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