turn 1271.5s turn · 68.3s LLM · 203.2s tools · tokens in=2,217 · out=5,586 · total=7,803
reasoning
Now let me write a custom CUDA kernel for this task. The operation involves:
1. For each batch element and each node i:
- For each pair (i, j):
- Concatenate [node_i, node_j, pair_feat_ij]
- Apply MLP: Linear(2*D + P -> H), SiLU, Linear(H -> D)
- Mask invalid edges
- Max pool over j
- LayerNorm
- Add residual (nodes)
Key considerations:
- B=4, N=24, D=96, P=16, H=192
- Total fused computations: B * N * N = 4 * 24 * 24 = 2304 calls to MLP
- Input size per fused: 2*D + P = 2*96 + 16 = 208
- Output size per fused: D = 96
Strategy:
- Fuse the concatenation, MLP computation, and masking into one kernel
- The MLP has two linear layers:
- Linear1: (208 -> 192) weight shape (192, 208)
- Linear2: (192 -> 96) weight shape (96, 192)
I'll write a kernel that:
1. Each thread handles one (b, i, j) triple for computing fused[i,j]
2. Then use parallel reduction for max-pooling
3. Apply LayerNorm and residual
Actually, let me think more carefully about the memory access patterns. The operations are:
For each batch b:
For each i in range(N):
For each j in range(N):
if edge_mask[b,i,j] > 0.5:
fused[b,i,j] = MLP([nodes[b,i], nodes[b,j], pair_feats[b,i,j]])
pooled[b,i] = max_j fused[b,i,j] (only considering valid edges)
output[b] = nodes[b] + LayerNorm(pooled[b])
Let me write a fused kernel:
1. First kernel: compute fused values with MLP and mask
- Each thread handles one (b, i, j)
- Store intermediate results in shared memory or global memory
2. Second kernel or same kernel: max pooling
- Could be done in same kernel with shared memory reduction
Actually, let me think about this more carefully. The problem is that we need:
- fused[b,i,j] for all j before we can compute pooled[b,i]
So one approach:
- Kernel 1: Compute all fused[b,i,j] = masked MLP output
- Kernel 2: Max reduce over j dimension
Or a single fused kernel with shared memory:
- Assign one thread block per (b, i) pair
- Each thread handles one j
- Threads cooperatively compute MLP and then reduce for max
Let me go with the single fused kernel approach for better efficiency.
Kernel design:
- Grid: (B * N) blocks, each handling one (b, i) pair
- Block: N threads, each handling one j value
- Shared memory: store fused values for reduction
Wait, with B=4, N=24, that's only 96 blocks, which might not fully utilize the GPU. Let me think...
Actually, let's compute:
- 96 blocks
- 24 threads per block
- That's 2304 total threads
The H100 has 132 SMs, and each SM can handle multiple blocks. With 96 blocks, we'd have less than 1 block per SM on average. That's not ideal for full utilization.
Alternative approach:
- Grid: (B * N * N) = 2304 blocks (one per (b, i, j))
- Each block has enough threads to compute the MLP
But then max-pooling would require synchronization across blocks, which is complex.
Let me try a different approach:
- Launch with thread blocks that handle multiple (b, i) pairs
- Or use a simpler approach with separate kernels
Actually, for small problem sizes like this, the overhead of kernel launches is relatively small, so separate kernels might be fine. Let me try:
1. Kernel for MLP fusion: each thread handles one (b, i, j)
2. Kernel for max pooling: each thread handles one (b, i)
3. LayerNorm and residual could be fused with max pooling
Let me write this step by step.
Actually, I realize I should be careful about the sizes. With B=4, N=24, D=96, the total data sizes are:
- nodes: 4 * 24 * 96 * 4 = 36,864 bytes = 36 KB
- pair_feats: 4 * 24 * 24 * 16 * 4 = 147,456 bytes = 144 KB
- edge_mask: 4 * 24 * 24 * 4 = 9,216 bytes = 9 KB
- fused intermediate: 4 * 24 * 24 * 96 * 4 = 884,736 bytes = 864 KB
These are small enough to fit in L2 cache easily.
Let me write the kernel now.
```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
import math
# Define CUDA source
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <cfloat>
// SiLU activation: x * sigmoid(x)
__device__ __forceinline__ float silu(float x) {
return x * (1.0f / (1.0f + expf(-x)));
}
// Fused MLP kernel: compute fused[b,i,j] for all (b,i,j)
// Grid: (B * N * N) blocks
// Block: threads cooperate to compute MLP
__global__ void fused_mlp_kernel(
const float* __restrict__ nodes, // (B, N, D)
const float* __restrict__ pair_feats, // (B, N, N, P)
const float* __restrict__ edge_mask, // (B, N, N)
const float* __restrict__ w1, // (hidden, 2*D + P)
const float* __restrict__ b1, // (hidden,)
const float* __restrict__ w2, // (D, hidden)
const float* __restrict__ b2, // (D,)
float* __restrict__ fused, // (B, N, N, D)
int B, int N, int D, int P, int hidden
) {
int idx = blockIdx.x;
int b = idx / (N * N);
idx %= (N * N);
int i = idx / N;
int j = idx % N;
int input_size = 2 * D + P;
// Load input: [node_i, node_j, pair_feat_ij]
extern __shared__ float input[];
// Cooperative loading
for (int k = threadIdx.x; k < input_size; k += blockDim.x) {
if (k < D) {
input[k] = nodes[b * N * D + i * D + k];
} else if (k < 2 * D) {
input[k] = nodes[b * N * D + j * D + (k - D)];
} else {
input[k] = pair_feats[b * N * N * P + i * N * P + j * P + (k - 2 * D)];
}
}
__syncthreads();
// Check edge mask
float mask = edge_mask[b * N * N + i * N + j];
// Compute first linear layer: hidden = SiLU(input @ w1.T + b1)
extern __shared__ float hidden_buf[];
float* hidden_out = hidden_buf;
for (int h = threadIdx.x; h < hidden; h += blockDim.x) {
float sum = b1[h];
for (int k = 0; k < input_size; k++) {
sum += input[k] * w1[h * input_size + k];
}
hidden_out[h] = silu(sum);
}
__syncthreads();
// Compute second linear layer: out = hidden_out @ w2.T + b2
for (int d = threadIdx.x; d < D; d += blockDim.x) {
float sum = b2[d];
for (int h = 0; h < hidden; h++) {
sum += hidden_out[h] * w2[d * hidden + h];
}
// Apply mask
float neg_inf = -CUDART_INF_F;
fused[b * N * N * D + i * N * D + j * D + d] = (mask >= 0.5f) ? sum : neg_inf;
}
}
// Max pooling kernel: pooled[b,i] = max_j fused[b,i,j]
// Grid: (B * N) blocks
// Block: N threads, each handles one j for reduction
__global__ void max_pool_kernel(
const float* __restrict__ fused, // (B, N, N, D)
float* __restrict__ pooled, // (B, N, D)
int B, int N, int D
) {
int b = blockIdx.x / N;
int i = blockIdx.x % N;
int d = threadIdx.x; // Each thread handles one dimension
if (d >= D) return;
float max_val = -CUDART_INF_F;
for (int j = 0; j < N; j++) {
float val = fused[b * N * N * D + i * N * D + j * D + d];
if (val > max_val) max_val = val;
}
pooled[b * N * D + i * D + d] = max_val;
}
// LayerNorm kernel: out = LN(pooled) + nodes
__global__ void layernorm_residual_kernel(
const float* __restrict__ pooled, // (B, N, D)
const float* __restrict__ nodes, // (B, N, D)
const float* __restrict__ gamma, // (D,)
const float* __restrict__ beta, // (D,)
float* __restrict__ out, // (B, N, D)
int B, int N, int D, float eps
) {
int b = blockIdx.x / N;
int i = blockIdx.x % N;
// Compute mean and variance
extern __shared__ float sdata[];
float* s_mean = sdata;
float* s_var = sdata + 1;
float local_sum = 0.0f;
float local_sum_sq = 0.0f;
for (int d = threadIdx.x; d < D; d += blockDim.x) {
float val = pooled[b * N * D + i * D + d];
local_sum += val;
local_sum_sq += val * val;
}
// Reduce within block
// ... (simplified, using atomicAdd for now)
__shared__ float mean, var;
if (threadIdx.x == 0) {
mean = 0.0f;
var = 0.0f;
}
__syncthreads();
// Use warp reduce or atomic
atomicAdd(&mean, local_sum);
atomicAdd(&var, local_sum_sq);
__syncthreads();
if (threadIdx.x == 0) {
mean /= D;
var = var / D - mean * mean;
}
__syncthreads();
// Apply LayerNorm and residual
float inv_std = rsqrtf(var + eps);
for (int d = threadIdx.x; d < D; d += blockDim.x) {
float val = pooled[b * N * D + i * D + d];
float ln = (val - mean) * inv_std * gamma[d] + beta[d];
out[b * N * D + i * D + d] = nodes[b * N * D + i * D + d] + ln;
}
}
"""
# But this is getting complicated. Let me try a simpler approach.
# For small tensors, we can use a single kernel that does everything.
```
Actually, let me reconsider. The problem is small enough that we might benefit from a different approach. Let me write a more integrated kernel:
1. For each (b, i), we need to compute pooled[b,i] = max_j MLP([node_i, node_j, pair_ij])
2. Then apply LayerNorm and residual
The key insight is that for each (b, i), the computation across j is independent until the max pooling. So we can:
1. Launch one block per (b, i) pair
2. Each thread in the block handles one j
3. Compute MLP for that j, store in shared memory
4. Do parallel max reduction in shared memory
5. Compute LayerNorm and store result
Let me write this more carefully.
assistantLooking at this task, I need to implement a fused CUDA kernel for the GF-VLA style scene graph readout. The operation involves:
1. MLP fusion of `[node_i, node_j, pair_feat_ij]` for all pairs
2. Masking invalid edges
3. Max-pooling over j dimension
4. LayerNorm + residual connection
Let me write an optimized kernel:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.cpp_extension import load_inline\nimport math\nimport os\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cfloat>\n\n__device__ __forceinline__ float silu(float x) {\n return x * (1.0f / (1.0f + expf(-x)));\n}\n\n// Warp reduce for max\ntemplate <int BLOCK_SIZE>\n__device__ float warp_reduce_max(float val) {\n for (int offset = 16; offset > 0; offset /= 2) {\n val = fmaxf(val, __shfl_down_sync(0xffffffff, val, offset));\n }\n return val;\n}\n\n// Fused kernel: one block per (b, i), each thread handles one j\n// Computes MLP for all j, max-pools, applies LayerNorm, adds residual\n__global__ void fused_graph_kernel(\n const float* __restrict__ nodes, // (B, N, D)\n const float* __restrict__ pair_feats, // (B, N, N, P)\n const float* __restrict__ edge_mask, // (B, N, N)\n const float* __restrict__ w1, // (hidden, 2*D + P)\n const float* __restrict__ b1, // (hidden,)\n const float* __restrict__ w2, // (D, hidden)\n const float* __restrict__ b2, // (D,)\n const float* __restrict__ gamma, // (D,)\n const float* __restrict__ beta, // (D,)\n float* __restrict__ out, // (B, N, D)\n int B, int N, int D, int P, int hidden\n) {\n // Block indices\n int b = blockIdx.x / N;\n int i = blockIdx.x % N;\n int j = threadIdx.x; // Each thread handles one j\n int tidx = threadIdx.x;\n \n const int input_size = 2 * D + P;\n const float neg_inf = -CUDART_INF_F;\n const float eps = 1e-5f;\n \n // Shared memory for intermediate results\n // Layout: [hidden_buf (hidden * N), pooled (D * N), shared_reduce (D)]\n extern __shared__ char smem[];\n float* hidden_buf = (float*)smem; // hidden * N\n float* pooled_shared = hidden_buf + hidden * N; // D * N\n float* reduce_buf = pooled_shared + D * N; // D * 2 (for mean/var)\n \n float* s_mean = reduce_buf;\n float* s_var = reduce_buf + 1;\n \n // Each thread computes MLP for its j\n // Thread j computes: fused[i,j] = MLP([node_i, node_j, pair_ij])\n \n // Load node_i (all threads need this)\n // We'll compute input vector on-the-fly during matmul\n \n float mask = edge_mask[b * N * N + i * N + j];\n bool valid = (mask >= 0.5f) && (j < N);\n \n // Compute first linear layer: hidden = SiLU(input @ w1.T + b1)\n // Store result in shared memory\n float* my_hidden = hidden_buf + j * hidden;\n \n for (int h = 0; h < hidden; h++) {\n float sum = b1[h];\n \n // node_i contribution (first D elements of input)\n for (int k = 0; k < D; k++) {\n float node_i_val = nodes[b * N * D + i * D + k];\n sum += node_i_val * w1[h * input_size + k];\n }\n \n // node_j contribution (next D elements)\n if (valid) {\n for (int k = 0; k < D; k++) {\n float node_j_val = nodes[b * N * D + j * D + k];\n sum += node_j_val * w1[h * input_size + D + k];\n }\n }\n \n // pair_feats contribution (last P elements)\n if (valid) {\n for (int k = 0; k < P; k++) {\n float pair_val = pair_feats[b * N * N * P + i * N * P + j * P + k];\n sum += pair_val * w1[h * input_size + 2 * D + k];\n }\n }\n \n my_hidden[h] = silu(sum);\n }\n \n // Compute second linear layer: out_d = hidden @ w2.T + b2\n // We need max over j for each dimension d\n \n float my_out[96]; // Max D=96, store result for this thread's j\n \n for (int d = 0; d < D; d++) {\n float sum = b2[d];\n for (int h = 0; h < hidden; h++) {\n sum += my_hidden[h] * w2[d * hidden + h];\n }\n // Apply mask: use neg_inf for invalid edges\n my_out[d] = valid ? sum : neg_inf;\n }\n \n __syncthreads();\n \n // Parallel max reduction over j (threadIdx.x)\n // Each dimension d needs max across threads\n // We'll do this cooperatively\n \n // Store my_out in shared memory for reduction\n float* my_pooled = pooled_shared + j * D;\n for (int d = 0; d < D; d++) {\n my_pooled[d] = my_out[d];\n }\n \n __syncthreads();\n \n // Now reduce: each thread computes max for some dimensions\n // Thread tidx computes max for dimensions [tidx, tidx+blockDim.x, ...]\n float max_val[96]; // Max D=96\n \n for (int d = tidx; d < D; d += blockDim.x) {\n float m = neg_inf;\n for (int jj = 0; jj < N; jj++) {\n float val = pooled_shared[jj * D + d];\n if (val > m) m = val;\n }\n max_val[d] = m;\n }\n \n // Store reduced max in first N slots of pooled_shared\n if (tidx < D) {\n pooled_shared[tidx] = max_val[tidx];\n }\n __syncthreads();\n \n // Now compute LayerNorm\n // First compute mean and variance\n if (tidx == 0) {\n float sum = 0.0f;\n float sum_sq = 0.0f;\n for (int d = 0; d < D; d++) {\n float val = pooled_shared[d];\n sum += val;\n sum_sq += val * val;\n }\n *s_mean = sum / D;\n *s_var = sum_sq / D - (*s_mean) * (*s_mean);\n }\n __syncthreads();\n \n float mean = *s_mean;\n float inv_std = rsqrtf(*s_var + eps);\n \n // Apply LayerNorm and residual\n for (int d = tidx; d < D; d += blockDim.x) {\n float pooled_val = pooled_shared[d];\n float ln = (pooled_val - mean) * inv_std * gamma[d] + beta[d];\n float node_val = nodes[b * N * D + i * D + d];\n out[b * N * D + i * D + d] = node_val + ln;\n }\n}\n\n// Host function\nvoid fused_graph_cuda(\n const torch::Tensor& nodes,\n const torch::Tensor& pair_feats,\n const torch::Tensor& edge_mask,\n const torch::Tensor& w1,\n const torch::Tensor& b1,\n const torch::Tensor& w2,\n const torch::Tensor& b2,\n const torch::Tensor& gamma,\n const torch::Tensor& beta,\n torch::Tensor& out,\n int B, int N, int D, int P, int hidden\n) {\n int blocks = B * N;\n int threads = N; // One thread per j value\n \n // Shared memory: hidden*N + D*N + 2\n size_t smem_size = hidden * N * sizeof(float) + D * N * sizeof(float) + 2 * sizeof(float);\n \n fused_graph_kernel<<<blocks, threads, smem_size>>>(\n nodes.data_ptr<float>(),\n pair_feats.data_ptr<float>(),\n edge_mask.data_ptr<float>(),\n w1.data_ptr<float>(),\n b1.data_ptr<float>(),\n w2.data_ptr<float>(),\n b2.data_ptr<float>(),\n gamma.data_ptr<float>(),\n beta.data_ptr<float>(),\n out.data_ptr<float>(),\n B, N, D, P, hidden\n );\n}\n\"\"\"\n\n# Load the CUDA extension\nfused_ext = load_inline(\n name='fused_graph',\n cpp_sources=\"\",\n cuda_sources=cuda_source,\n functions=[\"fused_graph_cuda\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\n\nclass ModelNew(nn.Module):\n def __init__(self, dim: int, pair_dim: int, hidden: int):\n super().__init__()\n self.dim = dim\n self.pair_dim = pair_dim\n self.hidden = hidden\n \n # Define layers same as reference\n self.fuse = nn.Sequential(\n nn.Linear(2 * dim + pair_dim, hidden),\n nn.SiLU(),\n nn.Linear(hidden, dim),\n )\n self.out_norm = nn.LayerNorm(dim)\n \n # Pre-compute weights for faster access\n self._weights_cached = False\n \n def _cache_weights(self):\n # Get weights from Sequential module\n linear1 = self.fuse[0]\n linear2 = self.fuse[2]\n \n self.w1 = linear1.weight # (hidden, 2*D + P)\n self.b1 = linear1.bias # (hidden,)\n self.w2 = linear2.weight # (D, hidden)\n self.b2 = linear2.bias # (D,)\n \n self.gamma = self.out_norm.weight # (D,)\n self.beta = self.out_norm.bias # (D,)\n \n self._weights_cached = True\n \n def forward(\n self,\n nodes: torch.Tensor,\n pair_feats: torch.Tensor,\n edge_mask: torch.Tensor,\n ) -> torch.Tensor:\n B, N, D = nodes.shape\n \n # Fall back to PyTorch for non-CUDA or unsupported dimensions\n if not nodes.is_cuda:\n return self._pytorch_forward(nodes, pair_feats, edge_mask)\n \n if not self._weights_cached:\n self._cache_weights()\n \n # Ensure contiguous and correct dtype\n nodes_c = nodes.contiguous().float()\n pair_feats_c = pair_feats.contiguous().float()\n edge_mask_c = edge_mask.contiguous().float()\n w1_c = self.w1.contiguous().float()\n b1_c = self.b1.contiguous().float()\n w2_c = self.w2.contiguous().float()\n b2_c = self.b2.contiguous().float()\n gamma_c = self.gamma.contiguous().float()\n beta_c = self.beta.contiguous().float()\n \n out = torch.empty_like(nodes_c)\n \n fused_ext.fused_graph_cuda(\n nodes_c, pair_feats_c, edge_mask_c,\n w1_c, b1_c, w2_c, b2_c,\n gamma_c, beta_c, out,\n B, N, D, self.pair_dim, self.hidden\n )\n \n return out\n \n def _pytorch_forward(self, nodes, pair_feats, edge_mask):\n # Reference implementation for fallback\n B, N, D = nodes.shape\n hi = nodes.unsqueeze(2).expand(-1, -1, N, -1)\n hj = nodes.unsqueeze(1).expand(-1, N, -1, -1)\n fused = self.fuse(torch.cat([hi, hj, pair_feats], dim=-1))\n neg = torch.finfo(fused.dtype).min\n fused = fused.masked_fill(edge_mask.unsqueeze(-1) < 0.5, neg)\n pooled, _ = fused.max(dim=2)\n return nodes + self.out_norm(pooled)"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'fused_graph': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=fused_graph -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_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/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=fused_graph -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_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/main.cpp: In function 'void pybind11_init_fused_graph(pybind11::module_&)':
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/main.cpp:4:55: error: 'fused_graph_cuda' was not declared in this scope
4 | m.def("fused_graph_cuda", torch::wrap_pybind_function(fused_graph_cuda), "fused_graph_cuda");
| ^~~~~~~~~~~~~~~~
[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=fused_graph -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' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/cuda.cu -o cuda.cuda.o
FAILED: [code=2] cuda.cuda.o
/usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=fused_graph -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' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/cuda.cu -o cuda.cuda.o
nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used
/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/cuda.cu(45): error: identifier "CUDART_INF_F" is undefined
const float neg_inf = -CUDART_INF_F;
^
1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/pop_l123_default_gpt/popcorn/FW-GLM-5-1/level_2_problem_10_cache/k_b03bfb9cc5e0/fused_graph/cuda.cu".
ninja: build stopped: subcommand failed.