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79 lines
2.2 KiB
79 lines
2.2 KiB
import jax |
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from jax import numpy as jnp |
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import numpy |
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from .models.dalle_bart_encoder_flax import DalleBartEncoderFlax |
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from .models.dalle_bart_decoder_flax import DalleBartDecoderFlax |
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def encode_flax( |
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text_tokens: numpy.ndarray, |
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config: dict, |
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params: dict |
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) -> jnp.ndarray: |
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print("loading flax encoder") |
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encoder: DalleBartEncoderFlax = DalleBartEncoderFlax( |
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attention_head_count = config['encoder_attention_heads'], |
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embed_count = config['d_model'], |
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glu_embed_count = config['encoder_ffn_dim'], |
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text_token_count = config['max_text_length'], |
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text_vocab_count = config['encoder_vocab_size'], |
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layer_count = config['encoder_layers'] |
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).bind({'params': params.pop('encoder')}) |
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print("encoding text tokens") |
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encoder_state = encoder(text_tokens) |
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del encoder |
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return encoder_state |
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def decode_flax( |
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text_tokens: jnp.ndarray, |
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encoder_state: jnp.ndarray, |
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config: dict, |
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seed: int, |
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params: dict |
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) -> jnp.ndarray: |
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print("loading flax decoder") |
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decoder = DalleBartDecoderFlax( |
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image_token_count = config['image_length'], |
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text_token_count = config['max_text_length'], |
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image_vocab_count = config['image_vocab_size'], |
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attention_head_count = config['decoder_attention_heads'], |
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embed_count = config['d_model'], |
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glu_embed_count = config['decoder_ffn_dim'], |
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layer_count = config['decoder_layers'], |
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start_token = config['decoder_start_token_id'] |
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) |
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print("sampling image tokens") |
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image_tokens = decoder.sample_image_tokens( |
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text_tokens, |
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encoder_state, |
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jax.random.PRNGKey(seed), |
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params.pop('decoder') |
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) |
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del decoder |
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return image_tokens |
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def generate_image_tokens_flax( |
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text_tokens: numpy.ndarray, |
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seed: int, |
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config: dict, |
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params: dict |
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) -> numpy.ndarray: |
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encoder_state = encode_flax( |
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text_tokens, |
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config, |
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params |
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) |
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image_tokens = decode_flax( |
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text_tokens, |
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encoder_state, |
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config, |
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seed, |
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params |
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) |
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image_tokens = numpy.array(image_tokens) |
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print("image tokens", list(image_tokens)) |
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return image_tokens |