Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

updated Encoder and Decoder signature. #66

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

updated Encoder and Decoder signature. #66

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
86 changes: 14 additions & 72 deletions ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -103,17 +103,7 @@ def forward(self, value, key, query, mask):


class Encoder(nn.Module):
def __init__(
self,
src_vocab_size,
embed_size,
num_layers,
heads,
device,
forward_expansion,
dropout,
max_length,
):
def __init__(self,src_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,):

super(Encoder, self).__init__()
self.embed_size = embed_size
Expand All @@ -122,25 +112,15 @@ def __init__(
self.position_embedding = nn.Embedding(max_length, embed_size)

self.layers = nn.ModuleList(
[
TransformerBlock(
embed_size,
heads,
dropout=dropout,
forward_expansion=forward_expansion,
)
for _ in range(num_layers)
]
)
[TransformerBlock(embed_size, heads, dropout=dropout, forward_expansion=forward_expansion,)
for _ in range(num_layers)])

self.dropout = nn.Dropout(dropout)

def forward(self, x, mask):
N, seq_length = x.shape
positions = torch.arange(0, seq_length).expand(N, seq_length).to(self.device)
out = self.dropout(
(self.word_embedding(x) + self.position_embedding(positions))
)
out = self.dropout((self.word_embedding(x) + self.position_embedding(positions)))

# In the Encoder the query, key, value are all the same, it's in the
# decoder this will change. This might look a bit odd in this case.
Expand Down Expand Up @@ -168,28 +148,15 @@ def forward(self, x, value, key, src_mask, trg_mask):


class Decoder(nn.Module):
def __init__(
self,
trg_vocab_size,
embed_size,
num_layers,
heads,
forward_expansion,
dropout,
device,
max_length,
):
def __init__(self, trg_vocab_size, embed_size, num_layers, heads, forward_expansion,dropout, device, max_length,):
super(Decoder, self).__init__()
self.device = device
self.word_embedding = nn.Embedding(trg_vocab_size, embed_size)
self.position_embedding = nn.Embedding(max_length, embed_size)

self.layers = nn.ModuleList(
[
DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
for _ in range(num_layers)
]
)
[DecoderBlock(embed_size, heads, forward_expansion, dropout, device)
for _ in range(num_layers)])
self.fc_out = nn.Linear(embed_size, trg_vocab_size)
self.dropout = nn.Dropout(dropout)

Expand Down Expand Up @@ -223,28 +190,8 @@ def __init__(
):

super(Transformer, self).__init__()

self.encoder = Encoder(
src_vocab_size,
embed_size,
num_layers,
heads,
device,
forward_expansion,
dropout,
max_length,
)

self.decoder = Decoder(
trg_vocab_size,
embed_size,
num_layers,
heads,
forward_expansion,
dropout,
device,
max_length,
)
self.encoder = Encoder(src_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,)
self.decoder = Decoder(trg_vocab_size, embed_size, num_layers, heads, forward_expansion, dropout, device, max_length,)

self.src_pad_idx = src_pad_idx
self.trg_pad_idx = trg_pad_idx
Expand All @@ -257,10 +204,7 @@ def make_src_mask(self, src):

def make_trg_mask(self, trg):
N, trg_len = trg.shape
trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(
N, 1, trg_len, trg_len
)

trg_mask = torch.tril(torch.ones((trg_len, trg_len))).expand(N, 1, trg_len, trg_len)
return trg_mask.to(self.device)

def forward(self, src, trg):
Expand All @@ -275,17 +219,15 @@ def forward(self, src, trg):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(device)

x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0], [1, 8, 7, 3, 4, 5, 6, 7, 2]]).to(
device
)
x = torch.tensor([[1, 5, 6, 4, 3, 9, 5, 2, 0],[1, 8, 7, 3, 4, 5, 6, 7, 2]]).to(device)
trg = torch.tensor([[1, 7, 4, 3, 5, 9, 2, 0], [1, 5, 6, 2, 4, 7, 6, 2]]).to(device)

src_pad_idx = 0
trg_pad_idx = 0
src_vocab_size = 10
trg_vocab_size = 10
model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device=device).to(
device
)

model = Transformer(src_vocab_size, trg_vocab_size, src_pad_idx, trg_pad_idx, device=device).to(device)

out = model(x, trg[:, :-1])
print(out.shape)