349 lines
16 KiB
C++
349 lines
16 KiB
C++
/* Copyright 2025 SGLang Team. All Rights Reserved.
|
|
|
|
Licensed under the Apache License, Version 2.0 (the "License");
|
|
you may not use this file except in compliance with the License.
|
|
You may obtain a copy of the License at
|
|
|
|
http://www.apache.org/licenses/LICENSE-2.0
|
|
|
|
Unless required by applicable law or agreed to in writing, software
|
|
distributed under the License is distributed on an "AS IS" BASIS,
|
|
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
See the License for the specific language governing permissions and
|
|
limitations under the License.
|
|
==============================================================================*/
|
|
|
|
#pragma once
|
|
|
|
#include <ATen/Tensor.h>
|
|
#include <cuda_runtime.h>
|
|
#include <torch/all.h>
|
|
|
|
#include <sstream>
|
|
|
|
#ifndef USE_ROCM
|
|
// Adapt from FlashInfer
|
|
#ifdef FLASHINFER_ENABLE_F16
|
|
#define _DISPATCH_CASE_F16(c_type, ...) \
|
|
case at::ScalarType::Half: { \
|
|
using c_type = nv_half; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
#else
|
|
#define _DISPATCH_CASE_F16(c_type, ...)
|
|
#endif
|
|
|
|
#ifdef FLASHINFER_ENABLE_BF16
|
|
#define _DISPATCH_CASE_BF16(c_type, ...) \
|
|
case at::ScalarType::BFloat16: { \
|
|
using c_type = nv_bfloat16; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
#else
|
|
#define _DISPATCH_CASE_BF16(c_type, ...)
|
|
#endif
|
|
|
|
#ifdef FLASHINFER_ENABLE_FP8_E4M3
|
|
#define _DISPATCH_CASE_FP8_E4M3(c_type, ...) \
|
|
case at::ScalarType::Float8_e4m3fn: { \
|
|
using c_type = __nv_fp8_e4m3; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
#else
|
|
#define _DISPATCH_CASE_FP8_E4M3(c_type, ...)
|
|
#endif
|
|
|
|
#ifdef FLASHINFER_ENABLE_FP8_E5M2
|
|
#define _DISPATCH_CASE_FP8_E5M2(c_type, ...) \
|
|
case at::ScalarType::Float8_e5m2: { \
|
|
using c_type = __nv_fp8_e5m2; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
#else
|
|
#define _DISPATCH_CASE_FP8_E5M2(c_type, ...)
|
|
#endif
|
|
|
|
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP16(pytorch_dtype, c_type, ...) \
|
|
[&]() -> bool { \
|
|
switch (pytorch_dtype) { \
|
|
_DISPATCH_CASE_F16(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_BF16(c_type, __VA_ARGS__) \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch data type " << pytorch_dtype; \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
|
|
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FP8(pytorch_dtype, c_type, ...) \
|
|
[&]() -> bool { \
|
|
switch (pytorch_dtype) { \
|
|
_DISPATCH_CASE_FP8_E4M3(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_FP8_E5M2(c_type, __VA_ARGS__) \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch fp8 data type " << pytorch_dtype; \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
|
|
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE(pytorch_dtype, c_type, ...) \
|
|
[&]() -> bool { \
|
|
switch (pytorch_dtype) { \
|
|
_DISPATCH_CASE_F16(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_BF16(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_FP8_E4M3(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_FP8_E5M2(c_type, __VA_ARGS__) \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch data type " << pytorch_dtype; \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
|
|
#define _DISPATCH_SWITCH(var_name, cond, ...) \
|
|
[&]() -> bool { \
|
|
switch (cond) { \
|
|
__VA_ARGS__ \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch " var_name " " << int(cond); \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
|
|
#define _DISPATCH_SWITCH_U16x2(var1_name, var2_name, cond1, cond2, ...) \
|
|
[&]() -> bool { \
|
|
switch (pack_u16(cond1, cond2)) { \
|
|
__VA_ARGS__ \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch (" var1_name ", " var2_name "): (" << int(cond1) << ", " \
|
|
<< int(cond2) << ")"; \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
|
|
#define _DISPATCH_CASE(case_expr, case_var, ...) \
|
|
case case_expr: { \
|
|
constexpr auto case_var = case_expr; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
|
|
#define _DISPATCH_CASE_U16x2(case_expr1, case_expr2, case_var1, case_var2, ...) \
|
|
case pack_u16(case_expr1, case_expr2): { \
|
|
constexpr auto case_var1 = case_expr1; \
|
|
constexpr auto case_var2 = case_expr2; \
|
|
return __VA_ARGS__(); \
|
|
}
|
|
|
|
#define DISPATCH_BOOL(expr, const_expr, ...) \
|
|
[&]() -> bool { \
|
|
if (expr) { \
|
|
constexpr bool const_expr = true; \
|
|
return __VA_ARGS__(); \
|
|
} else { \
|
|
constexpr bool const_expr = false; \
|
|
return __VA_ARGS__(); \
|
|
} \
|
|
}()
|
|
|
|
inline void check_shape(const at::Tensor& a, const at::Tensor& b, const char* a_name, const char* b_name) {
|
|
TORCH_CHECK(a.dim() == b.dim(), a_name, ".dim() != ", b_name, ".dim(). ", a.dim(), " vs ", b.dim());
|
|
for (int i = 0; i < a.dim(); ++i) {
|
|
TORCH_CHECK(a.size(i) == b.size(i), a_name, ".size(", i, ") != ", b_name, ".size(", i, ")");
|
|
}
|
|
}
|
|
|
|
inline constexpr uint32_t pack_u16(uint16_t a, uint16_t b) {
|
|
return (uint32_t(a) << 16) | uint32_t(b);
|
|
}
|
|
|
|
#define CHECK_GQA_HEAD_DIVISIBLE(num_qo_heads, num_kv_heads) \
|
|
TORCH_CHECK( \
|
|
num_qo_heads % num_kv_heads == 0, \
|
|
"num_qo_heads(", \
|
|
num_qo_heads, \
|
|
") must be divisible by num_kv_heads(", \
|
|
num_kv_heads, \
|
|
")")
|
|
|
|
#define CHECK_CUDA(x) TORCH_CHECK(x.is_cuda(), #x " must be a CUDA tensor")
|
|
|
|
#define CHECK_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
|
|
#define CHECK_LAST_DIM_CONTIGUOUS(x) \
|
|
TORCH_CHECK(x.strides()[x.strides().size() - 1] == 1, #x "must be contiguous at last dimension")
|
|
|
|
#define CHECK_INPUT(x) \
|
|
CHECK_CUDA(x); \
|
|
CHECK_CONTIGUOUS(x)
|
|
#define CHECK_LAST_DIM_CONTIGUOUS_INPUT(x) \
|
|
CHECK_CUDA(x); \
|
|
CHECK_LAST_DIM_CONTIGUOUS(x)
|
|
|
|
#define CHECK_DIM(d, x) TORCH_CHECK(x.dim() == d, #x " must be a " #d "D tensor")
|
|
|
|
#define CHECK_SHAPE(a, b) check_shape(a, b, #a, #b)
|
|
|
|
#define CHECK_EQ(a, b) TORCH_CHECK((a) == (b), "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
|
|
|
|
#define CHECK_GE(a, b) TORCH_CHECK((a) >= (b), "CHECK_GE(" #a ", " #b ") failed. ", a, " vs ", b)
|
|
|
|
inline bool is_float8_tensor(const at::Tensor& tensor) {
|
|
return tensor.scalar_type() == at::ScalarType::Float8_e4m3fn || tensor.scalar_type() == at::ScalarType::Float8_e5m2;
|
|
}
|
|
#endif
|
|
|
|
struct cuda_error : public std::runtime_error {
|
|
/**
|
|
* @brief Constructs a `cuda_error` object with the given `message`.
|
|
*
|
|
* @param message The error char array used to construct `cuda_error`
|
|
*/
|
|
cuda_error(const char* message) : std::runtime_error(message) {}
|
|
/**
|
|
* @brief Constructs a `cuda_error` object with the given `message` string.
|
|
*
|
|
* @param message The `std::string` used to construct `cuda_error`
|
|
*/
|
|
cuda_error(std::string const& message) : cuda_error{message.c_str()} {}
|
|
};
|
|
|
|
#define CHECK_CUDA_SUCCESS(cmd) \
|
|
do { \
|
|
cudaError_t e = cmd; \
|
|
if (e != cudaSuccess) { \
|
|
std::stringstream _message; \
|
|
auto s = cudaGetErrorString(e); \
|
|
_message << std::string(s) + "\n" << __FILE__ << ':' << __LINE__; \
|
|
throw cuda_error(_message.str()); \
|
|
} \
|
|
} while (0)
|
|
|
|
#define CHECK_IS_CUDA(x) TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor")
|
|
#define CHECK_IS_CONTIGUOUS(x) TORCH_CHECK(x.is_contiguous(), #x " must be contiguous")
|
|
#define CHECK_CUDA_INPUT(x) \
|
|
CHECK_IS_CUDA(x); \
|
|
CHECK_IS_CONTIGUOUS(x)
|
|
|
|
inline int getSMVersion() {
|
|
int device{-1};
|
|
CHECK_CUDA_SUCCESS(cudaGetDevice(&device));
|
|
int sm_major = 0;
|
|
int sm_minor = 0;
|
|
CHECK_CUDA_SUCCESS(cudaDeviceGetAttribute(&sm_major, cudaDevAttrComputeCapabilityMajor, device));
|
|
CHECK_CUDA_SUCCESS(cudaDeviceGetAttribute(&sm_minor, cudaDevAttrComputeCapabilityMinor, device));
|
|
return sm_major * 10 + sm_minor;
|
|
}
|
|
|
|
// SGLANG_SHFL_XOR_* adapted from https://github.com/vllm-project/vllm/blob/v0.7.3/csrc/cuda_compat.h#L19-L28
|
|
#ifndef USE_ROCM
|
|
#define SGLANG_SHFL_XOR_SYNC(mask, var, lane_mask) __shfl_xor_sync((mask), (var), (lane_mask))
|
|
#define SGLANG_SHFL_XOR_SYNC_WIDTH(mask, var, lane_mask, width) __shfl_xor_sync((mask), (var), (lane_mask), (width))
|
|
#else
|
|
#define SGLANG_SHFL_XOR_SYNC(mask, var, lane_mask) __shfl_xor((var), (lane_mask))
|
|
#define SGLANG_SHFL_XOR_SYNC_WIDTH(mask, var, lane_mask, width) __shfl_xor((var), (lane_mask), (width))
|
|
#endif
|
|
|
|
#ifndef USE_ROCM
|
|
#define DISPATCH_PYTORCH_DTYPE_TO_CTYPE_FLOAT_FP16(pytorch_dtype, c_type, ...) \
|
|
[&]() -> bool { \
|
|
switch (pytorch_dtype) { \
|
|
case at::ScalarType::Float: { \
|
|
using c_type = float; \
|
|
return __VA_ARGS__(); \
|
|
} \
|
|
_DISPATCH_CASE_F16(c_type, __VA_ARGS__) \
|
|
_DISPATCH_CASE_BF16(c_type, __VA_ARGS__) \
|
|
default: \
|
|
std::ostringstream oss; \
|
|
oss << __PRETTY_FUNCTION__ << " failed to dispatch data type " << pytorch_dtype; \
|
|
TORCH_CHECK(false, oss.str()); \
|
|
return false; \
|
|
} \
|
|
}()
|
|
#endif
|
|
|
|
#define DISPATCH_CASE_INTEGRAL_TYPES(...) \
|
|
AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__) \
|
|
AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__) \
|
|
AT_DISPATCH_CASE(at::ScalarType::Short, __VA_ARGS__) \
|
|
AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__) \
|
|
AT_DISPATCH_CASE(at::ScalarType::Long, __VA_ARGS__)
|
|
|
|
#define DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \
|
|
AT_DISPATCH_SWITCH(TYPE, NAME, DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__))
|
|
|
|
#define CEILDIV(x, y) (((x) + (y) - 1) / (y))
|
|
#define WARP_SIZE 32
|
|
|
|
#ifndef USE_ROCM
|
|
#include <c10/util/Float8_e4m3fn.h>
|
|
using FP8_TYPE = c10::Float8_e4m3fn;
|
|
C10_HOST_DEVICE constexpr auto FP8_E4M3_MAX = std::numeric_limits<FP8_TYPE>::max();
|
|
#else
|
|
#include <c10/util/Float8_e4m3fnuz.h>
|
|
|
|
using FP8_TYPE = c10::Float8_e4m3fnuz;
|
|
constexpr auto FP8_E4M3_MAX = 224.0f;
|
|
#endif
|
|
|
|
#ifndef USE_ROCM
|
|
__device__ __forceinline__ float atomicMaxFloat(float* addr, float value) {
|
|
float old;
|
|
old = (value >= 0) ? __int_as_float(atomicMax((int*)addr, __float_as_int(value)))
|
|
: __uint_as_float(atomicMin((unsigned int*)addr, __float_as_uint(value)));
|
|
return old;
|
|
}
|
|
|
|
__device__ __forceinline__ float warpReduceMax(float max_value) {
|
|
max_value = fmaxf(max_value, SGLANG_SHFL_XOR_SYNC(0xffffffff, max_value, 16));
|
|
max_value = fmaxf(max_value, SGLANG_SHFL_XOR_SYNC(0xffffffff, max_value, 8));
|
|
max_value = fmaxf(max_value, SGLANG_SHFL_XOR_SYNC(0xffffffff, max_value, 4));
|
|
max_value = fmaxf(max_value, SGLANG_SHFL_XOR_SYNC(0xffffffff, max_value, 2));
|
|
max_value = fmaxf(max_value, SGLANG_SHFL_XOR_SYNC(0xffffffff, max_value, 1));
|
|
return max_value;
|
|
}
|
|
|
|
__device__ __forceinline__ float blockReduceMax(float max_value) {
|
|
static __shared__ float warpLevelMaxs[WARP_SIZE];
|
|
const int laneId = threadIdx.x % WARP_SIZE;
|
|
const int warpId = threadIdx.x / WARP_SIZE;
|
|
|
|
max_value = warpReduceMax(max_value);
|
|
|
|
if (laneId == 0) warpLevelMaxs[warpId] = max_value;
|
|
__syncthreads();
|
|
|
|
max_value = (threadIdx.x < blockDim.x / WARP_SIZE) ? warpLevelMaxs[laneId] : 0;
|
|
if (warpId == 0) max_value = warpReduceMax(max_value);
|
|
|
|
return max_value;
|
|
}
|
|
#endif
|
|
|
|
// Pads to a multiple of `alignment` rows.
|
|
inline torch::Tensor pad_tensor(const torch::Tensor& tensor, int64_t alignment = 4, bool is_column_major = false) {
|
|
int64_t rows = tensor.size(0);
|
|
int64_t cols = tensor.size(1);
|
|
int64_t pad_rows = (alignment - (rows % alignment)) % alignment; // Compute padding size
|
|
|
|
if (pad_rows == 0) {
|
|
return tensor; // Already aligned
|
|
}
|
|
|
|
torch::Tensor padding = torch::zeros({pad_rows, cols}, tensor.options());
|
|
torch::Tensor tensor_padded = torch::cat({tensor, padding}, 0); // Pad along rows
|
|
|
|
// Ensure column-major layout
|
|
if (is_column_major) {
|
|
return tensor_padded.t().contiguous().t();
|
|
}
|
|
return tensor_padded;
|
|
}
|