GEMM, Triton and hipBLASlt and Transformer engine concept

1. GEMM (General Matrix Multiplication):

- This is the basic operation: C = A × B (matrix multiplication)

- Fundamental operation in deep learning, especially transformers

- Core computation in attention mechanisms, linear layers, etc.

2. Triton:

- A programming language for writing GPU kernels

- Lets you write your own custom GEMM implementation

- You control memory layout, tiling, etc.

- Example use: When you need a very specific matrix operation

3. hipBLASLt:

- A specialized library just for matrix operations

- Pre-built, highly optimized GEMM implementations

- Focuses on performance for common matrix sizes

- Example use: When you need fast, standard matrix multiplication

4. Transformer Engine:

- NVIDIA's specialized library for transformer models

- Automatically handles precision switching (FP8/FP16/FP32)

- Optimizes GEMM operations specifically for transformer architectures

- Includes specialized kernels for attention and linear layers

- Example use: When building large language models

The relationship:

```

Transformer Model

    ↓

Transformer Engine

    ↓

GEMM Operations (can be implemented via:)

    ↓

hipBLASLt / Triton / Other libraries

    ↓

GPU Hardware

```

the same matrix multiplication would be implemented using different approaches:

1. Basic GEMM Operation (what we want to compute):

```python

# C = A × B

# Where A is (M×K) and B is (K×N)

```

2. Using Triton (Custom implementation):

```python

@triton.jit

def matmul_kernel(

    a_ptr, b_ptr, c_ptr,    # Pointers to matrices

    M, N, K,                # Matrix dimensions

    stride_am, stride_ak,   # Memory strides for A

    stride_bk, stride_bn,   # Memory strides for B

    stride_cm, stride_cn,   # Memory strides for C

    BLOCK_SIZE: tl.constexpr,

):

    # Get program ID

    pid = tl.program_id(0)

    # Calculate block indices

    block_i = pid // (N // BLOCK_SIZE)

    block_j = pid % (N // BLOCK_SIZE)

    # Load blocks from A and B

    a = tl.load(a_ptr + ...)  # Load block from A

    b = tl.load(b_ptr + ...)  # Load block from B

    # Compute block multiplication

    c = tl.dot(a, b)          # Matrix multiply

    # Store result

    tl.store(c_ptr + ..., c)

```

3. Using hipBLASLt:

```cpp

// Initialize hipBLASLt

hipblasLtHandle_t handle;

hipblasLtCreate(&handle);

// Define matrix layout

hipblasLtMatrixLayout_t matA, matB, matC;

hipblasLtMatrixLayoutCreate(&matA, HIPBLAS_LT_R_16F, M, K, M);

hipblasLtMatrixLayoutCreate(&matB, HIPBLAS_LT_R_16F, K, N, K);

hipblasLtMatrixLayoutCreate(&matC, HIPBLAS_LT_R_16F, M, N, M);

// Execute GEMM

hipblasLtMatmul(

    handle,

    matmulDesc,

    &alpha,        // Scale factor

    A, matA,       // Input matrix A

    B, matB,       // Input matrix B

    &beta,         // Scale factor

    C, matC,       // Output matrix C

    workspace,     // Temporary workspace

    streams        // CUDA stream

);

```

4. Using Transformer Engine:

```python

import transformer_engine.pytorch as te

# Create TE layers

linear = te.Linear(in_features, out_features)

# Automatic precision handling

with te.fp8_autocast():

    output = linear(input)  # Internally uses optimized GEMM

```

Key differences:

1. Triton: You control everything (memory, blocks, compute)

2. hipBLASLt: Pre-optimized, you just call it

3. Transformer Engine: High-level, handles precision automatically

Performance comparison (general case):

```

Speed: hipBLASLt > Transformer Engine > Custom Triton

Flexibility: Triton > hipBLASLt > Transformer Engine

Ease of use: Transformer Engine > hipBLASLt > Triton

```