# Copyright 2024 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
#
#     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 tempfile
import shutil
import torch
import copy
import torch.distributed
from torch.distributed import init_device_mesh
from verl.utils.distributed import initialize_global_process_group
from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import Qwen2Config

from torch.distributed.fsdp import FullyShardedDataParallel as FSDP, ShardingStrategy, MixedPrecision, \
            CPUOffload


def test_fsdp_ckpt():
    assert torch.cuda.device_count() >= 2, "need at least 2 gpus for test"
    local_rank, rank, world_size = initialize_global_process_group()
    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=1)

    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)

    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)

    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_before_load = model(input_ids=input_ids2, attention_mask=attention_mask2).logits

    # Step 3: Load checkpoint and repeat second update
    checkpoint_manager.load_checkpoint(checkpoint_path)

    # 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_after_load = model(input_ids=input_ids2, attention_mask=attention_mask2).logits

    # Step 4: Verify outputs match
    torch.testing.assert_close(logits_before_load, logits_after_load, atol=0.0, rtol=0.0)
    print("Checkpoint save/load test passed!")

    # Cleanup
    shutil.rmtree(temp_dir)
    torch.distributed.barrier()


if __name__ == '__main__':
    test_fsdp_ckpt()