[example] update opt example using booster api (#3918)

examples/language/opt/opt_benchmark.py

@@ -0,0 +1,146 @@
+import time
+
+import torch
+import transformers
+from transformers import AutoConfig, OPTForCausalLM
+from transformers.utils.versions import require_version
+import tqdm
+
+import colossalai
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.logging import disable_existing_loggers, get_dist_logger
+from colossalai.tensor import ProcessGroup, ShardSpec
+from colossalai.utils import get_current_device
+from colossalai.zero import ColoInitContext
+from colossalai.booster import Booster
+from colossalai.booster.plugin import GeminiPlugin, LowLevelZeroPlugin, TorchDDPPlugin
+from colossalai.cluster import DistCoordinator
+
+from args import parse_benchmark_args
+
+require_version("transformers>=4.20.0", "To fix: pip install -r requirements.txt")
+
+
+def format_num(num: int, bytes=False):
+    """Scale bytes to its proper format, e.g. 1253656 => '1.20MB'"""
+    factor = 1024 if bytes else 1000
+    suffix = "B" if bytes else ""
+    for unit in ["", " K", " M", " G", " T", " P"]:
+        if num < factor:
+            return f"{num:.2f}{unit}{suffix}"
+        num /= factor
+
+
+def get_data(batch_size, seq_len, vocab_size):
+    input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
+    attention_mask = torch.ones_like(input_ids)
+    return input_ids, attention_mask
+
+
+def colo_memory_cap(size_in_GB):
+    from colossalai.utils import colo_device_memory_capacity, colo_set_process_memory_fraction, get_current_device
+    cuda_capacity = colo_device_memory_capacity(get_current_device())
+    if size_in_GB * (1024**3) < cuda_capacity:
+        colo_set_process_memory_fraction(size_in_GB * (1024**3) / cuda_capacity)
+        print(f"Limiting GPU memory usage to {size_in_GB} GB")
+
+
+def main():
+
+    args = parse_benchmark_args()
+
+    # Launch ColossalAI
+    colossalai.launch_from_torch(config={}, seed=args.seed)
+    coordinator = DistCoordinator()
+    world_size = coordinator.world_size
+
+    # Manage loggers
+    disable_existing_loggers()
+    logger = get_dist_logger()
+    if coordinator.is_master():
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        transformers.utils.logging.set_verbosity_error()
+    
+    # Whether to set limit of memory capacity
+    if args.mem_cap > 0:
+        colo_memory_cap(args.mem_cap)
+    
+    # Build OPT model
+    # Initialize the model under ColoInitContext if using GeminiPlugin
+    config = AutoConfig.from_pretrained(args.model_name_or_path)
+    if args.plugin == 'gemini':
+        shard_pg = ProcessGroup(tp_degree=world_size)
+        default_dist_spec = ShardSpec([-1], [world_size])
+        with ColoInitContext(device='cpu',
+                            default_dist_spec=default_dist_spec,
+                            default_pg=shard_pg):
+            model = OPTForCausalLM(config)
+    else:
+        model = OPTForCausalLM(config)
+    logger.info(f"Finish loading model from {args.model_name_or_path}", ranks=[0])
+
+    # Enable gradient checkpointing
+    model.gradient_checkpointing_enable()
+
+    # Set plugin
+    booster_kwargs = {}
+    if args.plugin == 'torch_ddp_fp16':
+        booster_kwargs['mixed_precision'] = 'fp16'
+    if args.plugin.startswith('torch_ddp'):
+        plugin = TorchDDPPlugin()
+    elif args.plugin == 'gemini':
+        plugin = GeminiPlugin(device=get_current_device(),
+                        placement_policy='cpu',
+                        pin_memory=True,
+                        strict_ddp_mode=True,
+                        initial_scale=2**5)
+    elif args.plugin == 'low_level_zero':
+        plugin = LowLevelZeroPlugin(initial_scale=2**5)
+    logger.info(f"Set plugin as {args.plugin}", ranks=[0])
+
+    # Set optimizer
+    optimizer = HybridAdam(model.parameters(), lr=args.learning_rate)
+
+    # Set booster
+    booster = Booster(plugin=plugin, **booster_kwargs)
+    model, optimizer, _, _, _ = booster.boost(model, optimizer)
+    
+    SEQ_LEN = 1024
+    VOCAB_SIZE = 50257
+
+    # Start training.
+    logger.info(f"Start testing", ranks=[0])
+    progress_bar = tqdm.tqdm(total=args.max_train_steps, desc="Training Step", disable=not coordinator.is_master())
+    
+    torch.cuda.synchronize()
+    model.train()
+    start_time = time.time()
+   
+    for _ in range(args.max_train_steps):
+
+        input_ids, attn_mask = get_data(args.batch_size, SEQ_LEN, VOCAB_SIZE)
+        optimizer.zero_grad()
+        outputs = model(input_ids=input_ids, attention_mask=attn_mask, labels=input_ids, use_cache=False)
+        loss = outputs['loss']
+        booster.backward(loss, optimizer)
+        optimizer.step()
+
+        torch.cuda.synchronize()
+        progress_bar.update(1)
+       
+    # Compute Statistics   
+    end_time = time.time()
+    throughput = "{:.4f}".format((world_size * args.max_train_steps * args.batch_size) / (end_time - start_time))
+    max_mem = format_num(torch.cuda.max_memory_allocated(device=torch.cuda.current_device()), bytes=True)
+    
+    logger.info(f"Testing finished, " 
+                f"batch size per gpu: {args.batch_size}, "
+                f"plugin: {args.plugin}, "
+                f"throughput: {throughput}, "
+                f"maximum memory usage per gpu: {max_mem}.",
+                ranks=[0])
+
+
+if __name__ == "__main__":
+    main()