error
18_SqueezeNet.py
started
2026-05-05T05:47:41Z
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 011.9s turn · 11.9s LLM · 0.0s tools
reasoning
**Developing SqueezeNet-like model**
I need to create a model similar to SqueezeNet and begin by replacing the forward method. I'll ensure the weights are copied from the original modules. It seems crucial for the constructor to accept num_classes. We should define PyTorch modules, and ModelNew might need to handle the state_dict for evaluation harnesses. I'll think about using nn.Module for identical weights and focus on implementing custom forward pass with CUDA optimization. It’s a complex task, so I might explore leveraging optimized PyTorch operations and consider the fixed input size specified.
**Exploring CUDA kernel optimization**
I need to think about replacing the forward method with a custom CUDA kernel, which could help with speed. To grasp the sizes, the input is 64x3x512x512. For Conv7 with stride 2 and no padding, the output dimensions turn out to be approximately 96x253x253 after calculations.
MaxPooling then reduces that to 126, and further processing continues to narrow down the dimensions through layers. I'm considering heavy FLOPs with Conv3x3 in PyTorch, which uses cuDNN for efficiency. Custom implementations might be slower, but maybe I can explore Tensor cores and TF32 for optimization?
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 1588.1s turn · 588.1s LLM · 0.0s tools
feedbackLLM call failed: _WorkItemTimeout: work item exceeded soft timeout budget
final submitted kernel (111 lines, 4,346 chars) — full source, untruncated
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.cpp_extension import load_inline
_cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
__global__ void relu_avg_kernel(const float* __restrict__ x, float* __restrict__ out,
long N, long C, long H, long W,
long sN, long sC, long sH, long sW) {
long idx = blockIdx.x;
long c = idx % C;
long n = idx / C;
float sum = 0.0f;
long HW = H * W;
for (long i = threadIdx.x; i < HW; i += blockDim.x) {
long h = i / W;
long w = i - h * W;
float v = x[n * sN + c * sC + h * sH + w * sW];
sum += v > 0.0f ? v : 0.0f;
}
__shared__ float sh[256];
sh[threadIdx.x] = sum;
__syncthreads();
for (int offset = 128; offset > 0; offset >>= 1) {
if (threadIdx.x < offset) sh[threadIdx.x] += sh[threadIdx.x + offset];
__syncthreads();
}
if (threadIdx.x == 0) out[n * C + c] = sh[0] / (float)HW;
}
torch::Tensor relu_avg(torch::Tensor x) {
TORCH_CHECK(x.is_cuda(), "x must be CUDA");
TORCH_CHECK(x.scalar_type() == at::kFloat, "only float32 supported");
TORCH_CHECK(x.dim() == 4, "expected NCHW tensor");
const long N = x.size(0), C = x.size(1), H = x.size(2), W = x.size(3);
auto out = torch::empty({N, C}, x.options());
dim3 block(256);
dim3 grid(N * C);
relu_avg_kernel<<<grid, block, 0, at::cuda::getCurrentCUDAStream()>>>(
x.data_ptr<float>(), out.data_ptr<float>(), N, C, H, W,
x.stride(0), x.stride(1), x.stride(2), x.stride(3));
return out;
}
'''
_ext = load_inline(
name='squeezenet_relu_avg_ext_v1',
cpp_sources='',
cuda_sources=_cuda_src,
functions=['relu_avg'],
extra_cuda_cflags=['-O3'],
verbose=False,
)
class FireModule(nn.Module):
def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels):
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):
x = self.squeeze_activation(self.squeeze(x))
return torch.cat((self.expand1x1_activation(self.expand1x1(x)),
self.expand3x3_activation(self.expand3x3(x))), 1)
class ModelNew(nn.Module):
def __init__(self, num_classes=1000):
super(ModelNew, 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))
)
# Keep the public module/state_dict identical, but use NHWC kernels where cuDNN is faster.
self.to(memory_format=torch.channels_last)
def forward(self, x):
# Convolution-heavy model: channels_last usually selects faster cuDNN kernels.
if x.is_cuda:
x = x.contiguous(memory_format=torch.channels_last)
x = self.features(x)
conv = self.classifier[1]
x = F.conv2d(x, conv.weight, conv.bias, stride=1, padding=0)
# Fused ReLU + adaptive average pool + flatten, avoiding materializing extra tensors/launches.
return _ext.relu_avg(x)