turn 027.8s turn · 27.8s LLM · 0.0s tools
reasoning
**Evaluating model implementation**
I need to create a Python file for `ModelNew`, and I’m considering using a linear ReLU divide. I’m wondering if the weights from `nn.Linear` should be initialized the same way. The constructor for `ModelNew` should include `nn.Linear`, and I'll probably need a copy of it too.
For evaluation, I'll need to instantiate both `Model` and `ModelNew`, ensuring their `state_dict` stays in sync. I also thought about defining parameters with matching names because `ModelNew` should have `self.linear = nn.Linear`. The forward method will call a custom extension with its inputs, weights, biases, and a divisor.
The matrix operations in this model will be quite large; I’ll need around 68 billion operations, so optimizing is key. My goal is to exceed the performance of the PyTorch reference by implementing cuBLAS matmul effectively. If I’m using a naive implementation, it’ll be tough to beat PyTorch’s performance. However, I see that I'll require a custom CUDA kernel, possibly involving a wrapper for `torch::matmul` or add. I should also consider fusing operations with cuBLASLt for better speed and implementing that using `at::addmm` with bias followed by the ReLU and division in a CUDA kernel.
**Exploring optimization strategies**
I’m considering how the reference implementation uses `Linear` followed by `addmm` and two operations, while I could speed things up with a custom kernel that fuses `relu` and division. It seems that using PyTorch's C++ `at::linear/addmm` could be acceptable, but I might still need a kernel for the expensive `matmul`.
If I use `torch.addmm` in Python, I could then implement a custom `relu_div` in a CUDA extension, but I need to manage output allocation carefully.
The goal is to avoid two separate PyTorch launches by calling `addmm` directly in the extension and then executing one custom kernel. The matrix operation is dominant, but fusing operations like `RELU + bias` with cuBLASLt could offer speed improvements. However, I’m unsure about scaling the output with alpha and beta before the ReLU operation.
**Analyzing scaling for ReLU**
If I set alpha to be `1/divisor` and beta as `1/divisor` for the bias, I can express the operation as `relu((xW + bias)/divisor)`. For a positive divisor, such as 2, this would be equivalent to performing `relu(xW + bias)` first and then dividing by the divisor afterward. This allows me to effectively manage the operations while maintaining the expected results, which is essential for both performance and accuracy in calculations.
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
final submitted kernel (136 lines, 5,239 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>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
#include <cublasLt.h>
#include <mutex>
#include <stdexcept>
void div_launcher(torch::Tensor y, double divisor);
static cublasLtHandle_t get_lt_handle() {
static cublasLtHandle_t handle = nullptr;
static std::once_flag flag;
std::call_once(flag, [](){ cublasLtCreate(&handle); });
return handle;
}
#define CHECK_CUBLAS(call) do { cublasStatus_t _s = (call); if (_s != CUBLAS_STATUS_SUCCESS) throw std::runtime_error("cublasLt error"); } while(0)
torch::Tensor linear_relu_div(torch::Tensor x, torch::Tensor weight, torch::Tensor bias, double divisor) {
const int64_t M64 = x.size(0);
const int64_t K64 = x.size(1);
const int64_t N64 = weight.size(0);
const int M = (int)M64;
const int N = (int)N64;
const int K = (int)K64;
auto y = torch::empty({M64, N64}, x.options());
cublasLtHandle_t lt = get_lt_handle();
cudaStream_t stream = at::cuda::getCurrentCUDAStream();
cublasLtMatmulDesc_t opDesc;
cublasLtMatrixLayout_t aDesc, bDesc, cDesc, dDesc;
CHECK_CUBLAS(cublasLtMatmulDescCreate(&opDesc, CUBLAS_COMPUTE_32F, CUDA_R_32F));
cublasOperation_t transa = CUBLAS_OP_T;
cublasOperation_t transb = CUBLAS_OP_N;
CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(transa)));
CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(transb)));
cublasLtEpilogue_t epi = CUBLASLT_EPILOGUE_RELU_BIAS;
CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(epi)));
const void* bias_ptr = bias.data_ptr<float>();
CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(opDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias_ptr, sizeof(bias_ptr)));
// Row-major tensors represented as column-major matrices. Computes
// y^T = weight * x^T, which is equivalent to y = x * weight^T.
CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&aDesc, CUDA_R_32F, K, N, K));
CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&bDesc, CUDA_R_32F, K, M, K));
CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&cDesc, CUDA_R_32F, N, M, N));
CHECK_CUBLAS(cublasLtMatrixLayoutCreate(&dDesc, CUDA_R_32F, N, M, N));
cublasLtMatmulPreference_t pref;
CHECK_CUBLAS(cublasLtMatmulPreferenceCreate(&pref));
size_t workspaceSize = 32 * 1024 * 1024;
CHECK_CUBLAS(cublasLtMatmulPreferenceSetAttribute(pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &workspaceSize, sizeof(workspaceSize)));
cublasLtMatmulHeuristicResult_t heuristic;
int returned = 0;
CHECK_CUBLAS(cublasLtMatmulAlgoGetHeuristic(lt, opDesc, aDesc, bDesc, cDesc, dDesc, pref, 1, &heuristic, &returned));
if (returned == 0) throw std::runtime_error("no cublasLt algorithm");
auto workspace = torch::empty({(long long)workspaceSize}, x.options().dtype(torch::kUInt8));
float alpha = 1.0f;
float beta = 0.0f;
CHECK_CUBLAS(cublasLtMatmul(lt, opDesc, &alpha,
weight.data_ptr<float>(), aDesc,
x.data_ptr<float>(), bDesc,
&beta,
y.data_ptr<float>(), cDesc,
y.data_ptr<float>(), dDesc,
&heuristic.algo,
workspace.data_ptr(), workspaceSize,
stream));
cublasLtMatmulPreferenceDestroy(pref);
cublasLtMatrixLayoutDestroy(aDesc);
cublasLtMatrixLayoutDestroy(bDesc);
cublasLtMatrixLayoutDestroy(cDesc);
cublasLtMatrixLayoutDestroy(dDesc);
cublasLtMatmulDescDestroy(opDesc);
div_launcher(y, divisor);
return y;
}
'''
cuda_src = r'''
#include <torch/extension.h>
#include <ATen/cuda/CUDAContext.h>
#include <cuda_runtime.h>
__global__ void div_kernel(float* __restrict__ y, long long n, float inv) {
long long i = (long long)blockIdx.x * blockDim.x + threadIdx.x;
long long stride = (long long)blockDim.x * gridDim.x;
for (; i < n; i += stride) {
y[i] *= inv;
}
}
void div_launcher(torch::Tensor y, double divisor) {
const long long n = y.numel();
if (n == 0) return;
const int threads = 256;
int blocks = (int)((n + threads - 1) / threads);
if (blocks > 4096) blocks = 4096;
const float inv = 1.0f / (float)divisor;
div_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(y.data_ptr<float>(), n, inv);
}
'''
_ext = load_inline(
name='linear_relu_div_lt_final',
cpp_sources=cpp_src,
cuda_sources=cuda_src,
functions=['linear_relu_div'],
extra_cuda_cflags=['-O3'],
extra_cflags=['-O3'],
extra_ldflags=['-lcublas', '-lcublasLt'],
verbose=False,
)
class ModelNew(nn.Module):
def __init__(self, in_features, out_features, divisor):
super(ModelNew, self).__init__()
self.linear = nn.Linear(in_features, out_features)
self.divisor = divisor
def forward(self, x):
return _ext.linear_relu_div(x, self.linear.weight, self.linear.bias, float(self.divisor))