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bgmv_impl.cuh
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bgmv_impl.cuh
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#pragma once
#include <ATen/cuda/CUDAContext.h>
#ifndef USE_ROCM
#include <cooperative_groups.h>
#else
#include <hip/hip_cooperative_groups.h>
#endif
#ifndef USE_ROCM
#include <cuda/pipeline>
#endif
#include <cuda_runtime.h>
#include <iostream>
#include <stdio.h>
#include "vec_dtypes.cuh"
namespace cg = cooperative_groups;
#ifdef USE_ROCM
template <size_t len>
__host__ __device__
inline void* memcpy_blocking(void *dst, const void *src) {
// Does not handle the case of long datatypes
char *d = reinterpret_cast<char *>(dst);
const char *s = reinterpret_cast<const char *>(src);
size_t i = 0;
#pragma unroll
for (i = 0; i < len; ++i) {
d[i] = s[i];
}
return dst;
}
#endif
#ifndef USE_ROCM
// nthrs = (32, 4)
template <int feat_in, int feat_out, size_t vec_size, size_t X_copy_size,
size_t W_copy_size, int tx, int ty, int tz, typename in_T,
typename out_T, typename W_T>
__global__ void
bgmv_shrink_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
const W_T *__restrict__ W,
const int64_t *__restrict__ indicies, int64_t y_offset,
int64_t full_y_size, int64_t num_layers, int64_t layer_idx,
float scale) {
size_t batch_idx = blockIdx.y;
int64_t idx = indicies[batch_idx] * num_layers + layer_idx;
if (idx < 0) {
return;
}
auto block = cg::this_thread_block();
size_t j = blockIdx.x;
constexpr size_t num_pipeline_stages = 2;
constexpr size_t tile_size = tx * ty * vec_size;
__shared__ W_T W_shared[num_pipeline_stages * tile_size];
__shared__ in_T X_shared[num_pipeline_stages * tile_size];
__shared__ float y_warpwise[ty];
size_t W_shared_offset[num_pipeline_stages] = {0U, 1U * tile_size};
size_t X_shared_offset[num_pipeline_stages] = {0U, 1U * tile_size};
auto pipe = cuda::make_pipeline();
// pipeline load W/X and compute WX;
pipe.producer_acquire();
cuda::memcpy_async(W_shared + (threadIdx.y * tx + threadIdx.x) * vec_size,
W + (idx * feat_out + j) * feat_in +
(threadIdx.y * tx + threadIdx.x) * vec_size,
cuda::aligned_size_t<W_copy_size>(W_copy_size), pipe);
cuda::memcpy_async(X_shared + (threadIdx.y * tx + threadIdx.x) * vec_size,
X + (batch_idx * feat_in) +
(threadIdx.y * tx + threadIdx.x) * vec_size,
cuda::aligned_size_t<X_copy_size>(X_copy_size), pipe);
pipe.producer_commit();
size_t copy_idx, compute_idx;
float y = 0.f;
vec_t<in_T, vec_size> x_vec;
vec_t<W_T, vec_size> w_vec;
size_t tile_idx;
#pragma unroll
for (tile_idx = 1; tile_idx < (feat_in + tile_size - 1) / tile_size;
++tile_idx) {
copy_idx = tile_idx % num_pipeline_stages;
// pipeline stage: async copy W fragment
pipe.producer_acquire();
if (tile_idx * tile_size + threadIdx.y * tx * vec_size < feat_in) {
cuda::memcpy_async(W_shared + W_shared_offset[copy_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size,
W + (idx * feat_out + j) * feat_in +
tile_idx * tile_size +
(threadIdx.y * tx + threadIdx.x) * vec_size,
cuda::aligned_size_t<W_copy_size>(W_copy_size), pipe);
cuda::memcpy_async(X_shared + X_shared_offset[copy_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size,
X + (batch_idx * feat_in) + tile_idx * tile_size +
(threadIdx.y * tx + threadIdx.x) * vec_size,
cuda::aligned_size_t<X_copy_size>(X_copy_size), pipe);
}
pipe.producer_commit();
compute_idx = (tile_idx - 1) % num_pipeline_stages;
// pipeline stage: compute WX
pipe.consumer_wait();
block.sync();
x_vec.load(X_shared + X_shared_offset[compute_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size);
w_vec.load(W_shared + W_shared_offset[compute_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size);
float sum = 0.f;
#pragma unroll
for (size_t i = 0; i < vec_size; ++i) {
sum += float(w_vec[i]) * float(x_vec[i]) * scale;
}
#pragma unroll
for (size_t offset = tx / 2; offset > 0; offset /= 2) {
sum += __shfl_down_sync(0xffffffff, sum, offset);
}
y_warpwise[threadIdx.y] = sum;
block.sync();
#pragma unroll
for (size_t i = 0; i < ty; ++i) {
y += y_warpwise[i];
}
block.sync();
pipe.consumer_release();
}
compute_idx = (tile_idx - 1) % num_pipeline_stages;
// final pipeline stage
pipe.consumer_wait();
block.sync();
x_vec.load(X_shared + X_shared_offset[compute_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size);
w_vec.load(W_shared + W_shared_offset[compute_idx] +
(threadIdx.y * tx + threadIdx.x) * vec_size);
float sum = 0.f;
#pragma unroll
for (size_t i = 0; i < vec_size; ++i) {
sum += float(w_vec[i]) * float(x_vec[i]) * scale;
}
#pragma unroll
for (size_t offset = tx / 2; offset > 0; offset /= 2) {
sum += __shfl_down_sync(0xffffffff, sum, offset);
}
y_warpwise[threadIdx.y] =
((tile_idx - 1) * tile_size + threadIdx.y * tx * vec_size < feat_in)
? sum
: 0.f;
block.sync();
#pragma unroll
for (size_t i = 0; i < ty; ++i) {
y += y_warpwise[i];
}
block.sync();
pipe.consumer_release();
// write Y;
if (block.thread_rank() == 0) {
Y[batch_idx * full_y_size + y_offset + j] += static_cast<out_T>(y);
}
}
#else
template <int feat_in, int feat_out, size_t vec_size, size_t X_copy_size,
size_t W_copy_size, int tx, int ty, int tz, typename in_T,
typename out_T, typename W_T>
__global__ void
bgmv_shrink_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
const W_T *__restrict__ W,
const int64_t *__restrict__ indicies, int64_t y_offset,
int64_t full_y_size, int64_t num_layers, int64_t layer_idx,
float scale) {
size_t batch_idx = blockIdx.y;
int64_t idx = indicies[batch_idx] * num_layers + layer_idx;
if (idx < 0) {
return;
}
size_t j = blockIdx.x;
constexpr size_t tile_size = tx * ty * vec_size;
constexpr size_t num_tiles = (feat_in + tile_size - 1) / tile_size;
__shared__ float y_warpwise[ty];
float y = 0;
vec_t<in_T, vec_size> x_vec;
vec_t<W_T, vec_size> w_vec;
size_t tile_idx;
#pragma unroll
for (tile_idx = 0; tile_idx < num_tiles; ++tile_idx) {
if (tile_idx * tile_size + (threadIdx.y * tx + threadIdx.x + 1) * vec_size - 1 < feat_in) {
x_vec.load(X + (batch_idx * feat_in) +
tile_idx * tile_size +
(threadIdx.y * tx + threadIdx.x) * vec_size);
w_vec.load(W + (idx * feat_out + j) * feat_in +
tile_idx * tile_size +
(threadIdx.y * tx + threadIdx.x) * vec_size);
}
float sum = 0.f;
#pragma unroll
for (size_t i = 0; i < vec_size; ++i) {
sum += convert_type<W_T, float>(w_vec[i]) * convert_type<in_T, float>(x_vec[i]) * scale;
}
#pragma unroll
for (size_t offset = tx / 2; offset > 0; offset /= 2) {
sum += VLLM_SHFL_DOWN_SYNC(sum, offset);
}
__syncthreads();
if (tile_idx * tile_size + (threadIdx.y * tx + threadIdx.x + 1) * vec_size - 1 < feat_in) {
y += sum;
}
}
if (threadIdx.x == 0) {
y_warpwise[threadIdx.y] = y;
}
__syncthreads();
float y_write = 0.f;
#pragma unroll
for (size_t i = 0; i < ty; ++i) {
y_write += y_warpwise[i];
}
// write Y;
if (threadIdx.x == 0 && threadIdx.y == 0) {
size_t y_idx = batch_idx * full_y_size + y_offset + j;
Y[y_idx] = vllm_add<out_T>(Y[y_idx], convert_type<float, out_T>(y_write));
}
}
#endif
// nthrs = (2, 16, 4)
template <int feat_in, int feat_out, size_t vec_size, int tx, int ty, int tz,
typename in_T, typename out_T, typename W_T>
__global__ void
bgmv_expand_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
const W_T *__restrict__ W,
const int64_t *__restrict__ indicies, int64_t y_offset,
int64_t full_y_size, int64_t num_layers, int64_t layer_idx,
float scale) {
size_t batch_idx = blockIdx.y;
int64_t idx = indicies[batch_idx] * num_layers + layer_idx;
if (idx < 0) {
return;
}
auto block = cg::this_thread_block();
size_t tile_idx = blockIdx.x;
// load X;
vec_t<in_T, vec_size> x_vec;
x_vec.load(X + batch_idx * feat_in + threadIdx.x * vec_size);
// load W;
vec_t<W_T, vec_size> w_vec;
w_vec.load(W + (idx * feat_out + tile_idx * tz * ty) * feat_in +
block.thread_rank() * vec_size);
float sum = 0.f;
#pragma unroll
for (size_t i = 0; i < vec_size; ++i) {
#ifndef USE_ROCM
sum += float(w_vec[i]) * float(x_vec[i]) * scale;
#else
sum += convert_type<W_T, float>(w_vec[i]) * convert_type<in_T, float>(x_vec[i]) * scale;
#endif
}
cg::thread_block_tile g = cg::tiled_partition<tx>(block);
#pragma unroll
for (size_t offset = tx / 2; offset > 0; offset /= 2) {
sum += g.shfl_down(sum, offset);
}
sum = g.shfl(sum, 0);
if (threadIdx.x == 0) {
#ifndef USE_ROCM
Y[batch_idx * full_y_size + y_offset + tile_idx * (tz * ty) +
threadIdx.z * ty + threadIdx.y] += static_cast<out_T>(sum);
#else
size_t y_idx = batch_idx * full_y_size + y_offset + tile_idx * (tz * ty) +
threadIdx.z * ty + threadIdx.y;
Y[y_idx] = vllm_add<out_T>(Y[y_idx], convert_type<float, out_T>(sum));
#endif
}
}
template <int feat_in, int feat_out, typename in_T, typename out_T,
typename W_T>
void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
const W_T *__restrict__ W,
const int64_t *__restrict__ indicies, int64_t y_offset,
int64_t full_y_size, int64_t batch_size, int64_t num_layers,
int64_t layer_idx, float scale) {
constexpr size_t vec_size = 8;
constexpr int tz = 4;
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if constexpr (feat_in <= feat_out) {
static_assert(feat_in % vec_size == 0);
constexpr int tx = feat_in / vec_size;
static_assert((32 % tx == 0 && feat_out % (32 / tx * tz) == 0) ||
(16 % tx == 0 && feat_out % (16 / tx * tz) == 0) ||
(8 % tx == 0 && feat_out % (8 / tx * tz) == 0));
if constexpr (32 % tx == 0 && feat_out % (32 / tx * tz) == 0) {
constexpr int ty = 32 / tx;
dim3 nblks(feat_out / (ty * tz), batch_size);
dim3 nthrs(tx, ty, tz);
bgmv_expand_kernel<feat_in, feat_out, vec_size, tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
} else if (16 % tx == 0 && feat_out % (16 / tx * tz) == 0) {
constexpr int ty = 16 / tx;
dim3 nblks(feat_out / (ty * tz), batch_size);
dim3 nthrs(tx, ty, tz);
bgmv_expand_kernel<feat_in, feat_out, vec_size, tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
} else {
constexpr int ty = 8 / tx;
dim3 nblks(feat_out / (ty * tz), batch_size);
dim3 nthrs(tx, ty, tz);
bgmv_expand_kernel<feat_in, feat_out, vec_size, tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
}
} else {
#ifndef USE_ROCM
static_assert(feat_in % (vec_size * 32) == 0 ||
feat_in % (vec_size * 16) == 0 ||
feat_in % (vec_size * 8) == 0);
if constexpr (feat_in % (vec_size * 32) == 0) {
constexpr int tx = 32;
constexpr int ty = 4;
dim3 nblks(feat_out, batch_size);
dim3 nthrs(tx, ty);
bgmv_shrink_kernel<feat_in, feat_out, vec_size, vec_size * sizeof(in_T),
vec_size * sizeof(W_T), tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
} else if constexpr (feat_in % (vec_size / 2 * 32) == 0) {
constexpr int tx = 32;
constexpr int ty = 4;
dim3 nblks(feat_out, batch_size);
dim3 nthrs(tx, ty);
bgmv_shrink_kernel<feat_in, feat_out, vec_size / 2,
vec_size * sizeof(in_T) / 2,
vec_size * sizeof(W_T) / 2, tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
} else if constexpr (feat_in % (vec_size / 2 * 16) == 0) {
constexpr int tx = 16;
constexpr int ty = 4;
dim3 nblks(feat_out, batch_size);
dim3 nthrs(tx, ty);
bgmv_shrink_kernel<feat_in, feat_out, vec_size / 2,
vec_size * sizeof(in_T) / 2,
vec_size * sizeof(W_T) / 2, tx, ty, tz>
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,
full_y_size, num_layers, layer_idx,
scale);
}
#else
constexpr size_t rocm_warp_size = warpSize;
#define CHECK_INPUT_TILEABLE_BY(vec_size_) \
feat_in % (rocm_warp_size * vec_size_) == 0
#define LAUNCH_BGMV_SHRINK_KERNELS_ROCM(factor_, vec_size_, tx_, ty_) \
if constexpr (CHECK_INPUT_TILEABLE_BY(factor_)) { \
constexpr size_t vec_size_shrink = vec_size_; \
constexpr int tx = tx_; \
constexpr int ty = ty_; \
dim3 nblks(feat_out, batch_size); \
dim3 nthrs(tx, ty); \
bgmv_shrink_kernel<feat_in, feat_out, vec_size_shrink, \
vec_size_shrink * sizeof(in_T), \
vec_size_shrink * sizeof(W_T), \
tx, ty, tz> \
<<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset, \
full_y_size, num_layers, layer_idx, \
scale); \
}
static_assert(CHECK_INPUT_TILEABLE_BY(32) ||
CHECK_INPUT_TILEABLE_BY(16) ||
CHECK_INPUT_TILEABLE_BY( 8) ||
CHECK_INPUT_TILEABLE_BY( 4) ||
CHECK_INPUT_TILEABLE_BY( 2) ||
CHECK_INPUT_TILEABLE_BY( 1));
LAUNCH_BGMV_SHRINK_KERNELS_ROCM(32, vec_size, rocm_warp_size, 32/vec_size)
else
LAUNCH_BGMV_SHRINK_KERNELS_ROCM(16, vec_size, rocm_warp_size, 16/vec_size)
else
LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 8, vec_size, rocm_warp_size, 8/vec_size)
else
LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 4, vec_size, rocm_warp_size/(vec_size/4), vec_size/4)
else
LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 2, vec_size, rocm_warp_size/(vec_size/2), vec_size/2)
else
LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 1, vec_size, rocm_warp_size/(vec_size/1), vec_size/1)
#undef CHECK_INPUT_TILEABLE_BY
#undef LAUNCH_BGMV_SHRINK_KERNELS_ROCM
#endif
}
}
#define INST_BGMV(feat_in, feat_out, in_T, out_T, W_T) \
template void bgmv_kernel<feat_in, feat_out>( \
out_T * __restrict__ Y, const in_T *__restrict__ X, \
const W_T *__restrict__ W, const int64_t *__restrict__ indicies, \
int64_t y_offset, int64_t full_y_size, int64_t batch_size, \
int64_t num_layers, int64_t layer_idx, float scale);
#define INST_BGMV_ONESIDE(in_T, out_T, W_T, feat_in, feat_out) \
INST_BGMV(feat_in, feat_out, in_T, out_T, W_T)
#define INST_BGMV_TWOSIDE(in_T, out_T, W_T, narrow, wide) \
INST_BGMV(narrow, wide, in_T, out_T, W_T) \
INST_BGMV(wide, narrow, in_T, out_T, W_T)