_iters):

    # every once in a while evaluate the loss on train and val sets
    if iter % eval_interval == 0 or iter == max_iters - 1:
        losses = estimate_loss()
        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    # sample a batch of data
    xb, yb = get_batch('train')

    # evaluate the loss
    logits, loss = model(xb, yb)
    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()

# generate from the model
context = torch.zeros((1, 1), dtype=torch.long, device=device)
print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))
</code></pre>
<p>0.203279 M parameters<br>
step 0: train loss 3.0684, val loss 3.0634<br>
step 100: train loss 0.9685, val loss 0.9290<br>
step 200: train loss 0.6753, val loss 0.6564<br>
step 300: train loss 0.5599, val loss 0.5882<br>
step 400: train loss 0.3871, val loss 0.5186<br>
step 500: train loss 0.2547, val loss 0.3679<br>
step 600: train loss 0.2038, val loss 0.2841<br>
step 700: train loss 0.1839, val loss 0.2364<br>
step 800: train loss 0.1704, val loss 0.2210<br>
step 900: train loss 0.1627, val loss 0.2135<br>
step 1000: train loss 0.1568, val loss 0.1984</p>
<pre><code class="lang-auto">import torch
import torch.nn as nn
from torch.nn import functional as F

# hyperparameters
batch_size = 16  # how many independent sequences will we process in parallel?
block_size = 32  # what is the maximum context length for predictions?
max_iters = 1000
eval_interval = 100
learning_rate = 1e-3
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 64
n_head = 4
n_layer = 4
dropout = 0.0
# ------------

torch.manual_seed(1337)

# Assuming input.txt is already downloaded and available
with open('input.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# here are all the unique characters that occur in this text
chars = sorted(list(set(text)))
vocab_size = len(chars)
# create a mapping from characters to integers
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}
encode = lambda s: [stoi[c] for c in s]  # encoder: take a string, output a list of integers
decode = lambda l: ''.join([itos[i] for i in l])  # decoder: take a list of integers, output a string

# Train and test splits
data = torch.tensor(encode(text), dtype=torch.long)
n = int(0.9 * len(data))  # first 90% will be train, rest val
train_data = data[:n]
val_data = data[n:]

# data loading
def get_batch(split):
    # generate a small batch of data of inputs x and targets y
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i + block_size] for i in ix])
    y = torch.stack([data[i + 1:i + block_size + 1] for i in ix])
    x, y = x.to(device), y.to(device)
    return x, y

u/torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()
    return out

class TransformerDecoderModel(nn.Module):
    def __init__(self, vocab_size, n_embd, n_head, n_layer, block_size, dropout=0.1):
        super().__init__()
        self.vocab_size = vocab_size
        self.n_embd = n_embd
        self.n_head = n_head
        self.n_layer = n_layer
        self.block_size = block_size

        self.token_embedding = nn.Embedding(vocab_size, n_embd)
        self.position_embedding = nn.Embedding(block_size, n_embd)

        decoder_layer = nn.TransformerDecoderLayer(d_model=n_embd, nhead=n_head, dropout=dropout)
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=n_layer)

        self.ln_f = nn.LayerNorm(n_embd)  # Final LayerNorm
        self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)

    def generate_square_subsequent_mask(self, sz):
        mask = torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)
        return mask

    def forward(self, idx, targets=None):
        B, T = idx.shape
        device = idx.device

        # Create mask with shape [block_size, block_size]
        mask = self.generate_square_subsequent_mask(T).to(device)

        tok_emb = self.token_embedding(idx)  # [B, T, C]
        pos_emb = self.position_embedding(torch.arange(T, dtype=torch.long, device=device))  # [T, C]
        pos_emb = pos_emb.unsqueeze(0).repeat(B, 1, 1)  # Expand to match batch size: [B, T, C]
        x = tok_emb + pos_emb

        # Transformer requires input shape as [T, B, C]
        x = x.permute(1, 0, 2)  # [T, B, C]
        x = self.transformer_decoder(x, x, tgt_mask=mask)

        # Revert shape to [B, T, C] for the linear layer
        x = x.permute(1, 0, 2)

        x = self.ln_f(x)
        logits = self.lm_head(x)

        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, self.vocab_size), targets.view(-1))
            return logits, loss
        else:
            return logits, None

    def generate(self, idx, max_new_tokens):
        idx = idx.to(device)
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.block_size:]
            logits = self.forward(idx_cond)[0]
            probs = F.softmax(logits[:, -1, :], dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat([idx, idx_next], dim=1)
        return idx


model = TransformerDecoderModel(vocab_size=vocab_size, n_embd=n_embd, n_head=n_head, n_layer=n_layer, block_size=block_size, dropout=dropout)

m = model.to(device)
# print the number of parameters in the model
print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')

# create a PyTorch optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)

for iter in range(max_iters):

    # every once in a while evaluate the loss on train and val sets
    if iter % eval_interval == 0 or iter == max_iters - 1:
        losses = estimate_loss()
        print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    # sample a batch of data
    xb, yb = get_batch('train')

    # evaluate the loss
    logits, loss = model(xb, yb)
    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()

# generate from the model
context = torch.zeros((1, 1), dtype=torch.long, device=device)
print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))
</code></pre>
<p>step 0: train loss 3.1871, val loss 3.2023<br>
step 100: train loss 0.9042, val loss 0.8612<br>
step 200: train loss 0.1210, val loss 0.1222<br>
step 300: train loss 0.0269, val loss 0.0262<br>
step 400: train loss 0.0178, val loss 0.0231<br>
step 500: train loss 0.0153, val loss 0.0221<br>
step 600: train loss 0.0072, val loss 0.0173<br>
step 700: train loss 0.0110, val loss 0.0102<br>
step 800: train loss 0.0027, val loss 0.0041<br>
step 900: train loss 0.0019, val loss 0.0015<br>
step 999: train loss 0.0006, val loss 0.0006</p>
<p>even though I implemented one thing form scratch and the other with transformer decoder layers both loss is different and I see PyTorch transformer decoder  implementation wrong as its generating same token repeatedly why?</p>