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from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_rope_utils import rope_config_validation
from transformers.utils import logging
logger = logging.get_logger(__name__)
class CoDAConfig(PretrainedConfig):
model_type = "CoDA"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=151936,
head_dim=128,
hidden_act="silu",
hidden_size=2048,
intermediate_size=6144,
num_attention_heads=16,
num_hidden_layers=28,
num_key_value_heads=8,
max_position_embeddings=40960,
initializer_range=0.02,
use_cache=True,
use_sliding_window=False,
tie_word_embeddings=True,
rms_norm_eps=1e-6,
rope_scaling=None,
rope_theta=1000000,
sliding_window=None,
max_window_layers=28,
attention_bias=False,
attention_dropout=0.0,
bos_token_id=151643,
eos_token_id=151645,
pad_token_id=151643,
mask_token_id=151669,
attention_kernel="flash_attention",
prefix_probability=0,
truncate_probability=0,
block_masking_probability=[0.25, 0.5, 0.5, 0.75, 0.25],
mask_block_sizes=[4, 8, 16, 32],
sampling_eps=[0.001, 0.25, 0.5, 0.25, 0.001], # minimum noise level
**kwargs,
):
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.use_sliding_window = use_sliding_window
self.sliding_window = sliding_window if use_sliding_window else None
self.max_window_layers = max_window_layers
# for backward compatibility
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
self.num_key_value_heads = num_key_value_heads
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.rms_norm_eps = rms_norm_eps
self.use_cache = use_cache
self.rope_theta = rope_theta
self.rope_scaling = rope_scaling
self.attention_dropout = attention_dropout
# Validate the correctness of rotary position embeddings parameters
# BC: if there is a 'type' field, move it to 'rope_type'.
if self.rope_scaling is not None and "type" in self.rope_scaling:
self.rope_scaling["rope_type"] = self.rope_scaling["type"]
rope_config_validation(self)
self.head_dim = head_dim
self.attention_bias = attention_bias
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.attention_kernel = attention_kernel
self.prefix_probability = prefix_probability
self.truncate_probability = truncate_probability
self.block_masking_probability = block_masking_probability
self.mask_block_sizes = mask_block_sizes
self.sampling_eps = sampling_eps
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
self.mask_token_id = mask_token_id
self.pad_token_id = pad_token_id
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