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Showing content from https://pytorch.org/cppdocs/notes/inference_mode.html below:

Inference Mode — PyTorch main documentation

c10::InferenceMode is a new RAII guard analogous to NoGradMode to be used when you are certain your operations will have no interactions with autograd (e.g. model training). Compared to NoGradMode, code run under this mode gets better performance by disabling autograd related work like view tracking and version counter bumps. However, tensors created inside c10::InferenceMode have more limitations when interacting with autograd system as well.

InferenceMode can be enabled for a given block of code. Inside InferenceMode all newly allocated (non-view) tensors are marked as inference tensors. Inference tensors:

• do not have a version counter so an error will be raised if you try to read their version (e.g., because you saved this tensor for backward).

• are immutable outside InferenceMode. So an error will be raised if you try to: - mutate their data outside InferenceMode. - mutate them into requires_grad=True outside InferenceMode. To work around you can make a clone outside InferenceMode to get a normal tensor before mutating.

A non-view tensor is an inference tensor if and only if it was allocated inside InferenceMode. A view tensor is an inference tensor if and only if it is a view of an inference tensor.

Inside an InferenceMode block, we make the following performance guarantees:

• Like NoGradMode, all operations do not record grad_fn even if their inputs have requires_grad=True. This applies to both inference tensors and normal tensors.

• View operations on inference tensors do not do view tracking. View and non-view inference tensors are indistinguishable.

• Inplace operations on inference tensors are guaranteed not to do a version bump.

For more implementation details of InferenceMode please see the RFC-0011-InferenceMode.

In production use of PyTorch for inference workload, we have seen a proliferation of uses of the C++ guard AutoNonVariableTypeMode (now AutoDispatchBelowADInplaceOrView), which disables autograd, view tracking and version counter bumps. Unfortunately, current colloquial of this guard for inference workload is unsafe: it’s possible to use AutoNonVariableTypeMode to bypass PyTorch’s safety checks and result in silently wrong results, e.g. PyTorch throws an error when tensors saved for backwards are subsequently mutated, but mutation happens inside AutoNonVariableTypeMode will silently bypass the check and returns wrong gradient to users.

When current users of AutoNonVariableTypeMode think about migrating, the following steps might help you decide the best alternatives:

1. Users trying to run workload in inference only mode (like loading a pretrained JIT model and run inference in C++ runtime) should add c10::InferenceMode guard to guard all operations on tensors (including model loading). See an inference workload example below:

c10::InferenceMode guard;
model.load_jit(saved_model);
auto inputs = preprocess_tensors(data);
auto out = model.forward(inputs);
auto outputs = postprocess_tensors(out);

Note c10::InferenceMode offers a drop in replacement for AutoNonVariableTypeMode which preserves the performance characteristics of AutoNonVariableTypeMode. But they also have some differences that users should pay additional attention to:

• Both guards affects tensor execution process to skip work not related to inference, but InferenceMode also affects tensor creation while AutoNonVariableTypeMode doesn’t. In other words, tensors created inside InferenceMode are marked as inference tensors so that certain limitation can be applied after exiting InferenceMode.

• Enabled/disabled InferenceMode states can be nested while AutoNonVariableTypeMode only allows enabled state.

{
  InferenceMode guard(true);
  // InferenceMode is on
  {
    InferenceMode guard(false);
    // InferenceMode is off
  }
  // InferenceMode is on
}
// InferenceMode is off

1. Users trying to implement a customized kernel who want to redispatch under Autograd dispatch keys should use AutoDispatchBelowADInplaceOrView instead. Note AutoDispatchBelowADInplaceOrView is just a new name of AutoNonVariableTypeMode since it explains the guard’s functionality better. We’re deprecating AutoNonVariableTypeMode and it’ll be removed in 1.10 release. See customized kernel ROIAlignFunction in pytorch/vision for an example:

class ROIAlignFunction : public torch::autograd::Function<ROIAlignFunction> {
 public:
  static torch::autograd::variable_list forward(
      torch::autograd::AutogradContext* ctx,
      const torch::autograd::Variable& input,
      const torch::autograd::Variable& rois,
      double spatial_scale,
      int64_t pooled_height,
      int64_t pooled_width,
      int64_t sampling_ratio,
      bool aligned) {
    ctx->saved_data["spatial_scale"] = spatial_scale;
    ctx->saved_data["pooled_height"] = pooled_height;
    ctx->saved_data["pooled_width"] = pooled_width;
    ctx->saved_data["sampling_ratio"] = sampling_ratio;
    ctx->saved_data["aligned"] = aligned;
    ctx->saved_data["input_shape"] = input.sizes();
    ctx->save_for_backward({rois});
    // Used to be at::AutoNonVariableTypeMode g;
    at::AutoDispatchBelowADInplaceOrView guard;
    auto result = roi_align(
        input, rois, spatial_scale, pooled_height,
        pooled_width, sampling_ratio, aligned);
    return {result};
  }

Customized inplace & view kernels need some special handling in addition to the guard above, see custom kernel tutorial for more details.

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