139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
from typing import List, Optional
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from transformers import PretrainedConfig
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#TODO (ywang96): Remove this file and import it from
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# transformers once the new release with Chameleon support
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# is available.
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class ChameleonConfig(PretrainedConfig):
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model_type = "chameleon"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=65536,
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hidden_size=4096,
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intermediate_size=11008,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=32,
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hidden_act="silu",
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max_position_embeddings=4096,
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initializer_range=0.02,
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rms_norm_eps=1e-05,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_theta=10000.0,
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rope_scaling=None,
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attention_bias=False,
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attention_dropout=0.0,
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model_parallel_size=1,
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swin_norm=False,
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vq_config=None,
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vocabulary_map=None,
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mlp_bias=False,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.mlp_bias = mlp_bias
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.attention_bias = attention_bias
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self.attention_dropout = attention_dropout
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self.model_parallel_size = model_parallel_size
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self.swin_norm = swin_norm
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if vq_config is None:
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vq_config = {}
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self.vq_config = ChameleonVQVAEConfig(**vq_config)
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self.vocabulary_map = vocabulary_map
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling,
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dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with with two fields, "
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f"`type` and `factor`, got {self.rope_scaling}")
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in [
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"linear", "dynamic"
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]:
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raise ValueError(
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"`rope_scaling`'s type field must be one of ['linear', "
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f"'dynamic'], got {rope_scaling_type}")
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if rope_scaling_factor is None or not isinstance(
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rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(
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"`rope_scaling`'s factor field must be a float > 1, "
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f"got {rope_scaling_factor}")
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class ChameleonVQVAEConfig(PretrainedConfig):
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model_type = "chameleon_vqgan"
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def __init__(
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self,
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embed_dim: int = 256,
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num_embeddings: int = 8192,
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double_latent: bool = False,
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latent_channels: int = 256,
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resolution: int = 512,
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in_channels: int = 3,
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base_channels: int = 128,
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channel_multiplier: List[int] = [1, 1, 2, 2, 4], #noqa
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num_res_blocks: int = 2,
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attn_resolutions: Optional[List[int]] = None,
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dropout: float = 0.0,
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attn_type: str = "vanilla",
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.embed_dim = embed_dim
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self.num_embeddings = num_embeddings
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self.double_latent = double_latent
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self.latent_channels = latent_channels
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self.resolution = resolution
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self.in_channels = in_channels
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self.base_channels = base_channels
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self.channel_multiplier = channel_multiplier
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self.num_res_blocks = num_res_blocks
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self.attn_resolutions = attn_resolutions
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self.dropout = dropout
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self.attn_type = attn_type
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self.initializer_range = initializer_range
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