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# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 sys
import unittest
from unittest.mock import patch
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from .testing_utils import is_peft_available, require_peft
class DummyDataset(torch.utils.data.Dataset):
def __init__(self, query_data, response_data):
self.query_data = query_data
self.response_data = response_data
def __len__(self):
return len(self.query_data)
def __getitem__(self, idx):
return self.query_data[idx], self.response_data[idx]
EXPECTED_STATS = [
"objective/kl",
"objective/kl_dist",
"objective/logprobs",
"objective/ref_logprobs",
"objective/kl_coef",
"objective/entropy",
"ppo/mean_non_score_reward",
"ppo/loss/policy",
"ppo/loss/value",
"ppo/loss/total",
"ppo/policy/entropy",
"ppo/policy/approxkl",
"ppo/policy/policykl",
"ppo/policy/clipfrac",
"ppo/policy/advantages",
"ppo/policy/advantages_mean",
"ppo/policy/ratio",
"ppo/returns/mean",
"ppo/returns/var",
"ppo/val/vpred",
"ppo/val/error",
"ppo/val/clipfrac",
"ppo/val/mean",
"ppo/val/var",
"ppo/val/var_explained",
"time/ppo/forward_pass",
"time/ppo/compute_rewards",
"time/ppo/optimize_step",
"time/ppo/calc_stats",
"time/ppo/total",
"ppo/learning_rate",
]
@require_peft
class TestPeftDependancy(unittest.TestCase):
def setUp(self):
self.causal_lm_model_id = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM"
self.seq_to_seq_model_id = "trl-internal-testing/tiny-random-T5ForConditionalGeneration"
if is_peft_available():
from peft import LoraConfig, get_peft_model
lora_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
)
causal_lm_model = AutoModelForCausalLM.from_pretrained(self.causal_lm_model_id)
self.peft_model = get_peft_model(causal_lm_model, lora_config)
def test_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead
# Check that loading a model with `peft` will raise an error
with self.assertRaises(ModuleNotFoundError):
import peft # noqa
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(self.causal_lm_model_id) # noqa
trl_seq2seq_model = AutoModelForSeq2SeqLMWithValueHead.from_pretrained(self.seq_to_seq_model_id) # noqa
def test_imports_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import ( # noqa
AutoModelForCausalLMWithValueHead,
AutoModelForSeq2SeqLMWithValueHead,
PPOConfig,
PPOTrainer,
PreTrainedModelWrapper,
)
def test_ppo_trainer_no_peft(self):
with patch.dict(sys.modules, {"peft": None}):
from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer
ppo_model_id = "trl-internal-testing/dummy-GPT2-correct-vocab"
trl_model = AutoModelForCausalLMWithValueHead.from_pretrained(ppo_model_id)
tokenizer = AutoTokenizer.from_pretrained(ppo_model_id)
tokenizer.pad_token_id = tokenizer.eos_token_id
ppo_config = PPOConfig(batch_size=2, mini_batch_size=1, log_with=None)
dummy_dataset = DummyDataset(
[torch.LongTensor([0, 1, 0, 1, 0, 1]), torch.LongTensor([0, 1, 0, 1, 0, 1])],
[torch.LongTensor([1, 0, 1, 0, 1, 0]), torch.LongTensor([0, 1, 0, 1, 0, 1])],
)
ppo_trainer = PPOTrainer(
config=ppo_config,
model=trl_model,
ref_model=None,
tokenizer=tokenizer,
dataset=dummy_dataset,
)
dummy_dataloader = ppo_trainer.dataloader
for query_tensor, response_tensor in dummy_dataloader:
# define a reward for response
# (this could be any reward such as human feedback or output from another model)
reward = [torch.tensor(1.0), torch.tensor(0.0)]
# train model
train_stats = ppo_trainer.step([q for q in query_tensor], [r for r in response_tensor], reward)
break
# check gradients are not None
for _, param in trl_model.named_parameters():
if param.requires_grad:
self.assertIsNotNone(param.grad)
# check expected stats
for stat in EXPECTED_STATS:
self.assertIn(stat, train_stats)
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