moe_cuda_kernel.cu

#include <torch/extension.h>
#include <torch/torch.h>
#include <cstdio>
#include <iostream>
#include <vector>


#include <cuda.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>                                                                                          
#include <helper_cuda.h> 

// #include "timer.hh"

#define CEIL(_x_,_y_) (((_x_)-1)/(_y_)+1)

template <typename scalar_t>
__global__
void generate_ptr_offset_kernel(size_t n, const scalar_t* base, size_t stride, const int* offset, const scalar_t** ptrs) {
	size_t idx = threadIdx.x + blockDim.x * blockIdx.x;
	if (idx < n) {
		ptrs[idx] = base + stride * offset[idx];
	}
}


inline cublasStatus_t cublasXgemmBatched(cublasHandle_t handle,
                                  cublasOperation_t transa,
                                  cublasOperation_t transb,
                                  int m, int n, int k,
                                  const float           *alpha,
                                  const float           *Aarray[], int lda,
                                  const float           *Barray[], int ldb,
                                  const float           *beta,
                                  float           *Carray[], int ldc,
                                  int batchCount)
{
    return cublasSgemmBatched(handle, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount);
}

inline cublasStatus_t cublasXgemmBatched(cublasHandle_t handle,
                                  cublasOperation_t transa,
                                  cublasOperation_t transb,
                                  int m, int n, int k,
                                  const double           *alpha,
                                  const double           *Aarray[], int lda,
                                  const double           *Barray[], int ldb,
                                  const double           *beta,
                                  double           *Carray[], int ldc,
                                  int batchCount)
{
    return cublasDgemmBatched(handle, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount);
}

inline cublasStatus_t cublasXgemmBatched(cublasHandle_t handle,
                                  cublasOperation_t transa,
                                  cublasOperation_t transb,
                                  int m, int n, int k,
                                  const __half           *alpha,
                                  const __half           *Aarray[], int lda,
                                  const __half           *Barray[], int ldb,
                                  const __half           *beta,
                                  __half           *Carray[], int ldc,
                                  int batchCount)
{
    return cublasHgemmBatched(handle, transa, transb, m, n, k, alpha, Aarray, lda, Barray, ldb, beta, Carray, ldc, batchCount);
}


template <typename scalar_t>
void moe1_cuda_forward_impl(
        const scalar_t* input,
        const int* gate,
        const scalar_t* weight,
        scalar_t* output,
        const size_t batch_size,
        const size_t top_k,
        const size_t in_feat,
        const size_t out_feat) {
    
    cublasHandle_t handle;
	cudaStream_t st;
	cudaStreamCreate(&st);
    checkCudaErrors(cublasCreate(&handle));
    checkCudaErrors(cublasSetStream(handle, st));

    // setup Aarray, Barray and Carray
	std::vector<const scalar_t*> aptrs;
    std::vector<scalar_t*> cptrs;
	
    const scalar_t **Aarray;
    const scalar_t **Barray;
    scalar_t **Carray;
	checkCudaErrors(cudaMalloc(&Aarray, batch_size * sizeof(const scalar_t*) * top_k));
    checkCudaErrors(cudaMalloc(&Barray, batch_size * sizeof(const scalar_t*) * top_k));
    checkCudaErrors(cudaMalloc(&Carray, batch_size * sizeof(scalar_t*) * top_k));

	for (size_t i=0; i<batch_size; ++i) {
        for (size_t k=0; k<top_k; ++k) {
            aptrs.push_back(input + in_feat * i);
            // bptrs.push_back(weight + out_feat * in_feat * gate[i * top_k + k]);
            cptrs.push_back(output + out_feat * (i * top_k + k));
        }
	}
	checkCudaErrors(cudaMemcpy(Aarray, aptrs.data(), batch_size * sizeof(const scalar_t*) * top_k, cudaMemcpyHostToDevice));
	// checkCudaErrors(cudaMemcpy(ptrs + batch_size * top_k, bptrs.data(), batch_size * sizeof(scalar_t*) * top_k, cudaMemcpyHostToDevice));
	checkCudaErrors(cudaMemcpy(Carray, cptrs.data(), batch_size * sizeof(scalar_t*) * top_k, cudaMemcpyHostToDevice));

	dim3 griddim(CEIL(batch_size * top_k, 256));
	dim3 blockdim(256);
    generate_ptr_offset_kernel<<<griddim, blockdim, 0, st>>>(batch_size * top_k, weight, out_feat * in_feat, gate, Barray);

    scalar_t alpha = 1, beta = 0;
	checkCudaErrors(cublasXgemmBatched(handle, 
			CUBLAS_OP_N,
			CUBLAS_OP_T,
			1, out_feat, in_feat,
			&alpha,
			Aarray, 1,
			Barray, out_feat,
			&beta,
			Carray, 1,
			batch_size * top_k));

	checkCudaErrors(cudaStreamSynchronize(st));
    checkCudaErrors(cudaStreamDestroy(st));
    checkCudaErrors(cublasDestroy(handle));
}


std::vector<torch::Tensor> moe1_cuda_forward(
        torch::Tensor input,
        torch::Tensor gate,
        torch::Tensor weight) {
    const auto batch_size = input.size(0);
    const auto top_k = gate.size(1);
    const auto num_expert = weight.size(0);
    const auto out_feat = weight.size(1);
    const auto in_feat = weight.size(2);
            
    // printf("b=%ld, expert=%ld, in_feat (d_model)=%ld, out_feat (d_ffn)=%ld, topk=%ld\n", batch_size, num_expert, in_feat, out_feat, top_k);
    auto output = input.new_zeros({batch_size, top_k, out_feat});
    
    AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "moe1_forward_cuda", ([&] {
                moe1_cuda_forward_impl<scalar_t>(
                    input.data_ptr<scalar_t>(),
                    gate.data_ptr<int>(),
                    weight.data_ptr<scalar_t>(),
                    output.data_ptr<scalar_t>(),
                    batch_size,
                    top_k,
                    in_feat,
                    out_feat
                );
    }));
    
    return {output, };           
}


/*
int main() {
    typedef float data_t;
    size_t batch_size = 4096;
    size_t top_k = 2;
    size_t num_expert = 128;
    size_t in_feat = 1024;
    size_t out_feat = 4096;
	data_t *input, *weight;
	data_t *output;
	size_t *gate;

	checkCudaErrors(cudaMalloc(&input, batch_size * in_feat * sizeof(data_t)));
	checkCudaErrors(cudaMalloc(&weight, num_expert * in_feat * out_feat * sizeof(data_t)));	
	checkCudaErrors(cudaMalloc(&output, batch_size * top_k * out_feat * sizeof(data_t)));
    checkCudaErrors(cudaMalloc(&gate, batch_size * top_k * sizeof(size_t)));
    
    size_t nt = 16;
    double tsum = 0, tmax = 0;

    size_t *gate_host = new size_t[batch_size * top_k];
    for (size_t i=0; i<batch_size * top_k; ++i) {
        gate_host[i] = rand() % num_expert;
    } 
    checkCudaErrors(cudaMemcpy(gate, gate_host, batch_size * top_k * sizeof(size_t), cudaMemcpyHostToDevice));

    moe_first_linear_cuda_forward<data_t>(input, gate, weight, output, batch_size, top_k, in_feat, out_feat);
    
    for (size_t i=0; i<nt; ++i) {
        timestamp(start);
		moe_first_linear_cuda_forward<data_t>(input, gate, weight, output, batch_size, top_k, in_feat, out_feat);
		timestamp(end);
		auto t = getDuration(start, end);
		tsum += t;
		if (t > tmax) tmax = t;
    }
    printf("Mean %.3lf us, max %.3lf us\n", tsum / nt * 1e6, tmax * 1e6);
	double tflops = (double)batch_size * top_k * in_feat * out_feat * nt * 2e-12 / tsum;
	printf("%.3lf TFLOPs\n", tflops);
}
*/