def _match(self, G: GraphView, set_identity: bool = True, **kwargs): modified_graph = False candidates = [node for node in G.nodes() if len(G.indexed_out_edges(node.name)) == 1 and len(G.out_edges(node.name)) > 1] while candidates: node = candidates.pop(0) strings = self.explore(G, [node]) if not strings: continue modified_graph = True primary = strings.pop(0) for pnode in primary: if pnode in candidates: candidates.remove(pnode) out_edges = [] for other in strings: out_edges.extend(G.out_edges(other[-1].name)) for other_node in other: if other_node in candidates: candidates.remove(other_node) G.remove(other_node) nid = NodeId(other_node) if G.quantization and nid in G.quantization: del G.quantization[nid] LOG.info( f'removed duplicates from {primary[0].name} {",".join(node.name for node in other)}') pend = primary[-1] for edge in out_edges: G.add_edge( NNEdge(from_node=pend, to_node=edge.to_node, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): fragment = GraphMatcher( match_function=lambda state, frag: (frag, state['match'])) fragment.add_node(MatScaleNodeMatch()) has_modified_graph = False for frag, match in fragment.match_graph(G): match_edges = [ G.indexed_in_edges(node.name)[idx] for node, idx in match['inputs'] ] matched_node = list(frag.nodes())[0] out_edges = G.out_edges(matched_node.name) has_modified_graph = True G.remove(matched_node) fnode = MatScaleFusionParameters( "{}_fusion".format(matched_node.name), fusion_type=match['type'], subgraph=frag, input_mapping=[[(matched_node, 0)], [(matched_node, 1)]]) G.add_node(fnode) for idx, edge in enumerate(match_edges): edge.to_node = fnode edge.to_idx = idx G.add_edge(edge) for edge in out_edges: edge.from_node = fnode G.add_edge(edge) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False for node in G.nodes(node_classes=MatMulOpParameters): in_edges = [edge for edge in G.indexed_in_edges(node.name)] trans_node = in_edges[1].from_node if not isinstance(trans_node, TransposeParameters): continue if isinstance(node, MatMulTransposedParameters): new_node = MatMulOpParameters(node.name) else: new_node = MatMulTransposedParameters(node.name) in_trans_edge = [ edge for edge in G.indexed_in_edges(trans_node.name) ][0] G.replace_node(node.name, new_node) G.remove(trans_node) G.add_edge( NNEdge(in_trans_edge.from_node, new_node, from_idx=in_trans_edge.from_idx, to_idx=1)) has_modified_graph = True if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False for node in [ node for node in G.nodes() if self.node_does_nothing(G, node) ]: has_modified_graph = True in_edge = G.in_edges(node.name)[0] G.remove_edge(in_edge) for out_edge in G.out_edges(node.name): G.remove_edge(out_edge) G.add_edge( NNEdge(in_edge.from_node, out_edge.to_node, from_idx=in_edge.from_idx, to_idx=out_edge.to_idx)) LOG.info(f'removing {node.name} that does nothing') G.remove(node) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False for node in G.nodes(node_classes=tuple(VALID_FUSIONS.keys())): node_list = self.get_node_list(G, node, FusionMatch(self._default_ktype)) if node_list is None or len(node_list.order) < 2: continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() last_node = None for snode in node_list.order: if last_node is not None: subgraph.add_edge( NNEdge(from_node=last_node, to_node=snode)) last_node = snode # assumption here is that the first node could have multiple inputs but definitely has only # one output input_mapping = [[ (node_list.node, idx) ] for idx in range(G.num_in_edges(node_list.node.name))] output_mapping = [(last_node, 0)] pnode = node_list.fusions_class(node_list.node.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: # TODO - stats qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = QRec.copy_ktype(qrecs[0], in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for fnode in pnode.contained_nodes(): G.quantization.move_to_fusion(fnode, pnode) G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.node.name) out_edges = G.out_edges(last_node.name) for snode in node_list.order: G.remove(snode) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge( NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): has_modified_graph = False for conv_node in [params for params in G.nodes() if isinstance(params, Conv2DParameters)]: node_list = self.get_node_list(G, conv_node) if node_list is None or len(node_list.order) < 2: continue if node_list.fusion_type == 'conv_active_pool': if node_list.pool.pool_type == "average": node_list.order = node_list.order[:2:] node_list.pool = None elif node_list.fusion_type == 'conv_pool_active': if node_list.pool.pool_type == "average" and node_list.active.activation != "relu": continue LOG.info("fusing nodes %s", ",".join((node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() last_node = None for node in node_list.order: if last_node is not None: subgraph.add_edge(NNEdge(from_node=last_node, to_node=node)) last_node = node input_mapping = [[(node_list.conv, idx)] for idx in range(3)] output_mapping = [(last_node, 0)] pnode = ConvFusionParameters( node_list.conv.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, in_dims_hint=node_list.conv.in_dims_hint, out_dims_hint=node_list.conv.out_dims_hint, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = None if isinstance(qrecs[0], (SymmetricQuantizationRecord, SymmetricScalableFilterQuantizationRecord)): prec = SymmetricQuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (MultQuantizationRecord, MultScalableFilterQuantizationRecord)): prec = MultQuantizationRecord(in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (Float32QuantizationRecord, Float32ScalableFilterQuantizationRecord)): prec = Float32QuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.conv.name) out_edges = G.out_edges(last_node.name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge(NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge(NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): rnn_nodes = [ self.find_unpack(G, node) for node in G.nodes() if isinstance(node, RNNBaseParameters) and node.n_output_cells > 1 ] rnn_nodes_by_slice = self.validate_slices(G, rnn_nodes) rnn_nodes_by_slice = self.validate_multi_branch(G, rnn_nodes_by_slice) if not rnn_nodes_by_slice: return False for unpack_node, rnn_unpacks in rnn_nodes_by_slice.items(): modified_nodes = set() for rnn_unpack in rnn_unpacks: self.process_path(G, rnn_unpack, modified_nodes) # since process path will have removed all unnecessary nodes the edges will be correct here out_edges = G.out_edges(unpack_node.name) in_edges = G.in_edges(unpack_node.name) assert len(in_edges ) == 1, "expecting unpack node to have only one in edge" in_edge = in_edges[0] changes_shape = unpack_node.changes_shape if isinstance( unpack_node, StridedSliceParameters) else False LOG.info("Eliminating last cell unpack: %s", unpack_node.name) G.remove(unpack_node) # Here the strided slice can change the output shape of the RNN # so insert a reshape to do the shape change if changes_shape: reshape = ReshapeParameters( unpack_node.name + '_reshape', old_shape=Dim.unnamed(unpack_node.post_slice_shape), shape=Dim.unnamed(unpack_node.out_shape)) G.add_edge( NNEdge(from_node=in_edge.from_node, to_node=reshape, from_idx=in_edge.from_idx)) for out_edge in out_edges: G.add_edge( NNEdge(from_node=reshape, to_node=out_edge.to_node, to_idx=out_edge.to_idx)) if G.quantization: G.quantization[NodeId(reshape)] = G.quantization[NodeId( unpack)] else: for out_edge in out_edges: G.add_edge( NNEdge(from_node=in_edge.from_node, to_node=out_edge.to_node, from_idx=in_edge.from_idx, to_idx=out_edge.to_idx)) if G.quantization: del G.quantization[NodeId(unpack_node)] if set_identity: self.set_identity(G) return True
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False group_identity = kwargs.get('group_identity') if group_identity == 'pow2_match_group': valid_activations = VALID_ACTIVATIONS_POW2 else: valid_activations = VALID_ACTIVATIONS_SQ8 for fc_node in [params for params in G.nodes() if isinstance(params, FcParameters)]: node_list = self.get_node_list(G, fc_node, valid_activations) if node_list is None or len(node_list.order) < 2: continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() last_node = None for node in node_list.order: if last_node is not None: subgraph.add_edge( NNEdge(from_node=last_node, to_node=node)) last_node = node input_mapping = [[(node_list.linear, idx)] for idx in range(3)] output_mapping = [(last_node, 0)] pnode = LinearFusionParameters( node_list.linear.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: # TODO - stats qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = QRec.copy_ktype( qrecs[0], in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.linear.name) out_edges = G.out_edges(last_node.name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge(NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge(NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False for split_node in set( [node for node in G.nodes() if isinstance(node, SplitParameters)]): in_edges = G.in_edges(split_node.name) if len(in_edges) > 1: continue in_edge = in_edges[0] if not isinstance(in_edge.from_node, ConcatParameters): continue concat_node = in_edge.from_node if len(G.out_edges(concat_node.name)) > 1: continue if concat_node.transpose_out or split_node.transpose_in: continue if concat_node.axis != split_node.axis: continue axis = concat_node.axis split_out_sizes = [ out_shape[axis] for out_shape in split_node.out_shapes ] if len(split_out_sizes) != len(concat_node.in_dims): continue if not all(split_out_sizes[idx] == in_dim.shape[axis] for idx, in_dim in enumerate(concat_node.in_dims)): continue has_modified_graph = True LOG.info("removing unnecessary concat/split pair %s/%s", concat_node.name, split_node.name) concat_in_edges = G.indexed_in_edges(concat_node.name) split_out_edges = G.indexed_out_edges(split_node.name) G.remove(split_node) G.remove(concat_node) for idx, in_edge in enumerate(concat_in_edges): for out_edge in split_out_edges[idx]: G.add_edge( NNEdge(from_node=in_edge.from_node, from_idx=in_edge.from_idx, to_node=out_edge.to_node, to_idx=out_edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): # Only works for reverses connected to one RNN node reverse_nodes = set([ node for node in G.nodes() if (isinstance(node, ReverseParameters) and len(G.out_edges(node.name)) == 1 and isinstance( G.out_edges(node.name)[0].to_node, RNNBaseParameters)) ]) has_modified_graph = False for reverse_node in reverse_nodes: in_edges = G.in_edges(reverse_node.name) rnn_edge = G.out_edges(reverse_node.name)[0] if rnn_edge.to_idx != 0: LOG.warning("reverse on rnn input %s", rnn_edge.to_idx) continue assert not rnn_edge.to_node.revert, "RNN node is already reversed!" rnn_edge.to_node.revert = True LOG.info("fusing reverses into node %s", rnn_edge.to_node.name) has_modified_graph = True G.remove(reverse_node) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, rnn_edge.to_node, from_idx=edge.from_idx, to_idx=rnn_edge.to_idx)) for edge in G.out_edges(rnn_edge.to_node.name): if not isinstance(edge.to_node, ReverseParameters): continue if edge.from_idx != 0: LOG.warning("reverse on rnn output %s", edge.from_idx) continue rev_edges = G.out_edges(edge.to_node.name) G.remove(edge.to_node) for rev_edge in rev_edges: G.add_edge( NNEdge(edge.from_node, rev_edge.to_node, from_idx=edge.from_idx, to_idx=rev_edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): visited_edges = {} nodes_to_remove = [] has_modified_graph = False for node in G.inputs(): # check if constantinput. if is then check if positive and check max value if isinstance(node, ConstantInputParameters): if node.value is not None: if G.has_quantized_parameters: qrec = G.quantization[NodeId(node)] qtype = qrec.out_qs[0] if hasattr(qtype, 'wrapped'): qtype = qtype.wrapped val = qtype.dequantize(node.value) else: val = node.value if val.min() >= 0: status = (True, val.max()) else: status = (False, False) else: status = (False, False) for edge in G.out_edges(node.name): visited_edges[edge] = status nodes_to_remove += find_redundant_relus( G, edge.to_node, visited_edges) for node in nodes_to_remove: has_modified_graph = True # Only relus so only one in edge in_edge = G.in_edges(node.name)[0] for edge in G.out_edges(node.name): G.add_edge( NNEdge(from_node=in_edge.from_node, from_idx=in_edge.from_idx, to_node=edge.to_node, to_idx=edge.to_idx)) G.remove(node) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): fac = MatScalePairMatchFactory() has_modified_graph = False for frag, match in fac.get_matcher().match_graph(G): match_edges = [ G.indexed_in_edges(node.name)[idx] for node, idx in match ] first_node = frag.inputs()[0] last_node = frag.outputs()[0] out_edges = G.out_edges(last_node.name) for node in frag.nodes(): G.remove(node) input_mapping = MatScaleFusionParameters.get_mapping_from_edges( match_edges) fnode = MatScaleFusionParameters( "{}_{}_fusion".format(first_node.name, last_node.name), fusion_type="vec_scalar", subgraph=frag, input_mapping=MatScaleFusionParameters.convert_input_mapping( input_mapping)) has_modified_graph = True G.add_node(fnode) fnode.in_dims_hint = [None] * 3 for idx, edge in enumerate(match_edges): new_edge = edge.clone( to_node=fnode, to_idx=list(input_mapping[edge.to_node].keys())[0]) if new_edge.from_node.out_dims_hint: fnode.in_dims_hint[idx] = new_edge.from_node.out_dims_hint[ edge.from_idx] G.add_edge(new_edge) for edge in out_edges: new_edge = edge.clone(from_node=fnode) G.add_edge(new_edge) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): modified_graph = True while modified_graph: modified_graph = False for reshape in G.nodes(node_classes=(ReshapeParameters, )): if not reshape.has_transpose and reshape.shape.shape == reshape.old_shape.shape: modified_graph = True LOG.info('removing reshape that does nothing %s', reshape.name) G.remove_and_reconnect(reshape, edge_class=NNEdge) nid = NodeId(reshape) if G.quantization and nid in G.quantization: del G.quantization[nid] res = None for reshape in G.nodes(node_classes=(ReshapeParameters, )): res = self.validate_reshape(G, reshape) if res: LOG.info('unnecessary reshape found after %s', reshape.name) modified_graph = True (reshape, candidates, out_shape) = res for candidate in candidates: LOG.info( 'removing unnecessary reshape or transpose %s', candidate.name) edges = G.out_edges(candidate.name) G.remove(candidate) nid = NodeId(candidate) if G.quantization and nid in G.quantization: del G.quantization[nid] for edge in edges: G.add_edge( NNEdge(from_node=reshape, to_node=edge.to_node, to_idx=edge.to_idx)) reshape.shape = Dim.unnamed(out_shape) break if set_identity: self.set_identity(G) return modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False for node in G.nodes(node_classes=SplitParameters): same_op_edges = self.moveable_same_operation_edges(G, node) if not same_op_edges: continue has_modified_graph = True in_edges = G.in_edges(node.name) assert len(in_edges) == 1 # sort by name to ensure that operation is repeatable same_op_edges.sort(key=lambda x: x.to_node.name) keep_node = same_op_edges[0].to_node LOG.info('split node %s has duplicate operations on its out edges', node.name) LOG.info('moving %s before split node %s', keep_node.name, node.name) for edge in G.out_edges(node.name): node_out_edges = G.out_edges(edge.to_node.name) G.remove(edge.to_node) if edge.to_node != keep_node: LOG.info('deleting duplicate node %s', edge.to_node.name) if G.quantization: nid = NodeId(edge.to_node) if nid in G.quantization: del G.quantization[nid] for out_edge in node_out_edges: G.add_edge( NNEdge(from_node=node, from_idx=edge.from_idx, to_node=out_edge.to_node, to_idx=out_edge.to_idx)) G.insert_node_at_edge(keep_node, in_edges[0], edge_class=NNEdge) if G.quantization: quantizer = NewQuantizer.from_quantized_graph(G) quantizer.quantize() if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): nodes = list(G.nodes(node_classes=GlobalPoolingParameters)) modified_graph = False while nodes: node = nodes.pop() node_group = self.reductions(G, node) if len(node_group) <= 1: continue modified_graph = True reduction_axes, new_shape, has_keepdims, _ = reduce( reduce_reduction, node_group, None) new_node = node_group[0] new_node.axis = sorted(list(reduction_axes)) new_node.keep_dims = has_keepdims out_edges = G.out_edges(node_group[-1].name) if G.quantization: last_qrec = G.quantization[NodeId(node_group[-1])] G.quantization[NodeId(new_node)].out_qs = last_qrec.out_qs for node in node_group[1::]: G.remove(node.name) nid = NodeId(node) if G.quantization and nid in G.quantization: del G.quantization[nid] if has_keepdims and len(new_shape) != len( new_node.in_dims[0].shape): rparams = ReshapeParameters( G.unique_name(f'{new_node.name}_reshape'), shape=Dim.unnamed(new_shape)) if G.quantization: G.quantization.copy_qrec(last_qrec, 'out', 0, rparams) G.add_edge(NNEdge(new_node, rparams)) new_node = rparams for edge in out_edges: G.add_edge(NNEdge(new_node, edge.to_node, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): visited_edges = {} nodes_to_remove = [] has_modified_graph = False for node in G.inputs(): # check if constantinput. if is then check if positive and check max value if isinstance(node, ConstantInputParameters): if node.value is not None: val = node.dqvalue if np.min(val) >= 0: status = (True, np.max(val)) else: status = (False, False) else: status = (False, False) else: status = (False, False) for edge in G.out_edges(node.name): visited_edges[edge] = status nodes_to_remove += find_redundant_relus( G, edge.to_node, visited_edges) for node in nodes_to_remove: has_modified_graph = True # Only relus so only one in edge LOG.info("removing redundant relu %s", node.name) in_edge = G.in_edges(node.name)[0] out_edges = G.out_edges(node.name) G.remove(node) for edge in out_edges: G.add_edge(NNEdge(from_node=in_edge.from_node, from_idx=in_edge.from_idx, to_node=edge.to_node, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False has_transposed = False for params in G.nodes(node_classes=MatMulOpParameters): while True: out_edges = G.out_edges(params.name) # can't fuse if there is a branch if len(out_edges) > 1: break out_edge = out_edges[0] op_node = out_edge.to_node # must be a valid matrix op if not isinstance(op_node, (MatrixAddParameters, MatrixMulParameters)): break # other edge to the op must be a constant other_idx = 1 if out_edge.to_idx == 0 else 0 other_in_edge = G.indexed_in_edges(op_node.name)[other_idx] if not isinstance(other_in_edge.from_node, ConstantInputParameters): break const_node = other_in_edge.from_node remove_constant = len(G.out_edges(const_node.name)) flat_value = const_node.dqvalue.flatten() out_shape = params.out_dims[0].shape if len(out_shape) != 2: raise ValueError( f'strange outputs shape of {out_shape} for matmul {params.name}' ) if len(flat_value) != out_shape[0] and len( flat_value) != out_shape[1]: LOG.info( "can't fuse %s into %s - value shape is not correct for bias", const_node.name, params.name) break has_bias = len(params.in_dims) == 3 if isinstance(op_node, MatrixAddParameters): if has_bias: if len(flat_value.shape) != len(params.in_dims[2]): LOG.info( "can't fuse %s into %s - bias shape is not the same", const_node.name, params.name) break bias_node = G.indexed_in_edges( params.name)[2].from_node LOG.info( "folding additive bias from %s into existing bias on %s", op_node.name, params.name) bias_node.value = bias_node.dq_value + flat_value else: if len(flat_value) == out_shape[1]: # matmul needs to be transposed to fuse this reverse_matmul(G, params) has_transposed = True bias_node = ConstantInputParameters( G.unique_name(f'{params.name}_bias'), value=flat_value, dims=Dim.unnamed(flat_value.shape)) G.add_edge( NNEdge(from_node=bias_node, to_node=params, to_idx=2)) # extend the inward transpose if params.transpose_in: params.transpose_in = params.transpose_in + [None] LOG.info( "folding additive bias from %s into new bias on %s", op_node.name, params.name) else: params_in = G.indexed_in_edges(params.name) consts = [ isinstance(edge.from_node, ConstantInputParameters) for edge in params_in ] if not any(consts): break mult_const_node = params_in[1].from_node if consts[ 1] else params_in[0].from_node mult_const_node.value = mult_const_node.dqvalue * const_node.dqvalue if has_bias: bias_node = params_in[2].from_node bias_node.value = bias_node.dqvalue * const_node.dqvalue LOG.info( "folding multaplicative bias from %s into new bias on %s", op_node.name, params.name) out_edges = G.out_edges(op_node.name) G.remove(op_node) if remove_constant: G.remove(const_node) for edge in out_edges: G.add_edge( NNEdge(from_node=params, to_node=edge.to_node, to_idx=edge.to_idx)) G.add_dimensions() if G.quantization: quantizer = UnifiedQuantizer.from_quantized_graph(G) quantizer.quantize(G, start_nodes=[params]) RemoveUnnecessaryQuantizeOperators().match(G) if has_transposed: G.adjust_order() if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False group_identity = kwargs.get('group_identity') if group_identity == 'pow2_match_group': valid_activations = VALID_ACTIVATIONS_POW2 else: valid_activations = VALID_ACTIVATIONS_SQ8 for conv_node in [ params for params in G.nodes() if isinstance(params, Conv2DParameters) ]: node_list = self.get_node_list(G, conv_node, valid_activations) if node_list is None or len(node_list.order) < 2: continue if node_list.fusion_type == 'conv_active_pool': if node_list.pool.pool_type == "average": node_list.order = node_list.order[:2:] node_list.pool = None elif node_list.fusion_type == 'conv_pool_active': # NOTE: This is only for old POW2 kernels - SQ8 can handle this if node_list.pool.pool_type == "average" and node_list.active.activation != "relu": continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() last_node = None for node in node_list.order: if last_node is not None: subgraph.add_edge(NNEdge(from_node=last_node, to_node=node)) last_node = node input_mapping = [[(node_list.conv, idx)] for idx in range(3)] output_mapping = [(last_node, 0)] pnode = ConvFusionParameters( node_list.conv.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, in_dims_hint=node_list.conv.in_dims_hint, out_dims_hint=node_list.conv.out_dims_hint, in_dims=deepcopy(node_list.conv.in_dims), out_dims=deepcopy(node_list.order[-1].out_dims), input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: # TODO - stats prec = QRec.copy_ktype(qrecs[0], in_qs=deepcopy(qrecs[0].in_qs), out_qs=deepcopy(qrecs[-1].out_qs)) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.conv.name) out_edges = G.out_edges(last_node.name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge( NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): rnn_nodes = [ self.find_unpack(G, node) for node in G.nodes() if isinstance(node, RNNBaseParameters) ] has_modified_graph = False for rnn_unpack in rnn_nodes: if not rnn_unpack: continue unpack_node = rnn_unpack[-1] rnn_node = rnn_unpack[0] time_axis = rnn_node.transpose_out[0].index( 0) if rnn_node.transpose_out else 0 if isinstance(unpack_node, StridedSliceParameters): if unpack_node.act_slice[time_axis][1] != rnn_node.n_cells: LOG.debug("can't remove %s. Slice not equal to cells", unpack_node.name) continue if unpack_node.act_slice[time_axis][2] != 1: LOG.debug("can't remove %s. Slice not of length 1", unpack_node.name) continue if unpack_node.act_slice[time_axis][0] != rnn_node.n_cells - 1: LOG.debug("can't remove %s. Slice isn't last cell", unpack_node.name) continue out_edge = G.out_edges(unpack_node.name)[0] elif isinstance(unpack_node, SplitParameters): out_edges = G.out_edges(unpack_node.name) if len(out_edges) > 1: LOG.debug("can't remove %s. More than one output edge", unpack_node.name) continue out_edge = out_edges[0] if out_edge.from_idx != len(unpack_node.act_slices) - 1: LOG.debug("can't remove %s. Not last output", unpack_node.name) continue act_slice = unpack_node.act_slices[-1] if act_slice[time_axis][1] != rnn_node.n_cells: LOG.debug("can't remove %s. Slice not equal to cells", unpack_node.name) continue if act_slice[time_axis][0] != rnn_node.n_cells - 1: LOG.debug("can't remove %s. Slice isn't last cell", unpack_node.name) continue out_edge = G.out_edges(unpack_node.name)[0] else: continue has_modified_graph = True for node in rnn_unpack[1::]: LOG.info("Eliminating last cell unpack: %s", node.name) if G.quantization: del G.quantization[NodeId(node)] G.remove(node) rnn_node.n_output_cells = 1 rnn_node.out_dims[0] = unpack_node.out_dims[out_edge.from_idx] rnn_node.out_dims_hint = [ unpack_node.out_dims_hint[out_edge.from_idx] ] rnn_node.transpose_out = None G.add_edge( NNEdge(rnn_node, out_edge.to_node, to_idx=out_edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): if G.quantization: LOG.warning( 'match_duplicate_operations does not handle quantized graphs') return False def same_source_edge_fn(x): return f"{x.from_node.__hash__()}##{x.from_idx}" def same_dest_edge(x): return f"{x.to_node.__hash__()}##{x.to_idx}" modified_graph = False while True: found_more = False same_source_edges = [ list(edge_list) for _, edge_list in groupby( sorted(G.edges(), key=same_source_edge_fn), same_source_edge_fn) ] # all have the same origin same_source_edges = [ elem for elem in same_source_edges if len(elem) > 1 ] same_dest_edges = [] same_dest_group_edges = [] for same_source_edge in same_source_edges: same_source_edge = [ edge for edge in same_source_edge if isinstance(edge.to_node, ComparableParameters) ] while same_source_edge: first = same_source_edge.pop(0) others = list( filter( partial( lambda x, y: x.to_node != y.to_node and y. to_node.is_same_operation_as(G, x.to_node), first), same_source_edge)) if others: same_dest_edges.append(tuple([first] + others)) for other in others: same_source_edge.remove(other) continue other_groups = list( filter( partial( lambda x, y: x.to_node != y.to_node and y. to_node.can_be_grouped_with(x.to_node), first), same_source_edge)) if other_groups: same_dest_group_edges.append( tuple([first] + other_groups)) for other in other_groups: same_source_edge.remove(other) # all are multiple edges that go to something comparable save_same_dest_edges = same_dest_edges.copy() while same_dest_edges: edge_set = same_dest_edges.pop(0) keep_node = edge_set[0].to_node other_edge_sets = [ edges for edges in same_dest_edges if any(edge.to_node == keep_node for edge in edges) ] for other_edge_set in other_edge_sets: same_dest_edges.remove(other_edge_set) nodes_to_delete = set() for edge_set in [edge_set] + other_edge_sets: for edge in edge_set: other_node = edge.to_node if other_node == keep_node or other_node in nodes_to_delete: continue nodes_to_delete.add(other_node) for out_edge in G.out_edges(other_node): G.add_edge( NNEdge(from_node=keep_node, to_node=out_edge.to_node, to_idx=out_edge.to_idx)) LOG.info( f'removed duplicates {",".join(node.name for node in nodes_to_delete)} to {keep_node.name}' ) for node in nodes_to_delete: G.remove(node) # # all are multiple edges that go to something comparable # for edge_set in same_dest_edges: # modified_graph = True # found_more = True # first = edge_set[0] # first_node = first.to_node # dup_nodes = [] # for other in edge_set[1::]: # dest_node = other.to_node # dup_nodes.append(dest_node.name) # out_edges = G.out_edges(dest_node.name) # G.remove(dest_node) # for out_edge in out_edges: # G.add_edge(NNEdge(from_node=first_node, to_node=out_edge.to_node, # from_idx=out_edge.from_idx, to_idx=out_edge.to_idx)) # LOG.info( # f'removed duplicates {",".join(dup_nodes)} to {first_node.name}') for edge_set in same_dest_group_edges: modified_graph = True found_more = True # we will merge all the convolutions into one first = edge_set[0] first_node = first.to_node in_edges = G.indexed_in_edges(first_node.name) first_filter = first_node.filter weights_node = in_edges[1].from_node biases_node = in_edges[2].from_node dup_nodes = [] num_convs = len(edge_set) out_shape = deepcopy(first_node.out_dims[0]) out_shape.c *= num_convs # create a split after the first node splitting on channel axis act_slices, out_shapes, axis = SplitParameters.get_splits( out_shape, out_shape.get_order_idx('c'), num_splits=num_convs) split1 = SplitParameters( G.unique_name(f'{first_node.name}_split'), act_slices=act_slices, out_shapes=out_shapes, axis=axis) out_num = 0 # first node out edge goes to split out_edges = G.out_edges(first_node.name) for edge in out_edges: G.remove_edge(edge) G.add_edge( NNEdge(from_node=split1, from_idx=out_num, to_node=edge.to_node, to_idx=edge.to_idx)) G.add_edge(NNEdge(from_node=first_node, to_node=split1)) # first split output goes to original output for other in edge_set[1::]: out_num += 1 node_other = other.to_node dup_nodes.append(node_other.name) in_edges = G.indexed_in_edges(node_other.name) weights_other = in_edges[1].from_node biases_other = in_edges[2].from_node # merge the weights and biases diwn output channel weights_node.value = np.concatenate( (weights_node.value, weights_other.value), axis=first_filter.get_order_idx('out_c')) weights_node.dims = Dim.unnamed(weights_node.value.shape) biases_node.value = np.concatenate( (biases_node.value, biases_other.value)) biases_node.dims = Dim.unnamed(biases_node.value.shape) first_filter.out_c += node_other.filter.out_c # wire edge from split out_edges = G.out_edges(node_other.name) G.remove(node_other) G.remove(weights_other) G.remove(biases_other) for edge in out_edges: G.add_edge( NNEdge(from_node=split1, from_idx=out_num, to_node=edge.to_node, to_idx=edge.to_idx)) LOG.info( f'merged convolutions {",".join(dup_nodes)} into {first_node.name}' ) if not found_more: break if set_identity: self.set_identity(G) return modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False slices_by_origin = {} for slice_node in [ node for node in G.nodes() if isinstance(node, StridedSliceParameters) ]: in_edge = G.in_edges(slice_node.name)[0] group = slices_by_origin.setdefault( (in_edge.from_node, in_edge.from_idx), []) group.append(slice_node) for in_edge, slice_nodes in slices_by_origin.items(): slices = list(zip(*[node.act_slice for node in slice_nodes])) if len(slice_nodes) == 1: self.slice_to_split(G, slice_nodes, slices) continue diff_slices = [(idx, elems) for idx, elems in enumerate(slices) if not all(elems[0] == elem for elem in elems[1::])] if len(diff_slices) != 1: continue # strides must be one if any(sl[2] != 1 for sl in diff_slices[0][1]): continue # check if slices are consecutive and non overlapping slices = sorted(diff_slices[0][1], key=lambda x: x[0]) if not all(sl[0] + sl[1] == slices[i + 1][0] for i, sl in enumerate(slices[:-1:])): continue szes = [sl[1] - sl[0] for sl in slices] axis = diff_slices[0][0] slice_nodes = sorted(slice_nodes, key=lambda x: x.act_slice[axis][0]) act_slices, out_shapes, axis = SplitParameters.get_splits( slice_nodes[0].in_dims[0].shape, axis, splits=szes) params = SplitParameters(slice_nodes[0].name + '_split', act_slices=act_slices, out_shapes=out_shapes, axis=axis) in_edge = G.in_edges(slice_nodes[0].name)[0] G.add_edge( NNEdge(from_node=in_edge.from_node, to_node=params, from_idx=in_edge.from_idx)) sub_names = [] for idx, node in enumerate(slice_nodes): sub_names.append(node.name) out_edges = G.out_edges(node.name) G.remove(node) for out_edge in out_edges: G.add_edge( NNEdge(from_node=params, to_node=out_edge.to_node, from_idx=idx, to_idx=out_edge.to_idx)) if G.quantization: G.add_dimensions() quantizer = UnifiedQuantizer.from_quantized_graph(G) quantizer.quantize(G, start_nodes=[params]) RemoveUnnecessaryQuantizeOperators().match(G) LOG.info( f'replaced slice nodes {",".join(sub_names)} with split node {sub_names[0]}' ) has_modified_graph = True if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True) -> bool: has_modified_graph = False for pad_params in [ pad for pad in G.nodes() if isinstance(pad, PadParameters) ]: pad_in_edges = G.in_edges(pad_params.name) pad_out_edges = G.out_edges(pad_params.name) dont_delete = False for pad_out_edge in pad_out_edges: filter_like_node, is_1d = self.find_conv( G, pad_out_edge.to_node) if not filter_like_node: dont_delete = True continue if not filter_like_node.in_dims_hint or not filter_like_node.in_dims_hint[ 0]: raise ValueError( f"filter {filter_like_node.name} doesn't have a input hint" ) in_hint = filter_like_node.in_dims_hint[0] if is_1d: if len(pad_params.padding) != 2: LOG.warning( "pad node %s is applied to 1d convolution but has length %s", pad_params.name, len(pad_params.padding)) dont_delete = True continue expanded_padding = [ pad_params.padding[0], (0, 0), pad_params.padding[1] ] else: if len(pad_params.padding) != 3: LOG.warning( "pad node %s is applied to 2d convolution but has length %s", pad_params.name, len(pad_params.padding)) dont_delete = True continue expanded_padding = pad_params.padding hinted_pad = { in_hint[idx]: pad for idx, pad in enumerate(expanded_padding) if sum(pad) > 0 } key_set = set(hinted_pad.keys()) key_set -= set(['h', 'w']) if len(key_set) > 0: dont_delete = True LOG.error( "node %s has padding on axes %s and cannot be fused with filter %s", pad_params.name, key_set, filter_like_node.name) continue if any(pval != 0 for val in pad_params.pad_vals for pval in val): dont_delete = True LOG.error( "node %s has non zero pad values and cannot be fused with filter %s", pad_params.name, filter_like_node.name) continue LOG.info("adding padding from: %s to %s filter: %s", pad_params.name, is_1d and "1D" or "2D", filter_like_node.name) for key in ['h', 'w']: if key not in hinted_pad: hinted_pad[key] = (0, 0) filter_like_node.padding = PadDim(*(list(hinted_pad['h']) + list(hinted_pad['w']))) filter_like_node.pad_type = "zero" has_modified_graph = True G.remove_edge(pad_out_edge) if is_1d: reshape_node = pad_out_edge.to_node reshape_node.old_shape = self.remove_padding( reshape_node.old_shape, pad_params.padding) reshape_node.shape = self.remove_padding( reshape_node.shape, expanded_padding) for in_edge in pad_in_edges: G.add_edge( NNEdge(from_node=in_edge.from_node, to_node=pad_out_edge.to_node, from_idx=in_edge.from_idx, to_idx=pad_out_edge.to_idx)) if not dont_delete: G.remove(pad_params) if G.quantization: G.quantization.remove_node(pad_params) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False for pad_params in [pad for pad in G.nodes() if isinstance(pad, PadParameters)]: pad_in_edges = G.in_edges(pad_params.name) pad_out_edges = G.out_edges(pad_params.name) dont_delete = False if len(pad_in_edges) == 1 and all(sum(padding) == 0 for padding in pad_params.padding): LOG.info("removing zero padding node %s", pad_params.name) G.remove(pad_params) if G.quantization: G.quantization.remove_node(pad_params) dont_delete = True in_edge = pad_in_edges[0] for out_edge in pad_out_edges: G.add_edge(NNEdge(from_node=in_edge.from_node, to_node=out_edge.to_node, from_idx=in_edge.from_idx, to_idx=out_edge.to_idx)) else: for pad_out_edge in pad_out_edges: filter_like_node, expanded_padding, reshapes = self.find_conv( G, pad_out_edge.to_node, pad_params.padding) if not filter_like_node: dont_delete = True continue if not filter_like_node.in_dims_hint or not filter_like_node.in_dims_hint[0]: raise ValueError( f"filter {filter_like_node.name} doesn't have a input hint") in_hint = filter_like_node.in_dims_hint[0] hinted_pad = {in_hint[idx]: pad for idx, pad in enumerate(expanded_padding) if sum(pad) > 0} key_set = set(hinted_pad.keys()) key_set -= set(['h', 'w']) if len(key_set) > 0: dont_delete = True LOG.error("node %s has padding on axes %s and cannot be fused with filter %s", pad_params.name, key_set, filter_like_node.name) continue if any(pval != 0 for val in pad_params.pad_vals for pval in val): dont_delete = True LOG.error("node %s has non zero pad values and cannot be fused with filter %s", pad_params.name, filter_like_node.name) continue LOG.info("adding padding from: %s to filter: %s - has %s reshapes", pad_params.name, filter_like_node.name, len(reshapes)) for key in ['h', 'w']: if key not in hinted_pad: hinted_pad[key] = (0, 0) filter_like_node.padding = PadDim( *(list(hinted_pad['h']) + list(hinted_pad['w']))) filter_like_node.pad_type = "zero" has_modified_graph = True G.remove_edge(pad_out_edge) for reshape_node, old_padding, new_padding in reshapes: reshape_node.old_shape = self.remove_padding( reshape_node.old_shape, old_padding) reshape_node.shape = self.remove_padding( reshape_node.shape, new_padding) for in_edge in pad_in_edges: G.add_edge(NNEdge(from_node=in_edge.from_node, to_node=pad_out_edge.to_node, from_idx=in_edge.from_idx, to_idx=pad_out_edge.to_idx)) if not dont_delete: G.remove(pad_params) if G.quantization: G.quantization.remove_node(pad_params) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): has_modified_graph = False for pad_node in [ params for params in G.nodes() if isinstance(params, PadParameters) ]: node_list = self.get_node_list(G, pad_node) if node_list is None or len(node_list.order) < 2: continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() padded_input_idx = G.out_edges(node_list.pad.name)[0].to_idx subgraph.add_edge( NNEdge(from_node=node_list.pad, to_node=node_list.add, to_idx=padded_input_idx)) last_node = node_list.add node_list.add.force_quantized_index = 0 if node_list.active: subgraph.add_edge( NNEdge(from_node=node_list.add, to_node=node_list.active)) last_node = node_list.active if padded_input_idx == 0: input_mapping = [[(node_list.pad, 0)], [(node_list.add, 1)]] else: input_mapping = [[(node_list.add, 0)], [(node_list.pad, 1)]] output_mapping = [(last_node, 0)] pnode = PaddedAddFusionParameters( "PADDED_" + node_list.add.name, fusion_type=node_list.fusion_type, subgraph=subgraph, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = None if isinstance(qrecs[0], (SymmetricQuantizationRecord, SymmetricScalableFilterQuantizationRecord)): prec = SymmetricQuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (MultQuantizationRecord, MultScalableFilterQuantizationRecord)): prec = MultQuantizationRecord(in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (Float32QuantizationRecord, Float32ScalableFilterQuantizationRecord)): prec = Float32QuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) G.quantization[NodeId(pnode)] = prec if padded_input_idx == 0: in_edges = G.in_edges(node_list.pad.name) + G.indexed_in_edges( node_list.add.name)[1::] else: in_edges = G.indexed_in_edges( node_list.add.name)[0:1:] + G.in_edges(node_list.pad.name) out_edges = G.out_edges(last_node.name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge( NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): has_modified_graph = False for matmul_node in [ params for params in G.nodes() if isinstance(params, MatMulOpParameters) ]: node_list = self.get_node_list(G, matmul_node) if node_list is None or len(node_list.order) < 2: continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() if node_list.active is not None: subgraph.add_edge( NNEdge(from_node=node_list.matmul, to_node=node_list.active)) input_mapping = [[(node_list.matmul, idx)] for idx in range(2)] if node_list.add: input_mapping += [[(node_list.matmul, 2)]] output_mapping = [(node_list.active, 0)] if node_list.active else [(node_list.matmul, 0)] pnode = MatMulOpFusionParameters(node_list.matmul.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = None if isinstance(qrecs[0], (SymmetricQuantizationRecord, SymmetricScalableFilterQuantizationRecord)): prec = SymmetricQuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (MultQuantizationRecord, MultScalableFilterQuantizationRecord)): prec = MultQuantizationRecord(in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) elif isinstance(qrecs[0], (Float32QuantizationRecord, Float32ScalableFilterQuantizationRecord)): prec = Float32QuantizationRecord( in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.matmul.name) if node_list.add: bias_edge = [ add_edge for add_edge in G.in_edges(node_list.add.name) if isinstance(add_edge.from_node, ConstantInputParameters) ][0] out_edges = G.out_edges(node_list.order[-1].name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) if node_list.add: G.add_edge( NNEdge(bias_edge.from_node, pnode, from_idx=bias_edge.from_idx, to_idx=2)) for edge in out_edges: G.add_edge( NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False filter_nodes = [ node for node in G.nodes() if isinstance(node, FilterParameters) ] for filter_node in filter_nodes: while True: out_edges = G.out_edges(filter_node.name) # can't fuse if there is a branch if len(out_edges) > 1: break out_edge = out_edges[0] op_node = out_edge.to_node # must be a valid matrix op if not isinstance(op_node, tuple(OPS.keys())): break # other edge to the op must be a constant other_idx = 1 if out_edge.to_idx == 0 else 0 other_in_edge = G.indexed_in_edges(op_node.name)[other_idx] if not isinstance(other_in_edge.from_node, ConstantInputParameters): break const_node = other_in_edge.from_node remove_constant = len(G.out_edges(const_node.name)) flat_value = const_node.dqvalue.flatten() out_c = filter_node.filter.out_c op, weights_and_biases = OPS[op_node.__class__] # it would be possible to support mult bias addition by out channel but only supporting a # scalar at present if len(flat_value) != 1 and (weights_and_biases or len(flat_value) != out_c): LOG.warning('could not absorb %s into %s', const_node.name, filter_node.name) break # If there is quantization then essentially the output of the filter # takes the quantization of the output of the operation. # The biases will not change since their quantization depends on the weights # and input fnid = NodeId(filter_node) opnid = NodeId(op_node) if G.quantization and (fnid in G.quantization or opnid in G.quantization): if not (fnid in G.quantization and opnid in G.quantization): LOG.warning( 'could not absorb %s into %s - graph is partially quantized', const_node.name, filter_node.name) break fqrec = G.quantization[fnid] opqrec = G.quantization[opnid] fqrec.out_qs[0] = opqrec.out_qs[0] has_modified_graph = True LOG.info("fusing bias in %s into %s", const_node.name, filter_node.name) self.fuse_bias(G, filter_node, other_idx, op, flat_value, 2) if weights_and_biases: # TODO - need to adjust weights quantization here LOG.info("fusing multiplicative bias in %s into %s", const_node.name, filter_node.name) self.fuse_bias(G, filter_node, other_idx, op, flat_value, 1) out_edges = G.out_edges(op_node.name) G.remove(op_node) if remove_constant: G.remove(const_node) for edge in out_edges: G.add_edge( NNEdge(from_node=filter_node, to_node=edge.to_node, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False slices_by_origin = {} for slice_node in [ node for node in G.nodes() if isinstance(node, StridedSliceParameters) ]: in_edge = G.in_edges(slice_node.name)[0] group = slices_by_origin.setdefault( (in_edge.from_node, in_edge.from_idx), []) group.append(slice_node) for in_edge, slice_nodes in slices_by_origin.items(): slices = list(zip(*[node.act_slice for node in slice_nodes])) if len(slice_nodes) == 1: self.slice_to_split(G, slice_nodes, slices) continue # strides must be one if any(sl[2] != 1 for sl_axis in slices for sl in sl_axis): continue diff_axes = list([ idx for idx, elems in enumerate(slices) if not all(elems[0] == elem for elem in elems[1::]) ]) not_diff_axes = [ idx for idx in range(len(slices)) if idx not in diff_axes ] diff_slices = [ sl for idx, sl in enumerate(slices) if idx in diff_axes ] axis_lengths = in_edge[0].out_dims[in_edge[1]].shape if not_diff_axes and min(not_diff_axes) < max(diff_axes): transpose_from = tuple(range(len(slices))) transpose_to = tuple(diff_axes + not_diff_axes) axis_lengths = [axis_lengths[idx] for idx in transpose_to] else: transpose_from = transpose_to = None diff_axis_lengths = axis_lengths[0:len(diff_axes):] diff_slices = combine_slices(diff_axis_lengths, diff_slices, slice_nodes) if diff_slices is None: continue if len(diff_axes) > 1: reshape_from = axis_lengths reshape_to = [np.prod(diff_axis_lengths)] + \ axis_lengths[len(diff_axes)::] else: reshape_from = None reshape_to = slice_nodes[0].in_dims[0].shape if transpose_from: reshape_to = [reshape_to[idx] for idx in transpose_to] sizes, shapes, sorted_nodes = slices_to_sizes( diff_slices, axis_lengths[len(diff_axes)::]) name_prefix = sorted_nodes[0].name in_edge = G.in_edges(sorted_nodes[0].name)[0] in_node = in_edge.from_node in_idx = in_edge.from_idx if transpose_from: params = TransposeParameters(G.unique_name(name_prefix + '_tin'), transpose=transpose_to) G.add_edge( NNEdge(from_node=in_node, to_node=params, from_idx=in_idx)) in_node = params in_idx = 0 if reshape_from: params = ReshapeParameters(G.unique_name(name_prefix + '_reshape'), old_shape=Dim.unnamed(reshape_from), shape=Dim.unnamed(reshape_to)) G.add_edge( NNEdge(from_node=in_node, to_node=params, from_idx=in_idx)) in_node = params in_idx = 0 act_slices, out_shapes, axis = SplitParameters.get_splits( reshape_to, 0, splits=sizes) split_node = SplitParameters(G.unique_name(name_prefix + '_split'), act_slices=act_slices, out_shapes=out_shapes, axis=axis) G.add_edge( NNEdge(from_node=in_node, from_idx=in_idx, to_node=split_node)) sub_names = [] for idx, node in enumerate(sorted_nodes): sub_names.append(node.name) out_edges = G.out_edges(node.name) G.remove(node) for out_edge in out_edges: params = split_node out_idx = idx if reshape_from: from_node = params params = ReshapeParameters( G.unique_name(name_prefix + f'_reshape{idx}'), shape=Dim.unnamed(shapes[idx])) G.add_edge( NNEdge(from_node=from_node, to_node=params, from_idx=out_idx)) out_idx = 0 if transpose_from: from_node = params params = TransposeParameters( G.unique_name(name_prefix + f'_tout{idx}'), transpose=reverse_transpose(transpose_to)) G.add_edge( NNEdge(from_node=from_node, to_node=params, from_idx=out_idx)) out_idx = 0 G.add_edge( NNEdge(from_node=params, to_node=out_edge.to_node, from_idx=out_idx, to_idx=out_edge.to_idx)) if G.quantization: G.add_dimensions() quantizer = NewQuantizer.from_quantized_graph(G) quantizer.quantize() RemoveUnnecessaryQuantizeOperators().match(G) LOG.info( f'replaced slice nodes {",".join(sub_names)} with split node {split_node.name}' ) has_modified_graph = True if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs) -> bool: has_modified_graph = False gathers_by_origin = {} for gather in [ node for node in G.nodes() if isinstance(node, GatherParameters) ]: in_edge = G.in_edges(gather.name)[0] group = gathers_by_origin.setdefault( (in_edge.from_node, in_edge.from_idx), []) group.append(gather) for in_edge, gathers in gathers_by_origin.items(): # This is too difficult to handle if there are multiple slices axis = gathers[0].axis if not all(gather.axis == axis and len(gather.indices.shape) <= 1 for gather in gathers[1::]): continue # sort all the indices gathers = sorted(gathers, key=lambda x: x.indices if len(x.indices.shape) == 0 else x.indices[0]) indices = [ elem for gather in gathers for elem in ([int(gather.indices)] if len(gather.indices.shape) == 0 else list(gather.indices)) ] # All the indices must be independant and sum to the out dim (this could be relaxed but # then needs to handle gaps) in_shape = in_edge[0].out_dims[in_edge[1]].shape in_shape_without_axis = in_shape[:axis:] + in_shape[axis + 1::] if len(set(indices)) != len(indices) and len( set(indices)) == in_shape[axis]: continue # good for a split LOG.info("gathers from %s[%s] converted to a split", in_edge[0].name, in_edge[1]) splits = [] shapes = [] out_edges = [] for gather in gathers: splits.append( [tuple([int(gather.indices), int(gather.indices) + 1, 1])]) shapes.append(in_shape_without_axis) out_edges.append(G.out_edges(gather.name)) G.remove(gather) params = SplitParameters("%s_split" % in_edge[0].name, act_slices=splits, out_shapes=shapes, axis=axis) if axis != 0: trans = [axis] + list(range(0, axis)) + list( range(axis, len(in_shape))) params.transpose_out = [[ trans.index(idx) for idx in range(len(trans)) ]] params.transpose_in = [trans] for idx, edges in enumerate(out_edges): for edge in edges: G.add_edge( NNEdge(from_node=params, to_node=edge.to_node, from_idx=idx, to_idx=edge.to_idx)) G.add_edge( NNEdge(from_node=in_edge[0], to_node=params, from_idx=in_edge[1])) has_modified_graph = True if set_identity: self.set_identity(G) return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs): has_modified_graph = False group_identity = kwargs.get('group_identity') if group_identity == 'pow2_match_group': valid_activations = VALID_ACTIVATIONS_POW2 valid_activations_wo_pool = VALID_ACTIVATIONS_POW2_WO_POOL else: valid_activations = VALID_ACTIVATIONS_SQ8 valid_activations_wo_pool = VALID_ACTIVATIONS_SQ8_WO_POOL for pool_node in G.nodes(node_classes=(PoolingParameters, GlobalPoolingParameters)): node_list = self.get_node_list(G, pool_node, valid_activations, valid_activations_wo_pool) if node_list is None or len(node_list.order) < 2: continue LOG.info("fusing nodes %s", ",".join( (node.name for node in node_list.order))) has_modified_graph = True subgraph = GraphView() last_node = None for node in node_list.order: if last_node is not None: subgraph.add_edge(NNEdge(from_node=last_node, to_node=node)) last_node = node input_mapping = [[(node_list.pool, 0)]] output_mapping = [(last_node, 0)] pnode = ActivationFusion(node_list.pool.name + '_fusion', fusion_type=node_list.fusion_type, subgraph=subgraph, input_mapping=input_mapping, output_mapping=output_mapping) if G.quantization: # TODO - stats qrecs = G.quantization.get_all(pnode.contained_nodes()) if qrecs: prec = QRec.copy_ktype(qrecs[0], in_qs=qrecs[0].in_qs, out_qs=qrecs[-1].out_qs) for node in pnode.contained_nodes(): G.quantization.move_to_fusion(node, pnode) if isinstance(node, GlobalPoolingParameters): # Global pooling fused with activations need to have only the activation scale G.quantization[NodeId(pnode, node)].out_qs[0] = deepcopy( G.quantization[NodeId( pnode, node)].in_qs[0]) G.quantization[NodeId( pnode, node)].out_qs[0].dtype = np.int32 G.quantization[NodeId(pnode)] = prec in_edges = G.in_edges(node_list.pool.name) out_edges = G.out_edges(last_node.name) for node in node_list.order: G.remove(node) for edge in in_edges: G.add_edge( NNEdge(edge.from_node, pnode, from_idx=edge.from_idx, to_idx=edge.to_idx)) for edge in out_edges: G.add_edge( NNEdge(pnode, edge.to_node, from_idx=edge.from_idx, to_idx=edge.to_idx)) if set_identity: self.set_identity(G) return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True): def same_source_edge(x): return f"{x.from_node.__hash__()}##{x.from_idx}" def same_dest_edge(x): return f"{x.to_node.__hash__()}##{x.to_idx}" modified_graph = False same_source_edges = [ list(edge_list) for _, edge_list in groupby( sorted(G.edges(), key=same_source_edge), same_source_edge) ] # all have the same origin same_source_edges = [ elem for elem in same_source_edges if len(elem) > 1 ] same_dest_edges = [] for same_source_edge in same_source_edges: same_source_edge = [ edge for edge in same_source_edge if isinstance(edge.to_node, ComparableParameters) ] while same_source_edge: first = same_source_edge.pop(0) others = list( filter( partial( lambda x, y: y.to_node.is_same_operation_as( x.to_node), first), same_source_edge)) if others: same_dest_edges.append(tuple([first] + others)) for other in others: same_source_edge.remove(other) # all are multiple edges that go to something comparable for edge_set in same_dest_edges: first = edge_set[0] first_node = first.to_node dup_nodes = [] for other in edge_set[1::]: modified_graph = True dest_node = other.to_node dup_nodes.append(dest_node.name) out_edges = G.out_edges(dest_node.name) G.remove(dest_node) for out_edge in out_edges: G.add_edge( NNEdge(from_node=first_node, to_node=out_edge.to_node, from_idx=out_edge.from_idx, to_idx=out_edge.to_idx)) LOG.info( f'removed duplicates {",".join(dup_nodes)} to {first_node.name}' ) if set_identity: self.set_identity(G) return modified_graph