test_general_checkpoint_io.py

import tempfile
import pytest
import torch
import logging
from torch.optim import Adam
from torchvision.models import resnet18
from pathlib import Path
import os
import subprocess

from colossalai.checkpoint_io import GeneralCheckpointIO
from colossalai.testing import clear_cache_before_run, parameterize

# ========
# Note:
# 1. due to checkpoint IO can be quite slow if tested with all models, we will only test on resnet for now
# 2. we will test on both sharded and unsharded checkpoints
# 3. implement sharded checkpoint and test it
# ========


@clear_cache_before_run()
@parameterize('use_safetensors', [True, False])
def test_unsharded_checkpoint(use_safetensors: bool):
    # create a model and optimizer
    model = resnet18()
    optimizer = Adam(model.parameters(), lr=0.001)

    # create test data sample
    x = torch.randn(1, 3, 224, 224)

    # run fwd and bwd
    y = model(x)
    loss = y.sum()
    loss.backward()
    optimizer.step()

    # create a temp file for checkpoint
    if use_safetensors:
        suffix = ".safetensors"
    else:
        suffix = ".bin"
    model_ckpt_tempfile = tempfile.NamedTemporaryFile(suffix=suffix)
    optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile()

    # save the model and optimizer
    ckpt_io = GeneralCheckpointIO()
    ckpt_io.save_model(model, model_ckpt_tempfile.name, use_safetensors=use_safetensors)
    ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name)

    # create new model
    new_model = resnet18()
    new_optimizer = Adam(new_model.parameters(), lr=0.001)

    # load the model and optimizer
    ckpt_io.load_model(new_model, model_ckpt_tempfile.name)
    ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name)


    # check for model and optimizer state dict recursively
    recursive_check(model.state_dict(), new_model.state_dict())
    recursive_check(optimizer.state_dict(), new_optimizer.state_dict())

@pytest.mark.parametrize('use_safetensors', [True, False])
def test_sharded_checkpoint(use_safetensors: bool):
    # create a model and optimizer
    model = resnet18()
    optimizer = Adam(model.parameters(), lr=0.001)
    # create test data sample
    x = torch.randn(1, 3, 224, 224)

    # run fwd and bwd
    y = model(x)
    loss = y.sum()
    loss.backward()
    optimizer.step()

    # create a temp file for checkpoint
    if use_safetensors:
        suffix = ".safetensors"
        SAFE_WEIGHTS_INDEX_NAME = "model.safetensors.index.json"
    else:
        suffix = ".bin"
        WEIGHTS_INDEX_NAME = "model.bin.index.json"
    
    # model_ckpt_dir = tempfile.TemporaryDirectory(suffix=suffix)
    model_ckpt_dir = tempfile.TemporaryDirectory()
    optimizer_ckpt_tempfile = tempfile.NamedTemporaryFile()

    # save the model and optimizer
    ckpt_io = GeneralCheckpointIO()

    ckpt_io.save_model(model, model_ckpt_dir.name, True, True, "", 10, use_safetensors=use_safetensors)
    ckpt_io.save_optimizer(optimizer, optimizer_ckpt_tempfile.name, shard=False)
    
    # create new model
    new_model = resnet18()
    new_optimizer = Adam(new_model.parameters(), lr=0.001)

    ckpt_io.load_model(new_model, str(model_ckpt_dir.name), strict=True)
    ckpt_io.load_optimizer(new_optimizer, optimizer_ckpt_tempfile.name)

    # check for model and optimizer state dict recursively
    recursive_check(model.state_dict(), new_model.state_dict())
    recursive_check(optimizer.state_dict(), new_optimizer.state_dict())


# do recursive check for the optimizer state dict
# if the value is a dict, compare its values
# if the value is a list, comapre all elements one-by-one
# if the value is a torch.Tensor, use torch.equal
# otherwise use assertEqual
def recursive_check(d1, d2):
    for k, v in d1.items():
        if isinstance(v, dict):
            recursive_check(v, d2[k])
        elif isinstance(v, list):
            for i in range(len(v)):
                if isinstance(v[i], torch.Tensor):
                    assert torch.equal(v[i], d2[k][i])
                else:
                    assert v[i] == d2[k][i]
        elif isinstance(v, torch.Tensor):
            assert torch.equal(v, d2[k])
        else:
            assert v == d2[k]