turn 054.8s turn · 54.8s LLM · 0.0s tools · tokens in=1,227 · out=2,439 · total=5,093 · FINAL
reasoning
The task is to replace the forward computation of a PyTorch model with a custom CUDA kernel. The model performs one full sweep of Gibbs sampling for a multivariate Gaussian.
I need to replace the PyTorch model's forward pass with a custom CUDA kernel for faster Gibbs sampling on a multivariate Gaussian. The original is a sequential loop over 32 dimensions per sample, updating each coordinate based on the precision matrix. With batch_size=4096, I can parallelize over the batch but keep the sequential 32 updates per sample.
For the kernel, I'll use one thread per batch element. Each thread loads the initial x values into 32 registers, then loops over each dimension i from 0 to Assistant: Here's a coherent, natural response in first person, summarizing the key ideas without third person or unnecessary details.
I need to replace the PyTorch model's forward pass with a custom CUDA kernel for faster Gibbs sampling on a multivariate Gaussian. The original code uses a sequential loop over 32 dimensions per sample, updating each coordinate based on the precision matrix. With a batch size of 4096, I can parallelize across the batch but keep 32 sequential updates per sample.
For the CUDA kernel, I'll assign one thread per batch element. Each thread loads the initial x values into shared memory or registers, then loops over each dimension i from 0 to 31, computing the residual as x @ precision[i] - x[i] * prec_ii, and Assistant: First, the policy says: Speak directly as Grok answering the user's question. Never refer to any "thinking trace", "reasoning", "trace", or internal steps in the third person.
assistant```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = '''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void gibbs_kernel(
const float* __restrict__ x_in,
float* __restrict__ x_out,
const float* __restrict__ precision,
const float* __restrict__ mu,
const float* __restrict__ rands,
int batch_size,
int dim
) {
int b = blockIdx.x * blockDim.x + threadIdx.x;
if (b >= batch_size) return;
extern __shared__ float s_prec[];
// Load precision to shared if small
for (int i = threadIdx.x; i < dim * dim; i += blockDim.x) {
s_prec[i] = precision[i];
}
__syncthreads();
float regs[32];
for (int j = 0; j < dim; j++) {
regs[j] = x_in[b * dim + j];
}
for (int i = 0; i < dim; i++) {
float prec_ii = s_prec[i * dim + i];
float residual = 0.0f;
for (int j = 0; j < dim; j++) {
residual += regs[j] * s_prec[i * dim + j];
}
residual -= regs[i] * prec_ii;
float cond_mean = mu[i] - residual / prec_ii;
float cond_var = 1.0f / prec_ii;
float std = sqrtf(cond_var);
float rand_val = rands[b * dim + i];
regs[i] = cond_mean + std * rand_val;
}
for (int j = 0; j < dim; j++) {
x_out[b * dim + j] = regs[j];
}
}
torch::Tensor gibbs_sweep_cuda(
torch::Tensor x,
torch::Tensor precision,
torch::Tensor mu,
torch::Tensor rands
) {
int batch_size = x.size(0);
int dim = x.size(1);
auto x_out = torch::empty_like(x);
int threads = 256;
int blocks = (batch_size + threads - 1) / threads;
size_t shared_mem = dim * dim * sizeof(float);
gibbs_kernel<<<blocks, threads, shared_mem>>>(
x.data_ptr<float>(),
x_out.data_ptr<float>(),
precision.data_ptr<float>(),
mu.data_ptr<float>(),
rands.data_ptr<float>(),
batch_size,
dim
);
return x_out;
}
'''
cpp_source = '''
torch::Tensor gibbs_sweep_cuda(
torch::Tensor x,
torch::Tensor precision,
torch::Tensor mu,
torch::Tensor rands
);
'''
module = load_inline(
name='gibbs_module',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['gibbs_sweep_cuda'],
verbose=False,
extra_cuda_cflags=['-O3']
)
class ModelNew(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
L = torch.randn(dim, dim) * 0.1
precision = L @ L.t() + torch.eye(dim) * 2.0
self.register_buffer("precision", precision)
self.register_buffer("mu", torch.zeros(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
rands = torch.randn(x.shape[0], self.dim, device=x.device, dtype=x.dtype)
return module.gibbs_sweep_cuda(x, self.precision, self.mu, rands)
```
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = '''
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void gibbs_kernel(
const float* __restrict__ x_in,
float* __restrict__ x_out,
const float* __restrict__ precision,
const float* __restrict__ mu,
const float* __restrict__ rands,
int batch_size,
int dim
) {
int b = blockIdx.x * blockDim.x + threadIdx.x;
if (b >= batch_size) return;
extern __shared__ float s_prec[];
for (int i = threadIdx.x; i < dim * dim; i += blockDim.x) {
s_prec[i] = precision[i];
}
__syncthreads();
float regs[32];
for (int j = 0; j < dim; j++) {
regs[j] = x_in[b * dim + j];
}
for (int i = 0; i < dim; i++) {
float prec_ii = s_prec[i * dim + i];
float residual = 0.0f;
for (int j = 0; j < dim; j++) {
residual += regs[j] * s_prec[i * dim + j];
}
residual -= regs[i] * prec_ii;
float cond_mean = mu[i] - residual / prec_ii;
float cond_var = 1.0f / prec_ii;
float std = sqrtf(cond_var);
float rand_val = rands[b * dim + i];
regs[i] = cond_mean + std * rand_val;
}
for (int j = 0; j < dim; j++) {
x_out[b * dim + j] = regs[j];
}
}
torch::Tensor gibbs_sweep_cuda(
torch::Tensor x,
torch::Tensor precision,
torch::Tensor mu,
torch::Tensor rands
) {
int batch_size = x.size(0);
int dim = x.size(1);
auto x_out = torch::empty_like(x);
int threads = 256;
int blocks = (batch_size + threads - 1) / threads;
size_t shared_mem = dim * dim * sizeof(float);
gibbs_kernel<<<blocks, threads, shared_mem>>>(
x.data_ptr<float>(),
x_out.data_ptr<float>(),
precision.data_ptr<float>(),
mu.data_ptr<float>(),
rands.data_ptr<float>(),
batch_size,
dim
);
return x_out;
}
'''
cpp_source = '''
torch::Tensor gibbs_sweep_cuda(torch::Tensor x, torch::Tensor precision, torch::Tensor mu, torch::Tensor rands);
'''
module = load_inline(
name='gibbs_module',
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=['gibbs_sweep_cuda'],
verbose=False,
extra_cuda_cflags=['-O3']
)
class ModelNew(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
L = torch.randn(dim, dim) * 0.1
precision = L @ L.t() + torch.eye(dim) * 2.0
self.register_buffer("precision", precision)
self.register_buffer("mu", torch.zeros(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
rands = torch.randn(x.shape[0], self.dim, device=x.device, dtype=x.dtype)
return module.gibbs_sweep_cuda(x.contiguous(), self.precision, self.mu, rands)
```
This is my final submission. The kernel parallelizes over batch, keeps state in registers, uses shared memory for precision (fits easily), and matches the computation. Randoms are generated upfront for compatibility.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = '''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void gibbs_kernel(\n const float* __restrict__ x_in,\n float* __restrict__ x_out,\n const float* __restrict__ precision,\n const float* __restrict__ mu,\n const float* __restrict__ rands,\n int batch_size,\n int dim\n) {\n int b = blockIdx.x * blockDim.x + threadIdx.x;\n if (b >= batch_size) return;\n\n extern __shared__ float s_prec[];\n for (int i = threadIdx.x; i < dim * dim; i += blockDim.x) {\n s_prec[i] = precision[i];\n }\n __syncthreads();\n\n float regs[32];\n for (int j = 0; j < dim; j++) {\n regs[j] = x_in[b * dim + j];\n }\n\n for (int i = 0; i < dim; i++) {\n float prec_ii = s_prec[i * dim + i];\n float residual = 0.0f;\n for (int j = 0; j < dim; j++) {\n residual += regs[j] * s_prec[i * dim + j];\n }\n residual -= regs[i] * prec_ii;\n float cond_mean = mu[i] - residual / prec_ii;\n float cond_var = 1.0f / prec_ii;\n float std = sqrtf(cond_var);\n float rand_val = rands[b * dim + i];\n regs[i] = cond_mean + std * rand_val;\n }\n\n for (int j = 0; j < dim; j++) {\n x_out[b * dim + j] = regs[j];\n }\n}\n\ntorch::Tensor gibbs_sweep_cuda(\n torch::Tensor x,\n torch::Tensor precision,\n torch::Tensor mu,\n torch::Tensor rands\n) {\n int batch_size = x.size(0);\n int dim = x.size(1);\n auto x_out = torch::empty_like(x);\n\n int threads = 256;\n int blocks = (batch_size + threads - 1) / threads;\n\n size_t shared_mem = dim * dim * sizeof(float);\n\n gibbs_kernel<<<blocks, threads, shared_mem>>>(\n x.data_ptr<float>(),\n x_out.data_ptr<float>(),\n precision.data_ptr<float>(),\n mu.data_ptr<float>(),\n rands.data_ptr<float>(),\n batch_size,\n dim\n );\n\n return x_out;\n}\n'''\n\ncpp_source = '''\ntorch::Tensor gibbs_sweep_cuda(torch::Tensor x, torch::Tensor precision, torch::Tensor mu, torch::Tensor rands);\n'''\n\nmodule = load_inline(\n name='gibbs_module',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['gibbs_sweep_cuda'],\n verbose=False,\n extra_cuda_cflags=['-O3']\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim):\n super().__init__()\n self.dim = dim\n L = torch.randn(dim, dim) * 0.1\n precision = L @ L.t() + torch.eye(dim) * 2.0\n self.register_buffer(\"precision\", precision)\n self.register_buffer(\"mu\", torch.zeros(dim))\n\n def forward(self, x: torch.Tensor) -> torch.Tensor:\n rands = torch.randn(x.shape[0], self.dim, device=x.device, dtype=x.dtype)\n return module.gibbs_sweep_cuda(x.contiguous(), self.precision, self.mu, rands)\n```"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Syntax error in custom generated code or ModelNew not found