Streaming offloading in (q)lora single device

docs/source/tutorials/memory_optimizations.rst

@@ -83,6 +83,35 @@ and in most cases training can slow-down quite a bit as a result of this activat
 To enable activation checkpointing, use the ``enable_activation_checkpointing`` config entry or flag
 in any of our recipes, e.g. ``enable_activation_checkpointing=True``.
 
+.. _glossary_act_off:
+
+Activation Offloading
+---------------------
+
+*What's going on here?*
+
+You may have just read about activation checkpointing! Similar to checkpointing, offloading is a memory
+efficiency technique that allows saving GPU VRAM by temporarily moving activations to CPU and bringing
+them back when needed in the backward pass.
+
+See `PyTorch autograd hook tutorial <https://pytorch.org/tutorials/intermediate/autograd_saved_tensors_hooks_tutorial.html#saving-tensors-to-cpu>`_
+for more details about how this is implemented through saved_tensors_hooks.
+
+This setting is especially helpful for larger batch sizes, or longer context lengths when you're memory constrained.
+However, these savings in memory can come at the cost of training speed (i.e. tokens per-second), as it takes runtime
+and resources to move Tensors from GPU to CPU and back. The implementation in torchtune has the ``offload_with_streams``
+option to use multiple CUDA streams in order to overlap the extra communication with the computation to hide the extra
+runtime. As the communication workload is variable depending on the number and size of tensors being offloaded, it is
+common to not offload every single activation. In fact, once can use offloading in conjunction with activations
+checkpointing, where all activations will either be recomputed later in the backward or brought back from the CPU.
+
+*Sounds great! How do I use it?*
+
+To enable activation offloading, use the ``enable_activation_offloading`` config entry or flag
+in our lora finetuning single device recipe, e.g. ``enable_activation_offloading=True``. To allow
+usage of streams, make sure you are on a torch version later than PyTorch 2.5.0.dev20240907 and
+specify ``offload_with_streams=True``.
+
 .. _glossary_grad_accm:
 
 Gradient Accumulation

pyproject.toml

@@ -25,6 +25,7 @@ dependencies = [
     "numpy<=1.26.4", # Pin here until https://github.com/tensorflow/tensorboard/issues/6869 is addressed
     "tqdm",
     "omegaconf",
+    "psutil",
 
 ]
 dynamic = ["version"]

recipes/configs/code_llama2/7B_lora_single_device.yaml

@@ -73,6 +73,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 dtype: bf16
 
 # Logging

recipes/configs/code_llama2/7B_qlora_single_device.yaml

@@ -73,6 +73,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 dtype: bf16
 
 # Logging

recipes/configs/gemma/2B_lora_single_device.yaml

@@ -70,6 +70,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/gemma/2B_qlora_single_device.yaml

@@ -70,6 +70,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/gemma/7B_lora_single_device.yaml

@@ -72,6 +72,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/gemma/7B_qlora_single_device.yaml

@@ -72,6 +72,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/llama2/13B_qlora_single_device.yaml

@@ -80,7 +80,9 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/configs/llama2/7B_lora_single_device.yaml

@@ -80,7 +80,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/configs/llama2/7B_qlora_single_device.yaml

@@ -79,7 +79,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/configs/llama3/8B_lora_single_device.yaml

@@ -79,7 +79,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Profiler (disabled)
 profiler:

recipes/configs/llama3/8B_qlora_single_device.yaml

@@ -78,7 +78,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: True
 
 # Profiler (disabled)
 profiler:

recipes/configs/llama3_1/8B_lora_single_device.yaml

@@ -82,7 +82,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Profiler (disabled)
 profiler:

recipes/configs/llama3_1/8B_qlora_single_device.yaml

@@ -81,7 +81,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Offloading
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Profiler (disabled)
 profiler:

recipes/configs/mistral/7B_lora_single_device.yaml

@@ -76,6 +76,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/mistral/7B_qlora_single_device.yaml

@@ -77,6 +77,7 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Reduced precision
 dtype: bf16

recipes/configs/phi3/mini_lora_single_device.yaml

@@ -71,6 +71,9 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
+
+# Reduced precision
 dtype: bf16
 
 # Logging

recipes/configs/phi3/mini_qlora_single_device.yaml

@@ -71,6 +71,9 @@ device: cuda
 
 # Memory management
 enable_activation_checkpointing: True
+enable_activation_offloading: False
+
+# Reduced precision
 dtype: bf16
 
 # Logging

recipes/configs/qwen2/0.5B_lora_single_device.yaml

@@ -79,7 +79,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/configs/qwen2/1.5B_lora_single_device.yaml

@@ -77,7 +77,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Memory
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/configs/qwen2/7B_lora_single_device.yaml

@@ -81,7 +81,10 @@ log_peak_memory_stats: False
 # Environment
 device: cuda
 dtype: bf16
+
+# Activations Offloading
 enable_activation_checkpointing: True
+enable_activation_offloading: False
 
 # Show case the usage of pytorch profiler
 # Set enabled to False as it's only needed for debugging training

recipes/lora_finetune_single_device.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import contextlib
 import sys
 import time
 
@@ -30,8 +31,12 @@
     validate_missing_and_unexpected_for_lora,
 )
 from torchtune.recipe_interfaces import FTRecipeInterface
-from torchtune.training import DummyProfiler, PROFILER_KEY
-
+from torchtune.training import (
+    DummyProfiler,
+    NoOpManager,
+    OffloadActivations,
+    PROFILER_KEY,
+)
 from tqdm import tqdm
 
 log = utils.get_logger("DEBUG")
@@ -43,13 +48,25 @@ class LoRAFinetuneRecipeSingleDevice(FTRecipeInterface):
     for single GPU training. Training on CPU is not supported.
 
     Features:
-        - Activation Checkpointing. This can be controlled using the ``activation_checkpointing``
+        - Activation Checkpointing. This can be controlled using the ``enable_activation_checkpointing``
             flag. Activation checkpointing helps reduce the memory footprint since we no longer keep
             activations in memory and instead recompute them during the backward pass. This is especially
             helpful for larger batch sizes when you're memory constrained. But these savings in memory
             come at the cost of training performance. In most cases training can slow-down quite a bit as
             a result of this activation recomputation.
 
+        - Activation Offloading. This can be controlled using the ``enable_activation_offloading``
+            flag. Activation offloading is a technique similar to activations checkpointing that helps
+            reduce the memory footprint to prevent OOMs and enable bigger batches. Where activations
+            checkpointing drops the activation in the forward to recompute it later in the backward,
+            activations offloading will drop the activation in the forward to the CPU and bring it
+            back during the backward pass. As always, there is a tradeoff--these savings in memory can
+            come at the cost of training performance and CPU resources. To recover some runtime cost,
+            specify ``offload_with_streams: True`` to enable offloading on a different stream to permit
+            overlapping with the computation. This option is currently only available on PyTorch nightly
+            version 2.5.0.dev20240907 or later. Activation offloading can be used in conjunction with
+            activation checkpointing.
+
         - Precision. Full fp32 and bf16 training are supported. Precision is controlled using the ``dtype``
             flag. When ``dtype=bf16``, all activations, gradients and optimizer states are in bfloat16. In
             most cases this should halve the memory footprint of full precision (fp32) training, without
@@ -222,6 +239,8 @@ def setup(self, cfg: DictConfig) -> None:
         self._model = self._setup_model(
             cfg_model=cfg.model,
             enable_activation_checkpointing=cfg.enable_activation_checkpointing,
+            enable_activation_offloading=cfg.get("enable_activation_offloading", False),
+            offload_with_streams=cfg.get("offload_with_streams", False),
             compile_model=cfg.compile,
             base_model_state_dict=checkpoint_dict[training.MODEL_KEY],
             lora_weights_state_dict=(
@@ -367,6 +386,8 @@ def _setup_model(
         self,
         cfg_model: DictConfig,
         enable_activation_checkpointing: bool,
+        enable_activation_offloading: bool,
+        offload_with_streams: bool,
         compile_model: bool,
         base_model_state_dict: Dict[str, Any],
         lora_weights_state_dict: Optional[Dict[str, Any]] = None,
@@ -420,6 +441,25 @@ def _setup_model(
             self.adapter_params.items(), dtype=self._dtype
         )
 
+        self.activations_handling_ctx = contextlib.nullcontext()
+        if enable_activation_offloading:
+            self.activations_handling_ctx = OffloadActivations(
+                use_streams=offload_with_streams
+            )
+
+            # Below is our hack to disable offloading the last output Linear in every
+            # step, as the cost for offloading the activation and then soon after bringing
+            # it back is expensive. Moreover, due to heuristics in our streaming API,
+            # we actually use more memory if we offload it as it interferes with chunkedCE.
+            if hasattr(model, "output") and isinstance(model.output, nn.Module):
+                noop_ctx = NoOpManager()
+                model.output.register_forward_pre_hook(
+                    lambda *args: noop_ctx.__enter__()
+                )
+                model.output.register_forward_hook(
+                    lambda *args: noop_ctx.__exit__(), always_call=True
+                )
+
         log.info(f"Model is initialized with precision {self._dtype}.")
 
         if self._device.type == "cuda":
@@ -576,7 +616,8 @@ def _loss_step(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
         input_pos = batch.get("input_pos", None)  # shape [b, s]
 
         # run model
-        logits = self._model(tokens, mask=mask, input_pos=input_pos)
+        with self.activations_handling_ctx:
+            logits = self._model(tokens, mask=mask, input_pos=input_pos)
 
         # Shift labels to compute loss
         # equivalent to doing labels[..., 1:] and logits[..., :-1, :]

torchtune/training/__init__.py

@@ -3,6 +3,7 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+from torchtune.training._activation_offloading import NoOpManager, OffloadActivations
 from torchtune.training._compile import compile_loss, compile_model
 from torchtune.training._distributed import (
     contains_fsdp,
@@ -122,4 +123,6 @@
     "setup_torch_profiler",
     "compile_loss",
     "compile_model",
+    "NoOpManager",
+    "OffloadActivations",
 ]