chop.passes.transform.pruning

prune_transform_pass

chop.passes.graph.transforms.pruning.prune.prune_transform_pass(graph, pass_args: dict = {})[source]

Apply pruning transformation to the given graph. This is achieved by adding a register_parametrization hook to weights and a register_pre_forward hook to activations

Parameters:

graph (MaseGraph) – The input graph to be pruned.
pass_args (dict) – Optional arguments for the pruning transformation.

pass_args should take the following form:

pass_config = {
    "weight" : {
        "scope": "local", # ["local, "global"] are available
        "granularity": "element", # ["element"] are available
        "method": "l1", # ["l1", "random"] are available
        "sparsity": 0.5, # a float between 0.0 and 1.0
    }
    "activation" : {
        "scope": "local", # ["local, "global"] are available
        "granularity": "element", # ["element"] are available
        "method": "l1", # ["l1", "random"] are available
        "sparsity": 0.5, # a float between 0.0 and 1.0
}

Returns:: The pruned graph and an empty dictionary.
Return type:: tuple

prune_detach_hook_transform_pass

chop.passes.graph.transforms.pruning.prune_detach_hook.prune_detach_hook_transform_pass(graph, pass_args: dict = {})[source]

Apply pruning transformation to the given graph. This is achieved by adding a register_parametrization hook to weights and a register_pre_forward hook to activations

Parameters:: graph (MaseGraph) – The input graph to be pruned.

pass_args can be None or an empty dictionary.

Parameters:: pass_args (dict) – Optional arguments for the pruning transformation.
Returns:: The pruned graph and an empty dictionary.
Return type:: tuple