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Contains code from https://github.com/punica-ai/punica
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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, mt_bfloat16, mt_bfloat16, mt_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, mt_bfloat16, mt_bfloat16, mt_bfloat16)

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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, mt_bfloat16, float, mt_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, mt_bfloat16, float, mt_bfloat16)

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#pragma once
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);
// clang-format off
#define FOR_BGMV_WIDE(f, in_T, out_T, W_T, narrow) \
f(in_T, out_T, W_T, narrow, 128) \
f(in_T, out_T, W_T, narrow, 256) \
f(in_T, out_T, W_T, narrow, 512) \
f(in_T, out_T, W_T, narrow, 640) \
f(in_T, out_T, W_T, narrow, 768) \
f(in_T, out_T, W_T, narrow, 1024) \
f(in_T, out_T, W_T, narrow, 1152) \
f(in_T, out_T, W_T, narrow, 1280) \
f(in_T, out_T, W_T, narrow, 1536) \
f(in_T, out_T, W_T, narrow, 1728) \
f(in_T, out_T, W_T, narrow, 1792) \
f(in_T, out_T, W_T, narrow, 2048) \
f(in_T, out_T, W_T, narrow, 2304) \
f(in_T, out_T, W_T, narrow, 2560) \
f(in_T, out_T, W_T, narrow, 2752) \
f(in_T, out_T, W_T, narrow, 2816) \
f(in_T, out_T, W_T, narrow, 3072) \
f(in_T, out_T, W_T, narrow, 3456) \
f(in_T, out_T, W_T, narrow, 3584) \
f(in_T, out_T, W_T, narrow, 4096) \
f(in_T, out_T, W_T, narrow, 4608) \
f(in_T, out_T, W_T, narrow, 5120) \
f(in_T, out_T, W_T, narrow, 5504) \
f(in_T, out_T, W_T, narrow, 5632) \
f(in_T, out_T, W_T, narrow, 6144) \
f(in_T, out_T, W_T, narrow, 6848) \
f(in_T, out_T, W_T, narrow, 6912) \
f(in_T, out_T, W_T, narrow, 7168) \
f(in_T, out_T, W_T, narrow, 8192) \
f(in_T, out_T, W_T, narrow, 9216) \
f(in_T, out_T, W_T, narrow, 10240) \
f(in_T, out_T, W_T, narrow, 11008) \
f(in_T, out_T, W_T, narrow, 12288) \
f(in_T, out_T, W_T, narrow, 13696) \
f(in_T, out_T, W_T, narrow, 13824) \
f(in_T, out_T, W_T, narrow, 14336) \
f(in_T, out_T, W_T, narrow, 15360) \
f(in_T, out_T, W_T, narrow, 16384) \
f(in_T, out_T, W_T, narrow, 20480) \
f(in_T, out_T, W_T, narrow, 22016) \
f(in_T, out_T, W_T, narrow, 24576) \
f(in_T, out_T, W_T, narrow, 27392) \
f(in_T, out_T, W_T, narrow, 28672) \
f(in_T, out_T, W_T, narrow, 32000) \
f(in_T, out_T, W_T, narrow, 32256) \
f(in_T, out_T, W_T, narrow, 32512) \
f(in_T, out_T, W_T, narrow, 32768) \
f(in_T, out_T, W_T, narrow, 33024) \
f(in_T, out_T, W_T, narrow, 36864) \
f(in_T, out_T, W_T, narrow, 43264) \
f(in_T, out_T, W_T, narrow, 49152) \
f(in_T, out_T, W_T, narrow, 64000) \
f(in_T, out_T, W_T, narrow, 64256) \
f(in_T, out_T, W_T, narrow, 64512) \
f(in_T, out_T, W_T, narrow, 102400) \
f(in_T, out_T, W_T, narrow, 102656) \
f(in_T, out_T, W_T, narrow, 102912) \
f(in_T, out_T, W_T, narrow, 128000) \
f(in_T, out_T, W_T, narrow, 128256) \
f(in_T, out_T, W_T, narrow, 128512) \
// Keep above in sync with vllm/lora/layers::LogitsProcessorWithLoRA
// and vllm/tests/lora/test_punica.py
// Used for defining kernels going from the variety of
// dim in to the narrow dim out
// Using it for the fully sharded column
// parallel LoRA A which splits the rank dim
#define FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, narrow) \
f(in_T, out_T, W_T, 128, narrow) \
f(in_T, out_T, W_T, 256, narrow) \
f(in_T, out_T, W_T, 512, narrow) \
f(in_T, out_T, W_T, 640, narrow) \
f(in_T, out_T, W_T, 768, narrow) \
f(in_T, out_T, W_T, 1024, narrow) \
f(in_T, out_T, W_T, 1152, narrow) \
f(in_T, out_T, W_T, 1280, narrow) \
f(in_T, out_T, W_T, 1536, narrow) \
f(in_T, out_T, W_T, 1728, narrow) \
f(in_T, out_T, W_T, 1792, narrow) \
f(in_T, out_T, W_T, 2048, narrow) \
f(in_T, out_T, W_T, 2304, narrow) \
f(in_T, out_T, W_T, 2560, narrow) \
f(in_T, out_T, W_T, 2752, narrow) \
f(in_T, out_T, W_T, 2816, narrow) \
f(in_T, out_T, W_T, 3072, narrow) \
f(in_T, out_T, W_T, 3456, narrow) \
f(in_T, out_T, W_T, 3584, narrow) \
f(in_T, out_T, W_T, 4096, narrow) \
f(in_T, out_T, W_T, 4608, narrow) \
f(in_T, out_T, W_T, 5120, narrow) \
f(in_T, out_T, W_T, 5504, narrow) \
f(in_T, out_T, W_T, 5632, narrow) \
f(in_T, out_T, W_T, 6144, narrow) \
f(in_T, out_T, W_T, 6848, narrow) \
f(in_T, out_T, W_T, 6912, narrow) \
f(in_T, out_T, W_T, 7168, narrow) \
f(in_T, out_T, W_T, 8192, narrow) \
f(in_T, out_T, W_T, 9216, narrow) \
f(in_T, out_T, W_T, 10240, narrow) \
f(in_T, out_T, W_T, 11008, narrow) \
f(in_T, out_T, W_T, 12288, narrow) \
f(in_T, out_T, W_T, 13696, narrow) \
f(in_T, out_T, W_T, 13824, narrow) \
f(in_T, out_T, W_T, 14336, narrow) \
f(in_T, out_T, W_T, 15360, narrow) \
f(in_T, out_T, W_T, 16384, narrow) \
f(in_T, out_T, W_T, 20480, narrow) \
f(in_T, out_T, W_T, 22016, narrow) \
f(in_T, out_T, W_T, 24576, narrow) \
f(in_T, out_T, W_T, 27392, narrow) \
f(in_T, out_T, W_T, 28672, narrow) \
f(in_T, out_T, W_T, 32000, narrow) \
f(in_T, out_T, W_T, 32256, narrow) \
f(in_T, out_T, W_T, 32512, narrow) \
f(in_T, out_T, W_T, 32768, narrow) \
f(in_T, out_T, W_T, 33024, narrow) \
f(in_T, out_T, W_T, 36864, narrow) \
f(in_T, out_T, W_T, 43264, narrow) \
f(in_T, out_T, W_T, 49152, narrow) \
f(in_T, out_T, W_T, 64000, narrow) \
f(in_T, out_T, W_T, 64256, narrow) \
f(in_T, out_T, W_T, 64512, narrow) \
f(in_T, out_T, W_T, 102400, narrow) \
f(in_T, out_T, W_T, 102656, narrow) \
f(in_T, out_T, W_T, 102912, narrow) \
f(in_T, out_T, W_T, 128000, narrow) \
f(in_T, out_T, W_T, 128256, narrow) \
f(in_T, out_T, W_T, 128512, narrow) \
// Keep above in sync with vllm/lora/layers::SamplerWithLoRA
// Keep this in sync with vllm/config::LoRAConfig
#define FOR_BGMV_WIDE_NARROW(f, in_T, out_T, W_T) \
FOR_BGMV_WIDE(f, in_T, out_T, W_T, 8) \
FOR_BGMV_WIDE(f, in_T, out_T, W_T, 16) \
FOR_BGMV_WIDE(f, in_T, out_T, W_T, 32) \
FOR_BGMV_WIDE(f, in_T, out_T, W_T, 64)
#define FOR_INST_BGMV_WIDE_NARROW(f, in_T, out_T, W_T) \
FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 1) \
FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 2) \
FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 4) \
f(in_T, out_T, W_T, 8, 64) \
f(in_T, out_T, W_T, 16, 64) \
f(in_T, out_T, W_T, 32, 64) \
f(in_T, out_T, W_T, 64, 64)
// clang-format on

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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, nv_half, nv_half)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_half, nv_half, nv_half)

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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, float, nv_half)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_half, float, nv_half)

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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, mt_bfloat16, mt_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, float, mt_bfloat16, mt_bfloat16)

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#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_half, nv_half)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, float, nv_half, nv_half)

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#pragma once
#include "torch_musa/csrc/aten/musa/MUSAContext.h"
#include <cooperative_groups.h>
#include <cuda/pipeline>
#include <musa_runtime.h>
#include <iostream>
#include <stdio.h>
#include "vec_dtypes.cuh"
namespace cg = cooperative_groups;
// 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);
}
}
// 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) {
sum += float(w_vec[i]) * float(x_vec[i]) * scale;
}
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) {
Y[batch_idx * full_y_size + y_offset + tile_idx * (tz * ty) +
threadIdx.z * ty + threadIdx.y] += static_cast<out_T>(sum);
}
}
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 musaStream_t stream = at::musa::getCurrentMUSAStream();
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 {
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);
}
}
}
#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)

48
csrc_musa/punica/bgmv/generator.py Normal file
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DTYPES = ["fp16", "bf16", "fp32"]
DTYPE_MAP = {
"fp16": "nv_half",
"bf16": "mt_bfloat16",
"fp32": "float",
}
TEMPLATE = """
#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, {input_dtype}, {output_dtype}, {weight_dtype})
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, {input_dtype}, {output_dtype}, {weight_dtype})
""".lstrip() # noqa: E501
for input_dtype in DTYPES:
for output_dtype in DTYPES:
for weight_dtype in DTYPES:
if weight_dtype == "fp32":
# FP32 weights are not supported.
continue
if output_dtype == "fp32":
# LoRA A matrix.
if input_dtype != weight_dtype:
# NOTE(woosuk): While Punica supports the case where the
# input and weight dtypes are different, we only generate
# the kernels the same dtypes to reduce the binary size.
continue
elif input_dtype == "fp32":
# LoRA B matrix.
if output_dtype != weight_dtype:
# NOTE(woosuk): While Punica supports the case where the
# output and weight dtypes are different, we only generate
# the kernels the same dtypes to reduce the binary size.
continue
elif not (input_dtype == output_dtype == weight_dtype):
# NOTE(woosuk): While Punica supports mixed data types for
# input, output, and weight, we only generate the kernels with
# the same data types to reduce the binary size.
continue
kernel_definition = TEMPLATE.format(
input_dtype=DTYPE_MAP[input_dtype],
output_dtype=DTYPE_MAP[output_dtype],
weight_dtype=DTYPE_MAP[weight_dtype])
filename = f"bgmv_{input_dtype}_{output_dtype}_{weight_dtype}.cu"
with open(filename, "w") as f:
f.write(kernel_definition)

1324
csrc_musa/punica/bgmv/vec_dtypes.muh Normal file
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582
csrc_musa/punica/punica_ops.cc Normal file
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#include <musa_bf16.h>
#include <musa_fp16.h>
#include <torch/extension.h>
#include "torch_musa/csrc/core/MUSAGuard.h"
#include <cstdint>
#include "bgmv/bgmv_config.h"
namespace {
//====== utils ======
inline void check_shape(const torch::Tensor &a, const torch::Tensor &b,
const char *a_name, const char *b_name) {
TORCH_CHECK(a.dim() == b.dim(), a_name, ".dim() != ", b_name, ".dim(). ",
a.dim(), " vs ", b.dim());
for (int i = 0; i < a.dim(); ++i) {
TORCH_CHECK(a.size(i) == b.size(i), a_name, ".size(", i, ") != ", b_name,
".size(", i, ")");
}
}
inline constexpr uint64_t pack_u32(uint32_t a, uint32_t b) {
return (uint64_t(a) << 32) | uint64_t(b);
}
#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
#define CHECK_CONTIGUOUS(x) \
TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
#define CHECK_INPUT(x) \
CHECK_CUDA(x); \
CHECK_CONTIGUOUS(x)
#define CHECK_DIM(d, x) \
TORCH_CHECK(x.dim() == d, #x " must be a " #d "D tensor")
#define CHECK_SHAPE(a, b) check_shape(a, b, #a, #b)
#define CHECK_EQ(a, b) \
TORCH_CHECK(a == b, "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
//====== bgmv ======
template <typename in_T, typename out_T, typename W_T>
inline bool launch_bgmv_kernel(out_T *Y, const in_T *X, const W_T *W,
const int64_t *lora_indices,
uint32_t in_features, uint32_t out_features,
int64_t y_offset, int64_t full_y_size,
int64_t batch_size, int64_t num_layers,
int64_t layer_idx, float scale) {
// NOTE(woosuk): While Punica supports various combinations of input/output
// data types, we limit the supported data types to reduce the binary size.
constexpr bool is_input_float = std::is_same<in_T, float>::value;
constexpr bool is_output_float = std::is_same<out_T, float>::value;
if (is_input_float) {
if (!std::is_same<out_T, W_T>::value) {
return false;
}
} else if (is_output_float) {
if (!std::is_same<in_T, W_T>::value) {
return false;
}
} else if (!(std::is_same<in_T, W_T>::value &&
std::is_same<out_T, W_T>::value)) {
return false;
}
switch (pack_u32(in_features, out_features)) {
#define CASE_ONESIDE(_in_T, _out_T, _W_T, feat_in, feat_out) \
case pack_u32(feat_in, feat_out): \
bgmv_kernel<feat_in, feat_out>(Y, X, W, lora_indices, y_offset, \
full_y_size, batch_size, num_layers, \
layer_idx, scale); \
break;
#define CASE(_in_T, _out_T, _W_T, narrow, wide) \
CASE_ONESIDE(in_T, out_T, W_T, narrow, wide) \
CASE_ONESIDE(in_T, out_T, W_T, wide, narrow)
FOR_BGMV_WIDE_NARROW(CASE, _, _, _)
FOR_INST_BGMV_WIDE_NARROW(CASE_ONESIDE, _, _, _)
#undef CASE
#undef CASE_ONESIDE
default:
return false;
}
return true;
}
void dispatch_bgmv(torch::Tensor y, torch::Tensor x, torch::Tensor w,
torch::Tensor indicies, int64_t layer_idx, float scale) {
CHECK_INPUT(y);
CHECK_INPUT(x);
CHECK_INPUT(w);
CHECK_INPUT(indicies);
CHECK_DIM(2, y);
CHECK_DIM(2, x);
CHECK_DIM(4, w);
CHECK_DIM(1, indicies);
int64_t B = x.size(0);
int64_t h_in = x.size(1);
int64_t h_out = y.size(1);
int64_t num_layers = w.size(1);
CHECK_EQ(w.size(3), h_in);
CHECK_EQ(w.size(2), h_out);
CHECK_EQ(indicies.size(0), x.size(0));
CHECK_EQ(y.size(0), x.size(0));
const at::musa::OptionalMUSAGuard device_guard(device_of(x));
bool ok = false;
if (h_in <= 128512 && h_out <= 128512) {
// TODO: See if we can get rid of this massive nested switch
switch (x.scalar_type()) {
case at::ScalarType::Half:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out, 0,
h_out, B, num_layers, layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
default:
break;
}
}
TORCH_CHECK(ok, "No suitable kernel.", " h_in=", h_in, " h_out=", h_out,
" dtype=", x.scalar_type(), " out_dtype=", y.scalar_type());
}
void dispatch_bgmv_low_level(torch::Tensor y, torch::Tensor x, torch::Tensor w,
torch::Tensor indicies, int64_t layer_idx,
float scale, int64_t h_in, int64_t h_out,
int64_t y_offset) {
CHECK_INPUT(y);
CHECK_INPUT(x);
CHECK_INPUT(w);
CHECK_INPUT(indicies);
CHECK_DIM(2, y);
CHECK_DIM(2, x);
CHECK_DIM(4, w);
CHECK_DIM(1, indicies);
int64_t B = x.size(0);
int64_t num_layers = w.size(1);
int64_t full_y_size = y.size(1);
CHECK_EQ(w.size(3), h_in);
CHECK_EQ(w.size(2), h_out);
CHECK_EQ(indicies.size(0), x.size(0));
CHECK_EQ(y.size(0), x.size(0));
const at::musa::OptionalMUSAGuard device_guard(device_of(x));
bool ok = false;
if (h_in <= 128512 && h_out <= 128512) {
// TODO: See if we can get rid of this massive nested switch
switch (x.scalar_type()) {
case at::ScalarType::Half:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<nv_half *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<mt_bfloat16 *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (y.scalar_type()) {
case at::ScalarType::Half:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<nv_half *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::BFloat16:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<mt_bfloat16 *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
case at::ScalarType::Float:
switch (w.scalar_type()) {
case at::ScalarType::Half:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<nv_half *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
case at::ScalarType::BFloat16:
ok = launch_bgmv_kernel(static_cast<float *>(y.data_ptr()),
static_cast<float *>(x.data_ptr()),
static_cast<mt_bfloat16 *>(w.data_ptr()),
indicies.data_ptr<int64_t>(), h_in, h_out,
y_offset, full_y_size, B, num_layers,
layer_idx, scale);
break;
default:
break;
}
break;
default:
break;
}
break;
default:
break;
}
}
TORCH_CHECK(ok, "No suitable kernel.", " h_in=", h_in, " h_out=", h_out,
" dtype=", x.scalar_type(), " out_dtype=", y.scalar_type());
}
} // namespace
//====== pybind ======
#define DEFINE_pybind(name) m.def(#name, &name, #name);
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("dispatch_bgmv", &dispatch_bgmv, "dispatch_bgmv");
m.def("dispatch_bgmv_low_level", &dispatch_bgmv_low_level,
"dispatch_bgmv_low_level");
}