File size: 11,534 Bytes
bdb11fe | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | """Environment-driven training configuration."""
from __future__ import annotations
import os
import math
import uuid
from dataclasses import dataclass, field
from pathlib import Path
from transformers import PretrainedConfig
DEFAULT_VOCAB_SIZE = 4096
DEFAULT_HIDDEN_SIZE = 192
DEFAULT_NUM_HIDDEN_LAYERS = 7
DEFAULT_NUM_ATTENTION_HEADS = 3
DEFAULT_NUM_KEY_VALUE_HEADS = 1
DEFAULT_HEAD_DIM = DEFAULT_HIDDEN_SIZE // DEFAULT_NUM_ATTENTION_HEADS
DEFAULT_INTERMEDIATE_SIZE = DEFAULT_HIDDEN_SIZE * 5 // 2
DEFAULT_BLOCK_SIZE = 512
DEFAULT_ROPE_THETA = 5000.0
class GPTConfig(PretrainedConfig):
model_type = "gpt"
def __init__(
self,
vocab_size: int = DEFAULT_VOCAB_SIZE,
hidden_size: int = DEFAULT_HIDDEN_SIZE,
num_hidden_layers: int = DEFAULT_NUM_HIDDEN_LAYERS,
num_attention_heads: int = DEFAULT_NUM_ATTENTION_HEADS,
num_key_value_heads: int | None = DEFAULT_NUM_KEY_VALUE_HEADS,
intermediate_size: int | None = DEFAULT_INTERMEDIATE_SIZE,
head_dim: int | None = None,
block_size: int = DEFAULT_BLOCK_SIZE,
rope_theta: float = DEFAULT_ROPE_THETA,
rms_norm_eps: float = 1e-6,
xsa_projection: bool = True,
tie_word_embeddings: bool = True,
labels_are_shifted: bool = False,
**kwargs,
):
if num_key_value_heads is None:
num_key_value_heads = num_attention_heads
if head_dim is None:
if hidden_size % num_attention_heads != 0:
raise ValueError("hidden_size must be divisible by num_attention_heads")
head_dim = hidden_size // num_attention_heads
if intermediate_size is None:
intermediate_size = hidden_size * 4
if num_attention_heads % num_key_value_heads != 0:
raise ValueError("num_attention_heads must be divisible by num_key_value_heads")
if head_dim % 2 != 0:
raise ValueError("head_dim must be even for RoPE")
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
self.vocab_size = int(vocab_size)
self.hidden_size = int(hidden_size)
self.num_hidden_layers = int(num_hidden_layers)
self.num_attention_heads = int(num_attention_heads)
self.num_key_value_heads = int(num_key_value_heads)
self.intermediate_size = int(intermediate_size)
self.head_dim = int(head_dim)
self.block_size = int(block_size)
self.max_position_embeddings = int(block_size)
self.rope_theta = float(rope_theta)
self.rms_norm_eps = float(rms_norm_eps)
self.xsa_projection = bool(xsa_projection)
self.labels_are_shifted = bool(labels_are_shifted)
def _bool_env(name: str, default: bool) -> bool:
raw = os.environ.get(name)
if raw is None:
return default
return raw.strip().lower() in {"1", "true", "yes", "on"}
def _path_env(name: str, default: str) -> str:
return str(Path(os.environ.get(name, default)).expanduser())
@dataclass
class Hyperparameters:
data_dir: str = field(default_factory=lambda: _path_env("DATA_DIR", "."))
tokenized_dir: str = field(default_factory=lambda: _path_env("TOKENIZED_DIR", "tokenized"))
tokenizer_dir: str = field(default_factory=lambda: _path_env("TOKENIZER_DIR", "tokenizer_4k"))
tokenizer_path: str = field(default_factory=lambda: os.environ.get("TOKENIZER_PATH", ""))
curriculum_path: str = field(default_factory=lambda: os.environ.get("CURRICULUM_PATH", ""))
mix_weights_path: str = field(default_factory=lambda: os.environ.get("MIX_WEIGHTS_PATH", ""))
run_id: str = field(default_factory=lambda: os.environ.get("RUN_ID", str(uuid.uuid4())))
seed: int = field(default_factory=lambda: int(os.environ.get("SEED", "1337")))
rank: int = field(init=False)
iterations: int = field(default_factory=lambda: int(os.environ.get("ITERATIONS", "10000")))
requested_train_tokens: int = field(init=False)
train_tokens: int = field(init=False)
decay_start_frac: float = field(default_factory=lambda: float(os.environ.get("DECAY_START_FRAC", "0.7")))
warmup_steps: int = field(default_factory=lambda: int(os.environ.get("WARMUP_STEPS", "0")))
lr_warmup_steps: int = field(default_factory=lambda: int(os.environ.get("LR_WARMUP_STEPS", "500")))
train_batch_tokens: int = field(default_factory=lambda: int(os.environ.get("TRAIN_BATCH_TOKENS", str(DEFAULT_BLOCK_SIZE * 512))))
train_seq_len: int = field(init=False)
eval_seq_len: int = field(init=False)
grad_accum_steps: int = field(default_factory=lambda: int(os.environ.get("GRAD_ACCUM_STEPS", "2")))
train_log_every: int = field(default_factory=lambda: int(os.environ.get("TRAIN_LOG_EVERY", "100")))
train_log_first_steps: int = field(default_factory=lambda: int(os.environ.get("TRAIN_LOG_FIRST_STEPS", "500")))
val_batch_tokens: int = field(default_factory=lambda: int(os.environ.get("VAL_BATCH_TOKENS", str(DEFAULT_BLOCK_SIZE * 256))))
val_loss_every: int = field(default_factory=lambda: int(os.environ.get("VAL_LOSS_EVERY", "1000")))
val_max_tokens: int = field(default_factory=lambda: int(os.environ.get("VAL_MAX_TOKENS", "10_000_000")))
vocab_size: int = field(default_factory=lambda: int(os.environ.get("VOCAB_SIZE", str(DEFAULT_VOCAB_SIZE))))
hidden_size: int = field(default_factory=lambda: int(os.environ.get("HIDDEN_SIZE", os.environ.get("MODEL_DIM", str(DEFAULT_HIDDEN_SIZE)))))
num_hidden_layers: int = field(default_factory=lambda: int(os.environ.get("NUM_HIDDEN_LAYERS", os.environ.get("NUM_LAYERS", str(DEFAULT_NUM_HIDDEN_LAYERS)))))
num_attention_heads: int = field(default_factory=lambda: int(os.environ.get("NUM_ATTENTION_HEADS", os.environ.get("NUM_HEADS", str(DEFAULT_NUM_ATTENTION_HEADS)))))
num_key_value_heads: int = field(default_factory=lambda: int(os.environ.get("NUM_KEY_VALUE_HEADS", os.environ.get("NUM_KV_HEADS", str(DEFAULT_NUM_KEY_VALUE_HEADS)))))
head_dim: int = field(init=False)
intermediate_size: int = field(default_factory=lambda: int(os.environ.get("INTERMEDIATE_SIZE", os.environ.get("INTERMEDIATE", str(DEFAULT_INTERMEDIATE_SIZE)))))
block_size: int = field(default_factory=lambda: int(os.environ.get("BLOCK_SIZE", str(DEFAULT_BLOCK_SIZE))))
rope_theta: float = field(default_factory=lambda: float(os.environ.get("ROPE_THETA", os.environ.get("ROPE_BASE", str(DEFAULT_ROPE_THETA)))))
rms_norm_eps: float = field(default_factory=lambda: float(os.environ.get("RMS_NORM_EPS", "1e-6")))
xsa_projection: bool = field(default_factory=lambda: _bool_env("XSA_PROJECTION", True))
tie_word_embeddings: bool = field(default_factory=lambda: _bool_env("TIE_WORD_EMBEDDINGS", _bool_env("TIE_EMBEDDINGS", True)))
min_lr: float = field(default_factory=lambda: float(os.environ.get("MIN_LR", "0.0")))
lr: float = field(default_factory=lambda: float(os.environ.get("LR", "0.004")))
beta1: float = field(default_factory=lambda: float(os.environ.get("BETA1", "0.9")))
beta2: float = field(default_factory=lambda: float(os.environ.get("BETA2", "0.95")))
adam_eps: float = field(default_factory=lambda: float(os.environ.get("ADAM_EPS", "1e-8")))
weight_decay: float = field(default_factory=lambda: float(os.environ.get("WEIGHT_DECAY", "0.005")))
compile_model: bool = field(default_factory=lambda: _bool_env("COMPILE_MODEL", True))
autocast: bool = field(default_factory=lambda: _bool_env("AUTOCAST", True))
bf16: bool = field(default_factory=lambda: _bool_env("BF16", True))
device: str = field(default_factory=lambda: os.environ.get("DEVICE", "auto"))
output_dir: str = field(default_factory=lambda: _path_env("OUTPUT_DIR", "outputs"))
checkpoint_name: str = field(default_factory=lambda: os.environ.get("CHECKPOINT_NAME", "final_model"))
logfile: str = field(init=False)
model_path: str = field(init=False)
is_main_process: bool = True
train_files: str = field(init=False)
val_files: str = field(init=False)
def __post_init__(self) -> None:
self.rank = int(os.environ.get("RANK", "0"))
if self.rank < 0:
raise ValueError("RANK must be non-negative")
self.is_main_process = self.rank == 0
self.head_dim = int(os.environ.get("HEAD_DIM", str(self.hidden_size // self.num_attention_heads)))
self.train_seq_len = int(os.environ.get("TRAIN_SEQ_LEN", str(self.block_size)))
self.eval_seq_len = int(os.environ.get("EVAL_SEQ_LEN", os.environ.get("TRAIN_SEQ_LEN", str(self.train_seq_len))))
token_alignment = self.train_seq_len * self.grad_accum_steps
if self.train_batch_tokens % token_alignment != 0:
raise ValueError(
"TRAIN_BATCH_TOKENS must be divisible by TRAIN_SEQ_LEN * GRAD_ACCUM_STEPS"
)
requested_train_tokens = int(os.environ.get("TRAIN_TOKENS", "0"))
self.requested_train_tokens = requested_train_tokens or self.iterations * self.train_batch_tokens
if self.requested_train_tokens <= 0:
raise ValueError("TRAIN_TOKENS must be positive")
self.train_tokens = self.requested_train_tokens - (self.requested_train_tokens % token_alignment)
if self.train_tokens <= 0:
raise ValueError(
"TRAIN_TOKENS must provide at least TRAIN_SEQ_LEN * GRAD_ACCUM_STEPS tokens"
)
self.iterations = math.ceil(self.train_tokens / self.train_batch_tokens)
tokenized = Path(self.tokenized_dir)
self.train_files = os.environ.get("TRAIN_FILES", str(tokenized / "*" / "shard_*.bin"))
self.val_files = os.environ.get("VAL_FILES", os.environ.get("TRAIN_FILES", self.train_files))
explicit_legacy_mix = bool(os.environ.get("MIX_WEIGHTS_PATH"))
if not self.curriculum_path and not explicit_legacy_mix:
tokenized_curriculum = tokenized / "curriculum.json"
default_curriculum = Path("pretraining_curriculum.json")
if tokenized_curriculum.exists():
self.curriculum_path = str(tokenized_curriculum)
elif default_curriculum.exists():
self.curriculum_path = str(default_curriculum)
if not self.mix_weights_path and not self.curriculum_path:
mix_weights_path = tokenized / "mix_weights.json"
self.mix_weights_path = str(mix_weights_path) if mix_weights_path.exists() else ""
if not self.tokenizer_path:
tok_dir = Path(self.tokenizer_dir)
json_path = tok_dir / "tokenizer.json"
self.tokenizer_path = str(json_path if json_path.exists() else tok_dir)
out = Path(self.output_dir)
self.logfile = os.environ.get("LOGFILE", str(out / "logs" / f"{self.run_id}.txt"))
self.model_path = os.environ.get("MODEL_PATH", str(out / self.checkpoint_name))
def to_model_config(self) -> GPTConfig:
return GPTConfig(
vocab_size=self.vocab_size,
hidden_size=self.hidden_size,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
num_key_value_heads=self.num_key_value_heads,
head_dim=self.head_dim,
intermediate_size=self.intermediate_size,
block_size=self.block_size,
rope_theta=self.rope_theta,
rms_norm_eps=self.rms_norm_eps,
xsa_projection=self.xsa_projection,
tie_word_embeddings=self.tie_word_embeddings,
labels_are_shifted=True,
)
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