Spaces:
Runtime error
Runtime error
File size: 4,543 Bytes
f04dcd7 | 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 | # Copyright © 2023 Apple Inc.
import glob
import json
import logging
from pathlib import Path
from typing import Generator
import mlx.core as mx
import mlx.nn as nn
import models.phi2 as phi2
import transformers
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer
# Constants
MODEL_MAPPING = {
"phi": phi2,
}
def _get_classes(config: dict):
"""
Retrieve the model and model args classes based on the configuration.
Args:
config (dict): The model configuration.
Returns:
A tuple containing the Model class and the ModelArgs class.
"""
model_type = config["model_type"]
if model_type not in MODEL_MAPPING:
msg = f"Model type {model_type} not supported."
logging.error(msg)
raise ValueError(msg)
arch = MODEL_MAPPING[model_type]
return arch.Model, arch.ModelArgs
def fetch_from_hub(hf_path: str):
model_path = snapshot_download(
repo_id=hf_path,
allow_patterns=["*.json", "*.safetensors", "tokenizer.model"],
)
weight_files = glob.glob(f"{model_path}/*.safetensors")
if len(weight_files) == 0:
raise FileNotFoundError("No safetensors found in {}".format(model_path))
weights = {}
for wf in weight_files:
weights.update(mx.load(wf).items())
config = transformers.AutoConfig.from_pretrained(hf_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(
hf_path,
)
return weights, config.to_dict(), tokenizer
def make_shards(weights: dict, max_file_size_gibibyte: int = 15):
max_file_size_bytes = max_file_size_gibibyte << 30
shards = []
shard, shard_size = {}, 0
for k, v in weights.items():
if shard_size + v.nbytes > max_file_size_bytes:
shards.append(shard)
shard, shard_size = {}, 0
shard[k] = v
shard_size += v.nbytes
shards.append(shard)
return shards
def save_model(save_dir: str, weights, tokenizer, config):
save_dir = Path(save_dir)
save_dir.mkdir(parents=True, exist_ok=True)
shards = make_shards(weights, max_file_size_gibibyte=5)
shards_count = len(shards)
shard_file_format = (
"model-{:05d}-of-{:05d}.safetensors"
if shards_count > 1
else "model.safetensors"
)
for i, shard in enumerate(shards):
shard_name = shard_file_format.format(i + 1, shards_count)
mx.save_safetensors(str(save_dir / shard_name), shard)
tokenizer.save_pretrained(save_dir)
with open(save_dir / "config.json", "w") as fid:
json.dump(config, fid, indent=4)
def load(path):
model_path = Path(path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Load the config
with open(model_path / "config.json", "r") as f:
config = json.load(f)
# Get the appropriate model and ModelArgs classes
model_class, model_args_class = _get_classes(config)
# Create ModelArgs instance
model_args = model_args_class.from_dict(config)
# Create model instance
model = model_class(model_args)
# Load weights from .safetensors files
weight_files = glob.glob(str(model_path / "*.safetensors"))
if not weight_files:
raise FileNotFoundError(f"No .safetensors files found in {model_path}")
weights = {}
for wf in weight_files:
weights.update(mx.load(wf))
if "quantization" in config:
print("[INFO] Loading quantized model")
group_size = config["quantization"]["group_size"]
bits = config["quantization"]["bits"]
nn.quantize(model, group_size, bits)
model.load_weights(list(weights.items()))
return model, tokenizer, model_args
def generate(
prompt: mx.array, model: nn.Module, temp: float = 0.0
) -> Generator[mx.array, None, None]:
"""
Generate text based on the given prompt and model.
Args:
prompt (mx.array): The input prompt.
model (nn.Module): The model to use for generation.
temp (float): The temperature for sampling. If temp is 0, use max sampling.
Yields:
mx.array: The generated text.
"""
def sample(logits: mx.array) -> mx.array:
return (
mx.argmax(logits, axis=-1)
if temp == 0
else mx.random.categorical(logits * (1 / temp))
)
y = prompt
cache = None
while True:
logits, cache = model(y[None], cache=cache)
logits = logits[:, -1, :]
y = sample(logits)
yield y
|