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3239 lines (2892 loc) · 118 KB
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Copy pathmath.cpp
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3239 lines (2892 loc) · 118 KB
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/******************************************************************************
* Copyright (c) 2025 AISS Group at Harbin Institute of Technology. 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.
*****************************************************************************/
#include <acl/acl.h>
#include <aclnn/aclnn_base.h>
#include <asnumpy/math/math.hpp>
#include <fmt/base.h>
#include <fmt/format.h>
#include <shape.h>
#include <stdexcept>
NPUArray Cumprod(const NPUArray& a, int64_t axis, py::dtype dtype) {
auto shape = a.shape;
auto axis_scalar = aclCreateScalar(&axis, ACL_INT64);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnCumprodGetWorkspaceSize(a.tensorPtr, axis_scalar, result.aclDtype, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnCumprod(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Cumsum(const NPUArray& a, int64_t axis, py::dtype dtype) {
auto shape = a.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnCumsumGetWorkspaceSize(a.tensorPtr, axis, result.aclDtype, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnCumsum(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Nancumprod(const NPUArray& a, int64_t axis, py::dtype dtype) {
auto shape = a.shape;
auto axis_scalar = aclCreateScalar(&axis, ACL_INT64);
float scalar = 1.0;
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnNanToNum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnCumprodGetWorkspaceSize(temp.tensorPtr, axis_scalar, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnCumprod(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Nancumsum(const NPUArray& a, int64_t axis, py::dtype dtype) {
auto shape = a.shape;
float scalar = 0.0;
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnNanToNum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnCumsumGetWorkspaceSize(temp.tensorPtr, axis, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnCumsum(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Cross(const NPUArray& a, const NPUArray& b, int64_t axisa, int64_t axisb, int64_t axisc, int64_t axis) {
auto broadcast = GetBroadcastShape(a, b);
auto result = NPUArray(broadcast, a.aclDtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLinalgCrossGetWorkspaceSize(a.tensorPtr, b.tensorPtr, axis, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLinalgCross(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Exp(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnExpGetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnExp(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Expm1(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnExpm1GetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnExpm1(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Exp2(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnExp2GetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnExp2(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Log(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLogGetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLog(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Log10(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLog10GetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLog10(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Log2(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLog2GetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLog2(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Log1p(const NPUArray& x, py::dtype dtype) {
auto shape = x.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLog1pGetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLog1p(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Logaddexp(const NPUArray& x1, const NPUArray& x2, py::dtype dtype) {
auto broadcast = GetBroadcastShape(x1, x2);
auto result = NPUArray(broadcast, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLogAddExpGetWorkspaceSize(x1.tensorPtr, x2.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLogAddExp(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Logaddexp2(const NPUArray& x1, const NPUArray& x2, py::dtype dtype) {
auto broadcast = GetBroadcastShape(x1, x2);
auto result = NPUArray(broadcast, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnLogAddExp2GetWorkspaceSize(x1.tensorPtr, x2.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnLogAddExp2(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Sinc(const NPUArray& x) {
auto shape = x.shape;
auto result = NPUArray(shape, x.aclDtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnSincGetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnSinc(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Real(const NPUArray& val) {
auto shape = val.shape;
auto result = NPUArray(shape, val.aclDtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnRealGetWorkspaceSize(a.tensorPtr, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnReal(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Prod(const NPUArray& a, int64_t axis, py::dtype dtype, bool keepdims) {
auto shape = a.shape;
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnProdDimGetWorkspaceSize(a.tensorPtr, axis, keepdims, result.aclDtype, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnProdDim(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Prod(const NPUArray& a, py::dtype dtype) {
auto shape = (1, );
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnProdGetWorkspaceSize(a.tensorPtr, result.aclDtype, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnProd(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Sum(const NPUArray& a, const std::vector<int64_t>& axis, py::dtype dtype, bool keepdims) {
auto shape = a.shape;
aclIntArray* axis_array = aclCreateIntArray(axis.data(), axis.size());
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnReduceSumGetWorkspaceSize(a.tensorPtr, axis_array, keepdims, result.aclDtype, result.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnReduceSum(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
return result;
}
NPUArray Sum(const NPUArray& a, py::dtype dtype) {
auto shape = a.shape;
std::vector<aclTensor *> tmp{a};
auto input = aclCreateTensorList(tmp.data(), tmp.size());
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnSumGetWorkspaceSize(input, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnSum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnCastGetWorkspaceSize(temp.tensorPtr, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnCast(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Nanprod(const NPUArray& a, int64_t axis, py::dtype dtype, bool keepdims) {
auto shape = a.shape;
float scalar = 1.0;
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnNanToNum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnProdDimGetWorkspaceSize(temp.tensorPtr, axis, keepdims, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnProdDim(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Nanprod(const NPUArray& a, py::dtype dtype) {
auto shape = (1, );
float scalar = 1.0;
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnNanToNum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnProdGetWorkspaceSize(temp.tensorPtr, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnProd(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Nansum(const NPUArray& a, const std::vector<int64_t>& axis, py::dtype dtype, bool keepdims) {
auto shape = a.shape;
float scalar = 0.0;
aclIntArray* axis_array = aclCreateIntArray(axis.data(), axis.size());
auto temp = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnNanToNum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnReduceSumGetWorkspaceSize(temp.tensorPtr, axis_array, keepdims, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnReduceSum(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
NPUArray Nansum(const NPUArray& a, py::dtype dtype) {
auto shape = a.shape;
float scalar = 0.0;
auto temp1 = NPUArray(shape, a.aclDtype);
auto temp2 = NPUArray(shape, a.aclDtype);
auto result = NPUArray(shape, dtype);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
auto error = aclnnNanToNumGetWorkspaceSize(a.tensorPtr, scalar, scalar, scalar, temp1.tensorPtr, &workspaceSize, &executor);
void* workspaceAddr = nullptr;
if(workspaceSize > 0) {
error = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
}
error = aclnnNanToNum(workspaceAddr, workspaceSize, executor, nullptr);
error = aclrtSynchronizeDevice();
std::vector<aclTensor *> tmp{temp1};
auto input = aclCreateTensorList(tmp.data(), tmp.size());
uint64_t workspaceSize1 = 0;
aclOpExecutor* executor1;
auto error1 = aclnnSumGetWorkspaceSize(tmp, temp2.tensorPtr, &workspaceSize1, &executor1);
void* workspaceAddr1 = nullptr;
if(workspaceSize1 > 0) {
error1 = aclrtMalloc(&workspaceAddr1, workspaceSize1, ACL_MEM_MALLOC_HUGE_FIRST);
}
error1 = aclnnSum(workspaceAddr1, workspaceSize1, executor1, nullptr);
error1 = aclrtSynchronizeDevice();
uint64_t workspaceSize2 = 0;
aclOpExecutor* executor2;
auto error2 = aclnnCastGetWorkspaceSize(temp2.tensorPtr, result.aclDtype, result.tensorPtr, &workspaceSize2, &executor2);
void* workspaceAddr2 = nullptr;
if(workspaceSize2 > 0) {
error2 = aclrtMalloc(&workspaceAddr2, workspaceSize2, ACL_MEM_MALLOC_HUGE_FIRST);
}
error2 = aclnnCast(workspaceAddr2, workspaceSize2, executor2, nullptr);
error2 = aclrtSynchronizeDevice();
return result;
}
/**
* @brief Compute element-wise square root of sum of squares of two arrays.
*
* Equivalent to numpy.hypot(a, b), computes √(a² + b²) for each element.
* This is the hypotenuse of a right-angled triangle with legs a and b.
*
* @param a NPUArray, first input array (one leg of the right triangle)
* @param b NPUArray, second input array (other leg of the right triangle)
* @return NPUArray Element-wise result of √(a² + b²)
*/
NPUArray Hypot(const NPUArray& a, const NPUArray& b) {
// 检查输入形状是否匹配
if (a.shape != b.shape) {
throw invalid_argument("Hypot: a and b must have the same shape");
}
// 初始化结果数组
auto shape = a.shape;
auto dtype = a.dtype;
NPUArray result(shape, dtype);
// 步骤1: 计算a的平方 (a²)
NPUArray a_squared(shape, dtype);
uint64_t a_sq_workspace_size = 0;
aclOpExecutor* a_sq_executor = nullptr;
auto error = aclnnMulGetWorkspaceSize(
a.tensorPtr, a.tensorPtr,
a_squared.tensorPtr,
&a_sq_workspace_size,
&a_sq_executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: a² workspace size failed, error={}", error));
}
void* a_sq_workspace = nullptr;
if (a_sq_workspace_size > 0) {
error = aclrtMalloc(&a_sq_workspace, a_sq_workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: a² workspace malloc failed, error={}", error));
}
}
error = aclnnMul(a_sq_workspace, a_sq_workspace_size, a_sq_executor, nullptr);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: a² computation failed, error={}", error));
}
// 步骤2: 计算b的平方 (b²)
NPUArray b_squared(shape, dtype);
uint64_t b_sq_workspace_size = 0;
aclOpExecutor* b_sq_executor = nullptr;
error = aclnnMulGetWorkspaceSize(
b.tensorPtr, b.tensorPtr,
b_squared.tensorPtr,
&b_sq_workspace_size,
&b_sq_executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: b² workspace size failed, error={}", error));
}
void* b_sq_workspace = nullptr;
if (b_sq_workspace_size > 0) {
error = aclrtMalloc(&b_sq_workspace, b_sq_workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: b² workspace malloc failed, error={}", error));
}
}
error = aclnnMul(b_sq_workspace, b_sq_workspace_size, b_sq_executor, nullptr);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: b² computation failed, error={}", error));
}
// 步骤3: 计算平方和 (a² + b²)
NPUArray sum_squares(shape, dtype);
uint64_t add_workspace_size = 0;
aclOpExecutor* add_executor = nullptr;
int32_t alpha = 1;
auto alpha_scalar = aclCreateScalar(&alpha, a.aclDtype);
error = aclnnAddGetWorkspaceSize(
a_squared.tensorPtr, b_squared.tensorPtr,
alpha_scalar, sum_squares.tensorPtr,
&add_workspace_size, &add_executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sum workspace size failed, error={}", error));
}
void* add_workspace = nullptr;
if (add_workspace_size > 0) {
error = aclrtMalloc(&add_workspace, add_workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sum workspace malloc failed, error={}", error));
}
}
error = aclnnAdd(add_workspace, add_workspace_size, add_executor, nullptr);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sum computation failed, error={}", error));
}
// 步骤4: 计算平方根 (√(a² + b²))
uint64_t sqrt_workspace_size = 0;
aclOpExecutor* sqrt_executor = nullptr;
error = aclnnSqrtGetWorkspaceSize(
sum_squares.tensorPtr, result.tensorPtr,
&sqrt_workspace_size, &sqrt_executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sqrt workspace size failed, error={}", error));
}
void* sqrt_workspace = nullptr;
if (sqrt_workspace_size > 0) {
error = aclrtMalloc(&sqrt_workspace, sqrt_workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sqrt workspace malloc failed, error={}", error));
}
}
error = aclnnSqrt(sqrt_workspace, sqrt_workspace_size, sqrt_executor, nullptr);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Hypot: sqrt computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
aclDestroyScalar(alpha_scalar);
if (a_sq_workspace) aclrtFree(a_sq_workspace);
if (b_sq_workspace) aclrtFree(b_sq_workspace);
if (add_workspace) aclrtFree(add_workspace);
if (sqrt_workspace) aclrtFree(sqrt_workspace);
return result;
}
/**
* @brief Compute element-wise arctangent of y/x considering quadrant.
*
* Equivalent to numpy.arctan2(y, x), returns values in [-π, π] radians.
*
* @param y NPUArray, numerator (y-coordinate)
* @param x NPUArray, denominator (x-coordinate)
* @return NPUArray Result of element-wise arctan2(y, x)
*/
NPUArray Arctan2(const NPUArray& y, const NPUArray& x) {
// 检查输入形状是否匹配
if (y.shape != x.shape) {
throw invalid_argument("Arctan2: y and x must have the same shape");
}
// 初始化结果数组
auto shape = y.shape;
auto dtype = y.dtype;
NPUArray result(shape, dtype);
// 获取工作空间大小
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
auto error = aclnnAtan2GetWorkspaceSize(
y.tensorPtr, x.tensorPtr,
result.tensorPtr,
&workspace_size, &executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arctan2: workspace size failed, error={}", error));
}
// 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arctan2: workspace malloc failed, error={}", error));
}
}
// 执行计算
error = aclnnAtan2(workspace, workspace_size, executor, nullptr);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arctan2: computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace) aclrtFree(workspace);
return result;
}
/**
* @brief Convert angles from degrees to radians.
*
* Equivalent to numpy.radians(x), computes x * (π / 180).
*
* @param x NPUArray, input angles in degrees
* @return NPUArray Angles converted to radians
*/
NPUArray Radians(const NPUArray& x) {
// 初始化结果数组
auto shape = x.shape;
auto dtype = x.dtype;
auto acl_dtype = x.aclDtype;
NPUArray result(shape, dtype);
// 1. 创建转换系数标量 (π / 180)
void* scalar_data = nullptr;
if (acl_dtype == ACL_FLOAT) {
static const float val = static_cast<float>(M_PI / 180.0);
scalar_data = const_cast<float*>(&val);
} else if (acl_dtype == ACL_DOUBLE) {
static const double val = M_PI / 180.0;
scalar_data = const_cast<double*>(&val);
} else {
throw invalid_argument(fmt::format("Radians: unsupported dtype={}", acl_dtype));
}
// 2. 将标量转换为1D张量（解决参数类型不匹配问题）
aclTensorDesc* scalar_desc = aclCreateTensorDesc(acl_dtype, 0, nullptr); // 0维标量描述符
aclTensor* scalar_tensor = nullptr;
auto error = aclCreateTensorWithData(scalar_desc, scalar_data,
aclGetDataTypeSize(acl_dtype),
ACL_MEMCPY_HOST_TO_DEVICE,
&scalar_tensor);
if (error != ACL_SUCCESS) {
aclDestroyTensorDesc(scalar_desc);
throw runtime_error(fmt::format("Radians: create scalar tensor failed, error={}", error));
}
// 3. 获取工作空间大小（现在第二个参数是aclTensor*类型，匹配接口要求）
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
error = aclnnMulGetWorkspaceSize(
x.tensorPtr, // 输入张量
scalar_tensor, // 转换为张量的标量（修复类型不匹配）
result.tensorPtr, // 输出张量
&workspace_size,
&executor
);
if (error != ACL_SUCCESS) {
aclDestroyTensor(scalar_tensor);
aclDestroyTensorDesc(scalar_desc);
throw runtime_error(fmt::format("Radians: workspace size failed, error={}", error));
}
// 4. 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
aclDestroyTensor(scalar_tensor);
aclDestroyTensorDesc(scalar_desc);
throw runtime_error(fmt::format("Radians: workspace malloc failed, error={}", error));
}
}
// 5. 执行计算 (x * (π / 180))
error = aclnnMul(workspace, workspace_size, executor, nullptr);
if (error != ACL_SUCCESS) {
aclrtFree(workspace);
aclDestroyTensor(scalar_tensor);
aclDestroyTensorDesc(scalar_desc);
throw runtime_error(fmt::format("Radians: computation failed, error={}", error));
}
// 6. 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace) aclrtFree(workspace);
aclDestroyTensor(scalar_tensor);
aclDestroyTensorDesc(scalar_desc);
return result;
}
/**
* @brief Compute element-wise hyperbolic sine.
*
* Equivalent to numpy.sinh(x), calculates sinh(x) = (e^x - e^(-x))/2 for each element.
*
* @param x NPUArray, input array
* @return NPUArray Element-wise hyperbolic sine of x
*/
NPUArray Sinh(const NPUArray& x) {
// 初始化结果数组（形状和数据类型与输入一致）
auto shape = x.shape;
auto dtype = x.dtype;
NPUArray result(shape, dtype);
// 获取工作空间大小
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
auto error = aclnnSinhGetWorkspaceSize(
x.tensorPtr,
result.tensorPtr,
&workspace_size,
&executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Sinh: get workspace size failed, error={}", error));
}
// 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Sinh: malloc workspace failed, error={}", error));
}
}
// 执行双曲正弦计算
error = aclnnSinh(
workspace,
workspace_size,
executor,
nullptr // 无需回调
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Sinh: computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace != nullptr) {
aclrtFree(workspace);
}
return result;
}
/**
* @brief Compute element-wise hyperbolic cosine.
*
* Equivalent to numpy.cosh(x), calculates cosh(x) = (e^x + e^(-x))/2 for each element.
*
* @param x NPUArray, input array
* @return NPUArray Element-wise hyperbolic cosine of x
*/
NPUArray Cosh(const NPUArray& x) {
// 初始化结果数组（形状和数据类型与输入一致）
auto shape = x.shape;
auto dtype = x.dtype;
NPUArray result(shape, dtype);
// 获取工作空间大小
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
auto error = aclnnCoshGetWorkspaceSize(
x.tensorPtr,
result.tensorPtr,
&workspace_size,
&executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Cosh: get workspace size failed, error={}", error));
}
// 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Cosh: malloc workspace failed, error={}", error));
}
}
// 执行双曲余弦计算
error = aclnnCosh(
workspace,
workspace_size,
executor,
nullptr // 无需回调
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Cosh: computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace != nullptr) {
aclrtFree(workspace);
}
return result;
}
/**
* @brief Compute element-wise hyperbolic tangent.
*
* Equivalent to numpy.tanh(x), calculates tanh(x) = sinh(x)/cosh(x) for each element.
*
* @param x NPUArray, input array
* @return NPUArray Element-wise hyperbolic tangent of x
*/
NPUArray Tanh(const NPUArray& x) {
// 初始化结果数组（形状和数据类型与输入一致）
auto shape = x.shape;
auto dtype = x.dtype;
NPUArray result(shape, dtype);
// 获取工作空间大小
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
auto error = aclnnTanhGetWorkspaceSize(
x.tensorPtr,
result.tensorPtr,
&workspace_size,
&executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Tanh: get workspace size failed, error={}", error));
}
// 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Tanh: malloc workspace failed, error={}", error));
}
}
// 执行双曲正切计算
error = aclnnTanh(
workspace,
workspace_size,
executor,
nullptr // 无需回调
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Tanh: computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace != nullptr) {
aclrtFree(workspace);
}
return result;
}
/**
* @brief Compute element-wise inverse hyperbolic sine.
*
* Equivalent to numpy.arcsinh(x), calculates arcsinh(x) = ln(x + √(x² + 1)) for each element.
*
* @param x NPUArray, input array
* @return NPUArray Element-wise inverse hyperbolic sine of x
*/
NPUArray Arcsinh(const NPUArray& x) {
// 初始化结果数组（形状和数据类型与输入一致）
auto shape = x.shape;
auto dtype = x.dtype;
NPUArray result(shape, dtype);
// 获取工作空间大小
uint64_t workspace_size = 0;
aclOpExecutor* executor = nullptr;
auto error = aclnnAsinhGetWorkspaceSize(
x.tensorPtr,
result.tensorPtr,
&workspace_size,
&executor
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arcsinh: get workspace size failed, error={}", error));
}
// 分配工作空间
void* workspace = nullptr;
if (workspace_size > 0) {
error = aclrtMalloc(&workspace, workspace_size, ACL_MEM_MALLOC_HUGE_FIRST);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arcsinh: malloc workspace failed, error={}", error));
}
}
// 执行反双曲正弦计算
error = aclnnAsinh(
workspace,
workspace_size,
executor,
nullptr // 无需回调
);
if (error != ACL_SUCCESS) {
throw runtime_error(fmt::format("Arcsinh: computation failed, error={}", error));
}
// 同步设备并释放资源
aclrtSynchronizeDevice();
if (workspace != nullptr) {
aclrtFree(workspace);
}
return result;
}
/**
* @brief Compute element-wise inverse hyperbolic cosine.
*
* Equivalent to numpy.arccosh(x), calculates arccosh(x) = ln(x + √(x² - 1)) for x ≥ 1.
*
* @param x NPUArray, input array (must contain values ≥ 1)