mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 17:31:18 +08:00
e56d9015ef
* Add --lora-all-linear option to apply LoRa to all linear transfer block layers * Moved to YAML config and added specification of rank & alpha * nits in conifg, more tests * nit * run tests for prs --------- Co-authored-by: Awni Hannun <awni@apple.com>
94 lines
2.9 KiB
Python
94 lines
2.9 KiB
Python
# Copyright © 2024 Apple Inc.
|
|
|
|
import os
|
|
import tempfile
|
|
import unittest
|
|
|
|
import mlx.core as mx
|
|
import mlx.nn as nn
|
|
from mlx.utils import tree_flatten
|
|
from mlx_lm import utils
|
|
|
|
HF_MODEL_PATH = "mlx-community/Qwen1.5-0.5B-Chat-4bit"
|
|
|
|
|
|
class TestUtils(unittest.TestCase):
|
|
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls.test_dir_fid = tempfile.TemporaryDirectory()
|
|
cls.test_dir = cls.test_dir_fid.name
|
|
if not os.path.isdir(cls.test_dir):
|
|
os.mkdir(cls.test_dir_fid.name)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
cls.test_dir_fid.cleanup()
|
|
|
|
def test_load(self):
|
|
model, _ = utils.load(HF_MODEL_PATH)
|
|
|
|
model_lazy, _ = utils.load(HF_MODEL_PATH, lazy=True)
|
|
|
|
mx.eval(model_lazy.parameters())
|
|
|
|
p1 = model.layers[0].mlp.up_proj.weight
|
|
p2 = model_lazy.layers[0].mlp.up_proj.weight
|
|
self.assertTrue(mx.allclose(p1, p2))
|
|
|
|
def test_make_shards(self):
|
|
from mlx_lm.models import llama
|
|
|
|
args = llama.ModelArgs(
|
|
model_type="llama",
|
|
hidden_size=2048,
|
|
num_hidden_layers=32,
|
|
intermediate_size=4096,
|
|
num_attention_heads=32,
|
|
rms_norm_eps=1e-5,
|
|
vocab_size=30_000,
|
|
)
|
|
model = llama.Model(args)
|
|
weights = tree_flatten(model.parameters())
|
|
gb = sum(p.nbytes for _, p in weights) // 2**30
|
|
shards = utils.make_shards(dict(weights), 1)
|
|
self.assertTrue(gb <= len(shards) <= gb + 1)
|
|
|
|
def test_quantize(self):
|
|
from mlx_lm.models import llama
|
|
|
|
args = llama.ModelArgs(
|
|
model_type="llama",
|
|
hidden_size=1024,
|
|
num_hidden_layers=4,
|
|
intermediate_size=2048,
|
|
num_attention_heads=4,
|
|
rms_norm_eps=1e-5,
|
|
vocab_size=10_000,
|
|
)
|
|
model = llama.Model(args)
|
|
weights, config = utils.quantize_model(model, {}, 64, 4)
|
|
self.assertTrue("model.layers.2.mlp.up_proj.scales" in weights)
|
|
self.assertTrue("model.layers.2.mlp.up_proj.biases" in weights)
|
|
self.assertEqual(config["quantization"]["group_size"], 64)
|
|
self.assertEqual(config["quantization"]["bits"], 4)
|
|
|
|
def test_convert(self):
|
|
mlx_path = os.path.join(self.test_dir, "mlx_model")
|
|
|
|
utils.convert(HF_MODEL_PATH, mlx_path=mlx_path, quantize=True)
|
|
model, _ = utils.load(mlx_path)
|
|
self.assertTrue(isinstance(model.layers[0].mlp.up_proj, nn.QuantizedLinear))
|
|
self.assertTrue(isinstance(model.layers[-1].mlp.up_proj, nn.QuantizedLinear))
|
|
|
|
# Check model weights have right type
|
|
utils.convert(HF_MODEL_PATH, mlx_path=mlx_path, dtype="bfloat16")
|
|
model, _ = utils.load(mlx_path)
|
|
|
|
self.assertEqual(model.layers[0].mlp.up_proj.weight.dtype, mx.bfloat16)
|
|
self.assertEqual(model.layers[-1].mlp.up_proj.weight.dtype, mx.bfloat16)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|