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xc-llm-ascend/csrc/aclnn_torch_adapter/op_api_common.h
Slightwind 9fdabb7b60 [feature] Add Custom Op grouped_matmul_swiglu_quant (#4431) 
This PR introduces the `EXEC_NPU_CMD` macro, serving as an adapter layer
to simplify the invocation of `aclnn` operators on Ascend NPUs.

**Key Changes:**
* **Adapter Layer:** Added `EXEC_NPU_CMD` macro and related dependencies
to standardize `aclnn` calls.
* **Operator Support:** Integrated `grouped_matmul_swiglu_quant` as a
reference implementation to demonstrate the usage of the new macro.

---


- vLLM version: v0.11.2

---------

Signed-off-by: SlightwindSec <slightwindsec@gmail.com>
2025-11-27 21:56:18 +08:00

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// Copyright (c) 2023 Huawei Technologies Co., Ltd
// All rights reserved.
//
// Licensed under the BSD 3-Clause License (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// https://opensource.org/licenses/BSD-3-Clause
//
// 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.
#ifndef OP_API_COMMON_ADAPTER
#define OP_API_COMMON_ADAPTER
#include <torch/types.h>
#include <ATen/Tensor.h>
#include <acl/acl_base.h>
#include <c10/util/Exception.h>
#include <dlfcn.h>
#include <functional>
#include <type_traits>
#include <vector>
#include <torch_npu/csrc/framework/utils/CalcuOpUtil.h>
#include <torch_npu/csrc/framework/utils/OpAdapter.h>
#include "torch_npu/csrc/aten/NPUNativeFunctions.h"
#include "torch_npu/csrc/core/npu/NPUStream.h"
#include "torch_npu/csrc/framework/OpCommand.h"
#include "torch_npu/csrc/framework/interface/EnvVariables.h"
#include "torch_npu/csrc/framework/utils/CalcuOpUtil.h"
#include "torch_npu/csrc/framework/utils/OpPreparation.h"
#include "NPUBridge.h"
#include "NPUStorageImpl.h"
#define NPU_NAME_SPACE at_npu::native
using namespace at;
typedef struct aclOpExecutor aclOpExecutor;
typedef struct aclTensor aclTensor;
typedef struct aclScalar aclScalar;
typedef struct aclIntArray aclIntArray;
typedef struct aclFloatArray aclFloatArray;
typedef struct aclBoolArray aclBoolArray;
typedef struct aclTensorList aclTensorList;
typedef aclTensor *(*_aclCreateTensor)(
const int64_t *view_dims, uint64_t view_dims_num, aclDataType data_type,
const int64_t *stride, int64_t offset, aclFormat format,
const int64_t *storage_dims, uint64_t storage_dims_num, void *tensor_data);
typedef aclScalar *(*_aclCreateScalar)(void *value, aclDataType data_type);
typedef aclIntArray *(*_aclCreateIntArray)(const int64_t *value, uint64_t size);
typedef aclFloatArray *(*_aclCreateFloatArray)(const float *value,
uint64_t size);
typedef aclBoolArray *(*_aclCreateBoolArray)(const bool *value, uint64_t size);
typedef aclTensorList *(*_aclCreateTensorList)(const aclTensor *const *value,
uint64_t size);
typedef int (*_aclDestroyTensor)(const aclTensor *tensor);
typedef int (*_aclDestroyScalar)(const aclScalar *scalar);
typedef int (*_aclDestroyIntArray)(const aclIntArray *array);
typedef int (*_aclDestroyFloatArray)(const aclFloatArray *array);
typedef int (*_aclDestroyBoolArray)(const aclBoolArray *array);
typedef int (*_aclDestroyTensorList)(const aclTensorList *array);
constexpr int kHashBufSize = 8192;
constexpr int kHashBufMaxSize = kHashBufSize + 1024;
extern thread_local char g_hashBuf[kHashBufSize];
extern thread_local int g_hashOffset;
#ifdef MMCV_WITH_XLA
#define DEVICE_TYPE at_npu::key::NativeDeviceType
#else
#define DEVICE_TYPE c10::DeviceType::PrivateUse1
#endif
#define AT_ALL_SCALAR_TYPE_AND_ACL_DATATYPE_PAIR(_) \
_(at::ScalarType::Byte, ACL_UINT8) \
_(at::ScalarType::Char, ACL_INT8) \
_(at::ScalarType::Short, ACL_INT16) \
_(at::ScalarType::Int, ACL_INT32) \
_(at::ScalarType::Long, ACL_INT64) \
_(at::ScalarType::Half, ACL_FLOAT16) \
_(at::ScalarType::Float, ACL_FLOAT) \
_(at::ScalarType::Double, ACL_DOUBLE) \
_(at::ScalarType::ComplexHalf, ACL_DT_UNDEFINED) \
_(at::ScalarType::ComplexFloat, ACL_COMPLEX64) \
_(at::ScalarType::ComplexDouble, ACL_COMPLEX128) \
_(at::ScalarType::Bool, ACL_BOOL) \
_(at::ScalarType::QInt8, ACL_DT_UNDEFINED) \
_(at::ScalarType::QUInt8, ACL_DT_UNDEFINED) \
_(at::ScalarType::QInt32, ACL_DT_UNDEFINED) \
_(at::ScalarType::BFloat16, ACL_BF16) \
_(at::ScalarType::QUInt4x2, ACL_DT_UNDEFINED) \
_(at::ScalarType::QUInt2x4, ACL_DT_UNDEFINED) \
_(at::ScalarType::Undefined, ACL_DT_UNDEFINED) \
_(at::ScalarType::NumOptions, ACL_DT_UNDEFINED)
constexpr aclDataType kATenScalarTypeToAclDataTypeTable
[static_cast<int64_t>(at::ScalarType::NumOptions) + 1] = {
#define DEFINE_ENUM(_1, n) n,
AT_ALL_SCALAR_TYPE_AND_ACL_DATATYPE_PAIR(DEFINE_ENUM)
#undef DEFINE_ENUM
};
#define GET_OP_API_FUNC(apiName) \
reinterpret_cast<_##apiName>(GetOpApiFuncAddr(#apiName))
#define MEMCPY_TO_BUF(data_expression, size_expression) \
if (g_hashOffset + (size_expression) > kHashBufSize) { \
g_hashOffset = kHashBufMaxSize; \
return; \
} \
memcpy(g_hashBuf + g_hashOffset, data_expression, size_expression); \
g_hashOffset += size_expression;
bool IsOpInputBaseFormat(const at::Tensor &tensor)
{
if (!tensor.is_privateuseone()) {
return true;
}
const auto format = vllm_ascend::NPUBridge::GetNpuStorageImplDesc(tensor).npu_format_;
return (format == ACL_FORMAT_ND) || (format == ACL_FORMAT_NCHW) || (format == ACL_FORMAT_NHWC) ||
(format == ACL_FORMAT_NCDHW);
}
inline const char *GetOpApiLibName(void) { return "libopapi.so"; }
inline const char *GetCustOpApiLibName(void) { return "libcust_opapi.so"; }
inline void *GetOpApiFuncAddrInLib(void *handler, const char *libName,
const char *apiName) {
auto funcAddr = dlsym(handler, apiName);
return funcAddr;
}
inline void *GetOpApiLibHandler(const char *libName) {
auto handler = dlopen(libName, RTLD_LAZY);
return handler;
}
inline void *GetOpApiFuncAddr(const char *apiName) {
static auto custOpApiHandler = GetOpApiLibHandler(GetCustOpApiLibName());
if (custOpApiHandler != nullptr) {
auto funcAddr =
GetOpApiFuncAddrInLib(custOpApiHandler, GetCustOpApiLibName(), apiName);
if (funcAddr != nullptr) {
return funcAddr;
}
}
static auto opApiHandler = GetOpApiLibHandler(GetOpApiLibName());
if (opApiHandler == nullptr) {
return nullptr;
}
return GetOpApiFuncAddrInLib(opApiHandler, GetOpApiLibName(), apiName);
}
inline c10::Scalar ConvertTensorToScalar(const at::Tensor &tensor) {
c10::Scalar expScalar;
const at::Tensor *aclInput = &tensor;
if (aclInput->scalar_type() == at::ScalarType::Double) {
double value = *(double *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::Long) {
int64_t value = *(int64_t *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::Float) {
float value = *(float *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::Int) {
int value = *(int *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::Half) {
c10::Half value = *(c10::Half *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::Bool) {
int8_t value = *(int8_t *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::ComplexDouble) {
c10::complex<double> value = *(c10::complex<double> *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::ComplexFloat) {
c10::complex<float> value = *(c10::complex<float> *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
} else if (aclInput->scalar_type() == at::ScalarType::BFloat16) {
c10::BFloat16 value = *(c10::BFloat16 *)aclInput->data_ptr();
c10::Scalar scalar(value);
expScalar = scalar;
}
return expScalar;
}
inline at::Tensor CopyTensorHostToDevice(const at::Tensor &cpu_tensor) {
at::Tensor cpuPinMemTensor = cpu_tensor.pin_memory();
int deviceIndex = 0;
return cpuPinMemTensor.to(c10::Device(DEVICE_TYPE, deviceIndex),
cpuPinMemTensor.scalar_type(), true, true);
}
inline at::Tensor CopyScalarToDevice(const c10::Scalar &cpu_scalar,
at::ScalarType scalar_data_type) {
return CopyTensorHostToDevice(
scalar_to_tensor(cpu_scalar).to(scalar_data_type));
}
inline aclTensor *ConvertType(const at::Tensor &at_tensor) {
static const auto aclCreateTensor = GET_OP_API_FUNC(aclCreateTensor);
if (aclCreateTensor == nullptr) {
return nullptr;
}
if (!at_tensor.defined()) {
return nullptr;
}
at::ScalarType scalar_data_type = at_tensor.scalar_type();
aclDataType acl_data_type =
kATenScalarTypeToAclDataTypeTable[static_cast<int64_t>(scalar_data_type)];
TORCH_CHECK(
acl_data_type != ACL_DT_UNDEFINED,
std::string(c10::toString(scalar_data_type)) + " has not been supported")
c10::SmallVector<int64_t, 5> storageDims;
// if acl_data_type is ACL_STRING, storageDims is empty.
auto itemsize = at_tensor.itemsize();
TORCH_CHECK(itemsize != 0, "When ConvertType, tensor item size cannot be zero.");
const auto dimNum = at_tensor.sizes().size();
aclFormat format = ACL_FORMAT_ND;
if (!IsOpInputBaseFormat(at_tensor)) {
format = vllm_ascend::NPUBridge::GetNpuStorageImpl(at_tensor)->npu_desc_.npu_format_;
if (acl_data_type != ACL_STRING) {
storageDims = vllm_ascend::NPUBridge::GetNpuStorageImpl(at_tensor)->npu_desc_.storage_sizes_;
}
} else {
switch (dimNum) {
case 3:
format = ACL_FORMAT_NCL;
break;
case 4:
format = ACL_FORMAT_NCHW;
break;
case 5:
format = ACL_FORMAT_NCDHW;
break;
default:
format = ACL_FORMAT_ND;
}
if (acl_data_type != ACL_STRING) {
storageDims.push_back(at_tensor.storage().nbytes() / itemsize);
}
}
if (at_tensor.unsafeGetTensorImpl()->is_wrapped_number()) {
c10::Scalar expScalar = ConvertTensorToScalar(at_tensor);
at::Tensor aclInput = CopyScalarToDevice(expScalar, scalar_data_type);
return aclCreateTensor(aclInput.sizes().data(), aclInput.sizes().size(),
acl_data_type, aclInput.strides().data(),
aclInput.storage_offset(), format,
storageDims.data(), storageDims.size(),
const_cast<void *>(aclInput.storage().data()));
}
auto acl_tensor = aclCreateTensor(
at_tensor.sizes().data(), at_tensor.sizes().size(), acl_data_type,
at_tensor.strides().data(), at_tensor.storage_offset(), format,
storageDims.data(), storageDims.size(),
const_cast<void *>(at_tensor.storage().data()));
return acl_tensor;
}
inline aclScalar *ConvertType(const at::Scalar &at_scalar) {
static const auto aclCreateScalar = GET_OP_API_FUNC(aclCreateScalar);
if (aclCreateScalar == nullptr) {
return nullptr;
}
at::ScalarType scalar_data_type = at_scalar.type();
aclDataType acl_data_type =
kATenScalarTypeToAclDataTypeTable[static_cast<int64_t>(scalar_data_type)];
TORCH_CHECK(
acl_data_type != ACL_DT_UNDEFINED,
std::string(c10::toString(scalar_data_type)) + " has not been supported")
aclScalar *acl_scalar = nullptr;
switch (scalar_data_type) {
case at::ScalarType::Double: {
double value = at_scalar.toDouble();
acl_scalar = aclCreateScalar(&value, acl_data_type);
break;
}
case at::ScalarType::Long: {
int64_t value = at_scalar.toLong();
acl_scalar = aclCreateScalar(&value, acl_data_type);
break;
}
case at::ScalarType::Bool: {
bool value = at_scalar.toBool();
acl_scalar = aclCreateScalar(&value, acl_data_type);
break;
}
case at::ScalarType::ComplexDouble: {
auto value = at_scalar.toComplexDouble();
acl_scalar = aclCreateScalar(&value, acl_data_type);
break;
}
default:
acl_scalar = nullptr;
break;
}
return acl_scalar;
}
inline aclIntArray *ConvertType(const at::IntArrayRef &at_array) {
static const auto aclCreateIntArray = GET_OP_API_FUNC(aclCreateIntArray);
if (aclCreateIntArray == nullptr) {
return nullptr;
}
auto array = aclCreateIntArray(at_array.data(), at_array.size());
return array;
}
template <std::size_t N>
inline aclBoolArray *ConvertType(const std::array<bool, N> &value) {
static const auto aclCreateBoolArray = GET_OP_API_FUNC(aclCreateBoolArray);
if (aclCreateBoolArray == nullptr) {
return nullptr;
}
auto array = aclCreateBoolArray(value.data(), value.size());
return array;
}
inline aclBoolArray *ConvertType(const at::ArrayRef<bool> &value) {
static const auto aclCreateBoolArray = GET_OP_API_FUNC(aclCreateBoolArray);
if (aclCreateBoolArray == nullptr) {
return nullptr;
}
auto array = aclCreateBoolArray(value.data(), value.size());
return array;
}
inline aclTensorList *ConvertType(const at::TensorList &at_tensor_list) {
static const auto aclCreateTensorList = GET_OP_API_FUNC(aclCreateTensorList);
if (aclCreateTensorList == nullptr) {
return nullptr;
}
std::vector<const aclTensor *> tensor_list(at_tensor_list.size());
for (size_t i = 0; i < at_tensor_list.size(); i++) {
tensor_list[i] = ConvertType(at_tensor_list[i]);
}
auto acl_tensor_list =
aclCreateTensorList(tensor_list.data(), tensor_list.size());
return acl_tensor_list;
}
inline aclTensor *ConvertType(const c10::optional<at::Tensor> &opt_tensor) {
if (opt_tensor.has_value() && opt_tensor.value().defined()) {
return ConvertType(opt_tensor.value());
}
return nullptr;
}
inline aclIntArray *ConvertType(
const c10::optional<at::IntArrayRef> &opt_array) {
if (opt_array.has_value()) {
return ConvertType(opt_array.value());
}
return nullptr;
}
inline aclScalar *ConvertType(const c10::optional<at::Scalar> &opt_scalar) {
if (opt_scalar.has_value()) {
return ConvertType(opt_scalar.value());
}
return nullptr;
}
inline aclDataType ConvertType(const at::ScalarType scalarType) {
return kATenScalarTypeToAclDataTypeTable[static_cast<int64_t>(scalarType)];
}
template <typename T>
T ConvertType(T value) {
return value;
}
template <typename Tuple, size_t... I>
auto ConvertToOpApiFunc(const Tuple &params, void *opApiAddr,
std::index_sequence<I...>) {
typedef int (*OpApiFunc)(
typename std::decay<decltype(std::get<I>(params))>::type...);
auto func = reinterpret_cast<OpApiFunc>(opApiAddr);
return func;
}
template <typename Tuple>
auto ConvertToOpApiFunc(const Tuple &params, void *opApiAddr) {
static constexpr auto size = std::tuple_size<Tuple>::value;
return ConvertToOpApiFunc(params, opApiAddr,
std::make_index_sequence<size>{});
}
inline void Release(aclTensor *p) {
static const auto aclDestroyTensor = GET_OP_API_FUNC(aclDestroyTensor);
if (aclDestroyTensor == nullptr) {
return;
}
aclDestroyTensor(p);
}
inline void Release(aclScalar *p) {
static const auto aclDestroyScalar = GET_OP_API_FUNC(aclDestroyScalar);
if (aclDestroyScalar == nullptr) {
return;
}
aclDestroyScalar(p);
}
inline void Release(aclIntArray *p) {
static const auto aclDestroyIntArray = GET_OP_API_FUNC(aclDestroyIntArray);
if (aclDestroyIntArray == nullptr) {
return;
}
aclDestroyIntArray(p);
}
inline void Release(aclBoolArray *p) {
static const auto aclDestroyBoolArray = GET_OP_API_FUNC(aclDestroyBoolArray);
if (aclDestroyBoolArray == nullptr) {
return;
}
aclDestroyBoolArray(p);
}
inline void Release(aclTensorList *p) {
static const auto aclDestroyTensorList =
GET_OP_API_FUNC(aclDestroyTensorList);
if (aclDestroyTensorList == nullptr) {
return;
}
aclDestroyTensorList(p);
}
template <typename T>
void Release(T value) {
(void)value;
}
template <typename Tuple, size_t... I>
void CallRelease(Tuple t, std::index_sequence<I...>) {
(void)std::initializer_list<int>{(Release(std::get<I>(t)), 0)...};
}
template <typename Tuple>
void ReleaseConvertTypes(Tuple &t) {
static constexpr auto size = std::tuple_size<Tuple>::value;
CallRelease(t, std::make_index_sequence<size>{});
}
template <typename... Ts>
constexpr auto ConvertTypes(Ts &... args) {
return std::make_tuple(ConvertType(args)...);
}
template <typename Function, typename Tuple, size_t... I>
auto call(Function f, Tuple t, std::index_sequence<I...>) {
return f(std::get<I>(t)...);
}
template <typename Function, typename Tuple>
auto call(Function f, Tuple t) {
static constexpr auto size = std::tuple_size<Tuple>::value;
return call(f, t, std::make_index_sequence<size>{});
}
template <std::size_t N>
void AddParamToBuf(const std::array<bool, N> &value) {
MEMCPY_TO_BUF(value.data(), value.size() * sizeof(bool));
}
template <typename T>
void AddParamToBuf(const T &value) {
MEMCPY_TO_BUF(&value, sizeof(T));
}
void AddParamToBuf(const at::Tensor &);
void AddParamToBuf(const at::Scalar &);
void AddParamToBuf(const at::IntArrayRef &);
void AddParamToBuf(const at::ArrayRef<bool> &);
void AddParamToBuf(const at::TensorList &);
void AddParamToBuf(const c10::optional<at::Tensor> &);
void AddParamToBuf(const c10::optional<at::IntArrayRef> &);
void AddParamToBuf(const c10::optional<at::Scalar> &);
void AddParamToBuf(const at::ScalarType);
void AddParamToBuf(const string &);
void AddParamToBuf();
template <typename T, typename... Args>
void AddParamToBuf(const T &arg, Args &... args) {
AddParamToBuf(arg);
AddParamToBuf(args...);
}
uint64_t CalcHashId();
typedef int (*InitHugeMemThreadLocal)(void *, bool);
typedef void (*UnInitHugeMemThreadLocal)(void *, bool);
typedef void (*ReleaseHugeMem)(void *, bool);
#define EXEC_NPU_CMD(aclnn_api, ...) \
do { \
static const auto getWorkspaceSizeFuncAddr = \
GetOpApiFuncAddr(#aclnn_api "GetWorkspaceSize"); \
static const auto opApiFuncAddr = GetOpApiFuncAddr(#aclnn_api); \
static const auto initMemAddr = \
GetOpApiFuncAddr("InitHugeMemThreadLocal"); \
static const auto unInitMemAddr = \
GetOpApiFuncAddr("UnInitHugeMemThreadLocal"); \
static const auto releaseMemAddr = GetOpApiFuncAddr("ReleaseHugeMem"); \
TORCH_CHECK( \
getWorkspaceSizeFuncAddr != nullptr && opApiFuncAddr != nullptr, \
#aclnn_api, " or ", #aclnn_api "GetWorkspaceSize", " not in ", \
GetOpApiLibName(), ", or ", GetOpApiLibName(), "not found."); \
auto acl_stream = c10_npu::getCurrentNPUStream().stream(false); \
uint64_t workspace_size = 0; \
uint64_t *workspace_size_addr = &workspace_size; \
aclOpExecutor *executor = nullptr; \
aclOpExecutor **executor_addr = &executor; \
InitHugeMemThreadLocal initMemFunc = \
reinterpret_cast<InitHugeMemThreadLocal>(initMemAddr); \
UnInitHugeMemThreadLocal unInitMemFunc = \
reinterpret_cast<UnInitHugeMemThreadLocal>(unInitMemAddr); \
if (initMemFunc) { \
initMemFunc(nullptr, false); \
} \
auto converted_params = \
ConvertTypes(__VA_ARGS__, workspace_size_addr, executor_addr); \
static auto getWorkspaceSizeFunc = \
ConvertToOpApiFunc(converted_params, getWorkspaceSizeFuncAddr); \
auto workspace_status = call(getWorkspaceSizeFunc, converted_params); \
TORCH_CHECK(workspace_status == 0, \
"call " #aclnn_api " failed, detail:", aclGetRecentErrMsg()); \
void *workspace_addr = nullptr; \
if (workspace_size != 0) { \
at::TensorOptions options = \
at::TensorOptions(torch_npu::utils::get_npu_device_type()); \
auto workspace_tensor = \
at::empty({workspace_size}, options.dtype(kByte)); \
workspace_addr = const_cast<void *>(workspace_tensor.storage().data()); \
} \
auto acl_call = [converted_params, workspace_addr, workspace_size, \
acl_stream, executor]() -> int { \
typedef int (*OpApiFunc)(void *, uint64_t, aclOpExecutor *, \
const aclrtStream); \
OpApiFunc opApiFunc = reinterpret_cast<OpApiFunc>(opApiFuncAddr); \
auto api_ret = \
opApiFunc(workspace_addr, workspace_size, executor, acl_stream); \
TORCH_CHECK(api_ret == 0, "call " #aclnn_api " failed, detail:", \
aclGetRecentErrMsg()); \
ReleaseConvertTypes(converted_params); \
ReleaseHugeMem releaseMemFunc = \
reinterpret_cast<ReleaseHugeMem>(releaseMemAddr); \
if (releaseMemFunc) { \
releaseMemFunc(nullptr, false); \
} \
return api_ret; \
}; \
at_npu::native::OpCommand cmd; \
cmd.Name(#aclnn_api); \
cmd.SetCustomHandler(acl_call); \
cmd.Run(); \
if (unInitMemFunc) { \
unInitMemFunc(nullptr, false); \
} \
} while (false)
#endif
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