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.. _tvmscript-arch:

TVMScript
=========

TVMScript is a Python-based domain-specific language (DSL) for writing TVM IR. It lets users
define ``IRModule``\ s — containing both Relax functions and TIR ``PrimFunc``\ s — using
familiar Python syntax. Although TVMScript *looks* like Python, it is **not executed by the
Python interpreter**. Instead, Python decorators extract the AST from the source code and
transform it into TVM IR through a dedicated parser and IR builder pipeline.

TVMScript serves two roles in the TVM stack:

- **Authoring**: users write TIR kernels and Relax programs directly in TVMScript.
- **Roundtrip**: every ``IRModule`` can be printed back to TVMScript via ``mod.script()`` and
  re-parsed to produce an equivalent module. This makes TVMScript the primary tool for
  inspecting, debugging, and serializing IR.


Overview
--------

The TVMScript system has three components:

.. code-block:: text

   Parsing (Python source → TVM IR):

   Python source (TVMScript)
        │
        ▼  ast.parse + convert
        │
   Doc AST (mirror of Python AST)
        │
        ▼  Parser (dispatch by token: ir / tirx / relax)
        │
        ▼  IR Builder (frame stack)
        │
   TVM IR (IRModule, PrimFunc, relax.Function)


   Printing (TVM IR → Python source):

   TVM IR
        │
        ▼  IRDocsifier (C++, dispatch by token + type)
        │
   Doc tree (ExprDoc, StmtDoc, ...)
        │
        ▼  DocToPythonScript
        │
   TVMScript text

- **Parser** (Python): reads Python source, converts it to a ``Doc AST`` (a mirror of
  Python's ``ast`` module), then walks the tree using dialect-specific handlers that call
  into the IR builder.
- **IR Builder** (Python + C++): provides a frame-stack API where each ``with`` block or
  decorator pushes a frame. When the frame exits, the constructed IR is finalized. The builder
  is shared across dialects — TIR and Relax each register their own frame types.
- **Printer** (C++): converts TVM IR objects to a ``Doc`` tree (an intermediate representation
  of Python syntax), then formats the tree into valid TVMScript text.


Decorators
----------

TVMScript uses three import aliases by convention:

.. code-block:: python

   from tvm.script import ir as I       # module-level constructs
   from tvm.script import tirx as T     # TIR constructs
   from tvm.script import relax as R    # Relax constructs

The primary decorators are:

- ``@I.ir_module``: marks a Python class as an ``IRModule``. Each method inside becomes a
  function in the module.
- ``@T.prim_func``: marks a function as a TIR ``PrimFunc``.
- ``@R.function``: marks a function as a ``relax.Function``.

These can be composed:

.. code-block:: python

   @I.ir_module
   class MyModule:
       @T.prim_func
       def add_kernel(A: T.Buffer((128,), "float32"),
                      B: T.Buffer((128,), "float32"),
                      C: T.Buffer((128,), "float32")):
           for i in range(128):
               with T.sblock("compute"):
                   vi = T.axis.spatial(128, i)
                   C[vi] = A[vi] + B[vi]

       @R.function
       def main(x: R.Tensor((128,), "float32"),
                y: R.Tensor((128,), "float32")) -> R.Tensor((128,), "float32"):
           with R.dataflow():
               out = R.call_tir(cls.add_kernel, (x, y),
                                out_ty=R.Tensor((128,), "float32"))
               R.output(out)
           return out

When Python encounters ``@I.ir_module``, the decorator does **not** execute the class body.
Instead, it calls ``tvm.script.parse()`` which extracts the source code of the class,
builds a Doc AST, and hands it to the parser.


Parser Architecture
-------------------

The parser lives in ``python/tvm/script/parser/``.

Dispatch mechanism
~~~~~~~~~~~~~~~~~~

Different IR dialects (TIR, Relax) need different handling for the same Python syntax. For
example, ``if ... else`` inside ``@T.prim_func`` creates a TIR ``If`` branch, while the same
syntax inside ``@R.function`` creates a Relax ``If`` node with different semantics.

The parser maintains a **dispatch token** stack (``["default"]`` initially). When it encounters
a decorated function, it inspects the decorator to determine the token — ``"tirx"`` for
``@T.prim_func``, ``"relax"`` for ``@R.function`` — and pushes it onto the stack.

Each AST node type is dispatched via a virtual table:

.. code-block:: text

   ParseVTable[(token, node_type)] → handler function

   Lookup order:
     1. (current_token, node_type)    e.g. ("tirx", "For")
     2. ("default", node_type)        e.g. ("default", "For")
     3. generic_visit                  fallback

Dialect-specific parsers (``parser/tirx/parser.py``, ``parser/relax/parser.py``) register
handlers using ``@dispatch.register(token, type_name)`` decorators.

Parse flow
~~~~~~~~~~

The entry point is ``parse(program, extra_vars)``:

1. **Source extraction**: the program's source code is extracted (from a class, function, or
   string) and converted to a Doc AST via Python's ``ast`` module.

2. **AST walking**: the ``Parser`` (a subclass of ``doc.NodeVisitor``) walks the Doc AST.
   For each node, it looks up the handler in the dispatch table.

3. **Expression evaluation**: expressions like ``T.grid(128, 128)`` are evaluated by the
   ``ExprEvaluator``, which resolves names against the variable table and the ``T.``/``R.``
   module namespaces.

4. **Value binding**: assignment statements (``A = T.match_buffer(...)`` in TIR,
   ``lv = R.add(x, y)`` in Relax) go through dialect-specific ``bind_*_value()`` functions
   that register the resulting TVM objects in the parser's ``VarTable``.

5. **Scoping**: the ``VarTable`` maintains a stack of frames. Entering a ``with`` block,
   ``for`` loop, or function body pushes a new frame; exiting pops it. This ensures variables
   are scoped correctly.

Variable table
~~~~~~~~~~~~~~

The ``VarTable`` is the parser's symbol table:

.. code-block:: text

   VarTable
   ├── frames: [VarTableFrame, ...]    ← stack of scopes
   └── name2value: {str: [Any, ...]}   ← name → value stack (for shadowing)

When a name is looked up, the most recent binding wins. When a frame is popped, all bindings
introduced in that frame are removed.


IR Builder Architecture
-----------------------

The IR builder (``python/tvm/script/ir_builder/``, backed by C++ in ``src/script/ir_builder/``)
provides a frame-stack API for constructing IR incrementally.

Frame stack
~~~~~~~~~~~

The core idea: each IR scope (module, function, block, loop) is a **frame**. Frames are pushed
on ``__enter__`` and popped on ``__exit__``. When a frame exits, it finalizes the IR it
represents and attaches it to the parent frame.

.. code-block:: text

   IRBuilder (thread-local singleton)
   └── frame stack:
       ├── IRModuleFrame          ← @I.ir_module
       │   ├── PrimFuncFrame      ← @T.prim_func
       │   │   ├── ForFrame       ← T.grid(...) / T.serial(...)
       │   │   │   └── SBlockFrame ← T.sblock(...)
       │   │   └── ...
       │   └── FunctionFrame      ← @R.function
       │       └── BindingBlockFrame ← R.dataflow()
       └── ...

This design means the parser never needs to build a complete IR tree in memory — it
constructs IR top-down by entering and exiting frames, and each frame handles its own
finalization.

TIR builder
~~~~~~~~~~~

The TIR builder (``ir_builder/tirx/ir.py``) provides functions that map directly to TVMScript
syntax. Key categories:

**Function and block**:

- ``T.prim_func()`` → ``PrimFuncFrame``
- ``T.sblock(name)`` → ``SBlockFrame`` (spatial block)
- ``T.init()`` → ``BlockInitFrame`` (reduction initialization)
- ``T.reads(...)``, ``T.writes(...)`` → declare buffer access regions

**Loops**:

- ``T.grid(*extents)`` → ``ForFrame`` returning loop variables
- ``T.serial(start, stop)``, ``T.parallel(...)``, ``T.vectorized(...)``,
  ``T.unroll(...)``, ``T.thread_binding(...)`` → loop with specific iterator type

**Block axes**:

- ``T.axis.spatial(dom, binding)`` — spatial iteration axis
- ``T.axis.reduce(dom, binding)`` — reduction axis
- ``T.axis.remap(kinds, bindings)`` — shorthand for multiple axes

**Buffers**:

- ``T.match_buffer(param, shape, dtype)`` — match function parameter to buffer
- ``T.alloc_buffer(shape, dtype)`` — allocate intermediate buffer
- ``T.Buffer(shape, dtype)`` — buffer type annotation in function signatures

Relax builder
~~~~~~~~~~~~~

The Relax builder (``ir_builder/relax/ir.py``) provides:

**Function and dataflow**:

- ``R.function()`` → ``FunctionFrame``
- ``R.dataflow()`` → ``BindingBlockFrame``
- ``R.output(*vars)`` → expose variables from a dataflow block

**Emit**:

- ``R.emit(value)`` → emit a binding, returns a ``Var``
- ``R.emit_match_cast(value, ty)`` → emit with type assertion

**Type annotations**:

- ``R.Tensor(shape, dtype)`` — tensor type
- ``R.Tuple(*fields)`` — tuple type
- ``R.Shape(values)`` — shape type
- ``R.Any()`` — any Relax value type

**Calling conventions**:

- ``R.call_tir(func, args, out_ty)`` — call a TIR function
- ``R.call_packed(name, *args)`` — call a PackedFunc
- ``R.call_dps_packed(func, *args)`` — call using destination-passing style

**Operators**: the ``R`` module also re-exports all Relax operators
(``R.add``, ``R.matmul``, ``R.nn.conv2d``, etc.) so they can be used directly in TVMScript.


Printer Architecture
--------------------

The printer converts TVM IR back to TVMScript text. It is implemented primarily in C++
(``src/script/printer/``) for performance.

Doc tree
~~~~~~~~

The printer does **not** generate text directly. Instead, it first builds a ``Doc`` tree — an
intermediate representation that mirrors Python syntax:

- **Expression docs**: ``IdDoc``, ``AttrAccessDoc``, ``CallDoc``, ``IndexDoc``,
  ``OperationDoc``, ``LiteralDoc``, ``TupleDoc``, ``ListDoc``, etc.
- **Statement docs**: ``AssignDoc``, ``ForDoc``, ``IfDoc``, ``ScopeDoc`` (``with`` blocks),
  ``FunctionDoc``, ``ClassDoc``, ``ReturnDoc``, ``CommentDoc``, etc.

For example, ``T.axis.spatial(128, i)`` is represented as:

.. code-block:: text

   CallDoc(
     callee=AttrAccessDoc(AttrAccessDoc(IdDoc("T"), "axis"), "spatial"),
     args=[LiteralDoc(128), IdDoc("i")]
   )

IRDocsifier
~~~~~~~~~~~

The ``IRDocsifier`` (``include/tvm/script/printer/ir_docsifier.h``) is the main dispatcher.
It maintains:

- A dispatch table mapping ``(token, type_index)`` pairs to converter functions.
- A frame stack for tracking the current scope (similar to the builder's frame stack).
- A variable-to-name mapping to produce readable names.

Each IR dialect registers its own converters:

- ``src/script/printer/tirx/`` — converts PrimFunc, Buffer, SBlock, loops, expressions.
- ``src/script/printer/relax/`` — converts relax.Function, bindings, types, operators.
- ``src/script/printer/ir/`` — converts IRModule, shared types.

The final step calls ``DocToPythonScript()`` (``src/script/printer/doc_printer/python_doc_printer.cc``)
to format the Doc tree into properly indented Python text.

Roundtrip guarantee
~~~~~~~~~~~~~~~~~~~

For any ``IRModule`` constructed through the compiler:

.. code-block:: python

   text = mod.script()           # IR → TVMScript text
   reparsed = tvm.script.from_source(text)  # text → IR
   tvm.ir.assert_structural_equal(mod, reparsed)

This roundtrip property is relied upon by testing infrastructure and serialization workflows.
Note that the printed text may differ from hand-written TVMScript — the printer uses canonical
forms (e.g., explicit ``R.emit`` calls, fully qualified buffer annotations) that are not required
in hand-written code.


Supported Python Syntax
-----------------------

TVMScript supports a subset of Python syntax. The table below summarizes what is supported
and how each construct is interpreted:

.. list-table::
   :header-rows: 1
   :widths: 25 15 60

   * - Python Syntax
     - TIR
     - Relax
   * - ``for i in range(n)``
     - Serial loop nest
     - Not supported (no Relax-level ``for`` handler)
   * - ``with T.sblock(...)``
     - Spatial block scope
     - N/A
   * - ``with R.dataflow()``
     - N/A
     - Dataflow block
   * - ``if ... else``
     - TIR ``If`` branch (PrimExpr condition) or static eval (Python bool)
     - Relax ``If`` node (plain Python ``if cond:`` syntax)
   * - ``while``
     - ``T.While`` loop
     - Not supported
   * - ``x = expr``
     - Variable binding
     - Emit binding (implicit ``R.emit``)
   * - ``x: T.Buffer(...)``
     - Buffer annotation
     - N/A
   * - ``x: R.Tensor(...)``
     - N/A
     - Struct info annotation
   * - ``return``
     - Not used
     - Function return value
   * - ``A[i, j]``
     - Buffer load
     - Not applicable (use operators)
   * - ``A[i, j] = expr``
     - Buffer store
     - Not applicable
   * - Arithmetic (``+``, ``-``, etc.)
     - PrimExpr operations
     - Calls to Relax operators
   * - Function calls
     - ``T.*`` intrinsics
     - ``R.*`` operators or ``call_tir`` / ``call_packed``

**Not supported**: ``class`` definitions (except for ``@I.ir_module``), ``try/except``,
``yield``, ``async/await``, list comprehensions, ``lambda``, ``import``, and ``global``
statements.


TIR Syntax Reference
---------------------

Function definition
~~~~~~~~~~~~~~~~~~~

.. code-block:: python

   @T.prim_func
   def func_name(a: T.handle, b: T.handle):
       A = T.match_buffer(a, (m, n), "float32")
       B = T.match_buffer(b, (m,), "float32")
       # function body

- ``T.handle`` — opaque handle parameter (matched to a buffer inside the function).
- ``T.Buffer(shape, dtype)`` — can also be used directly in the signature:
  ``def func(A: T.Buffer((128,), "float32"))``.

Block and axes
~~~~~~~~~~~~~~

.. code-block:: python

   for i, j in T.grid(128, 128):
       with T.sblock("block_name"):
           vi = T.axis.spatial(128, i)
           vj = T.axis.reduce(128, j)
           T.reads(A[vi, vj])
           T.writes(B[vi])
           # compute

- ``T.axis.spatial`` / ``T.axis.reduce`` / ``T.axis.scan`` — declare axis variables with
  their iteration domain and binding to outer loop variables.
- ``T.axis.remap("SR", [i, j])`` — shorthand: ``S`` = spatial, ``R`` = reduce.
- ``T.reads(...)``, ``T.writes(...)`` — declare buffer regions accessed by this block.

Loop types
~~~~~~~~~~

.. code-block:: python

   for i in T.serial(0, 128):          # sequential
   for i in T.parallel(0, 128):        # parallel
   for i in T.vectorized(0, 128):      # vectorized
   for i in T.unroll(0, 128):          # unrolled
   for i in T.thread_binding(0, 128, thread="threadIdx.x"):  # GPU thread

Buffer operations
~~~~~~~~~~~~~~~~~

.. code-block:: python

   C = T.alloc_buffer((128, 128), "float32")  # intermediate buffer
   val = A[i, j]                               # buffer load
   B[i] = val + 1.0                            # buffer store

Common intrinsics
~~~~~~~~~~~~~~~~~

.. code-block:: python

   T.exp(x), T.log(x), T.sqrt(x), T.tanh(x), ...  # math functions
   T.cast(x, "float16")                              # type cast
   T.if_then_else(cond, true_val, false_val)          # conditional expression
   T.min(a, b), T.max(a, b)                           # min/max
   T.call_extern("func_name", *args)                  # external function call
   T.call_packed("func_name", *args)                   # packed function call
   T.tvm_storage_sync("shared")                        # GPU memory fence


Relax Syntax Reference
-----------------------

Function definition
~~~~~~~~~~~~~~~~~~~

.. code-block:: python

   @R.function
   def main(x: R.Tensor((128, 128), "float32"),
            y: R.Tensor((128,), "float32")) -> R.Tensor((128, 128), "float32"):
       # function body
       return result

- ``R.Tensor(shape, dtype)`` — tensor type annotation.
- ``R.Tuple(...)``, ``R.Shape(...)``, ``R.Any()`` — other Relax type annotations.
- ``R.function(private=True)`` — marks the function as module-private.
- ``R.function(pure=False)`` — marks the function as having side effects.

Dataflow blocks
~~~~~~~~~~~~~~~

.. code-block:: python

   with R.dataflow():
       lv0 = R.add(x, y)
       lv1 = R.nn.relu(lv0)
       R.output(lv1)

Variables inside a ``R.dataflow()`` block are local to that block. ``R.output(...)`` exposes
variables to the outer scope.

Calling TIR functions
~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

   out = R.call_tir(cls.my_kernel, (x, y), out_ty=R.Tensor((128,), "float32"))

- ``cls.my_kernel`` — references a TIR ``PrimFunc`` in the same module.
- ``out_ty`` — the type (shape and dtype) of the output tensor.

Control flow
~~~~~~~~~~~~

Relax ``if`` uses plain Python ``if`` syntax. The condition must be a Relax variable with
boolean type. Both branches are required.

.. code-block:: python

   @R.function
   def f(cond: R.Tensor((), "bool"), x: R.Tensor((128,), "float32")):
       if cond:
           result = R.add(x, x)
       else:
           result = R.multiply(x, x)
       return result


Source Code Map
---------------

.. list-table::
   :header-rows: 1
   :widths: 50 50

   * - Path
     - Contents
   * - ``python/tvm/script/parser/core/``
     - Core parser: dispatch, expression evaluator, variable table, Doc AST
   * - ``python/tvm/script/parser/tirx/``
     - TIR-specific parser handlers and value binding
   * - ``python/tvm/script/parser/relax/``
     - Relax-specific parser handlers and value binding
   * - ``python/tvm/script/parser/ir/``
     - ``@I.ir_module`` entry point and module-level parsing
   * - ``python/tvm/script/ir_builder/base.py``
     - IRBuilder base class and frame stack mechanism
   * - ``python/tvm/script/ir_builder/tirx/``
     - TIR frame types and builder functions (``T.*``)
   * - ``python/tvm/script/ir_builder/relax/``
     - Relax frame types and builder functions (``R.*``)
   * - ``python/tvm/script/ir_builder/ir/``
     - IRModule builder (``I.*``)
   * - ``src/script/printer/``
     - C++ printer: Doc tree, IRDocsifier, Python code generation
   * - ``src/script/printer/tirx/``
     - TIR-specific IR-to-Doc converters
   * - ``src/script/printer/relax/``
     - Relax-specific IR-to-Doc converters
   * - ``src/script/ir_builder/``
     - C++ backend for frame stack and IR construction
   * - ``include/tvm/script/printer/``
     - C++ headers: Doc classes, IRDocsifier, dispatch functor
   * - ``include/tvm/script/ir_builder/``
     - C++ headers: builder base, dialect-specific frame types
