Custom Kernels

class ElementwiseKernel:
    """
    User-defined elementwise kernel.

    Parameters:
    - in_params: string of input parameters
    - out_params: string of output parameters  
    - operation: string of element-wise operation
    - name: kernel name
    - reduce_dims: whether to reduce dimensions
    - options: compiler options
    - preamble: code to insert at beginning
    - loop_prep: code to insert before loop
    - after_loop: code to insert after loop
    """
    def __init__(self, in_params, out_params, operation, name='kernel', 
                 reduce_dims=True, options=(), preamble='', loop_prep='',
                 after_loop='', **kwargs): ...
    
    def __call__(self, *args, **kwargs): ...

class ReductionKernel:
    """
    User-defined reduction kernel.

    Parameters:
    - in_params: string of input parameters
    - out_params: string of output parameters
    - map_expr: mapping expression
    - reduce_expr: reduction expression
    - post_map_expr: post-mapping expression
    - identity: identity value for reduction
    - name: kernel name
    - reduce_type: type for reduction
    - reduce_dims: whether to reduce dimensions
    - preamble: code to insert at beginning
    - options: compiler options
    """
    def __init__(self, in_params, out_params, map_expr, reduce_expr,
                 post_map_expr='', identity=None, name='kernel',
                 reduce_type=None, reduce_dims=True, preamble='',
                 options=(), **kwargs): ...
    
    def __call__(self, *args, **kwargs): ...

class RawKernel:
    """
    User-defined raw CUDA kernel.

    Parameters:
    - code: CUDA kernel code
    - name: kernel function name
    - options: compiler options
    - backend: compiler backend ('nvcc' or 'nvrtc')
    - translate_cucomplex: translate cuComplex types
    - enable_cooperative_groups: enable cooperative groups
    - jitify: use Jitify for compilation
    """
    def __init__(self, code, name, options=(), backend='nvcc',
                 translate_cucomplex=True, enable_cooperative_groups=False,
                 jitify=False, **kwargs): ...
    
    def __call__(self, grid, block, args, **kwargs): ...

class RawModule:
    """
    User-defined raw CUDA module.

    Parameters:
    - code: CUDA module code
    - options: compiler options  
    - backend: compiler backend
    - translate_cucomplex: translate cuComplex types
    - enable_cooperative_groups: enable cooperative groups
    - jitify: use Jitify for compilation
    """
    def __init__(self, code, options=(), backend='nvcc',
                 translate_cucomplex=True, enable_cooperative_groups=False,
                 jitify=False, **kwargs): ...
    
    def get_function(self, name): ...

def create_ufunc(name, ops, routine=None, preamble='', doc=None,
                default_casting=None, loop_prep='', out_ops=None):
    """
    Create universal function from operations.

    Parameters:
    - name: function name
    - ops: operation specifications
    - routine: routine function
    - preamble: preamble code
    - doc: documentation string
    - default_casting: default casting rule
    - loop_prep: loop preparation code
    - out_ops: output operations

    Returns:
    ufunc: created universal function
    """

def create_reduction_func(name, ops, routine=None, identity=None,
                         preamble='', **kwargs):
    """
    Create reduction function.

    Parameters:
    - name: function name
    - ops: operation specifications
    - routine: routine function  
    - identity: identity value
    - preamble: preamble code

    Returns:
    function: created reduction function
    """

def clear_memo():
    """Clear memoization cache."""

def memoize(for_each_device=False):
    """
    Memoization decorator.

    Parameters:
    - for_each_device: memoize for each device separately

    Returns:
    decorator: memoization decorator
    """

import cupy as cp

# Simple element-wise operation
add_kernel = cp.ElementwiseKernel(
    'float32 x, float32 y',  # input parameters
    'float32 z',             # output parameters
    'z = x + y',             # operation
    'add_kernel'             # kernel name
)

# Use the kernel
a = cp.random.random(1000).astype(cp.float32)
b = cp.random.random(1000).astype(cp.float32)
c = cp.empty(1000, dtype=cp.float32)

add_kernel(a, b, c)

# Verify result
expected = a + b
error = cp.linalg.norm(c - expected)
print(f"Add kernel error: {error}")

import cupy as cp

# More complex element-wise operation with conditionals
clamp_kernel = cp.ElementwiseKernel(
    'float32 x, float32 min_val, float32 max_val',
    'float32 y',
    '''
    if (x < min_val) {
        y = min_val;
    } else if (x > max_val) {
        y = max_val;
    } else {
        y = x;
    }
    ''',
    'clamp_kernel'
)

# Test the kernel
data = cp.random.uniform(-2, 2, 1000).astype(cp.float32)
result = cp.empty_like(data)

clamp_kernel(data, -1.0, 1.0, result)

# Verify clamping
print(f"Min value: {cp.min(result)}")  # Should be >= -1.0
print(f"Max value: {cp.max(result)}")  # Should be <= 1.0

import cupy as cp

# Kernel with helper functions in preamble
sigmoid_kernel = cp.ElementwiseKernel(
    'float32 x',
    'float32 y',
    'y = sigmoid(x)',
    'sigmoid_kernel',
    preamble='''
    __device__ float sigmoid(float x) {
        return 1.0f / (1.0f + expf(-x));
    }
    '''
)

# Use the kernel
x = cp.linspace(-5, 5, 1000).astype(cp.float32)
y = cp.empty_like(x)

sigmoid_kernel(x, y)

# Compare with CuPy implementation
expected = 1 / (1 + cp.exp(-x))
error = cp.linalg.norm(y - expected)
print(f"Sigmoid kernel error: {error}")

import cupy as cp

# Custom sum reduction
sum_kernel = cp.ReductionKernel(
    'float32 x',        # input
    'float32 y',        # output
    'x',                # map expression (identity)
    'a + b',            # reduce expression
    'y = a',            # post-map expression
    '0',                # identity value
    'sum_kernel'        # kernel name
)

# Test the kernel
data = cp.random.random(10000).astype(cp.float32)
result = sum_kernel(data)

# Compare with built-in sum
expected = cp.sum(data)
error = abs(result - expected)
print(f"Sum kernel error: {error}")

import cupy as cp

# Root mean square reduction
rms_kernel = cp.ReductionKernel(
    'float32 x',
    'float32 y',
    'x * x',            # map: square each element
    'a + b',            # reduce: sum squares  
    'y = sqrt(a / _in_ind.size())',  # post: sqrt of mean
    '0',                # identity
    'rms_kernel'
)

# Test the kernel
data = cp.random.random(1000).astype(cp.float32)
rms_result = rms_kernel(data)

# Compare with manual calculation
manual_rms = cp.sqrt(cp.mean(data * data))
error = abs(rms_result - manual_rms)
print(f"RMS kernel error: {error}")

import cupy as cp

# Matrix multiplication kernel
matmul_code = '''
extern "C" __global__
void matmul(float* A, float* B, float* C, int M, int N, int K) {
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    
    if (row < M && col < N) {
        float sum = 0.0f;
        for (int k = 0; k < K; k++) {
            sum += A[row * K + k] * B[k * N + col];
        }
        C[row * N + col] = sum;
    }
}
'''

matmul_kernel = cp.RawKernel(matmul_code, 'matmul')

# Test the kernel
M, N, K = 512, 512, 512
A = cp.random.random((M, K)).astype(cp.float32)
B = cp.random.random((K, N)).astype(cp.float32)
C = cp.zeros((M, N), dtype=cp.float32)

# Launch kernel
block = (16, 16)
grid = ((N + block[0] - 1) // block[0], (M + block[1] - 1) // block[1])

matmul_kernel(grid, block, (A, B, C, M, N, K))

# Compare with CuPy matmul
expected = cp.dot(A, B)
error = cp.linalg.norm(C - expected) / cp.linalg.norm(expected)
print(f"Matmul kernel relative error: {error}")

import cupy as cp

# Convolution kernel with shared memory
conv_code = '''
extern "C" __global__
void conv2d(float* input, float* kernel, float* output, 
           int height, int width, int kernel_size) {
    
    extern __shared__ float shared_mem[];
    
    int tx = threadIdx.x;
    int ty = threadIdx.y;
    int bx = blockIdx.x;
    int by = blockIdx.y;
    int bdx = blockDim.x;
    int bdy = blockDim.y;
    
    int row = by * bdy + ty;
    int col = bx * bdx + tx;
    
    int shared_width = bdx + kernel_size - 1;
    int shared_height = bdy + kernel_size - 1;
    
    // Load data into shared memory (simplified)
    if (row < height && col < width) {
        shared_mem[ty * shared_width + tx] = input[row * width + col];
    }
    
    __syncthreads();
    
    // Perform convolution
    if (row < height && col < width) {
        float sum = 0.0f;
        int half_kernel = kernel_size / 2;
        
        for (int ky = 0; ky < kernel_size; ky++) {
            for (int kx = 0; kx < kernel_size; kx++) {
                int y_idx = ty + ky;
                int x_idx = tx + kx;
                
                if (y_idx < shared_height && x_idx < shared_width) {
                    sum += shared_mem[y_idx * shared_width + x_idx] * 
                           kernel[ky * kernel_size + kx];
                }
            }
        }
        
        output[row * width + col] = sum;
    }
}
'''

conv_kernel = cp.RawKernel(conv_code, 'conv2d')

# Test the kernel (simplified example)
height, width = 256, 256
kernel_size = 3

input_data = cp.random.random((height, width)).astype(cp.float32)
kernel_data = cp.random.random((kernel_size, kernel_size)).astype(cp.float32)
output_data = cp.zeros((height, width), dtype=cp.float32)

# Calculate shared memory size
block = (16, 16)
shared_size = (block[0] + kernel_size - 1) * (block[1] + kernel_size - 1) * 4  # 4 bytes per float

grid = ((width + block[0] - 1) // block[0], (height + block[1] - 1) // block[1])

conv_kernel(grid, block, (input_data, kernel_data, output_data, height, width, kernel_size),
           shared_mem=shared_size)

import cupy as cp

# Module with multiple related kernels
vector_ops_code = '''
extern "C" __global__
void vector_add(float* a, float* b, float* c, int n) {
    int idx = blockDim.x * blockIdx.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] + b[idx];
    }
}

extern "C" __global__
void vector_mult(float* a, float* b, float* c, int n) {
    int idx = blockDim.x * blockIdx.x + threadIdx.x;
    if (idx < n) {
        c[idx] = a[idx] * b[idx];
    }
}

extern "C" __global__
void vector_norm(float* a, float* result, int n) {
    extern __shared__ float sdata[];
    
    int tid = threadIdx.x;
    int idx = blockDim.x * blockIdx.x + threadIdx.x;
    
    // Load and square
    sdata[tid] = (idx < n) ? a[idx] * a[idx] : 0.0f;
    __syncthreads();
    
    // Reduction in shared memory
    for (int s = blockDim.x / 2; s > 0; s >>= 1) {
        if (tid < s) {
            sdata[tid] += sdata[tid + s];
        }
        __syncthreads();
    }
    
    // Write result
    if (tid == 0) {
        atomicAdd(result, sdata[0]);
    }
}
'''

vector_module = cp.RawModule(code=vector_ops_code)

# Get individual kernels
add_kernel = vector_module.get_function('vector_add')
mult_kernel = vector_module.get_function('vector_mult')
norm_kernel = vector_module.get_function('vector_norm')

# Test the kernels
n = 1000000
a = cp.random.random(n).astype(cp.float32)
b = cp.random.random(n).astype(cp.float32)
c = cp.zeros(n, dtype=cp.float32)

block_size = 256
grid_size = (n + block_size - 1) // block_size

# Vector addition
add_kernel((grid_size,), (block_size,), (a, b, c, n))
add_expected = a + b
add_error = cp.linalg.norm(c - add_expected)
print(f"Add error: {add_error}")

# Vector multiplication  
mult_kernel((grid_size,), (block_size,), (a, b, c, n))
mult_expected = a * b
mult_error = cp.linalg.norm(c - mult_expected)
print(f"Mult error: {mult_error}")

# Vector norm
norm_result = cp.zeros(1, dtype=cp.float32)
shared_mem_size = block_size * 4  # 4 bytes per float

norm_kernel((grid_size,), (block_size,), (a, norm_result, n), 
           shared_mem=shared_mem_size)

norm_expected = cp.linalg.norm(a)**2
norm_error = abs(float(norm_result) - float(norm_expected))
print(f"Norm error: {norm_error}")

import cupy as cp
import time

# Compare ElementwiseKernel vs built-in operations
n = 10000000

# Built-in operation timing
a = cp.random.random(n).astype(cp.float32)
b = cp.random.random(n).astype(cp.float32)

start = time.time()
for _ in range(100):
    c_builtin = a + b
cp.cuda.Device().synchronize()
builtin_time = time.time() - start

# Custom kernel timing
add_kernel = cp.ElementwiseKernel(
    'float32 x, float32 y',
    'float32 z', 
    'z = x + y',
    'custom_add'
)

c_custom = cp.empty_like(a)
start = time.time()
for _ in range(100):
    add_kernel(a, b, c_custom)
cp.cuda.Device().synchronize()
custom_time = time.time() - start

print(f"Built-in time: {builtin_time:.4f}s")
print(f"Custom kernel time: {custom_time:.4f}s")
print(f"Speedup: {builtin_time/custom_time:.2f}x")

# Verify correctness
error = cp.linalg.norm(c_builtin - c_custom)
print(f"Accuracy error: {error}")