cuda-kernels

CUDA Kernels for Diffusers & Transformers

This skill provides patterns and guidance for developing optimized CUDA kernels targeting NVIDIA GPUs (H100, A100, T4) for use with HuggingFace diffusers and transformers libraries.

Quick Start

Diffusers (Video/Image Generation)

For benchmarking kernel performance:

# Benchmark with optimized kernels (6% end-to-end speedup)
python generate_video.py --use-optimized-kernels

# Benchmark baseline with torch.compile (34% speedup)
python generate_video.py --no-optimized-kernels --compile

# Compare configurations (note: --compile and --use-optimized-kernels are mutually exclusive)
python generate_video.py --use-optimized-kernels && \
python generate_video.py --no-optimized-kernels --compile

For a minimal diffusers integration example (~150 lines):

python scripts/ltx_kernel_injection_example.py

Transformers (LLMs)

For a minimal transformers integration example (~120 lines):

python scripts/transformers_injection_example.py

HuggingFace Kernels Hub

Load pre-compiled kernels from HuggingFace Hub (no local compilation):

from kernels import get_kernel

# Load optimized activation kernels
activation = get_kernel("kernels-community/activation", version=1)

# Use the kernel
y = torch.empty_like(x)
activation.gelu_fast(y, x)

For a complete HuggingFace Kernels example:

python scripts/huggingface_kernels_example.py

Isolated Kernel Micro-benchmarks

python benchmark_rmsnorm.py

Supported Libraries & Models

Library	Supported Models	Key Kernels
diffusers	LTX-Video, Stable Diffusion, FLUX, DiT	RMSNorm, GEGLU, RoPE, AdaLN
transformers	LLaMA, Mistral, Qwen, Falcon	RMSNorm, Attention

GPU	Compute Capability	Guide
H100	sm_90	h100-optimization-guide.md
A100	sm_80	a100-optimization-guide.md
T4	sm_75	t4-optimization-guide.md

When This Skill Applies

Use this skill when:

• Benchmarking kernel performance against baseline implementations
• Writing new CUDA kernels for diffusion models or LLMs
• Optimizing existing kernels for H100, A100, or T4 architecture
• Implementing custom attention, normalization, or activation layers
• Integrating kernels with diffusers pipelines (LTX-Video, Stable Diffusion, FLUX, DiT)
• Integrating kernels with transformers models (LLaMA, Mistral, Qwen)
• Debugging kernel performance issues on NVIDIA GPUs

Working Example

A complete working example is available at examples/ltx_video/. This demonstrates:

• Custom CUDA kernels (RMSNorm, RoPE 3D, GEGLU, AdaLN)
• Build system setup with setup.py, build.toml, and flake.nix
• PyTorch C++ bindings and Python API
• Benchmarking script for comparing optimized vs baseline performance

Benchmarking Kernels

Use the benchmark script to measure kernel performance:

# Full benchmark with all options
python scripts/benchmark_example.py \
    --use-optimized-kernels \
    --compile \
    --batch-size 1 \
    --num-frames 161 \
    --height 512 \
    --width 768 \
    --steps 50 \
    --warmup-iterations 2

Benchmark Script Options

Option	Default	Description
--use-optimized-kernels	auto	Use custom H100 CUDA kernels
--no-optimized-kernels	-	Use baseline implementation
--compile	false	Enable torch.compile on transformer
--batch-size	1	Number of videos per prompt
--num-frames	161	Number of frames to generate
--height	512	Video height in pixels
--width	768	Video width in pixels
--steps	50	Denoising steps
--warmup-iterations	2	Warmup runs before benchmark

Example Benchmark Results

End-to-End Video Generation (49 frames, 30 steps, H100 80GB):

Configuration	Time (s)	it/s	Speedup	Notes
Baseline (no compile)	2.87	12.58	1.00x	Reference
Optimized Kernels	2.70	13.52	1.06x	6% faster
Baseline + torch.compile	2.14	19.05	1.34x	34% faster

Important: --use-optimized-kernels and --compile are currently mutually exclusive. Custom kernels require PyTorch custom op registration to work with torch.compile.

Key metrics to capture:

• Device: GPU model (e.g., NVIDIA H100 80GB HBM3)
• Precision: Data type used (e.g., bfloat16)
• Resolution: Width x Height (e.g., 768x512)
• Frames: Number of frames generated (e.g., 49, 161)

RMSNorm Micro-benchmarks

The vectorized RMSNorm kernel achieves 2.67x average speedup over PyTorch baseline:

Shape	Custom (ms)	PyTorch (ms)	Speedup
[1×1024×2048]	0.019	0.065	3.37x
[2×1024×2048]	0.024	0.073	3.04x
[4×1024×2048]	0.036	0.093	2.58x
[2×4096×3072]	0.087	0.208	2.41x
[4×4096×3072]	0.157	0.392	2.49x

Bandwidth efficiency: 38% of H100's theoretical 3.35 TB/s

Why end-to-end speedup is smaller: RMSNorm accounts for ~5% of total compute in LTX-Video. The remaining time is spent in attention (Flash Attention/SDPA), linear projections, and VAE decode.

Project Structure

.claude/skills/cuda-kernels/
├── scripts/
│   ├── benchmark_example.py              # End-to-end video generation benchmark
│   ├── benchmark_rmsnorm.py              # Isolated RMSNorm micro-benchmark
│   ├── ltx_kernel_injection_example.py   # Minimal diffusers integration (~150 lines)
│   ├── transformers_injection_example.py # Minimal transformers integration (~120 lines)
│   └── huggingface_kernels_example.py    # HuggingFace Kernels Hub integration
├── references/
│   ├── diffusers-integration.md          # Complete diffusers integration guide
│   ├── transformers-integration.md       # Complete transformers integration guide
│   ├── huggingface-kernels-integration.md # HuggingFace Kernels Hub (get_kernel) guide
│   ├── troubleshooting.md                # Common issues and solutions
│   ├── kernel-templates.md               # CUDA kernel templates (includes vectorized)
│   ├── h100-optimization-guide.md        # H100 (Hopper) optimization deep dive
│   ├── a100-optimization-guide.md        # A100 (Ampere) optimization deep dive
│   └── t4-optimization-guide.md          # T4 (Turing) optimization deep dive
└── SKILL.md                              # This file

examples/ltx_video/                  # Complete working example
├── kernel_src/
│   └── rmsnorm.cu                  # Vectorized RMSNorm kernel (2.67x faster)
├── torch-ext/                      # PyTorch bindings
├── generate_video.py               # Full benchmark script
├── benchmark_rmsnorm.py            # Isolated kernel benchmark
└── setup.py                        # pip install -e .

GPU Architecture Reference

H100 (Hopper) - Primary Target

Spec	Value	Optimization Impact
SMs	132	Grid sizing: aim for multiples of 132
Threads/SM	2048	Max 16 blocks of 128 threads per SM
Shared Memory	192 KB/SM	Large tiles possible
L2 Cache	50 MB	Reuse across blocks
Memory BW	3.35 TB/s	Coalesced access critical
Warp Size	32	All reductions use warp shuffles

Quick Comparison (H100 vs A100 vs T4)

Spec	H100	A100	T4
SMs	132	108	40
Memory BW	3.35 TB/s	2.0 TB/s	320 GB/s
Shared Mem/SM	192 KB	164 KB	64 KB
BF16 Support	Yes	Yes	No (FP16 only)
Compute Cap	sm_90	sm_80	sm_75

See detailed guides: H100 | A100 | T4

Core Kernel Patterns

Vectorized Memory Access (Critical for Performance)

BFloat16 vectorization using __nv_bfloat162:

// Load 2 bfloat16 elements at once (32-bit load)
const __nv_bfloat162* vec_input = reinterpret_cast<const __nv_bfloat162*>(row_input);

#pragma unroll 4
for (int i = tid; i < vec_hidden; i += stride) {
    __nv_bfloat162 v = vec_input[i];
    float v0 = __bfloat162float(v.x);
    float v1 = __bfloat162float(v.y);
    sum_sq += v0 * v0 + v1 * v1;
}

FP16 vectorization using __half2:

const __half2* vec_input = reinterpret_cast<const __half2*>(row_input);
__half2 v = vec_input[i];
float v0 = __half2float(v.x);
float v1 = __half2float(v.y);

FP32 vectorization using float4:

const float4* vec_input = reinterpret_cast<const float4*>(row_input);
float4 v = vec_input[i];
sum_sq += v.x * v.x + v.y * v.y + v.z * v.z + v.w * v.w;

Warp Shuffle Reductions

template <typename T>
__device__ __forceinline__ T warp_reduce_sum(T val) {
    #pragma unroll
    for (int offset = 16; offset > 0; offset >>= 1) {
        val += __shfl_xor_sync(0xffffffff, val, offset);
    }
    return val;
}

Block Sizes for Attention

• BLOCK_SIZE_M = 128, BLOCK_SIZE_N = 64, BLOCK_SIZE_K = 64
• NUM_WARPS = 8

Thread Configuration

For element-wise ops (RoPE, GEGLU):

constexpr int BLOCK_SIZE = 256;
int num_blocks = (total_elements + BLOCK_SIZE - 1) / BLOCK_SIZE;

For reduction ops (LayerNorm, RMSNorm) with vectorization:

// Divide by 2 for bf16/fp16 vectorized access
int threads = min(hidden_size / 2, MAX_THREADS);
threads = max(threads, WARP_SIZE);
threads = (threads + 32 - 1) / 32 * 32;  // Round to warp boundary

Supported Data Types

All kernels support three precision modes:

• __half (FP16) - Default for inference
• __nv_bfloat16 (BF16) - Preferred for training
• float (FP32) - Reference/debugging

Building Kernels

With Nix (Recommended)

nix run .#build-and-copy --max-jobs 2 --cores 8 -L

With pip/uv

uv pip install -e .

build.toml Configuration

[general]
name = "ltx_kernels"
backends = ["cuda"]

[kernel.your_kernel]
backend = "cuda"
src = ["kernel_src/your_kernel.cu"]
cuda-capabilities = ["9.0"]

Library Integration

HuggingFace Kernels Hub (get_kernel)

See huggingface-kernels-integration.md for the complete guide.

Load pre-compiled, optimized kernels directly from HuggingFace Hub without local compilation:

from kernels import get_kernel, has_kernel

# Check availability and load
if has_kernel("kernels-community/activation"):
    activation = get_kernel("kernels-community/activation", version=1)

    # Use the kernel
    x = torch.randn((4, 4), dtype=torch.float16, device="cuda")
    y = torch.empty_like(x)
    activation.gelu_fast(y, x)

Key functions:

• get_kernel(repo_id, version=None) - Download and load kernel from Hub
• has_kernel(repo_id) - Check if compatible build exists
• get_local_kernel(path) - Load from local directory (development)

Popular community kernels:

• kernels-community/activation - GELU, SiLU, etc.
• kernels-community/flash-attn - Flash Attention 2
• kernels-community/triton-layer-norm - LayerNorm, RMSNorm

Diffusers Integration (Video/Image Generation)

See diffusers-integration.md for the complete guide.

Transformers Integration (LLMs)

See transformers-integration.md for the complete guide.

Key differences from diffusers:

• Transformers RMSNorm always has weights (no elementwise_affine=False)
• Use 'RMSNorm' in class_name to match LlamaRMSNorm, MistralRMSNorm, etc.
• Check for variance_epsilon (LLaMA) or eps (others) for epsilon
• No set_processor() pattern - use Flash Attention 2 instead

Minimal transformers pattern:

from transformers import AutoModelForCausalLM
from ltx_kernels import rmsnorm

def patch_rmsnorm(model):
    for name, module in model.named_modules():
        if 'RMSNorm' in type(module).__name__:
            eps = getattr(module, 'variance_epsilon', None) or getattr(module, 'eps', 1e-6)
            def make_forward(mod, epsilon):
                def forward(x):
                    return rmsnorm(x, mod.weight, eps=epsilon)
                return forward
            module.forward = make_forward(module, eps)

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf", torch_dtype=torch.bfloat16)
patch_rmsnorm(model)

Diffusers Critical Pitfalls

1. RMSNorm Weight May Be None

LTX-Video uses elementwise_affine=False for some RMSNorm modules:

# Transformer blocks: NO WEIGHT
self.norm1 = RMSNorm(dim, elementwise_affine=False)

# Attention modules: HAS WEIGHT
self.norm_q = torch.nn.RMSNorm(..., elementwise_affine=True)

Solution: Handle both cases:

has_weight = hasattr(module, 'weight') and module.weight is not None
if has_weight:
    output = rmsnorm(x, module.weight, eps=eps)
else:
    weight = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
    output = rmsnorm(x, weight, eps=eps)

2. Diffusers RMSNorm != torch.nn.RMSNorm

# WRONG - misses diffusers RMSNorm
if isinstance(module, torch.nn.RMSNorm):

# CORRECT - catches all RMSNorm variants
if type(module).__name__ == 'RMSNorm':

3. LTX-Video Uses GELU, Not GEGLU

LTX-Video uses activation_fn="gelu-approximate". Don't patch GEGLU for LTX-Video.

4. Inject Kernels BEFORE CPU Offloading

pipe = LTXPipeline.from_pretrained(...)
pipe.to("cuda")
inject_optimized_kernels(pipe)  # BEFORE offloading
pipe.enable_model_cpu_offload()  # Now safe

Minimal Integration Pattern

from diffusers import LTXPipeline
from ltx_kernels import rmsnorm

def patch_rmsnorm_modules(model):
    """Patch all RMSNorm modules to use custom kernel."""
    for name, module in model.named_modules():
        if type(module).__name__ == 'RMSNorm':
            eps = getattr(module, 'eps', 1e-6)
            has_weight = hasattr(module, 'weight') and module.weight is not None

            if has_weight:
                def make_forward(mod, epsilon):
                    def forward(x):
                        return rmsnorm(x, mod.weight, eps=epsilon)
                    return forward
                module.forward = make_forward(module, eps)
            else:
                def make_forward(epsilon):
                    def forward(x):
                        w = torch.ones(x.shape[-1], device=x.device, dtype=x.dtype)
                        return rmsnorm(x, w, eps=epsilon)
                    return forward
                module.forward = make_forward(eps)

# Usage
pipe = LTXPipeline.from_pretrained("Lightricks/LTX-Video", torch_dtype=torch.bfloat16)
pipe.to("cuda")
patch_rmsnorm_modules(pipe.transformer)
pipe.enable_model_cpu_offload()

Kernel-Specific Guidelines

RMSNorm

• Input layout: [..., hidden_size]
• Epsilon default: 1e-6
• Weight may be None if elementwise_affine=False
• Vectorization: Use __nv_bfloat162 for BF16, __half2 for FP16, float4 for FP32
• Performance: 2.67x faster than PyTorch with vectorized implementation
• Bandwidth: Achieves ~38% of H100's 3.35 TB/s theoretical bandwidth

RoPE

• 1D: [batch, seq, heads, head_dim] - for text
• 3D: [batch, t*h*w, heads, head_dim] - for video
• LTX-Video computes its own RoPE via LTXVideoRotaryPosEmbed

GEGLU vs GELU

• GEGLU: Input [batch, seq, 2*hidden] -> Output [batch, seq, hidden]
• GELU: Standard activation
• LTX-Video uses GELU, NOT GEGLU

AdaLN

• Formula: norm(x) * weight * (1 + scale) + shift
• Used in DiT blocks for conditioning

Performance Profiling

# NVIDIA Nsight Systems
nsys profile -o profile python your_script.py

# NVIDIA Nsight Compute
ncu --set full -o metrics python your_script.py

Common Issues

See troubleshooting.md for all common issues and solutions.

Quick fixes:

• "NoneType has no attribute contiguous": RMSNorm weight is None, create ones
• isinstance() not matching: Use type(module).__name__ instead
• GEGLU not called: Model uses GELU, not GEGLU
• Patching doesn't persist: Inject before enable_model_cpu_offload()
• torch.compile fails with custom kernels: See below

torch.compile Compatibility

Custom CUDA kernels and torch.compile are mutually exclusive unless you register the kernel as a PyTorch custom op.

Error message:

torch._dynamo.exc.Unsupported: Attempted to call function marked as skipped

Workaround options:

1. Use --use-optimized-kernels without --compile (6% speedup)
2. Use --compile without custom kernels (34% speedup)
3. Register kernel as custom op (advanced, requires torch.library)

To register as custom op (for torch.compile compatibility):

import torch

@torch.library.custom_op("ltx_kernels::rmsnorm", mutates_args={"out"})
def rmsnorm(out: torch.Tensor, input: torch.Tensor, weight: torch.Tensor, eps: float) -> None:
    ops.rmsnorm_forward(out, input.contiguous(), weight.contiguous(), eps)

@rmsnorm.register_fake
def _(out, input, weight, eps):
    pass  # No shape changes

See Also

Scripts

• benchmark_example.py - Benchmarking script for comparing optimized vs baseline - START HERE
• ltx_kernel_injection_example.py - Minimal diffusers integration (~150 lines)
• transformers_injection_example.py - Minimal transformers/LLM integration (~120 lines)
• huggingface_kernels_example.py - HuggingFace Kernels Hub integration

Integration Guides

• huggingface-kernels-integration.md - HuggingFace Kernels Hub (get_kernel) - load pre-compiled kernels
• diffusers-integration.md - Complete diffusers pipeline integration
• transformers-integration.md - Complete transformers/LLM integration

GPU Optimization Guides

• h100-optimization-guide.md - H100 (Hopper, sm_90) deep dive
• a100-optimization-guide.md - A100 (Ampere, sm_80) deep dive
• t4-optimization-guide.md - T4 (Turing, sm_75) deep dive

Reference

• troubleshooting.md - Common issues and solutions
• kernel-templates.md - Complete kernel templates
• examples/ltx_video/ - Full LTX-Video example directory

External Resources

• HuggingFace Kernels Documentation
• HuggingFace Kernels GitHub
• Community Kernels on Hub