reasoning
reasoning (truncated 61495 chars): Let me analyze the reference implementation to understand what needs to be computed:
1. **Target distribution p**: A mixture of Gaussians with `num_components` components
- Each component has weight `mix_weights[k]`, mean `mix_mus[k]`, and std `mix_stds[k]`
- Log density: `log_p(x) = logsumexp_k(log(mix_weights[k]) + log_component_k(x))`
- Where `log_component_k(x) = sum_d(-0.5 * ((x[d] - mu[k,d]) / std[k,d])^2 - log(std[k,d]))`
2. **Proposal distribution q**: Single Gaussian
- Mean `q_mu`, std `q_std`
- Log density: `log_q(x) = sum_d(-0.5 * ((x[d] - q_mu[d]) / q_std[d])^2 - log(q_std[d]))`
3. **Forward computation**:
- Input: `samples` of shape `(N, dim)`
- Compute `log_w = log_p(samples) - log_q(samples)` (shape: `(N,)`)
- Normalize: `log_w = log_w - logsumexp(log_w)`
- Compute weights: `w = exp(log_w)`
- Compute weighted mean: `weighted_mean = sum_n(w[n] * samples[n,:])` (shape: `(dim,)`)
- Compute ESS: `ess = 1.0 / sum_n(w[n]^2)`
Key challenges:
1. Computing `log_p` requires computing log densities for all components and then logsumexp
2. Need numerical stability for logsumexp operations
3. The computation involves reduction operations
Let me design a CUDA kernel for this:
**Kernel 1: Compute log_p and log_q**
- Each thread handles one sample
- Compute log_q (simple element-wise operations and sum)
- Compute log_p by iterating over components and computing logsumexp
- Store intermediate results
**Kernel 2: Compute logsumexp reduction**
- Parallel reduction to compute `logsumexp(log_w)`
- Could use shared memory for efficiency
**Kernel 3: Compute normalized weights and final results**
- Normalize log_w, compute exp to get w
- Accumulate weighted mean and sum of w^2 for ESS
Actually, let me think about this more carefully. The operations are:
1. For each sample i, compute log_p[i] and log_q[i]
2. Compute log_w[i] = log_p[i] - log_q[i]
3. Compute max_log_w = max(log_w)
4. Compute sum_exp = sum(exp(log_w - max_log_w))
5. Compute log_sum = max_log_w + log(sum_exp)
6. Normalize: log_w[i] = log_w[i] - log_sum
7. Compute w[i] = exp(log_w[i])
8. Compute weighted_mean = sum(w[i] * samples[i, :])
9. Compute sum_w2 = sum(w[i]^2)
10. Compute ESS = 1.0 / sum_w2
I'll structure this as:
1. A kernel to compute log_p and log_q for each sample
2. A reduction kernel (or Thrust/CUB) for logsumexp
3. A kernel for final computations
Actually, for better efficiency, I might combine some operations. Let me think about the memory access patterns:
- samples: (N, dim) - read many times
- mix_mus, mix_stds: (K, dim) - read once per sample
- Output: weighted_mean (dim,), ess (scalar)
Let me write a fused kernel approach:
```cpp
// Phase 1: Compute log_p and log_q, and initial reductions
// Phase 2: Finalize with normalized weights
```
Actually, let me write a single kernel that handles everything with atomic operations or shared memory for reductions.
Let me think about a cleaner approach:
1. First kernel: Compute log_p and log_q for each sample, store in memory
2. Use a device-wide reduction for logsumexp (could use a simple kernel or thrust)
3. Second kernel: Normalize, compute weighted mean and ESS
For simplicity and correctness, let me start with a straightforward implementation:
```cpp
__global__ void compute_log_weights(
const float* samples, // (N, dim)
int N, int D, int K,
const float* mix_weights, const float* mix_mus, const float* mix_stds,
const float* q_mu, const float* q_std,
float* log_p, float* log_q) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= N) return;
// Compute log_q
float log_q_val = 0.0f;
for (int d = 0; d < D; d++) {
float diff = samples[i * D + d] - q_mu[d];
float std = q_std[d];
log_q_val += -0.5f * (diff * diff) / (std * std) - logf(std);
}
log_q[i] = log_q_val;
// Compute log_p (mixture)
// Need logsumexp over components
float max_log_comp = -INFINITY;
float log_comp[K]; // Can't use VLA, need to handle differently
// ...
}
```
Actually, since K (num_components) is small (3 by default), I can compute this in a loop.
Let me write the complete kernel:
```cpp
__global__ void compute_log_weights(
const float* samples,
int N, int D, int K,
const float* mix_weights, const float* mix_mus, const float* mix_stds,
const float* q_mu, const float* q_std,
float* log_w) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= N) return;
// Compute log_q
float log_q_val = 0.0f;
for (int d = 0; d < D; d++) {
float diff = samples[i * D + d] - q_mu[d];
float s = q_std[d];
log_q_val += -0.5f * diff * diff / (s * s) - logf(s);
}
// Compute log_p (mixture) - need logsumexp over components
float max_log_comp = -INFINITY;
float log_comps[10]; // Assume K <= 10
for (int k = 0; k < K; k++) {
float log_comp = logf(mix_weights[k]);
for (int d = 0; d < D; d++) {
float diff = samples[i * D + d] - mix_mus[k * D + d];
float s = mix_stds[k * D + d];
log_comp += -0.5f * diff * diff / (s * s) - logf(s);
}
log_comps[k] = log_comp;
if (log_comp > max_log_comp) max_log_comp = log_comp;
}
// Logsumexp
float sum_exp = 0.0f;
for (int k = 0; k < K; k++) {
sum_exp += expf(log_comps[k] - max_log_comp);
}
float log_p_val = max_log_comp + logf(sum_exp);
log_w[i] = log_p_val - log_q_val;
}
```
Then I need to:
1. Compute logsumexp of log_w
2. Normalize and compute final results
For the logsumexp reduction, I can use a parallel reduction or atomic operations.
Let me think about an alternative approach using atomic operations for the final mean computation:
```cpp
__global__ void compute_final(
const float* samples,
const float* log_w,
int N, int D,
float log_sum,
float* weighted_mean,
float* sum_w2) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= N) return;
float log_w_i = log_w[i] - log_sum;
float w = expf(log_w_i);
float w2 = w * w;
for (int d = 0; d < D; d++) {
atomicAdd(&weighted_mean[d], w * samples[i * D + d]);
}
atomicAdd(sum_w2, w2);
}
```
But atomic operations might be slow. Let me think about using shared memory reductions.
Actually, for better performance, I should use a block-level reduction approach. But for simplicity and getting something working first, let me use atomics or a simple approach.
Let me write the full implementation:
```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 <cmath>
__global__ void compute_log_weights_kernel(
const float* __restrict__ samples,
int N, int D, int K,
const float* __restrict__ mix_weights,
const float* __restrict__ mix_mus,
const float* __restrict__ mix_stds,
const float* __restrict__ log_mix_weights,
const float* __restrict__ q_mu,
const float* __restrict__ q_std,
float* __restrict__ log_w) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
if (i >= N) return;
// Compute log_q
float log_q_val = 0.0f;
for (int d = 0; d < D; d++) {
float diff = samples[i * D + d] - q_mu[d];
float s = q_std[d];
log_q_val += -0.5f * diff * diff / (s * s) - logf(s);
}
// Compute log_p (mixture) - need logsumexp over components
float max_log_comp = -INFINITY;
float log_comps[10]; // Assume K <= 10
for (int k = 0; k < K; k++) {
float log_comp = log_mix_weights[k]; // Precomputed log(mix_weights[k])
for (int d = 0; d < D; d++) {
float diff = samples[i * D + d] - mix_mus[k * D + d];
float s = mix_stds[k * D + d];
log_comp += -0.5f * diff * diff / (s * s) - logf(s);
...[ omitted 61495 characters ]...
const float s = __ldg(&mix_stds[k * D + d]);
const float inv_s = 1.0f / s;
log_comp += -0.5f * diff * diff * inv_s * inv_s - logf(s);
}
log_comps[k] = log_comp;
max_log_comp = fmaxf(max_log_comp, log_comp);
}
float sum_exp = 0.0f;
for (int k = 0; k < K; k++) {
sum_exp += expf(log_comps[k] - max_log_comp);
}
const float log_p_val = max_log_comp + logf(sum_exp);
log_w[i] = log_p_val - log_q_val;
}
__global__ void compute_weighted_stats_kernel(
const float* __restrict__ samples,
const float* __restrict__ log_w,
const int N, const int D,
const float log_sum,
float* __restrict__ weighted_mean,
float* __restrict__ sum_w2) {
extern __shared__ float smem[];
float* s_mean = smem;
float* s_w2 = &smem[blockDim.x * D];
const int tid = threadIdx.x;
const int i = blockIdx.x * blockDim.x + tid;
float w = 0.0f;
if (i < N) {
w = expf(log_w[i] - log_sum);
}
const float w2 = w * w;
s_w2[tid] = w2;
for (int d = 0; d < D; d++) {
s_mean[tid * D + d] = (i < N) ? w * __ldg(&samples[i * D + d]) : 0.0f;
}
__syncthreads();
for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
if (tid < stride) {
s_w2[tid] += s_w2[tid + stride];
for (int d = 0; d < D; d++) {
s_mean[tid * D + d] += s_mean[(tid + stride) * D + d];
}
}
__syncthreads();
}
if (tid == 0) {
atomicAdd(sum_w2, s_w2[0]);
for (int d = 0; d < D; d++) {
atomicAdd(&weighted_mean[d], s_mean[d]);
}
}
}
std::tuple<torch::Tensor, torch::Tensor> importance_sampling_forward(
torch::Tensor samples,
torch::Tensor log_mix_weights,
torch::Tensor mix_mus,
torch::Tensor mix_stds,
torch::Tensor q_mu,
torch::Tensor q_std) {
samples = samples.contiguous();
log_mix_weights = log_mix_weights.contiguous();
mix_mus = mix_mus.contiguous();
mix_stds = mix_stds.contiguous();
q_mu = q_mu.contiguous();
q_std = q_std.contiguous();
const int N = samples.size(0);
const int D = samples.size(1);
const int K = log_mix_weights.size(0);
auto options = samples.options();
auto log_w = torch::empty({N}, options);
auto weighted_mean = torch::zeros({D}, options);
auto sum_w2 = torch::zeros({1}, options);
const int threads = 256;
const int blocks = (N + threads - 1) / threads;
compute_log_weights_kernel<<<blocks, threads>>>(
samples.data_ptr<float>(),
N, D, K,
log_mix_weights.data_ptr<float>(),
mix_mus.data_ptr<float>(),
mix_stds.data_ptr<float>(),
q_mu.data_ptr<float>(),
q_std.data_ptr<float>(),
log_w.data_ptr<float>()
);
const float log_sum = torch::logsumexp(log_w, 0).item<float>();
const size_t shared_mem_size = threads * (D + 1) * sizeof(float);
compute_weighted_stats_kernel<<<blocks, threads, shared_mem_size>>>(
samples.data_ptr<float>(),
log_w.data_ptr<float>(),
N, D,
log_sum,
weighted_mean.data_ptr<float>(),
sum_w2.data_ptr<float>()
);
const float ess_val = 1.0f / sum_w2.item<float>();
return std::make_tuple(weighted_mean, torch::tensor(ess_val, options));
}
"""
importance_sampling_ext = load_inline(
name="importance_sampling_ext",
cpp_sources="",
cuda_sources=cuda_source,
functions=["forward"],
verbose=False
)
class ModelNew(nn.Module):
def __init__(self, dim, num_components=3):
super().__init__()
self.dim = dim
self.num_components = num_components
self.register_buffer("mix_weights", torch.ones(num_components) / num_components)
mus = torch.randn(num_components, dim) * 2.0
self.register_buffer("mix_mus", mus)
self.register_buffer("mix_stds", torch.ones(num_components, dim))
self.register_buffer("q_mu", torch.zeros(dim))
self.register_buffer("q_std", torch.ones(dim) * 3.0)
def forward(self, samples: torch.Tensor) -> tuple:
log_mix_weights = torch.log(self.mix_weights)
weighted_mean, ess = importance_sampling_ext.forward(
samples, log_mix_weights, self.mix_mus, self.mix_stds, self.q_mu, self.q_std
)
return weighted_mean, ess
```
Actually, I just realized that there might be an issue with the model initialization. The `mus` variable is computed with `torch.randn`, which gives different random values each time the model is instantiated. But this is the same as the reference model, so it should be fine for testing.
Wait, actually looking at the reference more carefully:
```python
def get_init_inputs():
return [dim]
```
The model is initialized with just `dim`, and `num_components` uses the default value of 3. My `ModelNew` should match this behavior.
Also, I notice that the random initialization in `__init__` uses `torch.randn`, which will produce different values each time. This is fine as long as both the reference and new model are initialized with the same random state (which they should be if tested properly).
Let me also double-check the shape of the returned ESS:
- Reference returns `ess` which is a scalar tensor
- My implementation returns `torch::tensor(ess_val, options)` which is also a scalar tensor
This should match.
One more thing - I should make sure the CUDA extension is compiled only once, not on every forward call. The `load_inline` call at module level handles this.
I think the implementation is ready. Let me submit it.
Actually, wait - I should double check the initialization. The reference model uses:
```python
mus = torch.randn(num_components, dim) * 2.0
self.register_buffer("mix_mus", mus)
```
And my ModelNew does the same. However, since `torch.randn` uses the global RNG state, the values should be consistent between both models if they're instantiated with the same random state.
But wait, the problem description says:
> Your `ModelNew` class must:
> 1. Accept the same constructor arguments as `Model`.
So `ModelNew.__init__` should have the same signature as `Model.__init__`, which is `(dim, num_components=3)`. Let me verify:
Reference:
```python
def __init__(self, dim, num_components=3):
```
My implementation:
```python
def __init__(self, dim, num_components=3):
```
Yes, they match.
Now let me think about potential issues:
1. The CUDA kernel uses `__ldg` which requires compute capability >= 3.5. This should be fine for modern GPUs.
2. The shared memory kernel uses `extern __shared__` which is standard.
3. The atomic operations use `atomicAdd` for floats, which is standard CUDA.
I think the implementation is correct. Let me submit it.
Hmm, actually I want to reconsider one thing. Looking at the shared memory kernel more carefully:
```cpp
extern __shared__ float smem[];
float* s_mean = smem;
float* s_w2 = &smem[blockDim.x * D];
```
This assumes that `smem` is a contiguous block of size `threads * (D + 1) * sizeof(float)`. The `s_mean` array has `threads * D` elements, and `s_w2` has `threads` elements. This is correct.
But I should also check the indexing:
- `s_mean[tid * D + d]` accesses elements from 0 to `threads * D - 1`
- `s_w2[tid]` accesses elements from 0 to `threads - 1`
And `s_w2 = &smem[blockDim.x * D]` points to the start of the second part, which is correct.
Alright, the implementation looks correct. Let me finalize and submit.
One final optimization I could consider is using vector loads (float4) for the samples, but since D=16 is not a multiple of 4 in a way that aligns well with the thread indexing, I'll skip this for now.
Let me also add error handling for CUDA:
Actually, I'll keep it simple for now and trust that the CUDA code compiles and runs correctly. If there are issues, I'll debug them.
Here's the final implementation:
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 <cmath>\n#include <cfloat>\n\n__global__ void compute_log_weights_kernel(\n const float* __restrict__ samples,\n const int N, const int D, const int K,\n const float* __restrict__ log_mix_weights,\n const float* __restrict__ mix_mus,\n const float* __restrict__ mix_stds,\n const float* __restrict__ q_mu,\n const float* __restrict__ q_std,\n float* __restrict__ log_w) {\n \n const int i = blockIdx.x * blockDim.x + threadIdx.x;\n if (i >= N) return;\n \n // Compute log_q = sum_d [-0.5 * ((x_d - q_mu_d) / q_std_d)^2 - log(q_std_d)]\n float log_q_val = 0.0f;\n for (int d = 0; d < D; d++) {\n const float diff = __ldg(&samples[i * D + d]) - __ldg(&q_mu[d]);\n const float s = __ldg(&q_std[d]);\n const float inv_s = 1.0f / s;\n log_q_val += -0.5f * diff * diff * inv_s * inv_s - logf(s);\n }\n \n // Compute log_p (mixture) using logsumexp over components\n float max_log_comp = -FLT_MAX;\n float log_comps[32]; // Support up to 32 mixture components\n \n for (int k = 0; k < K; k++) {\n float log_comp = __ldg(&log_mix_weights[k]);\n for (int d = 0; d < D; d++) {\n const float diff = __ldg(&samples[i * D + d]) - __ldg(&mix_mus[k * D + d]);\n const float s = __ldg(&mix_stds[k * D + d]);\n const float inv_s = 1.0f / s;\n log_comp += -0.5f * diff * diff * inv_s * inv_s - logf(s);\n }\n log_comps[k] = log_comp;\n max_log_comp = fmaxf(max_log_comp, log_comp);\n }\n \n float sum_exp = 0.0f;\n for (int k = 0; k < K; k++) {\n sum_exp += expf(log_comps[k] - max_log_comp);\n }\n \n const float log_p_val = max_log_comp + logf(sum_exp);\n log_w[i] = log_p_val - log_q_val;\n}\n\n__global__ void compute_weighted_stats_kernel(\n const float* __restrict__ samples,\n const float* __restrict__ log_w,\n const int N, const int D,\n const float log_sum,\n float* __restrict__ weighted_mean,\n float* __restrict__ sum_w2) {\n \n extern __shared__ float smem[];\n float* s_mean = smem;\n float* s_w2 = &smem[blockDim.x * D];\n \n const int tid = threadIdx.x;\n const int i = blockIdx.x * blockDim.x + tid;\n \n // Compute normalized weight\n float w = 0.0f;\n if (i < N) {\n w = expf(log_w[i] - log_sum);\n }\n const float w2 = w * w;\n \n // Store contributions in shared memory\n s_w2[tid] = w2;\n for (int d = 0; d < D; d++) {\n s_mean[tid * D + d] = (i < N) ? w * __ldg(&samples[i * D + d]) : 0.0f;\n }\n \n __syncthreads();\n \n // Parallel reduction within block\n for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {\n if (tid < stride) {\n s_w2[tid] += s_w2[tid + stride];\n for (int d = 0; d < D; d++) {\n s_mean[tid * D + d] += s_mean[(tid + stride) * D + d];\n }\n }\n __syncthreads();\n }\n \n // Thread 0 writes block result\n if (tid == 0) {\n atomicAdd(sum_w2, s_w2[0]);\n for (int d = 0; d < D; d++) {\n atomicAdd(&weighted_mean[d], s_mean[d]);\n }\n }\n}\n\nstd::tuple<torch::Tensor, torch::Tensor> importance_sampling_forward(\n torch::Tensor samples,\n torch::Tensor log_mix_weights,\n torch::Tensor mix_mus,\n torch::Tensor mix_stds,\n torch::Tensor q_mu,\n torch::Tensor q_std) {\n \n samples = samples.contiguous();\n log_mix_weights = log_mix_weights.contiguous();\n mix_mus = mix_mus.contiguous();\n mix_stds = mix_stds.contiguous();\n q_mu = q_mu.contiguous();\n q_std = q_std.contiguous();\n \n const int N = samples.size(0);\n const int D = samples.size(1);\n const int K = log_mix_weights.size(0);\n \n auto options = samples.options();\n auto log_w = torch::empty({N}, options);\n auto weighted_mean = torch::zeros({D}, options);\n auto sum_w2 = torch::zeros({1}, options);\n \n const int threads = 256;\n const int blocks = (N + threads - 1) / threads;\n \n // Kernel 1: Compute log weights\n compute_log_weights_kernel<<<blocks, threads>>>(\n samples.data_ptr<float>(),\n N, D, K,\n log_mix_weights.data_ptr<float>(),\n mix_mus.data_ptr<float>(),\n mix_stds.data_ptr<float>(),\n q_mu.data_ptr<float>(),\n q_std.data_ptr<float>(),\n log_w.data_ptr<float>()\n );\n \n // Compute logsumexp normalization factor\n const float log_sum = torch::logsumexp(log_w, 0).item<float>();\n \n // Kernel 2: Compute weighted mean and sum of squared weights\n const size_t shared_mem_size = threads * (D + 1) * sizeof(float);\n compute_weighted_stats_kernel<<<blocks, threads, shared_mem_size>>>(\n samples.data_ptr<float>(),\n log_w.data_ptr<float>(),\n N, D,\n log_sum,\n weighted_mean.data_ptr<float>(),\n sum_w2.data_ptr<float>()\n );\n \n // Compute ESS = 1 / sum(w^2)\n const float ess_val = 1.0f / sum_w2.item<float>();\n return std::make_tuple(weighted_mean, torch::tensor(ess_val, options));\n}\n\"\"\"\n\nimportance_sampling_ext = load_inline(\n name=\"importance_sampling_ext\",\n cpp_sources=\"\",\n cuda_sources=cuda_source,\n functions=[\"forward\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, dim, num_components=3):\n super().__init__()\n self.dim = dim\n self.num_components = num_components\n self.register_buffer(\"mix_weights\", torch.ones(num_components) / num_components)\n mus = torch.randn(num_components, dim) * 2.0\n self.register_buffer(\"mix_mus\", mus)\n self.register_buffer(\"mix_stds\", torch.ones(num_components, dim))\n self.register_buffer(\"q_mu\", torch.zeros(dim))\n self.register_buffer(\"q_std\", torch.ones(dim) * 3.0)\n \n def forward(self, samples: torch.Tensor) -> tuple:\n log_mix_weights = torch.log(self.mix_weights)\n weighted_mean, ess = importance_sampling_ext.forward(\n samples, log_mix_weights, self.mix_mus, self.mix_stds, self.q_mu, self.q_std\n )\n return weighted_mean, ess"
}
result ← submit_kernel (fail)
failsubmit_kernel
submit_kernel FAILED: kernel did not compile.
Error building extension 'importance_sampling_ext': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=importance_sampling_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp -o main.o
FAILED: [code=1] main.o
/opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=importance_sampling_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp: In function 'void pybind11_init_importance_sampling_ext(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp:4:46: error: 'forward' was not declared in this scope
4 | m.def("forward", torch::wrap_pybind_function(forward), "forward");
| ^~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp:4:46: note: suggested alternatives:
In file included from /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/bits/stl_function.h:60,
from /opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/functional:49,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/headeronly/core/DeviceType.h:13,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/c10/core/DeviceType.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/c10/core/Device.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/TensorBody.h:12,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/Tensor.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/Tensor.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/function_hook.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:3,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/variable.h:7,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/autograd/autograd.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/extension.h:6,
from /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/main.cpp:1:
/opt/conda/lib/gcc/x86_64-conda-linux-gnu/14.2.0/include/c++/bits/move.h:82:5: note: 'std::forward'
82 | forward(typename std::remove_reference<_Tp>::type&& __t) noexcept
| ^~~~~~~
In file included from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/ir.h:19,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/api/function_impl.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/api/method.h:8,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/api/object.h:6,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/jit/api/module.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/serialize/input-archive.h:6,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/serialize/archive.h:4,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/data/samplers/serialize.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/data/samplers.h:9,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/data/datasets/chunk.h:7,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/data/datasets.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/data.h:5,
from /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include/torch/all.h:10:
/scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/ATen/core/interned_strings.h:353:1: note: 'c10::attr::forward'
353 | FORALL_NS_SYMBOLS(DEFINE_SYMBOL)
| ^~~~~~~~~~~~~~~~~
[2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=importance_sampling_ext -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_st_gpt/popcorn/FW-GLM-5-1/level_1_problem_26_cache/k_ae4b09fce822/importance_sampling_ext/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
ninja: build stopped: subcommand failed.