File size: 4,335 Bytes
b24b632 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 | from typing import Optional
from transformers.modeling_rope_utils import rope_config_validation
from transformers.configuration_utils import layer_type_validation
from transformers.utils import logging
from transformers import PretrainedConfig
import transformers.configuration_utils as configuration_util
logger = logging.get_logger(__name__)
class PMNetConfig(PretrainedConfig):
model_type = "PMNet"
keys_to_ignore_at_inference = ["past_key_values"]
base_model_tp_plan = {
"layers.*.self_attn.q_proj": "colwise",
"layers.*.self_attn.k_proj": "colwise",
"layers.*.self_attn.v_proj": "colwise",
"layers.*.self_attn.o_proj": "rowwise",
"layers.*.mlp.gate_proj": "colwise",
"layers.*.mlp.up_proj": "colwise",
"layers.*.mlp.down_proj": "rowwise",
}
base_model_pp_plan = {
"embed_tokens": (["input_ids"], ["inputs_embeds"]),
"layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
"norm": (["hidden_states"], ["hidden_states"]),
}
def __init__(
self,
vocab_size: Optional[int] = 151936,
hidden_size: Optional[int] = 4096,
intermediate_size: Optional[int] = 22016,
num_hidden_layers: Optional[int] = 32,
num_attention_heads: Optional[int] = 32,
num_key_value_heads: Optional[int] = 32,
head_dim: Optional[int] = 128,
memory_size: Optional[int] = 64,
num_memory: Optional[int] = 32,
num_memory_read_heads: Optional[int] = 8,
memory_write_period: Optional[int] = 4,
hidden_act: Optional[str] = "silu",
max_position_embeddings: Optional[int] = 32768,
initializer_range: Optional[float] = 0.02,
rms_norm_eps: Optional[int] = 1e-6,
use_cache: Optional[bool] = True,
tie_word_embeddings: Optional[bool] = False,
rope_theta=10000.0,
rope_scaling=None,
attention_bias: Optional[bool] = False,
use_sliding_window: Optional[bool] = False,
sliding_window: Optional[int] = 4096,
max_window_layers: Optional[int] = 28,
layer_types: Optional[list[str]] = None,
attention_dropout: Optional[float] = 0.0,
memory_cumsum: bool = True,
**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 self.use_sliding_window else None
self.max_window_layers = max_window_layers
self.memory_size = memory_size
self.num_memory = num_memory
self.num_memory_read_heads = num_memory_read_heads
self.memory_write_period = memory_write_period
# 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.head_dim = head_dim
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_bias = attention_bias
self.attention_dropout = attention_dropout
self.memory_cumsum = memory_cumsum
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.layer_types = layer_types
if self.layer_types is None:
self.layer_types = [
(
"sliding_attention"
if self.sliding_window is not None and i >= self.max_window_layers
else "full_attention"
)
for i in range(self.num_hidden_layers)
]
layer_type_validation(self.layer_types, self.num_hidden_layers)
super().__init__(
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
__all__ = ["PMNetConfig"]
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