wehub-resource-sync
176 lines
7.8 KiB
Python
176 lines
7.8 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import unittest
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from tempfile import TemporaryDirectory
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import numpy as np
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import paddle
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from paddle.quantization import QAT, QuantConfig
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from paddle.quantization.config import SingleLayerConfig
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from paddle.quantization.quanters import FakeQuanterWithAbsMaxObserver
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from paddle.quantization.quanters.abs_max import FakeQuanterWithAbsMaxObserverLayer
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from paddlenlp.peft.lora import LoRAConfig, LoRALinear, LoRAModel
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from paddlenlp.peft.lora.lora_quant_layers import QuantedLoRALinear
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from paddlenlp.transformers import AutoModel
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class TestQuantedLoraLayer(unittest.TestCase):
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def test_forward(self):
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quant_lora_layer = QuantedLoRALinear(
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layer=LoRALinear(in_features=16, out_features=8, r=4, lora_alpha=8),
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q_config=SingleLayerConfig(weight=FakeQuanterWithAbsMaxObserver(moving_rate=0.9), activation=None),
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)
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x = paddle.randn([2, 4, 16], "float32")
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quant_output = quant_lora_layer(x)
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self.assertFalse(quant_lora_layer.lora_A.stop_gradient)
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self.assertFalse(quant_lora_layer.lora_B.stop_gradient)
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self.assertTrue(quant_lora_layer.weight.stop_gradient)
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self.assertFalse(quant_lora_layer.bias.stop_gradient)
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self.assertEqual(quant_output.shape, [2, 4, 8])
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def test_forward_no_quant(self):
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lora_layer = LoRALinear(
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in_features=16,
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out_features=8,
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r=4,
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lora_alpha=8,
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)
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quant_lora_layer = QuantedLoRALinear(
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layer=lora_layer, q_config=SingleLayerConfig(weight=None, activation=None)
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)
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x = paddle.randn([2, 4, 16], "float32")
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output = lora_layer(x)
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quant_output = quant_lora_layer(x)
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self.assertTrue(paddle.allclose(output, quant_output))
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def test_dropout_raise_exception(self):
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with self.assertRaises(ValueError):
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QuantedLoRALinear(
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layer=LoRALinear(in_features=16, out_features=8, r=4, lora_alpha=8, lora_dropout=0.1),
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q_config=SingleLayerConfig(weight=None, activation=None),
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)
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def test_save_load(self):
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with TemporaryDirectory() as tempdir:
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q_config = SingleLayerConfig(weight=FakeQuanterWithAbsMaxObserver(moving_rate=0.9), activation=None)
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quant_lora_layer = QuantedLoRALinear(
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layer=LoRALinear(in_features=16, out_features=8, r=4, lora_alpha=8), q_config=q_config
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)
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weights_path = os.path.join(tempdir, "model.pdparams")
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paddle.save(quant_lora_layer.state_dict(), weights_path)
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new_quant_lora_layer = QuantedLoRALinear(
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layer=LoRALinear(in_features=16, out_features=8, r=4, lora_alpha=8), q_config=q_config
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)
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state_dict = paddle.load(weights_path)
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new_quant_lora_layer.set_dict(state_dict)
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x = paddle.randn([2, 4, 16], "float32")
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self.assertTrue(paddle.allclose(new_quant_lora_layer(x), quant_lora_layer(x)))
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def test_merge_weights(self):
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lora_layer = LoRALinear(in_features=16, out_features=8, r=4, lora_alpha=8)
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quant_lora_layer = QuantedLoRALinear(
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layer=lora_layer, q_config=SingleLayerConfig(weight=None, activation=None)
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)
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x = paddle.randn([2, 4, 16], "float32")
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quant_lora_layer.merge()
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merge_output = lora_layer(x)
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quant_lora_layer.unmerge()
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unmerge_output = lora_layer(x)
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self.assertTrue(paddle.allclose(merge_output, unmerge_output))
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class TestQuantedLoRAModel(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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lora_config = LoRAConfig(
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target_modules=[".*q_proj.*", ".*v_proj.*"],
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r=4,
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lora_alpha=8,
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)
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cls.model = AutoModel.from_pretrained("__internal_testing__/tiny-random-bert")
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cls.lora_model = LoRAModel(cls.model, lora_config)
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cls.lora_model.mark_only_lora_as_trainable()
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# lora_B parameter is initialized to 0, therefore AB = 0 and W + AB = W
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# Since we want to test W + AB logic, we set lora_B to random values.
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lora_b_state_dict = {}
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for name, state in cls.lora_model.state_dict().items():
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if "lora_B" in name:
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lora_b_state_dict[name] = paddle.randn(state.shape)
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cls.lora_model.set_dict(lora_b_state_dict)
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def _count_layers(self, model, layer_type):
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count = 0
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for _layer in model.sublayers(True):
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if isinstance(_layer, layer_type):
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count += 1
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return count
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def test_count_model_layers(self):
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q_config = QuantConfig(activation=None, weight=None)
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q_config.add_qat_layer_mapping(LoRALinear, QuantedLoRALinear)
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q_config.add_type_config(LoRALinear, weight=FakeQuanterWithAbsMaxObserver(moving_rate=0.9))
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qat = QAT(q_config)
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self.lora_model.train()
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quant_lora_model = qat.quantize(self.lora_model, inplace=False)
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quantizer_cnt = self._count_layers(quant_lora_model, FakeQuanterWithAbsMaxObserverLayer)
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# 2 LoRA layers (q_proj, v_proj) per transformer layer
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self.assertEqual(quantizer_cnt, 2 * self.model.config.num_hidden_layers)
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def test_forward_no_quant(self):
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q_config = QuantConfig(activation=None, weight=None)
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q_config.add_qat_layer_mapping(LoRALinear, QuantedLoRALinear)
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q_config.add_type_config(LoRALinear, weight=None, activation=None)
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qat = QAT(q_config)
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self.lora_model.train()
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quant_lora_model = qat.quantize(self.lora_model, inplace=False)
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quant_lora_model.merge()
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self.lora_model.merge()
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quant_lora_model.eval()
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self.lora_model.eval()
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input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 5]))
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original_model_outputs = self.lora_model(input_ids)[0]
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quant_model_outputs = quant_lora_model(input_ids)[0]
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self.assertTrue(paddle.allclose(original_model_outputs, quant_model_outputs, atol=1e-5))
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def test_forward_weight_quant(self):
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q_config = QuantConfig(activation=None, weight=None)
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q_config.add_qat_layer_mapping(LoRALinear, QuantedLoRALinear)
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q_config.add_type_config(LoRALinear, weight=FakeQuanterWithAbsMaxObserver(moving_rate=0.9))
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qat = QAT(q_config)
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self.lora_model.train()
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quant_lora_model = qat.quantize(self.lora_model, inplace=False)
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quant_lora_model.eval()
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input_ids = paddle.to_tensor(np.random.randint(100, 200, [1, 5]))
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original_model_outputs = self.lora_model(input_ids)[0]
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quant_model_outputs = quant_lora_model(input_ids)[0]
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self.assertEqual(original_model_outputs.shape, quant_model_outputs.shape)
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def test_quant_lora_model_stop_gradient(self):
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q_config = QuantConfig(activation=None, weight=None)
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q_config.add_qat_layer_mapping(LoRALinear, QuantedLoRALinear)
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q_config.add_type_config(LoRALinear, weight=FakeQuanterWithAbsMaxObserver(moving_rate=0.9))
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qat = QAT(q_config)
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self.lora_model.train()
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quant_lora_model = qat.quantize(self.lora_model, inplace=False)
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for name, weight in quant_lora_model.state_dict().items():
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if "lora" in name:
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self.assertFalse(weight.stop_gradient)
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else:
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self.assertTrue(weight.stop_gradient)
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