first commit

models/dalle_bart_decoder_torch.py

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+import torch
+from torch import LongTensor, nn, FloatTensor, BoolTensor
+from typing import List, Tuple
+
+from .dalle_bart_encoder_torch import GLUTorch, AttentionTorch
+
+
+class DecoderCrossAttentionTorch(AttentionTorch):
+    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 DecoderSelfAttentionTorch(AttentionTorch):
+    def forward(self, 
+        decoder_state: FloatTensor,
+        keys_values: FloatTensor,
+        attention_mask: BoolTensor,
+        token_index: LongTensor
+    ) -> Tuple[FloatTensor, FloatTensor, FloatTensor]:
+        keys = self.k_proj.forward(decoder_state)
+        values = self.v_proj.forward(decoder_state)
+        queries = self.q_proj.forward(decoder_state)
+
+        batch_count = decoder_state.shape[0]
+        token_count = keys_values.shape[-1]
+        keys_values = torch.where(
+            (torch.arange(token_count) == token_index)[None, None, :], 
+            torch.cat([keys, values]).squeeze(2), 
+            keys_values
+        )
+        keys, values = keys_values[:batch_count, :, None], keys_values[batch_count:, :, None]
+
+        decoder_state = super().forward(keys, values, queries, attention_mask)
+        return decoder_state, keys_values
+
+
+class DecoderLayerTorch(nn.Module):
+    def __init__(self, 
+        image_token_count: int,
+        head_count: int, 
+        embed_count: int,
+        glu_embed_count: int
+    ):
+        super().__init__()
+        self.image_token_count = image_token_count
+        self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
+        self.self_attn = DecoderSelfAttentionTorch(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 = DecoderCrossAttentionTorch(head_count, embed_count)
+        self.encoder_attn_layer_norm = nn.LayerNorm(embed_count)
+        self.glu = GLUTorch(embed_count, glu_embed_count)
+
+    def forward(self,
+        decoder_state: FloatTensor,
+        encoder_state: FloatTensor,
+        keys_values_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)
+        self_attn_mask = torch.arange(self.image_token_count) < token_index + 1
+        self_attn_mask = torch.stack([self_attn_mask] * decoder_state.shape[0])
+        decoder_state = decoder_state.transpose(1, 2).unsqueeze(2)
+        # print("decoder_state", decoder_state.shape)
+        decoder_state, keys_values_state = self.self_attn.forward(
+            decoder_state,
+            keys_values_state,
+            self_attn_mask,
+            token_index
+        )
+        decoder_state = decoder_state.transpose(1, 3).squeeze(2)
+        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 = decoder_state.transpose(1, 2).unsqueeze(2)
+        decoder_state = self.encoder_attn.forward(
+            decoder_state,
+            encoder_state,
+            attention_mask
+        )
+        decoder_state = decoder_state.transpose(1, 3).squeeze(2)
+        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, keys_values_state
+
+
+class DalleBartDecoderTorch(nn.Module):
+    def __init__(self,
+        image_vocab_size: int,
+        image_token_count: int,
+        sample_token_count: int,
+        embed_count: int,
+        attention_head_count: int,
+        glu_embed_count: int,
+        layer_count: int,
+        batch_count: int,
+        start_token: int,
+        is_verbose: bool
+    ):
+        super().__init__()
+        self.is_verbose = is_verbose
+        self.layer_count = layer_count
+        self.sample_token_count = sample_token_count
+        self.start_token = torch.tensor([start_token]).to(torch.long)
+        self.pad_token = torch.tensor([1]).to(torch.long)
+        self.condition_factor = torch.tensor([10]).to(torch.float)
+        self.image_token_count = image_token_count
+        self.embed_tokens = nn.Embedding(image_vocab_size + 1, embed_count)
+        self.embed_positions = nn.Embedding(image_token_count, embed_count)
+        self.layers: List[DecoderLayerTorch] = nn.ModuleList([
+            DecoderLayerTorch(
+                image_token_count,
+                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_size + 1, bias=False)
+        self.keys_values_state_shape = (
+            layer_count * 2 * batch_count, 
+            embed_count,
+            image_token_count
+        )
+
+
+    def decode_step(self,
+        text_tokens: LongTensor,
+        encoder_state: FloatTensor,
+        keys_values_state: FloatTensor,
+        prev_token_and_index: LongTensor
+    ) -> Tuple[LongTensor, FloatTensor]:
+        attention_mask = text_tokens.not_equal(self.pad_token)
+        batch_count = encoder_state.shape[0]
+        prev_token = torch.cat([prev_token_and_index[:1]] * batch_count)
+        token_index = torch.cat([prev_token_and_index[1:]] * batch_count)
+        decoder_state = self.embed_tokens.forward(prev_token)
+        decoder_state += self.embed_positions.forward(token_index)
+        decoder_state = self.layernorm_embedding.forward(decoder_state)
+        decoder_state = decoder_state[:, None]          # (batch_count, 1, embed_count)
+        keys_values = []
+        for i, layer in enumerate(self.layers):
+            j1, j2 = i * 2 * batch_count, (i + 1) * 2 * batch_count
+            decoder_state, keys_values_layer = layer.forward(
+                decoder_state,
+                encoder_state,
+                keys_values_state[j1:j2],
+                attention_mask,
+                token_index[:1]
+            )
+            keys_values.append(keys_values_layer)
+        keys_values = torch.cat(keys_values, dim=0)
+        decoder_state = self.final_ln(decoder_state)    # (batch_count, 1, embed_count)
+        logits = self.lm_head(decoder_state)            # (batch_count, 1, vocab_size)
+        a = self.condition_factor
+        logits: FloatTensor = a * logits[0, -1] + (1 - a) * logits[1, -1]
+
+        top_logits = logits.sort(descending=True)[0][:50]
+        probs = torch.where(
+            logits < top_logits[-1],
+            torch.zeros([1]),
+            torch.exp(logits - top_logits[0])
+        )
+        return probs, keys_values
+
+
+    def forward(self,
+        text_tokens: LongTensor,
+        encoder_state: FloatTensor
+    ) -> LongTensor:
+        image_tokens: List[LongTensor] = []
+        keys_values_state = torch.zeros(self.keys_values_state_shape)
+        image_token = self.start_token
+        encoder_state = encoder_state.transpose(1, 2).unsqueeze(2)
+
+        for i in range(self.sample_token_count):
+            token_index = torch.tensor([i]).to(torch.long)
+            probs, keys_values_state = self.decode_step(
+                text_tokens = text_tokens,
+                encoder_state = encoder_state,
+                keys_values_state = keys_values_state,
+                prev_token_and_index = torch.cat([image_token, token_index])
+            )
+
+            image_token = torch.multinomial(probs, 1)
+            image_tokens += [image_token]
+        
+            if self.is_verbose:
+                token = int(image_token.detach().numpy())
+                print("image token {} is {}".format(i, token))
+            
+        return torch.cat(image_tokens)