From fbcd509ff9e529be6a45919dbed636d834e52dce Mon Sep 17 00:00:00 2001 From: li126com Date: Tue, 26 Sep 2023 16:11:56 +0800 Subject: [PATCH] test pp --- new_test.py | 324 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 324 insertions(+) create mode 100644 new_test.py diff --git a/new_test.py b/new_test.py new file mode 100644 index 0000000..909aa52 --- /dev/null +++ b/new_test.py @@ -0,0 +1,324 @@ +import copy +import multiprocessing as mp +import random + +import numpy as np +import pytest +import torch +from torch import nn +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.testing import assert_close + +import internlm +from internlm.core.context.parallel_context import Config +from internlm.core.trainer import Trainer + +from internlm.core.scheduler import ( + InterleavedPipelineScheduler, + NonPipelineScheduler, + PipelineScheduler, + SchedulerHook, +) +from internlm.data.utils import unpack_data +from internlm.core.scheduler.pipeline_scheduler import get_tensor_shape +from internlm.core.context import global_context as gpc +from internlm.core.context import ParallelMode +from internlm.core.scheduler import SchedulerMetricHook +from internlm.model.metrics import AccPerplex +from internlm.train import ( + get_train_data_loader, + get_validation_data_loader, + initialize_llm_profile, + initialize_model, + initialize_optimizer, + load_new_batch, + record_current_batch_training_metrics, +) +from internlm.core.engine import Engine +from internlm.model.loss import FlashGPTLMLoss +from internlm.core.gradient_handler import PipelineSharedModuleGradientHandler +from internlm.core.trainer import TrainState +from internlm.solver.pipeline_utils import partition_uniform + + +import torch.distributed as dist + +class MlpModel(nn.Module): + + def __init__(self, start, end): + super().__init__() + self.part = [start , end] + self.blocks = nn.ModuleList([nn.Linear(8, 8, bias=False) for lid in range(end -start)]) + + def forward(self, hidden_states=None, cu_seqlens=None, input_ids=None, indexes=None, inference_params=None): + print(f'pp stage: {gpc.get_local_rank(ParallelMode.PIPELINE)} MLP {self.part} fwd:', input_ids.shape, flush=True) + print(gpc.get_global_rank(), 'len_blocsk:', len(self.blocks), flush=True) + current_device = torch.cuda.current_device() + print(gpc.get_global_rank(), 'current_device:', current_device, flush=True) + input_ids = input_ids.to(current_device) + print(gpc.get_global_rank(), 'mlp_input_data:', input_ids, input_ids.shape, type(input_ids), flush=True) + x = self.blocks[0](input_ids) + self.blocks[1](input_ids) + print(gpc.get_global_rank(), 'mlp_output_data:', x, x.shape, flush=True) + return x + +config = Config( + dict( + HIDDEN_SIZE=4, + parallel=dict(zero1=1, pipeline=dict(size=8, interleaved_overlap=False), sequence_parallel=False, tensor=1), + model_type="INTERNLM", + data=dict(seq_len=8, micro_num=16, micro_bsz=1, pack_sample_into_one=False, min_length=0, total_steps=9999), + model=dict( + dtype=torch.bfloat16, + ), + resume_tb_folder="", + tensorboard_folder="", + alert_address=None, + monitor=dict(alert=dict(enable_feishu_alert=False, feishu_alert_address=None, light_monitor_address=None)), + grad_scaler=dict( + fp16=dict( + initial_scale=1, + min_scale=1, + growth_interval=1, + ), + growth_factor=1.1, + backoff_factor=0.9, + max_scale=1, + hysteresis=1, + ), + adam=dict( + lr=1e-4, + adam_beta1=0.9, + adam_beta2=0.95, + adam_beta2_c=0, + adam_eps=1e-8, + weight_decay=0.01, + ), + hybrid_zero_optimizer=dict( + overlap_sync_grad=False, + overlap_sync_param=False, + reduce_bucket_size=512 * 1024 * 1024, + clip_grad_norm=1.0, + ), + beta2_scheduler = dict( + init_beta2=0.95, + c=0, + cur_iter=-1, + ), + lr_scheduler = dict( + total_steps=100, + init_steps=0, # optimizer_warmup_step + warmup_ratio=0.01, + eta_min=1e-5, + last_epoch=-1, + ) + ) +) + + +def build_environment(rank, world_size): + import os + + os.environ["RANK"] = str(rank) + os.environ["LOCAL_RANK"] = str(rank) + os.environ["WORLD_SIZE"] = str(world_size) + os.environ["MASTER_ADDR"] = "127.0.0.1" + os.environ["MASTER_PORT"] = "33333" + torch.cuda.empty_cache() + # launcher="torch" + internlm.launch_from_torch(config=config, seed=1024) + + +def loose_close(a, b, dtype: torch.dtype = torch.float32): + + if dtype is torch.float32: + rtol = 1.3e-6 + atol = 1e-5 + elif dtype is torch.bfloat16: + rtol = 2e-2 + atol = 2e-2 + + if isinstance(a, torch.Tensor): + a = a.detach().to(dtype) + b = b.detach().to(dtype) + + assert_close(a, b, rtol=rtol, atol=atol) + +def seed_all(seed, cuda_deterministic=False): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + if cuda_deterministic: # slower, more reproducible + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + else: + torch.backends.cudnn.deterministic = False + torch.backends.cudnn.benchmark = True + + + +def _build_generic_model_1d(num_layers, num_chunks, device=torch.device("cuda"), **kwargs): + """ + build generic model 1d + + Args: + num_layers (int): The number of layer. + num_chunks (int): The number of partitions in pipeline parallel. + device (Optional[Union[str, torch.device]]): The device will be used. torch.device("cuda") by default. + + """ + pipeline_size = gpc.get_world_size(ParallelMode.PIPELINE) + pipeline_rank = gpc.get_local_rank(ParallelMode.PIPELINE) + + all_parts = partition_uniform(num_layers, pipeline_size, num_chunks) + parts = all_parts[pipeline_rank] + if gpc.is_rank_for_log(): + print(f"The layer sharding is {all_parts}.", flush=True) + + models = [] + for start, end in parts: + models.append(MlpModel(start, end).cuda()) + torch.distributed.barrier() + if len(models) == 1: + model = models[0] + else: + model = nn.ModuleList(models) + + return model + + +class MyLoss(nn.Module): + def __init__(self): + super().__init__() + + def forward(self, logits, labels): + loss = torch.nn.MSELoss(reduction='sum') + print(logits, flush=True) + print(labels, flush=True) + return loss(logits, labels) + +def exam_pipeline_parallel(args): + import os + # rank, world_size = args + + rank = os.environ["RANK"] + world_size = os.environ["WORLD_SIZE"] + + build_environment(rank, world_size) + local_rank = int(os.environ["LOCAL_RANK"]) + print('rank_com:', rank, local_rank) + device = torch.device(f"cuda:{local_rank}") + # print('device_id:', device) + # torch.cuda.set_device(device) + seed_all(1024) + dtype=gpc.config.model["dtype"] + + # torch_model = MlpModel().to(device) + # pp_model = copy.deepcopy(torch_model).to(dtype) + pp_model = _build_generic_model_1d(num_layers=16, num_chunks=1) + pp_model = pp_model.to(dtype) + print(pp_model, flush=True) + + scheduler_hooks = [ + SchedulerMetricHook( + skip=True + ), + ] + + micro_num = gpc.config.data.micro_num + seq_len = gpc.config.data.seq_len + gpc.config.NUM_MICRO_BATCHES = micro_num + scheduler = PipelineScheduler( + data_process_func=None, + num_microbatches=micro_num, + dtype=gpc.config.model["dtype"], + tensor_shape=None, + scatter_gather_tensors=False, + scheduler_hooks=scheduler_hooks, + ) + + print(f"gpc.config.hybrid_zero_optimizer: {gpc.config.hybrid_zero_optimizer}", flush=True) + # optimizer, beta2_scheduler, lr_scheduler = initialize_optimizer(model=pp_model) + # criterion = FlashGPTLMLoss(parallel_output=False, label_smoothing=0) + + from internlm.solver.optimizer.hybrid_zero_optim import BaseOptimizer + optimizer = BaseOptimizer(torch.optim.AdamW( + params=[{"params": pp_model.parameters()}], + lr=1e-4, + betas=(0.9, 0.95), + eps=1e-8, + )) + + engine = Engine( + model=pp_model, + optimizer=optimizer, + lr_scheduler=None, + beta2_scheduler=None, + criterion=MyLoss().to(dtype), + gradient_handlers= [dict(type="PipelineSharedModuleGradientHandler")], + clip_grad_norm=gpc.config.hybrid_zero_optimizer.get("clip_grad_norm", 0.0), + ) + + scheduler.pre_processing(engine) + engine.train() + # engine.zero_grad() + + x_list = [] + y_list = [] + for _ in range(micro_num): + x_list.append([i for i in range(seq_len)]) + y_list.append([i for i in range(seq_len)]) + xs = torch.tensor(x_list).to(device).to(dtype) + yx = torch.tensor(y_list).to(device).to(dtype) + xs.requires_grad_() + yx.requires_grad_() + print(xs.shape, yx.shape, flush=True) + input_list = [{'input_ids':xs}, yx] + + # torch_input = torch.tensor([[0,1,2,3]]).to(device).to(torch.float32) + # torch_label = torch.tensor([[1]]).to(device).to(torch.int64) + # print('label_shape:', input_list[1].shape) + # input_list = [{'input_ids':torch.rand(1, 4).cuda()}, torch.rand(1, 4).cuda()] + # input = input_list[0] + # print(input) + # output = torch_model(input) + # print(output) + print('local_rank:', gpc.get_local_rank(ParallelMode.PIPELINE), 'start schedule', flush=True) + _, _, loss = scheduler.forward_backward_step(engine, input_list, forward_only=False, return_loss=True, return_output_label=False) + print('local_rank:', gpc.get_local_rank(ParallelMode.PIPELINE), 'end schedule', flush=True) + + dist.barrier() + torch.cuda.synchronize() + engine.step() + torch.cuda.synchronize() + # torch_output = torch_model(input_ids=torch_input) + # torch_loss = criterion(torch_output, torch_label).unsqueeze(0) + + # if rank == 0: + # print('loss:', loss) + # print('torch_loss:', torch_loss) + #loose_close(loss, torch_loss, dtype=dtype) + # torch_loss.backward() + print('local_rank:', gpc.get_local_rank(ParallelMode.PIPELINE), 'everything3') + + + + + +# def test_pipeline_parallel(): +# ctx = mp.get_context("spawn") +# with ctx.Pool(processes=8) as pool: +# pool.map( +# exam_pipeline_parallel, +# [[rank, 8] for rank in range(8)], +# ) +# pool.close() + +# pool.join() + + +if __name__ == "__main__": + # pytest.main(["-s", "-q", "test_pipeline.py"]) + exam_pipeline_parallel(None)