import torch import torch.distributed as dist import deepspeed import argparse import pytest import json import os import numpy as np import time from common import distributed_test from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict, create_deepspeed_args TORCH_MAJOR = int(torch.__version__.split('.')[0]) TORCH_MINOR = int(torch.__version__.split('.')[1]) if TORCH_MAJOR < 1 or TORCH_MINOR < 8: pytest.skip("NCCL-based 1-bit compression requires torch 1.8 or higher", allow_module_level=True) def test_onebitadam_fp16_basic(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": "nccl" } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 } } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 model = SimpleModel(hidden_dim) @distributed_test(world_size=[1, 2]) def _test_onebitadam_fp16_basic(args, model, hidden_dim): model, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() _test_onebitadam_fp16_basic(args=args, model=model, hidden_dim=hidden_dim) def test_onebitadam_fp32_basic(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": "nccl" } }, "gradient_clipping": 1.0, } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 model = SimpleModel(hidden_dim) @distributed_test(world_size=[1, 2]) def _test_onebitadam_fp32_basic(args, model, hidden_dim): model, _, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device, dtype=torch.float) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() _test_onebitadam_fp32_basic(args=args, model=model, hidden_dim=hidden_dim) def test_onebitadam_exp_avg_mask(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": "nccl" } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 } } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 model = SimpleModel(hidden_dim) param_optimizer = list(model.named_parameters()) mask1 = torch.zeros_like(param_optimizer[0][1].data) for col in range(mask1.size()[1]): mask1[0][col] += 1 mask1 = torch.flatten(mask1) optimizer_grouped_parameters = [{ 'params': [param_optimizer[0][1]], 'weight_decay': 0.01, 'exp_avg_mask': mask1 }, { 'params': [param_optimizer[1][1]], 'weight_decay': 0.01 }] @distributed_test(world_size=[2]) def _test_onebitadam_exp_avg_mask(args, model, hidden_dim): model, optimizer, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=optimizer_grouped_parameters) data_loader = random_dataloader(model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() # Test whether the momentum mask works for v in optimizer.state.values(): if v['exp_avg'].size() == mask1.size(): assert torch.allclose(v['exp_avg'], v['exp_avg'].mul_(mask1.to(device=v['exp_avg'].device)), atol=1e-07), f"Momentum mask is not working properly" _test_onebitadam_exp_avg_mask(args=args, model=model, hidden_dim=hidden_dim) def test_onebitadam_checkpointing(tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": "nccl" } }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 } } args = args_from_dict(tmpdir, config_dict) hidden_dim = 10 model = SimpleModel(hidden_dim) param_optimizer = list(model.named_parameters()) mask1 = torch.zeros_like(param_optimizer[0][1].data) mask2 = torch.zeros_like(param_optimizer[0][1].data) for col in range(mask1.size()[1]): mask1[0][col] += 1 mask2[1][col] += 1 mask1 = torch.flatten(mask1) mask2 = torch.flatten(mask2) optimizer_grouped_parameters_1 = [{ 'params': [param_optimizer[0][1]], 'weight_decay': 0.01, 'exp_avg_mask': mask1 }, { 'params': [param_optimizer[1][1]], 'weight_decay': 0.01 }] optimizer_grouped_parameters_2 = [{ 'params': [param_optimizer[0][1]], 'weight_decay': 0.01, 'exp_avg_mask': mask2 }, { 'params': [param_optimizer[1][1]], 'weight_decay': 0.01 }] optimizer_grouped_parameters_3 = [{ 'params': [param_optimizer[0][1]], 'weight_decay': 0.01 }, { 'params': [param_optimizer[1][1]], 'weight_decay': 0.01 }] @distributed_test(world_size=[2]) def _test_onebitadam_checkpointing(mask1, mask2, args, model, hidden_dim): model_1, optimizer_1, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=optimizer_grouped_parameters_1) data_loader = random_dataloader(model=model_1, total_samples=10, hidden_dim=hidden_dim, device=model_1.device) for n, batch in enumerate(data_loader): loss = model_1(batch[0], batch[1]) model_1.backward(loss) model_1.step() # Test whether momentum mask still exist after saving checkpoint assert optimizer_1.optimizer.adam_freeze_key is True mask1 = mask1.to(device=optimizer_1.param_groups[0]['exp_avg_mask'].device) assert torch.allclose(optimizer_1.param_groups[0]['exp_avg_mask'], mask1, atol=1e-07), f"Incorrect momentum mask" save_folder = os.path.join(tmpdir, 'saved_checkpoint') # optimizer_1.optimizer.gather_compression_errors() model_1.save_checkpoint(save_folder, tag=None) time.sleep(5) assert torch.allclose(optimizer_1.param_groups[0]['exp_avg_mask'], mask1, atol=1e-07), f"Momentum mask should not change after saving checkpoint" model_2, optimizer_2, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=optimizer_grouped_parameters_2) # Test whether momentum mask stays the same after loading checkpoint mask2 = mask2.to(device=optimizer_2.param_groups[0]['exp_avg_mask'].device) assert torch.allclose(optimizer_2.param_groups[0]['exp_avg_mask'], mask2, atol=1e-07), f"Incorrect momentum mask" model_2.load_checkpoint(save_folder, tag=None, load_optimizer_states=True, load_lr_scheduler_states=True) assert torch.allclose(optimizer_2.param_groups[0]['exp_avg_mask'], mask2, atol=1e-07), f"Momentum mask should not change after loading checkpoint" # Test whether worker&server error is resetted for v in optimizer_2.state.values(): assert 'worker_error' not in v, f"Incorrect worker error" assert 'server_error' not in v, f"Incorrect server error" assert optimizer_2.optimizer.adam_freeze_key is True model_3, optimizer_3, _, _ = deepspeed.initialize(args=args, model=model, model_parameters=optimizer_grouped_parameters_3) optimizer_3.optimizer.freeze_step = 20 data_loader = random_dataloader(model=model_3, total_samples=50, hidden_dim=hidden_dim, device=model_3.device) for n, batch in enumerate(data_loader): loss = model_3(batch[0], batch[1]) model_3.backward(loss) model_3.step() assert optimizer_3.optimizer.adam_freeze_key is True # Test whether momentum mask stays the same after loading checkpoint assert 'exp_avg_mask' not in optimizer_3.param_groups[0], f"Incorrect momentum mask" model_3.load_checkpoint(save_folder, tag=None, load_optimizer_states=True, load_lr_scheduler_states=True) assert 'exp_avg_mask' not in optimizer_3.param_groups[0], f"Momentum mask should not change after loading checkpoint" # Test whether worker&server error is resetted for v in optimizer_3.state.values(): assert 'worker_error' not in v, f"Incorrect worker error" assert 'server_error' not in v, f"Incorrect server error" assert optimizer_3.optimizer.adam_freeze_key is False _test_onebitadam_checkpointing(mask1, mask2, args=args, model=model, hidden_dim=hidden_dim) def test_compressed_allreduce_basic(tmpdir): @distributed_test(world_size=[1, 2]) def _test_compressed_allreduce_basic(): from deepspeed.runtime.comm.nccl import NcclBackend size = dist.get_world_size() rank = dist.get_rank() backend = NcclBackend() local_rank = dist.get_rank() device = torch.device("cuda", dist.get_rank()) # A simulated compression function using torch.distributed def torch_sim(a): a_sign = a.sign().add_(1).bool().float().add_(-0.5).mul_(2.0) scale = a.norm() / np.sqrt(a.numel()) a_compressed = scale * a_sign a_sign = None worker_error = a - a_compressed dist.all_reduce(a_compressed) a_compressed.mul_(1 / dist.get_world_size()) a_server_sign = a_compressed.sign().add_(1).bool().float().add_(-0.5).mul_( 2.0) a_list = torch.chunk(a_compressed, chunks=dist.get_world_size()) server_scale = [ chunk_a.norm() / np.sqrt(chunk_a.numel()) for chunk_a in a_list ] a_sign_list = torch.chunk(a_server_sign, dist.get_world_size()) a_server_compressed = torch.cat( [server_scale[i] * a_sign_list[i] for i in range(dist.get_world_size())]) rank = dist.get_rank() server_error = a_list[rank] - server_scale[rank] * a_sign_list[rank] torch.cuda.synchronize() torch.distributed.barrier() return a_server_compressed, worker_error, server_error tensor_size = 300 * 2**20 server_size = int(tensor_size / size) if tensor_size % (8 * size) != 0: right_tensor_size = tensor_size + (8 * size - (tensor_size % (8 * size))) else: right_tensor_size = tensor_size right_server_size = right_tensor_size // size # Adding bias to the initialization of the gradient we are communicating # In order to get rid of the case where some elements in the gradient are too small a = (torch.rand(tensor_size, device=device) - 0.5) + 0.01 * rank worker_error = torch.zeros(right_tensor_size, device=device) server_error = torch.zeros(right_server_size, device=device) a_torch, worker_error_torch, server_error_torch = torch_sim(a) torch.cuda.empty_cache() a_after = backend.compressed_allreduce(a, worker_error, server_error, local_rank) threshold = 1e-6 magnitude_threshold = 1e-6 diff_mask = (a_after - a_torch) > threshold diff_server_mask = torch.chunk(diff_mask, size)[rank] mpi_server = torch.chunk(a_after, size)[rank] + server_error torch_server = torch.chunk(a_torch, size)[rank] + server_error_torch # If the number in the compensated_server_m is too small (e.g 1e-8), then calling sign() might be problematic # The test would skip those numbers that are too small in compensated_server_m check_mag_mask = mpi_server[diff_server_mask] > magnitude_threshold if torch.sum(check_mag_mask) != 0: print('Fails at {} of positions'.format(torch.sum(check_mag_mask))) assert torch.sum(diff_server_mask) == 0 or torch.sum(check_mag_mask) == 0 _test_compressed_allreduce_basic()