Mapping Attributes

Mapping Attributes

When to Use

Use this skill when the user asks about zero-copy writes, map_attribute, direct memory access, Warp/CUDA kernel writes into mapped tensors, or GPU attribute updates. Use attribute-bindings instead for repeated writes to the same prims/attribute when the caller already owns the data tensor and a copy into ovrtx is acceptable.

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, element type, semantic conversion, and whether the attribute is mappable.
• Mapping target: CPU tensor, linear CUDA memory, or a bound attribute reused for repeated map/unmap cycles.
• Synchronization and lifetime requirements: stream/event, map duration, whether data must outlive the mapping, and whether array attributes are involved.
• 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.
• Array attributes such as float3[] points are not mappable; use writing-attributes or attribute-bindings for array writes.
• Use attribute-bindings first when the user wants repeated updates but does not need zero-copy direct buffer access.

Instructions

1. Identify the concrete map/unmap target, language, prim list, attribute name, memory target, and synchronization requirement.
2. Read the matching source snippet and copy its map/write/unmap lifecycle rather than inventing equivalent calls.
3. Validate dtype, shape, semantic, mappability, and ownership rules before proposing or editing code.
4. Keep mapped tensor views alive only until unmap, and copy anything that must outlive the mapping.
5. For repeated map/unmap cycles, create a persistent binding first and map through it to avoid recreating the descriptor.
6. 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

• Array attributes such as float3[] points are not supported for map/unmap because their lengths vary per prim.
• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

Mapping gives you direct access to RTX's internal Fabric buffer for an attribute. Instead of copying data in (like write_attribute), you write directly into the buffer, then unmap to apply changes. This is the most efficient path for per-frame updates, especially with GPU compute (e.g., Warp kernels).

The pattern is: map -> write into tensor -> unmap.

Python

CPU mapping with NumPy

Source: tests/docs/python/test_attribute_bindings.py snippet doc-map-attribute-cpu

Context manager (recommended)

Source: tests/docs/python/test_attribute_bindings.py snippet doc-map-attribute-cpu

GPU mapping with Warp

From the planet-system example -- map on CUDA, compute with a Warp kernel, unmap with stream sync:

Source: tests/docs/python/test_attribute_bindings.py snippet doc-map-attribute-cuda

Using a persistent binding for mapping

More efficient for repeated map/unmap cycles (avoids recreating the binding descriptor):

Source: tests/docs/python/test_attribute_bindings.py snippet doc-map-bound-attribute

C

Map, write, unmap

Source: tests/docs/c/test_attribute_bindings.cpp snippet doc-map-attribute-cpu-c

GPU mapping with CUDA sync

Source: examples/c/vulkan-interop/src/main.cpp snippet map-rendered-output-cuda-array

Key Types / Functions

Tensor layout follows the API-specific attribute rules (see the writing-attributes skill for the full table):

• Python mapping tensors are NumPy-style: dtype="float64", shape=(4, 4) returns a tensor with shape (N, 4, 4) and scalar lanes.
• C mapping tensors are lane-based: a 4x4 double matrix attribute maps as shape=[N], dtype={kDLFloat, 64, 16}.
• The Python semantic= path reshapes automatically: Semantic.XFORM_MAT4x4 → (N, 4, 4), points → (N, 3), colors → (N, 4) for RGBA or (N, 3) for RGB.

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

Troubleshooting

• Tensor lifetime: The tensor from mapping.tensor is only valid while the mapping is active (inside the with block in Python, or before unmap in C). All reads, writes, and kernel launches that access the tensor must happen before the mapping is released. Accessing it after the with block exits is undefined behavior. If you need data to outlive the mapping, copy it while still inside the block.
• The canonical transform attribute name is "omni:xform". The legacy name "omni:fabric:localMatrix" (used in examples above) is also accepted. New code should prefer "omni:xform".
• Array attributes (e.g., float3[] points) are not supported for mapping because they have variable lengths per prim. Use write_array_attribute() instead.
• Data must be fully written before calling unmap(). For CUDA, pass stream or event so ovrtx knows when the GPU write is complete.
• event and stream are mutually exclusive on unmap -- use one or the other.
• Providing event/stream for a CPU-mapped attribute raises an error.
• Multiple mappings can be outstanding on the same attribute. The logical order of application depends on the order of unmap() calls.

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.