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#
# 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 unittest
import torch
import torch.nn as nn
from tensordict import TensorDict
from transformers import AutoModelForCausalLM, Qwen3Config
from verl import DataProto
from verl.utils.device import get_device_name
from verl.workers.actor.dp_actor import DataParallelPPOActor
from verl.workers.config import FSDPActorConfig, OptimizerConfig
class MockTransformerModel(nn.Module):
"""Mock transformer model for testing DataParallelPPOActor"""
def __init__(self, vocab_size=1000, hidden_size=64):
super().__init__()
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.embedding = nn.Embedding(vocab_size, hidden_size)
self.transformer = nn.TransformerEncoder(
nn.TransformerEncoderLayer(d_model=hidden_size, nhead=4, batch_first=True), num_layers=2
)
self.lm_head = nn.Linear(hidden_size, vocab_size)
def forward(self, input_ids, attention_mask=None, position_ids=None, use_cache=False, **kwargs):
batch_size, seq_len = input_ids.shape
embeddings = self.embedding(input_ids)
hidden_states = self.transformer(embeddings)
logits = self.lm_head(hidden_states)
class MockOutput:
def __init__(self, logits):
self.logits = logits
return MockOutput(logits)
class TestDataParallelPPOActor(unittest.TestCase):
"""Test DataParallelPPOActor compute_log_prob and update_policy methods"""
@classmethod
def setUpClass(cls):
"""Set up distributed environment"""
if get_device_name() == "cuda":
backend_name = "nccl"
elif get_device_name() == "npu":
backend_name = "hccl"
else:
backend_name = "gloo"
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend=backend_name, init_method="env://")
cls.rank = torch.distributed.get_rank()
cls.world_size = torch.distributed.get_world_size()
if get_device_name() == "cuda":
torch.cuda.set_device(cls.rank)
cls.device = torch.device(f"cuda:{cls.rank}")
elif get_device_name() == "npu":
torch.npu.set_device(cls.rank)
cls.device = torch.device(f"npu:{cls.rank}")
else:
cls.device = torch.device("cpu")
def setUp(self):
"""Set up test fixtures"""
self.config = FSDPActorConfig(
strategy="fsdp2",
ppo_mini_batch_size=4,
ppo_micro_batch_size_per_gpu=2,
ppo_epochs=1,
clip_ratio=0.2,
entropy_coeff=0.01,
grad_clip=1.0,
use_dynamic_bsz=False,
use_torch_compile=False, # Disable torch.compile for testing
ulysses_sequence_parallel_size=1,
optim=OptimizerConfig(lr=1e-6),
rollout_n=1,
)
self.mock_model = MockTransformerModel(vocab_size=1000, hidden_size=64).to(self.device)
self.mock_optimizer = torch.optim.Adam(self.mock_model.parameters(), lr=1e-4)
self.actor = DataParallelPPOActor(
config=self.config, actor_module=self.mock_model, actor_optimizer=self.mock_optimizer
)
@classmethod
def tearDownClass(cls):
"""Clean up distributed environment"""
if torch.distributed.is_initialized():
torch.distributed.destroy_process_group()
def _create_test_data_for_compute_log_prob(self):
"""Create test DataProto for compute_log_prob method"""
batch_size = 2
prompt_length = 8
response_length = 4
total_length = prompt_length + response_length
vocab_size = 1000
input_ids = torch.randint(0, vocab_size, (batch_size, total_length)).to(self.device)
attention_mask = torch.ones(batch_size, total_length).to(self.device)
position_ids = torch.arange(total_length).unsqueeze(0).expand(batch_size, -1).to(self.device)
responses = input_ids[:, -response_length:] # Last part is the response
tensor_dict = TensorDict(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids,
"responses": responses,
},
batch_size=[batch_size],
)
meta_info = {"micro_batch_size": batch_size, "temperature": 1.0, "use_dynamic_bsz": False}
return DataProto(batch=tensor_dict, meta_info=meta_info)
def _create_test_data_for_update_policy(self):
"""Create test DataProto for update_policy method"""
batch_size = 4 # Must match ppo_mini_batch_size
prompt_length = 8
response_length = 4
total_length = prompt_length + response_length
vocab_size = 1000
input_ids = torch.randint(0, vocab_size, (batch_size, total_length)).to(self.device)
attention_mask = torch.ones(batch_size, total_length).to(self.device)
position_ids = torch.arange(total_length).unsqueeze(0).expand(batch_size, -1).to(self.device)
responses = input_ids[:, -response_length:]
response_mask = torch.ones(batch_size, response_length).to(self.device)
old_log_probs = torch.randn(batch_size, response_length).to(self.device) * 0.1 # Small values
advantages = torch.randn(batch_size, response_length).to(self.device) * 0.5
tensor_dict = TensorDict(
{
"input_ids": input_ids,
"attention_mask": attention_mask,
"position_ids": position_ids,
"responses": responses,
"response_mask": response_mask,
"old_log_probs": old_log_probs,
"advantages": advantages,
},
batch_size=[batch_size],
)
meta_info = {"temperature": 1.0}
return DataProto(batch=tensor_dict, meta_info=meta_info)
def test_compute_log_prob(self):
"""Test compute_log_prob method"""
data = self._create_test_data_for_compute_log_prob()
outputs = self.actor.compute_log_prob(data, calculate_entropy=True)
log_probs = outputs["log_probs"]
entropys = outputs["entropys"]
batch_size = data.batch["responses"].shape[0]
response_length = data.batch["responses"].shape[1]
self.assertIsInstance(log_probs, torch.Tensor)
self.assertEqual(log_probs.shape, (batch_size, response_length))
self.assertTrue(torch.all(torch.isfinite(log_probs)))
self.assertIsInstance(entropys, torch.Tensor)
self.assertEqual(entropys.shape, (batch_size, response_length))
self.assertTrue(torch.all(torch.isfinite(entropys)))
self.assertTrue(torch.all(entropys >= 0)) # Entropy should be non-negative
def test_compute_log_prob_without_entropy(self):
"""Test compute_log_prob method without entropy calculation"""
data = self._create_test_data_for_compute_log_prob()
outputs = self.actor.compute_log_prob(data, calculate_entropy=False)
log_probs = outputs["log_probs"]
entropys = outputs.get("entropys", None)
batch_size = data.batch["responses"].shape[0]
response_length = data.batch["responses"].shape[1]
self.assertIsInstance(log_probs, torch.Tensor)
self.assertEqual(log_probs.shape, (batch_size, response_length))
self.assertTrue(torch.all(torch.isfinite(log_probs)))
self.assertIsNone(entropys)
def test_update_policy(self):
"""Test update_policy method"""
data = self._create_test_data_for_update_policy()
metrics = self.actor.update_policy(data)
self.assertIsInstance(metrics, dict)
expected_metric_keys = [
"actor/pg_loss",
"actor/pg_clipfrac",
"actor/ppo_kl",
"actor/pg_clipfrac_lower",
"actor/grad_norm",
]
for key in expected_metric_keys:
self.assertIn(key, metrics)
if isinstance(metrics[key], list):
self.assertTrue(all(torch.isfinite(torch.tensor(v)) for v in metrics[key]))
else:
self.assertIsInstance(metrics[key], (float, int))
self.assertTrue(torch.isfinite(torch.tensor(metrics[key])))
def test_dataparallelppoactor_initialization(self):
"""Test DataParallelPPOActor initialization"""
self.assertIsNotNone(self.actor.actor_module)
self.assertIsNotNone(self.actor.actor_optimizer)
self.assertEqual(self.actor.config, self.config)
self.assertEqual(self.actor.config.strategy, "fsdp2")
self.assertEqual(self.actor.config.ppo_mini_batch_size, 4)
self.assertEqual(self.actor.config.clip_ratio, 0.2)
def test_dataparallelppoactor_with_qwen3_model(self):
"""Test DataParallelPPOActor with real Qwen3ForCausalLM model"""
qwen_config = Qwen3Config(
vocab_size=1000,
hidden_size=64,
intermediate_size=128,
num_hidden_layers=2,
num_attention_heads=4,
num_key_value_heads=2,
max_position_embeddings=512,
torch_dtype=torch.float32,
use_cache=False,
)
with torch.device(self.device):
qwen_model = AutoModelForCausalLM.from_config(config=qwen_config, torch_dtype=torch.float32).to(self.device)
qwen_optimizer = torch.optim.Adam(qwen_model.parameters(), lr=1e-4)
qwen_actor = DataParallelPPOActor(config=self.config, actor_module=qwen_model, actor_optimizer=qwen_optimizer)
data = self._create_test_data_for_compute_log_prob()
outputs = qwen_actor.compute_log_prob(data, calculate_entropy=True)
log_probs = outputs["log_probs"]
entropys = outputs["entropys"]
batch_size = data.batch["responses"].shape[0]
response_length = data.batch["responses"].shape[1]
self.assertIsInstance(log_probs, torch.Tensor)
self.assertEqual(log_probs.shape, (batch_size, response_length))
self.assertTrue(torch.all(torch.isfinite(log_probs)))
self.assertIsInstance(entropys, torch.Tensor)
self.assertEqual(entropys.shape, (batch_size, response_length))
self.assertTrue(torch.all(torch.isfinite(entropys)))
self.assertTrue(torch.all(entropys >= 0))
policy_data = self._create_test_data_for_update_policy()
metrics = qwen_actor.update_policy(policy_data)
self.assertIsInstance(metrics, dict)
expected_metric_keys = [
"actor/pg_loss",
"actor/pg_clipfrac",
"actor/ppo_kl",
"actor/pg_clipfrac_lower",
"actor/grad_norm",
]
for key in expected_metric_keys:
self.assertIn(key, metrics)
if isinstance(metrics[key], list):
self.assertTrue(all(torch.isfinite(torch.tensor(v)) for v in metrics[key]))
else:
self.assertIsInstance(metrics[key], (float, int))
self.assertTrue(torch.isfinite(torch.tensor(metrics[key])))
if __name__ == "__main__":
unittest.main()
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