CuTe DSL

Requirements

Workflows

Workflow 0: Starting from Examples (Recommended)

For any non-trivial kernel (GEMM, attention, pipelined, fused ops), start by finding the most similar existing example to use as a starting point — study its structure, then rework it for your use case. Do not copy examples verbatim; they target specific dtypes, architectures, and problem shapes that likely differ.

1. Pick the closest example from the index below. Prefer examples matching the target GPU architecture (check with torch.cuda.get_device_capability()) when the operation is similar.

   Fetch via web_fetch with base URL https://raw.githubusercontent.com/NVIDIA/cutlass/main/examples/python/CuTeDSL

   Operation	Arch	Example path (append to base URL)
   Element-wise add	SM80	ampere/elementwise_add.py
   Element-wise + autotune	SM80	ampere/elementwise_add_autotune.py
   Element-wise apply	SM80	ampere/elementwise_apply.py
   SGEMM (scalar)	SM80	ampere/sgemm.py
   Tensor-core GEMM	SM80	ampere/tensorop_gemm.py
   Flash Attention v2	SM80	ampere/flash_attention_v2.py
   HSTU Attention	SM80	ampere/hstu_attention.py
   Shared memory allocator	SM80	ampere/smem_allocator.py
   CTA norm (LayerNorm)	SM90	hopper/cta_norm.py
   Dense GEMM	SM90	hopper/dense_gemm.py
   Dense GEMM persistent	SM90	hopper/dense_gemm_persistent.py
   Flash MHA	SM90	hopper/fmha.py
   Dense GEMM	SM100	blackwell/dense_gemm.py
   Dense GEMM persistent	SM100	blackwell/dense_gemm_persistent.py
   Dense GEMM + alpha/beta	SM100	blackwell/dense_gemm_alpha_beta_persistent.py
   RMSNorm	SM100	blackwell/rmsnorm.py
   Reduce	SM100	blackwell/reduce.py
   Flash MHA	SM100	blackwell/fmha.py
   Grouped GEMM	SM100	blackwell/grouped_gemm.py
   Mamba2 SSD	SM100	blackwell/mamba2_ssd/
   GEMM tutorial (notebook)	SM100	notebooks/tour_to_sol_gemm.ipynb

   Example: To fetch the Hopper dense GEMM:

   web_fetch https://raw.githubusercontent.com/NVIDIA/cutlass/main/examples/python/CuTeDSL/hopper/dense_gemm.py
   

2. Read reference materials first — before diving into example code, read the relevant references/ docs to understand the patterns and APIs:

   • For GEMM: references/patterns-gemm.md (3-level tiling, epilogue fusion, cute.compile with mark_layout_dynamic, shared memory layouts)
   • For reductions: references/patterns-reduction.md (warp reductions, cute.compile cache pattern)
   • For element-wise: references/patterns-elementwise.md (variations A–E)
   • Always: references/api-arch.md (available APIs, arch-specific caveats)

   This gives you the conceptual foundation so you can rework the example intelligently rather than trying to copy-paste complex pipelines.

3. Fetch and study the example source — read for structure, not to copy:

   • Identify: decorators, tiling strategy, shared memory usage, mainloop flow
   • Note which dtype/arch it targets (many examples are fp16/bf16-specific)
   • Check if it uses APIs tied to a specific arch (TMA → SM90+, tcgen05 → SM100)

4. Rework for the user's workload (do not copy-paste):

   • Change shapes, data types, tile sizes to match requirements
   • Replace compute logic (epilogue, activation fusion) as needed
   • If dtype differs (e.g., example is fp16, need fp32), expect vectorization and layout changes — the scalar-loop patterns in references/ may be a better starting point than adapting a vectorized example
   • Runtime wrapper must be lightweight: kernel_fn() should only call from_dlpack() + the compiled kernel. Never allocate intermediate tensors, copy data, or re-compile per call — these belong in one-time setup
   • Apply optimizations from this skill's reference docs

   ⛔ Blackwell/Hopper GEMM + extra tensors — STOP: If the target GPU is SM90+ (Hopper/Blackwell) and the GEMM requires extra tensors beyond A, B, C in the epilogue (e.g., bias vector, activation inputs), do not attempt it. These examples use TMA descriptors for all data movement — adding tensors requires modifying TMA descriptor setup, which is prohibitively complex. Instead, tell the user this limitation and suggest a two-kernel approach: run the GEMM kernel as-is, then apply bias + activation in a separate element-wise kernel (Workflow 1). Plain GEMM (just A×B→C with scalar alpha/beta) on Hopper/Blackwell is fine.

5. Validate and benchmark using companion scripts:

   python scripts/verify_kernel.py kernel.py --rtol 1e-3 --atol 1e-3
   python scripts/benchmark_kernel.py kernel.py
   

   The kernel file must export kernel_fn, reference_fn, and get_inputs().

When to skip examples: Pure element-wise operations (Workflow 1) have complete patterns in references/patterns-elementwise.md — no need to fetch external examples.

Reduction kernels (softmax, layernorm, RMSNorm): Use references/patterns-reduction.md which provides complete, proven patterns for float32 reductions using scalar loops + butterfly shuffle + shared memory.

Workflow 1: Element-wise Kernel

For unary/binary/in-place operations that map inputs to outputs 1:1.

1. Determine kernel structure: inputs/outputs count, tensor rank, target arch
2. Select pattern from references/patterns-elementwise.md (Variations A–E)
3. Write kernel applying all four invariant principles:
   • P1: from_dlpack(tensor, assumed_align=16) for vector loads
   • P2: Derive vec_size from element_type.width
   • P3: cute.zipped_divide(mA, tiler) for coalesced access
   • P4: cutlass.dynamic_expr(thread_idx < total) for bounds
4. Critical rules: No early return, no a * 2 (use a + a), no cute.math.sigmoid
5. Pre-compile with cute.compile(): Always pre-compile the kernel once using cute.compile() so that kernel_fn calls the compiled object, not @cute.jit directly. Without pre-compilation, every call recompiles (~20-50ms overhead). Use .mark_layout_dynamic() so a single compiled kernel handles arbitrary input shapes without recompilation:

   # Compile once with dynamic layouts — works for any shape
   fake_x = from_dlpack(torch.empty(1, 1, dtype=torch.float16, device="cuda"),
                         assumed_align=16).mark_layout_dynamic()
   fake_out = from_dlpack(torch.empty(1, 1, dtype=torch.float16, device="cuda"),
                           assumed_align=16).mark_layout_dynamic()
   compiled_kernel = cute.compile(host_fn, fake_x, fake_out)
   
   def kernel_fn(x):
       out = torch.empty_like(x)
       compiled_kernel(from_dlpack(x, assumed_align=16).mark_layout_dynamic(),
                       from_dlpack(out, assumed_align=16).mark_layout_dynamic())
       return out
   

6. Verify correctness using companion script:

   python scripts/verify_kernel.py kernel.py --rtol 1e-3 --atol 1e-3
   

   The kernel file must export kernel_fn, reference_fn, and get_inputs().
7. Benchmark using companion script:

   python scripts/benchmark_kernel.py kernel.py
   

Workflow 2: GEMM Kernel

For matrix multiplication with tiling, shared memory, and tensor cores.

1. Define problem: shapes (M, N, K), data types, target architecture
2. Choose tiling: CTA tile (bM, bN, bK), pipeline stages, cluster shape
3. Three-level partitioning (see references/patterns-gemm.md):
   • Level 1: CTA tiling with local_tile()
   • Level 2: Copy partitioning (global → shared) with TiledCopy
   • Level 3: Compute partitioning (shared → register) with TiledMMA
4. Shared memory: Use swizzled layouts (make_smem_layout_atom) to avoid bank conflicts
5. Mainloop: K-tile loop with copy → sync → MMA → sync
6. Pipeline: Use PipelineTmaAsync (Hopper) or PipelineTmaUmma (Blackwell). ⚠️ TMA-based pipelines manage data movement via TMA descriptors — adding extra tensors (bias, activation inputs) to the epilogue requires modifying descriptor setup, which is prohibitively complex. See the stop condition in Workflow 0 step 4.
7. Epilogue: Predicated store with alpha/beta scaling
8. Pre-compile with cute.compile(): Always pre-compile the GEMM kernel so kernel_fn calls the compiled object, not @cute.jit directly. Without pre-compilation, every call recompiles (~20-50ms overhead).
9. Autotune: Search over tile sizes, cluster shapes, pipeline depths

Workflow 3: Framework Integration

For wrapping CuTe DSL kernels as PyTorch/JAX custom operators.

1. Write kernel using Workflow 1 or 2
2. Create wrapper: Accept torch.Tensor, convert via from_dlpack, call host fn
3. For production: Compile with TVM FFI for zero-overhead tensor passing:

   compiled = cute.compile(host_fn, *fake_tensors, options="--enable-tvm-ffi")
   compiled(torch_a, torch_b)  # Direct torch.Tensor, no from_dlpack
   

4. For deployment: Use AOT compilation → export to .o → load at runtime

Workflow 4: Debugging & Profiling

1. Set environment: CUTE_DSL_PRINT_IR=1, CUTE_DSL_KEEP_PTX=1
2. Use cute.printf() for runtime values (not Python print)
3. Inspect generated code: compiled.__ptx__, compiled.__mlir__
4. Profile: Enable CUTE_DSL_LINEINFO=1, use Nsight Compute/Systems
5. Debug memory: Run with compute-sanitizer python script.py

Output Formats

A typical CuTe DSL kernel project:

kernel_dir/
  kernel.py          # @cute.kernel + @cute.jit functions
  test_kernel.py     # Correctness test vs PyTorch reference
  bench_kernel.py    # Benchmark with cute.compile() setup

Success indicators:

• Correctness test passes (torch.testing.assert_close)
• Nsight shows vector loads (LDG.128/LDG.256), not scalar loads
• For GEMM: tensor core utilization > 80% in Nsight Compute

Companion Script Contract

Kernel files used with scripts/verify_kernel.py and scripts/benchmark_kernel.py must export three names:

• kernel_fn(*inputs) — the CuTe DSL kernel wrapper (calls cute.compile + runs kernel)
• reference_fn(*inputs) — PyTorch reference implementation (same signature)
• get_inputs() — returns a list of CUDA tensors for testing

# Example kernel.py contract
import torch
import cutlass.cute as cute
from cutlass.cute.runtime import from_dlpack

def kernel_fn(x):
    out = torch.empty_like(x)
    # ... call compiled cute kernel ...
    return out

def reference_fn(x):
    return torch.nn.functional.gelu(x)

def get_inputs():
    return [torch.randn(1024, 512, dtype=torch.float16, device="cuda")]

Examples

Example: 2D Unary Element-wise (ReLU)

import torch, cutlass, cutlass.cute as cute
from cutlass.cute.runtime import from_dlpack

@cute.kernel
def relu_kernel(gA: cute.Tensor, gC: cute.Tensor):
    tidx, _, _ = cute.arch.thread_idx()
    bidx, _, _ = cute.arch.block_idx()
    bdim, _, _ = cute.arch.block_dim()
    idx = bidx * bdim + tidx
    m, n = gA.shape[1]
    total = m * n
    if cutlass.dynamic_expr(idx < total):
        a = gA[(None, (idx // n, idx % n))].load()
        gC[(None, (idx // n, idx % n))] = cute.where(a > 0, a, 0)

@cute.jit
def relu_host(mA: cute.Tensor, mC: cute.Tensor):
    vec = 16 // (mA.element_type.width // 8)
    gA = cute.zipped_divide(mA, (1, vec))
    gC = cute.zipped_divide(mC, (1, vec))
    T = 256
    N = cute.size(gA.shape[1])
    relu_kernel(gA, gC).launch(grid=((N+T-1)//T,1,1), block=(T,1,1))

x = torch.randn(1024, 512, dtype=torch.float16, device="cuda")
out = torch.empty_like(x)
relu_host(from_dlpack(x, assumed_align=16), from_dlpack(out, assumed_align=16))

Error Handling

See references/troubleshooting.md for the full error table and limitations.

Debugging rule: Never delete kernel.py during debugging. Use backup_file to save a checkpoint, then edit_file to iterate. If stuck, revert_file to restore the backup. A partially-working kernel is always better than no kernel.

Finding More Information

Tier 1: This File (SKILL.md)

Workflows above cover element-wise kernels, GEMM, framework integration, and debugging. Search this file first for procedural questions.

Tier 2: references/ Directory

Grep for keywords across references/. Headers are grep-friendly.

How to search: Grep for your keyword across references/. Read only the file and section that Grep points to.

Tier 3: Original Documentation

If Tiers 1–2 don't answer, consult the source:

• Web: https://docs.nvidia.com/cutlass/latest/
• GitHub: https://github.com/NVIDIA/cutlass
• Fetch specific doc pages or search for "CUTLASS CuTe DSL <topic>"
• Consider distilling the answer back into references/