![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS Code Organization")

[README](/README.md#documentation) > **CUTLASS Utilities**

Note: This document discusses utilities commonly used with code that targets CUTLASS 2.x.
Although CUTLASS 3.0's primary entry point APIs do not transact in these `cutlass::*` tensor types anymore,
users can still find them convenient for managing allocations with trivial affine layouts.
For more advanced host side tensor management, [`cute::Tensor`](/media/docs/cute/03_tensor.md)s
can be used on either host or device for any memory space and full expressive power of
[`cute::Layout`](/media/docs/cute/01_layout.md)s.

# CUTLASS Utilities

CUTLASS utilities are additional template classes that facilitate recurring tasks. These are
flexible implementations of needed functionality, but they are not expected to be efficient.

Applications should configure their builds to list `/tools/util/include` in their include
paths.

Source code is in [`/tools/util/include/cutlass/util/`](/tools/util/include/cutlass/util).

## Tensor Allocation and I/O

To allocate a tensor with storage in both host and device memory, use `HostTensor` in
[`cutlass/util/host_tensor.h`](/tools/util/include/cutlass/util/host_tensor.h)

```c++
template <typename Element, typename Layout>
class HostTensor;
```

This class is compatible with all CUTLASS numeric data types and layouts.

**Example:** column-major matrix storage of single-precision elements.
```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/util/host_tensor.h>

int main() {
  int rows = 32;
  int columns = 16;

  cutlass::HostTensor<float, cutlass::layout::ColumnMajor> tensor({rows, columns});

  return 0;
}
```

Internal host-side storage may be accessed via the following methods.
```c++
float *host_ptr = tensor.host_data();
cutlass::TensorRef<float, cutlass::layout::ColumnMajor> host_ref = tensor.host_ref();
cutlass::TensorView<float, cutlass::layout::ColumnMajor> host_view = tensor.host_view();
```

Device memory may be accessed similarly.
```c++
float *device_ptr = tensor.device_data();
cutlass::TensorRef<float, cutlass::layout::ColumnMajor> device_ref = tensor.device_ref();
cutlass::TensorView<float, cutlass::layout::ColumnMajor> device_view = tensor.device_view();
```

Printing to human-readable CSV output is accoplished with `std::ostream::operator<<()` defined in
[`cutlass/util/tensor_view_io.h`](/tools/util/include/cutlass/util/tensor_view_io.h). 
Note, this assumes all views refer to host memory.
```c++
#include <cutlass/util/tensor_view_io.h>

int main() {
  // Obtain a TensorView into host memory
  cutlass::TensorView<float, cutlass::layout::ColumnMajor> view = tensor.host_view();

  // Print to std::cout
  std::cout << view << std::endl;

  return 0;
}
```

Host and device memory must be explicitly synchronized by the application.
```c++
float idx = 0;

for (int i = 0; i < rows; ++i) {
  for (int j = 0; j < columns; ++j) {

    // Write the element at location {i, j} in host memory
    tensor.host_ref().at({i, j}) = idx;

    idx += 0.5f;
  } 
}

// Copy host memory to device memory
tensor.sync_device();

// Obtain a device pointer usable in CUDA kernels
float *device_ptr = tensor.device_data();
```

`HostTensor<>` is usable by all CUTLASS layouts including interleaved layouts.
```c++
int rows = 4;
int columns = 3;

cutlass::HostTensor<float, cutlass::layout::ColumnMajorInterleaved<4>> tensor({rows, columns});

for (int i = 0; i < rows; ++i) {
  for (int j = 0; j < columns; ++j) {

    // Write the element at location {i, j} in host memory
    tensor.host_ref().at({i, j}) = float(i) * 1.5f - float(j) * 2.25f;
  } 
}

std::cout << tensor.host_view() << std::endl;
```

## Device Allocations

To strictly allocate memory on the device using the smart pointer pattern to manage allocation and deallocation,
use `cutlass::DeviceAllocation<>`. 

**Example:** allocating an array in device memory.
```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/layout/tensor_view.h>
#include <cutlass/util/device_memory.h>

__global__ void kernel(float *device_ptr) {

}

int main() {

  size_t N = 1024;

  cutlass::DeviceAllocation<float> device_alloc(N);

  // Call a CUDA kernel passing device memory as a pointer argument
  kernel<<< grid, block >>>(alloc.get());

  if (cudaGetLastError() != cudaSuccess) {
    return -1;
  }

  // Device memory is automatically freed when device_alloc goes out of scope

  return 0;
}
```

## Tensor Initialization

CUTLASS defines several utility functions to initialize tensors to uniform, procedural,
or randomly generated elements. These have implementations using strictly host code and
implementations using strictly CUDA device code.

`TensorFill()` for uniform elements throughout a tensor.
```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/util/reference/host/tensor_fill.h>
#include <cutlass/util/reference/device/tensor_fill.h>
#include <cutlass/util/host_tensor.h>

int main() {
  int rows = 128;
  int columns = 64;

  float x = 3.14159f;

  cutlass::HostTensor<float, cutlass::layout::ColumnMajor> tensor({rows, columns});

  // Initialize in host memory
  cutlass::reference::host::TensorFill(tensor.host_view(), x);

  // Initialize in device memory
  cutlass::reference::device::TensorFill(tensor.device_view(), x);

  return 0;
}
```

`TensorFillRandomUniform()` for initializing elements to a random uniform distribution.
The device-side implementation uses CURAND to generate random numbers.
```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/util/reference/host/tensor_fill.h>
#include <cutlass/util/reference/device/tensor_fill.h>
#include <cutlass/util/host_tensor.h>

int main() {
  int rows = 128;
  int columns = 64;

  double maximum = 4;
  double minimum = -4;
  uint64_t seed = 0x2019;

  cutlass::HostTensor<float, cutlass::layout::ColumnMajor> tensor({rows, columns});

  // Initialize in host memory
  cutlass::reference::host::TensorFillRandomUniform(
    tensor.host_view(),
    seed,
    maximum,
    minimum);

  // Initialize in device memory
  cutlass::reference::device::TensorFillRandomUniform(
    tensor.device_view(),
    seed,
    maximum,
    minimum);

  return 0;
}
```


`TensorFillRandomGaussian()` for initializing elements to a random gaussian distribution.
The device-side implementation uses CURAND to generate random numbers.
```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/util/reference/host/tensor_fill.h>
#include <cutlass/util/reference/device/tensor_fill.h>
#include <cutlass/util/host_tensor.h>

int main() {

  int rows = 128;
  int columns = 64;

  double mean = 0.5;
  double stddev = 2.0;
  uint64_t seed = 0x2019;

  cutlass::HostTensor<float, cutlass::layout::ColumnMajor> tensor({rows, columns});

  // Initialize in host memory
  cutlass::reference::host::TensorFillRandomGaussian(
    tensor.host_view(),
    seed,
    mean,
    stddev);

  // Initialize in device memory
  cutlass::reference::device::TensorFillRandomGaussian(
    tensor.device_view(),
    seed,
    mean,
    stddev);

  return 0;
}
```

Each of these functions accepts an additional argument to specify how many bits of
the mantissa less than 1 are non-zero. This simplifies functional comparisons when
exact random distributions are not necessary, since elements may be restricted to
integers or values with exact fixed-point representations.

```c++
#include <cutlass/layout/matrix.h>
#include <cutlass/util/reference/host/tensor_fill.h>
#include <cutlass/util/reference/device/tensor_fill.h>
#include <cutlass/util/host_tensor.h>

int main() {

  int rows = 128;
  int columns = 64;

  double mean = 0.5;
  double stddev = 2.0;
  uint64_t seed = 0x2019;

  int bits_right_of_binary_decimal = 2;

  cutlass::HostTensor<float, cutlass::layout::ColumnMajor> tensor({rows, columns});

  // Initialize in host memory
  cutlass::reference::host::TensorFillRandomGaussian(
    tensor.host_view(),
    seed,
    mean,
    stddev,
    bits_right_of_binary_decimal);

  // Initialize in device memory
  cutlass::reference::device::TensorFillRandomGaussian(
    tensor.device_view(),
    seed,
    mean,
    stddev,
    bits_right_of_binary_decimal);

  return 0;
}
```

These utilities may be used for all data types.

**Example:** random half-precision tensor with Gaussian distribution.
```c++
#include <cutlass/numeric_types.h>
#include <cutlass/layout/matrix.h>
#include <cutlass/util/reference/host/tensor_fill.h>
#include <cutlass/util/reference/device/tensor_fill.h>
#include <cutlass/util/host_tensor.h>

int main() {
  int rows = 128;
  int columns = 64;

  double mean = 0.5;
  double stddev = 2.0;
  uint64_t seed = 0x2019;

  // Allocate a column-major tensor with half-precision elements
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> tensor({rows, columns});

  // Initialize in host memory
  cutlass::reference::host::TensorFillRandomGaussian(
    tensor.host_view(),
    seed,
    mean,
    stddev);

  // Initialize in device memory
  cutlass::reference::device::TensorFillRandomGaussian(
    tensor.device_view(),
    seed,
    mean,
    stddev);

  return 0;
}
```

## Reference Implementations

CUTLASS defines reference implementations usable with all data types and layouts. These are
used throughout the unit tests.

**Example:** Reference GEMM implementation with mixed precision internal computation.
```c++
#include <cutlass/numeric_types.h>
#include <cutlass/layout/matrix.h>

#include <cutlass/util/host_tensor.h>
#include <cutlass/util/reference/host/gemm.h>

int main() {

  int M = 64;
  int N = 32;
  int K = 16;

  float alpha = 1.5f;
  float beta = -1.25f;

  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> A({M, K});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> B({K, N});
  cutlass::HostTensor<cutlass::half_t, cutlass::layout::ColumnMajor> C({M, N});

  cutlass::reference::host::Gemm<
    cutlass::half_t, cutlass::layout::ColumnMajor,   // ElementA and LayoutA
    cutlass::half_t, cutlass::layout::ColumnMajor,   // ElementB and LayoutB
    cutlass::half_t, cutlass::layout::ColumnMajor,   // ElementC and LayoutC
    float,                                           // scalar type (alpha and beta)
    float> gemm_op;                                  // internal accumulation type

  gemm_op(
    {M, N, K},             // problem size
    alpha,                 // alpha scalar
    A.host_view(),         // TensorView to host memory
    B.host_view(),         // TensorView to host memory
    beta,                  // beta scalar
    C.host_view(),         // TensorView to host memory
    D.host_view());        // TensorView to device memory

  return 0;
}
```

# Copyright

Copyright (c) 2017 - 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
SPDX-License-Identifier: BSD-3-Clause

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