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15_DenseNet121 • 15_densenet121_mem_coalesce_base

Level 3 • Task 15
import torch
import torch.nn as nn
import torch.nn.functional as F


def module_fn(
    x: torch.Tensor,
    params: nn.ParameterDict,
    is_training: bool,
) -> torch.Tensor:
    """
    Implements the DenseNet121 module.

    Args:
        x (torch.Tensor): Input tensor, shape (batch_size, in_channels, height, width)
        params (nn.ParameterDict): Dictionary of parameters
        is_training (bool): Whether to use training mode

    Returns:
        torch.Tensor: Output tensor, shape (batch_size, num_classes)
    """
    # Initial features
    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, stride=1, padding=1)
        x = F.dropout(x, p=0.0, training=is_training)
        return x

    def transition_layer_fn(
        x, bn_weight, bn_bias, bn_mean, bn_var, conv_weight, is_training
    ):
        """
        Functional version of transition 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)
        x = F.avg_pool2d(x, kernel_size=2, stride=2)
        return x

    # Dense blocks and transitions
    for i in range(4):  # 4 dense blocks
        features = [x]
        for j in range(params[f"block{i}_num_layers"]):  # layers per block
            prefix = f"block{i}_layer{j}_"
            new_feature = dense_layer_fn(
                x,
                params[prefix + "bn_weight"],
                params[prefix + "bn_bias"],
                params[prefix + "bn_mean"],
                params[prefix + "bn_var"],
                params[prefix + "conv_weight"],
                is_training,
            )
            features.append(new_feature)
            x = torch.cat(features, 1)

        if i != 3:  # Apply transition after all blocks except last
            x = transition_layer_fn(
                x,
                params[f"transition{i}_bn_weight"],
                params[f"transition{i}_bn_bias"],
                params[f"transition{i}_bn_mean"],
                params[f"transition{i}_bn_var"],
                params[f"transition{i}_conv_weight"],
                is_training,
            )

    # Final layers
    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()
        block_layers = [6, 12, 24, 16]

        # 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())
        self.params["features_bn_weight"] = nn.Parameter(bn.weight.data.clone())
        self.params["features_bn_bias"] = nn.Parameter(bn.bias.data.clone())
        self.params["features_bn_mean"] = nn.Parameter(bn.running_mean.data.clone())
        self.params["features_bn_var"] = nn.Parameter(bn.running_var.data.clone())

        # Dense blocks parameters
        num_features = 64
        for i, num_layers in enumerate(block_layers):
            self.params[f"block{i}_num_layers"] = num_layers
            for j in range(num_layers):
                in_features = num_features + j * growth_rate
                prefix = f"block{i}_layer{j}_"

                bn = nn.BatchNorm2d(in_features)
                conv = nn.Conv2d(
                    in_features, growth_rate, kernel_size=3, padding=1, bias=False
                )

                self.params[prefix + "bn_weight"] = nn.Parameter(bn.weight.data.clone())
                self.params[prefix + "bn_bias"] = nn.Parameter(bn.bias.data.clone())
                self.params[prefix + "bn_mean"] = nn.Parameter(
                    bn.running_mean.data.clone()
                )
                self.params[prefix + "bn_var"] = nn.Parameter(
                    bn.running_var.data.clone()
                )
                self.params[prefix + "conv_weight"] = nn.Parameter(
                    conv.weight.data.clone()
                )

            num_features = num_features + num_layers * growth_rate

            # Transition layers parameters (except after last block)
            if i != len(block_layers) - 1:
                bn = nn.BatchNorm2d(num_features)
                conv = nn.Conv2d(
                    num_features, num_features // 2, kernel_size=1, bias=False
                )

                self.params[f"transition{i}_bn_weight"] = nn.Parameter(
                    bn.weight.data.clone()
                )
                self.params[f"transition{i}_bn_bias"] = nn.Parameter(
                    bn.bias.data.clone()
                )
                self.params[f"transition{i}_bn_mean"] = nn.Parameter(
                    bn.running_mean.data.clone()
                )
                self.params[f"transition{i}_bn_var"] = nn.Parameter(
                    bn.running_var.data.clone()
                )
                self.params[f"transition{i}_conv_weight"] = nn.Parameter(
                    conv.weight.data.clone()
                )

                num_features = num_features // 2

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

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

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


# Test configurations
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, 24, 16]  # Corresponding layers in DenseNet121

        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 DenseNet121 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]
Download Evaluation Download PyTorch Download CUDA Download Profiles

Kernel Information

Operation Name 15_DenseNet121
Level ID 3
Task ID 15
Kernel Name 15_densenet121_mem_coalesce_base
CUDA Speedup (Native) 1.410x
CUDA Speedup (Compile) 0.897x
CUDA Runtime 4.215 ms
PyTorch Runtime (Native) 5.944 ms
PyTorch Runtime (Compile) 3.782 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 15 • 15_DenseNet121)

Rank Kernel Name Runtime (ms) Speedup Native Speedup Compile
🥇 15_DenseNet121 4.19 1.42 0.90
🥇 optimized_dense_net_base 4.19 1.42 0.90
🥉 15_densenet121_warp_optimized_base_base 4.20 1.42 0.90
🥉 15_densenet121_atomic_optimized_base 4.20 1.42 0.90
5 15_densenet121_ldg_optimized_base 4.20 1.42 0.90
5 15_densenet121_fused_bn_relu_base 4.20 1.42 0.90
7 15_DenseNet121_unrolled_base 4.20 1.41 0.90
8 15_densenet121_unroll_batched_base_base 4.20 1.41 0.90
9 15_densenet121_reduced_sync_base_base 4.21 1.41 0.90
10 15_densenet121_mem_coalesce_base 4.21 1.41 0.90
11 15_DenseNet121_manual_unroll_base 4.22 1.41 0.90
12 15_densenet121_atomic_optimization_edit_1 4.22 1.41 0.90
13 15_DenseNet121_unroll_manual_base 4.23 1.41 0.89
13 densenet121_warp_aligned_base_base 4.23 1.41 0.89
15 15_densenet121_strided_base 4.23 1.41 0.89
16 15_densenet121_ldg_fused_relu_base 4.23 1.40 0.89
16 15_DenseNet121_coalesced_edit_1 4.23 1.40 0.89
18 15_densenet121_strided_edit_1 4.24 1.40 0.89
19 15_densenet121_strided_loops_base 4.24 1.40 0.89
20 15_densenet121_atomic_optimization_base 4.24 1.40 0.89 
#include <torch/extension.h>
#include <ATen/ATen.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <vector>
#include <string>
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <cmath>

namespace py = pybind11;

// Custom CUDA kernel for fused BatchNorm and ReLU
// Assumes input is a 4D tensor in NCHW format.
__global__ void fused_bn_relu_kernel(const float* __restrict__ input,
                                       float* __restrict__ output,
                                       int N, int C, int H, int W,
                                       const float* __restrict__ weight,
                                       const float* __restrict__ bias,
                                       const float* __restrict__ running_mean,
                                       const float* __restrict__ running_var,
                                       float eps) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  int numel = N * C * H * W;
  if (idx < numel) {
    int hw = H * W;
    // Compute channel index assuming contiguous NCHW layout
    int c = (idx / hw) % C;
    float x = input[idx];
    // Use __ldg to leverage read-only cache for parameters
    float mean = __ldg(&running_mean[c]);
    float var = __ldg(&running_var[c]);
    float gamma = __ldg(&weight[c]);
    float beta = __ldg(&bias[c]);
    float norm = (x - mean) / sqrtf(var + eps);
    float y = gamma * norm + beta;
    // Fused ReLU
    output[idx] = y > 0.0f ? y : 0.0f;
  }
}

// Fused BatchNorm and ReLU forward function
at::Tensor fused_bn_relu_forward(
    at::Tensor input,
    at::Tensor weight,
    at::Tensor bias,
    at::Tensor running_mean,
    at::Tensor running_var,
    bool training,
    double momentum,
    double eps) {
  // For training, fallback to standard operations to ensure correctness.
  if (training) {
    auto bn_out = at::batch_norm(input, weight, bias, running_mean, running_var,
                                 training, momentum, eps, true);
    return at::relu(bn_out);
  }
  
  // Ensure input is contiguous for coalesced accesses
  if (!input.is_contiguous()) {
    input = input.contiguous();
  }
  // Create output tensor
  auto output = at::empty_like(input);

  // Assuming input is 4D: N x C x H x W
  int N = input.size(0);
  int C = input.size(1);
  int H = input.size(2);
  int W = input.size(3);
  int numel = input.numel();

  const int threads = 256;
  const int blocks = (numel + threads - 1) / threads;

  fused_bn_relu_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
      input.data_ptr<float>(),
      output.data_ptr<float>(),
      N, C, H, W,
      weight.data_ptr<float>(),
      bias.data_ptr<float>(),
      running_mean.data_ptr<float>(),
      running_var.data_ptr<float>(),
      static_cast<float>(eps));
  
  // Synchronize to ensure kernel completion
  cudaDeviceSynchronize();
  return output;
}

// Dense layer function using fused BN+ReLU
at::Tensor dense_layer_fn(
    at::Tensor x,
    at::Tensor bn_weight,
    at::Tensor bn_bias,
    at::Tensor bn_mean,
    at::Tensor bn_var,
    at::Tensor conv_weight,
    bool is_training
) {
  x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
                              is_training, 0.1, 1e-5);
  x = at::conv2d(x, conv_weight, /*bias=*/{}, /*stride=*/{1, 1}, /*padding=*/{1, 1});
  x = at::dropout(x, /*p=*/0.0, is_training);
  return x;
}

// Transition layer function using fused BN+ReLU
at::Tensor transition_layer_fn(
    at::Tensor x,
    at::Tensor bn_weight,
    at::Tensor bn_bias,
    at::Tensor bn_mean,
    at::Tensor bn_var,
    at::Tensor conv_weight,
    bool is_training
) {
  x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
                              is_training, 0.1, 1e-5);
  x = at::conv2d(x, conv_weight);
  x = at::avg_pool2d(x, /*kernel_size=*/{2, 2}, /*stride=*/{2, 2});
  return x;
}

// Main module function implementing DenseNet121
at::Tensor module_fn(
    at::Tensor x,
    py::object params,
    bool is_training
) {
  auto get_param = [&](const std::string &key) -> at::Tensor {
    return params.attr("__getitem__")(key.c_str()).cast<at::Tensor>();
  };

  auto features_conv_weight = get_param("features_conv_weight");
  x = at::conv2d(x, features_conv_weight, /*bias=*/{}, 2, 3);

  auto features_bn_mean = get_param("features_bn_mean");
  auto features_bn_var = get_param("features_bn_var");
  auto features_bn_weight = get_param("features_bn_weight");
  auto features_bn_bias = get_param("features_bn_bias");

  x = fused_bn_relu_forward(x, features_bn_weight, features_bn_bias,
                             features_bn_mean, features_bn_var,
                             is_training, 0.1, 1e-5);
  x = at::max_pool2d(x, 3, 2, 1);

  std::vector<int> num_layers = {6, 12, 24, 16};

  for (int i = 0; i < 4; ++i) {
    std::vector<at::Tensor> features = {x};

    for (int j = 0; j < num_layers[i]; ++j) {
      std::string prefix = "block" + std::to_string(i) + "_layer" + std::to_string(j) + "_";

      auto bn_weight = get_param(prefix + "bn_weight");
      auto bn_bias = get_param(prefix + "bn_bias");
      auto bn_mean = get_param(prefix + "bn_mean");
      auto bn_var = get_param(prefix + "bn_var");
      auto conv_weight = get_param(prefix + "conv_weight");

      at::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);
    }

    if (i != 3) {
      std::string prefix = "transition" + std::to_string(i) + "_";

      auto bn_weight = get_param(prefix + "bn_weight");
      auto bn_bias = get_param(prefix + "bn_bias");
      auto bn_mean = get_param(prefix + "bn_mean");
      auto bn_var = get_param(prefix + "bn_var");
      auto conv_weight = get_param(prefix + "conv_weight");

      x = transition_layer_fn(x, bn_weight, bn_bias, bn_mean, bn_var, conv_weight, is_training);
    }
  }

  auto final_bn_mean = get_param("final_bn_mean");
  auto final_bn_var = get_param("final_bn_var");
  auto final_bn_weight = get_param("final_bn_weight");
  auto final_bn_bias = get_param("final_bn_bias");

  x = fused_bn_relu_forward(x, final_bn_weight, final_bn_bias,
                             final_bn_mean, final_bn_var,
                             is_training, 0.1, 1e-5);
  x = at::adaptive_avg_pool2d(x, {1, 1}).flatten(1);

  auto classifier_weight = get_param("classifier_weight");
  auto classifier_bias = get_param("classifier_bias");
  x = at::linear(x, classifier_weight, classifier_bias);

  return x;
}

PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("forward", &module_fn, "DenseNet121 forward");
}
Performance Metrics
Metric Value Unit Variance Samples
Analysis Rules
Rule Description
Operation / Metric Value Unit
aten::to
CPU Time 5468913.01 μs
Device Time 2671.23 μs
Self CPU Time 713.70 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::_to_copy
CPU Time 5468199.31 μs
Device Time 2671.23 μs
Self CPU Time 1278.20 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::empty_strided
CPU Time 5455711.48 μs
Device Time 0.00 μs
Self CPU Time 2249.72 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
cudaDeviceGetStreamPriorityRange
CPU Time 5506909.83 μs
Device Time 0.00 μs
Self CPU Time 5506909.83 μs
Self Device Time 0.00 μs
CPU Memory Usage 0 B
Device Memory Usage 0 B
Self CPU Memory Usage 0 B
Self Device Memory Usage 0 B
aten::conv2d
CPU Time 3361271.86 μs
Device Time 2921569.76 μs
Self CPU Time 134672.41 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 3226599.45 μs
Device Time 2921569.76 μs
Self CPU Time 168036.08 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 3058563.37 μs
Device Time 2921569.76 μs
Self CPU Time 194648.39 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 2863914.99 μs
Device Time 2921569.76 μs
Self CPU Time 1465195.68 μs
Self Device Time 2921569.76 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 3096005.05 μs
Device Time 1288926.42 μs
Self CPU Time 148499.33 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
Status: Completed 
45309 warnings generated when compiling for host.
Suppressed 45337 warnings (45290 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/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:18:40 bugprone-easily-swappable-parameters
18 | const float* __restrict__ weight,
| ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
19 | const float* __restrict__ bias,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
20 | const float* __restrict__ running_mean,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
21 | const float* __restrict__ running_var,
| ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:18:66: note: the first parameter in the range is 'weight'
18 | const float* __restrict__ weight,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:21:66: note: the last parameter in the range is 'running_var'
21 | const float* __restrict__ running_var,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:23:13: warning: narrowing conversion from 'unsigned int' to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
23 | int idx = blockIdx.x * blockDim.x + threadIdx.x;
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:67:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
67 | int N = input.size(0);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:68:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
68 | int C = input.size(1);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:69:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
69 | int H = input.size(2);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:70:11: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
70 | int W = input.size(3);
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:71:15: warning: narrowing conversion from 'int64_t' (aka 'long') to signed type 'int' is implementation-defined [bugprone-narrowing-conversions]
71 | int numel = input.numel();
| ^
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:97:5: warning: 2 adjacent parameters of 'dense_layer_fn' of similar type ('at::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
97 | at::Tensor bn_var,
| ^~~~~~~~~~~~~~~~~~
98 | at::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:97:16: note: the first parameter in the range is 'bn_var'
97 | at::Tensor bn_var,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:98:16: note: the last parameter in the range is 'conv_weight'
98 | at::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:98:16: 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]
98 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:101:32: warning: parameter 'bn_weight' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
5 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:101:43: warning: parameter 'bn_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
101 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:101:52: warning: parameter 'bn_mean' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
101 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:101:61: warning: parameter 'bn_var' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
101 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:114:5: warning: 2 adjacent parameters of 'transition_layer_fn' of similar type ('at::Tensor') are easily swapped by mistake [bugprone-easily-swappable-parameters]
114 | at::Tensor bn_var,
| ^~~~~~~~~~~~~~~~~~
115 | at::Tensor conv_weight,
| ~~~~~~~~~~~~~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:114:16: note: the first parameter in the range is 'bn_var'
114 | at::Tensor bn_var,
| ^~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:115:16: note: the last parameter in the range is 'conv_weight'
115 | at::Tensor conv_weight,
| ^~~~~~~~~~~
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:115:16: 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]
115 | at::Tensor conv_weight,
| ^
| const &
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:118:32: warning: parameter 'bn_weight' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
118 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:118:43: warning: parameter 'bn_bias' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
118 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:118:52: warning: parameter 'bn_mean' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
118 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )
/home/robert_sakana_ai/llm_cuda/experiments/20250203_optimize_b10_s4_e0_sweep/level_3/task_15/b7_s1_15_densenet121_mem_coalesce/base/base.cu:118:61: warning: parameter 'bn_var' is passed by value and only copied once; consider moving it to avoid unnecessary copies [performance-unnecessary-value-param]
118 | x = fused_bn_relu_forward(x, bn_weight, bn_bias, bn_mean, bn_var,
| ^
| std::move( )