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16_DenseNet201 • coalesced_densenet_bn_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 coalesced_densenet_bn_base
CUDA Speedup (Native) 1.010x
CUDA Speedup (Compile) 1.031x
CUDA Runtime 8.065 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>

#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;
    int blocks = (total_vec + threads - 1) / threads;
    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;
    int blocks = (total_vec + threads - 1) / threads;
    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;
    int blocks = (total_vec + threads - 1) / threads;
    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;
    int blocks = (total_vec + threads - 1) / threads;
    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 3703214.00 μs
Device Time 3351661.22 μs
Self CPU Time 147864.18 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 3555349.82 μs
Device Time 3351661.22 μs
Self CPU Time 182586.80 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 3372763.02 μs
Device Time 3351661.22 μs
Self CPU Time 207746.04 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 3165016.98 μs
Device Time 3351661.22 μs
Self CPU Time 1539573.81 μs
Self Device Time 3351661.22 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 3317667.80 μs
Device Time 1652335.83 μs
Self CPU Time 167575.03 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 1718529.06 μs
Self CPU Time 0.00 μs
Self Device Time 1718529.06 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/b5_s2_coalesced_densenet_bn/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.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/base/base.cu:95:62: 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]
95 | 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/base/base.cu:115:19: 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]
115 | 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/base/base.cu:119:5: warning: 2 adjacent parameters of 'transition_layer_fn' of similar type ('torch::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
119 | torch::Tensor bn_var,
| ^~~~~~~~~~~~~~~~~~~~~
120 | 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/base/base.cu:119:19: note: the first parameter in the range is 'bn_var'
119 | 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/base/base.cu:120:19: note: the last parameter in the range is 'conv_weight'
120 | 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/base/base.cu:120:19: 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]
120 | 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/base/base.cu:124:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/base/base.cu:124:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/base/base.cu:124:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/base/base.cu:124:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
124 | 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/base/base.cu:158:57: 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]
158 | 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/base/base.cu:174:11: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | 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/base/base.cu:174:25: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | 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/base/base.cu:174:39: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | 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/base/base.cu:174:53: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
174 | 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/base/base.cu:208:26: warning: narrowing conversion from 'size_t' (aka 'unsigned long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
208 | 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/base/base.cu:231:7: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
231 | 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/base/base.cu:231:21: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
231 | 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/base/base.cu:231:35: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
231 | 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/base/base.cu:231:49: warning: narrowing conversion from 'long' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
231 | N = sizes[0]; C = sizes[1]; H = sizes[2]; W = sizes[3];
| ^