L2 P43 · popcorn

correct 43_ContactMapPrediction.py

model

gpt-5.5-priority

turns / max

15 / 15

tool calls

tokens (total)

wall clock

—

truncated

backend

cuda fp32

variant

popcorn

runtime

0.03 μs

ref runtime

0.10 μs

speedup

3.47x

started

2026-05-04T03:13:04Z

timingkernelbench.timing

kernel runtime

0.03 μs

ref runtime

0.10 μs

speedup

3.47x

kernel mean / median / std

0.03 / — / 1.69e-04 μs

ref mean / median / std

0.10 / — / 0.02 μs

numerical precisioneval_kernel_against_ref

max abs error

1.19e-07

mean abs error

2.93e-09

max rel error

2.35e-07

mean rel error

5.70e-09

gpu memoryextended_metrics.measure_memory

kernel peak

37.09 MB

ref peak

49.03 MB

kernel / ref

0.76x

kernel launches / fusiontorch.profiler

kernel launches

ref launches

fusion ratio (ref / kernel)

41.00x

total cuda time

20 μs

SOL (speed-of-light)nsight_hardware_counters

SOL score (0–1)

0.209

dram util %

4.9%

compute util %

20.9%

bottleneck

compute-bound

dominant pipe

FP32

arithmetic intensity

—

ridge point

20.00

roofline / occupancynsight_hardware_counters

dram bandwidth

165.61 GB/s

dram util %

4.9%

fp32 tflops

—

fp32 util %

—

fp16 tflops

—

occupancy %

73.9%

bottleneck

compute-bound

dominant pipe %

20.9%

arithmetic intensity

—

ridge point

20.00

L1 hit %

—

L2 hit %

30.7%

ld sectors / req

2.55

st sectors / req

—

regs / thread

smem / block

1024 B

block size

128

peak bw

3350.00 GB/s

peak fp32 tflops

67.00

peak fp16 tflops

1979.00

warp stalls (top 5)nsight

short_scoreboard

593.1%

mio_throttle

504.8%

long_scoreboard

248.4%

reference PyTorch source (the prompt the agent sees)

import torch
import torch.nn as nn
import torch.nn.functional as F


class Model(nn.Module):
    """
    Contact map prediction head.  Takes residue-pair embeddings and
    predicts a symmetric binary contact probability matrix (residue i
    within 8 Å of residue j).  Uses a small MLP on the symmetrised pair
    representation followed by a sigmoid.  Common final stage in protein
    structure prediction pipelines.
    """

    def __init__(self, pair_dim, hidden_dim):
        super().__init__()
        self.layer_norm = nn.LayerNorm(pair_dim)
        self.mlp = nn.Sequential(
            nn.Linear(pair_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, 1),
        )

    def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:
        """
        Args:
            pair_repr: (B, N, N, pair_dim)
        Returns:
            contact_probs: (B, N, N) – symmetric contact probability
        """
        x = self.layer_norm(pair_repr)
        # Symmetrise
        x = (x + x.permute(0, 2, 1, 3)) / 2.0
        logits = self.mlp(x).squeeze(-1)  # (B, N, N)
        return torch.sigmoid(logits)


pair_dim = 64
hidden_dim = 32
seq_len = 64
batch_size = 4


def get_inputs():
    return [torch.randn(popcorn_pri.jitter_int(batch_size), popcorn_pri.jitter_int(seq_len), popcorn_pri.jitter_int(seq_len), pair_dim)]


def get_init_inputs():
    return [pair_dim, hidden_dim]

turn 019.5s turn · 19.5s LLM · 0.0s tools

reasoning

**Implementing neural network model** I’m focusing on producing a Python file for a model with a specific architecture. I need to access weights from the neural network modules and ensure that the new model has the same structure and layers. When benchmarking, I'll instantiate both models and copy the state_dict. I’ll define modules consistently and implement operations involving layer normalization and a multi-layer perceptron. The output should be efficient, requiring optimization for performance, especially to exceed PyTorch operations. **Optimizing vector computations** I’m planning to use shared arrays for normalized symmetric vectors and for layers h1 and h2. I’ll set up parallel reductions across blocks, aiming for efficient computation despite potential overhead from too many threads. Another approach could be using warp-level reductions to manage output calculation efficiently. Each warp will compute outputs and handle layer normalization over 64 dimensions. I'll ensure memory coalescing for data vectors to facilitate efficient access. Overall, this design seems promising for minimizing computational costs! **Setting up C++ and CUDA** I need to create a C++ wrapper for my project, focusing on incorporating CUDA support. I’ll utilize the `load_inline` function and ensure I'm using the appropriate PyTorch extension. This integration should allow smooth interaction between C++ functionality and CUDA operations. I want to ensure everything is optimized to work seamlessly together, so I’ll pay attention to how the components interact. It seems straightforward, but I’ll double-check everything to avoid potential issues!

call → get_gpu_specs

callget_gpu_specs {}

result ← get_gpu_specs (ok)

okget_gpu_specs GPU specs for NVIDIA H100 80GB HBM3: total memory (runtime): 79.1 GB spec entry: H100 GPU Architecture: Hopper GPU Memory: 80GB Memory Bandwidth: 3.35 TB/s FP64 TFLOPS: 34 FP64 Tensor Core TFLOPS: 67 FP32 TFLOPS: 67 TF32 Tensor Core TFLOPS: 989 with sparsity BFLOAT16 Tensore Core TFLOPS: 1979 with sparsity FP16 Tensor Core TFLOPS: 1979 with sparsity FP8 Tensor Core TFLOPS: 3958 with sparsity INT8 Tensor Core TOPS: 3958 with sparsity Register File Size: 64K 32-bit registers per SM Maximum number of registers per thread: 255 Maximum number of thread blocks per SM: 32 Shared memory capacity per SM: 228 KB Maximum shared memory per thread block: 227 KB

turn 1 · 1 tool call(s) · 68.6s turn · 68.6s LLM · 11,000 chars (click to expand)

turn 168.6s turn · 68.6s LLM · 0.0s tools

reasoning

**Considering Python coding issues** I’m working with a coding situation where C is set to 64. I’m thinking about a potential issue: does load_inline compile at module execution? I wonder if I need to ensure the name is unique. I should include imports like torch and nn, and define my extension globally. Alright, it looks like I need to write the kernel and compile everything properly. Let's get this sorted out!

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n\n__device__ __forceinline__ float warp_sum(float v) {\n unsigned mask = 0xffffffffu;\n v += __shfl_down_sync(mask, v, 16);\n v += __shfl_down_sync(mask, v, 8);\n v += __shfl_down_sync(mask, v, 4);\n v += __shfl_down_sync(mask, v, 2);\n v += __shfl_down_sync(mask, v, 1);\n return v;\n}\n\n__global__ void contact_kernel(const float* __restrict__ x,\n const float* __restrict__ ln_w,\n const float* __restrict__ ln_b,\n const float* __restrict__ w1,\n const float* __restrict__ b1,\n const float* __restrict__ w2,\n const float* __restrict__ b2,\n const float* __restrict__ w3,\n const float* __restrict__ b3,\n float* __restrict__ out,\n int B, int N) {\n __shared__ float sh1[WARPS_PER_BLOCK][H];\n __shared__ float sh2[WARPS_PER_BLOCK][H];\n\n int lane = threadIdx.x & 31;\n int warp = threadIdx.x >> 5;\n int gid = blockIdx.x * WARPS_PER_BLOCK + warp;\n int pairs_per_b = N * (N + 1) / 2;\n int total = B * pairs_per_b;\n if (gid >= total) return;\n\n int b = gid / pairs_per_b;\n int p = gid - b * pairs_per_b;\n\n // map p to upper-triangular (i,j), i <= j\n float a = (float)(2 * N + 1);\n int i = (int)floorf((a - sqrtf(a * a - 8.0f * (float)p)) * 0.5f);\n int start = i * N - (i * (i - 1)) / 2;\n int j = i + (p - start);\n\n long base_ij = (((long)b * N + i) * N + j) * C;\n long base_ji = (((long)b * N + j) * N + i) * C;\n\n int d0 = lane;\n int d1 = lane + 32;\n\n float xa0 = x[base_ij + d0];\n float xa1 = x[base_ij + d1];\n float xb0 = x[base_ji + d0];\n float xb1 = x[base_ji + d1];\n\n float sum_a = xa0 + xa1;\n float sum_b = xb0 + xb1;\n sum_a = warp_sum(sum_a);\n sum_b = warp_sum(sum_b);\n float mean_a = sum_a * (1.0f / 64.0f);\n float mean_b = sum_b * (1.0f / 64.0f);\n\n float da0 = xa0 - mean_a;\n float da1 = xa1 - mean_a;\n float db0 = xb0 - mean_b;\n float db1 = xb1 - mean_b;\n float var_a = da0 * da0 + da1 * da1;\n float var_b = db0 * db0 + db1 * db1;\n var_a = warp_sum(var_a);\n var_b = warp_sum(var_b);\n float rstd_a = rsqrtf(var_a * (1.0f / 64.0f) + 1.0e-5f);\n float rstd_b = rsqrtf(var_b * (1.0f / 64.0f) + 1.0e-5f);\n\n float v0 = 0.5f * (da0 * rstd_a + db0 * rstd_b) * ln_w[d0] + ln_b[d0];\n float v1 = 0.5f * (da1 * rstd_a + db1 * rstd_b) * ln_w[d1] + ln_b[d1];\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float acc = v0 * w1[k * C + d0] + v1 * w1[k * C + d1];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b1[k];\n sh1[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float h = sh1[warp][lane];\n float acc = h * w2[k * H + lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b2[k];\n sh2[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n float acc = sh2[warp][lane] * w3[lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n float logit = acc + b3[0];\n float y = 1.0f / (1.0f + expf(-logit));\n out[((long)b * N + i) * N + j] = y;\n out[((long)b * N + j) * N + i] = y;\n }\n}\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3) {\n TORCH_CHECK(pair_repr.is_cuda(), \"pair_repr must be CUDA\");\n TORCH_CHECK(pair_repr.scalar_type() == at::kFloat, \"pair_repr must be float32\");\n int B = (int)pair_repr.size(0);\n int N = (int)pair_repr.size(1);\n TORCH_CHECK(pair_repr.size(2) == N && pair_repr.size(3) == C, \"expected shape B,N,N,64\");\n auto out = torch::empty({B, N, N}, pair_repr.options());\n int pairs = B * (N * (N + 1) / 2);\n int blocks = (pairs + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK;\n contact_kernel<<<blocks, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n pair_repr.data_ptr<float>(), ln_w.data_ptr<float>(), ln_b.data_ptr<float>(),\n w1.data_ptr<float>(), b1.data_ptr<float>(), w2.data_ptr<float>(), b2.data_ptr<float>(),\n w3.data_ptr<float>(), b3.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"contact_head_ext_v1\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"contact_forward_cuda\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.layer_norm = nn.LayerNorm(pair_dim)\n self.mlp = nn.Sequential(\n nn.Linear(pair_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, 1),\n )\n\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),\n self.layer_norm.weight, self.layer_norm.bias,\n self.mlp[0].weight, self.mlp[0].bias,\n self.mlp[2].weight, self.mlp[2].bias,\n self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← compile_kernel (fail)

failcompile_kernel compile_kernel FAILED: RuntimeError. RuntimeError: Error building extension 'contact_head_ext_v1': [1/3] /opt/conda/bin/x86_64-conda-linux-gnu-c++ -MMD -MF main.o.d -DTORCH_EXTENSION_NAME=contact_head_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -fPIC -std=c++17 -O3 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_43_cache/k_1e3afced2bf3/contact_head_ext_v1/main.cpp -o main.o [2/3] /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=contact_head_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_43_cache/k_1e3afced2bf3/contact_head_ext_v1/cuda.cu -o cuda.cuda.o FAILED: [code=2] cuda.cuda.o /usr/local/cuda/bin/nvcc -MD -MF cuda.cuda.o.d -ccbin /opt/conda/bin/x86_64-conda-linux-gnu-cc -DTORCH_EXTENSION_NAME=contact_head_ext_v1 -DTORCH_API_INCLUDE_EXTENSION_H -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include -isystem /scratch/abaner/KernelBench/.venv/lib/python3.10/site-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -isystem /opt/conda/include/python3.10 -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_BFLOAT16_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr -gencode=arch=compute_90,code=compute_90 -gencode=arch=compute_90,code=sm_90 --compiler-options '-fPIC' -O3 -std=c++17 -c /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_43_cache/k_1e3afced2bf3/contact_head_ext_v1/cuda.cu -o cuda.cuda.o nvcc warning : incompatible redefinition for option 'compiler-bindir', the last value of this option was used /scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_43_cache/k_1e3afced2bf3/contact_head_ext_v1/cuda.cu(132): error: namespace "at::cuda" has no member "getCurrentCUDAStream" contact_kernel<<<blocks, 128, 0, at::cuda::getCurrentCUDAStream()>>>( ^ 1 error detected in the compilation of "/scratch/abaner/KernelBench/runs/l12_all_gpt/popcorn/gpt-5.5-priority/level_2_problem_43_cache/k_1e3afced2bf3/contact_head_ext_v1/cuda.cu". ninja: build stopped: subcommand failed.

turn 2 · 1 tool call(s) · 12.2s turn · 12.2s LLM · 7,851 chars (click to expand)

turn 212.2s turn · 12.2s LLM · 0.0s tools

reasoning

**Including necessary headers** I need to include the ATen/cuda/CUDAContext.h file in my code. It's also important to consider if I should use c10::cuda for this purpose. I think adding that include would enhance my code's functionality for CUDA operations, but I want to be sure that it aligns with my overall project requirements. I'll double-check to make sure I've got everything covered for proper integration!

call → compile_kernel

callcompile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n\n__device__ __forceinline__ float warp_sum(float v) {\n unsigned mask = 0xffffffffu;\n v += __shfl_down_sync(mask, v, 16);\n v += __shfl_down_sync(mask, v, 8);\n v += __shfl_down_sync(mask, v, 4);\n v += __shfl_down_sync(mask, v, 2);\n v += __shfl_down_sync(mask, v, 1);\n return v;\n}\n\n__global__ void contact_kernel(const float* __restrict__ x,\n const float* __restrict__ ln_w,\n const float* __restrict__ ln_b,\n const float* __restrict__ w1,\n const float* __restrict__ b1,\n const float* __restrict__ w2,\n const float* __restrict__ b2,\n const float* __restrict__ w3,\n const float* __restrict__ b3,\n float* __restrict__ out,\n int B, int N) {\n __shared__ float sh1[WARPS_PER_BLOCK][H];\n __shared__ float sh2[WARPS_PER_BLOCK][H];\n\n int lane = threadIdx.x & 31;\n int warp = threadIdx.x >> 5;\n int gid = blockIdx.x * WARPS_PER_BLOCK + warp;\n int pairs_per_b = N * (N + 1) / 2;\n int total = B * pairs_per_b;\n if (gid >= total) return;\n\n int b = gid / pairs_per_b;\n int p = gid - b * pairs_per_b;\n\n float aa = (float)(2 * N + 1);\n int i = (int)floorf((aa - sqrtf(aa * aa - 8.0f * (float)p)) * 0.5f);\n int start = i * N - (i * (i - 1)) / 2;\n int j = i + (p - start);\n\n long base_ij = (((long)b * N + i) * N + j) * C;\n long base_ji = (((long)b * N + j) * N + i) * C;\n\n int d0 = lane;\n int d1 = lane + 32;\n\n float xa0 = x[base_ij + d0];\n float xa1 = x[base_ij + d1];\n float xb0 = x[base_ji + d0];\n float xb1 = x[base_ji + d1];\n\n float sum_a = xa0 + xa1;\n float sum_b = xb0 + xb1;\n sum_a = warp_sum(sum_a);\n sum_b = warp_sum(sum_b);\n float mean_a = sum_a * (1.0f / 64.0f);\n float mean_b = sum_b * (1.0f / 64.0f);\n\n float da0 = xa0 - mean_a;\n float da1 = xa1 - mean_a;\n float db0 = xb0 - mean_b;\n float db1 = xb1 - mean_b;\n float var_a = da0 * da0 + da1 * da1;\n float var_b = db0 * db0 + db1 * db1;\n var_a = warp_sum(var_a);\n var_b = warp_sum(var_b);\n float rstd_a = rsqrtf(var_a * (1.0f / 64.0f) + 1.0e-5f);\n float rstd_b = rsqrtf(var_b * (1.0f / 64.0f) + 1.0e-5f);\n\n float v0 = 0.5f * (da0 * rstd_a + db0 * rstd_b) * ln_w[d0] + ln_b[d0];\n float v1 = 0.5f * (da1 * rstd_a + db1 * rstd_b) * ln_w[d1] + ln_b[d1];\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float acc = v0 * w1[k * C + d0] + v1 * w1[k * C + d1];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b1[k];\n sh1[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float h = sh1[warp][lane];\n float acc = h * w2[k * H + lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b2[k];\n sh2[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n float acc = sh2[warp][lane] * w3[lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n float logit = acc + b3[0];\n float y = 1.0f / (1.0f + expf(-logit));\n out[((long)b * N + i) * N + j] = y;\n out[((long)b * N + j) * N + i] = y;\n }\n}\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3) {\n TORCH_CHECK(pair_repr.is_cuda(), \"pair_repr must be CUDA\");\n TORCH_CHECK(pair_repr.scalar_type() == at::kFloat, \"pair_repr must be float32\");\n int B = (int)pair_repr.size(0);\n int N = (int)pair_repr.size(1);\n TORCH_CHECK(pair_repr.size(2) == N && pair_repr.size(3) == C, \"expected shape B,N,N,64\");\n auto out = torch::empty({B, N, N}, pair_repr.options());\n int pairs = B * (N * (N + 1) / 2);\n int blocks = (pairs + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK;\n contact_kernel<<<blocks, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n pair_repr.data_ptr<float>(), ln_w.data_ptr<float>(), ln_b.data_ptr<float>(),\n w1.data_ptr<float>(), b1.data_ptr<float>(), w2.data_ptr<float>(), b2.data_ptr<float>(),\n w3.data_ptr<float>(), b3.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"contact_head_ext_v2\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"contact_forward_cuda\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.layer_norm = nn.LayerNorm(pair_dim)\n self.mlp = nn.Sequential(\n nn.Linear(pair_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, 1),\n )\n\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),\n self.layer_norm.weight, self.layer_norm.bias,\n self.mlp[0].weight, self.mlp[0].bias,\n self.mlp[2].weight, self.mlp[2].bias,\n self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← compile_kernel (ok)

okcompile_kernel compile_kernel PASSED: kernel compiled without errors.

turn 3 · 1 tool call(s) · 17.2s turn · 17.2s LLM · 7,637 chars (click to expand)

turn 317.2s turn · 17.2s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n\n__device__ __forceinline__ float warp_sum(float v) {\n unsigned mask = 0xffffffffu;\n v += __shfl_down_sync(mask, v, 16);\n v += __shfl_down_sync(mask, v, 8);\n v += __shfl_down_sync(mask, v, 4);\n v += __shfl_down_sync(mask, v, 2);\n v += __shfl_down_sync(mask, v, 1);\n return v;\n}\n\n__global__ void contact_kernel(const float* __restrict__ x,\n const float* __restrict__ ln_w,\n const float* __restrict__ ln_b,\n const float* __restrict__ w1,\n const float* __restrict__ b1,\n const float* __restrict__ w2,\n const float* __restrict__ b2,\n const float* __restrict__ w3,\n const float* __restrict__ b3,\n float* __restrict__ out,\n int B, int N) {\n __shared__ float sh1[WARPS_PER_BLOCK][H];\n __shared__ float sh2[WARPS_PER_BLOCK][H];\n\n int lane = threadIdx.x & 31;\n int warp = threadIdx.x >> 5;\n int gid = blockIdx.x * WARPS_PER_BLOCK + warp;\n int pairs_per_b = N * (N + 1) / 2;\n int total = B * pairs_per_b;\n if (gid >= total) return;\n\n int b = gid / pairs_per_b;\n int p = gid - b * pairs_per_b;\n\n float aa = (float)(2 * N + 1);\n int i = (int)floorf((aa - sqrtf(aa * aa - 8.0f * (float)p)) * 0.5f);\n int start = i * N - (i * (i - 1)) / 2;\n int j = i + (p - start);\n\n long base_ij = (((long)b * N + i) * N + j) * C;\n long base_ji = (((long)b * N + j) * N + i) * C;\n\n int d0 = lane;\n int d1 = lane + 32;\n\n float xa0 = x[base_ij + d0];\n float xa1 = x[base_ij + d1];\n float xb0 = x[base_ji + d0];\n float xb1 = x[base_ji + d1];\n\n float sum_a = xa0 + xa1;\n float sum_b = xb0 + xb1;\n sum_a = warp_sum(sum_a);\n sum_b = warp_sum(sum_b);\n float mean_a = sum_a * (1.0f / 64.0f);\n float mean_b = sum_b * (1.0f / 64.0f);\n\n float da0 = xa0 - mean_a;\n float da1 = xa1 - mean_a;\n float db0 = xb0 - mean_b;\n float db1 = xb1 - mean_b;\n float var_a = da0 * da0 + da1 * da1;\n float var_b = db0 * db0 + db1 * db1;\n var_a = warp_sum(var_a);\n var_b = warp_sum(var_b);\n float rstd_a = rsqrtf(var_a * (1.0f / 64.0f) + 1.0e-5f);\n float rstd_b = rsqrtf(var_b * (1.0f / 64.0f) + 1.0e-5f);\n\n float v0 = 0.5f * (da0 * rstd_a + db0 * rstd_b) * ln_w[d0] + ln_b[d0];\n float v1 = 0.5f * (da1 * rstd_a + db1 * rstd_b) * ln_w[d1] + ln_b[d1];\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float acc = v0 * w1[k * C + d0] + v1 * w1[k * C + d1];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b1[k];\n sh1[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n #pragma unroll\n for (int k = 0; k < H; ++k) {\n float h = sh1[warp][lane];\n float acc = h * w2[k * H + lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n acc += b2[k];\n sh2[warp][k] = acc > 0.0f ? acc : 0.0f;\n }\n }\n __syncwarp();\n\n float acc = sh2[warp][lane] * w3[lane];\n acc = warp_sum(acc);\n if (lane == 0) {\n float logit = acc + b3[0];\n float y = 1.0f / (1.0f + expf(-logit));\n out[((long)b * N + i) * N + j] = y;\n out[((long)b * N + j) * N + i] = y;\n }\n}\n\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr,\n torch::Tensor ln_w, torch::Tensor ln_b,\n torch::Tensor w1, torch::Tensor b1,\n torch::Tensor w2, torch::Tensor b2,\n torch::Tensor w3, torch::Tensor b3) {\n TORCH_CHECK(pair_repr.is_cuda(), \"pair_repr must be CUDA\");\n TORCH_CHECK(pair_repr.scalar_type() == at::kFloat, \"pair_repr must be float32\");\n int B = (int)pair_repr.size(0);\n int N = (int)pair_repr.size(1);\n TORCH_CHECK(pair_repr.size(2) == N && pair_repr.size(3) == C, \"expected shape B,N,N,64\");\n auto out = torch::empty({B, N, N}, pair_repr.options());\n int pairs = B * (N * (N + 1) / 2);\n int blocks = (pairs + WARPS_PER_BLOCK - 1) / WARPS_PER_BLOCK;\n contact_kernel<<<blocks, 128, 0, at::cuda::getCurrentCUDAStream()>>>(\n pair_repr.data_ptr<float>(), ln_w.data_ptr<float>(), ln_b.data_ptr<float>(),\n w1.data_ptr<float>(), b1.data_ptr<float>(), w2.data_ptr<float>(), b2.data_ptr<float>(),\n w3.data_ptr<float>(), b3.data_ptr<float>(), out.data_ptr<float>(), B, N);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"contact_head_ext_v2\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"contact_forward_cuda\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.layer_norm = nn.LayerNorm(pair_dim)\n self.mlp = nn.Sequential(\n nn.Linear(pair_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, hidden_dim),\n nn.ReLU(),\n nn.Linear(hidden_dim, 1),\n )\n\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),\n self.layer_norm.weight, self.layer_norm.bias,\n self.mlp[0].weight, self.mlp[0].bias,\n self.mlp[2].weight, self.mlp[2].bias,\n self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← run_correctness (fail)

failrun_correctness run_correctness FAILED: (0 / 5) trials did not all match. correctness_issue: Output mismatch max_difference: ['0.024839', '0.030817', '0.025119', '0.036024', '0.030862'] avg_difference: ['0.003542', '0.003589', '0.003633', '0.003627', '0.003603']

turn 414.4s turn · 14.4s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n __shared__ float sh1[WARPS_PER_BLOCK][H]; __shared__ float sh2[WARPS_PER_BLOCK][H];\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WARPS_PER_BLOCK+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C; long base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sum_a=warp_sum(xa0+xa1), sum_b=warp_sum(xb0+xb1); float mean_a=sum_a*(1.0f/64.0f), mean_b=sum_b*(1.0f/64.0f);\n float da0=xa0-mean_a, da1=xa1-mean_a, db0=xb0-mean_b, db1=xb1-mean_b;\n float var_a=warp_sum(da0*da0+da1*da1), var_b=warp_sum(db0*db0+db1*db1); float rstd_a=rsqrtf(var_a*(1.0f/64.0f)+1.0e-5f), rstd_b=rsqrtf(var_b*(1.0f/64.0f)+1.0e-5f);\n float v0=0.5f*(da0*rstd_a+db0*rstd_b)*ln_w[d0]+ln_b[d0]; float v1=0.5f*(da1*rstd_a+db1*rstd_b)*ln_w[d1]+ln_b[d1];\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; sh1[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=sh1[warp][lane]*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; sh2[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n float acc=warp_sum(sh2[warp][lane]*w3[lane]); if(lane==0){ float y=1.0f/(1.0f+expf(-(acc+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){\n int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N}, pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WARPS_PER_BLOCK-1)/WARPS_PER_BLOCK;\n contact_kernel<<<blocks,128,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out;\n}\n\"\"\"\n_ext = load_inline(name=\"contact_head_ext_v3\", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=[\"contact_forward_cuda\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← run_correctness (fail)

turn 516.8s turn · 16.8s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float warp_bcast0(float v){ return __shfl_sync(0xffffffffu, v, 0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n __shared__ float sh1[WARPS_PER_BLOCK][H]; __shared__ float sh2[WARPS_PER_BLOCK][H];\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WARPS_PER_BLOCK+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C; long base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sum_a=warp_bcast0(warp_sum(xa0+xa1)), sum_b=warp_bcast0(warp_sum(xb0+xb1)); float mean_a=sum_a*(1.0f/64.0f), mean_b=sum_b*(1.0f/64.0f);\n float da0=xa0-mean_a, da1=xa1-mean_a, db0=xb0-mean_b, db1=xb1-mean_b;\n float var_a=warp_bcast0(warp_sum(da0*da0+da1*da1)), var_b=warp_bcast0(warp_sum(db0*db0+db1*db1)); float rstd_a=rsqrtf(var_a*(1.0f/64.0f)+1.0e-5f), rstd_b=rsqrtf(var_b*(1.0f/64.0f)+1.0e-5f);\n float v0=0.5f*(da0*rstd_a+db0*rstd_b)*ln_w[d0]+ln_b[d0]; float v1=0.5f*(da1*rstd_a+db1*rstd_b)*ln_w[d1]+ln_b[d1];\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; sh1[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=sh1[warp][lane]*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; sh2[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n float acc=warp_sum(sh2[warp][lane]*w3[lane]); if(lane==0){ float y=1.0f/(1.0f+expf(-(acc+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N}, pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WARPS_PER_BLOCK-1)/WARPS_PER_BLOCK; contact_kernel<<<blocks,128,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext = load_inline(name=\"contact_head_ext_v4\", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=[\"contact_forward_cuda\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=1.19e-07 mean_abs_err=2.87e-09 max_rel_err=2.34e-07

turn 6 · 1 tool call(s) · 12.4s turn · 12.4s LLM · 6,964 chars (click to expand)

turn 612.4s turn · 12.4s LLM · 0.0s tools

reasoning

**Optimizing performance** I’m considering the performance aspect here. Submitting might be enough right now, but I really want to ensure everything is optimized. Profiling could help identify any bottlenecks or areas where I can enhance efficiency. It feels important to not just settle for "good enough" but to strive for the best performance possible. I wonder what specific measures I can take to make sure everything runs smoothly and effectively. Let’s explore those options!

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WARPS_PER_BLOCK 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float warp_bcast0(float v){ return __shfl_sync(0xffffffffu, v, 0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n __shared__ float sh1[WARPS_PER_BLOCK][H]; __shared__ float sh2[WARPS_PER_BLOCK][H];\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WARPS_PER_BLOCK+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C; long base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sum_a=warp_bcast0(warp_sum(xa0+xa1)), sum_b=warp_bcast0(warp_sum(xb0+xb1)); float mean_a=sum_a*(1.0f/64.0f), mean_b=sum_b*(1.0f/64.0f);\n float da0=xa0-mean_a, da1=xa1-mean_a, db0=xb0-mean_b, db1=xb1-mean_b;\n float var_a=warp_bcast0(warp_sum(da0*da0+da1*da1)), var_b=warp_bcast0(warp_sum(db0*db0+db1*db1)); float rstd_a=rsqrtf(var_a*(1.0f/64.0f)+1.0e-5f), rstd_b=rsqrtf(var_b*(1.0f/64.0f)+1.0e-5f);\n float v0=0.5f*(da0*rstd_a+db0*rstd_b)*ln_w[d0]+ln_b[d0]; float v1=0.5f*(da1*rstd_a+db1*rstd_b)*ln_w[d1]+ln_b[d1];\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; sh1[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=sh1[warp][lane]*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; sh2[warp][k]=acc>0.0f?acc:0.0f;} }\n __syncwarp();\n float acc=warp_sum(sh2[warp][lane]*w3[lane]); if(lane==0){ float y=1.0f/(1.0f+expf(-(acc+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N}, pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WARPS_PER_BLOCK-1)/WARPS_PER_BLOCK; contact_kernel<<<blocks,128,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext = load_inline(name=\"contact_head_ext_v4\", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=[\"contact_forward_cuda\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 29.0μs (100%) calls=1 --- Timing --- GPU time: 27.04 μs --- Memory Bandwidth --- DRAM BW: 156.6/3350 GB/s (4.7%) read=156.6 write=0.0 GB/s Hit rates: L2=30.1% --- Compute Throughput --- Dominant pipe: FP32 util=18.5% Pipe util: FMA=18.5%, Tensor=0.2% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 78.1% Eligible warps/cycle: 2.05 Launch: regs/thread=32, smem/blk=2048B, block=128, grid=2080 --- Memory Access Quality --- Global ld coalescing: 2.85 sectors/req (2.9x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- short_scoreboard 1043.8% #################### mio_throttle 566.5% #################### long_scoreboard 154.4% #################### --- Optimization Hints --- -> 1044% stalls: shared memory / L1 / MIO dependency. Check smem layout for bank conflicts, double-buffer, or async copies. -> Global load coalescing=2.9x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics.

turn 734.6s turn · 34.6s LLM · 0.0s tools

reasoning

**Evaluating performance optimization** I'm considering how to reduce any mismatches while keeping performance robust. Should I use "no TORCH_CHECK" to save on overhead? I'm wondering if host overhead is actually being measured. It seems like the submit timing probably includes both Python and the kernel. It looks like TORCH_CHECK has already been mostly removed, which is good. I could also mark pair_repr as contiguous without running a check.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 8\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int N){\n __shared__ float sh1[WPB][H]; __shared__ float sh2[WPB][H];\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int i=blockIdx.y; int j=i + blockIdx.x*WPB + warp; int b=blockIdx.z; if(j>=N) return;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; sh1[warp][k]=acc>0?acc:0;} }\n __syncwarp();\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=sh1[warp][lane]*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; sh2[warp][k]=acc>0?acc:0;} }\n __syncwarp();\n float acc=warp_sum(sh2[warp][lane]*w3[lane]); if(lane==0){ float y=1.0f/(1.0f+expf(-(acc+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); dim3 grid((N+WPB-1)/WPB,N,B); contact_kernel<<<grid,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v5\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=1.19e-07 mean_abs_err=2.87e-09 max_rel_err=2.34e-07

turn 8 · 1 tool call(s) · 10.8s turn · 10.8s LLM · 6,380 chars (click to expand)

turn 810.8s turn · 10.8s LLM · 0.0s tools

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 8\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int N){\n __shared__ float sh1[WPB][H]; __shared__ float sh2[WPB][H];\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int i=blockIdx.y; int j=i + blockIdx.x*WPB + warp; int b=blockIdx.z; if(j>=N) return;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; sh1[warp][k]=acc>0?acc:0;} }\n __syncwarp();\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=sh1[warp][lane]*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; sh2[warp][k]=acc>0?acc:0;} }\n __syncwarp();\n float acc=warp_sum(sh2[warp][lane]*w3[lane]); if(lane==0){ float y=1.0f/(1.0f+expf(-(acc+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); dim3 grid((N+WPB-1)/WPB,N,B); contact_kernel<<<grid,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v5\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 29.0μs (100%) calls=1 --- Timing --- GPU time: 32.38 μs [↑5.3μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 130.7/3350 GB/s (3.9%) [↓0.8% REGRESSED] read=130.7 write=0.0 GB/s Hit rates: L2=29.7% --- Compute Throughput --- Dominant pipe: FP32 util=18.1% [↓0.4% REGRESSED] Pipe util: FMA=18.1%, Tensor=0.1% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 73.6% [↓4.5% REGRESSED] Eligible warps/cycle: 1.62 Launch: regs/thread=32, smem/blk=3072B, block=256, grid=2048 --- Memory Access Quality --- Global ld coalescing: 2.85 sectors/req (2.9x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- short_scoreboard 1090.3% #################### mio_throttle 627.0% #################### long_scoreboard 258.9% #################### --- Optimization Hints --- -> 1090% stalls: shared memory / L1 / MIO dependency. Check smem layout for bank conflicts, double-buffer, or async copies. -> Global load coalescing=2.9x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 27.04→32.38μs (↑5.34) REGRESSED DRAM BW%: 4.67→3.90% (↓0.77) REGRESSED Compute%: 18.50→18.11% (↓0.40) REGRESSED Occupancy: 78.06→73.57% (↓4.48) REGRESSED Eligible warps/cyc: 2.05→1.62 (↓0.43) REGRESSED L2 hit%: 30.11→29.75% (↓0.36) REGRESSED

turn 922.3s turn · 22.3s LLM · 0.0s tools

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v6\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=1.19e-07 mean_abs_err=2.93e-09 max_rel_err=2.35e-07

turn 10 · 1 tool call(s) · 12.0s turn · 12.0s LLM · 6,486 chars (click to expand)

turn 1012.0s turn · 12.0s LLM · 0.0s tools

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v6\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 41.0μs (100%) calls=1 --- Timing --- GPU time: 25.44 μs [↓6.9μs ok] --- Memory Bandwidth --- DRAM BW: 166.5/3350 GB/s (5.0%) [↑1.1% ok] read=166.5 write=0.0 GB/s Hit rates: L2=30.7% --- Compute Throughput --- Dominant pipe: FP32 util=20.8% [↑2.7% ok] Pipe util: FMA=20.8%, Tensor=0.3% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 73.9% [↑0.3% ok] Eligible warps/cycle: 2.39 Launch: regs/thread=32, smem/blk=1024B, block=128, grid=2080 --- Memory Access Quality --- Global ld coalescing: 2.55 sectors/req (2.6x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- short_scoreboard 596.7% #################### mio_throttle 500.1% #################### long_scoreboard 222.1% #################### --- Optimization Hints --- -> 597% stalls: shared memory / L1 / MIO dependency. Check smem layout for bank conflicts, double-buffer, or async copies. -> Global load coalescing=2.6x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 32.38→25.44μs (↓6.94) ok DRAM BW%: 3.90→4.97% (↑1.07) ok Compute%: 18.11→20.85% (↑2.74) ok Occupancy: 73.57→73.86% (↑0.29) ok Eligible warps/cyc: 1.62→2.39 (↑0.77) ok Ld sectors/req: 2.85→2.55 (↓0.30) ok L2 hit%: 29.75→30.66% (↑0.91) ok

turn 1114.0s turn · 14.0s LLM · 0.0s tools

reasoning

**Considering performance improvements** I'm thinking about how v6 might be better. I could try setting WPB to 8 with a triangular loop to reduce the number of blocks. If I cut the blocks in half, I'd end up with about 1040 blocks across 256 threads, keeping the same warps but potentially lowering occupancy. That could help improve overhead. It sounds like a good idea to test this quickly by only changing WPB to 8.

call → run_correctness

callrun_correctness { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 8\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v7\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← run_correctness (ok)

okrun_correctness run_correctness PASSED: (5 / 5) trials all matched the reference. Numerical precision: max_abs_err=1.19e-07 mean_abs_err=2.93e-09 max_rel_err=2.35e-07

turn 12 · 1 tool call(s) · 12.3s turn · 12.3s LLM · 6,976 chars (click to expand)

turn 1212.3s turn · 12.3s LLM · 0.0s tools

reasoning

**Finalizing submission plans** I need to submit everything final now. I wonder if I should profile WPB8 without smem? It seems like we might need to speed things up. Right now, we have 3 turns and 38 calls. Maybe it would help to profile v7 quickly? I think I need to ensure the tool calls happen in the same turn, so everything flows smoothly. Let’s see how we can streamline this process!

call → profile_kernel

callprofile_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n_cpp_src=r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n_cuda_src=r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 8\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext=load_inline(name=\"contact_head_ext_v7\",cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=[\"contact_forward_cuda\"],extra_cflags=[\"-O3\"],extra_cuda_cflags=[\"-O3\"],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self,pair_dim,hidden_dim):\n super().__init__(); self.layer_norm=nn.LayerNorm(pair_dim); self.mlp=nn.Sequential(nn.Linear(pair_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,hidden_dim),nn.ReLU(),nn.Linear(hidden_dim,1))\n def forward(self,pair_repr:torch.Tensor)->torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(),self.layer_norm.weight,self.layer_norm.bias,self.mlp[0].weight,self.mlp[0].bias,self.mlp[2].weight,self.mlp[2].bias,self.mlp[4].weight,self.mlp[4].bias)\n" }

result ← profile_kernel (ok)

okprofile_kernel profile_kernel PASSED: profiling complete. === Kernel Profile Summary === --- Kernel Breakdown (by GPU time) --- #1: aten::empty 36.0μs (100%) calls=1 --- Timing --- GPU time: 25.76 μs [↑0.3μs REGRESSED] --- Memory Bandwidth --- DRAM BW: 164.4/3350 GB/s (4.9%) [↓0.1% REGRESSED] read=164.4 write=0.0 GB/s Hit rates: L2=30.3% --- Compute Throughput --- Dominant pipe: FP32 util=20.8% [↓0.0% REGRESSED] Pipe util: FMA=20.8%, Tensor=0.3% --- Roofline Analysis --- Ridge point (FP32): 20.00 FLOPs/byte Classification: COMPUTE-BOUND --- Occupancy --- Achieved: 74.7% [↑0.8% ok] Eligible warps/cycle: 2.46 Launch: regs/thread=32, smem/blk=1024B, block=256, grid=1040 --- Memory Access Quality --- Global ld coalescing: 2.55 sectors/req (2.6x wasted BW) --- Warp Stall Reasons (% of issue cycles) --- short_scoreboard 591.2% #################### mio_throttle 519.0% #################### long_scoreboard 230.6% #################### --- Optimization Hints --- -> 591% stalls: shared memory / L1 / MIO dependency. Check smem layout for bank conflicts, double-buffer, or async copies. -> Global load coalescing=2.6x (ideal=1.0) -> 3x wasted DRAM BW. Make per-warp accesses contiguous. -> Compute-bound but NOT using tensor cores. If applicable, use FP16/BF16 with torch.matmul or WMMA intrinsics. --- Delta vs Previous Iteration --- GPU time: 25.44→25.76μs (↑0.32) REGRESSED DRAM BW%: 4.97→4.91% (↓0.06) REGRESSED Compute%: 20.85→20.82% (↓0.03) REGRESSED Occupancy: 73.86→74.66% (↑0.80) ok Eligible warps/cyc: 2.39→2.46 (↑0.07) ok L2 hit%: 30.66→30.26% (↓0.40) REGRESSED

turn 1313.7s turn · 13.7s LLM · 0.0s tools

reasoning

**Finalizing submission steps** I need to ensure we’re ready to submit. Should I perform a static check first? I think we have enough information. Now, should I use version 6 or 7? It seems like version 6 has a profile of 25.44 WPB4, while version 7 is at 25.76. Maybe it's just noise, but I think version 6 might be better. I'll finalize the code and ensure the extension name is unique before submitting. Do I really need checks, though? Might not be necessary.

call → static_check

callstatic_check { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext = load_inline(name=\"contact_head_ext_final\", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=[\"contact_forward_cuda\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.layer_norm = nn.LayerNorm(pair_dim)\n self.mlp = nn.Sequential(nn.Linear(pair_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, 1))\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← static_check (ok)

okstatic_check static_check PASSED: no violations or warnings detected.

turn 1415.4s turn · 15.4s LLM · 0.0s tools · FINAL

call → submit_kernel

callsubmit_kernel { "kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);\n\"\"\"\n\n_cuda_src = r\"\"\"\n#include <torch/extension.h>\n#include <ATen/cuda/CUDAContext.h>\n#include <cuda.h>\n#include <cuda_runtime.h>\n#include <math.h>\n#define C 64\n#define H 32\n#define WPB 4\n__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }\n__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }\n__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){\n int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;\n int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;\n long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;\n float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];\n float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);\n float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);\n float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];\n float h1_lane=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }\n float logit_part=0.0f;\n #pragma unroll\n for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }\n if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }\n}\ntorch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }\n\"\"\"\n_ext = load_inline(name=\"contact_head_ext_final\", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=[\"contact_forward_cuda\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self, pair_dim, hidden_dim):\n super().__init__()\n self.layer_norm = nn.LayerNorm(pair_dim)\n self.mlp = nn.Sequential(nn.Linear(pair_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, 1))\n def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:\n return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)\n" }

result ← submit_kernel (ok)

oksubmit_kernel submit_kernel PASSED: (5 / 5) correctness trials all passed. Kernel runtime: 0.03 μs Runtime stats: mean=0.03μs median=?μs std=0.00μs Numerical precision: max_abs_err=1.19e-07 mean_abs_err=2.93e-09 max_rel_err=2.35e-07 Memory: 37.09 MB peak (ref: 49.03 MB, ratio: 0.7565x) Kernel launches: 1 (ref: 41, fusion ratio: 41.0) SOL score: 0.2086

final submitted kernel (48 lines, 4,069 chars) — full source, untruncated

import torch
import torch.nn as nn
from torch.utils.cpp_extension import load_inline

_cpp_src = r"""
#include <torch/extension.h>
torch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3);
"""

_cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <math.h>
#define C 64
#define H 32
#define WPB 4
__device__ __forceinline__ float warp_sum(float v){ unsigned m=0xffffffffu; v+=__shfl_down_sync(m,v,16); v+=__shfl_down_sync(m,v,8); v+=__shfl_down_sync(m,v,4); v+=__shfl_down_sync(m,v,2); v+=__shfl_down_sync(m,v,1); return v; }
__device__ __forceinline__ float bcast0(float v){ return __shfl_sync(0xffffffffu,v,0); }
__global__ void contact_kernel(const float* __restrict__ x,const float* __restrict__ ln_w,const float* __restrict__ ln_b,const float* __restrict__ w1,const float* __restrict__ b1,const float* __restrict__ w2,const float* __restrict__ b2,const float* __restrict__ w3,const float* __restrict__ b3,float* __restrict__ out,int B,int N){
    int lane=threadIdx.x&31, warp=threadIdx.x>>5; int gid=blockIdx.x*WPB+warp; int ppb=N*(N+1)/2; int total=B*ppb; if(gid>=total) return;
    int b=gid/ppb; int rem=gid-b*ppb; int i=0; int len=N; while(rem>=len){rem-=len; i++; len--;} int j=i+rem;
    long base_ij=(((long)b*N+i)*N+j)*C, base_ji=(((long)b*N+j)*N+i)*C; int d0=lane,d1=lane+32;
    float xa0=x[base_ij+d0], xa1=x[base_ij+d1], xb0=x[base_ji+d0], xb1=x[base_ji+d1];
    float sa=bcast0(warp_sum(xa0+xa1)), sb=bcast0(warp_sum(xb0+xb1)); float ma=sa*(1.0f/64.0f), mb=sb*(1.0f/64.0f);
    float da0=xa0-ma, da1=xa1-ma, db0=xb0-mb, db1=xb1-mb; float va=bcast0(warp_sum(da0*da0+da1*da1)), vb=bcast0(warp_sum(db0*db0+db1*db1)); float ra=rsqrtf(va*(1.0f/64.0f)+1e-5f), rb=rsqrtf(vb*(1.0f/64.0f)+1e-5f);
    float v0=0.5f*(da0*ra+db0*rb)*ln_w[d0]+ln_b[d0], v1=0.5f*(da1*ra+db1*rb)*ln_w[d1]+ln_b[d1];
    float h1_lane=0.0f;
    #pragma unroll
    for(int k=0;k<H;k++){ float acc=v0*w1[k*C+d0]+v1*w1[k*C+d1]; acc=warp_sum(acc); if(lane==0){acc+=b1[k]; acc=acc>0.0f?acc:0.0f;} float hv=__shfl_sync(0xffffffffu,acc,0); if(lane==k) h1_lane=hv; }
    float logit_part=0.0f;
    #pragma unroll
    for(int k=0;k<H;k++){ float acc=h1_lane*w2[k*H+lane]; acc=warp_sum(acc); if(lane==0){acc+=b2[k]; acc=acc>0.0f?acc:0.0f; logit_part += acc*w3[k];} }
    if(lane==0){ float y=1.0f/(1.0f+expf(-(logit_part+b3[0]))); out[((long)b*N+i)*N+j]=y; out[((long)b*N+j)*N+i]=y; }
}
torch::Tensor contact_forward_cuda(torch::Tensor pair_repr, torch::Tensor ln_w, torch::Tensor ln_b, torch::Tensor w1, torch::Tensor b1, torch::Tensor w2, torch::Tensor b2, torch::Tensor w3, torch::Tensor b3){ int B=(int)pair_repr.size(0); int N=(int)pair_repr.size(1); auto out=torch::empty({B,N,N},pair_repr.options()); int pairs=B*(N*(N+1)/2); int blocks=(pairs+WPB-1)/WPB; contact_kernel<<<blocks,WPB*32,0,at::cuda::getCurrentCUDAStream()>>>(pair_repr.data_ptr<float>(),ln_w.data_ptr<float>(),ln_b.data_ptr<float>(),w1.data_ptr<float>(),b1.data_ptr<float>(),w2.data_ptr<float>(),b2.data_ptr<float>(),w3.data_ptr<float>(),b3.data_ptr<float>(),out.data_ptr<float>(),B,N); return out; }
"""
_ext = load_inline(name="contact_head_ext_final", cpp_sources=_cpp_src, cuda_sources=_cuda_src, functions=["contact_forward_cuda"], extra_cflags=["-O3"], extra_cuda_cflags=["-O3"], verbose=False)

class ModelNew(nn.Module):
    def __init__(self, pair_dim, hidden_dim):
        super().__init__()
        self.layer_norm = nn.LayerNorm(pair_dim)
        self.mlp = nn.Sequential(nn.Linear(pair_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, 1))
    def forward(self, pair_repr: torch.Tensor) -> torch.Tensor:
        return _ext.contact_forward_cuda(pair_repr.contiguous(), self.layer_norm.weight, self.layer_norm.bias, self.mlp[0].weight, self.mlp[0].bias, self.mlp[2].weight, self.mlp[2].bias, self.mlp[4].weight, self.mlp[4].bias)