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Support FP8-E5M2 KV Cache (#2279)

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Co-authored-by: zhaoyang <zhao.yang16@zte.com.cn>
Co-authored-by: Zhuohan Li <zhuohan123@gmail.com>
This commit is contained in:
zhaoyang-star 2024-01-29 08:43:54 +08:00 committed by GitHub
parent 7d648418b8
commit 9090bf02e7
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GPG Key ID: B5690EEEBB952194
26 changed files with 912 additions and 196 deletions
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8
benchmarks/benchmark_latency.py
View File

@ -24,6 +24,7 @@ def main(args: argparse.Namespace):
trust_remote_code=args.trust_remote_code,
dtype=args.dtype,
enforce_eager=args.enforce_eager,
kv_cache_dtype=args.kv_cache_dtype,
)
sampling_params = SamplingParams(
@ -117,6 +118,13 @@ if __name__ == '__main__':
parser.add_argument('--enforce-eager',
action='store_true',
help='enforce eager mode and disable CUDA graph')
parser.add_argument(
"--kv-cache-dtype",
type=str,
choices=['auto', 'fp8_e5m2'],
default='auto',
help=
'Data type for kv cache storage. If "auto", will use model data type.')
parser.add_argument(
'--profile',
action='store_true',

12
benchmarks/benchmark_throughput.py
View File

@ -71,6 +71,7 @@ def run_vllm(
dtype: str,
max_model_len: Optional[int],
enforce_eager: bool,
kv_cache_dtype: str,
) -> float:
from vllm import LLM, SamplingParams
llm = LLM(
@ -83,6 +84,7 @@ def run_vllm(
dtype=dtype,
max_model_len=max_model_len,
enforce_eager=enforce_eager,
kv_cache_dtype=kv_cache_dtype,
)
# Add the requests to the engine.
@ -206,7 +208,8 @@ def main(args: argparse.Namespace):
args.quantization, args.tensor_parallel_size,
args.seed, args.n, args.use_beam_search,
args.trust_remote_code, args.dtype,
args.max_model_len, args.enforce_eager)
args.max_model_len, args.enforce_eager,
args.kv_cache_dtype)
elif args.backend == "hf":
assert args.tensor_parallel_size == 1
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@ -284,6 +287,13 @@ if __name__ == "__main__":
parser.add_argument("--enforce-eager",
action="store_true",
help="enforce eager execution")
parser.add_argument(
"--kv-cache-dtype",
type=str,
choices=["auto", "fp8_e5m2"],
default="auto",
help=
'Data type for kv cache storage. If "auto", will use model data type.')
args = parser.parse_args()
if args.tokenizer is None:
args.tokenizer = args.model

33
benchmarks/kernels/benchmark_paged_attention.py
View File

@ -1,9 +1,11 @@
from typing import Optional
import argparse
import random
import time
import torch
from vllm.utils import STR_DTYPE_TO_TORCH_DTYPE, create_kv_caches_with_random
from vllm._C import ops
NUM_BLOCKS = 1024
@ -23,6 +25,7 @@ def main(
dtype: torch.dtype,
seed: int,
do_profile: bool,
kv_cache_dtype: Optional[str] = None,
) -> None:
random.seed(seed)
torch.random.manual_seed(seed)
@ -59,15 +62,10 @@ def main(
block_tables = torch.tensor(block_tables, dtype=torch.int, device="cuda")
# Create the KV cache.
x = 16 // torch.tensor([], dtype=dtype).element_size()
key_cache_shape = (NUM_BLOCKS, num_kv_heads, head_size // x, block_size, x)
key_cache = torch.empty(size=key_cache_shape, dtype=dtype, device="cuda")
key_cache.uniform_(-scale, scale)
value_cache_shape = (NUM_BLOCKS, num_kv_heads, head_size, block_size)
value_cache = torch.empty(size=value_cache_shape,
dtype=dtype,
device="cuda")
value_cache.uniform_(-scale, scale)
key_caches, value_caches = create_kv_caches_with_random(
NUM_BLOCKS, block_size, 1, num_kv_heads, head_size, kv_cache_dtype,
dtype)
key_cache, value_cache = key_caches[0], value_caches[0]
# Prepare for the paged attention kernel.
output = torch.empty_like(query)
@ -106,6 +104,7 @@ def main(
block_size,
max_context_len,
alibi_slopes,
kv_cache_dtype,
)
elif version == "v2":
ops.paged_attention_v2(
@ -123,6 +122,7 @@ def main(
block_size,
max_context_len,
alibi_slopes,
kv_cache_dtype,
)
else:
raise ValueError(f"Invalid version: {version}")
@ -168,16 +168,18 @@ if __name__ == '__main__':
default="half")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--profile", action="store_true")
parser.add_argument(
"--kv-cache-dtype",
type=str,
choices=["auto", "fp8_e5m2"],
default="auto",
help=
'Data type for kv cache storage. If "auto", will use model data type.')
args = parser.parse_args()
print(args)
if args.num_query_heads % args.num_kv_heads != 0:
raise ValueError("num_query_heads must be divisible by num_kv_heads")
dtype_to_torch_dtype = {
"half": torch.half,
"bfloat16": torch.bfloat16,
"float": torch.float,
}
main(
version=args.version,
num_seqs=args.batch_size,
@ -187,7 +189,8 @@ if __name__ == '__main__':
head_size=args.head_size,
block_size=args.block_size,
use_alibi=args.use_alibi,
dtype=dtype_to_torch_dtype[args.dtype],
dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
seed=args.seed,
do_profile=args.profile,
kv_cache_dtype=args.kv_cache_dtype,
)

1
csrc/attention/attention_dtypes.h
View File

@ -4,3 +4,4 @@
#include "dtype_float16.cuh"
#include "dtype_float32.cuh"
#include "dtype_bfloat16.cuh"
#include "dtype_fp8_e5m2.cuh"

259
csrc/attention/attention_kernels.cu
View File

@ -25,6 +25,7 @@
#include "attention_dtypes.h"
#include "attention_utils.cuh"
#include "../quantization/fp8_e5m2_kvcache/quant_utils.cuh"
#include <algorithm>
@ -79,17 +80,19 @@ inline __device__ float block_sum(float* red_smem, float sum) {
// Grid: (num_heads, num_seqs, max_num_partitions).
template<
typename scalar_t,
typename cache_t,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS,
bool IS_FP8_E5M2_KV_CACHE,
int PARTITION_SIZE = 0> // Zero means no partitioning.
__device__ void paged_attention_kernel(
float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
float* __restrict__ max_logits, // [num_seqs, num_heads, max_num_partitions]
scalar_t* __restrict__ out, // [num_seqs, num_heads, max_num_partitions, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
@ -145,6 +148,9 @@ __device__ void paged_attention_kernel(
constexpr int VEC_SIZE = MAX(16 / (THREAD_GROUP_SIZE * sizeof(scalar_t)), 1);
using K_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
using Q_vec = typename Vec<scalar_t, VEC_SIZE>::Type;
#ifdef ENABLE_FP8_E5M2
using Quant_vec = typename Vec<cache_t, VEC_SIZE>::Type;
#endif
constexpr int NUM_ELEMS_PER_THREAD = HEAD_SIZE / THREAD_GROUP_SIZE;
constexpr int NUM_VECS_PER_THREAD = NUM_ELEMS_PER_THREAD / VEC_SIZE;
@ -176,7 +182,7 @@ __device__ void paged_attention_kernel(
// x == THREAD_GROUP_SIZE * VEC_SIZE
// Each thread group fetches x elements from the key at a time.
constexpr int x = 16 / sizeof(scalar_t);
constexpr int x = 16 / sizeof(cache_t);
float qk_max = -FLT_MAX;
// Iterate over the key blocks.
@ -202,13 +208,23 @@ __device__ void paged_attention_kernel(
#pragma unroll
for (int j = 0; j < NUM_VECS_PER_THREAD; j++) {
const scalar_t* k_ptr = k_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride
+ physical_block_offset * x;
const cache_t* k_ptr = k_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride
+ physical_block_offset * x;
const int vec_idx = thread_group_offset + j * THREAD_GROUP_SIZE;
const int offset1 = (vec_idx * VEC_SIZE) / x;
const int offset2 = (vec_idx * VEC_SIZE) % x;
k_vecs[j] = *reinterpret_cast<const K_vec*>(k_ptr + offset1 * BLOCK_SIZE * x + offset2);
if constexpr (IS_FP8_E5M2_KV_CACHE) {
#ifdef ENABLE_FP8_E5M2
Quant_vec k_vec_quant = *reinterpret_cast<const Quant_vec*>(k_ptr + offset1 * BLOCK_SIZE * x + offset2);
// Vector conversion from Quant_vec to K_vec.
k_vecs[j] = fp8_e5m2_unscaled::vec_conversion<K_vec, Quant_vec>(k_vec_quant);
#else
assert(false);
#endif
} else {
k_vecs[j] = *reinterpret_cast<const K_vec*>(k_ptr + offset1 * BLOCK_SIZE * x + offset2);
}
}
// Compute dot product.
@ -282,6 +298,9 @@ __device__ void paged_attention_kernel(
constexpr int V_VEC_SIZE = MIN(16 / sizeof(scalar_t), BLOCK_SIZE);
using V_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
using L_vec = typename Vec<scalar_t, V_VEC_SIZE>::Type;
#ifdef ENABLE_FP8_E5M2
using V_quant_vec = typename Vec<cache_t, V_VEC_SIZE>::Type;
#endif
using Float_L_vec = typename FloatVec<L_vec>::Type;
constexpr int NUM_V_VECS_PER_ROW = BLOCK_SIZE / V_VEC_SIZE;
@ -307,14 +326,25 @@ __device__ void paged_attention_kernel(
L_vec logits_vec;
from_float(logits_vec, *reinterpret_cast<Float_L_vec*>(logits + token_idx - start_token_idx));
const scalar_t* v_ptr = v_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride;
const cache_t* v_ptr = v_cache + physical_block_number * kv_block_stride
+ kv_head_idx * kv_head_stride;
#pragma unroll
for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
if (row_idx < HEAD_SIZE) {
const int offset = row_idx * BLOCK_SIZE + physical_block_offset;
V_vec v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
V_vec v_vec;
if constexpr (IS_FP8_E5M2_KV_CACHE) {
#ifdef ENABLE_FP8_E5M2
V_quant_vec v_quant_vec = *reinterpret_cast<const V_quant_vec*>(v_ptr + offset);
// Vector conversion from V_quant_vec to V_vec.
v_vec = fp8_e5m2_unscaled::vec_conversion<V_vec, V_quant_vec>(v_quant_vec);
#else
assert(false);
#endif
} else {
v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
}
if (block_idx == num_context_blocks - 1) {
// NOTE(woosuk): When v_vec contains the tokens that are out of the context,
// we should explicitly zero out the values since they may contain NaNs.
@ -395,14 +425,16 @@ __device__ void paged_attention_kernel(
// Grid: (num_heads, num_seqs, 1).
template<
typename scalar_t,
typename cache_t,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS>
int NUM_THREADS,
bool IS_FP8_E5M2_KV_CACHE>
__global__ void paged_attention_v1_kernel(
scalar_t* __restrict__ out, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
@ -412,7 +444,7 @@ __global__ void paged_attention_v1_kernel(
const int q_stride,
const int kv_block_stride,
const int kv_head_stride) {
paged_attention_kernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>(
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, IS_FP8_E5M2_KV_CACHE>(
/* exp_sums */ nullptr, /* max_logits */ nullptr,
out, q, k_cache, v_cache, num_kv_heads, scale, block_tables, context_lens,
max_num_blocks_per_seq, alibi_slopes, q_stride, kv_block_stride, kv_head_stride);
@ -421,17 +453,19 @@ __global__ void paged_attention_v1_kernel(
// Grid: (num_heads, num_seqs, max_num_partitions).
template<
typename scalar_t,
typename cache_t,
int HEAD_SIZE,
int BLOCK_SIZE,
int NUM_THREADS,
bool IS_FP8_E5M2_KV_CACHE,
int PARTITION_SIZE>
__global__ void paged_attention_v2_kernel(
float* __restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
float* __restrict__ max_logits, // [num_seqs, num_heads, max_num_partitions]
scalar_t* __restrict__ tmp_out, // [num_seqs, num_heads, max_num_partitions, head_size]
const scalar_t* __restrict__ q, // [num_seqs, num_heads, head_size]
const scalar_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const scalar_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const cache_t* __restrict__ k_cache, // [num_blocks, num_kv_heads, head_size/x, block_size, x]
const cache_t* __restrict__ v_cache, // [num_blocks, num_kv_heads, head_size, block_size]
const int num_kv_heads, // [num_heads]
const float scale,
const int* __restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
@ -441,7 +475,7 @@ __global__ void paged_attention_v2_kernel(
const int q_stride,
const int kv_block_stride,
const int kv_head_stride) {
paged_attention_kernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, PARTITION_SIZE>(
paged_attention_kernel<scalar_t, cache_t, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, IS_FP8_E5M2_KV_CACHE, PARTITION_SIZE>(
exp_sums, max_logits, tmp_out, q, k_cache, v_cache, num_kv_heads, scale,
block_tables, context_lens, max_num_blocks_per_seq, alibi_slopes,
q_stride, kv_block_stride, kv_head_stride);
@ -550,10 +584,10 @@ __global__ void paged_attention_v2_reduce_kernel(
#define LAUNCH_PAGED_ATTENTION_V1(HEAD_SIZE) \
VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize( \
((void*)vllm::paged_attention_v1_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>), \
shared_mem_size); \
vllm::paged_attention_v1_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS> \
<<<grid, block, shared_mem_size, stream>>>( \
((void*)vllm::paged_attention_v1_kernel<T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, \
IS_FP8_E5M2_KV_CACHE>), shared_mem_size); \
vllm::paged_attention_v1_kernel<T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, \
IS_FP8_E5M2_KV_CACHE><<<grid, block, shared_mem_size, stream>>>( \
out_ptr, \
query_ptr, \
key_cache_ptr, \
@ -571,7 +605,9 @@ __global__ void paged_attention_v2_reduce_kernel(
// TODO(woosuk): Tune NUM_THREADS.
template<
typename T,
typename CACHE_T,
int BLOCK_SIZE,
bool IS_FP8_E5M2_KV_CACHE,
int NUM_THREADS = 128>
void paged_attention_v1_launcher(
torch::Tensor& out,
@ -602,8 +638,8 @@ void paged_attention_v1_launcher(
T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
CACHE_T* key_cache_ptr = reinterpret_cast<CACHE_T*>(key_cache.data_ptr());
CACHE_T* value_cache_ptr = reinterpret_cast<CACHE_T*>(value_cache.data_ptr());
int* block_tables_ptr = block_tables.data_ptr<int>();
int* context_lens_ptr = context_lens.data_ptr<int>();
@ -647,35 +683,35 @@ void paged_attention_v1_launcher(
}
}
#define CALL_V1_LAUNCHER(T, BLOCK_SIZE) \
paged_attention_v1_launcher<T, BLOCK_SIZE>( \
out, \
query, \
key_cache, \
value_cache, \
num_kv_heads, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
#define CALL_V1_LAUNCHER(T, CACHE_T, BLOCK_SIZE, IS_FP8_E5M2_KV_CACHE) \
paged_attention_v1_launcher<T, CACHE_T, BLOCK_SIZE, IS_FP8_E5M2_KV_CACHE>( \
out, \
query, \
key_cache, \
value_cache, \
num_kv_heads, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
alibi_slopes);
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
// 1, 2, 4, 64, 128, 256.
#define CALL_V1_LAUNCHER_BLOCK_SIZE(T) \
switch (block_size) { \
case 8: \
CALL_V1_LAUNCHER(T, 8); \
break; \
case 16: \
CALL_V1_LAUNCHER(T, 16); \
break; \
case 32: \
CALL_V1_LAUNCHER(T, 32); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
break; \
#define CALL_V1_LAUNCHER_BLOCK_SIZE(T, CACHE_T, IS_FP8_E5M2_KV_CACHE) \
switch (block_size) { \
case 8: \
CALL_V1_LAUNCHER(T, CACHE_T, 8, IS_FP8_E5M2_KV_CACHE); \
break; \
case 16: \
CALL_V1_LAUNCHER(T, CACHE_T, 16, IS_FP8_E5M2_KV_CACHE); \
break; \
case 32: \
CALL_V1_LAUNCHER(T, CACHE_T, 32, IS_FP8_E5M2_KV_CACHE); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
break; \
}
void paged_attention_v1(
@ -689,20 +725,36 @@ void paged_attention_v1(
torch::Tensor& context_lens, // [num_seqs]
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
if (query.dtype() == at::ScalarType::Float) {
CALL_V1_LAUNCHER_BLOCK_SIZE(float);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V1_LAUNCHER_BLOCK_SIZE(uint16_t);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V1_LAUNCHER_BLOCK_SIZE(__nv_bfloat16);
const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype) {
if (kv_cache_dtype == "auto") {
if (query.dtype() == at::ScalarType::Float) {
CALL_V1_LAUNCHER_BLOCK_SIZE(float, float, false);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V1_LAUNCHER_BLOCK_SIZE(uint16_t, uint16_t, false);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V1_LAUNCHER_BLOCK_SIZE(__nv_bfloat16, __nv_bfloat16, false);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else if (kv_cache_dtype == "fp8_e5m2") {
if (query.dtype() == at::ScalarType::Float) {
CALL_V1_LAUNCHER_BLOCK_SIZE(float, uint8_t, true);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V1_LAUNCHER_BLOCK_SIZE(uint16_t, uint8_t, true);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V1_LAUNCHER_BLOCK_SIZE(__nv_bfloat16, uint8_t, true);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
}
}
#define LAUNCH_PAGED_ATTENTION_V2(HEAD_SIZE) \
vllm::paged_attention_v2_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, PARTITION_SIZE> \
vllm::paged_attention_v2_kernel<T, CACHE_T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS, \
IS_FP8_E5M2_KV_CACHE, PARTITION_SIZE> \
<<<grid, block, shared_mem_size, stream>>>( \
exp_sums_ptr, \
max_logits_ptr, \
@ -730,7 +782,9 @@ void paged_attention_v1(
template<
typename T,
typename CACHE_T,
int BLOCK_SIZE,
bool IS_FP8_E5M2_KV_CACHE,
int NUM_THREADS = 128,
int PARTITION_SIZE = 512>
void paged_attention_v2_launcher(
@ -768,8 +822,8 @@ void paged_attention_v2_launcher(
float* max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
T* tmp_out_ptr = reinterpret_cast<T*>(tmp_out.data_ptr());
T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
CACHE_T* key_cache_ptr = reinterpret_cast<CACHE_T*>(key_cache.data_ptr());
CACHE_T* value_cache_ptr = reinterpret_cast<CACHE_T*>(value_cache.data_ptr());
int* block_tables_ptr = block_tables.data_ptr<int>();
int* context_lens_ptr = context_lens.data_ptr<int>();
@ -816,38 +870,38 @@ void paged_attention_v2_launcher(
}
}
#define CALL_V2_LAUNCHER(T, BLOCK_SIZE) \
paged_attention_v2_launcher<T, BLOCK_SIZE>( \
out, \
exp_sums, \
max_logits, \
tmp_out, \
query, \
key_cache, \
value_cache, \
num_kv_heads, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
#define CALL_V2_LAUNCHER(T, CACHE_T, BLOCK_SIZE, IS_FP8_E5M2_KV_CACHE) \
paged_attention_v2_launcher<T, CACHE_T, BLOCK_SIZE, IS_FP8_E5M2_KV_CACHE>( \
out, \
exp_sums, \
max_logits, \
tmp_out, \
query, \
key_cache, \
value_cache, \
num_kv_heads, \
scale, \
block_tables, \
context_lens, \
max_context_len, \
alibi_slopes);
// NOTE(woosuk): To reduce the compilation time, we omitted block sizes
// 1, 2, 4, 64, 128, 256.
#define CALL_V2_LAUNCHER_BLOCK_SIZE(T) \
switch (block_size) { \
case 8: \
CALL_V2_LAUNCHER(T, 8); \
break; \
case 16: \
CALL_V2_LAUNCHER(T, 16); \
break; \
case 32: \
CALL_V2_LAUNCHER(T, 32); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
break; \
#define CALL_V2_LAUNCHER_BLOCK_SIZE(T, CACHE_T, IS_FP8_E5M2_KV_CACHE) \
switch (block_size) { \
case 8: \
CALL_V2_LAUNCHER(T, CACHE_T, 8, IS_FP8_E5M2_KV_CACHE); \
break; \
case 16: \
CALL_V2_LAUNCHER(T, CACHE_T, 16, IS_FP8_E5M2_KV_CACHE); \
break; \
case 32: \
CALL_V2_LAUNCHER(T, CACHE_T, 32, IS_FP8_E5M2_KV_CACHE); \
break; \
default: \
TORCH_CHECK(false, "Unsupported block size: ", block_size); \
break; \
}
void paged_attention_v2(
@ -864,15 +918,30 @@ void paged_attention_v2(
torch::Tensor& context_lens, // [num_seqs]
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes) {
if (query.dtype() == at::ScalarType::Float) {
CALL_V2_LAUNCHER_BLOCK_SIZE(float);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V2_LAUNCHER_BLOCK_SIZE(uint16_t);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V2_LAUNCHER_BLOCK_SIZE(__nv_bfloat16);
const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype) {
if (kv_cache_dtype == "auto") {
if (query.dtype() == at::ScalarType::Float) {
CALL_V2_LAUNCHER_BLOCK_SIZE(float, float, false);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V2_LAUNCHER_BLOCK_SIZE(uint16_t, uint16_t, false);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V2_LAUNCHER_BLOCK_SIZE(__nv_bfloat16, __nv_bfloat16, false);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else if (kv_cache_dtype == "fp8_e5m2") {
if (query.dtype() == at::ScalarType::Float) {
CALL_V2_LAUNCHER_BLOCK_SIZE(float, uint8_t, true);
} else if (query.dtype() == at::ScalarType::Half) {
CALL_V2_LAUNCHER_BLOCK_SIZE(uint16_t, uint8_t, true);
} else if (query.dtype() == at::ScalarType::BFloat16) {
CALL_V2_LAUNCHER_BLOCK_SIZE(__nv_bfloat16, uint8_t, true);
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
}
} else {
TORCH_CHECK(false, "Unsupported data type: ", query.dtype());
TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
}
}

35
csrc/attention/dtype_fp8_e5m2.cuh Normal file
View File

@ -0,0 +1,35 @@
#pragma once
#include "attention_generic.cuh"
#include <stdint.h>
#ifdef ENABLE_FP8_E5M2
#include <cuda_fp8.h>
#endif
namespace vllm {
#ifdef ENABLE_FP8_E5M2
// fp8 vector types for quantization of kv cache
template<>
struct Vec<uint8_t, 1> {
using Type = uint8_t;
};
template<>
struct Vec<uint8_t, 2> {
using Type = uint16_t;
};
template<>
struct Vec<uint8_t, 4> {
using Type = uint32_t;
};
template<>
struct Vec<uint8_t, 8> {
using Type = uint2;
};
#endif // ENABLE_FP8_E5M2
} // namespace vllm

8
csrc/cache.h
View File

@ -20,7 +20,8 @@ void reshape_and_cache(
torch::Tensor& value,
torch::Tensor& key_cache,
torch::Tensor& value_cache,
torch::Tensor& slot_mapping);
torch::Tensor& slot_mapping,
const std::string& kv_cache_dtype);
void gather_cached_kv(
torch::Tensor& key,
@ -28,3 +29,8 @@ void gather_cached_kv(
torch::Tensor& key_cache,
torch::Tensor& value_cache,
torch::Tensor& slot_mapping);
// Just for unittest
void convert_fp8_e5m2(
torch::Tensor& src_cache,
torch::Tensor& dst_cache);

135
csrc/cache_kernels.cu
View File

@ -4,6 +4,7 @@
#include "cuda_compat.h"
#include "dispatch_utils.h"
#include "quantization/fp8_e5m2_kvcache/quant_utils.cuh"
#include <algorithm>
#include <cassert>
@ -131,7 +132,7 @@ void copy_blocks(
dim3 block(std::min(1024, numel_per_block));
const at::cuda::OptionalCUDAGuard device_guard(cache_device);
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
VLLM_DISPATCH_FLOATING_TYPES(
VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
key_cache_ptrs_tensor.data_ptr<int64_t>(),
@ -143,12 +144,12 @@ void copy_blocks(
namespace vllm {
template<typename scalar_t>
template<typename scalar_t, typename cache_t, bool is_fp8_e5m2_kv_cache>
__global__ void reshape_and_cache_kernel(
const scalar_t* __restrict__ key, // [num_tokens, num_heads, head_size]
const scalar_t* __restrict__ value, // [num_tokens, num_heads, head_size]
scalar_t* __restrict__ key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
scalar_t* __restrict__ value_cache, // [num_blocks, num_heads, head_size, block_size]
cache_t* __restrict__ key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
cache_t* __restrict__ value_cache, // [num_blocks, num_heads, head_size, block_size]
const int64_t* __restrict__ slot_mapping, // [num_tokens]
const int key_stride,
const int value_stride,
@ -185,19 +186,45 @@ __global__ void reshape_and_cache_kernel(
+ head_idx * head_size * block_size
+ head_offset * block_size
+ block_offset;
key_cache[tgt_key_idx] = key[src_key_idx];
value_cache[tgt_value_idx] = value[src_value_idx];
scalar_t tgt_key = key[src_key_idx];
scalar_t tgt_value = value[src_value_idx];
if constexpr (is_fp8_e5m2_kv_cache) {
#ifdef ENABLE_FP8_E5M2
key_cache[tgt_key_idx] = fp8_e5m2_unscaled::vec_conversion<uint8_t, scalar_t>(tgt_key);
value_cache[tgt_value_idx] = fp8_e5m2_unscaled::vec_conversion<uint8_t, scalar_t>(tgt_value);
#else
assert(false);
#endif
} else {
key_cache[tgt_key_idx] = tgt_key;
value_cache[tgt_value_idx] = tgt_value;
}
}
}
} // namespace vllm
#define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, IS_FP8_E5M2_KV_CACHE) \
vllm::reshape_and_cache_kernel<KV_T, CACHE_T, IS_FP8_E5M2_KV_CACHE><<<grid, block, 0, stream>>>( \
reinterpret_cast<KV_T*>(key.data_ptr()), \
reinterpret_cast<KV_T*>(value.data_ptr()), \
reinterpret_cast<CACHE_T*>(key_cache.data_ptr()), \
reinterpret_cast<CACHE_T*>(value_cache.data_ptr()), \
slot_mapping.data_ptr<int64_t>(), \
key_stride, \
value_stride, \
num_heads, \
head_size, \
block_size, \
x);
void reshape_and_cache(
torch::Tensor& key, // [num_tokens, num_heads, head_size]
torch::Tensor& value, // [num_tokens, num_heads, head_size]
torch::Tensor& key_cache, // [num_blocks, num_heads, head_size/x, block_size, x]
torch::Tensor& value_cache, // [num_blocks, num_heads, head_size, block_size]
torch::Tensor& slot_mapping) // [num_tokens]
torch::Tensor& slot_mapping, // [num_tokens]
const std::string& kv_cache_dtype)
{
int num_tokens = key.size(0);
int num_heads = key.size(1);
@ -212,23 +239,25 @@ void reshape_and_cache(
dim3 block(std::min(num_heads * head_size, 512));
const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
VLLM_DISPATCH_FLOATING_TYPES(
key.scalar_type(),
"reshape_and_cache_kernel",
[&] {
vllm::reshape_and_cache_kernel<scalar_t><<<grid, block, 0, stream>>>(
key.data_ptr<scalar_t>(),
value.data_ptr<scalar_t>(),
key_cache.data_ptr<scalar_t>(),
value_cache.data_ptr<scalar_t>(),
slot_mapping.data_ptr<int64_t>(),
key_stride,
value_stride,
num_heads,
head_size,
block_size,
x);
});
if (kv_cache_dtype == "auto") {
if (key.dtype() == at::ScalarType::Float) {
CALL_RESHAPE_AND_CACHE(float, float, false);
} else if (key.dtype() == at::ScalarType::Half) {
CALL_RESHAPE_AND_CACHE(uint16_t, uint16_t, false);
} else if (key.dtype() == at::ScalarType::BFloat16) {
CALL_RESHAPE_AND_CACHE(__nv_bfloat16, __nv_bfloat16, false);
}
} else if (kv_cache_dtype == "fp8_e5m2") {
if (key.dtype() == at::ScalarType::Float) {
CALL_RESHAPE_AND_CACHE(float, uint8_t, true);
} else if (key.dtype() == at::ScalarType::Half) {
CALL_RESHAPE_AND_CACHE(uint16_t, uint8_t, true);
} else if (key.dtype() == at::ScalarType::BFloat16) {
CALL_RESHAPE_AND_CACHE(__nv_bfloat16, uint8_t, true);
}
} else {
TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
}
}
namespace vllm {
@ -256,12 +285,12 @@ __global__ void gather_cached_kv_kernel(
for (int i = threadIdx.x; i < num_tokens; i += blockDim.x) {
const int tgt_key_idx = token_idx * key_stride + i;
const int tgt_value_idx = token_idx * value_stride + i;
const int head_idx = i / head_size;
const int head_offset = i % head_size;
const int x_idx = head_offset / x; // the offset of the [head_size/x] dimension
const int x_offset = head_offset % x;
const int src_key_idx = block_idx * num_heads * (head_size / x) * block_size * x
+ head_idx * (head_size / x) * block_size * x
+ x_idx * block_size * x
@ -373,7 +402,7 @@ void gather_cached_kv(
dim3 block(std::min(num_heads * head_size, 512));
const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
VLLM_DISPATCH_FLOATING_TYPES(
VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
key.scalar_type(),
"gather_cached_kv_kernel_optimized",
[&] {
@ -391,3 +420,55 @@ void gather_cached_kv(
x);
});
}
namespace vllm {
template<typename Tout, typename Tin>
__global__ void convert_fp8_e5m2_kernel(
const Tin* __restrict__ src_cache,
Tout* __restrict__ dst_cache,
const int64_t block_stride) {
const int64_t block_idx = blockIdx.x;
for (int i = threadIdx.x; i < block_stride; i += blockDim.x) {
int64_t idx = block_idx * block_stride + i;
#ifdef ENABLE_FP8_E5M2
dst_cache[idx] = fp8_e5m2_unscaled::vec_conversion<Tout, Tin>(src_cache[idx]);
#else
assert(false);
#endif
}
}
} // namespace vllm
#define CALL_CONVERT_FP8_E5M2(Tout, Tin) \
vllm::convert_fp8_e5m2_kernel<Tout, Tin><<<grid, block, 0, stream>>>( \
reinterpret_cast<Tin*>(src_cache.data_ptr()), \
reinterpret_cast<Tout*>(dst_cache.data_ptr()), \
block_stride);
void convert_fp8_e5m2(
torch::Tensor& src_cache,
torch::Tensor& dst_cache)
{
int64_t num_blocks = src_cache.size(0);
int64_t block_stride = src_cache.stride(0);
dim3 grid(num_blocks);
dim3 block(std::min(block_stride, int64_t(512)));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
if (src_cache.dtype() == at::ScalarType::Float) {
CALL_CONVERT_FP8_E5M2(uint8_t, float);
} else if (src_cache.dtype() == at::ScalarType::Half) {
CALL_CONVERT_FP8_E5M2(uint8_t, uint16_t);
} else if (src_cache.dtype() == at::ScalarType::BFloat16) {
CALL_CONVERT_FP8_E5M2(uint8_t, __nv_bfloat16);
} else if (dst_cache.dtype() == at::ScalarType::Float) {
CALL_CONVERT_FP8_E5M2(float, uint8_t);
} else if (dst_cache.dtype() == at::ScalarType::Half) {
CALL_CONVERT_FP8_E5M2(uint16_t, uint8_t);
} else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
CALL_CONVERT_FP8_E5M2(__nv_bfloat16, uint8_t);
}
}

10
csrc/dispatch_utils.h
View File

@ -14,3 +14,13 @@
#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
AT_DISPATCH_SWITCH( \
TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
#define VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(...) \
AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__) \
AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)
#define VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(TYPE, NAME, ...) \
AT_DISPATCH_SWITCH( \
TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(__VA_ARGS__))

6
csrc/ops.h
View File

@ -13,7 +13,8 @@ void paged_attention_v1(
torch::Tensor& context_lens,
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes);
const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype);
void paged_attention_v2(
torch::Tensor& out,
@ -29,7 +30,8 @@ void paged_attention_v2(
torch::Tensor& context_lens,
int block_size,
int max_context_len,
const c10::optional<torch::Tensor>& alibi_slopes);
const c10::optional<torch::Tensor>& alibi_slopes,
const std::string& kv_cache_dtype);
void rms_norm(
torch::Tensor& out,

4
csrc/pybind.cpp
View File

@ -75,6 +75,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
"gather_cached_kv",
&gather_cached_kv,
"Gather key and value from the cache into contiguous QKV tensors");
cache_ops.def(
"convert_fp8_e5m2",
&convert_fp8_e5m2,
"Convert the key and value cache to fp8_e5m2 data type");
// Cuda utils
pybind11::module cuda_utils = m.def_submodule("cuda_utils", "vLLM cuda utils");

278
csrc/quantization/fp8_e5m2_kvcache/quant_utils.cuh Normal file
View File

@ -0,0 +1,278 @@
#pragma once
#include <assert.h>
#include <stdint.h>
#include <float.h>
#include <type_traits>
#include "../../attention/attention_dtypes.h"
#include "../../attention/dtype_float32.cuh"
#include "../../attention/dtype_float16.cuh"
#include "../../attention/dtype_bfloat16.cuh"
#pragma once
namespace vllm {
#ifdef ENABLE_FP8_E5M2
namespace fp8_e5m2_unscaled {
template<typename Tout, typename Tin>
__inline__ __device__ Tout vec_conversion(const Tin& x)
{
return x;
}
// fp8 -> half
template<>
__inline__ __device__ uint16_t vec_conversion<uint16_t, uint8_t>(const uint8_t& a)
{
__half_raw res = __nv_cvt_fp8_to_halfraw(a, __NV_E5M2);
return res.x;
}
// fp8x2 -> half2
template<>
__inline__ __device__ uint32_t vec_conversion<uint32_t, uint16_t>(const uint16_t& a)
{
union {
uint16_t u16[2];
uint32_t u32;
} tmp;
__half2_raw res = __nv_cvt_fp8x2_to_halfraw2(a, __NV_E5M2);
tmp.u16[0] = res.x;
tmp.u16[1] = res.y;
return tmp.u32;
}
// fp8x4 -> half2x2
template<>
__inline__ __device__ uint2 vec_conversion<uint2, uint32_t>(const uint32_t& a)
{
union {
uint2 u32x2;
uint32_t u32[2];
} tmp;
tmp.u32[0] = vec_conversion<uint32_t, uint16_t>((uint16_t)a);
tmp.u32[1] = vec_conversion<uint32_t, uint16_t>((uint16_t)(a >> 16U));
return tmp.u32x2;
}
// fp8x8 -> half2x4
template<>
__inline__ __device__ uint4 vec_conversion<uint4, uint2>(const uint2& a)
{
union {
uint4 u64x2;
uint2 u64[2];
} tmp;
tmp.u64[0] = vec_conversion<uint2, uint32_t>(a.x);
tmp.u64[1] = vec_conversion<uint2, uint32_t>(a.y);
return tmp.u64x2;
}
// fp8 -> __nv_bfloat16
template<>
__inline__ __device__ __nv_bfloat16 vec_conversion<__nv_bfloat16, uint8_t>(const uint8_t& a)
{
// Note there is no direct convert function from fp8 to bf16.
// fp8 -> half
__half_raw res = __nv_cvt_fp8_to_halfraw(a, __NV_E5M2);
// half -> float -> bf16
float tmp = half_to_float(res.x);
return __float2bfloat16(tmp);
}
// fp8x2 -> __nv_bfloat162
template<>
__inline__ __device__ __nv_bfloat162 vec_conversion<__nv_bfloat162, uint16_t>(const uint16_t& a)
{
__nv_bfloat162 res;
res.x = vec_conversion<__nv_bfloat16, uint8_t>((uint8_t)a);
res.y = vec_conversion<__nv_bfloat16, uint8_t>((uint8_t)(a >> 8U));
return res;
}
// fp8x4 -> bf16_4_t
template<>
__inline__ __device__ bf16_4_t vec_conversion<bf16_4_t, uint32_t>(const uint32_t& a)
{
bf16_4_t res;
res.x = vec_conversion<__nv_bfloat162, uint16_t>((uint16_t)a);
res.y = vec_conversion<__nv_bfloat162, uint16_t>((uint16_t)(a >> 16U));
return res;
}
// fp8x8 -> bf16_8_t
template<>
__inline__ __device__ bf16_8_t vec_conversion<bf16_8_t, uint2>(const uint2& a)
{
bf16_4_t tmp1, tmp2;
tmp1 = vec_conversion<bf16_4_t, uint32_t>(a.x);
tmp2 = vec_conversion<bf16_4_t, uint32_t>(a.y);
bf16_8_t res;
res.x = tmp1.x;
res.y = tmp1.y;
res.z = tmp2.x;
res.w = tmp2.y;
return res;
}
// fp8 -> float
template<>
__inline__ __device__ float vec_conversion<float, uint8_t>(const uint8_t& a)
{
// fp8 -> half
uint16_t tmp = vec_conversion<uint16_t, uint8_t>(a);
// half -> float
return half_to_float(tmp);
}
// fp8x2 -> float2
template<>
__inline__ __device__ float2 vec_conversion<float2, uint16_t>(const uint16_t& a)
{
// fp8x2 -> half2
uint32_t tmp = vec_conversion<uint32_t, uint16_t>(a);
// half2 -> float2
return half2_to_float2(tmp);
}
// fp8x4 -> float4
template<>
__inline__ __device__ Float4_ vec_conversion<Float4_, uint32_t>(const uint32_t& a)
{
Float4_ res;
res.x = vec_conversion<float2, uint16_t>((uint16_t)a);
res.y = vec_conversion<float2, uint16_t>((uint16_t)(a >> 16U));
return res;
}
// fp8x8 -> float8
template<>
__inline__ __device__ Float8_ vec_conversion<Float8_, uint2>(const uint2& a)
{
Float4_ tmp1, tmp2;
tmp1 = vec_conversion<Float4_, uint32_t>(a.x);
tmp2 = vec_conversion<Float4_, uint32_t>(a.y);
Float8_ res;
res.x = tmp1.x;
res.y = tmp1.y;
res.z = tmp2.x;
res.w = tmp2.y;
return res;
}
// half -> fp8
template<>
__inline__ __device__ uint8_t vec_conversion<uint8_t, uint16_t>(const uint16_t& a)
{
__half_raw tmp;
tmp.x = a;
__nv_fp8_storage_t res = __nv_cvt_halfraw_to_fp8(tmp, __NV_SATFINITE, __NV_E5M2);
return (uint8_t)res;
}
// bf16 -> fp8
template<>
__inline__ __device__ uint8_t vec_conversion<uint8_t, __nv_bfloat16>(const __nv_bfloat16& a)
{
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
assert(false);
#else
__nv_fp8_storage_t res = __nv_cvt_bfloat16raw_to_fp8(__nv_bfloat16_raw(a), __NV_SATFINITE, __NV_E5M2);
return (uint8_t)res;
#endif
}
// float -> fp8
template<>
__inline__ __device__ uint8_t vec_conversion<uint8_t, float>(const float& a)
{
__nv_fp8_storage_t res = __nv_cvt_float_to_fp8(a, __NV_SATFINITE, __NV_E5M2);
return (uint8_t)res;
}
// fp8x4 -> float4
template<>
__inline__ __device__ float4 vec_conversion<float4, uint32_t>(const uint32_t& a)
{
Float4_ tmp = vec_conversion<Float4_, uint32_t>(a);
float4 res = make_float4(tmp.x.x, tmp.x.y, tmp.y.x, tmp.y.y);
return res;
}
template<>
__inline__ __device__ uint32_t vec_conversion<uint32_t, float2>(const float2& a)
{
union {
half2 float16;
uint32_t uint32;
};
float16 = __float22half2_rn(a);
return uint32;
}
template<>
__inline__ __device__ uint2 vec_conversion<uint2, Float4_>(const Float4_& a)
{
uint2 b;
float2 val;
val.x = a.x.x;
val.y = a.x.y;
b.x = vec_conversion<uint32_t, float2>(val);
val.x = a.y.x;
val.y = a.y.y;
b.y = vec_conversion<uint32_t, float2>(val);
return b;
}
template<>
__inline__ __device__ float4 vec_conversion<float4, Float4_>(const Float4_& a)
{
float4 b;
b.x = a.x.x;
b.y = a.x.y;
b.z = a.y.x;
b.w = a.y.y;
return b;
}
template<>
__inline__ __device__ uint4 vec_conversion<uint4, Float8_>(const Float8_& a)
{
uint4 b;
b.x = vec_conversion<uint32_t, float2>(a.x);
b.y = vec_conversion<uint32_t, float2>(a.y);
b.z = vec_conversion<uint32_t, float2>(a.z);
b.w = vec_conversion<uint32_t, float2>(a.w);
return b;
}
template<>
__inline__ __device__ __nv_bfloat162 vec_conversion<__nv_bfloat162, float2>(const float2 &a) {
__nv_bfloat162 b;
from_float(b, a);
return b;
}
template<>
__inline__ __device__ bf16_4_t vec_conversion<bf16_4_t, Float4_>(const Float4_ &a) {
bf16_4_t b;
from_float(b, a);
return b;
}
template<>
__inline__ __device__ bf16_8_t vec_conversion<bf16_8_t, Float8_>(const Float8_ &a) {
bf16_8_t b;
from_float(b, a);
return b;
}
} // namespace fp8_e5m2_unscaled
#endif // ENABLE_FP8_E5M2
} // namespace vllm

32
docs/source/quantization/fp8_e5m2_kv_cache.rst Normal file
View File

@ -0,0 +1,32 @@
.. _fp8_e5m2_kv_cache:
FP8 E5M2 KV Cache
==================
The int8/int4 quantization scheme requires additional scale GPU memory storage, which reduces the expected GPU memory benefits.
The FP8 data format retains 2~3 mantissa bits and can convert float/fp16/bflaot16 and fp8 to each other.
Here is an example of how to enable this feature:
.. code-block:: python
from vllm import LLM, SamplingParams
# Sample prompts.
prompts = [
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params object.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Create an LLM.
llm = LLM(model="facebook/opt-125m", kv_cache_dtype="fp8_e5m2")
# Generate texts from the prompts. The output is a list of RequestOutput objects
# that contain the prompt, generated text, and other information.
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

3
setup.py
View File

@ -253,6 +253,9 @@ if _is_cuda():
num_threads = min(os.cpu_count(), nvcc_threads)
NVCC_FLAGS += ["--threads", str(num_threads)]
if nvcc_cuda_version >= Version("11.8"):
NVCC_FLAGS += ["-DENABLE_FP8_E5M2"]
# changes for punica kernels
NVCC_FLAGS += torch_cpp_ext.COMMON_NVCC_FLAGS
REMOVE_NVCC_FLAGS = [

41
tests/kernels/conftest.py
View File

@ -1,44 +1,7 @@
from typing import List, Tuple
import pytest
import torch
def create_kv_caches(
num_blocks: int,
block_size: int,
num_layers: int,
num_heads: int,
head_size: int,
dtype: torch.dtype,
seed: int,
device: str,
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
scale = head_size**-0.5
x = 16 // torch.tensor([], dtype=dtype).element_size()
key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
key_caches = []
for _ in range(num_layers):
key_cache = torch.empty(size=key_cache_shape,
dtype=dtype,
device=device)
key_cache.uniform_(-scale, scale)
key_caches.append(key_cache)
value_cache_shape = (num_blocks, num_heads, head_size, block_size)
value_caches = []
for _ in range(num_layers):
value_cache = torch.empty(size=value_cache_shape,
dtype=dtype,
device=device)
value_cache.uniform_(-scale, scale)
value_caches.append(value_cache)
return key_caches, value_caches
from vllm.utils import create_kv_caches_with_random
@pytest.fixture()
def kv_cache_factory():
return create_kv_caches
return create_kv_caches_with_random

41
tests/kernels/test_attention.py
View File

@ -6,14 +6,16 @@ import torch
from xformers import ops as xops
from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
from vllm._C import ops
from vllm._C import ops, cache_ops
from vllm.utils import get_max_shared_memory_bytes
FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
# This will change depending on the compute capability.
# - 512 as a buffer
MAX_SEQ_LEN = get_max_shared_memory_bytes() // FLOAT32_BYTES - 512
NUM_BLOCKS = 12000 # Arbitrary values for testing
# There may not be enough gpu memory due to large NUM_BLOCKS.
# Reduce NUM_BLOCKS when it happens.
NUM_BLOCKS = 4321 # Arbitrary values for testing
PARTITION_SIZE = 512
DTYPES = [torch.half, torch.bfloat16, torch.float]
@ -23,6 +25,7 @@ NUM_HEADS = [(40, 40), (64, 8)] # Arbitrary values for testing
HEAD_SIZES = [64, 80, 96, 112, 128, 256]
BLOCK_SIZES = [16, 32]
USE_ALIBI = [False, True]
KV_CACHE_DTYPE = ["auto", "fp8_e5m2"]
SEEDS = [0]
DEVICES = [i for i in range(1 if torch.cuda.device_count() == 1 else 2)]
@ -105,6 +108,7 @@ def ref_single_query_cached_kv_attention(
@pytest.mark.parametrize("use_alibi", USE_ALIBI)
@pytest.mark.parametrize("block_size", BLOCK_SIZES)
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", DEVICES)
def test_paged_attention(
@ -116,6 +120,7 @@ def test_paged_attention(
use_alibi: bool,
block_size: int,
dtype: torch.dtype,
kv_cache_dtype: str,
seed: int,
device: int,
) -> None:
@ -158,8 +163,9 @@ def test_paged_attention(
# Create the KV caches.
key_caches, value_caches = kv_cache_factory(NUM_BLOCKS, block_size, 1,
num_kv_heads, head_size, dtype,
seed, gpu_id)
num_kv_heads, head_size,
kv_cache_dtype, dtype, seed,
gpu_id)
key_cache, value_cache = key_caches[0], value_caches[0]
# Call the paged attention kernel.
@ -177,6 +183,7 @@ def test_paged_attention(
block_size,
max_context_len,
alibi_slopes,
kv_cache_dtype,
)
elif version == "v2":
num_partitions = ((max_context_len + PARTITION_SIZE - 1) //
@ -209,11 +216,30 @@ def test_paged_attention(
block_size,
max_context_len,
alibi_slopes,
kv_cache_dtype,
)
else:
raise AssertionError(f"Unknown version: {version}")
# Run the reference implementation.
if kv_cache_dtype == "fp8_e5m2":
# Convert cache data back to dtype.
x = 16 // torch.tensor([], dtype=dtype).element_size()
key_cache_shape = (NUM_BLOCKS, num_kv_heads, head_size // x,
block_size, x)
dequantized_key_cache = torch.empty(size=key_cache_shape,
dtype=dtype,
device=gpu_id)
cache_ops.convert_fp8_e5m2(key_cache, dequantized_key_cache)
key_cache = dequantized_key_cache
value_cache_shape = value_cache.shape
dequantized_value_cache = torch.empty(size=value_cache_shape,
dtype=dtype,
device=gpu_id)
cache_ops.convert_fp8_e5m2(value_cache, dequantized_value_cache)
value_cache = dequantized_value_cache
ref_output = torch.empty_like(query)
ref_single_query_cached_kv_attention(
ref_output,
@ -230,7 +256,12 @@ def test_paged_attention(
# NOTE(woosuk): Due to the kernel-level differences in the two
# implementations, there is a small numerical difference in the two
# outputs. Thus, we use a relaxed tolerance for the test.
assert torch.allclose(output, ref_output, atol=1e-3, rtol=1e-5)
# NOTE(zhaoyang): FP8 KV Cache will introduce quantization error,
# so we use a relaxed tolerance for the test.
atol, rtol = 1e-3, 1e-5
if kv_cache_dtype == "fp8_e5m2":
atol, rtol = 1e-2, 1e-5
assert torch.allclose(output, ref_output, atol=atol, rtol=rtol)
def ref_multi_query_kv_attention(

10
tests/kernels/test_cache.py
View File

@ -15,6 +15,7 @@ NUM_BLOCKS = [1024, 3600] # Arbitrary values for testing
NUM_MAPPINGS = [256] # Arbitrary values for testing
SEEDS = [0]
DEVICES = [i for i in range(1 if torch.cuda.device_count() == 1 else 2)]
KV_CACHE_DTYPE = ["auto", "fp8_e5m2"]
@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
@ -26,6 +27,7 @@ DEVICES = [i for i in range(1 if torch.cuda.device_count() == 1 else 2)]
@pytest.mark.parametrize("dtype", DTYPES)
@pytest.mark.parametrize("seed", SEEDS)
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("kv_cache_dtype", KV_CACHE_DTYPE)
@torch.inference_mode()
def test_copy_blocks(
kv_cache_factory,
@ -38,6 +40,7 @@ def test_copy_blocks(
dtype: torch.dtype,
seed: int,
device: int,
kv_cache_dtype: str,
) -> None:
random.seed(seed)
torch.random.manual_seed(seed)
@ -59,7 +62,8 @@ def test_copy_blocks(
# Create the KV caches.
key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
num_layers, num_heads,
head_size, dtype, seed, gpu_id)
head_size, kv_cache_dtype,
dtype, seed, gpu_id)
# Clone the KV caches.
cloned_key_caches = [key_cache.clone() for key_cache in key_caches]
@ -124,7 +128,7 @@ def test_reshape_and_cache(
# Create the KV caches.
key_caches, value_caches = kv_cache_factory(num_blocks, block_size, 1,
num_heads, head_size, dtype,
seed, gpu_id)
None, seed, gpu_id)
key_cache, value_cache = key_caches[0], value_caches[0]
# Clone the KV caches.
@ -133,7 +137,7 @@ def test_reshape_and_cache(
# Call the reshape_and_cache kernel.
cache_ops.reshape_and_cache(key, value, key_cache, value_cache,
slot_mapping)
slot_mapping, "auto")
# Run the reference implementation.
reshaped_key = key.reshape(num_tokens, *key_cache[0, :, :, 0, :].shape)

29
vllm/config.py
View File

@ -1,13 +1,14 @@
from typing import Optional, Union, ClassVar
from dataclasses import dataclass
import os
from packaging.version import Version
import torch
from transformers import PretrainedConfig
from vllm.logger import init_logger
from vllm.transformers_utils.config import get_config
from vllm.utils import get_cpu_memory, is_hip
from vllm.utils import get_cpu_memory, is_hip, get_nvcc_cuda_version
logger = init_logger(__name__)
@ -275,6 +276,7 @@ class CacheConfig:
gpu_memory_utilization: Fraction of GPU memory to use for the
vLLM execution.
swap_space: Size of the CPU swap space per GPU (in GiB).
cache_dtype: Data type for kv cache storage.
"""
def __init__(
@ -282,13 +284,16 @@ class CacheConfig:
block_size: int,
gpu_memory_utilization: float,
swap_space: int,
cache_dtype: str,
sliding_window: Optional[int] = None,
) -> None:
self.block_size = block_size
self.gpu_memory_utilization = gpu_memory_utilization
self.swap_space_bytes = swap_space * _GB
self.cache_dtype = cache_dtype
self.sliding_window = sliding_window
self._verify_args()
self._verify_cache_dtype()
# Will be set after profiling.
self.num_gpu_blocks = None
@ -300,6 +305,28 @@ class CacheConfig:
"GPU memory utilization must be less than 1.0. Got "
f"{self.gpu_memory_utilization}.")
def _verify_cache_dtype(self) -> None:
if self.cache_dtype == "auto":
pass
elif self.cache_dtype == "fp8_e5m2":
nvcc_cuda_version = get_nvcc_cuda_version()
if nvcc_cuda_version < Version("11.8"):
raise ValueError(
"FP8 is not supported when cuda version is lower than 11.8."
)
device_name = torch.cuda.get_device_name()
if "AMD" in device_name:
raise NotImplementedError(
"FP8_E5M2 KV Cache on AMD GPU has not been supported yet.")
logger.info(
"Using fp8_e5m2 data type to store kv cache. It reduces "
"the GPU memory footprint and boosts the performance. "
"But it may cause slight accuracy drop. "
"Currently we only support fp8 without scaling factors and "
"make e5m2 as a default format.")
else:
raise ValueError(f"Unknown kv cache dtype: {self.cache_dtype}")
def verify_with_parallel_config(
self,
parallel_config: "ParallelConfig",

11
vllm/engine/arg_utils.py
View File

@ -17,6 +17,7 @@ class EngineArgs:
download_dir: Optional[str] = None
load_format: str = 'auto'
dtype: str = 'auto'
kv_cache_dtype: str = 'auto'
seed: int = 0
max_model_len: Optional[int] = None
worker_use_ray: bool = False
@ -122,6 +123,14 @@ class EngineArgs:
'The "auto" option will use FP16 precision '
'for FP32 and FP16 models, and BF16 precision '
'for BF16 models.')
parser.add_argument(
'--kv-cache-dtype',
type=str,
choices=['auto', 'fp8_e5m2'],
default='auto',
help='Data type for kv cache storage. If "auto", will use model '
'data type. Note FP8 is not supported when cuda version is '
'lower than 11.8.')
parser.add_argument('--max-model-len',
type=int,
default=None,
@ -269,7 +278,7 @@ class EngineArgs:
self.max_context_len_to_capture)
cache_config = CacheConfig(self.block_size,
self.gpu_memory_utilization,
self.swap_space,
self.swap_space, self.kv_cache_dtype,
model_config.get_sliding_window())
parallel_config = ParallelConfig(self.pipeline_parallel_size,
self.tensor_parallel_size,

6
vllm/engine/llm_engine.py
View File

@ -85,6 +85,7 @@ class LLMEngine:
f"disable_custom_all_reduce={parallel_config.disable_custom_all_reduce}, "
f"quantization={model_config.quantization}, "
f"enforce_eager={model_config.enforce_eager}, "
f"kv_cache_dtype={cache_config.cache_dtype}, "
f"seed={model_config.seed})")
# TODO(woosuk): Print more configs in debug mode.
@ -144,6 +145,7 @@ class LLMEngine:
rank=0,
distributed_init_method=distributed_init_method,
lora_config=self.lora_config,
kv_cache_dtype=self.cache_config.cache_dtype,
is_driver_worker=True,
)
self._run_workers("init_model")
@ -234,6 +236,7 @@ class LLMEngine:
model_config = copy.deepcopy(self.model_config)
parallel_config = copy.deepcopy(self.parallel_config)
scheduler_config = copy.deepcopy(self.scheduler_config)
cache_config = copy.deepcopy(self.cache_config)
for rank, (worker, (node_id,
_)) in enumerate(zip(self.workers,
@ -249,6 +252,7 @@ class LLMEngine:
rank,
distributed_init_method,
lora_config=self.lora_config,
cache_config=cache_config,
))
driver_rank = 0
@ -261,6 +265,7 @@ class LLMEngine:
driver_rank,
distributed_init_method,
lora_config=self.lora_config,
cache_config=cache_config,
is_driver_worker=True,
)
@ -306,6 +311,7 @@ class LLMEngine:
block_size=self.cache_config.block_size,
gpu_memory_utilization=self.cache_config.gpu_memory_utilization,
cpu_swap_space=self.cache_config.swap_space_bytes,
cache_dtype=self.cache_config.cache_dtype,
)
# Since we use a shared centralized controller, we take the minimum

6
vllm/model_executor/input_metadata.py
View File

@ -12,6 +12,7 @@ class InputMetadata:
max_context_len: The maximum context length.
context_lens: the length of attention context for each sequence.
block_tables: The block tables. (Seq id -> list of physical block)
kv_cache_dtype: Data type to store kv cache.
"""
def __init__(
@ -25,6 +26,7 @@ class InputMetadata:
context_lens: Optional[torch.Tensor],
block_tables: Optional[torch.Tensor],
use_cuda_graph: bool,
kv_cache_dtype: str,
) -> None:
self.is_prompt = is_prompt
self.prompt_lens = prompt_lens
@ -35,6 +37,7 @@ class InputMetadata:
self.context_lens = context_lens
self.block_tables = block_tables
self.use_cuda_graph = use_cuda_graph
self.kv_cache_dtype = kv_cache_dtype
# Set during the execution of the first attention op.
# FIXME(woosuk): This is a hack.
@ -47,4 +50,5 @@ class InputMetadata:
f"slot_mapping={self.slot_mapping}, "
f"context_lens={self.context_lens}, "
f"block_tables={self.block_tables}, "
f"use_cuda_graph={self.use_cuda_graph})")
f"use_cuda_graph={self.use_cuda_graph}, "
f"kv_cache_dtype={self.kv_cache_dtype})")

3
vllm/model_executor/layers/attention.py
View File

@ -98,6 +98,7 @@ class PagedAttention(nn.Module):
key_cache,
value_cache,
input_metadata.slot_mapping.flatten(),
input_metadata.kv_cache_dtype,
)
if input_metadata.is_prompt:
@ -265,6 +266,7 @@ def _paged_attention(
block_size,
input_metadata.max_context_len,
alibi_slopes,
input_metadata.kv_cache_dtype,
)
else:
# Run PagedAttention V2.
@ -295,5 +297,6 @@ def _paged_attention(
block_size,
input_metadata.max_context_len,
alibi_slopes,
input_metadata.kv_cache_dtype,
)
return output

110
vllm/utils.py
View File

@ -1,9 +1,11 @@
import enum
import os
import socket
import subprocess
import uuid
from platform import uname
from typing import List
from typing import List, Tuple, Union
from packaging.version import parse, Version
import psutil
import torch
@ -17,7 +19,17 @@ from typing import (
from collections import OrderedDict
from typing import Any, Hashable, Optional
from vllm.logger import init_logger
T = TypeVar("T")
logger = init_logger(__name__)
STR_DTYPE_TO_TORCH_DTYPE = {
"half": torch.half,
"bfloat16": torch.bfloat16,
"float": torch.float,
"fp8_e5m2": torch.uint8,
}
class Device(enum.Enum):
@ -167,3 +179,99 @@ def get_open_port() -> int:
def set_cuda_visible_devices(device_ids: List[int]) -> None:
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(map(str, device_ids))
def get_nvcc_cuda_version() -> Version:
cuda_home = os.environ.get('CUDA_HOME')
if not cuda_home:
cuda_home = '/usr/local/cuda'
logger.info(
f'CUDA_HOME is not found in the environment. Using {cuda_home} as CUDA_HOME.'
)
nvcc_output = subprocess.check_output([cuda_home + "/bin/nvcc", "-V"],
universal_newlines=True)
output = nvcc_output.split()
release_idx = output.index("release") + 1
nvcc_cuda_version = parse(output[release_idx].split(",")[0])
return nvcc_cuda_version
def _generate_random_fp8_e5m2(
tensor: torch.tensor,
low: float,
high: float,
) -> None:
# NOTE(zhaoyang): Due to NaN and Inf representation for fp8 data type,
# it may occur Inf or NaN if we directly use torch.randint
# to generate random data for fp8 data.
# For example, s.11111.00 in fp8e5m2 format repesents Inf.
# | E4M3 | E5M2
#-----|-------------|-------------------
# Inf | N/A | s.11111.00
# NaN | s.1111.111 | s.11111.{01,10,11}
from vllm._C import cache_ops
tensor_tmp = torch.empty_like(tensor, dtype=torch.float16)
tensor_tmp.uniform_(low, high)
cache_ops.convert_fp8_e5m2(tensor_tmp, tensor)
del tensor_tmp
def create_kv_caches_with_random(
num_blocks: int,
block_size: int,
num_layers: int,
num_heads: int,
head_size: int,
cache_dtype: Optional[Union[str, torch.dtype]],
model_dtype: Optional[Union[str, torch.dtype]] = None,
seed: Optional[int] = 0,
device: Optional[str] = "cuda",
) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
torch.random.manual_seed(seed)
torch.cuda.manual_seed(seed)
if isinstance(cache_dtype, str):
if cache_dtype == "auto":
if isinstance(model_dtype, str):
torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[model_dtype]
elif isinstance(model_dtype, torch.dtype):
torch_dtype = model_dtype
else:
raise ValueError(f"Invalid model dtype: {model_dtype}")
elif cache_dtype in ["half", "bfloat16", "float"]:
torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype]
elif cache_dtype == "fp8_e5m2":
torch_dtype = torch.uint8
else:
raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
elif isinstance(cache_dtype, torch.dtype):
torch_dtype = cache_dtype
else:
raise ValueError(f"Invalid kv cache dtype: {cache_dtype}")
scale = head_size**-0.5
x = 16 // torch.tensor([], dtype=torch_dtype).element_size()
key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
key_caches = []
for _ in range(num_layers):
key_cache = torch.empty(size=key_cache_shape,
dtype=torch_dtype,
device=device)
if cache_dtype in ["auto", "half", "bfloat16", "float"]:
key_cache.uniform_(-scale, scale)
elif cache_dtype == 'fp8_e5m2':
_generate_random_fp8_e5m2(key_cache, -scale, scale)
key_caches.append(key_cache)
value_cache_shape = (num_blocks, num_heads, head_size, block_size)
value_caches = []
for _ in range(num_layers):
value_cache = torch.empty(size=value_cache_shape,
dtype=torch_dtype,
device=device)
if cache_dtype in ["auto", "half", "bfloat16", "float"]:
value_cache.uniform_(-scale, scale)
elif cache_dtype == 'fp8_e5m2':
_generate_random_fp8_e5m2(value_cache, -scale, scale)
value_caches.append(value_cache)
return key_caches, value_caches

15
vllm/worker/cache_engine.py
View File

@ -6,7 +6,7 @@ import torch
from vllm._C import cache_ops
from vllm.config import CacheConfig, ModelConfig, ParallelConfig
from vllm.logger import init_logger
from vllm.utils import in_wsl
from vllm.utils import in_wsl, STR_DTYPE_TO_TORCH_DTYPE
logger = init_logger(__name__)
@ -34,12 +34,16 @@ class CacheEngine:
self.head_size = model_config.get_head_size()
self.num_layers = model_config.get_num_layers(parallel_config)
self.num_heads = model_config.get_num_kv_heads(parallel_config)
self.dtype = model_config.dtype
self.block_size = cache_config.block_size
self.num_gpu_blocks = cache_config.num_gpu_blocks
self.num_cpu_blocks = cache_config.num_cpu_blocks
if cache_config.cache_dtype == "auto":
self.dtype = model_config.dtype
else:
self.dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_config.cache_dtype]
# Initialize the cache.
self.gpu_cache = self.allocate_gpu_cache()
self.cpu_cache = self.allocate_cpu_cache()
@ -142,6 +146,7 @@ class CacheEngine:
@staticmethod
def get_cache_block_size(
block_size: int,
cache_dtype: str,
model_config: ModelConfig,
parallel_config: ParallelConfig,
) -> int:
@ -152,7 +157,11 @@ class CacheEngine:
key_cache_block = block_size * num_heads * head_size
value_cache_block = key_cache_block
total = num_layers * (key_cache_block + value_cache_block)
dtype_size = _get_dtype_size(model_config.dtype)
if cache_dtype == "auto":
dtype = model_config.dtype
else:
dtype = STR_DTYPE_TO_TORCH_DTYPE[cache_dtype]
dtype_size = _get_dtype_size(dtype)
return dtype_size * total

7
vllm/worker/model_runner.py
View File

@ -36,6 +36,7 @@ class ModelRunner:
parallel_config: ParallelConfig,
scheduler_config: SchedulerConfig,
lora_config: Optional[LoRAConfig],
kv_cache_dtype: Optional[str] = "auto",
is_driver_worker: bool = False,
):
self.model_config = model_config
@ -68,6 +69,7 @@ class ModelRunner:
self.graph_block_tables = None # Set after initial profiling.
# cache in_wsl result
self.in_wsl = in_wsl()
self.kv_cache_dtype = kv_cache_dtype
def load_model(self) -> None:
self.model = get_model(self.model_config, self.lora_config)
@ -223,6 +225,7 @@ class ModelRunner:
context_lens=context_lens_tensor,
block_tables=block_tables,
use_cuda_graph=False,
kv_cache_dtype=self.kv_cache_dtype,
)
return (input_tokens, input_positions, input_metadata, prompt_lens,
subquery_lens, lora_index_mapping, lora_prompt_mapping,
@ -350,6 +353,7 @@ class ModelRunner:
context_lens=context_lens,
block_tables=block_tables,
use_cuda_graph=use_captured_graph,
kv_cache_dtype=self.kv_cache_dtype,
)
return input_tokens, input_positions, input_metadata, lora_index_mapping, lora_prompt_mapping, lora_requests
@ -473,6 +477,7 @@ class ModelRunner:
"context_lens": input_metadata.context_lens,
"block_tables": input_metadata.block_tables,
"use_cuda_graph": input_metadata.use_cuda_graph,
"kv_cache_dtype": input_metadata.kv_cache_dtype,
"selected_token_indices":
sampling_metadata.selected_token_indices,
"lora_requests": lora_requests,
@ -495,6 +500,7 @@ class ModelRunner:
context_lens=metadata_dict["context_lens"],
block_tables=metadata_dict["block_tables"],
use_cuda_graph=metadata_dict["use_cuda_graph"],
kv_cache_dtype=metadata_dict["kv_cache_dtype"],
)
sampling_metadata = SamplingMetadata(
seq_groups=None,
@ -665,6 +671,7 @@ class ModelRunner:
context_lens=context_lens[:batch_size],
block_tables=block_tables[:batch_size],
use_cuda_graph=True,
kv_cache_dtype=self.kv_cache_dtype,
)
if self.lora_config:

5
vllm/worker/worker.py
View File

@ -37,6 +37,7 @@ class Worker:
rank: int,
distributed_init_method: str,
lora_config: Optional[LoRAConfig] = None,
kv_cache_dtype: Optional[str] = "auto",
is_driver_worker: bool = False,
) -> None:
self.model_config = model_config
@ -54,6 +55,7 @@ class Worker:
parallel_config,
scheduler_config,
lora_config=self.lora_config,
kv_cache_dtype=kv_cache_dtype,
is_driver_worker=is_driver_worker)
# Uninitialized cache engine. Will be initialized by
# self.init_cache_engine().
@ -95,6 +97,7 @@ class Worker:
block_size: int,
gpu_memory_utilization: float,
cpu_swap_space: int,
cache_dtype: str,
) -> Tuple[int, int]:
"""Profiles the peak memory usage of the model and returns the maximum
number of GPU and CPU cache blocks that can be allocated.
@ -119,7 +122,7 @@ class Worker:
peak_memory = total_gpu_memory - free_gpu_memory
cache_block_size = CacheEngine.get_cache_block_size(
block_size, self.model_config, self.parallel_config)
block_size, cache_dtype, self.model_config, self.parallel_config)
num_gpu_blocks = int(
(total_gpu_memory * gpu_memory_utilization - peak_memory) //
cache_block_size)