L3 · problem 18
gpt-5.5-priority · original · l3_default_gpt
← all variants← original← gpt-5.5-prioritygenerated 2026-05-28 03:33:18 UTC
error 18_SqueezeNet.py
model
gpt-5.5-priority
turns / max
5 / 15
tool calls
4
tokens (total)
0
wall clock
—
truncated
no
backend
cuda fp32
variant
original
runtime
—
ref runtime
0.00 μs
speedup
—
started
2026-05-04T20:53:32Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
import torch.nn.functional as F

class FireModule(nn.Module):
    def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):
        """
        :param in_channels: Number of input channels
        :param squeeze_channels: Number of output channels for the squeeze layer
        :param expand1x1_channels: Number of output channels for the 1x1 expand layer
        :param expand3x3_channels: Number of output channels for the 3x3 expand layer
        """
        super(FireModule, self).__init__()
        
        self.squeeze = nn.Conv2d(in_channels, squeeze_channels, kernel_size=1)
        self.squeeze_activation = nn.ReLU(inplace=True)
        
        self.expand1x1 = nn.Conv2d(squeeze_channels, expand1x1_channels, kernel_size=1)
        self.expand1x1_activation = nn.ReLU(inplace=True)
        
        self.expand3x3 = nn.Conv2d(squeeze_channels, expand3x3_channels, kernel_size=3, padding=1)
        self.expand3x3_activation = nn.ReLU(inplace=True)
    
    def forward(self, x):
        """
        :param x: Input tensor, shape (batch_size, in_channels, height, width)
        :return: Output tensor, shape (batch_size, expand1x1_channels + expand3x3_channels, height, width)
        """
        x = self.squeeze_activation(self.squeeze(x))
        return torch.cat([
            self.expand1x1_activation(self.expand1x1(x)),
            self.expand3x3_activation(self.expand3x3(x))
        ], 1)

class Model(nn.Module):
    def __init__(self, num_classes=1000):
        """
        :param num_classes: Number of output classes
        """
        super(Model, self).__init__()
        
        self.features = nn.Sequential(
            nn.Conv2d(3, 96, kernel_size=7, stride=2),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            FireModule(96, 16, 64, 64),
            FireModule(128, 16, 64, 64),
            FireModule(128, 32, 128, 128),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            FireModule(256, 32, 128, 128),
            FireModule(256, 48, 192, 192),
            FireModule(384, 48, 192, 192),
            FireModule(384, 64, 256, 256),
            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
            FireModule(512, 64, 256, 256),
        )
        
        self.classifier = nn.Sequential(
            nn.Dropout(p=0.0),
            nn.Conv2d(512, num_classes, kernel_size=1),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1))
        )
    
    def forward(self, x):
        """
        :param x: Input tensor, shape (batch_size, 3, height, width)
        :return: Output tensor, shape (batch_size, num_classes)
        """
        x = self.features(x)
        x = self.classifier(x)
        return torch.flatten(x, 1)

# Test code
batch_size = 64
input_channels = 3
height = 512
width = 512
num_classes = 1000

def get_inputs():
    return [torch.rand(batch_size, input_channels, height, width)]

def get_init_inputs():
    return [num_classes]
turn 030.8s turn · 30.8s LLM · 0.0s tools
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 1131.7s turn · 131.7s LLM · 0.0s tools
reasoning
**Analyzing convolution operations** I'm exploring a way to optimize convolution operations with a combination of 1x1 convolutions and average pooling in a custom matrix. Essentially, I can integrate average calculations into the convolution layers. I realize using ReLU might prevent linear fusion, but I’m considering writing a custom kernel for final computation. This could save significant memory and operations, with calculations approaching 33 billion floating-point multiplications and accumulations. It's an exciting challenge to streamline this process!
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_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor relu_avg_pool(torch::Tensor x);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void relu_avg_pool_kernel(const float* __restrict__ x, float* __restrict__ out,\n int N, int C, int H, int W,\n long sN, long sC, long sH, long sW) {\n int n = blockIdx.y;\n int k = blockIdx.x * 32 + threadIdx.x;\n if (n >= N || k >= C) return;\n const float* base = x + (long)n * sN + (long)k * sC;\n float sum = 0.0f;\n #pragma unroll 4\n for (int h = 0; h < H; ++h) {\n const float* row = base + (long)h * sH;\n for (int w = 0; w < W; ++w) {\n float v = row[(long)w * sW];\n sum += v > 0.0f ? v : 0.0f;\n }\n }\n out[(long)n * C + k] = sum / (float)(H * W);\n}\n\ntorch::Tensor relu_avg_pool(torch::Tensor x) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float32\");\n TORCH_CHECK(x.dim() == 4, \"x must be 4D\");\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int H = (int)x.size(2);\n int W = (int)x.size(3);\n auto out = torch::empty({N, C}, x.options());\n dim3 block(32);\n dim3 grid((C + 31) / 32, N);\n relu_avg_pool_kernel<<<grid, block>>>(x.data_ptr<float>(), out.data_ptr<float>(), N, C, H, W,\n (long)x.stride(0), (long)x.stride(1),\n (long)x.stride(2), (long)x.stride(3));\n return out;\n}\n'''\n\n_ext = load_inline(name='squeezenet_relu_avg_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['relu_avg_pool'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass FireModule(nn.Module):\n def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):\n super(FireModule, self).__init__()\n self.squeeze = nn.Conv2d(in_channels, squeeze_channels, kernel_size=1)\n self.squeeze_activation = nn.ReLU(inplace=True)\n self.expand1x1 = nn.Conv2d(squeeze_channels, expand1x1_channels, kernel_size=1)\n self.expand1x1_activation = nn.ReLU(inplace=True)\n self.expand3x3 = nn.Conv2d(squeeze_channels, expand3x3_channels, kernel_size=3, padding=1)\n self.expand3x3_activation = nn.ReLU(inplace=True)\n def forward(self, x):\n x = self.squeeze_activation(self.squeeze(x))\n return torch.cat([self.expand1x1_activation(self.expand1x1(x)),\n self.expand3x3_activation(self.expand3x3(x))], 1)\n\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew, self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(3, 96, kernel_size=7, stride=2),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(96, 16, 64, 64),\n FireModule(128, 16, 64, 64),\n FireModule(128, 32, 128, 128),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(256, 32, 128, 128),\n FireModule(256, 48, 192, 192),\n FireModule(384, 48, 192, 192),\n FireModule(384, 64, 256, 256),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(512, 64, 256, 256),\n )\n self.classifier = nn.Sequential(\n nn.Dropout(p=0.0),\n nn.Conv2d(512, num_classes, kernel_size=1),\n nn.ReLU(inplace=True),\n nn.AdaptiveAvgPool2d((1, 1))\n )\n self.to(memory_format=torch.channels_last)\n def forward(self, x):\n x = x.contiguous(memory_format=torch.channels_last)\n x = self.features(x)\n # dropout has p=0; use the stored classifier conv, then fuse ReLU + global average + flatten\n x = self.classifier[1](x)\n return _ext.relu_avg_pool(x)\n" }
result ← compile_kernel (ok)
okcompile_kernel compile_kernel PASSED: kernel compiled without errors.
turn 28.9s turn · 8.9s LLM · 0.0s tools
call → run_correctness
callrun_correctness { "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_cpp_src = r'''\n#include <torch/extension.h>\ntorch::Tensor relu_avg_pool(torch::Tensor x);\n'''\n\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n\n__global__ void relu_avg_pool_kernel(const float* __restrict__ x, float* __restrict__ out,\n int N, int C, int H, int W,\n long sN, long sC, long sH, long sW) {\n int n = blockIdx.y;\n int k = blockIdx.x * 32 + threadIdx.x;\n if (n >= N || k >= C) return;\n const float* base = x + (long)n * sN + (long)k * sC;\n float sum = 0.0f;\n #pragma unroll 4\n for (int h = 0; h < H; ++h) {\n const float* row = base + (long)h * sH;\n for (int w = 0; w < W; ++w) {\n float v = row[(long)w * sW];\n sum += v > 0.0f ? v : 0.0f;\n }\n }\n out[(long)n * C + k] = sum / (float)(H * W);\n}\n\ntorch::Tensor relu_avg_pool(torch::Tensor x) {\n TORCH_CHECK(x.is_cuda(), \"x must be CUDA\");\n TORCH_CHECK(x.scalar_type() == at::kFloat, \"x must be float32\");\n TORCH_CHECK(x.dim() == 4, \"x must be 4D\");\n int N = (int)x.size(0);\n int C = (int)x.size(1);\n int H = (int)x.size(2);\n int W = (int)x.size(3);\n auto out = torch::empty({N, C}, x.options());\n dim3 block(32);\n dim3 grid((C + 31) / 32, N);\n relu_avg_pool_kernel<<<grid, block>>>(x.data_ptr<float>(), out.data_ptr<float>(), N, C, H, W,\n (long)x.stride(0), (long)x.stride(1),\n (long)x.stride(2), (long)x.stride(3));\n return out;\n}\n'''\n\n_ext = load_inline(name='squeezenet_relu_avg_ext_v1', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['relu_avg_pool'], extra_cuda_cflags=['-O3'], verbose=False)\n\nclass FireModule(nn.Module):\n def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):\n super(FireModule, self).__init__()\n self.squeeze = nn.Conv2d(in_channels, squeeze_channels, kernel_size=1)\n self.squeeze_activation = nn.ReLU(inplace=True)\n self.expand1x1 = nn.Conv2d(squeeze_channels, expand1x1_channels, kernel_size=1)\n self.expand1x1_activation = nn.ReLU(inplace=True)\n self.expand3x3 = nn.Conv2d(squeeze_channels, expand3x3_channels, kernel_size=3, padding=1)\n self.expand3x3_activation = nn.ReLU(inplace=True)\n def forward(self, x):\n x = self.squeeze_activation(self.squeeze(x))\n return torch.cat([self.expand1x1_activation(self.expand1x1(x)),\n self.expand3x3_activation(self.expand3x3(x))], 1)\n\nclass ModelNew(nn.Module):\n def __init__(self, num_classes=1000):\n super(ModelNew, self).__init__()\n self.features = nn.Sequential(\n nn.Conv2d(3, 96, kernel_size=7, stride=2),\n nn.ReLU(inplace=True),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(96, 16, 64, 64),\n FireModule(128, 16, 64, 64),\n FireModule(128, 32, 128, 128),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(256, 32, 128, 128),\n FireModule(256, 48, 192, 192),\n FireModule(384, 48, 192, 192),\n FireModule(384, 64, 256, 256),\n nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),\n FireModule(512, 64, 256, 256),\n )\n self.classifier = nn.Sequential(\n nn.Dropout(p=0.0),\n nn.Conv2d(512, num_classes, kernel_size=1),\n nn.ReLU(inplace=True),\n nn.AdaptiveAvgPool2d((1, 1))\n )\n self.to(memory_format=torch.channels_last)\n def forward(self, x):\n x = x.contiguous(memory_format=torch.channels_last)\n x = self.features(x)\n # dropout has p=0; use the stored classifier conv, then fuse ReLU + global average + flatten\n x = self.classifier[1](x)\n return _ext.relu_avg_pool(x)\n" }
result ← run_correctness (ok)
okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=6.30e-07 mean_abs_err=4.72e-08 max_rel_err=8.10e-01
turn 3 · 1 tool call(s) · 40.6s turn · 40.6s LLM · 8,301 chars (click to expand)
turn 340.6s turn · 40.6s LLM · 0.0s tools
reasoning
**Optimizing threads and blocks** I'm considering the final conversation output with certain parameters, including a limit on channel output. It seems like there might be low occupancy with only 32 threads per block for 2048 blocks. Hm, I could improve this by using four warps per block instead, which could help reduce overhead and enhance occupancy. A block with 128 threads handling four warps might be a better approach. I’ll keep thinking about these optimizations! **Considering max pooling after convolution** I'm thinking about how the max pooling works after the convolution layer. So, if I have 127 as the output from a convolution input of 253, that’s computed with a stride of 2 and a ceiling function. Each pooled cell ends up using overlapping data from the convolution. It feels a bit tricky, so I might have to visualize it to fully understand how it affects the output. **Evaluating compilation strategies** I'm considering if I should compile forwards lazily, maybe using static flags for laziness, but I'm unsure if the compile overhead is worth it. Then, there's `torch.jit.trace` — wait, that might not fit my needs. I could explore using `cudnn_convolution_relu` directly instead. It looks like PyTorch has its own internal operations, like `torch.ops.aten._convolution`, but it seems there's no fusing happening there. So, hmm, what should my next step be? **Considering implementation details** If this works, I think it could greatly improve speed. For the 1x1 and 3x3 convolutions, I’ll first fuse the convolution and ReLU operations together. Max pooling will be separate, as will concatenation. I believe I don't need to try and compile anything until I'm certain about correctness. We should create a variant where the final convolution in the classifier is not fused since it uses a custom ReLU average. I’ll store the module structure the same, but the Fire forward will use an operation that could fail if it’s unavailable. Time to test!
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 relu_avg_pool(torch::Tensor x);\n'''\n_cuda_src = r'''\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n__global__ void relu_avg_pool_kernel(const float* __restrict__ x, float* __restrict__ out,\n int N, int C, int H, int W,\n long sN, long sC, long sH, long sW) {\n int n = blockIdx.y;\n int k = blockIdx.x * 32 + threadIdx.x;\n if (k >= C) return;\n const float* base = x + (long)n * sN + (long)k * sC;\n float sum = 0.0f;\n for (int h = 0; h < H; ++h) {\n const float* row = base + (long)h * sH;\n #pragma unroll 4\n for (int w = 0; w < W; ++w) {\n float v = row[(long)w * sW];\n sum += v > 0.0f ? v : 0.0f;\n }\n }\n out[(long)n * C + k] = sum / (float)(H * W);\n}\ntorch::Tensor relu_avg_pool(torch::Tensor x) {\n TORCH_CHECK(x.is_cuda() && x.scalar_type() == at::kFloat && x.dim() == 4, \"bad input\");\n int N=(int)x.size(0), C=(int)x.size(1), H=(int)x.size(2), W=(int)x.size(3);\n auto out=torch::empty({N,C}, x.options());\n relu_avg_pool_kernel<<<dim3((C+31)/32,N), dim3(32)>>>(x.data_ptr<float>(), out.data_ptr<float>(), N,C,H,W,\n (long)x.stride(0),(long)x.stride(1),(long)x.stride(2),(long)x.stride(3));\n return out;\n}\n'''\n_ext = load_inline(name='squeezenet_relu_avg_ext_v2', cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=['relu_avg_pool'], extra_cuda_cflags=['-O3'], verbose=False)\n\ndef _conv_relu(x, conv):\n return torch.ops.aten.cudnn_convolution_relu(x, conv.weight, conv.bias, list(conv.padding), list(conv.stride), list(conv.dilation), conv.groups)\n\nclass FireModule(nn.Module):\n def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):\n super().__init__()\n self.squeeze=nn.Conv2d(in_channels,squeeze_channels,kernel_size=1)\n self.squeeze_activation=nn.ReLU(inplace=True)\n self.expand1x1=nn.Conv2d(squeeze_channels,expand1x1_channels,kernel_size=1)\n self.expand1x1_activation=nn.ReLU(inplace=True)\n self.expand3x3=nn.Conv2d(squeeze_channels,expand3x3_channels,kernel_size=3,padding=1)\n self.expand3x3_activation=nn.ReLU(inplace=True)\n def forward(self,x):\n x=_conv_relu(x,self.squeeze)\n return torch.cat([_conv_relu(x,self.expand1x1), _conv_relu(x,self.expand3x3)], 1)\n\nclass ModelNew(nn.Module):\n def __init__(self,num_classes=1000):\n super().__init__()\n self.features=nn.Sequential(\n nn.Conv2d(3,96,kernel_size=7,stride=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),\n FireModule(96,16,64,64), FireModule(128,16,64,64), FireModule(128,32,128,128), nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),\n FireModule(256,32,128,128), FireModule(256,48,192,192), FireModule(384,48,192,192), FireModule(384,64,256,256), nn.MaxPool2d(kernel_size=3,stride=2,ceil_mode=True),\n FireModule(512,64,256,256))\n self.classifier=nn.Sequential(nn.Dropout(p=0.0), nn.Conv2d(512,num_classes,kernel_size=1), nn.ReLU(inplace=True), nn.AdaptiveAvgPool2d((1,1)))\n self.to(memory_format=torch.channels_last)\n def forward(self,x):\n x=x.contiguous(memory_format=torch.channels_last)\n # fused first conv/relu, then maxpool and fire modules\n x=_conv_relu(x,self.features[0])\n x=self.features[2](x)\n for i in range(3,6): x=self.features[i](x)\n x=self.features[6](x)\n for i in range(7,11): x=self.features[i](x)\n x=self.features[11](x)\n x=self.features[12](x)\n x=self.classifier[1](x)\n return _ext.relu_avg_pool(x)\n" }
result ← run_correctness (fail)
failrun_correctness run_correctness 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 365, in execute return ctx.eval_client.run_correctness(ctx, kernel_code) File "/scratch/abaner/KernelBench/src/kernelbench/agent/eval_client.py", line 58, in run_correctness return self._call("correctness", 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
turn 4415.7s turn · 415.7s LLM · 0.0s tools
feedbackLLM call failed: Empty: