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Add utility to remove unused values/nodes in IR (#1617)
Mostly a re-implementation of the existing proto-based optimization to remove unused-values/nodes to use the IR.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,93 @@ | ||
| # Copyright (c) Microsoft Corporation. | ||
| # Licensed under the MIT License. | ||
| from __future__ import annotations | ||
|
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| import logging | ||
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| import onnx | ||
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| from onnxscript import ir | ||
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| logger = logging.getLogger(__name__) | ||
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| def remove_unused_optional_outputs( | ||
| node: ir.Node, graph_outputs: frozenset[ir.Value], onnx_opset_version: int | ||
| ) -> None: | ||
| try: | ||
| if node.domain not in {"", "onnx.ai"}: | ||
| return | ||
| op_schema = onnx.defs.get_schema(node.op_type, onnx_opset_version, domain=node.domain) | ||
| except Exception: | ||
| return | ||
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| if node.op_type == "BatchNormalization": | ||
| # BatchNormalization op has 3 outputs: Y, running_mean, running_var | ||
| # If running_mean and running_var are not used, remove them, and the training_mode attribute | ||
| def is_used_output(i: int) -> bool: | ||
| if i < len(node.outputs): | ||
| val = node.outputs[i] | ||
| return val in graph_outputs or bool(val.uses()) | ||
| return False | ||
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| if is_used_output(1) or is_used_output(2): | ||
| return | ||
| node.outputs[1].name = "" | ||
| node.outputs[2].name = "" | ||
| node.attributes.pop("training_mode", None) | ||
| return | ||
|
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| optional_info = [] | ||
| for o in op_schema.outputs: | ||
| # Current ops do not have optional outputs if they have variable number of outputs | ||
| if o.option == onnx.defs.OpSchema.FormalParameterOption.Variadic: | ||
| return | ||
| optional_info.append(o.option == onnx.defs.OpSchema.FormalParameterOption.Optional) | ||
| # If no optional outputs in spec, skip delete operations | ||
| if len([o == 1 for o in optional_info]) == 0: | ||
| return | ||
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| for i, out in enumerate(node.outputs): | ||
| if out not in graph_outputs and (not out.uses()) and optional_info[i] is True: | ||
| out.name = "" | ||
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| def process_function_or_graph(function_or_graph: ir.Function | ir.Graph) -> int: | ||
| graph_outputs = frozenset(function_or_graph.outputs) | ||
| onnx_opset_version = function_or_graph.opset_imports.get("", None) | ||
| count = 0 | ||
| for node in reversed(function_or_graph): | ||
| removable = True | ||
| for output in node.outputs: | ||
| if output in graph_outputs or output.uses(): | ||
| removable = False | ||
| break | ||
| if removable: | ||
| function_or_graph.remove(node, safe=True) | ||
| count += 1 | ||
| else: | ||
| if onnx_opset_version is not None: | ||
| remove_unused_optional_outputs(node, graph_outputs, onnx_opset_version) | ||
| for attr in node.attributes.values(): | ||
| if isinstance(attr, ir.AttrGraph): | ||
| count += process_function_or_graph(attr.value) | ||
| elif isinstance(attr, ir.AttrGraphs): | ||
| for graph in attr.value: | ||
| count += process_function_or_graph(graph) | ||
| return count | ||
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| def remove_unused_nodes(model: ir.Model) -> None: | ||
| """Removes unused nodes from the model.""" | ||
| count = process_function_or_graph(model.graph) | ||
| graph_outputs = frozenset(model.graph.outputs) | ||
| initializers = model.graph.initializers | ||
| for init in list(initializers.values()): | ||
| if not (init in graph_outputs or init.uses()): | ||
| del initializers[init.name] # type: ignore[arg-type] | ||
| count += 1 | ||
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| for function in model.functions.values(): | ||
| count += process_function_or_graph(function) | ||
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| logger.info("Removed %s unused nodes", count) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,141 @@ | ||
| # Copyright (c) Microsoft Corporation. | ||
| # Licensed under the MIT License. | ||
| from __future__ import annotations | ||
|
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| import logging | ||
| from typing import Sequence | ||
|
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| import onnx | ||
| from google.protobuf.internal.containers import ( # type: ignore | ||
| RepeatedCompositeFieldContainer, | ||
| ) | ||
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| logger = logging.getLogger(__name__) | ||
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| def remove_unused_optional_outputs( | ||
| n: onnx.NodeProto, used: set, opset_import: Sequence[onnx.OperatorSetIdProto] | ||
| ) -> None: | ||
| try: | ||
| if n.domain not in {"", "onnx.ai"}: | ||
| return | ||
| onnx_opset_version = 1 | ||
| for opset in opset_import: | ||
| if opset.domain == n.domain: | ||
| onnx_opset_version = opset.version | ||
| op_schema = onnx.defs.get_schema(n.op_type, onnx_opset_version, domain=n.domain) | ||
| except Exception: | ||
| return | ||
|
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| if n.op_type == "BatchNormalization": | ||
| # BatchNormalization op has 3 outputs: Y, running_mean, running_var | ||
| # If running_mean and running_var are not used, remove them, and the training_mode attribute | ||
| def is_used_output(i: int) -> bool: | ||
| if i < len(n.output): | ||
| return n.output[i] in used | ||
| return False | ||
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| if is_used_output(1) or is_used_output(2): | ||
| return | ||
| del n.output[1:] | ||
| for j, attr in enumerate(n.attribute): | ||
| if attr.name == "training_mode": | ||
| del n.attribute[j] | ||
| break | ||
|
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| optional_info = [] | ||
| for o in op_schema.outputs: | ||
| # Current ops do not have optional outputs if they have variable number of outputs | ||
| if o.option == onnx.defs.OpSchema.FormalParameterOption.Variadic: | ||
| return | ||
| optional_info.append(o.option == onnx.defs.OpSchema.FormalParameterOption.Optional) | ||
| # If no optional outputs in spec, skip delete operations | ||
| if len([o == 1 for o in optional_info]) == 0: | ||
| return | ||
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| for i, out in enumerate(n.output): | ||
| if out not in used and optional_info[i] is True: | ||
| n.output[i] = "" | ||
| # Only delete trailing unused optional outputs | ||
| for o in n.output[::-1]: # type: ignore[assignment] | ||
| if o == "": | ||
| n.output.pop() | ||
| else: | ||
| return | ||
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| def compute_used_in_node(n: onnx.NodeProto) -> set[str]: | ||
| used = {n for n in n.input if n != ""} | ||
| for attr in n.attribute: | ||
| if attr.HasField("g"): | ||
| used |= compute_used_in_graph(attr.g) | ||
| elif len(attr.graphs) > 0: | ||
| for graph in attr.graphs: | ||
| used |= compute_used_in_graph(graph) | ||
| return used | ||
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| def compute_used_in_graph(g: onnx.GraphProto) -> set[str]: | ||
| used = set() | ||
| for n in g.node: | ||
| used |= compute_used_in_node(n) | ||
| return used | ||
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| def process_nodes( | ||
| nodes: RepeatedCompositeFieldContainer[onnx.NodeProto], | ||
| used: set, | ||
| opset_import: Sequence[onnx.OperatorSetIdProto], | ||
| ) -> int: | ||
| count = 0 | ||
| i = len(nodes) - 1 | ||
| while i >= 0: | ||
| node = nodes[i] | ||
| remove_unused_optional_outputs(node, used, opset_import) | ||
| used_outputs = [x for x in node.output if x in used] | ||
| if not used_outputs: | ||
| del nodes[i] | ||
| count += 1 | ||
| i -= 1 | ||
| continue | ||
| for attr in node.attribute: | ||
| if attr.HasField("g"): | ||
| process_graph(attr.g, opset_import) | ||
| elif len(attr.graphs) > 0: | ||
| for graph in attr.graphs: | ||
| process_graph(graph, opset_import) | ||
| used |= compute_used_in_node(node) | ||
| i -= 1 | ||
| return count | ||
|
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| def process_graph( | ||
| graph: onnx.GraphProto, opset_import: Sequence[onnx.OperatorSetIdProto] | ||
| ) -> int: | ||
| used = {output.name for output in graph.output} | ||
|
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| count = process_nodes(graph.node, used, opset_import) | ||
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| for i in range(len(graph.initializer) - 1, -1, -1): | ||
| if graph.initializer[i].name not in used: | ||
| del graph.initializer[i] | ||
| count += 1 | ||
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| return count | ||
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| def process_function( | ||
| function: onnx.FunctionProto, opset_import: Sequence[onnx.OperatorSetIdProto] | ||
| ) -> int: | ||
| used = set(function.output) | ||
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| return process_nodes(function.node, used, opset_import) | ||
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| def remove_unused_nodes(model: onnx.ModelProto) -> None: | ||
| """Removes unused nodes from the model.""" | ||
| count = process_graph(model.graph, model.opset_import) | ||
| for function in model.functions: | ||
| count += process_function(function, model.opset_import) | ||
|
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| logger.info("Removed %s unused nodes", count) |
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