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22510af | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | from __future__ import annotations
import math
from typing import Any, Iterable, TYPE_CHECKING
import torch
if TYPE_CHECKING:
from torch import Tensor
from .base import ModelBase, TextModel, gguf
@ModelBase.register("DeciLMForCausalLM")
class DeciModel(TextModel):
model_arch = gguf.MODEL_ARCH.DECI
@staticmethod
def _ffn_mult_to_intermediate_size(ffn_mult: float, n_embd: int) -> int:
# DeciLM-specific code
intermediate_size = int(2 * ffn_mult * n_embd / 3)
return DeciModel._find_multiple(intermediate_size, 256)
@staticmethod
def _find_multiple(n: int, k: int) -> int:
# DeciLM-specific code
if n % k == 0:
return n
return n + k - (n % k)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
_block_configs: list[dict[str,Any]] = self.hparams["block_configs"]
assert self.block_count == len(_block_configs)
self._num_kv_heads = list()
self._num_heads = list()
_ffn_multipliers = list()
# ***linear attention layer***
# if n_heads_in_group is None and replace_with_linear is True
# then _num_kv_heads[il] is 0 and _num_heads[il] is num_attention_heads
# ***attention-free layer***
# if n_heads_in_group is None and replace_with_linear is False
# then _num_kv_heads[il] is 0 and _num_heads[il] is 0
# ***normal attention-layer***
# if n_heads_in_group is not None, then
# _num_kv_heads[il] is num_attention_head // n_heads_in_group and
# _num_heads[il] is num_attention_head
# ***dummy layer*** for nemotron 253B
# if n_heads_in_group is None and ffn_mult is None
# then _num_kv_heads[il] is 0 and _num_heads[il] is 0 and _ffn_dims is 0
for il in range(len(_block_configs)):
if _block_configs[il]["attention"]["n_heads_in_group"] is None:
if _block_configs[il]["attention"]["replace_with_linear"] is True:
self._num_kv_heads.append(0)
self._num_heads.append(self.hparams["num_attention_heads"])
else:
self._num_kv_heads.append(0)
self._num_heads.append(0)
else:
self._num_kv_heads.append(self.hparams["num_attention_heads"] // _block_configs[il]["attention"]["n_heads_in_group"])
self._num_heads.append(self.hparams["num_attention_heads"])
if _block_configs[il]["ffn"]["ffn_mult"] is None: # dummy layer
_ffn_multipliers.append(0.0)
else:
_ffn_multipliers.append(_block_configs[il]["ffn"]["ffn_mult"])
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(_ffn_multipliers)
assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
assert isinstance(self._num_heads, list) and isinstance(self._num_heads[0], int)
assert isinstance(_ffn_multipliers, list) and isinstance(_ffn_multipliers[0], float)
self._ffn_dims: list[int] = [
DeciModel._ffn_mult_to_intermediate_size(multiplier, self.hparams["hidden_size"])
for multiplier in _ffn_multipliers
]
def set_vocab(self):
# Please change tokenizer_config.json of Llama-3_1-Nemotron-51B's
# eos_token from '|eot_id|' to '|end_of_text|'
if self.hparams.get("vocab_size", 128256) == 128256:
tokens, toktypes, tokpre = self.get_vocab_base()
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_tokenizer_pre(tokpre)
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
special_vocab.add_to_gguf(self.gguf_writer)
else:
# DeciLM-7B
self._set_vocab_llama_hf()
def set_gguf_parameters(self):
if "block_configs" in self.hparams: # Llama-3_1-Nemotron-51B
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_heads)
assert self.block_count == len(self._ffn_dims)
if (rope_theta := self.rope_parameters.get("rope_theta")) is not None:
self.gguf_writer.add_rope_freq_base(rope_theta)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
self.gguf_writer.add_head_count(self._num_heads)
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
self.gguf_writer.add_key_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_value_length(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_file_type(self.ftype)
else: # DeciLM-7B
super().set_gguf_parameters()
if "num_key_value_heads_per_layer" in self.hparams: # DeciLM-7B
self._num_kv_heads: list[int] = self.hparams["num_key_value_heads_per_layer"]
assert self.block_count == len(self._num_kv_heads)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
hparams = self.hparams
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if (rope_dim := hparams.get("head_dim")) is None:
rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
self.gguf_writer.add_rope_dimension_count(rope_dim)
@staticmethod
def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
if n_head_kv is not None and n_head != n_head_kv:
n_head = n_head_kv
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
n_head = self.hparams["num_attention_heads"]
if bid is not None:
if "num_key_value_heads_per_layer" in self.hparams:
n_kv_head = self.hparams["num_key_value_heads_per_layer"][bid]
elif "block_configs" in self.hparams:
n_kv_head = self._num_kv_heads[bid]
n_head = self._num_heads[bid]
else:
n_kv_head = self.hparams.get("num_key_value_heads")
else:
n_kv_head = self.hparams.get("num_key_value_heads")
if name.endswith(("q_proj.weight", "q_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_head)
if name.endswith(("k_proj.weight", "k_proj.bias")):
data_torch = DeciModel.permute(data_torch, n_head, n_kv_head)
yield from super().modify_tensors(data_torch, name, bid)
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
if rope_params.get("rope_type", '').lower() == "llama3":
base = rope_params.get("rope_theta", 10000.0)
if (dim := self.hparams.get("head_dim")) is None:
dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
factor = rope_params.get("factor", 8.0)
low_freq_factor = rope_params.get("low_freq_factor", 1.0)
high_freq_factor = rope_params.get("high_freq_factor", 4.0)
old_context_len = rope_params.get("original_max_position_embeddings", 8192)
low_freq_wavelen = old_context_len / low_freq_factor
high_freq_wavelen = old_context_len / high_freq_factor
assert low_freq_wavelen != high_freq_wavelen
rope_factors = []
for freq in freqs:
wavelen = 2 * math.pi / freq
if wavelen < high_freq_wavelen:
rope_factors.append(1)
elif wavelen > low_freq_wavelen:
rope_factors.append(factor)
else:
smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
rope_factors.append(1 / ((1 - smooth) / factor + smooth))
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
def prepare_tensors(self):
super().prepare_tensors()
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