File size: 11,767 Bytes
e2ef423 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 |
"""
BitTransformerLM CLI Argument Standards
Unified command-line interface standards for all BitTransformerLM scripts.
This module provides standardized argument parsers and naming conventions.
"""
import argparse
from typing import Optional, Callable
class BitTransformerCLI:
"""Standardized CLI argument parser for BitTransformerLM."""
@staticmethod
def add_model_args(parser: argparse.ArgumentParser) -> None:
"""Add standard model configuration arguments."""
model_group = parser.add_argument_group('Model Configuration')
model_group.add_argument('--model-size', choices=['tiny', 'small', 'medium', 'large'],
default='small', help='Model size preset')
model_group.add_argument('--d-model', type=int, default=128,
help='Model dimension')
model_group.add_argument('--num-heads', type=int, default=8,
help='Number of attention heads')
model_group.add_argument('--num-layers', type=int, default=6,
help='Number of transformer layers')
model_group.add_argument('--dropout', type=float, default=0.1,
help='Dropout rate')
model_group.add_argument('--max-seq-len', type=int, default=512,
help='Maximum sequence length')
@staticmethod
def add_training_args(parser: argparse.ArgumentParser) -> None:
"""Add standard training arguments."""
train_group = parser.add_argument_group('Training Configuration')
train_group.add_argument('--epochs', type=int, default=10,
help='Number of training epochs')
train_group.add_argument('--batch-size', type=int, default=16,
help='Training batch size')
train_group.add_argument('--learning-rate', type=float, default=1e-3,
help='Learning rate')
train_group.add_argument('--weight-decay', type=float, default=0.01,
help='Weight decay')
train_group.add_argument('--grad-clip', type=float, default=1.0,
help='Gradient clipping threshold')
train_group.add_argument('--warmup-steps', type=int, default=100,
help='Number of warmup steps')
@staticmethod
def add_dataset_args(parser: argparse.ArgumentParser) -> None:
"""Add standard dataset arguments."""
data_group = parser.add_argument_group('Dataset Configuration')
data_group.add_argument('--dataset-name', type=str, default='synthetic',
help='Dataset name or path')
data_group.add_argument('--dataset-size', type=int, default=10000,
help='Dataset size (number of samples)')
data_group.add_argument('--seq-length', type=int, default=64,
help='Sequence length for training')
data_group.add_argument('--validation-split', type=float, default=0.1,
help='Validation split ratio')
@staticmethod
def add_safety_args(parser: argparse.ArgumentParser) -> None:
"""Add safety and telemetry arguments."""
safety_group = parser.add_argument_group('Safety & Telemetry')
safety_group.add_argument('--enable-safety-gates', action='store_true',
help='Enable safety gates during inference')
safety_group.add_argument('--min-negentropy', type=float, default=0.1,
help='Minimum negentropy threshold')
safety_group.add_argument('--max-complexity', type=float, default=0.9,
help='Maximum LZ complexity threshold')
safety_group.add_argument('--min-symbiosis', type=float, default=0.3,
help='Minimum symbiosis score threshold')
safety_group.add_argument('--telemetry-logging', action='store_true',
help='Enable detailed telemetry logging')
@staticmethod
def add_optimization_args(parser: argparse.ArgumentParser) -> None:
"""Add optimization and performance arguments."""
opt_group = parser.add_argument_group('Optimization & Performance')
opt_group.add_argument('--use-amp', action='store_true',
help='Use automatic mixed precision')
opt_group.add_argument('--gradient-checkpointing', action='store_true',
help='Use gradient checkpointing')
opt_group.add_argument('--compile-model', action='store_true',
help='Use torch.compile for optimization')
opt_group.add_argument('--chunk-size', type=int, default=None,
help='Chunk size for chunked attention')
opt_group.add_argument('--num-workers', type=int, default=4,
help='Number of data loader workers')
@staticmethod
def add_distributed_args(parser: argparse.ArgumentParser) -> None:
"""Add distributed training arguments."""
dist_group = parser.add_argument_group('Distributed Training')
dist_group.add_argument('--distributed', action='store_true',
help='Enable distributed training')
dist_group.add_argument('--world-size', type=int, default=1,
help='Number of distributed processes')
dist_group.add_argument('--rank', type=int, default=0,
help='Process rank for distributed training')
dist_group.add_argument('--backend', choices=['nccl', 'gloo'], default='nccl',
help='Distributed backend')
@staticmethod
def add_io_args(parser: argparse.ArgumentParser) -> None:
"""Add input/output arguments."""
io_group = parser.add_argument_group('Input/Output')
io_group.add_argument('--input-path', type=str,
help='Input file or directory path')
io_group.add_argument('--output-path', type=str, default='./output',
help='Output directory path')
io_group.add_argument('--weights-path', type=str, default='./weights/model.pt',
help='Model weights file path')
io_group.add_argument('--checkpoint-dir', type=str, default='./checkpoints',
help='Checkpoint directory path')
io_group.add_argument('--log-level', choices=['DEBUG', 'INFO', 'WARNING', 'ERROR'],
default='INFO', help='Logging level')
@staticmethod
def add_huggingface_args(parser: argparse.ArgumentParser) -> None:
"""Add HuggingFace integration arguments."""
hf_group = parser.add_argument_group('HuggingFace Integration')
hf_group.add_argument('--hf-repo', type=str,
help='HuggingFace repository ID')
hf_group.add_argument('--hf-token', type=str,
help='HuggingFace access token')
hf_group.add_argument('--private-repo', action='store_true',
help='Create private HuggingFace repository')
hf_group.add_argument('--auto-upload', action='store_true',
help='Automatically upload to HuggingFace after training')
@staticmethod
def add_diffusion_args(parser: argparse.ArgumentParser) -> None:
"""Add diffusion mode arguments."""
diff_group = parser.add_argument_group('Diffusion Mode')
diff_group.add_argument('--diffusion-mode', action='store_true',
help='Enable diffusion training mode')
diff_group.add_argument('--diffusion-steps', type=int, default=8,
help='Number of diffusion steps')
diff_group.add_argument('--noise-schedule', choices=['linear', 'cosine', 'exponential'],
default='linear', help='Noise schedule type')
diff_group.add_argument('--diffusion-curriculum', action='store_true',
help='Use curriculum learning for diffusion')
@classmethod
def create_standard_parser(cls,
description: str,
include_groups: Optional[list] = None) -> argparse.ArgumentParser:
"""Create a standardized argument parser with specified groups.
Args:
description: Parser description
include_groups: List of group names to include. If None, includes all.
Options: ['model', 'training', 'dataset', 'safety', 'optimization',
'distributed', 'io', 'huggingface', 'diffusion']
"""
parser = argparse.ArgumentParser(
description=description,
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
# Default groups to include if none specified
if include_groups is None:
include_groups = ['model', 'training', 'dataset', 'safety', 'io']
# Add requested argument groups
group_methods = {
'model': cls.add_model_args,
'training': cls.add_training_args,
'dataset': cls.add_dataset_args,
'safety': cls.add_safety_args,
'optimization': cls.add_optimization_args,
'distributed': cls.add_distributed_args,
'io': cls.add_io_args,
'huggingface': cls.add_huggingface_args,
'diffusion': cls.add_diffusion_args,
}
for group_name in include_groups:
if group_name in group_methods:
group_methods[group_name](parser)
# Add common flags
parser.add_argument('--verbose', '-v', action='store_true',
help='Enable verbose output')
parser.add_argument('--debug', action='store_true',
help='Enable debug mode')
parser.add_argument('--seed', type=int, default=42,
help='Random seed for reproducibility')
return parser
# Pre-configured parsers for common use cases
def create_training_parser() -> argparse.ArgumentParser:
"""Create parser for training scripts."""
return BitTransformerCLI.create_standard_parser(
"BitTransformerLM Training Script",
['model', 'training', 'dataset', 'safety', 'optimization', 'distributed', 'io', 'huggingface']
)
def create_inference_parser() -> argparse.ArgumentParser:
"""Create parser for inference scripts."""
return BitTransformerCLI.create_standard_parser(
"BitTransformerLM Inference Script",
['model', 'safety', 'io', 'diffusion']
)
def create_evaluation_parser() -> argparse.ArgumentParser:
"""Create parser for evaluation scripts."""
return BitTransformerCLI.create_standard_parser(
"BitTransformerLM Evaluation Script",
['model', 'dataset', 'safety', 'io']
)
def create_workflow_parser() -> argparse.ArgumentParser:
"""Create parser for workflow/pipeline scripts."""
return BitTransformerCLI.create_standard_parser(
"BitTransformerLM Workflow Script",
['model', 'training', 'dataset', 'safety', 'optimization', 'io', 'huggingface', 'diffusion']
) |