# Copyright (c) Microsoft Corporation. # SPDX-License-Identifier: Apache-2.0 # DeepSpeed Team import torch import torch.nn as nn import deepspeed.comm as dist import deepspeed import pytest import copy import os import numpy as np from deepspeed.runtime.pipe.topology import PipeDataParallelTopology from deepspeed.ops.op_builder import OpBuilder from deepspeed.runtime.pipe.module import PipelineModule from unit.common import DistributedTest from unit.simple_model import SimpleModel, random_dataloader from unit.alexnet_model import AlexNetPipe, train_cifar from unit.util import required_minimum_torch_version from deepspeed.accelerator import get_accelerator PipeTopo = PipeDataParallelTopology if not required_minimum_torch_version(major_version=1, minor_version=8): pytest.skip( "NCCL-based 1-bit compression requires torch 1.8 or higher", allow_module_level=True, ) rocm_version = OpBuilder.installed_rocm_version() if rocm_version[0] > 4: pytest.skip("NCCL-based 1-bit compression is not yet supported w. ROCm 5 until cupy supports ROCm 5", allow_module_level=True) @pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=["fp32", "fp16"]) class TestOneBitAdamBasic(DistributedTest): world_size = 2 def test(self, dtype): 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": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": (dtype == torch.float16), "loss_scale": 0, "initial_scale_power": 16, }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader( model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device, dtype=dtype, ) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() class TestOneBitAdamExpAvgMask(DistributedTest): world_size = 2 def test(self): 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": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 }, ] model, optimizer, _, _ = deepspeed.initialize( config=config_dict, 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" class TestOneBitAdamCheckpointing(DistributedTest): world_size = 2 def test(self, 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": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 }, ] model_1, optimizer_1, _, _ = deepspeed.initialize( config=config_dict, 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") model_1.save_checkpoint(save_folder, tag=None) 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( config=config_dict, 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 reset 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( config=config_dict, 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 reset 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 def test_overflow(self, 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": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=100, hidden_dim=hidden_dim, device=model.device) save_folder = os.path.join(tmpdir, "saved_checkpoint") for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) if dist.get_rank() == 0 and n >= 10: loss = loss * 1000000.0 model.backward(loss) dist.barrier() model.step() dist.barrier() model.save_checkpoint(save_folder, tag=None) @pytest.mark.parametrize( "topo_config", [ { "num_pp": 1, "num_dp": 4 }, { "num_pp": 2, "num_dp": 2 }, { "num_pp": 4, "num_dp": 1 }, ], ) class TestOneBitAdamFP16Pipeline(DistributedTest): world_size = 4 def test(self, topo_config): config_dict = { "train_batch_size": 16, "train_micro_batch_size_per_gpu": 4, "steps_per_print": 20, "optimizer": { "type": "OneBitAdam", "params": { "lr": 0.00001, "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 3e-7, "freeze_step": 200, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "zero_optimization": { "stage": 0 }, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, "pipeline": { "seed_layers": True, "activation_checkpoint_interval": 1 }, } topo = PipeTopo(**topo_config) steps = 500 # Must be >=100 # Allocate model for consistent initial weights. init_net = AlexNetPipe() test_net = copy.deepcopy(init_net) test_model = PipelineModule(layers=test_net.to_layers(), topology=topo, loss_fn=nn.CrossEntropyLoss()) test_losses = train_cifar( test_model, config=config_dict, num_steps=steps, fp16=config_dict["fp16"]["enabled"], ) @pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=["fp32", "fp16"]) class TestZeroOneAdamBasic(DistributedTest): world_size = 2 def test(self, dtype): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "ZeroOneAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "var_freeze_step": 4, "var_update_scaler": 1, "local_step_scaler": 1, "local_step_clipper": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": (dtype == torch.float16), "loss_scale": 0, "initial_scale_power": 16, }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader( model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device, dtype=dtype, ) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() class TestZeroOneAdamExpAvgMask(DistributedTest): world_size = 2 def test(self): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "ZeroOneAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "var_freeze_step": 4, "var_update_scaler": 1, "local_step_scaler": 1, "local_step_clipper": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 }, ] model, optimizer, _, _ = deepspeed.initialize( config=config_dict, 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" class TestZeroOneAdamCheckpointing(DistributedTest): world_size = 2 def test(self, tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "ZeroOneAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "var_freeze_step": 4, "var_update_scaler": 1, "local_step_scaler": 1, "local_step_clipper": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 }, ] model_1, optimizer_1, _, _ = deepspeed.initialize( config=config_dict, 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 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") model_1.save_checkpoint(save_folder, tag=None) 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( config=config_dict, 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 reset 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" model_3, optimizer_3, _, _ = deepspeed.initialize( config=config_dict, 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() # 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 reset 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" def test_overflow(self, tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "ZeroOneAdam", "params": { "lr": 0.00015, "weight_decay": 0.01, "var_freeze_step": 4, "var_update_scaler": 1, "local_step_scaler": 1, "local_step_clipper": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=100, hidden_dim=hidden_dim, device=model.device) save_folder = os.path.join(tmpdir, "saved_checkpoint") for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) if dist.get_rank() == 0 and n >= 10: loss = loss * 1000000.0 model.backward(loss) dist.barrier() model.step() dist.barrier() model.save_checkpoint(save_folder, tag=None) @pytest.mark.parametrize( "topo_config", [ { "num_pp": 1, "num_dp": 4 }, { "num_pp": 2, "num_dp": 2 }, { "num_pp": 4, "num_dp": 1 }, ], ) class TestZeroOneAdamFP16Pipeline(DistributedTest): world_size = 4 def test(self, topo_config): config_dict = { "train_batch_size": 16, "train_micro_batch_size_per_gpu": 4, "steps_per_print": 20, "optimizer": { "type": "ZeroOneAdam", "params": { "lr": 0.00001, "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 3e-7, "var_freeze_step": 4, "var_update_scaler": 1, "local_step_scaler": 1, "local_step_clipper": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "zero_optimization": { "stage": 0 }, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, "pipeline": { "seed_layers": True, "activation_checkpoint_interval": 1 }, } topo = PipeTopo(**topo_config) steps = 500 # Must be >=100 # Allocate model for consistent initial weights. init_net = AlexNetPipe() test_net = copy.deepcopy(init_net) test_model = PipelineModule(layers=test_net.to_layers(), topology=topo, loss_fn=nn.CrossEntropyLoss()) test_losses = train_cifar( test_model, config=config_dict, num_steps=steps, fp16=config_dict["fp16"]["enabled"], ) @pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=["fp32", "fp16"]) class TestOneBitLambBasic(DistributedTest): world_size = 2 def test(self, dtype): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitLamb", "params": { "lr": 0.00015, "weight_decay": 0.01, "max_coeff": 0.3, "min_coeff": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), "coeff_beta": 0.9, "factor_max": 1.0, "factor_min": 0.5, "factor_threshold": 0.1, }, }, "gradient_clipping": 1.0, "fp16": { "enabled": (dtype == torch.float16), "loss_scale": 0, "initial_scale_power": 16, }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader( model=model, total_samples=50, hidden_dim=hidden_dim, device=model.device, dtype=dtype, ) for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) model.backward(loss) model.step() class TestOneBitLampExpAvgMask(DistributedTest): world_size = 2 def test(self): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitLamb", "params": { "lr": 0.00015, "weight_decay": 0.01, "max_coeff": 0.3, "min_coeff": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), "coeff_beta": 0.9, "factor_max": 1.0, "factor_min": 0.5, "factor_threshold": 0.1, }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 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 }, ] model, optimizer, _, _ = deepspeed.initialize( config=config_dict, 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" class TestOneBitLambCheckpointing(DistributedTest): world_size = 2 def test(self, tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitLamb", "params": { "lr": 0.00015, "weight_decay": 0.01, "max_coeff": 0.3, "min_coeff": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), "coeff_beta": 0.9, "factor_max": 1.0, "factor_min": 0.5, "factor_threshold": 0.1, }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } 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 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 }, ] model_1, optimizer_1, _, _ = deepspeed.initialize( config=config_dict, 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.lamb_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" scaling_coeff_1 = [] for v in optimizer_1.state.values(): assert "scaling_coeff" in v, f"Incorrect scaling_coeff" scaling_coeff_1.append(v["scaling_coeff"]) save_folder = os.path.join(tmpdir, "saved_checkpoint") model_1.save_checkpoint(save_folder, tag=None) 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( config=config_dict, 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 reset assert len(optimizer_2.optimizer.worker_errors) == 0, f"Incorrect worker error" assert len(optimizer_2.optimizer.server_errors) == 0, f"Incorrect server error" # Test whether scaling_coeffs is loaded correctly scaling_coeff_2 = [] for v in optimizer_2.state.values(): assert "scaling_coeff" in v, f"Incorrect scaling_coeff" scaling_coeff_2.append(v["scaling_coeff"]) assert list(sorted(scaling_coeff_2)) == list(sorted(scaling_coeff_1)), f"Incorrect scaling_coeffs" assert optimizer_2.optimizer.lamb_freeze_key is True model_3, optimizer_3, _, _ = deepspeed.initialize( config=config_dict, 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.lamb_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 reset assert len(optimizer_3.optimizer.worker_errors) == 0, f"Incorrect worker error" assert len(optimizer_3.optimizer.server_errors) == 0, f"Incorrect server error" # Test whether scaling_coeffs, lamb_coeff_freeze, last_factor are reset for v in optimizer_3.state.values(): assert v["lamb_coeff_freeze"] == 0.0, f"Incorrect lamb_coeff_freeze" assert v["last_factor"] == 1.0, f"Incorrect last_factor" assert "scaling_coeff" not in v, f"Incorrect scaling_coeff" assert optimizer_3.optimizer.lamb_freeze_key is False def test_overflow(self, tmpdir): config_dict = { "train_batch_size": 2, "steps_per_print": 1, "optimizer": { "type": "OneBitLamb", "params": { "lr": 0.00015, "weight_decay": 0.01, "max_coeff": 0.3, "min_coeff": 0.01, "freeze_step": 2, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), "coeff_beta": 0.9, "factor_max": 1.0, "factor_min": 0.5, "factor_threshold": 0.1, }, }, "gradient_clipping": 1.0, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, } hidden_dim = 10 model = SimpleModel(hidden_dim) model, _, _, _ = deepspeed.initialize(config=config_dict, model=model, model_parameters=model.parameters()) data_loader = random_dataloader(model=model, total_samples=100, hidden_dim=hidden_dim, device=model.device) save_folder = os.path.join(tmpdir, "saved_checkpoint") for n, batch in enumerate(data_loader): loss = model(batch[0], batch[1]) if dist.get_rank() == 0 and n >= 10: loss = loss * 1000000.0 model.backward(loss) dist.barrier() model.step() dist.barrier() model.save_checkpoint(save_folder, tag=None) @pytest.mark.parametrize( "topo_config", [ { "num_pp": 1, "num_dp": 4 }, { "num_pp": 2, "num_dp": 2 }, { "num_pp": 4, "num_dp": 1 }, ], ) class TestOneBitLambFP16Pipeline(DistributedTest): world_size = 4 def test(self, topo_config): config_dict = { "train_batch_size": 16, "train_micro_batch_size_per_gpu": 4, "steps_per_print": 20, "optimizer": { "type": "OneBitLamb", "params": { "lr": 0.00001, "betas": [0.9, 0.999], "eps": 1e-8, "weight_decay": 3e-7, "freeze_step": 200, "cuda_aware": False, "comm_backend_name": get_accelerator().communication_backend_name(), }, }, "gradient_clipping": 1.0, "zero_optimization": { "stage": 0 }, "fp16": { "enabled": True, "loss_scale": 0, "initial_scale_power": 16 }, "pipeline": { "seed_layers": True, "activation_checkpoint_interval": 1 }, } topo = PipeTopo(**topo_config) steps = 500 # Must be >=100 # Allocate model for consistent initial weights. init_net = AlexNetPipe() test_net = copy.deepcopy(init_net) test_model = PipelineModule(layers=test_net.to_layers(), topology=topo, loss_fn=nn.CrossEntropyLoss()) test_losses = train_cifar( test_model, config=config_dict, num_steps=steps, fp16=config_dict["fp16"]["enabled"], ) @pytest.mark.sequential class TestCompressedAllReduceBasic(DistributedTest): world_size = 2 def test(self, tmpdir): 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(get_accelerator().device_name(), dist.get_rank()) # A simulated compression function using deepspeed.comm 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] get_accelerator().synchronize() dist.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) get_accelerator().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