Gradient checkpointing

The forward pass typically caches all intermediate activations for the backward pass to reuse. However, activations scale with batch size and sequence length. Gradient checkpointing only saves certain activations and discards the rest. This forces the backward pass to recompute some of the activations on-the-fly as they’re needed.

Normal training:
  Forward:   [L1]→[L2]→[L3]→[L4]   (save ALL activations)
  Backward:  ←uses cached activations everywhere

Gradient checkpointing:
  Forward:   [L1]→[L2]→[L3]→[L4]   (save only at checkpoints, discard the rest)
  Backward:  ←reaches L2, recomputes L2→L3 from scratch, uses it, discards it

Training is typically slower because the backward pass recomputes discarded activations, but checkpointing reduces activation memory.

Set gradient_checkpointing=True to enable.

Use with gradient accumulation to further reduce memory usage.

from transformers import TrainingArguments

args = TrainingArguments(
    ...,
    gradient_checkpointing=True,
)

Partial checkpointing

Full gradient checkpointing recomputes every checkpointable layer. If your run has some memory headroom, checkpoint fewer layers to trade some of the memory savings for speed.

Pass every_n_layers to gradient_checkpointing_enable() to choose the checkpointing interval.

every_n_layers=2

Forward:   input  -> [L1] -> [L2] -> [L3] -> [L4] -> [L5] -> [L6]
                      CP     keep     CP     keep     CP     keep

Backward:  output <- [L6] <- [L5] <- [L4] <- [L3] <- [L2] <- [L1]
                     keep   rerun    keep   rerun    keep   rerun

With every_n_layers=2, the first layer and every second layer after it are checkpointed. Checkpointed layers discard their activations during the forward pass and recompute them during the backward pass, while the other layers keep their activations in memory.

model.gradient_checkpointing_enable(every_n_layers=2)

The default, every_n_layers=1, checkpoints every layer. Larger values checkpoint the first layer and then every n layers after it, leaving the other layers’ activations in memory. For example, every_n_layers=2 checkpoints layers 1, 3, 5, and so on. Only modules that inherit from GradientCheckpointingLayer are counted. Other modules that support gradient checkpointing remain enabled.

To use partial gradient checkpointing with Trainer, set every_n_layers in gradient_checkpointing_kwargs.

from transformers import TrainingArguments

args = TrainingArguments(
    ...,
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"every_n_layers": 4},
)

Offloading the saved activations

Gradient checkpointing keeps one activation per checkpointed layer on the GPU. At long sequence lengths, this can consume substantial memory (approximately layers x sequence x hidden x bytes_per_element). Set offload to hold those activations in pinned host memory instead, at the cost of a device-to-host copy during the forward pass and a host-to-device copy during the backward pass.

from transformers import TrainingArguments

args = TrainingArguments(
    ...,
    gradient_checkpointing=True,
    gradient_checkpointing_kwargs={"offload": True},
)

Both copies run on the compute stream, so this trades a slower step for the memory. Reach for it when a run does not fit otherwise, not to speed one up.

Next steps

• Read the GPU memory usage doc to understand what is driving memory usage on the GPU during training.
• See the Mixed precision training guide to learn how to use lower precision data types to further reduce memory and speed up training.
• See the Kernels guide to learn how to speed up training with custom fused kernels.