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Commit 12ee8fa5 authored by hhakim's avatar hhakim
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Fix multiple definitions of faust_torch module. 

The bug raised when introduced FaustCoreCpp<proc><scalar_type>.cpp.in (because it includes the faust_torch module multiple times for each proc and scalar type).
parent ebce23d6
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CMakeLists.txt
+ 3
− 0
View file @ 12ee8fa5
......@@ -674,6 +674,9 @@ if(NOT USE_GPU_MOD)
list(REMOVE_ITEM CPP_FILES ${GPU_CPP_FILE})
endforeach()
endif()
if(NOT FAUST_TORCH)
list(REMOVE_ITEM CPP_FILES ${FAUST_SRC_LINEAR_OPERATOR_DIR}/CPU/faust_torch.cpp)
endif()
message(STATUS "FAµST cpp files: ${CPP_FILES}")
### include directory
#include_directories(${FAUST_SRC_LINEAR_OPERATOR_DIR} ${FAUST_LINEAR_OPERATOR_CPU_SRC_DIR} ${EIGEN_INC_DIR} ${FAUST_ALGORITHM_FACTORIZATION_SRC_DIR} ${FAUST_UTILS_SRC_DIR})
......
src/faust_linear_operator/CMakeLists.txt
+ 4
− 0
View file @ 12ee8fa5
......@@ -3,6 +3,10 @@
file(GLOB CPP_FILES "*.cpp" "${FAUST_ALGORITHM_CONSTRAINT_SRC_DIR}/*.cpp" "${FAUST_LINEAR_OPERATOR_CPU_SRC_DIR}/*.cpp")
file(GLOB HPP_FILES "${FAUST_ALGORITHM_CONSTRAINT_SRC_DIR}/*.hpp" "${FAUST_LINEAR_OPERATOR_CPU_SRC_DIR}/*.hpp")
if(NOT FAUST_TORCH)
list(REMOVE_ITEM CPP_FILES "${FAUST_LINEAR_OPERATOR_CPU_SRC_DIR}/faust_torch.cpp")
endif()
if(USE_GPU_MOD)
include_directories(${GPU_MOD_INCLUDE_DIR} ${GPU_MOD_INCLUDE_DIR}/../build)
......
src/faust_linear_operator/CPU/faust_torch.cpp 0 → 100644
+ 147
− 0
View file @ 12ee8fa5
#include "faust_torch.h"
void display_TensorList(std::vector<torch::Tensor>& tl, const bool transpose /*= true*/)
{
if(transpose)
for (int i = tl.size()-1; i >= 0; i--) {cout << "Tensor: " << tl.size()-1-i << " [" << tl[i].size(1) << "x" << tl[i].size(0) << "] " << (tl[i].is_sparse()?"SPARSE":"DENSE"); cout << endl;}
else
for (int i = 0; i < tl.size(); i++) {cout << "Tensor: " << i << " [" << tl[i].size(0) << "x" << tl[i].size(1) << "] " << (tl[i].is_sparse()?"SPARSE":"DENSE"); cout << endl;};
}
torch::Tensor tensor_chain_mul(const std::vector<torch::Tensor>& tl, const torch::Tensor* op, at::DeviceType dev, const bool chain_opt, const bool contiguous_dense_to_torch, const bool op_on_left /*=true*/)
{
bool all_dense = true;
std::vector<torch::Tensor> tlc;
for(auto t: tl)
{
all_dense &= !t.is_sparse();
tlc.push_back(t);
}
if(op)
{
all_dense &= !op->is_sparse();
if(op_on_left)
tlc.insert(tlc.begin(), *op);
else
tlc.push_back(*op);
}
if(all_dense)
return torch::chain_matmul(tlc); //chain_opt is useless because I suppose torch does its own chain opt.
if(chain_opt)
return std::move(tensor_chain_mul_opt(tlc, nullptr, dev));
auto it = tlc.end()-1;
auto res = *(it);
if(res.is_sparse())
res = res.to_dense();
std::vector<torch::Tensor> dense_contiguous_facts;
while(it != tlc.begin())
{
auto f = *(--it);
if(f.is_sparse())
{
if(dense_contiguous_facts.size() > 0)
{
//multiply chain of dense tensors before
dense_contiguous_facts.push_back(res);
res = torch::chain_matmul(dense_contiguous_facts);
dense_contiguous_facts.erase(dense_contiguous_facts.begin(), dense_contiguous_facts.end());
}
res = at::_sparse_mm(f, res);
}
else
if(contiguous_dense_to_torch)
dense_contiguous_facts.insert(dense_contiguous_facts.begin(), f);
else
res = torch::matmul(f, res);
}
if(contiguous_dense_to_torch && dense_contiguous_facts.size() > 0)
{
//multiply chain of dense tensors at the end/start of tlc
dense_contiguous_facts.push_back(res);
res = torch::chain_matmul(dense_contiguous_facts);
dense_contiguous_facts.erase(dense_contiguous_facts.begin(), dense_contiguous_facts.end());
}
// don't worry assert is enabled only in debug mode (when DEBUG is defined)
assert((op != nullptr && op_on_left && res.size(0) == op->size(0)) || ((! op_on_left || op == nullptr) && res.size(0) == tl[0].size(0)) || op == nullptr);
assert(((op == nullptr || op_on_left) && res.size(1) == (*(tl.end()-1)).size(1)) || op != nullptr && res.size(1) == op->size(1));
return std::move(res); //explicit move but should work auto because Tensor class overrides move operator= and ctor
}
torch::Tensor tensor_chain_mul_opt(const std::vector<torch::Tensor>& ml, const torch::Tensor* op, at::DeviceType dev, const bool op_on_left /* = true */)
{
// cost to apply a on b
auto cost = [](const torch::Tensor &a, const torch::Tensor &b)
{
uint64_t a_cost = a.size(0)*a.size(1);
uint64_t b_cost;
if(b.is_sparse())
b_cost = b._nnz();
else
b_cost = b.size(1);
return a_cost*b_cost;
};
// argmin of costs v
auto argmin = [](std::vector<uint64_t> v)
{
int i=0 /* argmin*/, j=0 /* element index*/;
uint64_t min = numeric_limits<uint64_t>::max();
for(auto e: v)
{
if(e < min)
{
min = e;
i = j;
}
j++;
}
return i;
};
std::vector<const torch::Tensor*> mlc;
for(int i=0;i<ml.size();i++) mlc.push_back(&ml[i]);
if(op != nullptr)
if(op_on_left)
mlc.insert(mlc.begin(), op);
else
mlc.push_back(op);
std::vector<uint64_t> costs(mlc.size()-1);
for(int i=0;i<costs.size();i++)
costs[i] = cost(*mlc[i], *mlc[i+1]);
torch::Tensor* res = new torch::Tensor();
vector<torch::Tensor*> res_list;
res_list.push_back(res);
while(mlc.size() > 1)
{
auto i = argmin(costs);
auto f1 = mlc[i];
auto f2 = mlc[i+1];
if(res != f1 && res != f2)
{
res = new torch::Tensor();
res_list.push_back(res);
}
if(f2->is_sparse())
if(f1->is_sparse())
*res = at::_sparse_mm(*f1, f2->to_dense());
else
*res = torch::t(torch::matmul(torch::t(*f2), torch::t(*f1)));
else
if(f1->is_sparse())
*res = at::_sparse_mm(*f1, *f2);
else
*res = torch::matmul(*f1, *f2);
mlc.insert(mlc.begin()+i, res);
mlc.erase(mlc.begin()+i+1,mlc.begin()+i+3);
if(mlc.size() > i+1)
{
costs.insert(costs.begin()+i, cost(*res, *mlc[i+1]));
costs.erase(costs.begin()+i+1, costs.begin()+i+3);
}
else
costs.erase(costs.end()-1);
}
assert(mlc.size() == 1);
torch::Tensor t = *mlc[0];
for(auto res: res_list)
delete res;
return std::move(t);
}
src/faust_linear_operator/CPU/faust_torch.hpp
+ 0
− 145
View file @ 12ee8fa5
......@@ -116,144 +116,6 @@ namespace Faust
}
}
torch::Tensor tensor_chain_mul(const std::vector<torch::Tensor>& tl, const torch::Tensor* op, at::DeviceType dev, const bool chain_opt, const bool contiguous_dense_to_torch, const bool op_on_left /*=true*/)
{
bool all_dense = true;
std::vector<torch::Tensor> tlc;
for(auto t: tl)
{
all_dense &= !t.is_sparse();
tlc.push_back(t);
}
if(op)
{
all_dense &= !op->is_sparse();
if(op_on_left)
tlc.insert(tlc.begin(), *op);
else
tlc.push_back(*op);
}
if(all_dense)
return torch::chain_matmul(tlc); //chain_opt is useless because I suppose torch does its own chain opt.
if(chain_opt)
return std::move(tensor_chain_mul_opt(tlc, nullptr, dev));
auto it = tlc.end()-1;
auto res = *(it);
if(res.is_sparse())
res = res.to_dense();
std::vector<torch::Tensor> dense_contiguous_facts;
while(it != tlc.begin())
{
auto f = *(--it);
if(f.is_sparse())
{
if(dense_contiguous_facts.size() > 0)
{
//multiply chain of dense tensors before
dense_contiguous_facts.push_back(res);
res = torch::chain_matmul(dense_contiguous_facts);
dense_contiguous_facts.erase(dense_contiguous_facts.begin(), dense_contiguous_facts.end());
}
res = at::_sparse_mm(f, res);
}
else
if(contiguous_dense_to_torch)
dense_contiguous_facts.insert(dense_contiguous_facts.begin(), f);
else
res = torch::matmul(f, res);
}
if(contiguous_dense_to_torch && dense_contiguous_facts.size() > 0)
{
//multiply chain of dense tensors at the end/start of tlc
dense_contiguous_facts.push_back(res);
res = torch::chain_matmul(dense_contiguous_facts);
dense_contiguous_facts.erase(dense_contiguous_facts.begin(), dense_contiguous_facts.end());
}
// don't worry assert is enabled only in debug mode (when DEBUG is defined)
assert((op != nullptr && op_on_left && res.size(0) == op->size(0)) || ((! op_on_left || op == nullptr) && res.size(0) == tl[0].size(0)) || op == nullptr);
assert(((op == nullptr || op_on_left) && res.size(1) == (*(tl.end()-1)).size(1)) || op != nullptr && res.size(1) == op->size(1));
return std::move(res); //explicit move but should work auto because Tensor class overrides move operator= and ctor
}
torch::Tensor tensor_chain_mul_opt(const std::vector<torch::Tensor>& ml, const torch::Tensor* op, at::DeviceType dev, const bool op_on_left /* = true */)
{
// cost to apply a on b
auto cost = [](const torch::Tensor &a, const torch::Tensor &b)
{
uint64_t a_cost = a.size(0)*a.size(1);
uint64_t b_cost;
if(b.is_sparse())
b_cost = b._nnz();
else
b_cost = b.size(1);
return a_cost*b_cost;
};
// argmin of costs v
auto argmin = [](std::vector<uint64_t> v)
{
int i=0 /* argmin*/, j=0 /* element index*/;
uint64_t min = numeric_limits<uint64_t>::max();
for(auto e: v)
{
if(e < min)
{
min = e;
i = j;
}
j++;
}
return i;
};
std::vector<const torch::Tensor*> mlc;
for(int i=0;i<ml.size();i++) mlc.push_back(&ml[i]);
if(op != nullptr)
if(op_on_left)
mlc.insert(mlc.begin(), op);
else
mlc.push_back(op);
std::vector<uint64_t> costs(mlc.size()-1);
for(int i=0;i<costs.size();i++)
costs[i] = cost(*mlc[i], *mlc[i+1]);
torch::Tensor* res = new torch::Tensor();
vector<torch::Tensor*> res_list;
res_list.push_back(res);
while(mlc.size() > 1)
{
auto i = argmin(costs);
auto f1 = mlc[i];
auto f2 = mlc[i+1];
if(res != f1 && res != f2)
{
res = new torch::Tensor();
res_list.push_back(res);
}
if(f2->is_sparse())
if(f1->is_sparse())
*res = at::_sparse_mm(*f1, f2->to_dense());
else
*res = torch::t(torch::matmul(torch::t(*f2), torch::t(*f1)));
else
if(f1->is_sparse())
*res = at::_sparse_mm(*f1, *f2);
else
*res = torch::matmul(*f1, *f2);
mlc.insert(mlc.begin()+i, res);
mlc.erase(mlc.begin()+i+1,mlc.begin()+i+3);
if(mlc.size() > i+1)
{
costs.insert(costs.begin()+i, cost(*res, *mlc[i+1]));
costs.erase(costs.begin()+i+1, costs.begin()+i+3);
}
else
costs.erase(costs.end()-1);
}
assert(mlc.size() == 1);
torch::Tensor t = *mlc[0];
for(auto res: res_list)
delete res;
return std::move(t);
}
template<typename FPP, FDevice D>
void tensor_chain_mul(const std::vector<Faust::MatGeneric<FPP,D>*>& ml, Faust::MatDense<FPP,D> & out, const Faust::MatGeneric<FPP,D>* op, const bool on_gpu, const bool clone, const bool chain_opt, const bool contiguous_dense_to_torch, const bool transpose /* = true */)
{
......@@ -281,12 +143,5 @@ namespace Faust
convTensorToMatDense(tres, out, transpose);
}
void display_TensorList(std::vector<torch::Tensor>& tl, const bool transpose /*= true*/)
{
if(transpose)
for (int i = tl.size()-1; i >= 0; i--) {cout << "Tensor: " << tl.size()-1-i << " [" << tl[i].size(1) << "x" << tl[i].size(0) << "] " << (tl[i].is_sparse()?"SPARSE":"DENSE"); cout << endl;}
else
for (int i = 0; i < tl.size(); i++) {cout << "Tensor: " << i << " [" << tl[i].size(0) << "x" << tl[i].size(1) << "] " << (tl[i].is_sparse()?"SPARSE":"DENSE"); cout << endl;};
}
}
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