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# -*- coding: utf-8 -*-
from typing import Optional
from transformers.configuration_utils import PretrainedConfig
class MTPTransformerConfig(PretrainedConfig):
model_type = 'transformer'
keys_to_ignore_at_inference = ['past_key_values']
def __init__(
self,
hidden_size: int = 2048,
num_hidden_layers: int = 24,
num_heads: int = 32,
num_kv_heads: int = None,
qkv_bias: bool = False,
qk_norm: bool = False,
window_size: Optional[int] = None,
rope_theta: Optional[float] = 10000.,
max_position_embeddings: int = 2048,
hidden_ratio: Optional[int] = 4,
intermediate_size: Optional[int] = None,
hidden_act: str = "swish",
initializer_range: float = 0.006,
elementwise_affine: Optional[bool] = True,
norm_eps: float = 1e-6,
use_cache: bool = True,
pad_token_id: int = None,
bos_token_id: int = 1,
eos_token_id: int = 2,
tie_word_embeddings: bool = False,
fuse_norm: bool = True,
fuse_swiglu: bool = True,
fuse_cross_entropy: bool = True,
vocab_size: int = 32000,
**kwargs,
):
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_heads = num_heads
self.num_kv_heads = num_kv_heads
self.qkv_bias = qkv_bias
self.qk_norm = qk_norm
self.window_size = window_size
self.rope_theta = rope_theta
self.max_position_embeddings = max_position_embeddings
self.hidden_ratio = hidden_ratio
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.elementwise_affine = elementwise_affine
self.norm_eps = norm_eps
self.use_cache = use_cache
self.fuse_norm = fuse_norm
self.fuse_swiglu = fuse_swiglu
self.fuse_cross_entropy = fuse_cross_entropy
self.vocab_size = vocab_size
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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