|
|
|
@ -111,7 +111,6 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
self, |
|
|
|
|
image_vocab_count: int, |
|
|
|
|
image_token_count: int, |
|
|
|
|
sample_token_count: int, |
|
|
|
|
embed_count: int, |
|
|
|
|
attention_head_count: int, |
|
|
|
|
glu_embed_count: int, |
|
|
|
@ -121,7 +120,6 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
super().__init__() |
|
|
|
|
self.layer_count = layer_count |
|
|
|
|
self.embed_count = embed_count |
|
|
|
|
self.sample_token_count = sample_token_count |
|
|
|
|
self.condition_factor = 10.0 |
|
|
|
|
self.image_token_count = image_token_count |
|
|
|
|
self.embed_tokens = nn.Embedding(image_vocab_count + 1, embed_count) |
|
|
|
@ -139,7 +137,7 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
self.final_ln = nn.LayerNorm(embed_count) |
|
|
|
|
self.lm_head = nn.Linear(embed_count, image_vocab_count + 1, bias=False) |
|
|
|
|
self.zero_prob = torch.zeros([1]) |
|
|
|
|
self.token_indices = torch.arange(self.sample_token_count) |
|
|
|
|
self.token_indices = torch.arange(self.image_token_count) |
|
|
|
|
self.start_token = torch.tensor([start_token]).to(torch.long) |
|
|
|
|
if torch.cuda.is_available(): |
|
|
|
|
self.zero_prob = self.zero_prob.cuda() |
|
|
|
@ -185,11 +183,35 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
torch.exp(logits - top_logits[:, [0]]) |
|
|
|
|
) |
|
|
|
|
return probs, attention_state |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def decode_row( |
|
|
|
|
self, |
|
|
|
|
row_index: int, |
|
|
|
|
attention_mask: BoolTensor, |
|
|
|
|
encoder_state: FloatTensor, |
|
|
|
|
attention_state: FloatTensor, |
|
|
|
|
image_tokens_sequence: LongTensor |
|
|
|
|
) -> Tuple[FloatTensor, LongTensor]: |
|
|
|
|
for col_index in range(16): |
|
|
|
|
i = 16 * row_index + col_index |
|
|
|
|
probs, attention_state = self.decode_step( |
|
|
|
|
attention_mask = attention_mask, |
|
|
|
|
encoder_state = encoder_state, |
|
|
|
|
attention_state = attention_state, |
|
|
|
|
prev_tokens = image_tokens_sequence[:, i], |
|
|
|
|
token_index = self.token_indices[[i]] |
|
|
|
|
) |
|
|
|
|
|
|
|
|
|
image_tokens_sequence[:, i + 1] = torch.multinomial(probs, 1)[:, 0] |
|
|
|
|
|
|
|
|
|
return attention_state, image_tokens_sequence |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def forward( |
|
|
|
|
self, |
|
|
|
|
image_count: int, |
|
|
|
|
row_count: int, |
|
|
|
|
text_tokens: LongTensor, |
|
|
|
|
encoder_state: FloatTensor |
|
|
|
|
) -> LongTensor: |
|
|
|
@ -206,7 +228,7 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
) |
|
|
|
|
attention_state = torch.zeros(attention_state_shape) |
|
|
|
|
image_tokens_sequence = torch.full( |
|
|
|
|
(image_count, self.image_token_count), |
|
|
|
|
(image_count, self.image_token_count + 1), |
|
|
|
|
6965, # black token |
|
|
|
|
dtype=torch.long |
|
|
|
|
) |
|
|
|
@ -214,18 +236,15 @@ class DalleBartDecoder(nn.Module): |
|
|
|
|
attention_state = attention_state.cuda() |
|
|
|
|
image_tokens_sequence = image_tokens_sequence.cuda() |
|
|
|
|
|
|
|
|
|
image_tokens = self.start_token[[0] * image_count] |
|
|
|
|
|
|
|
|
|
for i in range(self.sample_token_count): |
|
|
|
|
probs, attention_state = self.decode_step( |
|
|
|
|
attention_mask = attention_mask, |
|
|
|
|
encoder_state = encoder_state, |
|
|
|
|
attention_state = attention_state, |
|
|
|
|
prev_tokens = image_tokens, |
|
|
|
|
token_index = self.token_indices[[i]] |
|
|
|
|
) |
|
|
|
|
image_tokens_sequence[:, 0] = self.start_token[0] |
|
|
|
|
|
|
|
|
|
image_tokens = torch.multinomial(probs, 1)[:, 0] |
|
|
|
|
image_tokens_sequence[:, i] = image_tokens |
|
|
|
|
for row_index in range(row_count): |
|
|
|
|
attention_state, image_tokens_sequence = self.decode_row( |
|
|
|
|
row_index, |
|
|
|
|
attention_mask, |
|
|
|
|
encoder_state, |
|
|
|
|
attention_state, |
|
|
|
|
image_tokens_sequence |
|
|
|
|
) |
|
|
|
|
|
|
|
|
|
return image_tokens_sequence |
|
|
|
|
return image_tokens_sequence[:, 1:] |