Computing shape, tag, constant, release and in-place information#

onnx-light derives several pieces of information about a graph before it is executed:

shape — the element type and shape of every value (onnx_light.onnx_core.shape_inference).
shape_tag — a semantic tag (shape, axes, weight or ambiguous) attached to every value and node, guessing what role a tensor plays (e.g. a tensor holding a shape versus a numerical weight).
constant — whether a value’s content is entirely known before inference starts (initializers, Constant outputs, and outputs of deterministic nodes whose inputs are all constant).
release — the last node after which a value is no longer needed and its buffer can be released.
inplace — which node outputs can reuse one of their input buffers instead of allocating a new one.

This example builds a small model that exercises all five analyses, runs them through ComputeContext and the free functions in onnx_light.onnx_core.shape_inference, and shows how to retrieve the results either as in-memory objects or as metadata_props entries written directly on the model.

The model computes:

two        = Constant(2)                     # constant scalar
const_prod = Mul(W, two)                      # constant (W is an initializer)
added      = Add(X, const_prod)               # not constant (depends on X)
relu_out   = Relu(added)                       # may reuse "added" in place
x_shape    = Shape(X)                          # tagged "shape"
Z          = Reshape(relu_out, x_shape)        # uses the "shape" tensor

from __future__ import annotations

import onnx_light.onnx as onnxl
import onnx_light.onnx.defs as defs
import onnx_light.onnx.helper as oh
from onnx_light.onnx_core.shape_inference import (
    INPLACE_REUSE_METADATA_KEY,
    RELEASE_AFTER_METADATA_KEY,
    RELEASE_AFTER_SHAPE_TAG_METADATA_KEY,
    ComputeContext,
    ShapesContext,
    apply_inferred_shapes_to_model,
    compute_inplace_reuse,
    compute_shape_model,
    write_constant_info_to_metadata,
    write_inplace_reuse_to_metadata,
)
from onnx_light.tools import pretty_onnx

# Not exposed as a Python constant yet; mirrors the C++
# ``onnx_compute::kNotUsedAfterMetadataKey`` used by
# :func:`write_inplace_reuse_to_metadata`.
NOT_USED_AFTER_METADATA_KEY = "onnx_light.not_used_after"


def _metadata_value(node, key: str) -> str:
    """Returns the ``metadata_props`` value for ``key`` on ``node``, or ``""``."""
    return next((entry.value for entry in node.metadata_props if entry.key == key), "")


# Make sure the built-in operator schemas are registered before running
# shape inference (the C++ dispatch table looks them up).
defs.register_onnx_operator_set_schema()

Build the model#

model = oh.make_model(
    oh.make_graph(
        [
            oh.make_node(
                "Constant",
                [],
                ["two"],
                value=oh.make_tensor("two", onnxl.TensorProto.FLOAT, [], [2.0]),
            ),
            oh.make_node("Mul", ["W", "two"], ["const_prod"]),
            oh.make_node("Add", ["X", "const_prod"], ["added"]),
            oh.make_node("Relu", ["added"], ["relu_out"]),
            oh.make_node("Shape", ["X"], ["x_shape"]),
            oh.make_node("Reshape", ["relu_out", "x_shape"], ["Z"]),
        ],
        "compute_information_demo",
        inputs=[oh.make_tensor_value_info("X", onnxl.TensorProto.FLOAT, [3, 4])],
        outputs=[oh.make_tensor_value_info("Z", onnxl.TensorProto.FLOAT, None)],
        initializer=[oh.make_tensor("W", onnxl.TensorProto.FLOAT, [4], [1.0, 2.0, 3.0, 4.0])],
    ),
    opset_imports=[oh.make_opsetid("", 18)],
    ir_version=8,
)

print(pretty_onnx(model))

opset: domain='' version=18
graph: name='compute_information_demo'
input: float[3,4] X
init: float[4] W
0: Constant() -> two
1: Mul(W, two) -> const_prod
2: Add(X, const_prod) -> added
3: Relu(added) -> relu_out
4: Shape(X) -> x_shape
5: Reshape(relu_out, x_shape) -> Z
output: float[] Z

1. shape#

compute_shape_model() seeds a ShapesContext from the model’s opset, initializers and inputs, then runs per-operator shape inference on every node. apply_inferred_shapes_to_model() writes the result back into model.graph.value_info and model.graph.output.

shapes_ctx = ShapesContext()
compute_shape_model(shapes_ctx, model)
apply_inferred_shapes_to_model(shapes_ctx, model)

print("Inferred shapes:")
for name in ("two", "const_prod", "added", "relu_out", "x_shape", "Z"):
    descriptor = shapes_ctx.get(name)
    print(f"  {name:<12} dtype={descriptor.dtype} shape={descriptor.shape.dims()}")

Inferred shapes:
  two          dtype=1 shape=[]
  const_prod   dtype=1 shape=[4]
  added        dtype=1 shape=[3, 4]
  relu_out     dtype=1 shape=[3, 4]
  x_shape      dtype=7 shape=[2]
  Z            dtype=1 shape=[3, 4]

2. shape_tag#

ComputeContext.compute_value_and_node_tags() classifies every value and node. x_shape is tagged "shape" because it is produced by a Shape node and consumed as a shape argument by Reshape.

compute_ctx = ComputeContext()
value_tags, node_tags = compute_ctx.compute_value_and_node_tags(model.graph)

print("\nValue tags:")
for name, tag in sorted(value_tags.items()):
    print(f"  {name:<12} {tag}")

print("\nNode tags:")
for node, tag in zip(model.graph.node, node_tags):
    print(f"  {node.op_type:<10} outputs={list(node.output)!s:<16} tag={tag}")

Value tags:
  W            weight
  X            weight
  Z            weight
  added        weight
  const_prod   weight
  relu_out     weight
  two          weight
  x_shape      shape

Node tags:
  Constant   outputs=['two']          tag=weight
  Mul        outputs=['const_prod']   tag=weight
  Add        outputs=['added']        tag=weight
  Relu       outputs=['relu_out']     tag=weight
  Shape      outputs=['x_shape']      tag=shape
  Reshape    outputs=['Z']            tag=weight

3. constant#

two and const_prod only depend on the Constant node and the initializer W, so both are constant. added, relu_out, x_shape and Z depend on the graph input X and are not. write_constant_info_to_metadata() records the result directly on the model, under the onnx_light.constant metadata key.

write_constant_info_to_metadata(model)


def _is_constant(value_infos) -> dict[str, bool]:
    """Returns ``{name: is_constant}`` read from ``onnx_light.constant`` metadata."""
    result = {}
    for value_info in value_infos:
        result[value_info.name] = any(
            entry.key == "onnx_light.constant" for entry in value_info.metadata_props
        )
    return result


constant_values = _is_constant(model.graph.value_info)
constant_values.update(_is_constant(model.graph.initializer))
constant_values.update(_is_constant(model.graph.output))

print("\nConstant values:")
for name in ("W", "two", "const_prod", "added", "relu_out", "x_shape", "Z"):
    print(f"  {name:<12} {constant_values.get(name, False)}")

Constant values:
  W            True
  two          True
  const_prod   True
  added        False
  relu_out     False
  x_shape      False
  Z            False

4. release / 5. inplace#

compute_inplace_reuse() returns, for every node, the list of InPlaceReuse opportunities (which output can reuse which input buffer). write_inplace_reuse_to_metadata() additionally records release information on metadata_props:

onnx_light.inplace_reuse — the in-place opportunities, as output_index:input_index:kind triplets.
onnx_light.release_after — the values that are no longer needed once the node has run.
onnx_light.release_after_shape_tag — the subset of those released values that carry the "shape" tag (from value_tags above).
onnx_light.not_used_after — declared graph inputs or initializers that reach their last use at this node.

Here relu_out = Relu(added) can overwrite the buffer of added in place (same element type and shape), and added is released right after Relu runs since nothing else reads it.

reuse = compute_inplace_reuse(shapes_ctx, model.graph)
write_inplace_reuse_to_metadata(shapes_ctx, model.graph, value_tags)

print("\nIn-place reuse and release information per node:")
for node, node_reuse in zip(model.graph.node, reuse):
    reuse_desc = ", ".join(
        f"out{r.output_index}=in{r.input_index}({r.kind.name})" for r in node_reuse
    )
    inplace_metadata = _metadata_value(node, INPLACE_REUSE_METADATA_KEY)
    release_after = _metadata_value(node, RELEASE_AFTER_METADATA_KEY)
    release_after_shape_tag = _metadata_value(node, RELEASE_AFTER_SHAPE_TAG_METADATA_KEY)
    not_used_after = _metadata_value(node, NOT_USED_AFTER_METADATA_KEY)
    print(
        f"  {node.op_type:<10} outputs={list(node.output)!s:<16} "
        f"inplace=[{reuse_desc}] (metadata={inplace_metadata!r}) "
        f"release_after={release_after!r} "
        f"release_after_shape_tag={release_after_shape_tag!r} "
        f"not_used_after={not_used_after!r}"
    )

In-place reuse and release information per node:
  Constant   outputs=['two']          inplace=[] (metadata='') release_after='' release_after_shape_tag='' not_used_after=''
  Mul        outputs=['const_prod']   inplace=[] (metadata='') release_after='two' release_after_shape_tag='' not_used_after='W'
  Add        outputs=['added']        inplace=[] (metadata='') release_after='const_prod' release_after_shape_tag='' not_used_after=''
  Relu       outputs=['relu_out']     inplace=[out0=in0(kEqual)] (metadata='0:0:equal') release_after='added' release_after_shape_tag='' not_used_after=''
  Shape      outputs=['x_shape']      inplace=[] (metadata='') release_after='' release_after_shape_tag='' not_used_after='X'
  Reshape    outputs=['Z']            inplace=[out0=in0(kEqual)] (metadata='0:0:equal') release_after='relu_out;x_shape' release_after_shape_tag='x_shape' not_used_after=''

Total running time of the script: (0 minutes 0.009 seconds)

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Example last updated

Date:: 2026-08-25