Writing Attributes

Writing Attributes

When to Use

Use this skill when the user asks to write an attribute, set a property, change a material, set a color, or modify mesh data.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, or both.
• Prim paths, attribute name, USD value type, scalar versus array shape, and desired values.
• Data source location, dtype, semantic conversion, and sync/async behavior when those details are part of the task.
• Whether the write is one-shot, repeated with a stable target, or needs direct mapped-buffer access.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• Use attribute-bindings for repeated writes to the same prims/attribute, and mapping-attributes for zero-copy direct writes.
• Use binding-materials when the request is specifically to set material:binding.

Instructions

1. Identify the target language, prim paths, attribute name, value kind, data shape, memory target, and sync/async requirement.
2. Read the matching source snippet and copy its lifecycle pattern rather than inventing equivalent calls.
3. Validate dtype, shape, semantic, and ownership rules before proposing or editing code.
4. Keep bindings, mapped buffers, operation handles, and C strings alive only for the lifetimes documented by the referenced examples.
5. When changing code, run the narrow docs test or example that owns the snippet whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• Runtime string arrays, asset arrays, and timecode attributes are not supported.
• Use token[] for string-like arrays.
• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

Beyond transforms, you can write arbitrary attributes to prims: colors, visibility, mesh geometry, string tokens, and more. ovrtx uses DLTensor as the data format, and the dtype must exactly match the USD attribute schema.

There are two categories:

• Scalar attributes -- one value per prim (e.g., a color, a transform)
• Array attributes -- variable-length per prim (e.g., mesh points, face vertex counts)

Tensor layout

Python and C expose different DLTensor layouts for multi-component attributes:

Python — NumPy-style shapes

Python accepts and returns NumPy-style shapes with dtype.lanes=1. Multi-component values live in trailing shape dimensions:

Source: tests/docs/python/test_attribute_shapes.py snippets doc-shape-scalar-int32, doc-shape-float3-array, doc-shape-mat4-array.

C — lane-based attributes

The C API uses DLDataType::lanes for multi-component attribute reads and writes. The shape counts logical attribute elements; the lane count holds the vector or matrix component count:

Source: tests/docs/c/test_attribute_shapes.cpp snippets doc-shape-scalar-int32-c, doc-shape-float3-array-c, doc-shape-mat4-array-c.

For a C point3f[] attribute with 10 points, write or read one tensor with shape=[10] and dtype={kDLFloat, 32, 3}. Rendered output/AOV tensors are not attribute tensors; in C they use channel-last shapes such as [height, width, channels] with dtype.lanes=1.

USD Type Lookup

Use this table to answer "how do I write USD type X?" For array-valued USD attributes, use write_array_attribute in Python and set binding.binding_desc.attribute_type.is_array = true in C. Python uses normal shape dimensions; C uses DLDataType::lanes.

Python

Array attribute write (mesh points)

Source: tests/docs/python/test_attribute_shapes.py snippet doc-shape-float3-array

Array attribute write (same pattern for other array schemas)

Source: tests/docs/python/test_attribute_shapes.py snippet doc-shape-float3-array

Prim modes

Source: tests/docs/python/test_attribute_bindings.py snippet doc-bind-attribute-write

Token array attribute

Source: tests/docs/python/test_attribute_bindings.py snippet doc-write-token-array

Path/relationship array attribute

Source: tests/docs/python/test_base.py snippet doc-bind-material

Supported authored attribute write snippets

Python raw snippets:

The snippets listed in this table live in tests/docs/python/test_all_attributes.py.

Python can write token attributes directly from strings. For scalar asset attributes, Python can write the raw (token, 0) pair only if you already have a valid token ID; there is no public Python path-dictionary token-creation helper for asset strings today. The C snippet below shows the fully supported path-dictionary flow.

C

Generic scalar write in C

Source: tests/docs/c/test_attribute_bindings.cpp snippet doc-write-bound-attribute-c

Supported authored attribute write snippets in C

C raw snippets:

The snippets listed in this table live in tests/docs/c/test_all_attributes.cpp.

Key Types / Functions

Semantics (Python: from ovrtx import Semantic; C: ovrtx_attribute_semantic_t):

• Semantic.NONE / OVRTX_SEMANTIC_NONE -- generic data. In Python, element shape comes from trailing tensor dimensions. In C, element component count comes from DLDataType::lanes. Use for colors, visibility, and other non-structured attributes.
• Semantic.XFORM_MAT4x4 / OVRTX_SEMANTIC_XFORM_MAT4x4 -- 4x4 double matrix
• OVRTX_SEMANTIC_XFORM_POS3d_ROT4f_SCALE3f -- decomposed transform (C only)
• OVRTX_SEMANTIC_XFORM_POS3d_ROT3x3f -- decomposed transform (C only)
• Semantic.PATH_STRING / OVRTX_SEMANTIC_PATH_STRING -- prim path strings
• Semantic.TOKEN_STRING / OVRTX_SEMANTIC_TOKEN_STRING -- token strings
• Semantic.TOKEN_ID / OVRTX_SEMANTIC_TOKEN_ID -- raw token IDs that are already present in the runtime path dictionary. In C, create them with path_dictionary_create_tokens_from_strings() before writing raw token arrays.

In C, ovrtx_write_attribute returns ovrtx_enqueue_result_t which contains both .status (check for OVRTX_API_ERROR) and .op_index (for async tracking via ovrtx_wait_op).

Data access modes (Python: from ovrtx import DataAccess):

• DataAccess.SYNC -- copies data during the call, safe to free after return
• DataAccess.ASYNC -- data accessed later during stream execution, must keep alive; pass cuda_stream= or cuda_event= for GPU synchronization. Not allowed with string data.

Troubleshooting

• Array attribute dtype must exactly match the USD schema. Using numpy's default float64 for a float3[] attribute (which expects float32) will cause errors.
• In the current runtime, authored scalar USD float3 values may be created but populated as zero by populateAllAuthoredAttributes. If a value needs to come from USD, author it as a role-bearing type such as vector3f, point3f, normal3f, or color3f. Direct runtime writes to scalar float3 still work.
• Quaternion tensors use ovrtx/Fabric lane order (i, j, k, real). USDA quat* values are authored as (real, i, j, k), so reading quatd, quatf, or quath attributes reorders the components into (i, j, k, real), and writes should use that runtime tensor order.
• String attributes (Semantic.PATH_STRING, Semantic.TOKEN_STRING) only support synchronous data access (DataAccess.SYNC).
• String arrays are not supported as runtime attributes. If a workflow needs an array of string-like labels or categories, author and write a token[] instead and pass is_token=True when writing list[list[str]] data.
• Asset arrays are not supported as runtime attributes. Scalar asset attributes may be represented internally, but there is no general string-list replacement for asset[].
• Timecode attributes are not supported as runtime attributes, including both scalar timecode and timecode[].
• Custom relationships are not populated by the generic authored-attribute path. Specific relationships used by supported schemas, such as material:binding and shader connections, are handled by their schema/population code paths; arbitrary custom relationships are ignored today.
• Unsupported authored types are covered by negative tests in tests/docs/python/test_all_attributes.py and tests/docs/c/test_all_attributes.cpp; if one starts populating, keep this documentation and those tests in sync.
• For array attributes in Python, pass a list of tensors (one per prim), not a single tensor. NumPy arrays, Warp arrays, and any __dlpack__-compatible objects are accepted directly.
• When creating attributes from scratch (prim_mode=PrimMode.CREATE_NEW), semantic= is required for structured types (transforms, packed structs) so the C library knows what USD attribute type to create.
• For write_array_attribute with list[list[str]] data, strings default to path/relationship arrays. Set is_token=True to write token arrays for custom token-list attributes.
• ovrtx_make_binding_desc borrows the C prim path array. Keep any ovx_string_t prim paths and the array passed to the binding descriptor alive until the write operation has completed, or until ovrtx_create_attribute_binding finishes for persistent bindings.
• dirty_bits is a bitvector with 1 bit per prim -- the byte array size must be (prim_count + 7) / 8.
• In C, dirty_bits support three combination modes via ovrtx_write_bits_t in the ovrtx_input_buffer_t.dirty_bits_mode field: OVRTX_DIRTY_MASK_REPLACE (default -- replace existing mask), OVRTX_DIRTY_MASK_OR (merge with existing), OVRTX_DIRTY_MASK_AND (intersect with existing).

C convenience helpers for string attributes (#include <ovrtx/ovrtx_attributes.h>):

• ovrtx_set_path_attributes(renderer, paths, count, attr_name, path_values) -- write path/relationship attributes. Each prim gets a single-element array (relationships are always arrays in USD).
• ovrtx_set_token_attributes(renderer, paths, count, attr_name, token_values) -- write token string attributes (one per prim).

Source: tests/docs/c/test_attribute_helpers.cpp snippet doc-set-token-attributes-c

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.