import argparse import resource import time import warnings from contextlib import nullcontext import torch import torch.distributed as dist from data_utils import RandomDataset from model_utils import format_numel_str, get_model_numel from performance_evaluator import PerformanceEvaluator, get_profile_context from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload, MixedPrecision from tqdm import tqdm from transformers import AutoConfig, AutoModelForCausalLM from transformers.models.llama.configuration_llama import LlamaConfig import colossalai from colossalai.accelerator import get_accelerator from colossalai.booster import Booster from colossalai.booster.plugin import GeminiPlugin, HybridParallelPlugin, TorchFSDPPlugin from colossalai.cluster import DistCoordinator from colossalai.lazy import LazyInitContext from colossalai.nn.optimizer import HybridAdam from colossalai.pipeline.schedule.v_schedule import PipelineGraph from colossalai.shardformer import PipelineGradientCheckpointConfig warnings.filterwarnings("ignore") # ============================== # Constants # ============================== # We have lots of llamas for your choice! MODEL_CONFIGS = { "100m": LlamaConfig( max_position_embeddings=4096, num_hidden_layers=4, num_attention_heads=32, intermediate_size=2048, hidden_size=1024, ), "5b": LlamaConfig(max_position_embeddings=4096, num_key_value_heads=8), "7b": LlamaConfig(max_position_embeddings=4096), # "7b": LlamaConfig(num_hidden_layers=4, max_position_embeddings=4096), "13b": LlamaConfig( hidden_size=5120, intermediate_size=13824, num_hidden_layers=40, num_attention_heads=40, max_position_embeddings=4096, ), "70b": LlamaConfig( hidden_size=8192, intermediate_size=28672, num_hidden_layers=80, num_attention_heads=64, max_position_embeddings=4096, num_key_value_heads=8, ), } def main(): # ============================== # Parse Arguments # ============================== parser = argparse.ArgumentParser() parser.add_argument("-c", "--config", type=str, default="7b", help="Model configuration") parser.add_argument( "-p", "--plugin", choices=["gemini", "gemini_auto", "fsdp", "fsdp_cpu", "3d", "3d_cpu"], default="gemini", help="Choose which plugin to use", ) parser.add_argument("-b", "--batch_size", type=int, default=2, help="Batch size") parser.add_argument("-s", "--num_steps", type=int, default=5, help="Number of steps to run") parser.add_argument("-i", "--ignore_steps", type=int, default=2, help="Number of steps to ignore") parser.add_argument("-g", "--grad_checkpoint", action="store_true", help="Use gradient checkpointing") parser.add_argument("-l", "--max_length", type=int, default=4096, help="Max sequence length") parser.add_argument( "-w", "--warmup_ratio", type=float, default=0.8, help="warm up ratio of non-model data. Only for gemini-auto" ) parser.add_argument("-m", "--memory_limit", type=int, help="Gemini memory limit in mb") parser.add_argument("-x", "--xformers", action="store_true", help="Use xformers") parser.add_argument("--shard_param_frac", type=float, default=1.0, help="Shard param fraction. Only for gemini") parser.add_argument("--offload_optim_frac", type=float, default=0.0, help="Offload optim fraction. Only for gemini") parser.add_argument("--offload_param_frac", type=float, default=0.0, help="Offload param fraction. Only for gemini") parser.add_argument("--tp", type=int, default=1, help="Tensor parallel size") parser.add_argument("--sp", type=int, default=1, help="Sequence parallel size") parser.add_argument("--extra_dp", type=int, default=1, help="Extra data parallel size, used for Gemini") parser.add_argument("--pp", type=int, default=1, help="Pipeline parallel size") parser.add_argument("--mbs", type=int, default=1, help="Micro batch size of pipeline parallel") parser.add_argument("--zero", type=int, default=0, help="Zero Stage when hybrid plugin is enabled") parser.add_argument("--custom-ckpt", action="store_true", help="Customize checkpoint", default=False) parser.add_argument("--pp_style", default="1f1b", choices=["1f1b", "interleaved", "zbv"]) parser.add_argument("--n_chunks", default=1, help="number of model chunks", type=eval) parser.add_argument("--profile", action="store_true", help="Profile the code") parser.add_argument( "--nsys", action="store_true", help="Use nsys for profiling. \ You should put something like this before colossalai launch: \ nsys profile -w true -t cuda,cudnn,cublas -s cpu --capture-range=cudaProfilerApi --capture-range-end=stop --cudabacktrace=true -x true --python-backtrace=cuda -o prof_out", ) parser.add_argument("--disable-async-reduce", action="store_true", help="Disable the asynchronous reduce operation") parser.add_argument("--prefetch_num", type=int, default=0, help="chunk prefetch max number") parser.add_argument("--no_cache", action="store_true") parser.add_argument("--use_fp8_comm", action="store_true", default=False, help="for using fp8 during communication") parser.add_argument("--use_fp8", action="store_true", default=False, help="for using fp8 linear") parser.add_argument("--overlap_p2p", action="store_true", default=True, help="for using overlap p2p") parser.add_argument("--overlap_allgather", action="store_true") parser.add_argument( "--sp_mode", default="all_to_all", choices=["all_to_all", "ring_attn", "ring", "split_gather"], help="Sequence parallelism mode", ) args = parser.parse_args() colossalai.launch_from_torch() coordinator = DistCoordinator() def empty_init(): pass # ckpt config for LLaMA3-70B on 64 H100 GPUs hybrid_kwargs = ( { "gradient_checkpoint_config": PipelineGradientCheckpointConfig( num_ckpt_layers_per_stage=[19, 19, 19, 13], ), "num_layers_per_stage": [19, 20, 20, 21], "pp_style": "interleaved", } if args.custom_ckpt else {} ) # ============================== # Initialize Booster # ============================== if args.config in MODEL_CONFIGS: config = MODEL_CONFIGS[args.config] else: config = AutoConfig.from_pretrained(args.config, trust_remote_code=True) use_empty_init = True if args.plugin == "gemini": plugin = GeminiPlugin( precision="bf16", shard_param_frac=args.shard_param_frac, offload_optim_frac=args.offload_optim_frac, offload_param_frac=args.offload_param_frac, tp_size=args.tp, extra_dp_size=args.extra_dp, enable_fused_normalization=torch.cuda.is_available(), enable_flash_attention=args.xformers, max_prefetch=args.prefetch_num, enable_async_reduce=not args.disable_async_reduce, use_fp8=args.use_fp8, fp8_communication=args.use_fp8_comm, ) elif args.plugin == "gemini_auto": plugin = GeminiPlugin( placement_policy="auto", precision="bf16", warmup_non_model_data_ratio=args.warmup_ratio, tp_size=args.tp, extra_dp_size=args.extra_dp, enable_fused_normalization=torch.cuda.is_available(), max_prefetch=args.prefetch_num, enable_async_reduce=not args.disable_async_reduce, enable_flash_attention=args.xformers, use_fp8=args.use_fp8, fp8_communication=args.use_fp8_comm, ) elif args.plugin == "fsdp": if use_empty_init: plugin = TorchFSDPPlugin( mixed_precision=MixedPrecision( param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16, ), param_init_fn=empty_init(), fp8_communication=args.use_fp8_comm, ) else: plugin = TorchFSDPPlugin( mixed_precision=MixedPrecision( param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16, ), fp8_communication=args.use_fp8_comm, ) elif args.plugin == "fsdp_cpu": if use_empty_init: plugin = TorchFSDPPlugin( mixed_precision=MixedPrecision( param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16, ), cpu_offload=CPUOffload(offload_params=True), param_init_fn=empty_init(), fp8_communication=args.use_fp8_comm, ) else: plugin = TorchFSDPPlugin( mixed_precision=MixedPrecision( param_dtype=torch.float16, reduce_dtype=torch.float16, buffer_dtype=torch.float16, ), cpu_offload=CPUOffload(offload_params=True), fp8_communication=args.use_fp8_comm, ) elif args.plugin == "3d": if args.pp_style == "zbv": mem_f = 34 * config.hidden_size + 5 * config.num_attention_heads * args.max_length mem_w = -32 * config.hidden_size mem_b = -mem_w - mem_f scheduler_nodes = PipelineGraph( n_stage=args.pp, n_micro=args.batch_size // args.mbs, f_cost=1000, b_cost=1000, w_cost=1000, c_cost=1, f_mem=mem_f * 1.5, b_mem=mem_b * 1.5, w_mem=mem_w * 1.5, ).get_v_schedule() else: scheduler_nodes = None plugin = HybridParallelPlugin( tp_size=args.tp, pp_size=args.pp, pp_style=args.pp_style, num_model_chunks=args.n_chunks, zero_stage=args.zero, sp_size=args.sp, sequence_parallelism_mode=args.sp_mode, enable_sequence_parallelism=args.sp > 1, enable_fused_normalization=torch.cuda.is_available(), enable_flash_attention=args.xformers, microbatch_size=args.mbs, precision="bf16", enable_metadata_cache=not args.no_cache, overlap_allgather=args.overlap_allgather, use_fp8=args.use_fp8, fp8_communication=args.use_fp8_comm, scheduler_nodes=scheduler_nodes, **hybrid_kwargs, ) elif args.plugin == "3d_cpu": plugin = HybridParallelPlugin( tp_size=args.tp, pp_size=args.pp, pp_style=args.pp_style, num_model_chunks=args.n_chunks, zero_stage=args.zero, cpu_offload=True, enable_fused_normalization=torch.cuda.is_available(), enable_flash_attention=args.xformers, microbatch_size=args.mbs, initial_scale=2**8, precision="bf16", overlap_p2p=args.overlap_p2p, use_fp8=args.use_fp8, fp8_communication=args.use_fp8_comm, ) else: raise ValueError(f"Unknown plugin {args.plugin}") booster = Booster(plugin=plugin) # ============================== # Initialize Dataset and Dataloader # ============================== dp_size = getattr(plugin, "dp_size", coordinator.world_size) if args.config in MODEL_CONFIGS: config = MODEL_CONFIGS[args.config] else: config = AutoConfig.from_pretrained(args.config, trust_remote_code=True) torch.cuda.manual_seed(42) dataset = RandomDataset( num_samples=args.batch_size * args.num_steps * dp_size, max_length=args.max_length, vocab_size=config.vocab_size ) dataloader = plugin.prepare_dataloader(dataset, batch_size=args.batch_size, shuffle=True, drop_last=True, seed=42) # ============================== # Initialize Model and Optimizer # ============================== init_ctx = ( LazyInitContext(default_device=get_accelerator().get_current_device()) if isinstance(plugin, (GeminiPlugin, HybridParallelPlugin)) else nullcontext() ) init_kwargs = {} if config.model_type == "chatglm": init_kwargs["empty_init"] = False with init_ctx: model = AutoModelForCausalLM.from_config( config, trust_remote_code=True, **init_kwargs, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, ) if args.grad_checkpoint: model.gradient_checkpointing_enable() if config.model_type == "chatglm": model.transformer.encoder.gradient_checkpointing = True model_numel = get_model_numel(model) coordinator.print_on_master(f"Model params: {format_numel_str(model_numel)}") performance_evaluator = PerformanceEvaluator( model_numel, model.config.num_hidden_layers, model.config.hidden_size, model.config.vocab_size, args.grad_checkpoint, args.ignore_steps, dp_world_size=dp_size, ) optimizer = HybridAdam(model.parameters()) torch.set_default_dtype(torch.bfloat16) model, optimizer, _, dataloader, _ = booster.boost(model, optimizer, dataloader=dataloader) torch.set_default_dtype(torch.float) coordinator.print_on_master( f"Booster init max CUDA memory: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB" ) coordinator.print_on_master( f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024:.2f} MB" ) with get_profile_context( args.profile, args.ignore_steps, 1, # avoid creating massive log files save_dir=f"./profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-llama-{args.config}", nsys=args.nsys, ) as prof: if isinstance(plugin, HybridParallelPlugin) and args.pp > 1: data_iter = iter(dataloader) for step in tqdm(range(len(dataloader)), desc="Step", disable=not coordinator.is_master()): performance_evaluator.on_step_start(step) outputs = booster.execute_pipeline( data_iter, model, criterion=lambda outputs, inputs: outputs[0], optimizer=optimizer, return_loss=True, ) loss = outputs["loss"] if args.pp_style == "zbv": if coordinator.is_master(): print(f"Step {step} loss: {loss}") else: if coordinator.is_last_process(): print(f"Step {step} loss: {loss}") optimizer.step() optimizer.zero_grad() performance_evaluator.on_step_end(input_ids=torch.empty(args.batch_size, args.max_length)) prof.step() else: for step, batch in enumerate(tqdm(dataloader, desc="Step", disable=not coordinator.is_master())): performance_evaluator.on_step_start(step) outputs = model(**batch) loss = outputs[0] del outputs # free memory if dist.get_rank() == dist.get_world_size() - 1: print(f"Step {step} loss: {loss}") booster.backward(loss, optimizer) optimizer.step() optimizer.zero_grad() performance_evaluator.on_step_end(**batch) prof.step() performance_evaluator.on_fit_end() coordinator.print_on_master(f"Max CUDA memory usage: {get_accelerator().max_memory_allocated()/1024**2:.2f} MB") if __name__ == "__main__": main()