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[pfto] Reduce redudant copy #751

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[pfto] Reduce redudant copy #751

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d65517d
[pfto] Reduce redudant copy
Oct 3, 2023
0c17d87
Merge branch 'master' into pfto_opt
take-cheeze Oct 5, 2023
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70 changes: 36 additions & 34 deletions pytorch_pfn_extras/onnx/pfto_exporter/export.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,7 +161,7 @@ def _type_to_proto(t: torch._C.TensorType) -> onnx.TypeProto:

if t.kind() == "IntType":
ret.tensor_type.elem_type = onnx.TensorProto.DataType.INT64 # type: ignore[attr-defined]
ret.tensor_type.shape.CopyFrom(onnx.TensorShapeProto())
ret.tensor_type.shape.SetInParent()
return ret

assert t.kind() == "TensorType", f"Not Tensor type(actual: {t.kind()}): {t}"
Expand All @@ -173,7 +173,7 @@ def _type_to_proto(t: torch._C.TensorType) -> onnx.TypeProto:
sym_hel.cast_pytorch_to_onnx[t.scalarType()] # type: ignore[index]
)

ret.tensor_type.shape.CopyFrom(onnx.TensorShapeProto())
ret.tensor_type.shape.SetInParent()
if t.sizes() is not None:
for s in t.sizes(): # type: ignore
d = ret.tensor_type.shape.dim.add()
Expand Down Expand Up @@ -936,23 +936,29 @@ def assign_onnx_values(
assert len(blocks) == 2
for attr_name, block in zip(["then_branch", "else_branch"], blocks):
sub_g = block2subgraph(f"{new_nd.name}_{attr_name}", block, new_nd.doc_string)
new_nd.attribute.append(onnx.helper.make_attribute(attr_name, sub_g))
attr = new_nd.attribute.add()
attr.name = attr_name
attr.type = onnx.AttributeProto.GRAPH
attr.g.CopyFrom(sub_g)
else:
assert len(list(n.blocks())) == 0, f"Node with block needs to be handled separately: {n}"
if n in self.node_doc_string:
new_nd.doc_string = self.node_doc_string[n]
for attr_name in n.attributeNames():
attr_kind = n.kindOf(attr_name)
if attr_kind == "t":
attr = onnx.helper.make_attribute(attr_name, _tensor_to_proto(n.t(attr_name)))
attr = new_nd.attribute.add()
attr.name = attr_name
attr.type = onnx.AttributeProto.TENSOR
attr.t.CopyFrom(_tensor_to_proto(n.t(attr_name)))
else:
if pytorch_pfn_extras.requires('1.13'):
attr_val = sym_hel._node_get(n, attr_name) # type: ignore[attr-defined]
else:
attr_val = n[attr_name]
# Could not use onnx.helper.make_attribute for
if isinstance(attr_val, list):
attr = onnx.AttributeProto()
attr = new_nd.attribute.add()
attr.name = attr_name
if attr_kind == "ss":
attr.type = onnx.AttributeProto.STRINGS
Expand All @@ -966,8 +972,8 @@ def assign_onnx_values(
else:
assert False, f"'{attr_kind}' typed attribute not supported"
else:
attr = onnx.helper.make_attribute(attr_name, attr_val)
new_nd.attribute.append(attr)
attr = new_nd.attribute.add()
attr.CopyFrom(onnx.helper.make_attribute(attr_name, attr_val))
assign_onnx_values(new_nd.input, new_nd.name, n.inputs())
assign_onnx_values(new_nd.output, new_nd.name, n.outputs())
onnx_nodes.append(new_nd)
Expand Down Expand Up @@ -1001,6 +1007,8 @@ def generate_onnx(self) -> onnx.ModelProto:

self.log("ONNX graph", self.g)

model = onnx.ModelProto()

with record("to_node_proto"):
onnx_nodes, onnx_vars, val_tab = self.generate_proto_nodes(self.g, {}, {})

Expand Down Expand Up @@ -1069,52 +1077,46 @@ def apply_dynamic_axes_info(out: onnx.ValueInfoProto, k: str) -> None:

with record("rename_onnx_vars"):
unique_onnx_vars: Dict[str, onnx.ValueInfoProto] = {}
identities: List[onnx.NodeProto] = []
for onnx_name, ox_v in onnx_vars.items():
if ox_v.name in unique_onnx_vars:
ox_n = onnx.NodeProto()
ox_n = model.graph.node.add()
ox_n.name = f"{val_tab[onnx_name]}_id"
ox_n.op_type = "Identity"
ox_n.input.append(ox_v.name)
ox_n.output.append(val_tab[onnx_name])
identities.append(ox_n)
else:
unique_onnx_vars[ox_v.name] = ox_v
onnx_nodes = identities + onnx_nodes

with record("make_graph"):
graph = onnx.helper.make_graph(
nodes=onnx_nodes,
name=self.traced.original_name,
inputs=onnx_inputs,
outputs=onnx_outputs,
initializer=[v for k, v in unique_onnx_vars.items()],
doc_string=None if self.strip_doc_string else self.graph_doc_string,
# TODO(twata): Use torch IR's value type info
# value_info=[
# self.values[k] for k in set(list(self.values.keys())) - set(inout_names)
# ],
)
graph = model.graph
graph.node.extend(onnx_nodes)
graph.name = self.traced.original_name
graph.input.extend(onnx_inputs)
graph.output.extend(onnx_outputs)
graph.initializer.extend([v for k, v in unique_onnx_vars.items()])
if not self.strip_doc_string:
graph.doc_string = self.graph_doc_string
# TODO(twata): Use torch IR's value type info
# graph.value_info=[
# self.values[k] for k in set(list(self.values.keys())) - set(inout_names)
# ]

self.log("ONNX printable graph", lambda: onnx.helper.printable_graph(graph))

def get_model_opset_imports(graph: onnx.GraphProto) -> List[onnx.OperatorSetIdProto]:
def set_model_opset_imports(model: onnx.ModelProto) -> None:
opsets = {onnx.defs.ONNX_DOMAIN: self.opset_version}
for node in graph.node:
for node in model.graph.node:
if node.domain != onnx.defs.ONNX_DOMAIN:
opsets[node.domain] = self.custom_opsets.get(node.domain, 1)
opset_imports = []
for domain, version in opsets.items():
opset_imports.append(onnx.helper.make_opsetid(domain, version))
return opset_imports
o = model.opset_import.add()
o.domain = domain
o.version = version

with record("make_model"):
model: onnx.ModelProto = onnx.helper.make_model_gen_version(
graph,
opset_imports=get_model_opset_imports(graph),
producer_name="pfto",
ir_version=_fix_ir_version,
)
set_model_opset_imports(model)
model.producer_name = "pfto"
model.ir_version = _fix_ir_version
with record("pfto.check_model"):
model = self.check_model(model)

Expand Down
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