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from transformers import PretrainedConfig
class GraniteConfig(PretrainedConfig):
model_type = "granite"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size: int = 50257,
n_positions: int = 1024,
n_embd: int = 768,
n_layer: int = 12,
n_head: int = 12,
num_key_value_heads: int = None,
n_inner: int = None,
activation_function: str = "gelu_pytorch_tanh",
attention_head_type: str = "mqa",
resid_pdrop: float = 0.1,
embd_pdrop: float = 0.1,
attn_pdrop: float = 0.1,
normalization_function: str = "layernorm",
layer_norm_epsilon: float = 1e-5,
initializer_range: float = 0.02,
scale_attn_weights: bool = True,
attention_multiplier: float = None,
use_cache: bool = True,
bos_token_id: int = 50256,
eos_token_id: int = 50256,
pad_token_id: int = 50256,
attention_softmax_in_fp32: bool = True,
scale_attention_softmax_in_fp32: bool = True,
add_bias: bool = True,
position_embedding_type: str = "learned_absolute",
rope_theta: int = 10000,
**kwargs,
) -> None:
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.num_key_value_heads = num_key_value_heads
self.n_inner = 4 * n_embd if n_inner is None else n_inner
self.activation_function = activation_function
self.attention_head_type = attention_head_type
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.normalization_function = normalization_function
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.attention_multiplier = attention_multiplier
self.use_cache = use_cache
self.attention_softmax_in_fp32 = attention_softmax_in_fp32
self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
self.position_embedding_type = position_embedding_type
self.add_bias = add_bias
self.rope_theta = rope_theta
if self.attention_multiplier is not None:
assert self.scale_attn_weights
# for compatibility with some features
self.multi_query = attention_head_type == "mqa"
if attention_head_type == "mha":
if self.num_key_value_heads is None:
self.num_key_value_heads = self.n_head
assert (
self.n_head == self.num_key_value_heads
), "MultiHeadAttention should have same number of heads for query, keys and values"
elif attention_head_type == "mqa":
if self.num_key_value_heads is None:
self.num_key_value_heads = 1
assert self.num_key_value_heads == 1, "MultiQueryAttention should have 1 head for keys and values"
elif attention_head_type == "gqa":
assert (
self.num_key_value_heads is not None
), "`num_key_value_heads` needs to be specified with GroupedQueryAttention"
assert (
self.n_head % self.num_key_value_heads == 0
), "GroupedQueryAttention should have more than 1 head for keys and values"
else:
raise ValueError(f"unexpected attention_head_type ({attention_head_type})")
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
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