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16_DenseNet201 • fuse_bn_relu_opt_edit_1

Level 3 • Task 16
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(x, params, is_training):
    """
    Functional version of Model forward pass
    """
    x = F.conv2d(x, params["features_conv_weight"], bias=None, stride=2, padding=3)
    x = F.batch_norm(
        x,
        params["features_bn_mean"],
        params["features_bn_var"],
        params["features_bn_weight"],
        params["features_bn_bias"],
        training=is_training,
    )
    x = F.relu(x, inplace=True)
    x = F.max_pool2d(x, kernel_size=3, stride=2, padding=1)

    def dense_layer_fn(
        x, bn_weight, bn_bias, bn_mean, bn_var, conv_weight, is_training
    ):
        """
        Functional version of a single dense layer
        """
        x = F.batch_norm(x, bn_mean, bn_var, bn_weight, bn_bias, training=is_training)
        x = F.relu(x, inplace=True)
        x = F.conv2d(x, conv_weight, bias=None, padding=1)
        x = F.dropout(x, p=0.0, training=is_training)
        return x

    def dense_block_fn(x, layer_params, is_training):
        """
        Functional version of DenseBlock
        """
        features = [x]
        for params in layer_params:
            new_feature = dense_layer_fn(x, *params, is_training)
            features.append(new_feature)
            x = torch.cat(features, 1)
        return x

    def transition_layer_fn(
        x, bn_weight, bn_bias, bn_mean, bn_var, conv_weight, is_training
    ):
        """
        Functional version of TransitionLayer
        """
        x = F.batch_norm(x, bn_mean, bn_var, bn_weight, bn_bias, training=is_training)
        x = F.relu(x, inplace=True)
        x = F.conv2d(x, conv_weight, bias=None)  # Removed kernel_size parameter
        x = F.avg_pool2d(x, kernel_size=2, stride=2)
        return x

    # Dense blocks and transitions
    for i in range(len(params["dense_blocks"])):
        x = dense_block_fn(x, params["dense_blocks"][i], is_training)
        if i != len(params["dense_blocks"]) - 1:
            x = transition_layer_fn(x, *params["transition_layers"][i], is_training)

    x = F.batch_norm(
        x,
        params["final_bn_mean"],
        params["final_bn_var"],
        params["final_bn_weight"],
        params["final_bn_bias"],
        training=is_training,
    )
    x = F.relu(x, inplace=True)
    x = F.adaptive_avg_pool2d(x, (1, 1)).view(x.size(0), -1)
    x = F.linear(x, params["classifier_weight"], params["classifier_bias"])
    return x


class Model(nn.Module):
    def __init__(self, growth_rate=32, num_classes=1000):
        super(Model, self).__init__()

        self.params = nn.ParameterDict()
        num_features = 64
        block_layers = [6, 12, 48, 32]
        device = "cuda"

        # Extract initial features parameters
        conv = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        bn = nn.BatchNorm2d(64)
        self.params["features_conv_weight"] = nn.Parameter(conv.weight.data.clone()).to(
            device
        )
        self.params["features_bn_weight"] = nn.Parameter(bn.weight.data.clone()).to(
            device
        )
        self.params["features_bn_bias"] = nn.Parameter(bn.bias.data.clone()).to(device)
        self.params["features_bn_mean"] = nn.Parameter(bn.running_mean.data.clone()).to(
            device
        )
        self.params["features_bn_var"] = nn.Parameter(bn.running_var.data.clone()).to(
            device
        )

        # Extract dense blocks parameters
        self.params["dense_blocks"] = []
        for num_layers in block_layers:
            block_params = []
            for i in range(num_layers):
                in_features = num_features + i * growth_rate
                bn = nn.BatchNorm2d(in_features)
                conv = nn.Conv2d(
                    in_features, growth_rate, kernel_size=3, padding=1, bias=False
                )
                layer_params = [
                    nn.Parameter(bn.weight.data.clone()).to(device),
                    nn.Parameter(bn.bias.data.clone()).to(device),
                    nn.Parameter(bn.running_mean.data.clone()).to(device),
                    nn.Parameter(bn.running_var.data.clone()).to(device),
                    nn.Parameter(conv.weight.data.clone()).to(device),
                ]
                block_params.append(layer_params)
            self.params["dense_blocks"].append(block_params)
            num_features = num_features + num_layers * growth_rate

            # Extract transition layer parameters if not last block
            if len(self.params.get("transition_layers", [])) < len(block_layers) - 1:
                bn = nn.BatchNorm2d(num_features)
                conv = nn.Conv2d(
                    num_features, num_features // 2, kernel_size=1, bias=False
                )
                if "transition_layers" not in self.params:
                    self.params["transition_layers"] = []
                self.params["transition_layers"].append(
                    [
                        nn.Parameter(bn.weight.data.clone()).to(device),
                        nn.Parameter(bn.bias.data.clone()).to(device),
                        nn.Parameter(bn.running_mean.data.clone()).to(device),
                        nn.Parameter(bn.running_var.data.clone()).to(device),
                        nn.Parameter(conv.weight.data.clone()).to(device),
                    ]
                )
                num_features = num_features // 2

        # Extract final layers parameters
        bn = nn.BatchNorm2d(num_features)
        self.params["final_bn_weight"] = nn.Parameter(bn.weight.data.clone()).to(device)
        self.params["final_bn_bias"] = nn.Parameter(bn.bias.data.clone()).to(device)
        self.params["final_bn_mean"] = nn.Parameter(bn.running_mean.data.clone()).to(
            device
        )
        self.params["final_bn_var"] = nn.Parameter(bn.running_var.data.clone()).to(
            device
        )

        linear = nn.Linear(num_features, num_classes)
        self.params["classifier_weight"] = nn.Parameter(linear.weight.data.clone()).to(
            device
        )
        self.params["classifier_bias"] = nn.Parameter(linear.bias.data.clone()).to(
            device
        )

    def forward(self, x, fn=module_fn):
        return fn(x, self.params, self.training)


batch_size = 10
num_classes = 10
height, width = 224, 224


def get_inputs():
    return [torch.randn(batch_size, 3, height, width)]


def get_init_inputs():
    return [32, num_classes]

import torch
import torch.nn as nn
import torch.nn.functional as F

class DenseBlock(nn.Module):
    def __init__(self, num_layers: int, num_input_features: int, growth_rate: int):
        """
        :param num_layers: The number of layers in the dense block
        :param num_input_features: The number of input feature maps
        :param growth_rate: The growth rate for the dense block (new features added per layer)
        """
        super(DenseBlock, self).__init__()
        layers = []
        for i in range(num_layers):
            layers.append(self._make_layer(num_input_features + i * growth_rate, growth_rate))
        self.layers = nn.ModuleList(layers)

    def _make_layer(self, in_features: int, growth_rate: int):
        """
        Creates a single layer with BatchNorm, ReLU, Conv2D, and Dropout.
        """
        return nn.Sequential(
            nn.BatchNorm2d(in_features),
            nn.ReLU(inplace=True),
            nn.Conv2d(in_features, growth_rate, kernel_size=3, padding=1, bias=False),
            nn.Dropout(0.0)
        )

    def forward(self, x):
        """
        :param x: Input tensor of shape (batch_size, num_input_features, height, width)
        :return: Concatenated output tensor with shape (batch_size, num_output_features, height, width)
        """
        features = [x]
        for layer in self.layers:
            new_feature = layer(x)
            features.append(new_feature)
            x = torch.cat(features, 1)  # Concatenate along channel axis
        return x

class TransitionLayer(nn.Module):
    def __init__(self, num_input_features: int, num_output_features: int):
        """
        :param num_input_features: The number of input feature maps
        :param num_output_features: The number of output feature maps
        """
        super(TransitionLayer, self).__init__()
        self.transition = nn.Sequential(
            nn.BatchNorm2d(num_input_features),
            nn.ReLU(inplace=True),
            nn.Conv2d(num_input_features, num_output_features, kernel_size=1, bias=False),
            nn.AvgPool2d(kernel_size=2, stride=2)
        )

    def forward(self, x):
        """
        :param x: Input tensor of shape (batch_size, num_input_features, height, width)
        :return: Downsampled tensor with reduced number of feature maps
        """
        return self.transition(x)

class Model(nn.Module):
    def __init__(self, growth_rate: int = 32, num_classes: int = 1000):
        """
        :param growth_rate: The growth rate of the DenseNet (new features added per layer)
        :param num_classes: The number of output classes for classification
        """
        super(Model, self).__init__()

        # Initial convolution and pooling
        self.features = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        )

        # Each dense block is followed by a transition layer, except the last one
        num_features = 64
        block_layers = [6, 12, 48, 32]  # Corresponding layers in DenseNet201

        self.dense_blocks = nn.ModuleList()
        self.transition_layers = nn.ModuleList()

        for i, num_layers in enumerate(block_layers):
            block = DenseBlock(num_layers=num_layers, num_input_features=num_features, growth_rate=growth_rate)
            self.dense_blocks.append(block)
            num_features = num_features + num_layers * growth_rate

            if i != len(block_layers) - 1:
                transition = TransitionLayer(num_input_features=num_features, num_output_features=num_features // 2)
                self.transition_layers.append(transition)
                num_features = num_features // 2

        # Final batch norm and classifier
        self.final_bn = nn.BatchNorm2d(num_features)
        self.classifier = nn.Linear(num_features, num_classes)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        :param x: Input tensor of shape (batch_size, 3, height, width)
        :return: Output tensor of shape (batch_size, num_classes)
        """
        x = self.features(x)

        for i, block in enumerate(self.dense_blocks):
            x = block(x)
            if i != len(self.dense_blocks) - 1:
                x = self.transition_layers[i](x)

        x = self.final_bn(x)
        x = F.relu(x, inplace=True)
        x = F.adaptive_avg_pool2d(x, (1, 1)).view(x.size(0), -1)
        x = self.classifier(x)
        return x

# Testing the DenseNet201 model
batch_size = 10
num_classes = 10
height, width = 224, 224  # Standard input size for DenseNet

def get_inputs():
    return [torch.randn(batch_size, 3, height, width)]

def get_init_inputs():
    return [32, num_classes]
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Kernel Information

Operation Name 16_DenseNet201
Level ID 3
Task ID 16
Kernel Name fuse_bn_relu_opt_edit_1
CUDA Speedup (Native) 1.002x
CUDA Speedup (Compile) 1.023x
CUDA Runtime 8.131 ms
PyTorch Runtime (Native) 8.144 ms
PyTorch Runtime (Compile) 8.315 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model o3-mini-2025-01-31
Temperature 0.00
View Experiment Progress Details

Related Kernels (Level 3, Task 16 • 16_DenseNet201)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 warp_optimized_densenet_op_base 8.04 1.01 1.03
🥈 optimized_densenet_cuda_edit_1 8.04 1.01 1.03
🥉 shared_memory_densenet_op_edit_1 8.04 1.01 1.03
4 constant_mem_densenet_edit_1_base 8.06 1.01 1.03
5 coalesced_densenet_bn_base 8.06 1.01 1.03
6 warp_broadcast_densenet_optimized_base 8.09 1.01 1.03
7 warp_uniform_edit_1 8.09 1.01 1.03
8 warp_uniform_base 8.09 1.01 1.03
9 coalesced_densenet_bn_edit_1 8.09 1.01 1.03
10 thread_synchronization_densenet_base 8.10 1.01 1.03
11 16_DenseNet201 8.10 1.01 1.03
12 configurable_blocksize_densenet_base 8.11 1.00 1.03
13 constant_mem_densenet_edit_1_edit_1 8.11 1.00 1.02
14 fuse_bn_relu_opt_base 8.12 1.00 1.02
15 fuse_bn_relu_opt_edit_1 8.13 1.00 1.02
16 stride_loop_densenet_edit_1 8.13 1.00 1.02
17 configurable_blocksize_densenet_edit_1 8.14 1.00 1.02
18 warp_reduction_densenet_base_edit_1 8.14 1.00 1.02
19 shared_memory_densenet_op_base 8.14 1.00 1.02
20 stride_loop_densenet_base 8.15 1.00 1.02 
#include <torch/extension.h>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <vector>
#include <functional>
#include <cuda_runtime.h>
#include <cmath>

namespace py = pybind11;

// Fused BatchNorm + ReLU CUDA kernel for inference mode
__global__ void fused_bn_relu_kernel(const float* __restrict__ input,
                                       float* __restrict__ output,
                                       const float* __restrict__ bn_weight,
                                       const float* __restrict__ bn_bias,
                                       const float* __restrict__ bn_mean,
                                       const float* __restrict__ bn_var,
                                       float eps,
                                       int n, int c, int h, int w) {
  int total = n * c * h * w;
  int stride = blockDim.x * gridDim.x;
  for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < total; index += stride) {
    int hw = h * w;
    int channel = (index / hw) % c;
    float val = input[index];
    float norm = (val - bn_mean[channel]) / sqrtf(bn_var[channel] + eps);
    float bnval = bn_weight[channel] * norm + bn_bias[channel];
    // Fused ReLU activation
    output[index] = bnval > 0.0f ? bnval : 0.0f;
  }
}

// Host function to launch the fused BN+ReLU kernel
torch::Tensor fused_bn_relu(torch::Tensor x,
                              torch::Tensor bn_weight,
                              torch::Tensor bn_bias,
                              torch::Tensor bn_mean,
                              torch::Tensor bn_var,
                              float eps) {
  // Ensure the input tensor is contiguous
  x = x.contiguous();
  auto output = torch::empty_like(x);
  int n = x.size(0);
  int c = x.size(1);
  int h = x.size(2);
  int w = x.size(3);
  int total = n * c * h * w;
  int threads = 256;
  int blocks = (total + threads - 1) / threads;
  fused_bn_relu_kernel<<<blocks, threads>>>(x.data_ptr<float>(), output.data_ptr<float>(),
                                             bn_weight.data_ptr<float>(), bn_bias.data_ptr<float>(),
                                             bn_mean.data_ptr<float>(), bn_var.data_ptr<float>(),
                                             eps, n, c, h, w);
  cudaDeviceSynchronize();
  return output;
}

// Dense layer: applies a BN+ReLU then a convolution and dropout
torch::Tensor dense_layer_fn(
    torch::Tensor x,
    torch::Tensor bn_weight,  // scale (gamma)
    torch::Tensor bn_bias,    // shift (beta)
    torch::Tensor bn_mean,    // running mean
    torch::Tensor bn_var,     // running variance
    torch::Tensor conv_weight,
    bool is_training) {
  if (!is_training) {
    // Use the fused kernel in inference mode
    x = fused_bn_relu(x, bn_weight, bn_bias, bn_mean, bn_var, 1e-5);
  } else {
    x = at::batch_norm(x, bn_weight, bn_bias, bn_mean, bn_var, is_training, 0.1, 1e-5, true);
    x = at::relu(x);
  }
  x = at::conv2d(x,
                 conv_weight,
                 c10::nullopt,
                 at::IntArrayRef(std::vector<int64_t>{1, 1}),
                 at::IntArrayRef(std::vector<int64_t>{1, 1}));
  x = at::dropout(x, 0.0, is_training);
  return x;
}

// Dense block: sequentially applies dense layers and concatenates the outputs
torch::Tensor dense_block_fn(torch::Tensor x, py::list layer_params, bool is_training) {
  std::vector<torch::Tensor> features;
  features.push_back(x);
  for (ssize_t i = 0; i < layer_params.size(); i++) {
    auto params_tuple = layer_params[i].cast<py::tuple>();
    if (params_tuple.size() != 5) {
      throw std::runtime_error("Each dense layer parameter set must have 5 elements.");
    }
    torch::Tensor bn_weight   = params_tuple[0].cast<torch::Tensor>();
    torch::Tensor bn_bias     = params_tuple[1].cast<torch::Tensor>();
    torch::Tensor bn_mean     = params_tuple[2].cast<torch::Tensor>();
    torch::Tensor bn_var      = params_tuple[3].cast<torch::Tensor>();
    torch::Tensor conv_weight = params_tuple[4].cast<torch::Tensor>();

    torch::Tensor new_feature = dense_layer_fn(x, bn_weight, bn_bias, bn_mean, bn_var, conv_weight, is_training);
    features.push_back(new_feature);
    x = at::cat(features, 1);
  }
  return x;
}

// Transition layer: applies BN+ReLU, convolution and average pooling
torch::Tensor transition_layer_fn(
    torch::Tensor x,
    torch::Tensor bn_weight,  // scale (gamma)
    torch::Tensor bn_bias,    // shift (beta)
    torch::Tensor bn_mean,    // running mean
    torch::Tensor bn_var,     // running variance
    torch::Tensor conv_weight,
    bool is_training) {
  if (!is_training) {
    x = fused_bn_relu(x, bn_weight, bn_bias, bn_mean, bn_var, 1e-5);
  } else {
    x = at::batch_norm(x, bn_weight, bn_bias, bn_mean, bn_var, is_training, 0.1, 1e-5, true);
    x = at::relu(x);
  }
  x = at::conv2d(x,
                 conv_weight,
                 c10::nullopt,
                 at::IntArrayRef(std::vector<int64_t>{1, 1}),
                 at::IntArrayRef(std::vector<int64_t>{0, 0}));
  x = at::avg_pool2d(x,
                     at::IntArrayRef(std::vector<int64_t>{2, 2}),
                     at::IntArrayRef(std::vector<int64_t>{2, 2}));
  return x;
}

// Forward function orchestrates the DenseNet201 forward pass
torch::Tensor forward(torch::Tensor x, py::object params_obj, bool is_training) {
  py::dict params = params_obj.cast<py::dict>();

  // Initial convolution and BN+ReLU block
  torch::Tensor features_conv_weight = params["features_conv_weight"].cast<torch::Tensor>();
  torch::Tensor features_bn_mean     = params["features_bn_mean"].cast<torch::Tensor>();
  torch::Tensor features_bn_var      = params["features_bn_var"].cast<torch::Tensor>();
  torch::Tensor features_bn_weight   = params["features_bn_weight"].cast<torch::Tensor>();
  torch::Tensor features_bn_bias     = params["features_bn_bias"].cast<torch::Tensor>();

  x = at::conv2d(x,
                 features_conv_weight,
                 c10::nullopt,
                 at::IntArrayRef(std::vector<int64_t>{2, 2}),
                 at::IntArrayRef(std::vector<int64_t>{3, 3}));
  if (!is_training) {
    x = fused_bn_relu(x, features_bn_weight, features_bn_bias, features_bn_mean, features_bn_var, 1e-5);
  } else {
    x = at::batch_norm(x, features_bn_weight, features_bn_bias, features_bn_mean, features_bn_var, is_training, 0.1, 1e-5, true);
    x = at::relu(x);
  }
  x = at::max_pool2d(x,
                     at::IntArrayRef(std::vector<int64_t>{3, 3}),
                     at::IntArrayRef(std::vector<int64_t>{2, 2}),
                     at::IntArrayRef(std::vector<int64_t>{1, 1}));

  // Dense blocks and transition layers
  py::list dense_blocks = params["dense_blocks"].cast<py::list>();
  py::list transition_layers = params["transition_layers"].cast<py::list>();
  int num_dense_blocks = dense_blocks.size();

  // Precompute transition functions to avoid divergent branching
  std::vector<std::function<torch::Tensor(torch::Tensor, bool)>> transition_funcs;
  transition_funcs.reserve(num_dense_blocks);

  for (int i = 0; i < num_dense_blocks; i++) {
    if (i < num_dense_blocks - 1) {
      auto trans_tuple = transition_layers[i].cast<py::tuple>();
      if (trans_tuple.size() != 5) {
        throw std::runtime_error("Each transition layer parameter set must have 5 elements.");
      }
      torch::Tensor t_bn_weight   = trans_tuple[0].cast<torch::Tensor>();
      torch::Tensor t_bn_bias     = trans_tuple[1].cast<torch::Tensor>();
      torch::Tensor t_bn_mean     = trans_tuple[2].cast<torch::Tensor>();
      torch::Tensor t_bn_var      = trans_tuple[3].cast<torch::Tensor>();
      torch::Tensor t_conv_weight = trans_tuple[4].cast<torch::Tensor>();
      transition_funcs.push_back([
          t_bn_weight, t_bn_bias, t_bn_mean, t_bn_var, t_conv_weight
      ](torch::Tensor inp, bool is_train) -> torch::Tensor {
        return transition_layer_fn(inp, t_bn_weight, t_bn_bias, t_bn_mean, t_bn_var, t_conv_weight, is_train);
      });
    } else {
      // Identity function for the final dense block
      transition_funcs.push_back([](torch::Tensor inp, bool) -> torch::Tensor {
        return inp;
      });
    }
  }

  // Process each dense block and apply the corresponding transition uniformly
  for (int i = 0; i < num_dense_blocks; i++) {
    py::list block_params = dense_blocks[i].cast<py::list>();
    x = dense_block_fn(x, block_params, is_training);
    x = transition_funcs[i](x, is_training);
  }

  // Final classifier block with fused BN+ReLU
  torch::Tensor final_bn_mean   = params["final_bn_mean"].cast<torch::Tensor>();
  torch::Tensor final_bn_var    = params["final_bn_var"].cast<torch::Tensor>();
  torch::Tensor final_bn_weight = params["final_bn_weight"].cast<torch::Tensor>();
  torch::Tensor final_bn_bias   = params["final_bn_bias"].cast<torch::Tensor>();

  if (!is_training) {
    x = fused_bn_relu(x, final_bn_weight, final_bn_bias, final_bn_mean, final_bn_var, 1e-5);
  } else {
    x = at::batch_norm(x, final_bn_weight, final_bn_bias, final_bn_mean, final_bn_var, is_training, 0.1, 1e-5, true);
    x = at::relu(x);
  }
  x = at::adaptive_avg_pool2d(x, at::IntArrayRef(std::vector<int64_t>{1, 1}));
  x = x.view({x.size(0), -1});

  torch::Tensor classifier_weight = params["classifier_weight"].cast<torch::Tensor>();
  torch::Tensor classifier_bias   = params["classifier_bias"].cast<torch::Tensor>();
  x = at::linear(x, classifier_weight, classifier_bias);

  return x;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &forward, "Custom CUDA forward function with fused BN and ReLU for reduced warp divergence");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv2d
CPU Time 3778946.68 μs
Device Time 3553785.81 μs
Self CPU Time 157241.89 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::convolution
CPU Time 3621704.79 μs
Device Time 3553785.81 μs
Self CPU Time 178641.83 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::_convolution
CPU Time 3443062.96 μs
Device Time 3553785.81 μs
Self CPU Time 215029.79 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::cudnn_convolution
CPU Time 3228033.17 μs
Device Time 3553785.81 μs
Self CPU Time 1588469.00 μs
Self Device Time 3553785.81 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::batch_norm
CPU Time 3369741.17 μs
Device Time 1751852.51 μs
Self CPU Time 173409.64 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
sm80_xmma_fprop_implicit_gemm_tf32f32_tf32f32_f32_nhwckrsc_nchw_tilesize64x32x64_stage5_warpsize2x2x1_g1_tensor16x8x8_alignc4_execute_kernel__5x_cudnn
CPU Time 0.00 μs
Device Time 1820285.72 μs
Self CPU Time 0.00 μs
Self Device Time 1820285.72 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Completed 
45296 warnings generated when compiling for host.
Suppressed 45323 warnings (45276 in non-user code, 47 NOLINT).
Use -header-filter=.* to display errors from all non-system headers. Use -system-headers to display errors from system headers as well.
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:15:40 bugprone-easily-swappable-parameters
15 | const float* __restrict__ bn_bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ bn_mean,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:15:66: note: the first parameter in the range is 'bn_bias'
15 | const float* __restrict__ bn_bias,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:16:66: note: the last parameter in the range is 'bn_mean'
16 | const float* __restrict__ bn_mean,
| ^~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:18:40: warning: 2 adjacent parameters of 'fused_bn_relu_kernel' of convertible types are easily swapped by mistake [bugprone-easily-swappable-parameters]
18 | float eps,
| ^~~~~~~~~~
19 | int n, int c, int h, int w) {
| ~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:18:46: note: the first parameter in the range is 'eps'
18 | float eps,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:19:44: note: the last parameter in the range is 'n'
19 | int n, int c, int h, int w) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:19:40: note: 'float' and 'int' may be implicitly converted
19 | int n, int c, int h, int w) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:21:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
21 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:22:20: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
22 | for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < total; index += stride) {
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:35:45: warning: the parameter 'bn_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
35 | torch::Tensor bn_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:36:45: warning: the parameter 'bn_bias' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
36 | torch::Tensor bn_bias,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:37:45: warning: the parameter 'bn_mean' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
37 | torch::Tensor bn_mean,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:38:45: warning: the parameter 'bn_var' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
38 | torch::Tensor bn_var,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:43:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
43 | int n = x.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:44:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
44 | int c = x.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:45:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
45 | int h = x.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:46:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
46 | int w = x.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:64:5: warning: 2 adjacent parameters of 'dense_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
64 | torch::Tensor bn_var, // running variance
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
65 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:64:19: note: the first parameter in the range is 'bn_var'
64 | torch::Tensor bn_var, // running variance
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:65:19: note: the last parameter in the range is 'conv_weight'
65 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:65:19: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
65 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:84:56: warning: the parameter 'layer_params' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
84 | torch::Tensor dense_block_fn(torch::Tensor x, py::list layer_params, bool is_training) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:111:5: warning: 2 adjacent parameters of 'transition_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
111 | torch::Tensor bn_var, // running variance
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
112 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:111:19: note: the first parameter in the range is 'bn_var'
111 | torch::Tensor bn_var, // running variance
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:112:19: note: the last parameter in the range is 'conv_weight'
112 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:112:19: warning: the parameter 'conv_weight' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
112 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:132:51: warning: the parameter 'params_obj' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
132 | torch::Tensor forward(torch::Tensor x, py::object params_obj, bool is_training) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:161:26: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
161 | int num_dense_blocks = dense_blocks.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250208_optimize_b5_s4_e1_sweep/level_3/task_16/b3_s2_fuse_bn_relu_opt/edit_1/edit_1.cu:180:23: warning: the parameter 'inp' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
180 | ](torch::Tensor inp, bool is_train) -> torch::Tensor {
| ^
| const &