// Read or write ONNX text format ("onnxtxt"). Use when authoring `.FunctionBody(R"ONNX(...)")` blocks, writing tests with `onnx.parser.parse_model` / `parse_graph`, using the C++ `OnnxParser`, debugging parser errors, or interpreting `Constant <value = ...>` and body-subgraph syntax.
Add a function body definition to an ONNX operator, defining how it decomposes into simpler ops. Use when asked to make an op decomposable, add a FunctionBody, implement SetContextDependentFunctionBodyBuilder, or express an op in terms of other ONNX operators.
Add a new ONNX operator or update an existing operator to a new opset version. Use when asked to define an operator schema, register an op, add inputs/outputs/attributes to an op, move an op to old.cc, or bump an op's opset version.
Add or update type and shape inference for an ONNX operator. Use when asked to implement TypeAndShapeInferenceFunction, propagate shapes, add shape inference tests, fix shape inference bugs, or handle broadcasting logic.
| name | onnxtxt |
| description | Read or write ONNX text format ("onnxtxt"). Use when authoring `.FunctionBody(R"ONNX(...)")` blocks, writing tests with `onnx.parser.parse_model` / `parse_graph`, using the C++ `OnnxParser`, debugging parser errors, or interpreting `Constant <value = ...>` and body-subgraph syntax. |
ONNX has a compact text format implemented by onnx/parser.py (Python) and onnx/defs/parser.{h,cc} (C++). The formal grammar lives in docs/Syntax.md; this skill captures the practical conventions, idioms, and gotchas that matter when authoring or reviewing code that uses it.
| Surface | API |
|---|---|
| C++ function bodies | .FunctionBody(R"ONNX( ... )ONNX") and FunctionBuilder::Add(...) |
| Python test fixtures | onnx.parser.parse_model("..."), onnx.parser.parse_graph("...") |
| C++ tests | OnnxParser in onnx/defs/parser.h — parse a model, then call shape_inference::InferShapes |
<var> = <OpName> <attr1 = value, attr2 = value> (<input1>, <input2>)
.Input(...) / .Output(...).Const = Constant <value = float {0.0}>() or Alpha = Constant <value_float: float = @alpha>().CastLike (not Cast) when the target dtype depends on another input.@attr_name (only inside function bodies, and only for attributes declared on the schema).Simple scalar/tensor attributes — keep the conventional Op<attrs>(inputs) form; reads well on one line:
Y = Transpose<perm = [2, 0, 1]>(X)
Subgraph attributes (Scan, Loop, If, ScanVarLen, …) — prefer Op(inputs)<body = ...>. The body spans multiple lines, so putting inputs first keeps the call site readable:
so, xo = Scan (s, x) <
num_scan_inputs = 1,
body = scan_body (float[1] s_in, float[1] x_in) => (float[1] s_out, float[1] x_out) {
s_out = Add(s_in, x_in)
x_out = Identity(x_in)
}
>
| Test file | Recommendation |
|---|---|
onnx/test/shape_inference_test.py, onnx/test/reference_evaluator_test.py | Use onnx.parser.parse_model(...) for one-off fixtures. |
onnx/backend/test/case/node/<op>.py | Keep the outer helper.make_node + expect(...) (it drives data generation). For body-subgraph ops, build the body with onnx.parser.parse_graph. |
onnx/test/cpp/shape_inference_test.cc | Use OnnxParser (onnx/defs/parser.h); pair with shape_inference::InferShapes. |
onnx/test/version_converter/automatic_upgrade_test.py and similar harnesses | Keep the established _test_op_upgrade / _test_op_downgrade style — do not rewrite. |
Empirical: PR #7962 (ScanVarLen) cut ~58–70% of test LOC by switching to parser-based fixtures.
import onnx
import onnx.parser
import onnx.shape_inference
model = onnx.parser.parse_model("""
<ir_version: 8, opset_import: ["" : 18]>
g (float[2, 3, 4] X) => (float[4, 2, 3] Y) {
Y = Transpose<perm = [2, 0, 1]>(X)
}
""")
inferred = onnx.shape_inference.infer_shapes(model, strict_mode=True)
body = onnx.parser.parse_graph("""
b (float[1] s, float[1] xi) => (float[1] so, float[1] xo) {
so = Identity(s)
xo = Identity(xi)
}
""")
node = onnx.helper.make_node("Scan", ["s", "x"], ["so", "xo"], body=body, num_scan_inputs=1)
#include "onnx/defs/parser.h"
#include "onnx/shape_inference/implementation.h"
ModelProto model;
OnnxParser parser(R"ONNX(
<ir_version: 8, opset_import: ["" : 18]>
g (float[2, 3, 4] X) => (Y) {
Y = Transpose<perm = [2, 0, 1]>(X)
}
)ONNX");
auto status = parser.Parse(model);
ASSERT_TRUE(status.IsOK()) << status.ErrorMessage();
shape_inference::InferShapes(model);
unk__* materialization in C++ shape-inference tests. Under InferShapes, unset output dims are materialized by MaterializeSymbolicShape into dim_param names like unk__0, unk__1, … Assertions on free dims must accept either an unset dim or an unk__* placeholder — write (or use) a helper that treats both forms as equivalent.@attr_name scope. Only valid inside a function body, and only for attributes declared on the enclosing schema's .Attr(...) calls.CastLike vs Cast. Use CastLike when the desired target dtype is determined by another input; Cast requires a static to attribute.| Resource | Path |
|---|---|
| Formal grammar | docs/Syntax.md |
| C++ parser | onnx/defs/parser.h, onnx/defs/parser.cc |
| Python parser | onnx/parser.py |
| C++ parser tests | onnx/test/cpp/parser_test.cc |
| Python parser tests | onnx/test/parser_test.py |
| Empirical LOC win | PR #7962 (ScanVarLen test rewrite) |