from typing import Tuple, List import torch from torch import nn, LongTensor, FloatTensor, BoolTensor torch.set_grad_enabled(False) from .dalle_bart_encoder import GLU, AttentionBase IMAGE_TOKEN_COUNT = 256 BLANK_TOKEN = 6965 class DecoderCrossAttention(AttentionBase): def forward( self, decoder_state: FloatTensor, encoder_state: FloatTensor, attention_mask: BoolTensor ) -> FloatTensor: keys = self.k_proj.forward(encoder_state) values = self.v_proj.forward(encoder_state) queries = self.q_proj.forward(decoder_state) return super().forward(keys, values, queries, attention_mask) class DecoderSelfAttention(AttentionBase): def __init__(self, head_count: int, embed_count: int): super().__init__(head_count, embed_count) token_indices = torch.arange(IMAGE_TOKEN_COUNT) if torch.cuda.is_available(): token_indices = token_indices.cuda() self.token_indices = token_indices def forward( self, decoder_state: FloatTensor, attention_state: FloatTensor, token_index: LongTensor ) -> Tuple[FloatTensor, FloatTensor]: keys = self.k_proj.forward(decoder_state) values = self.v_proj.forward(decoder_state) queries = self.q_proj.forward(decoder_state) attn_mask = self.token_indices < token_index + 1 attn_mask = attn_mask[None][[0] * decoder_state.shape[0]] attn_state_new = torch.cat([keys, values]).to(attention_state.dtype) attention_state[:, token_index] = attn_state_new batch_count = decoder_state.shape[0] keys = attention_state[:batch_count] values = attention_state[batch_count:] decoder_state = super().forward(keys, values, queries, attn_mask) return decoder_state, attention_state class DecoderLayer(nn.Module): def __init__( self, head_count: int, embed_count: int, glu_embed_count: int ): super().__init__() self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count) self.self_attn = DecoderSelfAttention(head_count, embed_count) self.self_attn_layer_norm = nn.LayerNorm(embed_count) self.pre_encoder_attn_layer_norm = nn.LayerNorm(embed_count) self.encoder_attn = DecoderCrossAttention(head_count, embed_count) self.encoder_attn_layer_norm = nn.LayerNorm(embed_count) self.glu = GLU(embed_count, glu_embed_count) def forward( self, decoder_state: FloatTensor, encoder_state: FloatTensor, attention_state: FloatTensor, attention_mask: BoolTensor, token_index: LongTensor ) -> Tuple[FloatTensor, FloatTensor]: # Self Attention residual = decoder_state decoder_state = self.pre_self_attn_layer_norm.forward(decoder_state) decoder_state, attention_state = self.self_attn.forward( decoder_state, attention_state, token_index ) decoder_state = self.self_attn_layer_norm.forward(decoder_state) decoder_state = residual + decoder_state # Cross Attention residual = decoder_state decoder_state = self.pre_encoder_attn_layer_norm.forward(decoder_state) decoder_state = self.encoder_attn.forward( decoder_state, encoder_state, attention_mask ) decoder_state = self.encoder_attn_layer_norm.forward(decoder_state) decoder_state = residual + decoder_state # Feed forward residual = decoder_state decoder_state = self.glu.forward(decoder_state) decoder_state = residual + decoder_state return decoder_state, attention_state class DalleBartDecoder(nn.Module): def __init__( self, image_vocab_count: int, embed_count: int, attention_head_count: int, glu_embed_count: int, layer_count: int, start_token: int ): super().__init__() self.layer_count = layer_count self.embed_count = embed_count self.image_vocab_count = image_vocab_count self.embed_tokens = nn.Embedding(image_vocab_count + 1, embed_count) self.embed_positions = nn.Embedding(IMAGE_TOKEN_COUNT, embed_count) self.layers: List[DecoderLayer] = nn.ModuleList([ DecoderLayer( attention_head_count, embed_count, glu_embed_count ) for _ in range(layer_count) ]) self.layernorm_embedding = nn.LayerNorm(embed_count) 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(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() self.token_indices = self.token_indices.cuda() self.start_token = self.start_token.cuda() def decode_step( self, log2_k: int, log2_supercondition_factor: int, attention_mask: BoolTensor, encoder_state: FloatTensor, attention_state: FloatTensor, prev_tokens: LongTensor, token_index: LongTensor ) -> Tuple[FloatTensor, FloatTensor]: image_count = encoder_state.shape[0] // 2 token_index_batched = token_index[[0] * image_count * 2] prev_tokens = prev_tokens[list(range(image_count)) * 2] prev_tokens = prev_tokens.clamp(0, self.image_vocab_count) decoder_state = self.embed_tokens.forward(prev_tokens) decoder_state += self.embed_positions.forward(token_index_batched) decoder_state = self.layernorm_embedding.forward(decoder_state) decoder_state = decoder_state[:, None] for i in range(self.layer_count): decoder_state, attention_state[i] = self.layers[i].forward( decoder_state, encoder_state, attention_state[i], attention_mask, token_index ) decoder_state = self.final_ln(decoder_state) logits = self.lm_head(decoder_state) a = 2 ** log2_supercondition_factor logits: FloatTensor = ( logits[:image_count, -1] * (1 - a) + logits[image_count:, -1] * a ) top_logits, _ = logits.topk(2 ** log2_k, dim=-1) probs = torch.where( logits < top_logits[:, [-1]], self.zero_prob, torch.exp(logits - top_logits[:, [0]]) ) return probs, attention_state def decode_row( self, row_index: int, log2_k: int, log2_supercondition_factor: int, encoder_state: FloatTensor, attention_mask: BoolTensor, 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( log2_k = log2_k, log2_supercondition_factor = log2_supercondition_factor, 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 decode_initial( self, seed: int, image_count: int, text_tokens: LongTensor, encoder_state: FloatTensor ) -> Tuple[FloatTensor, FloatTensor, FloatTensor, LongTensor]: expanded_indices = [0] * image_count + [1] * image_count text_tokens = text_tokens[expanded_indices] encoder_state = encoder_state[expanded_indices] attention_mask = text_tokens.not_equal(1) attention_state_shape = ( self.layer_count, image_count * 4, IMAGE_TOKEN_COUNT, self.embed_count ) attention_state = torch.zeros(attention_state_shape) image_tokens_sequence = torch.full( (image_count, IMAGE_TOKEN_COUNT + 1), BLANK_TOKEN, dtype=torch.long ) if torch.cuda.is_available(): attention_state = attention_state.cuda() image_tokens_sequence = image_tokens_sequence.cuda() image_tokens_sequence[:, 0] = self.start_token[0] if seed > 0: torch.manual_seed(seed) return encoder_state, attention_mask, attention_state, image_tokens_sequence