79 lines
2.2 KiB
Python
79 lines
2.2 KiB
Python
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 |