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": "nccl", "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"
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": "nccl", "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(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": "nccl", }, }, "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"