def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
softmaxes = [
node for node in G.nodes() if isinstance(node, SoftMaxParameters)
]
qrecs = [G.quantization[NodeId(node)] for node in softmaxes]
if not all(isinstance(qrec, MultQuantizationRecord) for qrec in qrecs):
return
for softmax, qrec in zip(softmaxes, qrecs):
in_q = qrec.in_qs[0]
in_q.scale_to_pow2()
for edge in G.in_edges(softmax.name):
propagate_qtype_up(G, in_q, edge)
for edge in G.out_edges(softmax.name):
assert isinstance(
edge.to_node,
(OutputParameters, QuantizeParameters
)), "Softmax is supported only at the end of the graph"
out_qrec = G.quantization[NodeId(edge.to_node)]
out_qrec.in_qs[0] = qrec.out_qs[0]
out_qrec.out_qs[0] = qrec.out_qs[0]
if set_identity:
self.set_identity(G)
return False
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
const_ops = [node for node in G.nodes()
if isinstance(node, MatrixMulParameters)
and any([isinstance(edge.from_node, ConstantInputParameters)
and check_equals(G, edge.from_node, 1.0/6.0)
for edge in G.in_edges(node.name)])]
oprecs = [oprec for oprec in (look_back(G, op)
for op in const_ops)
if oprec is not None]
has_modified_graph = False
for oprec in oprecs:
mul_edge = G.out_edges(oprec['mul'][0].name)
if len(mul_edge) == 1:
mul_edge = mul_edge[0]
if isinstance(mul_edge.to_node, ReluActivationParameters):
oprec['relu3'] = (mul_edge.to_node,
G.quantization[NodeId(mul_edge.to_node)])
has_modified_graph = True
process_rec(G, oprec)
if set_identity:
self.set_identity(G)
return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
for nid in [nid for nid, qrec in G.quantization.sorted_iterator(G) if qrec is None or not (qrec.in_qs and qrec.out_qs)]:
if nid.fnode_name:
LOG.warning("can't add quantization to fused node %s", nid.fnode_name)
continue
if nid.node_name not in G:
# previous fusions may have removed nodes from the graph
continue
node = nid.get_node(G)
predecessors = [NodeId(pred) for pred in G.predecessors(node.name)]
successors = [NodeId(succ) for succs in G.successors(node.name) for succ in succs]
go_back = not successors or (predecessors and all(pred in G.quantization for pred in predecessors))
go_forward = not predecessors or (successors and all(succ in G.quantization for succ in successors))
if not (go_back or go_forward):
LOG.warning("node %s is not connected to anything and has no quantization", node.name)
continue
if go_forward:
out_qrecs = set(G.quantization[nid] for nid in successors)
if not all(isinstance(out_qrec, MultQuantizationRecord) for out_qrec in out_qrecs):
continue
out_qtypes = reduce_qtypes([(edge.from_idx, G.quantization[NodeId(edge.to_node)].in_qs[edge.to_idx])
for edge in G.out_edges(node.name)])
else:
out_qtypes = None
if go_back:
in_qrecs = set(G.quantization[nid] for nid in predecessors)
if not all(isinstance(in_qrec, MultQuantizationRecord) for in_qrec in in_qrecs):
continue
in_qtypes = reduce_qtypes([(edge.to_idx, G.quantization[NodeId(edge.from_node)].out_qs[edge.from_idx])
for edge in G.in_edges(node.name)])
else:
in_qtypes = None
if not in_qtypes:
if not predecessors:
LOG.info("setting quantization on input node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(out_qtypes), out_qs=deepcopy(out_qtypes))
else:
raise NotImplementedError("propagating qrecs not implemented")
elif not out_qtypes:
if not successors:
LOG.info("setting quantization on output node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(in_qtypes), out_qs=deepcopy(in_qtypes))
else:
raise NotImplementedError("propagating qrecs not implemented")
else:
LOG.info("setting quantization on node %s", node.name)
qrec = MultQuantizationRecord(in_qs=deepcopy(in_qtypes), out_qs=deepcopy(out_qtypes))
G.quantization[nid] = qrec
if set_identity:
self.set_identity(G)
return False
def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
sigs_swishes = [
node for node in G.nodes()
if isinstance(node, (SigmoidActivationParameters,
HSigmoidActivationParameters,
HSwishActivationParameters))
]
qrecs = [G.quantization[NodeId(node)] for node in sigs_swishes]
for sig_swish, qrec in zip(sigs_swishes, qrecs):
in_edge = [
edge for edge in G.in_edges(sig_swish.name) if edge.to_idx == 0
][0]
in_q = qrec.in_qs[0]
min_val, max_val = in_q.min_val, in_q.max_val
if isinstance(
sig_swish,
(HSigmoidActivationParameters, SigmoidActivationParameters)):
# Hard sigmoid implements a RELU, be sure 6 can be representable
min_val, max_val = 0, 6
elif isinstance(sig_swish, HSwishActivationParameters):
min_val, max_val = 0, in_q.max_val * 6
new_in_q = QType.from_min_max_sq(min_val=min_val,
max_val=max_val,
dtype=in_q.dtype)
propagate_qtype_up(G, new_in_q, in_edge)
if set_identity:
self.set_identity(G)
return False
def match(self, G: GraphView, set_identity: bool = True):
# get a list of all the nodes that are transposable but not transposes
# Need to do this first to avoid mutating it when doing the modifications
tnodes = list(filter(lambda n: isinstance(n, Transposable) and\
not isinstance(n, TransposeParameters),
G.nodes()))
for node in tnodes:
if node.transpose_in:
for idx, edge in enumerate(G.in_edges(node.name)):
in_params = TransposeParameters("%s_TIN_%s" % (node.name, idx),
transpose=node.transpose_in)
if node.in_dims_hint:
in_hint = node.in_dims_hint[edge.to_idx]
out_hint = apply_reverse_transpose_to_hint(in_hint, node.transpose_in)
in_params.in_dims_hint = [in_hint.copy()]
in_params.out_dims_hint = [out_hint.copy()]
node.in_dims_hint[edge.to_idx] = out_hint
G.insert_node(in_params, edge.from_node.name, edge.to_node.name,
from_idx=edge.from_idx, to_idx=edge.to_idx)
node.transpose_in = None
if node.transpose_out:
for idx, edge in enumerate(G.out_edges(node.name)):
out_params = TransposeParameters("%s_TOUT_%s" % (node.name, idx),
transpose=node.transpose_out)
if node.out_dims_hint:
out_hint = node.out_dims_hint[edge.from_idx]
in_hint = apply_reverse_transpose_to_hint(out_hint, node.transpose_out)
out_params.in_dims_hint = [in_hint.copy()]
out_params.out_dims_hint = [out_hint.copy()]
node.out_dims_hint[edge.from_idx] = in_hint
G.insert_node(out_params, edge.from_node.name, edge.to_node.name,
from_idx=edge.from_idx, to_idx=edge.to_idx)
node.transpose_out = None
if set_identity:
self.set_identity(G)
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):
something_changed = False
for relu_node in [node for node in G.nodes(node_classes=ReluActivationParameters) if node.upper_bound == 6]:
out_edges = G.out_edges(relu_node)
if len(out_edges) != 1 or not isinstance(out_edges[0].to_node, MatrixMulParameters):
continue
mul_node = out_edges[0].to_node
in_edges = G.in_edges(mul_node)
if len(in_edges) != 2:
continue
other_edge = (set(in_edges) - {out_edges[0]}).pop()
constant_node = other_edge.from_node
if len(G.out_edges(constant_node)) != 1:
continue
if (not isinstance(constant_node, ConstantInputParameters) or
not check_equals(G, constant_node, 1.0/6.0)):
continue
something_changed = True
activation = HSigmoidActivationParameters(
G.unique_name(f'{mul_node.name}_hsigmoid'), offset=0)
in_edges = G.in_edges(relu_node)
out_edges = G.out_edges(mul_node)
nodes_to_replace = [relu_node, mul_node, constant_node]
LOG.info(f'fusing {", ".join(node.name for node in nodes_to_replace)} into HSIGMOID {activation.name}')
G.remove_all(nodes_to_replace)
for in_edge in in_edges:
G.add_edge(NNEdge.clone(in_edge, to_node=activation, to_idx=0))
for out_edge in out_edges:
G.add_edge(NNEdge.clone(
out_edge, from_node=activation, from_idx=0))
if G.quantization:
reluqrec = G.quantization[NodeId(relu_node)]
mulqrec = G.quantization[NodeId(mul_node)]
del G.quantization[NodeId(constant_node)]
pqrec = QRec.copy_ktype(
reluqrec, in_qs=reluqrec.in_qs, out_qs=mulqrec.out_qs)
G.quantization[NodeId(activation)] = pqrec
return something_changed
def match(self, G: GraphView, set_identity: bool = True):
# get a list of all the nodes that are transposable but not transposes
# Need to do this first to avoid mutating it when doing the modifications
tnodes = list(filter(lambda n: isinstance(n, Transposable) and
not isinstance(n, TransposeParameters),
G.nodes()))
has_modified_graph = False
for node in tnodes:
if node.transpose_in:
for idx, edge in enumerate(G.in_edges(node.name)):
if edge.to_idx >= len(node.transpose_in):
continue
trans = node.transpose_in[edge.to_idx]
if trans is None:
continue
has_modified_graph = True
in_params = TransposeParameters("%s_TIN_%s" % (node.name, idx),
transpose=trans)
if node.in_dims_hint and node.in_dims_hint[edge.to_idx]:
in_hint = node.in_dims_hint[edge.to_idx]
out_hint = apply_reverse_transpose_to_hint(in_hint, trans)
in_params.in_dims_hint = [in_hint.copy()]
in_params.out_dims_hint = [out_hint.copy()]
node.in_dims_hint[edge.to_idx] = out_hint
if G.quantization:
G.quantization.copy_to_node(node, in_params)
G.insert_node(in_params, edge.from_node.name, edge.to_node.name,
from_idx=edge.from_idx, to_idx=edge.to_idx,
edge_class=NNEdge)
node.transpose_in = None
if node.transpose_out:
for idx, edge in enumerate(G.out_edges(node.name)):
if edge.from_idx >= len(node.transpose_out):
continue
trans = node.transpose_out[edge.from_idx]
if trans is None:
continue
has_modified_graph = True
out_params = TransposeParameters("%s_TOUT_%s" % (node.name, idx),
transpose=trans)
if node.out_dims_hint:
out_hint = node.out_dims_hint[edge.from_idx]
in_hint = apply_reverse_transpose_to_hint(out_hint, trans)
out_params.in_dims_hint = [in_hint.copy()]
out_params.out_dims_hint = [out_hint.copy()]
node.out_dims_hint[edge.from_idx] = in_hint
if G.quantization:
G.quantization.copy_to_node(node, out_params)
G.insert_node(out_params, edge.from_node.name, edge.to_node.name,
from_idx=edge.from_idx, to_idx=edge.to_idx,
edge_class=NNEdge)
node.transpose_out = None
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 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, **kwargs):
candidates = [
node for node in G.nodes(node_classes=SplitParameters)
if search_up_for_input(G, node)
]
has_modified_graph = False
for node in candidates:
LOG.info("Insert copy on split input %s", node.name)
has_modified_graph = True
cnode = CopyParameters(G.unique_name(f'{node.name}_copy'))
G.insert_node_at_edge(cnode, G.in_edges(node.name)[0])
if G.quantization:
G.quantization.copy_qrec(node, 'in', 0, cnode)
if set_identity:
self.set_identity(G)
return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
if not G.quantization:
return
rnns = [node for node in G.nodes() if isinstance(
node, RNNBaseParameters)]
qrecs = [G.quantization[NodeId(node)] for node in rnns]
for rnn, qrec in zip(rnns, qrecs):
in_idx = rnn.INPUT_NAMES.index('input')
in_edge = [edge for edge in G.in_edges(
rnn.name) if edge.to_idx == in_idx][0]
in_q = qrec.in_qs[in_idx]
propagate_qtype_up(G, in_q, in_edge)
if set_identity:
self.set_identity(G)
return False
def _match(self,
G: GraphView,
set_identity: bool = True,
**kwargs) -> bool:
replaced = True
has_modified_graph = False
while replaced:
replaced = False
for subgraph in self.match_function(G):
# TODO - Save in and out edges here since the replace function may modify the
# subgraph
in_edges = [
in_edge for input_node in subgraph.inputs()
for in_edge in G.in_edges(input_node.name)
]
out_edges = [
out_edge for output_node in subgraph.outputs()
for out_edge in G.out_edges(output_node.name)
]
try:
replacement, edge_in_mapping, edge_out_mapping = self.replace_function(
G, subgraph)
if replacement is None:
G.remove_fragment(subgraph)
has_modified_graph = True
elif isinstance(replacement, Node):
# use saved in and out edges
G.replace_fragment(subgraph,
replacement,
frag_in_edges=in_edges,
frag_out_edges=out_edges,
edge_in_mapping=edge_in_mapping,
edge_out_mapping=edge_out_mapping)
has_modified_graph = True
else:
raise TypeError(
"unexcepted return value from replace_function")
replaced = True
break
except DontReplaceError:
pass
if set_identity:
self.set_identity(G)
return has_modified_graph
def match(self, G: GraphView, set_identity: bool = True):
if not G.quantization:
return
concats = [
node for node in G.nodes() if isinstance(node, ConcatParameters)
]
qrecs = [G.quantization[NodeId(node)] for node in concats]
if not all(isinstance(qrec, MultQuantizationRecord) for qrec in qrecs):
return
for concat, qrec in zip(concats, qrecs):
out_q = qrec.out_qs[0]
for edge in G.in_edges(concat.name):
in_q = qrec.in_qs[edge.to_idx]
if in_q != out_q:
propagate_qtype_up(G, out_q, edge)
if set_identity:
self.set_identity(G)
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):
activations = [
node for node in G.nodes()
if isinstance(node, ActivationParameters)
]
activations = filter(
lambda n: not isinstance(
G.in_edges(n.name)[0].from_node, VALID_FUSIONS), activations)
can_be_moved = []
for activation in activations:
try:
edges = list(self.find_home_for_activation(G, activation))
LOG.info("Activation %s can be moved", activation.name)
can_be_moved.append({'activation': activation, 'edges': edges})
except LocationNotFoundError:
LOG.info("Activation %s cannot be moved", activation.name)
for move in can_be_moved:
self.move_activation(G, move['activation'], move['edges'])
if set_identity:
self.set_identity(G)
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):
has_modified_graph = False
nodes_to_remove = []
for node in G.nodes(node_classes=CopyParameters):
out_edges = G.out_edges(node)
if len(out_edges) > 1:
continue
if (search_down(
G,
out_edges[0],
(OutputParameters, InputParameters, ConstantInputParameters,
SplitParameters, ConcatParameters),
can_pass=(ReshapeParameters, NoOPParameters),
can_pass_fn=lambda G, node: isinstance(
node, TransposeParameters) and node.does_nothing,
follow_multi=True) and search_up(
G,
G.in_edges(node)[0],
(InputParameters, OutputParameters,
ConstantInputParameters, SplitParameters,
ConcatParameters),
can_pass=(ReshapeParameters, NoOPParameters),
can_pass_fn=lambda G, node: isinstance(
node, TransposeParameters) and node.does_nothing,
follow_multi=True)):
continue
nodes_to_remove.append(node)
for node in nodes_to_remove:
LOG.info("remove redundant copy %s", node.name)
has_modified_graph = True
G.remove_and_reconnect(node, edge_class=NNEdge)
if G.quantization:
nid = NodeId(node)
if nid in G.quantization:
del G.quantization[nid]
if set_identity:
self.set_identity(G)
return has_modified_graph
def _match(self, G: GraphView, set_identity: bool = True, **kwargs):
target_nodes = [node for node in G.nodes(
) if self.execute_tests(G, self.ValidNodes, node)]
target_nodes = filter(lambda n: not isinstance(
G.in_edges(n.name)[0].from_node, self.ValidFusions), target_nodes)
can_be_moved = []
has_modified_graph = False
for node in target_nodes:
try:
edges = list(self.find_home_for_node(G, node))
LOG.info("Node %s can be moved", node.name)
can_be_moved.append({'node': node, 'edges': edges})
except LocationNotFoundError:
LOG.info("Node %s cannot be moved", node.name)
for move in can_be_moved:
has_modified_graph = True
self.move_node(G, move['node'], move['edges'])
if set_identity:
self.set_identity(G)
return has_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
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) -> 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):
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,
**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,
**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
