xpu-kernels

XPU Triton Kernels for Intel GPUs

This skill provides patterns and guidance for developing optimized Triton kernels targeting Intel XPU GPUs (Battlemage/Arc Pro B50). It integrates the Xe-Forge optimization framework — an LLM-driven loop that transforms PyTorch code into fast Triton kernels.

Quick Start

Optimize a Kernel (Xe-Forge Workflow)

The full optimization workflow analyzes a PyTorch baseline, generates Triton kernel variants in a branching trial tree, benchmarks each on XPU hardware, and finalizes the best result.

# 1. Analyze the baseline
python scripts/analyze_kernel.py test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py
 
# 2. Initialize trial tracking
python scripts/trial_manager.py init 70_Gemm_Sigmoid test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py
 
# 3. Validate a generated kernel (no GPU needed)
python scripts/validate_triton.py my_kernel.py
 
# 4. Benchmark correctness + performance
python scripts/benchmark.py test_kernels/70_Gemm_Sigmoid_Scaling_ResidualAdd_pytorch.py my_kernel.py
 
# 5. Profile with VTune (optional)
python scripts/xpu_profiler.py my_kernel.py
 
# 6. Finalize best trial
python scripts/trial_manager.py finalize 70_Gemm_Sigmoid optimized_triton.py

Supported Hardware

See the Intel XPU Backend for Triton for supported hardware.

When This Skill Applies

Use this skill when:

• Optimizing PyTorch operations into Triton kernels for Intel XPU
• Writing GEMM, fused kernels, reductions, or Flash Attention for Intel GPUs
• Running the Xe-Forge optimization loop (analyze → validate → benchmark → profile → finalize)
• Benchmarking kernel performance against PyTorch baseline on XPU

Xe-Forge Optimization Workflow

Transform PyTorch code into optimized Triton kernels for Intel XPU. Kernels must be numerically equivalent and faster than baseline.

Configuration — Read config.yaml first

At the start of every session, read scripts/config.yaml. It controls:

• max_trials — hard cap on optimization trials; always run all of them (use this instead of hardcoded "10")
• vtune_enabled — if false, skip ALL VTune profiling steps (Step 3.6 and profiler-related decisions)
• vtune_bin — path to the VTune binary (also settable via VTUNE_BIN env var)

Rules — Never Violate

1. ONLY create Triton kernel files (test_kernels/*_triton.py or trial files t<trial_id>.py).
2. NEVER create benchmark scripts, test scripts, helper utilities, or any other Python files.
3. NEVER write custom scripts to measure performance or test correctness — ONLY use scripts/benchmark.py.
4. If a tool fails, STOP and report the error. Do NOT work around it with custom scripts.
5. Generated kernels must be self-contained — all helper functions inline.
6. You MUST run all max_trials trials from config.yaml. Do NOT stop early due to plateau — LLM sampling can discover new ideas at any point. The only valid early stop is speedup > 5x.

Mandatory Tools

CRITICAL — Single-XPU serialization: There is only ONE XPU on this machine. You MUST NOT run multiple GPU workloads in parallel. benchmark.py and xpu_profiler.py must execute strictly one at a time — concurrent GPU jobs produce wrong results. CPU-only tools (analyze_kernel.py, validate_triton.py, trial_manager.py) are safe to parallelize with each other and with anything else.

Workflow Steps

Step 1: Analyze

• Read the baseline source file. Identify shapes, dtypes, operations, fusion opportunities.
• If baseline is PyTorch: run python scripts/analyze_kernel.py <pytorch_file>.
• If baseline is Triton (--triton-baseline): skip analyze_kernel.py (it only supports PyTorch). Read the Triton file directly.
• Read relevant knowledge base files: start with references/correctness.yaml and references/xpu_optimizations.yaml.
• Read references/implementation_reference.md for templates and the Model class pattern.

Step 2: Initialize

python scripts/trial_manager.py init <kernel_name> <baseline_file> [--triton-baseline]

Step 3: Trial Loop (always run all max_trials from config.yaml)

For each trial:

1. Write kernel — start from templates or modify previous trial. See references/implementation_reference.md.
2. Validate — python scripts/validate_triton.py <triton_file> (fix until passing; doesn't count as a trial).
3. Save — python scripts/trial_manager.py save <kernel_name> <triton_file> --parent <parent_id> --strategy "description". Omit --parent for the first trial (t0).
4. Benchmark (MANDATORY every trial):
   • Trial t0: python scripts/benchmark.py <baseline_file> <triton_file> [--triton-baseline] (measures both baseline and triton).
   • Trials t1+: Get cached baseline via python scripts/trial_manager.py baseline-us <kernel_name>, then run python scripts/benchmark.py <baseline_file> <triton_file> [--triton-baseline] --baseline-us <cached_value> (skips baseline perf, saves time).
   • After finalize: Re-run benchmark.py without --baseline-us for final accurate comparison.
5. Record — python scripts/trial_manager.py result <kernel_name> <trial_id> --validation pass --correctness <pass|fail> --speedup <float> --baseline_us <float> --triton_us <float> (runtimes from benchmark output).
6. Profile (MANDATORY after t1, if vtune_enabled is true in config.yaml) — Run python scripts/xpu_profiler.py <triton_file> after your first benchmarked trial. Use its output to guide subsequent trial strategies. Run again if speedup plateaus after 2+ additional trials. Skip this step entirely if vtune_enabled is false.
7. Decide next action (use profiler output from step 6 to inform decisions):
   • Speedup > 5x → stop (excellent), finalize
   • Speedup improved → continue on this branch, try next optimization level
   • Speedup regressed → branch back to best trial, try different strategy
   • Correctness failed → fix on same branch
   • Profiler says low occupancy (if vtune_enabled) → increase tile sizes, check references/xpu_optimizations.yaml
   • Profiler says overhead kernels dominate (if vtune_enabled) → pre-pack to bf16, see references/optimization_levels.yaml
   • Plateau → do NOT stop. Try a fundamentally different approach (different algorithm, tiling, fusion strategy). LLM sampling can discover new ideas.
   • See references/optimization_strategies.md for the full "try harder" decision tree

Step 4: Finalize

python scripts/trial_manager.py finalize <kernel_name> <name>_triton.py

Reference Docs — Read During Step 1

Existing Baselines Are Naive

The test_kernels/*.py Triton files (non-pytorch) are unoptimized baselines. They use manual pointer arithmetic, lack autotune, and miss XPU optimizations. Do NOT copy their patterns. Use references/implementation_reference.md instead.

Core XPU Kernel Patterns

Tensor Descriptors (Preferred on XPU)

Tensor descriptors produce better address generation and memory access codegen than block pointers on Intel XPU.

desc = tl.make_tensor_descriptor(
    base=ptr, shape=[M, N],
    strides=[stride_m, stride_n],
    block_shape=[BLOCK_M, BLOCK_N],
)
block = tl.load(desc, [pid_m, pid_n], boundary_check=(0, 1))

GRF Mode '256'

Use the large register file for compute-heavy kernels:

@triton.autotune(
    configs=[triton.Config({'BLOCK_M': 256, 'BLOCK_N': 256}, num_warps=32)],
    key=['M', 'N', 'K'],
)
@triton.jit(launch_metadata=lambda *args, **kwargs: {'grf_mode': '256'})
def kernel(...):
    ...

Register-Spill-Aware Autotune Pruning

For autotuned XPU kernels, prune configs that are likely to spill even after large-GRF recompilation. This avoids slow double-compile/benchmark cycles where the Intel backend reports spills, recompiles in large GRF mode, and still spills.

Use an early_config_prune that estimates per-thread GRF pressure from the live accumulator and operand tiles:

regs ~= (accumulators * BM * BN + stages * (BM * BK + weight_tiles * BN * BK))
        / (num_warps * warp_size)

Guidelines:

• Apply this only on XPU; leave other backends' configs unchanged.
• Read BM, BN, and BK from either autotune config kwargs or launch args.
• Count accumulator tiles explicitly: gate/up fused kernels often have 2 accumulators, down/simple GEMM usually has 1.
• Count weight tiles explicitly: gate/up fused kernels often load 2 weight tiles, down/simple GEMM usually loads 1.
• Use num_stages for pipelined MMA paths; use 1 for scalar/non-pipelined paths.
• Keep a never-empty fallback, such as the config with the lowest estimated register pressure.
• Treat the model as a coarse filter for catastrophic spillers, not a perfect predictor; leave borderline configs for real benchmarking.

This complements grf_mode='256': large GRF increases the register budget, while pruning removes configs that are still too register-heavy to be useful.

For an implementation pattern, see references/xpu_optimizations.yaml (xpu_register_spill_autotune_pruning).

Tile Swizzling

Use 1D grid with GROUP_SIZE_M for L2 locality:

grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
# Inside kernel:
pid = tl.program_id(0)
num_pid_n = tl.cdiv(N, BLOCK_N)
group_id = pid // (GROUP_SIZE_M * num_pid_n)

bf16 Inputs with fp32 Accumulation

a = tl.load(a_desc, [pid_m, k], boundary_check=(0, 1))
b = tl.load(b_desc, [k, pid_n], boundary_check=(0, 1))
acc += tl.dot(a.to(tl.bfloat16), b.to(tl.bfloat16), acc=acc)  # fp32 accumulator

Critical XPU Constraints

• NO default values for @triton.autotune meta-parameters in kernel signature
• 1D grid when using tile swizzling (GROUP_SIZE_M)
• boundary_check uses dimension indices (0, 1), not booleans
• Cast batch indices to int64 before stride multiplication
• Prefer tensor descriptors over block pointers for all new XPU kernels
• Do NOT mix block pointer and tensor descriptor APIs on same operation
• Pre-zero output buffers when using atomic accumulation
• Model class must be compatible with ai-bench (nn.Module with nn.Linear)
• Match get_inputs(), get_init_inputs(), and module-level constants from *_pytorch.py

Full constraint list: correctness.yaml

Performance Results

Measured on Intel Battlemage G21 / Arc Pro B50 (128 XVEs). All runtimes are median of benchmark trials.

KernelBench Level 2 — Fused Kernels (bf16)

Speedup is vs. PyTorch eager baseline. Includes GEMM+Sigmoid+Scaling, GEMM+GELU+Softmax, Conv+BatchNorm+ReLU, and other fused patterns.

Flash Attention Forward (fp16)

Baseline is the flash attention kernel from the Intel XPU Triton backend; speedup is vs. that kernel across multiple sequence lengths.

Full results: see the Xe-Forge repository.

Common Issues

Project Structure

xpu-kernels/
├── SKILL.md                                    # This file (skill definition + workflow)
├── manifest.txt                                # Files included in this skill
│
├── scripts/                                    # Standalone CLI tools
│   ├── analyze_kernel.py                       # PyTorch → operations, shapes, fusion opportunities
│   ├── validate_triton.py                      # Syntax + constraint checks
│   ├── benchmark.py                            # Correctness + performance via ai-bench
│   ├── trial_manager.py                        # Tree-structured trial management
│   ├── xpu_profiler.py                         # VTune GPU hardware counters
│   ├── config.py                               # Shared configuration loader
│   ├── config.yaml                             # Session config (max_trials, vtune)
│   └── requirements.txt                        # Python dependencies
│
└── references/                                 # Knowledge base + integration guides
    ├── correctness.yaml                        # Hard constraints for XPU Triton
    ├── xpu_optimizations.yaml                  # Tensor descriptors, GRF, swizzling
    ├── implementation_reference.md             # Code templates, Model class pattern
    ├── implementation_reference.md             # Code templates, Model class pattern
    ├── optimization_strategies.md              # Strategy reference + "try harder" tree
    ├── optimization_levels.yaml                # Progressive L1-L5 optimization levels
    ├── workflow_details.md                     # Detailed workflow and decision tree
    ├── fusion_patterns.yaml                    # When to fuse vs split
    ├── memory_patterns.yaml                    # Access patterns and coalescing
    ├── dtype_optimizations.yaml                # Mixed precision choices
    ├── persistent_kernel_patterns.yaml         # Stream K and persistent kernels
    ├── kernel-templates.md                     # Triton kernel templates for XPU
    └── kernelbench-classification.md           # KernelBench operator taxonomy

See Also

Xe-Forge Tools

• analyze_kernel.py — Static analysis of PyTorch reference
• validate_triton.py — Pre-benchmark constraint checks
• benchmark.py — Correctness + performance measurement
• xpu_profiler.py — VTune GPU hardware counters
• trial_manager.py — Branching trial tree management

XPU Optimization References

• correctness.yaml — Critical constraints
• xpu_optimizations.yaml — Tensor descriptors, GRF, swizzling
• optimization_strategies.md — Strategy reference
• optimization_levels.yaml — Progressive L1-L5 levels
• implementation_reference.md — Code templates

Other References

• kernelbench-classification.md — KernelBench operator taxonomy

External Resources

• Xe-Forge Repository
• AI-Bench — Benchmark harness for correctness + performance
• Intel XPU Backend for Triton
• Triton Language Guide