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16_DenseNet201 • coalesced_densenet_bn_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 coalesced_densenet_bn_edit_1
CUDA Speedup (Native) 1.006x
CUDA Speedup (Compile) 1.027x
CUDA Runtime 8.093 ms
PyTorch Runtime (Native) 8.144 ms
PyTorch Runtime (Compile) 8.315 ms
Correct True
Max Diff (vs. Reference) 0.000000
Model bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0
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 <cuda_runtime.h>

#define EPS 1e-5f

// Kernel that uses vectorized loads/stores (float4) to ensure memory coalescing.
// Assumes that the total number of elements (N*C*H*W) is divisible by 4.
__global__ void coalesced_bn_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 total_vec = total / 4;  // Each float4 covers 4 consecutive elements
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int stride = blockDim.x * gridDim.x;

  const float4* input_vec = reinterpret_cast<const float4*>(input);
  float4* output_vec = reinterpret_cast<float4*>(output);

  for (int i = idx; i < total_vec; i += stride) {
    int base_index = i * 4;  // starting index in the original array
    float4 in_val = input_vec[i];
    float4 out_val;
    
    // Process each of the 4 elements
    #pragma unroll
    for (int j = 0; j < 4; j++) {
      int global_index = base_index + j;
      // Compute channel index: in NCHW layout, channels change every H*W elements
      int channel = (global_index / (H * W)) % C;
      float inv_std = rsqrtf(var[channel] + EPS);
      float elem = (j == 0) ? in_val.x : (j == 1) ? in_val.y : (j == 2) ? in_val.z : in_val.w;
      float normalized = (elem - mean[channel]) * inv_std;
      float result = weight[channel] * normalized + bias[channel];
      if (j == 0) out_val.x = result;
      else if (j == 1) out_val.y = result;
      else if (j == 2) out_val.z = result;
      else out_val.w = result;
    }
    output_vec[i] = out_val;
  }
}

// Dense layer function: applies batch normalization (using the optimized, coalesced kernel in inference), relu, conv2d, then dropout.
torch::Tensor dense_layer_fn(
    torch::Tensor x,
    torch::Tensor bn_weight,  // gamma
    torch::Tensor bn_bias,    // beta
    torch::Tensor bn_mean,
    torch::Tensor bn_var,
    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);

  int total = N * C * H * W;
  if (!is_training && (total % 4 == 0)) {
    int total_vec = total / 4;
    const int threads = 256;
    const int max_blocks = 65535;  // Maximum number of blocks per grid
    int desired_blocks = (total_vec + threads - 1) / threads;
    int blocks = min(desired_blocks, max_blocks);
    coalesced_bn_kernel<<<blocks, threads>>>(
        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, EPS, true);
  }
  
  output = at::relu(output);
  output = at::conv2d(output,
                      conv_weight,
                      c10::nullopt,
                      at::IntArrayRef({1, 1}),
                      at::IntArrayRef({1, 1}));
  output = at::dropout(output, 0.0, is_training);
  return output;
}

// Dense block: iteratively applies dense layers and concatenates the features.
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>();
    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 batch normalization (optimized in inference), relu, conv2d and average pooling.
torch::Tensor transition_layer_fn(
    torch::Tensor x,
    torch::Tensor bn_weight,
    torch::Tensor bn_bias,
    torch::Tensor bn_mean,
    torch::Tensor bn_var,
    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);

  int total = N * C * H * W;
  if (!is_training && (total % 4 == 0)) {
    int total_vec = total / 4;
    const int threads = 256;
    const int max_blocks = 65535;  // Maximum number of blocks per grid
    int desired_blocks = (total_vec + threads - 1) / threads;
    int blocks = min(desired_blocks, max_blocks);
    coalesced_bn_kernel<<<blocks, threads>>>(
        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, EPS, true);
  }

  output = at::relu(output);
  output = at::conv2d(output,
                      conv_weight,
                      c10::nullopt,
                      at::IntArrayRef({1, 1}),
                      at::IntArrayRef({0, 0}));
  output = at::avg_pool2d(output,
                          at::IntArrayRef({2, 2}),
                          at::IntArrayRef({2, 2}));
  return output;
}

// Forward pass: processes initial conv, dense blocks with transition layers, final BN, pooling and linear classifier.
torch::Tensor forward(torch::Tensor x, pybind11::object params_obj, bool is_training) {
  pybind11::dict params = params_obj.cast<pybind11::dict>();

  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({2, 2}),
                 at::IntArrayRef({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);
  int total = N * C * H * W;
  if (!is_training && (total % 4 == 0)) {
    int total_vec = total / 4;
    const int threads = 256;
    const int max_blocks = 65535;  // Maximum number of blocks per grid
    int desired_blocks = (total_vec + threads - 1) / threads;
    int blocks = min(desired_blocks, max_blocks);
    coalesced_bn_kernel<<<blocks, threads>>>(
        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, EPS, true);
  }
  
  x = at::relu(x);
  x = at::max_pool2d(x,
                     at::IntArrayRef({3, 3}),
                     at::IntArrayRef({2, 2}),
                     at::IntArrayRef({1, 1}));

  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>();
      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);
    }
  }

  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);
  total = N * C * H * W;
  if (!is_training && (total % 4 == 0)) {
    int total_vec = total / 4;
    const int threads = 256;
    const int max_blocks = 65535;  // Maximum number of blocks per grid
    int desired_blocks = (total_vec + threads - 1) / threads;
    int blocks = min(desired_blocks, max_blocks);
    coalesced_bn_kernel<<<blocks, threads>>>(
        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, EPS, true);
  }
  
  x = at::relu(x);
  x = at::adaptive_avg_pool2d(x, at::IntArrayRef({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 coalesced memory accesses using vectorized loads and stores");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::conv2d
CPU Time 3568749.67 μs
Device Time 3404312.01 μs
Self CPU Time 140612.78 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::convolution
CPU Time 3428136.88 μs
Device Time 3404312.01 μs
Self CPU Time 175681.56 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 3252455.32 μs
Device Time 3404312.01 μs
Self CPU Time 204115.78 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 3048339.55 μs
Device Time 3404312.01 μs
Self CPU Time 1503874.76 μs
Self Device Time 3404312.01 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 3159638.66 μs
Device Time 1677191.02 μs
Self CPU Time 156664.58 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 1747885.26 μs
Self CPU Time 0.00 μs
Self Device Time 1747885.26 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
Status: Failed 
45269 warnings and 4 errors generated when compiling for host.
Error while processing /home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu.
Suppressed 45287 warnings (45240 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.
Found compiler error(s).
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:13:5 bugprone-easily-swappable-parameters
13 | const float* __restrict__ input,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
14 | const float* __restrict__ weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:13:31: note: the first parameter in the range is 'input'
13 | const float* __restrict__ input,
| ^~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:14:31: note: the last parameter in the range is 'weight'
14 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:15:5: warning: 3 adjacent parameters of 'coalesced_bn_kernel' of similar type ('const float *__restrict') are easily swapped by mistake [bugprone-easily-swappable-parameters]
15 | const float* __restrict__ bias,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
16 | const float* __restrict__ mean,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
17 | const float* __restrict__ var,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:15:31: note: the first parameter in the range is 'bias'
15 | const float* __restrict__ bias,
| ^~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:17:31: note: the last parameter in the range is 'var'
17 | const float* __restrict__ var,
| ^~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:22:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
22 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:23:16: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int stride = blockDim.x * gridDim.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:54:19: 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]
54 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:58:5: warning: 2 adjacent parameters of 'dense_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
58 | torch::Tensor bn_var,
| ^~~~~~~~~~~~~~~~~~~~~
59 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:58:19: note: the first parameter in the range is 'bn_var'
58 | torch::Tensor bn_var,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:59:19: note: the last parameter in the range is 'conv_weight'
59 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:59:19: 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]
59 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:63:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:63:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:63:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:63:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
63 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:72:18: error: no matching function for call to 'min' [clang-diagnostic-error]
72 | int blocks = min(desired_blocks, max_blocks);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:97:62: 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]
97 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:117:19: 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]
117 | torch::Tensor x,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:121:5: warning: 2 adjacent parameters of 'transition_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
121 | torch::Tensor bn_var,
| ^~~~~~~~~~~~~~~~~~~~~
122 | torch::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:121:19: note: the first parameter in the range is 'bn_var'
121 | torch::Tensor bn_var,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:122:19: note: the last parameter in the range is 'conv_weight'
122 | torch::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:122:19: 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]
122 | torch::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:126:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:126:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:126:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:126:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
126 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:135:18: error: no matching function for call to 'min' [clang-diagnostic-error]
135 | int blocks = min(desired_blocks, max_blocks);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:162:57: 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]
162 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:178:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:178:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:178:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:178:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
178 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:186:18: error: no matching function for call to 'min' [clang-diagnostic-error]
186 | int blocks = min(desired_blocks, max_blocks);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:214:26: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
214 | int num_dense_blocks = dense_blocks.size();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250212_optimize_b5_s4_e1_v2/level_3/task_16/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:237:7: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
237 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:237:21: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
237 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:237:35: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
237 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:237:49: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
237 | 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/b5_s2_coalesced_densenet_bn/edit_1/edit_1.cu:245:18: error: no matching function for call to 'min' [clang-diagnostic-error]
245 | int blocks = min(desired_blocks, max_blocks);
| ^~~
/home/common_modules/clang-tidy/20.0.0git/lib/clang/20/include/__clang_cuda_math.h:201:16: note: candidate function not viable: call to __device__ function from __host__ function
201 | __DEVICE__ int min(int __a, int __b) { return __nv_min(__a, __b); }
| ^
/usr/local/cuda/include/crt/math_functions.hpp:868:38: note: candidate function not viable: call to __device__ function from __host__ function
868 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:873:38: note: candidate function not viable: call to __device__ function from __host__ function
873 | __MATH_FUNCTIONS_DECL__ unsigned int min(const int a, const unsigned int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:878:38: note: candidate function not viable: call to __device__ function from __host__ function
878 | __MATH_FUNCTIONS_DECL__ unsigned int min(const unsigned int a, const int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:883:34: note: candidate function not viable: call to __device__ function from __host__ function
883 | __MATH_FUNCTIONS_DECL__ long int min(const long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:902:43: note: candidate function not viable: call to __device__ function from __host__ function
902 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:919:43: note: candidate function not viable: call to __device__ function from __host__ function
919 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const long int a, const unsigned long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:936:43: note: candidate function not viable: call to __device__ function from __host__ function
936 | __MATH_FUNCTIONS_DECL__ unsigned long int min(const unsigned long int a, const long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:953:39: note: candidate function not viable: call to __device__ function from __host__ function
953 | __MATH_FUNCTIONS_DECL__ long long int min(const long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:958:48: note: candidate function not viable: call to __device__ function from __host__ function
958 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:963:48: note: candidate function not viable: call to __device__ function from __host__ function
963 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const long long int a, const unsigned long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:968:48: note: candidate function not viable: call to __device__ function from __host__ function
968 | __MATH_FUNCTIONS_DECL__ unsigned long long int min(const unsigned long long int a, const long long int b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:973:31: note: candidate function not viable: call to __device__ function from __host__ function
973 | __MATH_FUNCTIONS_DECL__ float min(const float a, const float b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:978:32: note: candidate function not viable: call to __device__ function from __host__ function
978 | __MATH_FUNCTIONS_DECL__ double min(const double a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:983:32: note: candidate function not viable: call to __device__ function from __host__ function
983 | __MATH_FUNCTIONS_DECL__ double min(const float a, const double b)
| ^
/usr/local/cuda/include/crt/math_functions.hpp:988:32: note: candidate function not viable: call to __device__ function from __host__ function
988 | __MATH_FUNCTIONS_DECL__ double min(const double a, const float b)
| ^