Struct caffe2_opt::TvmTransformer

pub struct TvmTransformer { /* private fields */ }

Implementations

impl TvmTransformer

pub fn new(opts: &TvmTransformOptions) -> Self

pub fn build_tvm_op( &mut self, net: &NetDef, weights: &HashSet<String>, shape_hints: &ShapeInfoMap ) -> NetDef

| Given TVM runnable subnets, contract | them into one TVMJitOp |

pub fn transform( &mut self, ws: *mut Workspace, pred_net: *mut NetDef, weight_names: &Vec<String>, input_shape_hints: &ShapeInfoMap, blocklisted_ops: &HashSet<i32> )

| Given workspace and predict net, cluster | continuous parts that can be run by TVM | and create one TVMJit op for each clustered | subgraph. | | ———– | @param ws | | c2 workspace | ––––– | @param pred_net | | c2 predict net | ––––– | @param weight_names | | list of the names of the constant weights | ––––– | @param shape_hints | | User provided shape info, usually for | primary inputs so that bound shape inference | can have something to start | ––––– | @param blocklisted_ops | | a set of ops that we don’t want to lower | to TVM in terms of their net positions. | This is very useful for debugging but | for normal runs it should be empty | | Cutting off the runnable part and replace | with TVMJitOPs. Asssume the nets were | topologically sorted |

pub fn get_supported_ops(&mut self) -> &HashSet<String>

pub fn can_convert_full_graph( &mut self, net: &NetDef, blocklisted_ops: &HashSet<i32> ) -> bool

pub fn apply_tvm_transform( &mut self, pred_net: *mut NetDef, weights: &HashSet<String>, blocklisted_ops: &HashSet<i32>, shape_hints: &ShapeInfoMap ) -> NetDef

Apply transform to cluster connected TVM runnable ops into one TVMJitOp