correct
32_HardTanh.py
started
2026-05-02T02:14:38Z
timingkernelbench.timing
kernel mean / median / std
4.63 / — / 0.14 μs
ref mean / median / std
4.30 / — / 0.09 μs
numerical precisioneval_kernel_against_ref
gpu memoryextended_metrics.measure_memory
kernel launches / fusiontorch.profiler
fusion ratio (ref / kernel)
3.00x
energyNVML (pynvml)
ratio (ref / kernel)
1.05x
SOL (speed-of-light)nsight_hardware_counters
roofline / occupancynsight_hardware_counters
dram bandwidth
2834.59 GB/s
warp stalls (top 5)nsight
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):
"""
Simple model that performs a HardTanh activation.
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Applies HardTanh activation to the input tensor.
Args:
x (torch.Tensor): Input tensor of any shape.
Returns:
torch.Tensor: Output tensor with HardTanh applied, same shape as input.
"""
return F.hardtanh(x, min_val=-1., max_val=1.)
batch_size = 4096
dim = 393216
def get_inputs():
x = torch.rand(batch_size, dim)
return [x]
def get_init_inputs():
return [] # No special initialization inputs neededturn 0115.4s turn · 115.4s LLM · 0.0s tools
reasoning
We need to write a custom CUDA kernel for HardTanh on a tensor of shape [batch_size, dim], i.e., 4096 x 393216. That's a large tensor.
The reference uses `F.hardtanh(x, min_val=-1., max_val=1.)`, which is equivalent to:
```
out = x
if x < -1: out = -1
if x > 1: out = 1
```
Or mathematically: `clamp(x, -1, 1)`.
This is a very memory-bandwidth bound operation: read input, apply elementwise clamp, write output. Since it's a simple activation and the tensor is large, we need to write a fused CUDA kernel that does this efficiently. However, `F.hardtanh` is already fused in PyTorch (it's a standard elementwise operation, likely using a vectorized kernel). But we might be able to beat it or at least match it.
But the reference runtime is using PyTorch's standard `F.hardtanh`. PyTorch's elementwise operations are quite optimized (using ATen's vectorized kernels, multi-threading on CPU, CUDA kernels on GPU). However, for a large contiguous tensor, PyTorch likely uses a simple elementwise CUDA kernel that's already near memory bandwidth bound. So speedup may be hard but possible if we write an efficient kernel with vectorized loads/stores (float4) to saturate memory bandwidth. Or we might do in-place if possible, but the signature expects a return tensor.
Given the shape [4096, 393216], total elements = 4096 * 393216 = 1,610,612,736 (~1.61 billion). In fp32, that's ~6.4 GB of data (input + output). So memory bandwidth is key. Using float4 (16 bytes per load) can help reduce instruction overhead and better utilize memory bandwidth.
GPU specs might help. Let's get them.
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 1299.1s turn · 299.1s LLM · 0.0s tools
reasoning
This is a straightforward element-wise operation: HardTanh(x) = max(-1, min(1, x)). For a tensor of shape (4096, 393216), that's about 1.6 billion elements or 6.4 GB of fp32 data (input + output). Since it's memory bandwidth bound, the key is to maximize memory throughput.
The reference implementation uses F.hardtanh which is likely already a fused CUDA kernel. However, we can potentially write a custom kernel that uses vectorized loads/stores (e.g., float4) to maximize bandwidth utilization, and we can also do loop unrolling to saturate the memory pipeline.
Let me write a kernel that processes elements in a grid-stride loop with float4 vectorization. Since each element is independent, this is an ideal case for a simple elementwise kernel.
For the kernel:
- Use block size of 256 or 512 threads
- Each thread processes multiple float4 vectors (4 floats each)
- Compute HardTanh: `y = fminf(1.0f, fmaxf(-1.0f, x))`
- Since it's fp32, use `float` type
Let me write the code and compile it.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "\nimport torch\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\ntemplate <typename T>\n__device__ __forceinline__ T hardtanh_fwd(T x, T min_val, T max_val) {\n return x < min_val ? min_val : (x > max_val ? max_val : x);\n}\n\n__global__ void hardtanh_kernel(const float* __restrict__ input,\n float* __restrict__ output,\n int64_t n) {\n const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n const int64_t grid_size = gridDim.x * blockDim.x;\n \n // Process 4 elements per thread using float4 for vectorized memory access\n const int64_t n_vec = n / 4;\n const float4* in_vec = reinterpret_cast<const float4*>(input);\n float4* out_vec = reinterpret_cast<float4*>(output);\n \n for (int64_t i = idx; i < n_vec; i += grid_size) {\n float4 x = in_vec[i];\n float4 y;\n y.x = hardtanh_fwd(x.x, -1.0f, 1.0f);\n y.y = hardtanh_fwd(x.y, -1.0f, 1.0f);\n y.z = hardtanh_fwd(x.z, -1.0f, 1.0f);\n y.w = hardtanh_fwd(x.w, -1.0f, 1.0f);\n out_vec[i] = y;\n }\n \n // Handle remaining elements\n const int64_t remainder_start = n_vec * 4;\n for (int64_t i = remainder_start + idx; i < n; i += grid_size) {\n output[i] = hardtanh_fwd(input[i], -1.0f, 1.0f);\n }\n}\n\ntorch::Tensor hardtanh_cuda(torch::Tensor input) {\n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n if (n == 0) return output;\n \n // Ensure contiguous and properly aligned for float4\n auto input_contig = input.contiguous();\n \n const int threads = 256;\n const int blocks = 512; // Enough to saturate the GPU\n \n hardtanh_kernel<<<blocks, threads>>>(\n input_contig.data_ptr<float>(),\n output.data_ptr<float>(),\n n\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor hardtanh_cuda(torch::Tensor input);\n\"\"\"\n\n# Compile the extension\nhardtanh_module = load_inline(\n name=\"hardtanh_cuda\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"hardtanh_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(torch.nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return hardtanh_module.hardtanh_cuda(x)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 2120.7s turn · 120.7s LLM · 0.0s tools
reasoning
The compilation passed. Now let's check correctness.
call → run_correctness
callrun_correctness
{
"kernel_code": "\nimport torch\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\ntemplate <typename T>\n__device__ __forceinline__ T hardtanh_fwd(T x, T min_val, T max_val) {\n return x < min_val ? min_val : (x > max_val ? max_val : x);\n}\n\n__global__ void hardtanh_kernel(const float* __restrict__ input,\n float* __restrict__ output,\n int64_t n) {\n const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n const int64_t grid_size = gridDim.x * blockDim.x;\n \n // Process 4 elements per thread using float4 for vectorized memory access\n const int64_t n_vec = n / 4;\n const float4* in_vec = reinterpret_cast<const float4*>(input);\n float4* out_vec = reinterpret_cast<float4*>(output);\n \n for (int64_t i = idx; i < n_vec; i += grid_size) {\n float4 x = in_vec[i];\n float4 y;\n y.x = hardtanh_fwd(x.x, -1.0f, 1.0f);\n y.y = hardtanh_fwd(x.y, -1.0f, 1.0f);\n y.z = hardtanh_fwd(x.z, -1.0f, 1.0f);\n y.w = hardtanh_fwd(x.w, -1.0f, 1.0f);\n out_vec[i] = y;\n }\n \n // Handle remaining elements\n const int64_t remainder_start = n_vec * 4;\n for (int64_t i = remainder_start + idx; i < n; i += grid_size) {\n output[i] = hardtanh_fwd(input[i], -1.0f, 1.0f);\n }\n}\n\ntorch::Tensor hardtanh_cuda(torch::Tensor input) {\n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n if (n == 0) return output;\n \n // Ensure contiguous and properly aligned for float4\n auto input_contig = input.contiguous();\n \n const int threads = 256;\n const int blocks = 512; // Enough to saturate the GPU\n \n hardtanh_kernel<<<blocks, threads>>>(\n input_contig.data_ptr<float>(),\n output.data_ptr<float>(),\n n\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor hardtanh_cuda(torch::Tensor input);\n\"\"\"\n\n# Compile the extension\nhardtanh_module = load_inline(\n name=\"hardtanh_cuda\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"hardtanh_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(torch.nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return hardtanh_module.hardtanh_cuda(x)\n"
}
result ← run_correctness (ok)
okrun_correctness
run_correctness PASSED: (5 / 5) trials all matched the reference.
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
turn 35.8s turn · 5.8s LLM · 0.0s tools
reasoning
The kernel compiles and passes correctness checks. Let me try to optimize it further to see if I can get more speedup. The current kernel uses float4 vectorization, which is good for memory bandwidth. Let me think about what else I can try:
1. I can try tuning the number of blocks/threads
2. I can try grid-stride loop with unrolling
3. I can check if using __ldg() (load global through read-only cache) gives any benefit
4. I can try using different vector widths
Let me try a few optimizations to see what works best. One thing I notice is that float4 might require 16-byte alignment, and torch tensors are 16-byte aligned. Let me try a kernel with different configurations, or use the kernel I have and submit.
Actually, let me first do a static check to make sure there's no reward hacking.
call → static_check
callstatic_check
{
"kernel_code": "\nimport torch\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\ntemplate <typename T>\n__device__ __forceinline__ T hardtanh_fwd(T x, T min_val, T max_val) {\n return x < min_val ? min_val : (x > max_val ? max_val : x);\n}\n\n__global__ void hardtanh_kernel(const float* __restrict__ input,\n float* __restrict__ output,\n int64_t n) {\n const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n const int64_t grid_size = gridDim.x * blockDim.x;\n \n // Process 4 elements per thread using float4 for vectorized memory access\n const int64_t n_vec = n / 4;\n const float4* in_vec = reinterpret_cast<const float4*>(input);\n float4* out_vec = reinterpret_cast<float4*>(output);\n \n for (int64_t i = idx; i < n_vec; i += grid_size) {\n float4 x = in_vec[i];\n float4 y;\n y.x = hardtanh_fwd(x.x, -1.0f, 1.0f);\n y.y = hardtanh_fwd(x.y, -1.0f, 1.0f);\n y.z = hardtanh_fwd(x.z, -1.0f, 1.0f);\n y.w = hardtanh_fwd(x.w, -1.0f, 1.0f);\n out_vec[i] = y;\n }\n \n // Handle remaining elements\n const int64_t remainder_start = n_vec * 4;\n for (int64_t i = remainder_start + idx; i < n; i += grid_size) {\n output[i] = hardtanh_fwd(input[i], -1.0f, 1.0f);\n }\n}\n\ntorch::Tensor hardtanh_cuda(torch::Tensor input) {\n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n if (n == 0) return output;\n \n // Ensure contiguous and properly aligned for float4\n auto input_contig = input.contiguous();\n \n const int threads = 256;\n const int blocks = 512; // Enough to saturate the GPU\n \n hardtanh_kernel<<<blocks, threads>>>(\n input_contig.data_ptr<float>(),\n output.data_ptr<float>(),\n n\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor hardtanh_cuda(torch::Tensor input);\n\"\"\"\n\n# Compile the extension\nhardtanh_module = load_inline(\n name=\"hardtanh_cuda\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"hardtanh_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(torch.nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return hardtanh_module.hardtanh_cuda(x)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.
turn 413.6s turn · 13.6s LLM · 0.0s tools · FINAL
reasoning
The kernel compiles, passes correctness, and passes static check. Let me also try optimizing further with more blocks or a different configuration. But before that, let me submit this kernel since it's already passing all checks. Let me also consider if there's any optimization:
Actually, let me think about potential issues:
1. float4 loads need 16-byte aligned pointers - `torch::Tensor` should be 16-byte aligned for GPU tensors that are allocated by CUDA memory allocator. The entry pointer of a GPU PyTorch tensor is typically aligned to at least 256 bytes.
2. The input is `rand(batch_size, dim)` which is contiguous, so alignment should be fine.
3. n_vec = 1610612736, which is an even number, so n_vec * 4 should cover exactly n elements.
One more optimization: we could use more blocks to ensure better occupancy. With H100 having 132 SMs and each SM can run 32 threadblocks max, we have 132 * 32 = 4224 max concurrent thread blocks. But usually we want enough thread blocks to saturate the GPU. With 512 blocks * 256 threads = 131072 threads, and each thread processes on average 1610612736/4 / 131072 = 3072 float4 elements. That's a lot of work per thread, which is good for occupancy as each threadblock will take a while and the GPU can schedule other threadblocks.
Actually, for memory-bound kernels, we want more parallelism, not less. Let me increase to 1024 blocks to get more parallelism.
But wait, I should also check if using a grid-stride loop with 1024 blocks might help. Let me try a different version with more blocks to see if it helps. But actually, I can just submit what I have since it should already be faster than PyTorch's reference.
Wait, one more thing - I should check if PyTorch's F.hardtanh might have some optimization issues. Looking at the reference implementation, it uses `F.hardtanh(x, min_val=-1., max_val=1.)`. PyTorch's hardtanh for contiguous tensors usually maps to `at::native::hardtanh_out` which might use TensorIterator. For a simple element-wise operation, TensorIterator should be efficient, but it might have some overhead or might not use vectorized loads.
Let me submit the kernel.
call → submit_kernel
callsubmit_kernel
{
"kernel_code": "\nimport torch\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <cstdint>\n\ntemplate <typename T>\n__device__ __forceinline__ T hardtanh_fwd(T x, T min_val, T max_val) {\n return x < min_val ? min_val : (x > max_val ? max_val : x);\n}\n\n__global__ void hardtanh_kernel(const float* __restrict__ input,\n float* __restrict__ output,\n int64_t n) {\n const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n const int64_t grid_size = gridDim.x * blockDim.x;\n \n // Process 4 elements per thread using float4 for vectorized memory access\n const int64_t n_vec = n / 4;\n const float4* in_vec = reinterpret_cast<const float4*>(input);\n float4* out_vec = reinterpret_cast<float4*>(output);\n \n for (int64_t i = idx; i < n_vec; i += grid_size) {\n float4 x = in_vec[i];\n float4 y;\n y.x = hardtanh_fwd(x.x, -1.0f, 1.0f);\n y.y = hardtanh_fwd(x.y, -1.0f, 1.0f);\n y.z = hardtanh_fwd(x.z, -1.0f, 1.0f);\n y.w = hardtanh_fwd(x.w, -1.0f, 1.0f);\n out_vec[i] = y;\n }\n \n // Handle remaining elements\n const int64_t remainder_start = n_vec * 4;\n for (int64_t i = remainder_start + idx; i < n; i += grid_size) {\n output[i] = hardtanh_fwd(input[i], -1.0f, 1.0f);\n }\n}\n\ntorch::Tensor hardtanh_cuda(torch::Tensor input) {\n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n if (n == 0) return output;\n \n // Ensure contiguous and properly aligned for float4\n auto input_contig = input.contiguous();\n \n const int threads = 256;\n const int blocks = 512; // Enough to saturate the GPU\n \n hardtanh_kernel<<<blocks, threads>>>(\n input_contig.data_ptr<float>(),\n output.data_ptr<float>(),\n n\n );\n \n return output;\n}\n\"\"\"\n\ncpp_source = \"\"\"\ntorch::Tensor hardtanh_cuda(torch::Tensor input);\n\"\"\"\n\n# Compile the extension\nhardtanh_module = load_inline(\n name=\"hardtanh_cuda\",\n cpp_sources=cpp_source,\n cuda_sources=cuda_source,\n functions=[\"hardtanh_cuda\"],\n extra_cuda_cflags=[\"-O3\", \"--use_fast_math\"],\n verbose=False,\n)\n\nclass ModelNew(torch.nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x: torch.Tensor) -> torch.Tensor:\n return hardtanh_module.hardtanh_cuda(x)\n"
}
result ← submit_kernel (ok)
oksubmit_kernel
submit_kernel PASSED: (5 / 5) correctness trials all passed.
Kernel runtime: 4.63 μs
Runtime stats: mean=4.63μs median=?μs std=0.14μs
Numerical precision: max_abs_err=0.00e+00 mean_abs_err=0.00e+00 max_rel_err=0.00e+00
Memory: 12288.0 MB peak (ref: 12288.0 MB, ratio: 1.0x)
Kernel launches: 2 (ref: 6, fusion ratio: 3.0)
Energy: 1856.38 mJ/run (ref: 1955.4 mJ, ratio: 1.0533x)
SOL score: 0.8461
final submitted kernel (85 lines, 2,441 chars) — full source, untruncated
import torch
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <cstdint>
template <typename T>
__device__ __forceinline__ T hardtanh_fwd(T x, T min_val, T max_val) {
return x < min_val ? min_val : (x > max_val ? max_val : x);
}
__global__ void hardtanh_kernel(const float* __restrict__ input,
float* __restrict__ output,
int64_t n) {
const int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
const int64_t grid_size = gridDim.x * blockDim.x;
// Process 4 elements per thread using float4 for vectorized memory access
const int64_t n_vec = n / 4;
const float4* in_vec = reinterpret_cast<const float4*>(input);
float4* out_vec = reinterpret_cast<float4*>(output);
for (int64_t i = idx; i < n_vec; i += grid_size) {
float4 x = in_vec[i];
float4 y;
y.x = hardtanh_fwd(x.x, -1.0f, 1.0f);
y.y = hardtanh_fwd(x.y, -1.0f, 1.0f);
y.z = hardtanh_fwd(x.z, -1.0f, 1.0f);
y.w = hardtanh_fwd(x.w, -1.0f, 1.0f);
out_vec[i] = y;
}
// Handle remaining elements
const int64_t remainder_start = n_vec * 4;
for (int64_t i = remainder_start + idx; i < n; i += grid_size) {
output[i] = hardtanh_fwd(input[i], -1.0f, 1.0f);
}
}
torch::Tensor hardtanh_cuda(torch::Tensor input) {
auto output = torch::empty_like(input);
int64_t n = input.numel();
if (n == 0) return output;
// Ensure contiguous and properly aligned for float4
auto input_contig = input.contiguous();
const int threads = 256;
const int blocks = 512; // Enough to saturate the GPU
hardtanh_kernel<<<blocks, threads>>>(
input_contig.data_ptr<float>(),
output.data_ptr<float>(),
n
);
return output;
}
"""
cpp_source = """
torch::Tensor hardtanh_cuda(torch::Tensor input);
"""
# Compile the extension
hardtanh_module = load_inline(
name="hardtanh_cuda",
cpp_sources=cpp_source,
cuda_sources=cuda_source,
functions=["hardtanh_cuda"],
extra_cuda_cflags=["-O3", "--use_fast_math"],
verbose=False,
)
class ModelNew(torch.nn.Module):
def __init__(self):
super(ModelNew, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return hardtanh_module.hardtanh_cuda(x)