docs/tirx/api/layout.rst

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Layout IR
=========

Layouts describe how logical tensor coordinates map to storage and execution
axes.  See :doc:`../layout` for the programming model and worked examples.

The primary classes are re-exported from ``tvm_tirx.tirx``, but are defined in this
module:

.. autoclass:: tvm_tirx.tirx.layout.Layout
   :members:
   :no-index:

.. autoclass:: tvm_tirx.tirx.layout.Axis
   :members:
   :no-index:

.. autoclass:: tvm_tirx.tirx.layout.Iter
   :members:
   :no-index:

.. autoclass:: tvm_tirx.tirx.layout.TileLayout
   :members:
   :no-index:

.. autoclass:: tvm_tirx.tirx.layout.ComposeLayout
   :members:
   :no-index:

``S[...]`` and ``R[...]`` build shard and replica layout specifications,
respectively.  Named axes such as ``laneid``, ``warpid``, ``tid_in_wg``,
``TLane``, and ``TCol`` belong to CUDA. Use ``T.cuda.Axis.laneid`` or
import axes from ``tvm_tirx.backend.cuda.axis``. Trainium's ``P``, ``F``, and
``Bank`` belong to ``T.trn.Axis``. The generic memory axis remains ``T.Axis.m``.

CUDA memory/instruction layout factories are defined in
``tvm_tirx.backend.cuda.layout`` and exposed as ``T.cuda.layout``. Trainium
layouts use explicit partition, free and bank axes. For example::

   from tvm_tirx.backend.trn.axis import P, F, Bank
   from tvm_tirx.tirx.layout import TileLayout, S

   # A (128, 512) SBUF tile: partition p, free element f.
   sbuf_layout = TileLayout(S[(128, 512) : (1 @ P, 1 @ F)])
   # Two (128, 512) PSUM tiles, one per bank.
   psum_layout = TileLayout(S[(2, 128, 512) : (1 @ Bank, 1 @ P, 1 @ F)])

Annotation strings are not accepted by generic tensor constructors.

Omitting ``layout`` or passing ``layout=None`` leaves every storage scope
without a layout. ``layout="default"`` explicitly requests a compact
``TileLayout``. Ordinary views preserve shape, strides and element offsets
without creating a layout. ``TensorType.effective_layout()`` returns an
explicit layout or infers a temporary compact layout for a global tensor.
Tile instructions query it through ``tensor.ty.effective_layout()``, using
the full tensor shape before slicing an operand region. The query does not
change the tensor type or its ``layout=None`` field. Noncompact global tensors
and other storage scopes require an explicit layout when the instruction uses one.

.. automodule:: tvm_tirx.tirx.layout
   :members:
   :no-index:
   :exclude-members: Axis, ComposeLayout, Iter, Layout, TileLayout