Memory profiling

configs/neox_arguments.md

@@ -111,7 +111,7 @@ Logging Arguments
 
 - **git_hash**: str
 
- Default = 0eb8b39
+ Default = 8669123
 
  current git hash of repository
 
@@ -199,6 +199,54 @@ Logging Arguments
 
 
 
+- **memory_profiling**: bool
+
+ Default = False
+
+ Whether to take a memory snapshot of the model. Useful for debugging memory issues.
+
+
+
+- **memory_profiling_path**: str
+
+ Default = None
+
+ Path to save memory snapshot to.
+
+
+
+- **profile**: bool
+
+ Default = False
+
+ Enable nsys profiling. When using this option,
+ nsys options should be specified in commandline.
+ An example nsys commandline is
+ ```
+ nsys profile -s none -t nvtx,cuda -o <path/to/output_file>
+ --force-overwrite true
+ --capture-range=cudaProfilerApi
+ --capture-range-end=stop
+ ```
+
+
+
+- **profile_step_start**: int
+
+ Default = 10
+
+ Step to start profiling at.
+
+
+
+- **profile_step_stop**: int
+
+ Default = 12
+
+ Step to stop profiling at.
+
+
+
 ## NeoXArgsModel
 
 Model Arguments

megatron/neox_arguments/neox_args.py

@@ -573,6 +573,39 @@ class NeoXArgsLogging(NeoXArgsTemplate):
  Whether to offload the buffered gradients to cpu when measuring gradient noise scale.
  """
 
+ memory_profiling: bool = False
+ """
+ Whether to take a memory snapshot of the model. Useful for debugging memory issues.
+ """
+
+ memory_profiling_path: str = None
+ """
+ Path to save memory snapshot to.
+ """
+
+ profile: bool = False
+ """
+ Enable nsys profiling. When using this option,
+ nsys options should be specified in commandline.
+ An example nsys commandline is
+ ```
+ nsys profile -s none -t nvtx,cuda -o <path/to/output_file>
+ --force-overwrite true
+ --capture-range=cudaProfilerApi
+ --capture-range-end=stop
+ ```
+ """
+
+ profile_step_start: int = 10
+ """
+ Step to start profiling at.
+ """
+
+ profile_step_stop: int = 12
+ """
+ Step to stop profiling at.
+ """
+
 
 @dataclass
 class NeoXArgsOther(NeoXArgsTemplate):

megatron/training.py

@@ -56,6 +56,9 @@
 )
 from megatron.model.gpt2_model import cross_entropy
 
+from pickle import dump
+import os
+
 
 def mup_weights_reinit(neox_args, model):
  def has_method(o, name):
@@ -368,6 +371,8 @@ def forward_step(
  return model.eval_batch(data_iterator, return_logits=return_logits)
 
  # Get the batch.
+ if neox_args.memory_profiling and neox_args.it:
+ torch.cuda.nvtx.range_push(f"Get batch")
  if timers is not None:
  timers("batch generator").start()
  tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
@@ -376,7 +381,11 @@ def forward_step(
 
  if timers is not None:
  timers("batch generator").stop()
+ if neox_args.memory_profiling:
+ torch.cuda.nvtx.range_pop()
 
+ if neox_args.memory_profiling:
+ torch.cuda.nvtx.range_push(f"Forward pass")
  outputs = model((tokens, position_ids, attention_mask), neox_args=neox_args)
  if (
  is_train
@@ -388,6 +397,8 @@ def forward_step(
  loss = cross_entropy(
  outputs, (labels, loss_mask), _fp16=neox_args.fp16_lm_cross_entropy
  )
+ if neox_args.memory_profiling:
+ torch.cuda.nvtx.range_pop()
  if return_logits:
  return loss, outputs
  return loss
@@ -628,6 +639,15 @@ def get_learning_rate_scheduler(optimizer, neox_args):
 
 
 def setup_model_and_optimizer(neox_args, use_cache=False, iteration=None):
+ """Setup memory profiler"""
+ if neox_args.memory_profiling:
+ torch.cuda.memory._record_memory_history(
+ True,
+ # keep a maximum 100,000 alloc/free events from before the snapshot
+ trace_alloc_max_entries=100000,
+ trace_alloc_record_context=True,
+ )
+
  """Setup model and optimizer."""
  model = get_model(neox_args=neox_args, use_cache=use_cache)
  optimizer, param_groups = get_optimizer(model=model, neox_args=neox_args)
@@ -727,6 +747,13 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler)
  reduced_loss = train_step_pipe(
  neox_args=neox_args, timers=timers, model=model, data_iterator=data_iterator
  )
+ if (
+ neox_args.memory_profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ and torch.distributed.get_rank() == 0
+ ):
+ save_snapshot(neox_args)
  else:
  losses = []
  for _ in range(neox_args.gradient_accumulation_steps):
@@ -742,6 +769,12 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler)
  timers("forward").stop()
  losses.append(loss)
  # Calculate gradients, reduce across processes, and clip.
+ if (
+ neox_args.profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ ):
+ torch.cuda.nvtx.range_push(f"Backward pass")
  timers("backward").start()
  backward_step(
  neox_args=neox_args,
@@ -751,13 +784,38 @@ def train_step(neox_args, timers, data_iterator, model, optimizer, lr_scheduler)
  loss=loss,
  )
  timers("backward").stop()
+ if (
+ neox_args.profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ ):
+ torch.cuda.nvtx.range_pop()
  # Update parameters.
+ if (
+ neox_args.profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ ):
+ torch.cuda.nvtx.range_push(f"Optimizer step")
  timers("optimizer").start()
  if neox_args.deepspeed:
  model.step()
  else:
  raise ValueError("Must be using deepspeed to run neox")
  timers("optimizer").stop()
+ if (
+ neox_args.profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ ):
+ torch.cuda.nvtx.range_pop()
+ if (
+ neox_args.profiling
+ and neox_args.iteration >= neox_args.profile_step_start
+ and neox_args.iteration <= neox_args.profile_step_stop
+ and torch.distributed.get_rank() == 0
+ ):
+ save_snapshot(neox_args)
  reduced_loss = {
  "lm_loss": reduce_losses(losses).mean()
  } # reduces losses across machines for logging
@@ -819,6 +877,8 @@ def train(
  # to monitor if we've skipped many iterations in a row and trigger an early exit
  overflow_monitor = OverflowMonitor(optimizer)
  while iteration < neox_args.train_iters:
+ if neox_args.profile and iteration == neox_args.profile_step_start:
+ torch.cuda.cudart().cudaProfilerStart()
  loss_dict, skipped_iter = train_step(
  neox_args=neox_args,
  timers=timers,
@@ -827,6 +887,8 @@ def train(
  optimizer=optimizer,
  lr_scheduler=lr_scheduler,
  )
+ if neox_args.profile and iteration == neox_args.profile_step_stop:
+ torch.cuda.cudart().cudaProfilerStop()
  iteration += 1
  neox_args.iteration = iteration
  if neox_args.precision == "fp16":
@@ -1033,3 +1095,15 @@ def evaluate_and_print_results(
  print_rank_0("-" * length)
  print_rank_0(string)
  print_rank_0("-" * length)
+
+
+def save_snapshot(neox_args):
+ assert (
+ neox_args.memory_profiling_path is not None
+ ), "Must pass memory_profiling_path config arg to use profiling"
+ snapshot = torch.cuda.memory._snapshot()
+ snapshot_path = os.path.join(neox_args.memory_profiling_path)
+ if not os.path.exists(snapshot_path):
+ os.makedirs(snapshot_path)
+ with open(os.path.join(snapshot_path, "mem_snapshot.pickle"), "wb") as f:
+ dump(snapshot, f)