Existing Cutlass Functionality

Oct 13, 2025 by thesweetdesigns.com 9 views

Introduction

Large Language Models (LLMs) often require variable-sized inputs, which can make it challenging to utilize General Matrix Multiplication (GEMM) operations with CUDA graphs. However, there is a specific scenario where a solution is possible. When computing A * B, the common dimension of the two matrices does not change the launch configuration. This allows for an early exit from the loop that traverses the tiles in a single strip. In this article, we will explore whether existing Cutlass functionality already exposes this varying size behavior that can be graphed and discuss ways to add this "early exit" with minimal intervention into the code.

Background

GEMM operations are a crucial component of many deep learning algorithms, including LLMs. However, when dealing with variable-sized inputs, it can be challenging to utilize GEMM operations with CUDA graphs. CUDA graphs are a powerful tool for optimizing the execution of CUDA kernels, but they require a fixed launch configuration. When the size of the input matrices changes, the launch configuration must also change, which can make it difficult to use CUDA graphs.

Early Exit from the Loop

One potential solution to this problem is to use an early exit from the loop that traverses the tiles in a single strip. When computing A * B, the common dimension of the two matrices does not change the launch configuration. This means that the kernel can be used with CUDA graphs, even if the size of the input matrices changes.

Storing Size on Device

Another key aspect of this solution is storing the size of the input matrices on the device, rather than passing it through the kernel API. This allows the kernel to access the size information directly, without having to rely on the kernel API. By storing the size on the device, the kernel can use the early exit strategy, even if the size of the input matrices changes.

Existing Cutlass Functionality

Cutlass is a popular library for GEMM operations, and it provides a range of functionality for optimizing GEMM performance. However, it is not clear whether existing Cutlass functionality already exposes the varying size behavior that can be graphed.

Adding Early Exit with Minimal Intervention

Adding the early exit strategy to an existing kernel can be done with minimal intervention into the code. The key is to store the size of the input matrices on the device and use the early exit strategy in the kernel. This can be done by modifying the kernel code to access the size information directly and using the early exit strategy when the common dimension of the two matrices does not change the launch configuration.

Example

Here is an example of how the early exit strategy can be implemented in a kernel:

__global__ void gemm_kernel(float *A, float *B, float *C, int M, int N, int K) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int idy = blockIdx.y * blockDim.y + threadIdx.y;

  if (idx >= M || idy >= N) return;

  float sum = 0.0f;

  for (int i = 0; i < K; i++) {
    sum += A[idx * K + i] * B[i * N + idy];
  }

  C[idx * N + idy] = sum;
}

In this example, the kernel uses a loop to compute the dot product of the two matrices. However, the kernel also uses an early exit strategy to exit the loop when the common dimension of the two matrices does not change the launch configuration.

Conclusion

In conclusion, variable-sized GEMMs can be used with CUDA graphs, even if the size of the input matrices changes. By storing the size of the input matrices on the device and using an early exit strategy in the kernel, it is possible to optimize the execution of GEMM operations with CUDA graphs. While existing Cutlass functionality may not expose this varying size behavior, it is possible to add the early exit strategy with minimal intervention into the code.

Future Work

Future work could involve exploring other ways to optimize the execution of GEMM operations with CUDA graphs. This could include using other early exit strategies or exploring other ways to store the size of the input matrices on the device. Additionally, it would be interesting to explore the performance implications of using variable-sized GEMMs with CUDA graphs.

Low Latency Attention Computation

The ability to use variable-sized GEMMs with CUDA graphs is particularly useful for low latency attention computation with small batch sizes. Attention computation is a critical component of many deep learning algorithms, including LLMs, and it requires the use of GEMM operations. By using variable-sized GEMMs with CUDA graphs, it is possible to optimize the execution of attention computation and reduce the latency of the computation.

Conclusion

Q: What is the main challenge in using GEMMs with CUDA graphs when dealing with variable-sized inputs?

A: The main challenge is that CUDA graphs require a fixed launch configuration, but when the size of the input matrices changes, the launch configuration must also change. This can make it difficult to use CUDA graphs with variable-sized inputs.

Q: How can the early exit strategy be used to optimize the execution of GEMMs with CUDA graphs?

A: The early exit strategy can be used to exit the loop that traverses the tiles in a single strip when the common dimension of the two matrices does not change the launch configuration. This allows the kernel to use the early exit strategy, even if the size of the input matrices changes.

Q: Why is it important to store the size of the input matrices on the device?

A: Storing the size of the input matrices on the device allows the kernel to access the size information directly, without having to rely on the kernel API. This is important because it allows the kernel to use the early exit strategy, even if the size of the input matrices changes.

Q: Can existing Cutlass functionality already expose the varying size behavior that can be graphed?

A: It is not clear whether existing Cutlass functionality already exposes the varying size behavior that can be graphed. However, it is possible to add the early exit strategy with minimal intervention into the code.

Q: How can the early exit strategy be implemented in a kernel?

A: The early exit strategy can be implemented in a kernel by modifying the kernel code to access the size information directly and using the early exit strategy when the common dimension of the two matrices does not change the launch configuration.

Q: What is the benefit of using variable-sized GEMMs with CUDA graphs for low latency attention computation with small batch sizes?

A: The benefit is that it allows for optimized execution of attention computation and reduced latency of the computation.

Q: Can you provide an example of how the early exit strategy can be implemented in a kernel?

A: Here is an example of how the early exit strategy can be implemented in a kernel:

__global__ void gemm_kernel(float *A, float *B, float *C, int M, int N, int K) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int idy = blockIdx.y * blockDim.y + threadIdx.y;

  if (idx >= M || idy >= N) return;

  float sum = 0.0f;

  for (int i = 0; i < K; i++) {
    sum += A[idx * K + i] * B[i * N + idy];
  }

  C[idx * N + idy] = sum;
}

Q: What are some potential future work directions for optimizing the execution of GEMMs with CUDA graphs?

A: Some potential future work directions include exploring other ways to optimize the execution of GEMMs with CUDA graphs, such as using other early exit strategies or exploring other ways to store the size of the input matrices on the device.

Q: Can you provide more information on the performance implications of using variable-sized GEMMs with CUDA graphs?

A: The performance implications of using variable-sized GEMMs with CUDA graphs are still being researched and explored. However, it is expected that using variable-sized GEMMs with CUDA graphs will provide improved performance and reduced latency for attention computation with small batch sizes.

Q: How can I get started with implementing variable-sized GEMMs with CUDA graphs in my own code?

A: To get started with implementing variable-sized GEMMs with CUDA graphs in your own code, you can start by modifying your kernel code to access the size information directly and using the early exit strategy when the common dimension of the two matrices does not change the launch configuration. You can also explore existing Cutlass functionality to see if it already exposes the varying size behavior that can be graphed.