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docs/source/en/grad_accumulation.md

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+    http://www.apache.org/licenses/LICENSE-2.0
+
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+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+
+# Gradient accumulation
+
+Large batches produce large activations that exhaust GPU memory. Gradient accumulation lets you train with a larger effective batch size by spreading gradient computation across multiple mini-batches.
+
+Gradients accumulate across *n* mini-batches before the optimizer updates the weights. For example, with a per-device batch size of 8 and 4 accumulation steps, the effective batch size is 32.
+
+```text
+Step 1: mini-batch 1 → forward → backward → grads = G₁
+Step 2: mini-batch 2 → forward → backward → grads = G₁ + G₂
+Step 3: mini-batch 3 → forward → backward → grads = G₁ + G₂ + G₃
+Step 4: mini-batch 4 → forward → backward → grads = G₁ + G₂ + G₃ + G₄
+        → optimizer.step()  ← same update as if batch_size × 4
+        → zero_grad()
+```
+
+Use gradient accumulation only when a larger batch doesn't fit in memory. It doesn't improve throughput over training with a true large batch.
+
+Accumulate gradients for `gradient_accumulation_steps` across `per_device_train_batch_size`.
+
+```py
+from transformers import TrainingArguments
+
+args = TrainingArguments(
+    ...,
+    per_device_train_batch_size=8,
+    gradient_accumulation_steps=4,
+)
+```
+
+## Loss scaling
+
+For a [custom loss function](./trainer_recipes#custom-loss-function), include `num_items_in_batch` so [`Trainer`] divides the loss by the number of prediction targets across all mini-batches. This normalizes by tokens rather than a fixed step count with `gradient_accumulation_steps`. Otherwise, [`Trainer`] divides loss by `gradient_accumulation_steps`.
+
+```py
+import torch.nn.functional as F
+
+def compute_loss(outputs, labels, num_items_in_batch=None):
+    logits = outputs["logits"]
+    loss = F.cross_entropy(logits, labels, reduction="sum")
+    return loss / num_items_in_batch
+```
+
+For causal LM models, `num_items_in_batch` counts the *shifted* labels. The loss shifts labels so the prediction at position `i` targets the token at position `i + 1`, which leaves position 0 of every sequence without a target. [`Trainer`] excludes those positions and counts over `labels[..., 1:]`, so the denominator matches the number of prediction targets the loss uses. When a data collator supplies `shift_labels` directly, such as a padding-free collator, [`Trainer`] counts over that tensor instead. Other loss types, like masked LM and classification, count the full label tensor.
+
+## Next steps
+
+- Read the [GPU memory usage](./model_memory_anatomy) doc to understand what is driving memory usage on the GPU during training.
+- See the [Gradient checkpointing](./grad_checkpointing) guide to learn how to reduce activation memory by recomputing activations instead of caching them.
+- See the [Mixed precision training](./mixed_precision_training) guide to learn how to use lower precision data types to reduce memory and speed up training.
+- Read the [Gradient Accumulation Fix](https://unsloth.ai/blog/gradient) blog post to learn how gradient accumulation is computed.