[feature] add pytorch profiling (#2182)

* add pytorch profiling

* kick off the profiler asap since things may get allcoated before train start

* document feature

* add url for visualizer [skip ci]

docs/config.qmd

@@ -363,6 +363,10 @@ eval_table_size: # Approximate number of predictions sent to wandb depending on
 eval_max_new_tokens: # Total number of tokens generated for predictions sent to wandb. Default is 128
 eval_causal_lm_metrics: # HF evaluate metrics used during evaluation. Default is ["sacrebleu", "comet", "ter", "chrf", "perplexity"]
 
+profiler_steps: # enable the pytorch profiler to capture the first N steps of training to the output_dir.
+                # see https://pytorch.org/blog/understanding-gpu-memory-1/ for more information
+                # snapshots can be visualized @ https://pytorch.org/memory_viz
+
 loss_watchdog_threshold: # High loss value, indicating the learning has broken down (a good estimate is ~2 times the loss at the start of training)
 loss_watchdog_patience: # Number of high-loss steps in a row before the trainer aborts (default: 3)
 

src/axolotl/core/trainer_builder.py

@@ -65,6 +65,7 @@ from axolotl.utils.callbacks import (
     log_prediction_callback_factory,
 )
 from axolotl.utils.callbacks.lisa import lisa_callback_factory
+from axolotl.utils.callbacks.profiler import PytorchProfilerCallback
 from axolotl.utils.chat_templates import get_chat_template
 from axolotl.utils.collators import (
     BatchSamplerDataCollatorForSeq2Seq,
@@ -1363,6 +1364,13 @@ class TrainerBuilderBase(abc.ABC):
             plugin_manager.add_callbacks_pre_trainer(cfg=self.cfg, model=self.model)
         )
 
+        if self.cfg.profiler_steps:
+            callbacks.append(
+                PytorchProfilerCallback(
+                    steps_to_profile=self.cfg.profiler_steps,
+                )
+            )
+
         if self.cfg.use_wandb:
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)

src/axolotl/utils/callbacks/profiler.py

@@ -0,0 +1,43 @@
+"""
+HF Trainer callback for creating pytorch profiling snapshots
+"""
+from pathlib import Path
+from pickle import dump  # nosec B403
+
+import torch
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+
+class PytorchProfilerCallback(TrainerCallback):
+    """
+    PyTorch Profiler callback to create snapshots of GPU memory usage at specified steps.
+    """
+
+    def __init__(self, steps_to_profile: int = 5):
+        self.steps_to_profile = steps_to_profile
+        if self.steps_to_profile:
+            torch.cuda.memory._record_memory_history(  # pylint: disable=protected-access
+                enabled="all"
+            )
+
+    def on_step_end(  # pylint: disable=unused-argument
+        self,
+        args: TrainingArguments,  # pylint: disable=unused-argument
+        state: TrainerState,
+        control: TrainerControl,  # pylint: disable=unused-argument
+        **kwargs,  # pylint: disable=unused-argument
+    ):
+        if state.global_step == self.steps_to_profile:
+            snapshot = torch.cuda.memory._snapshot()  # pylint: disable=protected-access
+            with open(Path(args.output_dir) / "snapshot.pickle", "wb") as fout:
+                dump(snapshot, fout)
+
+            # tell CUDA to stop recording memory allocations now
+            torch.cuda.memory._record_memory_history(  # pylint: disable=protected-access
+                enabled=None
+            )

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -762,6 +762,7 @@ class AxolotlInputConfig(
     load_best_model_at_end: Optional[bool] = False
     save_only_model: Optional[bool] = False
     use_tensorboard: Optional[bool] = None
+    profiler_steps: Optional[int] = None
 
     neftune_noise_alpha: Optional[float] = None