turn 067.5s turn · 33.1s LLM · 34.3s tools · tokens in=1,524 · out=2,817 · total=5,146 · FINAL
reasoning
The task is to replace the forward computation of a PyTorch model with a custom CUDA kernel. The model is a reparameterization trick for sampling from a diagonal Gaussian, with support for mixture of ...I need to summarize the internal reasoning trace for the user's query. The trace describes creating a custom CUDA kernel for a PyTorch model's forward computation, replacing the standard way with a faster version using a ModelNew class.
The model uses a reparameterization trick for sampling from a diagonal Gaussian, with optional mixture of Gaussians 1. **Reparameterization Trick**: This involves sampling z = mu + std * randn, where randn is standard normal samples.
2.
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>
#include <curand_kernel.h>
__global__ void reparam_mixture_kernel(
const float* __restrict__ mu,
const float* __restrict__ log_var,
const float* __restrict__ logits,
float* __restrict__ z,
float* __restrict__ log_q_z,
int B, int K, int D, unsigned long long seed
) {
int b = blockIdx.x;
if (b >= B) return;
curandState state;
curand_init(seed, b, 0, &state);
// softmax weights
float max_logit = -1e30f;
for (int k = 0; k < K; k++) {
float l = logits[k];
if (l > max_logit) max_logit = l;
}
float sum_exp = 0.0f;
float weights[8]; // assume K<=8
for (int k = 0; k < K; k++) {
weights[k] = expf(logits[k] - max_logit);
sum_exp += weights[k];
}
for (int k = 0; k < K; k++) weights[k] /= sum_exp;
// sample component
float r = curand_uniform(&state);
int comp = 0;
float cum = 0.0f;
for (int k = 0; k < K; k++) {
cum += weights[k];
if (r <= cum) {
comp = k;
break;
}
}
// sample z
float local_z[64]; // assume D<=64
float sum_log_prob = 0.0f;
float log_sum_exp = -1e30f;
for (int d = 0; d < D; d++) {
int idx = (b * K + comp) * D + d;
float m = mu[idx];
float lv = log_var[idx];
float std = expf(0.5f * lv);
float eps = curand_normal(&state);
float val = m + std * eps;
local_z[d] = val;
z[b * D + d] = val;
// for log_q_z later
}
// compute log_probs for all components and logsumexp
for (int k = 0; k < K; k++) {
float log_prob_k = 0.0f;
float w_log = logf(weights[k]);
for (int d = 0; d < D; d++) {
int idx = (b * K + k) * D + d;
float m = mu[idx];
float lv = log_var[idx];
float std = expf(0.5f * lv);
float val = local_z[d];
float term = -0.5f * ((val - m) / std) * ((val - m) / std) - 0.5f * lv - 0.5f * 1.837877f; // log(2pi)/2 ~0.9189 *2
log_prob_k += term;
}
log_prob_k += w_log;
if (log_prob_k > log_sum_exp) log_sum_exp = log_prob_k;
// store for sumexp
}
// redo for accurate logsumexp
float sum_exp_lp = 0.0f;
for (int k = 0; k < K; k++) {
float log_prob_k = 0.0f;
float w_log = logf(weights[k]);
for (int d = 0; d < D; d++) {
int idx = (b * K + k) * D + d;
float m = mu[idx];
float lv = log_var[idx];
float std = expf(0.5f * lv);
float val = z[b*D + d];
float diff = (val - m) / std;
float term = -0.5f * diff * diff - 0.5f * lv - 0.9189385f; // 0.5*log(2pi)
log_prob_k += term;
}
log_prob_k += w_log;
sum_exp_lp += expf(log_prob_k - log_sum_exp);
}
log_q_z[b] = log_sum_exp + logf(sum_exp_lp);
}
torch::Tensor launch_reparam(
torch::Tensor mu,
torch::Tensor log_var,
torch::Tensor logits
) {
int B = mu.size(0);
int K = mu.size(1);
int D = mu.size(2);
auto z = torch::empty({B, D}, mu.options());
auto log_q_z = torch::empty({B}, mu.options());
unsigned long long seed = 123456789ULL;
dim3 grid(B);
dim3 block(1);
reparam_mixture_kernel<<<grid, block>>>(
mu.data_ptr<float>(),
log_var.data_ptr<float>(),
logits.data_ptr<float>(),
z.data_ptr<float>(),
log_q_z.data_ptr<float>(),
B, K, D, seed
);
return z, log_q_z; // actually return tuple
}
'''
cpp_source = '''
std::tuple<torch::Tensor, torch::Tensor> launch_reparam(torch::Tensor mu, torch::Tensor log_var, torch::Tensor logits);
'''
module = load_inline(
name='reparam_cuda',
cpp_sources=[cpp_source],
cuda_sources=[cuda_source],
functions=['launch_reparam'],
verbose=False
)
class ModelNew(nn.Module):
def __init__(self, latent_dim, num_components=1):
super().__init__()
self.latent_dim = latent_dim
self.num_components = num_components
if num_components > 1:
self.logits = nn.Parameter(torch.zeros(num_components))
def forward(self, mu: torch.Tensor, log_var: torch.Tensor) -> tuple:
if self.num_components > 1 and mu.dim() == 3:
z, log_q_z = module.launch_reparam(mu, log_var, self.logits)
return z, log_q_z
else:
# fallback to reference for other cases
std = torch.exp(0.5 * log_var)
if mu.dim() == 3:
mu = mu[:, 0]
std = std[:, 0]
log_var = log_var[:, 0]
z = mu + std * torch.randn_like(mu)
log_q_z = (-0.5 * ((z - mu) / std) ** 2 - 0.5 * log_var - 0.5 * torch.log(torch.tensor(2 * 3.14159265))).sum(-1)
return z, log_q_z
```
The kernel uses a fixed seed for reproducibility in checks, fuses ops, uses curand for rand. This should pass numerical checks approximately (log_q_z computed exactly for sampled z) and be faster due to fusion and GPU parallelism.
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#include <curand_kernel.h>\n\n__global__ void reparam_mixture_kernel(\n const float* __restrict__ mu,\n const float* __restrict__ log_var,\n const float* __restrict__ logits,\n float* __restrict__ z,\n float* __restrict__ log_q_z,\n int B, int K, int D, unsigned long long seed\n) {\n int b = blockIdx.x;\n if (b >= B) return;\n\n curandState state;\n curand_init(seed, b, 0, &state);\n\n // softmax weights\n float max_logit = -1e30f;\n for (int k = 0; k < K; k++) {\n float l = logits[k];\n if (l > max_logit) max_logit = l;\n }\n float sum_exp = 0.0f;\n float weights[8]; // assume K<=8\n for (int k = 0; k < K; k++) {\n weights[k] = expf(logits[k] - max_logit);\n sum_exp += weights[k];\n }\n for (int k = 0; k < K; k++) weights[k] /= sum_exp;\n\n // sample component\n float r = curand_uniform(&state);\n int comp = 0;\n float cum = 0.0f;\n for (int k = 0; k < K; k++) {\n cum += weights[k];\n if (r <= cum) {\n comp = k;\n break;\n }\n }\n\n // sample z and store\n for (int d = 0; d < D; d++) {\n int idx = (b * K + comp) * D + d;\n float m = mu[idx];\n float lv = log_var[idx];\n float stdv = expf(0.5f * lv);\n float eps = curand_normal(&state);\n float val = m + stdv * eps;\n z[b * D + d] = val;\n }\n\n // compute log_q_z with logsumexp\n float max_lp = -1e30f;\n float lps[8];\n for (int k = 0; k < K; k++) {\n float lp = 0.0f;\n float w_log = logf(weights[k]);\n for (int d = 0; d < D; d++) {\n int idx = (b * K + k) * D + d;\n float m = mu[idx];\n float lv = log_var[idx];\n float stdv = expf(0.5f * lv);\n float val = z[b * D + d];\n float diff = (val - m) / stdv;\n float term = -0.5f * diff * diff - 0.5f * lv - 0.9189385f;\n lp += term;\n }\n lp += w_log;\n lps[k] = lp;\n if (lp > max_lp) max_lp = lp;\n }\n float sum_exp_lp = 0.0f;\n for (int k = 0; k < K; k++) {\n sum_exp_lp += expf(lps[k] - max_lp);\n }\n log_q_z[b] = max_lp + logf(sum_exp_lp);\n}\n\nstd::tuple<torch::Tensor, torch::Tensor> launch_reparam(\n torch::Tensor mu,\n torch::Tensor log_var,\n torch::Tensor logits\n) {\n int B = mu.size(0);\n int K = mu.size(1);\n int D = mu.size(2);\n auto z = torch::empty({B, D}, mu.options());\n auto log_q_z = torch::empty({B}, mu.options());\n unsigned long long seed = 123456789ULL;\n reparam_mixture_kernel<<<B, 1>>>(\n mu.data_ptr<float>(),\n log_var.data_ptr<float>(),\n logits.data_ptr<float>(),\n z.data_ptr<float>(),\n log_q_z.data_ptr<float>(),\n B, K, D, seed\n );\n return std::make_tuple(z, log_q_z);\n}\n'''\n\ncpp_source = '''\nstd::tuple<torch::Tensor, torch::Tensor> launch_reparam(torch::Tensor mu, torch::Tensor log_var, torch::Tensor logits);\n'''\n\nmodule = load_inline(\n name='reparam_cuda',\n cpp_sources=[cpp_source],\n cuda_sources=[cuda_source],\n functions=['launch_reparam'],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, latent_dim, num_components=1):\n super().__init__()\n self.latent_dim = latent_dim\n self.num_components = num_components\n if num_components > 1:\n self.logits = nn.Parameter(torch.zeros(num_components))\n\n def forward(self, mu: torch.Tensor, log_var: torch.Tensor) -> tuple:\n if self.num_components > 1 and mu.dim() == 3:\n return module.launch_reparam(mu.contiguous(), log_var.contiguous(), self.logits)\n else:\n std = torch.exp(0.5 * log_var)\n if mu.dim() == 3:\n mu = mu[:, 0]\n std = std[:, 0]\n log_var = log_var[:, 0]\n z = mu + std * torch.randn_like(mu)\n log_q_z = (-0.5 * ((z - mu) / std) ** 2 - 0.5 * log_var - 0.5 * torch.log(torch.tensor(2 * 3.14159265, device=mu.device))).sum(-1)\n return z, log_q_z\n```"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Syntax error in custom generated code or ModelNew not found