fixed relative imports

min_dalle/models/dalle_bart_decoder_flax.py

@@ -0,0 +1,288 @@
+import jax, flax
+from jax import lax, numpy as jnp
+from flax import linen as nn
+from typing import Tuple
+
+from .dalle_bart_encoder_flax import GLUFlax, AttentionFlax
+
+
+class DecoderCrossAttentionFlax(AttentionFlax):
+    def __call__(
+        self,
+        decoder_state: jnp.ndarray,
+        encoder_state: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+    ) -> jnp.ndarray:
+        keys: jnp.ndarray = self.k_proj(encoder_state)
+        values: jnp.ndarray = self.v_proj(encoder_state)
+        queries: jnp.ndarray = self.q_proj(decoder_state)
+        query_shape = queries.shape[:2] + (self.head_count, -1)
+        key_value_shape = keys.shape[:2] + (self.head_count, -1)
+        keys = keys.reshape(key_value_shape)
+        values = values.reshape(key_value_shape)
+        queries = queries.reshape(query_shape)
+        queries /= queries.shape[-1] ** 0.5
+        return self.forward(keys, values, queries, attention_mask)
+
+
+class DecoderSelfAttentionFlax(AttentionFlax):
+    def __call__(self,
+        decoder_state: jnp.ndarray,
+        keys_state: jnp.ndarray,
+        values_state: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        state_index: tuple
+    ) -> Tuple[jnp.ndarray, Tuple[jnp.ndarray, jnp.ndarray]]:
+        shape_split = decoder_state.shape[:2] + (self.head_count, -1)
+        keys_state = lax.dynamic_update_slice(
+            keys_state, 
+            self.k_proj(decoder_state).reshape(shape_split), 
+            state_index
+        )
+        values_state = lax.dynamic_update_slice(
+            values_state, 
+            self.v_proj(decoder_state).reshape(shape_split), 
+            state_index
+        )
+        queries = self.q_proj(decoder_state).reshape(shape_split)
+        queries /= queries.shape[-1] ** 0.5
+        decoder_state = self.forward(
+            keys_state, 
+            values_state, 
+            queries, 
+            attention_mask
+        )
+        return decoder_state, (keys_state, values_state)
+
+
+class DalleBartDecoderLayerFlax(nn.Module):
+    image_token_count: int
+    attention_head_count: int
+    embed_count: int
+    glu_embed_count: int
+
+    def setup(self):
+        self.pre_self_attn_layer_norm = nn.LayerNorm(use_scale=False)
+        self.self_attn = DecoderSelfAttentionFlax(
+            self.attention_head_count,
+            self.embed_count
+        )
+        self.self_attn_layer_norm = nn.LayerNorm()
+        self.pre_encoder_attn_layer_norm = nn.LayerNorm(use_scale=False)
+        self.encoder_attn = DecoderCrossAttentionFlax(
+            self.attention_head_count,
+            self.embed_count,
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm()
+        self.glu = GLUFlax(self.embed_count, self.glu_embed_count)
+
+    @nn.compact
+    def __call__(self,
+        decoder_state: jnp.ndarray,
+        encoder_state: jnp.ndarray,
+        keys_state: jnp.ndarray,
+        values_state: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        token_index: int
+    ) -> Tuple[jnp.ndarray, Tuple[jnp.ndarray, jnp.ndarray]]:
+        # Self Attention
+        residual = decoder_state
+        decoder_state = self.pre_self_attn_layer_norm(decoder_state)
+        self_attention_mask = jnp.tile(
+            jnp.arange(self.image_token_count) < token_index + 1, 
+            (decoder_state.shape[0], 1)
+        )
+        decoder_state, keys_values_state = self.self_attn(
+            decoder_state,
+            keys_state,
+            values_state,
+            self_attention_mask,
+            (0, token_index, 0, 0)
+        )
+        decoder_state = self.self_attn_layer_norm(decoder_state)
+        decoder_state = residual + decoder_state
+
+        # Cross Attention
+        residual = decoder_state
+        decoder_state = self.pre_encoder_attn_layer_norm(decoder_state)
+        decoder_state = self.encoder_attn(
+            decoder_state,
+            encoder_state,
+            attention_mask
+        )
+        decoder_state = self.encoder_attn_layer_norm(decoder_state)
+        decoder_state = residual + decoder_state
+
+        # Feed forward
+        residual = decoder_state
+        decoder_state = self.glu(decoder_state)
+        decoder_state = residual + decoder_state
+
+        return decoder_state, keys_values_state
+
+
+@flax.struct.dataclass
+class SampleState:
+    prev_token: jnp.ndarray
+    prng_key: jnp.ndarray
+    keys_state: jnp.ndarray
+    values_state: jnp.ndarray
+
+def super_conditioned(logits: jnp.ndarray, a: float) -> jnp.ndarray:
+    return a * logits[0, -1] + (1 - a) * logits[1, -1]
+
+def keep_top_k(logits: jnp.ndarray, k: int) -> jnp.ndarray:
+    top_logits, top_tokens = lax.top_k(logits, k)
+    suppressed = -jnp.inf * jnp.ones_like(logits)
+    return lax.select(logits < top_logits[-1], suppressed, logits)
+
+class DalleBartDecoderFlax(nn.Module):
+    image_token_count: int
+    text_token_count: int
+    image_vocab_count: int
+    attention_head_count: int
+    embed_count: int
+    glu_embed_count: int
+    layer_count: int
+    start_token: int
+
+    def setup(self):
+        self.embed_tokens = nn.Embed(
+            self.image_vocab_count + 1,
+            self.embed_count
+        )
+        self.embed_positions = nn.Embed(
+            self.image_token_count,
+            self.embed_count
+        )
+        self.layers = nn.scan(
+            DalleBartDecoderLayerFlax,
+            variable_axes = { "params": 0, "cache": 0 },
+            split_rngs = { "params": True },
+            in_axes = (nn.broadcast, 0, 0, nn.broadcast, nn.broadcast),
+            out_axes = (0, 0),
+            length=self.layer_count,
+        )(
+            self.image_token_count,
+            self.attention_head_count,
+            self.embed_count,
+            self.glu_embed_count, 
+            name="FlaxBartDecoderLayers"
+        )
+        self.layernorm_embedding = nn.LayerNorm()
+        self.final_ln = nn.LayerNorm(use_scale=False)
+        self.lm_head = nn.Dense(self.image_vocab_count + 1, use_bias=False)
+
+    def __call__(self,
+        encoder_state: jnp.ndarray,
+        keys_state: jnp.ndarray,
+        values_state: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        prev_token: int,
+        token_index: int
+    ) -> Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]:
+        batch_count = encoder_state.shape[0]
+        ones = jnp.ones((batch_count, 1), dtype=jnp.int32)
+        decoder_state = self.embed_tokens(prev_token * ones) 
+        decoder_state += self.embed_positions(token_index * ones)
+        decoder_state = self.layernorm_embedding(decoder_state)
+        decoder_state, (keys_state, values_state) = self.layers(
+            decoder_state,
+            encoder_state,
+            keys_state,
+            values_state,
+            attention_mask,
+            token_index
+        )
+        decoder_state = self.final_ln(decoder_state)
+        decoder_state = self.lm_head(decoder_state)
+        return decoder_state, keys_state, values_state
+
+    def compute_logits(self,
+        text_tokens: jnp.ndarray,
+        encoder_state: jnp.ndarray,
+        params: dict
+    ) -> jnp.ndarray:
+        batch_count = encoder_state.shape[0]
+        state_shape = (
+            self.layer_count, 
+            batch_count, 
+            self.image_token_count, 
+            self.attention_head_count, 
+            self.embed_count // self.attention_head_count
+        )
+        keys_state = jnp.zeros(state_shape)
+        values_state = jnp.zeros(state_shape)
+           
+        logits, _, _ = self.apply(
+            { 'params': params },
+            encoder_state = encoder_state,
+            keys_state = keys_state,
+            values_state = values_state,
+            attention_mask = jnp.not_equal(text_tokens, 1),
+            prev_token = self.start_token,
+            token_index = 0
+        )
+
+        return super_conditioned(logits, 10.0)
+
+    def sample_image_tokens(self,
+        text_tokens: jnp.ndarray,
+        encoder_state: jnp.ndarray,
+        prng_key: jax.random.PRNGKey,
+        params: dict
+    ) -> jnp.ndarray:
+        attention_mask = jnp.not_equal(text_tokens, 1)
+        
+        def sample_next_image_token(
+            state: SampleState,
+            token_index: int
+        ) -> Tuple[SampleState, None]:
+            logits, keys_state, values_state = self.apply(
+                { 'params': params },
+                encoder_state = encoder_state,
+                keys_state = state.keys_state,
+                values_state = state.values_state,
+                attention_mask = attention_mask,
+                prev_token = state.prev_token,
+                token_index = token_index
+            )
+            
+            logits = super_conditioned(logits, 10.0)
+            logits = keep_top_k(logits, k=50)
+
+            prng_key, prng_key_next = jax.random.split(state.prng_key)
+            next_token = jax.random.categorical(prng_key, logits, axis=-1)
+
+            state = SampleState(
+                prev_token = next_token,
+                prng_key = prng_key_next,
+                keys_state = keys_state,
+                values_state = values_state
+            )
+
+            return state, next_token
+
+        batch_count = encoder_state.shape[0]
+        state_shape = (
+            self.layer_count, 
+            batch_count, 
+            self.image_token_count, 
+            self.attention_head_count, 
+            self.embed_count // self.attention_head_count
+        )
+
+        initial_state = SampleState(
+            prev_token = self.start_token,
+            prng_key = prng_key,
+            keys_state = jnp.zeros(state_shape),
+            values_state = jnp.zeros(state_shape)
+        )
+
+        _, image_tokens = lax.scan(
+            sample_next_image_token,
+            initial_state,
+            jnp.arange(self.image_token_count)
+        )       
+
+        return image_tokens

min_dalle/models/dalle_bart_decoder_torch.py

@@ -0,0 +1,214 @@
+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)

min_dalle/models/dalle_bart_encoder_flax.py

@@ -0,0 +1,143 @@
+from functools import partial
+import jax
+from jax import lax, numpy as jnp
+from flax import linen as nn
+
+
+class GLUFlax(nn.Module):
+    count_in_out: int
+    count_middle: int
+
+    def setup(self):
+        self.gelu = partial(nn.gelu, approximate=False)
+        self.ln0 = nn.LayerNorm(use_scale=False)
+        self.ln1 = nn.LayerNorm(use_scale=False)
+        self.fc0 = nn.Dense(self.count_middle, use_bias=False)
+        self.fc1 = nn.Dense(self.count_middle, use_bias=False)
+        self.fc2 = nn.Dense(self.count_in_out, use_bias=False)
+
+    @nn.compact
+    def __call__(self, z: jnp.ndarray) -> jnp.ndarray:
+        z = self.ln0(z)
+        z = self.ln1(self.gelu(self.fc0(z)) * self.fc1(z))
+        z = self.fc2(z)
+        return z
+
+class AttentionFlax(nn.Module):
+    head_count: int
+    embed_count: int
+
+    def setup(self):
+        self.q_proj = nn.Dense(self.embed_count, use_bias=False)
+        self.k_proj = nn.Dense(self.embed_count, use_bias=False)
+        self.v_proj = nn.Dense(self.embed_count, use_bias=False)
+        self.out_proj = nn.Dense(self.embed_count, use_bias=False)
+
+    def forward(self,
+        keys: jnp.ndarray,
+        values: jnp.ndarray,
+        queries: jnp.ndarray,
+        attention_mask: jnp.ndarray
+    ) -> jnp.ndarray:
+        attention_bias: jnp.ndarray = lax.select(
+            attention_mask,
+            jnp.full(attention_mask.shape, 0.0),
+            jnp.full(attention_mask.shape, -jnp.inf),
+        )
+        attention_weights: jnp.ndarray = jnp.einsum(
+            'bqhd,bkhd->bhqk', 
+            queries, 
+            keys
+        )
+        attention_weights += attention_bias[:, None, None, :]
+        attention_weights = jax.nn.softmax(attention_weights)
+        attention_output: jnp.ndarray = jnp.einsum(
+            "bhqk,bkhd->bqhd", 
+            attention_weights, 
+            values
+        )
+        shape = attention_output.shape[:2] + (self.embed_count,)
+        attention_output = attention_output.reshape(shape)
+        attention_output = self.out_proj(attention_output)
+        return attention_output
+
+class EncoderSelfAttentionFlax(AttentionFlax):
+    def __call__(
+        self,
+        encoder_state: jnp.ndarray,
+        attention_mask: jnp.ndarray
+    ) -> jnp.ndarray:
+        shape_split = encoder_state.shape[:2] + (self.head_count, -1)
+        keys = self.k_proj(encoder_state).reshape(shape_split)
+        values = self.v_proj(encoder_state).reshape(shape_split)
+        queries = self.q_proj(encoder_state).reshape(shape_split)
+        queries /= queries.shape[-1] ** 0.5
+        return self.forward(keys, values, queries, attention_mask)
+
+class DalleBartEncoderLayerFlax(nn.Module):
+    attention_head_count: int
+    embed_count: int
+    glu_embed_count: int
+
+    def setup(self):
+        self.pre_self_attn_layer_norm = nn.LayerNorm(use_scale=False)
+        self.self_attn = EncoderSelfAttentionFlax(
+            self.attention_head_count,
+            self.embed_count
+        )
+        self.self_attn_layer_norm = nn.LayerNorm()
+        self.glu = GLUFlax(self.embed_count, self.glu_embed_count)
+
+    @nn.compact
+    def __call__(self,
+        encoder_state: jnp.ndarray,
+        attention_mask: jnp.ndarray
+    ) -> jnp.ndarray:
+        residual = encoder_state
+        encoder_state = self.pre_self_attn_layer_norm(encoder_state)
+        encoder_state = self.self_attn(encoder_state, attention_mask)
+        encoder_state = self.self_attn_layer_norm(encoder_state)
+        encoder_state = residual + encoder_state
+        residual = encoder_state
+        encoder_state = self.glu(encoder_state)
+        encoder_state = residual + encoder_state
+        return encoder_state, None
+
+class DalleBartEncoderFlax(nn.Module):
+    attention_head_count: int
+    embed_count: int
+    glu_embed_count: int
+    text_token_count: int
+    text_vocab_count: int
+    layer_count: int
+
+    def setup(self):
+        self.embed_tokens = nn.Embed(self.text_vocab_count, self.embed_count)
+        self.embed_positions = nn.Embed(self.text_token_count, self.embed_count)
+        self.layers = nn.scan(
+            DalleBartEncoderLayerFlax,
+            variable_axes = { "params": 0, "cache": 0 },
+            split_rngs = { "params": True },
+            in_axes = nn.broadcast,
+            length = self.layer_count
+        )(
+            self.attention_head_count,
+            self.embed_count,
+            self.glu_embed_count, 
+            name="FlaxBartEncoderLayers"
+        )
+        self.layernorm_embedding = nn.LayerNorm()
+        self.final_ln = nn.LayerNorm(use_scale=False)
+
+    def __call__(self, text_tokens: jnp.ndarray) -> jnp.ndarray:
+        batch_count, token_count = text_tokens.shape
+        pose_tokens = jnp.tile(jnp.arange(token_count), (batch_count, 1))
+        attention_mask = jnp.not_equal(text_tokens, 1)
+        encoder_state = (
+            self.embed_tokens(text_tokens) +
+            self.embed_positions(pose_tokens)
+        )
+        encoder_state = self.layernorm_embedding(encoder_state)
+        encoder_state, _ = self.layers(encoder_state, attention_mask)
+        encoder_state = self.final_ln(encoder_state)
+        return encoder_state

min_dalle/models/dalle_bart_encoder_torch.py

@@ -0,0 +1,159 @@
+from typing import List
+import torch
+from torch import nn, BoolTensor, FloatTensor, LongTensor
+
+class GLUTorch(nn.Module):
+    def __init__(self, count_in_out, count_middle):
+        super().__init__()
+        self.gelu = nn.GELU()
+        self.ln0 = nn.LayerNorm(count_in_out)
+        self.ln1 = nn.LayerNorm(count_middle)
+        self.fc0 = nn.Linear(count_in_out, count_middle, bias=False)
+        self.fc1 = nn.Linear(count_in_out, count_middle, bias=False)
+        self.fc2 = nn.Linear(count_middle, count_in_out, bias=False)
+    
+    def forward(self, z: FloatTensor) -> FloatTensor:
+        z = self.ln0.forward(z)
+        w = self.fc0.forward(z)
+        w = self.gelu.forward(w)
+        v = self.fc1.forward(z)
+        z = self.ln1.forward(w * v)
+        z = self.fc2.forward(z)
+        return z
+
+class AttentionTorch(nn.Module):
+    def __init__(self, head_count: int, embed_count: int):
+        super().__init__()
+        self.head_count = head_count
+        self.embed_count = embed_count
+        self.head_dim = embed_count // head_count
+
+        self.k_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
+        self.v_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
+        self.q_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
+        self.out_proj = nn.Conv2d(embed_count, embed_count, 1, bias=False)
+    
+    def forward(self,
+        keys: FloatTensor,
+        values: FloatTensor,
+        queries: FloatTensor,
+        attention_mask: BoolTensor
+    ) -> FloatTensor:
+        batch_count = keys.shape[0]
+
+        # b(hc)1q -> bqhc
+        # print(keys.shape, "keys", values.shape, "values", queries.shape, "queries")
+        keys = keys.transpose(1, 3)
+        keys = keys.reshape(keys.shape[:2] + (self.head_count, -1))
+
+        # b(hc)1q -> bchq
+        shape = (batch_count, self.head_count, self.head_dim, -1)
+        values = values.reshape(shape)
+        values = values.transpose(1, 2)
+        queries = queries.reshape(shape)
+        queries = queries.transpose(1, 2)
+
+        # print(keys.shape, "keys", values.shape, "values", queries.shape, "queries")
+
+        attention_bias = torch.where(
+            attention_mask,
+            torch.zeros([1, 1]),
+            torch.ones([1, 1]) * (-torch.inf),
+        )
+        attention_weights: FloatTensor = torch.einsum(
+            'bchq,bkhc->bkhq',
+            queries / self.head_dim ** 0.5, 
+            keys
+        )
+        attention_weights += attention_bias[:, :, None, None]
+        attention_weights = torch.softmax(attention_weights, 1)
+        # print(attention_weights.shape, "attention_weights")
+        hidden_state: FloatTensor = torch.einsum(
+            "bkhq,bchk->bchq",
+            attention_weights, 
+            values
+        )
+        # bchq -> b(hc)1q
+        # print(hidden_state.shape, "hidden_state")
+        hidden_state = hidden_state.transpose(1, 2)
+        hidden_state = hidden_state.reshape(batch_count, self.embed_count, 1, -1)
+        hidden_state = self.out_proj.forward(hidden_state)
+        # print(hidden_state.shape, "hidden_state")
+        return hidden_state
+
+
+class EncoderSelfAttentionTorch(AttentionTorch):
+    def forward(
+        self,
+        encoder_state: FloatTensor,
+        attention_mask: BoolTensor
+    ) -> FloatTensor:
+        encoder_state = encoder_state.transpose(1, 2).unsqueeze(2)
+        # print(encoder_state.shape, "encoder_state")
+        keys = self.k_proj.forward(encoder_state)
+        values = self.v_proj.forward(encoder_state)
+        queries = self.q_proj.forward(encoder_state)
+        return super().forward(keys, values, queries, attention_mask)
+
+
+class EncoderLayerTorch(nn.Module):
+    def __init__(self, embed_count: int, head_count: int, glu_embed_count: int):
+        super().__init__()
+        self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
+        self.self_attn = EncoderSelfAttentionTorch(head_count, embed_count)
+        self.self_attn_layer_norm = nn.LayerNorm(embed_count)
+        self.glu = GLUTorch(embed_count, glu_embed_count)
+    
+    def forward(
+        self,
+        encoder_state: FloatTensor,
+        attention_mask: BoolTensor
+    ) -> FloatTensor:
+        residual = encoder_state
+        encoder_state = self.pre_self_attn_layer_norm.forward(encoder_state)
+        encoder_state = self.self_attn.forward(encoder_state, attention_mask)
+        encoder_state = encoder_state.transpose(1, 3).squeeze(2)
+        encoder_state = self.self_attn_layer_norm.forward(encoder_state)
+        encoder_state = residual + encoder_state
+        residual = encoder_state
+        encoder_state = self.glu.forward(encoder_state)
+        encoder_state = residual + encoder_state
+        return encoder_state
+
+
+class DalleBartEncoderTorch(nn.Module):
+    def __init__(self,
+        layer_count: int,
+        embed_count: int,
+        attention_head_count: int,
+        text_vocab_count: int,
+        text_token_count: int,
+        glu_embed_count: int
+    ):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(text_vocab_count, embed_count)
+        self.embed_positions = nn.Embedding(text_token_count, embed_count)
+        self.layers: List[EncoderLayerTorch] = nn.ModuleList([
+            EncoderLayerTorch(
+                embed_count = embed_count,
+                head_count = attention_head_count,
+                glu_embed_count = glu_embed_count
+            ) 
+            for _ in range(layer_count)
+        ])
+        self.layernorm_embedding = nn.LayerNorm(embed_count)
+        self.final_ln = nn.LayerNorm(embed_count)
+
+    def forward(self, text_tokens: LongTensor) -> FloatTensor:
+        attention_mask = text_tokens.not_equal(1)
+        batch_count, token_count = text_tokens.shape
+        pose_tokens = torch.stack([torch.arange(token_count)] * batch_count)
+        encoder_state = (
+            self.embed_tokens.forward(text_tokens) +
+            self.embed_positions.forward(pose_tokens)
+        )
+        encoder_state = self.layernorm_embedding.forward(encoder_state)
+        for layer in self.layers:
+            encoder_state = layer.forward(encoder_state, attention_mask)
+        encoder_state = self.final_ln.forward(encoder_state)
+        return encoder_state

min_dalle/models/vqgan_detokenizer.py

@@ -0,0 +1,175 @@
+import torch
+from torch import Tensor
+from torch.nn import Module, ModuleList, GroupNorm, Conv2d, Embedding
+
+batch_size: int = 1
+
+class ResnetBlock(Module):
+    def __init__(self, log2_count_in: int, log2_count_out: int):
+        super().__init__()
+        m, n = 2 ** log2_count_in, 2 ** log2_count_out
+        self.is_middle = m == n
+        self.norm1 = GroupNorm(2 ** 5, m)
+        self.conv1 = Conv2d(m, n, 3, padding=1)
+        self.norm2 = GroupNorm(2 ** 5, n)
+        self.conv2 = Conv2d(n, n, 3, padding=1)
+        if not self.is_middle:
+            self.nin_shortcut = Conv2d(m, n, 1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        h = x
+        h = self.norm1.forward(h)
+        h *= torch.sigmoid(h)
+        h = self.conv1.forward(h)
+        h = self.norm2.forward(h)
+        h *= torch.sigmoid(h)
+        h = self.conv2(h)
+        if not self.is_middle:
+            x = self.nin_shortcut.forward(x)
+        return x + h
+
+
+class AttentionBlock(Module):
+    def __init__(self):
+        super().__init__()
+        n = 2 ** 9
+        self.norm = GroupNorm(2 ** 5, n)
+        self.q = Conv2d(n, n, 1)
+        self.k = Conv2d(n, n, 1)
+        self.v = Conv2d(n, n, 1)
+        self.proj_out = Conv2d(n, n, 1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        n = 2 ** 9
+        h = x
+        h = self.norm(h)
+        q = self.q.forward(h)
+        k = self.k.forward(h)
+        v = self.v.forward(h)
+        q = q.reshape(batch_size, n, 2 ** 8)
+        q = q.permute(0, 2, 1)
+        k = k.reshape(batch_size, n, 2 ** 8)
+        w = torch.bmm(q, k)
+        w /= n ** 0.5
+        w = torch.softmax(w, dim=2)
+        v = v.reshape(batch_size, n, 2 ** 8)
+        w = w.permute(0, 2, 1)
+        h = torch.bmm(v, w)
+        h = h.reshape(batch_size, n, 2 ** 4, 2 ** 4)
+        h = self.proj_out.forward(h)
+        return x + h
+
+class MiddleLayer(Module):
+    def __init__(self):
+        super().__init__()
+        self.block_1 = ResnetBlock(9, 9)
+        self.attn_1 = AttentionBlock()
+        self.block_2 = ResnetBlock(9, 9)
+    
+    def forward(self, h: Tensor) -> Tensor:
+        h = self.block_1.forward(h)
+        h = self.attn_1.forward(h)
+        h = self.block_2.forward(h)
+        return h
+
+class Upsample(Module):
+    def __init__(self, log2_count):
+        super().__init__()
+        n = 2 ** log2_count
+        self.upsample = torch.nn.UpsamplingNearest2d(scale_factor=2)
+        self.conv = Conv2d(n, n, 3, padding=1)
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = self.upsample.forward(x)
+        x = self.conv.forward(x)
+        return x
+
+class UpsampleBlock(Module):
+    def __init__(
+        self, 
+        log2_count_in: int, 
+        log2_count_out: int, 
+        has_attention: bool, 
+        has_upsample: bool
+    ):
+        super().__init__()
+        self.has_attention = has_attention
+        self.has_upsample = has_upsample
+        self.block = ModuleList([
+            ResnetBlock(log2_count_in, log2_count_out),
+            ResnetBlock(log2_count_out, log2_count_out),
+            ResnetBlock(log2_count_out, log2_count_out)
+        ])
+        if has_attention:
+            self.attn = ModuleList([
+                AttentionBlock(),
+                AttentionBlock(),
+                AttentionBlock()
+            ])
+        else:
+            self.attn = ModuleList()
+
+        if has_upsample:
+            self.upsample = Upsample(log2_count_out)
+
+
+    def forward(self, h: Tensor) -> Tensor:
+        for j in range(3):
+            h = self.block[j].forward(h)
+            if self.has_attention:
+                h = self.attn[j].forward(h)
+        if self.has_upsample:
+            h = self.upsample.forward(h)
+        return h
+
+class Decoder(Module):
+    def __init__(self):
+        super().__init__()
+
+        self.conv_in = Conv2d(2 ** 8, 2 ** 9, 3, padding=1)
+        self.mid = MiddleLayer()
+
+        self.up = ModuleList([
+            UpsampleBlock(7, 7, False, False),
+            UpsampleBlock(8, 7, False, True),
+            UpsampleBlock(8, 8, False, True),
+            UpsampleBlock(9, 8, False, True),
+            UpsampleBlock(9, 9, True, True)
+        ])
+
+        self.norm_out = GroupNorm(2 ** 5, 2 ** 7)
+        self.conv_out = Conv2d(2 ** 7, 3, 3, padding=1)
+
+    def forward(self, z: Tensor) -> Tensor:
+        z = self.conv_in.forward(z)
+        z = self.mid.forward(z)
+
+        for i in reversed(range(5)):
+            z = self.up[i].forward(z)
+
+        z = self.norm_out.forward(z)
+        z *= torch.sigmoid(z)
+        z = self.conv_out.forward(z)
+        return z
+
+class VQGanDetokenizer(Module):
+    def __init__(self):
+        super().__init__()
+        m, n = 2 ** 14, 2 ** 8
+        self.embedding = Embedding(m, n)
+        self.post_quant_conv = Conv2d(n, n, 1)
+        self.decoder = Decoder()
+
+    def forward(self, z: Tensor) -> Tensor:
+        z = self.embedding.forward(z)
+        z = z.view((batch_size, 2 ** 4, 2 ** 4, 2 ** 8))
+        z = z.permute(0, 3, 1, 2).contiguous()
+        z = self.post_quant_conv.forward(z)
+        z = self.decoder.forward(z)
+        z = z.permute(0, 2, 3, 1)
+        # z = torch.concat((
+        #     torch.concat((z[0], z[1]), axis=1),
+        #     torch.concat((z[2], z[3]), axis=1)
+        # ), axis=0)
+        z = z.clip(0.0, 1.0) * 255
+        return z[0]