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243 lines
8.8 KiB
243 lines
8.8 KiB
from typing import Tuple, List |
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import torch |
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from torch import nn, LongTensor, FloatTensor, BoolTensor |
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from .dalle_bart_encoder import GLU, AttentionBase |
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IMAGE_TOKEN_COUNT = 256 |
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BLANK_TOKEN = 6965 |
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class DecoderCrossAttention(AttentionBase): |
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def forward( |
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self, |
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decoder_state: FloatTensor, |
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encoder_state: FloatTensor, |
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attention_mask: BoolTensor |
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) -> FloatTensor: |
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keys = self.k_proj.forward(encoder_state) |
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values = self.v_proj.forward(encoder_state) |
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queries = self.q_proj.forward(decoder_state) |
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return super().forward(keys, values, queries, attention_mask) |
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class DecoderSelfAttention(AttentionBase): |
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def __init__(self, head_count: int, embed_count: int): |
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super().__init__(head_count, embed_count) |
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token_indices = torch.arange(IMAGE_TOKEN_COUNT) |
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if torch.cuda.is_available(): token_indices = token_indices.cuda() |
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self.token_indices = token_indices |
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def forward( |
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self, |
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decoder_state: FloatTensor, |
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attention_state: FloatTensor, |
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token_index: LongTensor |
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) -> Tuple[FloatTensor, FloatTensor]: |
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keys = self.k_proj.forward(decoder_state) |
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values = self.v_proj.forward(decoder_state) |
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queries = self.q_proj.forward(decoder_state) |
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attn_mask = self.token_indices < token_index + 1 |
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attn_mask = attn_mask[None][[0] * decoder_state.shape[0]] |
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attn_state_new = torch.cat([keys, values]).to(attention_state.dtype) |
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attention_state[:, token_index] = attn_state_new |
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batch_count = decoder_state.shape[0] |
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keys = attention_state[:batch_count] |
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values = attention_state[batch_count:] |
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decoder_state = super().forward(keys, values, queries, attn_mask) |
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return decoder_state, attention_state |
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class DecoderLayer(nn.Module): |
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def __init__( |
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self, |
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head_count: int, |
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embed_count: int, |
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glu_embed_count: int |
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): |
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super().__init__() |
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self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.self_attn = DecoderSelfAttention(head_count, embed_count) |
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self.self_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.pre_encoder_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.encoder_attn = DecoderCrossAttention(head_count, embed_count) |
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self.encoder_attn_layer_norm = nn.LayerNorm(embed_count) |
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self.glu = GLU(embed_count, glu_embed_count) |
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def forward( |
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self, |
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decoder_state: FloatTensor, |
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encoder_state: FloatTensor, |
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attention_state: FloatTensor, |
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attention_mask: BoolTensor, |
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token_index: LongTensor |
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) -> Tuple[FloatTensor, FloatTensor]: |
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# Self Attention |
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residual = decoder_state |
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decoder_state = self.pre_self_attn_layer_norm.forward(decoder_state) |
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decoder_state, attention_state = self.self_attn.forward( |
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decoder_state, |
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attention_state, |
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token_index |
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) |
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decoder_state = self.self_attn_layer_norm.forward(decoder_state) |
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decoder_state = residual + decoder_state |
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# Cross Attention |
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residual = decoder_state |
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decoder_state = self.pre_encoder_attn_layer_norm.forward(decoder_state) |
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decoder_state = self.encoder_attn.forward( |
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decoder_state, |
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encoder_state, |
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attention_mask |
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) |
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decoder_state = self.encoder_attn_layer_norm.forward(decoder_state) |
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decoder_state = residual + decoder_state |
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# Feed forward |
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residual = decoder_state |
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decoder_state = self.glu.forward(decoder_state) |
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decoder_state = residual + decoder_state |
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return decoder_state, attention_state |
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class DalleBartDecoder(nn.Module): |
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def __init__( |
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self, |
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image_vocab_count: int, |
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embed_count: int, |
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attention_head_count: int, |
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glu_embed_count: int, |
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layer_count: int, |
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start_token: int |
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): |
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super().__init__() |
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self.layer_count = layer_count |
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self.embed_count = embed_count |
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self.image_vocab_count = image_vocab_count |
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self.embed_tokens = nn.Embedding(image_vocab_count + 1, embed_count) |
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self.embed_positions = nn.Embedding(IMAGE_TOKEN_COUNT, embed_count) |
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self.layers: List[DecoderLayer] = nn.ModuleList([ |
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DecoderLayer( |
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attention_head_count, |
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embed_count, |
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glu_embed_count |
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) |
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for _ in range(layer_count) |
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]) |
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self.layernorm_embedding = nn.LayerNorm(embed_count) |
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self.final_ln = nn.LayerNorm(embed_count) |
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self.lm_head = nn.Linear(embed_count, image_vocab_count + 1, bias=False) |
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self.zero_prob = torch.zeros([1]) |
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self.token_indices = torch.arange(IMAGE_TOKEN_COUNT) |
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self.start_token = torch.tensor([start_token]).to(torch.long) |
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if torch.cuda.is_available(): |
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self.zero_prob = self.zero_prob.cuda() |
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self.token_indices = self.token_indices.cuda() |
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self.start_token = self.start_token.cuda() |
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def decode_step( |
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self, |
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log2_k: int, |
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log2_supercondition_factor: int, |
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attention_mask: BoolTensor, |
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encoder_state: FloatTensor, |
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attention_state: FloatTensor, |
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prev_tokens: LongTensor, |
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token_index: LongTensor |
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) -> Tuple[FloatTensor, FloatTensor]: |
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image_count = encoder_state.shape[0] // 2 |
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token_index_batched = token_index[[0] * image_count * 2] |
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prev_tokens = prev_tokens[list(range(image_count)) * 2] |
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prev_tokens = prev_tokens.clamp(0, self.image_vocab_count) |
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decoder_state = self.embed_tokens.forward(prev_tokens) |
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decoder_state += self.embed_positions.forward(token_index_batched) |
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decoder_state = self.layernorm_embedding.forward(decoder_state) |
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decoder_state = decoder_state[:, None] |
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for i in range(self.layer_count): |
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decoder_state, attention_state[i] = self.layers[i].forward( |
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decoder_state, |
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encoder_state, |
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attention_state[i], |
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attention_mask, |
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token_index |
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) |
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decoder_state = self.final_ln(decoder_state) |
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logits = self.lm_head(decoder_state) |
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a = 2 ** log2_supercondition_factor |
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logits: FloatTensor = ( |
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logits[:image_count, -1] * (1 - a) + |
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logits[image_count:, -1] * a |
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) |
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top_logits, _ = logits.topk(2 ** log2_k, dim=-1) |
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probs = torch.where( |
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logits < top_logits[:, [-1]], |
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self.zero_prob, |
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torch.exp(logits - top_logits[:, [0]]) |
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) |
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probs[:, 2 ** 14:] = 0 # vqgan vocab_count is only 2 ** 14 |
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return probs, attention_state |
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def decode_row( |
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self, |
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row_index: int, |
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log2_k: int, |
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log2_supercondition_factor: int, |
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encoder_state: FloatTensor, |
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attention_mask: BoolTensor, |
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attention_state: FloatTensor, |
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image_tokens_sequence: LongTensor |
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) -> Tuple[FloatTensor, LongTensor]: |
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for col_index in range(16): |
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i = 16 * row_index + col_index |
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probs, attention_state = self.decode_step( |
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log2_k = log2_k, |
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log2_supercondition_factor = log2_supercondition_factor, |
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attention_mask = attention_mask, |
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encoder_state = encoder_state, |
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attention_state = attention_state, |
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prev_tokens = image_tokens_sequence[:, i], |
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token_index = self.token_indices[[i]] |
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) |
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image_tokens_sequence[:, i + 1] = torch.multinomial(probs, 1)[:, 0] |
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return attention_state, image_tokens_sequence |
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def decode_initial( |
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self, |
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seed: int, |
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image_count: int, |
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text_tokens: LongTensor, |
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encoder_state: FloatTensor |
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) -> Tuple[FloatTensor, FloatTensor, FloatTensor, LongTensor]: |
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expanded_indices = [0] * image_count + [1] * image_count |
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text_tokens = text_tokens[expanded_indices] |
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encoder_state = encoder_state[expanded_indices] |
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attention_mask = text_tokens.not_equal(1) |
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attention_state_shape = ( |
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self.layer_count, |
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image_count * 4, |
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IMAGE_TOKEN_COUNT, |
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self.embed_count |
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) |
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attention_state = torch.zeros(attention_state_shape) |
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image_tokens_sequence = torch.full( |
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(image_count, IMAGE_TOKEN_COUNT + 1), |
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BLANK_TOKEN, |
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dtype=torch.long |
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) |
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if torch.cuda.is_available(): |
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attention_state = attention_state.cuda() |
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image_tokens_sequence = image_tokens_sequence.cuda() |
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image_tokens_sequence[:, 0] = self.start_token[0] |
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if seed > 0: torch.manual_seed(seed) |
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return encoder_state, attention_mask, attention_state, image_tokens_sequence |