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\n\n# Define the CUDA kernel source\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <cmath>\n\nconstexpr int BLOCK_SIZE = 256;\n\n// LayerNorm kernel: normalized over last dimension\n__global__ void layer_norm_kernel(\n const float* __restrict__ input,\n float* __restrict__ output,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n int batch_size, int seq_len, int dim, float eps) {\n \n // Each block handles one (batch, seq) position\n int idx = blockIdx.x;\n int s = blockIdx.y;\n int b = blockIdx.z;\n \n if (b >= batch_size || s >= seq_len || idx >= seq_len) return;\n \n int base = (b * seq_len + s) * seq_len * dim + idx * dim;\n \n // Compute mean\n float mean = 0.0f;\n for (int d = 0; d < dim; d++) {\n mean += input[base + d];\n }\n mean /= dim;\n \n // Compute variance\n float var = 0.0f;\n for (int d = 0; d < dim; d++) {\n float diff = input[base + d] - mean;\n var += diff * diff;\n }\n var /= dim;\n \n // Normalize and apply affine transform\n float inv_std = rsqrtf(var + eps);\n for (int d = 0; d < dim; d++) {\n output[base + d] = (input[base + d] - mean) * inv_std * weight[d] + bias[d];\n }\n}\n\n// Fused proj + gate + sigmoid + mask kernel\n__global__ void fused_proj_gate_kernel(\n const float* __restrict__ input,\n const float* __restrict__ proj_weight,\n const float* __restrict__ proj_bias,\n const float* __restrict__ gate_weight,\n const float* __restrict__ gate_bias,\n float* __restrict__ output,\n const float* __restrict__ mask,\n int batch_size, int seq_len, int in_dim, int out_dim) {\n \n int d = threadIdx.x;\n int idx = blockIdx.x;\n int s = blockIdx.y;\n int b = blockIdx.z;\n \n if (b >= batch_size || s >= seq_len || idx >= seq_len || d >= out_dim) return;\n \n int in_base = (b * seq_len + s) * seq_len * in_dim + idx * in_dim;\n int out_base = (b * seq_len + s) * seq_len * out_dim + idx * out_dim;\n \n // Compute projection\n float proj_val = proj_bias[d];\n for (int i = 0; i < in_dim; i++) {\n proj_val += input[in_base + i] * proj_weight[d * in_dim + i];\n }\n \n // Compute gate\n float gate_val = gate_bias[d];\n for (int i = 0; i < in_dim; i++) {\n gate_val += input[in_base + i] * gate_weight[d * in_dim + i];\n }\n \n // Sigmoid\n gate_val = 1.0f / (1.0f + expf(-gate_val));\n \n // Apply mask and store\n float m = mask[(b * seq_len + s) * seq_len + idx];\n output[out_base + d] = proj_val * gate_val * m;\n}\n\n// Einsum kernel: \"bkih,bkjh->bijh\"\n// a: (B, N, N, H), b: (B, N, N, H) -> out: (B, N, N, H)\n// out[b,i,j,h] = sum_k a[b,k,i,h] * b[b,k,j,h]\n__global__ void einsum_kernel(\n const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int batch_size, int seq_len, int hidden_dim) {\n \n int h = threadIdx.x;\n int j = blockIdx.x;\n int i = blockIdx.y;\n int b = blockIdx.z;\n \n if (b >= batch_size || i >= seq_len || j >= seq_len || h >= hidden_dim) return;\n \n float sum = 0.0f;\n for (int k = 0; k < seq_len; k++) {\n int a_idx = ((b * seq_len + k) * seq_len + i) * hidden_dim + h;\n int b_idx = ((b * seq_len + k) * seq_len + j) * hidden_dim + h;\n sum += a[a_idx] * b[b_idx];\n }\n \n int out_idx = ((b * seq_len + i) * seq_len + j) * hidden_dim + h;\n out[out_idx] = sum;\n}\n\n// Output layer norm kernel\n__global__ void layer_norm_out_kernel(\n const float* __restrict__ input,\n float* __restrict__ output,\n const float* __restrict__ weight,\n const float* __restrict__ bias,\n int batch_size, int seq_len, int dim, float eps) {\n \n int idx = blockIdx.x;\n int s = blockIdx.y;\n int b = blockIdx.z;\n \n if (b >= batch_size || s >= seq_len || idx >= seq_len) return;\n \n int base = (b * seq_len + s) * seq_len * dim + idx * dim;\n \n float mean = 0.0f;\n for (int d = 0; d < dim; d++) {\n mean += input[base + d];\n }\n mean /= dim;\n \n float var = 0.0f;\n for (int d = 0; d < dim; d++) {\n float diff = input[base + d] - mean;\n var += diff * diff;\n }\n var /= dim;\n \n float inv_std = rsqrtf(var + eps);\n for (int d = 0; d < dim; d++) {\n output[base + d] = (input[base + d] - mean) * inv_std * weight[d] + bias[d];\n }\n}\n\n// Output projection + sigmoid gate kernel\n__global__ void output_proj_kernel(\n const float* __restrict__ normed,\n const float* __restrict__ out_proj_weight,\n const float* __restrict__ out_proj_bias,\n const float* __restrict__ pair_repr,\n const float* __restrict__ out_gate_weight,\n const float* __restrict__ out_gate_bias,\n float* __restrict__ output,\n int batch_size, int seq_len, int hidden_dim, int pair_dim) {\n \n int d = threadIdx.x;\n int idx = blockIdx.x;\n int s = blockIdx.y;\n int b = blockIdx.z;\n \n if (b >= batch_size || s >= seq_len || idx >= seq_len || d >= pair_dim) return;\n \n int norm_base = (b * seq_len + s) * seq_len * hidden_dim + idx * hidden_dim;\n int pair_base = (b * seq_len + s) * seq_len * pair_dim + idx * pair_dim;\n int out_base = (b * seq_len + s) * seq_len * pair_dim + idx * pair_dim;\n \n // Compute out_proj\n float proj_val = out_proj_bias[d];\n for (int h = 0; h < hidden_dim; h++) {\n proj_val += normed[norm_base + h] * out_proj_weight[d * hidden_dim + h];\n }\n \n // Compute out_gate\n float gate_val = out_gate_bias[d];\n for (int p = 0; p < pair_dim; p++) {\n gate_val += pair_repr[pair_base + p] * out_gate_weight[d * pair_dim + p];\n }\n \n // Sigmoid\n gate_val = 1.0f / (1.0f + expf(-gate_val));\n \n output[out_base + d] = gate_val * proj_val;\n}\n\n// Host functions\nvoid launch_layer_norm_in(\n const float* input, float* output,\n const float* weight, const float* bias,\n int batch_size, int seq_len, int dim, float eps, cudaStream_t stream) {\n \n dim3 grid(seq_len, seq_len, batch_size);\n layer_norm_kernel<<<grid, 1, 0, stream>>>(input, output, weight, bias, batch_size, seq_len, dim, eps);\n}\n\nvoid launch_fused_proj_gate(\n const float* input, float* output,\n const float* proj_weight, const float* proj_bias,\n const float* gate_weight, const float* gate_bias,\n const float* mask,\n int batch_size, int seq_len, int in_dim, int out_dim, cudaStream_t stream) {\n \n dim3 grid(seq_len, seq_len, batch_size);\n dim3 block(out_dim);\n fused_proj_gate_kernel<<<grid, block, 0, stream>>>(\n input, output, proj_weight, proj_bias, gate_weight, gate_bias, output, mask,\n batch_size, seq_len, in_dim, out_dim);\n}\n\nvoid launch_einsum(\n const float* a, const float* b, float* out,\n int batch_size, int seq_len, int hidden_dim, cudaStream_t stream) {\n \n dim3 grid(seq_len, seq_len, batch_size);\n dim3 block(hidden_dim);\n einsum_kernel<<<grid, block, 0, stream>>>(a, b, out, batch_size, seq_len, hidden_dim);\n}\n\nvoid launch_layer_norm_out(\n const float* input, float* output,\n const float* weight, const float* bias,\n int batch_size, int seq_len, int dim, float eps, cudaStream_t stream) {\n \n dim3 grid(seq_len, seq_len, batch_size);\n layer_norm_out_kernel<<<grid, 1, 0, stream>>>(input, output, weight, bias, batch_size, seq_len, dim, eps);\n}\n\nvoid launch_output_proj(\n const float* normed, float* output,\n const float* out_proj_weight, const float* out_proj_bias,\n const float* pair_repr,\n const float* out_gate_weight, const float* out_gate_bias,\n int batch_size, int seq_len, int hidden_dim, int pair_dim, cudaStream_t stream) {\n \n dim3 grid(seq_len, seq_len, batch_size);\n dim3 block(pair_dim);\n output_proj_kernel<<<grid, block, 0, stream>>>(\n normed, output, out_proj_weight, out_proj_bias,\n pair_repr, out_gate_weight, out_gate_bias, output,\n batch_size, seq_len, hidden_dim, pair_dim);\n}\n\"\"\"\n\ncpp_source = \"\"\"\nvoid launch_layer_norm_in(\n const float* input, float* output,\n const float* weight, const float* bias,\n int batch_size, int seq_len, int dim, float eps, cudaStream_t stream);\n\nvoid launch_fused_proj_gate(\n const float* input, float* output,\n const float* proj_weight, const float* proj_bias,\n const float* gate_weight, const float* gate_bias,\n const float* mask,\n int batch_size, int seq_len, int in_dim, int out_dim, cudaStream_t stream);\n\nvoid launch_einsum(\n const float* a, const float* b, float* out,\n int batch_size, int seq_len, int hidden_dim, cudaStream_t stream);\n\nvoid launch_layer_norm_out(\n const float* input, float* output,\n const float* weight, const float* bias,\n int batch_size, int seq_len, int dim, float eps, cudaStream_t stream);\n\nvoid launch_output_proj(\n const float* normed, float* output,\n const float* out_proj_weight, const float* out_proj_bias,\n const float* pair_repr,\n const float* out_gate_weight, const float* out_gate_bias,\n int batch_size, int seq_len, int hidden_dim, int pair_dim, cudaStream_t stream);\n\ntorch::Tensor layer_norm_in_cuda(\n torch::Tensor input, torch::Tensor weight, torch::Tensor bias,\n int batch_size, int seq_len, int dim, float eps) {\n \n auto output = torch::empty_like(input);\n launch_layer_norm_in(\n input.data_ptr<float>(), output.data_ptr<float>(),\n weight.data_ptr<float>(), bias.data_ptr<float>(),\n batch_size, seq_len, dim, eps,\n c10::cuda::getCurrentCUDAStream());\n return output;\n}\n\ntorch::Tensor fused_proj_gate_cuda(\n torch::Tensor input, torch::Tensor proj_weight, torch::Tensor proj_bias,\n torch::Tensor gate_weight, torch::Tensor gate_bias,\n torch::Tensor mask,\n int batch_size, int seq_len, int in_dim, int out_dim) {\n \n auto output = torch::empty({batch_size, seq_len, seq_len, out_dim}, input.options());\n launch_fused_proj_gate(\n input.data_ptr<float>(), output.data_ptr<float>(),\n proj_weight.data_ptr<float>(), proj_bias.data_ptr<float>(),\n gate_weight.data_ptr<float>(), gate_bias.data_ptr<float>(),\n mask.data_ptr<float>(),\n batch_size, seq_len, in_dim, out_dim,\n c10::cuda::getCurrentCUDAStream());\n return output;\n}\n\ntorch::Tensor einsum_cuda(\n torch::Tensor a, torch::Tensor b,\n int batch_size, int seq_len, int hidden_dim) {\n \n auto output = torch::empty({batch_size, seq_len, seq_len, hidden_dim}, a.options());\n launch_einsum(\n a.data_ptr<float>(), b.data_ptr<float>(), output.data_ptr<float>(),\n batch_size, seq_len, hidden_dim,\n c10::cuda::getCurrentCUDAStream());\n return output;\n}\n\ntorch::Tensor layer_norm_out_cuda(\n torch::Tensor input, torch::Tensor weight, torch::Tensor bias,\n int batch_size, int seq_len, int dim, float eps) {\n \n auto output = torch::empty_like(input);\n launch_layer_norm_out(\n input.data_ptr<float>(), output.data_ptr<float>(),\n weight.data_ptr<float>(), bias.data_ptr<float>(),\n batch_size, seq_len, dim, eps,\n c10::cuda::getCurrentCUDAStream());\n return output;\n}\n\ntorch::Tensor output_proj_cuda(\n torch::Tensor normed, torch::Tensor out_proj_weight, torch::Tensor out_proj_bias,\n torch::Tensor pair_repr, torch::Tensor out_gate_weight, torch::Tensor out_gate_bias,\n int batch_size, int seq_len, int hidden_dim, int pair_dim) {\n \n auto output = torch::empty({batch_size, seq_len, seq_len, pair_dim}, normed.options());\n launch_output_proj(\n normed.data_ptr<float>(), output.data_ptr<float>(),\n out_proj_weight.data_ptr<float>(), out_proj_bias.data_ptr<float>(),\n pair_repr.data_ptr<float>(),\n out_gate_weight.data_ptr<float>(), out_gate_bias.data_ptr<float>(),\n batch_size, seq_len, hidden_dim, pair_dim,\n c10::cuda::getCurrentCUDAStream());\n return output;\n}\n\"\"\"\n\n# Load the CUDA extension\ntriangular_update = load_inline(\n name='triangular_update',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['layer_norm_in_cuda', 'fused_proj_gate_cuda', 'einsum_cuda', \n 'layer_norm_out_cuda', 'output_proj_cuda'],\n verbose=False,\n extra_cuda_cflags=['-O3']\n)\n\n\nclass ModelNew(nn.Module):\n \"\"\"\n Triangular multiplicative update \u2013 incoming edges.\n Custom CUDA implementation for improved performance.\n \"\"\"\n\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.pair_dim = pair_dim\n self.hidden_dim = hidden_dim\n \n # Initialize parameters same as original model\n self.layer_norm_in = nn.LayerNorm(pair_dim)\n self.proj_a = nn.Linear(pair_dim, hidden_dim)\n self.gate_a = nn.Linear(pair_dim, hidden_dim)\n self.proj_b = nn.Linear(pair_dim, hidden_dim)\n self.gate_b = nn.Linear(pair_dim, hidden_dim)\n self.layer_norm_out = nn.LayerNorm(hidden_dim)\n self.out_proj = nn.Linear(hidden_dim, pair_dim)\n self.out_gate = nn.Linear(pair_dim, pair_dim)\n\n def forward(self, pair_repr: torch.Tensor, pair_mask: torch.Tensor) -> torch.Tensor:\n \"\"\"\n Args:\n pair_repr: (B, N, N, pair_dim)\n pair_mask: (B, N, N)\n Returns:\n updated pair representation (B, N, N, pair_dim)\n \"\"\"\n B, N, N_, pair_dim = pair_repr.shape\n H = self.hidden_dim\n \n # Ensure inputs are contiguous and on same device\n pair_repr = pair_repr.contiguous()\n pair_mask = pair_mask.contiguous()\n \n # Move parameters to device and ensure contiguous\n ln_in_weight = self.layer_norm_in.weight.contiguous()\n ln_in_bias = self.layer_norm_in.bias.contiguous()\n \n # Layer norm input\n x = triangular_update.layer_norm_in_cuda(\n pair_repr, ln_in_weight, ln_in_bias, B, N, pair_dim, 1e-5)\n \n # Fused proj_a + gate_a\n a = triangular_update.fused_proj_gate_cuda(\n x, \n self.proj_a.weight.contiguous(), self.proj_a.bias.contiguous(),\n self.gate_a.weight.contiguous(), self.gate_a.bias.contiguous(),\n pair_mask, B, N, pair_dim, H)\n \n # Fused proj_b + gate_b\n b = triangular_update.fused_proj_gate_cuda(\n x,\n self.proj_b.weight.contiguous(), self.proj_b.bias.contiguous(),\n self.gate_b.weight.contiguous(), self.gate_b.bias.contiguous(),\n pair_mask, B, N, pair_dim, H)\n \n # Einsum: bkih,bkjh->bijh\n out = triangular_update.einsum_cuda(a, b, B, N, H)\n \n # Layer norm output\n ln_out_weight = self.layer_norm_out.weight.contiguous()\n ln_out_bias = self.layer_norm_out.bias.contiguous()\n normed = triangular_update.layer_norm_out_cuda(\n out, ln_out_weight, ln_out_bias, B, N, H, 1e-5)\n \n # Output projection with gate\n result = triangular_update.output_proj_cuda(\n normed,\n self.out_proj.weight.contiguous(), self.out_proj.bias.contiguous(),\n pair_repr,\n self.out_gate.weight.contiguous(), self.out_gate.bias.contiguous(),\n B, N, H, pair_dim)\n \n return result"
}
result ← compile_kernel (fail)
failcompile_kernel
compile_kernel FAILED: RuntimeError.
RuntimeError: Error building extension 'triangular_update': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=triangular_update -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_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/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=triangular_update -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_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp -o main.o
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:6:54: error: 'cudaStream_t' has not been declared
6 | int batch_size, int seq_len, int dim, float eps, cudaStream_t stream);
| ^~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:13:59: error: 'cudaStream_t' has not been declared
13 | int batch_size, int seq_len, int in_dim, int out_dim, cudaStream_t stream);
| ^~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:17:50: error: 'cudaStream_t' has not been declared
17 | int batch_size, int seq_len, int hidden_dim, cudaStream_t stream);
| ^~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:22:54: error: 'cudaStream_t' has not been declared
22 | int batch_size, int seq_len, int dim, float eps, cudaStream_t stream);
| ^~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:29:64: error: 'cudaStream_t' has not been declared
29 | int batch_size, int seq_len, int hidden_dim, int pair_dim, cudaStream_t stream);
| ^~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp: In function 'at::Tensor layer_norm_in_cuda(at::Tensor, at::Tensor, at::Tensor, int, int, int, float)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:40:20: error: 'getCurrentCUDAStream' is not a member of 'c10::cuda'
40 | c10::cuda::getCurrentCUDAStream());
| ^~~~~~~~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp: In function 'at::Tensor fused_proj_gate_cuda(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:57:20: error: 'getCurrentCUDAStream' is not a member of 'c10::cuda'
57 | c10::cuda::getCurrentCUDAStream());
| ^~~~~~~~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp: In function 'at::Tensor einsum_cuda(at::Tensor, at::Tensor, int, int, int)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:69:20: error: 'getCurrentCUDAStream' is not a member of 'c10::cuda'
69 | c10::cuda::getCurrentCUDAStream());
| ^~~~~~~~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp: In function 'at::Tensor layer_norm_out_cuda(at::Tensor, at::Tensor, at::Tensor, int, int, int, float)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:82:20: error: 'getCurrentCUDAStream' is not a member of 'c10::cuda'
82 | c10::cuda::getCurrentCUDAStream());
| ^~~~~~~~~~~~~~~~~~~~
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp: In function 'at::Tensor output_proj_cuda(at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, at::Tensor, int, int, int, int)':
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/main.cpp:98:20: error: 'getCurrentCUDAStream' is not a member of 'c10::cuda'
98 | c10::cuda::getCurrentCUDAStream());
| ^~~~~~~~~~~~~~~~~~~~
[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=triangular_update -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_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/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=triangular_update -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_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/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_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(104): error: "b" has already been declared in the current scope
int b = blockIdx.z;
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(112): error: expression must have pointer-to-object type but it has type "int"
sum += a[a_idx] * b[b_idx];
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(214): error: argument of type "const float *" is incompatible with parameter of type "float *"
input, output, proj_weight, proj_bias, gate_weight, gate_bias, output, mask,
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(214): error: argument of type "const float *" is incompatible with parameter of type "int"
input, output, proj_weight, proj_bias, gate_weight, gate_bias, output, mask,
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(215): error: too many arguments in function call
batch_size, seq_len, in_dim, out_dim);
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(247): error: argument of type "const float *" is incompatible with parameter of type "float *"
pair_repr, out_gate_weight, out_gate_bias, output,
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(247): error: argument of type "float *" is incompatible with parameter of type "int"
pair_repr, out_gate_weight, out_gate_bias, output,
^
/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu(248): error: too many arguments in function call
batch_size, seq_len, hidden_dim, pair_dim);
^
8 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/pop_l123_all_gpt/popcorn/FW-GLM-5-1/level_2_problem_21_cache/k_849803177e7e/triangular_update/cuda.cu".
ninja: build stopped: subcommand failed.