correct
94_MSELoss.py
started
2026-05-04T05:48:23Z
timingkernelbench.timing
kernel mean / median / std
2.79 / — / 0.09 μs
ref mean / median / std
8.49 / — / 0.07 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
7.00x
energyNVML (pynvml)
ratio (ref / kernel)
2.00x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
3208.61 GB/s
warp stalls (top 5)nsight
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
class Model(nn.Module):
"""
A model that computes the Mean Squared Error loss for regression tasks.
Parameters:
None
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, predictions, targets):
return torch.mean((predictions - targets) ** 2)
batch_size = 32768
input_shape = (32768,)
dim = 1
def get_inputs():
scale = torch.rand(())
return [torch.rand(batch_size, *input_shape)*scale, torch.rand(batch_size, *input_shape)]
def get_init_inputs():
return []
turn 013.1s turn · 13.1s LLM · 0.0s tools
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) · 20.1s turn · 20.1s LLM · 6,577 chars (click to expand)
turn 120.1s turn · 20.1s LLM · 0.0s tools
reasoning
**Evaluating fast_math options**
I’m noticing that fast_math alters how division operates, but I’m not sure that’s the main focus here. I find that using fmaf(diff, diff, sum) only rounds once, which could save a bit of precision. In comparison, PyTorch seems to handle square and addition separately. The difference in output between the two methods seems quite small, so maybe I’ll just stick with what’s straightforward for now!
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 mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_reduce_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n4) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_reduce_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_finalize_kernel(const float* __restrict__ partial,\n float* __restrict__ out,\n int num_partials,\n double inv_n) {\n int tid = threadIdx.x;\n double sum = 0.0;\n for (int i = tid; i < num_partials; i += blockDim.x) {\n sum += (double)partial[i];\n }\n // reduce double in shared memory (one block only)\n __shared__ double smem[256];\n smem[tid] = sum;\n __syncthreads();\n for (int s = blockDim.x >> 1; s > 0; s >>= 1) {\n if (tid < s) smem[tid] += smem[tid + s];\n __syncthreads();\n }\n if (tid == 0) out[0] = (float)(smem[0] * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const auto n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) {\n return out;\n }\n const int threads = 256;\n int64_t work = n;\n bool vec4 = ((n & 3LL) == 0) && ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) && ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n if (vec4) work = n >> 2;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n auto partial = torch::empty({blocks}, predictions.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n if (vec4) {\n mse_reduce_vec4_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), work);\n } else {\n mse_reduce_scalar_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), n);\n }\n mse_finalize_kernel<<<1, 256, 0, stream>>>(partial.data_ptr<float>(), out.data_ptr<float>(), blocks, 1.0 / (double)n);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_v1\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2 · 1 tool call(s) · 9.8s turn · 9.8s LLM · 6,101 chars (click to expand)
turn 29.8s turn · 9.8s 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>\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_reduce_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n4) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_reduce_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_finalize_kernel(const float* __restrict__ partial,\n float* __restrict__ out,\n int num_partials,\n double inv_n) {\n int tid = threadIdx.x;\n double sum = 0.0;\n for (int i = tid; i < num_partials; i += blockDim.x) {\n sum += (double)partial[i];\n }\n __shared__ double smem[256];\n smem[tid] = sum;\n __syncthreads();\n for (int s = blockDim.x >> 1; s > 0; s >>= 1) {\n if (tid < s) smem[tid] += smem[tid + s];\n __syncthreads();\n }\n if (tid == 0) out[0] = (float)(smem[0] * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const auto n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) {\n return out;\n }\n const int threads = 256;\n int64_t work = n;\n bool vec4 = ((n & 3LL) == 0) && ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) && ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n if (vec4) work = n >> 2;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n auto partial = torch::empty({blocks}, predictions.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n if (vec4) {\n mse_reduce_vec4_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), work);\n } else {\n mse_reduce_scalar_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), n);\n }\n mse_finalize_kernel<<<1, 256, 0, stream>>>(partial.data_ptr<float>(), out.data_ptr<float>(), blocks, 1.0 / (double)n);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_v1\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.49e-08 mean_abs_err=2.98e-09 max_rel_err=9.25e-08
turn 3 · 1 tool call(s) · 9.7s turn · 9.7s LLM · 7,290 chars (click to expand)
turn 39.7s turn · 9.7s 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\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < (blockDim.x >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_reduce_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n4) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_reduce_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ partial,\n int64_t n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) partial[blockIdx.x] = sum;\n}\n\n__global__ void mse_finalize_kernel(const float* __restrict__ partial,\n float* __restrict__ out,\n int num_partials,\n double inv_n) {\n int tid = threadIdx.x;\n double sum = 0.0;\n for (int i = tid; i < num_partials; i += blockDim.x) {\n sum += (double)partial[i];\n }\n __shared__ double smem[256];\n smem[tid] = sum;\n __syncthreads();\n for (int s = blockDim.x >> 1; s > 0; s >>= 1) {\n if (tid < s) smem[tid] += smem[tid + s];\n __syncthreads();\n }\n if (tid == 0) out[0] = (float)(smem[0] * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const auto n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) {\n return out;\n }\n const int threads = 256;\n int64_t work = n;\n bool vec4 = ((n & 3LL) == 0) && ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) && ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n if (vec4) work = n >> 2;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n auto partial = torch::empty({blocks}, predictions.options());\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n if (vec4) {\n mse_reduce_vec4_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), work);\n } else {\n mse_reduce_scalar_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), partial.data_ptr<float>(), n);\n }\n mse_finalize_kernel<<<1, 256, 0, stream>>>(partial.data_ptr<float>(), out.data_ptr<float>(), blocks, 1.0 / (double)n);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_v1\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 15304.0μs (100%) calls=1
#2: aten::empty 12.0μs (0%) calls=1
--- Timing ---
GPU time: 2826.24 μs
--- Memory Bandwidth ---
DRAM BW: 3156.0/3350 GB/s (94.2%)
read=3155.0 write=1.1 GB/s
Hit rates: L2=78.6%
--- Compute Throughput ---
Pipe util: FMA=4.4%, Tensor=0.1%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 86.9%
Eligible warps/cycle: 0.10
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=54, smem/blk=4224B, block=512, grid=65536
--- Memory Access Quality ---
Global ld coalescing: 20.00 sectors/req (20.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 22466.1% ####################
short_scoreboard 238.0% ####################
--- Optimization Hints ---
-> 22466% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=20.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=20.0x (ideal=1.0) -> 20x wasted DRAM BW. Make per-warp accesses contiguous.
turn 420.1s turn · 20.1s 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 mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a, const float* __restrict__ b,\n float* __restrict__ out, int64_t n4, float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a, const float* __restrict__ b,\n float* __restrict__ out, int64_t n, float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const int64_t n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) return out;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);\n const int threads = 256;\n bool vec4 = ((n & 3LL) == 0) && ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) && ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n int64_t work = vec4 ? (n >> 2) : n;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n float inv_n = 1.0f / (float)n;\n if (vec4) mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);\n else mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mse_reduce_ext_v2\", cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=[\"mse_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.43e-06 mean_abs_err=7.63e-07 max_rel_err=6.77e-06
turn 5 · 1 tool call(s) · 8.9s turn · 8.9s LLM · 6,584 chars (click to expand)
turn 58.9s turn · 8.9s 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\n_cpp_src = r\"\"\"\n#include <torch/extension.h>\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a, const float* __restrict__ b,\n float* __restrict__ out, int64_t n4, float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a, const float* __restrict__ b,\n float* __restrict__ out, int64_t n, float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const int64_t n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) return out;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);\n const int threads = 256;\n bool vec4 = ((n & 3LL) == 0) && ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) && ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n int64_t work = vec4 ? (n >> 2) : n;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n float inv_n = 1.0f / (float)n;\n if (vec4) mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);\n else mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);\n return out;\n}\n\"\"\"\n\n_ext = load_inline(name=\"mse_reduce_ext_v2\", cpp_sources=_cpp_src, cuda_sources=_cuda_src,\n functions=[\"mse_forward\"], extra_cflags=[\"-O3\"], extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"], verbose=False)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2338.0μs (100%) calls=1
--- Timing ---
GPU time: 2724.54 μs [↓101.7μs ok]
--- Memory Bandwidth ---
DRAM BW: 3153.7/3350 GB/s (94.1%) [↓0.1% REGRESSED]
read=3152.8 write=0.9 GB/s
Hit rates: L2=1.0%
--- Compute Throughput ---
Pipe util: FMA=3.3%, Tensor=0.1%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 74.4% [↓12.5% REGRESSED]
Eligible warps/cycle: 0.08
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=38, smem/blk=1152B, block=256, grid=65535
--- Memory Access Quality ---
Global ld coalescing: 16.00 sectors/req (16.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 14411.8% ####################
short_scoreboard 45.4% #########
--- Optimization Hints ---
-> 14412% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 2826.24→2724.54μs (↓101.70) ok
DRAM BW%: 94.21→94.14% (↓0.07) REGRESSED
Occupancy: 86.90→74.39% (↓12.50) REGRESSED
Eligible warps/cyc: 0.10→0.08 (↓0.02) REGRESSED
Ld sectors/req: 20.00→16.00 (↓4.00) ok
L2 hit%: 78.63→1.00% (↓77.63) REGRESSED
turn 610.4s turn · 10.4s LLM · 0.0s tools
reasoning
**Exploring optimization options**
I’m looking into atomic operations that can be around 100 microseconds. Maybe vectorization could improve performance? My DRAM at 3153 MHz is near peak, and I calculated a minimum memory requirement. If I consider float2/float4 loads, coalescing tools suggest something odd about float4 requests. I’m thinking of testing with 512 threads or blocks to check occupancy. My previous v1 profile used 512 threads, but I need to analyze and profile further to optimize it better.
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='''#include <torch/extension.h>\\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\\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 <stdint.h>\nstatic __device__ __forceinline__ float warp_reduce_sum(float v){v+=__shfl_down_sync(0xffffffff,v,16);v+=__shfl_down_sync(0xffffffff,v,8);v+=__shfl_down_sync(0xffffffff,v,4);v+=__shfl_down_sync(0xffffffff,v,2);v+=__shfl_down_sync(0xffffffff,v,1);return v;}\nstatic __device__ __forceinline__ float block_reduce_sum(float v){__shared__ float shared[32];int lane=threadIdx.x&31;int wid=threadIdx.x>>5;v=warp_reduce_sum(v);if(lane==0)shared[wid]=v;__syncthreads();v=(threadIdx.x<((blockDim.x+31)>>5))?shared[lane]:0.0f;if(wid==0)v=warp_reduce_sum(v);return v;}\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n4,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;const float4* __restrict__ a4=reinterpret_cast<const float4*>(a);const float4* __restrict__ b4=reinterpret_cast<const float4*>(b);float sum=0.0f;for(int64_t i=idx;i<n4;i+=stride){float4 av=a4[i],bv=b4[i];float d0=av.x-bv.x,d1=av.y-bv.y,d2=av.z-bv.z,d3=av.w-bv.w;sum=fmaf(d0,d0,sum);sum=fmaf(d1,d1,sum);sum=fmaf(d2,d2,sum);sum=fmaf(d3,d3,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;float sum=0.0f;for(int64_t i=idx;i<n;i+=stride){float d=a[i]-b[i];sum=fmaf(d,d,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\ntorch::Tensor mse_forward(torch::Tensor predictions,torch::Tensor targets){const int64_t n=predictions.numel();auto out=torch::empty({},predictions.options());if(n==0)return out;cudaStream_t stream=at::cuda::getCurrentCUDAStream();cudaMemsetAsync(out.data_ptr<float>(),0,sizeof(float),stream);const int threads=512;bool vec4=((n&3LL)==0)&&((((uintptr_t)predictions.data_ptr<float>())&15)==0)&&((((uintptr_t)targets.data_ptr<float>())&15)==0);int64_t work=vec4?(n>>2):n;int blocks=(int)((work+threads-1)/threads);if(blocks<1)blocks=1;if(blocks>65535)blocks=65535;float inv_n=1.0f/(float)n;if(vec4)mse_atomic_vec4_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),work,inv_n);else mse_atomic_scalar_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),n,inv_n);return out;}\n'''\n_ext=load_inline(name='mse_reduce_ext_v3',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['mse_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super(ModelNew,self).__init__()\n def forward(self,predictions,targets): return _ext.mse_forward(predictions,targets)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=1.16e-06 mean_abs_err=6.97e-07 max_rel_err=7.06e-06
turn 77.7s turn · 7.7s 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='''#include <torch/extension.h>\\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\\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 <stdint.h>\nstatic __device__ __forceinline__ float warp_reduce_sum(float v){v+=__shfl_down_sync(0xffffffff,v,16);v+=__shfl_down_sync(0xffffffff,v,8);v+=__shfl_down_sync(0xffffffff,v,4);v+=__shfl_down_sync(0xffffffff,v,2);v+=__shfl_down_sync(0xffffffff,v,1);return v;}\nstatic __device__ __forceinline__ float block_reduce_sum(float v){__shared__ float shared[32];int lane=threadIdx.x&31;int wid=threadIdx.x>>5;v=warp_reduce_sum(v);if(lane==0)shared[wid]=v;__syncthreads();v=(threadIdx.x<((blockDim.x+31)>>5))?shared[lane]:0.0f;if(wid==0)v=warp_reduce_sum(v);return v;}\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n4,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;const float4* __restrict__ a4=reinterpret_cast<const float4*>(a);const float4* __restrict__ b4=reinterpret_cast<const float4*>(b);float sum=0.0f;for(int64_t i=idx;i<n4;i+=stride){float4 av=a4[i],bv=b4[i];float d0=av.x-bv.x,d1=av.y-bv.y,d2=av.z-bv.z,d3=av.w-bv.w;sum=fmaf(d0,d0,sum);sum=fmaf(d1,d1,sum);sum=fmaf(d2,d2,sum);sum=fmaf(d3,d3,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;float sum=0.0f;for(int64_t i=idx;i<n;i+=stride){float d=a[i]-b[i];sum=fmaf(d,d,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\ntorch::Tensor mse_forward(torch::Tensor predictions,torch::Tensor targets){const int64_t n=predictions.numel();auto out=torch::empty({},predictions.options());if(n==0)return out;cudaStream_t stream=at::cuda::getCurrentCUDAStream();cudaMemsetAsync(out.data_ptr<float>(),0,sizeof(float),stream);const int threads=512;bool vec4=((n&3LL)==0)&&((((uintptr_t)predictions.data_ptr<float>())&15)==0)&&((((uintptr_t)targets.data_ptr<float>())&15)==0);int64_t work=vec4?(n>>2):n;int blocks=(int)((work+threads-1)/threads);if(blocks<1)blocks=1;if(blocks>65535)blocks=65535;float inv_n=1.0f/(float)n;if(vec4)mse_atomic_vec4_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),work,inv_n);else mse_atomic_scalar_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),n,inv_n);return out;}\n'''\n_ext=load_inline(name='mse_reduce_ext_v3',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['mse_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super(ModelNew,self).__init__()\n def forward(self,predictions,targets): return _ext.mse_forward(predictions,targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 39474.0μs (100%) calls=1
--- Timing ---
GPU time: 2705.73 μs [↓18.8μs ok]
--- Memory Bandwidth ---
DRAM BW: 3175.7/3350 GB/s (94.8%) [↑0.7% ok]
read=3174.7 write=1.0 GB/s
Hit rates: L2=0.4%
--- Compute Throughput ---
Pipe util: FMA=3.9%, Tensor=0.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 73.9% [↓0.5% REGRESSED]
Eligible warps/cycle: 0.13
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=38, smem/blk=1152B, block=512, grid=65535
--- Memory Access Quality ---
Global ld coalescing: 16.00 sectors/req (16.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 9636.6% ####################
short_scoreboard 74.3% ##############
mio_throttle 4.2%
--- Optimization Hints ---
-> 9637% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 2724.54→2705.73μs (↓18.82) ok
DRAM BW%: 94.14→94.80% (↑0.66) ok
Occupancy: 74.39→73.92% (↓0.47) REGRESSED
Eligible warps/cyc: 0.08→0.13 (↑0.05) ok
L2 hit%: 1.00→0.45% (↓0.55) REGRESSED
turn 8 · 1 tool call(s) · 8.5s turn · 8.5s LLM · 6,352 chars (click to expand)
turn 88.5s turn · 8.5s LLM · 0.0s tools
reasoning
**Evaluating performance optimization**
I'm analyzing potential performance improvements and testing configurations. A setup with 512 might be slightly faster, but I'm considering trying 1024, even though that could reduce occupancy. With a setup of one block of 1024 threads leading to around 32 warps, occupancy could be about 50%. That results in about 67 million total threads, each with around four float4s. If I optimize atomics, it might help with memory latency. I'm curious to see how this will play out!
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='''#include <torch/extension.h>\\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\\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 <stdint.h>\nstatic __device__ __forceinline__ float warp_reduce_sum(float v){v+=__shfl_down_sync(0xffffffff,v,16);v+=__shfl_down_sync(0xffffffff,v,8);v+=__shfl_down_sync(0xffffffff,v,4);v+=__shfl_down_sync(0xffffffff,v,2);v+=__shfl_down_sync(0xffffffff,v,1);return v;}\nstatic __device__ __forceinline__ float block_reduce_sum(float v){__shared__ float shared[32];int lane=threadIdx.x&31;int wid=threadIdx.x>>5;v=warp_reduce_sum(v);if(lane==0)shared[wid]=v;__syncthreads();v=(threadIdx.x<((blockDim.x+31)>>5))?shared[lane]:0.0f;if(wid==0)v=warp_reduce_sum(v);return v;}\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n4,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;const float4* __restrict__ a4=reinterpret_cast<const float4*>(a);const float4* __restrict__ b4=reinterpret_cast<const float4*>(b);float sum=0.0f;for(int64_t i=idx;i<n4;i+=stride){float4 av=a4[i],bv=b4[i];float d0=av.x-bv.x,d1=av.y-bv.y,d2=av.z-bv.z,d3=av.w-bv.w;sum=fmaf(d0,d0,sum);sum=fmaf(d1,d1,sum);sum=fmaf(d2,d2,sum);sum=fmaf(d3,d3,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;float sum=0.0f;for(int64_t i=idx;i<n;i+=stride){float d=a[i]-b[i];sum=fmaf(d,d,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\ntorch::Tensor mse_forward(torch::Tensor predictions,torch::Tensor targets){const int64_t n=predictions.numel();auto out=torch::empty({},predictions.options());if(n==0)return out;cudaStream_t stream=at::cuda::getCurrentCUDAStream();cudaMemsetAsync(out.data_ptr<float>(),0,sizeof(float),stream);const int threads=1024;bool vec4=((n&3LL)==0)&&((((uintptr_t)predictions.data_ptr<float>())&15)==0)&&((((uintptr_t)targets.data_ptr<float>())&15)==0);int64_t work=vec4?(n>>2):n;int blocks=(int)((work+threads-1)/threads);if(blocks<1)blocks=1;if(blocks>65535)blocks=65535;float inv_n=1.0f/(float)n;if(vec4)mse_atomic_vec4_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),work,inv_n);else mse_atomic_scalar_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),n,inv_n);return out;}\n'''\n_ext=load_inline(name='mse_reduce_ext_v4',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['mse_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super(ModelNew,self).__init__()\n def forward(self,predictions,targets): return _ext.mse_forward(predictions,targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 2333.0μs (100%) calls=1
--- Timing ---
GPU time: 2678.08 μs [↓27.6μs ok]
--- Memory Bandwidth ---
DRAM BW: 3208.5/3350 GB/s (95.8%) [↑1.0% ok]
read=3207.5 write=1.0 GB/s
Hit rates: L2=0.3%
--- Compute Throughput ---
Dominant pipe: FP32 util=5.2%
Pipe util: FMA=5.2%, Tensor=0.5%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 48.9% [↓25.0% REGRESSED]
Eligible warps/cycle: 0.33
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=38, smem/blk=1152B, block=1024, grid=65535
--- Memory Access Quality ---
Global ld coalescing: 16.00 sectors/req (16.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 4046.8% ####################
short_scoreboard 108.3% ####################
mio_throttle 46.4% #########
--- Optimization Hints ---
-> 4047% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 2705.73→2678.08μs (↓27.65) ok
DRAM BW%: 94.80→95.78% (↑0.98) ok
Occupancy: 73.92→48.90% (↓25.03) REGRESSED
Eligible warps/cyc: 0.13→0.33 (↑0.20) ok
L2 hit%: 0.45→0.32% (↓0.13) REGRESSED
turn 911.4s turn · 11.4s 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='''#include <torch/extension.h>\\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\\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 <stdint.h>\nstatic __device__ __forceinline__ float warp_reduce_sum(float v){v+=__shfl_down_sync(0xffffffff,v,16);v+=__shfl_down_sync(0xffffffff,v,8);v+=__shfl_down_sync(0xffffffff,v,4);v+=__shfl_down_sync(0xffffffff,v,2);v+=__shfl_down_sync(0xffffffff,v,1);return v;}\nstatic __device__ __forceinline__ float block_reduce_sum(float v){__shared__ float shared[32];int lane=threadIdx.x&31;int wid=threadIdx.x>>5;v=warp_reduce_sum(v);if(lane==0)shared[wid]=v;__syncthreads();v=(threadIdx.x<((blockDim.x+31)>>5))?shared[lane]:0.0f;if(wid==0)v=warp_reduce_sum(v);return v;}\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n4,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;const float4* __restrict__ a4=reinterpret_cast<const float4*>(a);const float4* __restrict__ b4=reinterpret_cast<const float4*>(b);float sum=0.0f;for(int64_t i=idx;i<n4;i+=stride){float4 av=a4[i],bv=b4[i];float d0=av.x-bv.x,d1=av.y-bv.y,d2=av.z-bv.z,d3=av.w-bv.w;sum=fmaf(d0,d0,sum);sum=fmaf(d1,d1,sum);sum=fmaf(d2,d2,sum);sum=fmaf(d3,d3,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;float sum=0.0f;for(int64_t i=idx;i<n;i+=stride){float d=a[i]-b[i];sum=fmaf(d,d,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\ntorch::Tensor mse_forward(torch::Tensor predictions,torch::Tensor targets){const int64_t n=predictions.numel();auto out=torch::empty({},predictions.options());if(n==0)return out;cudaStream_t stream=at::cuda::getCurrentCUDAStream();cudaMemsetAsync(out.data_ptr<float>(),0,sizeof(float),stream);const int threads=1024;bool vec4=((n&3LL)==0)&&((((uintptr_t)predictions.data_ptr<float>())&15)==0)&&((((uintptr_t)targets.data_ptr<float>())&15)==0);int64_t work=vec4?(n>>2):n;int blocks=(int)((work+threads-1)/threads);if(blocks<1)blocks=1;if(blocks>32768)blocks=32768;float inv_n=1.0f/(float)n;if(vec4)mse_atomic_vec4_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),work,inv_n);else mse_atomic_scalar_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),n,inv_n);return out;}\n'''\n_ext=load_inline(name='mse_reduce_ext_v5',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['mse_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super(ModelNew,self).__init__()\n def forward(self,predictions,targets): return _ext.mse_forward(predictions,targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 1930.0μs (100%) calls=1
--- Timing ---
GPU time: 2688.45 μs [↑10.4μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 3196.1/3350 GB/s (95.4%) [↓0.4% REGRESSED]
read=3195.1 write=0.9 GB/s
Hit rates: L2=0.3%
--- Compute Throughput ---
Pipe util: FMA=4.0%, Tensor=0.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: UNKNOWN
--- Occupancy ---
Achieved: 49.5% [↑0.6% ok]
Eligible warps/cycle: 0.19
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=38, smem/blk=1152B, block=1024, grid=32768
--- Memory Access Quality ---
Global ld coalescing: 16.00 sectors/req (16.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 6341.4% ####################
short_scoreboard 78.3% ###############
mio_throttle 33.8% ######
--- Optimization Hints ---
-> 6341% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 2678.08→2688.45μs (↑10.37) REGRESSED
DRAM BW%: 95.78→95.40% (↓0.37) REGRESSED
Occupancy: 48.90→49.45% (↑0.56) ok
Eligible warps/cyc: 0.33→0.19 (↓0.14) REGRESSED
L2 hit%: 0.32→0.27% (↓0.05) REGRESSED
turn 109.0s turn · 9.0s 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='''#include <torch/extension.h>\\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets);\\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 <stdint.h>\nstatic __device__ __forceinline__ float warp_reduce_sum(float v){v+=__shfl_down_sync(0xffffffff,v,16);v+=__shfl_down_sync(0xffffffff,v,8);v+=__shfl_down_sync(0xffffffff,v,4);v+=__shfl_down_sync(0xffffffff,v,2);v+=__shfl_down_sync(0xffffffff,v,1);return v;}\nstatic __device__ __forceinline__ float block_reduce_sum(float v){__shared__ float shared[32];int lane=threadIdx.x&31;int wid=threadIdx.x>>5;v=warp_reduce_sum(v);if(lane==0)shared[wid]=v;__syncthreads();v=(threadIdx.x<((blockDim.x+31)>>5))?shared[lane]:0.0f;if(wid==0)v=warp_reduce_sum(v);return v;}\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n4,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;const float4* __restrict__ a4=reinterpret_cast<const float4*>(a);const float4* __restrict__ b4=reinterpret_cast<const float4*>(b);float sum=0.0f;for(int64_t i=idx;i<n4;i+=stride){float4 av=a4[i],bv=b4[i];float d0=av.x-bv.x,d1=av.y-bv.y,d2=av.z-bv.z,d3=av.w-bv.w;sum=fmaf(d0,d0,sum);sum=fmaf(d1,d1,sum);sum=fmaf(d2,d2,sum);sum=fmaf(d3,d3,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,const float* __restrict__ b,float* __restrict__ out,int64_t n,float inv_n){int tid=threadIdx.x;int64_t idx=(int64_t)blockIdx.x*blockDim.x+tid;int64_t stride=(int64_t)blockDim.x*gridDim.x;float sum=0.0f;for(int64_t i=idx;i<n;i+=stride){float d=a[i]-b[i];sum=fmaf(d,d,sum);}sum=block_reduce_sum(sum);if(tid==0)atomicAdd(out,sum*inv_n);}\ntorch::Tensor mse_forward(torch::Tensor predictions,torch::Tensor targets){const int64_t n=predictions.numel();auto out=torch::empty({},predictions.options());if(n==0)return out;cudaStream_t stream=at::cuda::getCurrentCUDAStream();cudaMemsetAsync(out.data_ptr<float>(),0,sizeof(float),stream);const int threads=1024;bool vec4=((n&3LL)==0)&&((((uintptr_t)predictions.data_ptr<float>())&15)==0)&&((((uintptr_t)targets.data_ptr<float>())&15)==0);int64_t work=vec4?(n>>2):n;int blocks=(int)((work+threads-1)/threads);if(blocks<1)blocks=1;if(blocks>131072)blocks=131072;float inv_n=1.0f/(float)n;if(vec4)mse_atomic_vec4_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),work,inv_n);else mse_atomic_scalar_kernel<<<blocks,threads,0,stream>>>(predictions.data_ptr<float>(),targets.data_ptr<float>(),out.data_ptr<float>(),n,inv_n);return out;}\n'''\n_ext=load_inline(name='mse_reduce_ext_v6',cpp_sources=_cpp_src,cuda_sources=_cuda_src,functions=['mse_forward'],extra_cflags=['-O3'],extra_cuda_cflags=['-O3','--use_fast_math'],verbose=False)\nclass ModelNew(nn.Module):\n def __init__(self): super(ModelNew,self).__init__()\n def forward(self,predictions,targets): return _ext.mse_forward(predictions,targets)\n"
}
result ← profile_kernel (ok)
okprofile_kernel
profile_kernel PASSED: profiling complete.
=== Kernel Profile Summary ===
--- Kernel Breakdown (by GPU time) ---
#1: aten::empty 1877.0μs (100%) calls=1
--- Timing ---
GPU time: 2751.33 μs [↑62.9μs REGRESSED]
--- Memory Bandwidth ---
DRAM BW: 3123.0/3350 GB/s (93.2%) [↓2.2% REGRESSED]
read=3122.1 write=0.9 GB/s
Hit rates: L2=0.1%
--- Compute Throughput ---
Dominant pipe: FP32 util=7.7%
Pipe util: FMA=7.7%, Tensor=1.2%
--- Roofline Analysis ---
Ridge point (FP32): 20.00 FLOPs/byte
Classification: MEMORY-BOUND
--- Occupancy ---
Achieved: 47.9% [↓1.6% REGRESSED]
Eligible warps/cycle: 0.62
WARNING: <1 eligible warp/cycle = latency-bound, needs more parallelism
Launch: regs/thread=38, smem/blk=1152B, block=1024, grid=131072
--- Memory Access Quality ---
Global ld coalescing: 16.00 sectors/req (16.0x wasted BW)
--- Warp Stall Reasons (% of issue cycles) ---
long_scoreboard 1891.9% ####################
short_scoreboard 124.4% ####################
mio_throttle 52.7% ##########
--- Optimization Hints ---
-> 1892% stalls: global memory latency (waiting for DRAM/L2). + sectors/req=16.0 -> uncoalesced global loads, restructure access pattern or use __ldg Try shared-memory tiling, prefetching, or reducing working set.
-> Global load coalescing=16.0x (ideal=1.0) -> 16x wasted DRAM BW. Make per-warp accesses contiguous.
--- Delta vs Previous Iteration ---
GPU time: 2688.45→2751.33μs (↑62.88) REGRESSED
DRAM BW%: 95.40→93.22% (↓2.18) REGRESSED
Occupancy: 49.45→47.85% (↓1.60) REGRESSED
Eligible warps/cyc: 0.19→0.62 (↑0.44) ok
L2 hit%: 0.27→0.08% (↓0.19) REGRESSED
turn 11 · 2 tool call(s) · 30.0s turn · 30.0s LLM · 10,683 chars (click to expand)
turn 1130.0s turn · 30.0s 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 mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n4,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const int64_t n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) return out;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);\n\n const int threads = 1024;\n bool vec4 = ((n & 3LL) == 0) &&\n ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) &&\n ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n int64_t work = vec4 ? (n >> 2) : n;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n float inv_n = 1.0f / (float)n;\n\n if (vec4) {\n mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);\n } else {\n mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);\n }\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_final\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
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 mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n4,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const int64_t n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) return out;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);\n\n const int threads = 1024;\n bool vec4 = ((n & 3LL) == 0) &&\n ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) &&\n ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n int64_t work = vec4 ? (n >> 2) : n;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n float inv_n = 1.0f / (float)n;\n\n if (vec4) {\n mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);\n } else {\n mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);\n }\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_final\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=9.54e-07 mean_abs_err=6.23e-07 max_rel_err=5.40e-06
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 12 · FINAL · 1 tool call(s) · 9.8s turn · 9.8s LLM · 6,206 chars (click to expand)
turn 129.8s turn · 9.8s LLM · 0.0s tools · FINAL
reasoning
**Submitting final code**
I’m thinking that we need to submit the final code now. I want to ensure there’s no issue with `cudaMemsetAsync`. That seems to be okay though. As for final verbosity, it might be irrelevant, but I still want to double-check. I recognize the importance of submitting without any problems, so I’ll focus on using the tool to submit the code properly. Let's get this done!
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 mse_forward(torch::Tensor predictions, torch::Tensor targets);\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 <stdint.h>\n\nstatic __device__ __forceinline__ float warp_reduce_sum(float v) {\n v += __shfl_down_sync(0xffffffff, v, 16);\n v += __shfl_down_sync(0xffffffff, v, 8);\n v += __shfl_down_sync(0xffffffff, v, 4);\n v += __shfl_down_sync(0xffffffff, v, 2);\n v += __shfl_down_sync(0xffffffff, v, 1);\n return v;\n}\n\nstatic __device__ __forceinline__ float block_reduce_sum(float v) {\n __shared__ float shared[32];\n int lane = threadIdx.x & 31;\n int wid = threadIdx.x >> 5;\n v = warp_reduce_sum(v);\n if (lane == 0) shared[wid] = v;\n __syncthreads();\n v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;\n if (wid == 0) v = warp_reduce_sum(v);\n return v;\n}\n\n__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n4,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);\n const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);\n float sum = 0.0f;\n for (int64_t i = idx; i < n4; i += stride) {\n float4 av = a4[i];\n float4 bv = b4[i];\n float d0 = av.x - bv.x;\n float d1 = av.y - bv.y;\n float d2 = av.z - bv.z;\n float d3 = av.w - bv.w;\n sum = fmaf(d0, d0, sum);\n sum = fmaf(d1, d1, sum);\n sum = fmaf(d2, d2, sum);\n sum = fmaf(d3, d3, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\n__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,\n const float* __restrict__ b,\n float* __restrict__ out,\n int64_t n,\n float inv_n) {\n int tid = threadIdx.x;\n int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;\n int64_t stride = (int64_t)blockDim.x * gridDim.x;\n float sum = 0.0f;\n for (int64_t i = idx; i < n; i += stride) {\n float d = a[i] - b[i];\n sum = fmaf(d, d, sum);\n }\n sum = block_reduce_sum(sum);\n if (tid == 0) atomicAdd(out, sum * inv_n);\n}\n\ntorch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {\n const int64_t n = predictions.numel();\n auto out = torch::empty({}, predictions.options());\n if (n == 0) return out;\n cudaStream_t stream = at::cuda::getCurrentCUDAStream();\n cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);\n\n const int threads = 1024;\n bool vec4 = ((n & 3LL) == 0) &&\n ((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) &&\n ((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);\n int64_t work = vec4 ? (n >> 2) : n;\n int blocks = (int)((work + threads - 1) / threads);\n if (blocks < 1) blocks = 1;\n if (blocks > 65535) blocks = 65535;\n float inv_n = 1.0f / (float)n;\n\n if (vec4) {\n mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);\n } else {\n mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(\n predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);\n }\n return out;\n}\n\"\"\"\n\n_ext = load_inline(\n name=\"mse_reduce_ext_final\",\n cpp_sources=_cpp_src,\n cuda_sources=_cuda_src,\n functions=[\"mse_forward\"],\n extra_cflags=[\"-O3\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n def forward(self, predictions, targets):\n return _ext.mse_forward(predictions, targets)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 2.79 μs
Runtime stats: mean=2.79μs median=?μs std=0.09μs
Numerical precision: max_abs_err=9.24e-07 mean_abs_err=6.02e-07 max_rel_err=5.40e-06
Memory: 8225.0 MB peak (ref: 16417.0 MB, ratio: 0.501x)
Kernel launches: 1 (ref: 7, fusion ratio: 7.0)
Energy: 2045.32 mJ/run (ref: 4095.6 mJ, ratio: 2.0024x)
SOL score: 0.9578
final submitted kernel (125 lines, 4,365 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 mse_forward(torch::Tensor predictions, torch::Tensor targets);
"""
_cuda_src = r"""
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda.h>
#include <cuda_runtime.h>
#include <stdint.h>
static __device__ __forceinline__ float warp_reduce_sum(float v) {
v += __shfl_down_sync(0xffffffff, v, 16);
v += __shfl_down_sync(0xffffffff, v, 8);
v += __shfl_down_sync(0xffffffff, v, 4);
v += __shfl_down_sync(0xffffffff, v, 2);
v += __shfl_down_sync(0xffffffff, v, 1);
return v;
}
static __device__ __forceinline__ float block_reduce_sum(float v) {
__shared__ float shared[32];
int lane = threadIdx.x & 31;
int wid = threadIdx.x >> 5;
v = warp_reduce_sum(v);
if (lane == 0) shared[wid] = v;
__syncthreads();
v = (threadIdx.x < ((blockDim.x + 31) >> 5)) ? shared[lane] : 0.0f;
if (wid == 0) v = warp_reduce_sum(v);
return v;
}
__global__ void mse_atomic_vec4_kernel(const float* __restrict__ a,
const float* __restrict__ b,
float* __restrict__ out,
int64_t n4,
float inv_n) {
int tid = threadIdx.x;
int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;
int64_t stride = (int64_t)blockDim.x * gridDim.x;
const float4* __restrict__ a4 = reinterpret_cast<const float4*>(a);
const float4* __restrict__ b4 = reinterpret_cast<const float4*>(b);
float sum = 0.0f;
for (int64_t i = idx; i < n4; i += stride) {
float4 av = a4[i];
float4 bv = b4[i];
float d0 = av.x - bv.x;
float d1 = av.y - bv.y;
float d2 = av.z - bv.z;
float d3 = av.w - bv.w;
sum = fmaf(d0, d0, sum);
sum = fmaf(d1, d1, sum);
sum = fmaf(d2, d2, sum);
sum = fmaf(d3, d3, sum);
}
sum = block_reduce_sum(sum);
if (tid == 0) atomicAdd(out, sum * inv_n);
}
__global__ void mse_atomic_scalar_kernel(const float* __restrict__ a,
const float* __restrict__ b,
float* __restrict__ out,
int64_t n,
float inv_n) {
int tid = threadIdx.x;
int64_t idx = (int64_t)blockIdx.x * blockDim.x + tid;
int64_t stride = (int64_t)blockDim.x * gridDim.x;
float sum = 0.0f;
for (int64_t i = idx; i < n; i += stride) {
float d = a[i] - b[i];
sum = fmaf(d, d, sum);
}
sum = block_reduce_sum(sum);
if (tid == 0) atomicAdd(out, sum * inv_n);
}
torch::Tensor mse_forward(torch::Tensor predictions, torch::Tensor targets) {
const int64_t n = predictions.numel();
auto out = torch::empty({}, predictions.options());
if (n == 0) return out;
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
cudaMemsetAsync(out.data_ptr<float>(), 0, sizeof(float), stream);
const int threads = 1024;
bool vec4 = ((n & 3LL) == 0) &&
((((uintptr_t)predictions.data_ptr<float>()) & 15) == 0) &&
((((uintptr_t)targets.data_ptr<float>()) & 15) == 0);
int64_t work = vec4 ? (n >> 2) : n;
int blocks = (int)((work + threads - 1) / threads);
if (blocks < 1) blocks = 1;
if (blocks > 65535) blocks = 65535;
float inv_n = 1.0f / (float)n;
if (vec4) {
mse_atomic_vec4_kernel<<<blocks, threads, 0, stream>>>(
predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), work, inv_n);
} else {
mse_atomic_scalar_kernel<<<blocks, threads, 0, stream>>>(
predictions.data_ptr<float>(), targets.data_ptr<float>(), out.data_ptr<float>(), n, inv_n);
}
return out;
}
"""
_ext = load_inline(
name="mse_reduce_ext_final",
cpp_sources=_cpp_src,
cuda_sources=_cuda_src,
functions=["mse_forward"],
extra_cflags=["-O3"],
extra_cuda_cflags=["-O3", "--use_fast_math"],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, predictions, targets):
return _ext.mse_forward(predictions, targets)