turn 1531.8s turn · 45.6s LLM · 486.2s tools · tokens in=2,427 · out=4,446 · total=6,873
reasoning
Now let me understand the problem better. The model:
1. Takes input of shape (batch_size, channels, seq_len) = (32, 256, 4096)
2. For each dilation power 0-8:
- Apply LayerNorm across (channels, seq_len)
- Apply GELU activation
- Apply dilated Conv1d with kernel_size=3, dilation=2^i
- Add residual connection
This is a very memory-intensive operation. Let me start with a basic implementation and then optimize.
Key observations:
- The dilated convolutions have kernel_size=3, so only 3 elements per output
- LayerNorm is applied to the entire (channels, seq_len) for each batch element
- GELU is element-wise
- The residual add is simple
Let me start with a fused kernel approach that handles each operation.
Actually, let me first think about what we're doing:
- 9 iterations of (LayerNorm -> GELU -> Conv1d -> Residual Add)
- Each conv1d has dilation 1, 2, 4, 8, 16, 32, 64, 128, 256
For dilation=256, kernel_size=3:
- The receptive field is 256*2 = 512 positions
- But padding=256, so the output has the same seq_len
I'll create a fused kernel that:
1. Takes pre-computed LayerNorm statistics (mean, rstd) - or computes them on the fly
2. Applies GELU
3. Applies dilated convolution (conv1d with kernel_size=3)
4. Adds residual
Actually, let me think about this differently. The key challenge is that LayerNorm requires computing mean and variance over (channels, seq_len) for each batch element. This is 256*4096 = 1M elements per batch.
A good approach:
1. Fuse all operations per batch element
2. Use parallel reduction for LayerNorm statistics
3. Fuse the rest (GELU, conv, residual)
Let me write the kernel:
```python
import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <torch/extension.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <c10/cuda/CUDAException.h>
#include <math.h>
// GELU approximation
__device__ __forceinline__ float gelu(float x) {
// GELU(x) = 0.5 * x * (1 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))
const float sqrt_2_over_pi = 0.7978845608028654f;
const float coeff = 0.044715f;
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coeff * x3);
return 0.5f * x * (1.0f + tanhf(inner));
}
// Kernel for fused operations
// Input: x [B, C, S], residual [B, C, S], weight [C, 1, K], bias [C]
// Output: out [B, C, S]
__global__ void fused_layernorm_gelu_conv_residual_kernel(
const float* __restrict__ x,
const float* __restrict__ residual,
const float* __restrict__ weight_conv, // [C, 1, K]
const float* __restrict__ bias_conv, // [C]
const float* __restrict__ ln_weight, // [C, S]
const float* __restrict__ ln_bias, // [C, S]
float* __restrict__ out,
int B, int C, int S, int K, int dilation,
int total_elements
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx >= total_elements) return;
int s = idx % S;
int c = (idx / S) % C;
int b = idx / (C * S);
// ... need to compute layernorm first
// This approach doesn't work because layernorm requires full statistics
}
"""
# Actually, let me think about this more carefully.
# LayerNorm over (C, S) means we compute mean and variance over all C*S elements
# for each batch element separately.
# So the statistics have shape [B] (mean and rstd)
# Then we normalize: x_norm = (x - mean) * rstd * ln_weight + ln_bias
# But wait, the reference code uses nn.LayerNorm([channels, seq_len])
# This means the weight and bias have shape [channels, seq_len]
# So the normalized shape is (channels, seq_len) - we normalize over this entire space
# Let me re-read the reference:
# self.norms.append(nn.LayerNorm([channels, seq_len]))
# This creates LayerNorm with normalized_shape=(channels, seq_len)
# So for each batch element, we compute mean and variance over all channels*seq_len elements
# Then multiply by weight (channels, seq_len) and add bias (channels, seq_len)
# So the computation is:
# mean_b = mean over (c, s) of x[b, c, s]
# var_b = var over (c, s) of x[b, c, s]
# rstd_b = 1 / sqrt(var_b + eps)
# out[b, c, s] = (x[b, c, s] - mean_b) * rstd_b * weight[c, s] + bias[c, s]
# Then GELU:
# out[b, c, s] = gelu(out[b, c, s])
# Then Conv1d with dilation:
# For output position s, we sum over k:
# out[b, c, s] += weight_conv[c_out, c_in, k] * gelu_out[b, c_in, s - dilation*(k - K//2)]
# But since channels = channels (input = output), and conv is depthwise-ish... wait, no.
# nn.Conv1d(channels, channels, kernel_size) is a full convolution, not depthwise.
# So weight_conv has shape [channels_out, channels_in, kernel_size] = [C, C, K]
# This is a lot of weights: 256 * 256 * 3 = 196,608 per layer
# Actually, let me re-check. Conv1d(channels, channels, kernel_size):
# input: [B, C_in, S] = [B, C, S]
# output: [B, C_out, S] = [B, C, S] (since C_in = C_out = C)
# weight: [C_out, C_in, K] = [C, C, K]
# bias: [C_out] = [C]
# So the convolution is:
# out[b, c_out, s] = sum_{c_in, k} weight[c_out, c_in, k] * input[b, c_in, s + k - padding]
# With padding=dilation, the output size matches the input.
# This is a heavy computation! For each output position (B, C, S), we need to sum over C_in * K = 256 * 3 = 768 elements.
# Total: 32 * 256 * 4096 * 256 * 3 = ~24 billion ops per layer, times 9 layers = 216 billion ops.
# Let me write a proper fused kernel that handles all this efficiently.
```
Let me write a more complete implementation:
assistantLet me write a fused CUDA kernel for this dilated convolution tower. I'll start with a complete implementation:
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\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <c10/cuda/CUDAException.h>\n#include <math.h>\n\n// Constants for GELU\nconstexpr float GELU_COEFF = 0.044715f;\nconstexpr float SQRT_2_OVER_PI = 0.7978845608028654f;\n\n__device__ __forceinline__ float fast_gelu(float x) {\n float x3 = x * x * x;\n float inner = SQRT_2_OVER_PI * (x + GELU_COEFF * x3);\n return 0.5f * x * (1.0f + tanhf(inner));\n}\n\n// Warp-level reduction for sum\n__device__ __forceinline__ float warp_reduce_sum(float val) {\n #pragma unroll\n for (int offset = 16; offset > 0; offset /= 2) {\n val += __shfl_down_sync(0xffffffff, val, offset);\n }\n return val;\n}\n\n// Block-level reduction for sum\n__device__ float block_reduce_sum(float val, float* shared) {\n int lane = threadIdx.x % 32;\n int wid = threadIdx.x / 32;\n \n val = warp_reduce_sum(val);\n \n if (lane == 0) {\n shared[wid] = val;\n }\n __syncthreads();\n \n val = (threadIdx.x < (blockDim.x + 31) / 32) ? shared[lane] : 0.0f;\n if (wid == 0) {\n val = warp_reduce_sum(val);\n }\n return val;\n}\n\n// LayerNorm kernel: compute mean and rstd for each batch element\n__global__ void layernorm_stats_kernel(\n const float* __restrict__ x,\n float* __restrict__ mean,\n float* __restrict__ rstd,\n int C, int S, int total_CS\n) {\n extern __shared__ float shared[];\n \n int b = blockIdx.x;\n int tid = threadIdx.x;\n \n float sum = 0.0f;\n float sum_sq = 0.0f;\n \n for (int i = tid; i < total_CS; i += blockDim.x) {\n float val = x[b * total_CS + i];\n sum += val;\n sum_sq += val * val;\n }\n \n sum = block_reduce_sum(sum, shared);\n sum_sq = block_reduce_sum(sum_sq, shared);\n \n if (tid == 0) {\n float m = sum / total_CS;\n float var = sum_sq / total_CS - m * m;\n mean[b] = m;\n rstd[b] = rsqrtf(var + 1e-5f);\n }\n}\n\n// Fused kernel: LayerNorm -> GELU -> Conv1d -> Residual Add\n// Grid: (B * C * S), Block: up to 256 threads\n__global__ void fused_norm_gelu_conv_residual_kernel(\n const float* __restrict__ x,\n const float* __restrict__ residual,\n const float* __restrict__ ln_weight,\n const float* __restrict__ ln_bias,\n const float* __restrict__ conv_weight,\n const float* __restrict__ conv_bias,\n const float* __restrict__ mean,\n const float* __restrict__ rstd,\n float* __restrict__ out,\n int B, int C, int S, int K, int dilation, int pad\n) {\n int idx = blockIdx.x * blockDim.x + threadIdx.x;\n int total = B * C * S;\n if (idx >= total) return;\n \n int s = idx % S;\n int c_out = (idx / S) % C;\n int b = idx / (C * S);\n \n float m = mean[b];\n float rs = rstd[b];\n \n // Conv1d with dilation\n float conv_out = conv_bias[c_out];\n \n int k_half = K / 2;\n \n for (int c_in = 0; c_in < C; c_in++) {\n for (int k = 0; k < K; k++) {\n int s_in = s + k - pad;\n // Actually, for conv1d: output[s] = sum over c_in, k of weight[c_out, c_in, k] * input[c_in, s - k + padding]\n // Wait, let me get this right. Conv1d with padding P:\n // output[s] = sum over c_in, k of weight[c_out, c_in, k] * input[c_in, s - k + P]\n // But with dilation D:\n // output[s] = sum over c_in, k of weight[c_out, c_in, k] * input[c_in, s - D*k + P]\n \n int s_conv = s - dilation * k + pad; // s - dilation*k + padding\n \n if (s_conv >= 0 && s_conv < S) {\n int x_idx = b * C * S + c_in * S + s_conv;\n float x_val = x[x_idx];\n \n // Apply LayerNorm and GELU\n float norm_val = (x_val - m) * rs * ln_weight[c_in * S + s_conv] + ln_bias[c_in * S + s_conv];\n float gelu_val = fast_gelu(norm_val);\n \n // Conv weight indexing: [C_out, C_in, K]\n int w_idx = c_out * C * K + c_in * K + k;\n conv_out += conv_weight[w_idx] * gelu_val;\n }\n }\n }\n \n // Residual add\n out[idx] = conv_out + residual[idx];\n}\n\n// Fused kernel with shared memory for weights\ntemplate<int BLOCK_SIZE>\n__global__ void fused_norm_gelu_conv_residual_shared_kernel(\n const float* __restrict__ x,\n const float* __restrict__ residual,\n const float* __restrict__ ln_weight,\n const float* __restrict__ ln_bias,\n const float* __restrict__ conv_weight,\n const float* __restrict__ conv_bias,\n const float* __restrict__ mean,\n const float* __restrict__ rstd,\n float* __restrict__ out,\n int B, int C, int S, int K, int dilation, int pad\n) {\n __shared__ float s_bias[256]; // Max channels\n __shared__ float s_mean[1], s_rstd[1];\n \n int b = blockIdx.x / C;\n int c_out = blockIdx.x % C;\n int s_out = blockIdx.y * BLOCK_SIZE + threadIdx.x;\n \n // Load bias and layernorm stats\n if (threadIdx.x == 0) {\n s_bias[0] = conv_bias[c_out];\n s_mean[0] = mean[b];\n s_rstd[0] = rstd[b];\n }\n __syncthreads();\n \n float conv_out = s_bias[0];\n float m = s_mean[0];\n float rs = s_rstd[0];\n \n // Process conv\n for (int c_in = 0; c_in < C; c_in++) {\n for (int k = 0; k < K; k++) {\n int s_conv = s_out - dilation * k + pad;\n \n if (s_conv >= 0 && s_conv < S && s_out < S) {\n int x_idx = b * C * S + c_in * S + s_conv;\n float x_val = x[x_idx];\n \n float norm_val = (x_val - m) * rs * ln_weight[c_in * S + s_conv] + ln_bias[c_in * S + s_conv];\n float gelu_val = fast_gelu(norm_val);\n \n int w_idx = c_out * C * K + c_in * K + k;\n conv_out += conv_weight[w_idx] * gelu_val;\n }\n }\n }\n \n if (s_out < S) {\n int out_idx = b * C * S + c_out * S + s_out;\n out[out_idx] = conv_out + residual[out_idx];\n }\n}\n\nstd::vector<torch::Tensor> layernorm_stats_cuda(\n torch::Tensor x,\n int C, int S\n) {\n int B = x.size(0);\n auto mean = torch::empty({B}, x.options());\n auto rstd = torch::empty({B}, x.options());\n \n int total_CS = C * S;\n int threads = 256;\n int shared_mem = (threads / 32) * sizeof(float);\n \n layernorm_stats_kernel<<<B, threads, shared_mem>>>(\n x.data_ptr<float>(),\n mean.data_ptr<float>(),\n rstd.data_ptr<float>(),\n C, S, total_CS\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n \n return {mean, rstd};\n}\n\ntorch::Tensor fused_norm_gelu_conv_residual_cuda(\n torch::Tensor x,\n torch::Tensor residual,\n torch::Tensor ln_weight,\n torch::Tensor ln_bias,\n torch::Tensor conv_weight,\n torch::Tensor conv_bias,\n torch::Tensor mean,\n torch::Tensor rstd,\n int dilation\n) {\n int B = x.size(0);\n int C = x.size(1);\n int S = x.size(2);\n int K = conv_weight.size(2);\n int pad = dilation;\n \n auto out = torch::empty_like(x);\n \n int total = B * C * S;\n int threads = 256;\n int blocks = (total + threads - 1) / threads;\n \n fused_norm_gelu_conv_residual_kernel<<<blocks, threads>>>(\n x.data_ptr<float>(),\n residual.data_ptr<float>(),\n ln_weight.data_ptr<float>(),\n ln_bias.data_ptr<float>(),\n conv_weight.data_ptr<float>(),\n conv_bias.data_ptr<float>(),\n mean.data_ptr<float>(),\n rstd.data_ptr<float>(),\n out.data_ptr<float>(),\n B, C, S, K, dilation, pad\n );\n \n C10_CUDA_CHECK(cudaGetLastError());\n \n return out;\n}\n\"\"\"\n\ncpp_source = \"\"\"\nstd::vector<torch::Tensor> layernorm_stats_cuda(torch::Tensor x, int C, int S);\ntorch::Tensor fused_norm_gelu_conv_residual_cuda(\n torch::Tensor x,\n torch::Tensor residual,\n torch::Tensor ln_weight,\n torch::Tensor ln_bias,\n torch::Tensor conv_weight,\n torch::Tensor conv_bias,\n torch::Tensor mean,\n torch::Tensor rstd,\n int dilation\n);\n\"\"\"\n\nmodule = load_inline(\n name='dilated_conv_tower',\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=['layernorm_stats_cuda', 'fused_norm_gelu_conv_residual_cuda'],\n extra_cuda_cflags=['-O3', '--use_fast_math'],\n verbose=True\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, channels, seq_len, kernel_size, max_dilation_power):\n super(ModelNew, self).__init__()\n self.channels = channels\n self.seq_len = seq_len\n self.kernel_size = kernel_size\n self.max_dilation_power = max_dilation_power\n \n # Store weights as parameters\n self.ln_weights = nn.ParameterList()\n self.ln_biases = nn.ParameterList()\n self.conv_weights = nn.ParameterList()\n self.conv_biases = nn.ParameterList()\n \n for i in range(max_dilation_power + 1):\n # LayerNorm weights\n ln_w = nn.Parameter(torch.ones(channels, seq_len))\n ln_b = nn.Parameter(torch.zeros(channels, seq_len))\n self.ln_weights.append(ln_w)\n self.ln_biases.append(ln_b)\n \n # Conv weights\n dilation = 2 ** i\n conv = nn.Conv1d(channels, channels, kernel_size, padding=dilation, dilation=dilation)\n self.conv_weights.append(conv.weight)\n self.conv_biases.append(conv.bias)\n \n def forward(self, x):\n residual = x\n for i in range(self.max_dilation_power + 1):\n dilation = 2 ** i\n \n # Compute layernorm stats\n mean, rstd = module.layernorm_stats_cuda(x, self.channels, self.seq_len)\n \n # Fused operation\n x = module.fused_norm_gelu_conv_residual_cuda(\n x, residual, self.ln_weights[i], self.ln_biases[i],\n self.conv_weights[i], self.conv_biases[i],\n mean, rstd, dilation\n )\n residual = x # Update residual for next iteration\n return x"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.