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# Copyright 2020-2025 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 tempfile
import unittest
import torch
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoModelForSequenceClassification, AutoTokenizer
from trl import RLOOConfig, RLOOTrainer
class RLOOTrainerTester(unittest.TestCase):
def setUp(self):
self.model_id = "trl-internal-testing/tiny-Qwen2ForCausalLM-2.5"
self.policy_model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.reward_model = AutoModelForSequenceClassification.from_pretrained(self.model_id)
self.policy_ref_model = AutoModelForCausalLM.from_pretrained(self.model_id)
self.tokenizer = AutoTokenizer.from_pretrained(self.model_id, padding_side="left")
self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
def test_rloo_checkpoint(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RLOOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
total_episodes=1,
report_to="none",
)
dummy_text = [{"content": "Hello World!", "role": "user"}]
dummy_data = self.tokenizer.apply_chat_template(dummy_text)
dummy_dataset = Dataset.from_dict({"input_ids": dummy_data})
trainer = RLOOTrainer(
config=training_args,
policy=self.policy_model,
reward_model=self.reward_model,
ref_policy=self.policy_ref_model,
processing_class=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
trainer._save_checkpoint(trainer.model, trial=None)
def test_rloo_reward(self):
local_batch_size = 3
rloo_k = 4
sequence_length = 5 # Add sequence length for testing token-level rewards
# fmt: off
rlhf_reward = torch.tensor([
1, 2, 3, # first rlhf reward for three prompts
2, 3, 4, # second rlhf reward for three prompts
5, 6, 7, # third rlhf reward for three prompts
8, 9, 10, # fourth rlhf reward for three prompts
]).float()
# Create padding mask where 1 indicates valid token, 0 indicates padding
padding_mask = torch.ones(local_batch_size * rloo_k, sequence_length)
# Set padding based on sequence lengths
sequence_lengths = torch.tensor([
3, 4, 3, # lengths for first batch
4, 3, 4, # lengths for second batch
3, 4, 3, # lengths for third batch
4, 3, 4, # lengths for fourth batch
])
for i, length in enumerate(sequence_lengths):
padding_mask[i, length:] = 0
# Add kl tensor for testing token-level rewards
kl = torch.ones(local_batch_size * rloo_k, sequence_length) # Dummy KL values
# fmt: on
# Test token-level KL rewards following OpenRLHF implementation
kl_coef = 0.1
kl_reward = -kl_coef * kl
# Find last non-padded position
eos_indices = padding_mask.size(1) - 1 - padding_mask.long().fliplr().argmax(dim=1, keepdim=True)
# Create last reward tensor
last_reward = torch.zeros_like(kl)
last_reward.scatter_(dim=1, index=eos_indices, src=rlhf_reward.reshape(-1, 1))
# Test last_reward - should have rlhf_reward at the last non-padded position
for i, (length, reward) in enumerate(zip(sequence_lengths, rlhf_reward)):
# Check reward is at correct position
self.assertEqual(last_reward[i, length - 1].item(), reward.item())
# Check zeros elsewhere
self.assertTrue(torch.all(last_reward[i, : length - 1] == 0))
self.assertTrue(torch.all(last_reward[i, length:] == 0))
# Combine rewards
reward = last_reward + kl_reward
non_score_reward = kl_reward.sum(1)
token_level_rlhf_reward = reward.sum(1)
# Test reward components
# KL reward should be -0.1 for each token in sequence length
expected_kl_reward = -0.1 * sequence_length # Each position gets -0.1 KL reward
torch.testing.assert_close(non_score_reward, torch.tensor(expected_kl_reward).expand_as(non_score_reward))
# Total reward should be rlhf_reward + kl_reward
expected_total = rlhf_reward + expected_kl_reward
torch.testing.assert_close(token_level_rlhf_reward, expected_total)
# Test sequence-level rewards (existing test)
baseline = (rlhf_reward.sum(0) - rlhf_reward) / (rloo_k - 1)
advantages = torch.zeros_like(rlhf_reward)
for i in range(0, len(advantages), local_batch_size):
other_response_rlhf_rewards = []
for j in range(0, len(advantages), local_batch_size):
if i != j:
other_response_rlhf_rewards.append(rlhf_reward[j : j + local_batch_size])
advantages[i : i + local_batch_size] = rlhf_reward[i : i + local_batch_size] - torch.stack(
other_response_rlhf_rewards
).mean(0)
self.assertLess((1 - (2 + 5 + 8) / 3 - advantages[0].item()), 1e-6)
self.assertLess((6 - (3 + 2 + 9) / 3 - advantages[7].item()), 1e-6)
# Test vectorized implementation
rlhf_reward = rlhf_reward.reshape(rloo_k, local_batch_size)
baseline = (rlhf_reward.sum(0) - rlhf_reward) / (rloo_k - 1)
vec_advantages = rlhf_reward - baseline
torch.testing.assert_close(vec_advantages.flatten(), advantages)
def test_rloo_training(self):
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RLOOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
total_episodes=1,
num_train_epochs=1,
max_steps=2,
report_to="none",
)
# Create a simple dataset
dummy_text = [{"content": "Hello World!", "role": "user"}]
dummy_data = self.tokenizer.apply_chat_template(dummy_text)
dummy_dataset = Dataset.from_dict({"input_ids": [dummy_data, dummy_data]})
trainer = RLOOTrainer(
config=training_args,
policy=self.policy_model,
reward_model=self.reward_model,
ref_policy=self.policy_ref_model,
processing_class=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
# Test that training completes without errors
trainer.train()
# Check if objective/rlhf_reward is available
self.assertIn("objective/rlhf_reward", trainer.state.log_history[-1])
def test_rloo_training_with_custom_reward(self):
# dummy reward function
def reward_function(texts):
# based on length of text
rewards = [len(text) for text in texts]
return rewards
with tempfile.TemporaryDirectory() as tmp_dir:
training_args = RLOOConfig(
output_dir=tmp_dir,
per_device_train_batch_size=2,
per_device_eval_batch_size=2,
total_episodes=1,
num_train_epochs=1,
max_steps=2,
report_to="none",
)
# Create a simple dataset
dummy_text = [{"content": "Hello World!", "role": "user"}]
dummy_data = self.tokenizer.apply_chat_template(dummy_text)
dummy_dataset = Dataset.from_dict({"input_ids": [dummy_data, dummy_data]})
trainer = RLOOTrainer(
config=training_args,
policy=self.policy_model,
reward_model=reward_function,
ref_policy=self.policy_ref_model,
processing_class=self.tokenizer,
train_dataset=dummy_dataset,
eval_dataset=dummy_dataset,
)
# Test that training completes without errors
trainer.train()
# Check if objective/rlhf_reward is available
self.assertIn("objective/rlhf_reward", trainer.state.log_history[-1])
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