# Why are gradient\_checkpointing and training bound?
**URL:**
**Author:**  [@Met4physics](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FMet4physics)%5C
**Post date:** [March 2, 2026, 9:17am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhy-are-gradient-checkpointing-and-training-bound%2F173942%2F1 "2026-03-02T09:17:24Z")
In GradientCheckpointingLayer [implementation](https://github.com/huggingface/transformers/blob/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py#L60) in transformers, gradient checkpointing only works when the model is in training mode:
```python
if self.gradient_checkpointing and self.training:
```
However, I would like to enable gradient checkpointing while the model is in eval mode (with all parameters frozen, i.e. `requires_grad=False` ). What would happen if I change it to:
```python
if self.gradient_checkpointing:
```
---
**Author:**  [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [March 2, 2026, 11:30pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhy-are-gradient-checkpointing-and-training-bound%2F173942%2F2 "2026-03-02T23:30:09Z")
Hmm… Maybe:
* * *
## What that `and self.training` guard is doing in Transformers
In `GradientCheckpointingLayer. __call__ `, Transformers only routes through checkpointing when:
```python
if self.gradient_checkpointing and self.training:
```
and, **when it does** , it also forcibly disables cache-related kwargs (`use_cache=False`, `past_key_values=None`, etc.) and logs a warning once. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py "raw.githubusercontent.com"))
That pairing (“only in training” + “disable caching”) exists for two practical reasons:
### 1) Checkpointing is a backward-time optimization
PyTorch checkpointing saves memory by **not storing activations** in forward and instead **recomputing forward during backward**. If you are not going to run backward (typical eval/inference), there is little to gain. ([PyTorch Docs](https://docs.pytorch.org/docs/stable/checkpoint.html "torch.utils.checkpoint"))
### 2) Caching (`past_key_values`) is often critical in eval/generation
Transformers treats KV caching as incompatible with checkpointing, and explicitly rewrites cache args when checkpointing is active. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py "raw.githubusercontent.com"))
If you allowed checkpointing in eval mode, you’d also be enabling this “turn cache off” behavior in eval, which can break assumptions and/or slow generation paths. A concrete example is a reported `generate()` misbehavior triggered by `gradient_checkpointing=True → use_cache=False → past_key_values=None`. ([GitHub](https://github.com/huggingface/transformers/issues/36051 "[generate] generate not working gradient\_checkpointing= ..."))
* * *
## What would happen if you change it to `if self.gradient_checkpointing:`?
You’d be enabling checkpointing **in eval mode too** , which has different outcomes depending on whether gradients are enabled and whether anything requires grad.
### Case A — `eval()` + `torch.no_grad()` / `inference_mode()` (most inference)
**Likely outcome: mostly downside**
- **No meaningful memory savings** , because there is no backward graph to save activations for. ([PyTorch Docs](https://docs.pytorch.org/docs/stable/checkpoint.html "torch.utils.checkpoint"))
- **Potential overhead/noise** : you may see warnings of the form “None of the inputs have requires\_grad=True. Gradients will be None” if checkpoint is invoked when nothing participates in autograd (this is a common complaint in PyTorch land). ([PyTorch Forums](https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117 "Checkpoint with no grad requiring inputs PROBLEM"))
- **Behavior change** : any call passing `use_cache=True` / `past_key_values` would get those rewritten (disabled) even in eval. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py "raw.githubusercontent.com"))
This is particularly harmful for autoregressive decoding, where caching is the main performance lever (and can even affect code paths, as in the `generate()` issue). ([GitHub](https://github.com/huggingface/transformers/issues/36051 "[generate] generate not working gradient\_checkpointing= ..."))
### Case B — `eval()` + grads enabled, but **all params frozen** and inputs don’t require grad
**Likely outcome: checkpointing is ineffective and may block gradients**
- With all parameters `requires_grad=False`, **nothing inside the checkpointed region may participate in autograd**.
- For PyTorch’s **reentrant** checkpoint variant (`use_reentrant=True`), PyTorch documents a hard requirement: **at least one input and one output must have `requires_grad=True`** or the checkpointed part will not produce gradients. ([PyTorch Docs](https://docs.pytorch.org/docs/stable/checkpoint.html "torch.utils.checkpoint"))
- Result: you can get warnings / “grads will be None” behavior and you still don’t get a useful gradient signal. ([PyTorch Forums](https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117 "Checkpoint with no grad requiring inputs PROBLEM"))
### Case C — `eval()` + grads enabled, and you _do_ want gradients (e.g., wrt inputs/embeddings or a small unfrozen subset)
**This is the one scenario where “eval + checkpointing” can make sense**
- There’s an active Transformers issue describing exactly this need: keep `eval()` (disable dropout for deterministic gradients) while using checkpointing to fit large models for gradient-based metrics. ([GitHub](https://github.com/huggingface/transformers/issues/43381 "Gradient checkpointing cannot be used in eval mode #43381"))
- But you must ensure **something requires grad through the checkpointed region** :
- You still have to accept that **KV cache will be disabled** when checkpointing is active. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py "raw.githubusercontent.com"))
* * *
## Why binding to `training` is a blunt but pragmatic choice
`self.training` is being used as a proxy for “this is a backward-producing regime where checkpointing is worth it, and we’re okay disabling caches.”
There are multiple ecosystem reports asking for a different proxy: gate checkpointing on “grad mode” instead of “training mode.” For example, a Diffusers issue proposes `if torch.is_grad_enabled() and self.gradient_checkpointing:` specifically to enable checkpointing in eval for LoRA/frozen-model scenarios while avoiding inference/`no_grad()` paths. ([GitHub](https://github.com/huggingface/diffusers/issues/9850 "make gradient checkpointing with frozen model possible"))
* * *
## What you should do instead (recommended approach)
If your goal is **eval-mode forward behavior + gradients** , the more semantically correct condition is:
- **Enable checkpointing when gradients are enabled** , not when `module.training` is true.
Conceptually: `self.gradient_checkpointing and torch.is_grad_enabled()` (often also combined with “any tensor input requires grad” to avoid warnings).
Also, strongly consider using the **non-reentrant** checkpoint variant where possible, because it removes the “input/output must require grad” requirement that bites frozen-model setups. PyTorch explicitly states that this requirement applies to reentrant but not non-reentrant. ([PyTorch Docs](https://docs.pytorch.org/docs/stable/checkpoint.html "torch.utils.checkpoint"))
Transformers supports passing checkpoint kwargs via `gradient_checkpointing_enable(gradient_checkpointing_kwargs=...)`. ([Hugging Face](https://huggingface.co/docs/transformers/en/main_classes/model "Models"))
* * *
## Bottom line for your exact stated setup (eval + _all_ params frozen)
- If you **are not computing gradients** (typical eval/inference): changing the condition will mostly add risk (cache disabled, possible warnings) with little benefit. ([GitHub](https://raw.githubusercontent.com/huggingface/transformers/5c773b8a84677192d4a52edc1e2c8823f9c1dcea/src/transformers/modeling_layers.py "raw.githubusercontent.com"))
- If you **are computing gradients** (e.g., wrt inputs/embeddings): it can work, but only if you ensure something requires grad through the checkpointed region (often `enable_input_require_grads()`), and you accept that caching will be forced off. ([Hugging Face](https://huggingface.co/docs/transformers/en/main_classes/model "Models"))
---
**Author:**  [@system](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fsystem)%5C
**Post date:** [March 4, 2026, 7:34am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fwhy-are-gradient-checkpointing-and-training-bound%2F173942%2F3 "2026-03-04T07:34:08Z")
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