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16_DenseNet201 • configurable_blocksize_densenet_base

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 configurable_blocksize_densenet_base
CUDA Speedup (Native) 1.005x
CUDA Speedup (Compile) 1.026x
CUDA Runtime 8.107 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 1.00
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 <cuda_runtime.h>

// Dynamic batch norm kernel using grid-stride loop
__global__ void batch_norm_dynamic_kernel(
    float* __restrict__ output,
    const float* __restrict__ input,
    const float* __restrict__ weight,
    const float* __restrict__ bias,
    const float* __restrict__ mean,
    const float* __restrict__ var,
    int N, int C, int H, int W) {
  int total = N * C * H * W;
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;
  for (int i = idx; i < total; i += stride) {
    int c = (i / (H * W)) % C;
    float norm = (input[i] - mean[c]) * rsqrtf(var[c] + 1e-5f);
    output[i] = weight[c] * norm + bias[c];
  }
}

// Helper function to launch the dynamic batch norm kernel with configurable block size
inline void launch_batch_norm(
    float* output,
    const float* input,
    const float* weight,
    const float* bias,
    const float* mean,
    const float* var,
    int N, int C, int H, int W) {
  int total = N * C * H * W;
  int block_size;
  // Choose block size based on total work
  if (total < 1000000) {
    block_size = 128;
  } else if (total < 10000000) {
    block_size = 256;
  } else {
    block_size = 512;
  }
  int grid_size = (total + block_size - 1) / block_size;
  batch_norm_dynamic_kernel<<<grid_size, block_size>>>(output, input, weight, bias, mean, var, N, C, H, W);
}

// Dense layer function applying batch norm, relu, conv, 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) {
  auto sizes = x.sizes();
  int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];

  auto output = torch::empty_like(x);
  if (!is_training) {
    launch_batch_norm(
        output.data_ptr<float>(),
        x.data_ptr<float>(),
        bn_weight.data_ptr<float>(),
        bn_bias.data_ptr<float>(),
        bn_mean.data_ptr<float>(),
        bn_var.data_ptr<float>(),
        N, C, H, W);
  } else {
    output = at::batch_norm(x, bn_weight, bn_bias, bn_mean, bn_var, is_training, 0.1, 1e-5, true);
  }
  
  output = at::relu(output);
  output = at::conv2d(output,
                      conv_weight,
                      c10::nullopt,
                      at::IntArrayRef(std::vector<int64_t>{1, 1}),
                      at::IntArrayRef(std::vector<int64_t>{1, 1}));
  output = at::dropout(output, 0.0, is_training);
  return output;
}

// Dense block function. Each element in layer_params is a tuple of 5 tensors for the dense layer
torch::Tensor dense_block_fn(torch::Tensor x, pybind11::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<pybind11::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: batch norm, relu, conv 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) {
  auto sizes = x.sizes();
  int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
  auto output = torch::empty_like(x);
  
  if (!is_training) {
    launch_batch_norm(
        output.data_ptr<float>(),
        x.data_ptr<float>(),
        bn_weight.data_ptr<float>(),
        bn_bias.data_ptr<float>(),
        bn_mean.data_ptr<float>(),
        bn_var.data_ptr<float>(),
        N, C, H, W);
  } else {
    output = at::batch_norm(x, bn_weight, bn_bias, bn_mean, bn_var, is_training, 0.1, 1e-5, true);
  }
  
  output = at::relu(output);
  output = at::conv2d(output,
                      conv_weight,
                      c10::nullopt,
                      at::IntArrayRef(std::vector<int64_t>{1, 1}),
                      at::IntArrayRef(std::vector<int64_t>{0, 0}));
  output = at::avg_pool2d(output,
                          at::IntArrayRef(std::vector<int64_t>{2, 2}),
                          at::IntArrayRef(std::vector<int64_t>{2, 2}));
  return output;
}

// Forward function implementing the DenseNet201 computation using dynamic block size for batch norm
torch::Tensor forward(torch::Tensor x, pybind11::object params_obj, bool is_training) {
  pybind11::dict params = params_obj.cast<pybind11::dict>();

  // Initial convolution and batch norm 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}));

  auto sizes = x.sizes();
  int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];

  auto output = torch::empty_like(x);
  if (!is_training) {
    launch_batch_norm(
        output.data_ptr<float>(),
        x.data_ptr<float>(),
        features_bn_weight.data_ptr<float>(),
        features_bn_bias.data_ptr<float>(),
        features_bn_mean.data_ptr<float>(),
        features_bn_var.data_ptr<float>(),
        N, C, H, W);
    x = output;
  } 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
  pybind11::list dense_blocks = params["dense_blocks"].cast<pybind11::list>();
  pybind11::list transition_layers = params["transition_layers"].cast<pybind11::list>();

  int num_dense_blocks = dense_blocks.size();
  for (int i = 0; i < num_dense_blocks; i++) {
    pybind11::list block_params = dense_blocks[i].cast<pybind11::list>();
    x = dense_block_fn(x, block_params, is_training);
    
    if (i != num_dense_blocks - 1) {
      auto trans_tuple = transition_layers[i].cast<pybind11::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>();
      
      x = transition_layer_fn(x, t_bn_weight, t_bn_bias, t_bn_mean, t_bn_var, t_conv_weight, is_training);
    }
  }

  // Final classifier block
  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>();

  sizes = x.sizes();
  N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
  output = torch::empty_like(x);
  if (!is_training) {
    launch_batch_norm(
        output.data_ptr<float>(),
        x.data_ptr<float>(),
        final_bn_weight.data_ptr<float>(),
        final_bn_bias.data_ptr<float>(),
        final_bn_mean.data_ptr<float>(),
        final_bn_var.data_ptr<float>(),
        N, C, H, W);
    x = output;
  } 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 configurable block sizes for batch normalization");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv2d
CPU Time 3495013.14 μs
Device Time 3282798.46 μs
Self CPU Time 143235.05 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 3351778.08 μs
Device Time 3282798.46 μs
Self CPU Time 162620.87 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 3189157.21 μs
Device Time 3282798.46 μs
Self CPU Time 203675.78 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 2985481.43 μs
Device Time 3282798.46 μs
Self CPU Time 1514155.60 μs
Self Device Time 3282798.46 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 3139039.50 μs
Device Time 1620020.49 μs
Self CPU Time 153981.83 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
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 1686107.52 μs
Self CPU Time 0.00 μs
Self Device Time 1686107.52 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 
45304 warnings generated when compiling for host.
Suppressed 45322 warnings (45275 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/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:10:5 bugprone-easily-swappable-parameters
10 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
11 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:10:31: note: the first parameter in the range is 'input'
10 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:11:31: note: the last parameter in the range is 'weight'
11 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:12:5: warning: 2 adjacent parameters of 'batch_norm_dynamic_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
12 | const float* __restrict__ bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
13 | const float* __restrict__ mean,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:12:31: note: the first parameter in the range is 'bias'
12 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:13:31: note: the last parameter in the range is 'mean'
13 | const float* __restrict__ mean,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:17:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
17 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:18:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
18 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:51:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
51 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:55:5: warning: 2 adjacent parameters of 'dense_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
55 | torch::Tensor bn_var, // running variance
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
56 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:55:19: note: the first parameter in the range is 'bn_var'
55 | torch::Tensor bn_var, // running variance
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:56:19: note: the last parameter in the range is 'conv_weight'
56 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:56:19: 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]
56 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:59:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:59:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:59:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:59:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
59 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:86:62: 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]
86 | torch::Tensor dense_block_fn(torch::Tensor x, pybind11::list layer_params, bool is_training) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:109:19: warning: the parameter 'x' is copied for each invocation but only used as a const reference; consider making it a const reference [performance-unnecessary-value-param]
109 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:113:5: warning: 2 adjacent parameters of 'transition_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
113 | torch::Tensor bn_var, // running variance
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
114 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:113:19: note: the first parameter in the range is 'bn_var'
113 | torch::Tensor bn_var, // running variance
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:114:19: note: the last parameter in the range is 'conv_weight'
114 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:114:19: 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]
114 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:117:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:117:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:117:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:117:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
117 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:146:57: 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]
146 | torch::Tensor forward(torch::Tensor x, pybind11::object params_obj, bool is_training) {
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:163:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
163 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:163:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
163 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:163:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
163 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:163:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
163 | int N = sizes[0], C = sizes[1], H = sizes[2], W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:190:26: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
190 | int num_dense_blocks = dense_blocks.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:217:7: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
217 | N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:217:21: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
217 | N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:217:35: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
217 | N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b4_s1_configurable_blocksize_densenet/base/base.cu:217:49: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
217 | N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
| ^