''' Copyright (c) 2024 Beijing Volcano Engine Technology Ltd. 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 base64 import os import tempfile import unittest from copy import deepcopy from unittest import TestCase import torch import veturboio class TestLoad(TestCase): @classmethod def setUpClass(cls): ENV_KMS_HOST = 'VETURBOIO_KMS_HOST' ENV_KMS_REGION = 'VETURBOIO_KMS_REGION' ENV_KMS_AK = 'VETURBOIO_KMS_ACCESS_KEY' ENV_KMS_SK = 'VETURBOIO_KMS_SECRET_KEY' ENV_KMS_KEYRING = 'VETURBOIO_KMS_KEYRING_NAME' ENV_KMS_KEY = 'VETURBOIO_KMS_KEY_NAME' os.environ[ENV_KMS_HOST] = 'open.volcengineapi.com' os.environ[ENV_KMS_REGION] = 'cn-beijing' os.environ[ENV_KMS_AK] = os.environ['CI_VENDOR_AK'] os.environ[ENV_KMS_SK] = os.environ['CI_VENDOR_SK'] os.environ[ENV_KMS_KEYRING] = 'datapipe_keyring' os.environ[ENV_KMS_KEY] = 'datapipe_key_ml_maas' cls.tempdir = tempfile.TemporaryDirectory() cls.tensors_0 = { "weight1": torch.randn(2000, 10), "weight2": torch.randn(2000, 10), } cls.tensors_1 = { "weight1": torch.randn(2000, 10), "weight2": torch.randn(2000, 10), "weight3": torch.randn(2000, 10), } cls.filepath_0 = os.path.join(cls.tempdir.name, "model_0.safetensors") cls.filepath_1 = os.path.join(cls.tempdir.name, "model_1.safetensors") veturboio.save_file(cls.tensors_0, cls.filepath_0) veturboio.save_file(cls.tensors_1, cls.filepath_1) cls.pt_filepath = os.path.join(cls.tempdir.name, "model.pt") torch.save(cls.tensors_0, cls.pt_filepath) # cipher os.environ["VETURBOIO_KEY"] = base64.b64encode(b"abcdefgh12345678").decode("ascii") os.environ["VETURBOIO_IV"] = base64.b64encode(b"1234567887654321").decode("ascii") cls.filepath_0_enc = os.path.join(cls.tempdir.name, "model_0_enc.safetensors") cls.filepath_1_enc = os.path.join(cls.tempdir.name, "model_1_enc.safetensors") veturboio.save_file(cls.tensors_0, cls.filepath_0_enc, use_cipher=True) veturboio.save_file(cls.tensors_1, cls.filepath_1_enc, use_cipher=True) cls.pt_filepath_enc = os.path.join(cls.tempdir.name, "model_enc.pt") veturboio.save_pt(cls.tensors_0, cls.pt_filepath_enc, use_cipher=True) # cipher with header os.environ["VETURBOIO_CIPHER_HEADER"] = "1" cls.filepath_0_enc_h = os.path.join(cls.tempdir.name, "model_0_enc_h.safetensors") veturboio.save_file(cls.tensors_0, cls.filepath_0_enc_h, use_cipher=True) cls.pt_filepath_enc_h = os.path.join(cls.tempdir.name, "model_enc_h.pt") veturboio.save_pt(cls.tensors_0, cls.pt_filepath_enc_h, use_cipher=True) if torch.cuda.is_available(): cls.cuda_tensors_0 = deepcopy(cls.tensors_0) cls.cuda_tensors_1 = deepcopy(cls.tensors_1) for key in cls.cuda_tensors_0.keys(): cls.cuda_tensors_0[key] = cls.cuda_tensors_0[key].cuda() for key in cls.cuda_tensors_1.keys(): cls.cuda_tensors_1[key] = cls.cuda_tensors_1[key].cuda() @classmethod def tearDownClass(cls): # cls.tempdir.cleanup() pass def _run_pipeline(self, tensors, filepath, map_location, use_cipher, enable_fast_mode=True): loaded_tensors = veturboio.load( filepath, map_location=map_location, use_cipher=use_cipher, enable_fast_mode=enable_fast_mode ) for key in tensors.keys(): self.assertTrue(torch.allclose(tensors[key], loaded_tensors[key])) return loaded_tensors def test_pipeline_cpu(self): self._run_pipeline(self.tensors_0, self.filepath_0, "cpu", use_cipher=False) self._run_pipeline(self.tensors_0, self.filepath_0_enc, "cpu", use_cipher=True) self._run_pipeline(self.tensors_0, self.filepath_0, "cpu", use_cipher=False, enable_fast_mode=False) self._run_pipeline(self.tensors_0, self.filepath_0_enc, "cpu", use_cipher=True, enable_fast_mode=False) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_pipeline_cuda(self): self._run_pipeline(self.cuda_tensors_0, self.filepath_0, "cuda:0", use_cipher=False) self._run_pipeline(self.cuda_tensors_0, self.filepath_0_enc, "cuda:0", use_cipher=True) self._run_pipeline(self.cuda_tensors_0, self.filepath_0, "cuda:0", use_cipher=False, enable_fast_mode=False) self._run_pipeline(self.cuda_tensors_0, self.filepath_0_enc, "cuda:0", use_cipher=True, enable_fast_mode=False) def test_read_multi_state_dict_cpu(self): load_tensor_0 = self._run_pipeline(self.tensors_0, self.filepath_0, "cpu", use_cipher=False) load_tensor_1 = self._run_pipeline(self.tensors_1, self.filepath_1, "cpu", use_cipher=False) self.assertEqual(len(load_tensor_0), 2) self.assertEqual(len(load_tensor_1), 3) load_tensor_0_enc = self._run_pipeline(self.tensors_0, self.filepath_0_enc, "cpu", use_cipher=True) load_tensor_1_enc = self._run_pipeline(self.tensors_1, self.filepath_1_enc, "cpu", use_cipher=True) self.assertEqual(len(load_tensor_0_enc), 2) self.assertEqual(len(load_tensor_1_enc), 3) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_read_multi_state_dict_cuda(self): load_tensor_0 = self._run_pipeline(self.cuda_tensors_0, self.filepath_0, "cuda:0", use_cipher=False) load_tensor_1 = self._run_pipeline(self.cuda_tensors_1, self.filepath_1, "cuda:0", use_cipher=False) self.assertEqual(len(load_tensor_0), 2) self.assertEqual(len(load_tensor_1), 3) load_tensor_0_enc = self._run_pipeline(self.cuda_tensors_0, self.filepath_0_enc, "cuda:0", use_cipher=True) load_tensor_1_enc = self._run_pipeline(self.cuda_tensors_1, self.filepath_1_enc, "cuda:0", use_cipher=True) self.assertEqual(len(load_tensor_0_enc), 2) self.assertEqual(len(load_tensor_1_enc), 3) def test_load_pt_cpu(self): loaded_tensors = veturboio.load(self.pt_filepath, map_location="cpu", use_cipher=False) for key in self.tensors_0.keys(): self.assertTrue(torch.allclose(self.tensors_0[key], loaded_tensors[key])) loaded_tensors_enc = veturboio.load(self.pt_filepath_enc, map_location="cpu", use_cipher=True) for key in self.tensors_0.keys(): self.assertTrue(torch.allclose(self.tensors_0[key], loaded_tensors_enc[key])) @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_load_pt_cuda(self): loaded_tensors = veturboio.load(self.pt_filepath, map_location="cuda:0", use_cipher=False) for key in self.tensors_0.keys(): self.assertTrue(torch.allclose(self.cuda_tensors_0[key], loaded_tensors[key])) loaded_tensors_enc = veturboio.load(self.pt_filepath_enc, map_location="cuda:0", use_cipher=True) for key in self.tensors_0.keys(): self.assertTrue(torch.allclose(self.cuda_tensors_0[key], loaded_tensors_enc[key])) def test_load_cipher_header_cpu(self): os.environ["VETURBOIO_CIPHER_HEADER"] = "1" self._run_pipeline(self.tensors_0, self.filepath_0_enc_h, "cpu", use_cipher=True) self._run_pipeline(self.tensors_0, self.pt_filepath_enc_h, "cpu", use_cipher=True) self._run_pipeline(self.tensors_0, self.filepath_0_enc_h, "cpu", use_cipher=True, enable_fast_mode=False) self._run_pipeline(self.tensors_0, self.pt_filepath_enc_h, "cpu", use_cipher=True, enable_fast_mode=False) del os.environ["VETURBOIO_CIPHER_HEADER"] @unittest.skipIf(not torch.cuda.is_available(), "CUDA not available") def test_load_cipher_header_cuda(self): os.environ["VETURBOIO_CIPHER_HEADER"] = "1" self._run_pipeline(self.cuda_tensors_0, self.filepath_0_enc_h, "cuda:0", use_cipher=True) self._run_pipeline(self.cuda_tensors_0, self.pt_filepath_enc_h, "cuda:0", use_cipher=True) self._run_pipeline( self.cuda_tensors_0, self.filepath_0_enc_h, "cuda:0", use_cipher=True, enable_fast_mode=False ) self._run_pipeline( self.cuda_tensors_0, self.pt_filepath_enc_h, "cuda:0", use_cipher=True, enable_fast_mode=False ) del os.environ["VETURBOIO_CIPHER_HEADER"]