README > Programming Guidelines

Programming Guidelines

Hierarchical Organization

The CUTLASS 3.0 GEMM API document explains CUTLASS 3.0's hierarchical organization, based conceptually on parallelization strategy. This differs from CUTLASS 2.x's approach, which more closely mirrors the GPU hardware hierarchy of thread blocks, warps, and threads.

Design Patterns

CUTLASS aims for the highest performance possible on NVIDIA GPUs. It also offers flexible components that can be assembled and customized to solve new problems related to deep learning and linear algebra. Given a tradeoff between simplicity and performance, CUTLASS chooses performance. Consequently, several design patterns are necessary to yield a composable structure while also satisfying these performance objectives.

Templates

CUDA C++ templates and modern generic programming techniques enable CUTLASS device code to span a large design space.

This design space includes:

Mixed precision arithmetic and data storage
Kernels specialized for layout and problem size
Support for kernel fusion

Moreover, templates provided a structured approach to collecting compile-time constants such as tile dimensions. These must be template arguments to target static array allocation and take advantage of loop unrolling, constant folding, and function inlining.

Constant Memory

Several CUTLASS template classes exhibit a pattern in which problem-specific internal state is known at kernel launch time and remains invariant throughout the execution of a kernel. For example, tile iterators compute several offsets based on the strides of the input tensor that is added to an internal pointer when loading the elements of a tile. These are computed from the tensor stride and never updated; the per-thread internal state consists only of the internal global memory pointer.

CUTLASS can take advantage of this CUDA grid-invariant property by constructing the object in host code and passing a composed parameters structure to the kernel. This confers two benefits: (1.) invariant state is held in constant memory, and (2.) there is no overhead to compute the initial state by each thread.

The design pattern in CUTLASS is for classes with nontrivial constructors to define struct Params as an inner class which contains grid-invariant state. These should define a constructor and an initialize() method. The Params structure should also include a data member corresponding to each data member in the parent class, so these too can be properly constructed in host code. The parent class should define a constructor which accepts Params const & as its first argument.

Composable Shared Memory

Shared memory requires explicit effort by the programmer to allocate and de-allocate. CUTLASS follows the paradigm introduced by CUB to define composed structures for storing data intended to be held in shared memory. Any object requiring shared memory storage for itself or its data members should define a child structure called SharedStorage. This holds data needed by the class and also instantiates SharedStorage objects for each data member.

To be consistent, this pattern defines a convention in which classes define internal shared memory storage requirements. Classes should consider all SharedStorage structures to be opaque other than their own child class. When the lifetimes of child objects are known to be non-overlapping, unions may be used to alias multiple SharedStorage objects to the same shared memory region and reduce overall shared memory capacity. Developers should carefully note that C++ union rules require that they only access the most recently written ("active") member of the union; this differs from C rules.

Loop Unrolling

CUTLASS requires tiles of data to be stored in registers for high-bandwidth access. Simultaneously, high-throughput math instructions must be issued concurrently with memory instructions to hide latency with relatively few concurrent threads. These objectives are achieved by unrolling loops whose iteration counts are known at compile time.

Consequently, most loops within the CUTLASS GEMM implementation are specified by constant values and template arguments. The CUDA compiler is able to unroll the loop bodies, map array elements to registers, and construct an efficient instruction schedule.

All loops expected to be unrolled should be annotated with CUTLASS_PRAGMA_UNROLL to explicitly direct the compiler to unroll them.

int const kN = 8;
Array<float, kN> x;                       // Array we would like to store in registers

CUTLASS_PRAGMA_UNROLL                     // Directs the CUDA compiler to unroll this loop.
for (int idx = 0; idx < kN; ++idx) {      // Loop has constant number of iterations.

  x[i] = float(idx);                      // Indirect access by induction variable results in
                                          // direct register access.
}

Style

No automatic code formatting

Do not use any kind of automatic code formatting, like clang-format, on CUTLASS code.

C++ style

CUTLASS is a C++ project

CUTLASS is a C++ project. CUDA C++ is a C++ dialect. Therefore, we write using standard C++ idioms as much as possible. We aim for portability to as many compilers as possible, by writing host code in Standard C++ and device code in CUDA C++ that resembles Standard C++ as much as possible. This improves usability for the general community of C++ developers, and makes it easier for new staff to join the project.

Follow Standard C++ idioms where possible

Regarding "standard C++ idioms," CUTLASS source code follows the following guidelines, with deviations only because of compiler limitations or where performance absolutely requires it. "Performance requires it" implies measurement. Deviations should be limited in scope and we should always strive to eliminate them.

C++ Core Guidelines
Google C++ Style Guide

Spacing and line length

Use spaces, not tabs.
Use 2 spaces to indent.
Max 100 characters per line.

Lines longer than 100 characters typically wrap unfavorably when viewed in Github's pretty printer.

Function indentation

When calling a function or function object with a long name, break the line right after the invoking open parenthesis. Here is an example.

detail::very_long_function_object_name<TemplateArgument>{}(
  params.long_parameter_name, some_operator.another_long_function_name());

When declaring functions, indent function parameters like this.

void possibly_an_unusually_long_function_name(
    std::uint32_t foo
    std::uint32_t const* bar,
    TypeA a,
    TypeB b,
    TypeC c) {
  // ... the function's body ...
}

A newline should not be inserted between the parenthesis that closes the function's parameters and the curly bracket that opens the function's body. Note the double indent for function parameters.

If-else brackets and spacing

Always use braces with conditionals such as if.
Use a space after control flow keywords such as if, for, and while.
Use a space after the parenthesis closing a conditional such as if, and the curly bracket opening a scope.
Use a new line between the closing brace of an if branch, and the else keyword.

if (condition) {
  // ... code ...
}
else {
  // ... other code ...
}

for (int k = 0; k < num_iters; ++k) {
  // ... still more code ...
}

East const

CUTLASS uses the "East const" convention. That is, the const or constexpr keyword goes after the type, not before. The general rule is that const or constexpr modifies the type to the left of it. Here are some examples.

float constexpr compile_time_constant = 42.3f;

float const const_float = /* whatever */;
float const& reference_to_const_float = const_float;
float const* pointer_to_const_float = &const_float;
float const* const const_pointer_to_const_float = &const_float;

float nonconst_float;
float& reference_to_nonconst_float = nonconst_float;
float* pointer_to_nonconst_float = &nonconst_float;
float* const pointer_to_nonconst_float = &nonconst_float;

Contrast this with "West const" style, e.g.,

const float const_float = /* whatever */;
const float* pointer_to_const_float = &const_float;

Alignment of reference and pointer types

For reference and pointer types, align the & resp. * flush against the type that it modifies. This is called "left alignment."

For example, do this:

int const& var;
int const* var;

and not this.

int const &var;
int const *var;

Avoid calling functions "fast" or "optimized"

Putting words like "fast" or "optimized" in the name of a function assumes that the "fast" path is actually faster. That might be true now, but later changes (in the code, compilers, or GPU hardware) might make it false. In that case, your name could be unintentionally misleading. Consider instead a name that briefly describes the algorithm or feature that is relevant for optimization. For example, compute_on_host is more meaningful than compute_slowly, and computing on host might be faster in some cases (e.g., if the data are already on host and the algorithm is not GPU-friendly).

CUTLASS code has not always followed this rule in the past. Some functions and classes might have words like "fast" in their name. New code should follow this rule, however.

Avoid creating unconstrained templated functions with common names

See C++ Core Guidelines T.47: "Avoid highly visible unconstrained templates with common names." Argument-dependent lookup (ADL) means that if users call a function name without specifying the namespace, the compiler can find overloads of that function in any namespace. This can lead to ambiguous overloads in users' code, just because they happened to include one of your header files that exposes an unconstrained function template. The following illustrates this with an unconstrained swap overload in the cutlass namespace.

#include <cassert>
#include <memory>
#include <utility>

// Uncomment the line below to observe unwarranted build errors.
//#define BAD_CUTLASS_SWAP 1

namespace cutlass {
struct Bar {
  float f;
};
} // namespace cutlass

#ifdef BAD_CUTLASS_SWAP
namespace cutlass {

// don't do this
template<class T>
void swap(T& a, T& b) {
  T tmp = a;
  a = b;
  b = tmp;
}

} // namespace cutlass
#endif // BAD_CUTLASS_SWAP

namespace other {

#ifdef BAD_CUTLASS_SWAP
using cutlass::swap;
#endif // BAD_CUTLASS_SWAP

// Imagine for the sake of this example
// that "foo" is a less common name,
// and that T is constrained via
// std::enable_if or a requires clause.
template<class T>
void foo(T& a, T& b) {
  // The usual idiom for using std::swap is the "swap two-step":
  //
  // 1. import std::swap into the current scope, then
  // 2. call swap without namespace qualification.
  //
  // That won't build if we have another swap
  // overload available in the scope already.

  using std::swap;
  swap(a, b); // OBSERVE UNWARRANTED BUILD ERROR HERE
}

} // namespace other

int main() {
  int x = 42;
  int y = 43;
  other::foo(x, y);
  assert(x == 43);
  assert(y == 42);

  cutlass::Bar a{42.0};
  cutlass::Bar b{43.0};
  other::foo(a, b);
  assert(a.f == 43.0);
  assert(b.f == 42.0);

  // GCC 7.5 std::unique_ptr::reset calls swap,
  // leading to the same issue as above.
  // GCC 12.2's implementation of std::unique_ptr
  // does not have this issue.  Nevertheless,
  // breaking the swap two-step will break users' code,
  // just by them happening to include your headers.
  auto ptr = std::make_unique<cutlass::Bar>(cutlass::Bar{666.0f});
  ptr.reset(new cutlass::Bar{777.0f}); // OBSERVE UNWARRANTED BUILD ERROR HERE

  return 0;
}

Function return values and in-out parameters

Prefer return values to output parameters

In general, avoid in-out mutable references to return a value. If you need to return multiple values, you can return them by struct or tuple, rather than by output references. This includes the special case of error reporting by returning either a value or an error code. Please see the next section for details.

// Instead of passing in-out mutable references ...
void not_preferred(float& input_and_output); // not preferred

// keep functions pure and return value types instead
float preferred(float input); // preferred

Return multiple values by struct or tuple

Sometimes a function needs to return multiple values. In that case, consider the following, in decreasing order of preference.

Return a struct. This lets you name the fields (for more self-documenting code), yet still permits use of structured binding.
Return a tuple. If you need a tuple type that works on device, use cute::tuple. (Please note that cute::tuple does not work for all the types that work in std::tuple. CuTe's documentation explains.)

Here is an example of the struct approach for named values. For a comparable example in the C++ Standard, please see std::allocate_at_least, which returns std::allocation_result.

struct my_computation_result {
  float value = 0.0f;
  float relative_error = 0.0f;
  bool success = false;
};

my_computation_result my_computation(float tolerance);

void foo(float tolerance) {
  // Approach 1: Use structured binding.  The names
  // you choose on the left-hand side have nothing
  // to do with the struct, so it's up to you
  // to get the order right.  On the other hand,
  // this code works whether my_computation returns
  // a struct or a tuple.
  auto [val, rel_err, ok] = my_computation(tolerance);

  // Approach 2: Keep the struct and use its named fields.
  // This approach prevents errors like mixing the order of return types.
  // However, it only works for structs, not for tuples.

  auto result = my_computation(tolerance);
  if (not result.success) {
    // computation did not succeed
  }
  else if (result.relative_error > tolerance) {
    // successful but relative error too large
  }
  else {
    // successful and relative error is in bounds
  }
}

Reporting errors from a function that returns one or more values

We may want to return one or more values from a function that could fail or otherwise report errors. That is, the function either

returns one or more valid values, or
does not return any values and reports an error,

but NOT BOTH. We contrast this with cases when it's meaningful to report both a result and whether the result is satisfactory. For example, when solving a system of nonlinear equations iteratively, users may want the approximate computed solution, even if the iteration did not succeed by converging to the desired tolerance in the desired number of steps. (Users may want to invest more steps, or use the current approximation to jump-start a different algorithm.)

We're talking here about the "either valid value(s), or error, but not both" case. For this case, C++ offers a few options.

Return the value(s), or throw an exception on error
std::expected (requiring C++23) or something like it
std::optional (for a Boolean error state) or something like it
std::variant (a C++17 fall-back for std::expected) or something like it
C-style interface: return an error code, and "return" the values as output parameters

We usually cannot or do not want to throw exceptions on device. Some code projects forbid exceptions entirely (on host or device) and tell the compiler to disable them. If we exclude a C-style interface (the last option) as not idiomatic C++, then for host-only code, std::expected, std::optional, and std::variant all work. For code that needs to build and run on device, we can fall back to libcu++ equivalents in the cuda::std:: namespace, when they exist. Otherwise, we must resort to returning a struct or tuple with the value and the error information, and ask users not to use the value on error. This is acceptable if the value can be constructed cheaply with a reasonable default.

Performance of different value-or-error reporting methods

P1886R0 (Ben Craig, "Error speed benchmarking") surveys different ways in Standard C++ to report errors from a function that returns one or more values, and compares their (host-only) performance with different compilers.

Use aggregate initialization when returning a struct or tuple

Use aggregate initialization when returning a struct or tuple. This avoids duplication of the return type name.

struct foo_result {
  float value = 0.0f;
  float error = 0.0f;
  bool success = false;
};

foo_result foo(std::span<const float> input) {
  // ... code  ...

  // Prefer this.  We know what type the function returns.
  return {val, err, ok}; // prefer this

  // Naming foo_result again here is unnecessary.
  // return foo_result{val, err, ok};
}

However, note that this won't work if the function returns auto. The general rule is to avoid code duplication.

auto foo(std::span<const float> input) {
  // ... code  ...

  if constexpr (some_condition) {
    return foo_result{val, err, ok};
  }
  else {
    return bar_result{val, err, ok};
  }
}

Prefer using the actual return type to auto, if you know the type

C++ lets you use auto to deduce the type returned from a function.

If you know the actual type, prefer using the type instead of auto.
Use Constructor Type Argument Deduction (CTAD) if you know that a function returns some type (e.g., Tensor), but don't know the type's template arguments.
Use auto in structured bindings (where you have to use it anyway). This also makes your code agnostic of whether the return type is a struct, tuple, pair, or other tuple-like type.
Be careful using auto with types that provide expression templates.

Contrast this with "Almost Always Auto" (AAA) style. We deliberately choose not to follow AAA style, for the following reasons.

Using the actual type when we know it can help prevent common loss-of-precision errors in mixed-precision computations, an important use case for CUTLASS.
CTAD gives us much of the brevity of AAA, with more clarity.
Using the actual type instead of auto can prevent common dangling errors with expression templates.

Classes and structs

Type names use CamelCase. That is, words start with capital letters. The remaining letters in the word are lower case, and words are joined with no intervening underscores. The only exception is when implementations are a drop-in replacement for C++ Standard Library components.

Follow the C++ Core Guidelines to decide whether to use class or struct.

Use class when the object must maintain an invariant. Data members related to the invariant should be private.
Use struct when the class has no invariant to maintain, and data members may vary arbitrarily with respect to each other.

Prefer nonmember functions and statelessness where possible. Member functions imply invariants. More invariants make code maintenance and testing harder.

Class members

Methods and members are written using snake_case.

Private data and function members have suffix _.

Class Member Order

Members within classes and structures should be organized as follows:

Type and constant definitions
Data members
Constructors
Other methods

This convention follows the CUB library and is also described by Howard Hinnant. It also approximates the usual ordering of chapters in a typical Systems and Controls textbook. That is, it

identifies relevant constants,
defines a state-space representation of the dynamical system under study (the class's data members), and then
devotes the remaining "chapters" to defining the system's dynamical behavior (the class's methods).

Here is an example class.

class A {
public:
  // type definitions
protected:
  // protected type definitions
private:
  // private type definitions

public:
  // data members
protected:
  // protected data members
  // STRONGLY TO BE AVOIDED;
  // please see C++ Core Guidelines
private:
  // private data members

public:
  // methods
protected:
  // protected methods
private:
  // private methods
};

Use scoped enums

Use scoped enums (a C++11 feature) for enumerated types. Use capital letters for the enumerated type name and prefix k for enumerators like other constants.

enum class MatrixOperation {
  kNone,
  kTranspose,
  kConjugate,
  kHermitian
};

Namespaces

Namespaces are all lower case. The top-level namespace is cutlass::. The second nested namespace refers to the general category of operation performed by its members: e.g., gemm::. The third nested namespace refers to the operations' position in the conceptual hierarchy: e.g., device::, kernel::, or collective::.

The bodies of namespace definitions should not be indented. Comments on the closing brace to indicate the namespace being closed are welcome.

namespace cutlass {
namespace gemm {
namespace kernel {

struct AnotherGemmKernel {
  // ... contents ...
};

} // namespace kernel
} // namespace gemm
} // namespace cutlass

File Names

New files should be named using snake_case with extension .hpp for header files, .cu for CUDA sources, and .cpp for C++ host-only source files.

Header files with extension .h are CUTLASS 2.x legacy headers.

Macros

Only use macros when the preprocessor is the only way to accomplish the task. Do not use macros for literal constants. Instead, if inside the body of a function, use constexpr values, and if at namespace scope, use inline constexpr variables (a C++17 feature).

"Namespace" macros by starting them with the module name, e.g., CUTLASS_. Macros and ONLY MACROS use all capital letters with underscores between words. For example:

#define CUTLASS_MACROS_USE_ALL_CAPS inline __host__ __device__

Header files such as cutlass/cutlass.h and cute/config.hpp offer macros for expressing compiler-dependent behavior. These include

replacements for __device__ and/or __host__ annotations:
- CUTLASS_HOST_DEVICE or CUTE_HOST_DEVICE for functions that run on the host and the device,
- CUTLASS_DEVICE or CUTE_DEVICE for functions that run on the device only,
- CUTE_HOST for functions that run on the host only, and
- CUTE_HOST_RTC for functions that run on the host only, but occur as unevaluated operands (of e.g., decltype or sizeof; see C++ Standard, [expr.context] 1) in device code; and
annotations to loop unrolling:
- CUTLASS_PRAGMA_UNROLL or CUTE_UNROLL for full unrolling of loops with constant trip counts, and
- CUTLASS_PRAGMA_NO_UNROLL or CUTE_NO_UNROLL to prevent unrolling.

Guard all headers with `#pragma once`

Use #pragma once to guard all headers.

CuTe Layout Comments

Right align CuTe layout comments at column 120.
If layout comment is too long do your best to align it.
If layout comment is too long and there are many related tensors that reader should read together, try to align the layout comments of related tensors.

    Tensor my_tensor = make_tensor<Type>(Layout<Shape<_2,_2>{}, Stride<_1,_2>>{});                       // (2,2):(1,2)
    
    // Related tensors
    Tensor my_tensor1 = make_tensor<Type>(ThisIsAVeryComplicatedLayoutWithAVeryLongName);         // ((Mode0_0,Mode0_1,Mode0_2),Mode1,Mode2,Mode3)
    Tensor my_tensor2_related = make_tensor<Type>(ThisIsAVeryComplicatedLayoutWithAVeryLongName); // ((Mode0_0,Mode0_1,Mode0_2),Mode1,Mode2,Mode3)

CUDA C++ style

CUDA Built-in Variables

Avoid direct access to CUDA built-in variables threadIdx, blockIdx, blockDim, and gridDim within CUTLASS components except in special circumstances.

Using built-in global variables directly within resuable components necessitates that all components use them consistently which may not be possible if CUTLASS components are used in other contexts.

Instead, components should accept a linear ID identifying threads, warps, and threadblocks from calling code. The top-level kernel may then decide how to map threads, warps, and blocks to the problem it is solving.

Use CUTLASS's and CuTe's fundamental types and operations

Use the fundamental types and operations defined in CUTLASS consistently. This contributes to a framework of interoperable, consistent components. It reduces code duplication, which reduces build and test times. It also saves developer effort.

CUTLASS's fundamental types and operations include

Numeric types to represent numeric data in host and device code, and
functional.h to perform numeric operations in generic code.

CUTLASS 3.0 uses CuTe components to represent data layouts and multidimensional arrays. Please refer to the CuTe Tutorial for details. CuTe has replaced CUTLASS 2.x components such as Containers, Layouts, and TensorRef and TensorView.

CUTLASS idioms

Detecting major mode

Developers sometimes need to detect whether a tensor is MN-major or K-major. (For definitions, see the CuTe GEMM tutorial.)

Correct: cutlass::detail::is_major<0, Stride>() or cutlass::detail::is_k_major() from include/cutlass/gemm/gemm.h
Incorrect: get<0>(stride) == 1

The second point is incorrect because it assumes that the mode is a single integer, not a multimode. This means that the code will fail to compile for tensor contractions. For example, suppose that a tensor A has shape ((X, Y), K) and stride ((1, X), X*Y). get<0>(stride) is the tuple (1, X), not a single integer. However, A is certainly M major if interpreted as a matrix.

Copyright

  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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