mirror of
https://github.com/ml-explore/mlx-examples.git
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949f63f309
* chore(mlx-lm): fix print_trainable_parameters for quant models * chore: clean up * refactor: use layer type to check quant bits * chore: address comment
125 lines
4.1 KiB
Python
125 lines
4.1 KiB
Python
# Copyright © 2024 Apple Inc.
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import sys
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import unittest
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from io import StringIO
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from unittest.mock import MagicMock
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import mlx.nn as nn
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from mlx.utils import tree_flatten
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from mlx_lm import lora, tuner
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from mlx_lm.tuner.lora import LoRALinear
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class TestLora(unittest.TestCase):
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def setUp(self):
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self.capturedOutput = StringIO()
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sys.stdout = self.capturedOutput
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def tearDown(self):
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sys.stdout = sys.__stdout__
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def test_to_lora(self):
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from mlx_lm.models import llama
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args = llama.ModelArgs(
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model_type="llama",
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hidden_size=1024,
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num_hidden_layers=4,
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intermediate_size=2048,
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num_attention_heads=4,
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rms_norm_eps=1e-5,
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vocab_size=10_000,
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)
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lora_layers = 4
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def check_config(params):
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n_keys = 2
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if "keys" in params:
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n_keys = len(params["keys"])
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model = llama.Model(args)
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model.freeze()
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tuner.utils.linear_to_lora_layers(model, lora_layers, params)
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trainable_params = sum(
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v.size for _, v in tree_flatten(model.trainable_parameters())
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)
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self.assertEqual(
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trainable_params, lora_layers * params["rank"] * 1024 * 2 * n_keys
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)
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params = {"rank": 8, "alpha": 16, "dropout": 0.0, "scale": 10.0}
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check_config(params)
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params["rank"] = 1
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check_config(params)
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params["keys"] = ["self_attn.k_proj"]
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check_config(params)
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def test_quantized_print_trainable_parameters(self):
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model = MagicMock()
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quantized_linear = MagicMock(spec=nn.QuantizedLinear)
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quantized_linear.weight = MagicMock(size=1e6)
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quantized_linear.bits = 8
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lora_linear = MagicMock(spec=LoRALinear)
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lora_linear.weight = MagicMock(size=2e6)
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lora_linear.parameters.return_value = [lora_linear.weight]
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linear = MagicMock(spec=nn.Linear)
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linear.weight = MagicMock(size=3e6)
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linear.parameters.return_value = [linear.weight]
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model.leaf_modules.return_value = {
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"quantized_linear": quantized_linear,
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"lora_linear": lora_linear,
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"linear": linear,
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}
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model.trainable_parameters.return_value = {
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"layer1.weight": MagicMock(size=1e6),
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"layer3.weight": MagicMock(size=2e6),
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}
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expected_output_8bits = "Trainable parameters: 33.333% (3.000M/9.000M)\n"
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lora.print_trainable_parameters(model)
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self.assertEqual(self.capturedOutput.getvalue(), expected_output_8bits)
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self.capturedOutput.truncate(0)
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self.capturedOutput.seek(0)
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quantized_linear.weight = MagicMock(size=1e6)
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quantized_linear.bits = 4
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expected_output_4bits = "Trainable parameters: 23.077% (3.000M/13.000M)\n"
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lora.print_trainable_parameters(model)
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self.assertEqual(self.capturedOutput.getvalue(), expected_output_4bits)
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self.capturedOutput.truncate(0)
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self.capturedOutput.seek(0)
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def test_print_trainable_parameters(self):
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model = MagicMock()
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linear1 = MagicMock(spec=nn.Linear)
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linear1.weight = MagicMock(size=1e6)
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linear1.parameters.return_value = [linear1.weight]
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linear2 = MagicMock(spec=nn.Linear)
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linear2.weight = MagicMock(size=2e6)
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linear2.parameters.return_value = [linear2.weight]
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lora_linear = MagicMock(spec=LoRALinear)
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lora_linear.weight = MagicMock(size=3e6)
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lora_linear.parameters.return_value = [lora_linear.weight]
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model.leaf_modules.return_value = {
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"linear1": linear1,
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"linear2": linear2,
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"lora_linear": lora_linear,
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}
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model.trainable_parameters.return_value = {
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"layer1.weight": MagicMock(size=1e6),
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"layer3.weight": MagicMock(size=2e6),
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}
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expected_output = "Trainable parameters: 50.000% (3.000M/6.000M)\n"
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lora.print_trainable_parameters(model)
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self.assertEqual(self.capturedOutput.getvalue(), expected_output)
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if __name__ == "__main__":
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unittest.main()
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