Triton ile Custom GPU Kernels: Softmax, Matmul, FlashAttention Mini Sıfırdan

Ders Haritası#

1. Triton nedir, niye?
2. GPU programming model basics
3. İlk Triton kernel: vector add
4. Memory access patterns: coalesced loads
5. Softmax kernel sıfırdan
6. Matmul tiling matematiği
7. Autotune ile hyperparameter search
8. FlashAttention block-wise mini implementation
9. PyTorch entegrasyonu: torch.autograd.Function ile
10. Benchmark ve profiling
11. Production patterns
12. Modül 37'ye köprü

1. Triton Nedir, Niye?#

Niye CUDA C++ değil?#

• Düşük seviye memory management (shared, register, global)
• Thread block / warp / tile mathematics manuel
• Compilation NVCC ile yavaş
• Debugging zor
• Verification (correctness) çok zaman alıyor

Triton avantajları#

1. Python syntax: işin yarısı zaten Python-friendly
2. Auto-tuning: block size, num warps Triton seçiyor
3. JIT compilation: runtime'da Python'dan PTX'e
4. Pythonic memory model: pointer + offset (CUDA pointer arithmetic kadar değil)
5. Performance: hand-tuned CUDA'ya %80-95 yakın

Adoption#

• FlashAttention (Dao 2022): Triton ile yazıldı
• vLLM PagedAttention: Triton
• PyTorch torch.compile: Inductor → Triton (default backend)
• OpenAI internal: birçok production kernel

Türk perspektifi#

2. GPU Programming Model Basics#

Hierarchy#

Grid (kernel launch)
└── Blocks (paralel)
    └── Warps (32 thread)
        └── Threads (paralel SM içinde)

Memory hierarchy#

Global memory:    GBs, slow (~500 GB/s)
L2 cache:         MBs, faster
Shared memory:    KBs per block, fast (~10 TB/s effective)
Registers:        per thread, fastest

Triton abstraction#

• tl.program_id(axis)
  : bu block'un grid'deki ID'si
• tl.arange(0, BLOCK)
  : block içinde paralel offset'ler
• Tüm operasyonlar block-level, thread'leri Triton handle ediyor

Block size tuning#

• Küçük (128): fazla overhead, az parallelism
• Büyük (4096): registers/shared memory dolar, occupancy düşer
• Optimal genelde 1024-2048 GPU'ya göre

3. İlk Triton Kernel: Vector Add#

import torch
import triton
import triton.language as tl

@triton.jit
def add_kernel(
    x_ptr,    # pointer to x
    y_ptr,    # pointer to y
    out_ptr,  # pointer to output
    n,        # toplam element sayısı
    BLOCK_SIZE: tl.constexpr,  # compile-time constant
):
    # Bu block'un ID'si
    pid = tl.program_id(axis=0)
    # Block içindeki offset'ler
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    # Boundary mask (son block için)
    mask = offsets < n
    # Load
    x = tl.load(x_ptr + offsets, mask=mask)
    y = tl.load(y_ptr + offsets, mask=mask)
    # Compute
    out = x + y
    # Store
    tl.store(out_ptr + offsets, out, mask=mask)


def add(x, y):
    out = torch.empty_like(x)
    n = x.numel()
    # Grid: kaç block lazım?
    grid = lambda meta: (triton.cdiv(n, meta["BLOCK_SIZE"]),)
    add_kernel[grid](x, y, out, n, BLOCK_SIZE=1024)
    return out

# Test
x = torch.randn(10000, device="cuda")
y = torch.randn(10000, device="cuda")
out = add(x, y)
print(torch.allclose(out, x + y))  # True

Analiz#

• @triton.jit
  : function compile edilecek
• tl.constexpr
  : compile-time'da bilinen sabit (block size)
• tl.program_id
  : hangi block çalışıyor
• tl.load/store
  : memory operations with mask
• Grid lambda: cdiv(n, BLOCK_SIZE) block'a bölünür

Performance#

4. Memory Access Patterns — Coalesced Loads#

Coalesced vs Non-coalesced#

Coalesced (good):
Thread 0 → addr 0
Thread 1 → addr 1
Thread 2 → addr 2
...

Non-coalesced (bad):
Thread 0 → addr 0
Thread 1 → addr 32
Thread 2 → addr 64
...

Triton'da pattern#

offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
x = tl.load(x_ptr + offsets, mask=mask)

2D access#

# Row-major matris, M × N
row_offsets = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
col_offsets = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
# 2D ptr
mat_ptr = X_ptr + row_offsets[:, None] * N + col_offsets[None, :]
data = tl.load(mat_ptr, mask=...)

Modern Triton: block pointer#

block_ptr = tl.make_block_ptr(
    base=X_ptr, shape=(M, N), strides=(N, 1),
    offsets=(pid_m * BLOCK_M, pid_n * BLOCK_N),
    block_shape=(BLOCK_M, BLOCK_N), order=(1, 0),
)
data = tl.load(block_ptr, boundary_check=(0, 1))

5. Softmax Kernel Sıfırdan#

@triton.jit
def softmax_kernel(
    output_ptr, input_ptr,
    row_stride,  # bytes between rows
    n_cols,      # number of columns
    BLOCK_SIZE: tl.constexpr,
):
    # Her block bir satır işler
    row_idx = tl.program_id(0)
    row_start_ptr = input_ptr + row_idx * row_stride
    col_offsets = tl.arange(0, BLOCK_SIZE)
    input_ptrs = row_start_ptr + col_offsets
    mask = col_offsets < n_cols

    # Load row
    row = tl.load(input_ptrs, mask=mask, other=-float('inf'))

    # Numerical stability: subtract max
    row_max = tl.max(row, axis=0)
    row_minus_max = row - row_max

    # Exp + sum
    numerator = tl.exp(row_minus_max)
    denominator = tl.sum(numerator, axis=0)

    # Normalize
    softmax_output = numerator / denominator

    # Store
    output_row_start_ptr = output_ptr + row_idx * row_stride
    output_ptrs = output_row_start_ptr + col_offsets
    tl.store(output_ptrs, softmax_output, mask=mask)


def softmax(x):
    n_rows, n_cols = x.shape
    BLOCK_SIZE = triton.next_power_of_2(n_cols)
    output = torch.empty_like(x)
    softmax_kernel[(n_rows,)](
        output, x,
        x.stride(0),
        n_cols,
        BLOCK_SIZE=BLOCK_SIZE,
    )
    return output


# Test
x = torch.randn(1000, 512, device="cuda")
out = softmax(x)
print(torch.allclose(out, torch.softmax(x, dim=-1), atol=1e-5))  # True

Niye PyTorch'tan hızlı?#

1. exp(x - max(x)) — yeni tensor, full memory write
2. sum(exp_x) — reduction, başka tensor
3. div(exp_x, sum) — division, başka tensor

1. Load row
2. Compute (max, exp, sum, divide) — registers
3. Store

Profil#

6. Matmul Tiling Matematiği#

Naive#

for i in range(M):
    for j in range(N):
        for k in range(K):
            C[i, j] += A[i, k] * B[k, j]

Tiling — block-level paralel#

A_tile = A[i*BLOCK_M:(i+1)*BLOCK_M, k*BLOCK_K:(k+1)*BLOCK_K]
B_tile = B[k*BLOCK_K:(k+1)*BLOCK_K, j*BLOCK_N:(j+1)*BLOCK_N]
C_tile += A_tile @ B_tile

Triton matmul kernel#

@triton.jit
def matmul_kernel(
    a_ptr, b_ptr, c_ptr,
    M, N, K,
    stride_am, stride_ak,
    stride_bk, stride_bn,
    stride_cm, stride_cn,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
    BLOCK_K: tl.constexpr,
):
    pid_m = tl.program_id(0)
    pid_n = tl.program_id(1)

    offs_am = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_bn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    offs_k = tl.arange(0, BLOCK_K)

    a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak
    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn

    accumulator = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
    for k in range(0, K, BLOCK_K):
        # Load tiles
        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k)
        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k)
        # Accumulate
        accumulator += tl.dot(a, b)
        # Advance K
        a_ptrs += BLOCK_K * stride_ak
        b_ptrs += BLOCK_K * stride_bk

    # Store result
    offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
    c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
    tl.store(c_ptrs, accumulator, mask=(offs_cm[:, None] < M) & (offs_cn[None, :] < N))

Pratik performance#

• Shared memory caching
• Register-level tiling
• Tensor Core utilization (BF16/FP16)
• Software pipelining

Realistic use#

7. Autotune ile Hyperparameter Search#

@triton.autotune(
    configs=[
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 32}, num_warps=4),
        triton.Config({"BLOCK_M": 64, "BLOCK_N": 256, "BLOCK_K": 32}, num_warps=8),
        triton.Config({"BLOCK_M": 256, "BLOCK_N": 64, "BLOCK_K": 32}, num_warps=8),
        triton.Config({"BLOCK_M": 128, "BLOCK_N": 64, "BLOCK_K": 64}, num_warps=4),
    ],
    key=["M", "N", "K"],  # Bu key'lere göre cache'le
)
@triton.jit
def matmul_kernel(...):
    ...

Behavior#

num_warps#

• Az: low occupancy
• Çok: register pressure

num_stages#

Auto-tuning cost#

• Warmup'ta autotune koş
• Cache disk'e kaydedilebilir (
  triton.runtime.cache
  )

8. FlashAttention Block-Wise Mini Implementation#

Klasik attention (slow + memory-heavy)#

S = Q @ K.T / sqrt(d)  # (N, N) matrix — büyük!
P = softmax(S)          # (N, N)
O = P @ V               # (N, d)

FlashAttention insight#

Mini Triton implementation (illustrative)#

@triton.jit
def flash_attention_kernel(
    Q_ptr, K_ptr, V_ptr, O_ptr,
    M_ptr, L_ptr,         # running max, log-sum-exp
    N_HEAD, N_SEQ, D_HEAD,
    BLOCK_M: tl.constexpr,
    BLOCK_N: tl.constexpr,
):
    pid = tl.program_id(0)
    head_idx = tl.program_id(1)

    # Q block'unu yükle
    q_offsets = pid * BLOCK_M + tl.arange(0, BLOCK_M)
    Q_block = tl.load(Q_ptr + ...)  # (BLOCK_M, D_HEAD)

    # Initialize accumulators
    m_i = tl.full([BLOCK_M], -float('inf'), dtype=tl.float32)
    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
    O_block = tl.zeros([BLOCK_M, D_HEAD], dtype=tl.float32)

    # K, V'yi block block dolaş
    for kv_start in range(0, N_SEQ, BLOCK_N):
        # K, V blocks
        K_block = tl.load(K_ptr + ...)
        V_block = tl.load(V_ptr + ...)

        # Compute S = Q @ K^T
        S = tl.dot(Q_block, K_block.T) / tl.sqrt(D_HEAD.to(tl.float32))

        # Online softmax — running max + sum
        m_ij = tl.maximum(m_i, tl.max(S, axis=1))
        p_ij = tl.exp(S - m_ij[:, None])
        l_ij = l_i * tl.exp(m_i - m_ij) + tl.sum(p_ij, axis=1)

        # Update output
        O_block = O_block * (tl.exp(m_i - m_ij) * l_i / l_ij)[:, None] + tl.dot(p_ij, V_block) / l_ij[:, None]

        # Update running stats
        m_i = m_ij
        l_i = l_ij

    # Store
    tl.store(O_ptr + ..., O_block)

Real FlashAttention complexity#

• Forward + backward kernel
• Causal masking
• Variable-length sequences
• Dropout
• Bias support
• ~2000 line Triton code

Performance#

• 4-10x faster (sequence length'e göre)
• O(N) memory (vs O(N²))
• Numerically identical (no approximation)

LLM impact#

9. PyTorch Entegrasyonu#

class TritonSoftmax(torch.autograd.Function):
    @staticmethod
    def forward(ctx, x):
        out = torch.empty_like(x)
        n_rows, n_cols = x.shape
        BLOCK_SIZE = triton.next_power_of_2(n_cols)
        softmax_kernel[(n_rows,)](
            out, x, x.stride(0), n_cols, BLOCK_SIZE=BLOCK_SIZE
        )
        ctx.save_for_backward(out)
        return out

    @staticmethod
    def backward(ctx, grad_output):
        out, = ctx.saved_tensors
        # Softmax backward: grad = out * (grad_output - sum(grad_output * out))
        # Bu da Triton kernel olabilir
        grad_input = ...  # custom backward kernel
        return grad_input

# Module
class TritonSoftmaxLayer(torch.nn.Module):
    def forward(self, x):
        return TritonSoftmax.apply(x)

torch.library.custom_op (modern)#

@torch.library.custom_op("mylib::triton_softmax", mutates_args=())
def triton_softmax(x: torch.Tensor) -> torch.Tensor:
    out = torch.empty_like(x)
    # ... Triton kernel ...
    return out

@triton_softmax.register_fake
def _(x):
    return torch.empty_like(x)

@triton_softmax.register_autograd
def _backward(ctx, grad_output):
    # ...
    return grad_input

10. Benchmark ve Profiling#

Triton'un kendi benchmark utility'si#

@triton.testing.perf_report(
    triton.testing.Benchmark(
        x_names=["N"],
        x_vals=[2**i for i in range(8, 14)],
        line_arg="provider",
        line_vals=["triton", "pytorch"],
        line_names=["Triton", "PyTorch"],
        styles=[("blue", "-"), ("green", "-")],
        ylabel="GB/s",
        plot_name="softmax-perf",
        args={},
    )
)
def benchmark(N, provider):
    x = torch.randn(1024, N, device="cuda", dtype=torch.float32)
    if provider == "triton":
        ms = triton.testing.do_bench(lambda: softmax(x))
    elif provider == "pytorch":
        ms = triton.testing.do_bench(lambda: torch.softmax(x, dim=-1))
    gbps = 2 * x.numel() * x.element_size() / ms / 1e6
    return gbps

benchmark.run(show_plots=True, print_data=True)

Manuel timing#

import time

# Warmup
for _ in range(10): triton_kernel(...)
torch.cuda.synchronize()

# Timing
start = time.perf_counter()
for _ in range(100): triton_kernel(...)
torch.cuda.synchronize()
elapsed = (time.perf_counter() - start) / 100 * 1000
print(f"Triton: {elapsed:.3f} ms")

Profiling#

• Occupancy
• Memory throughput
• Roofline analysis (compute-bound vs memory-bound)

11. Production Patterns#

1. Custom kernel ne zaman yazılır?#

• Profil'de bir kernel %30+ time alıyorsa
• PyTorch native + torch.compile yetmiyorsa
• Algorithmic innovation (FlashAttention gibi)
• Hardware-specific (Tensor Core, FP8)

2. Caching kernel artifact#

import os
os.environ["TRITON_CACHE_DIR"] = "/path/to/cache"

3. Versioning#

• Triton version pin:
  triton==3.0.0
• Backward compatibility test
• Regression suite

4. Numerical verification#

def verify(triton_fn, pytorch_fn, x):
    out_triton = triton_fn(x)
    out_pytorch = pytorch_fn(x)
    rel_err = (out_triton - out_pytorch).abs().max() / out_pytorch.abs().max()
    assert rel_err < 1e-4, f"Mismatch: {rel_err}"

5. Edge cases#

• Empty input (N=0)
• Single element
• Non-contiguous tensors
• Mixed dtype

12. Modül 37'ye Köprü#

• FlashAttention v3 detaylı walkthrough
• Quantization kernel'ları (Q4, AWQ, GPTQ)
• Tensor Core matrix ops
• MoE routing kernels
• Custom backward implementation
• Multi-GPU kernel patterns (cross-device synchronization)
• Production debugging (Nsight Compute)
• Performance modeling

Ön hazırlık checklist#

• Vector add Triton kernel yazıp PyTorch ile karşılaştırma
• Softmax kernel'ı verify et
• Naive matmul kernel + autotune
• FlashAttention mini implementation deneme
• Triton documentation skim (
  triton-lang.org
  )
• OpenAI Triton tutorials (5-10 tutorial)

Pratik öneri#

1. PyTorch native + torch.compile dene
2. Profile et
3. Bottleneck'i identify et
4. Sadece bottleneck custom yaz

13. Mini Egzersizler#

1. Vector add benchmark: 10M element add, PyTorch vs Triton. Kim hızlı?
2. Softmax bandwidth: 1024 row × 4096 col softmax, Triton kernel ile theoretical max GB/s'in % kaçı?
3. Matmul size sweet spot: Hangi M, N, K boyutu için Triton custom matmul cuBLAS'a yaklaşır?
4. FlashAttention vs vanilla: 8K sequence length attention. Memory ve compute farkı?
5. Production karar: Llama 3 fine-tuning'de custom Triton kernel yazmaya değer mi?

Bu Derste Neler Öğrendik?#

Sıradaki Ders#