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Unverified Commit 7ecee343 authored by Jee Jee Li's avatar Jee Jee Li Committed by GitHub
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[Kernel][RFC] Refactor the punica kernel based on Triton (#5036)

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.github/workflows/scripts/build.sh
View file @ 7ecee343
......@@ -13,8 +13,6 @@ $python_executable -m pip install -r requirements-cuda.txt
# Limit the number of parallel jobs to avoid OOM
export MAX_JOBS=1
# Make sure punica is built for the release (for LoRA)
export VLLM_INSTALL_PUNICA_KERNELS=1
# Make sure release wheels are built for the following architectures
export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
# Build
......
CMakeLists.txt
View file @ 7ecee343
......@@ -223,61 +223,7 @@ define_gpu_extension_target(
USE_SABI 3
WITH_SOABI)
#
# _punica_C extension
#
set(VLLM_PUNICA_EXT_SRC
"csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu"
"csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu"
"csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu"
"csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu"
"csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu"
"csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu"
"csrc/punica/punica_ops.cu"
"csrc/punica/torch_bindings.cpp")
#
# Copy GPU compilation flags+update for punica
#
set(VLLM_PUNICA_GPU_FLAGS ${VLLM_GPU_FLAGS})
list(REMOVE_ITEM VLLM_PUNICA_GPU_FLAGS
"-D__CUDA_NO_HALF_OPERATORS__"
"-D__CUDA_NO_HALF_CONVERSIONS__"
"-D__CUDA_NO_BFLOAT16_CONVERSIONS__"
"-D__CUDA_NO_HALF2_OPERATORS__")
#
# Filter out CUDA architectures < 8.0 for punica.
#
if (${VLLM_GPU_LANG} STREQUAL "CUDA")
set(VLLM_PUNICA_GPU_ARCHES)
foreach(ARCH ${VLLM_GPU_ARCHES})
string_to_ver(CODE_VER ${ARCH})
if (CODE_VER GREATER_EQUAL 8.0)
list(APPEND VLLM_PUNICA_GPU_ARCHES ${ARCH})
endif()
endforeach()
message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
elseif(${VLLM_GPU_LANG} STREQUAL "HIP")
set(VLLM_PUNICA_GPU_ARCHES ${VLLM_GPU_ARCHES})
message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
endif()
if (VLLM_PUNICA_GPU_ARCHES)
define_gpu_extension_target(
_punica_C
DESTINATION vllm
LANGUAGE ${VLLM_GPU_LANG}
SOURCES ${VLLM_PUNICA_EXT_SRC}
COMPILE_FLAGS ${VLLM_PUNICA_GPU_FLAGS}
ARCHITECTURES ${VLLM_PUNICA_GPU_ARCHES}
USE_SABI 3
WITH_SOABI)
else()
message(WARNING "Unable to create _punica_C target because none of the "
"requested architectures (${VLLM_GPU_ARCHES}) are supported, i.e. >= 8.0")
endif()
#
# Add the `default` target which detects which extensions should be
......@@ -301,12 +247,4 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
message(STATUS "Enabling moe extension.")
add_dependencies(default _moe_C)
# Enable punica if -DVLLM_INSTALL_PUNICA_KERNELS=ON or
# VLLM_INSTALL_PUNICA_KERNELS is set in the environment and
# there are supported target arches.
if (VLLM_PUNICA_GPU_ARCHES AND
(ENV{VLLM_INSTALL_PUNICA_KERNELS} OR VLLM_INSTALL_PUNICA_KERNELS))
message(STATUS "Enabling punica extension.")
add_dependencies(default _punica_C)
endif()
endif()
Dockerfile
View file @ 7ecee343
......@@ -88,8 +88,6 @@ ENV MAX_JOBS=${max_jobs}
# number of threads used by nvcc
ARG nvcc_threads=8
ENV NVCC_THREADS=$nvcc_threads
# make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
ARG buildkite_commit
ENV BUILDKITE_COMMIT=${buildkite_commit}
......
Dockerfile.rocm
View file @ 7ecee343
......@@ -131,8 +131,7 @@ COPY . .
RUN --mount=type=cache,target=/root/.cache/pip \
python3 -m pip install --upgrade numba scipy huggingface-hub[cli]
# Make sure punica kernels are built (for LoRA)
ENV VLLM_INSTALL_PUNICA_KERNELS=1
# Workaround for ray >= 2.10.0
ENV RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1
# Silences the HF Tokenizers warning
......
csrc/punica/LICENSE deleted 100644 → 0
View file @ 7eb0cb4a
Contains code from https://github.com/punica-ai/punica
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\ No newline at end of file
csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, nv_bfloat16, nv_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_bfloat16, nv_bfloat16, nv_bfloat16)
csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, float, nv_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_bfloat16, float, nv_bfloat16)
csrc/punica/bgmv/bgmv_config.h deleted 100644 → 0
View file @ 7eb0cb4a
#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, 896) \
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, 1216) \
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, 1664) \
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, 2240) \
f(in_T, out_T, W_T, narrow, 2304) \
f(in_T, out_T, W_T, narrow, 2368) \
f(in_T, out_T, W_T, narrow, 2432) \
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, 3328) \
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, 3712) \
f(in_T, out_T, W_T, narrow, 4096) \
f(in_T, out_T, W_T, narrow, 4480) \
f(in_T, out_T, W_T, narrow, 4608) \
f(in_T, out_T, W_T, narrow, 4736) \
f(in_T, out_T, W_T, narrow, 4864) \
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, 5888) \
f(in_T, out_T, W_T, narrow, 6144) \
f(in_T, out_T, W_T, narrow, 6400) \
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, 7424) \
f(in_T, out_T, W_T, narrow, 8192) \
f(in_T, out_T, W_T, narrow, 8960) \
f(in_T, out_T, W_T, narrow, 9216) \
f(in_T, out_T, W_T, narrow, 9472) \
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, 11264) \
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, 14784) \
f(in_T, out_T, W_T, narrow, 14848) \
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, 18944) \
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, 22528) \
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, 27648) \
f(in_T, out_T, W_T, narrow, 28672) \
f(in_T, out_T, W_T, narrow, 29568) \
f(in_T, out_T, W_T, narrow, 29696) \
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, 49408) \
f(in_T, out_T, W_T, narrow, 60544) \
f(in_T, out_T, W_T, narrow, 60672) \
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, 896, 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, 1216, 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, 1664, 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, 2240, narrow) \
f(in_T, out_T, W_T, 2304, narrow) \
f(in_T, out_T, W_T, 2368, narrow) \
f(in_T, out_T, W_T, 2432, 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, 3328, 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, 3712, narrow) \
f(in_T, out_T, W_T, 4096, narrow) \
f(in_T, out_T, W_T, 4480, narrow) \
f(in_T, out_T, W_T, 4608, narrow) \
f(in_T, out_T, W_T, 4736, narrow) \
f(in_T, out_T, W_T, 4864, 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, 5888, narrow) \
f(in_T, out_T, W_T, 6144, narrow) \
f(in_T, out_T, W_T, 6400, 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, 7424, narrow) \
f(in_T, out_T, W_T, 8192, narrow) \
f(in_T, out_T, W_T, 8960, narrow) \
f(in_T, out_T, W_T, 9216, narrow) \
f(in_T, out_T, W_T, 9472, 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, 11264, 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, 14784, narrow) \
f(in_T, out_T, W_T, 14848, 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, 18944, 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, 22528, 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, 27648, narrow) \
f(in_T, out_T, W_T, 28672, narrow) \
f(in_T, out_T, W_T, 29568, narrow) \
f(in_T, out_T, W_T, 29696, 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, 49408, narrow) \
f(in_T, out_T, W_T, 60544, narrow) \
f(in_T, out_T, W_T, 60672, 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
csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#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)
csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#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)
csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#include "bgmv_config.h"
#include "bgmv_impl.cuh"
FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_bfloat16, nv_bfloat16)
FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, float, nv_bfloat16, nv_bfloat16)
csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu deleted 100644 → 0
View file @ 7eb0cb4a
#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)
csrc/punica/bgmv/bgmv_impl.cuh deleted 100644 → 0
View file @ 7eb0cb4a
#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)
csrc/punica/bgmv/generator.py deleted 100644 → 0
View file @ 7eb0cb4a
DTYPES = ["fp16", "bf16", "fp32"]
DTYPE_MAP = {
"fp16": "nv_half",
"bf16": "nv_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)
csrc/punica/bgmv/vec_dtypes.cuh deleted 100644 → 0
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csrc/punica/punica_ops.cu deleted 100644 → 0
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csrc/punica/punica_ops.h deleted 100644 → 0
View file @ 7eb0cb4a
#pragma once
#include <torch/all.h>
void dispatch_bgmv(torch::Tensor y, torch::Tensor x, torch::Tensor w,
torch::Tensor indicies, int64_t layer_idx, double scale);
void dispatch_bgmv_low_level(torch::Tensor y, torch::Tensor x, torch::Tensor w,
torch::Tensor indicies, int64_t layer_idx,
double scale, int64_t h_in, int64_t h_out,
int64_t y_offset);
csrc/punica/torch_bindings.cpp deleted 100644 → 0
View file @ 7eb0cb4a
#include "registration.h"
#include "punica_ops.h"
TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
m.def(
"dispatch_bgmv(Tensor! y, Tensor x, Tensor w, Tensor indicies, int "
"layer_idx, float scale) -> ()");
m.impl("dispatch_bgmv", torch::kCUDA, &dispatch_bgmv);
m.def(
"dispatch_bgmv_low_level(Tensor! y, Tensor x, Tensor w,"
"Tensor indicies, int layer_idx,"
"float scale, int h_in, int h_out,"
"int y_offset) -> ()");
m.impl("dispatch_bgmv_low_level", torch::kCUDA, &dispatch_bgmv_low_level);
}
REGISTER_EXTENSION(TORCH_EXTENSION_NAME)
csrc/punica/type_convert.h deleted 100644 → 0
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#ifndef CSRC__PUNICA__TYPE_CONVERT_H__
#define CSRC__PUNICA__TYPE_CONVERT_H__
#ifndef USE_ROCM
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#else
#include <hip/hip_bf16.h>
#include <hip/hip_fp16.h>
#define __TYPE_CONVERT__HOST_DEVICE__ __host__ __device__
typedef __half nv_half;
typedef __hip_bfloat16 nv_bfloat16;
typedef __hip_bfloat162 nv_bfloat162;
__TYPE_CONVERT__HOST_DEVICE__
inline __hip_bfloat162 make_bfloat162(__hip_bfloat16 val) {
return __hip_bfloat162{val, val};
}
__TYPE_CONVERT__HOST_DEVICE__
inline __hip_bfloat162 make_bfloat162(__hip_bfloat16 vall, __hip_bfloat16 valr) {
return __hip_bfloat162{vall, valr};
}
template <typename T_src, typename T_dst>
__TYPE_CONVERT__HOST_DEVICE__
inline T_dst convert_type(T_src val) {
return static_cast<T_dst>(val);
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline float convert_type<__half, float>(__half val) {
return __half2float(val);
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline __half convert_type<float, __half>(float val) {
return __float2half(val);
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline float convert_type<__hip_bfloat16, float>(__hip_bfloat16 val) {
return __bfloat162float(val);
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline __hip_bfloat16 convert_type<float, __hip_bfloat16>(float val) {
return __float2bfloat16(val);
}
template <typename T>
__TYPE_CONVERT__HOST_DEVICE__
inline T vllm_add(T a, T b) {
return a + b;
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline __half vllm_add<__half>(__half a, __half b) {
return __hadd(a, b);
}
template <>
__TYPE_CONVERT__HOST_DEVICE__
inline __hip_bfloat16 vllm_add<__hip_bfloat16>(__hip_bfloat16 a, __hip_bfloat16 b) {
return __hadd(a, b);
}
#undef __TYPE_CONVERT__HOST_DEVICE__
#endif // USE_ROCM
#endif // CSRC__PUNICA__TYPE_CONVERT_H__
docs/source/getting_started/installation.rst
View file @ 7ecee343
......@@ -66,7 +66,6 @@ You can also build and install vLLM from source:
$ git clone https://github.com/vllm-project/vllm.git
$ cd vllm
$ # export VLLM_INSTALL_PUNICA_KERNELS=1 # optionally build for multi-LoRA capability
$ pip install -e . # This may take 5-10 minutes.
.. tip::
......
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