[gemini] add profiler in the demo (#2534)

examples/language/gpt/gemini/commons/utils.py

+import time
+from contextlib import nullcontext
+
 import torch
+from torch.profiler import ProfilerActivity, profile, schedule, tensorboard_trace_handler
+
+
+class DummyProfiler:
+
+    def __init__(self):
+        self.step_number = 0
+
+    def step(self):
+        self.step_number += 1
 
 
 # Randomly Generated Data
@@ -10,3 +23,19 @@ def get_data(batch_size, seq_len, vocab_size):
 
 def get_tflops(model_numel, batch_size, seq_len, step_time):
     return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12)
+
+
+def get_profile_context(enable_flag, warmup_steps, active_steps, save_dir):
+    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)
+    else:
+        return nullcontext(DummyProfiler())
+
+
+def get_time_stamp():
+    cur_time = time.strftime("%d-%H:%M", time.localtime())
+    return cur_time

examples/language/gpt/gemini/train_gpt_demo.py

@@ -6,7 +6,7 @@ import psutil
 import torch
 import torch.nn as nn
 from commons.model_zoo import model_builder
-from commons.utils import get_data, get_tflops
+from commons.utils import get_data, get_profile_context, get_tflops, get_time_stamp
 from packaging import version
 from torch.nn.parallel import DistributedDataParallel as DDP
 
@@ -201,7 +201,8 @@ def main():
 
     WARMUP_STEPS = 1
     assert WARMUP_STEPS < NUM_STEPS, "warmup steps should smaller than the total steps"
-    assert (NUM_STEPS - WARMUP_STEPS) % 2 == 1, "the number of valid steps should be odd to take the median "
+    assert (NUM_STEPS - WARMUP_STEPS) % 2 == 1, "the number of valid steps should be odd to take the median"
+    PROF_FLAG = False    # The flag of profiling, False by default
 
     disable_existing_loggers()
     colossalai.launch_from_torch(config={})
@@ -292,7 +293,8 @@ def main():
     torch.cuda.synchronize()
     model.train()
     tflops_list = []
-    for n in range(NUM_STEPS):
+
+    def train_step():
         # we just use randomly generated data here
         input_ids, attn_mask = get_data(BATCH_SIZE, SEQ_LEN, VOCAB_SIZE)
         optimizer.zero_grad()
@@ -331,6 +333,16 @@ def main():
         if n >= WARMUP_STEPS:
             tflops_list.append(step_tflops)
 
+    demo_profiler = get_profile_context(PROF_FLAG,
+                                        WARMUP_STEPS,
+                                        NUM_STEPS - WARMUP_STEPS,
+                                        save_dir=f"profile/{get_time_stamp()}-demo")
+
+    with demo_profiler as prof:
+        for n in range(NUM_STEPS):
+            train_step()
+            prof.step()
+
     tflops_list.sort()
     median_index = ((NUM_STEPS - WARMUP_STEPS) >> 1) + WARMUP_STEPS
     logger.info(f"Median TFLOPS is {tflops_list[median_index]:.3f}")