L2 P14 · original

error 14_Gemm_Divide_Sum_Scaling.py

model

gpt-5.5-priority

turns / max

15 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

original

runtime

—

ref runtime

0.00 μs

speedup

—

started

2026-05-04T04:44:11Z

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn

class Model(nn.Module):
    """
    Model that performs a matrix multiplication, division, summation, and scaling.
    """
    def __init__(self, input_size, hidden_size, scaling_factor):
        super(Model, self).__init__()
        self.weight = nn.Parameter(torch.randn(hidden_size, input_size))
        self.scaling_factor = scaling_factor

    def forward(self, x):
        """
        Args:
            x (torch.Tensor): Input tensor of shape (batch_size, input_size).
        Returns:
            torch.Tensor: Output tensor of shape (batch_size, hidden_size).
        """
        x = torch.matmul(x, self.weight.T)  # Gemm
        x = x / 2  # Divide
        x = torch.sum(x, dim=1, keepdim=True) # Sum
        x = x * self.scaling_factor  # Scaling
        return x


batch_size   = 1024  
input_size   = 8192  
hidden_size  = 8192 
scaling_factor = 1.5

def get_inputs():
    return [torch.rand(batch_size, input_size)]

def get_init_inputs():
    return [input_size, hidden_size, scaling_factor]

turn 010.3s turn · 10.3s LLM · 0.0s tools

reasoning

**Considering CUDA kernel approach** I'm thinking about utilizing a CUDA kernel that operates with a grid across columns and row blocks. This method could involve calculating partial sums for each combination of (column block, row block) and then reducing those partial sums. It seems like a potentially efficient strategy to handle parallel processing. It's all about leveraging the architecture to maximize performance, right? Let’s keep exploring this to ensure I'm on the right track!

call → get_gpu_specs

callget_gpu_specs {}

result ← get_gpu_specs (ok)

okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB

turn 1 · 1 tool call(s) · 32.6s turn · 32.6s LLM · 9,663 chars (click to expand)

turn 132.6s turn · 32.6s LLM · 0.0s tools

reasoning

**Evaluating model efficiency** I’m considering whether I should cache the column sum across forwards. I think it might be important for correctness if I weigh it initialized just once from reference weights. For evaluation, it seems like instantiating ModelNew and copying the weights would be the best approach. I wonder if this will improve performance or if there’s a different method to consider. It feels like there might be more efficient ways to handle this. **Considering warp reductions** I'm thinking about how to improve warp reductions, specifically looking at using a block size of 256 and achieving good occupancy with 1024 blocks. The dot product could handle about 8 million fused multiply-add operations, but there's also a significant amount of memory being read each time – about 33MB for x and 32MB for c. I need to factor in L2 cache efficiency too. Lastly, I should compile everything to ensure accuracy.

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be a CUDA tensor\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type() == at::kFloat, #x \" must be float32\")\n\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I) {\n int col = blockIdx.x * 32 + threadIdx.x;\n int row0 = blockIdx.y * 256;\n int tx = threadIdx.x;\n int ty = threadIdx.y;\n __shared__ float smem[8][32];\n float s = 0.0f;\n if (col < I) {\n #pragma unroll\n for (int rr = ty; rr < 256; rr += 8) {\n int row = row0 + rr;\n if (row < H) s += w[(long long)row * I + col];\n }\n }\n smem[ty][tx] = s;\n __syncthreads();\n if (ty == 0 && col < I) {\n float t = smem[0][tx] + smem[1][tx] + smem[2][tx] + smem[3][tx]\n + smem[4][tx] + smem[5][tx] + smem[6][tx] + smem[7][tx];\n partial[(long long)blockIdx.y * I + col] = t;\n }\n}\n\n__global__ void colsum_finish_kernel(const float* __restrict__ partial, float* __restrict__ csum, int tiles, int I) {\n int col = blockIdx.x * blockDim.x + threadIdx.x;\n if (col >= I) return;\n float s = 0.0f;\n for (int t = 0; t < tiles; ++t) s += partial[(long long)t * I + col];\n csum[col] = s;\n}\n\n__global__ void dot_kernel(const float* __restrict__ x, const float* __restrict__ csum, float* __restrict__ out, int B, int I, float scale) {\n int b = blockIdx.x;\n int tid = threadIdx.x;\n float s = 0.0f;\n const float* xb = x + (long long)b * I;\n for (int i = tid; i < I; i += blockDim.x) {\n s = fmaf(xb[i], csum[i], s);\n }\n __shared__ float shm[256];\n shm[tid] = s;\n __syncthreads();\n for (int stride = 128; stride > 0; stride >>= 1) {\n if (tid < stride) shm[tid] += shm[tid + stride];\n __syncthreads();\n }\n if (tid == 0) out[b] = shm[0] * scale;\n}\n\ntorch::Tensor compute_colsum(torch::Tensor weight) {\n CHECK_CUDA(weight); CHECK_FLOAT(weight);\n TORCH_CHECK(weight.dim() == 2, \"weight must be 2D\");\n auto wc = weight.contiguous();\n int H = (int)wc.size(0);\n int I = (int)wc.size(1);\n auto opts = wc.options();\n auto csum = torch::empty({I}, opts);\n int tiles = (H + 255) / 256;\n auto partial = torch::empty({tiles, I}, opts);\n dim3 block(32, 8);\n dim3 grid((I + 31) / 32, tiles);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n colsum_partial_kernel<<<grid, block, 0, stream>>>(wc.data_ptr<float>(), partial.data_ptr<float>(), H, I);\n colsum_finish_kernel<<<(I + 255) / 256, 256, 0, stream>>>(partial.data_ptr<float>(), csum.data_ptr<float>(), tiles, I);\n return csum;\n}\n\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale) {\n CHECK_CUDA(x); CHECK_CUDA(csum); CHECK_FLOAT(x); CHECK_FLOAT(csum);\n TORCH_CHECK(x.dim() == 2, \"x must be 2D\");\n auto xc = x.contiguous();\n int B = (int)xc.size(0);\n int I = (int)xc.size(1);\n TORCH_CHECK(csum.numel() == I, \"csum length mismatch\");\n auto out = torch::empty({B, 1}, xc.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n dot_kernel<<<B, 256, 0, stream>>>(xc.data_ptr<float>(), csum.data_ptr<float>(), out.data_ptr<float>(), B, I, (float)(0.5 * scale));\n return out;\n}\n'''\n\n_ext = load_inline(name='matmul_sum_scale_ext_v1', cpp_sources='', cuda_sources=_cuda_src,\n functions=['compute_colsum', 'apply_dot'],\n extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, scaling_factor):\n super(ModelNew, self).__init__()\n self.weight = nn.Parameter(torch.randn(hidden_size, input_size))\n self.scaling_factor = scaling_factor\n self._csum = None\n self._csum_version = -1\n def forward(self, x):\n v = self.weight._version\n if self._csum is None or self._csum_version != v or self._csum.device != self.weight.device:\n self._csum = _ext.compute_colsum(self.weight)\n self._csum_version = v\n return _ext.apply_dot(x, self._csum, float(self.scaling_factor))\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'matmul_sum_scale_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=matmul_sum_scale_ext_v1 -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/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=matmul_sum_scale_ext_v1 -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/main.cpp -o main.o /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/main.cpp: In function 'void pybind11_init_matmul_sum_scale_ext_v1(pybind11::module_&)': /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/main.cpp:4:53: error: 'compute_colsum' was not declared in this scope 4 | m.def("compute_colsum", torch::wrap_pybind_function(compute_colsum), "compute_colsum"); | ^~~~~~~~~~~~~~ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/main.cpp:5:48: error: 'apply_dot' was not declared in this scope 5 | m.def("apply_dot", torch::wrap_pybind_function(apply_dot), "apply_dot"); | ^~~~~~~~~ [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=matmul_sum_scale_ext_v1 -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_80c9e5fa576e/matmul_sum_scale_ext_v1/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.

turn 2 · 1 tool call(s) · 10.1s turn · 10.1s LLM · 6,086 chars (click to expand)

turn 210.1s turn · 10.1s LLM · 0.0s tools

reasoning

**Organizing C++ Declarations** I need to ensure there are proper declarations in my C++ sources. I should add prototypes like `torch::Tensor compute_colsum(torch::Tensor)` along with `#include <torch/extension.h>`. I think it's important for the module to recognize these declarations. I’ll load the inline auto module so it can see everything correctly. It seems straightforward, but I want to double-check that everything is in place to prevent any issues during compilation.

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be a CUDA tensor\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type() == at::kFloat, #x \" must be float32\")\n\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I) {\n int col = blockIdx.x * 32 + threadIdx.x;\n int row0 = blockIdx.y * 256;\n int tx = threadIdx.x;\n int ty = threadIdx.y;\n __shared__ float smem[8][32];\n float s = 0.0f;\n if (col < I) {\n #pragma unroll\n for (int rr = ty; rr < 256; rr += 8) {\n int row = row0 + rr;\n if (row < H) s += w[(long long)row * I + col];\n }\n }\n smem[ty][tx] = s;\n __syncthreads();\n if (ty == 0 && col < I) {\n float t = smem[0][tx] + smem[1][tx] + smem[2][tx] + smem[3][tx]\n + smem[4][tx] + smem[5][tx] + smem[6][tx] + smem[7][tx];\n partial[(long long)blockIdx.y * I + col] = t;\n }\n}\n\n__global__ void colsum_finish_kernel(const float* __restrict__ partial, float* __restrict__ csum, int tiles, int I) {\n int col = blockIdx.x * blockDim.x + threadIdx.x;\n if (col >= I) return;\n float s = 0.0f;\n for (int t = 0; t < tiles; ++t) s += partial[(long long)t * I + col];\n csum[col] = s;\n}\n\n__global__ void dot_kernel(const float* __restrict__ x, const float* __restrict__ csum, float* __restrict__ out, int B, int I, float scale) {\n int b = blockIdx.x;\n int tid = threadIdx.x;\n float s = 0.0f;\n const float* xb = x + (long long)b * I;\n for (int i = tid; i < I; i += blockDim.x) {\n s = fmaf(xb[i], csum[i], s);\n }\n __shared__ float shm[256];\n shm[tid] = s;\n __syncthreads();\n for (int stride = 128; stride > 0; stride >>= 1) {\n if (tid < stride) shm[tid] += shm[tid + stride];\n __syncthreads();\n }\n if (tid == 0) out[b] = shm[0] * scale;\n}\n\ntorch::Tensor compute_colsum(torch::Tensor weight) {\n CHECK_CUDA(weight); CHECK_FLOAT(weight);\n TORCH_CHECK(weight.dim() == 2, \"weight must be 2D\");\n auto wc = weight.contiguous();\n int H = (int)wc.size(0);\n int I = (int)wc.size(1);\n auto opts = wc.options();\n auto csum = torch::empty({I}, opts);\n int tiles = (H + 255) / 256;\n auto partial = torch::empty({tiles, I}, opts);\n dim3 block(32, 8);\n dim3 grid((I + 31) / 32, tiles);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n colsum_partial_kernel<<<grid, block, 0, stream>>>(wc.data_ptr<float>(), partial.data_ptr<float>(), H, I);\n colsum_finish_kernel<<<(I + 255) / 256, 256, 0, stream>>>(partial.data_ptr<float>(), csum.data_ptr<float>(), tiles, I);\n return csum;\n}\n\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale) {\n CHECK_CUDA(x); CHECK_CUDA(csum); CHECK_FLOAT(x); CHECK_FLOAT(csum);\n TORCH_CHECK(x.dim() == 2, \"x must be 2D\");\n auto xc = x.contiguous();\n int B = (int)xc.size(0);\n int I = (int)xc.size(1);\n TORCH_CHECK(csum.numel() == I, \"csum length mismatch\");\n auto out = torch::empty({B, 1}, xc.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n dot_kernel<<<B, 256, 0, stream>>>(xc.data_ptr<float>(), csum.data_ptr<float>(), out.data_ptr<float>(), B, I, (float)(0.5 * scale));\n return out;\n}\n'''\n\n_ext = load_inline(name='matmul_sum_scale_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['compute_colsum', 'apply_dot'],\n extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, scaling_factor):\n super(ModelNew, self).__init__()\n self.weight = nn.Parameter(torch.randn(hidden_size, input_size))\n self.scaling_factor = scaling_factor\n self._csum = None\n self._csum_version = -1\n def forward(self, x):\n v = self.weight._version\n if self._csum is None or self._csum_version != v or self._csum.device != self.weight.device:\n self._csum = _ext.compute_colsum(self.weight)\n self._csum_version = v\n return _ext.apply_dot(x, self._csum, float(self.scaling_factor))\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 310.5s turn · 10.5s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be a CUDA tensor\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type() == at::kFloat, #x \" must be float32\")\n\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I) {\n int col = blockIdx.x * 32 + threadIdx.x;\n int row0 = blockIdx.y * 256;\n int tx = threadIdx.x;\n int ty = threadIdx.y;\n __shared__ float smem[8][32];\n float s = 0.0f;\n if (col < I) {\n #pragma unroll\n for (int rr = ty; rr < 256; rr += 8) {\n int row = row0 + rr;\n if (row < H) s += w[(long long)row * I + col];\n }\n }\n smem[ty][tx] = s;\n __syncthreads();\n if (ty == 0 && col < I) {\n float t = smem[0][tx] + smem[1][tx] + smem[2][tx] + smem[3][tx]\n + smem[4][tx] + smem[5][tx] + smem[6][tx] + smem[7][tx];\n partial[(long long)blockIdx.y * I + col] = t;\n }\n}\n\n__global__ void colsum_finish_kernel(const float* __restrict__ partial, float* __restrict__ csum, int tiles, int I) {\n int col = blockIdx.x * blockDim.x + threadIdx.x;\n if (col >= I) return;\n float s = 0.0f;\n for (int t = 0; t < tiles; ++t) s += partial[(long long)t * I + col];\n csum[col] = s;\n}\n\n__global__ void dot_kernel(const float* __restrict__ x, const float* __restrict__ csum, float* __restrict__ out, int B, int I, float scale) {\n int b = blockIdx.x;\n int tid = threadIdx.x;\n float s = 0.0f;\n const float* xb = x + (long long)b * I;\n for (int i = tid; i < I; i += blockDim.x) {\n s = fmaf(xb[i], csum[i], s);\n }\n __shared__ float shm[256];\n shm[tid] = s;\n __syncthreads();\n for (int stride = 128; stride > 0; stride >>= 1) {\n if (tid < stride) shm[tid] += shm[tid + stride];\n __syncthreads();\n }\n if (tid == 0) out[b] = shm[0] * scale;\n}\n\ntorch::Tensor compute_colsum(torch::Tensor weight) {\n CHECK_CUDA(weight); CHECK_FLOAT(weight);\n TORCH_CHECK(weight.dim() == 2, \"weight must be 2D\");\n auto wc = weight.contiguous();\n int H = (int)wc.size(0);\n int I = (int)wc.size(1);\n auto opts = wc.options();\n auto csum = torch::empty({I}, opts);\n int tiles = (H + 255) / 256;\n auto partial = torch::empty({tiles, I}, opts);\n dim3 block(32, 8);\n dim3 grid((I + 31) / 32, tiles);\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n colsum_partial_kernel<<<grid, block, 0, stream>>>(wc.data_ptr<float>(), partial.data_ptr<float>(), H, I);\n colsum_finish_kernel<<<(I + 255) / 256, 256, 0, stream>>>(partial.data_ptr<float>(), csum.data_ptr<float>(), tiles, I);\n return csum;\n}\n\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale) {\n CHECK_CUDA(x); CHECK_CUDA(csum); CHECK_FLOAT(x); CHECK_FLOAT(csum);\n TORCH_CHECK(x.dim() == 2, \"x must be 2D\");\n auto xc = x.contiguous();\n int B = (int)xc.size(0);\n int I = (int)xc.size(1);\n TORCH_CHECK(csum.numel() == I, \"csum length mismatch\");\n auto out = torch::empty({B, 1}, xc.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n dot_kernel<<<B, 256, 0, stream>>>(xc.data_ptr<float>(), csum.data_ptr<float>(), out.data_ptr<float>(), B, I, (float)(0.5 * scale));\n return out;\n}\n'''\n\n_ext = load_inline(name='matmul_sum_scale_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['compute_colsum', 'apply_dot'],\n extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, scaling_factor):\n super(ModelNew, self).__init__()\n self.weight = nn.Parameter(torch.randn(hidden_size, input_size))\n self.scaling_factor = scaling_factor\n self._csum = None\n self._csum_version = -1\n def forward(self, x):\n v = self.weight._version\n if self._csum is None or self._csum_version != v or self._csum.device != self.weight.device:\n self._csum = _ext.compute_colsum(self.weight)\n self._csum_version = v\n return _ext.apply_dot(x, self._csum, float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.018738', '0.018555', '0.019592', '0.023804', '0.019775'] avg_difference: ['0.004611', '0.004592', '0.004644', '0.004560', '0.004740']

turn 418.0s turn · 18.0s LLM · 0.0s tools

reasoning

**Evaluating sum order in PyTorch** I'm figuring out the sum order in PyTorch. It seems like we need to use the same order for hidden-major, where it’s a sum over h of the products. The algebra for column sums uses sum_h w, followed by a dot product. I’m also thinking about how floating changes might affect this. It's pretty intricate, and I want to get it right to avoid any unexpected results!

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be CUDA\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" must be float\")\n__global__ void colsum_partial_kernel(const float* __restrict__ w, double* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x; int ty=threadIdx.y; __shared__ double sm[8][32]; double s=0.0;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += (double)w[(long long)row*I+col]; }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0 && col<I){ double t=0; #pragma unroll for(int k=0;k<8;k++) t+=sm[k][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void colsum_finish_kernel(const double* __restrict__ partial, double* __restrict__ csum, int tiles, int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; double s=0; for(int t=0;t<tiles;t++) s+=partial[(long long)t*I+col]; csum[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x, const double* __restrict__ csum, float* __restrict__ out, int B,int I,double scale){ int b=blockIdx.x; int tid=threadIdx.x; double s=0.0; const float* xb=x+(long long)b*I; for(int i=tid;i<I;i+=blockDim.x) s += (double)xb[i]*csum[i]; __shared__ double shm[256]; shm[tid]=s; __syncthreads(); for(int stride=128;stride>0;stride>>=1){ if(tid<stride) shm[tid]+=shm[tid+stride]; __syncthreads(); } if(tid==0) out[b]=(float)(shm[0]*scale); }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto wc=weight.contiguous(); int H=wc.size(0), I=wc.size(1); auto opts=torch::TensorOptions().device(wc.device()).dtype(torch::kFloat64); auto csum=torch::empty({I},opts); int tiles=(H+255)/256; auto partial=torch::empty({tiles,I},opts); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(wc.data_ptr<float>(),partial.data_ptr<double>(),H,I); colsum_finish_kernel<<<(I+255)/256,256,0,stream>>>(partial.data_ptr<double>(),csum.data_ptr<double>(),tiles,I); return csum; }\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0), I=xc.size(1); auto out=torch::empty({B,1},xc.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<double>(),out.data_ptr<float>(),B,I,0.5*scale); return out; }\n'''\n_ext=load_inline(name='matmul_sum_scale_ext_v3d', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._csum=None; self._csum_version=-1\n def forward(self,x):\n v=self.weight._version\n if self._csum is None or self._csum_version!=v or self._csum.device!=self.weight.device:\n self._csum=_ext.compute_colsum(self.weight); self._csum_version=v\n return _ext.apply_dot(x,self._csum,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: persistent build/lock contention after retries. Please try a different kernel.

turn 58.6s turn · 8.6s LLM · 0.0s tools

reasoning

**Investigating compile errors** I’m considering whether a compile error might be hidden due to "lock contention" related to the name. I think I should try compiling first with a unique name to see if that resolves the issue. Also, there might be an issue with the preprocessor directive `#pragma inline`. I’ll look into both aspects to figure out what's going wrong here, as it’s important to ensure I have a clean compile. Let’s see what happens!

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be CUDA\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" must be float\")\n__global__ void colsum_partial_kernel(const float* __restrict__ w, double* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x; int ty=threadIdx.y; __shared__ double sm[8][32]; double s=0.0;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += (double)w[(long long)row*I+col]; }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0 && col<I){ double t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void colsum_finish_kernel(const double* __restrict__ partial, double* __restrict__ csum, int tiles, int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; double s=0; for(int t=0;t<tiles;t++) s+=partial[(long long)t*I+col]; csum[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x, const double* __restrict__ csum, float* __restrict__ out, int B,int I,double scale){ int b=blockIdx.x; int tid=threadIdx.x; double s=0.0; const float* xb=x+(long long)b*I; for(int i=tid;i<I;i+=blockDim.x) s += (double)xb[i]*csum[i]; __shared__ double shm[256]; shm[tid]=s; __syncthreads(); for(int stride=128;stride>0;stride>>=1){ if(tid<stride) shm[tid]+=shm[tid+stride]; __syncthreads(); } if(tid==0) out[b]=(float)(shm[0]*scale); }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto wc=weight.contiguous(); int H=wc.size(0), I=wc.size(1); auto opts=torch::TensorOptions().device(wc.device()).dtype(torch::kFloat64); auto csum=torch::empty({I},opts); int tiles=(H+255)/256; auto partial=torch::empty({tiles,I},opts); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(wc.data_ptr<float>(),partial.data_ptr<double>(),H,I); colsum_finish_kernel<<<(I+255)/256,256,0,stream>>>(partial.data_ptr<double>(),csum.data_ptr<double>(),tiles,I); return csum; }\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0), I=xc.size(1); auto out=torch::empty({B,1},xc.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<double>(),out.data_ptr<float>(),B,I,0.5*scale); return out; }\n'''\n_ext=load_inline(name='matmul_sum_scale_ext_v4d_unique', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._csum=None; self._csum_version=-1\n def forward(self,x):\n v=self.weight._version\n if self._csum is None or self._csum_version!=v or self._csum.device!=self.weight.device:\n self._csum=_ext.compute_colsum(self.weight); self._csum_version=v\n return _ext.apply_dot(x,self._csum,float(self.scaling_factor))\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 67.1s turn · 7.1s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be CUDA\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" must be float\")\n__global__ void colsum_partial_kernel(const float* __restrict__ w, double* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x; int ty=threadIdx.y; __shared__ double sm[8][32]; double s=0.0;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += (double)w[(long long)row*I+col]; }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0 && col<I){ double t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void colsum_finish_kernel(const double* __restrict__ partial, double* __restrict__ csum, int tiles, int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; double s=0; for(int t=0;t<tiles;t++) s+=partial[(long long)t*I+col]; csum[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x, const double* __restrict__ csum, float* __restrict__ out, int B,int I,double scale){ int b=blockIdx.x; int tid=threadIdx.x; double s=0.0; const float* xb=x+(long long)b*I; for(int i=tid;i<I;i+=blockDim.x) s += (double)xb[i]*csum[i]; __shared__ double shm[256]; shm[tid]=s; __syncthreads(); for(int stride=128;stride>0;stride>>=1){ if(tid<stride) shm[tid]+=shm[tid+stride]; __syncthreads(); } if(tid==0) out[b]=(float)(shm[0]*scale); }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto wc=weight.contiguous(); int H=wc.size(0), I=wc.size(1); auto opts=torch::TensorOptions().device(wc.device()).dtype(torch::kFloat64); auto csum=torch::empty({I},opts); int tiles=(H+255)/256; auto partial=torch::empty({tiles,I},opts); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(wc.data_ptr<float>(),partial.data_ptr<double>(),H,I); colsum_finish_kernel<<<(I+255)/256,256,0,stream>>>(partial.data_ptr<double>(),csum.data_ptr<double>(),tiles,I); return csum; }\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0), I=xc.size(1); auto out=torch::empty({B,1},xc.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<double>(),out.data_ptr<float>(),B,I,0.5*scale); return out; }\n'''\n_ext=load_inline(name='matmul_sum_scale_ext_v4d_unique2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._csum=None; self._csum_version=-1\n def forward(self,x):\n v=self.weight._version\n if self._csum is None or self._csum_version!=v or self._csum.device!=self.weight.device:\n self._csum=_ext.compute_colsum(self.weight); self._csum_version=v\n return _ext.apply_dot(x,self._csum,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.018402', '0.017822', '0.019734', '0.022583', '0.019531'] avg_difference: ['0.004544', '0.004545', '0.004608', '0.004460', '0.004640']

turn 722.0s turn · 22.0s LLM · 0.0s tools

reasoning

**Calculating operations and error estimation** I'm analyzing a group size of 16 to compute 512 cumulative sum vectors. This would involve around 4.3 billion operations, which might still be faster than General Matrix Multiply (GEMM). Depending on the graphics card, the time for the computation could be around 0.1 milliseconds. However, if I take memory rereads into account, it could increase the total time. It's tricky; I must balance correctness while considering potential errors that scale with group size. I wonder about tolerances and dot product order too. **Exploring dot product calculations** I’m thinking about how to compute dot products for each h in a simple block and then sum them. However, it looks like I might not be able to use materialized results. There could be an error compared to the cuBLAS dot product, possibly around 0.01. I also need to consider that PyTorch's matrix multiplication uses TF32, which might affect how our FP32 dot product behaves. Balancing these calculations is crucial! **Considering computation methods** I'm thinking about how to compute the partial dot product for each group while interleaving. If I have each thread accumulate results across these groups, the final reduction might change the order of the group sums. I need to clarify how this will affect the overall computation and if it will yield accurate results. There's definitely a lot to consider in terms of efficiency and accuracy here!

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nGROUPS = 512\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor compute_gsum(torch::Tensor weight, int groups);\ntorch::Tensor apply_gdot(torch::Tensor x, torch::Tensor gsum, double scale);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void gsum_kernel(const float* __restrict__ w, float* __restrict__ gs, int H, int I, int G){\n int col=blockIdx.x*32+threadIdx.x; int g=blockIdx.y; int tx=threadIdx.x, ty=threadIdx.y; int start=(long long)g*H/G; int end=(long long)(g+1)*H/G; __shared__ float sm[8][32]; float s=0.f;\n if(col<I){ for(int r=start+ty; r<end; r+=8) s += w[(long long)r*I+col]; }\n sm[ty][tx]=s; __syncthreads(); if(ty==0 && col<I){ float t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; gs[(long long)g*I+col]=t; }\n}\n__global__ void gdot_kernel(const float* __restrict__ x, const float* __restrict__ gs, float* __restrict__ out, int B, int I, int G, float scale){\n int b=blockIdx.x, tid=threadIdx.x; const float* xb=x+(long long)b*I; __shared__ float shm[256]; float total=0.f;\n for(int g=0; g<G; ++g){ float s=0.f; const float* cg=gs+(long long)g*I; for(int i=tid;i<I;i+=256) s=fmaf(xb[i], cg[i], s); shm[tid]=s; __syncthreads(); for(int st=128; st>0; st>>=1){ if(tid<st) shm[tid]+=shm[tid+st]; __syncthreads(); } if(tid==0) total += shm[0]; __syncthreads(); }\n if(tid==0) out[b]=total*scale;\n}\ntorch::Tensor compute_gsum(torch::Tensor weight, int groups){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto w=weight.contiguous(); int H=w.size(0), I=w.size(1); auto gs=torch::empty({groups,I},w.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); gsum_kernel<<<dim3((I+31)/32,groups),dim3(32,8),0,stream>>>(w.data_ptr<float>(),gs.data_ptr<float>(),H,I,groups); return gs; }\ntorch::Tensor apply_gdot(torch::Tensor x, torch::Tensor gsum, double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0), I=xc.size(1), G=gsum.size(0); auto out=torch::empty({B,1},xc.options()); cudaStream_t stream=at::cuda::getCurrentCUDAStream(); gdot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),gsum.data_ptr<float>(),out.data_ptr<float>(),B,I,G,(float)(0.5*scale)); return out; }\n'''\n_ext=load_inline(name='group_ext_512_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_gsum','apply_gdot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._gs=None; self._ver=-1\n def forward(self,x):\n if self._gs is None or self._ver!=self.weight._version or self._gs.device!=self.weight.device:\n self._gs=_ext.compute_gsum(self.weight,GROUPS); self._ver=self.weight._version\n return _ext.apply_gdot(x,self._gs,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.018433', '0.017578', '0.020210', '0.024170', '0.020752'] avg_difference: ['0.004691', '0.004649', '0.004727', '0.004505', '0.004684']

turn 811.5s turn · 11.5s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[8][32]; float s=0.f;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += __float_to_tf32(w[(long long)row*I+col]); }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0&&col<I){ float t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void finish(const float* __restrict__ p,float* __restrict__ c,int tiles,int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; float s=0; for(int t=0;t<tiles;t++) s+=p[(long long)t*I+col]; c[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x,const float* __restrict__ c,float* __restrict__ out,int B,int I,float scale){ int b=blockIdx.x,tid=threadIdx.x; const float* xb=x+(long long)b*I; float s=0.f; for(int i=tid;i<I;i+=256) s=fmaf(__float_to_tf32(xb[i]), c[i], s); __shared__ float sh[256]; sh[tid]=s; __syncthreads(); for(int st=128;st>0;st>>=1){ if(tid<st) sh[tid]+=sh[tid+st]; __syncthreads(); } if(tid==0) out[b]=sh[0]*scale; }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto w=weight.contiguous(); int H=w.size(0),I=w.size(1); auto c=torch::empty({I},w.options()); int tiles=(H+255)/256; auto p=torch::empty({tiles,I},w.options()); auto stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(w.data_ptr<float>(),p.data_ptr<float>(),H,I); finish<<<(I+255)/256,256,0,stream>>>(p.data_ptr<float>(),c.data_ptr<float>(),tiles,I); return c; }\ntorch::Tensor apply_dot(torch::Tensor x,torch::Tensor csum,double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0),I=xc.size(1); auto out=torch::empty({B,1},xc.options()); auto stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<float>(),out.data_ptr<float>(),B,I,(float)(0.5*scale)); return out; }\n'''\n_ext=load_inline(name='tf32_col_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._cs=None; self._ver=-1\n def forward(self,x):\n if self._cs is None or self._ver!=self.weight._version or self._cs.device!=self.weight.device:\n self._cs=_ext.compute_colsum(self.weight); self._ver=self.weight._version\n return _ext.apply_dot(x,self._cs,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: persistent build/lock contention after retries. Please try a different kernel.

turn 9 · 1 tool call(s) · 8.1s turn · 8.1s LLM · 8,239 chars (click to expand)

turn 98.1s turn · 8.1s LLM · 0.0s tools

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[8][32]; float s=0.f;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += __float_to_tf32(w[(long long)row*I+col]); }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0&&col<I){ float t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void finish(const float* __restrict__ p,float* __restrict__ c,int tiles,int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; float s=0; for(int t=0;t<tiles;t++) s+=p[(long long)t*I+col]; c[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x,const float* __restrict__ c,float* __restrict__ out,int B,int I,float scale){ int b=blockIdx.x,tid=threadIdx.x; const float* xb=x+(long long)b*I; float s=0.f; for(int i=tid;i<I;i+=256) s=fmaf(__float_to_tf32(xb[i]), c[i], s); __shared__ float sh[256]; sh[tid]=s; __syncthreads(); for(int st=128;st>0;st>>=1){ if(tid<st) sh[tid]+=sh[tid+st]; __syncthreads(); } if(tid==0) out[b]=sh[0]*scale; }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto w=weight.contiguous(); int H=w.size(0),I=w.size(1); auto c=torch::empty({I},w.options()); int tiles=(H+255)/256; auto p=torch::empty({tiles,I},w.options()); auto stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(w.data_ptr<float>(),p.data_ptr<float>(),H,I); finish<<<(I+255)/256,256,0,stream>>>(p.data_ptr<float>(),c.data_ptr<float>(),tiles,I); return c; }\ntorch::Tensor apply_dot(torch::Tensor x,torch::Tensor csum,double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0),I=xc.size(1); auto out=torch::empty({B,1},xc.options()); auto stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<float>(),out.data_ptr<float>(),B,I,(float)(0.5*scale)); return out; }\n'''\n_ext=load_inline(name='tf32_col_ext_compile_unique', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._cs=None; self._ver=-1\n def forward(self,x):\n if self._cs is None or self._ver!=self.weight._version or self._cs.device!=self.weight.device:\n self._cs=_ext.compute_colsum(self.weight); self._ver=self.weight._version\n return _ext.apply_dot(x,self._cs,float(self.scaling_factor))\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'tf32_col_ext_compile_unique': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=tf32_col_ext_compile_unique -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 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/main.cpp -o main.o [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=tf32_col_ext_compile_unique -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/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=tf32_col_ext_compile_unique -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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/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/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/cuda.cu(13): error: identifier "__float_to_tf32" is undefined if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += __float_to_tf32(w[(long long)row*I+col]); }} ^ /scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/cuda.cu(17): error: identifier "__float_to_tf32" is undefined void dot_kernel(const float* __restrict__ x,const float* __restrict__ c,float* __restrict__ out,int B,int I,float scale){ int b=blockIdx.x,tid=threadIdx.x; const float* xb=x+(long long)b*I; float s=0.f; for(int i=tid;i<I;i+=256) s=fmaf(__float_to_tf32(xb[i]), c[i], s); ^ 2 errors detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/original/gpt-5.5-priority/level_2_problem_14_cache/k_4ec2fbae0e91/tf32_col_ext_compile_unique/cuda.cu". ninja: build stopped: subcommand failed.

turn 1011.1s turn · 11.1s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor compute_colsum(torch::Tensor weight);\ntorch::Tensor apply_dot(torch::Tensor x, torch::Tensor csum, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__device__ __forceinline__ float tf32_round(float v){ unsigned int x=__float_as_uint(v); unsigned int r=((x >> 13) & 1u) + 0x0FFFu; x = (x + r) & 0xFFFFE000u; return __uint_as_float(x); }\n__global__ void colsum_partial_kernel(const float* __restrict__ w, float* __restrict__ partial, int H, int I){\n int col=blockIdx.x*32+threadIdx.x; int row0=blockIdx.y*256; int tx=threadIdx.x,ty=threadIdx.y; __shared__ float sm[8][32]; float s=0.f;\n if(col<I){ for(int rr=ty; rr<256; rr+=8){ int row=row0+rr; if(row<H) s += tf32_round(w[(long long)row*I+col]); }}\n sm[ty][tx]=s; __syncthreads(); if(ty==0&&col<I){ float t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx]; partial[(long long)blockIdx.y*I+col]=t; }\n}\n__global__ void finish(const float* __restrict__ p,float* __restrict__ c,int tiles,int I){ int col=blockIdx.x*blockDim.x+threadIdx.x; if(col>=I)return; float s=0; for(int t=0;t<tiles;t++) s+=p[(long long)t*I+col]; c[col]=s; }\n__global__ void dot_kernel(const float* __restrict__ x,const float* __restrict__ c,float* __restrict__ out,int B,int I,float scale){ int b=blockIdx.x,tid=threadIdx.x; const float* xb=x+(long long)b*I; float s=0.f; for(int i=tid;i<I;i+=256) s=fmaf(tf32_round(xb[i]), c[i], s); __shared__ float sh[256]; sh[tid]=s; __syncthreads(); for(int st=128;st>0;st>>=1){ if(tid<st) sh[tid]+=sh[tid+st]; __syncthreads(); } if(tid==0) out[b]=sh[0]*scale; }\ntorch::Tensor compute_colsum(torch::Tensor weight){ CHECK_CUDA(weight); CHECK_FLOAT(weight); auto w=weight.contiguous(); int H=w.size(0),I=w.size(1); auto c=torch::empty({I},w.options()); int tiles=(H+255)/256; auto p=torch::empty({tiles,I},w.options()); auto stream=at::cuda::getCurrentCUDAStream(); colsum_partial_kernel<<<dim3((I+31)/32,tiles),dim3(32,8),0,stream>>>(w.data_ptr<float>(),p.data_ptr<float>(),H,I); finish<<<(I+255)/256,256,0,stream>>>(p.data_ptr<float>(),c.data_ptr<float>(),tiles,I); return c; }\ntorch::Tensor apply_dot(torch::Tensor x,torch::Tensor csum,double scale){ CHECK_CUDA(x); CHECK_FLOAT(x); auto xc=x.contiguous(); int B=xc.size(0),I=xc.size(1); auto out=torch::empty({B,1},xc.options()); auto stream=at::cuda::getCurrentCUDAStream(); dot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),csum.data_ptr<float>(),out.data_ptr<float>(),B,I,(float)(0.5*scale)); return out; }\n'''\n_ext=load_inline(name='tf32_soft_col_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=['compute_colsum','apply_dot'], extra_cuda_cflags=['-O3'], extra_cflags=['-O3'], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__(); self.weight=nn.Parameter(torch.randn(hidden_size,input_size)); self.scaling_factor=scaling_factor; self._cs=None; self._ver=-1\n def forward(self,x):\n if self._cs is None or self._ver!=self.weight._version or self._cs.device!=self.weight.device:\n self._cs=_ext.compute_colsum(self.weight); self._ver=self.weight._version\n return _ext.apply_dot(x,self._cs,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['2.839355', '2.454956', '2.633057', '2.418701', '2.882080'] avg_difference: ['0.613061', '0.670157', '0.632889', '0.624522', '0.646956']

turn 1127.4s turn · 27.4s LLM · 0.0s tools

reasoning

**Evaluating reference correctness** I'm wondering about how the reference correctness run constructs Model and ModelNew after execution. If changing the global torch backend affects reference matmul behavior, is that allowed? I feel like that's a tricky patching situation. It doesn’t affect timing or RNG, but adjusting global matmul precision could be questionable. Though, does the reference use current torch settings? If allow_tf32 is set to False, then maybe FP32 is the default? I should check if torch.backends.cuda.matmul.allow_tf32 is set appropriately. **Considering implementation options** I need to understand the sign or correlation, but the tool only provides absolute values. Should I manually write code for this? However, there's no way to execute arbitrary code except for the tools we have, which aren’t detailed here. I could focus on correctness instead of the sign. I might consider implementing Kahan summation or using different orders to see the differences. I'm thinking about the current direct sums regarding colsum, row tiles, and the dot product. I should choose an order that somewhat matches the hidden major.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nGROUPS=2048\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor compute_gsum(torch::Tensor weight, int groups);\ntorch::Tensor apply_gdot(torch::Tensor x, torch::Tensor gsum, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda_runtime.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void gsum_kernel(const float* __restrict__ w,float* __restrict__ gs,int H,int I,int G){int col=blockIdx.x*32+threadIdx.x,g=blockIdx.y,tx=threadIdx.x,ty=threadIdx.y;int start=(long long)g*H/G,end=(long long)(g+1)*H/G;__shared__ float sm[8][32];float s=0; if(col<I){for(int r=start+ty;r<end;r+=8)s+=w[(long long)r*I+col];} sm[ty][tx]=s;__syncthreads(); if(ty==0&&col<I){float t=sm[0][tx]+sm[1][tx]+sm[2][tx]+sm[3][tx]+sm[4][tx]+sm[5][tx]+sm[6][tx]+sm[7][tx];gs[(long long)g*I+col]=t;}}\n__global__ void gdot_kernel(const float* __restrict__ x,const float* __restrict__ gs,float* __restrict__ out,int I,int G,float scale){int b=blockIdx.x,tid=threadIdx.x;const float* xb=x+(long long)b*I;__shared__ float sh[256];float total=0; for(int g=0;g<G;++g){float s=0;const float* cg=gs+(long long)g*I;for(int i=tid;i<I;i+=256)s=fmaf(xb[i],cg[i],s);sh[tid]=s;__syncthreads();for(int st=128;st>0;st>>=1){if(tid<st)sh[tid]+=sh[tid+st];__syncthreads();}if(tid==0)total+=sh[0];__syncthreads();} if(tid==0)out[b]=total*scale;}\ntorch::Tensor compute_gsum(torch::Tensor weight,int groups){CHECK_CUDA(weight);CHECK_FLOAT(weight);auto w=weight.contiguous();int H=w.size(0),I=w.size(1);auto gs=torch::empty({groups,I},w.options());auto stream=at::cuda::getCurrentCUDAStream();gsum_kernel<<<dim3((I+31)/32,groups),dim3(32,8),0,stream>>>(w.data_ptr<float>(),gs.data_ptr<float>(),H,I,groups);return gs;}\ntorch::Tensor apply_gdot(torch::Tensor x,torch::Tensor gsum,double scale){CHECK_CUDA(x);CHECK_FLOAT(x);auto xc=x.contiguous();int B=xc.size(0),I=xc.size(1),G=gsum.size(0);auto out=torch::empty({B,1},xc.options());auto stream=at::cuda::getCurrentCUDAStream();gdot_kernel<<<B,256,0,stream>>>(xc.data_ptr<float>(),gsum.data_ptr<float>(),out.data_ptr<float>(),I,G,(float)(0.5*scale));return out;}\n'''\n_ext=load_inline(name='group_ext_2048_v1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['compute_gsum','apply_gdot'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__();self.weight=nn.Parameter(torch.randn(hidden_size,input_size));self.scaling_factor=scaling_factor;self._gs=None;self._ver=-1\n def forward(self,x):\n if self._gs is None or self._ver!=self.weight._version or self._gs.device!=self.weight.device:\n self._gs=_ext.compute_gsum(self.weight,GROUPS);self._ver=self.weight._version\n return _ext.apply_gdot(x,self._gs,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.021149', '0.018555', '0.025938', '0.023621', '0.020264'] avg_difference: ['0.004880', '0.004794', '0.004895', '0.004596', '0.005005']

turn 1221.2s turn · 21.2s LLM · 0.0s tools

reasoning

**Evaluating tensor precision** I need to ensure correctness in my calculations for the tensor operations. I'm referencing a matrix multiplication followed by a sum, and I wonder how floating-point order works in PyTorch with GEMM and TF32. A noticeable difference in outputs suggests the reference might use TF32 tensor cores with different rounding. Since the required tolerance is tight, matching outputs is crucial. I also wonder about using cuBLAS or custom CUDA to speed up the operations while maintaining precision. However, I'm concerned about the costs associated with my calculations. **Analyzing TF32 accumulation** I’m considering using TF32 for both multiplication, but I’m uncertain about the accumulation order in GEMM. It seems I should group by hidden rows, computing dot products and then summing up for each group. However, with 68 billion operations, using FP32 CUDA cores may be too slow. Reducing the group size could help with correctness, though I'm not sure if my approach is aligned with the reference. I wonder if using cuBLAS for GEMM would be more efficient, especially if I can implement a fused reduce method. **Matching cuBLAS settings** I’m trying to ensure that my matrix multiplication aligns with cuBLAS settings. It seems that PyTorch utilizes cuBLASLt, which may include TF32 support. To get everything working correctly, I need to set the cuBLAS math mode to TF32 tensor operations. Also, I’ll need to make sure that I compile and link everything properly for cuBLAS. It’s crucial that I get these settings right to match the expected performance and accuracy!

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gemm_reduce(torch::Tensor x, torch::Tensor weight, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/cuda/CUDABlas.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void reduce_kernel(const float* __restrict__ tmp,float* __restrict__ out,int B,int H,float scale){int b=blockIdx.x,tid=threadIdx.x;float s=0;const float* row=tmp+(long long)b*H;for(int i=tid;i<H;i+=256)s+=row[i];__shared__ float sh[256];sh[tid]=s;__syncthreads();for(int st=128;st>0;st>>=1){if(tid<st)sh[tid]+=sh[tid+st];__syncthreads();}if(tid==0)out[b]=sh[0]*scale;}\ntorch::Tensor gemm_reduce(torch::Tensor x, torch::Tensor weight, double scale){CHECK_CUDA(x);CHECK_CUDA(weight);CHECK_FLOAT(x);CHECK_FLOAT(weight);auto xc=x.contiguous();auto wc=weight.contiguous();int B=xc.size(0),I=xc.size(1),H=wc.size(0);auto tmp=torch::empty({B,H},xc.options());auto out=torch::empty({B,1},xc.options());auto handle=at::cuda::getCurrentCUDABlasHandle();cublasSetStream(handle, at::cuda::getCurrentCUDAStream());cublasSetMathMode(handle, CUBLAS_TF32_TENSOR_OP_MATH);float alpha=1.0f,beta=0.0f; cublasSgemm(handle,CUBLAS_OP_T,CUBLAS_OP_N,H,B,I,&alpha,wc.data_ptr<float>(),I,xc.data_ptr<float>(),I,&beta,tmp.data_ptr<float>(),H);reduce_kernel<<<B,256,0,at::cuda::getCurrentCUDAStream()>>>(tmp.data_ptr<float>(),out.data_ptr<float>(),B,H,(float)(0.5*scale));return out;}\n'''\n_ext=load_inline(name='gemm_reduce_ext_v1',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['gemm_reduce'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__();self.weight=nn.Parameter(torch.randn(hidden_size,input_size));self.scaling_factor=scaling_factor\n def forward(self,x):\n return _ext.gemm_reduce(x,self.weight,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['2.822632', '2.394958', '2.601074', '2.395020', '2.822021'] avg_difference: ['0.608892', '0.663825', '0.628189', '0.620905', '0.641484']

turn 1318.1s turn · 18.1s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r'''\n#include <torch/extension.h>\ntorch::Tensor gemm_reduce(torch::Tensor x, torch::Tensor weight, double scale);\n'''\n_cuda_src=r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/cuda/CUDABlas.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" cuda\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" float\")\n__global__ void reduce_kernel(const float* __restrict__ tmp,float* __restrict__ out,int H,float scale){int b=blockIdx.x,tid=threadIdx.x;float s=0;const float* row=tmp+(long long)b*H;for(int i=tid;i<H;i+=256)s+=row[i];__shared__ float sh[256];sh[tid]=s;__syncthreads();for(int st=128;st>0;st>>=1){if(tid<st)sh[tid]+=sh[tid+st];__syncthreads();}if(tid==0)out[b]=sh[0]*scale;}\ntorch::Tensor gemm_reduce(torch::Tensor x, torch::Tensor weight, double scale){CHECK_CUDA(x);CHECK_CUDA(weight);CHECK_FLOAT(x);CHECK_FLOAT(weight);auto xc=x.contiguous();auto wc=weight.contiguous();int B=xc.size(0),I=xc.size(1),H=wc.size(0);auto tmp=torch::empty({B,H},xc.options());auto out=torch::empty({B,1},xc.options());auto handle=at::cuda::getCurrentCUDABlasHandle();cublasSetStream(handle, at::cuda::getCurrentCUDAStream());cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH);float alpha=1.0f,beta=0.0f;cublasSgemm(handle,CUBLAS_OP_T,CUBLAS_OP_N,H,B,I,&alpha,wc.data_ptr<float>(),I,xc.data_ptr<float>(),I,&beta,tmp.data_ptr<float>(),H);reduce_kernel<<<B,256,0,at::cuda::getCurrentCUDAStream()>>>(tmp.data_ptr<float>(),out.data_ptr<float>(),H,(float)(0.5*scale));return out;}\n'''\n_ext=load_inline(name='gemm_reduce_ext_v2_default',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['gemm_reduce'],extra_cuda_cflags=['-O3'],extra_cflags=['-O3'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,input_size,hidden_size,scaling_factor):\n super().__init__();self.weight=nn.Parameter(torch.randn(hidden_size,input_size));self.scaling_factor=scaling_factor\n def forward(self,x):\n return _ext.gemm_reduce(x,self.weight,float(self.scaling_factor))\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (1 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.001709', '0.002197', '0.001709', '0.001953'] avg_difference: ['0.000420', '0.000442', '0.000421', '0.000427']

turn 14 · 1 tool call(s) · 16.2s turn · 16.2s LLM · 6,269 chars (click to expand)

turn 1416.2s turn · 16.2s LLM · 0.0s tools

reasoning

**Assessing computation correctness** I need to ensure that everything is submitted correctly. There's a mismatch with the cublas default, as the maximum tolerance needs to be matched exactly. I wonder if we can use PyTorch operations, which might require a custom extension but could be faster. The correctness tolerance is set to 1e-4, which is crucial. I’m considering whether using torch.sum on temporary variables will yield the same result as my reference after cublas calculations; this could help address the reduction order differences. **Exploring CUDA kernel optimizations** I'm considering whether using `torch.sum(tmp/2, dim=1) * scale` would produce a matching output after cublas operations. Although the forward pass includes the torch sum instead of a fully custom kernel, it might still yield a faster result. The task requires a custom CUDA kernel, so I'm unsure if I can submit while adhering to code requirements. I think the final kernel must use the extension for matrix multiplication and rely on PyTorch for division and summation, ensuring correctness throughout the calculations! **Determining submission strategy** I’m wondering whether to use `torch.matmul` in the forward pass, but it might not provide any speed benefits. My priority is submitting the best and most accurate approach. I think I should submit the extension for the gemm operation that returns the temporary result, along with the necessary torch operations. It seems like I don’t need to make the code static, so I can focus on submitting my code effectively. I really want to get this right!

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor do_gemm(torch::Tensor x, torch::Tensor weight);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <ATen/cuda/CUDABlas.h>\n#include <cuda_runtime.h>\n#include <cublas_v2.h>\n\n#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x \" must be CUDA\")\n#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x \" must be float32\")\n\ntorch::Tensor do_gemm(torch::Tensor x, torch::Tensor weight) {\n CHECK_CUDA(x); CHECK_CUDA(weight); CHECK_FLOAT(x); CHECK_FLOAT(weight);\n auto xc = x.contiguous();\n auto wc = weight.contiguous();\n int B = (int)xc.size(0);\n int I = (int)xc.size(1);\n int H = (int)wc.size(0);\n auto tmp = torch::empty({B, H}, xc.options());\n auto handle = at::cuda::getCurrentCUDABlasHandle();\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cublasSetStream(handle, stream);\n cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH);\n float alpha = 1.0f, beta = 0.0f;\n // Computes tmp = x @ weight.T. In cuBLAS' column-major view this is\n // tmp^T = weight * x^T, with dimensions H x B.\n cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N,\n H, B, I,\n &alpha,\n wc.data_ptr<float>(), I,\n xc.data_ptr<float>(), I,\n &beta,\n tmp.data_ptr<float>(), H);\n return tmp;\n}\n'''\n\n_ext = load_inline(name='gemm_then_torch_reduce_ext_final',\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=['do_gemm'],\n extra_cuda_cflags=['-O3'],\n extra_cflags=['-O3'],\n verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, input_size, hidden_size, scaling_factor):\n super(ModelNew, self).__init__()\n self.weight = nn.Parameter(torch.randn(hidden_size, input_size))\n self.scaling_factor = scaling_factor\n\n def forward(self, x):\n y = _ext.do_gemm(x, self.weight)\n y = y / 2.0\n y = torch.sum(y, dim=1, keepdim=True)\n y = y * self.scaling_factor\n return y\n" }

result ← submit_kernel (fail)

failsubmit_kernel submit_kernel FAILED: unexpected error during tool execution. _WorkItemTimeout: work item exceeded soft timeout budget Traceback (most recent call last): File "/scratch/abaner/KernelBench/src/kernelbench/agent/agent.py", line 627, in _execute_tool return tool.execute(self.ctx, **args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/tools.py", line 770, in execute return ctx.eval_client.submit_kernel(ctx, kernel_code) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 50, in submit_kernel return self._call("submit", args) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 117, in _call payload = self._response_q.get(timeout=remaining) File "<string>", line 2, in get File "/opt/conda/lib/python3.10/multiprocessing/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 250, in recv buf = self._recv_bytes() File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 414, in _recv_bytes buf = self._recv(4) File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 379, in _recv chunk = read(handle, remaining) File "/scratch/abaner/KernelBench/scripts/run_sweep.py", line 161, in _alarm_handler raise _WorkItemTimeout("work item exceeded soft timeout budget") __mp_main__._WorkItemTimeout: work item exceeded soft timeout budget

final submitted kernel (66 lines, 2,242 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cpp_src = r'''
#include <torch/extension.h>
torch::Tensor do_gemm(torch::Tensor x, torch::Tensor weight);
'''

_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDABlas.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>

#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be CUDA")
#define CHECK_FLOAT(x) TORCH_CHECK(x.scalar_type()==at::kFloat, #x " must be float32")

torch::Tensor do_gemm(torch::Tensor x, torch::Tensor weight) {
    CHECK_CUDA(x); CHECK_CUDA(weight); CHECK_FLOAT(x); CHECK_FLOAT(weight);
    auto xc = x.contiguous();
    auto wc = weight.contiguous();
    int B = (int)xc.size(0);
    int I = (int)xc.size(1);
    int H = (int)wc.size(0);
    auto tmp = torch::empty({B, H}, xc.options());
    auto handle = at::cuda::getCurrentCUDABlasHandle();
    cudaStream_t stream = at::cuda::getCurrentCUDAStream();
    cublasSetStream(handle, stream);
    cublasSetMathMode(handle, CUBLAS_DEFAULT_MATH);
    float alpha = 1.0f, beta = 0.0f;
    // Computes tmp = x @ weight.T.  In cuBLAS' column-major view this is
    // tmp^T = weight * x^T, with dimensions H x B.
    cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N,
                H, B, I,
                &alpha,
                wc.data_ptr<float>(), I,
                xc.data_ptr<float>(), I,
                &beta,
                tmp.data_ptr<float>(), H);
    return tmp;
}
'''

_ext = load_inline(name='gemm_then_torch_reduce_ext_final',
                   cpp_sources=_cpp_src,
                   cuda_sources=_cuda_src,
                   functions=['do_gemm'],
                   extra_cuda_cflags=['-O3'],
                   extra_cflags=['-O3'],
                   verbose=False)

class ModelNew(nn.Module):
    def __init__(self, input_size, hidden_size, scaling_factor):
        super(ModelNew, self).__init__()
        self.weight = nn.Parameter(torch.randn(hidden_size, input_size))
        self.scaling_factor = scaling_factor

    def forward(self, x):
        y = _ext.do_gemm(x, self.weight)
        y = y / 2.0
        y = torch.sum(y, dim=1, keepdim=True)
        y = y * self.scaling_factor
        return y