Release v4.0.0 (#2294)

This commit is contained in:
Kihiro Bando
2025-05-13 15:55:29 -04:00
committed by GitHub
parent ad7b2f5e84
commit f115c3f854
299 changed files with 51495 additions and 4413 deletions
@@ -0,0 +1,140 @@
.. _dsl_control_flow:
.. |DC| replace:: dynamic compilation
.. |IR| replace:: intermediate representation (IR)
.. |DSL| replace:: CuTe DSL
.. |Constexpr| replace:: **Constexpr** (compile-time Python value)
|DSL| Control Flow
==================
.. contents::
:depth: 2
:local:
Overview
--------
|DSL| walks Python’s AST and converts each control-flow construct it finds into
structured |IR|. You can therefore write ordinary Python loops and branches
while the compiler decides—statement by statement—whether to
* **evaluate at compile time** if the controlling value is a |Constexpr|, or
* **emit intermediate representation (IR)** when the value is dynamic.
For a high-level discussion of the overall pipeline, see
:doc:`the code-generation overview <dsl_code_generation>`.
For Loops
---------
|DSL| recognises three kinds of ranges for ``for`` loops:
* ``range`` – the Python built-in
* ``cutlass.range_dynamic`` – always lowers to |IR|
* ``cutlass.range_constexpr`` – always unrolls at compile time
range(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
The AST rewriter inserts a small helper stub. At runtime the loop bounds are
inspected:
* **Constant bounds** → the loop is unrolled at compile time.
* **Dynamic bounds** → the loop is emitted as structured |IR|.
cutlass.range_dynamic(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Use when you *always* want a loop in the generated |IR|, even if the bounds
look constant.
cutlass.range_constexpr(...)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Runs in the Python interpreter and is fully unrolled before code generation.
All loop indices must be |Constexpr|.
Limitations of Dynamic For Loops
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
* Early-exit ``break``, ``continue``, or raising exception are not yet supported.
* Operations in the loop body are traced only when tracing is active in that
region.
**Example:**
.. code-block:: python
@cute.jit
def loop_example():
n = 10
# ❌ This loop is dynamic, early-exit isn't allowed.
for i in cutlass.range_dynamic(n):
if i == 5:
break # Early-exit
cute.printf("%d\\n", i)
# ✅ This loop is constexpr, early-exit is allowed.
for i in cutlass.range_constexpr(n):
if i == 5:
break # Early-exit
cute.printf("%d\\n", i)
If-Else Statements
------------------
Standard Python ``if``/``else`` is supported.
* **Predicate is Constexpr (compile-time Python value)** → evaluated at compile time.
* **Predicate is dynamic** → lowered to |IR|.
**Example:**
.. code-block:: python
@cute.jit
def main(const_var: cutlass.Constexpr, dynamic_var: cutlass.Int32):
if const_var: # compile-time branch
cute.printf("Const branch\\n")
else:
cute.printf("Const else\\n")
if dynamic_var == 10: # dynamic branch
cute.printf("Dynamic True\\n")
else:
cute.printf("Dynamic False\\n")
Similarly to for-loops, the ``if cutlass.const_expr`` and ``if cutlass.dynamic_expr`` constructs can
be used to force the evaluation at compile-time or the generation of IR, respectively. Unstructured
control flow is only supported when using ``if cutlass.const_expr``.
While Loops
-----------
Python ``while`` loops are always treated as **dynamic** because the loop condition may become
dynamic after the first iteration. Similarly to for-loops and ``if``/``else``, the
``while cutlass.const_expr`` and ``while cutlass.dynamic_expr`` constructs are available.
Compile-Time Metaprogramming
----------------------------
Mix compile-time constructs with normal |DSL| code to generate specialised
kernels without runtime overhead. A compile-time flag can, for example, toggle
an optional **ReLU** epilogue:
.. code-block:: python
@cute.kernel
def gemm(..., do_relu: cutlass.Constexpr):
# main GEMM work
...
if const_expr(do_relu): # compile-time guard
# ReLU code is emitted only when do_relu is True
...
.. code-block:: text
gemm(..., False) # ReLU is omitted from the generated |IR|
gemm(..., True) # ReLU is included