wehub-resource-sync
348 lines
12 KiB
Python
348 lines
12 KiB
Python
# Copyright (c) 2024 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 functools import partial
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from tempfile import TemporaryDirectory
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from types import SimpleNamespace
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import paddle
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from llm.utils.data import convert_example_for_reft
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from paddlenlp.data import DataCollatorForSeq2Seq
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from paddlenlp.datasets import load_dataset
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from paddlenlp.peft.reft import (
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LoreftIntervention,
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LowRankRotateLayer,
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ReFTConfig,
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ReftDataCollator,
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ReFTModel,
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TinyIntervention,
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do_predict,
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)
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from paddlenlp.peft.reft.modeling_utils import (
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count_parameters,
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get_type_from_string,
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set_seed,
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)
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from paddlenlp.transformers import AutoModelForCausalLM, AutoTokenizer
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from paddlenlp.trl import SFTTrainer
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class TestReftDataCollator(unittest.TestCase):
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def test_call(self):
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model_name = "__internal_testing__/tiny-random-llama"
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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model_max_length=512,
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padding_side="right",
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)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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model = AutoModelForCausalLM.from_pretrained(model_name)
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data_collator = DataCollatorForSeq2Seq(
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tokenizer=tokenizer, model=model, label_pad_token_id=-100, padding="longest"
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)
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reft_data_collator = ReftDataCollator(data_collator)
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instances = [
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{
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"input_ids": paddle.to_tensor([[1, 2, 3], [4, 5, 6]]),
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"intervention_locations": paddle.to_tensor([[0, 1, 0], [1, 0, 1]]),
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},
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{
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"input_ids": paddle.to_tensor([[7, 8, 9], [10, 11, 12]]),
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"intervention_locations": paddle.to_tensor([[1, 0, 1], [0, 1, 0]]),
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},
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]
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batch_inputs = reft_data_collator(instances)
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self.assertIn("input_ids", batch_inputs)
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self.assertIn("intervention_locations", batch_inputs)
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self.assertIsInstance(batch_inputs["input_ids"], paddle.Tensor)
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self.assertIsInstance(batch_inputs["intervention_locations"], paddle.Tensor)
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class TestBasicUtils(unittest.TestCase):
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def test_get_type_from_string(self):
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class_str = "paddlenlp.peft.reft.LoreftIntervention"
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cls = get_type_from_string(class_str)
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self.assertIsInstance(cls, type(LoreftIntervention))
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def test_set_seed(self):
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set_seed(42)
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set_seed(66)
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def test_count_param(self):
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model = AutoModelForCausalLM.from_pretrained("__internal_testing__/tiny-random-llama")
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count_parameters(model)
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class TestReftConfig(unittest.TestCase):
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def test_reft_config(self):
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layers = [0, 1, 2]
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representations = [
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{
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"layer": l,
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"component": "block_output",
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"low_rank_dimension": 4,
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"intervention": LoreftIntervention(
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embed_dim=768,
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low_rank_dimension=4,
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dropout=0.00,
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dtype="float32",
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act_fn="linear",
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device="gpu",
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add_bias=False,
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),
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}
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for l in layers
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]
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reft_config = ReFTConfig(representations=representations)
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reft_config.__str__()
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class TestLoReftIntervention(unittest.TestCase):
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def setUp(self):
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self.kwargs = {
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"embed_dim": 64,
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"low_rank_dimension": 4,
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"dtype": paddle.float32,
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"dropout": 0.1,
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"act_fn": "linear",
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}
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def test_initialization(self):
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intervention = LoreftIntervention(**self.kwargs)
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self.assertIsInstance(intervention.rotate_layer, LowRankRotateLayer)
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self.assertIsInstance(intervention.learned_source, paddle.nn.Linear)
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self.assertEqual(intervention.dropout.p, self.kwargs["dropout"])
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def test_forward(self):
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base = paddle.randn([10, self.kwargs["embed_dim"]])
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intervention = LoreftIntervention(**self.kwargs)
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output = intervention.forward(base)
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self.assertEqual(output.shape, base.shape)
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self.assertEqual(output.dtype, self.kwargs["dtype"])
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def test_load_state_dict(self):
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model = LoreftIntervention(**self.kwargs)
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state_dict = {
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"learned_source.weight": paddle.randn([64, 4]),
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"learned_source.bias": paddle.zeros([4]),
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"rotate_layer.weight": paddle.randn([64, 4]),
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}
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model.load_state_dict(state_dict)
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self.assertTrue(paddle.allclose(model.learned_source.weight.data, state_dict["learned_source.weight"]))
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self.assertTrue(paddle.allclose(model.learned_source.bias.data, state_dict["learned_source.bias"]))
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self.assertTrue(
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paddle.allclose(
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model.rotate_layer.weight[:, : state_dict["rotate_layer.weight"].shape[-1]],
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state_dict["rotate_layer.weight"],
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)
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)
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class TestTinyIntervention(unittest.TestCase):
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def setUp(self):
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self.kwargs = {
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"embed_dim": 768,
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"low_rank_dimension": 4,
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"dtype": paddle.float32,
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"dropout": 0.1,
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"act_fn": "relu",
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}
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def test_initialization(self):
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intervention = TinyIntervention(**self.kwargs)
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self.assertEqual(intervention.rank, self.kwargs["low_rank_dimension"])
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self.assertEqual(intervention.hidden_size, self.kwargs["embed_dim"])
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self.assertEqual(intervention.param_A.shape, [self.kwargs["embed_dim"], self.kwargs["low_rank_dimension"]])
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self.assertEqual(intervention.param_B.shape, [self.kwargs["low_rank_dimension"], self.kwargs["embed_dim"]])
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self.assertEqual(intervention.param_a.shape, [self.kwargs["low_rank_dimension"]])
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self.assertEqual(intervention.param_b.shape, [self.kwargs["embed_dim"]])
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def test_forward(self):
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base = paddle.randn([10, self.kwargs["embed_dim"]])
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intervention = TinyIntervention(**self.kwargs)
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output = intervention.forward(base)
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self.assertEqual(output.shape, base.shape)
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self.assertEqual(output.dtype, self.kwargs["dtype"])
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def test_load_state_dict(self):
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model = TinyIntervention(**self.kwargs)
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state_dict = {
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"param_A": paddle.randn([768, 4]),
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"param_B": paddle.randn([4, 768]),
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"param_a": paddle.randn([4]),
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"param_b": paddle.randn([768]),
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}
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model.load_state_dict(state_dict)
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self.assertTrue(paddle.allclose(model.param_A, state_dict["param_A"]))
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self.assertTrue(paddle.allclose(model.param_B, state_dict["param_B"]))
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self.assertTrue(paddle.allclose(model.param_a, state_dict["param_a"]))
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self.assertTrue(paddle.allclose(model.param_b, state_dict["param_b"]))
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class TestReftModel(unittest.TestCase):
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def test_get_reft_model(self):
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model = AutoModelForCausalLM.from_pretrained("__internal_testing__/tiny-random-llama")
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layers = [0]
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representations = [
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{
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"layer": l,
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"component": "block_output",
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"low_rank_dimension": 4,
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"intervention": LoreftIntervention(
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embed_dim=768,
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low_rank_dimension=4,
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dropout=0.00,
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dtype="float32",
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act_fn="linear",
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device="gpu",
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add_bias=False,
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),
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}
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for l in layers
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]
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reft_config = ReFTConfig(representations=representations)
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reft_model = ReFTModel(reft_config, model)
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reft_model.print_trainable_parameters()
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self.assertTrue(type(reft_model), ReFTModel)
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def test_reft_model_forward(self):
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model = AutoModelForCausalLM.from_pretrained("__internal_testing__/tiny-random-llama")
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layers = [0]
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representations = [
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{
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"layer": l,
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"component": "block_output",
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"low_rank_dimension": 4,
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"intervention": LoreftIntervention(
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embed_dim=768,
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low_rank_dimension=4,
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dropout=0.00,
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dtype="float32",
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act_fn="linear",
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device="gpu",
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add_bias=False,
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),
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}
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for l in layers
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]
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reft_config = ReFTConfig(representations=representations)
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reft_model = ReFTModel(reft_config, model)
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reft_model.print_trainable_parameters()
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outputs = reft_model.model(**{"input_ids": paddle.randint(low=1, high=100, shape=(5, 10))})
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self.assertTrue(outputs[0].shape, [5, 10, 32000])
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class TestReFTModelPredict(unittest.TestCase):
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def test_reft_model_predict(self):
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tokenizer = AutoTokenizer.from_pretrained("__internal_testing__/tiny-random-llama")
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tokenizer.pad_token_id = tokenizer.eos_token_id
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train_ds = load_dataset(
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"json",
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data_files=os.path.join("./tests/fixtures/llm/data", "train.json"),
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lazy=False,
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)[0]
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dev_ds = load_dataset(
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"json",
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data_files=os.path.join("./tests/fixtures/llm/data", "dev.json"),
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lazy=False,
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)[0]
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trans_func = partial(
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convert_example_for_reft,
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tokenizer=tokenizer,
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data_args=SimpleNamespace(**{"max_length": 64, "src_length": 32, "autoregressive": False}),
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positions="f7",
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num_interventions=1,
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)
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train_ds = train_ds.map(
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partial(
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trans_func,
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is_test=False,
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zero_padding=False,
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flash_mask=False,
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)
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)
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dev_ds = dev_ds.map(
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partial(
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trans_func,
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is_test=False,
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zero_padding=False,
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flash_mask=False,
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)
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)
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model = AutoModelForCausalLM.from_pretrained("__internal_testing__/tiny-random-llama")
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layers = [0]
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representations = [
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{
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"layer": l,
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"component": "block_output",
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"low_rank_dimension": 4,
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"intervention": LoreftIntervention(
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embed_dim=768,
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low_rank_dimension=4,
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dropout=0.00,
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dtype="float32",
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act_fn="linear",
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device="gpu",
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add_bias=False,
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),
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}
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for l in layers
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]
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reft_config = ReFTConfig(representations=representations)
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reft_model = ReFTModel(reft_config, model)
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reft_model.disable_model_gradients()
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reft_model.model.train()
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reft_model.print_trainable_parameters()
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data_collator_fn = DataCollatorForSeq2Seq(
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tokenizer=tokenizer, model=model, label_pad_token_id=-100, padding="longest"
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)
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data_collator = ReftDataCollator(data_collator=data_collator_fn)
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trainer = SFTTrainer(
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model=reft_model,
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tokenizer=tokenizer,
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train_dataset=train_ds,
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data_collator=data_collator,
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eval_dataset=None,
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compute_metrics=None,
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gen_args=None,
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data_args=None,
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do_generation=False,
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)
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trainer.train()
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with TemporaryDirectory() as tempdir:
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reft_model.save_pretrained(tempdir)
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# 预测
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do_predict(
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intervenable=reft_model,
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tokenizer=tokenizer,
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eval_dataset=dev_ds,
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batch_size=1,
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predict_path=f"{tempdir}/pred_result.json",
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)
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if __name__ == "__main__":
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unittest.main()
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