reasoning
The kernel compiles and runs but the output doesn't match. The issue is likely the weight initialization - I need to match the exact weight layout of the reference model.
In the reference:
- `self.pointwise = nn.Conv1d(channels, expanded_channels, 1)` - this creates weights of shape [expanded_channels, channels, 1]
- `self.depthwise = nn.Conv1d(expanded_channels, expanded_channels, kernel_size, padding=kernel_size // 2, groups=expanded_channels)` - this creates weights of shape [expanded_channels, 1, kernel_size]
- `self.project_back = nn.Conv1d(expanded_channels, channels, 1)` - this creates weights of shape [channels, expanded_channels, 1]
The weights need to be passed from the reference model to my custom model, not just initialized the same way. The initialization depends on random state.
Wait, the issue is that I'm creating new parameters with my own initialization, but the reference model has its own weights. I need to copy the weights from the reference model to my custom model.
But actually, looking at the problem description, I think the weights will be loaded from the reference model. The problem is that my ModelNew is being initialized with the same arguments but I'm doing my own weight initialization.
Let me look at this more carefully. The reference model:
- `self.pointwise.weight` has shape `[expanded_channels, channels, 1]`
- `self.pointwise.bias` has shape `[expanded_channels]`
- `self.depthwise.weight` has shape `[expanded_channels, 1, kernel_size]`
- `self.depthwise.bias` has shape `[expanded_channels]`
- `self.project_back.weight` has shape `[channels, expanded_channels, 1]`
- `self.project_back.bias` has shape `[channels]`
My weight shapes:
- `self.pw_weight` has shape `[expanded_channels, channels]`
- `self.dw_weight` has shape `[expanded_channels, kernel_size]`
- `self.pb_weight` has shape `[channels, expanded_channels]`
The shapes are correct (just squeezed), but I need to make sure they can be loaded from a state dict.
Actually, I think the issue is that the testing framework will initialize the ModelNew class with the same arguments as Model, but then might load weights from the reference. I need to make sure my parameter names match, or I need to adapt the weights correctly.
Let me check if I should use the same parameter structure as the reference model (with Conv1d layers) and just use my custom kernel for forward pass.
Actually, let me reconsider. The problem says "Replace the forward computation of the PyTorch model below with a custom CUDA kernel". This suggests I should use the same weights/parameters as the reference, and just change the forward pass.
Let me try a different approach: keep the same Conv1d layers as in the reference but use my CUDA kernel for forward pass. This way the weights will be automatically compatible.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\nimport math\n\n# Define CUDA source - fused kernel\ncuda_source = \"\"\"\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <torch/extension.h>\n#include <cmath>\n\n// GELU activation function (approximate version matching PyTorch's default)\n__device__ __forceinline__ float gelu_approx(float x) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + 0.044715f * x3);\n float tanh_inner = tanhf(inner);\n return 0.5f * x * (1.0f + tanh_inner);\n}\n\n// Fused kernel: each block handles one (batch, position)\n__global__ void depthwise_separable_block_kernel(\n const float* __restrict__ input,\n const float* __restrict__ pw_weight, // [expanded_channels, channels]\n const float* __restrict__ pw_bias, // [expanded_channels]\n const float* __restrict__ dw_weight, // [expanded_channels, kernel_size]\n const float* __restrict__ dw_bias, // [expanded_channels]\n const float* __restrict__ pb_weight, // [channels, expanded_channels]\n const float* __restrict__ pb_bias, // [channels]\n float* __restrict__ output,\n int batch_size, int channels, int expanded_channels, int seq_len, int kernel_size, int padding\n) {\n int batch = blockIdx.y;\n int pos = blockIdx.x;\n int tid = threadIdx.x;\n \n extern __shared__ float smem[];\n float* input_smem = smem;\n float* pw_out_smem = &smem[channels];\n float* dw_out_smem = &smem[channels + expanded_channels];\n \n // Load input for this position\n for (int ch = tid; ch < channels; ch += blockDim.x) {\n input_smem[ch] = input[batch * channels * seq_len + ch * seq_len + pos];\n }\n __syncthreads();\n \n // Stage 1: Pointwise conv (channels -> expanded_channels)\n for (int exp_ch = tid; exp_ch < expanded_channels; exp_ch += blockDim.x) {\n float val = pw_bias[exp_ch];\n for (int in_ch = 0; in_ch < channels; in_ch++) {\n val += pw_weight[exp_ch * channels + in_ch] * input_smem[in_ch];\n }\n pw_out_smem[exp_ch] = val;\n }\n __syncthreads();\n \n // Stage 2: Depthwise conv with GELU\n int half_kernel = kernel_size / 2;\n for (int exp_ch = tid; exp_ch < expanded_channels; exp_ch += blockDim.x) {\n float val = dw_bias[exp_ch];\n for (int k = 0; k < kernel_size; k++) {\n int src_pos = pos - half_kernel + k;\n if (src_pos >= 0 && src_pos < seq_len) {\n float pw_val = pw_bias[exp_ch];\n for (int in_ch = 0; in_ch < channels; in_ch++) {\n float in_val = input[batch * channels * seq_len + in_ch * seq_len + src_pos];\n pw_val += pw_weight[exp_ch * channels + in_ch] * in_val;\n }\n val += dw_weight[exp_ch * kernel_size + k] * pw_val;\n }\n }\n dw_out_smem[exp_ch] = gelu_approx(val);\n }\n __syncthreads();\n \n // Stage 3: Project back with residual\n for (int out_ch = tid; out_ch < channels; out_ch += blockDim.x) {\n float val = pb_bias[out_ch];\n for (int exp_ch = 0; exp_ch < expanded_channels; exp_ch++) {\n val += pb_weight[out_ch * expanded_channels + exp_ch] * dw_out_smem[exp_ch];\n }\n val += input_smem[out_ch];\n output[batch * channels * seq_len + out_ch * seq_len + pos] = val;\n }\n}\n\ntorch::Tensor depthwise_separable_block_cuda(\n torch::Tensor input,\n torch::Tensor pw_weight, torch::Tensor pw_bias,\n torch::Tensor dw_weight, torch::Tensor dw_bias,\n torch::Tensor pb_weight, torch::Tensor pb_bias,\n int kernel_size, int padding\n) {\n int batch_size = input.size(0);\n int channels = input.size(1);\n int seq_len = input.size(2);\n int expanded_channels = pw_weight.size(0);\n \n auto output = torch::empty_like(input);\n \n int block_size = 256;\n dim3 grid(seq_len, batch_size);\n dim3 block(block_size);\n \n size_t smem_size = (channels + 2 * expanded_channels) * sizeof(float);\n \n depthwise_separable_block_kernel<<<grid, block, smem_size>>>(\n input.data_ptr<float>(),\n pw_weight.data_ptr<float>(),\n pw_bias.data_ptr<float>(),\n dw_weight.data_ptr<float>(),\n dw_bias.data_ptr<float>(),\n pb_weight.data_ptr<float>(),\n pb_bias.data_ptr<float>(),\n output.data_ptr<float>(),\n batch_size, channels, expanded_channels, seq_len, kernel_size, padding\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor depthwise_separable_block_cuda(\n torch::Tensor input,\n torch::Tensor pw_weight, torch::Tensor pw_bias,\n torch::Tensor dw_weight, torch::Tensor dw_bias,\n torch::Tensor pb_weight, torch::Tensor pb_bias,\n int kernel_size, int padding\n);\n\"\"\"\n\ndepthwise_ext = load_inline(\n name='depthwise_separable_ext_v4',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['depthwise_separable_block_cuda'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, expanded_channels, kernel_size):\n super(ModelNew, self).__init__()\n self.pointwise = nn.Conv1d(channels, expanded_channels, 1)\n self.depthwise = nn.Conv1d(expanded_channels, expanded_channels, kernel_size,\n padding=kernel_size // 2, groups=expanded_channels)\n self.activation = nn.GELU()\n self.project_back = nn.Conv1d(expanded_channels, channels, 1)\n \n def forward(self, x):\n # Use the custom CUDA kernel with weights from the Conv1d layers\n # pw_weight: [expanded_channels, channels, 1] -> squeeze to [expanded_channels, channels]\n # dw_weight: [expanded_channels, 1, kernel_size] -> squeeze to [expanded_channels, kernel_size]\n # pb_weight: [channels, expanded_channels, 1] -> squeeze to [channels, expanded_channels]\n \n return depthwise_ext.depthwise_separable_block_cuda(\n x,\n self.pointwise.weight.squeeze(2), # [expanded_channels, channels]\n self.pointwise.bias, # [expanded_channels]\n self.depthwise.weight.squeeze(1), # [expanded_channels, kernel_size]\n self.depthwise.bias, # [expanded_channels]\n self.project_back.weight.squeeze(2), # [channels, expanded_channels]\n self.project_back.bias, # [channels]\n self.depthwise.kernel_size[0],\n self.depthwise.padding[0]\n )"
}