deepspeed test init

tests/core/drivers/torch_driver/test_deepspeed.py

@@ -0,0 +1,475 @@
+import os
+
+import pytest
+from pathlib import Path
+
+from fastNLP.core.drivers.torch_driver.deepspeed import DeepSpeedDriver
+from fastNLP.core.samplers import (
+    RandomSampler,
+    UnrepeatedSampler,
+    BucketedBatchSampler,
+    UnrepeatedRandomSampler,
+    UnrepeatedSequentialSampler,
+)
+from tests.helpers.models.torch_model import TorchNormalModel_Classification_1
+from tests.helpers.datasets.torch_data import TorchNormalDataset, TorchNormalXYDataset
+from tests.helpers.utils import magic_argv_env_context
+from fastNLP.envs.distributed import rank_zero_rm
+from fastNLP import logger
+
+from fastNLP.envs.imports import _NEED_IMPORT_TORCH, _NEED_IMPORT_DEEPSPEED
+
+if _NEED_IMPORT_TORCH:
+    import torch
+    import torch.distributed as dist
+    from torch.utils.data import DataLoader, BatchSampler
+    
+if _NEED_IMPORT_DEEPSPEED:
+    import deepspeed
+
+def generate_driver(labels, features, device=[0,1], fp16=False, output_from_new_proc="all"):
+    torch_model = TorchNormalModel_Classification_1(labels, features)
+    torch_opt = torch.optim.Adam(params=torch_model.parameters(), lr=0.01)
+    device = [torch.device(i) for i in device]
+    driver = DeepSpeedDriver(
+        model=torch_model,
+        parallel_device=device,
+        fp16=fp16,
+        output_from_new_proc=output_from_new_proc
+    )
+    driver.set_optimizers(torch_opt)
+    driver.setup()
+
+    return driver
+
+def dataloader_with_bucketedbatchsampler(dataset, length, batch_size, shuffle, drop_last):
+    """
+    建立一个 batch_sampler 为 BucketedBatchSampler 的 dataloader
+    """
+    dataloader = DataLoader(
+        dataset=dataset,
+        batch_sampler=BucketedBatchSampler(
+            dataset,
+            length,
+            batch_size,
+            shuffle=shuffle,
+            drop_last=drop_last,
+        ),
+    )
+
+    return dataloader
+
+def dataloader_with_randomsampler(dataset, batch_size, shuffle, drop_last, seed=0, unrepeated=False):
+    """
+    建立一个 sampler 为 RandomSampler 的 dataloader
+    """
+    if unrepeated:
+        sampler = UnrepeatedRandomSampler(dataset, shuffle, seed)
+    else:
+        sampler = RandomSampler(dataset, shuffle, seed=seed)
+    dataloader = DataLoader(
+        dataset,
+        sampler=sampler,
+        drop_last=drop_last,
+        batch_size=batch_size
+    )
+    return dataloader
+
+############################################################################
+#
+# 测试 TorchDDPDriver 的一些函数
+#
+############################################################################
+
+@pytest.mark.torch
+@magic_argv_env_context
+def test_multi_drivers():
+    """
+    测试使用了多个 TorchDDPDriver 的情况。
+    """
+    generate_driver(10, 10)
+    generate_driver(20, 10)
+    
+    with pytest.raises(RuntimeError):
+        # 设备设置不同，应该报错
+        generate_driver(20, 3, device=[0,1,2])
+        assert False
+    dist.barrier()
+
+    if dist.is_initialized():
+        dist.destroy_process_group()
+
+@magic_argv_env_context
+def test_multi_optimizers():
+    torch_model = TorchNormalModel_Classification_1(10, 10)
+    torch_opt = torch.optim.Adam(params=torch_model.parameters(), lr=0.01)
+    device = [torch.device(i) for i in device]
+    driver = DeepSpeedDriver(
+        model=torch_model,
+        parallel_device=device,
+    )
+    driver.set_optimizers([torch_opt, torch_opt])
+    with pytest.raises(ValueError):
+        driver.setup()
+
+    if dist.is_initialized():
+        dist.destroy_process_group()
+
+@pytest.mark.torch
+class TestDeepSpeedDriverFunction:
+    """
+    测试 TorchDeepSpeedDriver 一些简单函数的测试类，基本都是测试能否运行、是否存在 import 错误等问题
+    """
+
+    @magic_argv_env_context
+    def test_simple_functions(self):
+        """
+        简单测试多个函数
+        """
+        driver = generate_driver(10, 10)
+
+        """
+        测试 move_data_to_device 函数。这个函数仅调用了 torch_move_data_to_device ，测试例在
+        tests/core/utils/test_torch_utils.py中，就不重复测试了
+        """
+        driver.move_data_to_device(torch.rand((32, 64)))
+        dist.barrier()
+
+        """
+        测试 is_distributed 函数
+        """
+        assert driver.is_distributed() == True
+        dist.barrier()
+
+        """
+        测试 get_no_sync_context 函数
+        """
+        res = driver.get_model_no_sync_context()
+        dist.barrier()
+
+        """
+        测试 is_global_zero 函数
+        """
+        driver.is_global_zero()
+        dist.barrier()
+
+        """
+        测试 unwrap_model 函数
+        """
+        driver.unwrap_model()
+        dist.barrier()
+
+        """
+        测试 get_local_rank 函数
+        """
+        driver.get_local_rank()
+        dist.barrier()
+
+        """
+        测试 all_gather 函数
+        详细的测试在 test_dist_utils.py 中完成
+        """
+        obj = {
+            "rank": driver.global_rank
+        }
+        obj_list = driver.all_gather(obj, group=None)
+        for i, res in enumerate(obj_list):
+            assert res["rank"] == i
+
+        """
+        测试 broadcast_object 函数
+        详细的函数在 test_dist_utils.py 中完成
+        """
+        if driver.global_rank == 0:
+            obj = {
+                "rank": driver.global_rank
+            }
+        else:
+            obj = None
+        res = driver.broadcast_object(obj, src=0)
+        assert res["rank"] == 0
+
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+############################################################################
+#
+# 测试 save 和 load 相关的功能
+#
+############################################################################
+@pytest.mark.torch
+class TestSaveLoad:
+    """
+    测试多卡情况下 save 和 load 相关函数的表现
+    """
+
+    def setup_method(self):
+        self.dataset = TorchNormalXYDataset(100)
+
+    @magic_argv_env_context
+    @pytest.mark.parametrize("only_state_dict", ([True, False]))
+    def test_save_and_load_model(self, only_state_dict):
+        """
+        测试 save_model 和 load_model 函数
+        """
+        try:
+            path = "model"
+
+            dataloader = DataLoader(self.dataset, batch_size=2)
+            driver1, driver2 = generate_driver(20, 1), generate_driver(20, 1)
+
+            driver1.save_model(path, only_state_dict)
+
+            # 同步
+            dist.barrier()
+            driver2.load_model(path, only_state_dict)
+
+            for idx, batch in enumerate(dataloader):
+                batch = driver1.move_data_to_device(batch)
+                res1 = driver1.model(
+                    batch,
+                    fastnlp_fn=driver1.model.module.model.evaluate_step,
+                    # Driver.model -> DataParallel.module -> _FleetWrappingModel.model
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+                res2 = driver2.model(
+                    batch,
+                    fastnlp_fn=driver2.model.module.model.evaluate_step,
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+
+                assert torch.equal(res1["preds"], res2["preds"])
+        finally:
+            rank_zero_rm(path)
+
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+    @magic_argv_env_context
+    @pytest.mark.parametrize("only_state_dict", ([True, False]))
+    @pytest.mark.parametrize("fp16", ([True, False]))
+    @pytest.mark.parametrize("device", ([[0,1]]))
+    def test_save_and_load_with_bucketedbatchsampler(self, device, only_state_dict, fp16):
+        """
+        测试save和load函数，主要测试 dataloader 被替换了 sampler 之后的情况
+        """
+
+        try:
+            path = "model.ckp"
+            num_replicas = len(device)
+
+            driver1, driver2 = generate_driver(20, 1, device=device, fp16=fp16), \
+                                            generate_driver(20, 1, device=device, fp16=False)
+            dataloader = dataloader_with_bucketedbatchsampler(
+                self.dataset,
+                length=[10 for i in range(len(self.dataset))],
+                batch_size=4,
+                shuffle=True,
+                drop_last=False
+            )
+            dataloader.batch_sampler.set_distributed(
+                num_replicas=driver1.world_size,
+                rank=driver1.global_rank,
+                pad=True
+            )
+            num_consumed_batches = 4
+
+            already_seen_x_set = set()
+            already_seen_y_set = set()
+            driver1.set_sampler_epoch(dataloader, 4)
+            for idx, batch in enumerate(dataloader):
+                if idx >= num_consumed_batches:
+                    break
+                already_seen_x_set.update(batch["x"].reshape(-1, ).tolist())
+                already_seen_y_set.update(batch["y"].reshape(-1, ).tolist())
+
+            # 同步
+            dist.barrier()
+
+            # 保存状态
+            sampler_states = dataloader.batch_sampler.state_dict()
+            save_states = {"num_consumed_batches": num_consumed_batches}
+            driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+            dist.barrier()
+            # 加载
+            # 更改 batch_size
+            dataloader = dataloader_with_bucketedbatchsampler(
+                self.dataset,
+                length=[10 for i in range(len(self.dataset))],
+                batch_size=2,
+                shuffle=True,
+                drop_last=False
+            )
+            dataloader.batch_sampler.set_distributed(
+                num_replicas=driver2.world_size,
+                rank=driver2.global_rank,
+                pad=True
+            )
+            dist.barrier()
+            load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
+            dist.barrier()
+            replaced_loader = load_states.pop("dataloader")
+            
+            # 1. 检查 optimizer 的状态
+            # TODO optimizer 的 state_dict 总是为空
+
+            # 2. 检查 batch_sampler 是否被正确地加载和替换
+            assert not (replaced_loader is dataloader)
+            assert replaced_loader.batch_sampler is dataloader.batch_sampler
+            assert isinstance(replaced_loader.batch_sampler, BucketedBatchSampler)
+            if os.environ['FASTNLP_GLOBAL_RANK'] == '0':
+                assert replaced_loader.batch_sampler.seed == sampler_states["seed"]
+            assert replaced_loader.batch_sampler.num_consumed_samples == num_consumed_batches * 4 * num_replicas
+
+            # 3. 检查 fp16 是否被加载
+            if fp16:
+                assert not isinstance(driver2.grad_scaler, torch.cuda.amp.GradScaler)
+
+            # 4. 检查 model 的参数是否正确
+            # 5. 检查 batch_idx
+            start_batch = load_states.pop('batch_idx_in_epoch')
+            assert start_batch == 2 * num_consumed_batches
+            left_x_batches = set()
+            left_y_batches = set()
+            driver2.set_sampler_epoch(replaced_loader, 4)
+            for idx, batch in enumerate(replaced_loader):
+
+                left_x_batches.update(batch["x"].reshape(-1, ).tolist())
+                left_y_batches.update(batch["y"].reshape(-1, ).tolist())
+                res1 = driver1.model(
+                    batch,
+                    fastnlp_fn=driver1.model.module.model.evaluate_step,
+                    # Driver.model -> DataParallel.module -> _FleetWrappingModel.model
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+                res2 = driver2.model(
+                    batch,
+                    fastnlp_fn=driver2.model.module.model.evaluate_step,
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+                assert torch.equal(res1["preds"], res2["preds"])
+
+            assert len(left_x_batches) + len(already_seen_x_set) == len(self.dataset) / num_replicas
+            assert len(left_x_batches | already_seen_x_set) == len(self.dataset) / num_replicas
+            assert len(left_y_batches) + len(already_seen_y_set) == len(self.dataset) / num_replicas
+            assert len(left_y_batches | already_seen_y_set) == len(self.dataset) / num_replicas
+            dist.barrier()
+        finally:
+            rank_zero_rm(path)
+
+        if dist.is_initialized():
+            dist.destroy_process_group()
+
+    @magic_argv_env_context
+    @pytest.mark.parametrize("only_state_dict", ([True, False]))
+    @pytest.mark.parametrize("fp16", ([True, False]))
+    @pytest.mark.parametrize("device", ([[0,1]]))
+    def test_save_and_load_with_randomsampler(self, device, only_state_dict, fp16):
+        """
+        测试save和load函数，主要测试 dataloader 被替换了 batch_sampler 的情况
+        """
+
+        try:
+            path = "checkpoints/"
+
+            num_replicas = len(device)
+
+            driver1 = generate_driver(20, 1, device=device, fp16=fp16)
+            driver2 = generate_driver(20, 1, device=device, fp16=False)
+
+            dataloader = dataloader_with_randomsampler(self.dataset, 4, True, False, unrepeated=False)
+            dataloader.batch_sampler.sampler.set_distributed(
+                num_replicas=driver1.world_size,
+                rank=driver1.global_rank,
+                pad=True
+            )
+            num_consumed_batches = 4
+
+            already_seen_x_set = set()
+            already_seen_y_set = set()
+            driver1.set_sampler_epoch(dataloader, 4)
+            for idx, batch in enumerate(dataloader):
+                if idx >= num_consumed_batches:
+                    break
+                already_seen_x_set.update(batch["x"].reshape(-1, ).tolist())
+                already_seen_y_set.update(batch["y"].reshape(-1, ).tolist())
+
+            # 同步
+            dist.barrier()
+
+            # 保存状态
+            sampler_states = dataloader.batch_sampler.sampler.state_dict()
+            save_states = {"num_consumed_batches": num_consumed_batches}
+            if only_state_dict:
+                driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True)
+            else:
+                driver1.save_checkpoint(Path(path), save_states, dataloader, only_state_dict, should_save_model=True, input_spec=[torch.ones((16, 10))])
+            dist.barrier()  # 等待save成功
+            # 加载
+            # 更改 batch_size
+            dataloader = dataloader_with_randomsampler(self.dataset, 2, True, False, unrepeated=False)
+            dataloader.batch_sampler.sampler.set_distributed(
+                num_replicas=driver2.world_size,
+                rank=driver2.global_rank,
+                pad=True
+            )
+            load_states = driver2.load_checkpoint(Path(path), dataloader, only_state_dict, should_load_model=True)
+            replaced_loader = load_states.pop("dataloader")
+
+            # 1. 检查 optimizer 的状态
+            # TODO optimizer 的 state_dict 总是为空
+
+            # 2. 检查 sampler 是否被正确地加载和替换
+            assert not (replaced_loader is dataloader)
+            assert isinstance(replaced_loader.batch_sampler.sampler, RandomSampler)
+            if os.environ['FASTNLP_GLOBAL_RANK'] == '0':
+                assert replaced_loader.batch_sampler.sampler.seed == sampler_states["seed"]
+                assert replaced_loader.batch_sampler.sampler.epoch == sampler_states["epoch"]
+                assert len(replaced_loader.batch_sampler.sampler.dataset) == sampler_states["length"]
+                assert replaced_loader.batch_sampler.sampler.shuffle == sampler_states["shuffle"]
+            assert replaced_loader.batch_sampler.sampler.num_consumed_samples == 4 * num_consumed_batches * num_replicas
+
+            # 3. 检查 fp16 是否被加载
+            if fp16:
+                assert not isinstance(driver2.grad_scaler, torch.cuda.amp.GradScaler)
+
+            # 4. 检查 model 的参数是否正确
+            # 5. 检查 batch_idx
+            start_batch = load_states.pop('batch_idx_in_epoch')
+            assert start_batch == 2 * num_consumed_batches
+            left_x_batches = set()
+            left_y_batches = set()
+            driver2.set_sampler_epoch(replaced_loader, 4)
+            for idx, batch in enumerate(replaced_loader):
+
+                left_x_batches.update(batch["x"].reshape(-1, ).tolist())
+                left_y_batches.update(batch["y"].reshape(-1, ).tolist())
+                res1 = driver1.model(
+                    batch,
+                    fastnlp_fn=driver1.model.module.model.evaluate_step,
+                    # Driver.model -> DataParallel.module -> _FleetWrappingModel.model
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+                res2 = driver2.model(
+                    batch,
+                    fastnlp_fn=driver2.model.module.model.evaluate_step,
+                    fastnlp_signature_fn=None,
+                    wo_auto_param_call=False,
+                )
+                assert torch.equal(res1["preds"], res2["preds"])
+
+            assert len(left_x_batches) + len(already_seen_x_set) == len(self.dataset) / num_replicas
+            assert len(left_x_batches | already_seen_x_set) == len(self.dataset) / num_replicas
+            assert len(left_y_batches) + len(already_seen_y_set) == len(self.dataset) / num_replicas
+            assert len(left_y_batches | already_seen_y_set) == len(self.dataset) / num_replicas
+
+        finally:
+            rank_zero_rm(path)
+
+        if dist.is_initialized():
+            dist.destroy_process_group()