moe_compute_kernel.cu

#include "moe_cuda_kernel.h"

#include <cstdio>
#include <iostream>
#include <vector>

#include <cuda.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <c10/cuda/CUDAGuard.h>

#include "timer.hh"

#include "cublas_wrapper.h"
#include "cuda_stream_manager.h"

#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 long* offset, const scalar_t** ptrs) { 
	size_t idx = threadIdx.x + blockDim.x * blockIdx.x;
	if (idx < n) {
		ptrs[idx] = base + stride * offset[idx];
	}
}

template <typename scalar_t>
__global__
void batch_scatter_kernel(size_t wid, const long* pos, 
		const scalar_t* inbuf, scalar_t* oubuf) { 
	inbuf += wid * pos[blockIdx.x];
	oubuf += wid * blockIdx.x;
	for (int i = threadIdx.x; i < wid; i += blockDim.x) {
		oubuf[i] = inbuf[i];
	}
}


/*
	This function is to be called with one block per each column
*/
template <typename scalar_t>
__global__ 
void column_reduce(const scalar_t * matrix, scalar_t * result, 
	int m /* lines */, int n /* columns*/) {
    extern __shared__ float sdata[];
    unsigned int tid = threadIdx.x; // line
    unsigned int i = blockIdx.x + threadIdx.x * n; // get to idx th line
    unsigned int offset = 0;
    unsigned int it = n * blockDim.x; // advanced blockDim.x threads vertically

    // sum all the values from that column to fit in one single block
    sdata[tid] = 0;
    while (i + offset < n*m) {
        sdata[tid] += matrix[i + offset];
        offset += it; 
        
    }
    __syncthreads();

    for (unsigned int s = 1; tid + s < blockDim.x; s *= 2) {
        if (tid % (2*s) == 0) {
            sdata[tid] += sdata[tid + s];
        }

        __syncthreads();
    }
    if (tid == 0) {result[blockIdx.x] = sdata[0];}

}

void moe_cuda_expert_count_impl(
        const int* d_gate,
		int* expert_count,
		int* d_pos,
		const size_t num_expert,
        const size_t batch_size) {
    int *gate = new int[batch_size];
	int *expert_ptr = new int[num_expert];
	memset(expert_count, 0, sizeof(int) * num_expert);

	checkCudaErrors(cudaMemcpy(gate, d_gate, sizeof(int) * batch_size,
				cudaMemcpyDeviceToHost));

	for (int i = 0; i < batch_size; ++i) {
		++expert_count[gate[i]];
	}
	expert_ptr[0] = 0;
	for (int i = 1; i < num_expert; ++i) {
		expert_ptr[i] = expert_ptr[i - 1] + expert_count[i - 1];
	}

	int *pos = new int[batch_size];

	for (int i = 0; i < batch_size; ++i) {
		pos[i] = expert_ptr[gate[i]]++;
	}
	for (int i = num_expert - 1; i > 0; --i) {
		expert_ptr[i] = expert_ptr[i - 1];
	}
	expert_ptr[0] = 0;
	checkCudaErrors(cudaMemcpy(d_pos, pos, sizeof(int) * batch_size,
				cudaMemcpyHostToDevice));
	delete [] gate;
	delete [] expert_ptr;
}

template <typename scalar_t>
void moe_cuda_local_scatter_impl(
        const scalar_t* input,
		const long* d_pos,
		scalar_t* input_buf,
		const long batch_size,
		const long in_feat, 
		CudaStreamManager* smgr) {
	batch_scatter_kernel<scalar_t>
		<<<batch_size, 256, 0, smgr->stream(0)>>>(in_feat, d_pos, input,
				input_buf); 
	smgr->sync(1);
}

template <typename scalar_t>
__global__
void batch_gather_kernel(size_t wid, const long* pos, 
		const scalar_t* inbuf, scalar_t* oubuf) { 
	inbuf += wid * blockIdx.x;
	oubuf += wid * pos[blockIdx.x];
	for (int i = threadIdx.x; i < wid; i += blockDim.x) {
		oubuf[i] = inbuf[i];
	}
}

template <typename scalar_t>
void moe_cuda_local_gather_impl(
        const scalar_t* output_buf,
		const long* d_pos,
		scalar_t* output,
		const size_t batch_size,
		const size_t out_feat,
		CudaStreamManager* smgr) {
	batch_gather_kernel<scalar_t>
		<<<batch_size, 256, 0, smgr->stream(0)>>>(out_feat, d_pos, output_buf,
				output); 
	smgr->sync(1);
}

template <typename scalar_t>
void moe_cuda_forward_impl(
        const scalar_t* input_buf,
        const scalar_t* weight,
		const long* expert_count,
        scalar_t* output_buf,
		const bool has_bias,
        const size_t in_feat,
        const size_t out_feat,
        const size_t num_expert,
		CudaStreamManager* smgr) {
	scalar_t alpha = 1, beta = has_bias ? 1 : 0; 

	for (int i = 0, ptr = 0; i < num_expert; ++i) {
		if (expert_count[i] == 0) {
			continue;
		}
		// Use T(B) x T(A) = T(C) to produce row-major C
		checkCudaErrors(cublasXgemm(
				smgr->handle(i),
				CUBLAS_OP_T,
				CUBLAS_OP_N,
				out_feat, expert_count[i], in_feat,
				&alpha,
				weight + i * in_feat * out_feat, in_feat,
				input_buf + ptr * in_feat, in_feat,
				&beta,
				output_buf + out_feat * ptr, out_feat
				));

		ptr += expert_count[i];
	}
	smgr->sync(num_expert);
}

template <typename scalar_t>
void moe_cuda_backward_impl(
        const scalar_t* grad_output_buf,
        const scalar_t* input_buf,
		const scalar_t* weight,
		const long* expert_count,
        scalar_t* grad_input_buf,
        scalar_t* grad_weight,
		scalar_t* grad_bias,
		const bool has_bias,
        const size_t batch_size,
        const size_t in_feat,
        const size_t out_feat,
        const size_t num_expert,
		CudaStreamManager* smgr) {
    scalar_t alpha = 1, beta = 0;

	for (int i = 0, ptr = 0; i < num_expert; ++i) {
		if (expert_count[i] == 0) {
			cudaMemset(grad_weight + i * in_feat * out_feat, 0, 
					sizeof(scalar_t) * in_feat * out_feat);
			cudaMemset(grad_bias + i * out_feat, 0, sizeof(scalar_t) * out_feat);
			continue;
		}
		// Use T(B) x T(A) = T(C) to produce row-major C

		// Backward input: g_i = w @ g_o
		checkCudaErrors(cublasXgemm(
				smgr->handle(i),
				CUBLAS_OP_N,
				CUBLAS_OP_N,
				in_feat, expert_count[i], out_feat,
				&alpha,
				weight + i * in_feat * out_feat, in_feat,
				grad_output_buf + ptr * out_feat, out_feat,
				&beta,
				grad_input_buf + in_feat * ptr, in_feat
				));

		// Backward weight: g_w = i @ g_o
		checkCudaErrors(cublasXgemm(
				smgr->handle(i),
				CUBLAS_OP_N,
				CUBLAS_OP_T,
				in_feat, out_feat, expert_count[i],
				&alpha,
				input_buf + in_feat * ptr, in_feat,
				grad_output_buf + ptr * out_feat, out_feat,
				&beta,
				grad_weight + i * in_feat * out_feat, in_feat
				));
		
		if (has_bias) {
			column_reduce
			<<<out_feat, 1024, sizeof(scalar_t)*1024, smgr->stream(0)>>>
			(
				grad_output_buf + ptr * out_feat,
				grad_bias + i * out_feat,
				expert_count[i],
				out_feat
			);
		}

		ptr += expert_count[i];
	}
	smgr->sync(num_expert);
}


std::vector<torch::Tensor> moe_cuda_expert_count(
		torch::Tensor gate, 
		size_t num_expert) {
	const auto batch_size = gate.size(0);

	auto ec_options = torch::TensorOptions().dtype(torch::kInt32);
	auto expert_count = torch::empty(num_expert, ec_options);

	auto pos_options = torch::TensorOptions()
		.device(gate.device())
		.dtype(torch::kInt32);
	auto pos = torch::empty(batch_size, pos_options);
	moe_cuda_expert_count_impl(
			gate.data_ptr<int>(),
			expert_count.data_ptr<int>(),
			pos.data_ptr<int>(),
			num_expert,
			batch_size);

	return {expert_count, pos};
}

std::vector<torch::Tensor> moe_cuda_local_scatter(
    torch::Tensor input,
	torch::Tensor pos) {
	auto smgr = getCudaStreamManager(input.device().index());
	const auto batch_size = pos.size(0);
    const auto in_feat = input.size(1);

	auto opt = torch::TensorOptions()
		.dtype(input.dtype())
		.device(input.device());
	auto input_buf = torch::empty({batch_size, in_feat}, opt);

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(input.scalar_type(), "moe_local_scatter_cuda", 
			([&] {
		moe_cuda_local_scatter_impl<scalar_t>(
			input.data_ptr<scalar_t>(),
			pos.data_ptr<long>(),
			input_buf.data_ptr<scalar_t>(),
			batch_size,
			in_feat,
			smgr);
	}));
	return {input_buf,};
}

std::vector<torch::Tensor> moe_cuda_local_gather(
	torch::Tensor output_buf,
	torch::Tensor pos) {
	auto smgr = getCudaStreamManager(output_buf.device().index());
	const auto batch_size = pos.size(0);
    const auto out_feat = output_buf.size(1);

	auto opt = torch::TensorOptions()
		.dtype(output_buf.dtype())
		.device(output_buf.device());
	auto output = torch::empty({batch_size, out_feat}, opt);

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(output_buf.scalar_type(), "moe_local_gather_cuda", 
			([&] {
		moe_cuda_local_gather_impl<scalar_t>(
			output_buf.data_ptr<scalar_t>(),
			pos.data_ptr<long>(),
			output.data_ptr<scalar_t>(),
			batch_size,
			out_feat,
			smgr);
	}));
	return {output,};
}

std::vector<torch::Tensor> moe_cuda_forward(
        torch::Tensor input_buf,
		torch::Tensor expert_count,
        torch::Tensor weight,
		at::optional<torch::Tensor> bias
		) {
	auto smgr = getCudaStreamManager(input_buf.device().index());
	const auto batch_size = input_buf.size(0);
    const auto num_expert = weight.size(0);
    const auto out_feat = weight.size(1);
    const auto in_feat = weight.size(2);
            
#ifdef MOE_DEBUG
    printf("[forward] expert=%ld, in_feat (d_model)=%ld, out_feat (d_ffn)=%ld\n", 
			num_expert, in_feat, out_feat);
#endif

    torch::Tensor output;
    
	if (bias.has_value()) {
		output = bias.value().repeat_interleave(expert_count.to(bias.value().device()), 0);
	} else{
		auto out_options = torch::TensorOptions()
			.device(input_buf.device())
			.dtype(input_buf.dtype());
		output = torch::empty({batch_size, out_feat}, out_options);
	}
		
    AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_forward_cuda", 
			([&] {
		moe_cuda_forward_impl<scalar_t>(
			input_buf.data_ptr<scalar_t>(),
			weight.data_ptr<scalar_t>(),
			expert_count.data_ptr<long>(),
			output.data_ptr<scalar_t>(),
			bias.has_value(),
			in_feat,
			out_feat,
			num_expert,
			smgr
		);
    }));
    
    return {output, };           
}

std::vector<torch::Tensor> moe_cuda_backward(
    torch::Tensor grad_output_buf, 	// [batch_size x out_feat]
    torch::Tensor input_buf, 		// [batch_size x out_feat]
	torch::Tensor expert_count,
    torch::Tensor weight, 			// [num_expert x out_feat x in_feat]
	at::optional<torch::Tensor> bias
) {
	auto smgr = getCudaStreamManager(input_buf.device().index());
    const auto batch_size = input_buf.size(0);
    const auto num_expert = weight.size(0);
    const auto out_feat = weight.size(1);
    const auto in_feat = weight.size(2);

#ifdef MOE_DEBUG
    printf("[backward] b=%ld, expert=%ld, in_feat (d_model)=%ld, "
			"out_feat (d_ffn)=%ld\n",
			batch_size, num_expert, in_feat, out_feat);
#endif

    auto grad_input_buf = grad_output_buf.new_empty({batch_size, in_feat}); 
    auto grad_weight = grad_output_buf.new_empty({num_expert, out_feat, in_feat});
	auto grad_bias = grad_output_buf.new_empty({num_expert, out_feat});

    AT_DISPATCH_FLOATING_TYPES_AND_HALF(input_buf.scalar_type(), "moe_cuda_backward", ([&] {
        moe_cuda_backward_impl<scalar_t>(
            grad_output_buf.data_ptr<scalar_t>(),
            input_buf.data_ptr<scalar_t>(),
            weight.data_ptr<scalar_t>(),
			expert_count.data_ptr<long>(),
            grad_input_buf.data_ptr<scalar_t>(),
            grad_weight.data_ptr<scalar_t>(),
			grad_bias.data_ptr<scalar_t>(),
			bias.has_value(),
            batch_size,
            in_feat,
            out_feat,
            num_expert,
			smgr
        );
    }));

	return {grad_input_buf, grad_weight, grad_bias};
}