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torch.utils.mobile_optimizer

Warning

This API is in beta and may change in the near future.

Torch mobile supports torch.utils.mobile_optimizer.optimize_for_mobile utility to run a list of optimization pass with modules in eval mode. The method takes the following parameters: a torch.jit.ScriptModule object, a blocklisting optimization set, a preserved method list, and a backend.

For CPU Backend, by default, if optimization blocklist is None or empty, optimize_for_mobile will run the following optimizations:
  • Conv2D + BatchNorm fusion (blocklisting option mobile_optimizer.MobileOptimizerType.CONV_BN_FUSION): This optimization pass folds Conv2d-BatchNorm2d into Conv2d in forward method of this module and all its submodules. The weight and bias of the Conv2d are correspondingly updated.
  • Insert and Fold prepacked ops (blocklisting option mobile_optimizer.MobileOptimizerType.INSERT_FOLD_PREPACK_OPS): This optimization pass rewrites the graph to replace 2D convolutions and linear ops with their prepacked counterparts. Prepacked ops are stateful ops in that, they require some state to be created, such as weight prepacking and use this state, i.e. prepacked weights, during op execution. XNNPACK is one such backend that provides prepacked ops, with kernels optimized for mobile platforms (such as ARM CPUs). Prepacking of weight enables efficient memory access and thus faster kernel execution. At the moment optimize_for_mobile pass rewrites the graph to replace Conv2D/Linear with 1) op that pre-packs weight for XNNPACK conv2d/linear ops and 2) op that takes pre-packed weight and activation as input and generates output activations. Since 1 needs to be done only once, we fold the weight pre-packing such that it is done only once at model load time. This pass of the optimize_for_mobile does 1 and 2 and then folds, i.e. removes, weight pre-packing ops.
  • ReLU/Hardtanh fusion: XNNPACK ops support fusion of clamping. That is clamping of output activation is done as part of the kernel, including for 2D convolution and linear op kernels. Thus clamping effectively comes for free. Thus any op that can be expressed as clamping op, such as ReLU or hardtanh, can be fused with previous Conv2D or linear op in XNNPACK. This pass rewrites graph by finding ReLU/hardtanh ops that follow XNNPACK Conv2D/linear ops, written by the previous pass, and fuses them together.
  • Dropout removal (blocklisting option mobile_optimizer.MobileOptimizerType.REMOVE_DROPOUT): This optimization pass removes dropout and dropout_ nodes from this module when training is false.
  • Conv packed params hoisting (blocklisting option mobile_optimizer.MobileOptimizerType.HOIST_CONV_PACKED_PARAMS): This optimization pass moves convolution packed params to the root module, so that the convolution structs can be deleted. This decreases model size without impacting numerics.
  • Add/ReLU fusion (blocklisting option mobile_optimizer.MobileOptimizerType.FUSE_ADD_RELU): This pass finds instances of relu ops that follow add ops and fuses them into a single add_relu.
for Vulkan Backend, by default, if optimization blocklist is None or empty, optimize_for_mobile will run the following optimization:
  • Automatic GPU Transfer (blocklisting option mobile_optimizer.MobileOptimizerType.VULKAN_AUTOMATIC_GPU_TRANSFER): This optimization pass rewrites the graph so that moving input and output data to and from the GPU becomes part of the model.

optimize_for_mobile will also invoke freeze_module pass which only preserves forward method. If you have other method to that needed to be preserved, add them into the preserved method list and pass into the method.

.. currentmodule:: torch.utils.mobile_optimizer

.. autofunction:: optimize_for_mobile