error
88_MinGPTNewGelu.py
started
2026-05-02T04:40:21Z
reference PyTorch source (the prompt the agent sees)
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
# From https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
class Model(nn.Module):
"""
Implementation of the GELU activation function currently in Google BERT repo (identical to OpenAI GPT).
Reference: Gaussian Error Linear Units (GELU) paper: https://arxiv.org/abs/1606.08415
"""
def __init__(self):
super(Model, self).__init__()
def forward(self, x):
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
batch_size = 8192
dim = 8192
def get_inputs():
return [torch.rand(batch_size, dim)]
def get_init_inputs():
return []turn 093.7s turn · 93.7s LLM · 0.0s tools
reasoning
We need to write a custom CUDA kernel for GELU as implemented in the reference. The reference is:
GELU(x) = 0.5 * x * (1.0 + tanh( sqrt(2/pi) * (x + 0.044715 * x^3) ) )
We need to write a torch.utils.cpp_extension.load_inline CUDA kernel and a ModelNew class.
Given the batch size 8192 and dim 8192, total elements = 8192*8192 = 67,108,864. That's about 67M elements. In fp32, 256 MB. Memory bandwidth limited. The kernel just applies elementwise transformation.
We need to write a fast CUDA kernel. We'll consider vectorized loads/stores (float4), grid-stride loops, fused computation. Need to match reference with fp32 precision. We need to use tanh from CUDA math library, but with float precision. TANH in math.h yields float if input is float? Actually `tanhf` is float. We can use `tanhf`. However, there is a difference between `tanh` (double overload) and `tanhf` (float). But using `tanhf` is fine. Need to ensure numerically equivalent to PyTorch. PyTorch's implementation uses double constants then computes in float? Actually PyTorch for CPU uses float tensors, but constants are Python floats (double) then cast to float in ops. The expression is `math.sqrt(2.0 / math.pi)` which is a double ~0.7978845608. Then multiplied by `(x + 0.044715 * x^3)`. Since x is fp32, PyTorch will perform compute in fp32. So we need to match using float precision and same constants (cast to float). The constant `0.044715` is also double literal but cast to float in PyTorch. Using float literal `0.044715f` is slightly different from double cast? Actually `0.044715` double truncated to float might differ slightly, but both are constants with same decimal. In CUDA we can define `const float k = 0.7978845608028654f;` and `const float c = 0.044715f;`. We can compute `float x3 = x * x * x; float inner = x + c * x3; float tanh_val = tanhf(k * inner); float out = 0.5f * x * (1.0f + tanh_val);`.
Alternatively, we can approximate tanh using fast math, but `tanhf` likely fast enough. Since memory bound, compute is negligible.
We can also consider using `__tanhf` intrinsic? There is no `__tanhf` intrinsic; we can use `tanhf`. With `-use_fast_math`, `tanhf` might be approximate but acceptable as long as within tolerance. But to be safe, use `tanhf` (accurate). PyTorch uses `tanh` in its elementwise kernel which likely also calls `tanhf` or a vectorized approximation. We need to match within 1e-4. `tanhf` should be fine.
To maximize bandwidth, we can use `float4` to load/store 4 floats per thread. Also use large block sizes (256 or 512). Since total elements is 67M, threads ~ enough.
One thing: the GPU specs. Let's call get_gpu_specs to see hardware. Then we can decide block size, etc.
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) · 556.9s turn · 556.9s LLM · 8,773 chars (click to expand)
turn 1556.9s turn · 556.9s LLM · 0.0s tools
reasoning
This is a GELU activation function that needs to be optimized. Let me analyze the reference implementation:
```python
return 0.5 * x * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3.0))))
```
This is the tanh-based GELU approximation:
GELU(x) = 0.5 * x * (1 + tanh(√(2/π) * (x + 0.044715*x³)))
For large tensors (8192 x 8192 = 67M elements), this is memory-bandwidth bound since it's an elementwise operation.
Let me write a CUDA kernel that does this in a single fused operation, minimizing memory traffic. The key is:
1. Load each element once
2. Compute the GELU
3. Store the result once
Since this is bandwidth-bound on an H100 (3.35 TB/s), we want to maximize memory throughput. We can use vectorized loads/stores (float4) to improve bandwidth utilization.
Let me write the kernel:
```cuda
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <math.h>
__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {
const float sqrt_2_over_pi = 0.7978845608028654f;
const float coef = 0.044715f;
int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
int64_t stride = gridDim.x * blockDim.x;
for (int64_t i = idx; i < n; i += stride) {
float x = input[i];
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coef * x3);
float tanh_val = tanhf(inner);
output[i] = 0.5f * x * (1.0f + tanh_val);
}
}
```
Actually, let me think about vectorization. We can process 4 floats at a time using float4:
```cuda
__global__ void gelu_kernel_float4(const float* __restrict__ input, float* __restrict__ output, int64_t n) {
const float sqrt_2_over_pi = 0.7978845608028654f;
const float coef = 0.044715f;
int64_t idx = (blockIdx.x * blockDim.x + threadIdx.x) * 4;
int64_t stride = gridDim.x * blockDim.x * 4;
const float4* in4 = reinterpret_cast<const float4*>(input);
float4* out4 = reinterpret_cast<float4*>(output);
int64_t n4 = n / 4;
for (int64_t i = blockIdx.x * blockDim.x + threadIdx.x; i < n4; i += gridDim.x * blockDim.x) {
float4 x4 = in4[i];
// Process each element
float x = x4.x;
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coef * x3);
float tanh_val = tanhf(inner);
x4.x = 0.5f * x * (1.0f + tanh_val);
x = x4.y;
x3 = x * x * x;
inner = sqrt_2_over_pi * (x + coef * x3);
tanh_val = tanhf(inner);
x4.y = 0.5f * x * (1.0f + tanh_val);
x = x4.z;
x3 = x * x * x;
inner = sqrt_2_over_pi * (x + coef * x3);
tanh_val = tanhf(inner);
x4.z = 0.5f * x * (1.0f + tanh_val);
x = x4.w;
x3 = x * x * x;
inner = sqrt_2_over_pi * (x + coef * x3);
tanh_val = tanhf(inner);
x4.w = 0.5f * x * (1.0f + tanh_val);
out4[i] = x4;
}
// Handle remaining elements
for (int64_t i = idx + n4 * 4; i < n; i += gridDim.x * blockDim.x) {
float x = input[i];
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coef * x3);
float tanh_val = tanhf(inner);
output[i] = 0.5f * x * (1.0f + tanh_val);
}
}
```
Wait, the alignment and vectorization can be tricky. Let me think about a simpler but still effective approach. Using 256 threads per block and enough blocks to saturate the GPU.
For 67M elements, with each thread processing 4 elements (via float4), we need about 67M/4 = 16.75M threads total. With a grid of maybe 256 blocks x 256 threads = 65536 threads, each doing 256 iterations, that's about 16.7M operations.
Actually, let me just use a simple approach first and see if it's fast enough.
For bandwidth-bound kernels on H100, we want to:
1. Use large blocks (512 or 1024 threads)
2. Use many SMs
3. Use vectorized memory access
Let me write a clean kernel:
```python
kernel_code = '''
import torch
from torch.utils.cpp_extension import load_inline
cuda_source = """
#include <torch/extension.h>
#include <cuda_runtime.h>
#include <math.h>
__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {
const float sqrt_2_over_pi = 0.7978845608028654f;
const float coef = 0.044715f;
int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
int64_t stride = gridDim.x * blockDim.x;
for (int64_t i = idx; i < n; i += stride) {
float x = input[i];
float x3 = x * x * x;
float inner = sqrt_2_over_pi * (x + coef * x3);
float tanh_val = tanhf(inner);
output[i] = 0.5f * x * (1.0f + tanh_val);
}
}
torch::Tensor gelu_forward(torch::Tensor input) {
auto output = torch::empty_like(input);
int64_t n = input.numel();
const int threads = 512;
const int blocks = (n + threads - 1) / threads;
const int max_blocks = 65536;
const int actual_blocks = std::min(blocks, max_blocks);
gelu_kernel<<<actual_blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);
return output;
}
"""
gelu_module = load_inline(
name="gelu_cuda",
cpp_sources="torch::Tensor gelu_forward(torch::Tensor input);",
cuda_sources=cuda_source,
functions=["gelu_forward"],
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):
return gelu_module.gelu_forward(x)
'''
```
Let me first compile and test this simple version. If it works, I can optimize further with vectorization.
Actually wait - I should be more careful about the tanh approximation. The reference uses torch.tanh which is exact. Using tanhf with --use_fast_math might give slightly different results. Let me first try without --use_fast_math to be safe, or test with it.
Also, for elementwise ops, PyTorch likely already has a fused kernel. But since the reference does it with separate ops (pow, mul, add, tanh, mul, add, mul), my fused kernel should still be faster.
Let me compile first.
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 285.3s turn · 85.3s LLM · 0.0s tools
reasoning
Good, it compiled. Now let me run the correctness check.
call → run_correctness
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=1.01e-10 max_rel_err=2.11e-07
turn 325.2s turn · 25.2s LLM · 0.0s tools
reasoning
The basic kernel is correct but likely not fast enough. The element count is 8192 * 8192 = 67,108,864 elements, which is about 256 MB of fp32 data (128MB input + 128MB output). This is memory-bound, so we need to maximize memory throughput. The basic approach uses a grid-stride loop with enough blocks to saturate the GPU. Let me optimize the kernel for better memory throughput by using float4 vectorized loads/stores, which can quadruple memory bandwidth utilization.
call → compile_kernel
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 416.3s turn · 16.3s LLM · 0.0s tools
reasoning
The compilation passed. Let me test the correctness again and then submit. Since the first version already passed correctness, let me just run a quickcheck and then submit.
Actually wait, I should think about whether the performance is good enough. The reference in PyTorch likely uses ATen's optimized ops which internally also use CUDA kernels. But PyTorch's gelu might not be the exact same formula as in the reference - the reference computes `0.5 * x * (1.0 + tanh(sqrt(2/pi) * (x + 0.044715 * x^3)))` while PyTorch's GELU uses a different implementation. Since we're matching this exact formula, the CUDA kernel should be competitive because we're fusing everything into a single kernel instead of multiple PyTorch ops.
Let me run correctness and then submit.
call → run_correctness
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=1.01e-10 max_rel_err=2.11e-07
turn 58.6s turn · 8.6s LLM · 0.0s tools
reasoning
Good. The correct kernel compiled and passed correctness. Now let me try a few more optimizations.
Looking at typical GELU implementations in CUDA kernels (like in CUTLASS), there's an optimization that computes the GELU approximation slightly differently to avoid the tanhf call, or uses a more efficient polynomial approximation.
Actually, I think a key optimization I haven't tried is using `__launch_bounds__` to help the compiler. Let me also try to vectorize reads/writes to better utilize memory bandwidth.
Let me think about the math more carefully. The GELU formula is:
0.5 * x * (1 + tanh(√(2/π) * (x + 0.044715 * x^3)))
An alternative formulation of the tanh GELU that might be faster uses the sigmoid-like expression:
tanh(x) = 1 - 2/(exp(2x) + 1)
So:
1 + tanh(x) = 2 - 2/(exp(2x) + 1) = 2 * exp(2x) / (exp(2x) + 1) = 2 * sigmoid(2x)
This means:
0.5 * x * (1 + tanh(x)) = 0.5 * x * 2 * sigmoid(2x) = x * sigmoid(2x)
So for the GELU:
y = 0.5 * x * (1 + tanh(a)) = x * sigmoid(2a)
where a = sqrt(2/π) * (x + 0.044715 * x^3)
2a = 2 * sqrt(2/π) * (x + 0.044715 * x^3)
So: y = x * sigmoid(2 * sqrt(2/π) * (x + 0.044715 * x^3))
And sigmoid(z) = 1 / (1 + exp(-z))
But sigmoid has precision issues for large positive/negative z. We can use the fused implementation.
Actually, PyTorch's GELU in CUDA uses the `tanh` formulation, so let me stick with that.
Let me try a kernel with vectorized loads using float4:
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n const float coef = 0.044715f;\n \n int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = gridDim.x * blockDim.x;\n \n // Treat as 4-element vectors for better bandwidth utilization\n typedef float4 vec_t;\n const int vec_size = 4;\n int64_t n_vec = n / vec_size;\n int64_t remainder = n % vec_size;\n \n const vec_t* in_vec = reinterpret_cast<const vec_t*>(input);\n vec_t* out_vec = reinterpret_cast<vec_t*>(output);\n \n for (int64_t i = idx; i < n_vec; i += stride) {\n vec_t in_val = in_vec[i];\n vec_t out_val;\n \n #pragma unroll\n for (int j = 0; j < vec_size; j++) {\n float x = (&in_val.x)[j];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n (&out_val.x)[j] = 0.5f * x * (1.0f + tanh_val);\n }\n \n out_vec[i] = out_val;\n }\n \n // Handle remainder\n for (int64_t i = n_vec * vec_size + idx; i < n; i += stride) {\n float x = input[i];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n output[i] = 0.5f * x * (1.0f + tanh_val);\n }\n}\n\ntorch::Tensor gelu_forward(torch::Tensor input) {\n TORCH_CHECK(input.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(input.dtype() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(input.is_contiguous(), \"Input must be contiguous\");\n \n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n const int threads = 512;\n const int blocks = 256;\n \n gelu_kernel<<<blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);\n return output;\n}\n\"\"\"\n\ngelu_module = load_inline(\n name=\"gelu_cuda_vec\",\n cpp_sources=\"torch::Tensor gelu_forward(torch::Tensor input);\",\n cuda_sources=cuda_source,\n functions=[\"gelu_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x):\n return gelu_module.gelu_forward(x)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 64.3s turn · 4.3s 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\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n const float coef = 0.044715f;\n \n int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = gridDim.x * blockDim.x;\n \n // Treat as 4-element vectors for better bandwidth utilization\n typedef float4 vec_t;\n const int vec_size = 4;\n int64_t n_vec = n / vec_size;\n int64_t remainder = n % vec_size;\n \n const vec_t* in_vec = reinterpret_cast<const vec_t*>(input);\n vec_t* out_vec = reinterpret_cast<vec_t*>(output);\n \n for (int64_t i = idx; i < n_vec; i += stride) {\n vec_t in_val = in_vec[i];\n vec_t out_val;\n \n #pragma unroll\n for (int j = 0; j < vec_size; j++) {\n float x = (&in_val.x)[j];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n (&out_val.x)[j] = 0.5f * x * (1.0f + tanh_val);\n }\n \n out_vec[i] = out_val;\n }\n \n // Handle remainder\n for (int64_t i = n_vec * vec_size + idx; i < n; i += stride) {\n float x = input[i];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n output[i] = 0.5f * x * (1.0f + tanh_val);\n }\n}\n\ntorch::Tensor gelu_forward(torch::Tensor input) {\n TORCH_CHECK(input.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(input.dtype() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(input.is_contiguous(), \"Input must be contiguous\");\n \n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n const int threads = 512;\n const int blocks = 256;\n \n gelu_kernel<<<blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);\n return output;\n}\n\"\"\"\n\ngelu_module = load_inline(\n name=\"gelu_cuda_vec\",\n cpp_sources=\"torch::Tensor gelu_forward(torch::Tensor input);\",\n cuda_sources=cuda_source,\n functions=[\"gelu_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x):\n return gelu_module.gelu_forward(x)\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=1.01e-10 max_rel_err=2.11e-07
turn 711.2s turn · 11.2s LLM · 0.0s tools
reasoning
The vectorized kernel passed correctness. Let me try an even more optimized version with:
1. More SMs (512 blocks instead of 256) to saturate the GPU
2. Using more threads (1024)
3. Or try a different approach - using `cudaOccupancyMaxActiveBlocksPerMultiprocessor` equivalent heuristic
Actually, the main issue I want to optimize is to maximize throughput. For H100 with 80GB, it likely has many more SMs. The key is to have enough total warps to hide latency while saturating memory bandwidth.
Let me try 1024 threads and maybe 512 or even 1024 blocks. For 67M elements, with vectorized loads of 4, that's 16.75M elements. With 512 threads per block and 1024 blocks = 524288 threads, that's about 32 elements each in the ideal case with no grid-stride needed.
Actually, let me think about this again. For an element-wise kernel, the main thing is bandwidth. Having thread count >= element count isn't necessary - grid-stride loops work fine. What matters is having enough threads in flight to saturate memory bandwidth.
For H100 (Hopper), there are 132 SMs (I think for PCIe version, or 144 for SXM). Each SM can have up to 32 blocks and up to 2048 threads. The max occupancy for 256 threads per block would be 8 blocks per SM * 132 SMs = 1056 blocks active.
Actually, let me try a much simpler approach: just tune the number of blocks to ensure we saturate the SMs. With the vectorized version, I'll try:
- 1024 threads per block, 512 blocks = 524288 total threads
This gives us plenty of thread-level parallelism while still being coalesced.
Let me also try using more blocks like 2048 to see if it helps.
call → compile_kernel
callcompile_kernel
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n const float coef = 0.044715f;\n \n int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = gridDim.x * blockDim.x;\n \n // Treat as 4-element vectors for better bandwidth utilization\n typedef float4 vec_t;\n const int vec_size = 4;\n int64_t n_vec = n / vec_size;\n \n const vec_t* in_vec = reinterpret_cast<const vec_t*>(input);\n vec_t* out_vec = reinterpret_cast<vec_t*>(output);\n \n for (int64_t i = idx; i < n_vec; i += stride) {\n vec_t in_val = in_vec[i];\n vec_t out_val;\n \n #pragma unroll\n for (int j = 0; j < vec_size; j++) {\n float x = (&in_val.x)[j];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n (&out_val.x)[j] = 0.5f * x * (1.0f + tanh_val);\n }\n \n out_vec[i] = out_val;\n }\n \n // Handle remainder\n for (int64_t i = n_vec * vec_size + idx; i < n; i += stride) {\n float x = input[i];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n output[i] = 0.5f * x * (1.0f + tanh_val);\n }\n}\n\ntorch::Tensor gelu_forward(torch::Tensor input) {\n TORCH_CHECK(input.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(input.dtype() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(input.is_contiguous(), \"Input must be contiguous\");\n \n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n const int threads = 1024;\n const int blocks = 512;\n \n gelu_kernel<<<blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);\n return output;\n}\n\"\"\"\n\ngelu_module = load_inline(\n name=\"gelu_cuda_v2\",\n cpp_sources=\"torch::Tensor gelu_forward(torch::Tensor input);\",\n cuda_sources=cuda_source,\n functions=[\"gelu_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x):\n return gelu_module.gelu_forward(x)\n"
}
result ← compile_kernel (ok)
okcompile_kernel
compile_kernel PASSED: kernel compiled without errors.
turn 85.0s turn · 5.0s LLM · 0.0s tools
reasoning
Let me run correctness for the latest version and then submit.
call → run_correctness
callrun_correctness
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n const float coef = 0.044715f;\n \n int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = gridDim.x * blockDim.x;\n \n typedef float4 vec_t;\n const int vec_size = 4;\n int64_t n_vec = n / vec_size;\n \n const vec_t* in_vec = reinterpret_cast<const vec_t*>(input);\n vec_t* out_vec = reinterpret_cast<vec_t*>(output);\n \n for (int64_t i = idx; i < n_vec; i += stride) {\n vec_t in_val = in_vec[i];\n vec_t out_val;\n \n #pragma unroll\n for (int j = 0; j < vec_size; j++) {\n float x = (&in_val.x)[j];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n (&out_val.x)[j] = 0.5f * x * (1.0f + tanh_val);\n }\n \n out_vec[i] = out_val;\n }\n \n for (int64_t i = n_vec * vec_size + idx; i < n; i += stride) {\n float x = input[i];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n output[i] = 0.5f * x * (1.0f + tanh_val);\n }\n}\n\ntorch::Tensor gelu_forward(torch::Tensor input) {\n TORCH_CHECK(input.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(input.dtype() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(input.is_contiguous(), \"Input must be contiguous\");\n \n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n const int threads = 1024;\n const int blocks = 512;\n \n gelu_kernel<<<blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);\n return output;\n}\n\"\"\"\n\ngelu_module = load_inline(\n name=\"gelu_cuda_final\",\n cpp_sources=\"torch::Tensor gelu_forward(torch::Tensor input);\",\n cuda_sources=cuda_source,\n functions=[\"gelu_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x):\n return gelu_module.gelu_forward(x)\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=1.01e-10 max_rel_err=2.11e-07
turn 94.7s turn · 4.7s LLM · 0.0s tools
reasoning
Let me do a final static check and then submit. Actually, let me just submit the kernel since it passed correctness. But first, let me do a quick static check to be safe.
call → static_check
callstatic_check
{
"kernel_code": "import torch\nimport torch.nn as nn\nfrom torch.utils.cpp_extension import load_inline\n\ncuda_source = \"\"\"\n#include <torch/extension.h>\n#include <cuda_runtime.h>\n#include <math.h>\n\n__global__ void gelu_kernel(const float* __restrict__ input, float* __restrict__ output, int64_t n) {\n const float sqrt_2_over_pi = 0.7978845608028654f;\n const float coef = 0.044715f;\n \n int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;\n int64_t stride = gridDim.x * blockDim.x;\n \n typedef float4 vec_t;\n const int vec_size = 4;\n int64_t n_vec = n / vec_size;\n \n const vec_t* in_vec = reinterpret_cast<const vec_t*>(input);\n vec_t* out_vec = reinterpret_cast<vec_t*>(output);\n \n for (int64_t i = idx; i < n_vec; i += stride) {\n vec_t in_val = in_vec[i];\n vec_t out_val;\n \n #pragma unroll\n for (int j = 0; j < vec_size; j++) {\n float x = (&in_val.x)[j];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n (&out_val.x)[j] = 0.5f * x * (1.0f + tanh_val);\n }\n \n out_vec[i] = out_val;\n }\n \n for (int64_t i = n_vec * vec_size + idx; i < n; i += stride) {\n float x = input[i];\n float x3 = x * x * x;\n float inner = sqrt_2_over_pi * (x + coef * x3);\n float tanh_val = tanhf(inner);\n output[i] = 0.5f * x * (1.0f + tanh_val);\n }\n}\n\ntorch::Tensor gelu_forward(torch::Tensor input) {\n TORCH_CHECK(input.is_cuda(), \"Input must be CUDA tensor\");\n TORCH_CHECK(input.dtype() == torch::kFloat32, \"Input must be float32\");\n TORCH_CHECK(input.is_contiguous(), \"Input must be contiguous\");\n \n auto output = torch::empty_like(input);\n int64_t n = input.numel();\n \n const int threads = 1024;\n const int blocks = 512;\n \n gelu_kernel<<<blocks, threads>>>(input.data_ptr<float>(), output.data_ptr<float>(), n);\n return output;\n}\n\"\"\"\n\ngelu_module = load_inline(\n name=\"gelu_cuda_final\",\n cpp_sources=\"torch::Tensor gelu_forward(torch::Tensor input);\",\n cuda_sources=cuda_source,\n functions=[\"gelu_forward\"],\n extra_cuda_cflags=[\"-O3\"],\n verbose=False\n)\n\nclass ModelNew(nn.Module):\n def __init__(self):\n super(ModelNew, self).__init__()\n \n def forward(self, x):\n return gelu_module.gelu_forward(x)\n"
}
result ← static_check (ok)
okstatic_check
static_check PASSED: no violations or warnings detected.