test_activation_offload.py

# Copyright 2025 Bytedance Ltd. and/or its affiliates
#
# 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
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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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.fsdp_utils import MixedPrecisionPolicy, apply_fsdp2, get_fsdp_wrap_policy


def _fsdp_activation_offloading_test(rank, world_size, rendezvous_file, strategy="fsdp"):
    torch.cuda.set_device(rank)
    torch.distributed.init_process_group(
        backend="nccl",
        init_method=f"file://{rendezvous_file}",
        rank=rank,
        world_size=world_size,
    )
    device_mesh = init_device_mesh("cuda", mesh_shape=(world_size,), mesh_dim_names=("dp",))

    model_name = "Qwen/Qwen2.5-0.5B-Instruct"
    config = Qwen2Config(num_hidden_layers=4)

    with torch.device("cuda"):
        model = AutoModelForCausalLM.from_config(
            config=config, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2"
        )
        model = model.to(device="cuda")

    # 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=torch.cuda.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 = torch.randint(0, vocab_size, (batch_size, seq_len), device="cuda")
    attention_mask1 = torch.ones_like(input_ids1)

    # Second input for verification
    input_ids2 = torch.randint(0, vocab_size, (batch_size, seq_len), device="cuda")
    attention_mask2 = torch.ones_like(input_ids2)

    # Step 1: Initial update and save checkpoint
    outputs1 = model(input_ids=input_ids1, attention_mask=attention_mask1)
    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, attention_mask=attention_mask2)
    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, attention_mask=attention_mask2).logits

    # Step 3: wrap module with activation offloading and load checkpoint
    enable_activation_offloading(model, "fsdp")
    checkpoint_manager.load_checkpoint(checkpoint_path)

    # Step 4: Repeat the second update with same input
    outputs3 = model(input_ids=input_ids2, attention_mask=attention_mask2)
    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, attention_mask=attention_mask2).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,
    )