HIGH-PERFORMANCE
GPU KERNEL FUSION
FOR LLM INFERENCE
SRAM-aware, block-tiled OpenAI Triton kernels eliminating High Bandwidth Memory (HBM) roundtrips. Built with real PyTorch autograd integration, stride-safe pointer math, and dynamic zero-crash fallback routing.
Memory Hierarchy & The Fusion Advantage
Why kernel fusion matters: On-chip registers and SRAM provide ~19 TB/s bandwidth at ~1-20 cycle latency, whereas global HBM provides only ~2 TB/s at 400-800 cycles latency.
The Triton Compilation Pipeline
Python AST & Block IR
Translates high-level tensor slices into Triton Block-level IR (TTIR).
_LLVM IR & Memory Coalescing
Auto-coalesces global memory into 128-byte transactions, bank-conflict-free shared memory.
PTX Assembly & Register Allocation
Low-level NVIDIA PTX scheduling, warp shuffle, register alloc ≤255 regs/thread.
Optimized GPU Kernel
Final kernel runs entirely in on-chip SRAM — zero HBM roundtrips for intermediates.
FUSED GPU OPERATORS (TRITON JIT)
Inspect Triton JIT kernels alongside naive PyTorch eager baselines. All kernels feature hardware-aware fallback routing and contiguity checking.
Fused RMSNorm
Memory-Bound · 8× FasterSingle-pass row normalization keeping intermediate reductions in registers/SRAM. Includes full custom backward pass via torch.autograd.Function and automatic contiguous stride memory safety.
Fused SwiGLU
Autotuned · 1.75× FasterGated activation fusing linear projections, the SiLU gate, and element-wise multiply into one kernel. Autotuned across 4 block configurations at runtime with multi-block tiling.
Tiled FlashAttention-2
O(N) Memory · 3.3× FasterBlock-tiled forward attention using online softmax streaming — no N×N matrix is ever materialized in HBM. Sequence length independence at inference time.
Fused RMSNorm
Memory-Bound · 8× FasterSingle-pass row normalization keeping intermediate reductions in registers/SRAM. Includes full custom backward pass via torch.autograd.Function and automatic contiguous stride memory safety.
RMSNorm(x) = x / √(Σxᵢ²/d + ε) ⊙ γ@triton_route(fallback_fn=pytorch_rmsnorm)
def fused_rmsnorm(x: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
orig_shape = x.shape
N = orig_shape[-1]
# Enforce contiguous row strides for 3D/non-contiguous tensors
x_contig = x.contiguous().view(-1, N)
weight_contig = weight.contiguous()
M = x_contig.shape[0]
y = torch.empty_like(x_contig)
BLOCK_SIZE = min(triton.next_power_of_2(N), 4096)
_rmsnorm_fwd_kernel[(M,)](
x_contig, y, weight_contig, None,
x_contig.stride(0), y.stride(0),
N, eps, BLOCK_SIZE=BLOCK_SIZE
)
return y.view(orig_shape)EMPIRICAL BENCHMARKS & SPEEDUPS
Standard PyTorch Eager (Float32) vs. TritonForge Fused Kernels
| Kernel & Dimension | PyTorch Eager | Triton Fused | Speedup | HBM Accesses | Status |
|---|---|---|---|---|---|
| RMSNorm N=512 | 0.74 ms | 0.09 ms | 8.2× | 3 passes → 1 pass | Pass |
| RMSNorm N=2048 | 3.59 ms | 0.44 ms | 8.1× | 3 passes → 1 pass | Pass |
| RMSNorm N=8192 | 13.76 ms | 1.71 ms | 8× | 3 passes → 1 pass | Pass |
| SwiGLU N=512 | 1.26 ms | 0.73 ms | 1.7× | 2 buffers → 0 | Pass |
| SwiGLU N=4096 | 13.24 ms | 7.84 ms | 1.7× | 2 buffers → 0 | Pass |
| Attention N=1024 | 14.72 ms | 5.38 ms | 2.7× | O(N²) → O(N) | Pass |
| Attention N=2048 | 61.35 ms | 18.4 ms | 3.3× | O(N²) → O(N) | Pass |
TritonForge: Automated GPU Kernel Optimization and SRAM-Aware Fusion for Large Language Model Inference
Large language model (LLM) inference is predominantly bounded by GPU High Bandwidth Memory (HBM) latency rather than peak arithmetic throughput. Memory-bound layers — specifically Root Mean Square Normalization (RMSNorm), gated activation functions (SwiGLU), and scaled dot-product attention — suffer from repeated memory transfers between HBM and on-chip Streaming Multiprocessors (SMs), yielding low hardware arithmetic intensity. We present TritonForge, an automated GPU kernel optimization and profiling workstation built on OpenAI Triton. TritonForge implements custom block-tiled kernels that fuse multiple sequential tensor operations into single-pass kernels executed entirely within on-chip Static RAM (SRAM) and register files. Across benchmark evaluations on NVIDIA Tesla T4 and A100 architectures, TritonForge achieves 4.47× speedup on RMSNorm, 1.75× speedup on SwiGLU, and 3.3× speedup on FlashAttention-2 with a 95.3% reduction in peak attention memory consumption. We demonstrate full numerical parity (atol=1e-5) against PyTorch eager baselines, implement custom backward passes via torch.autograd.Function, and present a dynamic 4-layer fallback routing architecture guaranteeing high availability across diverse deployment targets.
Complete Research Manuscript Sections
Systems Engineering Deep Dives
Why Your Normalization Layer is Slowing Down LLM Inference
RMSNorm only performs 2 arithmetic operations per byte of memory read from HBM. How kernel fusion eliminates 3 round trips to main memory.
Demystifying FlashAttention-2: Online Softmax in SRAM
The math behind Milakov-Gimelshein online softmax and why you never need to write an N×N attention matrix to global GPU memory.
Writing Production PyTorch Autograd Functions in OpenAI Triton
Step-by-step derivation of analytical backward passes and writing torch.autograd.Function wrappers that pass gradcheck.
4-LAYER HARDWARE-AWARE DYNAMIC FALLBACK ROUTER
In production environments (e.g. CI/CD runners, Mac/CPU instances, cloud edge), GPU hardware or CUDA drivers may be unavailable. TritonForge provides seamless `@triton_route` wrapping:
HAS_TRITON availability test
Safely traps environments without Triton installed and routes to PyTorch reference.
torch.cuda.is_available() & device placement
Confirms GPU runtime presence and ensures all input tensors reside in CUDA VRAM.
shape_validator(args, kwargs)
Validates matrix dimensions and alignment; routes non-compliant shapes safely.
CompilationError & Hardware Trap
Catches PTX syntax faults or driver errors and executes fallback transparently.
INSTALLATION & INTEGRATION MANUAL
Install TritonForge via pip from GitHub or local source:
# Clone repository git clone https://github.com/Gaurav711cgu/TritonForge.git cd TritonForge # Install dependencies with GPU support pip install -e ".[gpu]" # Run verification test suite (CPU fallback + GPU) pytest tests/ -v