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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import math
from typing import Any, Dict, List, Optional, Union
from transformers import PretrainedConfig
class MixFormerSequentialConfig(PretrainedConfig):
"""MixFormer (sequential for DeepSpeed) configuration."""
model_type = "mixformer-sequential"
attribute_map = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
"input_emb_layer": "embd_layer", # `input_emb_layer` key is for backward compatibility
"blocks": "architecture", # `blocks` key is for backward compatibility
}
def __init__(
self,
vocab_size: Optional[int] = 50304,
n_positions: Optional[int] = 2048,
n_embd: Optional[int] = 1024,
n_layer: Optional[int] = 20,
n_inner: Optional[int] = None,
n_head: Optional[int] = 16,
rotary_dim: Optional[int] = 32,
activation_function: Optional[str] = "gelu_new",
embd_layer: Optional[str] = "default",
architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
embd_pdrop: Optional[float] = 0.0,
resid_pdrop: Optional[float] = 0.0,
layer_norm_epsilon: Optional[float] = 1e-5,
initializer_range: Optional[float] = 0.02,
tie_word_embeddings: Optional[bool] = False,
pad_vocab_size_multiple: Optional[int] = 64,
**kwargs
) -> None:
self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_inner = n_inner
self.n_head = n_head
self.rotary_dim = min(rotary_dim, n_embd // n_head)
self.activation_function = activation_function
self.embd_layer = embd_layer
self.architecture = architecture
self.embd_pdrop = embd_pdrop
self.resid_pdrop = resid_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
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