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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 os
import shutil
import tempfile
import pytest
import torch
import torch.distributed
import torch.multiprocessing as mp
from torch.distributed import init_device_mesh
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import MixedPrecision, ShardingStrategy
from transformers import AutoModelForCausalLM, AutoTokenizer, Qwen2Config
from verl.utils.activation_offload import enable_activation_offloading
from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager
from verl.utils.device import get_device_name, get_nccl_backend, get_torch_device
from verl.utils.fsdp_utils import MixedPrecisionPolicy, apply_fsdp2, get_fsdp_wrap_policy
def create_random_input_ids(batch_size, seq_len, vocab_size):
if get_device_name() == "cuda":
from flash_attn.bert_padding import unpad_input
elif get_device_name() == "npu":
from verl.utils.attention_utils import unpad_input
from verl.utils.model import compute_position_id_with_mask, create_random_mask
input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=get_device_name())
attention_mask = create_random_mask(
input_ids, max_ratio_of_left_padding=0.1, min_ratio_of_valid_token=0.5, max_ratio_of_valid_token=0.7
)
position_ids = compute_position_id_with_mask(attention_mask)
input_ids = unpad_input(input_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1)
position_ids = unpad_input(position_ids.unsqueeze(-1), attention_mask)[0].transpose(0, 1)
return input_ids, position_ids
def _fsdp_activation_offloading_test(rank, world_size, rendezvous_file, strategy="fsdp"):
get_torch_device().set_device(rank)
torch.distributed.init_process_group(
backend=get_nccl_backend(),
init_method=f"file://{rendezvous_file}",
rank=rank,
world_size=world_size,
)
device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=("dp",))
model_name = os.path.expanduser("~/models/Qwen/Qwen2.5-0.5B-Instruct")
config = Qwen2Config(num_hidden_layers=4)
with torch.device(get_device_name()):
model = AutoModelForCausalLM.from_config(
config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
)
model = model.to(device=get_device_name())
# Wrap model with FSDP
mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32)
if strategy == "fsdp":
model = FSDP(
model,
use_orig_params=False,
device_id=get_torch_device().current_device(),
sharding_strategy=ShardingStrategy.FULL_SHARD,
mixed_precision=mixed_precision,
device_mesh=device_mesh,
auto_wrap_policy=get_fsdp_wrap_policy(module=model),
)
else:
mp_policy = MixedPrecisionPolicy(
param_dtype=torch.bfloat16, reduce_dtype=torch.float32, cast_forward_inputs=True
)
fsdp_kwargs = {
"mesh": device_mesh,
"mp_policy": mp_policy,
}
apply_fsdp2(model, fsdp_kwargs, {})
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9)
# Create checkpoint manager
tokenizer = AutoTokenizer.from_pretrained(model_name)
checkpoint_manager = FSDPCheckpointManager(
model=model, optimizer=optimizer, lr_scheduler=lr_scheduler, tokenizer=tokenizer
)
# Generate sample input
batch_size = 2
seq_len = 32
vocab_size = 32000
# First input for initial update
input_ids1, position_ids1 = create_random_input_ids(batch_size, seq_len, vocab_size)
# Second input for verification
input_ids2, position_ids2 = create_random_input_ids(batch_size, seq_len, vocab_size)
# Step 1: Initial update and save checkpoint
outputs1 = model(input_ids=input_ids1, position_ids=position_ids1)
loss1 = outputs1.logits.mean()
loss1.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Save checkpoint after first update
temp_dir = tempfile.mkdtemp()
checkpoint_path = os.path.join(temp_dir, "checkpoint")
checkpoint_manager.save_checkpoint(local_path=checkpoint_path, hdfs_path=None, global_step=0)
# Step 2: Second update and forward pass
outputs2 = model(input_ids=input_ids2, position_ids=position_ids2)
loss2 = outputs2.logits.mean()
loss2.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Record logits after second update
with torch.no_grad():
logits_without_offloading = model(input_ids=input_ids2, position_ids=position_ids2).logits
# Step 3: wrap module with activation offloading and load checkpoint
enable_activation_offloading(model, strategy=strategy)
checkpoint_manager.load_checkpoint(checkpoint_path)
# Step 4: Repeat the second update with same input
outputs3 = model(input_ids=input_ids2, position_ids=position_ids2)
loss3 = outputs3.logits.mean()
loss3.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# Record logits after loaded checkpoint and update
with torch.no_grad():
logits_with_offloading = model(input_ids=input_ids2, position_ids=position_ids2).logits
# Step 4: Verify outputs match
torch.testing.assert_close(logits_without_offloading, logits_with_offloading, atol=0.0, rtol=0.0)
print(f"Activaiton offloading for {strategy} test passed on {world_size} GPUs!")
# Cleanup
shutil.rmtree(temp_dir)
torch.distributed.barrier()
torch.distributed.destroy_process_group()
@pytest.mark.parametrize("world_size", (2, 4))
@pytest.mark.parametrize("strategy", ("fsdp", "fsdp2"))
def test_activation_offloading(world_size, strategy, tmp_path):
rendezvous_file = str(tmp_path / "rdzv_file")
os.makedirs(os.path.dirname(rendezvous_file), exist_ok=True)
mp.spawn(
fn=_fsdp_activation_offloading_test,
args=(world_size, rendezvous_file, strategy),
nprocs=world_size,
join=True,
)
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