[example] add profile util for llama

examples/language/llama/benchmark.py

@@ -1,11 +1,12 @@
 import argparse
 import resource
+import time
 from contextlib import nullcontext
 
 import torch
 from data_utils import RandomDataset
 from model_utils import format_numel_str, get_model_numel
-from performance_evaluator import PerformanceEvaluator
+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
@@ -76,6 +77,7 @@ def main():
     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("--profile", action="store_true", help="Enable profiling", default=False)
     args = parser.parse_args()
 
     colossalai.launch_from_torch()
@@ -110,6 +112,7 @@ def main():
             extra_dp_size=args.extra_dp,
             enable_fused_normalization=torch.cuda.is_available(),
             enable_flash_attention=args.xformers,
+            max_prefetch=10,
         )
     elif args.plugin == "gemini_auto":
         plugin = GeminiPlugin(
@@ -246,25 +249,37 @@ def main():
         f"Booster init max CPU memory: {resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1024:.2f} MB"
     )
 
-    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)
-            booster.execute_pipeline(
-                data_iter, model, criterion=lambda outputs, inputs: outputs[0], optimizer=optimizer, return_loss=False
-            )
-            optimizer.step()
-            optimizer.zero_grad()
-            performance_evaluator.on_step_end(input_ids=torch.empty(args.batch_size, args.max_length))
-    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]
-            booster.backward(loss, optimizer)
-            optimizer.step()
-            optimizer.zero_grad()
-            performance_evaluator.on_step_end(**batch)
+    with get_profile_context(
+        args.profile,
+        1,
+        len(dataloader) - 1,
+        save_dir=f"profile/{time.strftime('%H:%M', time.localtime())}-{args.plugin}-llama-{args.config}",
+    ) 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)
+                booster.execute_pipeline(
+                    data_iter,
+                    model,
+                    criterion=lambda outputs, inputs: outputs[0],
+                    optimizer=optimizer,
+                    return_loss=False,
+                )
+                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]
+                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")

examples/language/performance_evaluator.py

@@ -4,6 +4,7 @@ from typing import Optional
 import torch
 import torch.distributed as dist
 from torch import Tensor
+from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler
 
 from colossalai.accelerator import get_accelerator
 from colossalai.cluster import DistCoordinator
@@ -27,6 +28,27 @@ def all_reduce_mean(x: float, world_size: int) -> float:
     return tensor.item()
 
 
+def get_profile_context(enable_flag, warmup_steps, active_steps, save_dir):
+    class DummyProfiler:
+        def __init__(self):
+            self.step_number = 0
+
+        def step(self):
+            self.step_number += 1
+
+    if enable_flag:
+        return profile(
+            activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
+            schedule=schedule(wait=0, warmup=warmup_steps, active=active_steps),
+            on_trace_ready=tensorboard_trace_handler(save_dir),
+            # record_shapes=True,
+            # profile_memory=True,
+            with_stack=True,
+        )
+    else:
+        return DummyProfiler()
+
+
 class Timer:
     def __init__(self) -> None:
         self.start_time: Optional[float] = None