File size: 12,696 Bytes
302920f |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
# Copyright 2023-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import tempfile
import pytest
import torch
from torch.testing import assert_close
from transformers import AutoModelForCausalLM
from peft import get_peft_model
from peft.peft_model import PeftModel
from peft.tuners.multitask_prompt_tuning import MultitaskPromptTuningConfig, MultitaskPromptTuningInit
from peft.utils import infer_device
from peft.utils.other import WEIGHTS_NAME, prepare_model_for_kbit_training
from peft.utils.save_and_load import get_peft_model_state_dict
MODELS_TO_TEST = [
"trl-internal-testing/tiny-random-LlamaForCausalLM",
]
class TestMultiTaskPromptTuning:
"""
Tests for the MultiTaskPromptTuning model.
"""
@pytest.fixture
def config(cls) -> MultitaskPromptTuningConfig:
return MultitaskPromptTuningConfig(
task_type="CAUSAL_LM",
num_virtual_tokens=50,
num_tasks=3,
prompt_tuning_init_text=(
"classify the following into either positive or negative, or entailment, neutral or contradiction:"
),
)
transformers_class = AutoModelForCausalLM
torch_device = infer_device()
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_prepare_for_training(self, model_id, config):
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model = model.to(self.torch_device)
dummy_input = torch.LongTensor([[1, 1, 1]]).to(self.torch_device)
dummy_output = model.get_input_embeddings()(dummy_input)
assert not dummy_output.requires_grad
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_prepare_for_int8_training(self, model_id, config):
model = AutoModelForCausalLM.from_pretrained(model_id)
model = prepare_model_for_kbit_training(model)
model = model.to(self.torch_device)
for param in model.parameters():
assert not param.requires_grad
model = get_peft_model(model, config)
# For backward compatibility
if hasattr(model, "enable_input_require_grads"):
model.enable_input_require_grads()
else:
def make_inputs_require_grad(module, input, output):
output.requires_grad_(True)
model.get_input_embeddings().register_forward_hook(make_inputs_require_grad)
dummy_input = torch.LongTensor([[1, 1, 1]]).to(self.torch_device)
dummy_output = model.get_input_embeddings()(dummy_input)
assert dummy_output.requires_grad
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_save_pretrained(self, model_id, config):
seed = 420
torch.manual_seed(seed)
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model = model.to(self.torch_device)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname)
torch.manual_seed(seed)
model_from_pretrained = AutoModelForCausalLM.from_pretrained(model_id)
model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname)
# check if the state dicts are equal
state_dict = get_peft_model_state_dict(model)
state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained)
# check if same keys
assert state_dict.keys() == state_dict_from_pretrained.keys()
# Check that the number of saved parameters is 4 -- 2 layers of (tokens and gate).
assert len(state_dict) == 3
# check if tensors equal
for key in state_dict.keys():
assert torch.allclose(
state_dict[key].to(self.torch_device), state_dict_from_pretrained[key].to(self.torch_device)
)
# check if `adapter_model.safetensors` is present
assert os.path.exists(os.path.join(tmp_dirname, "adapter_model.safetensors"))
# check if `adapter_config.json` is present
assert os.path.exists(os.path.join(tmp_dirname, "adapter_config.json"))
# check if `pytorch_model.bin` is not present
assert not os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin"))
# check if `config.json` is not present
assert not os.path.exists(os.path.join(tmp_dirname, "config.json"))
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_save_pretrained_regression(self, model_id, config):
seed = 420
torch.manual_seed(seed)
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model = model.to(self.torch_device)
with tempfile.TemporaryDirectory() as tmp_dirname:
model.save_pretrained(tmp_dirname, safe_serialization=False)
torch.manual_seed(seed)
model_from_pretrained = AutoModelForCausalLM.from_pretrained(model_id)
model_from_pretrained = PeftModel.from_pretrained(model_from_pretrained, tmp_dirname)
# check if the state dicts are equal
state_dict = get_peft_model_state_dict(model)
state_dict_from_pretrained = get_peft_model_state_dict(model_from_pretrained)
# check if same keys
assert state_dict.keys() == state_dict_from_pretrained.keys()
# Check that the number of saved parameters is 4 -- 2 layers of (tokens and gate).
assert len(state_dict) == 3
# check if tensors equal
for key in state_dict.keys():
assert torch.allclose(
state_dict[key].to(self.torch_device), state_dict_from_pretrained[key].to(self.torch_device)
)
# check if `adapter_model.bin` is present for regression
assert os.path.exists(os.path.join(tmp_dirname, "adapter_model.bin"))
# check if `adapter_config.json` is present
assert os.path.exists(os.path.join(tmp_dirname, "adapter_config.json"))
# check if `pytorch_model.bin` is not present
assert not os.path.exists(os.path.join(tmp_dirname, "pytorch_model.bin"))
# check if `config.json` is not present
assert not os.path.exists(os.path.join(tmp_dirname, "config.json"))
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_generate(self, model_id, config):
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model = model.to(self.torch_device)
input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
task_ids = torch.LongTensor([1, 2]).to(self.torch_device)
# check if `generate` works
_ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)
# check if `generate` works if positional arguments are passed
_ = model.generate(input_ids, attention_mask=attention_mask, task_ids=task_ids)
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_use_cache(self, model_id, config):
"""Test that MultiTaskPromptTuning works when Llama config use_cache=True."""
torch.manual_seed(0)
input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
task_ids = torch.LongTensor([1, 2]).to(self.torch_device)
original = AutoModelForCausalLM.from_pretrained(model_id)
mpt = get_peft_model(original, config)
mpt = mpt.to(self.torch_device)
expected = mpt.generate(input_ids=input_ids, max_length=8, task_ids=task_ids)
# Set use_cache = True and generate output again.
mpt.base_model.config.use_cache = True
actual = mpt.generate(input_ids=input_ids, max_length=8, task_ids=task_ids)
assert_close(expected, actual, rtol=0, atol=0)
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_bf16_inference(self, model_id, config):
"""Test that MultiTaskPromptTuning works when Llama using a half-precision model."""
input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
task_ids = torch.tensor([1, 2]).to(self.torch_device)
original = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
mpt = get_peft_model(original, config)
mpt = mpt.to(self.torch_device)
_ = mpt.generate(input_ids=input_ids, task_ids=task_ids)
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_generate_text_with_random_init(self, model_id, config) -> None:
torch.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(model_id)
config.prompt_tuning_init = MultitaskPromptTuningInit.RANDOM
model = get_peft_model(model, config)
model = model.to(self.torch_device)
input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
task_ids = torch.LongTensor([0]).to(self.torch_device)
# check if `generate` works
_ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)
with pytest.raises(ValueError):
# check if `generate` raises an error if task_ids are not passed
_ = model.generate(input_ids, attention_mask=attention_mask)
@pytest.mark.parametrize(
"prompt_tuning_init",
[
MultitaskPromptTuningInit.AVERAGE_SOURCE_TASKS,
MultitaskPromptTuningInit.EXACT_SOURCE_TASK,
MultitaskPromptTuningInit.ONLY_SOURCE_SHARED,
],
)
@pytest.mark.parametrize("model_id", MODELS_TO_TEST)
def test_generate_text_with_other_init(self, prompt_tuning_init, model_id, config) -> None:
# This test is flaky, hence fixing the seed. The reason is somehow related to:
# https://github.com/huggingface/transformers/blob/e786844425b6b1112c76513d66217ce2fe6aea41/src/transformers/generation/utils.py#L2691
# When an EOS token is generated, the loop is exited and the pytest.raises at the bottom is not triggered
# because `forward` of the PEFT model, which should raise the error, is never called.
torch.manual_seed(42) # seed 43 fails with transformers v4.42.3 and torch v2.3.1
with tempfile.TemporaryDirectory() as tmp_dirname:
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model.save_pretrained(tmp_dirname, safe_serialization=False) # bc torch.load is used
config = MultitaskPromptTuningConfig(
task_type="CAUSAL_LM",
num_virtual_tokens=50,
num_tasks=1,
prompt_tuning_init_text=(
"classify the following into either positive or negative, or entailment, neutral or contradiction:"
),
prompt_tuning_init=prompt_tuning_init,
prompt_tuning_init_state_dict_path=os.path.join(tmp_dirname, WEIGHTS_NAME),
)
model = AutoModelForCausalLM.from_pretrained(model_id)
model = get_peft_model(model, config)
model = model.to(self.torch_device)
input_ids = torch.LongTensor([[1, 1, 1], [2, 1, 2]]).to(self.torch_device)
attention_mask = torch.LongTensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)
task_ids = torch.LongTensor([0]).to(self.torch_device)
# check if `generate` works
_ = model.generate(input_ids=input_ids, attention_mask=attention_mask, task_ids=task_ids)
with pytest.raises(ValueError, match="task_ids cannot be None"):
# check if `generate` raises an error if task_ids are not passed
_ = model.generate(input_ids, attention_mask=attention_mask)
|